Findable, Accessible, Interoperable And Reusable Principles Research Articles

One Digital Health for more FAIRness.

One Digital Health (ODH) aims to propose a framework that merges One Health's and Digital Health's specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or, in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention. This paper aims to elicit how the ODH framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup. An integrative analysis of the literature was conducted to extract instances of the need-or of the eventual already existing deployment-of FAIR principles, for each of the three layers (keys, perspectives and dimensions) of the ODH framework. The scope was to assess the extent of scatteredness in pursuing the many facets of FAIRness, descending from the lack of a unifying and balanced framework. A first attempt to interpret the different technological components existing in the different layers of the ODH framework, in the light of the FAIR principles, was conducted. Although the mature and working examples of workflows for data FAIRification processes currently retrievable in the literature provided a robust ground to work on, a nonsuitable capacity to fully assess FAIR aspects for highly interconnected scenarios, which the ODH-based ones are, has emerged. Rooms for improvement are anyway possible to timely deal with all the underlying features of topics like the delivery of health care in a syndemic scenario, the digital transformation of human and animal health data, or the digital nature conservation through digital technology-based intervention. ODH pillars account for the availability (findability, accessibility) of human, animal, and environmental data allowing a unified understanding of complex interactions (interoperability) over time (reusability). A vision of integration between these two worlds, under the vest of ODH Interventions featuring FAIRness characteristics, toward the development of a systemic lookup of health and ecology in a digitalized way, is therefore auspicable.

FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy

A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale® system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.

Open science and Big Data in South Africa.

With the Square Kilometer Array (SKA) project and the new Multi-Purpose Reactor (MPR) soon coming on-line, South Africa and other collaborating countries in Africa will need to make the management, analysis, publication, and curation of "Big Scientific Data" a priority. In addition, the recent draft Open Science policy from the South African Department of Science and Innovation (DSI) requires both Open Access to scholarly publications and research outputs, and an Open Data policy that facilitates equal opportunity of access to research data. The policy also endorses the deposit, discovery and dissemination of data and metadata in a manner consistent with the FAIR principles - making data Findable, Accessible, Interoperable and Re-usable (FAIR). The challenge to achieve Open Science in Africa starts with open access for research publications and the provision of persistent links to the supporting data. With the deluge of research data expected from the new experimental facilities in South Africa, the problem of how to make such data FAIR takes center stage. One promising approach to make such scientific datasets more "Findable" and "Interoperable" is to rely on the Dataset representation of the Schema.org vocabulary which has been endorsed by all the major search engines. The approach adds some semantic markup to Web pages and makes scientific datasets more "Findable" by search engines. This paper does not address all aspects of the Open Science agenda but instead is focused on the management and analysis challenges of the "Big Scientific Data" that will be produced by the SKA project. The paper summarizes the role of the SKA Regional Centers (SRCs) and then discusses the goal of ensuring reproducibility for the SKA data products. Experiments at the new MPR neutron source will also have to conform to the DSI's Open Science policy. The Open Science and FAIR data practices used at the ISIS Neutron source at the Rutherford Appleton Laboratory in the UK are then briefly described. The paper concludes with some remarks about the important role of interdisciplinary teams of research software engineers, data engineers and research librarians in research data management.

Introducing the FAIR Principles for research software

Research software is a fundamental and vital part of research, yet significant challenges to discoverability, productivity, quality, reproducibility, and sustainability exist. Improving the practice of scholarship is a common goal of the open science, open source, and FAIR (Findable, Accessible, Interoperable and Reusable) communities and research software is now being understood as a type of digital object to which FAIR should be applied. This emergence reflects a maturation of the research community to better understand the crucial role of FAIR research software in maximising research value. The FAIR for Research Software (FAIR4RS) Working Group has adapted the FAIR Guiding Principles to create the FAIR Principles for Research Software (FAIR4RS Principles). The contents and context of the FAIR4RS Principles are summarised here to provide the basis for discussion of their adoption. Examples of implementation by organisations are provided to share information on how to maximise the value of research outputs, and to encourage others to amplify the importance and impact of this work.

