European parliamentarians urge action on missing clinical trial results

“A cross-party group of members of the European parliament has sent an open letter to regulators urging them to not drop the ball on over 3,400 clinical trial results that are still missing on the EudraCT trial registry, in violation of long-standing transparency rules.

 

 

Under European rules, institutions running investigative drug trials must make their results public within 12 months of trial completion. While the rules are set at the European level, responsibility for encouraging and enforcing compliance lies with the national medicines regulators in each country….”

Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets

Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re)use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity.

OER Publishing and Libraries

Abstract:  This presentation explored current library Open Educational Resources (OER) publishing practices and presented research results on those practices. This original research surveyed academic librarians involved in OER publication projects to begin to address the need for expanded dialogue and the development of best practices for publishing OER. The survey results illustrate a broad picture of current practices and serves as a foundation for creating a best practice guide for library OER publishing. The presentation addressed author recruitment and marketing, publishing tools and platforms, and publishing support outside the library.

 

A Registry of Editorial Boards – a new trust signal for scholarly communications? – Crossref

“Whilst most journal websites only give the names of the editors, others possibly add a country, some include affiliations, very few link to a professional profile, an ORCID ID. Even when it’s clear when the editorial board details were updated, it’s hardly ever possible to find past editorial boards information and almost none lists declarations of competing interest.

We hear of instances where a researcher’s name has been listed on the board of a journal without their knowledge or agreement, potentially to deceive other researchers into submitting their manuscripts. Regular reports of impersonation, nepotism, collusion and conflicts of interest have become a cause for concern.

Similarly, recent studies on gender representation and gender and geographical disparity on editorial boards have highlighted the need to do better in this area and provide trusted, reliable and coherent information on editorial board members in order to add transparency, prevent unethical behaviour, maintain trust, promote and support research integrity….

We are proposing the creation of some form of Registry of Editorial Boards to encourage best practice around editorial boards’ information and governance that can easily be accessed and used by the community….”

The craft and coordination of data curation: complicating “workflow” views of data science

Abstract:  Data curation is the process of making a dataset fit-for-use and archiveable. It is critical to data-intensive science because it makes complex data pipelines possible, makes studies reproducible, and makes data (re)usable. Yet the complexities of the hands-on, technical and intellectual work of data curation is frequently overlooked or downplayed. Obscuring the work of data curation not only renders the labor and contributions of the data curators invisible; it also makes it harder to tease out the impact curators’ work has on the later usability, reliability, and reproducibility of data. To better understand the specific work of data curation — and thereby, explore ways of showing curators’ impact — we conducted a close examination of data curation at a large social science data repository, the Inter-university Consortium of Political and Social Research (ICPSR). We asked, What does curatorial work entail at ICPSR, and what work is more or less visible to different stakeholders and in different contexts? And, how is that curatorial work coordinated across the organization? We triangulate accounts of data curation from interviews and records of curation in Jira tickets to develop a rich and detailed account of curatorial work. We find that curators describe a number of craft practices needed to perform their work, which defies the rote sequence of events implied by many lifecycle or workflow models. Further, we show how best practices and craft practices are deeply intertwined.

 

Arcadia and ICOR: Experiments in Open Science

“With ICOR we will be working on distributed experiments for collective gain. Some of the key areas of alignment between Arcadia and ICOR are in:

 

Developing Open Science Best Practices: As we develop our open science program, we will contribute to ICOR’s library of guidelines, sharing our approach, our documentation, and our learnings.

Creating an IP Toolbox: We believe that open science and commercialization do not have to be mutually exclusive. Establishing a strong and creative IP strategy is essential for proving that open science can support and speed our commercial pursuits. We have already learned from the resources provided by ICOR and are working on developing agreements and means of tracking our progress, which we will share back with the community.

Building Research Output Management Systems (ROMS) and Using Persistent Identifiers (PIDs): Scientists have traditionally relied on journals and journal articles to house and disseminate their data, but the journal system wasn’t built with today’s diverse and ever-expanding datasets in mind. New systems are needed to share and organize scientific research. Arcadia is committed to using PIDs to facilitate discoverability and to depositing data in repositories that meet FAIR (Findability, Accessibility, Interoperability, and Reuse) principles. Working towards shared data schemas for all research outputs will help facilitate discussion, review, and reuse.

