Findable, Accessible, Interoperable, Reusable Principles Research Articles

FAIR environmental and health registry (FAIREHR)- supporting the science to policy interface and life science research, development and innovation.

The environmental impact on health is an inevitable by-product of human activity. Environmental health sciences is a multidisciplinary field addressing complex issues on how people are exposed to hazardous chemicals that can potentially affect adversely the health of present and future generations. Exposure sciences and environmental epidemiology are becoming increasingly data-driven and their efficiency and effectiveness can significantly improve by implementing the FAIR (findable, accessible, interoperable, reusable) principles for scientific data management and stewardship. This will enable data integration, interoperability and (re)use while also facilitating the use of new and powerful analytical tools such as artificial intelligence and machine learning in the benefit of public health policy, and research, development and innovation (RDI). Early research planning is critical to ensuring data is FAIR at the outset. This entails a well-informed and planned strategy concerning the identification of appropriate data and metadata to be gathered, along with established procedures for their collection, documentation, and management. Furthermore, suitable approaches must be implemented to evaluate and ensure the quality of the data. Therefore, the 'Europe Regional Chapter of the International Society of Exposure Science' (ISES Europe) human biomonitoring working group (ISES Europe HBM WG) proposes the development of a FAIR Environment and health registry (FAIREHR) (hereafter FAIREHR). FAIR Environment and health registry offers preregistration of studies on exposure sciences and environmental epidemiology using HBM (as a starting point) across all areas of environmental and occupational health globally. The registry is proposed to receive a dedicated web-based interface, to be electronically searchable and to be available to all relevant data providers, users and stakeholders. Planned Human biomonitoring studies would ideally be registered before formal recruitment of study participants. The resulting FAIREHR would contain public records of metadata such as study design, data management, an audit trail of major changes to planned methods, details of when the study will be completed, and links to resulting publications and data repositories when provided by the authors. The FAIREHR would function as an integrated platform designed to cater to the needs of scientists, companies, publishers, and policymakers by providing user-friendly features. The implementation of FAIREHR is expected to yield significant benefits in terms of enabling more effective utilization of human biomonitoring (HBM) data.

BIOREALM—An ontology of comparative biogeography: New insights into the semantics of biodiversity conservation

AbstractAimWe aimed to apply ontological techniques to address semantic ambiguities in protected area and conservation informatics. By doing so, we aimed to create a coherent, machine‐actionable semantic representation of the biogeographic areas (which often overlap protected areas) to support more efficient and standardized informatics, supporting research and decision‐making. We present BIOREALM, the first informatic ontology for comparative biogeography.LocationGlobal.TaxonAny taxon can be integrated in BIOREALM.MethodsWe convert a cladogram of biogeographic areas—generated by a process known as bioregionalization—into a series of ontological classes. Areas of endemism are treated as formal objects related by hierarchical relationships and constrained by a condition of monophyly. We use semantic web approaches to extend the Environment Ontology (ENVO) with classes for (often semantically confounded) biogeographic entities, including biogeographic areas, areas of endemism and endemic areas. We applied this approach to a bioregionalization of Australia as a case study. In all, 20 subregions which are part of the Austral Bioregionalisation Atlas have been selected for the study and integrated in BIOREALM.ResultsWe have created an ontology—formatted in the Web Ontology Language and adhering to the practices of the Open Biomedical and Biological Ontology Foundry—which provides a rigorous, extensible and machine‐actionable framework that can improve biogeographic analyses and interoperability between systems. One main class and 20 individuals per class were implemented.Main ConclusionsBIOREALM encodes a model‐theoretic view of endemism using semantic web approaches, offering new avenues to express and analyse biogeographic units. This approach offers a means to identify monophyletic biogeographic areas for conservation, based on specific combinations of monophyletic endemic taxa. Such an ontology provides knowledge representation solutions which supports interoperability along the FAIR (Findable, Accessible, Interoperable, Reusable) principles, thus fostering more consistent ecological informatics.

Sharing sensitive research data in the practice of personalised medicine

Fragmentation of health data and biomedical research data is a major obstacle for precision medicine based on data-driven decisions. The development of personalized medicine requires the efficient exploitation of health data resources that are extraordinary in size and complexity, but highly fragmented, as well as technologies that enable data sharing across institutions and even borders. Biobanks are both sample archives and data integration centers. The analysis of large biobank data warehouses in federated datasets promises to yield conclusions with higher statistical power. A prerequisite for data sharing is harmonization, i.e., the mapping of the unique clinical and molecular characteristics of samples into a unified data model and standard codes. These databases, which are aligned to a common schema, then make healthcare information available for privacy-preserving federated data sharing and learning. The re-evaluation of sensitive health data is inconceivable without the protection of privacy, the legal and conceptual framework for which is set out in the GDPR (General Data Protection Regulation) and the FAIR (findable, accessible, interoperable, reusable) principles. For biobanks in Europe, the BBMRI-ERIC (Biobanking and Biomolecular Research Infrastructure - European Research Infrastructure Consortium) research infrastructure develops common guidelines, which the Hungarian BBMRI Node joined in 2021. As the first step, a federation of biobanks can connect fragmented datasets, providing high-quality data sets motivated by multiple research goals. Extending the approach to real-word data could also allow for higher level evaluation of data generated in the real world of patient care, and thus take the evidence generated in clinical trials within a rigorous framework to a new level. In this publication, we present the potential of federated data sharing in the context of the Semmelweis University Biobanks joint project. Orv Hetil. 2023; 164(21): 811-819.

How data governance principles influence participation in biodiversity science

ABSTRACT Biodiversity science is in a pivotal period when diverse groups of actors – including researchers, businesses, national governments, and Indigenous Peoples – are negotiating wide-ranging norms for governing and managing biodiversity data in digital repositories. The management of these repositories, often called biodiversity data portals, can serve either to redress or to perpetuate the colonial history of biodiversity science and current inequities. Both researchers and Indigenous Peoples are implementing new strategies to influence whom biodiversity data portals recognise as salient participants in data management and use. Two notable efforts are the FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective benefit, Authority, Responsibility, Ethics) Data Principles. Actors use these principles to influence the governance of biodiversity data portals. ‘Fit-for-use’ data is a social status provided by groups of actors who approve whether the data meets specific purposes. Advocates for the FAIR and CARE Principles use them in a similar way to institutionalise the authority of different groups of actors. However, the FAIR Principles prioritise the ability of machine agents to understand the meanings of data, while the CARE Principles prioritise Indigenous Peoples and their data sovereignty. Together, FAIR and CARE illustrate a broader emerging strategy for institutionalising international norms for digital repositories about who they should recognise as having a formal role in determinations of the fitness-for-use of data.

FAIRness through automation: development of an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital

BackgroundSecondary use of routine medical data is key to large-scale clinical and health services research. In a maximum care hospital, the volume of data generated exceeds the limits of big data on a daily basis. This so-called “real world data” are essential to complement knowledge and results from clinical trials. Furthermore, big data may help in establishing precision medicine. However, manual data extraction and annotation workflows to transfer routine data into research data would be complex and inefficient. Generally, best practices for managing research data focus on data output rather than the entire data journey from primary sources to analysis. To eventually make routinely collected data usable and available for research, many hurdles have to be overcome. In this work, we present the implementation of an automated framework for timely processing of clinical care data including free texts and genetic data (non-structured data) and centralized storage as Findable, Accessible, Interoperable, Reusable (FAIR) research data in a maximum care university hospital.MethodsWe identify data processing workflows necessary to operate a medical research data service unit in a maximum care hospital. We decompose structurally equal tasks into elementary sub-processes and propose a framework for general data processing. We base our processes on open-source software-components and, where necessary, custom-built generic tools.ResultsWe demonstrate the application of our proposed framework in practice by describing its use in our Medical Data Integration Center (MeDIC). Our microservices-based and fully open-source data processing automation framework incorporates a complete recording of data management and manipulation activities. The prototype implementation also includes a metadata schema for data provenance and a process validation concept. All requirements of a MeDIC are orchestrated within the proposed framework: Data input from many heterogeneous sources, pseudonymization and harmonization, integration in a data warehouse and finally possibilities for extraction or aggregation of data for research purposes according to data protection requirements.ConclusionThough the framework is not a panacea for bringing routine-based research data into compliance with FAIR principles, it provides a much-needed possibility to process data in a fully automated, traceable, and reproducible manner.