Challenges for FAIR Digital Object Assessment

Challenges for FAIR Digital Object Assessment

How does software fit into the FDO landscape?

In academic research virtually every field has increased its use of digital and computational technology, leading to new scientific discoveries, and this trend is likely to continue. Reliable and efficient scholarly research requires researchers to be able to validate and extend previously generated research results. In the digital era, this implies that digital objectsKahn and Wilensky 2006 used in research should be Findable, Accessible, Interoperable and Reusable (FAIR). These objects include (but are not limited to) data, software, models (for example, machine learning), representations of physical objects, virtual research environments, workflows, etc. Leaving any of these digital objects out of the FAIR process may result in a loss of academic rigor and may have severe consequences in the long term for the field, such as a reproducibility crisis. In this extended abstract, we focus on research software as a FAIR digital object (FDO). The FDO framework De Smedt et al. 2020 describes FDOs as being actionable units of knowledge, which can be aggregated, analyzed, and processed by different types of algorithms. Such algorithms must be implemented by software in one form or another. The framework also describes large software stacks supporting FDOs enabling responsible data science and increasing reproducibility. This implies that software is a key ingredient of the FDO framework, and should adhere to the FAIR principles. Software plays multiple roles: it is a DO itself, it is responsible for creating new FDOs (e.g., data) and it helps to make them available to the public (e.g., via repositories and registries). However there is a need to specify in more detail how non-data DOs, in particular software, fit in this framework. Different classes of digital objects have different intrinsic properties and ways to relate to other DOs. This means that while they, in principle, are subject to the high-level FAIR principles, there are also differences depending on their type and properties, requiring an adaptation so FAIR implementations are more aligned to the digital object itself. This holds true in particular to software. Software has intrinsic properties (executability, composite nature, development practices, continuous evolution and versioning, and packaging and distribution) and specific needs that must be considered by the FDO framework. For example, open source software is typically developed in the open on social coding platforms, where releases are distributed through package management systems, unlike data that is typically published in archival repositories. These social coding platforms do not provide long term archiving, permanent identifiers, or metadata, and package management systems, while somewhat better, similarly do not make a commitment to long term archiving, do not use identifiers that fit the scholarly publication system well, and provide metadata that may be missing key elements. The FAIR for research software (FAIR4RS, Chue Hong et al. 2021) working group has dedicated significant effort in building a community consensus around developing FAIR principles that are customized for research software, providing methods for researchers to understand and address these gaps. In this presentation we will highlight the importance of software for the FAIR landscape and why different (but related) FAIR principles are needed for software (vs those originally developed for data). Our goal here is to contribute to building an FDO landscape together, where we consider all different types of digital objects that are essential in today's research, and we are enthusiastic about contributing our expertise on research software in helping shape this landscape.

A Novel Tightly Coupled Information System for Research Data Management

Most research projects are data driven. However, many organizations lack proper information systems (IS) for managing data, that is, planning, collecting, analyzing, storing, archiving, and sharing for use and re-use. Many research institutions have disparate and fragmented data that make it difficult to uphold the FAIR (findable, accessible, interoperable, and reusable) data management principles. At the same time, there is minimal practice of open and reproducible science. To solve these challenges, we designed and implemented an IS architecture for research data management. Through it, we have a centralized platform for research data management. The IS has several software components that are configured and unified to communicate and share data. The software components are, namely, common ontology, data management plan, data collectors, and the data warehouse. Results show that the IS components have gained global traction, 56.3% of the total web hits came from news users, and 259 projects had metadata (and 17 of those also had data resources). Moreover, the IS aligned the institution’s scientific data resources to universal standards such as the FAIR principles of data management and at the same time showcased open data, open science, and reproducible science. Ultimately, the architecture can be adopted by other organizations to manage research data.

