There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

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The FAIR Guiding Principles for scientific data management and stewardship

The last few years have witnessed significant changes in Pfam (https://pfam.xfam.org). The number of families has grown substantially to a total of 17,929 in release 32.0. New additions have been coupled with efforts to improve existing families, including refinement of domain boundaries, their classification into Pfam clans, as well as their functional annotation. We recently began to collaborate with the RepeatsDB resource to improve the definition of tandem repeat families within Pfam. We carried out a significant comparison to the structural classification database, namely the Evolutionary Classification of Protein Domains (ECOD) that led to the creation of 825 new families based on their set of uncharacterized families (EUFs). Furthermore, we also connected Pfam entries to the Sequence Ontology (SO) through mapping of the Pfam type definitions to SO terms. Since Pfam has many community contributors, we recently enabled the linking between authorship of all Pfam entries with the corresponding authors' ORCID identifiers. This effectively permits authors to claim credit for their Pfam curation and link them to their ORCID record.

The Pfam protein families database in 2019.

The GWAS Catalog delivers a high-quality curated collection of all published genome-wide association studies enabling investigations to identify causal variants, understand disease mechanisms, and establish targets for novel therapies. The scope of the Catalog has also expanded to targeted and exome arrays with 1000 new associations added for these technologies. As of September 2018, the Catalog contains 5687 GWAS comprising 71673 variant-trait associations from 3567 publications. New content includes 284 full P-value summary statistics datasets for genome-wide and new targeted array studies, representing 6 × 109 individual variant-trait statistics. In the last 12 months, the Catalog's user interface was accessed by ∼90000 unique users who viewed >1 million pages. We have improved data access with the release of a new RESTful API to support high-throughput programmatic access, an improved web interface and a new summary statistics database. Summary statistics provision is supported by a new format proposed as a community standard for summary statistics data representation. This format was derived from our experience in standardizing heterogeneous submissions, mapping formats and in harmonizing content. Availability: https://www.ebi.ac.uk/gwas/.

The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

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Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

The methods and results of systematic reviews should be reported in sufficient detail to allow users to assess the trustworthiness and applicability of the review findings. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement was developed to facilitate transparent and complete reporting of systematic reviews and has been updated (to PRISMA 2020) to reflect recent advances in systematic review methodology and terminology. Here, we present the explanation and elaboration paper for PRISMA 2020, where we explain why reporting of each item is recommended, present bullet points that detail the reporting recommendations, and present examples from published reviews. We hope that changes to the content and structure of PRISMA 2020 will facilitate uptake of the guideline and lead to more transparent, complete, and accurate reporting of systematic reviews.

https://www.bmj.com/content/bmj/372/bmj.n160.full.pdf

PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews.

The increasing interest in analysing, describing, and improving the research process requires the development of new forms of scholarly data publication and analysis that integrates lessons and approaches from the field of Semantic Technologies, Science of Science, Digital Libraries, and Artificial Intelligence. This editorial summarises the content of the Special Issue on Scholarly Data Analysis (Semantics, Analytics, Visualisation), which aims to showcase some of the most interesting research efforts in the field. This issue includes an extended version of the best papers of the last two editions of the “Semantics, Analytics, Visualisation: Enhancing Scholarly Dissemination” (SAVE-SD 2017 and 2018) workshop at The Web Conference.

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Editorial: Special Issue on Scholarly Data Analysis (Semantics, Analytics, Visualisation)

SemanticLMM: semantic modelling of linear mixed model analysis

The advent of affordable sequencing technology provides for a new generation of explorers who probe the world’s microbial diversity. Projects such as Tara Oceans, Moorea Biocode Project and Gut Microbiome rely on sequencing technologies to probe community diversity. Either targeted gene surveys (also known as community surveys) or complete metagenomes are evaluated. The former, being the less costly of the two methods, relies on the identification of specific genomic regions, which can be used as a proxy to estimate genetic distance between related species in a Phylum. For instance, 16S ribosomal RNA gene surveys are used to probe bacterial communities while Internal Transcribed Spacer (ITS) surveys, for example, can be used for probing fungal communities. With the explosion of projects and frenzy to explore new domains of life, scientists in the field have issued guidelines to report minimal information (following a checklist), ensuring that information is contextualized in a meaningful way. Yet the semantic of a checklist is not explicit. We demonstrate here how a tabular template can be used to collect information on microbial diversity using an explicit representation in the Resource Description Framework (RDF) that is consistent with community agreed-upon knowledge representation patterns found in the Ontology for Biomedical Investigations (OBI). Keywords— microbial diversity, targeted gene survey, ontology, assay, OBO Foundry, ISA framework ICBO 2014 Proceedings

Modeling a Microbial Community and Biodiversity Assay with OBI and PCO OBO Foundry Ontologies: the Interoperability Gains of a Modular Approach.

This article presents elements in the Data Tags Suite (DATS) metadata schema describing data access, data use conditions, and consent information. DATS is a product of the bioCADDIE Project, which created a data discovery index for searching across all types of biomedical data. 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. Data reuse is often controlled to protect the privacy of subjects and patients. We focus on the impact of data protection procedures on data users. However, these procedures are part of a larger environment around patient privacy protection, and this article puts DATS metadata into the context of the administrative, legal, and technical systems used to protect confidential data.

https://www.biorxiv.org/content/biorxiv/early/2019/01/11/518571.full.pdf

Discovering Data Access and Use Requirements Using the Data Tags Suite (DATS) Model1

This chapter introduces the problems experimentalists in all sectors face in utilizing third party data sets given the unhelpful wealth of formats and terminologies and the consequent mountain of technical frameworks needed to achieve data interoperability. We argue on the importance of a complementary set of open standards, the challenges we must overcome and the role the BioSharing effort is set to play. As an example of progress, we present the open source ISA software solution in action during the curation of the InnoMed PredTox data set, along with its growing active developer and user community, including academia and industrial sectors, such as The Novartis Institutes for BioMedical Research and Janssen Research & Development.

Alejandra Gonzalez-Beltran

Papers

Editorial: Special Issue on Scholarly Data Analysis (Semantics, Analytics, Visualisation)

SemanticLMM: semantic modelling of linear mixed model analysis

Modeling a Microbial Community and Biodiversity Assay with OBI and PCO OBO Foundry Ontologies: the Interoperability Gains of a Modular Approach.

Discovering Data Access and Use Requirements Using the Data Tags Suite (DATS) Model1

Investigation-Study-Assay, a toolkit for standardizing data capture and sharing