1. Data Sharing entails shared responsibilities
Six principles
Jean Bernard Minster
Gail Peretsman-Clement
On behalf of the Interest Group on Legal Interoperability
Ignorance of law and policy is no excuse, for anybody (including me and you!)
SciDataCon 2016

2. 1. It is incumbent upon law and policy makers to craft legislation, legally binding contracts and licenses, and policy statements regarding rights and responsibilities in research outputs in clear, explicit terms.
Prevailing laws in the jurisdiction where research is performed must directly address eligibility for, and ownership in, legal protection of research data as defined by the authoritative definition applied in that jurisdiction
Legal contracts that govern ownership and usage of intellectual property (e.g., employment contracts, sponsorship agreements, student enrollment agreements, visiting researcher agreements, etc.) must clearly make manifest the conditions under which the institution, the data creator, or other parties own the research data created while affiliated with a given institution or funded by a given sponsor.
All licenses governing usage of protected research, whether negotiated or ‘click through’ must clearly specify terms and conditions for reuse, remixing, republication, and requirements for attribution.
9/12/16
SciDataCon 2016

3. 2. Data providers and data users need to be familiar with and understand all relevant rules and policies, or at the least must know how to access up-to-date versions thereof.
Prevailing laws in the jurisdiction where work is performed, and where data and results are published.
Rules and policies imposed by funding agencies (public, and private sources).
Rules and policies imposed by employer.
Rules and policies imposed by publication channels.
Rules and policies of cognizant scientific organizations.
9/12/16
SciDataCon 2016

4. 3. It is the responsibility of data providers and consumers to understand essential concepts
Nature and meaning of relevant legal terms such as “copyright”, “license”, “waiver”.
Differences between “rights”, and “permissions”.
Semantic differences between “public records”, “open access data”, and “public domain data”.
Semantic differences between “public domain accessibility” and “right waivers”, and “fair-use”, “fair-dealing” or equivalent doctrines.
Different requirements for authorship and contributorship, and prevailing expectations for crediting both.
Various levels of “attribution” and the processes that are in place to adjudicate complaints of misappropriation
9/12/16
SciDataCon 2016

5. 4. It is the responsibility of data providers to take effective steps to make data discoverable, accessible and usable.
Use of standard format or tools and software to deal transparently with non-standard format. Proprietary software risks violating principles of open data access.
Openly resolvable, unique and persistent identifiers (e.g. DOI, ARK, GEO DMPs). Details depend on the relevant community of practice, and on the nature of the data.
Clear, explicit rights statements accompanying data documentation so that potential users and machines can quickly identify and understand any usage restrictions, and readily evaluate the resource for fitness of use.
9/12/16
SciDataCon 2016

6. 5. Researchers and research communities should also engage in the political processes that lead to laws and other norms ruling the access to and the reuse of research data, explain the needs of legal interoperability of research data, and explore legal frameworks —including enforcement of rules— that will facilitate scientific progress.
Assert right to be consulted by authorities before such laws are enacted.
Enforcement of rules can be a legal, policy, or normative action within each data community.
Engagement of professional researchers in supporting “citizen science” projects and thereby help educate a larger fraction of the public in data issues.
9/12/16
SciDataCon 2016

7. 6. As part of the responsible conduct of research, and in order to achieve a sustainable set of practices in the long term, a well-conceived educational process should be constructed and adopted by relevant institutions, that leads to a better prepared generations of future researchers.
Formal courses in Data Science targeted at students at all levels (e.g. RPI curriculum)
Institute seminars for supervisors and mentors
Seminars/webinars targeted at various stakeholders (editors, reviewers, librarians)  (e.g. WDS webinars)
Targeted research training programs (e.g. CODATA-RDA Summer Schools, NSF NRT program; Belmont forum)
Disseminate idea that management of research data might be considered professional career on its own (e.g. EDISON
9/12/16
SciDataCon 2016