Reuse Of Data Research Articles

7.Q. Workshop: What population health researchers’ need and how PHIRI federated research infrastructure can help

Abstract What population health researchers’ need? The population health research community has a vast experience in the reuse of data for health monitoring and surveillance or healthcare performance assessment. However, there is a gap in the extensive reuse of individual sensitive data, particularly when mobilising these data requires the linkage of multiple data sources curated in different sites. The gap is greater when it comes to using sensitive data in cross-national research. The usual arguments to explain the scarce extensive and continuous mobilisation of sensitive data are data privacy and safety issues, the difficulty to discover data sources of value, complex accessing rules, uneven data quality (particularly, non-harmonized data), and limited capacity (personnel and dedicated resources). In InfAct Joint Action, Information for Action, we demonstrated at a very small scale that mobilising individual sensitive data is possible, it is compliant with the legal and ethical requirements, and it yields the expected outputs. The instrument used for such an achievement was the design, implementation and deployment of a very small-scale federated infrastructure, where we could pilot all the legal, organisational, data quality and technological issues related to the mobilisation of individual sensitive data. (https://doi.org/10.1186/s13690-021-00731-z). Building on those achievements In PHIRI (see here https://www.phiri.eu/wp7) we are paving the way for a large-scale research infrastructure where multiple population health researchers with multiple research questions will need the mobilisation of multiple data sources from multiple sites across Europe. The PHIRI enhanced infrastructure will have to be prepared to provide a variety of services for data discovery, data access, data analysis and research outputs FAIR publication, while improving the capacity of population health researchers community in the use of advanced computing tools. In this workshop we will start describing the PHIRI federated research infrastructure achievements, the governance step-wise approach and the technological solutions provided. The workshop will discuss how an enhanced PHIRI could improve its services for the community of population health researchers; in particular improving the analytical capacity and the associated technological solutions. Finally, the workshop will touch ground on the future developments, in particular, the interaction of the PHIRI infrastructure with existen European-wide services providers, as EGI, and research infrastructures. Key messages • In the domain of population health sciences, the reuse of individual sensitive data for research purposes is very limited. • The PHIRI federated research infrastructure is paving the way for population health researchers to enhance their research when reusing individual sensitive data.

8.G. Workshop: European Health Data Space: Dialogue on TEHDAS JA data quality & semantic interoperability proposals

Abstract The EU Data Strategy foresees the development of shared European data infrastructures, enabling cross-border exchange and availability of comparable and high-quality data. The European Health Data Space (EHDS) is the first of these infrastructures to be launched, making use of the potential of health and social data for the purpose of advancing public health, research and innovation. Quality of digital health data has been the focus of research and debate for a long time and several proposals for improvement have been put forward. While these efforts are ongoing, secondary use or re-use of health data, combined with recent developments in data analytics, AI and real-world data (RWD), have raised new requirements on our understanding of data quality and the means for its assurance. Both the European Commission and Member States have underlined the need to support EU and national authorities, as well as the scientific community to agree on interoperability guidelines addressing data quality and semantic interoperability. The TEHDAS Joint Action has responded to these expectations through work undertaken in the context of WP6 - Excellence in data quality. With expected delivery deadline early 2023, the EUPHA conference offers an excellent platform to present the draft forthcoming proposals to the public health community and engage in meaningful dialogue with participants. Key members of the WP6 team will present the process through which the TEHDAS JA recommendations on data quality and semantic interoperability have been developed, the results achieved thus far and the remaining open questions. The workshop discussion section will allow the JA team to collect direct input and feedback for utilization in the production of the final version of the deliverables. Key messages • Increased awareness and understanding of background and rational of forthcoming proposals on data quality and semantic interoperability in the European Health Data Space. • Opportunity to raise concerns and proposals for improvement and thus have a direct impact on the final TEHDAS JA output on data quality.

