Findable, Accessible, Interoperable And Reusable Data Principles Research Articles

FAIRsoft-A practical implementation of FAIR principles for research software.

Software plays a crucial and growing role in research. Unfortunately, the computational component in Life Sciences research is often challenging to reproduce and verify. It could be undocumented, opaque, contain unknown errors that affect the outcome, or be directly unavailable and impossible to use for others. These issues are detrimental to the overall quality of scientific research. One step to address this problem is the formulation of principles that research software in the domain should meet to ensure its quality and sustainability, resembling the FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles. We present here a comprehensive series of quantitative indicators based on a pragmatic interpretation of the FAIR Principles and their implementation on OpenEBench, ELIXIR's open platform providing both support for scientific benchmarking and an active observatory of quality-related features for Life Sciences research software. The results serve to understand the current practices around research software quality-related features and provide objective indications for improving them. Software metadata, from 11 different sources, collected, integrated and analysed in the context of this manuscript are available at https://doi.org/10.5281/zenodo.7311067. Code used for software metadata retrieval and processing is available in the following repository https://gitlab.bsc.es/inb/elixir/software-observatory/FAIRsoft_ETL. Supplementary data are available at Bioinformatics online.

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.

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

Public access to well-organized migration data repositories is rare. While open migration data are available, most are dispersed, challenging their organization. This work uses a metadata modeling approach to organize migration-related data and research components hosted in an open-source, collaborative research environment. The proposed platform integrates data, computational models, application libraries, and research projects in a unified environment for research. An overview of the metadata modeling process and the XML technology used to integrate the platform components was provided, along with descriptions of the platform’s essential features, such as standardized exchange of information for data organization and sharing and collaborative authoring. The design of a prototype collaborative, open-source migration data and modeling research platform using sample data and software components was built and hosted on GitHub for testing and refinement purposes. It allows the authors to collaborate, improve, and assess the platform’s functionalities and capabilities. Our platform, COSMOS (Collaborative Open-Source Modeling System), adopted the creation and use of open data formats for alignment with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles to ensure that data at the prototype level is consistent with and can be used by the international migration research community in the future. By providing a standardized framework for data organization, data sharing, and collaboration, the platform can help accelerate scientific discovery and advance our understanding of complex social and cultural phenomena such as immigration.

Semantics-Enabled Data Federation: Bringing Materials Scientists Closer to FAIR Data

The development and discovery of new materials can be significantly enhanced through the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and the establishment of a robust data infrastructure in support of materials informatics. A FAIR data infrastructure and associated best practices empower materials scientists to access and make the most of a wealth of information on materials properties, structures, and behaviors, allowing them to collaborate effectively, and enable data-driven approaches to material discovery. To make data findable, accessible, interoperable, and reusable to materials scientists, we developed and are in the process of expanding a materials data infrastructure to capture, store, and link data to enable a variety of analytics and visualizations. Our infrastructure follows three key architectural design philosophies: (i) capture data across a federated storage layer to minimize the storage footprint and maximize the query performance for each data type, (ii) use a knowledge graph-based data fusion layer to provide a single logical interface above the federated data repositories, and (iii) provide an ensemble of FAIR data access and reuse services atop the knowledge graph to make it easy for materials scientists and other domain experts to explore, use, and derive value from the data. This paper details our architectural approach, open-source technologies used to build the capabilities and services, and describes two applications through which we have successfully demonstrated its use. In the first use case, we created a system to enable additive manufacturing data storage and process parameter optimization with a range of user-friendly visualizations. In the second use case, we created a system for exploring data from cathodic arc deposition experiments to develop a new steam turbine coating material, fusing a combination of materials data with physics-based equations to enable advanced reasoning over the combined knowledge using a natural language chatbot-like user interface.

