TY - JOUR
T1 - DataMed - an open source discovery index for finding biomedical datasets
AU - Chen, Xiaoling
AU - Gururaj, Anupama E.
AU - Ozyurt, Burak
AU - Liu, Ruiling
AU - Soysal, Ergin
AU - Cohen, Trevor
AU - Tiryaki, Firat
AU - Li, Yueling
AU - Zong, Nansu
AU - Jiang, Min
AU - Rogith, Deevakar
AU - Salimi, Mandana
AU - Kim, Hyeon eui
AU - Rocca-Serra, Philippe
AU - Gonzalez-Beltran, Alejandra
AU - Farcas, Claudiu
AU - Johnson, Todd
AU - Margolis, Ron
AU - Alter, George
AU - Sansone, Susanna Assunta
AU - Fore, Ian M.
AU - Ohno-Machado, Lucila
AU - Grethe, Jeffrey S.
AU - Xu, Hua
N1 - Publisher Copyright:
© The Author 2018. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.
PY - 2018/3/1
Y1 - 2018/3/1
N2 - Objective: Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain. Materials and Methods: DataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health-funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium. It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries. In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine. Results and Conclusion: Our manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community.
AB - Objective: Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain. Materials and Methods: DataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health-funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium. It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries. In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine. Results and Conclusion: Our manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community.
KW - Data discovery index
KW - Dataset
KW - Information dissemination
KW - Information storage and retrieval
KW - Metadata
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U2 - 10.1093/jamia/ocx121
DO - 10.1093/jamia/ocx121
M3 - Article
C2 - 29346583
AN - SCOPUS:85043344475
SN - 1067-5027
VL - 25
SP - 300
EP - 308
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 3
ER -