FAIR Guidelines and Data Regulatory Framework for Digital Health in Nigeria

AbstractAdopting the FAIR Guidelines—that data should be Findable, Accessible, Interoperable and Reusable (FAIR)—in the health data system in Nigeria will help protect data against use by unauthorised parties, while also making data more accessible to legitimate users. However, little is known about the FAIR Guidelines and their compatibility with data and health laws and policies in Nigeria. This study assesses the governance framework for digital and health/eHealth policies in Nigeria and explores the possibility of a policy window opening for the FAIR Guidelines to be adopted and implemented in Nigeria's eHealth sector. Ten Nigerian policy documents were examined for mention of the FAIR Guidelines (or FAIR Equivalent terminology) and the 15 sub-criteria or facets. The analysis found that although the FAIR Guidelines are not explicitly mentioned, 70% of the documents contain FAIR Equivalent terminology. The Nigeria Data Protection Regulation contained the most FAIR Equivalent principles (73%) and some of the remaining nine documents also contained some FAIR Equivalent principles (between 0–60%). Accordingly, it can be concluded that a policy window is open for the FAIR Guidelines to be adopted and implemented in Nigeria's eHealth sector.

Harnessing risk-informed data for disaster and climate resilience

Disaster and climate risks result from a complex interaction between hazard, exposure, and vulnerability in a broad context defined by socioeconomic, political, and ecological factors. To better understand the risk and manage it more effectively, we need to collect, store, analyse, and use risk-informed data. We identified challenges and opportunities for harnessing risk-informed data for disaster and climate resilience. The framework is inspired by the FAIR (findable, accessible, interoperable and reusable) and CARE (collective, authority to control, responsibility and ethics) principles to discuss opportunities how data could be available to inform risk-informed decision-making in climate and disaster risk management. Looking ahead, data could be developed and integrated with societal needs and participation. The use of data for risk management necessitates a common definition of risk to ensure a comparable research and development process. The world is shifting from a “for-profit” to a “for-benefit” operating model, which needs a Fifth Industrial Revolution driven by and for data for the benefit of society.

Establishing Plankton Imagery Dataflows Towards International Biodiversity Data Aggregators

Over the last decade, plankton research has experienced extensive developments in automatic image acquisition for identifying and quantifying plankton species. This information is useful for the reporting of plankton occurrences and ecological data. Imagery instruments can vary in the way they sample (benchtop or in situ imagers) and the particle’s size range they target (see Lombard et al. (2019) for an extensive comparison of instruments and specifications). However, due to the wide variety of instruments and their (automatic) output data and formats, it is challenging to integrate datasets that originate from different sources. For this reason, we developed recommendations for plankton imagery data management, which can promote the ability to make these datasets as FAIR (Findable, Accessible, Interoperable and Reusable principles), as possible. The workflow presented here could inspire other Biodiversity Information Standards TDWG communities working with (automated) imagery data (e.g., camera traps) such as the Audubon Core and Machine Observations Interest Group. The recommended data format follows the OBIS-ENV-DATA format (De Pooter et al. 2017), a Darwin Core-based approach to standardise biodiversity data (Wieczorek et al. 2012) used in EurOBIS, the European node of the Ocean Biodiversity Information System (OBIS) and EMODnet Biology, the European Marine Biodiversity Data Network. However, this format does not include sufficient information for imagery data, therefore we propose the use of additional Darwin Core terms. For example, by including the terms identifiedBy, identificationVerificationStatus and identificationReferences in the Occurrence table, more clarity is reported regarding the uncertainty of the classification made by an algorithm. Thus, data providers can publish manually validated datasets or datasets produced by fully automated plankton identification workflows; and users can choose to use validated or not validated data. See in Suppl. material 1 a practical example on how to report an imagery dataset following the best practices. Moreover, the OBIS-ENV-DATA format allows the ingestion of additional information thanks to the use of the Darwin Core (DwC) Extended Measurement Or Facts or eMoF extension in the DwC Event core. The eMoF stores biotic, abiotic and sampling measurements and facts that are related to the Event and Occurrence table. An important aspect of this extension is that it includes standardised terms and controlled vocabularies, such as the British Oceanographic Data Centre (BODC) vocabularies, to standardise parameters that are not covered by DwC. The advantages of these is to unambiguously report information and to include those measurements that cannot be reported in the Event and Occurrence table (e.g., reporting abundance or biomass of plankton), and that are crucial to investigate ecosystem functioning questions. As a consequence, biodiversity data aggregators can extend their scope beyond species occurrence data. Fig. 1 summarises a typical dataflow that goes from imagery data acquisition to publication in several steps: Images are cropped and classified with software. This can be done in EcoTaxa, a web application that allows users to taxonomically classify images of individual organisms. Data is formatted in OBIS-ENV-DATA format. This format can be exported from EcoTaxa through its API. Data is submitted to EurOBIS via the IPT (Integrated Publishing Toolkit). Data is quality controlled by the BioCheck tool. Data in EurOBIS can flow to EMODnet Biology, OBIS and GBIF (Global Biodiversity Information Facility). Images are cropped and classified with software. This can be done in EcoTaxa, a web application that allows users to taxonomically classify images of individual organisms. Data is formatted in OBIS-ENV-DATA format. This format can be exported from EcoTaxa through its API. Data is submitted to EurOBIS via the IPT (Integrated Publishing Toolkit). Data is quality controlled by the BioCheck tool. Data in EurOBIS can flow to EMODnet Biology, OBIS and GBIF (Global Biodiversity Information Facility). Plankton imagery instrument operators now have the possibility to format their data following the best practices and recommendations for plankton imagery data management (Martin-Cabrera et al. 2022). After a dataset is formatted following these guidelines, it can be submitted to the international biodiversity data aggregators, EurOBIS, EMODnet Biology and GBIF. Additionally a (semi) automated dataflow is presented where data providers can classify images in EcoTaxa and export the data in the required formats using an API before submission to EurOBIS. The next steps are to disseminate these best practices, encouraging plankton imagery data generators to implement these workflows to share their data easily, enriching these data portals and encouraging cross collaborations to create data products covering broader geographic scales and plankton species.

