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. Guidelines for vocabulary and term metadata are provided, as well as development and maintenance considerations. The rules are arranged as a stepwise recipe for creating a FAIR vocabulary based on the legacy vocabulary. By following these rules you can achieve the outcome of converting a legacy vocabulary into a standalone FAIR vocabulary, which can be used for unambiguous data annotation. In turn, this increases data interoperability and enables data integration.

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. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of 'following the science' are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline. Although developed during the COVID-19 pandemic, it allows easy annotation of any data as they are consumed by analyses, or conversely traces the provenance of scientific outputs back through the analytical or modelling source code to primary data. Such a tool provides a mechanism for the public, and fellow scientists, to better assess scientific evidence by inspecting its provenance, while allowing scientists to support policymakers in openly justifying their decisions. We believe that such tools should be promoted for use across all areas of policy-facing research. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

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. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science. AVAILABILITY AND IMPLEMENTATION: The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

EBI metagenomics--a new resource for the analysis and archiving of metagenomic data.

Metagenomics is a relatively recently established but rapidly expanding field that uses high-throughput next-generation sequencing technologies to characterize the microbial communities inhabiting different ecosystems (including oceans, lakes, soil, tundra, plants and body sites). Metagenomics brings with it a number of challenges, including the management, analysis, storage and sharing of data. In response to these challenges, we have developed a new metagenomics resource (http://www.ebi.ac.uk/metagenomics/) that allows users to easily submit raw nucleotide reads for functional and taxonomic analysis by a state-of-the-art pipeline, and have them automatically stored (together with descriptive, standards-compliant metadata) in the European Nucleotide Archive.

MetaboLights--an open-access general-purpose repository for metabolomics studies and associated meta-data.

MetaboLights (http://www.ebi.ac.uk/metabolights) is the first general-purpose, open-access repository for metabolomics studies, their raw experimental data and associated metadata, maintained by one of the major open-access data providers in molecular biology. Metabolomic profiling is an important tool for research into biological functioning and into the systemic perturbations caused by diseases, diet and the environment. The effectiveness of such methods depends on the availability of public open data across a broad range of experimental methods and conditions. The MetaboLights repository, powered by the open source ISA framework, is cross-species and cross-technique. It will cover metabolite structures and their reference spectra as well as their biological roles, locations, concentrations and raw data from metabolic experiments. Studies automatically receive a stable unique accession number that can be used as a publication reference (e.g. MTBLS1). At present, the repository includes 15 submitted studies, encompassing 93 protocols for 714 assays, and span over 8 different species including human, Caenorhabditis elegans, Mus musculus and Arabidopsis thaliana. Eight hundred twenty-seven of the metabolites identified in these studies have been mapped to ChEBI. These studies cover a variety of techniques, including NMR spectroscopy and mass spectrometry.

linkedISA: semantic representation of ISA-Tab experimental metadata.

BACKGROUND: Reporting and sharing experimental metadata- such as the experimental design, characteristics of the samples, and procedures applied, along with the analysis results, in a standardised manner ensures that datasets are comprehensible and, in principle, reproducible, comparable and reusable. Furthermore, sharing datasets in formats designed for consumption by humans and machines will also maximize their use. The Investigation/Study/Assay (ISA) open source metadata tracking framework facilitates standards-compliant collection, curation, visualization, storage and sharing of datasets, leveraging on other platforms to enable analysis and publication. The ISA software suite includes several components used in increasingly diverse set of life science and biomedical domains; it is underpinned by a general-purpose format, ISA-Tab, and conversions exist into formats required by public repositories. While ISA-Tab works well mainly as a human readable format, we have also implemented a linked data approach to semantically define the ISA-Tab syntax. RESULTS: We present a semantic web representation of the ISA-Tab syntax that complements ISA-Tab's syntactic interoperability with semantic interoperability. We introduce the linkedISA conversion tool from ISA-Tab to the Resource Description Framework (RDF), supporting mappings from the ISA syntax to multiple community-defined, open ontologies and capitalising on user-provided ontology annotations in the experimental metadata. We describe insights of the implementation and how annotations can be expanded driven by the metadata. We applied the conversion tool as part of Bio-GraphIIn, a web-based application supporting integration of the semantically-rich experimental descriptions. Designed in a user-friendly manner, the Bio-GraphIIn interface hides most of the complexities to the users, exposing a familiar tabular view of the experimental description to allow seamless interaction with the RDF representation, and visualising descriptors to drive the query over the semantic representation of the experimental design. In addition, we defined queries over the linkedISA RDF representation and demonstrated its use over the linkedISA conversion of datasets from Nature' Scientific Data online publication. CONCLUSIONS: Our linked data approach has allowed us to: 1) make the ISA-Tab semantics explicit and machine-processable, 2) exploit the existing ontology-based annotations in the ISA-Tab experimental descriptions, 3) augment the ISA-Tab syntax with new descriptive elements, 4) visualise and query elements related to the experimental design. Reasoning over ISA-Tab metadata and associated data will facilitate data integration and knowledge discovery.

