General MIHCSME template

Generic metadata template describing High Content Screening data that conform to the REMBI and ISA specification. LEI-MIHCSME empty template that can serve as basis for filling in metadata. This template was created and modified from templates produced by Leiden University.

Submitter: Rohola Hosseini

Assay type: high content screening

Technology type: Imaging

Investigation: Minimal Information for High Content Screening ...

Study: MIHCSME templates

LEI-MIHCSME - empty template

This excel template is for use of describing HCS microscopy data. It was created based on ISA methodology and modified to conform REMBI recomandations.

Creator: Rohola Hosseini

Submitter: Rohola Hosseini

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NFDI4Health Task Force COVID-19 Metadata Schema Mapping

The NFDI4Health Task Force COVID-19 Metadata Schema Mapping (Metadata Schema Mapping) contains a list of properties describing a resource being registered in the Study Hub of the NFDI4Health Task Force COVID-19 (Study Hub) and how those properties align with other standards (FHIRE, CDISK, DRKS, ITRCP)

Creators: None

Submitter: Martin Golebiewski

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NFDI4Health Task Force COVID-19 Metadata Schema

The NFDI4Health Task Force COVID-19 Metadata Schema (Metadata Schema) contains a list of properties describing a resource being registered in the Study Hub of the NFDI4Health Task Force COVID-19 (Study Hub).

Creators: Aliaksandra Shutsko, Carsten Oliver Schmidt, Johannes Darms, Martin Golebiewski, Moritz Lehne, Matthias Löbe, Sophie Klopfenstein, Carina Nina Vorisek

Submitter: Martin Golebiewski

DOI: 10.15490/fairdomhub.1.datafile.3972.1

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The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19 research

Abstract (Expand)

Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing segmentation of … information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the "German Corona Consensus Dataset" (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data. Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats. Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, anamnesis, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined. Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

Authors: Julian Sass, Alexander Bartschke, Moritz Lehne, Andrea Essenwanger, Eugenia Rinaldi, Stefanie Rudolph, Kai Uwe Heitmann, Joerg Janne Vehreschild, Christof von Kalle, Sylvia Thun

Date Published: 29th Jul 2020

Publication Type: Journal

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