Machine actionable metadata models.

Community-developed minimum information checklists are designed to drive the rich and consistent reporting of metadata, underpinning the reproducibility and reuse of the data. These reporting guidelines, however, are usually in the form of narratives intended for human consumption. Modular and reusable machine-readable versions are also needed.

FAIR data pipeline: provenance-driven data management for traceable scientific workflows.

Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used.

ISA API: An open platform for interoperable life science experimental metadata.

Background: The Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open source community specifications and software tools for enabling discovery, exchange, and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab-a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, the JSON serialization ISA-JSON was developed.

Ten simple rules for making a vocabulary FAIR.

We present ten simple rules that support converting a legacy vocabulary-a list of terms available in a print-based glossary or in a table not accessible using web standards-into a FAIR vocabulary. Various pathways may be followed to publish the FAIR vocabulary, but we emphasise particularly the goal of providing a globally unique resolvable identifier for each term or concept. A standard representation of the concept should be returned when the individual web identifier is resolved, using SKOS or OWL serialised in an RDF-based representation for machine-interchange and in a web-page for human consumption.

Radical collaboration during a global health emergency: development of the RDA COVID-19 data sharing recommendations and guidelines.

The coronavirus disease 2019 (COVID-19) global pandemic required a rapid and effective response. This included ethical and legally appropriate sharing of data. The European Commission (EC) called upon the Research Data Alliance (RDA) to recruit experts worldwide to quickly develop recommendations and guidelines for COVID-related data sharing.

Fostering global data sharing: highlighting the recommendations of the Research Data Alliance COVID-19 working group.

The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research.

Semantic concept schema of the linear mixed model of experimental observations.

In the information age, smart data modelling and data management can be carried out to address the wealth of data produced in scientific experiments. In this paper, we propose a semantic model for the statistical analysis of datasets by linear mixed models. We tie together disparate statistical concepts in an interdisciplinary context through the application of ontologies, in particular the Statistics Ontology (STATO), to produce FAIR data summaries.

The Data Tags Suite (DATS) model for discovering data access and use requirements.

Background: Data reuse is often controlled to protect the privacy of subjects and patients. Data discovery tools need ways to inform researchers about restrictions on data access and re-use.

Results: We present elements in the Data Tags Suite (DATS) metadata schema describing data access, data use conditions, and consent information.

Interoperable and scalable data analysis with microservices: applications in metabolomics.

Motivation: Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator.

Results: We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis.

PhenoMeNal: processing and analysis of metabolomics data in the cloud.

Background: Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools.

Data discovery with DATS: exemplar adoptions and lessons learned.

The DAta Tag Suite (DATS) is a model supporting dataset description, indexing, and discovery. It is available as an annotated serialization with schema.org, a vocabulary used by major search engines, thus making the datasets discoverable on the web.

The future of metabolomics in ELIXIR.

Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community.

Four simple recommendations to encourage best practices in research software.

Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research.

Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration.

DATS, the data tag suite to enable discoverability of datasets.

Today's science increasingly requires effective ways to find and access existing datasets that are distributed across a range of repositories. For researchers in the life sciences, discoverability of datasets may soon become as essential as identifying the latest publications via PubMed. Through an international collaborative effort funded by the National Institutes of Health (NIH)'s Big Data to Knowledge (BD2K) initiative, we have designed and implemented the DAta Tag Suite (DATS) model to support the DataMed data discovery index.

Finding useful data across multiple biomedical data repositories using DataMed.

The value of broadening searches for data across multiple repositories has been identified by the biomedical research community. As part of the US National Institutes of Health (NIH) Big Data to Knowledge initiative, we work with an international community of researchers, service providers and knowledge experts to develop and test a data index and search engine, which are based on metadata extracted from various data sets in a range of repositories. DataMed is designed to be, for data, what PubMed has been for the scientific literature.

The health care and life sciences community profile for dataset descriptions.

Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories.

BioSharing: curated and crowd-sourced metadata standards, databases and data policies in the life sciences.

BioSharing (http://www.biosharing.org) is a manually curated, searchable portal of three linked registries.

The Ontology for Biomedical Investigations.

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources.

Data standards can boost metabolomics research, and if there is a will, there is a way.

Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency.