How to be FAIR with your data

“This handbook was written and edited by a group of about 40 collaborators in a series of six book sprints that took place between 1 and 10 June 2021. It aims to support higher education institutions with the practical implementation of content relating to the FAIR principles in their curricula, while also aiding teaching by providing practical material, such as competence profiles, learning outcomes, lesson plans, and supporting information. It incorporates community feedback received during the public consultation which ran from 27 July to 12 September 2021.”

 

RAISE Project: a Game Changer for OS

The real value of open data for the research community is not to access them, but to process them as conveniently as possible in order to reduce time-to-result and increase productivity. RAISE project will provide the infrastructure for a distributed crowdsourced data processing system, moving from open data to open access data for processing. 

Case Study: ROR in FAIRsharing

“In this installment of the ROR Case Studies series, we talk with Allyson Lister, Content and Community Lead for FAIRsharing, a cross-disciplinary registry of scientific standards, databases, and policies, about how and why FAIRsharing used ROR to help make organizations first-class citizens in their data model….”

 

Water science must be Open Science | Nature Water

“Since water is a common good, the outcome of water-related research should be accessible to everyone. Since Open Science is more than just open access research articles, journals must work with the research community to enable fully open and FAIR science…”

An iterative and interdisciplinary categorisation process towards FAIRer digital resources for sensitive life-sciences data | Scientific Reports

Abstract:  For life science infrastructures, sensitive data generate an additional layer of complexity. Cross-domain categorisation and discovery of digital resources related to sensitive data presents major interoperability challenges. To support this FAIRification process, a toolbox demonstrator aiming at support for discovery of digital objects related to sensitive data (e.g., regulations, guidelines, best practice, tools) has been developed. The toolbox is based upon a categorisation system developed and harmonised across a cluster of 6 life science research infrastructures. Three different versions were built, tested by subsequent pilot studies, finally leading to a system with 7 main categories (sensitive data type, resource type, research field, data type, stage in data sharing life cycle, geographical scope, specific topics). 109 resources attached with the tags in pilot study 3 were used as the initial content for the toolbox demonstrator, a software tool allowing searching of digital objects linked to sensitive data with filtering based upon the categorisation system. Important next steps are a broad evaluation of the usability and user-friendliness of the toolbox, extension to more resources, broader adoption by different life-science communities, and a long-term vision for maintenance and sustainability.

 

FAIRPoints-FAIRPoints ‘Ask me Anything’ (AMA) – SciLifeLab

“This event is part of a series of “Ask Me Anything”-style events featuring keynote speakers from the RDA, and EOSC groups focused on RDA activities and EOSC solutions in relation to FAIR implementation and Open practices in Science.”

FAIRsharing | CommunityCuration

“The FAIRsharing the Community Curation Programme is a thriving community of domain and discipline experts who:

1 . act as advocates to promote the value of standards, databases and policies for digital objects (incl. data, software).

2 . create educational material escribing these resources helping researchers and other stakeholders to find, use and adopt them.

3 . enrich the content of FAIRsharing, adding and enhancing the description and discoverability of these resources.

The FAIRsharing Community Curators put their expertise into action in one of more disciplines or area of activities, according to their interest, and are credited for their contribution via visible attribution in their ORCID and FAIRsharing profiles. Their contribution to and engagement with the FAIRsharing team gives them more knowledge about the wealth of standards (terminologies, models/formats, guidelines, identifier schema), databases (repositories and knowledge bases) and policies (by institutions, funders, journals and other stakeholders) relevant to them.

Launched in the summer of 2022, and supported by the RDA FAIRsharing WG and the RDA/EOSC-Future Ambassadorship Programme, the FAIRsharing the Community Curation Programme shows the importance of cultivating and sharing the collective knowledge on standards, databases and policies to map this complex landscape that enables the FAIR Principles….”

figshare plus

“Figshare has been helping researchers make their data openly available for more than 10 years. We want to offer a way to get more support for sharing larger datasets in a trusted generalist repository.

Figshare+ offers data deposit as a one-time Data Publishing Charge (DPC) to share the datasets and materials supporting a specific publication or project. Added features and expert guidance are also included for sharing your data FAIR-ly….”

Galaxy Training: A powerful framework for teaching! | PLOS Computational Biology

Abstract:  There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

 

 

Frontiers | Open Science, Open Data, and Open Scholarship: European Policies to Make Science Fit for the Twenty-First Century

“Open science will make science more efficient, reliable, and responsive to societal challenges. The European Commission has sought to advance open science policy from its inception in a holistic and integrated way, covering all aspects of the research cycle from scientific discovery and review to sharing knowledge, publishing, and outreach. We present the steps taken with a forward-looking perspective on the challenges laying ahead, in particular the necessary change of the rewards and incentives system for researchers (for which various actors are co-responsible and which goes beyond the mandate of the European Commission). Finally, we discuss the role of artificial intelligence (AI) within an open science perspective.”

TIER2

“Enhancing Trust, Integrity and Efficiency in Research through next-level Reproducibility…

TIER2 aims to boost knowledge on reproducibility, create tools, engage communities, implement interventions and policy across different contexts to increase re-use and overall quality of research results….”

Boosting the reproducibility of research

“Recent years have seen perceptions of a “reproducibility crisis” grow in various disciplines. Scientists see poor levels of reproducibility as a severe threat to scientific self-correction, the efficiency of research processes, and societal trust in research results. A major Horizon Europe-funded project named TIER2 starts this month to study these issues and improve reproducibility across diverse scientific contexts….

The interdisciplinary TIER2 consortium comprises ten members from universities and research centers across Europe. They share a long history of successful cooperation and have extensive experience in completed EU projects, especially in the fields of Open Science, Research Integrity, and Science Policy: Know Center (Austria), Athena Research Center (Greece), Amsterdam University Medical Center (Netherlands), Aarhus University (Denmark), Pensoft Publishing (Bulgaria), GESIS Leibniz Institute for the Social Sciences (Germany), OpenAIRE (EU), Charite? – University of Medicine Berlin (Geramany), Oxford University (UK), and Alexander Fleming Biomedical Sciences Research Center (Greece)….”

 

[2301.01189] On the long-term archiving of research data

Abstract:  Accessing research data at any time is what FAIR (Findable Accessible Interoperable Reusable) data sharing aims to achieve at scale. Yet, we argue that it is not sustainable to keep accumulating and maintaining all datasets for rapid access, considering the monetary and ecological cost of maintaining repositories. Here, we address the issue of cold data storage: when to dispose of data for offline storage, how can this be done while maintaining FAIR principles and who should be responsible for cold archiving and long-term preservation.

 

FAIR service investigation survey

“The FAIR principles and “making data FAIR” have become common buzzwords in the biomedical field in recent years. Many organizations are striving to create fully FAIR data or to FAIRify existing data. In that process they often find themselves hindered by a range of challenges. To address those challenges, the FAIRplus consortium and other organizations have developed FAIR products and services in recent years. Now the question is: what is the need for new FAIR services or for improvement of the existing ones, in order to better serve life science organizations in their quest to accelerate biomedical research? To answer this, The Hyve run a survey to collect experience with the FAIR principles, main challenges and the products or services that help (or might have helped) to overcome those challenges….”