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In 2019, members of the Global Indigenous Data Alliance (GIDA) published the CARE Principles (Collective Benefit, Authority to Control, Responsibility, and Ethics) for Indigenous Data Governance (IDGov). CARE has since been referenced, leveraged, and adopted in various ways across disciplines and sectors worldwide. In this article, GIDA members from Aotearoa New Zealand, Australia, Canada, and the United States share examples of IDGov models that predate and emerged after the development of CARE. Together, we reflect upon the affordances and limitations of the broad uptake of CARE. We argue that renewed attention is needed to the original intent of CARE: to direct data actors to local communities' protocols and frameworks for IDGov, and to transform institutional policies and practices to fortify Indigenous Peoples' authority to control their data. [ABSTRACT FROM AUTHOR]

Data literacy, the ability to understand and effectively communicate with data, is crucial for researchers to interpret and validate data. However, low reproducibility in biomedical research is nowadays a significant issue, with major implications for scientific progress and the reliability of findings. Recognizing this, funding bodies such as the European Commission emphasize the importance of regular data management practices to enhance reproducibility. Establishing a standardized framework for statistical methods and data analysis is essential to minimize biases and inaccuracies. The FAIR principles (Findable, Accessible, Interoperable, Reusable) aim to enhance data interoperability and reusability, promoting transparent and ethical data practices. The study presented here aimed to train postgraduate students at the Universidad Europea de Madrid in data literacy skills and FAIR principles, assessing their application in master thesis projects. A total of 46 participants, including students and mentors, were involved in the study during the 2022–2023 academic year. Students were trained to prioritize FAIR data sources and implement Data Management Plans (DMPs) during their master's thesis. An 11-item questionnaire was developed to evaluate the FAIRness of research data, showing strong internal consistency. The study found that integrating FAIR principles into educational curricula is crucial for enhancing research reproducibility and transparency. This approach equips future researchers with essential skills for navigating a data-driven scientific environment and contributes to advancing scientific knowledge. [ABSTRACT FROM AUTHOR]

The study was conducted in Masvingo Province, Zimbabwe to interrogate the strategies being used to preserve indigenous knowledge, in the face of technological challenges. The researchers sought to ascertain the web presence of such entities and the awareness of indigenous datagovernance principles among information professionals. Qualitative research, using multiple case studies, was conducted. Web content analysis was done to identify such spaces, with four culture houses being purposively selected. Interviews were conducted with the participants who were available on the day of the researchers' visit when the latter observed each site. The findings showed that all culture houses did not have a web presence, and some administrators were aware of the principles for preserving indigenous knowledge in the digital era. The study recommends that in the future, cultural tourism can be better promoted, and indigenous communities empowered, by enhancing their access to related knowledge to achieve sustainable development goals. [ABSTRACT FROM AUTHOR]

Balancing speed and quality during crises pose challenges for ensuring the value and utility of data in social science research. The COVID-19 pandemic in particular underscores the need for high-quality data and rapid dissemination. Given the importance of behavioural measures and compliance with measures to contain the pandemic, social science research has played a key role in policymaking during this global crisis. This study addresses two key research questions: How FAIR (findable, accessible, interoperable and reusable) are social science data on the COVID-19 pandemic? Which study features are related to the level of FAIRness scores of datasets? We assess the FAIRness of n = 1131 articles, retrieved through a keyword search in the Web of Science database, employing both automated and manual coding methods. Our study inclusion criteria encompass empirical studies on the COVID-19 pandemic published between 2019 and 2023 with a social science focus and explicit reference to the underlying dataset(s). Our analysis of n = 45 datasets reveals substantial differences in FAIRness for different types of research on the COVID-19 pandemic. The overall FAIRness of data is acceptable, although particularly Reusability scores fall short, in both the manual and the automatic assessment. Further, articles explicitly linked to the Social Science concept in the OpenAlex database exhibit a higher mean overall FAIRness value. Based on these results, we derive recommendations for balancing ethical obligations and the potential tradeoff between speed and data (sharing) quality in social-scientific crisis research. [ABSTRACT FROM AUTHOR]

Abstract Data literacy, the ability to understand and effectively communicate with data, is crucial for researchers to interpret and validate data. However, low reproducibility in biomedical research is nowadays a significant issue, with major implications for scientific progress and the reliability of findings. Recognizing this, funding bodies such as the European Commission emphasize the importance of regular data management practices to enhance reproducibility. Establishing a standardized framework for statistical methods and data analysis is essential to minimize biases and inaccuracies. The FAIR principles (Findable, Accessible, Interoperable, Reusable) aim to enhance data interoperability and reusability, promoting transparent and ethical data practices. The study presented here aimed to train postgraduate students at the Universidad Europea de Madrid in data literacy skills and FAIR principles, assessing their application in master thesis projects. A total of 46 participants, including students and mentors, were involved in the study during the 2022–2023 academic year. Students were trained to prioritize FAIR data sources and implement Data Management Plans (DMPs) during their master’s thesis. An 11-item questionnaire was developed to evaluate the FAIRness of research data, showing strong internal consistency. The study found that integrating FAIR principles into educational curricula is crucial for enhancing research reproducibility and transparency. This approach equips future researchers with essential skills for navigating a data-driven scientific environment and contributes to advancing scientific knowledge.

