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The Ouest Data Hub (ODH) a project lead by GCS HUGO which is a cooperation group of University Hospitals in the French Grand Ouest region represents a groundbreaking initiative in this territory, advancing health data sharing and reuse to support research driven by real-world health data. Central to its structure are the Clinical Data Warehouses (CDWs) and Clinical Data Centers (CDCs), essential for analytics and as the linchpin of the ODH's status as an interregional Learning Health System. Aimed at fostering innovation and research, the ODH's collaborative and multi-institutional model effectively utilizes both local and shared resources. Yet, the path is not without challenges, especially in data quality and interoperability, where ongoing harmonization and standard adherence are critical. In 2023, this facilitated access to extensive health data from over 9.3 million patient records, demonstrating the ODH's capacity for both monocentric and multicentric research across various clinical fields, in close collaboration with physicians. The integration of healthcare professionals is crucial, ensuring data's clinical relevance and guiding accurate interpretations. Future expansions of the ODH to new hospitals and data types promise to enhance its model further, already inspiring similar frameworks across France. This scalable model for health data ecosystems showcases the ODH's potential as a foundation for national and supranational data sharing efforts.

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Bayesian Dynamic Borrowing (BDB) designs are being increasingly used in clinical drug development. These methods offer a mathematically rigorous and robust approach to increase efficiency and strengthen evidence by integrating existing trial data into a new clinical trial. The regulatory acceptability of BDB is evolving and varies between and within regulatory agencies. This paper describes how BDB can be used to design a new randomised clinical trial including external data to supplement the planned sample size and discusses key considerations related to data re-use and BDB in drug development programs. A case-study illustrating the planning and evaluation of a BDB approach to support registration of a new medicine with the Center for Drug Evaluation in China will be presented. Key steps and considerations for the use of BDB will be discussed and evaluated, including how to decide whether it is appropriate to borrow external data, which external data can be re-used, the weight to put on the external data and how to decide if the new study has successfully demonstrated treatment benefit.

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BACKGROUND: The FAIR data principles aim to make scientific data more Findable, Accessible, Interoperable, and Reusable. In the field of traumatic stress research, FAIR data practices can help accelerate scientific advances to improve clinical practice and can reduce participant burden. Previous studies have identified factors that influence data sharing and re-use among scientists, such as normative pressure, perceived career benefit, scholarly altruism, and availability of data repositories. No prior study has examined researcher views and practices regarding data sharing and re-use in the traumatic stress field. OBJECTIVE: To investigate the perspectives and practices of traumatic stress researchers around the world concerning data sharing, re-use, and the implementation of FAIR data principles in order to inform development of a FAIR Data Toolkit for traumatic stress researchers. METHOD: A total of 222 researchers from 28 countries participated in an online survey available in seven languages, assessing their views on data sharing and re-use, current practices, and potential facilitators and barriers to adopting FAIR data principles. RESULTS: The majority of participants held a positive outlook towards data sharing and re-use, endorsing strong scholarly altruism, ethical considerations supporting data sharing, and perceiving data re-use as advantageous for improving research quality and advancing the field. Results were largely consistent with prior surveys of scientists across a wide range of disciplines. A significant proportion of respondents reported instances of data sharing and re-use, but gold standard practices such as formally depositing data in established repositories were reported as infrequent. The study identifies potential barriers such as time constraints, funding, and familiarity with FAIR principles. CONCLUSIONS: These results carry crucial implications for promoting change and devising a FAIR Data Toolkit tailored for traumatic stress researchers, emphasizing aspects such as study planning, data preservation, metadata standardization, endorsing data re-use, and establishing metrics to assess scientific and societal impact.

Traumatic stress researchers worldwide responding to a survey held generally positive views on data sharing, endorsing scholarly altruism and pro-sharing ethical considerations, and rating data re-use as useful for advancing the field.While many respondents reported instances of sharing or re-using data, gold standard practices such as formally depositing data in established repositories were reported as infrequent.Barriers to data sharing and re-use included time constraints, funding, and a lack of familiarity with practices to make data more Findable, Accessible, Interoperable, and Re-usable (FAIR).

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The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs. We defined tissue as a distinct functional or structural region within an organ. Overall, the aggregated dataset contains 67 healthy tissues, corresponding to 3,119 mass spectrometry runs covering 498 samples from 489 individuals. We compared protein abundances between different organs and studied the distribution of proteins across these organs. We also compared the results with data generated in analogous studies. Additionally, we performed gene ontology and pathway-enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point, we have integrated the protein abundance results into the resource Expression Atlas, where they can be accessed and visualized either individually or together with gene expression data coming from transcriptomics datasets. We believe this is a good mechanism to make proteomics data more accessible for life scientists.

