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"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>One of the scientific disciplines behind the Science of Science studies is the study of scientific networks. We are concerned with scientific networks as a social network with connections representing either co-authorship (collaboration) or citations. Different measures of network analysis can be applied to these networks such as centrality (to find influential authors) or clustering coefficient (to show the connectedness of a group of scientists). The major limitation of the earlier studies has been lack of completeness of data set. Any study on co-authorship may not necessarily have all the citation links. This limits the analyses of different types on the same set of nodes. To overcome this, we have worked on publicly available citation metadata to develop a workflow to create different types of scientific networks. Our focus is primarily on the identification of prominent authors through centrality analysis, as well as whether this can be achieved using open metadata. To present our approach, we have analysed Scientometrics journal as a case study. We are not concerned with bibliometrics study of any field rather we aim to provide a replicable workflow (in form of Python scripts) to apply network analysis using OpenCitatons data. With the increasing popularity of open access and open metadata, we hypothesise that this workflow shall provide an avenue for understanding science in multiple dimensions.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Identifying prominent authors (gurus) of any field is one of the primary focus for young researchers in that particular field. Likewise, other researchers tend to follow research published by gurus of the field.</ns0:p><ns0:p>In this work, we aim to utilise open metadata <ns0:ref type='bibr' target='#b30'>(Peroni et al., 2015)</ns0:ref>, made available using Crossref, and utilise open access NetworkX <ns0:ref type='bibr' target='#b9'>(Hagberg et al., 2008)</ns0:ref> and SNAP <ns0:ref type='bibr' target='#b17'>(Leskovec and Sosi&#269;, 2016)</ns0:ref> libraries of Python for network analysis. Python is used based on its popularity with researchers as per survey results by <ns0:ref type='bibr' target='#b1'>AlNoamany and Borghi (2018)</ns0:ref>. This article provides minimal details of a case study for analysing collaboration network of Scientometrics journal metadata, for 10 years starting from 2003. All steps are described for replication of this study. This work shall lay the groundwork for further analyses of similar type on different journals, set of journals or a subject category using open metadata.</ns0:p><ns0:p>Defining a guru of the field is not an easy task, and any definition will be highly subjective. To this end, we focus on the definition of guru using the centrality measures of social network analysis. Details of different centrality measures are depicted in Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref> <ns0:ref type='bibr' target='#b25'>(Newman, 2010)</ns0:ref>. The following description was inspired by <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014)</ns0:ref>. Simply said, any author with a high citation count may be considered the guru. This can be achieved using degree centrality. Although another way of identifying a highly cited individual is to see whose paper is cited in top percentile within the domain we currently limit such definitions to degree centrality of articles. However, it is not always the case that all highly cited authors are equally influential. Those who are cited by other influential authors may also be termed as influential even though they may or may not have high citation count. Likewise, any author collaborating frequently with influential authors would also have some high influence in that field of study. This recursive influence definition is well captured by eigenvector centrality. Another centrality measure, namely betweenness PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:1:1:NEW 18 Nov 2020)</ns0:p><ns0:p>Manuscript to be reviewed Computer Science centrality would define an author as prominent in the field if the author is a collaborator with individuals of different clusters within the domain. Centrality measures of closeness and farness measure the extent to which an author is on average close to or far from other authors within the network, respectively.</ns0:p><ns0:p>Such analyses can be applied on a variety of scientific networks such as article citation network, author citation network or author collaboration network. These networks can be created using different data sources. Some data sources (such as Crossref) allows to fetch the metadata of articles cited by the article or that cited the original article. This allows expanding the breadth of the network. In Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref> neighbours of node (n) (namely node (k), (l), (m), (o) and (p)) will form its ego network. C. shows high betweenness centrality since the highest number of shortest paths will go from the node (i) and (k) since they are bridging two parts of the network. D. shows high degree centrality as both the nodes (e) and (n) have 5 neighbours. E. shows high eigenvector centrality since node (e) is connected to many neighbours with a relatively higher degree. F. shows high closeness centrality as the average distance from nodes (i), (j) and (k) are minimum to other nodes.</ns0:p><ns0:p>The remaining document is organised as follows: after giving some preliminary definitions we briefly provide details about the pipeline architecture in methodology before moving to detailed steps of acquiring data. First, we provide details of the citation index downloaded from the web and loaded in memory.</ns0:p><ns0:p>Next, we discuss how the data are fetched and filtered. Further, we provide a case study for finding gurus.</ns0:p><ns0:p>Lastly, we conclude with details of how this work can be further expanded.</ns0:p></ns0:div> <ns0:div><ns0:head>RELATED WORK</ns0:head><ns0:p>Visualising bibliometric data as a network is not new, <ns0:ref type='bibr' target='#b32'>Price (1965)</ns0:ref> introduced the work more than 50 years ago. Most recent studies are on co-authorship network <ns0:ref type='bibr' target='#b0'>(Abbasi et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b23'>Milojevi&#263;, 2014;</ns0:ref><ns0:ref type='bibr' target='#b16'>Lee, 2019;</ns0:ref><ns0:ref type='bibr' target='#b34'>Singh and Jolad, 2019;</ns0:ref><ns0:ref type='bibr' target='#b37'>Waheed et al., 2019)</ns0:ref>, however others have focused on citation network for authors <ns0:ref type='bibr' target='#b7'>(Ding, 2011;</ns0:ref><ns0:ref type='bibr' target='#b23'>Milojevi&#263;, 2014;</ns0:ref><ns0:ref type='bibr' target='#b41'>Xu and Pekelis, 2015;</ns0:ref><ns0:ref type='bibr' target='#b37'>Waheed et al., 2019)</ns0:ref> or journal <ns0:ref type='bibr' target='#b39'>(Waltman and Yan, 2014;</ns0:ref><ns0:ref type='bibr' target='#b18'>Leydesdorff et al., 2018)</ns0:ref>. Only a couple of studies have utilised more than one Scientific Network for analysis <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014;</ns0:ref><ns0:ref type='bibr' target='#b37'>Waheed et al., 2019)</ns0:ref>. Traditionally bibliometric analysis has been done using WoS and Scopus <ns0:ref type='bibr' target='#b38'>(Waltman and Larivi&#232;re, 2020)</ns0:ref>, and a similar case is seen in these studies where the data sources, primarily are WoS <ns0:ref type='bibr' target='#b7'>(Ding, 2011;</ns0:ref><ns0:ref type='bibr' target='#b23'>Milojevi&#263;, 2014;</ns0:ref><ns0:ref type='bibr' target='#b39'>Waltman and Yan, 2014;</ns0:ref><ns0:ref type='bibr' target='#b18'>Leydesdorff et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b22'>Massucci and Docampo, 2019)</ns0:ref> or Scopus <ns0:ref type='bibr' target='#b0'>(Abbasi et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b16'>Lee, 2019)</ns0:ref>, however, some recent studies have focused on open access data sources <ns0:ref type='bibr' target='#b34'>(Singh and Jolad, 2019;</ns0:ref><ns0:ref type='bibr' target='#b35'>Van den Besselaar and Sandstr&#246;m, 2019;</ns0:ref><ns0:ref type='bibr' target='#b37'>Waheed et al., 2019)</ns0:ref>. Other data sources such as PubMed, CiteSeerX and ACL are not discussed in this article as they are mostly used for text analysis instead of network analysis. Below we provide a brief account of work done on scientific networks using centrality measures in the past decade.</ns0:p><ns0:p>Details are summarized in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref> in chronological order. Some earlier studies such as <ns0:ref type='bibr' target='#b26'>(Newman, 2004)</ns0:ref> are not discussed here to only include recent studies. <ns0:ref type='bibr' target='#b7'>Ding (2011)</ns0:ref> proposed to analyse the author citation network with weighted PageRank. The author showed that their proposed strategy outperforms the conventional h-index and related citation count measures on predicting prize winners. <ns0:ref type='bibr' target='#b0'>Abbasi et al. (2012)</ns0:ref> discussed the use of betweenness centrality as a measure of getting more collaborators compared to degree and closeness centrality. They have used temporal co-authorship network in the steel research domain. Data was manually curated and downloaded from Scopus. <ns0:ref type='bibr' target='#b29'>Ortega (2014)</ns0:ref> analysed 500 co-authors' ego network and conclude that centrality measures are correlated with bibliometric indicators. They have used clustering coefficient, degree and betweenness centrality as local metrics while some global level metrics were also analysed due to a holistic view of ego network. It is one of the early studies using MAG.</ns0:p><ns0:p>Two book chapters provide hands-on details about centrality measures <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014)</ns0:ref> and PageRank <ns0:ref type='bibr' target='#b39'>(Waltman and Yan, 2014)</ns0:ref> using WoS data. <ns0:ref type='bibr' target='#b23'>Milojevi&#263; (2014)</ns0:ref> constructed the author collaboration network and calculated degree, betweenness, eigenvector and closeness centrality. <ns0:ref type='bibr' target='#b39'>Waltman and Yan (2014)</ns0:ref> provides details for applying PageRank on journal citation network. <ns0:ref type='bibr' target='#b41'>Xu and Pekelis (2015)</ns0:ref> used a manually curated dataset for authors of China and Taiwan in the field of Chinese Language Interpreting Studies. They have applied PageRank and degree centrality to find influential authors within different clusters identified using community detection. <ns0:ref type='bibr' target='#b18'>Leydesdorff et al. (2018)</ns0:ref> have used betweenness centrality as a measure of multidisciplinary of a journal using a journal citation network. Any journal is usually cited from its subject category but the journals cited/citing the other fields are considered a bridge between the subject categories. Authors have limited their approach with a diversity measure and evaluated it on data from JCR. <ns0:ref type='bibr' target='#b16'>Lee (2019)</ns0:ref> provide a case study for young researchers performance evaluation by analysing the collaboration network of these researchers. Using statistical analysis frequency of collaborators measured by degree centrality is shown to correspond with future publication count. This is akin to <ns0:ref type='bibr' target='#b20'>Li et al. (2019)</ns0:ref> who concludes that collaboration of young scientist with top-ranked co-authors has a huge probability of future success. <ns0:ref type='bibr' target='#b22'>Massucci and Docampo (2019)</ns0:ref> applies the PageRank algorithm on a university citation network.</ns0:p><ns0:p>Working on five different subject categories they show that their framework is more robust than existing university rankings while holding a high correlation with these accepted rankings. <ns0:ref type='bibr' target='#b34'>Singh and Jolad (2019)</ns0:ref> utilised data of APS journals to form collaboration network of Indian physicist. In this co-authorship network, they have applied different centrality measures and report the overlapping top authors. <ns0:ref type='bibr' target='#b35'>Van den Besselaar and Sandstr&#246;m (2019)</ns0:ref> discuss the potential use of clustering coefficient and eigenvector centrality of ego network of researchers and their supervisor. These measures provide a metric for gauging the independence of a researcher. They have used a small scale study of 4 pair of researchers handpicked for their comparison. Although the authors agree that there are numerous ways to capture independence, however, the use of clustering coefficient and eigenvector centrality could be a potential tool for evaluating independence over a large data set. <ns0:ref type='table' target='#tab_1'>2020:08:52217:1:1:NEW 18 Nov 2020)</ns0:ref> Manuscript to be reviewed Computer Science citation network to 5 levels in cited-by and citing directions. Using a large network available at AMiner they proposed a hybrid strategy for recommendations using different centrality measures on each network.</ns0:p><ns0:p>Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref> provides a summary of these studies stating the data source used to create the scientific network, as well as the measures which were applied for analysis. Case studies similar to our work are also available on the proprietary data source of WoS <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014)</ns0:ref> and Scopus <ns0:ref type='bibr' target='#b33'>(Rose and Kitchin, 2019)</ns0:ref>. Further, a set of graphical tools are also available as discussed by <ns0:ref type='bibr' target='#b24'>Moral-Mu&#241;oz et al. (2020)</ns0:ref> in a recent survey but most tools do not give access for Crossref apart from <ns0:ref type='bibr' target='#b36'>(Van Eck and Waltman, 2014;</ns0:ref><ns0:ref type='bibr' target='#b5'>Chen, 2005)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Study</ns0:head><ns0:p>Chen (2005) discusses identification of highly cited clusters of a scientific network. Also discusses the identification of pivotal points in the scientific network using betweenness centrality. The author uses clinical evidence data associated with reducing risks of heart disease to illustrate the approach. They have discussed the design of citeSpace tool and its new feature for identifying pivotal points. They used betweenness centrality to identify pathways between thematic clusters because by studying these pathways identifies how two clusters differ. High betweenness centrality nodes are good for pivotal points in a scientific network. We intend to approach similarly but instead of a graphical software tool, we propose to use Python scripts which give more flexibility for advance analysis. For a detailed survey of tools, we would refer the interested reader to <ns0:ref type='bibr' target='#b24'>(Moral-Mu&#241;oz et al., 2020)</ns0:ref>.</ns0:p><ns0:p>One of the recent studies that provide replicable Python scripts <ns0:ref type='bibr' target='#b33'>(Rose and Kitchin, 2019)</ns0:ref> focuses on using Scopus data for network analysis. They have provided a scripted interface for researchers to</ns0:p></ns0:div> <ns0:div><ns0:head>5/15</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:1:1:NEW 18 Nov 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science perform useful analysis. Although accessing Scopus is possible with Elsevier Developer API Key but it requires institutional or authenticated access. Such access is not possible, especially for developing countries <ns0:ref type='bibr' target='#b13'>(Herzog et al., 2020)</ns0:ref>. Although our work is similar to <ns0:ref type='bibr' target='#b33'>Rose and Kitchin (2019)</ns0:ref> that it provides a scripted interface for researchers, it is different in two aspects. Firstly, we are working with OpenCitatons data using Crossref. Secondly, we have not provided an API interface that needs maintenance and support since we believe that Crossref, NetworkX and SNAP APIs fulfil the purpose.</ns0:p><ns0:p>Overall these studies show that applying centrality measures is a useful analysis in bibliometrics, however, these approaches are mostly not scalable and would require considerable effort to apply the same analysis on bigger networks. In some cases, the tools limit the size of network analysed, whereas in other studies the data are manually curated. In comparison to our work most studies are limited to one type of network and the way dataset is acquired limits the analysis to expand to another type of networks.</ns0:p><ns0:p>As mentioned above in our representative literature review it is observed that rarely any study has used multiple networks or mentioned how it can be curated with the same data source. Although with WoS and Scopus data it is theoretically possible to create all networks with other data sources a dump is usually provided with limited metadata, thereby limiting the authors to confine their studies to this limitation.</ns0:p><ns0:p>On the other hand, publicly available metadata has its limitations when it comes to completeness and verification of available data. <ns0:ref type='bibr' target='#b14'>Iorio et al. (2019)</ns0:ref> concludes that using OpenCitatons data for evaluation purpose is not enough due to unavailability of complete data, however more than half of data are available in comparison to WoS and Scopus. A similar evaluation is also done by <ns0:ref type='bibr' target='#b27'>Nishioka and F&#228;rber (2019)</ns0:ref> and <ns0:ref type='bibr' target='#b21'>Mart&#237;n-Mart&#237;n et al. (2020)</ns0:ref>. Further, there are different approaches to augment the current OpenCitatons data <ns0:ref type='bibr' target='#b6'>(Daquino et al., 2018;</ns0:ref><ns0:ref type='bibr'>Heibi et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b31'>Peroni and Shotton, 2020)</ns0:ref>.</ns0:p><ns0:p>Using open metadata are gaining popularity. <ns0:ref type='bibr' target='#b15'>(Kami&#324;ska, 2018)</ns0:ref> discusses a case study for using</ns0:p><ns0:p>OpenCitatons data for visualising citation network. <ns0:ref type='bibr' target='#b42'>(Zhu et al., 2019)</ns0:ref> has used COCI to evaluate books scholarship. We hypothesise that with a scripted workflow provided below it would be easier for masses to adopt to OpenCitatons data for bibliometric analysis.</ns0:p></ns0:div> <ns0:div><ns0:head>METHODOLOGY</ns0:head><ns0:p>This section provides details of a systematic workflow from data fetching to analysis. To apply centrality analysis on the author collaboration and author citation networks a series of steps are required to create these networks using the OpenCitatons data which provide the article citation network. All scripts were executed on Windows Server machine having Quad-Core AMD Opteron(TM) Processor 6272 with 128 GB RAM installed. It is interesting to note that only the initial processing of data requires heavy computation and memory once. Later, the data are converted to a compressed binary format using libraries for processing large networks and thus can run on any standard laptop machine. Below we provide details of the workflow to create scientific networks for SCIM. A generic query on Crossref provided a huge amount of data so their analysis was outside the scope of this current article. We aim to provide details of our extended analysis in an upcoming publication and not clutter this workflow with unnecessary details. Although this case study is limited to data of SCIM, we have made every effort to keep the process automated such that applying the same script require minimum changes for other journals or set of journals.</ns0:p><ns0:p>Overview of the process is depicted in Figure <ns0:ref type='figure' target='#fig_2'>2</ns0:ref> and further details about each of the following step are provided separately. Each step is distributed with three sub-steps for clarity and batch execution.</ns0:p><ns0:p>Step 1 The first step is to download the citation index provided as COCI <ns0:ref type='bibr'>(Heibi et al., 2019)</ns0:ref>.</ns0:p><ns0:p>Step 2 The second step is to download the metadata for provided ISSN through Crossref.</ns0:p><ns0:p>Step 3 The third step is to fetch the ego network from COCI data for the DOIs of respective ISSN.</ns0:p><ns0:p>Step 4 The fourth step is to merge these data to create a different scientific network(s).</ns0:p><ns0:p>Step 5 Finally, the last step is to apply the centrality analysis on these networks.</ns0:p><ns0:p>Minimal set of Python scripts are provided as Supplemental Files, for not only replication of the current study, but also reuse of this study for other ISSN or other network types for bibliometric analyses.</ns0:p><ns0:p>Details are provided below for the understanding of this study and can be accessed online <ns0:ref type='bibr' target='#b4'>(Butt and Faizi, 2020)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>6/15</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:1:1:NEW 18 Nov 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science </ns0:p></ns0:div> <ns0:div><ns0:head>Fetching citation network</ns0:head><ns0:p>Summary of the sub-steps to fetch citation network is shown in Figure <ns0:ref type='figure' target='#fig_3'>3</ns0:ref>. Below we define the sub-steps to convert the COCI data to be used in Python libraries for network processing. This step is computation and memory intensive but needs to be performed only once. Convert COCI data to edge list This step is needed to convert the COCI data to an edge list format. It is an easy to process format with two nodes on each row signifying an edge. This format is supported by SNAP <ns0:ref type='bibr' target='#b17'>(Leskovec and Sosi&#269;, 2016)</ns0:ref> which is used for processing huge network data such as COCI. After this step edge list file is approx 35 GB. We convert the COCI from comma-separated-values (CSV) to space-separated-values having only citing and cited column. This is the only format supported by SNAP for bulk upload. Some formatting corrections are also made for removing extra CR/LF and quotes since it hampers the loading process of SNAP. We have tried to load the same files with other libraries which are</ns0:p></ns0:div> <ns0:div><ns0:head>7/15</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:1:1:NEW 18 Nov 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science relatively more intuitive but not as powerful as SNAP <ns0:ref type='bibr' target='#b17'>(Leskovec and Sosi&#269;, 2016)</ns0:ref>. However, we later discuss how this data can be used with other libraries and provide scripts to convert data to a format that is supported by the majority of network processing libraries.