Sharing human brain data can yield scientific benefits, but because of various disincentives, only a fraction of these data is currently shared. We profile three successful data-sharing experiences from the NIH BRAIN Initiative Research Opportunities in Humans (ROH) Consortium and demonstrate benefits to data producers and to users.

Abstract Background Genotyping of individuals plays a pivotal role in various biological analyses, with technology choice influenced by multiple factors including genomic constraints, number of targeted loci and individuals, cost considerations, and the ease of sample preparation and data processing. Target enrichment capture of specific polymorphic regions has emerged as a flexible and cost-effective genomic reduction method for genotyping, especially adapted to the case of very large genomes. However, this approach necessitates complex bioinformatics treatment to extract genotyping data from raw reads. Existing workflows predominantly cater to phylogenetic inference, leaving a gap in user-friendly tools for genotyping analysis based on capture methods. In response to these challenges, we have developed GeCKO (Genotyping Complexity Knocked-Out). To assess the effectiveness of combining target enrichment capture with GeCKO, we conducted a case study on durum wheat domestication history, involving sequencing, processing, and analyzing variants in four relevant durum wheat groups. Results GeCKO encompasses four distinct workflows, each designed for specific steps of genomic data processing: (i) read demultiplexing and trimming for data cleaning, (ii) read mapping to align sequences to a reference genome, (iii) variant calling to identify genetic variants, and (iv) variant filtering. Each workflow in GeCKO can be easily configured and is executable across diverse computational environments. The workflows generate comprehensive HTML reports including key summary statistics and illustrative graphs, ensuring traceable, reproducible results and facilitating straightforward quality assessment. A specific innovation within GeCKO is its 'targeted remapping' feature, specifically designed for efficient treatment of targeted enrichment capture data. This process consists of extracting reads mapped to the targeted regions, constructing a smaller sub-reference genome, and remapping the reads to this sub-reference, thereby enhancing the efficiency of subsequent steps. Conclusions The case study results showed the expected intra-group diversity and inter-group differentiation levels, confirming the method's effectiveness for genotyping and analyzing genetic diversity in species with complex genomes. GeCKO streamlined the data processing, significantly improving computational performance and efficiency. The targeted remapping enabled straightforward SNP calling in durum wheat, a task otherwise complicated by the species' large genome size. This illustrates its potential applications in various biological research contexts.

Modern bioimaging core facilities at research institutions are essential for managing and maintaining high‐end instruments, providing training and support for researchers in experimental design, image acquisition and data analysis. An important task for these facilities is the professional management of complex multidimensional bioimaging data, which are often produced in large quantity and very different file formats. This article details the process that led to successfully implementing the OME Remote Objects system (OMERO) for bioimage‐specific research data management (RDM) at the Core Facility Cellular Imaging (CFCI) at the Technische Universität Dresden (TU Dresden). Ensuring compliance with the FAIR (findable, accessible, interoperable, reusable) principles, we outline here the challenges that we faced in adapting data handling and storage to a new RDM system. These challenges included the introduction of a standardised group‐specific naming convention, metadata curation with tagging and Key–Value pairs, and integration of existing image processing workflows. By sharing our experiences, this article aims to provide insights and recommendations for both individual researchers and educational institutions intending to implement OMERO as a management system for bioimaging data. We showcase how tailored decisions and structured approaches lead to successful outcomes in RDM practices. [ABSTRACT FROM AUTHOR]

Persistent Identifiers (PIDs) are elementary ingredients in nowadays and future research data management processes. Looking at various data life cycles, identifiers help to achieve certain goals in a very efficient way, supporting steps to find, relate, integrate and publish data. Various identifier systems have been developed for this purpose, which are provided by various providers. This created the challenge of the complex and different methods of using the identifiers. Depending on the system, an identifier can, for example, point to a landing page or directly to a digital object. The PID Metaresolver unifies the use of PIDs from different providers and thus supports and simplifies the use of the referenced object. It is a service for resolving PIDs from various providers, validating the PID provider for a given PID and integrating additional providers. With the PID Metaresolver, workflows and analyses of research data can be extended to a larger list of providers. We present the design and implementation of the PID Metaresolver service and give an insight into the many possible uses. [ABSTRACT FROM AUTHOR]

Public sector data associated with health are a highly valuable resource with multiple potential end-users, from health practitioners, researchers, public bodies, policy makers, and industry. Data for infectious disease agents are used for epidemiological investigations, disease tracking and assessing emerging biological threats. Yet, there are challenges in collating and re-using it. Data may be derived from multiple sources, generated and collected for different purposes. While public sector data should be open access, providers from public health settings or from agriculture, food, or environment sources have sensitivity criteria to meet with ethical restrictions in how the data can be reused. Yet, sharable datasets need to describe the pathogens with sufficient contextual metadata for maximal utility, e.g. associated disease or disease potential and the pathogen source. As data comprise the physical resources of pathogen collections and potentially associated sequences, there is an added emerging technical issue of integration of omics 'big data'. Thus, there is a need to identify suitable means to integrate and safely access diverse data for pathogens. Established genomics alliances and platforms interpret and meet the challenges in different ways depending on their own context. Nonetheless, their templates and frameworks provide a solution for adaption to pathogen datasets. [ABSTRACT FROM AUTHOR]

