Saskia Hendriks1, Khara M Ramos2, Christine Grady2. 1. From the The Office of Neuroscience Communications and Engagement (S.H., K.M.R.), National Institute of Neurological Disorders and Stroke; and Department of Bioethics (S.H., C.G.), Clinical Center National Institute of Health, Bethesda, MD. saskia.hendriks@nih.gov. 2. From the The Office of Neuroscience Communications and Engagement (S.H., K.M.R.), National Institute of Neurological Disorders and Stroke; and Department of Bioethics (S.H., C.G.), Clinical Center National Institute of Health, Bethesda, MD.
Abstract
BACKGROUND AND OBJECTIVES: In the neurosciences, significant opportunities for sharing individual-level data are underexploited. Commentators suggest various barriers to data sharing, which may need to be addressed. Investigators' perspectives on the main barriers are unclear. Furthermore, bioethicists have raised concerns about the potential misuse of neuroscience data, although discussions are hampered by uncertainty about the potential risks. It is unclear how common sensitive data are obtained and whether investigators judge them as sensitive. METHODS: An online survey was disseminated among 1,190 principal investigators (PIs) of active National Institute of Neurological Disorders and Stroke, National Institute of Mental Health, or NIH Brain Research Through Advancing Innovative Neurotechnologies Initiative grants involving human subject research. RESULTS: A total of 397 investigators responded to the survey (response rate 33%). Most investigators (84%) support efforts to increase sharing of deidentified individual-level data. However, investigators perceive many barriers to data sharing. The largest barriers were costs and time; limited interpretation of the data without understanding the context of data collection; lack of incentives; limited standardization and norms for data acquisition, formatting, and description; and heterogeneity of data types. Several types of data described as sensitive in the literature are common among neuroscience studies, for example, neural correlates of behavior, emotions, or decision making (71%) and/or predictive data (54%). Although most investigators consider it unlikely or extremely unlikely for their research data to be misused to harm individual research participants (82%), the majority were at least slightly concerned about potential harm to individuals if their research data were misused (65%). Investigators with more easily reidentifiable data, data from vulnerable groups, and neural data were more concerned about the likelihood of misuse and/or magnitude of harm of misuse of their research data. DISCUSSION: We hope these data help prioritize the development of tools and strategies to overcome the main barriers to data sharing. Furthermore, these data provide input on what may be sensitive data for which additional safeguards should be considered.
BACKGROUND AND OBJECTIVES: In the neurosciences, significant opportunities for sharing individual-level data are underexploited. Commentators suggest various barriers to data sharing, which may need to be addressed. Investigators' perspectives on the main barriers are unclear. Furthermore, bioethicists have raised concerns about the potential misuse of neuroscience data, although discussions are hampered by uncertainty about the potential risks. It is unclear how common sensitive data are obtained and whether investigators judge them as sensitive. METHODS: An online survey was disseminated among 1,190 principal investigators (PIs) of active National Institute of Neurological Disorders and Stroke, National Institute of Mental Health, or NIH Brain Research Through Advancing Innovative Neurotechnologies Initiative grants involving human subject research. RESULTS: A total of 397 investigators responded to the survey (response rate 33%). Most investigators (84%) support efforts to increase sharing of deidentified individual-level data. However, investigators perceive many barriers to data sharing. The largest barriers were costs and time; limited interpretation of the data without understanding the context of data collection; lack of incentives; limited standardization and norms for data acquisition, formatting, and description; and heterogeneity of data types. Several types of data described as sensitive in the literature are common among neuroscience studies, for example, neural correlates of behavior, emotions, or decision making (71%) and/or predictive data (54%). Although most investigators consider it unlikely or extremely unlikely for their research data to be misused to harm individual research participants (82%), the majority were at least slightly concerned about potential harm to individuals if their research data were misused (65%). Investigators with more easily reidentifiable data, data from vulnerable groups, and neural data were more concerned about the likelihood of misuse and/or magnitude of harm of misuse of their research data. DISCUSSION: We hope these data help prioritize the development of tools and strategies to overcome the main barriers to data sharing. Furthermore, these data provide input on what may be sensitive data for which additional safeguards should be considered.
Authors: Peter Zuk; Clarissa E Sanchez; Kristin Kostick; Laura Torgerson; Katrina A Muñoz; Rebecca Hsu; Lavina Kalwani; Demetrio Sierra-Mercado; Jill O Robinson; Simon Outram; Barbara A Koenig; Stacey Pereira; Amy L McGuire; Gabriel Lázaro-Muñoz Journal: Front Hum Neurosci Date: 2020-12-17 Impact factor: 3.169
Authors: Mark D Wilkinson; Michel Dumontier; I Jsbrand Jan Aalbersberg; Gabrielle Appleton; Myles Axton; Arie Baak; Niklas Blomberg; Jan-Willem Boiten; Luiz Bonino da Silva Santos; Philip E Bourne; Jildau Bouwman; Anthony J Brookes; Tim Clark; Mercè Crosas; Ingrid Dillo; Olivier Dumon; Scott Edmunds; Chris T Evelo; Richard Finkers; Alejandra Gonzalez-Beltran; Alasdair J G Gray; Paul Groth; Carole Goble; Jeffrey S Grethe; Jaap Heringa; Peter A C 't Hoen; Rob Hooft; Tobias Kuhn; Ruben Kok; Joost Kok; Scott J Lusher; Maryann E Martone; Albert Mons; Abel L Packer; Bengt Persson; Philippe Rocca-Serra; Marco Roos; Rene van Schaik; Susanna-Assunta Sansone; Erik Schultes; Thierry Sengstag; Ted Slater; George Strawn; Morris A Swertz; Mark Thompson; Johan van der Lei; Erik van Mulligen; Jan Velterop; Andra Waagmeester; Peter Wittenburg; Katherine Wolstencroft; Jun Zhao; Barend Mons Journal: Sci Data Date: 2016-03-15 Impact factor: 6.444