The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19 research in university medicine and beyond

Saß, Julian; Bartschke, Alexander; Lehne, Moritz; Essenwanger, Andrea; Rinaldi, Eugenia; Rudolph, Stefanie; Heitmann, Kai U.; Vehreschild, Jörg Janne; Kalle, Christof von; Thun, Sylvia

doi:10.21203/rs.3.rs-51348/v3

Cited by 11 publications

(12 citation statements)

References 3 publications

(3 reference statements)

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“…However, a global standard data model that comprises all COVID-19 related terms is not available as of now. Therefore, we have used three controlled vocabularies to establish the interoperability and mapping towards various datasets and data points, namely, German Corona Consensus Dataset (GECCO) [18] , the COVID-19 Ontology [19] and the Corona Virus Vocabulary (COVOC) (https://github.com/EBISPOT/covoc/raw/master/covoc.owl).…”

Section: Methodsmentioning

confidence: 99%

Curating, collecting, and cataloguing global COVID-19 datasets for the aim of predicting personalized risk

Khatami

Sepehr

Russo

et al. 2021

Preprint

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The COVID-19 data catalogue is a repository that provides a landscape view of COVID-19 studies and datasets as a putative source to enable researchers to develop personalized COVID-19 predictive risk models. The COVID-19 data catalogue currently contains over 400 studies and their relevant information collected from a wide range of global sources such as global initiatives, clinical trial repositories, publications and data repositories. Further, the curated content stored in this data catalogue is complemented by a web application, providing visualizations of these studies, including their references, relevant information such as measured variables, and the geographical locations of where these studies were performed. This resource is one of the first to capture, organize and store studies, datasets and metadata in the area of COVID-19 in a comprehensive repository. We are convinced that our work will facilitate future research and development of personalized predictive risk models of COVID-19.

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Section: Methodsmentioning

confidence: 99%

Curating, collecting, and cataloguing global COVID-19 datasets for the aim of predicting personalized risk

Khatami

Sepehr

Russo

et al. 2021

Preprint

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“…Given the harmonized but individual data collection of the three NAPKON cohorts, each maintains its dedicated eCRF. All NAPKON eCRFs contain the German Corona Consensus Dataset (GECCO-83) [31], ensuring syntactic and semantic interoperability for a core dataset via diverse international terminologies (e.g., International Statistical Classi cation of Diseases and Related Health Problems, 10th revision, German modi cation (ICD-10-GM)[32], Logical Observation Identi ers Names and Codes (LOINC) [33], the Anatomical Therapeutic Chemical Classi cation System (ATC) [34], Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) [35]) and de ned Health Level 7 (HL7) standard Fast Healthcare Interoperability Resources (FHIR) pro les [36]. The cohorts selected additional data elements by incorporating international data sets (e.g., ISARIC [37]), already established German COVID-19 cohorts (e.g., LEOSS [38], Pa-COVID-19 [39]), and suggestions of scientists (see section "Governance").…”

Section: Local (Review A) and Central (Review B) Quality Assessmentmentioning

confidence: 99%

The German National Pandemic Cohort Network (NAPKON): rationale, study design and baseline characteristics.

Schons

Pilgram

Reese

et al. 2022

Preprint

Self Cite

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The German government initiated the Network University Medicine (NUM) in early 2020 to improve national research activities on the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. To this end, 36 German Academic Medical Centers started to collaborate on 13 projects, with the largest being the National Pandemic Cohort Network (NAPKON). The NAPKON’s goal is creating the most comprehensive Coronavirus Disease 2019 (COVID-19) cohort in Germany. Within NAPKON, adult and pediatric patients are observed in three complementary cohort platforms (Cross-Sectoral, High-Resolution and Population-Based) from the initial infection until up to three years of follow-up. Study procedures comprise comprehensive clinical and imaging diagnostics, quality-of-life assessment, patient-reported outcomes and biosampling. The three cohort platforms build on four infrastructure core units (Interaction, Biosampling, Epidemiology, and Integration) and collaborations with NUM projects. Key components of the data capture, regulatory, and data privacy are based on the German Centre for Cardiovascular Research. By December 01, 2021, 34 university and 34 non-university hospitals have enrolled 4,241 patients with local data quality reviews performed on 2,812 (66%). 47% were female, the median age was 53 (IQR: 38-63)) and 3 pediatric cases were included. 30% of patients were hospitalized, 11% admitted to an intensive care unit, and 4% of patients deceased while enrolled. 7,143 visits with biosampling in 3,595 patients were conducted by November 29, 2021. In this overview article, we summarize NAPKON’s design, relevant milestones including first study population characteristics, and outline the potential of NAPKON for German and international research activities.Trial registration:· https://clinicaltrials.gov/ct2/show/NCT04768998· https://clinicaltrials.gov/ct2/show/NCT04747366· https://clinicaltrials.gov/ct2/show/NCT04679584

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“…Akindtande et al [19] present a dataset that investigates the magnitude of the misinformation content influencing scepticisms about the novel COVID-19 pandemic in Africa and the data is collected via an electronic questionnaire method from twenty-one Africa countries. In medicine, Sass et al 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 [20].…”

Section: A Covid-19 Datasetsmentioning

confidence: 99%

Dataset of Coronavirus Content From Instagram With an Exploratory Analysis

et al. 2021

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The novel coronavirus (COVID-19) pandemic outbreak is drastically shaping and reshaping many aspects of our life, with a huge impact on our social life. In this era of lockdown policies in most of the major cities around the world, we see a huge increase in people and professionals' engagement in social media. Online Social Networks are playing an important role in news propagation as well as keeping people in contact. At the same time, social media is both a blessing and a curse as the coronavirus infodemic has become a major concern, and is already a topic that needs special attention and further research. In this study, we publish a multilingual coronavirus (COVID-19) Instagram dataset that we have continuously collected during the first wave of the pandemic from 5 January 2020 to 30 May 2020. The dataset contains 25.7K posts, 829K comments, and 3.2M likes in various subjects from different publishers such as 'public accounts', 'fake accounts (bots)', 'newsagencies', 'influencers', 'celebrities', 'business pages', etc. In addition to the dataset, this paper provides an analysis of the behaviour of the publishers. We study the behavioural aspects of the users in terms of their engagement, use of hashtags, activities, reactions as well as a full analysis of the published content related to the COVID-19. We believe this contribution helps the research community to better understand the dynamics behind this phenomenon in Instagram, as one of the major social media.

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The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19 research in university medicine and beyond

Cited by 11 publications

References 3 publications

Curating, collecting, and cataloguing global COVID-19 datasets for the aim of predicting personalized risk

Curating, collecting, and cataloguing global COVID-19 datasets for the aim of predicting personalized risk

The German National Pandemic Cohort Network (NAPKON): rationale, study design and baseline characteristics.

Dataset of Coronavirus Content From Instagram With an Exploratory Analysis

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