</ns0:p><ns0:p>Save COCI as binary Loading 35 GB edge list in-memory using SNAP takes approx 5.5 hours. Since the edge labels are DOI in the COCI data, therefore they are saved as strings. However, this slows down further processing so strings are converted to a hash file. There are two binary files generated when loading the COCI data in SNAP. First is DOIDirected.graph file which contains the directed citation network of COCI with integer node labels. Second is DOIMapping.hash which maps the integer node label to respective DOI. We save loaded graph as binary files for further computations. Loading binary file in-memory takes a few minutes as compared to a few hours for loading CSV data with the downside that additional columns of COCI are currently not being utilised. To keep things simple for novice and non-technical user DOIMapping.hash is simply a node list where node number is mapped to its label (DOI) while the DOIDirected.graph is an edge list on node number. This is the part which makes SNAP less intuitive but more powerful since computations are much faster when integer labels are used but for human consumption, a mapping to string labels is also provided.</ns0:p></ns0:div> <ns0:div><ns0:head>Fetching Crossref metadata</ns0:head><ns0:p>Summary of the sub-steps to download Crossref metadata are shown in Figure <ns0:ref type='figure'>4</ns0:ref>. Below we define the sub-steps to fetch the citation metadata and converting it to list of authors and DOIs. Although these steps only provide API string to fetch data for a single journal, however, it is possible to fetch data with other filters and query using Crossref. Details are provided in Crossref documentation, and the metadata downloaded via different filters is in a similar format which makes this script reusable for a variety of tasks.</ns0:p></ns0:div> <ns0:div><ns0:head>Figure 4.</ns0:head><ns0:p>Step 2 of the workflow with details of fetching metadata from Crossref API. Sub-steps are applied sequentially.</ns0:p><ns0:p>Create Crossref API string Crossref limits a one time query to 1000 records for a single ISSN. For queries with more than 1000 records, multiple API strings are needed which are created automatically.</ns0:p><ns0:p>Crossref data of SCIM is fetched via Crossref API which contains total 1857 records. These records are fetched by two API requests to create JSON of SCIM.</ns0:p></ns0:div> <ns0:div><ns0:head>Fetch author(s) list from data</ns0:head><ns0:p>Once data are fetched from Crossref as JSON we populate the list of authors. We extract authors from the previous downloaded JSON. It is important to note that we do not apply any technique for author name disambiguation and rely on Crossref to provide correct author names.</ns0:p><ns0:p>Although this is problematic for further analysis, in the long run, corrected data from a single source is much efficient than using different methods of cleaning. A similar approach is provided by MAG <ns0:ref type='bibr' target='#b40'>(Wang et al., 2020)</ns0:ref>.</ns0:p><ns0:p>Fetch DOI list from data Once data are fetched from Crossref as JSON we populate the list of DOI.</ns0:p><ns0:p>DOIs are extracted from the previously downloaded JSON. Although the purpose of fetching DOI is redundant but it's replica script is created to suggest that analysis with only provided DOI list is also</ns0:p></ns0:div> <ns0:div><ns0:head>8/15</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:1:1:NEW 18 Nov 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>possible. So the previous two sub-steps can be ignored if analysing a specific journal is not needed. If the list of DOIs is fetched from an external source then it can be easily incorporated in this workflow.</ns0:p></ns0:div> <ns0:div><ns0:head>Creating ego network</ns0:head><ns0:p>Summary of the sub-steps to create ego network are shown in Figure <ns0:ref type='figure' target='#fig_4'>5</ns0:ref>. Below we define the sub-steps to create Ego Network. This step can be iterated zero or more times to grow the network as desired. This step is not used in the case study, however, we provide the details in this section to show that with publicly accessible metadata it is relatively easier to scale our approach. Further, this step justifies our approach of using SNAP over other network processing libraries since the process of creating the ego network is not only fast but intuitive to code due to a variety of functions available in the extensive library documentation that makes it easier to access the nodes in both directions of an edge. Also, the integer labels make the computation faster than using string labels. Crossref dump for egonet We provide the fetching of Crossref data for all DOIs of article ego network created in the previous step. This way first we download all data and then process it to create the network.</ns0:p><ns0:p>Depending on the size of the network and the number of ego levels, as well as connectivity bandwidth available this process can take from a few hours to days. Once a local copy of data is available this delay can be reduced. Since we do not have access to complete dump of Crossref we could not identify whether these same scripts can be reused but we assume that there would be few changes required to access the data locally.</ns0:p></ns0:div> <ns0:div><ns0:head>DOI and author list extraction</ns0:head><ns0:p>We provide the creation of the ego network for authors. This is similar to nodes of SCIM downloaded earlier. However, here we add the connecting nodes fetched in subgraph above and download their respective author details.</ns0:p></ns0:div> <ns0:div><ns0:head>Creating scientific network(s)</ns0:head><ns0:p>Summary of the sub-steps to create scientific networks are shown in Figure <ns0:ref type='figure'>6</ns0:ref>. Once all the data are pre-processed this step creates different types of network. We can also add bibliographic coupling and co-citation network within the list but they are ignored for two reasons. First, we did not find much evidence of centrality analysis on these networks. Secondly, the processing time for creating these networks for a very large citation network is relatively much longer than creating author collaboration or author citation network. These networks are simply created by making an edge list for authors who have collaborated or cited each other.</ns0:p></ns0:div> <ns0:div><ns0:head>9/15</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:1:1:NEW 18 Nov 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:note type='other'>Computer Science Figure 6.</ns0:note><ns0:p>Step 4 of the workflow with details of creating different scientific networks. Sub-steps are applied sequentially.</ns0:p></ns0:div> <ns0:div><ns0:head>Create article citation network</ns0:head><ns0:p>Once the list of DOI is available it is used to fetch subgraph of article citation network for these DOIs. We provide details of fetching article citation network as a subgraph from COCI. Further, it saves the same graph as a binary file for further analysis. Also, the CSV file can be used with any graph processing library (such as NetworkX) while binary file can be read using SNAP.</ns0:p><ns0:p>Create author collaboration network Author collaboration is identified via a list of co-authors from JSON data fetched via Crossref. This refined data are further used in the case study in the subsequent section. It is important to note that the count of authors at this sub-step may vary from next sub-step of creating author citation network since the list of co-authors in Crossref is provided as a list of names and we do not include further metadata about these authors.</ns0:p><ns0:p>Create author citation network Using the subgraph of article citation network respective edges are made for authors to create author citation network. All co-authors are linked to use full counting method.</ns0:p><ns0:p>In case method of partial counting is to be utilised then this script needs to be modified. However, our workflow is not affected by the use of a partial or full counting method and hence we have picked simpler one for brevity <ns0:ref type='bibr' target='#b8'>(Glanzel, 2003)</ns0:ref>. In any case, this network shall supplement the analysis on a collaboration network that was constructed in the previous step, as well as article citation network that was originally provided.</ns0:p></ns0:div> <ns0:div><ns0:head>Centrality analysis</ns0:head><ns0:p>Summary of the sub-steps to apply centrality analysis are shown in Figure <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>. Below we define the sub-steps to apply different centrality measures on the scientific networks. This is one of the common method employed in the bibliometric analysis, however other methods of SNA can also be applied at this step. Any tool or wrapper API may restrict the functionality at this point, however, this work can be extended to use any functions in existing network processing libraries. Since using graphical tools is easier than the script so a future application of this study could be about creating a front end tool for ease of use. Below we provide details about how the different centrality measures applied by different studies can be accomplished. Each of the measures is separated in the different listing along with loading and initialisation.</ns0:p></ns0:div> <ns0:div><ns0:head>Applying centrality measures on article citation network</ns0:head><ns0:p>The article citation network is a Directed Acyclic Graph (DAG). Most centrality analyses are not meaningful on DAG. Two measures are presented.</ns0:p><ns0:p>First, degree centrality provides highly cited articles. Finding authors of these articles is also possible, however not provided for simplicity. Secondly, influence definition in DAG is captured via the recursive definition of Katz centrality which is also provided using NetworkX library. Manuscript to be reviewed</ns0:p><ns0:p>Computer Science Step 5 of the workflow with details of centrality measures that are applied on different scientific networks. Sub-steps may be applied as required as there is no dependency within steps.</ns0:p><ns0:p>(eigenvector centrality) and authors working in multiple domains (betweenness centrality).</ns0:p></ns0:div> <ns0:div><ns0:head>Applying centrality measures on author collaboration network</ns0:head><ns0:p>The author collaboration network has cyclic nature and most centrality analyses are possible. Five measures are presented, namely highly collaborative authors (degree centrality), influential collaborators (eigenvector centrality), authors working in multiple groups (betweenness centrality), well-knitted authors (closeness centrality), and solo authors (farness centrality). Ranks captured here are presented in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>. This work was done manually by sorting individual lists on respective centrality scores and identifying their rank position.</ns0:p></ns0:div> <ns0:div><ns0:head>Batch execution</ns0:head><ns0:p>All python scripts can be executed through a sample batch file by modifying the ISSN and date range. This batch processing will also be useful for developing a front-end tool, as well as modifying the sequence as per user need.</ns0:p><ns0:p>CASE STUDY USING SCIM <ns0:ref type='bibr' target='#b23'>Milojevi&#263; (2014)</ns0:ref> analysed collaboration network using WoS data of SCIM for 10 years starting from the year 2003. The outcome of their analysis was provided in a table having authors that had top 5 ranks in either of the centrality scores. The respective rank of those authors was also provided. To verify whether or not our workflow can capture a similar pattern we provide the results in a similar tabular form.</ns0:p><ns0:p>For each of the centrality measure we provide the rank given in <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014)</ns0:ref> using WoS data, as well as compare it with the rank obtained in our study using OpenCitatons data. We observe that the rank of authors for the degree, betweenness and closeness centrality is more or less similar, however, further analysis is required to inquire the reason for the difference of eigenvector centrality ranks. Such an analysis is outside the scope of this study.</ns0:p><ns0:p>Ranks in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref> are entered manually after processing the information separately. Author names are sorted in the same sequence as provided in the original study along with their respective ranks. Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref> has four sections for the degree, betweenness, eigenvector and closeness centrality, respectively. Each section has two columns with the left column showing rank from <ns0:ref type='bibr' target='#b23'>Milojevi&#263; (2014)</ns0:ref> and the right column shows the rank calculated for the same author using our workflow. It is pertinent to note that a very hand-on approach is provided by <ns0:ref type='bibr' target='#b23'>Milojevi&#263; (2014)</ns0:ref>, however, due to access restriction of WoS and its</ns0:p></ns0:div> <ns0:div><ns0:head>11/15</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:1:1:NEW 18 Nov 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science unaffordability for developing countries, such useful analysis are only limited to researchers of specific institutes having subscription <ns0:ref type='bibr' target='#b38'>(Waltman and Larivi&#232;re, 2020)</ns0:ref>.</ns0:p><ns0:p>This highlights the importance of our workflow to provide access to any individual who can download the publicly available metadata. Further, we do not discuss the reasons for why a specific author has topped the list and what the centrality measure signifies, and the interested reader is referred to <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014)</ns0:ref>. However, we intend to provide a detailed analysis in a separate publication using ego networks. <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014)</ns0:ref> and current study for each centrality measure. Table is divided into 4 sections for each centrality measure with the left column in each section showing the rank from <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014)</ns0:ref>, and the right column showing the rank calculated by our workflow.</ns0:p></ns0:div> <ns0:div><ns0:head>DISCUSSION</ns0:head><ns0:p>Based on numerous studies discussed in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref> it is evident that centrality measures are a popular way of identifying prominent authors. The results of a case study also show that accessing metadata of publishers which submit metadata to Crossref as public access (such as Springer) does not hamper analysis. However, the same would not be true for publishers whose metadata are not yet public although available with Crossref (such as Elsevier).</ns0:p><ns0:p>Scientific networks rely on completion of data, and although the field has existed for more than 50 years <ns0:ref type='bibr' target='#b32'>(Price, 1965)</ns0:ref>, however, the limitations on data access have not helped to reach its true potential.</ns0:p><ns0:p>We aim that with the availability of publicly available metadata <ns0:ref type='bibr' target='#b38'>(Waltman and Larivi&#232;re, 2020</ns0:ref>) and a workflow to access it, such as the one presented in this study, a researcher from any field will be able to analyse the prominent authors. It can further be used for identifying reviewers for a potential study (based on its references), as well as a graduate student finding a PhD supervisor.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSION AND FUTURE WORK</ns0:head><ns0:p>Once the citation network is fetched and saved as a binary file the time it takes to analyse authors list in a journal is well under an hour, barring the time to create ego network as it requires downloading Crossref files for each DOI. This provides a means for fast and interactive analysis for researchers of any field. This study currently does not provide a detailed analysis of the ego network, however, a brief comparison justifies the importance of systematic metadata harvesting workflow. For case study, some manual work was also done to sort and format the results, however, it can also be scripted in future as it Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>does not hamper the workflow and can be performed as a standalone. Likewise, techniques for author name disambiguation or partial counting have not been included but for effective analysis, these need to be incorporated in future.</ns0:p><ns0:p>We further aim to enhance this work to filter Crossref data based on subject categories instead of journal ISSN. It would enhance the capability and usefulness of this analysis for individual researchers. A web-based portal is also under construction where the user may be able to select the date range along with other filters and the system which initiates the scripts at the back-end. This way the users who are not familiar with programming can also benefit from this analysis.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. A toy network showing different nodes with high centrality for different measures. A. shows high farness centrality since the node (a) has the maximum average distance to other nodes. B. shows high clustering coefficient since neighbours of the node (c) are all connected as well.C. shows high betweenness centrality since the highest number of shortest paths will go from the node (i) and (k) since they are bridging two parts of the network. D. shows high degree centrality as both the nodes (e) and (n) have 5 neighbours. E. shows high eigenvector centrality since node (e) is connected to many neighbours with a relatively higher degree. F. shows high closeness centrality as the average distance from nodes (i), (j) and (k) are minimum to other nodes.</ns0:figDesc><ns0:graphic coords='3,141.73,172.49,413.57,232.28' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc><ns0:ref type='bibr' target='#b37'>Waheed et al. (2019)</ns0:ref> discusses the use of centrality measures on multiple scientific networks of author collaboration, author citation and article citation to improve article recommendation. They filter the 4/15 PeerJ Comput. Sci. reviewing PDF | (CS-</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Workflow to identify gurus of any Field. A pyramid shows the refinement of data at every step. COCI contains approx. 625 M edges which are refined to ego network for subset nodes fetched for respective ISSN. Finally, the top of the pyramid shows the output in form of a few nodes identified with high centrality.</ns0:figDesc><ns0:graphic coords='8,141.73,63.78,413.59,175.53' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. Step 1 of the workflow with details of creating the citation network. Sub-steps are applied sequentially.</ns0:figDesc><ns0:graphic coords='8,141.73,379.10,413.59,157.50' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5.Step 3 of the workflow with details of creating the ego network. Sub-steps are applied sequentially, and may be iterated over to create next level of ego network.</ns0:figDesc><ns0:graphic coords='10,141.73,216.53,413.59,153.36' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>Applying centrality measures on author citation networkThe author citation network has cyclic nature. Three measures are presented, namely highly cited authors (degree centrality), influential authors 10/15 PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:1:1:NEW 18 Nov 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7.Step 5 of the workflow with details of centrality measures that are applied on different scientific networks. Sub-steps may be applied as required as there is no dependency within steps.</ns0:figDesc><ns0:graphic coords='12,141.73,63.78,413.59,249.31' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF | (CS-2020:08:52217:1:1:NEW 18 Nov 2020)</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='9,141.73,355.50,413.59,150.71' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Review of studies applying social network analysis on scientific networks.</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell>Bibliometric</ns0:cell><ns0:cell>Scientific Network(s)</ns0:cell><ns0:cell>Social Network Analysis</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Data Source</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Ding (2011)</ns0:cell><ns0:cell>WoS</ns0:cell><ns0:cell>Author Citation</ns0:cell><ns0:cell>Weighted PageRank</ns0:cell></ns0:row><ns0:row><ns0:cell>Abbasi et al.</ns0:cell><ns0:cell>Scopus</ns0:cell><ns0:cell>Author Collaboration</ns0:cell><ns0:cell>Degree, Betweenness, Close-</ns0:cell></ns0:row><ns0:row><ns0:cell>(2012)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>ness Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>Ortega (2014)</ns0:cell><ns0:cell>MAG</ns0:cell><ns0:cell>Co-Author Ego Network</ns0:cell><ns0:cell>Clustering Coefficient, Degree</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>and Betweenness Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>Milojevi&#263;</ns0:cell><ns0:cell>WoS</ns0:cell><ns0:cell>Author Collaboration and Cita-</ns0:cell><ns0:cell>Degree, Betweenness, Close-</ns0:cell></ns0:row><ns0:row><ns0:cell>(2014)</ns0:cell><ns0:cell /><ns0:cell>tion, Article Citation</ns0:cell><ns0:cell>ness, Eigenvector Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>Waltman and</ns0:cell><ns0:cell>WoS</ns0:cell><ns0:cell>Journal Citation Network</ns0:cell><ns0:cell>PageRank</ns0:cell></ns0:row><ns0:row><ns0:cell>Yan (2014)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Xu and Peke-</ns0:cell><ns0:cell>Manual</ns0:cell><ns0:cell>Author Citation</ns0:cell><ns0:cell>PageRank and Degree Central-</ns0:cell></ns0:row><ns0:row><ns0:cell>lis (2015)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>ity</ns0:cell></ns0:row><ns0:row><ns0:cell>Leydesdorff</ns0:cell><ns0:cell>WoS/JCR</ns0:cell><ns0:cell>Journal Citation</ns0:cell><ns0:cell>Betweenness Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>et al. (2018)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Lee (2019)</ns0:cell><ns0:cell>Scopus</ns0:cell><ns0:cell>Author Collaboration</ns0:cell><ns0:cell>Degree and Betweenness Cen-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>trality, Clustering Coefficient</ns0:cell></ns0:row><ns0:row><ns0:cell>Massucci and</ns0:cell><ns0:cell>WoS</ns0:cell><ns0:cell>Institutional Citation</ns0:cell><ns0:cell>PageRank</ns0:cell></ns0:row><ns0:row><ns0:cell>Docampo</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>(2019)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Singh and Jo-</ns0:cell><ns0:cell>APS Journals</ns0:cell><ns0:cell>Author Collaboration</ns0:cell><ns0:cell>Centrality, Community Detec-</ns0:cell></ns0:row><ns0:row><ns0:cell>lad (2019)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>tion</ns0:cell></ns0:row><ns0:row><ns0:cell>Van den Besse-</ns0:cell><ns0:cell>Manual</ns0:cell><ns0:cell>Researchers Ego Network</ns0:cell><ns0:cell>Clustering coefficient, eigenvec-</ns0:cell></ns0:row><ns0:row><ns0:cell>laar and Sand-</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>tor Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>str&#246;m (2019)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Waheed et al.