Category Archives: oa.fair

Aspen Institute Launches Council for a Fair Data Future – The Aspen Institute

Posted on by
Reply

“In collaboration with Omidyar Network, the Aspen Institute is bringing together a diverse team of experts, advocates, and practitioners to imagine an equitable data economy that works for everyone. A project of the Aspen Digital program, the Council for a Fair Data Future will debate, deliberate, and collaborate on what is needed to create changes to systems and markets, and propose ways to infuse fairness and equitable benefits across institutions, technologies, policies, and social and economic frameworks….”

Knowledge Bites #15 : How to integrate an Open Science service or data-source into the EOSC portal? – EELISA

Posted on by
Reply

“European Open Science Cloud (EOSC) offers researchers, innovators, companies and the general public a federated and open multi-disciplinary environment. Using the e-infrastructure of this environment, users can publish, search and reuse data-sets, various tools and services for research, innovation and education. Data and related services in EOSC are established on FAIR principles.

In this presentation, we will give a short introduction to EOSC and show the process by which the providers – organizations like universities – of services can register themselves and then onboard their respective services. Some of the examples of such services will be shown. We will also present the benefits that the users of the EOSC gain by using the tools and e-infrastructure of the EOSC….”

WorldFAIR Project (D13.2) Cultural Heritage Image Sharing Recommendations Report | Zenodo

Posted on by
Reply

Abstract:  Deliverable 13.2 for the WorldFAIR Project’s Cultural Heritage Work Package (WP13). Although the cultural heritage sector has only recently begun to think of traditional gallery, library, archival and museum (‘GLAM’) collections as data, long established practices guiding the management and sharing of information resources has aligned the domain well with the FAIR principles for research data, evidenced in complementary workflows and standards that support discovery, access, reuse, and persistence. As explored in the previous report by Work Package 13 for the WorldFAIR Project, D13.1 Practices and policies supporting cultural heritage image sharing platforms, memory institutions are in an important position to influence cross-domain data sharing practices and raise critical questions about why and how those practices are implemented.

Deliverable 13.2 aims to build on our understanding of what it means to support FAIR in the sharing of image data derived from GLAM collections. This report looks at previous efforts by the sector towards FAIR alignment and presents 5 recommendations designed to be implemented and tested at the DRI that are also broadly applicable to the work of the GLAMs. The recommendations are ultimately a roadmap for the Digital Repository of Ireland (DRI) to follow in improving repository services, as well as a call for continued dialogue around ‘what is FAIR?’ within the cultural heritage research data landscape.

WorldFAIR Project (D13.2) Cultural Heritage Image Sharing Recommendations Report –

Posted on by
Reply

“Deliverable 13.2 for the WorldFAIR Project’s Cultural Heritage Work Package (WP13). Although the cultural heritage sector has only recently begun to think of traditional gallery, library, archival and museum (‘GLAM’) collections as data, long established practices guiding the management and sharing of information resources has aligned the domain well with the FAIR principles for research data, evidenced in complementary workflows and standards that support discovery, access, reuse, and persistence. As explored in the previous report by Work Package 13 for the WorldFAIR Project, D13.1 Practices and policies supporting cultural heritage image sharing platforms, memory institutions are in an important position to influence cross-domain data sharing practices and raise critical questions about why and how those practices are implemented.

Deliverable 13.2 aims to build on our understanding of what it means to support FAIR in the sharing of image data derived from GLAM collections. This report looks at previous efforts by the sector towards FAIR alignment and presents 5 recommendations designed to be implemented and tested at the DRI that are also broadly applicable to the work of the GLAMs. The recommendations are ultimately a roadmap for the Digital Repository of Ireland (DRI) to follow in improving repository services, as well as a call for continued dialogue around ‘what is FAIR?’ within the cultural heritage research data landscape.

The report is available on Zenodo.”

G7 Science and Technology Ministers’ Communique

Posted on by
Reply

“We share a growing concern that some actors may attempt to unfairly exploit or distort the open research environment and misappropriate research results for economic, strategic, geopolitical, or military purposes. This undermines the principles and values that underpin open, transparent, reciprocal, and accountable international research cooperation and the integrity of research and may pose security risks….

The G7 will collaborate in expanding open science with equitable dissemination of scientific knowledge and publicly funded research outputs including research data and scholarly publications in line with the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. This is so that researchers and people throughout the world can benefit from them as well as contribute to the creation of new knowledge, stimulation of innovation, democratization of access to knowledge by society and the development of solutions for global challenges. This will also help to build more reproducible and trusted research results.