Interpreting and reporting fission-track chronological data

Fission-track dating is based on the analysis of tracks—linear damage trails—produced by the spontaneous fission of 238U in a range of natural accessory minerals and glasses. The retention of tracks is sensitive to elevated temperatures, and the data serve principally as a tool for recording thermal histories of rocks, potentially over the range of ∼20−350 °C, depending on the specific minerals studied. As such, in most cases, fission-track data generally bear little or no direct relationship to the original formation age of the material studied. The age range of fission-track dating is related to the product of age and uranium content, and ages from several tens of years to older than 1 Ga are reported. Fission-track analysis led to the development of powerful modeling techniques. When used with appropriate geological constraints, these modeling techniques allow important geological processes to be addressed in a broad range of upper crustal settings. Since early attempts to standardize the treatment of fission-track data and system calibration over more than 30 years ago, major advancements were made in the methodology, necessitating the development of new, updated data reporting requirements. Inconsistencies in reporting impede public data transparency, accessibility and reuse, Big Data regional syntheses, and interlaboratory analytical comparisons. This paper briefly reviews the fundamentals of fission-track dating and applications to provide context for recommended guidelines for reporting and supporting essential meta fission-track data for publication and methodological archiving in structured formats that conform with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Adopting such practices will ensure that data can be readily accessed, interrogated, and reused, allowing for further integration with other numerical geoscience techniques.

A multi-omics data analysis workflow packaged as a FAIR Digital Object.

Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.

TopDownApp: An open and modular platform for analysis and visualisation of top-down proteomics data.

Although Top-down (TD) proteomics techniques, aimed at the analysis of intact proteins and proteoforms, are becoming increasingly popular, efforts are needed at different levels to generalise their adoption. In this context, there are numerous improvements that are possible in the area of open science practices, including a greater application of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. These include, for example, increased data sharing practices and readily available open data standards. Additionally, the field would benefit from the development of open data analysis workflows that can enable data reuse of public datasets, something that is increasingly common in other proteomics fields.

CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics.

Metabolite annotation continues to be the widely accepted bottleneck in nontargeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high-resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas-phase collision cross-section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS, and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Another unique aspect of CCSP 2.0 is its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73, and 1.87% for the 170 [M - H]-, 155 [M + H]+, and 138 [M + Na]+ adducts tested. For superclass-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a ΔCCS threshold of 2.8% and a mass error of 10 ppm.

PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.

British Antarctic Survey's aerogeophysical data: releasing 25 years of airborne gravity, magnetic, and radar datasets over Antarctica

Abstract. Over the past 50 years, the British Antarctic Survey (BAS) has been one of the major acquirers of aerogeophysical data over Antarctica, providing scientists with gravity, magnetic, and radar datasets that have been central to many studies of the past, present, and future evolution of the Antarctic Ice Sheet. Until recently, many of these datasets were not openly available, restricting further usage of the data for different glaciological and geophysical applications. Starting in 2020, scientists and data managers at BAS have worked on standardizing and releasing large swaths of aerogeophysical data acquired during the period 1994–2020, including a total of 64 datasets from 24 different surveys, amounting to ∼ 450 000 line-km (or 5.3 million km2) of data across West Antarctica, East Antarctica, and the Antarctic Peninsula. Amongst these are the extensive surveys over the fast-changing Pine Island (BBAS 2004–2005) and Thwaites (ITGC 2018–2019 & 2019–2020) glacier catchments, and the first ever surveys of the Wilkes Subglacial Basin (WISE-ISODYN 2005–2006) and Gamburtsev Subglacial Mountains (AGAP 2007–2009). Considerable effort has been made to standardize these datasets to comply with the FAIR (findable, accessible, interoperable and re-usable) data principles, as well as to create the Polar Airborne Geophysics Data Portal (https://www.bas.ac.uk/project/nagdp/, last access: 18 July 2022), which serves as a user-friendly interface to interact with and download the newly published data. This paper reviews how these datasets were acquired and processed, presents the methods used to standardize them, and introduces the new data portal and interactive tutorials that were created to improve the accessibility of the data. Lastly, we exemplify future potential uses of the aerogeophysical datasets by extracting information on the continuity of englacial layering from the fully published airborne radar data. We believe these newly released data will be a valuable asset to future glaciological and geophysical studies over Antarctica and will significantly extend the life cycle of the data. All datasets included in this data release are now fully accessible at https://data.bas.ac.uk (British Antarctic Survey, 2022).