</ns0:cell><ns0:cell>DBLP, ACM,</ns0:cell><ns0:cell>Author Collaborationand Cita-</ns0:cell><ns0:cell>Degree, Betweenness, Close-</ns0:cell></ns0:row><ns0:row><ns0:cell>(2019)</ns0:cell><ns0:cell>MAG</ns0:cell><ns0:cell>tionArticle Citation, Co-citation</ns0:cell><ns0:cell>ness, Eigenvector Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>and Bibliographic Coupling</ns0:cell><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Comparison of ranks by previous study</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Collaborator</ns0:cell><ns0:cell cols='8'>Degree Rank Betweenness Rank Eigenvector Rank Closeness Rank</ns0:cell></ns0:row><ns0:row><ns0:cell>Name</ns0:cell><ns0:cell cols='3'>Prev Curr Prev</ns0:cell><ns0:cell>Curr</ns0:cell><ns0:cell>Prev</ns0:cell><ns0:cell>Curr</ns0:cell><ns0:cell>Prev</ns0:cell><ns0:cell>Curr</ns0:cell></ns0:row><ns0:row><ns0:cell>Glanzel, W</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell></ns0:row><ns0:row><ns0:cell>Rousseau, R</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>2</ns0:cell></ns0:row><ns0:row><ns0:cell>DeMoya-Anegon, F</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>8</ns0:cell><ns0:cell>12</ns0:cell><ns0:cell>20</ns0:cell><ns0:cell>26</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell>15</ns0:cell></ns0:row><ns0:row><ns0:cell>Klingsporn, B</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>21</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>89</ns0:cell><ns0:cell>121</ns0:cell><ns0:cell>174</ns0:cell><ns0:cell>144</ns0:cell></ns0:row><ns0:row><ns0:cell>Ho, Ys</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>22</ns0:cell><ns0:cell>125</ns0:cell><ns0:cell>2096</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>613</ns0:cell><ns0:cell>575</ns0:cell></ns0:row><ns0:row><ns0:cell>Thijs, B</ns0:cell><ns0:cell>63</ns0:cell><ns0:cell>51</ns0:cell><ns0:cell>65</ns0:cell><ns0:cell>44</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>10</ns0:cell><ns0:cell>1710</ns0:cell></ns0:row><ns0:row><ns0:cell>Schubert,A</ns0:cell><ns0:cell>36</ns0:cell><ns0:cell>48</ns0:cell><ns0:cell>38</ns0:cell><ns0:cell>28</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>18</ns0:cell><ns0:cell>27</ns0:cell><ns0:cell>24</ns0:cell></ns0:row><ns0:row><ns0:cell>Debackere, K</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>15</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>5</ns0:cell></ns0:row><ns0:row><ns0:cell>Schlemmer, B</ns0:cell><ns0:cell>670</ns0:cell><ns0:cell>832</ns0:cell><ns0:cell>382</ns0:cell><ns0:cell>962</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>808</ns0:cell><ns0:cell>33</ns0:cell><ns0:cell>37</ns0:cell></ns0:row><ns0:row><ns0:cell>Meyer, M</ns0:cell><ns0:cell>43</ns0:cell><ns0:cell>39</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>14</ns0:cell><ns0:cell>9</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>3</ns0:cell></ns0:row><ns0:row><ns0:cell>Leydesdorff, L</ns0:cell><ns0:cell>54</ns0:cell><ns0:cell>35</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>46</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>42</ns0:cell><ns0:cell>44</ns0:cell></ns0:row><ns0:row><ns0:cell>Rafols,I</ns0:cell><ns0:cell>1058</ns0:cell><ns0:cell>387</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>23</ns0:cell><ns0:cell>83</ns0:cell><ns0:cell>239</ns0:cell><ns0:cell>45</ns0:cell><ns0:cell>49</ns0:cell></ns0:row></ns0:table></ns0:figure> </ns0:body> "

"Editor, PeerJ CS 18 Nov 2020 We would like to thank the editor and all reviewers for their time and effort for detailed comments. We have made appropriate changes to address the concerns. In summary, code and workflow documentation is made available via GitHub which is the primary focus of the manuscript. The analysis presented has been reshaped as a case study. We believe that the manuscript will now be as per the expectation of esteemed reviewers. Thanks, Bilal, Rafi and Sabih Reviewer 1 Basic reporting The basic reporting is good, the very fact that the authors provide Python scripts shall be commended. The English is more or less OK, but the use or misuse of the capital letters is surprising. I show only a few errors: 'apply Network Analysis using open citation' 'Eigen centrality'?? You do not mean here Manfred Eigen, don't you? 'python script' >> Response: Agreed. Capitalization is corrected Some awkward constructions 'use case' ​>> Response: The term “use case” is replaced with “case study” on line 137 ' instead of a software tool, we propose to use python libraries' But Python libraries ARE software tools ​>> Response: corrected to mean graphical tool on line 169 ' identification of highly cited clusters of scientific clusters' ​>> Response: correction made as scientific networks instead of scientific clusters on line 163 Experimental design The research goal is clear, the methods are desrcibed with sufficient details ​>> Response: Appreciated Validity of the findings Dubious. Allthough the technique is valid, the validation protocol is problematic. ​>> Response: As per overall comments, the comparative analysis is replaced as a case study. Comments for the author In this work B.H. Butt, M. Rafi, and M. Sabih demonstrate a software tool to download data from the Open Citation database and to process it. They demonstarte an example of such processing- measuring various kinds of centrality of the authors of a certain journalScientometrics. While the protocol of downloading the whole Open Citation database shall be commended, the scientific benefits that the authors draw from the processing of this database are not clear enough. Hence, I do not suggest to publish this paper in its present form. However, , if the authors report only their downloading protocol and do not report data processing, such abridged paper is publishable. >> Response: As per overall comments, the comparative analysis is replaced as a case study. Detailed comments. The opening sentence of the paper 'Identifying prominent authors (Gurus) of any field is one of the primary focus for researchers in that particular field' is blatantly wrong. A serious researcher working in some field shall know all prominent authors in his field without citation analysis of the corresponding databases. To identify prominent authors through citation databases- this is usually made by beginners or by the researchers in adjacent fields. >> Response: corrected for young researchers on line 27 Some of the statements of the paper look like typos: 'Eigen centrality'- as if the author thinks that Eigen is a person. >> Response: correction made as eigenvector centrality in manuscript 'APS The American Phytopathological Society'- This is a very naive error. The abbreviation of the American Phytopathological Society is indeed APS, but in the field of informatics, APS means the American Physical Society and this is what the Ref. Singh and Jolad deals with. ​>> Response: correction made as American Physical Society ​www.aps.org​ on line 88 'Data Source, primarily, is WoS or Scopus'. Two of the authors come from the Department of Computer Science, why they are not familiar with CiteSeer? >> Response: Data sources mentioned were related to scientific networks for which CiteSeer were not found in related articles. Details about some popular data sources not used are also mentioned in the revised manuscript on lines 111 & 112. Using their downloaded database, the authors choose to analyze the authors of the Scientometrics Journal. Thie goal is to identify the most prominent authors. However, one can't make such analysis basing on one journal- one needs to analyze the whole scientific field. There areseveral journals in this field: Scientometrics, Journal of Informetrics, Quantitative Science Studies, Journal of the American Society for Information Science and Technology. Prominent scientists in the the field of information science publish in these journals but due to rivalry between the journals, there is a certain association between some scientists and some journals. Moreover, european scientists tend to publish in the european journals. Hence, by analyzing citation network of one journal, it is impossible to find all prominent figures in the fieldone shall analyze all journals atogether. ​>> Response: Agreed. Focus of revised manuscript is workflow so it is not incorporated. However, with the provided scripts it will be possible to run it for multiple ISSN inorder to apply the analysis on a set of journals. Reviewer 2 Basic reporting The authors go in great detail to explain what they have done, yet the article is lacking on several points. The language is not always used appropriately, several figures are low-resolution, and most definitions are given without reference to the related papers nor formally (e.g., for centralities). ​>> Response: Language is improved, image resolution enhanced and reference is added for the definitions of centrality analysis (Newman, 2010) on line 38. Experimental design The research questions are well-defined, yet unfortunately they do not focus on novel contributions. >> Response: The RQs are rephrased as per overall comments on lines 98-99. RQ1 explores whether network centrality measures can be used to detect popular authors, or 'gurus', which is something that has been extensively explored in previous work. >> Response: Our aim in providing it as an RQ was to emphasize its importance, however, RQ has been rephrased to appropriately represent the contribution of the workflow on lines 98-99. RQ2 is, instead, more novel in that it attempt to use an open citation index, COCI, and compare its results with WoS. Yet, this study has also been recently performed (in much greater detail): https://arxiv.org/abs/2004.14329 >> Response: As per overall comments, the comparative analysis is replaced as a case study. The reference provided by the esteemed reviewer was published around 3 weeks after the submission of this manuscript, we have cited it appropriately. It is similar to our work in comparing the data source but the focus is on overall coverage rather than the limitations that come with a lack of coverage of open access metadata. Validity of the findings I do not have much to add in terms of findings, as the main issue with the paper is in its lack of novelty. I would, nevertheless, suggest to the authors to share their code (per se, a great thing to do) using online persistent repositories such as Zenodo. >> Response: code is being shared via GitHub Reviewer: Ludo Waltman Basic reporting See my general comments. Experimental design See my general comments. Validity of the findings See my general comments. Comments for the author The contribution of this paper is in providing a set of Python scripts for performing scientometric network analyses in a reproducible manner based on open data sources. The paper does not aim to make a substantive contribution by providing new scientometric insights. I value the work presented in the paper. However, the authors need to be more clear about the contribution and the scope of their paper. For instance, I believe the following sentence needs to be removed from the abstract: “We have shown that centrality analysis is a useful measure for identifying prominent authors.” The paper does not show this. Likewise, the authors claim to answer the following research question: “Is it possible to identify prominent authors (Gurus) of any field, by applying Centrality measures on Scientific Networks?” I don’t believe the paper answers this question (except by summarizing some earlier literature, but this is not an original contribution). It therefore seems to me that this research question needs to be removed from the paper. The discussion section at the end of the paper also needs to be revised accordingly. >> Response: Suggestions have been incorporated. Our aim in providing it as an RQ was to emphasize its importance, however, RQ has been rephrased to appropriately represent the contribution of the workflow on lines 98-99. “Does the coverage of CrossRef (for Open Access publishers), hamper the Network Analysis as compared to WoS, or it can be replicated?”: This research question needs to be rephrased. Crossref provides data not only for open access publishers but also for subscription-based publishers, and for both types of publishers most data is openly available. Also, since the empirical analysis presented in the paper focuses on a single journal (Scientometrics), the paper offers only a partial answer to the question whether WoS-based analyses can be replicated using Crossref data. >> Response: RQ is now removed. The comparative analysis section is hard to understand. According to the authors, Milojevic (2014) “have fetched the data at least 5 years earlier than us, therefore, the total citation count is different”. This is difficult to understand. It is not clear to me how exactly the authors collected their data, and in particular for which time period data was collected. I don’t understand why the authors didn’t organize their data collection in such a way that it is as similar as possible to the data collection performed by Milojevic (2014). Having data sets that are as similar as possible is essential for a meaningful comparative analysis. If there are basic differences in the time periods covered by two data sets, I don’t see the value of performing a comparative analysis. >> Response: Citations accrued after data collection by M ​ ilojevic (2014) analysis were also part of our analysis, as we did not apply additional filtering to keep the workflow targeted. Confusing sentences have been removed for clarity. Ego networks play a central role in the paper, but the paper doesn’t provide a proper explanation of what an ego network is. A one-sentence explanation is provided on p. 2, but this is not sufficient. The authors should provide a more extensive discussion of ego networks and their relevance in scientometric analyses. >> Response: Incorporated with toy network explanation in Figure 1 on lines 50 to 54. Also, while most of the paper is about analyzing ego networks, these networks are not considered at all in the empirical part of the paper. It would be very helpful if the authors could add a section to their paper in which they give a practical example of an analysis of an ego network. >> Response: Ego networks are normally restricted to a single ego node, however, the provided scripts generate and merge ego networks for all nodes. This creates a network similar to existing networks with rich details. A similar type of analysis is possible for ego networks, hence additional details were not provided. We have modified the text to reflect this understanding. Following table is from an upcoming publication that intends to explore the ego network in more detail. ​Nodes at second level refer to the articles (or authors) that are referenced by the original article(s) or which cite the original article(s). This provides a holistic view of the field instead of limited data of only articles within a specific journal. Network Type # Nodes at first level # Edges at first level # Nodes at second level # Edges at second level Second level Edge List Article Citation 1,284 2,312 166,023 274,866 Fetched 13 MB Author Collaboration 2,446 4,176 250,000 400,000 Estimated 30 MB Author Citation 1,985 14,250 200,000 1,000,000 Estimated 50 MB Table 1: Dataset Details “the results of a case study based on WoS data is reproduced to confirm that accessing metadata of publishers, which submit metadata to CrossRef as Open Access (such as Springer) does not hamper analysis as compared to WoS. However, the same would not be true for Publishers whose data is not yet available with CrossRef (such as Elsevier).”: The information in these sentences is not entirely correct. Both Springer and Elsevier make basic metadata such as titles and author lists of publications openly available in Crossref. They also both make reference lists of publications available in Crossref. The only difference between the two publishers is that Springer makes reference lists openly available, while Elsevier keeps reference lists closed. >> Response: Thank you for the clarification. We have modified the text to reflect this understanding on lines 378 to 380. Most readers won’t understand the discussion on partial vs. full counting on p. 9. This requires some additional explanation. >> Response: appropriate reference is provided for interested readers on line 322. Throughout the paper ‘CrossRef’ should be written as ‘Crossref’. >> Response: Incorporated in overall manuscript. The authors may be interested in a special issue on bibliographic data sources published in the first issue of Quantitative Science Studies:​ ​https://www.mitpressjournals.org/toc/qss/1/1​. This special issue offers a lot of information on data sources such as WoS, Crossref, and OpenCitations. ​>> Response: This special issue is indeed very helpful and has been included in the bibliography on lines 81, 82, 83, 85, 89, 107, 176, 194, 270, 370 and 383. Editor's Decision Major Revisions Dear authors, Thanks for submitting your work at PeerJ Computer Science. Three independent experts have assessed your work, and you can find their reviews attached. There are several points of interest in your work, which are counterbalanced by significative issues. All agree that the article submitted is not acceptable for publication in the present form, and needs extensive rewriting before being ready for publication. The main argument is about its main contribution. According to what you said, the contribution is twofold. On the one hand, to provide a workflow for retrieving open citation data and open bibliographic metadata for bibliometric/scientometric studies. On the other hand, you run an analysis using the data retrieved. All the reviewers agree that the second part of the contribution, i.e. the analysis, should not be the focus of the work due to several flaws. However, all of them have praised the first part of the contribution, i.e. the workflow to run to download data. Thus, my suggestion is to remove the part about the analysis and to solely focus on the workflow for downloading and processing the data. >> Response: Analysis part has been reshaped as a case study Of course, there are issues that should also be addressed in the workflow part. Several of them are highlighted by the reviewers. In addition to those, I would add that making scripts available is not enough for claiming about the replicability of the workflow the scripts implement, but other resources must be made available as well. The whole workflow should be carefully described in the paper, with examples of use, a discussion of possible applications, some measures with respect to the quality of the networks created (see the issues about the author-based network below), etc. I would also suggest the following additions: - availability: all the code should be available in an online repository (e.g. GitHub) >> Response: Incorporated via GitHub - reusability: all the code should be released with appropriate open source licence to be reused by anyone >> Response: Incorporated on GitHub - workflow documentation: the repository must include appropriate documentation to enable a programmer to understand which data he/she needs and how to run all the Python scripts better if they are defined as a proper protocol (e.g. see protocols.io) and cited in the article >> Response: Incorporated on GitHub - code documentation: all the code developed should be appropriately documented in order to understand how to use the various Python scripts and functions >> Response: Incorporated on GitHub - citation: the code developed should be citable, and thus it should be referred by using a DOI (e.g. via GitHub+Zenodo) - general principles: since the article describes a software, software citation principles (https://peerj.com/articles/cs-86/) should be followed >> Response: Python scripts are uploaded on GitHub and is cited on line 222 In addition to that, there is an aspect of the workflow, which is not clear to me, and that concerns the author collaboration and citation networks. As far as I know, Crossref does not have a systematic way to disambiguate authors by using some identifiers. Indeed, Crossref has some ORCIDs specified in some entities, but the main part of them do not have any. Thus the problem is that, in the creation of the network that involves authors: - how do you deal with the disambiguation of the authors? - are two authors with the same name recognised as the same author? - are two authors having the same family name and the same initial of the given name recognised as the same author? - how you deal with homonymous authors? All these issues should be taken into account when building such author-based networks in order to avoid mistakes. >> Response: Author name disambiguation is not the focus of this work and hence as mentioned in original manuscript Crossref provided name strings were used. We had added details about this in the original manuscript so that the user of our scripts is aware of the limitations. Also, I strongly suggest to properly check the text, since the reviewers have highlighted several issues in using English to this respect. >> Response: Changes have been made as pointed out by reviewers Some typos: - Open Citation data -> OpenCitations data ​>> Response: Incorporated "

Here is a paper. Please give your review comments after reading it.