3.Q. Workshop: Using foresight to anticipate future public health challenges

Abstract The COVID-19 pandemic brought great uncertainty about current and future health impacts. Uncertainty about how the virus would directly impact our health. And uncertainty about indirect impacts of COVID-19 on long-term health and wellbeing, including consequences due to delayed prevention, diagnosis, and medical treatment. At this point in time, we know a lot more about the immediate mortality and morbidity aspects. How we fare on the wider impacts on the longer term is more difficult to say. A structured tool for gaining in sight in this, is foresight. We use this tool within the Population Health Information Research Infrastructure (PHIRI). PHIRI supports research across Europe through the identification, access, assessment and reuse of population health and non-health data to underpin public health policy decisions. PHIRI is a practical use case and lays the foundation for ultimately developing a Distributed Infrastructure on Population Health (DIPoH). The joint workshop aims to show ways to better understand the complexity of the wider impacts of the SARS-COV-2 virus on population health across Europe; show how a research infrastructure might help researchers in Europe build capacity and helping each other to achieve higher goals. The workshop will begin with a presentation providing a brief overview of the PHIRI foresight work. This will be followed by two researchers who participated in the PHIRI foresight training program and undertook their own Public Health Foresight study. In the last presentation of the session foresight will be reflected on from a policy perspective. How can researchers and health policy makers better interact to have better foresight-informed policy making. Interaction with the audience will be guaranteed using Mentimeter questions. With this interactive element, insights art obtained what people find the most important trends to act upon, what they consider most important future challenges and how researchers and policymakers can better interact. Key messages • Foresight studies are essential to be better prepared for and to anticipate to future challenges. • With foresight-informed policy making direct and indirect health impacts of COVID-19 are addressed.

6.G. Workshop: The European Health Data Space 2: is Europe ready to maximise the re-use of health data?

Abstract The EU's Heads of State called in October 2020 to set up a European Health Data Space (EHDS): “The European Council welcomes the creation of common European data spaces in strategic sectors, and in particular invites the Commission to give priority to the health data space”. As a response, multiple initiatives were set-up to engage the views and expectations from EU member states in the development of the upcoming legislative proposal on the EHDS for primary (EHDS1) and secondary use of health data (EHDS2). One of these was the launch of the Joint Action (JA) Towards the European Health Data Space (TEHDAS) in February 2021. The aim of TEHDAS is to support the European Commission (EC) and Member States (MS), identifying the challenges and providing concepts and options for the EHDS2. It is built on the needs and expectations of national and international stakeholders, citizens’ engagement and existing infrastructure. The major expected outcome of this JA is a sustainable roadmap towards the EHDS2 implementation promoting the cross-border sharing of health data for secondary use. The EHDS2 will benefit population health by facilitating the timely exchange of and access to health data, improving not only the care of patients but also facilitating research, epidemiology, disease prevention and data-based policy decisions. This workshop aims to provide an overview of the preparedness of MS to join the EHDS for secondary use of health data, covering current state-of-play of health data management systems in the EU, barriers on the secondary use of health data, the ongoing work on the development and deployment of the EHDS2 infrastructure and finally, how population health research can benefit from the EHDS2. The workshop will begin with a short introduction on the TEHDAS JA, followed by a presentation on the outcomes of the TEHDAS country visits. In these country visits, the state-of-play of the health data management system and the preparedness to join the EHDS2 were mapped in 12 different EU MS. The next presentation will continue with the results from a study that collected more evidence on the barriers to cross-border sharing of health data for secondary use and options on how to overcome these. It will focus on the legal preparedness and barriers and will discuss how the options proposed by experts in the field align with the legislative proposal for the EHDS2. The audience will be able to give their opinion on the different options through an interactive voting poll during the session. Some of the options on how to overcome technical barriers will be taken further in the development of a pilot infrastructure for the EHDS2. Therefore, the last two presentations will focus on the development and deployment of this pilot infrastructure for the EHDS2 and the role and benefits that population health research will have in it. Throughout the workshop there will be discussion sessions between presentations to allow exchange of knowledge, experiences and opinions with the audience. Key messages • The main outputs will support EU countries to prepare their health data management system for the EHDS2 based on the best practices and actions presented. • The presentations will inform and align participants on the current work on the technical EHDS infrastructure that is being developed for secondary use of health data, the EHDS2.

