Introducing the Truth Effect Database (TED): An open trial-level resource promoting FAIR data in truth effect research.

The Horizon 2020 project Biodiversity Community Integrated Knowledge Library (BiCIKL) (started 1st of May 2021, duration 3 years) will build a new European community of key research infrastructures, researchers, citizen scientists and other stakeholders in biodiversity and life sciences. Together, the BiCIKL 14 partners will solidify open science practices by providing access to data, tools and services at each stage of, and along the entire biodiversity research and data life cycle (specimens, sequences, taxon names, analytics, publications, biodiversity knowledge graph) (Fig. 1, see also the BiCIKL kick-off presentation through Suppl. material 1), in compliance with the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. The existing services provided by the participating infrastructures will expand through development and adoption of shared, common or interoperable domain standards, resulting in liberated and enhanced flows of data and knowledge across these domains. BiCIKL puts a special focus on the biodiversity literature. Over the span of the project, BiCIKL will develop new methods and workflows for semantic publishing and integrated access to harvesting, liberating, linking, and re-using sub-article-level data extracted from literature (i.e., specimens, material citations, sequences, taxonomic names, taxonomic treatments, figures, tables). Data linkages may be realised with different technologies (e.g., data warehousing, linking between FAIR Data Objects, Linked Open Data) and can be bi-lateral (between two data infrastructures) or multi-lateral (among multiple data infrastructures). The main challenge of BiCIKL is to design, develop and implement a FAIR Data Place (FDP), a central tool for search, discovery and management of interlinked FAIR data across different domains. The key final output of BiCIKL will the future Biodiversity Knowledge Hub (BKH), a one-stop portal, providing access to the BiCIKL services, tools and workflows, beyond the lifetime of the project.

MODERATOR: Jenna Daenzer Peer Review Coordinator GENETICS & G3 SPEAKERS: Taunton Paine National Institutes of Health Gabriele Hayden University of Oregon Libraries Karen Yook WormBase and microPublication Biology Matt Giampoala American Geophysical Union REPORTER: Tony Alves HighWire Press The FAIR (Findable, Accessible, Interoperable, and Reusable) Data principles have become essential guidelines for modern data management and scholarly publishing. These principles are designed to enhance the quality and impact of research by ensuring data is managed and shared in a way that maximizes its utility and accessibility. The CSE 2024 Annual Meeting session on FAIR Data brought together experts from various fields to discuss the challenges and opportunities associated with implementing these principles. Each speaker provided unique insights into the practicalities and benefits of FAIR Data. NIH Data Management and Sharing Policy The National Institutes of Health (NIH) Data Management and Sharing Policy underscores the importance of data sharing to advance rigorous and reproducible research. Taunton Paine, Director in the Scientific Data Sharing Policy Division of the Office of Science Policy at the NIH, emphasized that sharing data enables the validation of research results, makes high-value datasets accessible, and accelerates future research directions. The policy also aims to increase opportunities for citation and collaboration, promoting public trust and transparency in research. The NIH has a long history of encouraging data sharing, with policies dating back to 2003 for data sharing plans and more specific policies for genomic data and clinical trials introduced in subsequent years. Despite these efforts, data accessibility remains […]

Solutions for the generation of FAIR (Findable, Accessible, Interoperable, and Reusable) data and metadata in experimental tribology are currently lacking. Nonetheless, FAIR data production is a promising path for implementing scalable data science techniques in tribology, which can lead to a deeper understanding of the phenomena that govern friction and wear. Missing community-wide data standards, and the reliance on custom workflows and equipment are some of the main challenges when it comes to adopting FAIR data practices. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building, and (4) numerous – seemingly – small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability, and reusability of experimental tribological data beyond typical publication practices.

The National Institutes of Health (NIH) Stimulating Peripheral Activity to Relieve Conditions (SPARC) program seeks to accelerate the development of therapeutic devices that modulate electrical activity in nerves to improve organ function. SPARC-funded researchers are generating rich datasets from neuromodulation research that are curated and shared according to FAIR (Findable, Accessible, Interoperable, and Reusable) guidelines and are accessible to the public on the SPARC data portal. Keeping track of the utilization of these datasets within the larger research community is a feature that will benefit data-generating researchers in showcasing the impact of their SPARC outcomes. This will also allow the SPARC program to display the impact of the FAIR data curation and sharing practices that have been implemented. This manuscript provides the methods and outcomes of SPARClink, our web tool for visualizing the impact of SPARC, which won the Second prize at the 2021 SPARC FAIR Codeathon. With SPARClink, we built a system that automatically and continuously finds new published SPARC scientific outputs (datasets, publications, protocols) and the external resources referring to them. SPARC datasets and protocols are queried using publicly accessible REST application programming interfaces (APIs, provided by Pennsieve and Protocols.io) and stored in a publicly accessible database. Citation information for these resources is retrieved using the NIH reporter API and National Center for Biotechnology Information (NCBI) Entrez system. A novel knowledge graph-based structure was created to visualize the results of these queries and showcase the impact that the FAIR data principles can have on the research landscape when they are adopted by a consortium.