Biodiversity science is in a pivotal period when diverse groups of actors – including researchers, businesses, national governments, and Indigenous Peoples – are negotiating wide-ranging norms for governing and managing biodiversity data in digital repositories. The management of these repositories, often called biodiversity data portals, can serve either to redress or to perpetuate the colonial history of biodiversity science and current inequities. Both researchers and Indigenous Peoples are implementing new strategies to influence whom biodiversity data portals recognise as salient participants in data management and use. Two notable efforts are the FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective benefit, Authority, Responsibility, Ethics) Data Principles. Actors use these principles to influence the governance of biodiversity data portals. 'Fit-for-use' data is a social status provided by groups of actors who approve whether the data meets specific purposes. Advocates for the FAIR and CARE Principles use them in a similar way to institutionalise the authority of different groups of actors. However, the FAIR Principles prioritise the ability of machine agents to understand the meanings of data, while the CARE Principles prioritise Indigenous Peoples and their data sovereignty. Together, FAIR and CARE illustrate a broader emerging strategy for institutionalising international norms for digital repositories about who they should recognise as having a formal role in determinations of the fitness-for-use of data. [ABSTRACT FROM AUTHOR]

This report describes the experience in implementing the FAIR principles for the GoTriple Discovery Platform for Social Sciences and Humanities (SSH). It shows how adherence to FAIR should be considered as a continuous process throughout the entire lifespan of any information management system, including GoTriple, rather than a static goal with decisions only made at the design time. This report presents an introduction highlighting the importance of the FAIR principles, indicating how they can be assessed in data management systems. Then, the GoTriple case is presented, with a general overview of this discovery platform before describing some of the implemented practices in support of FAIR. The Discussion Section shows, on the one hand, some virtuous reuse of GoTriple data, together with one major pitfall in the platform's FAIR implementation. In this sense, this report serves as a case study that can offer insights and actionable advice for those implementing information systems aligned, from the very outset, with the FAIR principles. [ABSTRACT FROM AUTHOR]

Research on novel tools for model-based development differs from a mere engineering task by not only developing a new tool, but by providing some form of evidence that it is effective. This is typically achieved by experimental evaluations. Following principles of good scientific practice, both the tool and the models used in the experiments should be made available along with a paper, aiming at the replicability of experimental results. We investigate to which degree recent research reporting on novel methods, techniques, or algorithms supporting model-based development with MATLAB/Simulink meets the requirements for replicability of experimental results. Our results from studying 65 research papers obtained through a systematic literature search are rather unsatisfactory. In a nutshell, we found that only 31% of the tools and 22% of the models used as experimental subjects are accessible. Given that both artifacts are needed for a replication study, only 9% of the tool evaluations presented in the examined papers can be classified to be replicable in principle. We found none of the experimental results presented in these papers to be fully replicable, and 6% partially replicable. Given that tools are still being listed among the major obstacles of a more widespread adoption of model-based principles in practice, we see this as an alarming signal. While we are convinced that this situation can only be improved as a community effort, this paper is meant to serve as starting point for discussion, based on the lessons learnt from our study. [ABSTRACT FROM AUTHOR]

Systematic reviews and other types of literature reviews are more prevalent in clinical medicine than in other fields. The recurring need for improvement and updates in these disciplines has led to the Living Systematic Review (LSR) concept to enhance the effectiveness of scientific synthesis efforts. While LSR was introduced in 2014, its adoption outside clinical medicine has been limited, with one exception. However, it is anticipated that this will change in the future, prompting a detailed exploration of four key dimensions for LSR development, regardless of the scientific domain. These dimensions include (a) compliance with FAIR principles, (b) interactivity to facilitate easier access to scientific knowledge, (c) public participation for a more comprehensive review, and (d) extending the scope beyond mere updates to living systematic reviews. Each field needs to establish clear guidelines for drafting literature reviews as independent studies, with discussions centring around the central theme of the Living Systematic Review. [ABSTRACT FROM AUTHOR]

Fish remains are a common component of coastal and inland archaeological assemblages from Australia and the Pacific Islands. Physical reference collections are the primary tool that researchers use to taxonomically identify fish bones but given the high biodiversity of fishes in this region, collections are frequently not complete at the genus- and species-level. Adjunct resources, such as online photograph repositories of fish bone reference collections and illustrated technical guides, provide useful supplementary aids. However, such 2D photographs and illustrations offer fixed perspectives of the bone and do not allow for ready manipulation and detailed examination of the specimen. Here, we introduce Fishboneviz, the first open access 3D fish bone reference collection, which was developed to reduce inequitable access to physical reference collections in line with the FAIR principles of data management (findable, accessible, interoperable, reusable). A 'best-practice' methodology was established to facilitate fast and effective segmentation (i.e. isolate regions of interest such as elements) of fish Computed Tomography (CT) scans. This way, fish bone elements of interest were segmented to allow digital manipulation and viewing of the complete element. To examine the effectiveness of the approach, image segmentation procedures were applied to a representative sample of 10 bone elements per fish: dentary, premaxilla, maxilla, articular, quadrate, hyomandibular, opercle, preopercle, last precaudal vertebra, and first caudal vertebra. For species within the family Labridae, the three pharyngeal grinding plates were also segmented. These elements were selected as they represent different regions of the skeleton, variable morphologies, and commonly recovered elements from archaeological sites. To date, the collection contains the skeletal elements of 26 fish species (18 families). In the future, it is hoped that the collection will be further expanded by a broader network of interested collaborators to ensure it grows according to the changing needs of research and teaching communities. [ABSTRACT FROM AUTHOR]

