Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1392&sort=award_id
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=award_id", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=award_id", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1393&sort=award_id", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1391&sort=award_id" }, "data": [ { "type": "Grant", "id": "10554", "attributes": { "award_id": "75N94021C00007-P00005-9999-1", "title": "DEVELOPMENT OF AN ELECTRONIC CARE PLAN FOR PERSONS WITH MULTIPLE CHRONIC CONDITIONS (E-CARE)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-30", "end_date": "2023-09-29", "award_amount": 111971, "principal_investigator": { "id": 26567, "first_name": "EVELYN", "last_name": "GALLEGO", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": null, "abstract": "Through funding made available from the Department of Health and Human Services’ Patient-Centered Outcomes Research Trust Fund (PCOR TF), the NIDDK has partnered with the Agency for Healthcare Research and Quality (AHRQ) on the development and testing of a pilot suite of interoperable electronic (e-) care planning tools to facilitate aggregation and sharing of critical patient-centered data across home-, community-, clinic- and research-based settings for persons with multiple chronic conditions (MCC), including chronic kidney disease (CKD), type 2 diabetes mellitus (T2D), cardiovascular disease (CVD), chronic pain, and post-acute sequelae of SARS-CoV-2 infection (PASC). Care plans are a prominent part of multifaceted, care coordination interventions that reduce mortality and hospitalizations and improve disease management and satisfaction. In addition, proactive care planning promotes person-centeredness, improves outcomes, and reduces the cost of care. Development of care plans based on standardized data—leveraging standards such as Fast Healthcare Interoperability Resources (FHIR) and Substitutable Medical Apps, Reusable Technology (SMART) (https://smarthealthit.org/) on FHIR—has been proposed as a method for enabling electronic systems to pull together and share data elements automatically and dynamically. Such aggregated data would not only provide actionable information to identify and achieve health and wellness goals for individuals with MCC, but also would reduce missingness and improve quality of point-of-care data for use in pragmatic research. The NIDDK is leading the development component of the project, while real-world testing is being led by AHRQ.", "keywords": [ "Cardiovascular Diseases", "Caregivers", "Caring", "Chronic", "Chronic Kidney Failure", "Coordination and Collaboration", "Data", "Data Aggregation", "Data Element", "Development", "Disease Management", "Fast Healthcare Interoperability Resources", "Funding", "Goals", "Health", "Health Status", "Home", "Hospitalization", "Individual", "Intervention", "Medical", "Methods", "National Institute of Diabetes and Digestive and Kidney Diseases", "Non-Insulin-Dependent Diabetes Mellitus", "Outcomes Research", "Patient-Focused Outcomes", "Patients", "Persons", "Post-Acute Sequelae of SARS-CoV-2 Infection", "Research", "System", "Technology", "Testing", "Trust", "United States Agency for Healthcare Research and Quality", "United States Dept. of Health and Human Services", "base", "care coordination", "care costs", "chronic pain", "community clinic", "data management", "data sharing", "data standards", "improved", "improved outcome", "interoperability", "mortality", "multiple chronic conditions", "patient oriented", "point of care", "satisfaction", "tool" ], "approved": true } }, { "type": "Grant", "id": "9362", "attributes": { "award_id": "75N94021F00006-P00001-0-1", "title": "SURVEY COLLECTION AND ANALYSIS REGARDING COVID-19 DRIVEN DRINKING HABITS", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Alcohol Abuse and Alcoholism (NIAAA)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2020-11-04", "end_date": "2021-07-03", "award_amount": 194513, "principal_investigator": { "id": 25110, "first_name": "CAROLINA", "last_name": "BARBOSA", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 809, "ror": "", "name": "RESEARCH TRIANGLE INSTITUTE", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 809, "ror": "", "name": "RESEARCH TRIANGLE INSTITUTE", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has created profound changes in a range of social, behavioral, and economic factors that are known to affect drinking behaviors, including incomes and employment, stress, opportunities for socializing, and alcohol availability. Simultaneous upheavals in these and other factors may create profound changes in drinking patterns and, in turn, the need for prevention and treatment services as well as health care for the wide range of alcohol-related health conditions. The National Institute on Alcohol Abuse and Alcoholism has a clear interest in monitoring and understanding these changes and their effects on public health. The purpose of this contract is to build on the findings of a previous survey in order to obtain insights into changes in alcohol consumption and closely related behaviors associated with the COVID-19 pandemic.", "keywords": [ "Affect", "Alcohol consumption", "Alcohols", "Behavior", "COVID-19", "COVID-19 pandemic", "Collection", "Contracts", "Economic Factors", "Employment", "Habits", "Health", "Healthcare", "Income", "Individual", "International", "Methods", "Monitor", "National Institute on Alcohol