Reuse Of Data Research Articles

How to enhance standardized and structured recording by elderly care physicians for the reuse of EHR data?: an interview study (Preprint)

How to enhance standardized and structured recording by elderly care physicians for the reuse of EHR data?: an interview study (Preprint)

Custom Biomedical FAIR Data Analysis in the Cloud Using CAVATICA.

The historically fragmented biomedical data ecosystem has moved towards harmonization under the findable, accessible, interoperable, and reusable (FAIR) data principles, creating more opportunities for cloud-based research. This shift is especially opportune for scientists across diverse domains interested in implementing creative, nonstandard computational analytic pipelines on large and varied datasets. However, executing custom cloud analyses may present difficulties, particularly for investigators lacking advanced computational expertise. Here, we present an accessible, streamlined approach for the cloud compute platform CAVATICA that offers a solution. We outline how we developed a custom workflow in the cloud, for analyzing whole genome sequences of case-parent trios to detect sex-specific genetic effects on orofacial cleft risk, which required several programming languages and custom software packages. The approach involves just three components: Docker to containerize software environments, tool creation for each analysis step, and a visual workflow editor to weave the tools into a Common Workflow Language (CWL) pipeline. Our approach should be accessible to any investigator with basic computational skills, is readily extended to implement any scalable high-throughput biomedical data analysis in the cloud, and is applicable to other commonly used compute platforms such as BioData Catalyst. We believe our approach empowers versatile data reuse and promotes accelerated biomedical discovery in a time of substantial FAIR data.

A multi-omics dataset of the response to early plant polysaccharide ingestion in rabbits

The transition from a milk-based diet to exclusive solid feeding deeply modifies microbiota-host crosstalk. Specifically, early ingestion of plant polysaccharides would be one of the main nutritional components to drive host-microbiota-interaction. To capture the effects of polysaccharides early-life nutrition (starch vs rapidly fermentable fiber) on the holobiont development, we investigated on the one hand the gut bacteriome and metabolome and on the other hand the transcriptome of two host gut tissues. Rabbit model was used to study post-natal co-development of the gut microbiota and its host around weaning transition. The assessment of the microbial composition of the gut appendix together with the caecum was provided for the first time. Gene expression signatures were analyzed along the gut (ileum and caecum) through high-throughput qPCR. The data collected were completed by the analysis of animal growth changes and time-series assessment of blood biomarkers. Those accessible and reusable data could help highlight the gut development dynamics as well as biological adaptation processes at the onset of solid feeding.

Implementing a Biomedical Data Warehouse From Blueprint to Bedside in a Regional French University Hospital Setting: Unveiling Processes, Overcoming Challenges, and Extracting Clinical Insight.

Biomedical data warehouses (BDWs) have become an essential tool to facilitate the reuse of health data for both research and decisional applications. Beyond technical issues, the implementation of BDWs requires strong institutional data governance and operational knowledge of the European and national legal framework for the management of research data access and use. In this paper, we describe the compound process of implementation and the contents of a regional university hospital BDW. We present the actions and challenges regarding organizational changes, technical architecture, and shared governance that took place to develop the Nantes BDW. We describe the process to access clinical contents, give details about patient data protection, and use examples to illustrate merging clinical insights. More than 68 million textual documents and 543 million pieces of coded information concerning approximately 1.5 million patients admitted to CHUN between 2002 and 2022 can be queried and transformed to be made available to investigators. Since its creation in 2018, 269 projects have benefited from the Nantes BDW. Access to data is organized according to data use and regulatory requirements. Data use is entirely determined by the scientific question posed. It is the vector of legitimacy of data access for secondary use. Enabling access to a BDW is a game changer for research and all operational situations in need of data. Finally, data governance must prevail over technical issues in institution data strategy vis-à-vis care professionals and patients alike.

Development of the CDISC Pediatrics User Guide: a CDISC and conect4children collaboration.