J’ai dû me renseigner sur la DSAC en effectuant des recherches sur Google

<h3>Abstract</h3> Software plays a crucial and growing role in research. Unfortunately, the computational component in Life Sciences research is challenging to reproduce and verify most of the time. It could be undocumented, opaque, may even contain unknown errors that affect the outcome, or be directly unavailable, and impossible to use by others. These issues are detrimental to the overall quality of scientific research. One step to address this problem is the formulation of principles that research software in the domain should meet to ensure its quality and sustainability, resembling the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles. Within the ELIXIR infrastructure, OpenEBench aims to be an open platform providing both support for scientific benchmarking and an active observatory of software quality for the universe of Life Sciences research software. We present our initial proposal to instantiate a FAIR-like framework for assessing software quality as the first step toward the implementation of such an observatory in OpenEBench.

The Role of FAIR Data towards Sustainable Agricultural Performance: A Systematic Literature Review

Feeding a growing global population requires improving agricultural production in the face of multidimensional challenges; and digital agriculture is increasingly seen as a strategy for better decision making. Agriculture and agricultural supply chains are increasingly reliant on data, including its access and provision from the farm to the consumer. Far-reaching data provision inevitably needs the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) that offer data originators and depository custodians with a set of guidelines to safeguard a progressive data availability and reusability. Through a systematic literature review it is apparent that although FAIR data principles can play a key role in achieving sustainable agricultural operational and business performance, there are few published studies on how they have been adopted and used. The investigation examines: (1) how FAIR data assimilate with the sustainability framework; and (2) whether the use of FAIR data by the agriculture industry, has an impact on agricultural performance. The work identifies a social science research gap and suggests a method to guide agriculture practitioners in identifying the specific barriers in making their data FAIR. By troubleshooting the barriers, the value propositions of adopting FAIR data in agriculture can be better understood and addressed.

SPARClink: an interactive tool to visualize the impact of the SPARC program.

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

PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations.

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.

Data Discovery and Reuse in Data Service Practices: A Global Perspective

AbstractThe proposed panel will address the issues of the discovery and reuse of publicly available data on the web in the context of data service practices from a global perspective. Thousands of data discovery services have appeared around the world since the promotion of “open science”, reproducible research, and the FAIR (Findable, Accessible, Interoperable and Reusable) data principles in the research sector. However, there is also increasing demand for transparency of search algorithms, and in the design, development, evaluation, and deployment of current data search services; this requires a better understanding of how users approach data discovery and interact with data in search settings. From a global perspective, we will identify and discuss the specific system design issues in data discovery and reuse, drawing on our organization of the NTCIR (NII Testbeds and Community for Information access Research) project of Data Search track, the design and evaluation of the data discovery service of the Australian Research Data Commons (ARDC), and studies examining researchers' practices of data discovery and reuse.

Towards Interlinked FAIR Biodiversity Knowledge: The BiCIKL perspective

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

Tissue Resources for the Functional Annotation of Animal Genomes

In order to generate an atlas of the functional elements driving genome expression in domestic animals, the Functional Annotation of Animal Genome (FAANG) strategy was to sample many tissues from a few animals of different species, sexes, ages, and production stages. This article presents the collection of tissue samples for four species produced by two pilot projects, at INRAE (National Research Institute for Agriculture, Food and Environment) and the University of California, Davis. There were three mammals (cattle, goat, and pig) and one bird (chicken). It describes the metadata characterizing these reference sets (1) for animals with origin and selection history, physiological status, and environmental conditions; (2) for samples with collection site and tissue/cell processing; (3) for quality control; and (4) for storage and further distribution. Three sets are identified: set 1 comprises tissues for which collection can be standardized and for which representative aliquots can be easily distributed (liver, spleen, lung, heart, fat depot, skin, muscle, and peripheral blood mononuclear cells); set 2 comprises tissues requiring special protocols because of their cellular heterogeneity (brain, digestive tract, secretory organs, gonads and gametes, reproductive tract, immune tissues, cartilage); set 3 comprises specific cell preparations (immune cells, tracheal epithelial cells). Dedicated sampling protocols were established and uploaded in https://data.faang.org/protocol/samples. Specificities between mammals and chicken are described when relevant. A total of 73 different tissues or tissue sections were collected, and 21 are common to the four species. Having a common set of tissues will facilitate the transfer of knowledge within and between species and will contribute to decrease animal experimentation. Combining data on the same samples will facilitate data integration. Quality control was performed on some tissues with RNA extraction and RNA quality control. More than 5,000 samples have been stored with unique identifiers, and more than 4,000 were uploaded onto the Biosamples database, provided that standard ontologies were available to describe the sample. Many tissues have already been used to implement FAANG assays, with published results. All samples are available without restriction for further assays. The requesting procedure is described. Members of FAANG are encouraged to apply a range of molecular assays to characterize the functional status of collected samples and share their results, in line with the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles.