Process-Sensitive Naming: Trait Descriptors and the Shifting Semantics of Plant (Data) Science

This paper examines classification practices in the domain of plant data semantics, and particularly methods used to label plant traits to foster the collection, management, linkage and analysis of data about crops across locations—which crucially inform research and interventions on plants and agriculture. The efforts required to share data place in sharp relief the forms of diversity characterizing the systems used to capture the biological and environmental characteristics of plant variants: particularly the biological, cultural, scientific and semantic diversity affecting the identification and description of plant traits, the methods used to generate and process data, and the goals and skills of those with relevant expertise—including farmers and breeders. Through a study of the Crop Ontology (which explicitly recognizes and negotiates diversity) and its application to cassava breeding, I argue for a process-sensitive approach to the naming of plant traits that focuses on documenting environmental processes instead of biological products. I claim that this approach can foster reliable linkage and robust re-use of plant data, while at the same time facilitating dialogue between data scientists, plant researchers, breeders, and other relevant experts in ways that crucially inform agricultural interventions. I conclude that the study of data semantics and related descriptors constitutes a productive and underexplored way to think about the epistemic import of naming traits within plant science. The effort to articulate semantic differences among plant varieties and methods of data processing can generate newly inclusive ways to develop and communicate biological knowledge. In turn, such practices have the potential to defy existing understandings of systematisation and hierarchies of expertise in biology, thus bolstering the extent to which plant science can support biodiversity and sustainable agriculture.

The role of PHIRI in the EHDS2

Abstract The Population Health Information Research Infrastructure (PHIRI) is setting the foundations for the design, implementation and deployment of a fully operative European research infrastructure on population health research in the next few years. In a nutshell, this effort represents setting up the principles and instruments for the governance of the reuse of data for research purposes in a number of aspects - the legal and ethical foundations for the mobilization of data, including sensitive data; how the different nodes in the infrastructure organize to respond multiple research questions in a federated manner; how to ensure high standards of data quality and data interoperability; and, how technological solutions can preserve a safe use of the data while enabling sound scientific outputs. PHIRI has been challenged to provide real-life solutions to each of these aspects. Acting as a demonstration project, PHIRI is currently providing solutions to researchers that are trying to solve their research questions. Building on this experience, in the future, PHIRI is expected to provide support and services to population health researchers that build their research on the secondary use of health data. Among those services: data discovery; data harmonization services (e.g., discovery and use of common data models and interoperability standards); technological services (e.g., playgrounds with synthetic data, secure process environments); research outputs archival (e.g., maintenance and dissemination of the knowledge base generated in the infrastructure); and, training and capacity building. Interestingly, PHIRI can be seen as a primer for the upcoming European Health Data Space for secondary use (EHDS2). In this workshop will be discussing how to line up PHIRI's vision and role within the EHDS2.

Recovering and Reusing Scientific Data

Legacy scientific data relevant to current and future scientific research exist scattered across memory institutions. This data remains unexplored and often faces the potential to be destroyed. This poster focuses on data rescue initiatives within the sciences, exploring how these efforts suggest the need for greater focus on scientific thinking and collaboration between the sciences and LIS.
 This research explores the efforts of digital curators and scientists to recover library and archival records housed on obsolete formats (both physical and digital) and data within unpublished documents. The ongoing "Recovering and Reusing Archival Data for Science" project (RRAD-S) draws on semi-structured interviews with scientists from atmospheric sciences to ornithology and data curators, including metadata librarians and archivists. These interviews document the catalysts, priorities, and challenges of participants' data reuse/recovery efforts. This poster reports on initial findings regarding how scientific data rescue entails a degree of digital curation expertise and planning on the part of scientific research teams, meaning that scientists thinking proactively about data preservation or reuse might collaborate with information professionals to develop data conservation plans.
 Historically, LIS education dedicates limited attention to understanding scientific thinking, which usually emphasizes generating new data over reuse. This poster asserts that LIS education can support long-term scientific scholarship by consciously educating students to participate in scientific data preservation initiatives. Additionally, educators can utilize scientific data practices to inform LIS pedagogy by prioritizing and centering proactive data creation within scientific endeavors.

Transcriptomic and metabolomic integration as a resource in grapevine to study fruit metabolite quality traits.

