This paper is in the following e-collection/theme issue:

Public Health Data Analytics (89) Clinical Informatics (1328) Theoretical Frameworks and Concepts (340) Data Science (358) Big Data (308) Public (e)Health, Digital Epidemiology and Public Health Informatics (1013) Datasets (19) Innovations in Clinical Trials and Research Data Management (124) Secondary Use of Clinical Data for Research and Surveillance (475) Health Care Quality and Health Services Research (337)

Published on in Vol 16 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56237, first published .
Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study

Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study

Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study

Authors of this article:

David Amadi1 Author Orcid Image ;   Sylvia Kiwuwa-Muyingo2 Author Orcid Image ;   Tathagata Bhattacharjee1 Author Orcid Image ;   Amelia Taylor3 Author Orcid Image ;   Agnes Kiragga2 Author Orcid Image ;   Michael Ochola2 Author Orcid Image ;   Chifundo Kanjala4 Author Orcid Image ;   Arofan Gregory5 Author Orcid Image ;   Keith Tomlin1 Author Orcid Image ;   Jim Todd1 Author Orcid Image ;   Jay Greenfield5 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Mugotitsa B, Andeso P, Owoko H, Momanyi R, Mailosi D, Ssebunnya J, Tsofa B, Greenfield J, Kiragga A, Todd J. Enhancing mental health outcomes through a theory of change: supporting open-source data science platforms to promote FAIR data and evidence-based decisions. BMC Health Services Research 2025;25(1) View
  2. Petit P, Vuillerme N. Datagraphy: toward a systematic approach to dataset discovery. GigaScience 2025;14 View