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. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.
Biomedical ontologists to date have concentrated on ontological descriptions of biomedical entities such as gene products and their attributes, phenotypes and so on. Recently, effort has diversified ...to descriptions of the laboratory investigations by which these entities were produced. However, much biological insight is gained from the analysis of the data produced from these investigations, and there is a lack of adequate descriptions of the wide range of software that are central to bioinformatics. We need to describe how data are analyzed for discovery, audit trails, provenance and reproducibility.
The Software Ontology (SWO) is a description of software used to store, manage and analyze data. Input to the SWO has come from beyond the life sciences, but its main focus is the life sciences. We used agile techniques to gather input for the SWO and keep engagement with our users. The result is an ontology that meets the needs of a broad range of users by describing software, its information processing tasks, data inputs and outputs, data formats versions and so on. Recently, the SWO has incorporated EDAM, a vocabulary for describing data and related concepts in bioinformatics. The SWO is currently being used to describe software used in multiple biomedical applications.
The SWO is another element of the biomedical ontology landscape that is necessary for the description of biomedical entities and how they were discovered. An ontology of software used to analyze data produced by investigations in the life sciences can be made in such a way that it covers the important features requested and prioritized by its users. The SWO thus fits into the landscape of biomedical ontologies and is produced using techniques designed to keep it in line with user's needs.
The Software Ontology is available under an Apache 2.0 license at http://theswo.sourceforge.net/; the Software Ontology blog can be read at http://softwareontology.wordpress.com.
The sharing and documentation of cardiovascular research data are essential for efficient use and reuse of data, thereby aiding scientific transparency, accelerating the progress of cardiovascular ...research and healthcare, and contributing to the reproducibility of research results. However, challenges remain. This position paper, written on behalf of and approved by the German Cardiac Society and German Centre for Cardiovascular Research, summarizes our current understanding of the challenges in cardiovascular research data management (RDM). These challenges include lack of time, awareness, incentives, and funding for implementing effective RDM; lack of standardization in RDM processes; a need to better identify meaningful and actionable data among the increasing volume and complexity of data being acquired; and a lack of understanding of the legal aspects of data sharing. While several tools exist to increase the degree to which data are findable, accessible, interoperable, and reusable (FAIR), more work is needed to lower the threshold for effective RDM not just in cardiovascular research but in all biomedical research, with data sharing and reuse being factored in at every stage of the scientific process. A culture of open science with FAIR research data should be fostered through education and training of early-career and established research professionals. Ultimately, FAIR RDM requires permanent, long-term effort at all levels. If outcomes can be shown to be superior and to promote better (and better value) science, modern RDM will make a positive difference to cardiovascular science and practice. The full position paper is available in the supplementary materials.
Saint is a web application which provides a lightweight annotation integration environment for quantitative biological models. The system enables modellers to rapidly mark up models with biological ...information derived from a range of data sources. Availability and Implementation: Saint is freely available for use on the web at http://www.cisban.ac.uk/saint. The web application is implemented in Google Web Toolkit and Tomcat, with all major browsers supported. The Java source code is freely available for download at http://saint-annotate.sourceforge.net. The Saint web server requires an installation of libSBML and has been tested on Linux (32-bit Ubuntu 8.10 and 9.04). Contact: helpdesk@cisban.ac.uk; a.l.lister@ncl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
The Horizon Europe project ISIDORe is dedicated to pandemic preparedness and responsiveness research. It brings together 17 research infrastructures (RIs) and networks to provide a broad range of ...services to infectious disease researchers. An efficient and structured treatment of data is central to ISIDORe’s aim to furnish seamless access to its multidisciplinary catalogue of services, and to ensure that users’ results are treated FAIRly. ISIDORe therefore requires a data management plan (DMP) covering both access management and research outputs, applicable over a broad range of disciplines, and compatible with the constraints and existing practices of its diverse partners.
Here, we describe how, to achieve that aim, we undertook an iterative, step-by-step, process to build a community-approved living document, identifying good practices and processes, on the basis of use cases, presented as proof of concepts. International fora such as the RDA and EOSC, and primarily the BY-COVID project, furnished registries, tools and online data platforms, as well as standards, and the support of data scientists. Together, these elements provide a path for building an umbrella, FAIR-compliant DMP, aligned as fully as possible with FAIR principles, which could also be applied as a framework for data management harmonisation in other large-scale, challenge-driven projects. Finally, we discuss how data management and reuse can be further improved through the use of knowledge models when writing DMPs and, how, in the future, an inter-RI network of data stewards could contribute to the establishment of a community of practice, to be integrated subsequently into planned trans-RI competence centres.
Thousands of community-developed (meta)data guidelines, models, ontologies,
schemas and formats have been created and implemented by several thousand data
repositories and knowledge-bases, across all ...disciplines. These resources are
necessary to meet government, funder and publisher expectations of greater
transparency and access to and preservation of data related to research
publications. This obligates researchers to ensure their data is FAIR, share
their data using the appropriate standards, store their data in sustainable and
community-adopted repositories, and to conform to funder and publisher data
policies. FAIR data sharing also plays a key role in enabling researchers to
evaluate, re-analyse and reproduce each other's work. We can map the
landscape of relationships between community-adopted standards and repositories,
and the journal publisher and funder data policies that recommend their use. In
this paper, we show how the work of the GO-FAIR FAIR Standards, Repositories and
Policies (StRePo) Implementation Network serves as a central integration and
cross-fertilisation point for the reuse of FAIR standards, repositories and data
policies in general. Pivotal to this effort, the FAIRsharing, an endorsed
flagship resource of the Research Data Alliance that maps the landscape of
relationships between community-adopted standards and repositories, and the
journal publisher and funder data policies that recommend their use. Lastly, we
highlight a number of activities around FAIR tools, services and educational
efforts to raise awareness and encourage participation.
The creation of accurate quantitative Systems Biology Markup Language (SBML) models is a time-intensive, manual process often complicated by the many data sources and formats required to annotate ...even a small and well-scoped model. Ideally, the retrieval and integration of biological knowledge for model annotation should be performed quickly, precisely, and with a minimum of manual effort.
Here we present rule-based mediation, a method of semantic data integration applied to systems biology model annotation. The heterogeneous data sources are first syntactically converted into ontologies, which are then aligned to a small domain ontology by applying a rule base. We demonstrate proof-of-principle of this application of rule-based mediation using off-the-shelf semantic web technology through two use cases for SBML model annotation. Existing tools and technology provide a framework around which the system is built, reducing development time and increasing usability.
Integrating resources in this way accommodates multiple formats with different semantics, and provides richly-modelled biological knowledge suitable for annotation of SBML models. This initial work establishes the feasibility of rule-based mediation as part of an automated SBML model annotation system.
Detailed information on the project files as well as further information on and comparisons with similar projects is available from the project page at http://cisban-silico.cs.ncl.ac.uk/RBM/.
...live blogging does not change what information is broadcast from a conference, merely how fast it is propagated. ...senior scientists are also encouraged to support their students in their ...initial forays into social networks as open interaction with other researchers not only provides excellent training, but also opens up venues for exhibiting their own research group to a wider community.
...the International Society for Computational Biology (ISCB), organizers of the Intelligent Systems for Molecular Biology/European Conference on Computational Biology (ISMB/ECCB) 2009 conference ...decided to actively support future blogging efforts. Other platforms such as Twitter (http://www.twitter.com) and personal blogs were used at the conference. Because it is difficult to retrieve Twitter statistics and this platform was not used extensively during the ISMB/ECCB 2009, we focus on the reporting activities in the FriendFeed room.
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