Machine-readable data and metadata of international key comparisons in radionuclide metrology

The digitalization of key comparisons in radionuclide metrology is being studied at the BIPM. Results of comparisons are made available on the BIPM KCDB platform where a PDF/A document can be downloaded. Access to the metadata contained in the PDF is not straightforward. Therefore, the XML and JSON versions of the report are now embedded in the PDF/A document, allowing direct access to the metadata. This achievement is a first step towards the implementation of FAIR (findable, accessible, interoperable, reusable) principles and does not affect the current policy of publishing comparison results. The combination of PDF/A and XML technologies seems to be an effective way to carry out digitalization. The strengths of both technologies can be brought together to meet the FAIR principles and ISO/IEC 17025 compliance. This approach benefits from robust data exchange features such as XML Schema and XML namespaces, as well as secure digital signatures and long-term archiving of PDF/A-3 documents.

Conceptual framework and documentation standards of cystoscopic media content for artificial intelligence

BackgroundThe clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine clinical practice. MethodsA conceptual framework was designed to document cystoscopy in a standardized manner with three major sections: data management, annotation management, and utilization management. A Swiss-cheese model was proposed for quality control and root cause analyses. We defined the infrastructure required to implement the framework with respect to FAIR (findable, accessible, interoperable, reusable) principles. We applied two scenarios exemplifying data sharing for research and educational projects to ensure compliance with FAIR principles. ResultsThe framework was successfully implemented while following FAIR principles. The cystoscopy atlas produced from the framework could be presented in an educational web portal; a total of 68 full-length qualitative videos and corresponding annotation data were sharable for artificial intelligence projects covering frame classification and segmentation problems at case, lesion, and frame levels. ConclusionOur study shows that the proposed framework facilitates the storage of visual documentation in a standardized manner and enables FAIR data for education and artificial intelligence research.

Making in silico predictive models for toxicology FAIR

In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) approaches to predict physico-chemical and ADME properties, toxicological effects and internal exposure. Such models are used to fill data gaps as part of chemical risk assessment. There is a growing need to ensure in silico predictive models for toxicology are available for use and that they are reproducible. This paper describes how the FAIR (Findable, Accessible, Interoperable, Reusable) principles, developed for data sharing, have been applied to in silico predictive models. In particular, this investigation has focussed on how the FAIR principles could be applied to improved regulatory acceptance of predictions from such models. Eighteen principles have been developed that cover all aspects of FAIR. It is intended that FAIRification of in silico predictive models for toxicology will increase their use and acceptance.

MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry

Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC–MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS2) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS2 spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and maw-py. The source code and documentation are available on GitHub (https://github.com/zmahnoor14/MAW). The performance of MAW is evaluated on two case studies. MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. Taken together, MAW could greatly facilitate automated metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

Hyperspectral reflectance dataset of pristine, weathered, and biofouled plastics

Abstract. This work presents a hyperspectral reflectance dataset of macroplastic samples acquired using Analytical Spectral Devices (ASD) FieldSpec 4. Samples analyzed consisted of pristine, artificially weathered, and biofouled plastic items and plastic debris samples collected in the docks of the Port of Antwerp and in the river Scheldt near Temse Bridge (Belgium). The hyperspectral signal of each sample was measured in controlled dry conditions in an optical calibration facility at the Vlaamse Instelling voor Technologisch Onderzoek (VITO; Flemish Institute for Technological Research) and, for a subset of plastics, under wet and submerged conditions in a silo tank at Flanders Hydraulics. The wet and submerged hyperspectral signals were measured in a mesocosm setting that mimicked environmentally relevant concentrations of freshwater microalgae and suspended sediment. The ASD was equipped with an 8∘ field of view at the calibration facility, and a 1∘ field of view was used in the mesocosm setting. The dataset obtained complies with the FAIR (Findable, Accessible, Interoperable, Reusable) principles and is available in the open-access repository Marine Data Archive (https://doi.org/10.14284/530, Leone et al., 2021).

Patpat: a public proteomics dataset search framework.

As the FAIR (Findable, Accessible, Interoperable, Reusable) principles have become widely accepted in the proteomics field, under the guidance of ProteomeXchange and The Human Proteome Organization Proteomics Standards Initiative, proteomics public databases have been providing Application Programming Interfaces for programmatic access. Based on generating logic from proteomics data, we present Patpat, an extensible framework for searching public datasets, merging results from multiple databases to help researchers find their proteins of interest in the vast mass spectrometry. Patpat's 2D strategy of combining results from multiple databases allows users to provide only protein identifiers to obtain metadata for relevant datasets, improving the 'Findable' of proteomics data. The Patpat framework is released under the Apache 2.0 license open source, and the source code is stored on GitHub (https://github.com/henry-leo/Patpat) and is freely available. Supplementary data are available at Bioinformatics online.

Using Ontologies to Create Machine-Actionable Datasets: Two Case Studies

Achieving the highest levels of compliance with the FAIR (findable, accessible, interoperable, reusable) principles for scientific data management and stewardship requires machine-actionable semantic representations of data and metadata. Human and machine interpretation and reuse of measurement datasets rely on metrological information that is often specified inconsistently or cannot be inferred automatically, while several ontologies to capture the metrological information are available, practical implementation examples are few. This work aims to close this gap by discussing how standardised measurement data and metadata could be presented using semantic web technologies. The examples provided in this paper are machine-actionable descriptions of Earth observation and bathymetry measurement datasets, based on two ontologies of quantities and units of measurement selected for their prominence in the semantic web. The selected ontologies demonstrated a good coverage of the concepts related to quantities, dimensions, and individual units as well as systems of units, but showed variations and gaps in the coverage, completeness and traceability of other metrology concept representations such as standard uncertainty, expanded uncertainty, combined uncertainty, coverage factor, probability distribution, etc. These results highlight the need for both (I) user-friendly tools for semantic representations of measurement datasets and (II) the establishment of good practices within each scientific community. Further work will consequently investigate how to support ontology modelling for measurement uncertainty and associated concepts.

Science Gateways and the Humanities: An Exploratory Study of Their Rare Partnership

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Researchers and educators in humanities such as computational linguists, digital humanists, and those doing historical reconstructions are increasingly heavy users of computational and/or data resources. Many know about activities, working groups, and initiatives around the FAIR (Findable, Accessible, Interoperable, Reusable) principles and are a driving force for improving the sharing of data and software. However, it seems humanities researchers are less aware of the science gateways community and the end-to-end solutions that science gateways could provide, therefore lacking a driving force for adoption of this technology. Some may be creating their own gateways outside the community; others may wish to use computational and data infrastructures but may perceive a lack of support or opportunities. Hypotheses about the reasons that humanities are not well represented as gateways builders and users include lack of funding and support by computer centers. This small-scale exploratory study will clarify some of the challenges and needs faced by computational researchers in the humanities that may explain their relatively low participation in the science gateways community. For this paper, we present the results of 19 interviews with 7 women, 11 men, and 1 non-binary individual from seven states and DC in the US with a range of professional backgrounds and roles.</i>

FAIR data station for lightweight metadata management and validation of omics studies.

The life sciences are one of the biggest suppliers of scientific data. Reusing and connecting these data can uncover hidden insights and lead to new concepts. Efficient reuse of these datasets is strongly promoted when they are interlinked with a sufficient amount of machine-actionable metadata. While the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles have been accepted by all stakeholders, in practice, there are only a limited number of easy-to-adopt implementations available that fulfill the needs of data producers. We developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It implements the ISA metadata framework and uses minimal information metadata standards to capture experiment metadata. The FAIR Data Station consists of 3 modules. Based on the minimal information model(s) selected by the user, the "form generation module" creates a metadata template Excel workbook with a header row of machine-actionable attribute names. The Excel workbook is subsequently used by the data producer(s) as a familiar environment for sample metadata registration. At any point during this process, the format of the recorded values can be checked using the "validation module." Finally, the "resource module" can be used to convert the set of metadata recorded in the Excel workbook in RDF format, enabling (cross-project) (meta)data searches and, for publishing of sequence data, in an European Nucleotide Archive-compatible XML metadata file. Turning FAIR into reality requires the availability of easy-to-adopt data FAIRification workflows that are also of direct use for data producers. As such, the FAIR Data Station provides, in addition to the means to correctly FAIRify (omics) data, the means to build searchable metadata databases of similar projects and can assist in ENA metadata submission of sequence data. The FAIR Data Station is available at https://fairbydesign.nl.

First steps towards FAIRization of product-focused sensory data

The emergence of open science makes it necessary to formalize data and knowledge in a way that adheres to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. The objective of this work was to lay the foundations for such a FAIRization of sensory data. After an explanation of the rationale, the main characteristics of sensory evaluation methods were identified, and a data-centric typology of the different types of measures was proposed. This typology enabled us to classify the sensory evaluation methods regardless of whether they were used in “sensory only” or “consumer-centric product-focused” research. To complete this conceptual typology, minimal information standards have been defined to help in the reporting and interpreting of sensory evaluation data. These standards include a data format and metadata related to sensory evaluation measures. Based on the typology and metadata, several aspects related to sensory data aggregation were discussed, and examples were presented to demonstrate the interest of such a grouping of datasets. The outputs of this work were intended to be implemented in the “sensory evaluation measures” branch of TransformON, a food ontology. Thus, this work opens up new perspectives related to the use of ontologies for data-driven research related to food sustainability. Moreover, it invites the sensory science community to adopt a broader perspective regarding sensory measures.

FAIR Points: From sdo:LearningResource to FAIR Digital Object

The FAIRPoints organization, co-founded by the authors, aims to provide a platform for conversations to take place around realistic and pragmatic implementations of the FAIR (Findable, Accessible, Interoperable, Reusable) principles. The uniqueness of the FAIRPoints effort stems from an additional aim: to capture conversation contributions in the form of “bite-sized” objects – “points” – in a way that facilitates dynamic composition by instructors for the delivery of audience-customized training experiences. Thus, FAIRPoints aims to cultivate pragmatic learning resources to help realize the FAIR principles in practice, both through inviting speakers to prime and lead discussions focused on choices/challenges regarding FAIR, and amplifying downstream value potential by serializing “points” made during such events as FAIR resources. inviting speakers to prime and lead discussions focused on choices/challenges regarding FAIR, and amplifying downstream value potential by serializing “points” made during such events as FAIR resources. Currently, event outcomes are serialized as LearningResource-typed JSON-LD objects in the schema.org sense, i.e. sdo:LearningResource, where @prefix sdo: &amp;lt;https://schema.org/&amp;gt; ., and conform to the bioschemas.org TrainingMaterial profile. However, any differences in participant perspectives must be reconciled, via git revision control, towards a single “view” of a sdo:LearningResource. This situation is at odds with other explicit aims of the FAIRPoints organization such as including diverse voices and collecting heterogeneous input from a global perspective. Using the FAIR Digital Object (FDO) approach, a FAIRPoints sdo:LearningResource instance may be the Object to which an Identifier points, through an FDO Identifier Record, and sdo:LearningResource may be the FDO Type. Crucially, there may be a multiplicity of Metadata records pointed to by an FDO Identifier Record and thus a formal mechanism to cultivate and publish diverse perspectives. This presentation will outline FAIRPoints’ approach to FDO implementation for learning resources and its relation to published practice. Specifically, in relation to the FAIR Digital Twins approach*1, our approach may be seen as the stewardship of a “fluid graph” of learning-resource “knowlets” with support for “qua” projection in service of e.g. an instructor’s dynamic composition of training material for a targeted workshop.

Towards an extensible FAIRness assessment of FAIR Digital Objects

The objective of the FAIR Digital Objects Framework (FDOF) is for objects published in a digital environment to comply with a set of requirements, such as identifiability, and the use of a rich metadata record (Santos 2021, Schultes and Wittenburg 2019, Schwardmann 2020). With the increasing prevalence of the FAIR (Findable, Accessible, Interoperable, Reusable) principles, and FAIR Digital Objects (FDO), used within different communities and domains (Wise et al. 2019), there will be a need to evaluate whether a FDO meets the requirements of the ecosystem in which it is used. Without a dedicated framework, communities will develop isolated assessment systems from the ground up (Sun et al. 2022, Bahim et al. 2020), which will cost them time, and lead to FAIRness assessments with limited interoperability and comparability. Previous work from the FAIR Metrics working group defined a framework for deploying individual FAIR metrics tests as separate services endpoints (Wilkinson et al. 2018, Wilkinson et al. 2019). To work in accordance with this framework, each test should take a subject URL as input, and return a score, either 0 or 1, a test version, and the test execution logs. A central service can then be used to assess the FAIRness of digital objects using collections of individual assessments. Such a framework could be easily extended, but there are currently no guidelines or tools to implement and publish new FAIRness assessments complying with this framework. To amend this problem, we published the fair-test library in python and its documentation, which help with developing and deploying individual FAIRness assessments. With this library, developers define their metric tests using custom python objects, which will guide them to provide all required metadata for their test as attributes, and implement the test evaluation logic as a function. The library also provides additional helper functions for common tasks, such as retrieving metadata from a URL, or testing a metric test. These tests can then be deployed as a web API, and registered in a central FAIR evaluation service supporting the FAIR metrics working group framework, such as FAIR enough or the FAIR evaluator. Finally, users of the evaluation services will be able to group the registered metrics tests in collections used to assess the quality of publicly available digital objects. There are currently as many as 47 tests that have been defined to assess compliance with various FAIR metrics, from which 25 have been defined using the fair-test library, including assessing if the identifier used is persistent, or if the metadata record attached to a digital object complies with a specific schema. This presentation introduces a user-friendly and extensible tool, which can assess whether specific requirements are met for a digital resource. Our contributions are: Developing and publishing the fair-test library to make the development and deployment of independent FAIRness assessment tests easier. Developing and publishing tests in python for existing FAIR metrics: 23 generic tests covering most of the FAIR metrics, and 2 domain-specific tests for the Rare Disease research community. Developing and publishing the fair-test library to make the development and deployment of independent FAIRness assessment tests easier. Developing and publishing tests in python for existing FAIR metrics: 23 generic tests covering most of the FAIR metrics, and 2 domain-specific tests for the Rare Disease research community. We aim to engage with the FDO community to explore potential use-cases for an extensible tool to evaluate FDOs, and discuss their expectations related to the evaluation of digital objects. Insights and guidelines from the FDO community would contribute to further improving the fair-test ecosystem. Among improvements that are currently being under consideration, we can cite improving the collaborative aspect of metadata extraction, or adding new metadata to be returned by the tests.