We recognize openness, freedom, and inclusiveness should be enhanced globally for the sound development of scientific research. When making decisions about openness, the respect for universal human rights and the protection of national security are essential, and principles and rules related to academic freedom, research integrity, privacy, and protection of intellectual property rights should be applied and upheld.

We acknowledge that open science platforms can allow the rapid sharing of pathogen samples and pathogen genetic sequence data on a global scale. They should also enable early development and more rapid, effective, and equitable access to MCMs for the prevention and control of emerging and re-emerging infectious diseases. Robust multilateral data sharing is needed to ensure continued societal resilience to the global issues of today and the future….

The G7 also supports immediate open and public access to government-funded scholarly publications and scientific data, and supports the endeavors of the scientific community to address challenges in scholarly publishing for broader sharing of appropriate scientific outputs. To this end, we support the efforts of the G7 Open Science Working Group in promoting the interoperability and sustainability of infrastructure for research outputs, supporting research assessment approaches that incentivize and reward open science practices, and encouraging “research on research”, aimed at helping to shape a more effective evidence-based research policy…. ”

Frontiers | Editorial: Data science and artificial intelligence for (better) science

Posted on by
Reply

“Meaningful and explainable AI in research can only be fulfilled when as much data as possible is made FAIR (Findable, Accessible, Interoperable, and Reusable). How meaning is communicated in science “as precisely as possible” to machines when we formulate scientific concepts is a key question. Machine readability and interpretability is needed in order to make data and information “Fully AI-Ready” and support data-intensive research (Schultes et al.). The future of science is where there is only “one computer” and FAIR services see all FAIR data and effectively access a global FAIR database….

Finally, the question is how to enable (better) open science. Increasingly relevant today than ever before is the greater reliance on access to data, artificial intelligence (AI) and machine learning (ML). Data access increasingly determines scientific discoveries and advancements. Data reuse is at the forefront of an emerging “third wave of open data” (Verhulst et al., 2020). But despite progress in implementing open data and FAIR principles, science data asymmetries (as in disparities in access to science data) are a growing problem and can undermine scientific progress. Comparative research is needed to document (Verhulst and Young) for instance, investigating the creation of new types of data asymmetries by, e.g., new private-sector investments in data platforms and knowledge repositories, how data portability and interoperability impact the practice of data collaboration, the relationship and interplay between existing asymmetries and technological and societal drivers. Finally, new methods for achieving a social license for data use and reuse toward the public good are needed, capturing multiple stakeholders’ acceptance of standard practices and procedures.”

English – Knowledge Equity Network

Posted on by
Reply

“For Higher Education Institutions

Publish a Knowledge Equity Statement for your institution by 2025, incorporating tangible commitments aligned with the principles and objectives below.
Commit to institutional action(s) to support a sustained increase of published educational material being open and freely accessible for all to use and reuse for teaching, learning, and research.
Commit to institutional action(s) to support a sustained increase of new research outputs being transparent, open and freely accessible for all, and which meet the expectations of funders.
Use openness as an explicit criteria in reaching hiring, tenure, and promotion decisions. Reward and recognise open practices across both research and research-led education. This should include the importance of interdisciplinary and/or collaborative activities, and the contribution of all individuals to activities.
Define Equity, Diversity and Inclusion targets that will contribute towards open and inclusive Higher Education practices, and report annually on progress against these targets.
To create new mechanisms in and between Higher Education Institutions that allow for further widening participation and increased diversity of staff and student populations.
Review the support infrastructure for open Higher Education, and invest in the human, technical, and digital infrastructure that is needed to make open Higher Education a success.
Promote the use of open interoperability principles for any research or education software/system that you procure or develop, explicitly highlighting the option of making all or parts of content open for public consumption.
Ensure that all research data conforms to the FAIR Data Principles: ‘findable’, accessible, interoperable, and re-useable.

For Funding Agencies

Publish a statement that open dissemination of research findings is a critical component in evaluating the productivity and integrity of research.
Incorporate open research practices into assessment of funding proposals.
Incentivise the adoption of Open Research through policies, frameworks and mandates that require open access for publications, data, and other outputs, with as liberal a licence as possible for maximum reuse.
Actively manage funding schemes to support open infrastructures and open dissemination of research findings, educational resources, and underpinning data.
Explicitly define reward and recognition mechanisms for globally co-produced and co-delivered open educational resources that benefit society….”

 

Knowledge Pixels

Posted on by
Reply

“We aim to kick-start the next revolution in scientific publishing and knowledge sharing. With nanopublications as our core technology, we are taking the first steps towards our vision of the knowledge space to make the use of research results radically more efficient and effective….

