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Correction: Toward a Domain-Overarching Metadata Schema for Making Health Research Studies FAIR (Findable, Accessible, Interoperable, and Reusable): Development of the NFDI4Health Metadata Schema

Correction: Toward a Domain-Overarching Metadata Schema for Making Health Research Studies FAIR (Findable, Accessible, Interoperable, and Reusable): Development of the NFDI4Health Metadata Schema

Haitham Abaza, Aliaksandra Shutsko, Sophie A I Klopfenstein, Carina N Vorisek, Carsten Oliver Schmidt, Claudia Brünings-Kuppe, Vera Clemens, Johannes Darms, Sabine Hanß, Timm Intemann, Franziska Jannasch, Elisa Kasbohm, Birte Lindstädt, Matthias Löbe, Katharina Nimptsch, Ute Nöthlings, Marisabel Gonzalez Ocanto, Tracy Bonsu Osei, Ines Perrar, Manuela Peters, Tobias Pischon, Ulrich Sax, Matthias B Schulze, Florian Schwarz, Carolina Schwedhelm, Sylvia Thun, Dagmar Waltemath, Atinkut A Zeleke, Wolfgang Müller, Martin Golebiewski

JMIR Med Inform 2025;13:e78151

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Toward a Domain-Overarching Metadata Schema for Making Health Research Studies FAIR (Findable, Accessible, Interoperable, and Reusable): Development of the NFDI4Health Metadata Schema

Toward a Domain-Overarching Metadata Schema for Making Health Research Studies FAIR (Findable, Accessible, Interoperable, and Reusable): Development of the NFDI4Health Metadata Schema

Haitham Abaza, Aliaksandra Shutsko, Sophie A I Klopfenstein, Carina N Vorisek, Carsten Oliver Schmidt, Claudia Brünings-Kuppe, Vera Clemens, Johannes Darms, Sabine Hanß, Timm Intemann, Franziska Jannasch, Elisa Kasbohm, Birte Lindstädt, Matthias Löbe, Katharina Nimptsch, Ute Nöthlings, Marisabel Gonzalez Ocanto, Tracy Bonsu Osei, Ines Perrar, Manuela Peters, Tobias Pischon, Ulrich Sax, Matthias B Schulze, Florian Schwarz, Carolina Schwedhelm, Sylvia Thun, Dagmar Waltemath, Hannes Wünsche, Atinkut A Zeleke, Wolfgang Müller, Martin Golebiewski

JMIR Med Inform 2025;13:e63906

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A New Natural Language Processing–Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study

A New Natural Language Processing–Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study

Bruno Paiva, Marcos André Gonçalves, Leonardo Chaves Dutra da Rocha, Milena Soriano Marcolino, Fernanda Cristina Barbosa Lana, Maira Viana Rego Souza-Silva, Jussara M Almeida, Polianna Delfino Pereira, Claudio Moisés Valiense de Andrade, Angélica Gomides dos Reis Gomes, Maria Angélica Pires Ferreira, Frederico Bartolazzi, Manuela Furtado Sacioto, Ana Paula Boscato, Milton Henriques Guimarães-Júnior, Priscilla Pereira dos Reis, Felício Roberto Costa, Alzira de Oliveira Jorge, Laryssa Reis Coelho, Marcelo Carneiro, Thaís Lorenna Souza Sales, Silvia Ferreira Araújo, Daniel Vitório Silveira, Karen Brasil Ruschel, Fernanda Caldeira Veloso Santos, Evelin Paola de Almeida Cenci, Luanna Silva Monteiro Menezes, Fernando Anschau, Maria Aparecida Camargos Bicalho, Euler Roberto Fernandes Manenti, Renan Goulart Finger, Daniela Ponce, Filipe Carrilho de Aguiar, Luiza Margoto Marques, Luís César de Castro, Giovanna Grünewald Vietta, Mariana Frizzo de Godoy, Mariana do Nascimento Vilaça, Vivian Costa Morais

JMIR Med Inform 2024;12:e54246

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