Evaluating FAIR Maturity Through a Scalable, Automated, Community-governed Framework
Transparent evaluations of FAIRness are increasingly required by a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers. We propose a scalable, automatable framework to evaluate digital resources that encompasses measurable indicators, open source tools, and participation guidelines, which come together to accommodate domain relevant community-defined FAIR assessments. The components of the framework are: (1) Maturity Indicators - community-authored specifications that delimit a specific automatically-measurable FAIR behavior; (2) Compliance Tests - small Web apps that test digital resources against individual Maturity Indicators; and (3) the Evaluator, a Web application that registers, assembles, and applies community-relevant sets of Compliance Tests against a digital resource, and provides a detailed report about what a machine "sees" when it visits that resource. We discuss the technical and social considerations of FAIR assessments, and how this translates to our community-driven infrastructure. We then illustrate how the output of the Evaluator tool can serve as a roadmap to assist data stewards to incrementally and realistically improve the FAIRness of their resources.
Implementing a Data Policy: a How-to Guide for Publishers - OASPA
OASPA is pleased to publish this guest post on the subject of open data and data sharing, providing helpful practical advice drawn from a wealth of resources, to enable publishers and editors to play a key role in the important movement to make data accessible.
The Citation Advantage of Linking Publications to Research Data
Efforts to make research results open and reproducible are increasingly reflected by journal policies encouraging authors to provide data availability statements. As a consequence of this, there has been a strong recent uptake of data availability statements, but it is still unclear what proportion of these statements actually contain well-formed links to data, and if there is an added value in providing them.
The goal of the Open Science Graphs Interest Group (OSG IG) is to build on the outcomes and broaden the challenges of the Data Description Registry Interoperability (DDRI) and Scholarly Link Exchange (Scholix) RDA Working Groups to investigate the open issues and identify solutions towards achieving interoperability between services and information models of Open Science Graph initiatives.
Do Swiss researchers share their data with other researchers and with the public? And if not, why? Which data repositories and other channels do they use for data sharing? A large-scale survey by the SNSF and swissuniversities offers some answers.
FAIRsharing As a Community Approach to Standards, Repositories and Policies
Community-developed standards, such as those for the identification, citation and reporting of data, underpin reproducible and reusable research, aid scholarly publishing, and drive both the discovery and the evolution of scientific practice.
Introducing Five Essential Factors, our latest white paper. Over the past two years, we've heard from more than 11,000 researchers about their views on data sharing, what they do in practice and the challenges they face. Building on that understanding, today we have released a whitepaper which proposes five key factors to make data management and sharing "business as usual" for all researchers.
Ten Principles for Machine-Actionable Data Management Plans
Data management plans can have thematic, machine-actionable richness with added value for all stakeholders: researchers, funders, repository managers, research administrators, data librarians, and others.
Implementing Publisher Policies that Inform, Support and Encourage Authors to Share Data
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. In this article we present two case studies which examine the experiences of Taylor & Francis and Springer Nature rolling out data-sharing policies.
Data Sharing: Better for Everyone, Especially You!! | PLOS Biologue
Happy Open Data Day 2019! It's that special day of the year again! Well, every day should be Open Data Day, but today lots of motivated folk come together around the world to remind us all why Open Data, Open Science, and sharing of data and science in general is better for everyone. Better for reuse, better for tracking public money flows, better for open mapping and development, and also, lest we lost sight, better for the researcher who produced the data! Why better for the researchers who generated the data? Better because the value add from sharing is multifold. Others can reuse and reanalyse your data. If you've placed the data in a repository with a persistent identifier, you'll get attributed when they are reused and you can get credit for this - and even citations. What may not be immediately obvious is that taking a little bit of time to ensure your data are 'sharable' is good practise that ensures that when you want to use
Europe Wrestles with a Plan to Build the 'Amazon of Science'
The European Open Science Cloud is a giant effort to provide a single point of access to all scientific data. But getting all the infrastructures to integrate and engendering a culture of sharing is a daunting task, say those involved in its creation.
Despite some notable progress in data sharing policies and practices, restrictions are still often placed on the open and unconditional use of various genomic data after they have received official approval for release to the public domain or to public databases.