Biodiversity Informatics: A success story for the long-term financial sustainability of the European Open Science Cloud (EOSC)

The European Open Science Cloud's (EOSC) ambition to provide European researchers, innovators, companies, and citizens with a federated and open multi-disciplinary environment where they can publish, find and reuse data, tools and services for research, innovation, and educational purposes. To enable this, EOSC promotes the use of the FAIR (Findable, Accessible, Interoperable and Re-usable) principles. The EOSC Sustainability Working Group under the EOSC Secretariat published the FAIR Lady Report (European Commission, Directorate-General for Research and Innovation, 2020), formulating recommendations for sustainability in the form of a Minimal Viable EOSC (MVE) including criteria for its sustainable implementation, formed by three essential components: EOSC Core, containing the minimum set of components necessary to provide researchers with the means to discover, share, access and reuse data and services; ESOC Exchange, a marketplace built on top of the EOSC Core to extend the set of public and private providers, and EOSC Data federation. EOSC Core, containing the minimum set of components necessary to provide researchers with the means to discover, share, access and reuse data and services; ESOC Exchange, a marketplace built on top of the EOSC Core to extend the set of public and private providers, and EOSC Data federation. The EOSC Association was established in July 2020 as the legal entity to govern EOSC. The Association appointed several Advisory Groups to provide recommendations towards the implementation of EOSC, including two Task Forces on Long Term Data Preservation and on the Financial Sustainability for Sustaining EOSC. This presentation will focus on the Financial Sustainability Task Force. The Task Force formed three "Working Streams" following the recommendation of the FAIR Lady Report to tackle the EOSC Core, Exchange, and Data Federation in smaller groups. Within the Data Federation Working Stream, the experts have identified many thematic data federations that have exisisted for quite some time, notably in environmental sciences, biodiversity, and social sciences. The case of Biodiversity Informatics is presented here as one of these sucess stories, retracing the history of major data federations such as GBIF (Global Biodiversity Information Facility), the CoL (Catalogue of Life), BHL (Biodiversity Hertiage Library) and how they managed to move from community-driven initiatitves to structurally funded research infrastructures supported by national and international governmental authorities with a long term perspective, leading to the establishment of infrastructures like the Distributed System of Scientific Collections (DiSSCo) and LifeWatch. This presentation will also address the essential contributions of associations like TDWG (Biodiversity Information Standards) and CETAF (Consortium of European Taxonomic Facilities) to achieving long-term sustainability and on how to successfully obtain complementary funds from EU projects in Horizon 2020 and its successor, Horizon Europe, with programs such as Synthesis of Systematic Resources (SYNTHESYS), DiSSCo Prepare, Biodiversity Community Integrated Knowledge Library (BiCiKL), and Transforming European Taxonomy through Training, Research, and Innovations (TETTRIs), to name a few.

