Reuse Of Data Research Articles

Exploring barriers and ethical challenges to medical data sharing: perspectives from Chinese researchers.

The impetus for policies promoting medical data sharing in China has gained significant traction. Nonetheless, the present legal and ethical framework governing the research use of medical data in China, is characterized by a more restrictive rather than permissive approach. The proportion of Chinese medical data being leveraged for scientific research still has room for improvement at present, indicating a significant untapped potential for advancing medical knowledge and improving healthcare outcomes. Building upon this research, we aim to delve deeper into the challenges researchers encounter in the sharing of medical data through focus group interviews. We conducted two focus group interviews study with researchers representing diverse disciplines to explore their perspectives on 21 June 2021 and 28 July 2021. A total of seventeen researchers willingly participated in this study, representing various professional backgrounds. Similar codes were merged. Research team discussions were also utilized to select interviewees' statements that were regarded as typical or representative. The respondents demonstrated a strong understanding that medical data should not be disseminated arbitrarily, recognizing the importance of sharing data in compliance with laws. Through the interview, we found that although respondents stressed the importance of careful consideration regarding if and when this information can be responsibly released, none of the respondents raised the issue of necessitating consent from data subjects for the research use of medical data. This observation sharply contrasts with the stringent separate consent provisions for secondary data use outlined in the PIPL. The findings from the focus group studies shed light on researchers' barriers and ethical challenges towards medical data sharing for scientific research, highlighting their deep concern for data security and cautious approach to sharing. The key objectives aimed at facilitating and enabling the reuse of medical data encompass enhancing interoperability, harmonizing data standards, improving data quality, safeguarding privacy, ensuring informed consent, incentivizing patients, and establishing explicit regulations pertaining to data access and utilization.

JPOST environment accelerates the reuse and reanalysis of public proteome mass spectrometry data.

jPOST (https://jpostdb.org/) comprises jPOSTrepo (https://repository.jpostdb.org/) (over 2000 projects), a repository for proteome mass spectrometry data, the reanalysis of raw proteome data based on a standardised protocol using UniScore, and jPOSTdb (https://globe.jpostdb.org/) (over 600 datasets), a database that integrates the reanalysed data. The jPOST reanalysis protocol rescores MS/MS spectra using a new scale, UniScore, to evaluate the extent to which the spectral peaks correspond to the amino acid sequences identified by search engines. However, the metadata registered in the repository database is insufficient for conducting the reanalysis. To address this issue, the Japanese Proteomics Society launched a data journal, the Journal of Proteome Data and Methods (JPDM), which accepts data descriptor articles detailing metadata that can be reanalysed. Within jPOST, raw proteome data is reanalysed based on the metadata described in the JPDM data descriptor articles, utilising UniScore. The reanalysed data is deposited in jPOSTdb, and a link to the JPDM articles is added to jPOSTrepo. These reanalysis accelerations within the jPOST environment will promote FAIR data principles and open science.

Collectively Working towards Plant-Pollinator Interactions Data Interoperability and Reuse: Lessons Learned from the WorldFAIR Project

The WorldFAIR project worked with eleven case studies to advance the implementation of the FAIR data principles (Findable, Accessible, Interoperable, and Reusable; Wilkinson et al. 2016). The Case Study on Agricultural Biodiversity (Drucker et al. 2022) addressed the challenges of advancing interoperability and mobilizing plant-pollinator interaction data for reuse. In the first phase of the project we performed a landscape analysis and found that a significant amount of data on plant-pollinator interaction was available as supplementary files of research articles, in a range of formats such as PDFs, Excel® spreadsheets, and text files. The diversity of approaches and the lack of appropriate data vocabularies led to confusion, information loss, and the need for complex data interpretation and transformation. In the second phase of the project, we performed six studies to adopt standards recommended in the first phase. Our approach for plant-pollinator data standardization was based on Darwin Core (Darwin Core Task Group 2009) and Ecological Data Language (EML, Jones et al. 2019), in conjunction with a data model and vocabulary proposed by the Brazilian Network of Plant-Pollinator Interactions (REBIPP). The studies underwent a process of “FAIRification” (i.e., transforming data into a format that adheres to the FAIR data principles) using the Global Biotic Interactions (GloBI, Poelen et al. 2014) platform. Two studies also explored the publishing model for Biotic Interactions as part of the GBIF New Data Model. The standardization strategy was successful and increased the interoperability of plant-pollinator interaction data, resulting in a process that allows tracking the provenance of the data, as well as facilitates the reuse of datasets (Drucker et al. 2024). This effort led to the development of "FAIR best practices" and guidelines for sharing plant-pollinator interaction data, as well as a FAIR assessment tool designed to help researchers and institutions evaluate adherence to the FAIR principles (Drucker et al. 2024). A tutorial to standardize plant-pollinator datasets (Gonzalez-Vaquero et al. 2024) and a cookbook with guidelines and recommendations for publishing agriculture-related plant-pollinator data (Salim et al. 2024) were developed to help different actors standardize plant-pollinator interaction data in accordance with the FAIR principles. Moving from diverse approaches and siloed initiatives to widely available FAIR plant-pollination interaction data for scientists and decision-makers will enable the development of integrative studies that enhance our understanding of species biology, behavior, ecology, phenology, and evolution.

