The Big Data revolution requires that biomedical scientists be able to locate, analyze, and integrate the large datasets that now pervade biomedicine. Such work is possible only when experimental datasets are made available online and when they are annotated with metadata that explain how the data are organized, what the data represent, and how the data were collected. The Center for Expanded Data Annotation and Retrieval (CEDAR) will take advantage of the recent growth in community-driven metadata standards to develop innovative computational methods to ease the authoring and use of metadata annotations.
Our specific aims focus on working with communities of investigators to standardize descriptions of the data generated through biomedical studies; creating a computational collective for development, evaluation, use, and refinement of metadata templates for describing laboratory studies; developing a comprehensive and open repository of metadata that will inform the learning algorithms that will drive much of our Center's technology; training the biomedical community in the use of metadata and in CEDAR's resources; and evaluating our work in the context of ImmPort, an NIAID-supported multi-assay data repository that will offer end-to-end opportunities to demonstrate and validate our ideas. We anticipate a growing community of users, starting with the Human Immunology Project Consortium, then the BD2K Center Consortium, then the Stanford Digital Repository, growing until we have developed a wide user base leading to measurable changes in the quality of the metadata used to annotate online datasets. The Overall description of our project provides a synopsis of CEDAR's activities and overall specific aims.

Public Health Relevance

The ability to locate, analyze, and integrate Big Data depends on the metadata that describe data sets and the experiments that have been performed. This project will facilitate annotation of data with high quality metadata. The results of our work will lead to better data and, thus, to better science. Ultimately, such results will led to better health.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54AI117925-05
Application #
9475722
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Mathur, Punam
Project Start
2014-09-29
Project End
2019-08-31
Budget Start
2018-05-01
Budget End
2019-08-31
Support Year
5
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Stanford University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
Bukhari, Syed Ahmad Chan; Martínez-Romero, Marcos; O' Connor, Martin J et al. (2018) CEDAR OnDemand: a browser extension to generate ontology-based scientific metadata. BMC Bioinformatics 19:268
Sweeney, Timothy E; Azad, Tej D; Donato, Michele et al. (2018) Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters. Crit Care Med 46:915-925
Sweeney, Timothy E; Wynn, James L; Cernada, María et al. (2018) Validation of the Sepsis MetaScore for Diagnosis of Neonatal Sepsis. J Pediatric Infect Dis Soc 7:129-135
Bukhari, Syed Ahmad Chan; O'Connor, Martin J; Martínez-Romero, Marcos et al. (2018) The CAIRR Pipeline for Submitting Standards-Compliant B and T Cell Receptor Repertoire Sequencing Studies to the National Center for Biotechnology Information Repositories. Front Immunol 9:1877
Sweeney, Timothy E; Haynes, Winston A; Vallania, Francesco et al. (2017) Methods to increase reproducibility in differential gene expression via meta-analysis. Nucleic Acids Res 45:e1
Haynes, Winston A; Vallania, Francesco; Liu, Charles et al. (2017) EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY. Pac Symp Biocomput 22:144-153
Panahiazar, Maryam; Dumontier, Michel; Gevaert, Olivier (2017) Predicting biomedical metadata in CEDAR: A study of Gene Expression Omnibus (GEO). J Biomed Inform 72:132-139
Martínez-Romero, Marcos; O'Connor, Martin J; Shankar, Ravi D et al. (2017) Fast and Accurate Metadata Authoring Using Ontology-Based Recommendations. AMIA Annu Symp Proc 2017:1272-1281
Raymond, Steven L; López, María Cecilia; Baker, Henry V et al. (2017) Unique transcriptomic response to sepsis is observed among patients of different age groups. PLoS One 12:e0184159
Sweeney, Timothy E; Khatri, Purvesh (2017) The authors reply. Crit Care Med 45:e457-e458

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