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The biomedical domain has shown that in silico analyses over vast data pools enhances the speed and scale of scientific innovation. This can hold true in agricultural research and guide similar multi-stakeholder action in service of global food security as well (Streich et al. Curr Opin Biotechnol 61:217–225. Retrieved from https://doi.org/10.1016/j.copbio.2020.01.010, 2020). However, entrenched research culture and data and standards governance issues to enable data interoperability and ease of reuse continue to be roadblocks in the agricultural research for development sector. Effective operationalization of the FAIR Data Principles towards Findable, Accessible, Interoperable, and Reusable data requires that agricultural researchers accept that their responsibilities in a digital age include the stewardship of data assets to assure long-term preservation, access and reuse. The development and adoption of common agricultural data standards are key to assuring good stewardship, but face several challenges, including limited awareness about standards compliance; lagging data science capacity; emphasis on data collection rather than reuse; and limited fund allocation for data and standards management. Community-based hurdles around the development and governance of standards and fostering their adoption also abound. This chapter discusses challenges and possible solutions to making FAIR agricultural data assets the norm rather than the exception to catalyze a much-needed revolution towards “translational agriculture”.

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labelling to be interoperable. As recommended by the FAIR principles, data, labels and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no agreed full set of ontologies recommended for data annotation across agricultural research disciplines, which may span genetics, environment, agroecology, biology and socioeconomics. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant ontology expertise. This CoP aims to stimulate knowledge sharing and directly support platform development teams by producing ontologies or contributing missing concepts, recommending best practices and identifying mitigation solutions when gold standard datasets are difficult to attain.

Purpose There is as yet no computer-processable resource to describe treatment end points in cancer, hindering our ability to systematically capture and share outcomes data to inform better patient care. To address these unmet needs, we have built an ontology, the Cancer Care Treatment Outcome Ontology (CCTOO), to organize high-level concepts of treatment end points with structured knowledge representation to facilitate standardized sharing of real-world data. Methods End points from oncology trials in ClinicalTrials.gov were extracted, queried using the keyword cancer, and followed by an expert appraisal. Synonyms and relevant terms were imported from the National Cancer Institute Thesaurus and Common Terminology Criteria for Adverse Events. Logical relationships among concepts were manually represented by production rules. The applicability of 1,847 rules was tested in an index case. Results After removing duplicated terms from 54,705 trial entries, an ontology holding 1,133 terms was built. CCTOO organized concepts into four domains (cancer treatment, health services, physical, and psychosocial health–related concepts), 13 subgroups (including efficacy, safety, and quality of life), and two (taxonomic and evaluative) concept hierarchies. This ontology has a comprehensive term coverage in the cancer trial literature: at least one term was mentioned in 98% of MEDLINE abstracts of phase I to III trials, whereas concepts about efficacy were mentioned in 7,208 (79%) phase I, 15,051 (92%) phase II, and 3,884 (86%) phase III trials. The event sequence of the index case was readily convertible to a comprehensive profile incorporating response, treatment toxicity, and survival by applying the set of production rules curated in the CCTOO. Conclusion CCTOO categorizes high-level treatment end points used in oncology and provides a mechanism for profiling individual patient data by outcomes to facilitate translational analysis.

In this document we aim at composing a high level overview of the use of semantics in the production, exchange and use of agricultural data. In particular, we focus on the use of semantics for data management, and the resources, tools and services available for its production. ​We ​also look at the current research trends in the area.

Over the past decade the biological sciences have been widely embracing Systems Biology and its various data integration approaches to discover new knowledge. Molecular Systems Biology aims to develop hypotheses based on integrated, or modelled data. These hypotheses can be subsequently used to design new experiments for testing, leading to an improved understanding of the biology; a more accurate model of the biological system and therefore an improved ability to develop hypotheses. During the same period the biosciences have also eagerly taken up the emerging Semantic Web as evidenced by the dedicated exploitation of Semantic Web technologies for data integration and sharing in the Life Sciences. We describe how these two approaches merged in Semantic Systems Biology: a data integration and analysis approach complementary to model-based Systems Biology. Semantic Systems Biology augments the integration and sharing of knowledge, and opens new avenues for computational support in quality checking and automated reasoning, and to develop new, testable hypotheses.