Mental health disorders remain a significant public health challenge in low- and middle-income countries (LMICs) such as Kenya and Uganda due to limited resources, stigma, and inadequate service integration. The INSPIRE Mental Health (MH) Project aims to address these challenges by developing an open-source data science platform that aligns with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. A Theory of Change (ToC) was developed to guide project implementation. A participatory approach was employed, involving targeted intrest holder engagements through workshops, focus group discussions, and key informant interviews with policymakers, healthcare providers, Village Health Team (VHT) members, data managers, researchers, and community representatives. All participants provided informed consent be- fore inclusion in the study. Collected data was transcribed, cleaned, and analyzed using Atlas.ti, employing thematic analysis to identify emerging themes. The insights gained in- formed the development of the ToC, which visually maps causal linkages between project activities, assumptions, and anticipated outcomes. Interest-holders identified key challenges in mental health service delivery, including fragmented services, inadequate funding, and limited data management capacity. The findings were categorized into five themes: (1) mental health program design, (2) bur- den assessment, (3) screening and assessment tools, (4) data reporting and utilization, (5) prospects for longitudinal studies. Participants emphasized the need for standardized data collection, improved reporting systems, and capacity building in data science techniques to support evidence-based decision-making. The ToC framework offers a strategic roadmap to enhance mental health service delivery and data utilization, fostering collaboration and sustainability. By promoting FAIR data principles and interest holder engagement, the project aims to support informed mental health interventions in Kenya and Uganda.

The urgency of the climate crisis and the need to accelerate learning and climate action requires that we build on previous knowledge, rather than replicating it. There is an abundance of knowledge on climate change adaptation and mitigation dispersed across websites, projects, platforms, and documents. There is either too much information that is not easily discoverable (sitting in silos) or it is too technical or complex, and not ‘usable’ or fit for purpose in terms (e.g. language or format). Both issues cause redundancy and sometimes replication of work, wasting resources. In the worst case, they can also cause unintended consequences such as maladaptation, or increased vulnerability. However, the issue is not a lack of information, but rather how to organise and connect such knowledge to allow people to discover what already exists and put it to effective use. As such, our goal is to make climate action knowledge findable, accessible, interoperable and reusable (FAIR) and reduce climate change knowledge silos. The recently awarded FAIR2Adapt Project aims to establish a comprehensive FAIR and open data framework for CCA and to demonstrate the impact of FAIR data on CCA strategies. By making CCA data FAIR, FAIR2Adapt will accelerate adaptation actions so that they are visible, understandable, and actionable for various purposes and different types of stakeholders. FAIR taxonomies are one approach to help tackle this issue by making climate change knowledge FAIR and by ensuring, that going forward, platforms have a way to make their knowledge FAIR and thus more reusable by the climate change community. The Climate Connectivity Hub and Taxonomy seek to visualize and connect online platform data (e.g. Cordis, Climate-ADAPT, weADAPT, PreventionWeb) to increase discoverability, interoperability and a shared understanding of the research results and their potential application in future policy, research and practice. It builds on past knowledge to scale up and accelerate climate action whilst also identifying key knowledge gaps. This presentation will show that: 1) taxonomies are useful supporting the interoperability of online climate knowledge and can usefully emerge from combined expert and machine learning of project results (e.g. Cordis); 2) shared vocabularies and different interpretations of language and terminology add value to project planning and implementation ; and, 3) the visualization of these elements for decision-makers, planners, researchers, policy makers, etc. can help to enable and scale accelerated climate action. 