The conect4children (c4c) project aims to facilitate efficient planning and delivery of paediatric clinical trials. One objective of c4c is data standardization and reuse. Interoperability and reusability of paediatric clinical trial data is challenging due to a lack of standardization. The Clinical Data Interchange Standards Consortium (CDISC) standards that are required or recommended for regulatory submissions in several countries lack paediatric specificity with limited awareness within academic institutions. To address this, c4c and CDISC collaborated to develop the Pediatrics User Guide (PUG) consisting of cross-cutting data items that are routinely collected in paediatric clinical trials, factoring in all paediatric age ranges. The development of the PUG consisted of six stages. During the scoping phase, subtopics (each containing several clinically relevant concepts) were suggested and debated for inclusion in the PUG. Ninety concepts were selected for the modelling phase. Concept maps describing the Research Topic and representation procedure were developed for the 19 concepts that had no (or partial) previous modelling in CDISC. Next, metadata and implementation examples were developed for concepts. This was followed by a CDISC internal review and a public review. For both these review stages, the feedback comments were either implemented or rejected based on budget, timelines, expert review, and scope. The PUG was published on the CDISC website on February 23, 2023. The PUG is a first step in bridging the lack of child specific CDISC standards, particularly within academia. Several academic and industrial partners were involved in the development of the PUG, and c4c has undertaken multiple steps to publicize the PUG within its academic partner organizations - in particular, the European Reference Networks (ERNs) that are developing registries and dictionaries in 24 disease areas. In the long term, continued use of the PUG in paediatric clinical trials will enable the pooling of data from multiple trials, which is particularly important for medical domains with small populations.

A MATLAB Toolbox for Findable, Accessible, Interoperable, and Reusable Atom Probe Data Science.

Atom probe tomography (APT) data analytics have traditionally been based on manual analytics by researchers. As newer atom probes together with focused ion beam-based specimen preparation have opened APT to many more materials, yielding much more complex mass spectra, building up a systematic understanding of the pathway from raw data to final interpretation has increasingly become important. This demands a system in which the data and treatment can be traced, ideally by any interested party. Such an approach of findable, accessible, interoperable, and reusable (FAIR) data and analysis policies is becoming increasingly important, not just in APT. In this paper, we present a toolbox, written in MATLAB, which allows the user to store the raw and processed data in a standardized FAIR format (hierarchical data format 5) and process the data in a largely scriptable environment to minimize manual user input. This allows for the experiment data to be interchanged without owner explanations and the analysis to be reproduced. We have devised a metadata scheme that is extensible to other experiments in the materials science domain. With this toolbox, collective knowledge can be built up, and a large number of data sets can be analyzed in a fully automated fashion.

A Qualitative Study of Physicians' Views on the Reuse of Electronic Health Record Data for Secondary Analysis.

Electronic health records (EHRs) have become ubiquitous in clinical practice. Given the rich biomedical data captured for a large panel of patients, secondary analysis of these data for health research is also commonplace. Yet, there are many caveats to EHR data that the researchers must be aware of, such as the accuracy of and motive for documentation, and the reason for patients' visits to the clinic. The clinician-the author of the documentation-is thus central to the correct interpretation of EHR data for research purposes. In this study, I interviewed 11 physicians in various clinical specialties to bring attention to their view on the validity of research using EHR data. Qualitative, in-depth, one-on-one interviews were conducted with practicing physicians in inpatient and outpatient medicine. Content analysis using a data-driven, inductive approach to identify themes related to challenges and opportunities in the reuse of EHR data for secondary analysis generated seven themes. Themes that reflected challenges of EHRs for research included (1) audience, (2) accuracy of data, (3) availability of data, (4) documentation practices, and (5) representativeness. Themes that reflected opportunities of EHRs for research included (6) endorsement and (7) enablers. The greatest perceived barriers reflected the intended audience of the EHR, the interpretation and meaning of the data, and the quality of the data for research purposes. Physicians generally expressed more perceived challenges than opportunities in the reuse of EHR data for research purposes; however, they remained optimistic.