Transcriptomics and metabolomics are methodologies being increasingly chosen to perform molecular studies in grapevine (Vitis vinifera L.), focusing either on plant and fruit development or on interaction with abiotic or biotic factors. Currently, the integration of these approaches has become of utmost relevance when studying key plant physiological and metabolic processes. The results from these analyses can undoubtedly be incorporated in breeding programs whereby genes associated with better fruit quality (e.g., those enhancing the accumulation of health-promoting compounds) or with stress resistance (e.g., those regulating beneficial responses to environmental transition) can be used as selection markers in crop improvement programs. Despite the vast amount of data being generated, integrative transcriptome/metabolome meta-analyses (i.e., the joint analysis of several studies) have not yet been fully accomplished in this species, mainly due to particular specificities of metabolomic studies, such as differences in data acquisition (i.e., different compounds being investigated), unappropriated and unstandardized metadata, or simply no deposition of data in public repositories. These meta-analyses require a high computational capacity for data mining a priori, but they also need appropriate tools to explore and visualize the integrated results. This perspective article explores the universe of omics studies conducted in V. vinifera, focusing on fruit-transcriptome and metabolome analyses as leading approaches to understand berry physiology, secondary metabolism, and quality. Moreover, we show how omics data can be integrated in a simple format and offered to the research community as a web resource, giving the chance to inspect potential gene-to-gene and gene-to-metabolite relationships that can later be tested in hypothesis-driven research. In the frame of the activities promoted by the COST Action CA17111 INTEGRAPE, we present the first grapevine transcriptomic and metabolomic integrated database (TransMetaDb) developed within the Vitis Visualization (VitViz) platform (https://tomsbiolab.com/vitviz). This tool also enables the user to conduct and explore meta-analyses utilizing different experiments, therefore hopefully motivating the community to generate Findable, Accessible, Interoperable and Reusable (F.A.I.R.) data to be included in the future.

Promoting data quality and reuse in archaeology through collaborative identifier practices

Investments in data management infrastructure often seek to catalyze new research outcomes based on the reuse of research data. To achieve the goals of these investments, we need to better understand how data creation and data quality concerns shape the potential reuse of data. The primary audience for this paper centers on scientific domain specialists that create and (re)use datasets documenting archaeological materials. This paper discusses practices that promote data quality in support of more open-ended reuse of data beyond the immediate needs of the creators. We argue that identifier practices play a key, but poorly recognized, role in promoting data quality and reusability. We use specific archaeological examples to demonstrate how the use of globally unique and persistent identifiers can communicate aspects of context, avoid errors and misinterpretations, and facilitate integration and reuse. We then discuss the responsibility of data creators and data reusers to employ identifiers to better maintain the contextual integrity of data, including professional, social, and ethical dimensions.

Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data Reuse: Consensus Study

Despite the many opportunities data reuse offers, its implementation presents many difficulties, and raw data cannot be reused directly. Information is not always directly available in the source database and needs to be computed afterwards with raw data for defining an algorithm. The main purpose of this article is to present a standardized description of the steps and transformations required during the feature extraction process when conducting retrospective observational studies. A secondary objective is to identify how the features could be stored in the schema of a data warehouse. This study involved the following 3 main steps: (1) the collection of relevant study cases related to feature extraction and based on the automatic and secondary use of data; (2) the standardized description of raw data, steps, and transformations, which were common to the study cases; and (3) the identification of an appropriate table to store the features in the Observation Medical Outcomes Partnership (OMOP) common data model (CDM). We interviewed 10 researchers from 3 French university hospitals and a national institution, who were involved in 8 retrospective and observational studies. Based on these studies, 2 states (track and feature) and 2 transformations (track definition and track aggregation) emerged. "Track" is a time-dependent signal or period of interest, defined by a statistical unit, a value, and 2 milestones (a start event and an end event). "Feature" is time-independent high-level information with dimensionality identical to the statistical unit of the study, defined by a label and a value. The time dimension has become implicit in the value or name of the variable. We propose the 2 tables "TRACK" and "FEATURE" to store variables obtained in feature extraction and extend the OMOP CDM. We propose a standardized description of the feature extraction process. The process combined the 2 steps of track definition and track aggregation. By dividing the feature extraction into these 2 steps, difficulty was managed during track definition. The standardization of tracks requires great expertise with regard to the data, but allows the application of an infinite number of complex transformations. On the contrary, track aggregation is a very simple operation with a finite number of possibilities. A complete description of these steps could enhance the reproducibility of retrospective studies.

Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences

The humanities and social sciences (HSS) have recently witnessed an exponential growth in data-driven research. In response, attention has been afforded to datasets and accompanying data papers as outputs of the research and dissemination ecosystem. In 2015, two data journals dedicated to HSS disciplines appeared in this landscape: Journal of Open Humanities Data (JOHD) and Research Data Journal for the Humanities and Social Sciences (RDJ). In this paper, we analyse the state of the art in the landscape of data journals in HSS using JOHD and RDJ as exemplars by measuring performance and the deep impact of data-driven projects, including metrics (citation count; Altmetrics, views, downloads, tweets) of data papers in relation to associated research papers and the reuse of associated datasets. Our findings indicate: that data papers are published following the deposit of datasets in a repository and usually following research articles; that data papers have a positive impact on both the metrics of research papers associated with them and on data reuse; and that Twitter hashtags targeted at specific research campaigns can lead to increases in data papers’ views and downloads. HSS data papers improve the visibility of datasets they describe, support accompanying research articles, and add to transparency and the open research agenda.

An interactive mass spectrometry atlas of histone posttranslational modifications in T-cell acute leukemia

The holistic nature of omics studies makes them ideally suited to generate hypotheses on health and disease. Sequencing-based genomics and mass spectrometry (MS)-based proteomics are linked through epigenetic regulation mechanisms. However, epigenomics is currently mainly focused on DNA methylation status using sequencing technologies, while studying histone posttranslational modifications (hPTMs) using MS is lagging, partly because reuse of raw data is impractical. Yet, targeting hPTMs using epidrugs is an established promising research avenue in cancer treatment. Therefore, we here present the most comprehensive MS-based preprocessed hPTM atlas to date, including 21 T-cell acute lymphoblastic leukemia (T-ALL) cell lines. We present the data in an intuitive and browsable single licensed Progenesis QIP project and provide all essential quality metrics, allowing users to assess the quality of the data, edit individual peptides, try novel annotation algorithms and export both peptide and protein data for downstream analyses, exemplified by the PeptidoformViz tool. This data resource sets the stage for generalizing MS-based histone analysis and provides the first reusable histone dataset for epidrug development.

