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.”

 

Researcher and Academic Library Roles and User Beliefs in the Pandemic: Designing the Open-Access and Library Usage Scale (OALU) | DeZouche | Journal of Intellectual Freedom & Privacy

Abstract:  We investigated whether individuals believe they have a right to information during a crisis, and whether attitudes about crisis-related information sharing differ by age and one’s role in providing or consuming information. We measured attitudes about aspects of data sharing related to COVID-19: researchers’ obligation to share data, publishers’ obligation to share information, and libraries’ responsibility to provide them. We predicted younger individuals, especially students as consumers of information, would report stronger preference for open access to pandemic-related information. A principal components analysis was performed, and two predicted factors emerged: information-sharing obligations and libraries’ responsibility to provide resources. Age was not significantly correlated with attitudes about libraries or information-sharing. Planned analyses comparing students, faculty, and community members unaffiliated with the university revealed no differences in their attitudes regarding library resources or information-sharing. A lack of age and university affiliation-related differences can be explained by universally strong attitudes in favor of both information-sharing and library resources, with a greater desire for information-sharing. Knowing that individuals demonstrate a strong preference for open access to information and that these attitudes do not differ between those who are providing (faculty), and consuming information (students/community) can contribute to funding for these resources. This research is innovative and timely, as attitudes about access when information is urgently and globally needed, as during a pandemic, is likely to differ from those observed under different circumstances.

 

ARIADNE PLUS – Ariadne infrastructure

“The ARIADNEplus project is the extension of the previous ARIADNE Integrating Activity, which successfully integrated archaeological data infrastructures in Europe, indexing in its registry about 2.000.000 datasets (ARIADNE portal). ARIADNEplus will build on the ARIADNE results, extending and supporting the research community that the previous project created and further developing the relationships with key stakeholders such as the most important European archaeological associations, researchers, heritage professionals, national heritage agencies and so on. The new enlarged partnership of ARIADNEplus covers all of Europe. It now includes leaders in different archaeological domains like palaeoanthropology, bioarchaeology and environmental archaeology as well as other sectors of archaeological sciences, including all periods of human presence from the appearance of hominids to present times. Transnational Activities together with the planned training will further reinforce the presence of ARIADNEplus as a key actor.

The ARIADNEplus data infrastructure will be embedded in a cloud that will offer the availability of Virtual Research Environments where data-based archaeological research may be carried out. The project will furthermore develop a Linked Data approach to data discovery, making available to users innovative services, such as visualization, annotation, text mining and geo-temporal data management. Innovative pilots will be developed to test and demonstrate the innovation potential of the ARIADNEplus approach.

ARIADNEplus is funded by the European Commission under the H2020 Programme, contract no. H2020-INFRAIA-2018-1-823914….”

The ideal model, if you ask me | Openjournals.nl

“We invited a number of (lead) editors to tell us about their journals and the reasons why they chose to work with Openjournals.nl. Sible Andringa, editor-in-chief of the Dutch Journal of Applied Linguistics, kicks off. He feels that the journal has become more attractive to authors since switching to Openjournals and he explains why his editors quit working with a traditional publisher.

Sible Andringa: ‘The journal Dutch Journal of Applied Linguistics (DuJAL) has been around for a long time. It started as the Journal of Applied Linguistics in Articles. The first volume was published in-house in 1976. From the beginning, the journal was published by the Dutch Association of Applied Linguistics Anéla (see www.anela.nl). In 2012, it was decided to change its name. The journal was renamed Dutch Journal of Applied Linguistics and it has since been published by  John Benjamins. In January 2021, the journal moved to Openjournals….

With Openjournals, you can choose to offer all that together: pre- and post-prints are not necessary, and all data and instruments can be co-published. The ideal model, if you ask me. We can now also think about all kinds of new forms of publishing, such as publishing conference posters and the like. Those conversations we can now have, because we know it is possible and allowed by the publisher. We find that we have become more attractive to authors now that we are open access and publish on an ongoing basis.  There are not huge numbers of submissions right away, but a steady stream of good quality.”

Coalition Publica Call for Projects 2023: Textual data in Social Sciences and Humanities (SSH) – Public Knowledge Project

“Coalition Publica announced the 2023 Call for projects – Textual data in SSH with the goal of promoting access to massive research resources. As a part of Coalition Publica, the Public Knowledge Project would like to extend the invitation to all non-commercial projects to submit research proposals and apply for access to the large collection of textual data that Érudit, together with Library and Archives Canada, Bibliothèque et Archives Nationales du Québec, Canadiana / CRKN and the Bibliothèque de l’Assemblée Nationale du Québec is developing.”

Finding The ‘Rights’ Balance:. 7 ideas to harmonize debates… | by Open Data Charter | opendatacharter | Jan, 2023 | Medium

“The importance of the right to access to information and the right to personal data protection, both of which are fundamental human rights, raises the need to strike a balance to be able to exercise both in a complementary manner, that ensures that one of them does not jeopardize the guarantees enshrined by the other. There are different proposals and techniques that allow us to continue working on open data for a more democratic and just society, respecting the right to privacy. We explore some ideas below….”

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. 