Building an open-source collaborative platform for migration research: A metadata modeling approach using XML

The management of our soils plays a crucial role in addressing some of our greatest challenges, such as food security and climate change. Working together across national borders is essential for securing the health and quality of our soil in the future. With multi-stakeholder processes, language and the use of language is very important since the capability of understanding each other is critical. Developing a common language facilitates learning together, trust-building, defining common viewpoints, resolving disagreements, and arriving at technically sound and practical solutions.   This document represents the initial attempt to create a common soil domain ontology and data model to support the creation of a user-friendly infrastructure in accordance with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles for uploading, managing, and providing comparable soil data at global scale. To meet these requirements, the review and application of accepted and widely used standards, conventions, and guidelines for the ingestion, organization, and sharing of soil data is necessary. The document should be regarded as a living document that evolves along with the progress of the project and in response to stakeholder feedback.   This project began with the development of an ontology for the soil domain and the identification of representational terms. This was done by following a data-driven approach, which allowed the determination of relevant entities by making strategic decisions based on data analysis and organization, while facilitating a modular expansion of the knowledge base. Next, a standardized soil vocabulary was defined to facilitate data sharing and discovery.  Furthermore, soil analytical information is collected and modelled across a variety of data sources, as well as SI units and non-SI units accepted for use with the International System of Units. Finally, a list of relevant conversion factors, regressions, and pedotransfer functions was collected to facilitate data comparison across different data bases.  

The effects of repetition spacing on the illusory truth effect

<p>Making research data FAIR (Findable, Accessible, Interoperable, and Reusable) is critical to maximizing its impact. However, since the FAIR principles are designed as guidelines and do not specify implementation rules, it is difficult to verify the practice of these principles. Therefore, metrics and associated tools need to be developed to enable the assessment of FAIR compliance of services and datasets. Such practical solutions are important for many stakeholders to assess the quality of data-related services. They are important for selecting such services, but can also be used to iteratively improve data offerings, e.g., as part of FAIR advisory processes. With the increasing number of published datasets and the need to test them repeatedly, there is a growing body of literature that recognizes this importance of automated FAIR assessment tools. Our goal is to contribute to this area of FAIR through the development of an open source tool called F-UJI.  F-UJI supports programmatic FAIR assessment of research data based on a set of core metrics against which the implementation of FAIR principles can be assessed. This paper presents the development and application of F-UJI and the underlying metrics. For each of the metrics, we have designed and implemented practical tests based on existing standards and best practices for research data. The tests are important to our expanded understanding of how to test FAIR metrics in practice that have not been fully addressed in previous work on FAIR data assessment. We demonstrate the use of the tool by assessing several multidisciplinary datasets from selected trusted digital repositories, followed by recommendations for improving the FAIRness of these datasets. We summarize the experience and lessons learned from the development and testing.</p>

The illusory truth effect requires semantic coherence across repetitions

PurposeThe data economy mainly relies on the surveillance capitalism business model, enabling companies to monetize their data. The surveillance allows for transforming private human experiences into behavioral data that can be harnessed in the marketing sphere. This study aims to focus on investigating the domain of data economy with the methodological lens of quantitative bibliometric analysis of published literature.Design/methodology/approachThe bibliometric analysis seeks to unravel trends and timelines for the emergence of the data economy, its conceptualization, scientific progression and thematic synergy that could predict the future of the field. A total of 591 data between 2008 and June 2021 were used in the analysis with the Biblioshiny app on the web interfaced and VOSviewer version 1.6.16 to analyze data from Web of Science and Scopus.FindingsThis study combined findable, accessible, interoperable and reusable (FAIR) data and data economy and contributed to the literature on big data, information discovery and delivery by shedding light on the conceptual, intellectual and social structure of data economy and demonstrating data relevance as a key strategic asset for companies and academia now and in the future.Research limitations/implicationsFindings from this study provide a steppingstone for researchers who may engage in further empirical and longitudinal studies by employing, for example, a quantitative and systematic review approach. In addition, future research could expand the scope of this study beyond FAIR data and data economy to examine aspects such as theories and show a plausible explanation of several phenomena in the emerging field.Practical implicationsThe researchers can use the results of this study as a steppingstone for further empirical and longitudinal studies.Originality/valueThis study confirmed the relevance of data to society and revealed some gaps to be undertaken for the future.