A portal for indexing distributed FAIR digital objects for catalysis research

A research object (RO) is defined as a semantically rich aggregation of (potentially distributed) resources that provide a layer of structure on top of information delivered as linked data (Bechhofer et al. 2013, Soiland-Reyes et al. 2022). A RO provides a container for the aggregation of resources, produced, and consumed by common services and shareable within and across organisational boundaries. This work sees research digital objects as composites which may consist of objects hosted in different repositories. In catalysis research, the characterisation of a sample may require analysing experimental data obtained from an instrument, data from a computer model, and/or comparing to data from a specialized database. Additionally, data may need to be reduced and cleaned before analysis, resulting in intermediate data. In this scenario the composite research object is integrated by all these data objects and their corresponding metadata. UK Catalysis Hub (UKCH) researchers perform these tasks as part of their day-to-day work. However, most of the time they need to manually collect, catalogue, and preserve all these data assets. The UKCH aims to support researchers with tools and services for the management and processing of data, through the development of the Catalysis Data Infrastructure (CDI Nieva de la Hidalga et al. 2022a) and the Catalysis Research Workbench (CRW Nieva de la Hidalga et al. 2022b). This work is integrated in the context of the Physical Sciences Data Infrastructure (PSDI Coles and Knight 2022). The PSDI aims to provide a layer that enables transparent access to existing resources whilst ensuring that they remain dedicated to its specific application. The intention is to explore the concept of the composite research digital object and the services required to facilitate both human and programmatic interactions with those objects to browse, review, retrieve, and use digital objects in the context of the research produced by UKCH scientists. The CDI will act as a thematic portal presenting data managed through the PSDI and serve as an example for the development of similar portals targeting specific research domains. In this case, the CDI is in the process of being redesigned with a sematic metadata model. The basic ontologies being considered for this model are: DCAT (Albertoni et al. 2022) will encode the metadata of digital objects; PROV-O (Belhajjame et al. 2013) will track the generation of digital objects. SPAR (Peroni and Shotton 2018) to encode publications data; SCHOLIX to encode the links between publications and data objects (Burton et al. 2017); FOAF (Brickley and Miller 2014) to encode researcher information; the Organization Ontology (ORG Reynolds 2014) to encode institution information; EXPO (Soldatova et al. 2006) to encode experiment information; and various domain specific ontologies for adding metadata about experiments, for instance CHEBI (Hastings et al. 2011), CHEMINF (Hastings et al. 2015), and FIX (Chebi-Administrators 2005). The implementation of the CDI using these ontologies will provide a roadmap for the integration of FAIR data object repositories with a service infrastructure which supports reproducibility, reuse of data, reuse of processing tools and implementation of advanced processing tools. The integration of the CDI and CRW with existing and new infrastructures will further support the work of catalysis scientists. In this context, a researcher can access the CDI to look for publications, see if there are data objects linked to them, and then look for processing tools which can be used to reproduce the results. An experiment for an early use case demonstrated the feasibility of reproducing published results using data and metadata linked to existing publications (Nieva de la Hidalga et al. 2022b). In the experiment, papers citing processable data were used to retrieve, process, and reproduce published results with no need for contacting the authors. Fig. 1 presents a view of the experiment performed. The current practices of publishing catalysis research data can be seen as aligned to the FAIR data principles, for instance Fig. 1 above can be also seen as Fig. 2 Reproducing results required several human-centered activities, partly due to the encoding of the metadata as text documents. The challenge is to accelerate and automate these processes. It is important to highlight the role of cataloguing interfaces, such as the CDI, containing DO crates with only metadata and links to the different data assests that constitute the composite digital objects. The users of these interfaces will in turn rely on transparent services which do not require them to manually track the location and formats of the data assets they want to retrieve and use.

Emerging FAIR Ecosystem(s): A Practical Perspective

There is broad acceptance that FAIR data (Wilkinson et al. 2016) reuse is desirable, with considerable interest and energy being devoted to its realization, but many questions remain on the part of prospective implementers. Fundamental to explaining how best to implement FAIR is an overview of how the many FAIR models (and the technologies that support them) fit together into a coherent "FAIR ecosystem". How do FAIR Implementation Profiles (FIPs) (Schultes et al. 2020) relate to FAIR Data Points (FDPs) (Bonino da Silva Santo et al. 2022), and how do these relate to the concept of FAIR Digital Objects (FDOs) (Hudson et al. 2018)? What is their relationship to other diverse FAIR resources and digital assets (metadata, datasets, repositories, and the complex web of services that run them)? How are these novel and legacy systems intended to interoperate? These questions are often encountered by those involved in the growing number of projects looking at FAIR implementation (ENVRI-FAIR 2019, CODATA 2022, SciDataCon 2022, Seoul Korea 2022b, Health-Holland Project 2021, Health-Holland Project 2022, Swiss Personalized Health Network 2022, Maxwell 2021, VODAN 2020). Such an overview could also inform the development of specifications for the different models involved in a FAIR ecosystem, such as FIPs, FDPs, and the description of digital resources (data and services) at various levels. With an agreed picture of the FAIR-reuse ecosystem, the points of contact and "hand-off" would be easier to describe and coordinate. This presentation looks at questions from FAIR implementation across various settings, and proposes a view of the overall ecosystem which could be agreed and communicated to prospective implementers. It suggests the relationship between various artefacts being discussed in the FAIR community today (FIPs, FDPs, FDOs, and other digital assets) and looks at how these can be connected to the business layer to support the development of services and applications within the envisioned FAIR ecosystem. Notably, this includes how the Cross-Domain Interoperability Framework (CDIF) being developed through the WorldFAIR project can connect to the underlying FAIR ecosystem in practical terms (Weise et al. 2022). The presentation will address high-level considerations around the major technology components of a FAIR ecosystem, their roles within a range of common user scenarios (often having unavoidable legacy technology), and their relationship to each other and to the set of models needed to provide practical services for FAIR interoperation at the business level. Three basic scenarios are examined, in order to understand the practical requirements of different communities. The first scenario is one which has received a good deal of attention during initial efforts to implement the FAIR principles, a domain or user community without a strong pre-existing culture of data sharing and resume, wishes to become FAIR. The second scenario is one where a community with a strong existing culture of data sharing and reuse is looking to integrate its current approaches with those advocated by the FAIR community. The third scenario looks at FAIR from the perspective of the implementer of FAIR services from an “industrial” perspective: how does FAIR provide the kind of market which is needed to support full-scale services and application development? Each of these scenarios provide valid, but different views of what it will take to implement the FAIR in practical terms. In order to understand them, we can draw parallels with other large-scale data-sharing efforts in other communities - the Internet and the Web itself can be understood as a useful example of how vision, standards, and implementation combine to provide successful infrastructure at this scale. Indeed, the concept of Digital Objects (and now FAIR Digital Objects) has its roots in this analogy (Kahn and Wilensky 2006). Other, smaller examples also exist, which focus more specifically on the exchange of data and metadata: for example the Statistical Data and Metadata Exchange (SDMX) Initiative (SDMX community 2022) or the emerging Cross-Domain Interoperability Framework (SciDataCon 2022, Seoul Korea 2022a). Although implemented within targeted communities, these efforts exchange a wide range of data and metadata not entirely dissimilar to what is envisioned in FAIR. There is currently no single exact parallel for FAIR ecosystems, but there are examples from which we can learn in terms of making large-scale data reuse a practical reality. Core to these is a vision of all of the component pieces, and how they can act in concert to provide a scalable infrastructure which will address the needs of the many different communities of users. Such a common vision may be implicitly agreed among those working on FAIR implementation today, but in the interests of clear communication, it is time to document it - and in keeping with FAIR, this documentation should be itself machine-actionable. As we move toward the specification of the many components of the FAIR ecosystem, it seems only common sense to have an agreed roadmap.