GCM and gcType in 2024: comprehensive resources for microbial strains and genomic data.

Microbial culture collections play a crucial role in the collection, maintenance, and distribution of quality-assured living microbial strains, along with their associated phenotypic and omics data. To enhance the find-able, accessible, interoperable, and re-usable (FAIR) data utilization of microbial resources, the World Data Center for Microorganisms (WDCM) has developed the Global Catalogue of Microorganisms (GCM) and the Global Catalogue of Type Strains (gcType). These platforms provide interactive interfaces for cataloging the holdings of collections, along with detailed annotations of type strain genomes and curated metadata, including ecosystems, growth conditions, and collection locations. The system maximizes the scientific impact of microbial resources and culture collections through an integrated data mining tool that links strain- and species-related information from various public resources. Currently, the GCM and gcType include 574422 strains from 154 culture collections across 51 countries and regions, along with 25980 genomes from type species. Additionally, 2702655 articles and 103337 patents are integrated with these microbial resources.The system supports microbial taxonomic research and provides evidence for implementing the Nagoya Protocol in the field of microbial resources and their digital sequence information (DSI). Access is freely available at gcm.wdcm.org and gctype.wdcm.org.

A Real-Time Associative Feature-Based Customer Relationship Management and Enterprise Resource Planning Integration Model for Small- and Medium-Sized Enterprises

Abstract Customer relationship management (CRM) and enterprise resource planning (ERP) have been extensively discussed in the research literature respectively. However, existing studies do not reach a complete agreement on the CRM/ERP integration method, especially for small- and medium-sized enterprises (SMEs). Generally, this work proposed a novel method for CRM/ERP integration approach via associative feature technology. A new feature type, Real-time Data Link Board (RDLB), is developed and detailed as a generic information carrier solution for multisystem integration between CRM and ERP packages. Valuable data elements are mapped into such data boards dynamically and synchronized across different data sources, structures, and databases. The contribution of this work is to present a well-defined and generically reusable data carrier definition, alongside related methods for ensuring consistent data modeling during the system integration phase across various digitalization engineering implementation projects. With this approach, the consistent management of data integration across intricate systems is achievable throughout application lifecycles, supported by the object-oriented software engineering foundation.

Ethical considerations related to personal data collection and reuse: trust and transparency in language and speech technologies

Ethical considerations related to personal data collection and reuse: trust and transparency in language and speech technologies

SSBD: an ecosystem for enhanced sharing and reuse of bioimaging data.

SSBD (https://ssbd.riken.jp) is a platform for the sharing and reuse of bioimaging data. As part of efforts to build a bioimaging data ecosystem, SSBD has recently been updated to a two-tiered data resource comprising SSBD:repository, a public repository for the sharing of all types of bioimaging data reported in journals, and SSBD:database, an added-value database for the sharing of curated, highly reusable, metadata-rich data. This update addresses the conflicting demands of rapid data publication and sharing of richly annotated data, thereby promoting bioimaging data sharing and reuse. With this update, SSBD is now positioned as a core repository and database within the foundingGIDE, an international consortium working to establish a global image data ecosystem. Harmonizing metadata between data resources enables cross-searching and data exchange with data resources from other countries and regions.

