Update 2024 annual baseline data URLs

README.md

@@ -27,7 +27,7 @@ pretty_name: PubMed
 tags:
 - citation-estimation
 dataset_info:
-- config_name: '2023'
+- config_name: '2024'
   features:
   - name: MedlineCitation
     struct:
@@ -135,10 +135,10 @@ dataset_info:
         dtype: int32
   splits:
   - name: train
-    num_bytes: 52199025303
-    num_examples: 34960700
-  download_size: 41168762331
-  dataset_size: 52199025303
+    num_bytes: 54723097181
+    num_examples: 36555430
+  download_size: 45202943276
+  dataset_size: 54723097181
 ---
 
 # Dataset Card for PubMed
@@ -174,11 +174,18 @@ dataset_info:
 - **Repository:**
 - **Paper:**
 - **Leaderboard:**
-- **Point of Contact:**
+- **Point of Contact:** [National Center for Biotechnology Information](mailto:[email protected])
 
 ### Dataset Summary
 
-NLM produces a baseline set of MEDLINE/PubMed citation records in XML format for download on an annual basis. The annual baseline is released in December of each year. Each day, NLM produces update files that include new, revised and deleted citations. See our documentation page for more information.
+PubMed comprises more than 36 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.
+
+NLM produces a baseline set of PubMed citation records in XML format for download on an annual basis. The annual baseline is released in December of each year.
+- Last Updated December 15, 2023
+
+Each day, NLM produces update files that include new, revised, and deleted citations.
+
+Source: https://ftp.ncbi.nlm.nih.gov/pubmed/README.txt
 
 ### Supported Tasks and Leaderboards
 
@@ -260,13 +267,15 @@ There are no splits in this dataset. It is given as is.
 
 ### Curation Rationale
 
-[More Information Needed]
+The use of "Medline" in an element name does not mean the record represents a citation from a MEDLINE-selected journal. When the NLM DTDs and XML elements were first created, MEDLINE records were the only data exported. Now NLM exports citations other than MEDLINE records. To minimize unnecessary disruption to users of the data, NLM has retained the original element names (e.g., MedlineCitation, MedlineJournalInfo, MedlineTA).
+
+Policies affecting data creation have evolved over the years. Some PubMed records are added or revised well after the cited article was first published. In these cases, on occasion an element that had not yet been created when the article was published may appear on the record. For example, the Abstract element was not created until 1975, but some records published before 1975 but added to PubMed after 1975 contain <Abstract>. It is also possible that an element may be treated differently from the way it would have been treated had the record been created or maintained near the time the article was published. For example, the number of <Author> occurrences can diverge from the policies stated in the NLM author indexing policy (https://pubmed.ncbi.nlm.nih.gov/help/#author-indexing-policy). Lastly, as of October 2016, the publisher of the original article has the capability to edit the PubMed record’s citation data, with the exception of MeSH data, using the PubMed Data Management system. PubMed record data for older citations, therefore, may contain data for elements that didn’t exist when the citation was created.
 
 ### Source Data
 
 #### Initial Data Collection and Normalization
 
-[https://www.nlm.nih.gov/databases/download/pubmed_medline_faq.html]()
+[More Information Needed]
 
 #### Who are the source language producers?
 
@@ -308,7 +317,33 @@ There are no splits in this dataset. It is given as is.
 
