Responsible Research Network, Finland | DORA

“Finland is among the first countries to have developed national recommendations on responsible research evaluation. In 2020, a task force formed by the Federation of Finnish Learned Societies published the “Good Practice in Researcher Evaluation: Recommendation for Responsible Evaluation of a Researcher in Finland.”1 A major driver for the national recommendation was the need to make conscious decisions in evaluation processes. Although many national entities were involved in developing the Recommendation, the approach is considered “bottom-up” and there was broad and enthusiastic buy-in among Finnish academic stakeholders….

A national task force was founded based on shared concerns identified by learned societies, research funders, policy organizations, publishers, national open science coordination, and the national research integrity board. While many national entities were involved in the Recommendation’s creation, the approach is considered “bottom-up”; in Finland there is a historic culture of autonomy for academic stakeholders….

In addition, the Recommendation timing coincided with the uptake of FAIR (findable, accessible, interoperable, and reusable) data and open science initiatives in Finland. These initiatives incentivize and reward researchers for producing open and FAIR data, and align with the Recommendation. In the coming years, the focus will be on building the capacity to move evaluation practices beyond quantitative publication metrics and in closer alignment with the goals of the Recommendation….”

Four key challenges in the open?data revolution – Salguero?Gómez – 2021 – Journal of Animal Ecology – Wiley Online Library

Abstract:  In Focus: Culina, A., Adriaensen, F., Bailey, L. D., et al. (2021) Connecting the data landscape of long-term ecological studies: The SPI-Birds data hub. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.13388. Long-term, individual-based datasets have been at the core of many key discoveries in ecology, and calls for the collection, curation and release of these kinds of ecological data are contributing to a flourishing open-data revolution in ecology. Birds, in particular, have been the focus of international research for decades, resulting in a number of uniquely long-term studies, but accessing these datasets has been historically challenging. Culina et al. (2021) introduce an online repository of individual-level, long-term bird records with ancillary data (e.g. genetics), which will enable key ecological questions to be answered on a global scale. As well as these opportunities, however, we argue that the ongoing open-data revolution comes with four key challenges relating to the (1) harmonisation of, (2) biases in, (3) expertise in and (4) communication of, open ecological data. Here, we discuss these challenges and how key efforts such as those by Culina et al. are using FAIR (Findable, Accessible, Interoperable and Reproducible) principles to overcome them. The open-data revolution will undoubtedly reshape our understanding of ecology, but with it the ecological community has a responsibility to ensure this revolution is ethical and effective.

 

 

 

F-UJI Automated FAIR Data Assessment Tool | FAIRsFAIR

“The F-UJI assessment is based on 16 out of 17 core FAIR object assessment metrics developed within FAIRsFAIR and each corresponding to a part or the whole of a FAIR principle. F-UJI adheres to existing web standards and PID resolution services best practices and utilises external registries and resources such as re3data1 and Datacite2 APIs, SPDX License List3, RDA Metadata Standards Catalog4, and Linked Open Vocabularies (LOV)5   For information on the practical tests implemented against the metrics, see Devaraju, Huber, et al., 2020.

The source code is now available with a free license through Github. Any feedback on improving the tool and associated metrics can be added as an issue on Github. …”

FAIRsFAIR Repository Support Series Webinars | FAIRsFAIR

“FAIRsFAIR webinar series aims to help repository managers become familiar with FAIR-enabling practices. Each webinar will provide an overview of a specific FAIR-enabling activity, share information on recent developments within FAIRsFAIR and other initiatives as well as offering examples of good practice, practical tips and recommendations. Each webinar will last a maximum in 1.5 hours and include time for questions and discussion. Registration is free and open to all however the main audience is repository managers and service providers. Data stewards and developers may also find the session informative.”

Announcing the FAIR Data Stewardship Listserv |

“Today 09 September 2021, the GO FAIR US office is announcing the FAIR Data Stewardship listserv, fair-data-stewardship@mit.edu. Subscribe to the listserv at http://mailman.mit.edu/mailman/listinfo/fair-data-stewardship.

The idea for this listserv emerged out of the FAIR Data Stewardship interest group meeting hosted in late March, 2021, by Katie Knight and Chris Erdmann.

One of the pillars to emerge from discussions at this meeting was the decision to host an open email list about Data Stewardship. Inpsiration for the list initially stems from stories such as Katie’s where she found it difficult to find an open list where nuanced questions around topics such as metadata practices, can be asked or found via community discussion. An example Katie used was the Autocat listserv where cataloging experts share best practices around cataloging material. Having a virtual community, like Autocat, full of individuals with deep and broad domain knowledge available to ask for expertise, professional advice (anecdotal or otherwise), training events or webinars, and so on was essential, especially since at that time Katie worked as the sole metadata librarian at her institution and had no in-person colleagues to engage in these discussions with. Even monitoring such a listserv was educational, as she was able to follow certain discussion threads and learn by proxy….”

