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About

On January 26, 2017 in Barcelona, the EU project LEARN (LEaders Activating Research Networks: Implementing the LERU Research Data Roadmap and Toolkit) are organizing their 5th workshop on Research Data Management, entitled “Shaping and aligning research data management policies”, to which this document is a contribution. The workshop has been live streamed, and the videos archived. There have also been tweets under the #learnbcn hashtag.

Title

Integrating policy and infrastructure in research data management: the perspective of researchers

Abstract

As the interest in research data management is growing in many parts of the research ecosystem, policies and infrastructures are being put in place in a growing number of contexts. I will comment on these developments from the perspective of researchers who are supposed to abide by the policies and to use the infrastructures for their research. Drawing on examples from the ongoing Zika virus outbreak, I will pay special attention to policy and infrastructure elements that are facilitating or complicating research data management and explore avenues for standardization.

License

CC0/ Public Domain; all kinds of sharing and feedback welcome; attribution appreciated

Comments on previous talks

Paul Ayris

Slides available?

Discoverability

Research cycle

Data availability

  • some funders now require that data be kept for 10 years after last consultation
  • we should pay more attention to what should be deleted, and when (cf. CERN)

Multitude of policies

  • see slide 17 of Paul Ayris talk, which summarized Research funder policies relevant to University College London
  • I'll zoom in on that

Marie Timmermann

Slides?

Drivers of change

DMP framework

  • Framework was "submitted to several communities" but amongst the search results for "DMP framework", none are obviously related to Science Europe?
  • Using Data management plans to enforce policies
    • Not practical at the moment
    • Need to move towards machine readable DMPs that are versioned and public
    • I'll zoom in a bit here as well
  • Domain Data Protocols?

Bureaucracy

  • Open science is often perceived as yet another layer of bureaucracy
  • Lots of bureaucratic procedures exist for reasons closely tied to the lack of transparency
  • Look for opportunities to reduce bureaucracy by increasing openness and transparency

Sharing policies relevant to NIH

At the international level

At the U.S. Federal level

At the HHS level

At the NIH level

At NIH Institutes and Centers

  • 27 of them, e.g. the National Library of Medicine (NLM), where I am an intramural researcher
  • The report on the strategic vision for the National Library of Medicine recommends that NLM should
    • "be a leader and innovator in open science efforts worldwide"
    • "lead efforts to support and catalyze open science, data sharing, and research reproducibility, striving to promote the concept that biomedical information and its transparent analysis are public"
    • and, in particular, "lead efforts to promulgate and implement best practices in open source, open science, standards, and data harmonization, forming partnerships across communities, stakeholder organizations, agencies, and countries" as well as "be an active participant in the design and oversight of programs that incentivize and celebrate the open sharing of data and resources."
  • My comments on how to do this in practice.

Interactions of policies

Data management plans

Data sharing in public health emergencies

Making ethics data FAIR

Interaction between policies and infrastructure

Data integration

See also