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• HGSOC genome-wide SNP (project ITH)

  SNP array genotyping of multi-site HGSOC samples

  Dataset EGAD00010002482

• Melanoma multi site metastases

  These are caveman, pindel, battenberg and brass calls for index patients' metastatic melanoma genomes within this study.

  Dataset EGAD00001005483

• Anaplastic meingioma methylation

  Methylation of anaplastic meningiona samples

  Dataset EGAD00010001629

• Error-corrected sequencing of 26 driver genes

  387 swab samples were sequenced. The bam files contain consensus reads.

  Dataset EGAD50000000079

• WGS in insulinomas

  Whole-Genome Sequencing datasets of insulinoma samples and paired blood controls

  Dataset EGAD50000000464

• Resuscitation Outcomes Consortium (ROC) Cardiac Epidemiologic Registry (Cardiac Epistry) Version 3 (ROC-Cardiac Epistry 3-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Available Data The ROC Cardiac Epistry version 3 include all cardiac arrest cases entered into the ROC database from April 1, 2011 to June 30, 2015. This is separate from versions 1 and 2 because of the major changes in how and which data were collected. Version 3 represents the following major changes to the structure and data collection: separate ED Arrival and Hospital Admits forms, mandatory ED and Hospital elements and a revamped CPR Process form. For ROC traumatic injury Epistry data please see: ROC-Trauma Epistry Objectives To build a prospective population-based registry of participants with out-of-hospital cardiac arrest responded to by Emergency Medical Services (EMS). Specific aims included: to establish whether the results of Resuscitation Outcomes Consortium (ROC) trials can be generalized to the larger population of participants that experience cardiac arrest; to more fully establish the burden of cardiac arrest; and to examine the relationships between variation in EMS structure and process, regional and periodic factors, and participant outcomes. Background Cardiac arrest is a common, serious, debilitating and costly public health problem. Although there has been a steady decline in morbidity and mortality from most cardiovascular diseases, high mortality rates for out-of-hospital cardiac arrests continue to pose a challenge for healthcare providers and a significant public health burden. The Resuscitation Outcomes Consortium (ROC) was established in 2004 to conduct clinical research in the areas of cardiopulmonary arrest and life-threatening traumatic injury with the overall goal of improving resuscitation outcomes. Participant and care characteristics can predict favorable outcomes in cardiac arrests, but there is still a wide variation in outcomes that is not well understood. EMS factors such as service level, number of responding providers, use of procedures or drugs in the field, training, quality assurance/feedback, and response time intervals also vary significantly by region. Variations in geographic, socioeconomic and periodic factors may also be associated with differences in outcomes. Prior to ROC Cardiac Epistry, there were no North American population-based registries for out-of-hospital cardiac arrests. Therefore there was a need for standardized data collection of out-of-hospital cardiac arrests in diverse geographic locations in order to identify the independent effects of prognostic or treatment factors accounting for variations in survival. Participants There were 120,306 participants. Design ROC Epistry collected standardized data regarding episode-specific factors, participant demographics, clinical information, pre-hospital interventions and disposition, hospital information, and participant outcome for all out-of-hospital cardiac arrests in the ROC regions. Each ROC site had to ensure capture of all eligible cases within the EMS service areas. Out-of-hospital data were extracted from existing databases whenever possible and augmented with targeted review of EMS reports. Hospital data were abstracted directly from the hospital file in most cases. Alternative methods included linkage to death registries and obituaries if the death occurred within 30 days. Sites submitted data using a web-based interface or batch uploads. Participants were not contacted directly.