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BACKGROUND: Assessing the safety of engineered nanomaterials (ENMs) is an interdisciplinary and complex process producing huge amounts of information and data. To make such data and metadata reusable for researchers, manufacturers, and regulatory authorities, there is an urgent need to record and provide this information in a structured, harmonized, and digitized way. RESULTS: This study aimed to identify appropriate description standards and quality criteria for the special use in nanosafety. There are many existing standards and guidelines designed for collecting data and metadata, ranging from regulatory guidelines to specific databases. Most of them are incomplete or not specifically designed for ENM research. However, by merging the content of several existing standards and guidelines, a basic catalogue of descriptive information and quality criteria was generated. In an iterative process, our interdisciplinary team identified deficits and added missing information into a comprehensive schema. Subsequently, this overview was externally evaluated by a panel of experts during a workshop. This whole process resulted in a minimum information table (MIT), specifying necessary minimum information to be provided along with experimental results on effects of ENMs in the biological context in a flexible and modular manner. The MIT is divided into six modules: general information, material information, biological model information, exposure information, endpoint read out information and analysis and statistics. These modules are further partitioned into module subdivisions serving to include more detailed information. A comparison with existing ontologies, which also aim to electronically collect data and metadata on nanosafety studies, showed that the newly developed MIT exhibits a higher level of detail compared to those existing schemas, making it more usable to prevent gaps in the communication of information. CONCLUSION: Implementing the requirements of the MIT into e.g., electronic lab notebooks (ELNs) would make the collection of all necessary data and metadata a daily routine and thereby would improve the reproducibility and reusability of experiments. Furthermore, this approach is particularly beneficial regarding the rapidly expanding developments and applications of novel non-animal alternative testing methods.

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BACKGROUND: Herbarium collections and the data they hold are the main sources of plant biodiversity information. These collections contain taxonomical and spatial data on living and extinct species; consequently, they are the fundamental basis for temporal and spatial biogeographical studies of plants. Mega projects focused on providing digital and free access to accurate biodiversity data have transformed plant science research, mainly in the past two decades. In this sense, researchers today are overwhelmed by the many different datasets in online repositories. There are also several challenges involved in using these data for biogeographical analyses. Analyses performed on the data available in the repositories show that 70-75% of the total amount of data have spatial deficiencies and a high number of records lack coordinates. This shortage of reliable primary biogeographical information creates serious impediments for biogeographical analyses and conservation assessments and taxonomic revisions consequently produces obstacles for evaluations of threats to biodiversity at global, regional and local levels. With the aim of contributing to botanical and biogeographical research, this paper provides georeferenced spatial data for angiosperm species endemic to Brazil. The information from two reliable online databases, i.e. the Flora do Brasil 2020 floristic database (BFG) and Plantas do Brasil: Resgate Histórico e Herbário Virtual para o Conhecimento e Conservação da Flora Brasileira (REFLORA), which are both based on records collected over the course of the last two centuries, is used to create this spatial dataset. NEW INFORMATION: We provide three taxonomically-edited and georeferenced datasets for basal angiosperms, monocots and eudicots, covering a total of 14,992 endemic species from Brazil. Producing this consolidated dataset involved several months of detailed revision of coordinates and nomenclaturally updating of the names in these datasets. The information provided in this geo-referenced dataset, covering two centuries of specimen collections, will contribute to several botanical and mainly biogeographical studies.

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OBJECTIVE: Anonymised patient-level data from clinical research are increasingly recognised as a fundamental and valuable resource. It has value beyond the original research project and can help drive scientific research and innovations and improve patient care. To support responsible data sharing, we need to develop systems that work for all stakeholders. The members of the Independent Review Panel (IRP) for the data sharing platform Clinical Study Data Request (CSDR) describe here some summary metrics from the platform and challenge the research community on why the promised demand for data has not been observed. SUMMARY OF DATA: From 2014 to the end of January 2019, there were a total of 473 research proposals (RPs) submitted to CSDR. Of these, 364 met initial administrative and data availability checks, and the IRP approved 291. Of the 90 research teams that had completed their analyses by January 2018, 41 reported at least one resulting publication to CSDR. Less than half of the studies ever listed on CSDR have been requested. CONCLUSION: While acknowledging there are areas for improvement in speed of access and promotion of the platform, the total number of applications for access and the resulting publications have been low and challenge the sustainability of this model. What are the barriers for data contributors and secondary analysis researchers? If this model does not work for all, what needs to be changed? One thing is clear: that data access can realise new and unforeseen contributions to knowledge and improve patient health, but this will not be achieved unless we build sustainable models together that work for all.