The EU-funded action to strengthen health information systems

Abstract The European Commission initiative to build a European Health Union recognizes the need to address in a coordinated fashion the effects of the pandemic in health and social sectors and to strengthen the resilience, accessibility and effectiveness of health systems through co-operation, best practice exchange, training schemes, technical support and improved data and knowledge management. To make the most of the potential of health data, the European Health Data Space (EHDS) is being established as one of the central building blocks of a strong European Health Union. The EHDS sets out rules, common standards, infrastructures and a governance framework for the use of electronic health data for healthcare, research, innovation and policy making. In line with these policy goals, the European Commission is funding the Action ‘Supporting Member States in the WHO European Region in strengthening health information systems and boosting health data governance’. This Action will be implemented by WHO Regional Office for Europe and aims to boost the use and reuse of health data, including by promoting, where possible, the principles and solutions developed and implemented for the EHDS. It will also contribute to the objectives of the EU Global Health Strategy to improve global health security and deliver better health for all in a changing world. In addition, the Action is designed to strengthen countries’ capacity across critical programmatic areas of health data and digital health such as data quality (including appropriate data management and analytics) and digital health literacy across the 53 Member States in the WHO European region. The main objectives of the Action are to establish a Health Information Network for strategic guidance; and develop and implement tools and capacities to strengthen health information systems, health data governance, data quality and interoperability in countries.

10.B. Scientific session: The European Health Data Space (EHDS): A sea of change for health data in Europe

Abstract A wealth of data is generated every day throughout our health systems which, if leveraged well, can underpin the digital solutions and policies that will transform health and care. However, the complexity and divergence of rules, structures and processes within and across European Member States make it difficult to fully harness that potential at European scale. The European Health Data Space will address this challenge by empowering Europeans to access and control their health data and by offering a harmonised and simplified framework for the reuse of health data, including for research, innovation, and policymaking purposes. During the workshop, we will present an overview of the European Health Data Space framework and its implementation phases and discuss its benefits for targeted stakeholders, including citizens, researchers and policymakers in public health. Objectives 1. Emphasise the importance of leveraging health data for research, innovation and policymaking; 2. Present the European Health Data Space regulation and what actions are being undertaken to prepare for its implementation; 3. Discuss how access to their health data can enable patients and individuals play an active role in their own health journey; 4. Discuss what will be the main benefits for policymakers and researchers in public health resulting from enhanced access to health data. Introduction by Chair for presenting the European Health Data Space and the setting the scene for the session. Short presentations by speakers: 1. Implementing the EHDS: lessons from the EHDS2 pilot - Marianne Benderra, Health Data Hub, presenting his experience on developing and piloting the EHDS infrastructure for secondary use of health data, highlighting key project developments; 2. How can the EHDS enable participatory healthcare? - Henrique Martins, ISCTE, xShare project on how the xShare “Yellow Button” will empower individuals with access to and control over their health data and may foster the development of knowledge and tools necessary for prevention and management of their health; 3. What will the EHDS enable for research and policymaking? - Nienke Schutte, Scienciano, bringing the perspective of a leading European public health institute how the benefits the EHDS framework can bring for policymaking, research and innovation. 15 min Q&A session, taking questions from the audience, possibly also through Slido. Possible guiding questions: How can stakeholders contribute more broadly to the development of the EHDS? What support is needed to ensure that data users and data holders are prepared when the EHDS framework becomes operational? Conclusions 2 min by Chair. Speakers/Panelists Marianne Benderra French Health Data Hub, Paris, France Henrique Manuel Martins ISCTE, Lisbon, Portugal Nienke Schutte Sciensano, Brussels, Belgium

Enhancing Data Quality through Electronic Health Records in Prison Settings: Towards a Learning Health System

Abstract Utilizing electronic health records (EHRs) in prison settings offers a unique opportunity to establish a learning health system (LHS) aimed at enhancing healthcare quality for incarcerated individuals. Despite the challenges inherent in prison healthcare, leveraging EHRs can facilitate data-driven decision-making and foster continuous improvement initiatives. This study describes the results of an intervention aimed at improving EHR data quality in prisons and explores the potential to enhance the development of a robust LHS. We assessed EHR data quality in 2015 (38 prisons) and 2022 (35 prisons) in the Netherlands over six months. Data were extracted, de-identified, and analyzed across demographic, medication use, (history of) diagnosis, and healthcare usage categories. We measured indicator scores and evaluated variation between prisons in data quality. This project was commissioned by the Custodial Institutions Agency. In 2015, age and gender were recorded for 93% of prisoners, increasing to 100% in 2022. Diagnostic coding use rose from an average of 82% in 2015 to 98% in 2022, with reduced variation between prisons. Consultation frequency per inmate ranged from 3 to 13, reflecting challenges in constructing a uniform denominator due to institutional differences. Mental health problems accounted for 17.7% of recorded disease episodes in 2022. In addition, consultations were most often related to oral health (4.4%), drug abuse (3.2%), sleep (1.8%), fear of diseases (1.6%), and respiratory infections (1.5%). Our findings indicate improvements in EHR data quality in Dutch prisons between 2015 to 2022. Integrating EHR data reuse into prison healthcare systems is pivotal for realizing the vision of an LHS in correctional facilities. This study underscores the potential of advancing EHR recording habits and the reuse of EHR data to enhance healthcare quality in prison settings.

