Piwowar AMIA 2008: Identifying data sharing in biomedical literature

Identifying data sharing in the biomedical literature Heather Piwowar and Wendy Chapman Department of Biomedical Informatics, U of Pittsburgh

Our full paper: Visualized as a “Wordle” (font size ~ word frequency, location and orientation are random)

Created at IBM’s data sharing and visualization site Many Eyes

Our aim: Identify research articles for which the authors have shared their datasets For this research: sharing = submitted to centralized databases

Links between article and data are important

The data provides detail for the results of the article

The article provides detail for the data

Specialized searching methods help us ﬁnd articles OR data... but what about when we want articles WITH data?

How can we ﬁnd articles that have shared their datasets?

Sometimes the links are easy to discover

1. Through database citations: When authors upload data to a database, they have the opportunity to cite the paper that describes the data collection

Unfortunately, the citation is often left blank because the data is submitted before Text the paper is published

2. Through hyperlink urls in the text Authors often reference their datasets within their paper with a website url

But the meaning of the hyperlinks is ambiguous. Sometimes they point to datasets that have been accessed, rather than submitted.

And often the text contains no hyperlinks at all:

3. Through text mining

What if we could extract phrases like “data of the experiment can be accessed at”

full-text phrases containing “... accessed”

“can be accessed” suggests data is shared

BUT “was/were accessed” suggests data reuse!

full-text phrases containing “... downloaded”

“was/were downloaded” suggests data reuse

while “can be downloaded” suggests data sharing

Our aim: Identify research articles for which the authors have shared their raw datasets. Proposed approach: Develop a system to identify statements of shared data from an article’s full text.

Materials: Full text from a subset of the open access literature Database submission citations from ﬁve databases: • Genbank • Protein Data Bank • Gene Expression Omnibus • ArrayExpress • Stanford Microarray Database

Our Gold standard: An article was considered to have a “shared dataset” if the article was cited within the primary submission ﬁeld of a database entry (+ a small amount of manual screening to ﬁnd additional positives based on full text)

Approach: For those articles that mention database names, • Extract a 300-character window around every mention of a database name • Apply various mining algorithms to decide if there is evidence that the authors deposited data from this study in the database

Results: • queried 24 000 articles across 27 journals • 25% of all open access articles mentioned one of the database names (50% Genbank) • development set of 4434 articles training set of 2000 test set of 1028

True positives: 23% of the articles that mentioned a database were cited from within a database submission ﬁeld = evidence that article shared its data!

Three simple methods for identifying sharing Does the excerpt surrounding the database name contain: 1. the word “accession” 2. an accession number 3. a URL

Two complex methods: 4. A manually-derived regular expression to match lexical cues that suggest sharing 5. An automatically-derived bag of words decision tree

Snippet of manually-developed regular expression accessioned added archived we assigned deposited have entered has imported is included + are inserted loaded was lodged were placed be posted been provided registered reported to stored submitted uploaded to

How accurately were these methods able to identify papers with evidence of public database submissions?

Recall: % of papers cited in database submission ﬁelds that were found by our methods

Recall: % of papers cited in database submission ﬁelds that were found by our methods Best method for recall depends on database

Recall: % of papers cited in database submission ﬁelds that were found by our methods “accession” good for some, <url> for others

Recall: % of papers cited in database submission ﬁelds that were found by our methods lexical regular expressions do well overall

Precision: % of papers found by our methods that were cited in database submissions ﬁelds

Precision: % of papers found by our methods that were cited in database submissions ﬁelds lexical regular expressions do well overall, bag-of- words does even better

Precision: % of papers found by our methods that were cited in database submissions ﬁelds Precision of simple patterns depends on database

Precision: % of papers found by our methods that were cited in database submissions ﬁelds Simple patterns do poorly on the most popular databases (those with the most statements of reuse?)

Precision vs. Recall plot of all methods for each database. Diverse!