Background: Genotyping of individuals plays a pivotal role in various biological analyses, with technology choice influenced by multiple factors including genomic constraints, number of targeted loci and individuals, cost considerations, and the ease of sample preparation and data processing. Target enrichment capture of specific polymorphic regions has emerged as a flexible and cost-effective genomic reduction method for genotyping, especially adapted to the case of very large genomes. However, this approach necessitates complex bioinformatics treatment to extract genotyping data from raw reads. Existing workflows predominantly cater to phylogenetic inference, leaving a gap in user-friendly tools for genotyping analysis based on capture methods. In response to these challenges, we have developed GeCKO (Genotyping Complexity Knocked-Out). To assess the effectiveness of combining target enrichment capture with GeCKO, we conducted a case study on durum wheat domestication history, involving sequencing, processing, and analyzing variants in four relevant durum wheat groups. Results: GeCKO encompasses four distinct workflows, each designed for specific steps of genomic data processing: (i) read demultiplexing and trimming for data cleaning, (ii) read mapping to align sequences to a reference genome, (iii) variant calling to identify genetic variants, and (iv) variant filtering. Each workflow in GeCKO can be easily configured and is executable across diverse computational environments. The workflows generate comprehensive HTML reports including key summary statistics and illustrative graphs, ensuring traceable, reproducible results and facilitating straightforward quality assessment. A specific innovation within GeCKO is its 'targeted remapping' feature, specifically designed for efficient treatment of targeted enrichment capture data. This process consists of extracting reads mapped to the targeted regions, constructing a smaller sub-reference genome, and remapping the reads to this sub-reference, thereby enhancing the efficiency of subsequent steps. Conclusions: The case study results showed the expected intra-group diversity and inter-group differentiation levels, confirming the method's effectiveness for genotyping and analyzing genetic diversity in species with complex genomes. GeCKO streamlined the data processing, significantly improving computational performance and efficiency. The targeted remapping enabled straightforward SNP calling in durum wheat, a task otherwise complicated by the species' large genome size. This illustrates its potential applications in various biological research contexts. [ABSTRACT FROM AUTHOR]

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts. [ABSTRACT FROM AUTHOR]

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Metadata is critical throughout the research process, from study design to corpus selection/compilation, result interpretability and cumulative research. To date, however, learner corpus research has not developed community standards or best practices for metadata collection and sharing. In this article, we present the results of a collaborative project aimed at addressing this issue by developing a standardised metadata schema for learner corpora. We first describe the procedure implemented to design the schema, including the ways in which we continuously involved learner corpus researchers in this initiative. We then introduce the Core Metadata Schema for Learner Corpora (LC-meta, Version 2), which consists in a set of obligatory and optional variables that encapsulate crucial information about L2 data (administrative details, corpus design, text-related variables, learner-related variables, annotations, annotators, or transcribers). Finally, we discuss future developments and emphasise the importance of continued maintenance and further refinement of this schema by the research community. [ABSTRACT FROM AUTHOR]

FAIRification of personal health data is of utmost importance to improve health research and political as well as medical decision-making, which ultimately contributes to a better health of the general population. Despite the many advances in information technology, several obstacles such as interoperability problems remain and relevant research on the health topic of interest is likely to be missed out due to time-consuming search and access processes. A recent example is the COVID-19 pandemic, where a better understanding of the virus' transmission dynamics as well as preventive and therapeutic options would have improved public health and medical decision-making. Consequently, the NFDI4Health Task Force COVID-19 was established to foster the FAIRification of German COVID-19 studies. This paper describes the various steps that have been taken to create low barrier workflows for scientists in finding and accessing German COVID-19 research. It provides an overview on the building blocks for FAIR health research within the Task Force COVID-19 and how this initial work was subsequently expanded by the German consortium National Research Data Infrastructure for Personal Health Data (NFDI4Health) to cover a wider range of studies and research areas in epidemiological, public health and clinical research. Lessons learned from the Task Force helped to improve the respective tasks of NFDI4Health. [ABSTRACT FROM AUTHOR]

IntroductionData-driven medicine is essential for enhancing the accessibility and quality of the healthcare system. The availability of data plays a crucial role in achieving this goal.MethodsWe propose implementing a robust data infrastructure of FAIRification and data fusion for clinical, genomic, and imaging data. This will be embedded within the framework of a distributed analytics platform for healthcare data analysis, utilizing the Personal Health Train paradigm.ResultsThis infrastructure will ensure the findability, accessibility, interoperability, and reusability of data, metadata, and results among multiple medical centers participating in the BETTER Horizon Europe project. The project focuses on studying rare diseases, such as intellectual disability and inherited retinal dystrophies.ConclusionThe anticipated impacts will benefit a wide range of healthcare practitioners and potentially influence health policymakers.