In the landscape of general-purpose repositories, Zenodo was built at the European Laboratory for Particle Physics' (CERN) data center to facilitate the sharing and preservation of the long tail of research across all disciplines and scientific domains. Given Zenodo’s long tradition of making research artifacts FAIR (Findable, Accessible, Interoperable, and Reusable), there are still challenges in applying these principles effectively when serving the needs of specific research domains. Plazi’s biodiversity taxonomic literature processing pipeline liberates data from publications, making it FAIR via extensive metadata, the minting of a DataCite Digital Object Identifier (DOI), a licence and both human- and machine-readable output provided by Zenodo, and accessible via the Biodiversity Literature Repository community at Zenodo. The deposits (e.g., taxonomic treatments, figures) are an example of how local networks of information can be formally linked to explicit resources in a broader context of other platforms like GBIF (Global Biodiversity Information Facility). In the context of biodiversity taxonomic literature data workflows, a general-purpose repository’s traditional submission approach is not enough to preserve rich metadata and to capture highly interlinked objects, such as taxonomic treatments and digital specimens. As a prerequisite to serve these use cases and ensure that the artifacts remain FAIR, Zenodo introduced the concept of custom metadata, which allows enhancing submissions such as figures or taxonomic treatments (see as an example the treatment of Eurygyrus peloponnesius) with custom keywords, based on terms from common biodiversity vocabularies like Darwin Core and Audubon Core and with an explicit link to the respective vocabulary term. The aforementioned pipelines and features are designed to be served first and foremost using public Representational State Transfer Application Programming Interfaces (REST APIs) and open web technologies like webhooks. This approach allows researchers and platforms to integrate existing and new automated workflows into Zenodo and thus empowers research communities to create self-sustained cross-platform ecosystems. The BiCIKL project (Biodiversity Community Integrated Knowledge Library) exemplifies how repositories and tools can become building blocks for broader adoption of the FAIR principles. Starting with the above literature processing pipeline, the concepts of and resulting FAIR data, with a focus on the custom metadata used to enhance the deposits, will be explained.

Introduction: As the intelligence community observed the evolution of open-source intelligence (OSINT) and the development of the extensive data landscape, several new challenges to traditional approaches to warfighting emerged. One of these challenges was the increasing intensity of the illusory truth effect and its effects on operational timeline planning. How effective are these Emerging Disruptive Technologies (EDT)-driven open sources in projecting a battlespace deception, and what are the limitations and risks? To offer a substantive answer to this question, this study will use the Russian war of aggression against Ukraine as a case study specifically focusing on a projected deception using OSINT in support of the Ukrainian Kherson Offensive, which began on August 29, 2022, and the Kharkiv Offensive, which started in September 2022.Methods: The analysis aimed to identify and quantify instances where the Ukrainian deception storyline was repeated across various media outlets and social media platforms. It used an OSINT scraper to aggregate and filter the data. Then, a simple quantitative analysis was used to cross-reference the intensity of the illusory truth effect with the Russian operational timeline for troop movements, and a conclusion was drawn.Results: The number of ‘hits’ scrapped during a specific period was unexpectedly high, indicating a high level of engagement from both mobile and desktop devices. The data revealed a clear connection between increasing illusory truth intensity and Russian troop movements in the field. The study also pointed out the limitations of large-scale social media data in confidently establishing cause-and-effect relationships between influence and physical actions. It also demonstrated how the growing risk of the illusory truth effect, driven by OSINT and unrestricted military access to social media, could potentially compromise the compartmentalised operational command and control of military organisations through the personal devices of individuals involved in the command-and-control processes. Conclusion: In an information environment enhanced by EDT, the illusory truth effect is a powerful tool for deceptive projection in the information domain. This effect is amplified if access is gained to compartmentalised operational decision-making processes within the target warfighting organisation. Consequently, from an operational security perspective, the intelligence community must address the threat of an illusory truth breach of command & control processes via OSINT collection in an EDT-enhanced information environment.

It is crucial to optimize global mental health research to address the high burden of mental health challenges and mental illness for individuals and societies. Data sharing and reuse have demonstrated value for advancing science and accelerating knowledge development. The FAIR (Findable, Accessible, Interoperable, and Reusable) Guiding Principles for scientific data provide a framework to improve the transparency, efficiency, and impact of research. In this review, we describe ethical and equity considerations in data sharing and reuse, delineate the FAIR principles as they apply to mental health research, and consider the current state of FAIR data practices in global mental health research, identifying challenges and opportunities. We describe noteworthy examples of collaborative efforts, often across disciplinary and national boundaries, to improve Findability and Accessibility of global mental health data, as well as efforts to create integrated data resources and tools that improve Interoperability and Reusability. Based on this review, we suggest a vision for the future of FAIR global mental health research and suggest practical steps for researchers with regard to study planning, data preservation and indexing, machine-actionable metadata, data reuse to advance science and improve equity, metrics and recognition.