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The main objective of this article is to describe the legal principles governing the selection by European public authorities, such as National Health Services (NHS) of third parties, when entering into agreements for the transfer of health data. According to Directive 2003/98/EC, and in light of the provisions of the Treaties of the European Union, the choice as to how a public authority makes its data available to third parties needs to be transparent, non-discriminatory and may not in any case benefit a specific company at the expense of others. For this reason, we maintain that a hypothetical agreement by which a public authority grants exclusive access to a large amount of health data to a private company selected with non-transparent criteria appears highly questionable. We advocate that the NHS should adopt more appropriate data policies aimed at promoting the sustainability of the NHS, following the legal framework analysed in this article.

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This paper analyses the role of information security (IS) in shaping the dissemination and re-use of biomedical data, as well as the embedding of such data in material, social and regulatory landscapes of research. We consider data management practices adopted by two UK-based data linkage infrastructures: the Secure Anonymised Information Linkage, a Welsh databank that facilitates appropriate re-use of health data derived from research and routine medical practice in the region, and the Medical and Environmental Data Mash-up Infrastructure, a project bringing together researchers to link and analyse complex meteorological, environmental and epidemiological data. Through an in-depth analysis of how data are sourced, processed and analysed in these two cases, we show that IS takes two distinct forms: epistemic IS, focused on protecting the reliability and reusability of data as they move across platforms and research contexts, and infrastructural IS, concerned with protecting data from external attacks, mishandling and use disruption. These two dimensions are intertwined and mutually constitutive, and yet are often perceived by researchers as being in tension with each other. We discuss how such tensions emerge when the two dimensions of IS are operationalized in ways that put them at cross purpose with each other, thus exemplifying the vulnerability of data management strategies to broader governance and technological regimes. We also show that whenever biomedical researchers manage to overcome the conflict, the interplay between epistemic and infrastructural IS prompts critical questions concerning data sources, formats, metadata and potential uses, resulting in an improved understanding of the wider context of research and the development of relevant resources. This informs and significantly improves the reusability of biomedical data, while encouraging exploratory analyses of secondary data sources.

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Clusters of differentiation (CD) are cell surface biomarkers that denote key biological differences between cell types and disease state. CD-targeting therapeutic monoclonal antibodies (mABs) afford rich trans-disease repositioning opportunities. Within a compendium of systemic lupus erythematous (SLE) patients, we applied the Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB) to profile de novo gene expression features affecting CD20, CD22 and CD30 gene aberrance. First, a novel Relief-based algorithm identified interdependent features(p=681) predicting treatment-naïve SLE patients (balanced accuracy=0.822). We then compiled CD-associated expression profiles using regularized logistic regression and pathway enrichment analyses. On an independent general cell line model system data, we replicated associations (in silico) of BCL7A (padj=1.69e-9) and STRBP(padj=4.63e-8) with CD22; NCOA2(padj=7.00e-4), ATN1 (padj=1.71e-2), and HOXC4(padj=3.34e-2) with CD30; and PHOSPHO1, a phosphatase linked to bone mineralization, with both CD22(padj=4.37e-2) and CD30(padj=7.40e-3). Utilizing carefully aggregated secondary data and leveraging a priori hypotheses, i-mAB fostered robust biomarker profiling among interdependent biological features.

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BACKGROUND: The reproducibility of research is essential to rigorous science, yet significant concerns of the reliability and verifiability of biomedical research have been recently highlighted. Ongoing efforts across several domains of science and policy are working to clarify the fundamental characteristics of reproducibility and to enhance the transparency and accessibility of research. METHODS: The aim of the proceeding work is to develop an assessment tool operationalizing key concepts of research transparency in the biomedical domain, specifically for secondary biomedical data research using electronic health record data. The tool (RepeAT) was developed through a multi-phase process that involved coding and extracting recommendations and practices for improving reproducibility from publications and reports across the biomedical and statistical sciences, field testing the instrument, and refining variables. RESULTS: RepeAT includes 119 unique variables grouped into five categories (research design and aim, database and data collection methods, data mining and data cleaning, data analysis, data sharing and documentation). Preliminary results in manually processing 40 scientific manuscripts indicate components of the proposed framework with strong inter-rater reliability, as well as directions for further research and refinement of RepeAT. CONCLUSIONS: The use of RepeAT may allow the biomedical community to have a better understanding of the current practices of research transparency and accessibility among principal investigators. Common adoption of RepeAT may improve reporting of research practices and the availability of research outputs. Additionally, use of RepeAT will facilitate comparisons of research transparency and accessibility across domains and institutions.

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