Open science is becoming one of the modern trends and a permanent object of study, including awareness and attitude of scientists from different countries, involvement of subject areas and individual scientific institutions. The purpose of the study is to investigate the attitude of users engaged in research activities in institutions of higher professional education, research organizations and manufacturing enterprises, united in the world-class West Siberian Interregional Scientific and Educational Center, to open science. When composing the questionnaire consisting of 38 questions, a comprehensive approach to the study of the problem was used, The questionnaire has six sections that reflect the most important aspects necessary to study the attitude of researchers to open scientific publications and their awareness of this issue. The survey involves 563 respondents whose research interests are related to various subject areas. The survey revealed their attitudes towards open access publishing of research results, their knowledge of open access resources, open science communications and data sharing principles, as well as emerging issues and concerns about open access initiatives. The study is practically important for the development of recommendations for forming information culture of users from higher education institutions in the field of open science. It is also important for the development of new directions in the activities of university libraries, and in this regard to that of methodology for the basis of training programs for library professionals in the framework of additional and professional education.

Research data management (RDM) is central to the implementation of the FAIR (Findable Accessible, Interoperable, Reusable) and Open Science principles. Recognising the importance of RDM, ELIXIR Platforms and Nodes have invested in RDM and launched various projects and initiatives to ensure good data management practices for scientific excellence. These projects have resulted in a rich set of tools and resources highly valuable for FAIR data management. However, these resources remain scattered across projects and ELIXIR structures, making their dissemination and application challenging. Therefore, it becomes imminent to coordinate these efforts for sustainable and harmonised RDM practices with dedicated forums for RDM professionals to exchange knowledge and share resources. The proposed ELIXIR RDM Community will bring together RDM experts to develop ELIXIR’s vision and coordinate its activities, taking advantage of the available assets. It aims to coordinate RDM best practices and illustrate how to use the existing ELIXIR RDM services. The Community will be built around three integral pillars, namely, a network of RDM professionals, RDM knowledge management and RDM training expertise and resources. It will also engage with external stakeholders to leverage benefits and provide a forum to RDM professionals for regular knowledge exchange, capacity building and development of harmonised RDM practices, keeping in line with the overall scope of the RDM Community. In the short term, the Community aims to build upon the existing resources and ensure that the content of these remain up to date and fit for purpose. In the long run, the Community will aim to strengthen the skills and knowledge of its RDM professionals to support the emerging needs of the scientific community. The Community will also devise an effective strategy to engage with other ELIXIR structures and international stakeholders to influence and align with developments and solutions in the RDM field.

The relationship between people, place, and data presents challenges and opportunities for science and society. While there has been general enthusiasm for and work toward Findable, Accessible, Interoperable, and Reusable (FAIR) data for open science, only more recently have these data-centric principles been extended into dimensions important to people and place-notably, the CARE Principles for Indigenous Data Governance, which affect collective benefit, authority to control, responsibility, and ethics. The FAIR Island project seeks to translate these ideals into practice, leveraging the institutional infrastructure provided by scientific field stations. Starting with field stations in French Polynesia as key use cases that are exceptionally well connected to international research networks, FAIR Island builds interoperability between different components of critical research infrastructure, helping connect these to societal benefit areas. The goal is not only to increase reuse of scientific data and the awareness of work happening at the field stations but more generally to accelerate place-based research for sustainable development. FAIR Island works reflexively, aiming to scale horizontally through networks of field stations and to serve as a model for other sites of intensive long-term scientific study.

Recent advances in microscopy imaging and image analysis motivate more and more institutes worldwide to establish dedicated core‐facilities for bioimage analysis. To maximise the benefits research groups at these institutes gain from their core‐facilities, they should be established to fit well into their respective environment. In this article, we introduce common collaborator requests and corresponding potential services core‐facilities can offer. We also discuss potential competing interests between the targeted missions and implementations of services to guide decision makers and core‐facility founders to circumvent common pitfalls. LAY DESCRIPTION: Bioimage analysis has become a field of research which is essential for driving current scientific projects and discoveries in the life sciences. It is a fast‐moving, diverse and highly multidisciplinary domain. Hence, single individuals that are proficient in analysing image data are scarce and often overwhelmed with providing support for their local communities while also developing their individual career paths. To support these talents, their knowledge and expertise, institutes and decision makers are increasingly creating core‐facilities for bioimage analysis. The success of a core‐facility to preserve, incubate and spread knowledge in bioimage analysis can be ensured by providing an appropriate set of services the core‐facility offers. In our manuscript, we summarise the requirements which a bioimage analysis core‐facilities are confronted with, and the services it can provide to satisfy these. We introduce categories of services in order to help core‐facility founders, members and decision makers to define their strategy and tailor their core‐facilities towards the specific needs of their local communities. Lastly, we summarise a set of competing interests which may arise in the process of shaping or managing a core‐facility and provide argumentative support towards their resolution. [ABSTRACT FROM AUTHOR]

