Are Research Datasets FAIR in the Long Run?

Authors : Dennis Wehrle, Klaus Rechert

Currently, initiatives in Germany are developing infrastructure to accept and preserve dissertation data together with the dissertation texts (on state level – bwDATA Diss, on federal level – eDissPlus).

In contrast to specialized data repositories, these services will accept data from all kind of research disciplines. To ensure FAIR data principles (Wilkinson et al., 2016), preservation plans are required, because ensuring accessibility, interoperability and re-usability even for a minimum ten year data redemption period can become a major challenge.

Both for longevity and re-usability, file formats matter. In order to ensure access to data, the data’s encoding, i.e. their technical and structural representation in form of file formats, needs to be understood. Hence, due to a fast technical lifecycle, interoperability, re-use and in some cases even accessibility depends on the data’s format and our future ability to parse or render these.

This leads to several practical questions regarding quality assurance, potential access options and necessary future preservation steps. In this paper, we analyze datasets from public repositories and apply a file format based long-term preservation risk model to support workflows and services for non-domain specific data repositories.

URL : Are Research Datasets FAIR in the Long Run?


Remediation Data Management Plans : A Tool for Recovering Research Data from Messy, Messy Projects

Author : Clara Llebot

Data Management Plans (DMPs) have been used in the last decade to encourage good data management practices among researchers. DMPs are widely used, preventive tools that encourage good data management practices. DMPs are traditionally used to manage data during the planning stage of the project, often required for grant proposals, and prior to data collection.

In this paper we will use a case study to argue that Data Management Plans can be useful in improving the management of the data of research projects that have moved beyond the planning stage of the research life cycle.

In particular, we focus on the case of active projects where data has already been collected and is still being analyzed.

We discuss the differences and commonalities in structure between preventive Data Management Plans and remedial Data Management Plans, and describe in detail the additional considerations that are needed when writing remedial Data Management Plans: the goals and audience of the document, the data inventory, and an implementation plan.

URL : Remediation Data Management Plans : A Tool for Recovering Research Data from Messy, Messy Projects


Construction(s) et contradictions des données de recherche en SHS

Auteurs/Authors : Marie-Laure Malingre, Morgane Mignon, Cécile Pierre, Alexandre Serres

La structuration et le partage des données s’imposent depuis cinq ans au monde de la recherche, à travers des injonctions politiques (de Horizon 2020 au Plan national pour la science ouverte).

L’analyse de l’enquête menée en 2017 auprès des chercheurs de l’université Rennes 2 sur leurs pratiques, représentations et attentes en matière de données conduit à interroger le terme lui-même. Variable et complexe, contrairement à ce que suggère le mot « donnée », la notion ne va pas de soi.

L’article s’efforcera de montrer qu’elle fait l’objet d’une triple construction, épistémologique, intellectuelle et politique, dans les discours des chercheurs et des acteurs institutionnels, en tension avec les pratiques constatées sur le terrain.


Implementing publisher policies that inform, support and encourage authors to share data: two case studies

Authors: Leila Jones, Rebecca Grant, Iain Hrynaszkiewicz

Open research data is one of the key areas in the expanding open scholarship movement. Scholarly journals and publishers find themselves at the heart of the shift towards openness, with recent years seeing an increase in the number of scholarly journals with data-sharing policies aiming to increase transparency and reproducibility of research.

In this article we present two case studies which examine the experiences that two leading academic publishers, Taylor & Francis and Springer Nature, have had in rolling out data-sharing policies.

We illustrate some of the considerations involved in providing consistent policies across journals of many disciplines, reflecting on successes and challenges.

URL : Implementing publisher policies that inform, support and encourage authors to share data: two case studies


Ten principles for machine-actionable data management plans

Authors : Tomasz Miksa, Stephanie Simms, Daniel Mietchen, Sarah Jones

Data management plans (DMPs) are documents accompanying research proposals and project outputs. DMPs are created as free-form text and describe the data and tools employed in scientific investigations. They are often seen as an administrative exercise and not as an integral part of research practice.

There is now widespread recognition that the DMP can have more thematic, machine-actionable richness with added value for all stakeholders: researchers, funders, repository managers, research administrators, data librarians, and others.

The research community is moving toward a shared goal of making DMPs machine-actionable to improve the experience for all involved by exchanging information across research tools and systems and embedding DMPs in existing workflows.

This will enable parts of the DMP to be automatically generated and shared, thus reducing administrative burdens and improving the quality of information within a DMP.

This paper presents 10 principles to put machine-actionable DMPs (maDMPs) into practice and realize their benefits. The principles contain specific actions that various stakeholders are already undertaking or should undertake in order to work together across research communities to achieve the larger aims of the principles themselves.

We describe existing initiatives to highlight how much progress has already been made toward achieving the goals of maDMPs as well as a call to action for those who wish to get involved.

URL : Ten principles for machine-actionable data management plans


Responsible data sharing in international health research: a systematic review of principles and norms

Authors : Shona Kalkman, Menno Mostert, Christoph Gerlinger, Johannes J. M. van Delden, Ghislaine J. M. W. van Thiel


Large-scale linkage of international clinical datasets could lead to unique insights into disease aetiology and facilitate treatment evaluation and drug development.

Hereto, multi-stakeholder consortia are currently designing several disease-specific translational research platforms to enable international health data sharing.

Despite the recent adoption of the EU General Data Protection Regulation (GDPR), the procedures for how to govern responsible data sharing in such projects are not at all spelled out yet. In search of a first, basic outline of an ethical governance framework, we set out to explore relevant ethical principles and norms.


We performed a systematic review of literature and ethical guidelines for principles and norms pertaining to data sharing for international health research.


We observed an abundance of principles and norms with considerable convergence at the aggregate level of four overarching themes: societal benefits and value; distribution of risks, benefits and burdens; respect for individuals and groups; and public trust and engagement.

However, at the level of principles and norms we identified substantial variation in the phrasing and level of detail, the number and content of norms considered necessary to protect a principle, and the contextual approaches in which principles and norms are used.


While providing some helpful leads for further work on a coherent governance framework for data sharing, the current collection of principles and norms prompts important questions about how to streamline terminology regarding de-identification and how to harmonise the identified principles and norms into a coherent governance framework that promotes data sharing while securing public trust.

URL : Responsible data sharing in international health research: a systematic review of principles and norms