FAIR Digital Objects (FDO) Forum IN: New GO FAIR Implementation Network – GO FAIR

“Check out the most recent addition to GO FAIR’s Implementation Networks: FAIR Digital Objects (FDO) Forum IN.

The FDO Forum IN is a group of international experts from relevant research institutions and infrastructure initiatives committed to specify FAIR Digital Objects (FDO) and its components and to foster the implementation of an infrastructure ready to support FDOs practically.

The FDO Forum will actively work together with working groups from other initiatives such as RDA, OAI, W3C, ISO, and IETF to achieve these goals. As the name already indicates, the FAIR principles are the starting point of the IN’s considerations. They will look for a variety of implementation models that meet the FDO requirements….”

MEMORANDUM FOR THE HEADS OF EXECUTIVE DEPARTMENTS AND AGENCIES

“Advancing climate science to improve understanding of Earth’s changing climate and changes that pose the greatest risk to society. This includes: facilitating public access to climate-related information that will assist Federal, State, local, and Tribal governments in climate planning and resilience activities, coupled with capacity building and training to increase access to and support the use of data, information, and climate services; research to advance understanding of the societal and economic impacts of climate change (e.g., human and ecosystem health, wildlife and fisheries); improving observational networks to create carbon inventories and baselines; improving modeling capabilities for local-scale, regional climate and related extreme weather events; and disaster attribution science, including in potential tipping points in physical, natural, and human systems….

For example, open science and other participatory modes of research, such as community-based datahubs that give citizens access to information and data, as well as community-engaged research that respectfully provides opportunities for the participation in science and technology of those historically excluded from the scientific enterprise. Public participation in science is critical for the health of the nation and leads to more innovative research of all kinds, including research that addresses the needs of diverse communities…. 

Relevant agencies should develop data infrastructure that facilitates identification of inequities across sectors at scale, especially in underserved rural and urban communities, including through data linkage across Federal agencies, creation of interoperable data systems, and efforts to make data more available to the public, while preserving privacy and upholding ethical principles. This includes a focus on the underutilized, inaccessible, or missing data needed to measure and promote equity. Finally, agencies should also take steps to improve diversity and equity in the research workforce…. 

To build a trustworthy and engaged U.S. science and technology (S&T) enterprise, agencies should prioritize making Federally funded R&D: open to the public in a findable, accessible, interoperable, and reusable way; more rigorous, reproducible, and transparent; safe and secure; grounded in assessment of ethical, legal, and societal implications; and free from improper political interference—all while minimizing administrative burden….”

Open Research Infrastructure Programs at LYRASIS

“Academic libraries, and institutional repositories in particular, play a key role in the ongoing quest for ways to gather metrics and connect the dots between researchers and research contributions in order to measure “institutional impact,” while also streamlining workflows to reduce administrative burden. Identifying accurate metrics and measurements for illustrating “impact” is a goal that many academic research institutions share, but these goals can only be met to the extent that all organizations across the research and scholarly communication landscape are using best practices and shared standards in research infrastructure. For example, persistent identifiers (PIDs) such as ORCID iDs (Open Researcher and Contributor Identifier) and DOIs (Digital Object Identifiers) have emerged as crucial best practices for establishing connections between researchers and their contributions while also serving as a mechanism for interoperability in sharing data across systems. The more institutions using persistent identifiers (PIDs) in their workflows, the more connections can be made between entities, making research objects more FAIR (findable, accessible, interoperable, and reusable). Also, when measuring institutional repository usage, clean, comparable, standards-based statistics are needed for accurate internal assessment, as well as for benchmarking with peer institutions….”

Open Research Infrastructure Programs at LYRASIS

“Academic libraries, and institutional repositories in particular, play a key role in the ongoing quest for ways to gather metrics and connect the dots between researchers and research contributions in order to measure “institutional impact,” while also streamlining workflows to reduce administrative burden. Identifying accurate metrics and measurements for illustrating “impact” is a goal that many academic research institutions share, but these goals can only be met to the extent that all organizations across the research and scholarly communication landscape are using best practices and shared standards in research infrastructure. For example, persistent identifiers (PIDs) such as ORCID iDs (Open Researcher and Contributor Identifier) and DOIs (Digital Object Identifiers) have emerged as crucial best practices for establishing connections between researchers and their contributions while also serving as a mechanism for interoperability in sharing data across systems. The more institutions using persistent identifiers (PIDs) in their workflows, the more connections can be made between entities, making research objects more FAIR (findable, accessible, interoperable, and reusable). Also, when measuring institutional repository usage, clean, comparable, standards-based statistics are needed for accurate internal assessment, as well as for benchmarking with peer institutions….”