All digital objects that result from the modeling and simulation field are valid sets of research data. In general, research data are the result of intense intellectual activity that is worth communicating. This communication is an essential research practice that, whether with the aim of understanding, critiquing or further developing results, smoothly leads to collaboration, which not only involves discussions, and sharing institutional resources, but also the sharing of data and information at several stages of the research process. Data sharing is intended to improve and facilitate collaboration but quickly introduces challenges like reproducibility, reusability, interoperability, and standardization. These challenges are deeply rooted in an apparent reproducibility standard, about which there is a debate worth considering before emphasizing how the modeling and simulation workflow commonly occurs. Although that workflow is almost natural for practitioners, the sharing practices still require special attention because the principles (known as FAIR principles) that guide research practices towards data sharing also guide the requirements for machine actionable results. The FAIR principles, however, do not address the actual implementation of the data sharing process. This implementation requires careful consideration of characteristics of the sharing platforms for benefiting the most of the data sharing activity. This article serves as an invitation to integrate data sharing practices into the established routines of researchers and elaborates on the perspectives, and guidelines surrounding data sharing implementation. [ABSTRACT FROM AUTHOR]

The expansive scientific software ecosystem, characterized by millions of titles across various platforms and formats, poses significant challenges in maintaining reproducibility and provenance in scientific research. The diversity of independently developed applications, evolving versions and heterogeneous components highlights the need for rigorous methodologies to navigate these complexities. In response to these challenges, the SBGrid team builds, installs and configures over 530 specialized software applications for use in the on‐premises and cloud‐based computing environments of SBGrid Consortium members. To address the intricacies of supporting this diverse application collection, the team has developed the Capsule Software Execution Environment, generally referred to as Capsules. Capsules rely on a collection of programmatically generated bash scripts that work together to isolate the runtime environment of one application from all other applications, thereby providing a transparent cross‐platform solution without requiring specialized tools or elevated account privileges for researchers. Capsules facilitate modular, secure software distribution while maintaining a centralized, conflict‐free environment. The SBGrid platform, which combines Capsules with the SBGrid collection of structural biology applications, aligns with FAIR goals by enhancing the findability, accessibility, interoperability and reusability of scientific software, ensuring seamless functionality across diverse computing environments. Its adaptability enables application beyond structural biology into other scientific fields. [ABSTRACT FROM AUTHOR]

In the era of digital innovation, the preservation of indigenous knowledge stands at a compelling intersection of heritage and technology. Librarians play a crucial role in navigating the complexities of technology while respecting cultural protocols. Through a comprehensive exploration of librarian practices, this study sought to unpack how indigenous knowledge can be ethically preserved in the digital landscape. A qualitative study was conducted with 20 librarians in Zimbabwe, who were purposively selected. Interviews and observation were used to collect data, which was analysed using thematic content analysis. The findings reveal that libraries preserved indigenous knowledge in the form of poetry, folklore, drama and artefacts; demonstrated traditional dances; and sometimes recorded live sessions and special collections. The major challenges being faced were the ethical issues in documenting indigenous knowledge. It is recommended that librarians actively work with indigenous communities in indigenous knowledge projects to avoid resistance from knowledge holders. [ABSTRACT FROM AUTHOR]

Introduction: Sharingmicrobiome data among researchers fosters new innovations and reduces cost for research. Practically, this means that the (meta)data will have to be standardized, transparent and readily available for researchers. The microbiome data and associated metadata will then be described with regards to composition and origin, in order tomaximize the possibilities for application in various contexts of research. Here, we propose a set of tools and protocols to develop a real-time FAIR (Findable. Accessible, Interoperable and Reusable) compliant database for the handling and storage of human microbiome and host-associated data. Methods: The conflicts arising from privacy laws with respect to metadata, possible human genome sequences in the metagenome shotgun data and FAIR implementations are discussed. Alternate pathways for achieving compliance in such conflicts are analyzed. Sample traceable and sensitive microbiome data, such as DNA sequences or geolocalized metadata are identified, and the role of the GDPR (General Data Protection Regulation) data regulations are considered. For the construction of the database, procedures have been realized to make data FAIR compliant, while preserving privacy of the participants providing the data. Results and discussion: An open-source development platform, Supabase, was used to implement the microbiome database. Researchers can deploy this realtime database to access, upload, download and interact with human microbiome data in a FAIR complaint manner. In addition, a large language model (LLM) powered by ChatGPT is developed and deployed to enable knowledge dissemination and non-expert usage of the database. [ABSTRACT FROM AUTHOR]

Due to the increasing availability of data, information pollution and disinformation have become more prevalent in digital journalism. In the past, access to information was emphasized as a right, but today, the right to information pollution protection has emerged. The primary reasons for this are the inability to monitor the vast amount of news circulating in the digital environment and the absence of a structure to govern the responsibilities of digital news sharing. Our research has developed a data model for digital journalism to address such issues. In addition to providing the FAIR principles, the data model we propose for digital journalism creates a trustworthy social media network by enabling the news to have a logical relationship with each other and all news processes to be observed. For a data model to support an observable and traceable social media environment, it must be compatible with graph structures containing massive amounts of data. Although existing blockchain technologies allow for observation and monitoring, they do not support graph data structures. The model and data are separated in the proposed structure, and indexing mechanisms are supported to address these issues. The proposed data model was compared to the blockchain technology data model. In terms of time and space complexity, sustainability, and maintenance costs, it was determined that the model we created for digital journalism was superior to the data model of blockchain technology. [ABSTRACT FROM AUTHOR]