Abuse and Alcoholism", "Pattern", "Public Health", "Reporting", "Socialization", "Stress", "Surveys", "United States", "alcohol availability", "behavioral economics", "drinking", "drinking behavior", "insight", "interest", "prevention service", "response", "social", "treatment services" ], "approved": true } }, { "type": "Grant", "id": "12416", "attributes": { "award_id": "75N94023F00171-0-0-1", "title": "NICHD STANDARDIZE GOVERNANCE METADATA FOR PEDIATRIC COVID-19 DATA LINKAGE", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-28", "end_date": "2024-11-27", "award_amount": 567015, "principal_investigator": { "id": 28364, "first_name": "", "last_name": "", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2099, "ror": "", "name": "MITRE CORPORATION", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Centers for Medicare & Medicaid Services (CMS) Alliance to Modernize Healthcare Federally Funded Research and Development Center (Health FFRDC) Strategic Services in Support of Standardize Governance Metadata for Pediatric COVID-19 Data Linkage for the National Institutes of Health (NIH), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) The National Institutes of Health (NIH), a part of the U.S. Department of Health and Human Services (HHS), is the nation’s primary medical research agency, making important discoveries that improve health and saves lives. NIH is now one of the world’s foremost biomedical research agencies and serves as the focal point for biomedical research within the Federal Government. NIH began in 1887, and today, is comprised of 27 separate Institutes and Centers (ICs), most of which are located in Bethesda, Maryland. The NIH works toward that mission by: 1) conducting research in its own laboratories; 2) supporting non-Federal scientists at universities, teaching hospitals, and other academic institutions around the world; 3) sponsoring training programs for research investigators; and 4) fostering the communication of research-based health information. The Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) is leading an effort to assess the usage of privacy preserving record linkage (PPRL) for pediatric patient-centered outcomes research, with a focus on pediatric COVID research. PPRL holds significant promise for enhancing the value of de novo clinical research data collection, through linkages across different studies and linkages with HHS administrative and survey datasets. NIH has determined that individual-level dataset linkages could enable researchers to deduplicate subjects across studies, introduce new variables into analysis plans, and reduce costly redundancies in the generation of genomic sequencing data. In order for individual-level datasets to be linked using PPRL or any other linkage method, however, researchers and data stewards must ensure that the linkages are appropriate, based on if or how the data were consented for use by the research participant, whether the scope of linkage encompasses other data sources, and if there are regulatory and/or legal frameworks that apply to the use of the data. It is important to understand how the resulting linked dataset inherits consent and/or regulation-based data use limitations that are associated with the original datasets and if new limitations arise, for example through increased identifiability of linked datasets. This project shall develop a technical approach to streamline decisions made by researchers, data stewards, and linkage honest brokers about the acceptable linkage of clinical research and administrative health datasets and the subsequent usage of linked datasets. The broader purpose of the project is to enhance appropriate development and analysis of research datasets that track research participants’ demographics, treatments, health outcomes, multiple modalities of –omics data, and other related variables such as social determinants of health, over time. The project may develop a governance database for multiple NIH and HHS clinical and administrative datasets, providing structure and provenance for all dataset governance information regarding linkage and use of those data. The project shall summarize the relevant standards in use or in development by US and international research organizations. The project shall then develop and validate approaches to standardize and digitize as contextual metadata consent, policy, and regulation-based data use requirements, based on initial analyses of pediatric COVID-19 and other high-priority HHS clinical and administrative datasets. The approach will include the development of a generalizable governance metadata schema, the integration of that governance metadata schema into real-world data collection tool(s), and a pilot evaluation of the tool enhancement to support record linkage decision making. Standardized, digitized consent and regulatory metadata shall provide the foundation for streamlining decision making processes about the appropriateness of linking individual-level datasets and using the linked data for research purposes. This project is intended to provide a biomedical and health research data governance use case and supporting technology resource to the broad field of privacy preserving data sharing and analytics. NIH NICHD requires the support of a qualified Health FFRDC to address specific long-term business and technical needs that cannot be met by any other contractor or in-house resources. The unique institutional