"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>One of the scientific disciplines behind the Science of Science studies is the study of scientific networks. We are concerned with scientific networks as a social network with connections representing either co-authorship (collaboration) or citations. Different measures of network analysis can be applied to these networks such as centrality (to find influential authors) or clustering coefficient (to show the connectedness of a group of scientists). Any study on co-authorship may not need all the citation links, however, for a holistic view citation links may also be needed. One of the major limitations of the earlier studies has been the lack of completeness of the data set. This limits the analyses of different types on the same set of nodes. To overcome this, we have worked on publicly available citation metadata to develop a workflow to create different types of scientific networks. Our focus is primarily on the identification of prominent authors through centrality analysis, as well as whether this can be achieved using open metadata. To present our approach, we have analysed Scientometrics journal as a case study. We are not concerned with bibliometrics study of any field rather we aim to provide a replicable workflow (in form of Python scripts) to apply network analysis using OpenCitatons data.</ns0:p><ns0:p>With the increasing popularity of open access and open metadata, we hypothesise that this workflow shall provide an avenue for understanding science in multiple dimensions.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Identifying prominent authors (gurus) of any field is one of the primary focus for young researchers in that particular field. Likewise, other researchers tend to follow research published by gurus of the field. To achieve this objective, using network analysis, comprehensive access to citation metadata is required. This can be accomplished using publicly available citation metadata using Crossref <ns0:ref type='bibr' target='#b12'>(Hendricks et al., 2020)</ns0:ref>. However, applying network analysis on this data requires a series of steps that may not be intuitive to a common researcher. We aim to provide these steps, with thorough details, so that it is easy for a common researcher to supplement it with different analyses. The workflow presented in this article is part of a larger study on the influence of scholarly research artefacts. To this end, we primarily limit our focus on the research goal to have a systematic workflow to identify prominent authors (gurus) using publicly available metadata for citations. In this work, we aim to utilise open metadata <ns0:ref type='bibr' target='#b30'>(Peroni et al., 2015)</ns0:ref>, made available using Crossref, and utilise open access NetworkX <ns0:ref type='bibr' target='#b9'>(Hagberg et al., 2008)</ns0:ref> and SNAP <ns0:ref type='bibr' target='#b17'>(Leskovec and Sosi&#269;, 2016)</ns0:ref> libraries of Python for network analysis. Python is used based on its popularity with researchers as per survey results by <ns0:ref type='bibr' target='#b1'>AlNoamany and Borghi (2018)</ns0:ref>. This article provides minimal details of a case study for analysing collaboration network of Scientometrics journal metadata, for 10 years starting from 2003. All steps are described for replication of this study. This work shall lay the groundwork for further analyses of similar type on different journals, set of journals or a subject category using open metadata.</ns0:p><ns0:p>Defining a guru of the field is not an easy task, and any definition will be highly subjective. To this end, we focus on the definition of guru using the centrality measures of social network analysis. Details PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:2:0:NEW 1 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed Computer Science of different centrality measures are depicted in Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref> <ns0:ref type='bibr' target='#b25'>(Newman, 2010)</ns0:ref>. The following description was inspired by <ns0:ref type='bibr' target='#b22'>(Milojevi&#263;, 2014)</ns0:ref>. Simply said, any author with a high citation count may be considered the guru. This can be achieved using degree centrality. Although another way of identifying a highly cited individual is to see whose paper is cited in top percentile within the domain we currently limit such definitions to degree centrality of articles. However, it is not always the case that all highly cited authors are equally influential. Those who are cited by other influential authors may also be termed as influential even though they may or may not have high citation count. Likewise, any author collaborating frequently with influential authors would also have some high influence in that field of study. This recursive influence definition is well captured by eigenvector centrality. Another centrality measure, namely betweenness centrality would define an author as prominent in the field if the author is a collaborator with individuals of different clusters within the domain. Centrality measures of closeness and farness measure the extent to which an author is on average close to or far from other authors within the network, respectively.</ns0:p><ns0:p>Such analyses can be applied on a variety of scientific networks such as article citation network, author citation network or author collaboration network. These networks can be created using different data sources. Some data sources (such as Crossref) allows to fetch the metadata of articles cited by the article or that cited the original article. This allows expanding the breadth of the network. In Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref> C. shows high betweenness centrality since the highest number of shortest paths will go from the node (i) and (k) since they are bridging two parts of the network. D. shows high degree centrality as both the nodes (e) and (n) have 5 neighbours. E. shows high eigenvector centrality since node (e) is connected to many neighbours with a relatively higher degree. F. shows high closeness centrality as the average distance from nodes (i), (j) and (k) are minimum to other nodes.</ns0:p></ns0:div> <ns0:div><ns0:head>RELATED WORK</ns0:head><ns0:p>Visualising bibliometric data as a network is not new, <ns0:ref type='bibr' target='#b32'>Price (1965)</ns0:ref> introduced the work more than 50 years ago. Most recent studies are on co-authorship network <ns0:ref type='bibr' target='#b0'>(Abbasi et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b22'>Milojevi&#263;, 2014;</ns0:ref><ns0:ref type='bibr' target='#b16'>Lee, 2019;</ns0:ref><ns0:ref type='bibr' target='#b34'>Singh and Jolad, 2019;</ns0:ref><ns0:ref type='bibr' target='#b37'>Waheed et al., 2019)</ns0:ref>, however others have focused on citation network for authors <ns0:ref type='bibr' target='#b7'>(Ding, 2011;</ns0:ref><ns0:ref type='bibr' target='#b22'>Milojevi&#263;, 2014;</ns0:ref><ns0:ref type='bibr' target='#b41'>Xu and Pekelis, 2015;</ns0:ref><ns0:ref type='bibr' target='#b37'>Waheed et al., 2019)</ns0:ref> or journal <ns0:ref type='bibr' target='#b39'>(Waltman and Yan, 2014;</ns0:ref><ns0:ref type='bibr' target='#b18'>Leydesdorff et al., 2018)</ns0:ref>. Only a couple of studies have utilised more than one Scientific Network for analysis <ns0:ref type='bibr' target='#b22'>(Milojevi&#263;, 2014;</ns0:ref><ns0:ref type='bibr' target='#b37'>Waheed et al., 2019)</ns0:ref>. Traditionally bibliometric analysis has been done using WoS and Scopus <ns0:ref type='bibr' target='#b38'>(Waltman and Larivi&#232;re, 2020)</ns0:ref>, and a similar case is seen in these studies where the data sources, primarily are WoS <ns0:ref type='bibr' target='#b7'>(Ding, 2011;</ns0:ref><ns0:ref type='bibr' target='#b22'>Milojevi&#263;, 2014;</ns0:ref><ns0:ref type='bibr' target='#b39'>Waltman and Yan, 2014;</ns0:ref><ns0:ref type='bibr' target='#b18'>Leydesdorff et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b21'>Massucci and Docampo, 2019)</ns0:ref> or Scopus <ns0:ref type='bibr' target='#b0'>(Abbasi et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b16'>Lee, 2019)</ns0:ref>, however, some recent studies have focused on open access data sources <ns0:ref type='bibr' target='#b34'>(Singh and Jolad, 2019;</ns0:ref><ns0:ref type='bibr' target='#b35'>Van den Besselaar and Sandstr&#246;m, 2019;</ns0:ref><ns0:ref type='bibr' target='#b37'>Waheed et al., 2019)</ns0:ref>. Other data sources such as PubMed, CiteSeerX and ACL are not discussed in this article as they are mostly used for text analysis instead of network analysis. Below we provide a brief account of work done on scientific networks using centrality measures in the past decade.</ns0:p><ns0:p>Details are summarized in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref> in chronological order. Some earlier studies such as <ns0:ref type='bibr' target='#b26'>(Newman, 2004)</ns0:ref> are not discussed here to only include recent studies. <ns0:ref type='bibr' target='#b7'>Ding (2011)</ns0:ref> proposed to analyse the author citation network with weighted PageRank. The author showed that their proposed strategy outperforms the conventional h-index and related citation count measures on predicting prize winners. <ns0:ref type='bibr' target='#b0'>Abbasi et al. (2012)</ns0:ref> discussed the use of betweenness centrality as a measure of getting more collaborators compared to degree and closeness centrality. They have used temporal co-authorship network in the steel research domain. Data was manually curated and downloaded from Scopus. <ns0:ref type='bibr' target='#b29'>Ortega (2014)</ns0:ref> analysed 500 co-authors' ego network and conclude that centrality measures are correlated with bibliometric indicators. They have used clustering coefficient, degree and betweenness centrality as local metrics while some global level metrics were also analysed due to a holistic view of ego network. It is one of the early studies using MAG.</ns0:p><ns0:p>Two book chapters provide hands-on details about centrality measures <ns0:ref type='bibr' target='#b22'>(Milojevi&#263;, 2014)</ns0:ref> and PageRank <ns0:ref type='bibr' target='#b39'>(Waltman and Yan, 2014)</ns0:ref> using WoS data. <ns0:ref type='bibr' target='#b22'>Milojevi&#263; (2014)</ns0:ref> constructed the author collaboration network and calculated degree, betweenness, eigenvector and closeness centrality. <ns0:ref type='bibr' target='#b39'>Waltman and Yan (2014)</ns0:ref> provides details for applying PageRank on journal citation network. <ns0:ref type='bibr' target='#b41'>Xu and Pekelis (2015)</ns0:ref> used a manually curated dataset for authors of China and Taiwan in the field of Chinese Language Interpreting Studies. They have applied PageRank and degree centrality to find influential authors within different clusters identified using community detection. <ns0:ref type='bibr' target='#b18'>Leydesdorff et al. (2018)</ns0:ref> have used betweenness centrality as a measure of multidisciplinary of a journal using a journal citation network. Any journal is usually cited from its subject category but the journals cited/citing the other fields are considered a bridge between the subject categories. Authors have limited their approach with a diversity measure and evaluated it on data from JCR. <ns0:ref type='bibr' target='#b16'>Lee (2019)</ns0:ref> provide a case study for young researchers performance evaluation by analysing the collaboration network of these researchers. Using statistical analysis frequency of collaborators measured by degree centrality is shown to correspond with future publication count. This is akin to <ns0:ref type='bibr' target='#b19'>Li et al. (2019)</ns0:ref> who concludes that collaboration of young scientist with top-ranked co-authors has a huge probability of future success. <ns0:ref type='bibr' target='#b21'>Massucci and Docampo (2019)</ns0:ref> applies the PageRank algorithm on a university citation network.</ns0:p><ns0:p>Working on five different subject categories they show that their framework is more robust than existing university rankings while holding a high correlation with these accepted rankings. <ns0:ref type='bibr' target='#b34'>Singh and Jolad (2019)</ns0:ref> utilised data of APS journals to form collaboration network of Indian physicist. In this co-authorship network, they have applied different centrality measures and report the overlapping top authors. <ns0:ref type='bibr' target='#b35'>Van den Besselaar and Sandstr&#246;m (2019)</ns0:ref> discuss the potential use of clustering coefficient and eigenvector centrality of ego network of researchers and their supervisor. These measures provide a metric for gauging the independence of a researcher. They have used a small scale study of 4 pair of researchers handpicked for their comparison. Although the authors agree that there are numerous ways to capture independence, however, the use of clustering coefficient and eigenvector centrality could be a potential tool for evaluating independence over a large data set. <ns0:ref type='table' target='#tab_1'>2020:08:52217:2:0:NEW 1 Jan 2021)</ns0:ref> Manuscript to be reviewed Computer Science citation network to 5 levels in cited-by and citing directions. Using a large network available at AMiner they proposed a hybrid strategy for recommendations using different centrality measures on each network.</ns0:p><ns0:p>Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref> provides a summary of these studies stating the data source used to create the scientific network, as well as the measures which were applied for analysis. Case studies similar to our work are also available on the proprietary data source of WoS <ns0:ref type='bibr' target='#b22'>(Milojevi&#263;, 2014)</ns0:ref> and Scopus <ns0:ref type='bibr' target='#b33'>(Rose and Kitchin, 2019)</ns0:ref>. Further, a set of graphical tools are also available as discussed by <ns0:ref type='bibr' target='#b24'>Moral-Mu&#241;oz et al. (2020)</ns0:ref> in a recent survey but most tools do not give access for Crossref apart from <ns0:ref type='bibr' target='#b36'>(Van Eck and Waltman, 2014;</ns0:ref><ns0:ref type='bibr' target='#b5'>Chen, 2005)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Study</ns0:head><ns0:p>Chen (2005) discusses identification of highly cited clusters of a scientific network. Also discusses the identification of pivotal points in the scientific network using betweenness centrality. The author uses clinical evidence data associated with reducing risks of heart disease to illustrate the approach. They have discussed the design of citeSpace tool and its new feature for identifying pivotal points. They used betweenness centrality to identify pathways between thematic clusters because by studying these pathways identifies how two clusters differ. High betweenness centrality nodes are good for pivotal points in a scientific network. We intend to approach similarly but instead of a graphical software tool, we propose to use Python scripts which give more flexibility for advance analysis. For a detailed survey of tools, we would refer the interested reader to <ns0:ref type='bibr' target='#b24'>(Moral-Mu&#241;oz et al., 2020)</ns0:ref>.</ns0:p><ns0:p>One of the recent studies that provide replicable Python scripts <ns0:ref type='bibr' target='#b33'>(Rose and Kitchin, 2019)</ns0:ref> focuses on using Scopus data for network analysis. They have provided a scripted interface for researchers to</ns0:p></ns0:div> <ns0:div><ns0:head>5/14</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:2:0:NEW 1 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science perform useful analysis. Although accessing Scopus is possible with Elsevier Developer API Key but it requires institutional or authenticated access. Such access is not possible, especially for developing countries <ns0:ref type='bibr' target='#b13'>(Herzog et al., 2020)</ns0:ref>. Although our work is similar to <ns0:ref type='bibr' target='#b33'>Rose and Kitchin (2019)</ns0:ref> that it provides a scripted interface for researchers, it is different in two aspects. Firstly, we are working with OpenCitatons data using Crossref. Secondly, we have not provided an API interface that needs maintenance and support since we believe that Crossref, NetworkX and SNAP APIs fulfil the purpose.</ns0:p><ns0:p>Overall these studies show that applying centrality measures is a useful analysis in bibliometrics, however, these approaches are mostly not scalable and would require considerable effort to apply the same analysis on bigger networks. In some cases, the tools limit the size of network analysed, whereas in other studies the data are manually curated. In comparison to our work most studies are limited to one type of network and the way dataset is acquired limits the analysis to expand to another type of networks.</ns0:p><ns0:p>As mentioned above in our representative literature review it is observed that rarely any study has used multiple networks or mentioned how it can be curated with the same data source. Although with WoS and Scopus data it is theoretically possible to create all networks with other data sources a dump is usually provided with limited metadata, thereby limiting the authors to confine their studies to this limitation.</ns0:p><ns0:p>On the other hand, publicly available metadata has its limitations when it comes to completeness and verification of available data. <ns0:ref type='bibr' target='#b14'>Iorio et al. (2019)</ns0:ref> concludes that using OpenCitatons data for evaluation purpose is not enough due to unavailability of complete data, however more than half of data are available in comparison to WoS and Scopus. A similar evaluation is also done by <ns0:ref type='bibr' target='#b27'>Nishioka and F&#228;rber (2019)</ns0:ref> and <ns0:ref type='bibr' target='#b20'>Mart&#237;n-Mart&#237;n et al. (2020)</ns0:ref>. Further, there are different approaches to augment the current OpenCitatons data <ns0:ref type='bibr' target='#b6'>(Daquino et al., 2018;</ns0:ref><ns0:ref type='bibr'>Heibi et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b31'>Peroni and Shotton, 2020)</ns0:ref>.</ns0:p><ns0:p>Using open metadata are gaining popularity. <ns0:ref type='bibr' target='#b15'>(Kami&#324;ska, 2018)</ns0:ref> discusses a case study for using</ns0:p><ns0:p>OpenCitatons data for visualising citation network. <ns0:ref type='bibr' target='#b42'>(Zhu et al., 2019)</ns0:ref> has used COCI to evaluate books scholarship. We hypothesise that with a scripted workflow provided below it would be easier for masses to adopt to OpenCitatons data for bibliometric analysis.