Falling costs in genomic laboratory experiments have led to a steady increaseof genomic feature and variation data. Multiple genomic data formats exist forsharing these data, and whilst they are similar, they are addressing slightly differentdata viewpoints and are consequently not fully compatible with each other. Thefragmentation of data format specifications makes it hard to integrate and interpretdata for further analysis with information from multiple data providers. As asolution, a new ontology is presented here for annotating and representing genomicfeature and variation dataset contents. The Genomic Feature and Variation Ontology(GFVO) specifically addresses genomic data as it is regularly shared using the GFF3(incl. FASTA), GTF, GVF and VCF file formats. GFVO simplifies data integration andenables linking of genomic annotations across datasets through common semanticsof genomic types and relations.Availability and implementation.The latest stable release of the ontology isavailable via its base URI; previous and development versions are available at theontology’s GitHub repository: https://github.com/BioInterchange/Ontologies;versions of the ontology are indexed through BioPortal (without externalclass-/property-equivalences due to BioPortal release 4.10 limitations); examplesand reference documentation is provided on a separate web-page: http://www.biointerchange.org/ontologies.html. GFVO version 1.0.2 is licensed under theCC0 1.0 Universal license (https://creativecommons.org/publicdomain/zero/1.0)and thereforede factowithin the public domain; the ontology can be appropriatedwithout attribution for commercial and non-commercial use. DOI 10.7717/peerj.933. Copyright 2015 Baran et al. Distributed under Creative Commons CC-BY 4.0

e18137 Background: Systematic enhancement of knowledge about outcomes of cancer therapy is a central goal for oncology research, practice and audit. However, there is no resource standardizing the disparate vocabulary used to describe treatment outcomes, which prevents translating the data acquired into value-adding information for better cancer care. Methods: To build an application ontology that organizes high-level concepts describing treatment outcomes in solid tumors, provisionally named Cancer Care Treatment Outcomes (CCTO), we extracted all trial endpoints listed in ClinicalTrials.gov, queried it using the keyword ‘cancer’, followed by an expert appraisal. Term definitions and synonyms were automatically imported from the National Cancer Institute thesaurus, and extra terms were imported from Common Toxicity Criteria for Adverse Events. Logical relationships between concepts were represented by forward chaining rules, aiding conversion between concepts. The coverage of CCTO was tested against MEDLINE abstracts, and the applicability of 1825 rules was tested in a case of metastatic colon cancer (index case). Results: After removing duplicated terms from 54705 trial entries, an ontology (CCTO), holding 1145 terms and organized into 13 concept groups (including but not limited to efficacy, safety, and quality of life), was built. CCTO not only captures a comprehensive taxonomic hierarchy (through is_a relationships) but also an evaluative hierarchy ( is_assessed_by) to aid the acquisition of treatment outcome data. At least one CCTO term was mentioned in 29493 of 29951 (98%) MEDLINE abstracts on phase 1-3 cancer trials; concepts about efficacy were mentioned in 7208 (79%) phase 1, 15051 (92%) phase 2, and 3884 (86%) phase 3 trials. We show that, by applying the relationship rules in an expert system shell, the event sequence of the index case can be readily converted into a comprehensive profile that incorporates survival measures, treatment responses, and adverse events. Conclusions: CCTO efficiently captures and categorizes high-level concepts of treatment endpoints in oncologic practice and enables rapid profiling of clinical outcomes for cancer patients.

Background BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research. Results The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization. Conclusion We provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end-consumer.

The Cell Cycle Ontology (CCO) is an application ontology that automatically captures and integrates detailed knowledge on the cell cycle process by combining, interlinking and enriching knowledge from various sources. CCO uses Semantic Web technologies, and it is accessible via the web for browsing, visualising, advanced querying, and computational reasoning. CCO facilitates a detailed analysis of cell cycle related molecular network components. Through querying and automated reasoning, it may provide new hypotheses to help steer a systems biology approach to biological network building. The ontology is available on "http://www.cellcycleontology.org":http://www.cellcycleontology.org. Visual exploration can be done via the BioPortal, the Ontology Lookup Service, the Ontology Online service, or the DIAMONDS platform.*The Cell Cycle Ontology*The Cell Cycle Ontology captures detailed information (in terms and relationships) of the cell cycle process by combining representations from several, public sources.1 CCO supports four model organisms (H. sapiens, A. thaliana, S. pombe and S. cerevisiae) with separate ontologies and one integrated ontology. It is an application ontology that is supplied as an integrated turnkey system for exploratory analysis, advanced querying, and automated reasoning.CCO holds more than 13,000 concepts and 30 types of relationships. CCO comprises data from existing resources such as the Gene Ontology (GO), the Relations Ontology (RO), the IntAct database (MI), the NCBI taxonomy, the UniProt Knowledge Base as well as orthology data. An automatic pipeline builds CCO from scratch periodically: initially some existing ontologies (GO, RO, MI, in-house ones) are automatically fetched, integrated and merged, producing a core cell cycle ontology. Then, organism-specific protein and gene data are added from UniProt and from the GO Annotation files, generating four organism-specific ontologies. Those four ontologies are merged and more terms are included from an ontology built automatically from the OrthoMCL execution on the cell cycle proteins.*Formats and queries*CCO is built in the OBOF format with ONTO-PERL and exported to other formats later.2 CCO is available in: OBOF, RDF, XML, OWL, GML, and DOT. The Semantic Web formats RDF and OWL allow queries on CCO. In a SPARQL endpoint complex queries on the RDF format can be formulated, such as “retrieve all the core cell cycle proteins in S. cerevisiae that are located in the cytoplasm and that have a hydrolysisrelated function”.Relational closures are pre-inferenced in the RDF triple store, by operating SPARUL update queries over CCO and Metarel. This allows for very simple and responsive queries over long chains of relations in CCO.Finally, during the maintenance phase, a semantic improvement on the OWL version is carried out: Ontology Design Patterns are included using the Ontology Pre-Processor Language. The resulting CCO is designed to provide a richer view of the cell cycle regulatory process, in particular by accommodating the intrinsic dynamics of this process.*References*1. Antezana E, Egaña M, Blondé W et al. The Cell Cycle Ontology: An application ontology for the representation and integrated analysis of the cell cycle process, Genome Biology, 2009, 10:52. Antezana E, Egaña M, De Baets B, Kuiper M, Mironov V. ONTO-PERL: an api supporting the development and analysis of bio-ontologies. Bioinformatics, 2008, pp. 885–887.