Facilitated Collaboration: Collaboration is central to Arcadia’s success, and we aim to collaborate widely while maintaining our commitment to open science. We are in the process of developing our Collaborator Agreement, and will work with ICOR to share it, to track its success and any necessary revisions.

Modular Data and Review: It is current standard practice to release data and solicit peer review at the end of a project. We believe that releasing data more frequently and gathering and integrating community feedback more often and earlier in a project’s lifespan will accelerate science and produce better results.

Tracking Nano-Contributions: Author lists on journal articles do not accurately reflect a scientist’s contribution to a project and can promote territorialism and competition, rather than collaboration. Arcadia will be developing new methods for mapping contributions. These methods will provide a richer, more substantive picture of a person’s contribution, and ICOR will aid in measuring and tracking the success of these methods.

Metrics of Utilization: Knowing if and how people are using the data you produce is key to providing a valuable resource for the community. As we develop our ROMS, we will incorporate meaningful metrics to track utilization and will learn how to improve our data products to increase accessibility and reuse. …”

D7.4 How to be FAIR with your data. A teaching and training handbook for higher education institutions | Zenodo

Abstract:  This handbook aims to support higher education institutions with the integration of FAIR-related content in their curricula and teaching.  It was written and edited by a group of about 40 collaborators in a series of six book sprint events that took place between 1 and 10 June 2021. The document provides practical material, such as competence profiles, learning outcomes and lesson plans, and supporting information. It incorporates community feedback received during the public consultation which ran from 27 July to 12 September 2021.

 

FAIRsFAIR project publishes FAIR teaching and training adoption handbook and good practices report

“In December 2021, partners of FAIRsFAIR’s WP7 on “Data Science and Professionalisation” published the two final deliverables of the WP: the adoption handbook “How to be FAIR with your data: a teaching and training handbook for higher education institutions” and the report “Good Practices in FAIR Competence Education”.

Both documents represent practical tools for universities to develop new teaching and training activities that can support the uptake of research data management (RDM) and FAIR data skills at the bachelor, master and doctoral level….

The adoption handbook provides ready-to-use model lesson plans on a variety of topics, including FAIR data, Data Management Plans (DMPs), repositories, data creation and reuse. In addition, it offers FAIR competence profiles and learning outcomes for the bachelor, master and doctoral levels as well as information on course design and the implementation of the FAIR principles on the institutional level. 

The deliverable is available on Zenodo, here. …”

Preprint publications: waste in haste or pragmatic progress? | Royal College of Physicians of Edinburgh

“Proposed best practices for preprint publications

Consider posting preprints to establish the primacy of an idea or to rapidly share novel findings.
Ensure the results are final before submitting a preprint.
Link the preprint to the eventual journal submission.
If eventually published in a peer-reviewed journal this should be indicated at the website where the preprint is located.
Undertake reasonable efforts to publish preprints as peer-reviewed journal articles.
Address major concerns raised during pre-publication peer review in the manuscript submitted to a journal.
Cite peer-reviewed journal publications rather than preprints.

To conclude, preprints represent important progress in the landscape of scientific publishing provided they are optimally utilised. Guidance from societies regulating scientific publishing should be generated and updated to recommend best practices for preprint publications.”