(Invited) Combining Machine Learning, DFT, EFM, and Modeling to Design Nanodielectric Behavior

Polymer Nanodielectrics are class of materials with intriguing combinations of properties. Predicting and designing the properties, however, is complex due to the number of parameters controlling the properties. This makes it difficult to compare results across groups, validate models, and develop a design methodology. This presentation will share a recent approach to developing a data driven design methodology grounded in physics-based models and experimental calibration. We combine finite element modeling of dielectric constant and loss functions with a Monte Carlo multi-scale simulation of carrier hopping to predict break down strength. Filler dispersion, filler geometry, isotropy and interface properties are explicitly taken into to account to compute objective functions for ideal nanodielectric insulators. Further, we have used machine learning to develop an EFM technique for directly measuring the dielectric constant of the nanoscale interfacial region as critical input to our models. Ultimately, we calculate the Pareto frontier with respect to nanocomposite constitute properties and geometry to optimize properties.The finite element approach can be used to forward predict properties based on the properties of the interfacial region and filler dispersion, or as an inverse tool to calculate interface properties or develop optimized filler morphologies. The breakdown strength model critically depends on the energy distribution of trap states that inhibit space charge motion. We have developed an ab initio approach to determine the trap states at amorphous interfaces, and have used that to do a systematic analyses of trap distributions in composites with functionalized particle interfaces. The models all use 3D particle distributions based on 2D imaging (TEM) of the composites and publicly available tools for binarization and characterization of filler morphology. Finally, we use a design of experiment approach (Latin Hybercube Design) to sample the complete experimental design space, and using calibrated models and morphologies, create a data set spanning the full set of parameters. We use the data from the DOE to train a Gaussian Process metamodel and a genetic algorithm to determine which designs fulfill the design parameters. The talk will present several case studies as examples.The data for this work was accessed from a new data resource, MaterialsMine, using FAIR (Findable, Accessible, Interoperable, and Reusable) principles. MaterialsMine is an open source data repository, and resource. It includes unique visualization tools as well as modeling and characterization resources.

Development and Governance of FAIR Thresholds for a Data Federation

The FAIR (findable, accessible, interoperable, and re-usable) principles and practice recommendations provide high level guidance and recommendations that are not research-domain specific in nature. There remains a gap in practice at the data provider and domain scientist level demonstrating how the FAIR principles can be applied beyond a set of generalist guidelines to meet the needs of a specific domain community. We present our insights developing FAIR thresholds in a domain specific context for self-governance by a community (agricultural research). ‘Minimum thresholds’ for FAIR data are required to align expectations for data delivered from providers’ distributed data stores through a community-governed federation (the Agricultural Research Federation, AgReFed). Data providers were supported to make data holdings more FAIR. There was a range of different FAIR starting points, organisational goals, and end user needs, solutions, and capabilities. This informed the distilling of a set of FAIR criteria ranging from ‘Minimum thresholds’ to ‘Stretch targets’. These were operationalised through consensus into a framework for governance and implementation by the agricultural research domain community. Improving the FAIR maturity of data took resourcing and incentive to do so, highlighting the challenge for data federations to generate value whilst reducing costs of participation. Our experience showed a role for supporting collective advocacy, relationship brokering, tailored support, and low-bar tooling access particularly across the areas of data structure, access and semantics that were challenging to domain researchers. Active democratic participation supported by a governance framework like AgReFed’s will ensure participants have a say in how federations can deliver individual and collective benefits for members.