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases.

This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging.

Google dataset search and DOI for data in the ESA space science archives

Reproducibility is at the core of science. For this reason, a common practice for authors is to provide links to the data used in their manuscripts upon submission to peer-reviewed journals. In space science, reference to archives’ websites of major data providers like space agencies are still accepted by most journals. However, even these websites are not persistent links and will eventually not be accepted once journals require full compliance with the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. On the contrary, Digital Object Identifiers (DOIs) associated to data could provide persistent links. But so far, the publishing industry is systematically minting DOIs related to their articles or books without requiring DOIs for the data used in their publications. Furthermore, DOIs related to data would not only link refereed publications to the data location. It could also enable a more precise attribution to the data producers and publishers while providing the capability to eventually trace their use. These latter points require that journals eventually request or even enforce the use of these DOIs. For data producers, proper attribution and usage are important in the context of securing research grants or extension of projects. Additionally, structured metadata included in the DOI landing pages offer the possibility for datasets to be discoverable by the new Google Dataset Search (GDS) engine, available since January 2020. This paper describes the on-going activities at the European Space Agency (ESA) to mint DOIs for datasets served across the space science archives, and how a subset of these DOIs are already discoverable by GDS.

Initiatives, Concepts, and Implementation Practices of FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles in Health Data Stewardship Practice: Protocol for a Scoping Review.

BackgroundData stewardship is an essential driver of research and clinical practice. Data collection, storage, access, sharing, and analytics are dependent on the proper and consistent use of data management principles among the investigators. Since 2016, the FAIR (findable, accessible, interoperable, and reusable) guiding principles for research data management have been resonating in scientific communities. Enabling data to be findable, accessible, interoperable, and reusable is currently believed to strengthen data sharing, reduce duplicated efforts, and move toward harmonization of data from heterogeneous unconnected data silos. FAIR initiatives and implementation trends are rising in different facets of scientific domains. It is important to understand the concepts and implementation practices of the FAIR data principles as applied to human health data by studying the flourishing initiatives and implementation lessons relevant to improved health research, particularly for data sharing during the coronavirus pandemic.ObjectiveThis paper aims to conduct a scoping review to identify concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in the health data domain.MethodsThe Arksey and O’Malley stage-based methodological framework for scoping reviews will be used for this review. PubMed, Web of Science, and Google Scholar will be searched to access relevant primary and grey publications. Articles written in English and published from 2014 onwards with FAIR principle concepts or practices in the health domain will be included. Duplication among the 3 data sources will be removed using a reference management software. The articles will then be exported to a systematic review management software. At least two independent authors will review the eligibility of each article based on defined inclusion and exclusion criteria. A pretested charting tool will be used to extract relevant information from the full-text papers. Qualitative thematic synthesis analysis methods will be employed by coding and developing themes. Themes will be derived from the research questions and contents in the included papers.ResultsThe results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews) reporting guidelines. We anticipate finalizing the manuscript for this work in 2021.ConclusionsWe believe comprehensive information about the FAIR data principles, initiatives, implementation practices, and lessons learned in the FAIRification process in the health domain is paramount to supporting both evidence-based clinical practice and research transparency in the era of big data and open research publishing.International Registered Report Identifier (IRRID)PRR1-10.2196/22505