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Developing a standardized but extendable framework to increase the findability of infectious disease datasets.
Tsueng, Ginger; Cano, Marco A Alvarado; Bento, José; Czech, Candice; Kang, Mengjia; Pache, Lars; Rasmussen, Luke V; Savidge, Tor C; Starren, Justin; Wu, Qinglong; Xin, Jiwen; Yeaman, Michael R; Zhou, Xinghua; Su, Andrew I; Wu, Chunlei; Brown, Liliana; Shabman, Reed S; Hughes, Laura D.
Afiliação
  • Tsueng G; Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA. gtsueng@scripps.edu.
  • Cano MAA; Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.
  • Bento J; Department of Computer Science, Boston College, 245 Beacon St, Chestnut Hill, MA, 02467, USA.
  • Czech C; Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.
  • Kang M; Division of Pulmonary and Critical Care, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.
  • Pache L; Infectious and Inflammatory Disease Center, Immunity and Pathogenesis Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA.
  • Rasmussen LV; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
  • Savidge TC; Texas Children's Microbiome Center & Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX, 77030, USA.
  • Starren J; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
  • Wu Q; Texas Children's Microbiome Center & Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX, 77030, USA.
  • Xin J; Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.
  • Yeaman MR; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Zhou X; Divisions of Molecular Medicine and Infectious Diseases, Harbor-UCLA Medical Center, Torrance, CA, 90502, USA.
  • Su AI; Lundquist Institute for Infection & Immunity at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA.
  • Wu C; Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.
  • Brown L; Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.
  • Shabman RS; Scripps Research Translational Institute, La Jolla, CA, 92037, USA.
  • Hughes LD; Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, 92037, USA.
Sci Data ; 10(1): 99, 2023 02 23.
Article em En | MEDLINE | ID: mdl-36823157
ABSTRACT
Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças Transmissíveis / Conjuntos de Dados como Assunto Tipo de estudo: Guideline Limite: Humans Idioma: En Revista: Sci Data Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças Transmissíveis / Conjuntos de Dados como Assunto Tipo de estudo: Guideline Limite: Humans Idioma: En Revista: Sci Data Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos