DataMed – an open source discovery index for finding biomedical datasets

Chen, Xiaoling; Gururaj, Anupama E.; Özyurt, İbrahim Burak; Liu, Ruiling; Soysal, Ergin; Cohen, Trevor; Tiryaki, Firat; Li, Yueling; Zong, Nansu; Jiang, Min; Rogith, Deevakar; Salimi, Mandana; Kim, Hyeoneui; Rocca-Serra, Philippe; González-Beltrán, Alejandra; Farcas, Claudiu; Johnson, Todd R.; Margolis, Ron; Alter, George; Sansone, Susanna Assunta; Fore, Ian; Ohno-Machado, Lucila; Grethe, Jeffrey S.; Xu, Hua

doi:10.1093/jamia/ocx121

Cited by 59 publications

(41 citation statements)

References 36 publications

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“…This does not mean that all data providers must expose metadata about their holdings in DATS. The bioCADDIE Project has demonstrated the flexibility of DATS by mapping and ingesting metadata from more than 70 data repositories into DataMed (Chen et al, 2018). However, the capabilities of data repositories vary widely.…”

Section: Discussionmentioning

confidence: 99%

Discovering Data Access and Use Requirements Using the Data Tags Suite (DATS) Model¹

Alter

González-Beltrán

Ohno-Machado

et al. 2019

Preprint

Self Cite

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This article presents elements in the Data Tags Suite (DATS) metadata schema describing data access, data use conditions, and consent information. DATS is a product of the bioCADDIE Project, which created a data discovery index for searching across all types of biomedical data. The "access and use" metadata items in DATS are designed from the perspective of a researcher who wants to find and re-use existing data. Data reuse is often controlled to protect the privacy of subjects and patients. We focus on the impact of data protection procedures on data users. However, these procedures are part of a larger environment around patient privacy protection, and this article puts DATS metadata into the context of the administrative, legal, and technical systems used to protect confidential data.

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Section: Discussionmentioning

confidence: 99%

Discovering Data Access and Use Requirements Using the Data Tags Suite (DATS) Model¹

Alter

González-Beltrán

Ohno-Machado

et al. 2019

Preprint

Self Cite

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“…Apache Solr is an open source search platform built on Apache Lucene library. Apache Lucene provides rich features to handle document such as full-text search and real-time indexing for various applications [18,19]. The Reuters news data were filtered in Apache Solr to retrieve articles that mentioned the 10 public health issues.…”

Section: Filtering News Articles On Public Health Issuesmentioning

confidence: 99%

Mining news media for understanding public health concerns

Zolnoori

Huang

Patten

et al. 2019

J. Clin. Trans. Sci.

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Introduction: News media play an important role in raising public awareness, framing public opinions, affecting policy formulation, and acknowledgment of public health issues. Traditional qualitative content analysis for news sentiments and focuses are time-consuming and may not efficiently convey sentiments nor the focuses of news media. Methods: We used descriptive statistics and state-of-art text mining to conduct sentiment analysis and topic modeling, to efficiently analyze over 3 million Reuters news articles during 2007–2017 for identifying their coverage, sentiments, and focuses for public health issues. Based on the top keywords from public health scientific journals, we identified 10 major public health issues (i.e., “air pollution,” “alcohol drinking,” “asthma,” “depression,” “diet,” “exercise,” “obesity,” “pregnancy,” “sexual behavior,” and “smoking”). Results: The news coverage for seven public health issues, “Smoking,” “Exercise,” “Alcohol drinking,” “Diet,” “Obesity,” “Depression,” and “Asthma” decreased over time. The news coverage for “Sexual behavior,” “Pregnancy,” and “Air pollution” fluctuated during 2007–2017. The sentiments of the news articles for three of the public health issues, “exercise,” “alcohol drinking,” and “diet” were predominately positive and associated such as “energy.” Sentiments for the remaining seven public health issues were mainly negative, linked to negative terms, e.g., diseases. The results of topic modeling reflected the media’s focus on public health issues. Conclusions: Text mining methods may address the limitations of traditional qualitative approaches. Using big data to understand public health needs is a novel approach that could help clinical and translational science awards programs focus on community-engaged research efforts to address community priorities.

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“…DataMed [16,17] is an open source system that facilitates discovery and indexing of biomedical datasets. In this system, users can access an integrated data using unified schema (called Data Tag Suite) and search for biomedical articles using a proposed search engine.…”

Section: Related Workmentioning

confidence: 99%

DiiS: A Biomedical Data Access Framework for Aiding Data Driven Research Supporting FAIR Principles

Deshpande

Rasin

Furst

et al. 2019

Data

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Vast amounts of clinical and biomedical research data are produced daily. These data can help enable data driven healthcare through novel biomedical discoveries, improved diagnostics processes, epidemiology, and education. However, finding, and gaining access to these data and relevant metadata that are necessary to achieve these goals remains a challenge. Furthermore, data management and enabling widespread, albeit controlled, use poses a major challenge for data producers. These data sources are often geographically distributed, with diverse characteristics, and are controlled by a host of logistical and legal factors that require appropriate governance and access control guarantees. To overcome these obstacles, a set of guiding principles under the term FAIR has been previously introduced. The primary desirable dataset properties are thus that the data should be Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we introduce and describe an abstract framework that models these ideal goals, and could be a step toward supporting data driven research. We also develop a system instantiated on our framework called the Data integration and indexing System (DiiS). The system provides an integration model for making healthcare data available on a global scale. Our research work describes the challenges inhibiting data producers, data stewards, and data brokers in achieving FAIR goals for sharing biomedical data. We attempt to address some of the key challenges through the proposed system. We evaluated our framework using the software architecture testing technique and also looked at how different challenges in data integration are addressed by our system. Our evaluation shows that the DiiS framework is a user friendly data integration system that would greatly contribute to biomedical research.

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DataMed – an open source discovery index for finding biomedical datasets

Cited by 59 publications

References 36 publications

Discovering Data Access and Use Requirements Using the Data Tags Suite (DATS) Model¹

Discovering Data Access and Use Requirements Using the Data Tags Suite (DATS) Model¹

Mining news media for understanding public health concerns

DiiS: A Biomedical Data Access Framework for Aiding Data Driven Research Supporting FAIR Principles

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DataMed – an open source discovery index for finding biomedical datasets

Cited by 59 publications

References 36 publications

Discovering Data Access and Use Requirements Using the Data Tags Suite (DATS) Model1

Discovering Data Access and Use Requirements Using the Data Tags Suite (DATS) Model1

Mining news media for understanding public health concerns

DiiS: A Biomedical Data Access Framework for Aiding Data Driven Research Supporting FAIR Principles

Contact Info

Product

Resources

About

Discovering Data Access and Use Requirements Using the Data Tags Suite (DATS) Model¹

Discovering Data Access and Use Requirements Using the Data Tags Suite (DATS) Model¹