 ### Licensing Information
 
-[https://www.nlm.nih.gov/databases/download/terms_and_conditions.html]()
+[National Library of Medicine Terms and Conditions](https://www.nlm.nih.gov/databases/download/terms_and_conditions.html)
+
+Downloading PubMed data from the National Library of Medicine FTP servers indicates your acceptance of the following Terms and Conditions. No charges, usage fees or royalties are paid to NLM for these data.
+
+#### PubMed Specific Terms:
+
+NLM freely provides PubMed data. Please note some abstracts may be protected by copyright.
+
+#### General Terms and Conditions
+
+Users of the data agree to:
+- acknowledge NLM as the source of the data in a clear and conspicuous manner,
+- NOT use the PubMed wordmark or the PubMed logo in association or in connection with user's or any other party's product or service.
+- NOT adopt, use, or seek to register any mark or trade name confusingly similar to or suggestive of the PubMed wordmark or PubMed logo
+- NOT to indicate or imply that NLM/NIH/HHS has endorsed its products/services/applications.
+
+Users who republish or redistribute the data (services, products or raw data) agree to:
+- maintain the most current version of all distributed data, or
+- make known in a clear and conspicuous manner that the products/services/applications do not reflect the most current/accurate data available from NLM.
+
+These data are produced with a reasonable standard of care, but NLM makes no warranties express or implied, including no warranty of merchantability or fitness for particular purpose, regarding the accuracy or completeness of the data. Users agree to hold NLM and the U.S. Government harmless from any liability resulting from errors in the data. NLM disclaims any liability for any consequences due to use, misuse, or interpretation of information contained or not contained in the data.
+
+NLM does not provide legal advice regarding copyright, fair use, or other aspects of intellectual property rights. See the NLM Copyright page:  https://www.nlm.nih.gov/web_policies.html#copyright
+
+NLM reserves the right to change the type and format of its machine-readable data. NLM will take reasonable steps to inform users of any changes to the format of the data before the data are distributed via the announcement section or subscription to email and RSS updates.
+
+The PubMed wordmark and the PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.
 
 ### Citation Information
 

pubmed.py

@@ -16,6 +16,7 @@
 
 
 import copy
+import gzip
 import xml.etree.ElementTree as ET
 
 import datasets
@@ -36,10 +37,7 @@ _HOMEPAGE = "https://www.nlm.nih.gov/databases/download/pubmed_medline.html"
 
 _LICENSE = ""
 
-# The HuggingFace dataset library don't host the datasets but only point to the original files
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-# Note these URLs here are used by MockDownloadManager.create_dummy_data_list
-_URLs = [f"https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed23n{i:04d}.xml.gz" for i in range(1, 1167)]
+_URLs = [f"https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed24n{i:04d}.xml.gz" for i in range(1, 1220)]
 
 
 # Copyright Ferry Boender, released under the MIT license.
@@ -146,7 +144,7 @@ class Pubmed(datasets.GeneratorBasedBuilder):
     """Pubmed citations records"""
 
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="2023", description="The 2023 annual record", version=datasets.Version("3.0.0")),
+        datasets.BuilderConfig(name="2024", description="The 2024 annual record", version=datasets.Version("4.0.0")),
     ]
 
     # FILLED automatically from features
@@ -315,7 +313,7 @@ class Pubmed(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        dl_dir = dl_manager.download_and_extract(_URLs)
+        dl_dir = dl_manager.download(_URLs)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -358,28 +356,29 @@ class Pubmed(datasets.GeneratorBasedBuilder):
         """Yields examples."""
         id_ = 0
         for filename in filenames:
-            try:
-                tree = ET.parse(filename)
-                root = tree.getroot()
-                xmldict = self.xml_to_dictionnary(root)
-            except ET.ParseError:
-                logger.warning(f"Ignoring file {filename}, it is malformed")
-                continue
-
-            for article in xmldict["PubmedArticleSet"]["PubmedArticle"]:
-                self.update_citation(article)
-                new_article = default_article()
-
+            with gzip.open(filename) as f:
                 try:
-                    deepupdate(new_article, article)
-                except Exception:
-                    logger.warning(f"Ignoring article {article}, it is malformed")
+                    tree = ET.parse(f)
+                    root = tree.getroot()
+                    xmldict = self.xml_to_dictionnary(root)
+                except ET.ParseError:
+                    logger.warning(f"Ignoring file {filename}, it is malformed")
                     continue
 
-                try:
-                    _ = self.info.features.encode_example(new_article)
-                except Exception as e:
-                    logger.warning(f"Ignore example because {e}")
-                    continue
-                yield id_, new_article
-                id_ += 1
+                for article in xmldict["PubmedArticleSet"]["PubmedArticle"]:
+                    self.update_citation(article)
+                    new_article = default_article()
+
+                    try:
+                        deepupdate(new_article, article)
+                    except Exception:
+                        logger.warning(f"Ignoring article {article}, it is malformed")
+                        continue
+
+                    try:
+                        _ = self.info.features.encode_example(new_article)
+                    except Exception as e:
+                        logger.warning(f"Ignore example because {e}")
+                        continue
+                    yield id_, new_article
+                    id_ += 1