Good Practices Primer – Code and Software (community enhanced).docx

“As organizations develop open science policies pertaining to code and software, they can maximize their open source investments by considering the following issues: ? Timing . Does the funder or institutional policy require that code or software be made openly available immediately upon the posting of research findings (e.g., publication of an article, deposit of a dataset), or with some embargo (noting that open components remain open throughout)? Will institutions and researchers develop policies for community development of code throughout the entire lifecycle? ? Financial Support. W ill the relevant policy maker provide funding to defray costs of preparing and/or depositing the code or software, as well as providing the ongoing support to the community that receives or supports the code ? I f so, is there a cap on the amount? Must the researcher explicitly account for these expenses at the time of proposal development or project design? ? Viability, Sustainability , Future Proofing and Maintenance . Is there an existing community of developers or users that could be engaged or leveraged? If necessary, what is the viability of forming a new community with skill, interest, capacity and freedom to develop and maintain the code? What are the expectations for the duration and extent to which code should be kept up to date? Is there funding to support community development, ongoing maintenance of the software, or dependencies of the software? Is there a plan for sustainability of the community of developers and users? ? Proprietary Software. To the extent that some or all of the code base upon which research relies cannot be put under an open source license, what steps can be taken to reduce restrictions on its reuse? ? Licensing. W hat type of licensing requirements will the policy include to facilitate reuse? What are the goals of the researcher, university, funder, and society and what licenses support these goals? What resources are available to the researcher? How can institutions support the researchers? What support is available to support researchers trying to ensure compliance with licenses of underlying software dependencies? ? Metadata. What documentation and descriptive details are needed to understand and execute the code or run the software program? How will the computational environment in which software or code was originally executed be described and archived? Is the documentation and accompanying material prepared in a manner such that any reasonably adept programmer and systems engineer could easily set up, compile and run it? ? Preservation. What constitutes an appropriate deposit location for the code or software? Is there a repository that is appropriate for the subject matter in question, and/or has emerged within a specific research community as the default resource in that field? Is the repository secure, stable, open and discoverable for all to access? ? Attribution. How will the creators of the software be credited for their work, and how will the code be referred to using identifiers? How will the provenance of non-code contributions, such as design or funding, be recorded? What mechanisms exist for persistent citation? How do identifiers and provenance work interoperate with other systems? ? Further contributions. How will the project build in processes or allocate funds to give back to open source tools which it uses, in order to make a more sustainable ecosystem as a whole? ? Integration. How will an open source programs office (OSPO) integrate with the university community? How does the OSPO support the creation of software inventories, metrics, assessment, etc.? How does the OSPO work with research administration including issues such as ethical use of software? …”

Data Repository Attributes WG Case Statement | RDA

“A complete and current description of a research data repository is important to help a user discover a repository; to understand the repository’s purpose, policies, functionality, and other characteristics; and to evaluate the fitness for their use of the repository and the data that it stewards. Many repositories do not provide adequate descriptions in their websites, structured metadata, and documentation, which can make this challenging. Descriptive attributes may be expressed and exposed in different ways, making it difficult to compare repositories and to enable interoperability among repositories and other infrastructures such as registries. Incomplete and proprietary repository descriptions present challenges for stakeholders such as researchers, repository managers, repository developers, publishers, funders, and registries to enable the discovery and comparison of data repositories. For example:

 

As a researcher, I would like to be able to generate a list of repositories to determine where I can deposit my data based on a query of descriptive attributes that are important to me.
As a repository manager, I would like to know what attributes are important for me to provide to users in order to advertise my repository, its services, and its data collections.
As a repository developer, I would like to know how to express and serialize these attributes as structured metadata for reuse by users and user agents in a manner that is integrated into the functionality of my repository software platform.
As a publisher, I would like to inform journal editors and authors of what repositories are appropriate to deposit their datasets that are associated with manuscripts that are being submitted.
As a funder, I would like to be able to recommend and monitor data repositories to be utilized in conjunction with public access plans and data management plans for the research that I am sponsoring.
As a registry, I would like to be able to easily harvest and index attributes of data repositories to help users find the best repository for their purpose.

 

While this is not an exhaustive list of stakeholders and potential use cases, the value of identifying and harmonizing a list of descriptive attributes of data repositories and highlighting current approaches being taken by repositories would help the community address these important challenges and move towards developing a standard for the description and interoperability of information about data repositories. The statements of interest below demonstrate that there is a significant interest in this work….

Many sets of attributes have been identified by different initiatives with differing scopes and motivations.[2] These attributes have included information about data repositories such as terms of deposit, subject classifications, geographic coverage, API and protocol support, funding models, governance, preservation services and policies, openness of the underlying infrastructure, adherence to relevant standards and certifications, and more….”

Data-sharing practices in publications funded by the Canadian Institutes of Health Research: a descriptive analysis | CMAJ Open

Abstract:  Background: As Canada increases requirements for research data management and sharing, there is value in identifying how research data are shared and what has been done to make them findable and reusable. This study aimed to understand Canada’s data-sharing landscape by reviewing how data funded by the Canadian Institutes of Health Research (CIHR) are shared and comparing researchers’ data-sharing practices to best practices for research data management and sharing.

Methods: We performed a descriptive analysis of CIHR-funded publications from PubMed and PubMed Central published between 1946 and Dec. 31, 2019, that indicated that the research data underlying the results of the publication were shared. We analyzed each publication to identify how and where data were shared, who shared data and what documentation was included to support data reuse.