This note suggests that decimal latitude/longitude [dLL] locations should be used to identify features of interest, landforms, sample and investigations sites, in an 'information landscape' provided by the geomorphological literature. All the information associated with a labelled, or tagged, geolocation should be available for examination as part of information landscapes that can be explored and represented in books, papers and other publications. This note also outlines the 'open' and FAIR data that are findable, accessible, interoperable and reusable and how the principles can be used to better explain landscapes, especially in the mountain landscape domain. Tors and rock glaciers illustrate [dLL] geolocation to identify sites and inform fieldwork and literature searching. Any [dLL]‐specified location is an identifying label, as are names given to landforms and toponyms. Two letters (digraph) are used as landform labels: TO for tors and RG for rock glaciers. Citations, (author–date–title–source) attributions, are also labels. The note shows how these attributions can be linked to [dLL] geolocations specifying locations in time and space and in the literature. The addition of [dLL] will facilitate future literature searches and modelling to explore 'unknowns' in the landscape, and this paper suggests ways in which this can be achieved, including geoheritage and geotourism. [ABSTRACT FROM AUTHOR]

The conect4children (c4c) initiative was established to facilitate the development of new drugs and other therapies for paediatric patients. It is widely recognised that there are not enough medicines tested for all relevant ages of the paediatric population. To overcome this, it is imperative that clinical data from different sources are interoperable and can be pooled for larger post hoc studies. c4c has collaborated with the Clinical Data Interchange Standards Consortium (CDISC) to develop cross-cutting data resources that build on existing CDISC standards in an effort to standardise paediatric data. The natural next step was an extension to disease-specific data items. c4c brought together several existing initiatives and resources relevant to disease-specific data and analysed their use for standardising disease-specific data in clinical trials. Several case studies that combined disease-specific data from multiple trials have demonstrated the need for disease-specific data standardisation. We identified three relevant initiatives. These include European Reference Networks, European Joint Programme on Rare Diseases, and Pistoia Alliance. Other resources reviewed were National Cancer Institute Enterprise Vocabulary Services, CDISC standards, pharmaceutical company-specific data dictionaries, Human Phenotype Ontology, Phenopackets, Unified Registry for Inherited Metabolic Disorders, Orphacodes, Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP), and Observational Medical Outcomes Partnership. The collaborative partners associated with these resources were also reviewed briefly. A plan of action focussed on collaboration was generated for standardising disease-specific paediatric clinical trial data. A paediatric data standards multistakeholder and multi-project user group was established to guide the remaining actions—FAIRification of metadata, a Phenopackets pilot with RDCA-DAP, applying Orphacodes to case report forms of clinical trials, introducing CDISC standards into European Reference Networks, testing of the CDISC Pediatric User Guide using data from the mentioned resources and organisation of further workshops and educational materials. [ABSTRACT FROM AUTHOR]

Research data management (RDM) is central to the implementation of the FAIR (Findable Accessible, Interoperable, Reusable) and Open Science principles. Recognising the importance of RDM, ELIXIR Platforms and Nodes have invested in RDM and launched various projects and initiatives to ensure good data management practices for scientific excellence. These projects have resulted in a rich set of tools and resources highly valuable for FAIR data management. However, these resources remain scattered across projects and ELIXIR structures, making their dissemination and application challenging. Therefore, it becomes imminent to coordinate these efforts for sustainable and harmonised RDM practices with dedicated forums for RDM professionals to exchange knowledge and share resources. The proposed ELIXIR RDM Community will bring together RDM experts to develop ELIXIR’s vision and coordinate its activities, taking advantage of the available assets. It aims to coordinate RDM best practices and illustrate how to use the existing ELIXIR RDM services. The Community will be built around three integral pillars, namely, a network of RDM professionals, RDM knowledge management and RDM training expertise and resources. It will also engage with external stakeholders to leverage benefits and provide a forum to RDM professionals for regular knowledge exchange, capacity building and development of harmonised RDM practices, keeping in line with the overall scope of the RDM Community. In the short term, the Community aims to build upon the existing resources and ensure that the content of these remain up to date and fit for purpose. In the long run, the Community will aim to strengthen the skills and knowledge of its RDM professionals to support the emerging needs of the scientific community. The Community will also devise an effective strategy to engage with other ELIXIR structures and international stakeholders to influence and align with developments and solutions in the RDM field.

Research data management and data curation need strong relationships with colleagues, collaborators, and researchers. Because change comes fast, assessment of current services and practices are important so that new paths and initiatives can be developed. Creating strong metadata will support the FAIR Principles and provide a more equitable and accessible path to data.

This data article presents a set of primary, analyzed, and digitalized mechanical testing datasets for nine copper alloys. The mechanical testing methods including the Brinell and Vickers hardness, tensile, stress relaxation, and low-cycle fatigue (LCF) testing were performed according to the DIN/ISO standards. The obtained primary testing data (84 files) mainly contain the raw measured data along with the testing metadata of the processes, materials, and testing machines. Five secondary datasets were also provided for each testing method by collecting the main meta- and measurement data from the primary data and the outputs of data analyses. These datasets give materials scientists beneficial data for comparative material selection analyses by clarifying the wide range of mechanical properties of copper alloys, including Brinell and Vickers hardness, yield and tensile strengths, elongation, reduction of area, relaxed and residual stresses, and LCF fatigue life. Furthermore, both the primary and secondary datasets were digitalized by the approach introduced in the research article entitled “Toward a digital materials mechanical testing lab” [1]. The resulting open-linked data are the machine-processable semantic descriptions of data and their generation processes and can be easily queried by semantic searches to enable advanced data-driven materials research.