Relative strength of methods for this task across databases bag of words <lexical patterns> <accession> <url> “accession”

Limitations: • bias due to manual screening of negatives • database-centric classiﬁer • approach requires computational access to literature full text!

Impact: • A recent version that runs in PubMed Central: • could increase GEO article links by 2.6% • by 5.5% annually when all NIH in PMC • double the recall (to 80%), double these estimates • 40 links already added by GEO staff!

Ongoing work: 1. Continue focusing on methods that use existing full-text query interfaces, like PubMed Central 2. Use this tool to evaluate the patterns and prevalence of biomedical research data sharing and reuse

Thanks to the Dept of Biomedical Informatics at the U of Pittsburgh, the NLM for funding through training grant 5 T15 LM007059-22, and everyone who publishes “gold” open access, thereby facilitates reuse of article full text for studies like this. My shared data: www.dbmi.pitt.edu/piwowar Share your research data too!

Our manual filter for additional positive classifications identified more cases in some databases than others: we reclassified 19% of [article,database] cases from ArrayExpress as positive despite an omitted literature link, compared to 11%, 7%, 2%, and 1% for GEO, Genbank, PDB, and SMD respectively (see Table 2 for raw number of cases). The most common situations included: the database entry listed a citation for another paper by the same authors, the entry listed an erroneous PubMed ID, the entry included a citation without a PubMed ID, or the entry had a blank citation field.

Usage? • scientists looking for datasets for reuse • curators looking for primary citations • researchers studying data sharing behaviour

Regular expression • Precise one + • "(b(accession.{0,20}(for|at).{0,100}(is|are)))", • r"(b(raw|original|our|complete|detailed).{0,20}data)", • r"(b(we|have|is|was|were|is|are|be|have|has|been).(exported|gave|given|listed|provided|reported))" • ]) + ")"

Precise Regular expression • we have has is are was were be been accessioned|added|archived|assigned|deposited|entered|imported|included|inserted|loaded|lodged|placed| posted|provided|registered|reported.to|stored|submitted|uploaded.to))", is|are|will.be|made).{0,20}(available|accessible) (be).(accessed|browsed|downloaded|found|obtained|queried|retrieved|searched|viewed) (through|under|as).{0,20}accession (given)|new|received|assigned).{0,20}(accession) (data.{0,20}availability|for public distribution|for.{0,20}release upon publication|for the.{0,20}data.{0,20} generated|from this study have.{0,20}accession|data.{0,10}from this study|access to.{0,20}data.

Stopwords are important!

Recall

Precision

Evaluation • queried 24 000 articles across 27 journals • 25% mentioned one of the database names • development set of 4434 training set of 2000 test set of 1028

Research data http://upload.wikimedia.org/wikipedia/commons/7/76/PeptideMSMS.jpg; http://en.wikipedia.org/wiki/Image:Helices.png; http://en.wikipedia.org/wiki/Image:Heatmap.png; http://en.wikipedia.org/wiki/Image:Microarray2.gif; http://zellig.cpmc.columbia.edu/medlee/demo/; htp://www.plosone.org/article/fetchArticle.action?articleURI=info:doi/10.1371/journal.pone.0000441

Research data PAST MEDICAL HISTORY: Past medical history showed she had superﬁcial phlebitis times two in the past, had non-insulin dependent diabetes mellitus for four years. She had been hypothyroid for three years. HISTORY OF PRESENT ILLNESS: The patient is a 58-year-old female, … http://upload.wikimedia.org/wikipedia/commons/7/76/PeptideMSMS.jpg; http://en.wikipedia.org/wiki/Image:Helices.png; http://en.wikipedia.org/wiki/Image:Heatmap.png; http://en.wikipedia.org/wiki/Image:Microarray2.gif; http://zellig.cpmc.columbia.edu/medlee/demo/; htp://www.plosone.org/article/fetchArticle.action?articleURI=info:doi/10.1371/journal.pone.0000441

Piwowar AMIA 2008: Identifying data sharing in biomedical literature

0 comments

Notes on slide 1

Favorites, Groups & Events