Development of a core outcome set for hypertensive intracerebral hemorrhage in clinical trials of traditional Chinese medicine: a study protocol

BackgroundIntracerebral hemorrhage (ICH) is a devastating disease, its mortality and disability rate are high. In China, hypertensive intracerebral hemorrhage (HICH) is responsible for 75% of all the cases of primary ICH. A lot of randomized controlled trials (RCTs) of traditional Chinese medicine (TCM) for treating HICH have been carried out. However, these RCTs have a lot of problems, such as heterogeneous outcomes, non-uniform point of measurement. These lead to systematic review/meta-analysis only can include a small number of studies. And outcome measures did not take the wishes of patients and other stakeholders into account. The aim of this study is to establish the core outcome set (COS) for future TCM clinical trials of HICH.Methods and analysisFirst, we will develop a long list of general outcomes by making systematic literature review and semi-structured interviews. Then healthcare professionals and patients with HICH will be invited to participate in two rounds of the Delphi survey to determine the importance of the outcome. Finally, a face-to-face consensus meeting will be conducted to determine the final COS of HICH, including what outcomes should be measured and when and how to measure the outcomes.ResultsWe aim to develop a COS that includes TCM core syndrome for HICH to determine what outcomes should be reported and when and how to measure them.ConclusionBy doing this, we can increase the reporting consistency and reduce the reporting bias in the outcome, which leads to the reuse of research data in meta-analysis and the making of informed healthcare decisions.Ethics and disseminationThe entire project has received approval from the Ethics Committee of Xiyuan Hospital, China Academy of Chinese Medical Sciences. The final COS will be published and reported at the national and international conferences.Trial registrationThis study is registered with the Core Outcome Measures in Effectiveness Trials database as study 1475. Registered on December 2019.