A catalogue with semantic annotations makes multilabel datasets FAIR

Multilabel classification (MLC) is a machine learning task where the goal is to learn to label an example with multiple labels simultaneously. It receives increasing interest from the machine learning community, as evidenced by the increasing number of papers and methods that appear in the literature. Hence, ensuring proper, correct, robust, and trustworthy benchmarking is of utmost importance for the further development of the field. We believe that this can be achieved by adhering to the recently emerged data management standards, such as the FAIR (Findable, Accessible, Interoperable, and Reusable) and TRUST (Transparency, Responsibility, User focus, Sustainability, and Technology) principles. We introduce an ontology-based online catalogue of MLC datasets originating from various application domains following these principles. The catalogue extensively describes many MLC datasets with comprehensible meta-features, MLC-specific semantic descriptions, and different data provenance information. The MLC data catalogue is available at: http://semantichub.ijs.si/MLCdatasets.

Towards a Digital Twin of the Earth System: Geo-Soft-CoRe, a Geoscientific Software & Code Repository

The immense advances in computer power achieved in the last decades have had a significant impact in Earth science, providing valuable research outputs that allow the simulation of complex natural processes and systems, and generating improved forecasts. The development and implementation of innovative geoscientific software is currently evolving towards a sustainable and efficient development by integrating models of different aspects of the Earth system. This will set the foundation for a future digital twin of the Earth. The codification and update of this software require great effort from research groups and therefore, it needs to be preserved for its reuse by future generations of geoscientists. Here, we report on Geo-Soft-CoRe, a Geoscientific Software & Code Repository, hosted at the archive DIGITAL.CSIC. This is an open source, multidisciplinary and multiscale collection of software and code developed to analyze different aspects of the Earth system, encompassing tools to: 1) analyze climate variability; 2) assess hazards, and 3) characterize the structure and dynamics of the solid Earth. Due to the broad range of applications of these software packages, this collection is useful not only for basic research in Earth science, but also for applied research and educational purposes, reducing the gap between the geosciences and the society. By providing each software and code with a permanent identifier (DOI), we ensure its self-sustainability and accomplish the FAIR (Findable, Accessible, Interoperable and Reusable) principles. Therefore, we aim for a more transparent science, transferring knowledge in an easier way to the geoscience community, and encouraging an integrated use of computational infrastructure.Systematic Review Registration: https://digital.csic.es/handle/10261/193580.

Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic

BackgroundThe COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data ‘silos’ that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR.ResultsIn this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors’ research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital.ConclusionsOur work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.

A metadata schema for lattice thermal conductivity from first-principles calculations

Materials genome engineering databases represent fundamental infrastructures for data-driven materials design, in which the data resources should satisfy the FAIR (Findable, Accessible, Interoperable and Reusable) principles. However, a variety of challenges, such as data standardization, veracity and longevity, still impede the progress of data-driven materials science, including both high-throughput experiments and simulations. In this work, we propose a metadata schema for lattice thermal conductivity from first-principles calculations. The calculation workflow for lattice thermal conductivity includes structural optimization and the calculation of interatomic force constants and lattice thermal conductivity. The data generated during the calculation process corresponds to the virtual sample information, virtual source data and processed data, respectively, as specified in the General rule for materials genome engineering data of the Chinese Society for Testing and Materials. Following this general rule, the metadata structure and schema for each action are systematically defined and all metadata elements can be collected completely. Although this metadata schema is specific to lattice thermal conductivity calculations, it provides general rules and insights for other computational materials data in materials genome engineering.

New system for archiving integrative structures.