Enhancing oncology research through data sharing and re-use: A five-year review of Vivli's global data platform.

e13670 Background: The last decade has seen a paradigm shift in clinical research with the accessibility of trial data, facilitating secondary analyses and the development of novel predictive models. Vivli a non-profit entity established in 2018, epitomizes this movement with its global platform (vivli.org) dedicated to the facilitating access to and re-use of individual participant data from completed clinical trials for use by researchers worldwide. Methods: Commencing with approximately 2,500 trials from 15 members, Vivli has expanded over five years to encompass more than 7,000 trials involving 3.6 million participants across 50 members who contribute trial data from various sectors (industry, academic, government and non-profit foundation). Results: Oncology trials constitute 11% of the Vivli data pool of 7,000 trials, constituting 36% of Vivli’s research proposals due to the therapeutic area's high-impact potential. Researchers accessing data in the Vivli platform have produced a significant body of scientific work, with 74 oncology-focused publications emerging from their analyses. Notably, Roche's data has been a key resource, denoted by the 28% of all Vivli proposals requesting access to Roche data. Fifty-eight publications have resulted from secondary analysis of Roche oncology data. The motivation for secondary analyses in oncology have been predominantly for new research into treatment effectiveness or safety (39%). Other reasons include confirmation of existing research (15%), individual participant data (IPD) meta-analysis (14%), statistical method (10%), clinical trial design (5%), and summary-level meta-analysis (4%), training/testing (7%), other (5%). Conclusions: The Vivli platform has proven to be a key resource in recent years for the oncology community enabling the integration and publication of new insights. The sharing of IPD is in part motivated to ensure reproducibility of findings; our experience demonstrates that a minority of researchers are interested in reproducibility analysis but the majority of research is focused on new insight generation. The platform's success in facilitating the findability, accessibility and reuse of patient level data is signified by ever increasing numbers of scientific publications emerging from analyses on the Vivli platform and this underscores its role in advancing scientific discovery and development within the field of oncology.

Secondary use of data for data analysis: A case of modeling medical data for treatment analysis and assessment

Collecting and structuring data on which to perform specific data analyses is a long and complicated process. It requires substantial advanced planning of what data and what data characteristics will be needed for the study. It is important to specify in what format they should be collected. On the other side, there are huge amounts of data stored in a variety of applications. This data is clean and consistent. In many cases, it can be used for new analyses, different from the original purposes for which the information was collected. This is particularly true for data in healthcare. Medical information accumulated in multiple systems can be used for further research and analysis.This paper presents a methodology, appropriate data models, and relevant software decisions that support the reuse and explorations of medical data for specific treatment assessments based on data routinely collected from clinical systems. Some initial results are presented and discussed.

ReactomeGSA: new features to simplify public data reuse.

ReactomeGSA is part of the Reactome knowledgebase and one of the leading multi-omics pathway analysis platforms. ReactomeGSA provides access to quantitative pathway analysis methods supporting different 'omics data types. Additionally, ReactomeGSA can process different datasets simultaneously, leading to a comparative pathway analysis that can also be performed across different species. We present a major update to the ReactomeGSA analysis platforms that greatly simplifies the reuse and direct integration of public data. In order to increase the number of available datasets, we developed the new grein_loader Python application that can directly fetch experiments from the GREIN resource. This enabled us to support both EMBL-EBI's Expression Atlas and GEO RNA-seq Experiments Interactive Navigator within ReactomeGSA. To further increase the visibility and simplify the reuse of public datasets, we integrated a novel search function into ReactomeGSA that enables users to search for public datasets across both supported resources. Finally, we completely re-developed ReactomeGSA's web-frontend and R/Bioconductor package to support the new search and loading features, and greatly simplify the use of ReactomeGSA. The new ReactomeGSA web frontend is available at https://www.reactome.org/gsa with an built-in, interactive tutorial. The ReactomeGSA R package (https://bioconductor.org/packages/release/bioc/html/ReactomeGSA.html) is available through Bioconductor and shipped with detailed documentation and vignettes. The grein_loader Python application is available through the Python Package Index (pypi). The complete source code for all applications is available on GitHub at https://github.com/grisslab/grein_loader and https://github.com/reactome.