ECBD: European chemical biology database.

The European Chemical Biology Database (ECBD, https://ecbd.eu) serves as the central repository for data generated by the EU-OPENSCREEN research infrastructure consortium. It is developed according to FAIR principles, which emphasize findability, accessibility, interoperabilityand reusability of data. This data is made available to the scientific community following open access principles. The ECBD stores both positive and negative results from the entire chemical biology project pipeline, including data from primary or counter-screening assays. The assays utilize a defined and diverse library of over 107000 compounds, the annotations of which are continuously enriched by external user supported screening projects and by internal EU-OPENSCREEN bioprofiling efforts. These compounds were screened in 89 currently deposited datasets (assays), with 48 already being publicly accessible, while the remaining will be published after a publication embargo period of up to 3 years. Together these datasets encompass ∼4.3 million experimental data points. All public data within ECBD can be accessed through its user interface, API or by database dump under the CC-BY 4.0 license.

Telemedicine data secure sharing scheme based on heterogeneous federated learning

The forward triage characteristic of telemedicine highlights its importance again in the COVID-19 pandemic. Telemedicine can provide timely emergency response in the case of environmental or biological hazards, and the patient’s medical privacy data generated in this process can also accelerate the establishment of models for preventing and treating infectious diseases. However, the reuse process of telemedicine user privacy data based on federated learning also faces significant challenges. Differences in regions, economic levels, and grades lead to heterogeneous data and resource-constrained environments, seriously damaging the federated learning process. Besides, the weak password authentication of medical terminals and eavesdropping attacks on transmission channels may cause illegal access to terminals and platforms and leakage of sensitive data. This paper proposed a telemedicine data secure-sharing scheme based on heterogeneous federated learning. Specifically, we proposed a heterogeneous federated learning scheme with model alignment to guide telemedicine practice through the reuse of telemedicine data; in addition, we designed an SM9 threshold identity authentication scheme to guarantee that the patient’s medical privacy data is protected from leakage during the federated learning process. We evaluated our scheme using two third-party medical datasets. The evaluation results indicate that this scheme can still assist the federated learning process in resisting data heterogeneity and resource constraints with almost no performance cost.

An assessment of whether educated non-researcher audiences understand how to reuse research data

PurposeThe purpose of this study is to assess whether educated non-researcher audiences understand how to reuse research data stored in a data repository.Design/methodology/approachA total of 44 participants in two user studies were asked to study a data set accessed from re3data.org. The participants were non-researcher audiences of the disciplines of the selected data sets. They were asked to figure out whether they understood how to reuse a data set after reading all the metadata or contextual information about the data set.FindingsMost participants reported that they figured out how to reuse the data, although their self-reports can be an overestimated assessment. However, the participants understand how to reuse a data set either numerically or statistically significantly worse than what the data set is, how it was collected or created and its purpose. Data set type tends to play a role in understanding how to reuse data sets and the purpose of data sets. Participants reported that unless a data set is self-explanatory, instructions on data set reuse and the purpose of data set were necessary for understanding how to reuse data set. However, because data reuse requires domain knowledge and data processing skills, some non-researcher audiences who lack domain knowledge and data processing skills may not understand how to reuse the data set in any way.Research limitations/implicationsThis study’s findings enrich the theoretical framework of data sharing and reuse by expanding the necessary information to be included in data documentation to support non-researchers’ data reuse. The findings of the study complement previous literature.Practical implicationsThis study extended previous literature by suggesting detailed data reuse instructions be included in data documentation if data producers and data curators wish to support educated non-researchers’ data reuse. This study’s findings enable policymakers of research data management (RDM) to formulate guidelines for supporting non-researchers’ data reuse. If data curators need to work with data producers to prepare the instructions on data reuse for non-researcher audiences, they probably need computing and data processing skills. This has implications for Library and Information Science schools to educate data librarians.Originality/valueThe research question is original because non-researcher audiences in the context of RDM have not been studied before. This study extended previous literature by suggesting detailed data reuse instructions be included in data documentation if data curators and data producers and data curators wish to support educated non-researchers’ data reuse. This study’s findings enable policymakers of RDM to formulate guidelines for supporting non-researchers’ data reuse.

