Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop

Jagodnik, Kathleen M.; Koplev, Simon; Jenkins, Sherry L.; Ohno-Machado, Lucila; Paten, Benedict; Schürer, Stephan C.; Dumontier, Michel; Verborgh, Ruben; Bui, Alex A. T.; Ping, Peipei; McKenna, Neil J.; Madduri, Ravi; Pillai, Ajay S.; Ma’ayan, Avi

doi:10.1016/j.jbi.2017.05.006

Cited by 25 publications

(17 citation statements)

References 51 publications

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“…The FAIR Guiding Principles 9 seek to enhance the ability to find, access, integrate/interoperate with, and reuse datasets. The LINCS DCIC is actively involved in BD2K and NIH Commons initiatives including development and evaluation of data FAIRness 38 . LINCS datasets and metadata reagents are indexed and mapped by cross-domain-interest initiatives such as DataMed 39 , OMICS Discovery Index 40 , ChEMBL 26 , ChEBI 25 , CLO 34 , and Cellosaurus ( http://web.expasy.org/cellosaurus/ ).…”

Section: Resultsmentioning

confidence: 99%

Sustainable data and metadata management at the BD2K-LINCS Data Coordination and Integration Center

et al. 2018

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The NIH-funded LINCS Consortium is creating an extensive reference library of cell-based perturbation response signatures and sophisticated informatics tools incorporating a large number of perturbagens, model systems, and assays. To date, more than 350 datasets have been generated including transcriptomics, proteomics, epigenomics, cell phenotype and competitive binding profiling assays. The large volume and variety of data necessitate rigorous data standards and effective data management including modular data processing pipelines and end-user interfaces to facilitate accurate and reliable data exchange, curation, validation, standardization, aggregation, integration, and end user access. Deep metadata annotations and the use of qualified data standards enable integration with many external resources. Here we describe the end-to-end data processing and management at the DCIC to generate a high-quality and persistent product. Our data management and stewardship solutions enable a functioning Consortium and make LINCS a valuable scientific resource that aligns with big data initiatives such as the BD2K NIH Program and concords with emerging data science best practices including the findable, accessible, interoperable, and reusable (FAIR) principles.

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Section: Resultsmentioning

confidence: 99%

Sustainable data and metadata management at the BD2K-LINCS Data Coordination and Integration Center

et al. 2018

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“…Moreover, FAIRness assessments can be automated by simply examining the compliance of a digital object within the Datasets2Tools database with the FAIR criteria. The effort placed forth to design and implement Datasets2Tools is contributing toward realizing the vision of the NIH Data Commons 20 .…”

Section: Discussionmentioning

confidence: 99%

Datasets2Tools, repository and search engine for bioinformatics datasets, tools and canned analyses

et al. 2018

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Biomedical data repositories such as the Gene Expression Omnibus (GEO) enable the search and discovery of relevant biomedical digital data objects. Similarly, resources such as OMICtools, index bioinformatics tools that can extract knowledge from these digital data objects. However, systematic access to pre-generated ‘canned’ analyses applied by bioinformatics tools to biomedical digital data objects is currently not available. Datasets2Tools is a repository indexing 31,473 canned bioinformatics analyses applied to 6,431 datasets. The Datasets2Tools repository also contains the indexing of 4,901 published bioinformatics software tools, and all the analyzed datasets. Datasets2Tools enables users to rapidly find datasets, tools, and canned analyses through an intuitive web interface, a Google Chrome extension, and an API. Furthermore, Datasets2Tools provides a platform for contributing canned analyses, datasets, and tools, as well as evaluating these digital objects according to their compliance with the findable, accessible, interoperable, and reusable (FAIR) principles. By incorporating community engagement, Datasets2Tools promotes sharing of digital resources to stimulate the extraction of knowledge from biomedical research data. Datasets2Tools is freely available from: http://amp.pharm.mssm.edu/datasets2tools.

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“…The FAIR principles serve as a guide for data producers, stewards, and disseminators for enhancing reusability of data, inclusive of data algorithms, tools, and workflows that are essential for good data lifecycle management [ 48 ]. A biomedical data ecosystem should have capabilities for indexing of data, metadata, software, and other digital objects—a hallmark of the NIH Big Data to Knowledge (BD2K) initiative [ 49 ].…”

Section: Developing a Cloud-based Digital Ecosystem For Biomedical Rementioning

confidence: 99%

Cloud computing applications for biomedical science: A perspective

Navale

Bourne

2018

PLoS Comput Biol

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Biomedical research has become a digital data–intensive endeavor, relying on secure and scalable computing, storage, and network infrastructure, which has traditionally been purchased, supported, and maintained locally. For certain types of biomedical applications, cloud computing has emerged as an alternative to locally maintained traditional computing approaches. Cloud computing offers users pay-as-you-go access to services such as hardware infrastructure, platforms, and software for solving common biomedical computational problems. Cloud computing services offer secure on-demand storage and analysis and are differentiated from traditional high-performance computing by their rapid availability and scalability of services. As such, cloud services are engineered to address big data problems and enhance the likelihood of data and analytics sharing, reproducibility, and reuse. Here, we provide an introductory perspective on cloud computing to help the reader determine its value to their own research.

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Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop

Cited by 25 publications

References 51 publications

Sustainable data and metadata management at the BD2K-LINCS Data Coordination and Integration Center

Sustainable data and metadata management at the BD2K-LINCS Data Coordination and Integration Center

Datasets2Tools, repository and search engine for bioinformatics datasets, tools and canned analyses

Cloud computing applications for biomedical science: A perspective

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