  Study phs003726

• Resuscitation Outcomes Consortium (ROC) Cardiac Epidemiologic Registry (Cardiac Epistry) Versions 1 and 2 (ROC-Cardiac Epistry 1 and 2-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Available Data: The ROC Cardiac Epistry versions 1 and 2 include all cardiac arrest cases entered into the ROC database from December 1, 2005 to April 1, 2011. ROC Cardiac Epistry 3 includes cardiac arrest cases captured between 2011 and 2015, and introduced significant changes in how and which data were collected.For ROC traumatic injury Epistry data, please see: ROC-Trauma Epistry Objectives: To build a prospective population-based registry of participants with out-of-hospital cardiac arrest responded to by Emergency Medical Services (EMS). Specific aims: to establish whether the results of Resuscitation Outcomes Consortium (ROC) trials can be generalized to the larger population of participants that experience cardiac arrest;to more fully establish the burden of cardiac arrest; and to examine the relationships between variation in EMS structure and process, regional and periodic factors, and participant outcomes.Background: Cardiac arrest is a common, serious, debilitating and costly public health problem. Although there has been a steady decline in morbidity and mortality from most cardiovascular diseases, high mortality rates for out-of-hospital cardiac arrests continue to pose a challenge for healthcare providers and a significant public health burden. The Resuscitation Outcomes Consortium (ROC) was established in 2004 to conduct clinical research in the areas of cardiopulmonary arrest and life-threatening traumatic injury with the overall goal of improving resuscitation outcomes. Participant and care characteristics can predict favorable outcomes in cardiac arrests, but there is still a wide variation in outcomes that is not well understood. EMS factors such as service level, number of responding providers, use of procedures or drugs in the field, training, quality assurance/feedback, and response time intervals also vary significantly by region. Variations in geographic, socioeconomic and periodic factors may also be associated with differences in outcomes.Prior to ROC Cardiac Epistry, there were no North American population-based registries for out-of-hospital cardiac arrests. Therefore there was a need for standardized data collection of out-of-hospital cardiac arrests in diverse geographic locations in order to identify the independent effects of prognostic or treatment factors accounting for variations in survival. Participants: The registry included 109,326 cardiac arrest events from 264 EMS agencies transporting to 287 acute care hospitals from the following regional centers: Birmingham, Alabama; Dallas, Texas; Iowa City, Iowa; Milwaukee, Wisconsin; Pittsburgh, Pennsylvania; Portland, Oregon; San Diego, California; Seattle/King County, Washington; Ottawa, Ontario; Toronto, Ontario; and Vancouver, British Columbia.Design: ROC Epistry collected standardized data regarding episode-specific factors, participant demographics, clinical information, pre-hospital interventions and disposition, hospital information, and participant outcome for all out-of-hospital cardiac arrests in the ROC regions. Each ROC site had to ensure capture of all eligible cases within the EMS service areas. Out-of-hospital data were extracted from existing databases whenever possible and augmented with targeted review of EMS reports. Hospital data were abstracted directly from the hospital file in most cases. Alternative methods included linkage to death registries and obituaries if the death occurred within 30 days. Sites submitted data using a web-based interface or batch uploads. Participants were not contacted directly.

  Study phs003803

• Rapid Acceleration of Diagnostics - Digital Health Technologies (RADx-DHT): NIH Digital Health Solutions for COVID-19: Clear2Go - A Digital Identity Wallet for Health Status

  Clear2Go is a solution/app that provides digital, non-refutable cryptographic proof of testing or vaccination that can be used to evaluate risk of allowing individuals to return to normal work, travel, and public life activities. It is a mobile service that maps a person's vetted identity and biometrics to the phone and then cryptographically binds it with their COVID-19 test and vaccination records. The person can then prove their status by utilizing the credentials with a QRCode or NFC tap.DOI: https://rapids.ll.mit.edu/10.57895/b2d6-8060

  Study phs002628

• SF3B1 splicing signature

  We describe individuals with de novo pathogenic variants in SF3B1. SF3B1 is a core component of the U2 snRNP complex that ensures accurate branchpoint and 3′ splice-site recognition, and its mutations induce aberrant splicing that contributes to tumorigenesis, notably in myelodysplastic syndromes. We aim to study splicing defects associated with constitutional pathogenic variants.

  Study EGAS50000001473

• Data distribution Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue Here, we show the amount of data distributed by the EGA. Data Distributed / Live EGA Data Distribution / Live SFTP Aspera HTSGET lineChartLive("svg#ega-dist-live", document.querySelector("#ega-figure-dist-live figcaption")); Data Distributed The below figure represents the cumulative EGA data distribution via the download clients(v2 and v3.x api versions) using HTTPS, FTP, and download boxes using ASPERA. You can see cumulative values by clicking "Cumulative". EGA Data Distribution //lineChart('#ega-data-distribution svg.stats', 'https://stats.ega-archive.org/distribution', // ['Amount of Distributed Data', ['Aspera', 'Download Clients', 'Total']]) curveChart('#ega-data-distribution', [ { domId: 'stats-aspera', label: 'Aspera', url: 'https://stats.ega-archive.org/distribution/aspera' }, { domId: 'stats-sftp' , label: 'SFTP', url: 'https://stats.ega-archive.org/distribution/sftp' }, //{ domId: 'stats-htsget', label: 'HTSGET', url: 'https://stats.ega-archive.org/distribution/htsget' }, { domId: 'stats-streamer' , label: 'Download Client', url: 'https://stats.ega-archive.org/distribution/streamer' }, { domId: 'stats-total' , label: 'Total', url: 'https://stats.ega-archive.org/distribution/total' } ] );

  Documentation about/statistics/distribution