Open Science for Better FAIRness: A biodiversity virtual research environment point of view

"FAIR (Findable, Accessible, Interoperable, Reusable) principles" (Wilkinson et al. 2016) and "open science" are two complementary movements in biodiversity science. Although we need to transition to making scientific data and associated material more FAIR, this does not necessarily imply open data or open source algorithms. Here, based on the experience of the French Biodiversity Data Hub ("Pôle national de données de Biodiversité" - PNDB), which is an e-infrastructure for and by researchers, we want to showcase how focusing on openness can be a strategy to efficiently reach greater FAIRness. Following DataOne guidance, we can build a complete data/metadata ecosystem allowing us to structure heterogeneous environmental information systems. Using the Galaxy analysis platform and its related initiatives (Galaxy training network, European Erasmus+ Gallantries project, bioconda, bioContainer), we can thus illustrate how we can create transparent, peer-reviewed and accessible tools and workflows and collaborative training material. The Galaxy platform also facilitates use of high performance computing infrastructure through notably the European Open Science Cloud marketplace. Finally, through our experiences contributing to open source projects like EML (Ecological Metadata Language (Michener et al. 1997)) Assembly Line, EDI (Environmental Data Initiative, or PAMPA (Indicators of Marine Protected Areas performance for managing coastal ecosystems, resources and their uses), a French platform to help protected areas managers to standardize and analyse their data, we also show how building open source "doors" through the R Shiny programming language to these environments can be beneficial for all.

Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey.

Background: Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. Methods: An online survey was conducted with a mixed question-type design. We created a questionnaire tailored to relevant topics of the bioimaging community, including specific questions on bioimaging methods and bioimage analysis, as well as more general questions on RDM principles and tools. 203 survey entries were included in the analysis covering the perspectives from various life and biomedical science disciplines and from participants at different career levels. Results: The results highlight the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. The survey participants request metadata guidelines and annotation tools and endorse the usage of image data management platforms. At present, OMERO (Open Microscopy Environment Remote Objects) is the best known and most widely used platform. Most respondents rely on image processing and analysis, which they regard as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans is limited, respondents acknowledge their potential value for data handling and publication. Conclusion: The bioimaging community acknowledges and endorses the value of RDM and data sharing. Still, there is a need for information, guidance, and standardization to foster the adoption of FAIR data handling. This survey may help inspiring targeted measures to close this gap.

OSGEO, PERSISTENT IDENTIFIERS AND THE SHAPE OF THINGS TO COME

Abstract. The Open Source Geospatial Foundation (OSGeo) is an international community, which maintains and develops a growing number of quality-assured open source geospatial software tools. OSGeo supports Open Science and the FAIR (Findable, Accessible, Interoperable, Reusable) principles. The open source software projects of OSGeo are drivers for innovation, but also benefit from technical advances made in other fields to foster FAIR. We report on the of adoption of persistent identifiers (PID) by OSGeo communities, which is accelerating and diversifying. Persistent referencing by PID was introduced to OSGeo for scientific video in 2014. Since 2017 it is being adopted by a growing number of OSGeo software projects to enable scientific citation of software repositories, individuals software releases and involved persons. This enables tangible scientific credit for OSGeo volunteer work and is expected to affect OSGeo workflows, such as the incubation and graduation process. For illustration, examples from the OSGeo projects GRASS GIS and actinia are provided.