The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again.

BACKGROUND: The ISA-Tab format and software suite have been developed to break the silo effect induced by technology-specific formats for a variety of data types and to better support experimental metadata tracking. Experimentalists seldom use a single technique to monitor biological signals. Providing a multi-purpose, pragmatic and accessible format that abstracts away common constructs for describing Investigations, Studies and Assays, ISA is increasingly popular. To attract further interest towards the format and extend support to ensure reproducible research and reusable data, we present the Risa package, which delivers a central component to support the ISA format by enabling effortless integration with R, the popular, open source data crunching environment. RESULTS: The Risa package bridges the gap between the metadata collection and curation in an ISA-compliant way and the data analysis using the widely used statistical computing environment R. The package offers functionality for: i) parsing ISA-Tab datasets into R objects, ii) augmenting annotation with extra metadata not explicitly stated in the ISA syntax; iii) interfacing with domain specific R packages iv) suggesting potentially useful R packages available in Bioconductor for subsequent processing of the experimental data described in the ISA format; and finally v) saving back to ISA-Tab files augmented with analysis specific metadata from R. We demonstrate these features by presenting use cases for mass spectrometry data and DNA microarray data. CONCLUSIONS: The Risa package is open source (with LGPL license) and freely available through Bioconductor. By making Risa available, we aim to facilitate the task of processing experimental data, encouraging a uniform representation of experimental information and results while delivering tools for ensuring traceability and provenance tracking. SOFTWARE AVAILABILITY: The Risa package is available since Bioconductor 2.11 (version 1.0.0) and version 1.2.1 appeared in Bioconductor 2.12, both along with documentation and examples. The latest version of the code is at the development branch in Bioconductor and can also be accessed from GitHub https://github.com/ISA-tools/Risa, where the issue tracker allows users to report bugs or feature requests.

OntoMaton: a bioportal powered ontology widget for Google Spreadsheets.

MOTIVATION: Data collection in spreadsheets is ubiquitous, but current solutions lack support for collaborative semantic annotation that would promote shared and interdisciplinary annotation practices, supporting geographically distributed players. RESULTS: OntoMaton is an open source solution that brings ontology lookup and tagging capabilities into a cloud-based collaborative editing environment, harnessing Google Spreadsheets and the NCBO Web services. It is a general purpose, format-agnostic tool that may serve as a component of the ISA software suite. OntoMaton can also be used to assist the ontology development process. AVAILABILITY: OntoMaton is freely available from Google widgets under the CPAL open source license; documentation and examples at: https://github.com/ISA-tools/OntoMaton.

Federated ontology-based queries over cancer data.

BACKGROUND: Personalised medicine provides patients with treatments that are specific to their genetic profiles. It requires efficient data sharing of disparate data types across a variety of scientific disciplines, such as molecular biology, pathology, radiology and clinical practice. Personalised medicine aims to offer the safest and most effective therapeutic strategy based on the gene variations of each subject. In particular, this is valid in oncology, where knowledge about genetic mutations has already led to new therapies. Current molecular biology techniques (microarrays, proteomics, epigenetic technology and improved DNA sequencing technology) enable better characterisation of cancer tumours. The vast amounts of data, however, coupled with the use of different terms - or semantic heterogeneity - in each discipline makes the retrieval and integration of information difficult. RESULTS: Existing software infrastructures for data-sharing in the cancer domain, such as caGrid, support access to distributed information. caGrid follows a service-oriented model-driven architecture. Each data source in caGrid is associated with metadata at increasing levels of abstraction, including syntactic, structural, reference and domain metadata. The domain metadata consists of ontology-based annotations associated with the structural information of each data source. However, caGrid's current querying functionality is given at the structural metadata level, without capitalising on the ontology-based annotations. This paper presents the design of and theoretical foundations for distributed ontology-based queries over cancer research data. Concept-based queries are reformulated to the target query language, where join conditions between multiple data sources are found by exploiting the semantic annotations. The system has been implemented, as a proof of concept, over the caGrid infrastructure. The approach is applicable to other model-driven architectures. A graphical user interface has been developed, supporting ontology-based queries over caGrid data sources. An extensive evaluation of the query reformulation technique is included. CONCLUSIONS: To support personalised medicine in oncology, it is crucial to retrieve and integrate molecular, pathology, radiology and clinical data in an efficient manner. The semantic heterogeneity of the data makes this a challenging task. Ontologies provide a formal framework to support querying and integration. This paper provides an ontology-based solution for querying distributed databases over service-oriented, model-driven infrastructures.

