ArticlePloS one2015
Sizing the Problem of Improving Discovery and Access to NIH-Funded Data: A Preliminary Study.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Metastatic castration-resistant prostate cancer: Academic insights and perspectives through bibliometric analysis.Medicine · 2020Pooled it
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- The NYU Data Catalog: a modular, flexible infrastructure for data discovery.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- RCSB Protein Data Bank resources for structure-facilitated design of mRNA vaccines for existing and emerging viral pathogens.Structure (London, England : 1993) · 2022Article
- Open-access data: A cornerstone for artificial intelligence approaches to protein structure prediction.Structure (London, England : 1993) · 2021Review
- Scientist and data architect collaborate to curate and archive an inner ear electrophysiology data collection.PloS one · 2019Article
- How to earn a reputation as a great partner.Journal of the Medical Library Association : JMLA · 2018Article
- What Do We Know About the Stewardship Gap.Data science journal · 2018Article
- RCSB Protein Data Bank: Sustaining a living digital data resource that enables breakthroughs in scientific research and biomedical education.Protein science : a publication of the Protein Society · 2018Review
- Reproducibility, sharing and progress in nanomaterial databases.Nature nanotechnology · 2017Review
- Developing a data sharing community for spinal cord injury research.Experimental neurology · 2017Review
- PubChem BioAssay: A Decade's Development toward Open High-Throughput Screening Data Sharing.SLAS discovery : advancing life sciences R & D · 2017Article
- Influenza Research Database: An integrated bioinformatics resource for influenza virus research.Nucleic acids research · 2017Article
- Badges to Acknowledge Open Practices: A Simple, Low-Cost, Effective Method for Increasing Transparency.PLoS biology · 2016Article
- Data sharing in psychology.The American psychologistReview
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectiveThis 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.
methodsWe 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.
resultsAbout 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.
conclusionIn 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.