</ns0:p></ns0:div> <ns0:div><ns0:head>METHODOLOGY</ns0:head><ns0:p>This section provides details of a systematic workflow from data fetching to analysis. To apply centrality analysis on the author collaboration and author citation networks a series of steps are required to create these networks using the OpenCitatons data which provide the article citation network. All scripts were executed on Windows Server machine having Quad-Core AMD Opteron(TM) Processor 6272 with 128 GB RAM installed. It is interesting to note that only the initial processing of data requires heavy computation and memory once. Later, the data are converted to a compressed binary format using libraries for processing large networks and thus can run on any standard laptop machine. Below we provide details of the workflow to create scientific networks for SCIM. A generic query on Crossref provided a huge amount of data so their analysis was outside the scope of this current article. We aim to provide details of our extended analysis in an upcoming publication and not clutter this workflow with unnecessary details. Although this case study is limited to data of SCIM, we have made every effort to keep the process automated such that applying the same script require minimum changes for other journals or set of journals.</ns0:p><ns0:p>Overview of the process is depicted in Figure <ns0:ref type='figure' target='#fig_3'>2</ns0:ref> and further details about each of the following step are provided separately. Each step is distributed with three sub-steps for clarity and batch execution.</ns0:p><ns0:p>Step 1 The first step is to download the citation index provided as COCI <ns0:ref type='bibr'>(Heibi et al., 2019)</ns0:ref>.</ns0:p><ns0:p>Step 2 The second step is to download the metadata for provided ISSN through Crossref.</ns0:p><ns0:p>Step 3 The third step is to fetch the ego network from COCI data for the DOIs of respective ISSN.</ns0:p><ns0:p>Step 4 The fourth step is to merge these data to create a different scientific network(s).</ns0:p><ns0:p>Step 5 Finally, the last step is to apply the centrality analysis on these networks.</ns0:p><ns0:p>Minimal set of Python scripts are provided as Supplemental Files, for not only replication of the current study, but also reuse of this study for other ISSN or other network types for bibliometric analyses.</ns0:p><ns0:p>Details are provided below for the understanding of this study and can be accessed online <ns0:ref type='bibr' target='#b4'>(Butt and Faizi, 2020)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>6/14</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:2:0:NEW 1 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science </ns0:p></ns0:div> <ns0:div><ns0:head>Fetching citation network</ns0:head><ns0:p>Summary of the sub-steps to fetch citation network is shown in Figure <ns0:ref type='figure' target='#fig_4'>3</ns0:ref>. Below we define the sub-steps to convert the COCI data to be used in Python libraries for network processing. This step is computation and memory intensive but needs to be performed only once. Convert COCI data to edge list This step is needed to convert the COCI data to an edge list format. It is an easy to process format with two nodes on each row signifying an edge. This format is supported by SNAP <ns0:ref type='bibr' target='#b17'>(Leskovec and Sosi&#269;, 2016)</ns0:ref> which is used for processing huge network data such as COCI. After this step edge list file is approx 35 GB. We convert the COCI from comma-separated-values (CSV) to space-separated-values having only citing and cited column. This is the only format supported by SNAP for bulk upload. Some formatting corrections are also made for removing extra CR/LF and quotes since it hampers the loading process of SNAP. We have tried to load the same files with other libraries which are</ns0:p></ns0:div> <ns0:div><ns0:head>7/14</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:2:0:NEW 1 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science relatively more intuitive but not as powerful as SNAP <ns0:ref type='bibr' target='#b17'>(Leskovec and Sosi&#269;, 2016)</ns0:ref>. However, we later discuss how this data can be used with other libraries and provide scripts to convert data to a format that is supported by the majority of network processing libraries.</ns0:p><ns0:p>Save COCI as binary Loading 35 GB edge list in-memory using SNAP takes approx 5.5 hours. Since the edge labels are DOI in the COCI data, therefore they are saved as strings. However, this slows down further processing so strings are converted to a hash file. There are two binary files generated when loading the COCI data in SNAP. First is DOIDirected.graph file which contains the directed citation network of COCI with integer node labels. Second is DOIMapping.hash which maps the integer node label to respective DOI. We save loaded graph as binary files for further computations. Loading binary file in-memory takes a few minutes as compared to a few hours for loading CSV data with the downside that additional columns of COCI are currently not being utilised. To keep things simple for novice and non-technical user DOIMapping.hash is simply a node list where node number is mapped to its label (DOI) while the DOIDirected.graph is an edge list on node number. This is the part which makes SNAP less intuitive but more powerful since computations are much faster when integer labels are used but for human consumption, a mapping to string labels is also provided.</ns0:p></ns0:div> <ns0:div><ns0:head>Fetching Crossref metadata</ns0:head><ns0:p>Summary of the sub-steps to download Crossref metadata are shown in Figure <ns0:ref type='figure'>4</ns0:ref>. Below we define the sub-steps to fetch the citation metadata and converting it to list of authors and DOIs. Although these steps only provide API string to fetch data for a single journal, however, it is possible to fetch data with other filters and query using Crossref. Details are provided in Crossref documentation, and the metadata downloaded via different filters is in a similar format which makes this script reusable for a variety of tasks.</ns0:p></ns0:div> <ns0:div><ns0:head>Figure 4.</ns0:head><ns0:p>Step 2 of the workflow with details of fetching metadata from Crossref API. Sub-steps are applied sequentially.</ns0:p><ns0:p>Create Crossref API string Crossref limits a one time query to 1000 records for a single ISSN. For queries with more than 1000 records, multiple API strings are needed which are created automatically.</ns0:p><ns0:p>Crossref data of SCIM is fetched via Crossref API which contains total 1857 records. These records are fetched by two API requests to create JSON of SCIM.</ns0:p></ns0:div> <ns0:div><ns0:head>Fetch author(s) list from data</ns0:head><ns0:p>Once data are fetched from Crossref as JSON we populate the list of authors. We extract authors from the previous downloaded JSON. It is important to note that we do not apply any technique for author name disambiguation and rely on Crossref to provide correct author names.</ns0:p><ns0:p>Although this is problematic for further analysis, in the long run, corrected data from a single source is much efficient than using different methods of cleaning. A similar approach is provided by MAG <ns0:ref type='bibr' target='#b40'>(Wang et al., 2020)</ns0:ref>.</ns0:p><ns0:p>Fetch DOI list from data Once data are fetched from Crossref as JSON we populate the list of DOI.</ns0:p><ns0:p>DOIs are extracted from the previously downloaded JSON. Although the purpose of fetching DOI is redundant but it's replica script is created to suggest that analysis with only provided DOI list is also</ns0:p></ns0:div> <ns0:div><ns0:head>8/14</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:2:0:NEW 1 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>possible. So the previous two sub-steps can be ignored if analysing a specific journal is not needed. If the list of DOIs is fetched from an external source then it can be easily incorporated in this workflow.</ns0:p></ns0:div> <ns0:div><ns0:head>Creating ego network</ns0:head><ns0:p>Summary of the sub-steps to create ego network are shown in Figure <ns0:ref type='figure' target='#fig_5'>5</ns0:ref>. Below we define the sub-steps to create Ego Network. This step can be iterated zero or more times to grow the network as desired. This step is not used in the case study, however, we provide the details in this section to show that with publicly accessible metadata it is relatively easier to scale our approach. Further, this step justifies our approach of using SNAP over other network processing libraries since the process of creating the ego network is not only fast but intuitive to code due to a variety of functions available in the extensive library documentation that makes it easier to access the nodes in both directions of an edge. Also, the integer labels make the computation faster than using string labels. Crossref dump for egonet We provide the fetching of Crossref data for all DOIs of article ego network created in the previous step. This way first we download all data and then process it to create the network.</ns0:p><ns0:p>Depending on the size of the network and the number of ego levels, as well as connectivity bandwidth available this process can take from a few hours to days. Once a local copy of data is available this delay can be reduced. Since we do not have access to complete dump of Crossref we could not identify whether these same scripts can be reused but we assume that there would be few changes required to access the data locally.</ns0:p></ns0:div> <ns0:div><ns0:head>DOI and author list extraction</ns0:head><ns0:p>We provide the creation of the ego network for authors. This is similar to nodes of SCIM downloaded earlier. However, here we add the connecting nodes fetched in subgraph above and download their respective author details.</ns0:p></ns0:div> <ns0:div><ns0:head>Creating scientific network(s)</ns0:head><ns0:p>Summary of the sub-steps to create scientific networks are shown in Figure <ns0:ref type='figure'>6</ns0:ref>. Once all the data are pre-processed this step creates different types of network. We can also add bibliographic coupling and co-citation network within the list but they are ignored for two reasons. First, we did not find much evidence of centrality analysis on these networks. Secondly, the processing time for creating these networks for a very large citation network is relatively much longer than creating author collaboration or author citation network. These networks are simply created by making an edge list for authors who have collaborated or cited each other.</ns0:p></ns0:div> <ns0:div><ns0:head>9/14</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:2:0:NEW 1 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:note type='other'>Computer Science Figure 6.</ns0:note><ns0:p>Step 4 of the workflow with details of creating different scientific networks. Sub-steps are applied sequentially.</ns0:p></ns0:div> <ns0:div><ns0:head>Create article citation network</ns0:head><ns0:p>Once the list of DOI is available it is used to fetch subgraph of article citation network for these DOIs. We provide details of fetching article citation network as a subgraph from COCI. Further, it saves the same graph as a binary file for further analysis. Also, the CSV file can be used with any graph processing library (such as NetworkX) while binary file can be read using SNAP.</ns0:p><ns0:p>Create author collaboration network Author collaboration is identified via a list of co-authors from JSON data fetched via Crossref. This refined data are further used in the case study in the subsequent section. It is important to note that the count of authors at this sub-step may vary from next sub-step of creating author citation network since the list of co-authors in Crossref is provided as a list of names and we do not include further metadata about these authors.</ns0:p><ns0:p>Create author citation network Using the subgraph of article citation network respective edges are made for authors to create author citation network. All co-authors are linked to use full counting method.</ns0:p><ns0:p>In case method of partial counting is to be utilised then this script needs to be modified. However, our workflow is not affected by the use of a partial or full counting method and hence we have picked simpler one for brevity <ns0:ref type='bibr' target='#b8'>(Glanzel, 2003)</ns0:ref>. In any case, this network shall supplement the analysis on a collaboration network that was constructed in the previous step, as well as article citation network that was originally provided.</ns0:p></ns0:div> <ns0:div><ns0:head>Centrality analysis</ns0:head><ns0:p>Summary of the sub-steps to apply centrality analysis are shown in Figure <ns0:ref type='figure' target='#fig_7'>7</ns0:ref>. Below we define the sub-steps to apply different centrality measures on the scientific networks. This is one of the common method employed in the bibliometric analysis, however other methods of SNA can also be applied at this step. Any tool or wrapper API may restrict the functionality at this point, however, this work can be extended to use any functions in existing network processing libraries. Since using graphical tools is easier than the script so a future application of this study could be about creating a front end tool for ease of use. Below we provide details about how the different centrality measures applied by different studies can be accomplished. Each of the measures is separated in the different listing along with loading and initialisation.</ns0:p></ns0:div> <ns0:div><ns0:head>Applying centrality measures on article citation network</ns0:head><ns0:p>The article citation network is a Directed Acyclic Graph (DAG). Most centrality analyses are not meaningful on DAG. Two measures are presented.</ns0:p><ns0:p>First, degree centrality provides highly cited articles. Finding authors of these articles is also possible, however not provided for simplicity. Secondly, influence definition in DAG is captured via the recursive definition of Katz centrality which is also provided using NetworkX library. Manuscript to be reviewed</ns0:p><ns0:p>Computer Science Step 5 of the workflow with details of centrality measures that are applied on different scientific networks. Sub-steps may be applied as required as there is no dependency within steps.</ns0:p><ns0:p>(eigenvector centrality) and authors working in multiple domains (betweenness centrality).</ns0:p></ns0:div> <ns0:div><ns0:head>Applying centrality measures on author collaboration network</ns0:head><ns0:p>The author collaboration network has cyclic nature and most centrality analyses are possible. Five measures are presented, namely highly collaborative authors (degree centrality), influential collaborators (eigenvector centrality), authors working in multiple groups (betweenness centrality), well-knitted authors (closeness centrality), and solo authors (farness centrality). Ranks captured here are presented in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>. This work was done manually by sorting individual lists on respective centrality scores and identifying their rank position.</ns0:p></ns0:div> <ns0:div><ns0:head>Batch execution</ns0:head><ns0:p>All python scripts can be executed through a sample batch file by modifying the ISSN and date range. This batch processing will also be useful for developing a front-end tool, as well as modifying the sequence as per user need.</ns0:p></ns0:div> <ns0:div><ns0:head>CASE STUDY USING SCIM</ns0:head><ns0:p>Milojevi&#263; (2014) analysed collaboration network using WoS data of SCIM for 10 years starting from the year 2003. The outcome of their analysis was provided in a table having authors that had top 5 ranks in either of the centrality scores. The respective rank of those authors was also provided. To verify whether or not our workflow can capture a similar pattern we provide the results in a similar tabular form.</ns0:p><ns0:p>For each of the centrality measure we provide the rank given in <ns0:ref type='bibr' target='#b22'>(Milojevi&#263;, 2014)</ns0:ref> using WoS data, as well as compare it with the rank obtained in our study using OpenCitatons data. We observe that the rank of authors for the degree, betweenness and closeness centrality is more or less similar, however, further analysis is required to inquire the reason for the difference of eigenvector centrality ranks. Such an analysis is outside the scope of this study.</ns0:p><ns0:p>Ranks in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref> are entered manually after processing the information separately. Author names are sorted in the same sequence as provided in the original study along with their respective ranks. Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref> has four sections for the degree, betweenness, eigenvector and closeness centrality, respectively. Each section has two columns with the left column showing rank from <ns0:ref type='bibr' target='#b22'>Milojevi&#263; (2014)</ns0:ref> and the right column shows the rank calculated for the same author using our workflow. It is pertinent to note that a very hand-on approach is provided by <ns0:ref type='bibr' target='#b22'>Milojevi&#263; (2014)</ns0:ref>, however, due to access restriction of WoS and its</ns0:p></ns0:div> <ns0:div><ns0:head>11/14</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:2:0:NEW 1 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science unaffordability for developing countries, such useful analysis are only limited to researchers of specific institutes having subscription <ns0:ref type='bibr' target='#b38'>(Waltman and Larivi&#232;re, 2020)</ns0:ref>.</ns0:p><ns0:p>This highlights the importance of our workflow to provide access to any individual who can download the publicly available metadata. Further, we do not discuss the reasons for why a specific author has topped the list and what the centrality measure signifies, and the interested reader is referred to <ns0:ref type='bibr' target='#b22'>(Milojevi&#263;, 2014)</ns0:ref>. However, we intend to provide a detailed analysis in a separate publication using ego networks. <ns0:ref type='bibr' target='#b22'>(Milojevi&#263;, 2014)</ns0:ref> and current study for each centrality measure. Table is divided into 4 sections for each centrality measure with the left column in each section showing the rank from <ns0:ref type='bibr' target='#b22'>(Milojevi&#263;, 2014)</ns0:ref>, and the right column showing the rank calculated by our workflow.