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. Firstly, to provide the necessary quantitative and verifiable measures of the degree to which the metadata descriptors meet these community requirements, a requirement of the FAIR Principles. Secondly, to encourage the creation of standards-driven templates for metadata authoring, especially when describing complex experiments that require multiple reporting guidelines to be used in combination or extended. We present new functionalities to support the creation and improvements of machine-readable models. We apply the approach to an exemplar set of reporting guidelines in Life Science and discuss the challenges. Our work, targeted to developers of standards and those familiar with standards, promotes the concept of compositional metadata elements and encourages the creation of community-standards which are modular and interoperable from the onset.

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. RESULTS: In this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python object classes. We describe the ISA API feature set, early adopters, and its growing user community. CONCLUSIONS: The ISA API provides users with rich programmatic metadata-handling functionality to support automation, a common interface, and an interoperable medium between the 2 ISA formats, as well as with other life science data formats required for depositing data in public databases.

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

Background: 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. Purpose: The purpose of the present work was to explore how the RDA succeeded in engaging the participation of its community of scientists in a rapid response to the EC request. Methods: A survey questionnaire was developed and distributed among RDA COVID-19 work group members. A mixed-methods approach was used for analysis of the survey data. Results: The three constructs of radical collaboration (inclusiveness, distributed digital practices, productive and sustainable collaboration) were found to be well supported in both the quantitative and qualitative analyses of the survey data. Other social factors, such as motivation and group identity were also found to be important to the success of this extreme collaborative effort. Conclusions: Recommendations and suggestions for future work were formulated for consideration by the RDA to strengthen effective expert collaboration and interdisciplinary efforts.

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. DATS metadata are explained in terms of the administrative, legal, and technical systems used to protect confidential data. CONCLUSIONS: The access and use metadata items in DATS are designed from the perspective of a researcher who wants to find and re-use existing data. We call for standard ways of describing informed consent and data use agreements that will enable automated systems for managing research data.

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. We hope to improve the general understanding of statistical modelling and thus contribute to a better description of the statistical conclusions from data analysis, allowing their efficient exploration and automated processing.

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. These guidelines include recommendations for clinicians, researchers, policy- and decision-makers, funders, publishers, public health experts, disaster preparedness and response experts, infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations), and other potential users. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community.

Community standards for open cell migration data.

Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited owing to the diversity of experimental protocols and non-standardized output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardized data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardization efforts by the Cell Migration Standardisation Organisation (CMSO), an open community-driven organization to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration.

Semantic web data warehousing for caGrid.

The National Cancer Institute (NCI) is developing caGrid as a means for sharing cancer-related data and services. As more data sets become available on caGrid, we need effective ways of accessing and integrating this information. Although the data models exposed on caGrid are semantically well annotated, it is currently up to the caGrid client to infer relationships between the different models and their classes. In this paper, we present a Semantic Web-based data warehouse (Corvus) for creating relationships among caGrid models. This is accomplished through the transformation of semantically-annotated caBIG Unified Modeling Language (UML) information models into Web Ontology Language (OWL) ontologies that preserve those semantics. We demonstrate the validity of the approach by Semantic Extraction, Transformation and Loading (SETL) of data from two caGrid data sources, caTissue and caArray, as well as alignment and query of those sources in Corvus. We argue that semantic integration is necessary for integration of data from distributed web services and that Corvus is a useful way of accomplishing this. Our approach is generalizable and of broad utility to researchers facing similar integration challenges.

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. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. FINDINGS: PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. CONCLUSIONS: PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains.

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. DATS underlies DataMed, the National Institutes of Health Big Data to Knowledge Data Discovery Index prototype, which aims to provide a "PubMed for datasets." The experience gained while indexing a heterogeneous range of >60 repositories in DataMed helped in evaluating DATS's entities, attributes, and scope. In this work, 3 additional exemplary and diverse data sources were mapped to DATS by their representatives or experts, offering a deep scan of DATS fitness against a new set of existing data. The procedure, including feedback from users and implementers, resulted in DATS implementation guidelines and best practices, and identification of a path for evolving and optimizing the model. Finally, the work exposed additional needs when defining datasets for indexing, especially in the context of clinical and observational information.