Results: Of 4144 CIHR-funded publications identified, 1876 (45.2%) included accessible data, 935 (22.6%) stated that data were available via request or application, and 300 (7.2%) stated that data sharing was not applicable or possible; we found no evidence of data sharing in 1558 publications (37.6%). Frequent data-sharing methods included via a repository (1549 [37.4%]), within supplementary files (1048 [25.3%]) and via request or application (935 [22.6%]). Overall, 554 publications (13.4%) included documentation that would facilitate data reuse.

Interpretation: Publications funded by the CIHR largely lack the metadata, access instructions and documentation to facilitate data discovery and reuse. Without measures to address these concerns and enhanced support for researchers seeking to implement best practices for research data management and sharing, much CIHR-funded research data will remain hidden, inaccessible and unusable.

Gathering input for an online dashboard highlighting good practices in research assessment | DORA

“As institutions experiment with and refine academic assessment policies and practices, there is a need for knowledge sharing and tools to support culture change. On September 9, 2021, we held a community call to gather early-stage input for a new resource: an interactive online dashboard to identify, track, and display good practices for academic career assessment. The dashboard is one of the key outputs of Tools to Advance Research Assessment (TARA), which is a DORA project sponsored by Arcadia – a charitable fund of Lisbet Rausing and Peter Baldwin to facilitate the development of new policies and practices for academic career assessment….

It comes as no surprise that academic assessment reform is complex. Institutions are at different stages of readiness for reform and have implemented new practices in a variety of academic disciplines, career stages, and evaluation processes. The dashboard aims to capture this progress and provide counter-mapping to common proxy measures of success (e.g., Journal Impact Factor (JIF), H-index, and university rankings). Currently, we picture the general uses of the dashboard will include:

Tracking policies: Collecting academic institutional standards for hiring, promotion, and tenure.
Capturing new and innovative policies: Enabling the ability to share new assessment policies and practices.
Visualizing content: Displaying source material to see or identify patterns or trends in assessment reform.

 

Because the dashboard will highlight positive trends and examples in academic career assessment, it is important to define what constitutes good practice. One idea comes from the 2020 working paper from the Research on Research Institute (RoRI), where the authors define responsible research assessment as: approaches to assessment which incentivize, reflect and reward the plural characteristics of high-quality research, in support of diverse and inclusive research cultures….”

The importance of adherence to international standards for depositing open data in public repositories | BMC Research Notes | Full Text

Abstract:  There has been an important global interest in Open Science, which include open data and methods, in addition to open access publications. It has been proposed that public availability of raw data increases the value and the possibility of confirmation of scientific findings, in addition to the potential of reducing research waste. Availability of raw data in open repositories facilitates the adequate development of meta-analysis and the cumulative evaluation of evidence for specific topics. In this commentary, we discuss key elements about data sharing in open repositories and we invite researchers around the world to deposit their data in them.

 

Sharing published short academic works in institutional repositories after six months | LIBER Quarterly: The Journal of the Association of European Research Libraries

Abstract:  The ambition of the Netherlands, laid down in the National Plan Open Science, is to achieve 100% open access for academic publications. The ambition was to be achieved by 2020. However, it is to be expected that for the year 2020 between 70% and 75% of the articles will be open access. Until recently, the focus of the Netherlands has been on the gold route – open access via journals and publishers’ platforms. This is likely to be costly and it is also impossible to cover all articles and other publication types this way. Since 2015, Dutch Copyright Act has offered an alternative with the implementation of Article 25fa (also known as the ‘Taverne Amendment’), facilitating the green route, i.e. open access via (trusted) repositories. This amendment allows researchers to share short scientific works (e.g. articles and book chapters in edited collections), regardless of any restrictive guidelines from publishers. From February 2019 until August 2019 all Dutch universities participated in the pilot ‘You Share, we Take Care!’ to test how this copyright amendment could be interpreted and implemented by institutions as a policy instrument to enhance green open access and “self-archiving”. In 2020 steps were taken to scale up further implementation of the amendment. This article describes the outcomes of this pilot and shares best practices on implementation and awareness activities in the period following the pilot until early 2021, in which libraries have played an instrumental role in building trust and working on effective implementations on an institutional level. It concludes with some possible next steps for alignment, for example on a European level.