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Independent data stewardship remains a core component of good data governance practice. Yet, there is a need for more robust independent data stewardship models that are able to oversee data-driven, multi-party data sharing, usage and re-usage, which can better incorporate citizen representation, especially in relation to personal data. We propose that data foundations – inspired by Channel Islands' foundations laws – provide a workable model for good data governance not only in the Channel Islands but also elsewhere. A key advantage of this model – in addition to leveraging existing legislation and building on established precedent – is the statutory role of the guardian that is a unique requirement in the Channel Islands, and when interpreted in a data governance model provides the independent data steward. The principal purpose for this paper therefore is to demonstrate why data foundations are well suited to the needs of data sharing initiatives. We further examine how data foundations could be established in practice – and provide key design principles that should be used to guide the design and development of any data foundation

Qualitative data provide rich information on research questions in diverse fields. Recent calls for increased transparency and openness in research emphasize data sharing. However, qualitative data sharing has yet to become the norm internationally and is particularly uncommon in the United States. Guidance for archiving and secondary use of qualitative data is required for progress in this regard. In this study, we review the benefits and concerns associated with qualitative data sharing and then describe the results of a content analysis of guidelines from international repositories that archive qualitative data. A minority of repositories provide qualitative data sharing guidelines. Of the guidelines available, there is substantial variation in whether specific topics are addressed. Some topics, such as removing direct identifiers, are consistently addressed, while others, such as providing an anonymization log, are not. We discuss the implications of our study for education, best practices, and future research.

Presented at the National data integrity conference: data sharing: the how, why, when and when not to share held on June 2-3, 2016 at University of Colorado, Denver, Colorado. The National Data Integrity Conference is a gathering of people sharing new challenges and solutions regarding research data and integrity. This conference aims to provide attendees with both an understanding of data integrity issues and impart practical tools and skills to deal with them. Topics addressed will include data privacy, openness, policy, education and the impacts of sharing data, how to do it, when to do it, and when not to. Speakers and audience members come from diverse fields such as: Academic Research; Information Technology; Quality Assurance; Regulatory Compliance; Private Industry; Grant Funding; Government. ; Professor Scott Denning received his B.A. in Geological Sciences from the University of Maine in 1984, and his M.S. and Ph.D. degrees in Atmospheric Science from Colorado State University in 1993 and 1994. He studied radiometric geochronology, surface water geochemistry, and mountain hydrology before becoming interested in global climate and biogeochemical dynamics. After a two-year postdoctoral appointment modeling global sources and sinks of atmospheric CO2, he spent two years as an Assistant Professor in the Donald Bren School of Environmental Science and Management at the University of California at Santa Barbara. He joined the Atmospheric Science faculty at Colorado State University in 1998, and has served as Director of Education for CMMAP since 2006. He does a lot of outreach about climate change, and takes special delight in engaging hostile audiences. ; PowerPoint presentation given on June 3, 2016.

Presented at the National data integrity conference: data sharing: the how, why, when and when not to share held on June 2-3, 2016 at University of Colorado, Denver, Colorado. The National Data Integrity Conference is a gathering of people sharing new challenges and solutions regarding research data and integrity. This conference aims to provide attendees with both an understanding of data integrity issues and impart practical tools and skills to deal with them. Topics addressed will include data privacy, openness, policy, education and the impacts of sharing data, how to do it, when to do it, and when not to. Speakers and audience members come from diverse fields such as: Academic Research; Information Technology; Quality Assurance; Regulatory Compliance; Private Industry; Grant Funding; Government. ; Professor Scott Denning received his B.A. in Geological Sciences from the University of Maine in 1984, and his M.S. and Ph.D. degrees in Atmospheric Science from Colorado State University in 1993 and 1994. He studied radiometric geochronology, surface water geochemistry, and mountain hydrology before becoming interested in global climate and biogeochemical dynamics. After a two-year postdoctoral appointment modeling global sources and sinks of atmospheric CO2, he spent two years as an Assistant Professor in the Donald Bren School of Environmental Science and Management at the University of California at Santa Barbara. He joined the Atmospheric Science faculty at Colorado State University in 1998, and has served as Director of Education for CMMAP since 2006. He does a lot of outreach about climate change, and takes special delight in engaging hostile audiences. ; PowerPoint presentation given on June 3, 2016.

Along with technological progress, one can observe socio-economic changes taking place, and the transformation of the EU economy into a digital economy is an eloquent example. The scope of this transformation includes data, which plays an important role in the economy. This may be readily inferred from the European Strategy for Data published by the European Commission, which envisages a data-driven economy. The transformation towards a data-agile economy results in certain modification in the legal space. For instance, the proposal for a data governance regulation introduces an entity referred to as a data altruism organisation. The proposed act also requires EU Member States to designate a competent authority. This paper examines the functioning of said organisations and attempts to define their status, and discusses the duties of competent authorities which may possibly supervise the activities of data altruism organisations.

AbstractThis article aimed to capture and understand individual's intentions to share data, focusing on data individuals perceive as most sensitive: healthcare data. The study reviews literature related to the decision‐making process with regard to sharing personal data. The context is the UK National Health Service, and measures from literature are used to analyze individual's intention to share healthcare data. A scale is developed and applied to evaluate the decision to share healthcare data. Measurement constructs include intention to disclose, perceived protection, benefits, risk, subjective norms, and perception of use. Analysis draws on data from 129 survey respondents. Though numerous measurements are reported in literature and used in this study, two predictors dominate intention to disclose healthcare data: perceived information risk (PIR) and perceived societal benefit (PSB), and both are significant. PIR contributes negatively, whereas PSB contributes positively to predict intention. For personal healthcare, the privacy paradox applies as though risk may outweigh benefit people rarely opt out of data sharing. Individuals consciously or unconsciously consider their perception of the risk and broader benefits of data sharing. Both risk and benefit are both significant and important; perceived risk carries more weight than perceived benefits. Organizations need to develop campaigns to very clearly explain risks and benefits of personal data sharing to ensure that individuals can make truly informed decisions.

This special section of JERHRE is in response to the needs of institutions to develop advanced data sharing capabilities. On October 1, 2003, the National Institutes of Health (NIH) initiated a requirement that investigator-initiated proposals for grants with direct costs over $500,000 in any year incorporate plans to accommodate sharing research data. The requirement stipulates that such plans describe the procedures through which shared data would be rendered "free of identifiers that would permit linkages to individual research participants and variables that could lead to deductive disclosure of the identity of individual subjects." ( http://grants2.nih.gov/grants/policy/data_sharing ). We expect that many researchers who deal with human research data are unfamiliar with the procedures presented in the ensuing articles. These sophisticated procedures have been developed to help protect confidentiality of subjects' data in files that are shared, while simultaneously preserving the analytic value of data for secondary users. Among these are procedures developed by government statisticians that include innovative methods to prevent deductive disclosure of identities. More recently, academic researchers and data experts have adapted or extended these methods. Together, these methods aim to achieve both disclosure limitation and retention of key analytic usefulness of the shared data.