Best Practice for Publishing Environmental DNA (eDNA) Data According to FAIR Principles

Reversing global biodiversity loss will require transformational human actions and robust measurements of their effectiveness. Diversity assessment using environmental DNA (eDNA) has emerged as a cutting-edge technique with the potential to address the challenges of measuring biodiversity. Vast amounts of eDNA sequences and eDNA-based species detections are generated in scientific studies. These datasets are typically stored in a variety of different repositories in multiple formats, hindering their reuse (Berry et al. 2020). Ensuring the publication of eDNA data following the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson et al. 2016) would revolutionise environmental assessment, including monitoring of biodiversity, individual species, and interactions across extensive spatial and temporal scales, and generate critical knowledge for evidence-based management. Archiving FAIR eDNA data requires standardising data formats and vocabularies, cyberinfrastructures, guidelines, data sharing policy, and collaboration among scientists and institutions. Some of these requirements are addressed by existing data standards and infrastructures, including Darwin Core (DwC) (Wieczorek et al. 2012), Minimum Information about any (x) Sequence (MIxS) (Yilmaz et al. 2011), the Global Biodiversity Information Facility (GBIF) network, and International Nucleotide Sequence Database Collaboration (INSDC) partners (Arita et al. 2020). However, multiple challenges remain and FAIR data practices have yet to be established among the eDNA community. This is partly because critical attributes unique to eDNA data are not adequately accommodated by existing standards. For example, monitoring contamination and excluding non-target taxa, and the parameters used for quality filtering and species detection vary greatly between studies, depending on the study scopes and the associated financial and ecological costs of incorrectly inferring presence or absence. Making such information FAIR is needed for future studies reusing data and requiring high confidence levels in species detection and taxonomic assignment. Furthermore, the procedures of targeted-taxon detection approach (e.g., interpretations of quantitative polymerase chain reaction (qPCR) results in detecting the presence of DNA from individual taxa) have not yet been fully captured by existing standards. Increasing efforts have been made to establish minimum reporting requirements to validate eDNA study methods and data (Klymus et al. 2020, Thalinger et al. 2021). These requirements need further development to be translated into data standards and formats to enhance machine readability and reusability, and to support and guide the eDNA community, so that they are effectively utilised. In this talk, we share our best practice guide for formatting and publishing eDNA data, developed by an international multidisciplinary working-group comprising eDNA researchers, journal editors, and biodiversity and omics data scientists. We identified required data types, formats and metadata checklists through reviewing and integrating existing data standards, devising subject-specific vocabularies, and introducing additional terms to accommodate the distinctive properties of eDNA data. Implementing the FAIR eDNA data best practice guide, offers a pivotal step towards standardising and enhancing the publication and re-use of eDNA data.

Access to longitudinal mental health data in Africa: Lessons from a landscape analysis by the INSPIRE network datahub

Background Data from longitudinal mental health research in Africa is critical to understanding the complexities of mental health disorders in the continent’s diverse contexts. To be useful, data need to adhere to the FAIR principles of Findability, Accessibility, Interoperability, and Reusability. Methods A literature search from 1970 to 2022 identified longitudinal studies on depression, anxiety, and psychosis in was done. Using Artificial Intelligence (AI) and Natural Language Processing techniques, the search found data from more studies. The search engaged stakeholders in understanding data sharing practices and barriers and categorizing methods and challenges for sharing data. Results The initial search yielded 18,019 articles, of which 284 were eligible for review, and 226 passed quality assessments. A significant effort to access data directly from researchers yielded positive responses for 100 articles with available data statements, from which datasets were requested through online repositories and direct correspondence. Analysis revealed significant disparities in the distribution of mental health research across countries, with a concentration of studies in specific areas and on certain conditions. The study also highlighted a varied adherence to FAIR principles, with only 16 datasets adhering to data-sharing practices. Conclusions Despite the challenges encountered in data accessibility and the manual adjustments required, the study’s findings irradiate the path toward a more equitable and effective mental health research ecosystem on the continent. By fostering collaboration and embracing advanced methodologies and technologies, this study advocates for a concerted effort to improve the accessibility, interoperability, and reusability of mental health data. Ultimately, the project aims to contribute to understanding data-sharing dynamics in Africa, paving the way for informed interventions and policies.

