Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only
Introduction In January, the National Institutes of Health (NIH) implemented a Data Management and Sharing Policy aiming to leverage data collected during NIH-funded research. The COVID-19 pandemic illustrated that this practice is equally vital for augmenting patient research. In addition, data sha...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2023-12-01
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| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/30/1/e100771.full |
| _version_ | 1797364558536376320 |
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| author | Leo Anthony Celi Yuan Lai Hope Watson Jack Gallifant Alexander P Radunsky Cleva Villanueva Nicole Martinez Judy Gichoya Uyen Kim Huynh |
| author_facet | Leo Anthony Celi Yuan Lai Hope Watson Jack Gallifant Alexander P Radunsky Cleva Villanueva Nicole Martinez Judy Gichoya Uyen Kim Huynh |
| author_sort | Leo Anthony Celi |
| collection | DOAJ |
| description | Introduction In January, the National Institutes of Health (NIH) implemented a Data Management and Sharing Policy aiming to leverage data collected during NIH-funded research. The COVID-19 pandemic illustrated that this practice is equally vital for augmenting patient research. In addition, data sharing acts as a necessary safeguard against the introduction of analytical biases. While the pandemic provided an opportunity to curtail critical research issues such as reproducibility and validity through data sharing, this did not materialise in practice and became an example of ‘Open Data in Appearance Only’ (ODIAO). Here, we define ODIAO as the intent of data sharing without the occurrence of actual data sharing (eg, material or digital data transfers).Objective Propose a framework that states the main risks associated with data sharing, systematically present risk mitigation strategies and provide examples through a healthcare lens.Methods This framework was informed by critical aspects of both the Open Data Institute and the NIH’s 2023 Data Management and Sharing Policy plan guidelines.Results Through our examination of legal, technical, reputational and commercial categories, we find barriers to data sharing ranging from misinterpretation of General Data Privacy Rule to lack of technical personnel able to execute large data transfers. From this, we deduce that at numerous touchpoints, data sharing is presently too disincentivised to become the norm.Conclusion In order to move towards Open Data, we propose the creation of mechanisms for incentivisation, beginning with recentring data sharing on patient benefits, additional clauses in grant requirements and committees to encourage adherence to data reporting practices. |
| first_indexed | 2024-03-08T16:36:58Z |
| format | Article |
| id | doaj.art-27b252f00a6d4e248d79cbe2a36145dd |
| institution | Directory Open Access Journal |
| issn | 2632-1009 |
| language | English |
| last_indexed | 2024-03-08T16:36:58Z |
| publishDate | 2023-12-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj.art-27b252f00a6d4e248d79cbe2a36145dd2024-01-05T13:50:09ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092023-12-0130110.1136/bmjhci-2023-100771Delivering on NIH data sharing requirements: avoiding Open Data in Appearance OnlyLeo Anthony Celi0Yuan Lai1Hope Watson2Jack Gallifant3Alexander P Radunsky4Cleva Villanueva5Nicole Martinez6Judy Gichoya7Uyen Kim Huynh8Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USADepartment of Urban Planning and Design, Tsinghua University, Beijing, ChinaDBT Labs, Boston, Massachusetts, USAInstitute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USAUniversity of Texas Southwestern Medical Center, Dallas, Texas, USADepartment of Medicine, Instituto Politécnico Nacional, Ciudad de Mexico, MexicoCenter for Biomedical Ethics, Stanford University School of Medicine, Stanford, California, USADepartment of Radiology, Emory University, Atlanta, Georgia, USAUNICEF, New York, New York, USAIntroduction In January, the National Institutes of Health (NIH) implemented a Data Management and Sharing Policy aiming to leverage data collected during NIH-funded research. The COVID-19 pandemic illustrated that this practice is equally vital for augmenting patient research. In addition, data sharing acts as a necessary safeguard against the introduction of analytical biases. While the pandemic provided an opportunity to curtail critical research issues such as reproducibility and validity through data sharing, this did not materialise in practice and became an example of ‘Open Data in Appearance Only’ (ODIAO). Here, we define ODIAO as the intent of data sharing without the occurrence of actual data sharing (eg, material or digital data transfers).Objective Propose a framework that states the main risks associated with data sharing, systematically present risk mitigation strategies and provide examples through a healthcare lens.Methods This framework was informed by critical aspects of both the Open Data Institute and the NIH’s 2023 Data Management and Sharing Policy plan guidelines.Results Through our examination of legal, technical, reputational and commercial categories, we find barriers to data sharing ranging from misinterpretation of General Data Privacy Rule to lack of technical personnel able to execute large data transfers. From this, we deduce that at numerous touchpoints, data sharing is presently too disincentivised to become the norm.Conclusion In order to move towards Open Data, we propose the creation of mechanisms for incentivisation, beginning with recentring data sharing on patient benefits, additional clauses in grant requirements and committees to encourage adherence to data reporting practices.https://informatics.bmj.com/content/30/1/e100771.full |
| spellingShingle | Leo Anthony Celi Yuan Lai Hope Watson Jack Gallifant Alexander P Radunsky Cleva Villanueva Nicole Martinez Judy Gichoya Uyen Kim Huynh Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only BMJ Health & Care Informatics |
| title | Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only |
| title_full | Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only |
| title_fullStr | Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only |
| title_full_unstemmed | Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only |
| title_short | Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only |
| title_sort | delivering on nih data sharing requirements avoiding open data in appearance only |
| url | https://informatics.bmj.com/content/30/1/e100771.full |
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