Recommended data elements for health registries: a survey from a German funding initiative.

The selection of data elements is a decisive task within the development of a health registry. Having the right metadata is crucial for answering the particular research questions. Furthermore, the set of data elements determines the registries' readiness of interoperability and data reusability to a major extent. Six health registries shared and published their metadata within a German funding initiative. As one step in the direction of a common set of data elements, a selection of those metadata was evaluated with regard to their appropriateness for a broader usage. Each registry was asked to contribute a 10%-selection of their data elements to an evaluation sample. The survey was set up with the online survey tool "LimeSurvey Cloud". The registries and an accompanying project participated in the survey with one vote for each project. The data elements were offered in content groups along with the question of whether the data element is appropriate for health registries on a broader scale. The question could be answered using a Likert scale with five options. Furthermore, "no answer" was allowed. The level of agreement was assessed using weighted Cohen's kappa and Kendall's coefficient of concordance. The evaluation sample consisted of 269 data elements. With a grade of "perhaps recommendable" or higher in the mean, 169 data elements were selected. These data elements belong preferably to groups' demography, education/occupation, medication, and nutrition. Half of the registries lost significance compared with their percentage of data elements in the evaluation sample, one remained stable. The level of concordance was adequate. The survey revealed a set of 169 data elements recommended for health registries. When developing a registry, this set could be valuable help in selecting the metadata appropriate to answer the registry's research questions. However, due to the high specificity of research questions, data elements beyond this set will be needed to cover the whole range of interests of a register. A broader discussion and subsequent surveys are needed to establish a common set of data elements on an international scale.

FAIR assessment of nanosafety data reusability with community standards.

Nanomaterials hold great promise for improving our society, and it is crucial to understand their effects on biological systems in order to enhance their properties and ensure their safety. However, the lack of consistency in experimental reporting, the absence of universally accepted machine-readable metadata standards, and the challenge of combining such standards hamper the reusability of previously produced data for risk assessment. Fortunately, the research community has responded to these challenges by developing minimum reporting standards that address several of these issues. By converting twelve published minimum reporting standards into a machine-readable representation using FAIR maturity indicators, we have created a machine-friendly approach to annotate and assess datasets' reusability according to those standards. Furthermore, our NanoSafety Data Reusability Assessment (NSDRA) framework includes a metadata generator web application that can be integrated into experimental data management, and a new web application that can summarize the reusability of nanosafety datasets for one or more subsets of maturity indicators, tailored to specific computational risk assessment use cases. This approach enhances the transparency, communication, and reusability of experimental data and metadata. With this improved FAIR approach, we can facilitate the reuse of nanosafety research for exploration, toxicity prediction, and regulation, thereby advancing the field and benefiting society as a whole.

Artificial intelligence based data curation: enabling a patient-centric European health data space.

The emerging European Health Data Space (EHDS) Regulation opens new prospects for large-scale sharing and re-use of health data. Yet, the proposed regulation suffers from two important limitations: it is designed to benefit the whole population with limited consideration for individuals, and the generation of secondary datasets from heterogeneous, unlinked patient data will remain burdensome. AIDAVA, a Horizon Europe project that started in September 2022, proposes to address both shortcomings by providing patients with an AI-based virtual assistant that maximises automation in the integration and transformation of their health data into an interoperable, longitudinal health record. This personal record can then be used to inform patient-related decisions at the point of care, whether this is the usual point of care or a possible cross-border point of care. The personal record can also be used to generate population datasets for research and policymaking. The proposed solution will enable a much-needed paradigm shift in health data management, implementing a 'curate once at patient level, use many times' approach, primarily for the benefit of patients and their care providers, but also for more efficient generation of high-quality secondary datasets. After 15 months, the project shows promising preliminary results in achieving automation in the integration and transformation of heterogeneous data of each individual patient, once the content of the data sources managed by the data holders has been formally described. Additionally, the conceptualization phase of the project identified a set of recommendations for the development of a patient-centric EHDS, significantly facilitating the generation of data for secondary use.

