title: Extraction of data deposition statements from the literature: a method for automatically tracking research results.
abstract: BACKGROUND
  Research in the biomedical domain can have a major impact through open sharing of the data produced. For this reason, it is important to be able to identify instances of data production and deposition for potential re-use. Herein, we report on the automatic identification of data deposition statements in research articles.
  
  
  RESULTS
  We apply machine learning algorithms to sentences extracted from full-text articles in PubMed Central in order to automatically determine whether a given article contains a data deposition statement, and retrieve the specific statements. With an Support Vector Machine classifier using conditional random field determined deposition features, articles containing deposition statements are correctly identified with 81% F-measure. An error analysis shows that almost half of the articles classified as containing a deposition statement by our method but not by the gold standard do indeed contain a deposition statement. In addition, our system was used to process articles in PubMed Central, predicting that a total of 52 932 articles report data deposition, many of which are not currently included in the Secondary Source Identifier [si] field for MEDLINE citations.
  
  
  BACKGROUND
  All annotated datasets described in this study are freely available from the NLM/NCBI website at http://www.ncbi.nlm.nih.gov/CBBresearch/Fellows/Neveol/DepositionDataSets.zip
  
  
  BACKGROUND
  aurelie.neveol@nih.gov; john.wilbur@nih.gov; zhiyong.lu@nih.gov
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: National Center for Biotechnology Information, National Library of Medicine, Bethesda, Maryland 20894, USA. aurelie.neveol@nih.gov
authors: Aurélie
authors: A
authors: Névéol
authors: W John
authors: WJ
authors: Wilbur
authors: Zhiyong
authors: Z
authors: Lu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D030541:Databases, Genetic; D016239:MEDLINE; D008969:Molecular Sequence Data; D009317:National Library of Medicine (U.S.); D039781:PubMed; D060388:Support Vector Machine; D014481:United States