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Workshop on Data Governance: Final Report

• Arlington, VA
• December 14-15, 2011
• Supported by NSF #0753138 and #0830944

Abstract

The Internet and related technologies have created new opportunities to advance scientific research, in part by sharing research data sooner and more widely. The ability to discover, access and reuse existing research data has the potential to both improve the reproducibility of research as well as enable new research that builds on prior results in novel ways. Because of this potential there is increased interest from across the research enterprise (researchers, universities, funders, societies, publishers, etc.) in data sharing and related issues. This applies to all types of research, but particularly data-intensive or “big science”, and where data is expensive to produce or is not reproducible. However, our understanding of the legal, regulatory and policy environment surrounding research data lags behind that of other research outputs like publications or conference proceedings. This lack of shared understanding is hindering our ability to develop good policy and improve data sharing and reusability, but it is not yet clear who should take the lead in this area and create the framework for data governance that we currently lack. This workshop was a first attempt to define the issues of data governance, identify short- term activities to clarify and improve the situation, and suggest a long-term research agenda that would allow the research enterprise to create the vision of a truly scalable and interoperable “Web of data" that we believe can take scientific progress to new heights.

Introduction

Data governance is the system of decision rights and responsibilities that describe who can take what actions with what data, when, under what circumstances, and using what methods. It includes laws and policies associated with data, as well as strategies for data quality control and management in the context of an organization. It includes the processes that insure important data are formally managed throughout an organization, including business processes and risk management. Organizations managing data are both traditional and well-defined (e.g. universities) as well as cultural or virtual (e.g. a scientific disciplines or large, international research collaborations). Data governance ensures that data can be trusted and that people are made accountable for actions affecting the data.

Sharing and integrating scientific research data are common requirements for international and interdisciplinary data intensive research collaborations but are often difficult for a variety of technical, cultural, policy and legal reasons. For example, the NSF’s INTEROP and DataNet programs are addressing many of the technical and cultural issues through their funded projects, including DataONE, but the legal and policy issues surrounding data are conspicuously missing from that work. The ultimate success of programs like DataNet depends on scalable data sharing that includes data governance.

Reproducing research – a core scientific principle – also depends on effective sharing of research data along with documentation on its production, processing and analysis workflow (i.e. its provenance) and its formatting and structure. Without access to the supporting data and the means to interpret and compare it, scientific research is not entirely credible and trustworthy, and this access again depends on data governance.

The research community recognizes that data governance issues, such as legal licensing and the related technical issue of attribution of Web-based resources would benefit from wider community discussion. The Data Governance Workshop was convened to discuss:

• Legal/policy issues (e.g. copyrights, sui generis database rights, confidentiality restrictions, licensing and contracts for data);
• Attribution and/or citation requirements (e.g. as required by legal license or desired by researchers);
• Repositories and Preservation (e.g. persistence of data and its citability, persistence of identifiers for data and data creators);
• Discovery and provenance metadata, including its governance (e.g. licenses for metadata);
• Schema/ontology discovery and sharing, including governance (e.g. licenses for ontologies)