Research Data Management Challenges in Citizen Science Projects and Recommendations for Library Support Services. A Scoping Review and Case Study

Abstract:  Citizen science (CS) projects are part of a new era of data aggregation and harmonisation that facilitates interconnections between different datasets. Increasing the value and reuse of CS data has received growing attention with the appearance of the FAIR principles and systematic research data management (RDM) practises, which are often promoted by university libraries. However, RDM initiatives in CS appear diversified and if CS have special needs in terms of RDM is unclear. Therefore, the aim of this article is firstly to identify RDM challenges for CS projects and secondly, to discuss how university libraries may support any such challenges.

A scoping review and a case study of Danish CS projects were performed to identify RDM challenges. 48 articles were selected for data extraction. Four academic project leaders were interviewed about RDM practices in their CS projects.

Challenges and recommendations identified in the review and case study are often not specific for CS. However, finding CS data, engaging specific populations, attributing volunteers and handling sensitive data including health data are some of the challenges requiring special attention by CS project managers. Scientific requirements or national practices do not always encompass the nature of CS projects.

Based on the identified challenges, it is recommended that university libraries focus their services on 1) identifying legal and ethical issues that the project managers should be aware of in their projects, 2) elaborating these issues in a Terms of Participation that also specifies data handling and sharing to the citizen scientist, and 3) motivating the project manager to good data handling practises. Adhering to the FAIR principles and good RDM practices in CS projects will continuously secure contextualisation and data quality. High data quality increases the value and reuse of the data and, therefore, the empowerment of the citizen scientists.

Consultant FAIR research data: building on open science (0.8 FTE)

As a Consultant, you work proactively within the various networks and communities in the field of FAIR research data. Researchers are part of interdisciplinary communities where it is common practice to collect, analyze, share, find and store data together in a systematic way. Good data management, based on the FAIR principles, is indispensable here. As a Consultant FAIR research data, you implement services organized by the University Library: you inform, advise and train researchers, research groups, and faculties across disciplines in data management, during the various stages of research. You will also participate in projects to further expand or optimize services in the field of research data. A specific part of your duties is the role of community manager, a pivotal function for current and future data specialists within the UU. As a spider in the web, you ensure a continuous and varied program for and by researchers, data support staff, and students. Examples include organizing the introduction program for new UU employees in this area and data community meetings. You take the lead, keep your eyes open for new initiatives, ensure good organization, enthuse and connect people. You work closely with colleagues within other support services such as Information & Technology Services (ITS). The Consultant FAIR research data is part of Research Data Management (RDM) Support, a multidisciplinary network of data experts within the University and University Medical Center Utrecht, see also Research Data Management Support – Universiteit Utrecht.

Understanding Open Data | Open Research Europe

“Open Research Europe endorses the FAIR Data Principles, alongside an open data policy, as a framework to promote the broadest reuse of research data. We believe that sharing research data can accelerate the pace of discovery, provide credibility and recognition for authors, and lead to increased public trust in research. This also brings benefits for wider society, including driving innovation in technology, better evidence-based policymaking, and economic benefits. 

What is Open Data?  

Open Data is data that is available for everyone to access, use and share. For researchers, this refers to any information or materials that have been collected or created as part of your research project – such as survey results, gene sequences, software, code, neuro-images, even audio files. In research, open data practices are also known as ‘data sharing’. 

What is FAIR data?  

The FAIR Guiding Principles were published in Scientific Data in 2016, providing a new framework for research data management, designed to maximize its reuse and support open data practices.  
 
FAIR data is Findable, Accessible, Interoperable, and Reusable. FAIR data goes beyond open data, aiming to make the data itself more useful and user-friendly. …”

Promoting FAIR Data Through Community-driven Agile Design: the Open Data Commons for Spinal Cord Injury (odc-sci.org) | SpringerLink

Abstract:  The past decade has seen accelerating movement from data protectionism in publishing toward open data sharing to improve reproducibility and translation of biomedical research. Developing data sharing infrastructures to meet these new demands remains a challenge. One model for data sharing involves simply attaching data, irrespective of its type, to publisher websites or general use repositories. However, some argue this creates a ‘data dump’ that does not promote the goals of making data Findable, Accessible, Interoperable and Reusable (FAIR). Specialized data sharing communities offer an alternative model where data are curated by domain experts to make it both open and FAIR. We report on our experiences developing one such data-sharing ecosystem focusing on ‘long-tail’ preclinical data, the Open Data Commons for Spinal Cord Injury (odc-sci.org). ODC-SCI was developed with community-based agile design requirements directly pulled from a series of workshops with multiple stakeholders (researchers, consumers, non-profit funders, governmental agencies, journals, and industry members). ODC-SCI focuses on heterogeneous tabular data collected by preclinical researchers including bio-behaviour, histopathology findings and molecular endpoints. This has led to an example of a specialized neurocommons that is well-embraced by the community it aims to serve. In the present paper, we provide a review of the community-based design template and describe the adoption by the community including a high-level review of current data assets, publicly released datasets, and web analytics. Although odc-sci.org is in its late beta stage of development, it represents a successful example of a specialized data commons that may serve as a model for other fields.