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSION AND FUTURE WORK</ns0:head><ns0:p>Scientific networks rely on completion of data, and although the field has existed for more than 50 years <ns0:ref type='bibr' target='#b32'>(Price, 1965)</ns0:ref>, however, the limitations on data access have not helped to reach its true potential. We aim that with the availability of publicly available metadata <ns0:ref type='bibr' target='#b38'>(Waltman and Larivi&#232;re, 2020)</ns0:ref> and a workflow to access it, such as the one presented in this study, a researcher from any field will be able to analyse the prominent authors. Based on numerous studies discussed in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref> it is evident that centrality measures are a popular way of identifying prominent authors. It can further be used for identifying reviewers for a potential study (based on its references), as well as a graduate student finding a PhD supervisor. Once the citation network is fetched and saved as a binary file the time it takes to analyse authors list in a journal is well under an hour, barring the time to create ego network as it requires downloading Crossref files for each DOI. This provides a means for fast and interactive analysis for researchers of any field. This study currently does not provide a detailed analysis of the ego network, however, a brief comparison justifies the importance of systematic metadata harvesting workflow. For case study, some manual work was also done to sort and format the results, however, it can also be scripted in future as it does not hamper the workflow and can be performed as a standalone. Likewise, techniques for author name disambiguation or partial counting have not been included but for effective analysis, these need to be incorporated in future.</ns0:p><ns0:p>We further aim to enhance this work to filter Crossref data based on subject categories instead of journal ISSN. It would enhance the capability and usefulness of this analysis for individual researchers. A web-based portal is also under construction where the user may be able to select the date range along with other filters and the system which initiates the scripts at the back-end. This way the users who are not familiar with programming can also benefit from this analysis.</ns0:p><ns0:p>12/14</ns0:p><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:2:0:NEW 1 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>neighbours of node (n) (namely node (k), (l), (m), (o) and (p)) will form its ego network.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. A toy network showing different nodes with high centrality for different measures. A. shows high farness centrality since the node (a) has the maximum average distance to other nodes. B. shows high clustering coefficient since neighbours of the node (c) are all connected as well.C. shows high betweenness centrality since the highest number of shortest paths will go from the node (i) and (k) since they are bridging two parts of the network. D. shows high degree centrality as both the nodes (e) and (n) have 5 neighbours. E. shows high eigenvector centrality since node (e) is connected to many neighbours with a relatively higher degree. F. shows high closeness centrality as the average distance from nodes (i), (j) and (k) are minimum to other nodes.</ns0:figDesc><ns0:graphic coords='3,141.73,279.84,413.57,232.28' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc><ns0:ref type='bibr' target='#b37'>Waheed et al. (2019)</ns0:ref> discusses the use of centrality measures on multiple scientific networks of author collaboration, author citation and article citation to improve article recommendation. They filter the 4/14 PeerJ Comput. Sci. reviewing PDF | (CS-</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Workflow to identify gurus of any Field. A pyramid shows the refinement of data at every step. COCI contains approx. 625 M edges which are refined to ego network for subset nodes fetched for respective ISSN. Finally, the top of the pyramid shows the output in form of a few nodes identified with high centrality.</ns0:figDesc><ns0:graphic coords='8,141.73,63.78,413.59,175.53' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. Step 1 of the workflow with details of creating the citation network. Sub-steps are applied sequentially.</ns0:figDesc><ns0:graphic coords='8,141.73,379.10,413.59,157.50' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5.Step 3 of the workflow with details of creating the ego network. Sub-steps are applied sequentially, and may be iterated over to create next level of ego network.</ns0:figDesc><ns0:graphic coords='10,141.73,216.53,413.59,153.36' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>Applying centrality measures on author citation networkThe author citation network has cyclic nature. Three measures are presented, namely highly cited authors (degree centrality), influential authors 10/14 PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:2:0:NEW 1 Jan 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7.Step 5 of the workflow with details of centrality measures that are applied on different scientific networks. Sub-steps may be applied as required as there is no dependency within steps.</ns0:figDesc><ns0:graphic coords='12,141.73,63.78,413.59,249.31' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='9,141.73,355.50,413.59,150.71' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Review of studies applying social network analysis on scientific networks.</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell>Bibliometric</ns0:cell><ns0:cell>Scientific Network(s)</ns0:cell><ns0:cell>Social Network Analysis</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Data Source</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Ding (2011)</ns0:cell><ns0:cell>WoS</ns0:cell><ns0:cell>Author Citation</ns0:cell><ns0:cell>Weighted PageRank</ns0:cell></ns0:row><ns0:row><ns0:cell>Abbasi et al.</ns0:cell><ns0:cell>Scopus</ns0:cell><ns0:cell>Author Collaboration</ns0:cell><ns0:cell>Degree, Betweenness, Close-</ns0:cell></ns0:row><ns0:row><ns0:cell>(2012)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>ness Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>Ortega (2014)</ns0:cell><ns0:cell>MAG</ns0:cell><ns0:cell>Co-Author Ego Network</ns0:cell><ns0:cell>Clustering Coefficient, Degree</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>and Betweenness Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>Milojevi&#263;</ns0:cell><ns0:cell>WoS</ns0:cell><ns0:cell>Author Collaboration and Cita-</ns0:cell><ns0:cell>Degree, Betweenness, Close-</ns0:cell></ns0:row><ns0:row><ns0:cell>(2014)</ns0:cell><ns0:cell /><ns0:cell>tion, Article Citation</ns0:cell><ns0:cell>ness, Eigenvector Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>Waltman and</ns0:cell><ns0:cell>WoS</ns0:cell><ns0:cell>Journal Citation Network</ns0:cell><ns0:cell>PageRank</ns0:cell></ns0:row><ns0:row><ns0:cell>Yan (2014)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Xu and Peke-</ns0:cell><ns0:cell>Manual</ns0:cell><ns0:cell>Author Citation</ns0:cell><ns0:cell>PageRank and Degree Central-</ns0:cell></ns0:row><ns0:row><ns0:cell>lis (2015)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>ity</ns0:cell></ns0:row><ns0:row><ns0:cell>Leydesdorff</ns0:cell><ns0:cell>WoS/JCR</ns0:cell><ns0:cell>Journal Citation</ns0:cell><ns0:cell>Betweenness Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>et al. (2018)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Lee (2019)</ns0:cell><ns0:cell>Scopus</ns0:cell><ns0:cell>Author Collaboration</ns0:cell><ns0:cell>Degree and Betweenness Cen-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>trality, Clustering Coefficient</ns0:cell></ns0:row><ns0:row><ns0:cell>Massucci and</ns0:cell><ns0:cell>WoS</ns0:cell><ns0:cell>Institutional Citation</ns0:cell><ns0:cell>PageRank</ns0:cell></ns0:row><ns0:row><ns0:cell>Docampo</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>(2019)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Singh and Jo-</ns0:cell><ns0:cell>APS Journals</ns0:cell><ns0:cell>Author Collaboration</ns0:cell><ns0:cell>Centrality, Community Detec-</ns0:cell></ns0:row><ns0:row><ns0:cell>lad (2019)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>tion</ns0:cell></ns0:row><ns0:row><ns0:cell>Van den Besse-</ns0:cell><ns0:cell>Manual</ns0:cell><ns0:cell>Researchers Ego Network</ns0:cell><ns0:cell>Clustering coefficient, eigenvec-</ns0:cell></ns0:row><ns0:row><ns0:cell>laar and Sand-</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>tor Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>str&#246;m (2019)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Waheed et al.</ns0:cell><ns0:cell>DBLP, ACM,</ns0:cell><ns0:cell>Author Collaborationand Cita-</ns0:cell><ns0:cell>Degree, Betweenness, Close-</ns0:cell></ns0:row><ns0:row><ns0:cell>(2019)</ns0:cell><ns0:cell>MAG</ns0:cell><ns0:cell>tionArticle Citation, Co-citation</ns0:cell><ns0:cell>ness, Eigenvector Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>and Bibliographic Coupling</ns0:cell><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Comparison of ranks by previous study</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Collaborator</ns0:cell><ns0:cell cols='8'>Degree Rank Betweenness Rank Eigenvector Rank Closeness Rank</ns0:cell></ns0:row><ns0:row><ns0:cell>Name</ns0:cell><ns0:cell cols='3'>Prev Curr Prev</ns0:cell><ns0:cell>Curr</ns0:cell><ns0:cell>Prev</ns0:cell><ns0:cell>Curr</ns0:cell><ns0:cell>Prev</ns0:cell><ns0:cell>Curr</ns0:cell></ns0:row><ns0:row><ns0:cell>Glanzel, W</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell></ns0:row><ns0:row><ns0:cell>Rousseau, R</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>2</ns0:cell></ns0:row><ns0:row><ns0:cell>DeMoya-Anegon, F</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>8</ns0:cell><ns0:cell>12</ns0:cell><ns0:cell>20</ns0:cell><ns0:cell>26</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell>15</ns0:cell></ns0:row><ns0:row><ns0:cell>Klingsporn, B</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>21</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>89</ns0:cell><ns0:cell>121</ns0:cell><ns0:cell>174</ns0:cell><ns0:cell>144</ns0:cell></ns0:row><ns0:row><ns0:cell>Ho, Ys</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>22</ns0:cell><ns0:cell>125</ns0:cell><ns0:cell>2096</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>613</ns0:cell><ns0:cell>575</ns0:cell></ns0:row><ns0:row><ns0:cell>Thijs, B</ns0:cell><ns0:cell>63</ns0:cell><ns0:cell>51</ns0:cell><ns0:cell>65</ns0:cell><ns0:cell>44</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>10</ns0:cell><ns0:cell>1710</ns0:cell></ns0:row><ns0:row><ns0:cell>Schubert,A</ns0:cell><ns0:cell>36</ns0:cell><ns0:cell>48</ns0:cell><ns0:cell>38</ns0:cell><ns0:cell>28</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>18</ns0:cell><ns0:cell>27</ns0:cell><ns0:cell>24</ns0:cell></ns0:row><ns0:row><ns0:cell>Debackere, K</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>15</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>5</ns0:cell></ns0:row><ns0:row><ns0:cell>Schlemmer, B</ns0:cell><ns0:cell>670</ns0:cell><ns0:cell>832</ns0:cell><ns0:cell>382</ns0:cell><ns0:cell>962</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>808</ns0:cell><ns0:cell>33</ns0:cell><ns0:cell>37</ns0:cell></ns0:row><ns0:row><ns0:cell>Meyer, M</ns0:cell><ns0:cell>43</ns0:cell><ns0:cell>39</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>14</ns0:cell><ns0:cell>9</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>3</ns0:cell></ns0:row><ns0:row><ns0:cell>Leydesdorff, L</ns0:cell><ns0:cell>54</ns0:cell><ns0:cell>35</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>46</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>42</ns0:cell><ns0:cell>44</ns0:cell></ns0:row><ns0:row><ns0:cell>Rafols,I</ns0:cell><ns0:cell>1058</ns0:cell><ns0:cell>387</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>23</ns0:cell><ns0:cell>83</ns0:cell><ns0:cell>239</ns0:cell><ns0:cell>45</ns0:cell><ns0:cell>49</ns0:cell></ns0:row></ns0:table></ns0:figure> </ns0:body> "

"Editor, PeerJ CS 1 Jan 2021 We acknowledge the time and effort given by the editor and all reviewers in improving the manuscript. We believe that the manuscript will now be acceptable for publication in PeerJ CS. Thanks, Bilal, Rafi and Sabih Editor's Decision Minor Revisions Dear authors, Thanks for submitting your revised work at PeerJ Computer Science. The same three independent experts who assessed your initial submission were able to review your revision again, and their reviews are attached. All of them praised the work and the extensive rewriting you did in the article. However, there are still some issues to address before this article is acceptable for publication in PeerJ Computer Science. >> Response: Authors acknowledge the effort put in by the editor for a timely conduct of the review process. Please read carefully the reviewers' comments and address all of them in the new revision of your work. Please let me know if you need more time for preparing the revision. >> Response: All suggested changes have been addressed, as below. Thanks again for having submitted to PeerJ Computer Science. Have a nice day :-) Silvio Peroni https://orcid.org/0000-0003-0530-4305 Reviewer 1 Basic reporting Although the authors reworked their submission according to suggestions of the reviewers, I find too many inconsistencies to recommend its publication in its present form. >> Response: Authors acknowledge the critical feedback provided by the reviewer that has shaped the current study. While the review of previous studies and description of the workflow are good, the formulation of the research goals and the case study are problematic. >> Response: Research Question is merged with the introduction section. Indeed, the abstract formulates the research goal as developing a methodology to construct a multinetwork from the same dataset. Namely, citation network overlayed with authorship network and collaboration network. This is an ambitious program and the authors seem to be able to show the workflow, algorithm how they perform this task. Such algorithms are known and the authors shall be commended for their thorough description of how these algorithms work together, in tandem or pipeline. >> Response: Appreciated. The authors present the example of such workflow, the case study. They downloaded the authors writing to Scientometrics, calculated different centralities associated with them, and compared their measurements to previous study (Milojevic, 2014). Table 1 show dramatic difference between their ranking of prominent authors and those of Milojevic, In other words, the authors shoot themselves in the foot, since discrepancies in Table 1 invalidate their algorithm. My feeling is that either description of some details of their algorithm is missing, or they do not explain in which aspect their measurements are different from those of Milojevic. >> Response: As mentioned in line no. 369 (of the previous submission) due to the unaffordability of the WoS dataset we were not able to perform a detailed comparison. This highlights the importance of our workflow to provide access to any individual who can download the publicly available metadata. Ranks for different measures (other than eigenvector centrality) are similar in pattern apart from a couple of anomalies which could not be investigated further. ​Experimental design good. >> Response: Appreciated. Validity of the findings Problematic. Probably, some details of the algorithm are missing. >> Response: In our first submission we did mention a possible reason for difference in ranks of eigenvector centrality (of using PageRank implementation with a damping factor of 0.85). We have later removed it since the comparative analysis was reshaped as a case study (as per overall comments) and the case study presents only a sample execution of our workflow. We believe that such details may confuse the reader. Reviewer 2 Basic reporting See below. Experimental design See below. Validity of the findings See below. Comments for the author Dear authors, thank you for a thoughtful and extensive revision of your article. I believe its goals and scope are now clear, as well as its contribution. The article is also now well-embedded into previous literature, and the publication of the code on GitHub is crucial. >> Response: Authors acknowledge the thoughtful comments of the reviewer. While I remain skeptical about the actual scientific contribution of this work, which I consider somewhat narrow, I believe that the authors have substantially improved on their previous submission and, if the editor considers their work of interest to PeerJ readers, I now support acceptance. >> Response: We hypothesize that the workflow, once published as open access, shall give the community a means of supplementing it with different analyses. Reviewer: Ludo Waltman Basic reporting See my general comments. Experimental design See my general comments. Validity of the findings See my general comments. Comments for the author I would like to thank the authors for the improvements they have made to their paper. Before I can recommend this paper for publication, there are some further improvements that I consider to be necessary. >> Response: Authors acknowledge the invaluable comments provided that have improved this study. The introduction of the paper, in particular the first paragraph of the introduction, needs to provide a better explanation of what the paper is about. The introduction is largely focused on discussing the problem of identifying ‘gurus’. This gives the incorrect impression that the paper may provide in-depth analyses of different approaches to identifying gurus. The introduction does not make sufficiently clear that the paper is almost entirely about providing a standardized workflow for identifying gurus. This needs to be made much more clear in the introduction. >> Response: Starting of introduction section is modified. The subsection ‘Research question’ in the section ‘Preliminaries’ can best be removed. If the authors want to specify an explicit research question, it is best to do so in the introduction. >> Response: Research Question is merged with Introduction. The distinction between the section ‘Discussion’ and the section ‘Conclusion and future work’ is not very clear. My recommendation is to merge the two sections in a single section. >> Response: Sections have been merged. “Any study on co-authorship may not necessarily have all the citation links.”: I don’t understand this sentence. To study co-authorship, there is no need to have information about citation links. A study of co-authorship requires data on co-authorship. It doesn’t require citation data. >> Response: This is correct, however, the purpose of providing workflow is to fetch all data needed for a holistic view. A researcher looking for citation details of highly collaborative authors may not be able to utilize the mono dimensional data of co-authorship only. Sentences in the abstract have been rephrased. “the same would not be true for publishers whose metadata are not yet public although available with Crossref (such as Elsevier).”: As I pointed out in my previous review report, this is incorrect. Elsevier does make metadata openly available in Crossref. The only exception is citation data, which Elsevier currently doesn’t make openly available in Crossref. (By the way, this will change soon. See​ ​https://www.elsevier.com/connect/advancing-responsible-research-assessment​.) >> Response: These sentences have been removed. "

Here is a paper. Please give your review comments after reading it.