Information reuse of nondestructive evaluation (NDE) data sets

Abstract. To achieve added value from data spaces and data sets in general, an essential condition is to ensure the high quality of the stored information and its continuous availability. Nondestructive evaluation (NDE) processes represent an information source with potential for reuse. These provide essential information for the evaluation and characterization of materials and components. This information, along with others such as process parameters, is a valuable resource for data-driven added value, e.g., for process optimization or as training data for artificial intelligence (AI) applications. However, this use requires the continuous availability of NDE data sets as well as their structuring and readability. This paper describes the steps necessary to realize an NDE data cycle from the generation of information to the reuse of data.

Sociotechnical Imaginaries of Sharing and Emerging Postdigital Meaning-Making Practices in the Astronomy Community

For decades, science communities have had digital technology embedded in their everyday work. However, new research infrastructures are amplifying the presence and use of digital technologies for scientists. In that respect, radio astronomy is undergoing a major transformation causing the community to enter a phase of postdigital work, due to the construction of the most sensitive telescope, the Square Kilometer Array Observatory (SKAO), which scales the presence of digital technology and the amount of data generated. As new digital research infrastructure is set up, sociotechnical imaginaries—symbols and visions of a shared future—emerge, while others become obsolete thus impacting structures and practices of meaning-making. In this paper, we explore the disruptive potential of sociotechnical imaginaries and how astronomers using SKA pathfinder and precursor telescope data respond to these imaginaries and incorporate them into their meaning-making. The analysis shows that postdigital imaginaries related to data circulation, storage, archiving, and reuse have been amplified as SKA facilities and services are set up. Two changes are highlighted regarding a new postdigital condition within the astronomy community. Firstly, as astronomers engage in new postdigital forms of collaboration, they need to reach a consensus on what types of analyses to use by agreeing which methods are appropriate. This affects how scientific questions and research proposals are negotiated collectively, impacting the agency of astronomers. Secondly, as digital tools are increasingly part of astronomers’ daily work, they have to rely on new data analysis methods, which determine what evidence is uninteresting. Overall, these changes pose new questions regarding how meaning-making processes are altered and the way science is undertaken because of these new entanglements of human and non-human actors.

Re-use of research data in the social sciences. Use and users of digital data archive.

The aim of this paper is to investigate the re-use of research data deposited in digital data archive in the social sciences. The study examines the quantity, type, and purpose of data downloads by analyzing enriched user log data collected from Swiss data archive. The findings show that quantitative datasets are downloaded increasingly from the digital archive and that downloads focus heavily on a small share of the datasets. The most frequently downloaded datasets are survey datasets collected by research organizations offering possibilities for longitudinal studies. Users typically download only one dataset, but a group of heavy downloaders form a remarkable share of all downloads. The main user group downloading data from the archive are students who use the data in their studies. Furthermore, datasets downloaded for research purposes often, but not always, serve to be used in scholarly publications. Enriched log data from data archives offer an interesting macro level perspective on the use and users of the services and help understanding the increasing role of repositories in the social sciences. The study provides insights into the potential of collecting and using log data for studying and evaluating data archive use.