Abstract The increasing number of articles on adverse interactions that may occur when specific foods are consumed with certain drugs makes it difficult to keep up with the latest findings. Conflicting information is available in the scientific literature and specialized knowledge bases because interactions are described in an unstructured or semi-structured format. The FIDEO ontology aims to integrate and represent information about food-drug interactions in a structured way. This article reports on the new version of this ontology in which more than 1700 interactions are integrated from two online resources: DrugBank and Hedrine. These food-drug interactions have been represented in FIDEO in the form of precompiled concepts, each of which specifies both the food and the drug involved. Additionally, competency questions that can be answered are reviewed, and avenues for further enrichment are discussed.

The quality of modern software relies heavily on the effective use of static code analysis tools. To improve their usefulness, these tools should be evaluated using a framework that prioritizes collaboration, user-friendliness, and long-term sustainability. In this paper, we suggest applying the FAIR principles—Findability, Accessibility, Interoperability, and Reusability—as a foundation for assessing static code analysis tools. We specifically focus on Python-based tools, analyzing their features and how well they align with FAIR guidelines. Our findings indicate that it is important to expand the FAIR principles to include thorough documentation, performance assessments, and robust testing frameworks for a more complete evaluation. As Internet of Things (IoT) applications and technologies become increasingly common, these tools must adapt to meet the unique challenges posed by complex and interconnected systems. Addressing these issues is vital for ensuring security and scalability within IoT environments. By implementing this FAIR-based approach, we aim to support the development of static code analysis tools that cater to the evolving needs of the software engineering community while ensuring they remain sustainable and reliable.

Data that are restricted are historically challenging for researchers to find and even more difficult to access. While efforts to support open data have expanded in Canada, the same cannot be said for restricted data. To better understand the landscape of restricted data in Canada, this study aimed to accomplish two primary goals: (1) identify data sources where data were restricted and (2) assess a subset of health sciences data sources to determine how well they make their data discoverable and accessible. Our study identified 137 Canadian data sources, where 48 health sciences sources were evaluated for discoverability/accessibility. Data sources received poor grades with respect to data discovery due to a lack of metadata standards (38/48, 79%), an inability to find datasets through searching and browsing (32/46, 70%), and a lack of data documentation to support reuse (27/48, 56%). The absence of pricing information (31/48, 65%) and opaque dataset restrictions (25/48, 52%) were identified as key barriers to the data access request process. This study highlights significant room for improvement with respect to improving the discovery of and access to restricted data in Canada and makes recommendations for how to better support restricted data sources on a national scale.

In Bangladesh, agriculture is a crucial driver for addressing Sustainable Development Goal 1 (no poverty) and 2 (zero hunger), playing a fundamental role in the economy and people’s livelihoods. To enhance the sustainability and resilience of the agriculture industry through data-driven insights, the Bangladesh Bureau of Statistics, open data portal, and other organizations consistently collect and publish agricultural data on the Web. Nevertheless, the current datasets encounter various challenges: 1) they are presented in an unsustainable, static, read-only, and aggregated format, 2) they do not conform to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles, and 3) they do not facilitate interactive analysis and integration with other data sources. In this paper, we present a thorough solution, delineating a systematic procedure for developing BDAKG: a knowledge graph that semantically and multidimensionally integrates Bangladesh agricultural data. BDAKG incorporates multidimensional semantics, is linked with external knowledge graphs, is compatible with OLAP, and adheres to the FAIR principles. Our experimental evaluation centers on evaluating the integration process and assessing the quality of the resultant knowledge graph in terms of completeness, timeliness, FAIRness, OLAP compatibility, correctness, and data-driven analysis. Our federated data analyses recommend a strategic approach focused on decreasing CO2 emissions, fostering economic growth, and promoting sustainable forestry.

The challenge of defining and cataloging the building blocks of the brain requires a standardized approach to naming neurons and organizing knowledge about their properties. The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, and others are generating vast amounts of data and characterizing large numbers of neurons throughout the nervous system. The neuroscientific literature contains many neuron names (e.g. parvalbumin-positive interneuron or layer 5 pyramidal cell) that are commonly used and generally accepted. However, it is often unclear how such common usage types relate to many evidence-based types that are proposed based on the results of new techniques. Further, comparing different types across labs remains a significant challenge. Here, we propose an interoperable knowledge representation, the Neuron Phenotype Ontology (NPO), that provides a standardized and automatable approach for naming cell types and normalizing their constituent phenotypes using identifiers from community ontologies as a common language. The NPO provides a framework for systematically organizing knowledge about cellular properties and enables interoperability with existing neuron naming schemes. We evaluate the NPO by populating a knowledge base with three independent cortical neuron classifications derived from published data sets that describe neurons according to molecular, morphological, electrophysiological, and synaptic properties. Competency queries to this knowledge base demonstrate that the NPO knowledge model enables interoperability between the three test cases and neuron names commonly used in the literature.