"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>One of the disciplines behind the science of science is the study of scientific networks. This work focuses on scientific networks as a social network having different nodes and connections. Nodes can be represented by authors, articles or journals while connections by citation, co-citation or co-authorship. One of the challenges in creating scientific networks is the lack of publicly available comprehensive data set. It limits the variety of analyses on the same set of nodes of different scientific networks. To supplement such analyses we have worked on publicly available citation metadata from Crossref and OpenCitatons. Using this data a workflow is developed to create scientific networks.</ns0:p><ns0:p>Analysis of these networks gives insights into academic research and scholarship. Different techniques of social network analysis have been applied in the literature to study these networks. It includes centrality analysis, community detection, and clustering coefficient.</ns0:p><ns0:p>We have used metadata of Scientometrics journal, as a case study, to present our workflow. We did a sample run of the proposed workflow to identify prominent authors using centrality analysis. This work is not a bibliometric study of any field rather it presents replicable Python scripts to perform network analysis. With an increase in the popularity of open access and open metadata, we hypothesise that this workflow shall provide an avenue for understanding scientific scholarship in multiple dimensions.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Scientific networks provide useful information in understanding the dynamics of science <ns0:ref type='bibr' target='#b34'>(Price, 1965)</ns0:ref>.</ns0:p><ns0:p>With the advent of numerous bibliographic data sources <ns0:ref type='bibr' target='#b40'>(Waltman and Larivi&#232;re, 2020)</ns0:ref>, it is now possible to analyse different scientific networks. The proposed study focuses on article citation network, author citation network, and co-authorship network. Usually, studies that focus on co-authorship do not require information about the citation. However, having citation links would enable a more complete and holistic view of the possible relations among authors <ns0:ref type='bibr' target='#b45'>(Zingg et al., 2020)</ns0:ref>. To achieve this objective comprehensive access to citation metadata is required. This can be accomplished using publicly available citation metadata available via Crossref <ns0:ref type='bibr' target='#b13'>(Hendricks et al., 2020)</ns0:ref>. However, applying network analysis on it requires a series of steps that may not be intuitive. Proposed study furnish details of these steps so that it is easy to supplement it with different analyses.</ns0:p><ns0:p>Social network analysis techniques are applied to study scientific networks. It includes citation networks of article or author, and author collaboration network. Usually, these networks are build using different data sources. However, our workflow can create all these networks using OpenCitations data and Crossref. The workflow presented in this article is part of a study on the influence of scholarly research artefacts. To this end, we primarily limit our research goal to have a systematic workflow for analysing scientific networks. In this work, we aim to utilise open metadata <ns0:ref type='bibr' target='#b32'>(Peroni et al., 2015)</ns0:ref>, made available using Crossref. Also, we utilise open source Python libraries for network analysis, namely, NetworkX <ns0:ref type='bibr' target='#b10'>(Hagberg et al., 2008)</ns0:ref> and SNAP <ns0:ref type='bibr' target='#b18'>(Leskovec and Sosi&#269;, 2016)</ns0:ref>. Python is used based on its popularity with researchers as per survey results by <ns0:ref type='bibr' target='#b2'>AlNoamany and Borghi (2018)</ns0:ref>. Although graphical software has PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:3:1:NEW 27 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science an ease of use, we prefer to provide workflow as a set of Python scripts to facilitate advance analysis.</ns0:p><ns0:p>Details of batch execution of workflow scripts are available on GitHub for researchers with programming background <ns0:ref type='bibr' target='#b5'>(Butt and Faizi, 2020)</ns0:ref>. This article outlines details of a case study for analysing collaboration network of Scientometrics journal metadata. All steps are documented for the replication of this study. This work shall lay the groundwork for analysing scientific networks using metadata of different journals, set of journals or a subject category. One such analysis is the identification of prominent authors (gurus).</ns0:p><ns0:p>Identifying prominent authors of any field is one of the primary focus for young researchers. Likewise, other researchers tend to follow research published by gurus of the field. Defining a guru of the field is not an easy task, and the definition of guru will be very subjective. To this end, we focus on the definition of guru using the centrality measures of social network analysis. Details of different centrality measures are depicted in Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref> <ns0:ref type='bibr' target='#b26'>(Newman, 2010)</ns0:ref>. The following description was inspired by <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014)</ns0:ref>.</ns0:p><ns0:p>Simply put, any author with a high citation count may be considered the guru. It can be achieved using degree centrality. Another way of identifying a highly cited individual is to calculate whose paper is in top percentile within the domain. However, we currently limit such definitions to degree centrality of articles. It is not always the case that all highly cited authors are equally influential. Those who are cited by other influential authors may also be termed as influential even though they may or may not have high citation count. Likewise, any author frequently collaborating with influential authors would also influence that field. This recursive definition of influence is well captured by eigenvector centrality.</ns0:p><ns0:p>Another centrality measure, namely betweenness centrality would define an author as prominent if author collaborates with different groups. Centrality measures of closeness and farness is the extent to which an author is on average close to or far from other authors within the network, respectively. C. shows high betweenness centrality since the highest number of shortest paths will go from the node (i) and (k) since they are bridging two parts of the network. D. shows high degree centrality as both the nodes (e) and (n) have highest count of neighbours. E. shows high eigenvector centrality since node (e) is connected to many neighbours with a relatively higher degree. F. shows high closeness centrality as the average distance from nodes (i), (j) and (k) are minimum to other nodes.</ns0:p><ns0:p>In the case of analysing the citation network with a limited snapshot of data, this could be supplemented by creating the ego-centered network <ns0:ref type='bibr' target='#b27'>(Newman, 2003)</ns0:ref>. Citation index allow fetching the metadata of</ns0:p></ns0:div> <ns0:div><ns0:head>RELATED WORK</ns0:head><ns0:p>Visualising bibliographic data as a network is not new, <ns0:ref type='bibr' target='#b34'>Price (1965)</ns0:ref> introduced the work more than 50 years ago. Most recent studies are on co-authorship network <ns0:ref type='bibr' target='#b0'>(Abbasi et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b23'>Milojevi&#263;, 2014;</ns0:ref><ns0:ref type='bibr' target='#b17'>Lee, 2019;</ns0:ref><ns0:ref type='bibr' target='#b36'>Singh and Jolad, 2019;</ns0:ref><ns0:ref type='bibr' target='#b39'>Waheed et al., 2019)</ns0:ref>, however others have focused on citation network of authors <ns0:ref type='bibr' target='#b8'>(Ding, 2011;</ns0:ref><ns0:ref type='bibr' target='#b23'>Milojevi&#263;, 2014;</ns0:ref><ns0:ref type='bibr' target='#b43'>Xu and Pekelis, 2015;</ns0:ref><ns0:ref type='bibr' target='#b39'>Waheed et al., 2019)</ns0:ref> or citation network of journals <ns0:ref type='bibr' target='#b41'>(Waltman and Yan, 2014;</ns0:ref><ns0:ref type='bibr' target='#b19'>Leydesdorff et al., 2018)</ns0:ref>. Only a couple of studies have utilised more than one scientific network for analysis <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014;</ns0:ref><ns0:ref type='bibr' target='#b39'>Waheed et al., 2019)</ns0:ref>. Traditionally bibliometric analysis has been done using WoS and Scopus <ns0:ref type='bibr' target='#b40'>(Waltman and Larivi&#232;re, 2020)</ns0:ref>. A similar case has been observed in studies on scientific network analysis where the data sources used are Scopus <ns0:ref type='bibr' target='#b0'>(Abbasi et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b17'>Lee, 2019)</ns0:ref> or WoS <ns0:ref type='bibr' target='#b8'>(Ding, 2011;</ns0:ref><ns0:ref type='bibr' target='#b23'>Milojevi&#263;, 2014;</ns0:ref><ns0:ref type='bibr' target='#b41'>Waltman and Yan, 2014;</ns0:ref><ns0:ref type='bibr' target='#b19'>Leydesdorff et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b22'>Massucci and Docampo, 2019)</ns0:ref>. However, some recent studies have focused on open access data sources <ns0:ref type='bibr' target='#b36'>(Singh and Jolad, 2019;</ns0:ref><ns0:ref type='bibr' target='#b37'>Van den Besselaar and Sandstr&#246;m, 2019;</ns0:ref><ns0:ref type='bibr' target='#b39'>Waheed et al., 2019)</ns0:ref>. Other data sources such as PubMed, CiteSeerX and ACL are not discussed in this article. They are used mostly for text analysis instead of network analysis. Below we list a brief account of work done on scientific networks using centrality measures. Details are summarized in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref> in chronological order. Some earlier studies such as <ns0:ref type='bibr' target='#b28'>(Newman, 2004)</ns0:ref> are not included as we have focused on studies published in the last decade. <ns0:ref type='bibr' target='#b8'>Ding (2011)</ns0:ref> proposed to analyse the author citation network with weighted PageRank. The author proposed the strategy on predicting prize winners that outperforms the conventional h-index and related citation count measures. <ns0:ref type='bibr' target='#b0'>Abbasi et al. (2012)</ns0:ref> discussed the use of betweenness centrality as a measure of getting more collaborators compared to degree and closeness centrality. They have used temporal co-authorship network in the steel research domain. Data was manually curated and downloaded from Scopus. <ns0:ref type='bibr' target='#b31'>Ortega (2014)</ns0:ref> analysed 500 co-authors' ego network and conclude that bibliometric indicators and centrality measures are correlated. They have used clustering coefficient, degree and betweenness centrality as local metrics. Some global level metrics were also analysed using the ego network. It is one of the early studies using MAG.</ns0:p><ns0:p>Two book chapters provide hands-on details about centrality measures <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014)</ns0:ref> and PageRank <ns0:ref type='bibr' target='#b41'>(Waltman and Yan, 2014)</ns0:ref> using WoS data. <ns0:ref type='bibr' target='#b23'>Milojevi&#263; (2014)</ns0:ref> constructed the author collaboration network and calculated degree, betweenness, eigenvector and closeness centrality. <ns0:ref type='bibr' target='#b41'>Waltman and Yan (2014)</ns0:ref> details applying PageRank on journal citation network. <ns0:ref type='bibr' target='#b43'>Xu and Pekelis (2015)</ns0:ref> used a manually curated dataset for authors of China and Taiwan in the field of Chinese Language Interpreting Studies. They have applied PageRank and degree centrality to find influential authors within different clusters identified using community detection. <ns0:ref type='bibr' target='#b19'>Leydesdorff et al. (2018)</ns0:ref> have used betweenness centrality to measure multidisciplinary journals.</ns0:p><ns0:p>Authors have limited their approach with a diversity measure and evaluated it on JCR data. Usually, a journal gets citation within its subject category but those journals cited/citing the other fields are considered a bridge between the subject categories.</ns0:p><ns0:p>A case study for young researchers performance evaluation is presented by <ns0:ref type='bibr' target='#b17'>Lee (2019)</ns0:ref>. The author analysed the collaboration network of these researchers using statistical analysis for the frequency of collaborators. The degree centrality is showed to correspond with future publication count. It is akin to Li Working on five different subject categories, they proposed a framework which is more robust than existing university rankings. It holds a high correlation with these accepted rankings. <ns0:ref type='bibr' target='#b36'>Singh and Jolad (2019)</ns0:ref> utilised data of APS journals to form collaboration network of Indian physicist. In this co-authorship network, they have applied different centrality measures and report the overlapping top authors.</ns0:p><ns0:p>Van den Besselaar and Sandstr&#246;m (2019) discuss the potential use of clustering coefficient and eigenvector centrality in ego network of research students and their supervisor. Both metrics are used to gauge the independence of a researcher. They have handpicked 4 pairs of researchers. The authors suggested that there are numerous ways to capture the researcher's autonomy. However, when evaluating large data sets the clustering coefficient and eigenvector centrality can be effective. Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref> summarises the studies in three aspects. First, the bibliographic data source used. Second, the scientific network created. Last, details of techniques applied for analysis. Studies show that applying centrality measures is a useful analysis in bibliometrics. However, these approaches are mostly not scalable and require considerable effort to apply the same analysis on bigger networks. In some cases, the tools limit the size of network analysed, whereas in other studies the data sets are manually curated. In comparison to our work, most of the studies are limited to one type of network. The way data sets are acquired limits the analysis to expand to another type of networks <ns0:ref type='bibr' target='#b45'>(Zingg et al., 2020)</ns0:ref>. We observe that very few studies have either used multiple networks or mentioned that if these can be curated with the same data source. With WoS and Scopus, it is theoretically possible to create all networks. However, with other data sources, a dump is usually uploaded with limited metadata. It restricts the authors to confine their studies.</ns0:p><ns0:p>Case studies similar to our workflow are also available on the proprietary data source of WoS <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014)</ns0:ref> and Scopus <ns0:ref type='bibr' target='#b35'>(Rose and Kitchin, 2019)</ns0:ref>. Further, a set of graphical tools are also available as discussed by <ns0:ref type='bibr' target='#b24'>Moral-Mu&#241;oz et al. (2020)</ns0:ref> in a recent survey. Most tools do not give access for Crossref apart from (Van Eck and <ns0:ref type='bibr'>Waltman, 2014;</ns0:ref><ns0:ref type='bibr' target='#b6'>Chen, 2005)</ns0:ref>.</ns0:p><ns0:p>One of the recent studies <ns0:ref type='bibr' target='#b35'>(Rose and Kitchin, 2019)</ns0:ref> focuses on using Scopus data for network analysis.</ns0:p><ns0:p>They have provided an API for researchers to perform useful analyses. Accessing Scopus is possible with Elsevier Developer API Key. However, it requires institutional or authenticated access. Such access is not possible, especially for developing countries <ns0:ref type='bibr' target='#b14'>(Herzog et al., 2020)</ns0:ref>. Although our work is similar to <ns0:ref type='bibr' target='#b35'>Rose and Kitchin (2019)</ns0:ref> in using Python for analysing scientific network, it is different in two aspects.</ns0:p><ns0:p>Firstly, we are working with OpenCitatons data using Crossref. Secondly, we have not developed an API interface that needs maintenance and support since Crossref, NetworkX and SNAP fulfil the purpose. <ns0:ref type='bibr' target='#b6'>Chen (2005)</ns0:ref> discusses the identification of highly cited clusters of a scientific network. The pivotal points in the scientific network are captured using betweenness centrality. The author uses clinical evidence data associated with reducing risks of heart disease to illustrate the approach. They have discussed the design of the CiteSpace tool and its new feature for identifying pivotal points. They used betweenness centrality to identify pathways between thematic clusters. Nodes with high betweenness centrality are potential pivotal points in clustering the scientific network. Instead of a graphical software tool, we propose to use Python scripts. It gives more flexibility for advance analysis. For a detailed survey, we would refer the interested reader to <ns0:ref type='bibr' target='#b24'>(Moral-Mu&#241;oz et al., 2020)</ns0:ref>.</ns0:p><ns0:p>Usage of open metadata are gaining popularity. On the other hand, publicly available metadata has its limitations with completeness and verification. <ns0:ref type='bibr' target='#b15'>Iorio et al. (2019)</ns0:ref> concludes that using OpenCitatons data for evaluation purpose is not enough due to the unavailability of complete data. However, more than half of the records are available in comparison to WoS and Scopus. A similar evaluation is also done by <ns0:ref type='bibr' target='#b29'>Nishioka and F&#228;rber (2019)</ns0:ref> and <ns0:ref type='bibr' target='#b21'>Mart&#237;n-Mart&#237;n et al. (2020)</ns0:ref>. Further, there are different approaches to augment the current OpenCitatons data <ns0:ref type='bibr' target='#b7'>(Daquino et al., 2018;</ns0:ref><ns0:ref type='bibr'>Heibi et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b33'>Peroni and Shotton, 2020)</ns0:ref>. <ns0:ref type='bibr' target='#b16'>Kami&#324;ska (2018)</ns0:ref> discusses a case study for using OpenCitatons data for visualising citation network. <ns0:ref type='bibr' target='#b44'>Zhu et al. (2020)</ns0:ref> hypothesise that it would be easier for masses to adopt OpenCitatons data for bibliometric analysis.</ns0:p></ns0:div> <ns0:div><ns0:head>METHODOLOGY</ns0:head><ns0:p>This section details a systematic workflow from data fetching to analysis. A series of steps are required to apply centrality analysis on the author collaboration and author citation networks. Utilising the article citation network, available as citation index, these networks get created. All scripts were executed on</ns0:p><ns0:p>Windows Server machine having Quad-Core AMD Opteron(TM) Processor 6272 with 128 GB RAM installed. The initial processing of data requires heavy computation and memory once. Later, the data are converted to a compressed binary format using libraries for processing large networks. It can run on any standard laptop machine. Below, we provide details of the workflow to create scientific networks.</ns0:p><ns0:p>Although the case study is limited to data of SCIM, we have made the process automated. This automation helps applying the same script for other journals with minimum changes.</ns0:p><ns0:p>Overview of the process is shown in Figure <ns0:ref type='figure' target='#fig_3'>2</ns0:ref> and further details about each of the following steps are documented separately. Each of the steps is further distributed with three sub-steps for clarity and batch execution.</ns0:p><ns0:p>Step 1 Download the citation index available as COCI <ns0:ref type='bibr'>(Heibi et al., 2019)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>6/14</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:3:1:NEW 27 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>Step 2 Download the metadata for given ISSN through Crossref.</ns0:p><ns0:p>Step 3 Fetch the ego network from COCI data for the DOIs of respective ISSN.</ns0:p><ns0:p>Step 4 Merge these data to create scientific networks.</ns0:p><ns0:p>Step 5 Apply the centrality analysis on these networks. Python scripts are uploaded as Supplemental Files which can also be accessed on GitHub <ns0:ref type='bibr' target='#b5'>(Butt and Faizi, 2020)</ns0:ref>. It gives replication and reuse of this study for other ISSN or bibliometric analyses for different network types. Details are provided below for the understanding of this study.</ns0:p></ns0:div> <ns0:div><ns0:head>Load citation network</ns0:head><ns0:p>Summary of the sub-steps to load citation network is shown in Figure <ns0:ref type='figure'>3</ns0:ref>. Below we define the sub-steps to convert the COCI data to use in Python libraries for network processing. This step is computation and memory intensive but needs to be performed only once.</ns0:p></ns0:div> <ns0:div><ns0:head>Figure 3.</ns0:head><ns0:p>Step 1 of the workflow with details of creating the citation network. Sub-steps are applied sequentially.</ns0:p></ns0:div> <ns0:div><ns0:head>7/14</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:3:1:NEW 27 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>Download COCI data COCI is manually downloaded from (OpenCitations, 2020). The 15 GB Zip file extracts to 98 GB set of files. Loading this data in-memory resulted in memory-overflow even when using 128 GB RAM. Therefore, in the next step, we remove the columns other than citing and cited. These two columns are used to create the article citation network.</ns0:p><ns0:p>Convert COCI data to edge list This step is needed to convert the COCI data to an edge list format. In this format, two nodes on each row signify an edge. This format is supported by SNAP <ns0:ref type='bibr' target='#b18'>(Leskovec and Sosi&#269;, 2016)</ns0:ref> for processing large-scale network data such as COCI. After this step, the edge list file is approx 35 GB. We convert the COCI from comma-separated-values (CSV) to space-separated-values having citing and cited columns. It is the only format supported by SNAP for bulk upload. Some formatting corrections are done for removing extra CR/LF and quotes. It hampers the loading process of SNAP. We have failed to load the same files with other libraries which are relatively more intuitive but not as powerful as SNAP <ns0:ref type='bibr' target='#b18'>(Leskovec and Sosi&#269;, 2016)</ns0:ref>. However, we later discuss how this data can be used with other libraries. Details to save network in a format supported by most network processing libraries is provided in subsequent steps.</ns0:p><ns0:p>Save COCI as binary Loading 35 GB edge list in-memory using SNAP takes approx 5.5 hours. Since the edge labels are DOI in the COCI data, therefore they are saved as strings. However, this slows down further processing so strings are converted to a hash file. There are two binary files generated when loading the COCI data in SNAP. First is DOIDirected.graph file which contains the directed citation network of COCI with integer node labels. Second is DOIMapping.hash which maps the integer node label to respective DOI. We save loaded graph as binary files for further computations. Loading binary file in-memory takes a few minutes, compared to hours for loading CSV data. Downside is that additional columns of COCI are currently not being utilised. DOIMapping.hash is simply a node list where node number is mapped to its label (DOI). DOIDirected.graph is an edge list on node number. Using numeric labels makes SNAP less intuitive but more powerful since computations are much faster when integer labels are used. The mapping to string labels is possible with the node list.</ns0:p></ns0:div> <ns0:div><ns0:head>Fetching Crossref metadata</ns0:head><ns0:p>Summary of the sub-steps to download Crossref metadata are shown in Figure <ns0:ref type='figure' target='#fig_4'>4</ns0:ref>. Below, we define the sub-steps to fetch the citation metadata and converting it to list of authors and DOIs. These steps only</ns0:p><ns0:p>give API string to fetch data for a single journal. However, it is possible to fetch data with other filters and details are available in Crossref documentation. The metadata downloaded via different filters is in a similar format which makes this script reusable for a variety of tasks. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>Fetch author(s) list from data Once data are fetched from Crossref as JSON we populate the list of authors. We extract authors from the previous downloaded JSON. It is important to note that we do not apply any technique for author name disambiguation and rely on Crossref for correct author names.</ns0:p><ns0:p>Although this is problematic for further analysis. Corrected data from a single source is much more efficient than using local methods of cleaning. A similar approach is used by MAG <ns0:ref type='bibr' target='#b42'>(Wang et al., 2020)</ns0:ref>.</ns0:p><ns0:p>Fetch DOI list from data Once data are fetched from Crossref as JSON we populate the list of DOI.</ns0:p><ns0:p>DOIs are extracted from the previously downloaded JSON. Although the purpose of fetching DOI is redundant, it's replica script is created to suggest that analysis with only given DOI list is also possible.</ns0:p><ns0:p>So the previous two sub-steps can be ignored if analysing a specific journal is not needed. If the list of DOI is fetched from an external source then it can be easily incorporated in this workflow.</ns0:p></ns0:div> <ns0:div><ns0:head>Creating ego network</ns0:head><ns0:p>Summary of the sub-steps to create ego network are shown in Figure <ns0:ref type='figure'>5</ns0:ref>. Below, we define the sub-steps to create ego network. This is an optional step. Iterating this step multiple times will grow the network as desired. This step is not used in the case study, however, with publicly accessible metadata it is easier to scale our approach. Further, this step justifies our approach of using SNAP over other network processing libraries. The process of creating the ego network is not only fast but intuitive to code due to a variety of functions available in the extensive library documentation. These functions make it easier to access the nodes in both directions of an edge. Also, the hash with integer labels makes the sub-graph computation faster than using string labels.</ns0:p></ns0:div> <ns0:div><ns0:head>Figure 5.</ns0:head><ns0:p>Step 3 of the workflow with details of creating the ego network. Sub-steps are applied sequentially, and may be iterated to create the next level of ego network.</ns0:p><ns0:p>Load COCI binary to fetch subgraph After loading a binary file of COCI, a subset of the graph is fetched with nodes linked at one level apart. These nodes are either cited-by or cite the existing articles.</ns0:p><ns0:p>Processing a subgraph from 625M edges takes a few minutes on a Core i5 laptop with 16 GB RAM.</ns0:p><ns0:p>Crossref dump for egonet Crossref data is fetched for all DOIs of article ego network created in the previous step. First, all data is downloaded and then it is processed to create the network. Depending on the size of the network, the number of ego levels, and connectivity bandwidth this process may continue from hours to days. Once a local copy of data is available this delay can be reduced. Since we do not have access to complete dump of Crossref, we could not identify that whether these same scripts can be reused.</ns0:p><ns0:p>We assume that there would be few changes required to access the data locally. Manuscript to be reviewed</ns0:p><ns0:p>Computer Science co-citation network within the list but they are ignored for two reasons. First, we did not find much evidence of centrality analysis on these networks. Secondly, the processing time for creating these networks for a very large citation network is relatively much longer than creating author collaboration or author citation network. These networks are created by making an edge list for authors who have collaborated or cited each other, respectively.</ns0:p></ns0:div> <ns0:div><ns0:head>Figure 6.</ns0:head><ns0:p>Step 4 of the workflow with details of creating different scientific networks. Sub-steps are applied sequentially.</ns0:p></ns0:div> <ns0:div><ns0:head>Create article citation network</ns0:head><ns0:p>Once the list of DOI is available it is used to fetch subgraph of article citation network. Article citation network is fetched as a subgraph from COCI. Further, it saves the same graph as a binary file for further analysis. Also, the CSV file can be used with any graph processing library (such as NetworkX) while binary file can be read using SNAP.</ns0:p><ns0:p>betweenness, eigenvector and closeness centrality, respectively. Each section has two columns with the left column showing rank from <ns0:ref type='bibr' target='#b23'>Milojevi&#263; (2014)</ns0:ref> and the right column shows the rank calculated for the same author using our workflow. It is pertinent to note that a very hand-on approach is given in Milojevi&#263; (2014). However, due to access restriction of WoS and its unaffordability for developing countries such useful analysis is only limited to researchers of specific institutes having WoS subscription <ns0:ref type='bibr' target='#b40'>(Waltman and Larivi&#232;re, 2020)</ns0:ref>. This highlights the importance of our workflow to provide access to any individual who can download the publicly available metadata. <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014)</ns0:ref> and current study. Table is divided into 4 sections for each centrality measure. The left column in each section showing the rank from <ns0:ref type='bibr' target='#b23'>(Milojevi&#263;, 2014)</ns0:ref>, and the right column showing the rank calculated by our workflow.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSION AND FUTURE WORK</ns0:head><ns0:p>Scientific networks rely on completion of data <ns0:ref type='bibr' target='#b45'>(Zingg et al., 2020)</ns0:ref>. Although the field has existed for more than 50 years <ns0:ref type='bibr' target='#b34'>(Price, 1965)</ns0:ref> but the limitations on data access have not helped to reach its true potential.</ns0:p><ns0:p>We aim that with the availability of publicly available citation metadata <ns0:ref type='bibr' target='#b40'>(Waltman and Larivi&#232;re, 2020)</ns0:ref> and a scripted workflow to access it <ns0:ref type='bibr' target='#b5'>(Butt and Faizi, 2020)</ns0:ref> a researcher from any field will be able to analyse the scientific networks. Its application can be vast, from identifying reviewers for a manuscript (based on article's references) to a graduate student finding a supervisor (through collaboration network).</ns0:p><ns0:p>The time it takes to completely execute the workflow scripts is well under an hour, barring the two time intensive steps. First, saving citation index as a binary file which needs to be done only once. Second, downloading Crossref DOI files for individual nodes of ego-centered network can be optimised with a local copy. The workflow provides a means for fast and interactive analysis.</ns0:p><ns0:p>Since using graphical tools is easier than executing the scripts so a future application of this study is to create a front-end tool. A web-based portal is also under construction where the user may be able to select the date range along with other filters, and the system will initiate the scripts at the back-end. This way the researchers who are not familiar with programming can also benefit. It would enhance the capability and usefulness of this workflow. Techniques for author name disambiguation and partial counting have not been included. For effective analysis these need to be incorporated in future.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. A toy network showing nodes with high centrality for different measures. A. shows high farness centrality since the node (a) has the maximum average distance to other nodes. B. shows high clustering coefficient since neighbours of the node (c) are all connected as well.C. shows high betweenness centrality since the highest number of shortest paths will go from the node (i) and (k) since they are bridging two parts of the network. D. shows high degree centrality as both the nodes (e) and (n) have highest count of neighbours. E. shows high eigenvector centrality since node (e) is connected to many neighbours with a relatively higher degree. F. shows high closeness centrality as the average distance from nodes (i), (j) and (k) are minimum to other nodes.</ns0:figDesc><ns0:graphic coords='3,141.73,330.41,413.57,232.28' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF | (CS-2020:08:52217:3:1:NEW 27 Jan 2021) Manuscript to be reviewed Computer Science et al. (2019) who concludes that collaboration of young scientist with top-ranked co-authors has a high probability of future success. Massucci and Docampo (2019) applied the PageRank algorithm on a university citation network.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc><ns0:ref type='bibr' target='#b39'>Waheed et al. (2019)</ns0:ref> discusses the use of centrality measures on multiple scientific networks to improve article recommendation. They filter the citation network to five levels in cited-by and citing directions. Evaluating a large-scale network available at AMiner they proposed a hybrid recommendations strategy. It includes different centrality measures on author collaboration network, author citation network and article citation network.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Workflow for analysing scientific networks. The pyramid shows the refinement of data at every step. COCI contains approximately 625 M edges. It gets reduced as a subset of nodes fetched for respective ISSN. Finally, the top of the pyramid shows the output in the form of a few nodes identified with high centrality.</ns0:figDesc><ns0:graphic coords='8,141.73,166.17,413.59,175.53' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. Step 2 of the workflow with details of fetching metadata from Crossref API. Sub-steps are applied sequentially.</ns0:figDesc><ns0:graphic coords='9,141.73,477.38,413.59,150.71' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>DOI</ns0:head><ns0:label /><ns0:figDesc>and author list extraction Processing of ego network for authors is similar to nodes of SCIM downloaded earlier. However, the connecting nodes fetched in subgraph above are added and their respective author details are downloaded.Creating scientific network(s)Summary of the sub-steps to create scientific networks are shown in Figure6. Once all the data are pre-processed this step creates different types of network. We can also add bibliographic coupling and 9/14 PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:3:1:NEW 27 Jan 2021)</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='11,141.73,137.30,413.59,183.64' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='12,141.73,63.78,413.59,249.31' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>has used COCI to evaluate books scholarship. With a scripted workflow, we Review of studies applying social network analysis on scientific networks.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Study</ns0:cell><ns0:cell>Bibliographic</ns0:cell><ns0:cell>Scientific Network(s)</ns0:cell><ns0:cell>Social Network Analysis</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Data Source</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Ding (2011)</ns0:cell><ns0:cell>WoS</ns0:cell><ns0:cell>Author Citation</ns0:cell><ns0:cell>Weighted PageRank</ns0:cell></ns0:row><ns0:row><ns0:cell>Abbasi et al.</ns0:cell><ns0:cell>Scopus</ns0:cell><ns0:cell>Author Collaboration</ns0:cell><ns0:cell>Degree, Betweenness, Close-</ns0:cell></ns0:row><ns0:row><ns0:cell>(2012)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>ness Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>Ortega (2014)</ns0:cell><ns0:cell>MAG</ns0:cell><ns0:cell>Co-Author Ego Network</ns0:cell><ns0:cell>Clustering Coefficient, Degree</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>and Betweenness Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>Milojevi&#263;</ns0:cell><ns0:cell>WoS</ns0:cell><ns0:cell>Author Collaboration and Cita-</ns0:cell><ns0:cell>Degree, Betweenness, Close-</ns0:cell></ns0:row><ns0:row><ns0:cell>(2014)</ns0:cell><ns0:cell /><ns0:cell>tion, Article Citation</ns0:cell><ns0:cell>ness, Eigenvector Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>Waltman and</ns0:cell><ns0:cell>WoS</ns0:cell><ns0:cell>Journal Citation Network</ns0:cell><ns0:cell>PageRank</ns0:cell></ns0:row><ns0:row><ns0:cell>Yan (2014)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Xu and Peke-</ns0:cell><ns0:cell>Manual</ns0:cell><ns0:cell>Author Citation</ns0:cell><ns0:cell>PageRank and Degree Central-</ns0:cell></ns0:row><ns0:row><ns0:cell>lis (2015)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>ity</ns0:cell></ns0:row><ns0:row><ns0:cell>Leydesdorff</ns0:cell><ns0:cell>WoS/JCR</ns0:cell><ns0:cell>Journal Citation</ns0:cell><ns0:cell>Betweenness Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>et al. (2018)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Lee (2019)</ns0:cell><ns0:cell>Scopus</ns0:cell><ns0:cell>Author Collaboration</ns0:cell><ns0:cell>Degree and Betweenness Cen-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>trality, Clustering Coefficient</ns0:cell></ns0:row><ns0:row><ns0:cell>Massucci and</ns0:cell><ns0:cell>WoS</ns0:cell><ns0:cell>Institutional Citation</ns0:cell><ns0:cell>PageRank</ns0:cell></ns0:row><ns0:row><ns0:cell>Docampo</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>(2019)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Singh and Jo-</ns0:cell><ns0:cell>APS Journals</ns0:cell><ns0:cell>Author Collaboration</ns0:cell><ns0:cell>Centrality, Community Detec-</ns0:cell></ns0:row><ns0:row><ns0:cell>lad (2019)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>tion</ns0:cell></ns0:row><ns0:row><ns0:cell>Van den Besse-</ns0:cell><ns0:cell>Manual</ns0:cell><ns0:cell>Researchers Ego Network</ns0:cell><ns0:cell>Clustering coefficient, eigenvec-</ns0:cell></ns0:row><ns0:row><ns0:cell>laar and Sand-</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>tor Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell>str&#246;m (2019)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Waheed et al.</ns0:cell><ns0:cell>DBLP, ACM,</ns0:cell><ns0:cell>Author Collaboration and Ci-</ns0:cell><ns0:cell>Degree, Betweenness, Close-</ns0:cell></ns0:row><ns0:row><ns0:cell>(2019)</ns0:cell><ns0:cell>MAG</ns0:cell><ns0:cell>tation, Article Citation, Co-</ns0:cell><ns0:cell>ness, Eigenvector Centrality</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>citation and Bibliographic Cou-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>pling</ns0:cell><ns0:cell /></ns0:row></ns0:table><ns0:note>5/14PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:3:1:NEW 27 Jan 2021)Manuscript to be reviewed Computer Science</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Comparison of ranks by previous study</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Collaborator</ns0:cell><ns0:cell cols='8'>Degree Rank Betweenness Rank Eigenvector Rank Closeness Rank</ns0:cell></ns0:row><ns0:row><ns0:cell>Name</ns0:cell><ns0:cell cols='3'>Prev Curr Prev</ns0:cell><ns0:cell>Curr</ns0:cell><ns0:cell>Prev</ns0:cell><ns0:cell>Curr</ns0:cell><ns0:cell>Prev</ns0:cell><ns0:cell>Curr</ns0:cell></ns0:row><ns0:row><ns0:cell>Glanzel, W</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1</ns0:cell></ns0:row><ns0:row><ns0:cell>Rousseau, R</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>2</ns0:cell></ns0:row><ns0:row><ns0:cell>DeMoya-Anegon, F</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>8</ns0:cell><ns0:cell>12</ns0:cell><ns0:cell>20</ns0:cell><ns0:cell>26</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell>15</ns0:cell></ns0:row><ns0:row><ns0:cell>Klingsporn, B</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>21</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>89</ns0:cell><ns0:cell>121</ns0:cell><ns0:cell>174</ns0:cell><ns0:cell>144</ns0:cell></ns0:row><ns0:row><ns0:cell>Ho, Ys</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>22</ns0:cell><ns0:cell>125</ns0:cell><ns0:cell>2096</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>613</ns0:cell><ns0:cell>575</ns0:cell></ns0:row><ns0:row><ns0:cell>Thijs, B</ns0:cell><ns0:cell>63</ns0:cell><ns0:cell>51</ns0:cell><ns0:cell>65</ns0:cell><ns0:cell>44</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>10</ns0:cell><ns0:cell>1710</ns0:cell></ns0:row><ns0:row><ns0:cell>Schubert,A</ns0:cell><ns0:cell>36</ns0:cell><ns0:cell>48</ns0:cell><ns0:cell>38</ns0:cell><ns0:cell>28</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>18</ns0:cell><ns0:cell>27</ns0:cell><ns0:cell>24</ns0:cell></ns0:row><ns0:row><ns0:cell>Debackere, K</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>15</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>5</ns0:cell></ns0:row><ns0:row><ns0:cell>Schlemmer, B</ns0:cell><ns0:cell>670</ns0:cell><ns0:cell>832</ns0:cell><ns0:cell>382</ns0:cell><ns0:cell>962</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>808</ns0:cell><ns0:cell>33</ns0:cell><ns0:cell>37</ns0:cell></ns0:row><ns0:row><ns0:cell>Meyer, M</ns0:cell><ns0:cell>43</ns0:cell><ns0:cell>39</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>14</ns0:cell><ns0:cell>9</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>3</ns0:cell></ns0:row><ns0:row><ns0:cell>Leydesdorff, L</ns0:cell><ns0:cell>54</ns0:cell><ns0:cell>35</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>46</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>42</ns0:cell><ns0:cell>44</ns0:cell></ns0:row><ns0:row><ns0:cell>Rafols,I</ns0:cell><ns0:cell>1058</ns0:cell><ns0:cell>387</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>23</ns0:cell><ns0:cell>83</ns0:cell><ns0:cell>239</ns0:cell><ns0:cell>45</ns0:cell><ns0:cell>49</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot' n='14'>/14 PeerJ Comput. Sci. reviewing PDF | (CS-2020:08:52217:3:1:NEW 27 Jan 2021) Manuscript to be reviewed Computer Science</ns0:note> </ns0:body> "

"Editor, PeerJ CS 21 Jan 2021 Thank you for your encouraging comments. We have revised language of the manuscript. Abstract and introduction have been restructured to conform to the comments. Also, GitHub readme has been updated. Please see point-wise response below. Thanks, Bilal, Rafi and Sabih ############################################################################ Thanks again for your revision. I think that the current form of the article is fine for being accepted for publication in PeerJ Computer Science, pending a few additional suggestions that can be addressed directly on the camera-ready version of the paper – and that I will personally check before publishing the article. 1. In the new part of the introduction, you refer to a 'common researcher'. However, it is not clear at all to what that 'common' actually refers to, in particular in comparison to what? What is an 'uncommon researcher' then? I think it is important to clarify explicitly what kind of users you are going to help with the implementation of the workflow you are proposing. Is your work of any help to researchers (in what? Scientometrics?) with no expertise in programming? Or, does it address issues that researchers with expertise in programming but no expertise in Scientometrics may have in retrieving and analysing these data? Or, again, is that done for helping data scientists? etc. Thus, you need to clarify in the introduction which specific users (i.e. kinds of researchers) are you going to help with your computational workflow. >> response: Our primary focus is to target bibliometricians, however, researchers looking to inspect their own field may utilize the workflow as well. A basic understanding of programming is required. Having said that, this workflow may be utilized by a front-end developer to create a graphical tool. Therefore, we do not want to explicitly mention a user base. We developed a prototype dashboard and willing to enhance it in future. Just sharing a sample image. 2. When you refer to open source software, such as NetworkX and SNAP, please mention it as it is. In particular, 'open access' should not be used with software, 'open source' should. >> response: Correction done. 3. The license specified in the release on GitHub (https://github.com/bilal-dsu/Guru-workflow/tree/v1.0) is CC0, which does not apply to software applications. Please, choose an appropriate license to use for releasing the software - e.g. see the list at https://opensource.org/licenses. >> response: Updated to MIT License. 4. In the GitHub readme, there should be explicitly stated how to call every single Python script developed, since some of them actually take in input parameters, and they are not defined in the text. Suppose to be one of your users. By reading the text it is clear what each script does, but it is not clear how to run it properly. >> response: A separate batch file is provided. Details have also been added on readme. 5. You say that your focus is 'to have a systematic workflow to identify prominent authors (gurus) using publicly available metadata for citations'. However, as far as I understood, the point is slightly different. Indeed, the workflow you devised is for collecting data and calculating metrics that then **can be used** to identify gurus, but the identification of gurus is not the focus of the present work. Thus, please avoid stressing too much on this aspect. This is also reflected by the fact you are presenting a case study to show one possible use of the workflow and not full comparative research on gurus identification. Please, relax a bit your claim about what this article is about. >> response: Along with restructuring abstract and introduction, we have also modified the title to reflect this understanding. 6. In your answers to reviewers, you say that 'Research Question is merged with the introduction section'. However, I cannot see any research question stated there. Actually, it seems that you have totally removed it from the article. While this can be fine, after looking at your answer I expected to find it in the introduction of the revision. Is the current form (i.e. no research questions) correct, or did you miss to add it in the introduction? >> response: Current form is correct. 7. In the conclusion you state that 'for case study, some manual work was also done to sort and format the results, however, it can also be scripted in future as it does not hamper the workflow and can be performed as a standalone'. I thought that the workflow was complete, and thus it could retrieve all the data you need to perform the case study. But here you say that 'manual work' (outside of the workflow, I presume) was needed to address the case study. I think you should clarify this passage, otherwise, it seems that, in the end, the case study is not reproducible despite the workflow you implemented. >> response: Manual work incorporated was to enter the ranks in Table 2. Workflow retrieves the complete data. 8. I would suggest revising the sentence in the abstract, i.e. 'Any study on co-authorship may not need all the citation links, however, for a holistic view citation links may also be needed', in a way which is more compliant with the reviewer's comment in the previous round of reviews. In particular, it may be stated that while studies on co-authorship do not need, usually, information about the citation network, having citation links would enable a more complete and holistic view of the possible relations among authors. >> response: We have placed the suggested sentence in the introduction and have also made a relevant citation to it. 9. Please, check again the English of the whole article, since there are several typos, mistakes and long and ambiguous sentences that should be rewritten to drastically improve the readability of the text. >> response: Text has been rewritten. 10. Typos: - libraries of Python --> Python libraries - line 91: the citation should be to (Heibi et al., 2019), not to (Peroni and Shotton, 2020) - It seems that the DOI of the Guru script is not correct - References: please, check the consistency of all the references, and add DOI URLs when possible >> response: Incorporated. "

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