{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:22:47Z","timestamp":1772252567567,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:00:00Z","timestamp":1706486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"COPERIMOplus","award":["Anti-Corona 840266"],"award-info":[{"award-number":["Anti-Corona 840266"]}]},{"name":"Fraunhofer \u2018Internal Programs Fraunhofer vs Corona\u2019","award":["Anti-Corona 840266"],"award-info":[{"award-number":["Anti-Corona 840266"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Although hundreds of datasets have been published since the beginning of the coronavirus pandemic, there is a lack of centralized resources where these datasets are listed and harmonized to facilitate their applicability and uptake by predictive modeling approaches. Firstly, such a centralized resource provides information about data owners to researchers who are searching datasets to develop their predictive models. Secondly, the harmonization of the datasets supports simultaneously taking advantage of several similar datasets. This, in turn, does not only ease the imperative external validation of data-driven models but can also be used for virtual cohort generation, which helps to overcome data sharing impediments. Here, we present that 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 related to COVID-19 in a comprehensive repository. We believe that our work will facilitate future research and development of personalized predictive risk models for COVID-19.<\/jats:p>","DOI":"10.3390\/data9020025","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T08:54:03Z","timestamp":1706518443000},"page":"25","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Curating, Collecting, and Cataloguing Global COVID-19 Datasets for the Aim of Predicting Personalized Risk"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7606-5228","authenticated-orcid":false,"given":"Sepehr Golriz","family":"Khatami","sequence":"first","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany"},{"name":"Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5860-6369","authenticated-orcid":false,"given":"Astghik","family":"Sargsyan","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany"},{"name":"Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria Francesca","family":"Russo","sequence":"additional","affiliation":[{"name":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2046-6145","authenticated-orcid":false,"given":"Daniel","family":"Domingo-Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany"},{"name":"Fraunhofer Center for Machine Learning, 53757 Sankt Augustin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1740-8390","authenticated-orcid":false,"given":"Andrea","family":"Zaliani","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), 22525 Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abish","family":"Kaladharan","sequence":"additional","affiliation":[{"name":"Causality Biomodels, Kinfra Hi-Tech Park, Kalamassery, Cochin 683503, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4983-6728","authenticated-orcid":false,"given":"Priya","family":"Sethumadhavan","sequence":"additional","affiliation":[{"name":"Causality Biomodels, Kinfra Hi-Tech Park, Kalamassery, Cochin 683503, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah","family":"Mubeen","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany"},{"name":"Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113 Bonn, Germany"},{"name":"Fraunhofer Center for Machine Learning, 53757 Sankt Augustin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7683-0452","authenticated-orcid":false,"given":"Yojana","family":"Gadiya","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), 22525 Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1815-0037","authenticated-orcid":false,"given":"Reagon","family":"Karki","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), 22525 Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephan","family":"Gebel","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ram Kumar","family":"Ruppa Surulinathan","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany"},{"name":"Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vanessa","family":"Lage-Rupprecht","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saulius","family":"Archipovas","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Digital Medicine (MEVIS), 28359 Bremen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geltrude","family":"Mingrone","sequence":"additional","affiliation":[{"name":"Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy"},{"name":"Department of Diabetes Research, School of Life Course Sciences, Faculty of Life Sciences & Medicine, King\u2019s College London, London WC2R 2LS, UK"},{"name":"Medicina e Chirurgia \u201cA.Gemelli\u201d, Dipartimento di Medicina e Chirurgia Traslazionale, Universit\u00e0 Cattolica del Sacro Cuore, 00168 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4153-3930","authenticated-orcid":false,"given":"Marc","family":"Jacobs","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5831-8498","authenticated-orcid":false,"given":"Carsten","family":"Claussen","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), 22525 Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Hofmann-Apitius","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, 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mortal COVID-19 cases: A systematic literature review and meta-analysis","volume":"81","author":"Zheng","year":"2020","journal-title":"J. Infect."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1016\/j.numecd.2020.07.031","article-title":"Common cardiovascular risk factors and in-hospital mortality in 3894 patients with COVID-19: Survival analysis and machine learning-based findings from the multicentre Italian CORIST Study","volume":"30","author":"Bonaccio","year":"2020","journal-title":"Nutr. Metab. Cardiovasc. Dis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1093\/cid\/ciaa539","article-title":"Risk factors associated with clinical outcomes in 323 coronavirus disease 2019 (COVID-19) hospitalized patients in Wuhan, China","volume":"71","author":"Hu","year":"2020","journal-title":"Clin. Infect. Dis."