https://researchdata.se/sv/catalogue Search results sv This is a comprehensive catalog of species-cluster representative genomes. To make this catalog, we generated 527 metagenome-assembled genomes (MAGs) from 204 microbial model communities. Moreover, we binned 305 MAGs from the metagenomes corresponding to the samples from which we established the microbial model communities. To make the genomic catalog comprehensive, we added 771 MAGs from the publicly available metagenomes collected from the Baltic Sea (Alneberg et al., 2018, 2020). Altogether, the 1603 MAGs were dereplicated into 701 species-clusters (average nucleotide identity - ANI ≥ 95%), which form the Baltic Sea genomic catalog (BalticMAG catalog). In this Figshare, we share all 701 species-cluster representative genomes. Moreover, you will find a table with metadata for the genomes. Fri, 25 Apr 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28746086 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28746086 Stockholms universitet Sarahi Garcia Armando Pacheco-Valenciana Jennah Dharamshi Luis Fernando Delgado Zambrano Anders Andersson Projektets syfte är att utveckla bättre metoder för prevention av hjärtkärlsjukdom. Den bygger på hypotesen att bildbaserad information om subklinisk åderförkalkning (i) ökar precisionen i bedömningen av risk för hjärtkärlsjukdom, (ii) förbättrar kommunikationen och förståelsen av risken, och som en följd (iii) ökar motivation för och följsamhet till evidensbaserad läkemedelsbehandling och levnadsvaneförändring. 3500 friska deltagare med låg/måttlig risk för hjärtkärlsjukdom har, utöver konventionell riskfaktorbedömning och prevention inom ramen för Västerbottens Hälsoundersökningar, under 2013-2016 genomgått ultraljudsundersökning av halskärlen och randomiserats till två grupper. I interventionsgruppen informerades deltagaren och hens läkare med bild och grafik i färg om individens subkliniska åderförkalkning. Till kontrollgruppen gavs ingen information om ultraljudsresultaten. Uppföljning efter 1, 3 och 6 år omfattar provtagning avseende kliniska riskfaktorer, blodprov för analys av biomarkörer, omfattande enkäter samt intervjuer. Efter 3 och 6 år upprepades ultraljudsundersökningen och samtliga deltagare och deras läkare informerades om resultatet. I databasen ingår även registerdata avseende förskrivning av läkemedel, exponeringsdata för luftföroreningar, hälsoundersökningsdata 10 och 20 år före ingång i studien, samt för männen mönstringsdata. Efter 10 år inhämtas registerdata avseende endpoints: insjuknande och död i hjärtkärlsjukdom. Åtkomst till VIPVIZAs dataportal och forskningsdata från VIPVIZA är möjlig i samarbete med forskare inom VIPVIZA-projektet. För närmare information kontakta PI Ulf Näslund ulf.naslund@umu.se Fri, 19 Nov 2021 12:03:13 GMT https://researchdata.se/sv/catalogue/dataset/2020-204-1 https://researchdata.se/sv/catalogue/dataset/2020-204-1 Umeå universitet Margareta Norberg Ulf Näslund Patrik Wennberg Dataset with annotated 12-lead ECG records. The exams were taken in 811 counties in the state of Minas Gerais/Brazil by the Telehealth Network of Minas Gerais (TNMG) between 2010 and 2016. And organized by the CODE (Clinical outcomes in digital electrocardiography) group. Requesting access Researchers affiliated to educational or research institutions might make requests to access this data dataset. Requests will be analyzed on an individual basis and should contain: Name of PI and host organisation; Contact details (including your name and email); and, the scientific purpose of data access request. If approved, a data user agreement will be forwarded to the researcher that made the request (through the email that was provided). After the agreement has been signed (by the researcher or by the research institution) access to the dataset will be granted. Openly available subset: A subset of this dataset (with 15% of the patients) is openly available. See: "CODE-15%: a large scale annotated dataset of 12-lead ECGs" https://doi.org/10.5281/zenodo.4916206. Content The folder contains: A column separated file containing basic patient attributes. The ECG waveforms in the wfdb format. Additional references The dataset is described in the paper "Automatic diagnosis of the 12-lead ECG using a deep neural network". https://www.nature.com/articles/s41467-020-15432-4. Related publications also using this dataset are: - [1] G. Paixao et al., “Validation of a Deep Neural Network Electrocardiographic-Age as a Mortality Predictor: The CODE Study,” Circulation, vol. 142, no. Suppl_3, pp. A16883–A16883, Nov. 2020, doi: 10.1161/circ.142.suppl_3.16883.