characteristics of the Health FFRDC provide the following essential benefits to NIH NICHD: Objectivity and Freedom from Conflicts-of-Interest - The work described in this statement of work (SOW) necessitates the engagement of the Health FFRDC because this work contains sensitive information that requires an impartial, objective party that has no conflicts of interest in the planning and decision-making to best meet the needs of NIH and advances the taxpayer interest. NIH has considered and determined that a private contractor would not be suited for this work because of the potential self-interest in benefiting from either competing for downstream work or indirectly benefiting from connections to other entities that may benefit from the competition. As such, NIH has determined that the Health FFRDC is uniquely positioned to provide objective and unbiased advice because it maintains a not-for-profit status, does not compete for downstream work, and does not have financial or other interests related to the program, thus ensuring that the advice and support provided are solely in NIH’s best interest. Access to Propriety and Sensitive Information – By design, FFRDCs are given access to information that is beyond that which is common to the normal contractual relationship. This work may contain government sensitive information that a contractor is not allowed to access and commercial proprietary information that a contractor may use for competitive advantage, thus necessitating the engagement of the Health FFRDC. Comprehensive Knowledge of NIH needs and Long-term Continuity - This project requires an understanding of NIH’s key mission and objectives as well as understanding of technology expertise. The Health FFRDC has access to a strong team of members who are well-versed in the technical aspects of the work as well as are knowledgeable about HHS agencies’ mission and work. Developing a technical approach to streamline dataset linkage decision making requires this expertise. This project is funded by the HHS Office of the Secretary’s Patient Centered Outcome Research Trust Fund (OS-PCORTF) as part of the OS-PCORTF strategic plan for building data capacity for patient-centered outcomes research through coordinated, systematic efforts across federal agencies. Data capacity, in PCOR context, refers to the availability and sustainability of data and analytic resources to address national health priorities. The OS-PCORTF strategic plan addresses a broad range of data sources, including clinical, clinical trial, social services, administrative and claims data, and notes that issues of availability, quality, accessibility, and interoperability are significant hurdles to PCOR research. Health data sources, data linkage, and data analysis are the cornerstones of the PCOR data infrastructure. This project addresses the “Linking of Clinical and Other Data for Research” goal within the OS-PCORTF plan.", "keywords": [ "Access to Information", "Address", "Biomedical Research", "Businesses", "COVID-19", "Characteristics", "Childhood", "Clinical", "Clinical Research", "Clinical Trials", "Communication", "Conflict of Interest", "Consent", "Contractor", "Data", "Data Analyses", "Data Analytics", "Data Collection", "Data Linkages", "Data Set", "Data Sources", "Databases", "Decision Making", "Development", "Elements", "Ensure", "Evaluation", "Federal Government", "Fostering", "Foundations", "Freedom", "Funding", "Generations", "Genomics", "Goals", "Government", "Health", "Health Priorities", "Healthcare", "Human", "Individual", "Inherited", "Institution", "International", "Knowledge", "Laboratories", "Legal", "Link", "Maryland", "Medical Research", "Metadata", "Methods", "Mission", "Modality", "Modernization", "National Institute of Child Health and Human Development", "Outcome", "Outcomes Research", "Participant", "Patient-Focused Outcomes", "Policies", "Positioning Attribute", "Privatization", "Process", "Qualifying", "Regulation", "Research", "Research Personnel", "Resources", "Scientist", "Services", "Social Work", "Source", "Standardization", "Strategic Planning", "Structure", "Surveys", "Teaching Hospitals", "Technology", "Time", "Training Programs", "Trust", "United States Centers for Medicare and Medicaid Services", "United States Dept. of Health and Human Services", "United States National Institutes of Health", "Universities", "Work", "coronavirus disease", "cost", "data infrastructure", "data sharing", "data standards", "demographics", "design", "genetic linkage analysis", "health data", "improved", "interest", "interoperability", "member", "pediatric patients", "privacy preservation", "programs", "research and development", "social health determinants", "tool" ], "approved": true } }, { "type": "Grant", "id": "9094", "attributes": { "award_id": "75N95020C00010-0-9999-1", "title": "NCATS RESEARCH SERVICES SECTION SUPPORT 2.0 (RSSS-2.0) POP 6/8/20 - 12/31/20.", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Center for Advancing Translational Sciences (NCATS)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2020-06-08", "end_date": "2020-12-31", "award_amount": 686597, "principal_investigator": { "id": 24128, "first_name": "SUHAS", "last_name": "SHARMA", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1701, "ror": "", "name": "AXLE INFORMATICS, LLC", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1701, "ror": "", "name": "AXLE INFORMATICS, LLC", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "The NCATS National COVID Cohort Collaborative (N3C) Data Enclave, a centralized and secure data platform featuring powerful analytics capabilities for online discovery, visualization and collaboration for researchers studying COVID-19. The data are robust in scale and scope and are transformed into a harmonized data set to help scientists study COVID 19, including potential risk factors, protective factors and long-term health consequences. The N3C Data Enclave is anticipated to be one of the largest collections of data on COVID-19 patients in the United States. Data analysis within the enclave is supported by both R and Python, the most widely used open-source platforms for statistical analysis and data science. Researchers requesting access to, or working within, the enclave are encouraged to assemble collaborative teams with diverse expertise in such areas as clinical research, statistical analysis and informatics to make the best use of the N3C Data Enclave. A core tenet of the enclave is that it is both accessible and secure, allowing researchers to pursue research in a safe environment conducive to collaborative discovery while also allowing for the deployment of a wide variety of open source tools and components.", "keywords": [ "Area", "COVID-19", "Clinical Research", "Collaborations", "Data", "Data Analyses", "Data Collection", "Data Science", "Data Set", "Environment", "Health", "Informatics", "Patients", "Pythons", "Research", "Research Personnel", "Risk Factors", "Scientist", "Secure", "Services", "Statistical Data Interpretation", "United States", "Visualization", "cohort", "coronavirus disease", "data enclave", "data harmonization", "open source", "protective factors", "tool" ], "approved": true } }, { "type": "Grant", "id": "9093", "attributes": { "award_id": "75N95020C00010-P00005-9999-3", "title": "MODIFICATION TO DEOBLIGATE FUNDS.", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Center for Advancing Translational Sciences (NCATS)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2020-06-08", "end_date": "2021-02-28", "award_amount": 188591, "principal_investigator": { "id": 24128, "first_name": "SUHAS", "last_name": "SHARMA", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1701, "ror": "", "name": "AXLE INFORMATICS, LLC", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1701, "ror": "", "name": "AXLE INFORMATICS, LLC", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "The NCATS National COVID Cohort Collaborative (N3C) Data Enclave, a centralized and secure data platform featuring powerful analytics capabilities for online discovery, visualization and collaboration for researchers studying COVID-19. The data are robust in scale and scope and are transformed into a harmonized data set to help scientists study COVID 19, including potential risk factors, protective factors and long-term health consequences. The N3C Data Enclave is anticipated to be one of the largest collections of data on COVID-19 patients in the United States. Data analysis within the enclave is supported by both R and Python, the most widely used open-source platforms for statistical analysis and data science. Researchers requesting access to, or working within, the enclave are encouraged to assemble collaborative teams with diverse expertise in such areas as clinical research, statistical analysis and informatics to make the best use of the N3C Data Enclave. A core tenet of the enclave is that it is both accessible and secure, allowing researchers to pursue research in a safe environment conducive to collaborative discovery while also allowing for the deployment of a wide variety of open source tools and components.", "keywords": [ "Area", "COVID-19", "COVID-19 patient", "Clinical Research", "Collaborations", "Data", "Data Analyses", "Data Collection", "Data Science", "Data Set", "Environment", "Funding", "Health", "Informatics", "Pythons", "Research", "Research Personnel", "Risk Factors", "Scientist", "Secure", "Statistical Data Interpretation", "United States", "Visualization", "cohort", "coronavirus disease", "data enclave", "data harmonization", "open source", "open source tool", "protective factors" ], "approved": true } }, { "type": "Grant", "id": "7751", "attributes": { "award_id": "75N95020D00003-0-759502000003-1", "title": "NCATS COLLABORATIVE SCIENTIFIC PLATFORM AS A SERVICE CONTINUATION - COVID-19 DATASET AGGREGATION PROOF OF CONCEPT", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Center for Advancing Translational Sciences (NCATS)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2020-05-04", "end_date": "2020-09-27", "award_amount": 2030622, "principal_investigator": { "id": 23550, "first_name": "JULIE", "last_name": "BUSH", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1618, "ror": "", "name": "PALANTIR TECHNOLOGIES INC.", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1618, "ror": "", "name": "PALANTIR TECHNOLOGIES INC.", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The NCATS National COVID Cohort Collaborative (N3C) Data Enclave, a centralized and secure data platform featuring powerful analytics capabilities for online discovery, visualization and collaboration for researchers studying COVID-19. The data are robust in scale and scope and are transformed into a harmonized data set to help scientists study COVID 19, including potential risk factors, protective factors and long-term health consequences. The N3C Data Enclave is anticipated