Literature DB >> 29036325

It's all in the timing: calibrating temporal penalties for biomedical data sharing.

Weiyi Xia1,2, Zhiyu Wan3,2, Zhijun Yin3,4,2, James Gaupp1,2, Yongtai Liu3,2, Ellen Wright Clayton5,6,7,2, Murat Kantarcioglu8, Yevgeniy Vorobeychik1,3,2, Bradley A Malin1,3,4,2.   

Abstract

Objective: Biomedical science is driven by datasets that are being accumulated at an unprecedented rate, with ever-growing volume and richness. There are various initiatives to make these datasets more widely available to recipients who sign Data Use Certificate agreements, whereby penalties are levied for violations. A particularly popular penalty is the temporary revocation, often for several months, of the recipient's data usage rights. This policy is based on the assumption that the value of biomedical research data depreciates significantly over time; however, no studies have been performed to substantiate this belief. This study investigates whether this assumption holds true and the data science policy implications.
Methods: This study tests the hypothesis that the value of data for scientific investigators, in terms of the impact of the publications based on the data, decreases over time. The hypothesis is tested formally through a mixed linear effects model using approximately 1200 publications between 2007 and 2013 that used datasets from the Database of Genotypes and Phenotypes, a data-sharing initiative of the National Institutes of Health.
Results: The analysis shows that the impact factors for publications based on Database of Genotypes and Phenotypes datasets depreciate in a statistically significant manner. However, we further discover that the depreciation rate is slow, only ∼10% per year, on average.
Conclusion: The enduring value of data for subsequent studies implies that revoking usage for short periods of time may not sufficiently deter those who would violate Data Use Certificate agreements and that alternative penalty mechanisms may need to be invoked.
© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Keywords:  biomedical data science; data sharing; economics of data; genomics; policy

Mesh:

Year:  2018        PMID: 29036325      PMCID: PMC6080807          DOI: 10.1093/jamia/ocx101

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


  15 in total

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Journal:  J Am Med Inform Assoc       Date:  2015-07-03       Impact factor: 4.497

2.  The history and meaning of the journal impact factor.

Authors:  Eugene Garfield
Journal:  JAMA       Date:  2006-01-04       Impact factor: 56.272

3.  The NCBI dbGaP database of genotypes and phenotypes.

Authors:  Matthew D Mailman; Michael Feolo; Yumi Jin; Masato Kimura; Kimberly Tryka; Rinat Bagoutdinov; Luning Hao; Anne Kiang; Justin Paschall; Lon Phan; Natalia Popova; Stephanie Pretel; Lora Ziyabari; Moira Lee; Yu Shao; Zhen Y Wang; Karl Sirotkin; Minghong Ward; Michael Kholodov; Kerry Zbicz; Jeffrey Beck; Michael Kimelman; Sergey Shevelev; Don Preuss; Eugene Yaschenko; Alan Graeff; James Ostell; Stephen T Sherry
Journal:  Nat Genet       Date:  2007-10       Impact factor: 38.330

4.  Moving forward: breaking the cycle of mistrust between American Indians and researchers.

Authors:  Christina M Pacheco; Sean M Daley; Travis Brown; Melissa Filippi; K Allen Greiner; Christine M Daley
Journal:  Am J Public Health       Date:  2013-10-17       Impact factor: 9.308

5.  Data archiving is a good investment.

Authors:  Heather A Piwowar; Todd J Vision; Michael C Whitlock
Journal:  Nature       Date:  2011-05-19       Impact factor: 49.962

6.  Research ethics. The complexities of genomic identifiability.

Authors:  Laura L Rodriguez; Lisa D Brooks; Judith H Greenberg; Eric D Green
Journal:  Science       Date:  2013-01-18       Impact factor: 47.728

7.  The inevitable application of big data to health care.

Authors:  Travis B Murdoch; Allan S Detsky
Journal:  JAMA       Date:  2013-04-03       Impact factor: 56.272

8.  Database resources of the National Center for Biotechnology Information.

Authors: 
Journal:  Nucleic Acids Res       Date:  2013-11-19       Impact factor: 16.971

9.  The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data.

Authors:  Ronald Margolis; Leslie Derr; Michelle Dunn; Michael Huerta; Jennie Larkin; Jerry Sheehan; Mark Guyer; Eric D Green
Journal:  J Am Med Inform Assoc       Date:  2014-07-09       Impact factor: 4.497

10.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.

Authors: 
Journal:  Nature       Date:  2007-06-07       Impact factor: 49.962

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  4 in total

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Authors:  Patricia Flatley Brennan; Michael F Chiang; Lucila Ohno-Machado
Journal:  J Am Med Inform Assoc       Date:  2018-01-01       Impact factor: 4.497

2.  Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care.

Authors:  Fadila Zerka; Samir Barakat; Sean Walsh; Marta Bogowicz; Ralph T H Leijenaar; Arthur Jochems; Benjamin Miraglio; David Townend; Philippe Lambin
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4.  Distributed deep learning networks among institutions for medical imaging.

Authors:  Ken Chang; Niranjan Balachandar; Carson Lam; Darvin Yi; James Brown; Andrew Beers; Bruce Rosen; Daniel L Rubin; Jayashree Kalpathy-Cramer
Journal:  J Am Med Inform Assoc       Date:  2018-08-01       Impact factor: 7.942

  4 in total

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