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105949","DOI":"10.1016\/j.ijantimicag.2020.105949","article-title":"Hydroxychloroquine and azithromycin as a treatment of COVID-19: Results of an open-label non-randomized clinical trial","volume":"56","author":"Gautret","year":"2020","journal-title":"Int. J. Antimicrob. Agents"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"573044","DOI":"10.3389\/fphys.2020.573044","article-title":"Heparin therapy improving hypoxia in COVID-19 patients\u2013a case series","volume":"11","author":"Negri","year":"2020","journal-title":"Front. Physiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"JC63","DOI":"10.7326\/ACPJ202006160-063","article-title":"In COVID-19, adding lopinavir\u2013ritonavir to usual care did not shorten time to clinical improvement","volume":"172","author":"Yang","year":"2020","journal-title":"Ann. Intern. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"154","DOI":"10.3947\/ic.2020.52.2.154","article-title":"Age-related morbidity and mortality among patients with COVID-19","volume":"52","author":"Kang","year":"2020","journal-title":"Infect. Chemother."},{"key":"ref_9","first-page":"178","article-title":"COVID-19 and Cardiovascular Comorbidities","volume":"130","author":"Marx","year":"2020","journal-title":"Exp. Clin. Endocrinol. Diabetes"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Remppis, J., Ganzenmueller, T., Vasconcelos, M.K., Heinzel, O., Handgretinger, R., and Renk, H. (2021). A case series of children and young people admitted to a tertiary care hospital in Germany with COVID-19. BMC Infect. Dis., 21.","DOI":"10.1186\/s12879-021-05791-8"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"The Lancet Rheumatology (2020). High-stakes heterogeneity in COVID-19. Lancet. Rheumatol., 2, e577.","DOI":"10.1016\/S2665-9913(20)30310-6"},{"key":"ref_12","first-page":"132","article-title":"Personalized predictive modeling and risk factor identification using patient similarity","volume":"2015","author":"Ng","year":"2015","journal-title":"AMIA Summits Transl. Sci. Proc."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fr\u00f6hlich, H., Balling, R., Beerenwinkel, N., Kohlbacher, O., Kumar, S., Lengauer, T., Maathuis, M.H., Moreau, Y., Murphy, S.A., and Przytycka, T.M. (2018). From hype to reality: Data science enabling personalized medicine. BMC Med., 16.","DOI":"10.1186\/s12916-018-1122-7"},{"key":"ref_14","unstructured":"Sun, K., Zhu, Z., and Lin, Z. (2019). Towards understanding adversarial examples systematically: Exploring data size, task and model factors. arXiv."},{"key":"ref_15","first-page":"149","article-title":"A machine learning perspective on Personalized Medicine: An automized, comprehensive knowledge base with ontology for pattern recognition","volume":"1","author":"Dehmer","year":"2019","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sun, Y., Butler, A., Stewart, L.A., Liu, H., Yuan, C., Southard, C.T., Kim, J.H., and Weng, C. (2021). Building an OMOP common data model-compliant annotated corpus for COVID-19 clinical trials. J. Biomed. Inform., 118.","DOI":"10.1016\/j.jbi.2021.103790"},{"key":"ref_17","first-page":"469","article-title":"Risk factors for COVID-19","volume":"28","author":"Rashedi","year":"2020","journal-title":"Infez. Med."},{"key":"ref_18","unstructured":"Lin, A.Y., Gebel, S., Li, Q.L., Madan, S., Darms, J., Bolton, E., Smith, B., Hofmann-Apitius, M., He, Y.O., and Kodamullil, A.T. (2020, January 17). CTO: A Community-Based Clinical Trial Ontology and its Applications in PubChemRDF and SCAIView. Proceedings of the 11th International Conference on Biomedical Ontologies (ICBO), Bolzano, Italy."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-020-01374-w","article-title":"The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19 research in university medicine and beyond","volume":"20","author":"Sass","year":"2020","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5703","DOI":"10.1093\/bioinformatics\/btaa1057","article-title":"The COVID-19 Ontology","volume":"36","author":"Sargsyan","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Moutchia, J., Pokharel, P., Kerri, A., McGaw, K., Uchai, S., Nji, M., and Goodman, M. (2020). Clinical laboratory parameters associated with severe or critical novel coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis. PLoS ONE, 15.","DOI":"10.1101\/2020.04.24.20078782"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.medcli.2020.05.017","article-title":"Crucial laboratory parameters in COVID-19 diagnosis and prognosis: An updated meta-analysis","volume":"155","author":"Soraya","year":"2020","journal-title":"Med. Clin."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci. Data"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kang, I.S., and Kong, K.A. (2021). Body mass index and severity\/fatality from coronavirus disease 2019: A nationwide epidemiological study in Korea. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0253640"},{"key":"ref_25","first-page":"100020","article-title":"Machine Learning-Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases","volume":"1","author":"Linden","year":"2021","journal-title":"Artif. Intell. Life Sci."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/9\/2\/25\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:51:00Z","timestamp":1760104260000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/9\/2\/25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,29]]},"references-count":25,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["data9020025"],"URL":"https:\/\/doi.org\/10.3390\/data9020025","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.11.14.21265797","asserted-by":"object"}]},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,29]]}}}