Structures of many complex biological assemblies are increasingly determined using integrative approaches, in which data from multiple experimental methods are combined. A standalone system, called PDB-Dev, has been developed for archiving integrative structures and making them publicly available. Here, the data standards and software tools that support PDB-Dev are described along with the new and updated components of the PDB-Dev data-collection, processing and archiving infrastructure. Following the FAIR (Findable, Accessible, Interoperable and Reusable) principles, PDB-Dev ensures that the results of integrative structure determinations are freely accessible to everyone.

FAIRification of nanosafety data to improve applicability of (Q)SAR approaches: A case study on in vitro Comet assay genotoxicity data

(Quantitative) structure-activity relationship ([Q]SAR) methodologies are widely applied to predict the (eco)toxicological effects of chemicals, and their use is envisaged in different regulatory frameworks for filling data gaps of untested substances. However, their application to the risk assessment of nanomaterials is still limited, also due to the scarcity of large and curated experimental datasets. Despite a great amount of nanosafety data having been produced over the last decade in international collaborative initiatives, their interpretation, integration and reuse has been hampered by several obstacles, such as poorly described (meta)data, non-standard terminology, lack of harmonized reporting formats and criteria.Recently, the FAIR (Findable, Accessible, Interoperable, and Reusable) principles have been established to guide the scientific community in good data management and stewardship. The EU H2020 Gov4Nano project, together with other international projects and initiatives, is addressing the challenge of improving nanosafety data FAIRness, for maximizing their availability, understanding, exchange and ultimately their reuse. These efforts are largely supported by the creation of a common Nanosafety Data Interface, which connects a row of project-specific databases applying the eNanoMapper data model. A wide variety of experimental data relating to characterization and effects of nanomaterials are stored in the database; however, the methods, protocols and parameters driving their generation are not fully mature. This article reports the progress of an ongoing case study in the Gov4nano project on the reuse of in vitro Comet genotoxicity data, focusing on the issues and challenges encountered in their FAIRification through the eNanoMapper data model. The case study is part of an iterative process in which the FAIRification of data supports the understanding of the phenomena underlying their generation and, ultimately, improves their reusability.

Biodiversity Literature Repository: Building the customized FAIR repository by using custom metadata

In the landscape of general-purpose repositories, Zenodo was built at the European Laboratory for Particle Physics' (CERN) data center to facilitate the sharing and preservation of the long tail of research across all disciplines and scientific domains. Given Zenodo’s long tradition of making research artifacts FAIR (Findable, Accessible, Interoperable, and Reusable), there are still challenges in applying these principles effectively when serving the needs of specific research domains. Plazi’s biodiversity taxonomic literature processing pipeline liberates data from publications, making it FAIR via extensive metadata, the minting of a DataCite Digital Object Identifier (DOI), a licence and both human- and machine-readable output provided by Zenodo, and accessible via the Biodiversity Literature Repository community at Zenodo. The deposits (e.g., taxonomic treatments, figures) are an example of how local networks of information can be formally linked to explicit resources in a broader context of other platforms like GBIF (Global Biodiversity Information Facility). In the context of biodiversity taxonomic literature data workflows, a general-purpose repository’s traditional submission approach is not enough to preserve rich metadata and to capture highly interlinked objects, such as taxonomic treatments and digital specimens. As a prerequisite to serve these use cases and ensure that the artifacts remain FAIR, Zenodo introduced the concept of custom metadata, which allows enhancing submissions such as figures or taxonomic treatments (see as an example the treatment of Eurygyrus peloponnesius) with custom keywords, based on terms from common biodiversity vocabularies like Darwin Core and Audubon Core and with an explicit link to the respective vocabulary term. The aforementioned pipelines and features are designed to be served first and foremost using public Representational State Transfer Application Programming Interfaces (REST APIs) and open web technologies like webhooks. This approach allows researchers and platforms to integrate existing and new automated workflows into Zenodo and thus empowers research communities to create self-sustained cross-platform ecosystems. The BiCIKL project (Biodiversity Community Integrated Knowledge Library) exemplifies how repositories and tools can become building blocks for broader adoption of the FAIR principles. Starting with the above literature processing pipeline, the concepts of and resulting FAIR data, with a focus on the custom metadata used to enhance the deposits, will be explained.