- [2] A. L. P. Ribeiro et al., “Tele-electrocardiography and bigdata: The CODE (Clinical Outcomes in Digital Electrocardiography) study,” Journal of Electrocardiology, Sep. 2019, doi: 10/gf7pwg.- [3] D. M. Oliveira, A. H. Ribeiro, J. A. O. Pedrosa, G. M. M. Paixao, A. L. P. Ribeiro, and W. Meira Jr, “Explaining end-to-end ECG automated diagnosis using contextual features,” in Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Ghent, Belgium, Sep. 2020, vol. 12461, pp. 204--219. doi: 10.1007/978-3-030-67670-4_13.- [4] D. M. Oliveira, A. H. Ribeiro, J. A. O. Pedrosa, G. M. M. Paixao, A. L. Ribeiro, and W. M. Jr, “Explaining black-box automated electrocardiogram classification to cardiologists,” in 2020 Computing in Cardiology (CinC), 2020, vol. 47. doi: 10.22489/CinC.2020.452.- [5] G. M. M. Paixão et al., “Evaluation of mortality in bundle branch block patients from an electronic cohort: Clinical Outcomes in Digital Electrocardiography (CODE) study,” Journal of Electrocardiology, Sep. 2019, doi: 10/dcgk.- [6] G. M. M. Paixão et al., “Evaluation of Mortality in Atrial Fibrillation: Clinical Outcomes in Digital Electrocardiography (CODE) Study,” Global Heart, vol. 15, no. 1, p. 48, Jul. 2020, doi: 10.5334/gh.772.- [7] G. M. M. Paixão et al., “Electrocardiographic Predictors of Mortality: Data from a Primary Care Tele-Electrocardiography Cohort of Brazilian Patients,” Hearts, vol. 2, no. 4, Art. no. 4, Dec. 2021, doi: 10.3390/hearts2040035.- [8] G. M. Paixão et al., “ECG-AGE FROM ARTIFICIAL INTELLIGENCE: A NEW PREDICTOR FOR MORTALITY? THE CODE (CLINICAL OUTCOMES IN DIGITAL ELECTROCARDIOGRAPHY) STUDY,” Journal of the American College of Cardiology, vol. 75, no. 11 Supplement 1, p. 3672, 2020, doi: 10.1016/S0735-1097(20)34299-6.- [9] E. M. Lima et al., “Deep neural network estimated electrocardiographic-age as a mortality predictor,” Nature Communications, vol. 12, 2021, doi: 10.1038/s41467-021-25351-7.- [10] W. Meira Jr, A. L. P. Ribeiro, D. M. Oliveira, and A. H. Ribeiro, “Contextualized Interpretable Machine Learning for Medical Diagnosis,” Communications of the ACM, 2020, doi: 10.1145/3416965.- [11] A. H. Ribeiro et al., “Automatic diagnosis of the 12-lead ECG using a deep neural network,” Nature Communications, vol. 11, no. 1, p. 1760, 2020, doi: 10/drkd.- [12] A. H. Ribeiro et al., “Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network,” Machine Learning for Health (ML4H) Workshop at NeurIPS, 2018.- [13] A. H. Ribeiro et al., “Automatic 12-lead ECG classification using a convolutional network ensemble,” 2020. doi: 10.22489/CinC.2020.130.- [14] V. Sangha et al., “Automated Multilabel Diagnosis on Electrocardiographic Images and Signals,” medRxiv, Sep. 2021, doi: 10.1101/2021.09.22.21263926.- [15] S. Biton et al., “Atrial fibrillation risk prediction from the 12-lead ECG using digital biomarkers and deep representation learning,” European Heart Journal - Digital Health, 2021, doi: 10.1093/ehjdh/ztab071. Code: The following github repositories perform analysis that use this dataset: - https://github.com/antonior92/automatic-ecg-diagnosis- https://github.com/antonior92/ecg-age-prediction Related Datasets: - CODE-test: An annotated 12-lead ECG dataset (https://doi.org/10.5281/zenodo.3765780)- CODE-15%: a large scale annotated dataset of 12-lead ECGs (https://doi.org/10.5281/zenodo.4916206)- Sami-Trop: 12-lead ECG traces with age and mortality annotations (https://doi.org/10.5281/zenodo.4905618) Ethics declarations The CODE Study was approved by the Research Ethics Committee of the Universidade Federal de Minas Gerais, protocol 49368496317.7.0000.5149. Tue, 23 Nov 2021 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-15169716 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-15169716 Uppsala universitet Antonio H. Ribeiro Manoel Horta Ribeiro Gabriela M. Paixão Derick M. Oliveira Paulo R. Gomes Jéssica A. Canazart Milton P. Ferreira Carl R. Andersson Peter W. Macfarlane Wagner Meira Jr. Thomas B. Schön Antonio Luiz P. Ribeiro