Colorectal cancer health and care quality indicators in a federated setting using the Personal Health Train

ObjectiveHospitals and healthcare providers should assess and compare the quality of care given to patients and based on this improve the care. In the Netherlands, hospitals provide data to national quality registries, which in return provide annual quality indicators. However, this process is time-consuming, resource intensive and risks patient privacy and confidentiality. In this paper, we presented a multicentric ‘Proof of Principle’ study for federated calculation of quality indicators in patients with colorectal cancer. The findings suggest that the proposed approach is highly time-efficient and consume significantly lesser resources.Materials and methodsTwo quality indicators are calculated in an efficient and privacy presevering federated manner, by i) applying the Findable Accessible Interoperable and Reusable (FAIR) data principles and ii) using the Personal Health Train (PHT) infrastructure. Instead of sharing data to a centralized registry, PHT enables analysis by sending algorithms and sharing only insights from the data.ResultsETL process extracted data from the Electronic Health Record systems of the hospitals, converted them to FAIR data and hosted in RDF endpoints within each hospital. Finally, quality indicators from each center are calculated using PHT and the mean result along with the individual results plotted.Discussion and conclusionPHT and FAIR data principles can efficiently calculate quality indicators in a privacy-preserving federated approach and the work can be scaled up both nationally and internationally. Despite this, application of the methodology was largely hampered by ELSI issues. However, the lessons learned from this study can provide other hospitals and researchers to adapt to the process easily and take effective measures in building quality of care infrastructures.

A dataset for measuring the impact of research data and their curation

Science funders, publishers, and data archives make decisions about how to responsibly allocate resources to maximize the reuse potential of research data. This paper introduces a dataset developed to measure the impact of archival and data curation decisions on data reuse. The dataset describes 10,605 social science research datasets, their curation histories, and reuse contexts in 94,755 publications that cover 59 years from 1963 to 2022. The dataset was constructed from study-level metadata, citing publications, and curation records available through the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. The dataset includes information about study-level attributes (e.g., PIs, funders, subject terms); usage statistics (e.g., downloads, citations); archiving decisions (e.g., curation activities, data transformations); and bibliometric attributes (e.g., journals, authors) for citing publications. This dataset provides information on factors that contribute to long-term data reuse, which can inform the design of effective evidence-based recommendations to support high-impact research data curation decisions.

Creative usage of Vivli’s Antimicrobial Resistance Surveillance Open Data through the Re-Use Data Challenge

The escalation of infections caused by drug-resistant bacteria worldwide poses a formidable challenge to global health, necessitating innovative approaches to combat this issue. In 2022, Vivli launched the Antimicrobial Resistance (AMR) Register, a platform that consolidates antimicrobial susceptibility data from pharmaceutical industry surveillance programs. This register provides a freely available and fully searchable user-friendly repository to facilitate scientific understanding of resistance patterns and support development of innovative strategies to address AMR globally. To promote the register’s utility, raise AMR awareness and encourage further re-use of the available data, Wellcome funded the Vivli AMR Surveillance Open Data Re-Use Data Challenge in 2023. This data challenge successfully fostered diverse, impactful, and innovative solutions that leveraged the AMR Register data to contribute to resolving the AMR crisis. The outcomes of the initiative underscore the importance of access to open data in stimulating collaboration and innovation in the public health domain, particularly in combatting the growing threat of drug-resistant infections.

The W3C Data Catalog Vocabulary, Version 2: Rationale, Design Principles, and Uptake

ABSTRACT DCAT is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web. Since its first release in 2014 as a W3C Recommendation, DCAT has seen a wide adoption across communities and domains, particularly in conjunction with implementing the FAIR data principles (for findable, accessible, interoperable and reusable data). These implementation experiences, besides demonstrating the fitness of DCAT to meet its intended purpose, helped identify existing issues and gaps. Moreover, over the last few years, additional requirements emerged in data catalogs, given the increasing practice of documenting not only datasets but also data services and APIs. This paper illustrates the new version of DCAT, explaining the rationale behind its main revisions and extensions, based on the collected use cases and requirements, and outlines the issues yet to be addressed in future versions of DCAT.