Data Management Plans: Implications for Automated Analyses

Abstract:  Data management plans (DMPs) are an essential part of planning data-driven research projects and ensuring long-term access and use of research data and digital objects; however, as text-based documents, DMPs must be analyzed manually for conformance to funder requirements. This study presents a comparison of DMPs evaluations for 21 funded projects using 1) an automated means of analysis to identify elements that align with best practices in support of open research initiatives and 2) a manually-applied scorecard measuring these same elements. The automated analysis revealed that terms related to availability (90% of DMPs), metadata (86% of DMPs), and sharing (81% of DMPs) were reliably supplied. Manual analysis revealed 86% (n = 18) of funded DMPs were adequate, with strong discussions of data management personnel (average score: 2 out of 2), data sharing (average score 1.83 out of 2), and limitations to data sharing (average score: 1.65 out of 2). This study reveals that the automated approach to DMP assessment yields less granular yet similar results to manual assessments of the DMPs that are more efficiently produced. Additional observations and recommendations are also presented to make data management planning exercises and automated analysis even more useful going forward.

 

Reminder: NIH Policy for Data Management and Sharing effective on January 25, 2023.

“The purpose of this notice is to remind the community of the effective date of the NIH Policy for Data Management and Sharing (DMS Policy) and summarize available key resources.

As noted in the Final NIH Policy for Data Management and Sharing (NOT-OD-21-013), the effective date of the DMS Policy is January 25, 2023 for competing grant applications submitted to NIH for the January 25, 2023 and subsequent receipt dates; proposals for contracts  submitted to NIH on or after January 25, 2023; NIH Intramural Research Projects conducted on or after January 25, 2023; and other funding agreements (e.g., Other Transactions)  executed on or after January 25, 2023, unless otherwise stipulated by NIH.

The DMS Policy applies to all NIH research, funded or conducted in whole or in part by NIH, that results in the generation of scientific data. Note that the DMS Policy does not apply to research and other activities that do not generate scientific data, for example: research training, fellowships, infrastructure development, and non-research activities. See Research Covered Under the Data Management & Sharing Policy for more details.

The DMS Policy has two basic requirements:

Submission of a Data Management and Sharing (DMS) Plan outlining how scientific data and any accompanying metadata will be managed and shared, considering any potential restrictions or limitations. 
Compliance with the Plan approved by the funding NIH Institute, Center, or Office.

DMS Plans should describe how data will be managed and appropriately shared. See Writing a Data Management & Sharing Plan for details, sample Plans, and an optional format page which includes six elements recommended to be included in a Data Management and Sharing Plan. Guidance on planning and budgeting and selecting a data repository are available on the NIH Scientific Data Sharing website. Application Guide instructions have been updated to provide instructions for DMS policy implementation.

Ultimately, the new DMS Policy promotes transparency and accountability in research by setting a minimum set of expectations for data management and sharing. This means that other NIH policies or NIH Institutes, Centers, Offices, or programs may build upon these expectations, for instance, by specifying scientific data to share, relevant standards, repository timelines, and/or shorter data sharing timelines for meeting programmatic needs, the DMS Policy sets a consistent baseline across NIH.

In preparing for DMS Policy implementation, NIH has developed a number of helpful resources that we encourage investigators and institutions to review:

DMS Policy Overview
DMS Policy FAQs
Learning Resources including 2-part webinar series on DMS Policy
Statements and Guide Notices …”

An open database on global coal and metal mine production | Scientific Data

Abstract:  While the extraction of natural resources has been well documented and analysed at the national level, production trends at the level of individual mines are more difficult to uncover, mainly due to poor availability of mining data with sub-national detail. In this paper, we contribute to filling this gap by presenting an open database on global coal and metal mine production on the level of individual mines. It is based on manually gathered information from more than 1900 freely available reports of mining companies, where every data point is linked to its source document, ensuring full transparency. The database covers 1171 individual mines and reports mine-level production for 80 different materials in the period 2000–2021. Furthermore, also data on mining coordinates, ownership, mineral reserves, mining waste, transportation of mining products, as well as mineral processing capacities (smelters and mineral refineries) and production is included.

 

Canadian policy: Data management requirement takes effect in March

“Canadian institutions are preparing for a research data management policy developed by three major federal granting agencies to go into effect this March. The policy of the Tri-Agency Council, comprising the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Social Sciences and Humanities Research Council of Canada (SSHRC), asserts that “research data collected through the use of public funds should be responsibly and securely managed and be, where ethical, legal and commercial obligations allow, available for reuse by others.” Dryad would be pleased to assist any Canadian institution seeking a solution to help support their affiliated researchers with this policy….”

The Semantic Scholar Open Data Platform

The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field. Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover and understand scientific literature. We combine public and proprietary data sources using state-of-theart techniques for scholarly PDF content extraction and automatic knowledge graph construction to build the Semantic Scholar Academic Graph, the largest open scientific literature graph to-date, with 200M+ papers, 80M+ authors, 550M+ paper-authorship edges, and 2.4B+ citation edges. The graph includes advanced semantic features such as structurally parsed text, natural language summaries, and vector embeddings. In this paper, we describe the components of the S2 data processing pipeline and the associated APIs offered by the platform. We will update this living document to reflect changes as we add new data offerings and improve existing services.