TY - JOUR
T1 - Sizing the problem of improving discovery and access to NIH-Funded data
T2 - A preliminary study
AU - Read, Kevin B.
AU - Sheehan, Jerry R.
AU - Huerta, Michael F.
AU - Knecht, Lou S.
AU - Mork, James G.
AU - Humphreys, Betsy L.
AU - Abhyankar, Swapna
AU - Akiwumi, Olubumi
AU - Bodenreider, Olivier
AU - Davidson, Sally
AU - Fushman, Dina Demner
AU - Edinger, Tracy
AU - Farber, Greg
AU - Gutzman, Karen
AU - Hantakas, Mary Ann
AU - Kochar, Preeti
AU - Larkin, Jennie
AU - Lyster, Peter
AU - McAuliffe, Matt
AU - Mohary, Shari
AU - Ochej, Helen
AU - Printseva, Olga
AU - Rodionov, Oleg
AU - Rodriguez, Laritza
AU - Roy, Suzy
AU - Schmidt, Susan
AU - Shooshan, Sonya
AU - Simpson, Matthew
AU - Sinnot, Corinn
AU - Tate, Samantha
AU - Ward, Janice
AU - Yorks, Melissa
N1 - Funding Information:
This research was supported by the Intramural Research Program of the U.S. National Institutes of Health, National Library of Medicine (NLM) and in part by an appointment to the NLM Associate Fellowship Program sponsored by the National Library of Medicine and administered by the Oak Ridge Institute for Science and Education.
PY - 2015/7/24
Y1 - 2015/7/24
N2 - Objective This study informs efforts to improve the discoverability of and access to biomedical datasets by providing a preliminary estimate of the number and type of datasets generated annually by research funded by the U.S. National Institutes of Health (NIH). It focuses on those datasets that are "invisible" or not deposited in a known repository. Methods We analyzed NIH-funded journal articles that were published in 2011, cited in PubMed and deposited in PubMed Central (PMC) to identify those that indicate data were submitted to a known repository. After excluding those articles, we analyzed a random sample of the remaining articles to estimate how many and what types of invisible datasets were used in each article. Results About 12% of the articles explicitly mention deposition of datasets in recognized repositories, leaving 88% that are invisible datasets. Among articles with invisible datasets, we found an average of 2.9 to 3.4 datasets, suggesting there were approximately 200,000 to 235,000 invisible datasets generated from NIH-funded research published in 2011. Approximately 87% of the invisible datasets consist of data newly collected for the research reported; 13% reflect reuse of existing data. More than 50% of the datasets were derived from live human or non-human animal subjects. Conclusion In addition to providing a rough estimate of the total number of datasets produced per year by NIH-funded researchers, this study identifies additional issues that must be addressed to improve the discoverability of and access to biomedical research data: the definition of a "dataset," determination of which (if any) data are valuable for archiving and preservation, and better methods for estimating the number of datasets of interest. Lack of consensus amongst annotators about the number of datasets in a given article reinforces the need for a principled way of thinking about how to identify and characterize biomedical datasets.
AB - Objective This study informs efforts to improve the discoverability of and access to biomedical datasets by providing a preliminary estimate of the number and type of datasets generated annually by research funded by the U.S. National Institutes of Health (NIH). It focuses on those datasets that are "invisible" or not deposited in a known repository. Methods We analyzed NIH-funded journal articles that were published in 2011, cited in PubMed and deposited in PubMed Central (PMC) to identify those that indicate data were submitted to a known repository. After excluding those articles, we analyzed a random sample of the remaining articles to estimate how many and what types of invisible datasets were used in each article. Results About 12% of the articles explicitly mention deposition of datasets in recognized repositories, leaving 88% that are invisible datasets. Among articles with invisible datasets, we found an average of 2.9 to 3.4 datasets, suggesting there were approximately 200,000 to 235,000 invisible datasets generated from NIH-funded research published in 2011. Approximately 87% of the invisible datasets consist of data newly collected for the research reported; 13% reflect reuse of existing data. More than 50% of the datasets were derived from live human or non-human animal subjects. Conclusion In addition to providing a rough estimate of the total number of datasets produced per year by NIH-funded researchers, this study identifies additional issues that must be addressed to improve the discoverability of and access to biomedical research data: the definition of a "dataset," determination of which (if any) data are valuable for archiving and preservation, and better methods for estimating the number of datasets of interest. Lack of consensus amongst annotators about the number of datasets in a given article reinforces the need for a principled way of thinking about how to identify and characterize biomedical datasets.
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U2 - 10.1371/journal.pone.0132735
DO - 10.1371/journal.pone.0132735
M3 - Article
C2 - 26207759
AN - SCOPUS:84941670899
SN - 1932-6203
VL - 10
JO - PloS one
JF - PloS one
IS - 7
M1 - e0132735
ER -