Multilinguality and LLOD: A survey across linguistic description levels

Limited accessibility to language resources and technologies represents a challenge for the analysis, preservation, and documentation of natural languages other than English. Linguistic Linked (Open) Data (LLOD) holds the promise to ease the creation, linking, and reuse of multilingual linguistic data across distributed and heterogeneous resources. However, individual language resources and technologies accommodate or target different linguistic description levels, e.g., morphology, syntax, phonology, and pragmatics. In this comprehensive survey, the state-of-the-art of multilinguality and LLOD is being represented with a particular focus on linguistic description levels, identifying open challenges and gaps as well as proposing an ideal ecosystem for multilingual LLOD across description levels. This survey seeks to contribute an introductory text for newcomers to the field of multilingual LLOD, uncover gaps and challenges to be tackled by the LLOD community in reference to linguistic description levels, and present a solid basis for a future best practice of multilingual LLOD across description levels.

Semantic Web and its role in facilitating ICT data sharing for the circular economy: An ontology survey

The environmental pressure, CO2 emissions (including embodied energy) and delivery risks of our digital infrastructures are increasing. The exponentially growing digitisation of services that drive the transition from industry 4.0 to industry 5.0 has resulted in a rising materials demand for ICT hardware manufacturing. ICT devices such as laptops and data servers are being used on average for 3 and 4–5 years respectively (van Driel (2020)), while research shows that they should last 7 years before replacement (Journal of Cleaner Production 69 (2014), 10–16). A solution is to transition from a linear to a circular economy (CE), through which materials that were previously disposed of as waste are re-entered back into product life-cycles through processes such as reuse, recycling, remanufacturing, repurposing. However, the adoption of the CE in the ICT sector is currently limited due to the lack of tools that support knowledge exchange between sustainability, ICT and technology experts in a standardised manner and the limited data availability, accessibility and interoperability needed to build such tools. Further, the already existing knowledge of the domain is fragmented into silos and the lack of a common terminology restricts its interoperability and usability. These also lead to transparency and responsibility issues along the supply chain. For many years now, the Semantic Web has been known to provide solutions to such issues in the form of ontologies. Several ontologies for the ICT, materials and CE domains have been build and successfully utilised to support processes such as predictive maintenance. However, there is a lack of a systematic analysis of the existing ontologies in these domains. Motivated by this, we present a literature survey and analysis of, but not limited to, existing ontologies for ICT devices such as laptops, materials and the CE. In addition, we discuss the need for findable, accessible, interoperable, reusable (FAIR) data in the CE, different factors such as data privacy and security that affect this and the role of ontologies.

Categorical misalignment: Making autism(s) in big data biobanking.

The opaque relationship between biology and behavior is an intractable problem for psychiatry, and it increasingly challenges longstanding diagnostic categorizations. While various big data sciences have been repeatedly deployed as potential solutions, they have so far complicated more than they have managed to disentangle. Attending to categorical misalignment, this article proposes one reason why this is the case: Datasets have to instantiate clinical categories in order to make biological sense of them, and they do so in different ways. Here, I use mixed methods to examine the role of the reuse of big data in recent genomic research on autism spectrum disorder (ASD). I show how divergent regimes of psychiatric categorization are innately encoded within commonly used datasets from MSSNG and 23andMe, contributing to a rippling disjuncture in the accounts of autism that this body of research has produced. Beyond the specific complications this dynamic introduces for the category of autism, this paper argues for the necessity of critical attention to the role of dataset reuse and recombination across human genomics and beyond.

Advancing healthcare through data: the BETTER project's vision for distributed analytics.

Data-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. We 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. This 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. The anticipated impacts will benefit a wide range of healthcare practitioners and potentially influence health policymakers.

The Human‐Data Interaction Driven by Data Reuse

ABSTRACTThe data‐intensive research paradigm is sweeping through the scientific community, and new interaction challenges for interacting with data have emerged. Data reuse emerges as a critical driver of human‐data interaction, positioning data repositories at the forefront as the optimal solution for facilitating this process. This study adopts a diary study methodology to analyze the data repository‐supported human‐data interaction behaviors driven. The findings reveal that human‐data interaction extends beyond mere engagement with data, encapsulating elements of human‐computer interaction, engagement with literature, interactions with agent systems, and interpersonal communication.