to be one of the largest collections of data on COVID-19 patients in the United States. Data analysis within the enclave is supported by both R and Python, the most widely used open-source platforms for statistical analysis and data science. Researchers requesting access to, or working within, the enclave are encouraged to assemble collaborative teams with diverse expertise in such areas as clinical research, statistical analysis and informatics to make the best use of the N3C Data Enclave. A core tenet of the enclave is that it is both accessible and secure, allowing researchers to pursue research in a safe environment conducive to collaborative discovery while also allowing for the deployment of a wide variety of open source tools and components.", "keywords": [ "Area", "COVID-19", "Clinical Research", "Collaborations", "Data", "Data Analyses", "Data Collection", "Data Science", "Data Set", "Environment", "Health", "Informatics", "Patients", "Pythons", "Research", "Research Personnel", "Risk Factors", "Scientist", "Secure", "Services", "Statistical Data Interpretation", "United States", "Visualization", "cohort", "coronavirus disease", "data enclave", "data harmonization", "open source", "protective factors", "tool" ], "approved": true } }, { "type": "Grant", "id": "13498", "attributes": { "award_id": "75N95020D00011-0-759502300002-1", "title": "NCATS CYBERSECURITY SERVICES DIVISION PROGRAM SUPPORT", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Center for Advancing Translational Sciences (NCATS)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-29", "end_date": "2024-02-28", "award_amount": 1427260, "principal_investigator": { "id": 29624, "first_name": "", "last_name": "", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": null, "abstract": "National COVID-19 Cohort Collaborative (N3C): The National COVID-19 Cohort Collaborative (N3C) sponsors the NIH COVID-19 Data Enclave, https://covid.cd2h.org/, one of the largest data enclaves in the world supporting COVID-19 research. N3C is a partnership among the NCATS-supported Clinical and Translational Science Awards (CTSA) Program hubs, the National Center for Data to Health (CD2H), and the NIGMS-supported Institutional Development Award Networks for Clinical and Translational Research (IDeA-CTR), with overall stewardship by NCATS. The N3C program is essentially a medium sized business, consisting of thousands of researchers, requiring enterprise level information technology (IT) support as part of a virtual research organization (VRO). This contract is necessary to ensure that NCATS and N3C can continue to provide adequate support for a secure, collaborative, VRO. This contract allows for continued support of the VRO which supports all of the required information technology functions to support an environment of over 4,000 users, including cloud-based productivity tools, a service desk, commercial and open-source deployments of analytical tools for the community to use, and expansion of the data types available for analysis, such as imaging, viral variant genomic sequences, etc. The common need is to share a collaborative cloud environment capable of ingesting billions of data points and performing a variety of complex analyses against multimodal data types, ranging from pathology and radiology data, synthetic data, genomic information, electronic health records (EHRs) and a wide variety of others. All of this must be done while meeting the highest levels of security and privacy, given the sensitivity of some of the data types being collected and the importance of the work being done in the environment. This contract provides IT security support for all of these enterprise IT efforts.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "7750", "attributes": { "award_id": "75N95020D00016-0-759502000003-1", "title": "SUPPORT FOR THE N3C DATA ENCLAVE", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Center for Advancing Translational Sciences (NCATS)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2020-09-28", "end_date": "2020-12-24", "award_amount": 2373492, "principal_investigator": { "id": 23550, "first_name": "JULIE", "last_name": "BUSH", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1618, "ror": "", "name": "PALANTIR TECHNOLOGIES INC.", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1618, "ror": "", "name": "PALANTIR TECHNOLOGIES INC.", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The NCATS National COVID Cohort Collaborative (N3C) Data Enclave, a centralized and secure data platform featuring powerful analytics capabilities for online discovery, visualization and collaboration for researchers studying COVID-19. The data are robust in scale and scope and are transformed into a harmonized data set to help scientists study COVID 19, including potential risk factors, protective factors and long-term health consequences. The N3C Data Enclave is anticipated to be one of the largest collections of data on COVID-19 patients in the United States. Data analysis within the enclave is supported by both R and Python, the most widely used open-source platforms for statistical analysis and data science. Researchers requesting access to, or working within, the enclave are encouraged to assemble collaborative teams with diverse expertise in such areas as clinical research, statistical analysis and informatics to make the best use of the N3C Data Enclave. 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