While OWL, the Web Ontology Language, is often regarded as the preferred language for Knowledge Representation in the world of the Semantic Web, the potential of direct representation in RDF, the Resource Description Framework, is underestimated. Here we show how ontologies adequately represented in RDF could be semantically enriched with SPARUL. To deal with the semantics of relations we created Metarel, a meta-ontology for relations. The utility of the approach is demonstrated by an application on Gene Ontology Annotation (GOA) RDF graphs in the RDF Knowledge Base BioGateway. We show that Metarel can facilitate inferencing in BioGateway, which allows for queries that are otherwise not possible. Metarel is available on http://www.metarel.org.

A software resource for the analysis of cell cycle related molecular networks., The Cell Cycle Ontology ( is an application ontology that automatically captures and integrates detailed knowledge on the cell cycle process. Cell Cycle Ontology is enabled by semantic web technologies, and is accessible via the web for browsing, visualizing, advanced querying, and computational reasoning. Cell Cycle Ontology facilitates a detailed analysis of cell cycle-related molecular network components. Through querying and automated reasoning, it may provide new hypotheses to help steer a systems biology approach to biological network building.

The aMAZE LightBench (http://www.amaze.ulb. ac.be/) is a web interface to the aMAZE relational database, which contains information on gene expression, catalysed chemical reactions, regulatory interactions, protein assembly, as well as metabolic and signal transduction pathways. It allows the user to browse the information in an intuitive way, which also reflects the underlying data model. Moreover links are provided to literature references, and whenever appropriate, to external databases.

As ontologies are developed there is a common need to transform them, especially from those that are axiomatically lean to those that are axiomatically rich. Such transformations often require large numbers of axioms to be generated that affect many different parts of the ontology. This paper describes the Ontology Pre-Processor Language (OPPL), a domain-specific macro language, based in the Manchester OWL Syntax, for manipulating ontologies written in OWL. OPPL instructions can add/remove entities, and add/remove axioms (semantics or annotations) to/from entities in an OWL ontology. OPPL is suitable for applying the same change to different ontologies or at different development stages, and for keeping track of the changes made (e.g. in pipelines). It is also suitable for defining independent modelling macros (e.g. Ontology Design Patterns) that can be applied at will and systematically across an ontology. The presented OPPL Instruction Manager is a Java library that processes OPPL instructions making the changes to an OWL ontology. A reference implementation that uses the OPPL Instruction Manager is also presented. The use of OPPL has been demonstrated in the Cell Cycle Ontology

Background Life scientists need help in coping with the plethora of fast growing and scattered knowledge resources. Ideally, this knowledge should be integrated in a form that allows them to pose complex questions that address the properties of biological systems, independently from the origin of the knowledge. Semantic Web technologies prove to be well suited for knowledge integration, knowledge production (hypothesis formulation), knowledge querying and knowledge maintenance. Results We implemented a semantically integrated resource named BioGateway, comprising the entire set of the OBO foundry candidate ontologies, the GO annotation files, the SWISS-PROT protein set, the NCBI taxonomy and several in-house ontologies. BioGateway provides a single entry point to query these resources through SPARQL. It constitutes a key component for a Semantic Systems Biology approach to generate new hypotheses concerning systems properties. In the course of developing BioGateway, we faced challenges that are common to other projects that involve large datasets in diverse representations. We present a detailed analysis of the obstacles that had to be overcome in creating BioGateway. We demonstrate the potential of a comprehensive application of Semantic Web technologies to global biomedical data. Conclusion The time is ripe for launching a community effort aimed at a wider acceptance and application of Semantic Web technologies in the life sciences. We call for the creation of a forum that strives to implement a truly semantic life science foundation for Semantic Systems Biology. Access to the system and supplementary information (such as a listing of the data sources in RDF, and sample queries) can be found at http://www.semantic-systems-biology.org/biogateway.