We provide software and services to publish scientific findings in a way that is human readable and machine actionable at the same time. Our approach is open, decentralized, and in full accordance with the FAIR principles….”

Introducing FAIR findings – Innovators and publishers join forces to make scientific articles’ findings machine-interpretable

Posted on by
Reply

“First pilot project by Knowledge Pixels starts with scholarly publishers Pensoft and IOS Press to publish findings as knowledge graph snippets by means of nanopublications….

Until AI technology “learns” how to interpret complex scientific literature and evaluates the data, methodology and evidence behind it, it is our responsibility to make sure what we know today is optimally available to the computer algorithms. In turn, those algorithms would be extremely helpful in assisting researchers to build on the knowledge of yesterday by delivering the right information at the right time in a ready-to-use format.

This is why the team behind Knowledge Pixels, a recent startup that develops software and services, devised a framework to publish scientific findings in a way that is simultaneously human-readable and machine-actionable. To do this, the duo teamed up with forward-looking scholarly publishers Pensoft and IOS Press to implement its goal.

A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences

Posted on by
Reply

Abstract:  Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine the FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated the feasibility of our method on three real use cases where researchers looked for six datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress, Gene Expression Omnibus, eNanoMapper, caNanoLab, NanoCommons and ChEMBL), a registry of repositories, and a searchable resource (Google Dataset Search) via application program interfaces (API) wherever possible. With our analysis, we found that the six datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and the presence of specific attributes in registries of repositories could increase FAIRness of datasets.

 

Accelerating and Deepening Approaches to FAIR Data Sharing A Workshop | National Academies

Posted on by
Reply

“The National Academies of Sciences, Engineering, and Medicine’s Board on Research Data and Information (BRDI) will convene a half-day public workshop, Accelerating and Deepening Approaches to FAIR Data Sharing on April 20, 2023 (Thursday) in a hybrid format. The public is invited to join virtually. Please register to receive information on how to attend the workshop. 

The workshop will feature invited presentations and discussions on new approaches that research funders, research institutions, researchers, scientific societies, and other stakeholders are taking to facilitate and encourage sharing of FAIR (findable-accessible-interoperable-reusable) research data within and across disciplines. The discussions will explore new initiatives to support FAIR data sharing, the need for innovative approaches, potential obstacles to success and how obstacles might be overcome. A Proceedings of a Workshop-in Brief will be prepared by a designated rapporteur in accordance with institutional guidelines.”

Systematic review of marine environmental DNA metabarcoding studies: toward best practices for data usability and accessibility [PeerJ]

Posted on by
Reply

Abstract:  The emerging field of environmental DNA (eDNA) research lacks universal guidelines for ensuring data produced are FAIR–findable, accessible, interoperable, and reusable–despite growing awareness of the importance of such practices. In order to better understand these data usability challenges, we systematically reviewed 60 peer reviewed articles conducting a specific subset of eDNA research: metabarcoding studies in marine environments. For each article, we characterized approximately 90 features across several categories: general article attributes and topics, methodological choices, types of metadata included, and availability and storage of sequence data. Analyzing these characteristics, we identified several barriers to data accessibility, including a lack of common context and vocabulary across the articles, missing metadata, supplementary information limitations, and a concentration of both sample collection and analysis in the United States. While some of these barriers require significant effort to address, we also found many instances where small choices made by authors and journals could have an outsized influence on the discoverability and reusability of data. Promisingly, articles also showed consistency and creativity in data storage choices as well as a strong trend toward open access publishing. Our analysis underscores the need to think critically about data accessibility and usability as marine eDNA metabarcoding studies, and eDNA projects more broadly, continue to proliferate.

 

FAIR-IMPACT Open calls for support | FAIR-IMPACT

Posted on by
Reply

“In recent years, a number of tools, solutions and approaches have been developed to help different stakeholders implement the FAIR principles. Many of these have been developed through European Commission supported activities such as the regional and thematic INFRAEOSC projects and international initiatives such as the Research Data Alliance.

It can be challenging to know which tools and/or approaches to use for different purposes and how they should be implemented most effectively. To help take some of the guesswork out of implementing FAIR, the FAIR-IMPACT project will provide a set of clearly defined support actions that will help successful applicants learn how to use specific tools/methods/approaches which will help them start (or continue) their journey to becoming more FAIR-enabling and active contributors to the European Open Science Cloud.

Each of the defined support actions will provide dedicated guidance and assistance to a cohort of selected applicants on implementing one or more tools and approaches. The details of the support actions will be shared on this page as they are confirmed. There will be at least three open calls for participation in the defined support actions over the lifetime of the FAIR-IMPACT project….”