FAIR-IMPACT

In this poster we present the FAIR-IMPACT project, “Expanding FAIR solutions across EOSC”, which is funded by the European Commission Horizon Europe programme. The acronym FAIR stands for Findable, Accessible, Interoperable and Reusable. The project is coordinated by DANS and supported by 27 additional partners from 11 countries. FAIR-IMPACT will build on the successful practices, policies, tools and technical specifications arising from FAIRsFAIR other H2020 projects and initiatives, and from the FAIR and other relevant Working Groups of the former European Open Science Cloud (EOSC) Executive Board. The FAIR-IMPACT project is active between June 2022 and May 2025. Cascading grants will be available to support uptake of FAIR solutions and practices. The overall objective of FAIR-IMPACT is to realise a FAIR EOSC, that is an EOSC of FAIR data and services, by supporting the implementation of FAIR-enabling practices across scientific communities and research outputs at a European, national, and international level. Advancing findability, accessibility, interoperability and reusability (“FAIRness”) of data and other research objects are at the core of this project, which closely collaborates with the FAIRCORE4ESOC project (https://faircore4eosc.eu/ ). We will coordinate the implementation of frameworks and the alignment of FAIR data practices on metadata and persistent identifiers (PIDs) in order to achieve the wide uptake of and compliance with FAIR principles by national and European research data and metadata providers and repositories. In the poster we present our work on the implementation of the FAIR principles and practices. Among other things we aim at a coherent implementation of PIDs and more exact data citation, as services then are able to support better data quality with suitable PID solutions. A broader and more targeted use of PIDs, based on end-user needs, can support trust and risk management and requires collaboration among PID service providers and developing PID policies. Special attention will be paid to reproducibility and to sensitive data. As semantic artefacts are an important element in creating semantic interoperability, they are also regarded as digital objects with their own recommendations for FAIR implementation, as are software. Working together with the FAIRCORE4ESOC project, we can address things like kernel information profiles that are highly relevant for FAIR digital objects. The project will also focus on increasing data accessibility through enhancing interoperability on all levels, with specific steps taken to address recommendations outlined in the EOSC (The EOSC Interoperability Framework). Validation of core interoperability components through metadata mechanisms across scientific disciplines, fostering interoperability alignment with the nine European Data Spaces, and the DAMA framework (DAMA-DMBoK), i.e. the EOSC ecosystem, are of relevance. Metrics and FAIR assessment are also addressed in this project, that will extend and adapt the FAIRsFAIR data object assessment metrics and F-UJI tool to be more disciplinary-context aware and to include more discipline specific tests. The FAIR-IMPACT project will closely work with how the FAIR principles are implemented within the EOSC and how digital objects can be identified and managed.

Updating Linked Data practices for FAIR Digital Object principles

Updating Linked Data practices for FAIR Digital Object principles

Facing the Challenges in simulation-based Earth System Sciences and the Role of FAIR Digital Objects

Facing the Challenges in simulation-based Earth System Sciences and the Role of FAIR Digital Objects

Design and Evaluation of Coastal Web Atlases: Best Practices and Future Opportunities for Map Representation, Interaction, and Usability

In this article, we examine best practices and future opportunities for the design of coastal web atlases (CWAs) supporting adaptive management. Coastal zones face significant challenges, and CWAs have emerged as a resource to organize maps and geospatial data in support of education, exploration, and decision-making about coastal issues. Our research is motivated by the Wisconsin Coastal Atlas (https://www.wicoastalatlas.net/)—one of several U.S. state-based CWAs that are members of the broader International Coastal Atlas Network (ICAN: https://ican.iode.org/). Specifically, we conducted a needs assessment that bridges adaptive coastal management user needs with three tenets of interactive cartographic design relevant to CWAs: map representation, interaction, and usability. The needs assessment included two stages: a competitive analysis of 10 state CWAs and a user survey with stakeholders from those states about their experiences with and opinions on CWA design. In addition to characterizing design patterns and values, the needs assessment identified important gaps informing future CWAs, such as: inclusion of a wider range of thematic maps; provision of hybrid basemaps providing context about the land and water sides of the coastline; implementation of spatial calculations and temporal sequencing for analysis and exploration; use of story maps to support CWA learnability; improved responsiveness between mobile and non-mobile devices; and customization of advanced analytical tools that support decision making about the most pressing issues facing our coasts. This research serves coastal managers, planners, researchers, educators, outreach specialists, and related stakeholders who benefit from findable, accessible, interoperable, and reusable (FAIR) data and effective decision tools to guide management of coastal resources.

The Neurodata Without Borders ecosystem for neurophysiological data science.

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB's impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.