Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1419&sort=keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1420&sort=keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1418&sort=keywords" }, "data": [ { "type": "Grant", "id": "9536", "attributes": { "award_id": "2030139", "title": "Compounding Crises: Facing Hurricane Season in the Era of COVID-19", "funder": null, "funder_divisions": [], "program_reference_codes": [ "CK090", "RND123" ], "program_officials": [], "start_date": null, "end_date": null, "award_amount": 199890, "principal_investigator": null, "other_investigators": [], "awardee_organization": null, "abstract": "Test", "keywords": [ "covid", "research" ], "approved": true } }, { "type": "Grant", "id": "9169", "attributes": { "award_id": "75N92020C00015-P00001-9999-1", "title": "RAPID ACCELERATION OF DIAGNOSTICS ( RADX) PROGRAM: TECH PROJECT #2322 MICROGEM (MICROLAB)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Biomedical Imaging and Bioengineering (NIBIB)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2020-08-28", "end_date": "2021-08-27", "award_amount": 24369230, "principal_investigator": { "id": 24932, "first_name": "STUART HELLYAR HELLYAR", "last_name": "HELLYAR", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": null, "abstract": "A battery powered, POC/at home device for the quantitation and detection of SARS-CoV-2 simultaneously with Influenza A & B, with the accuracy, specificity and sensitivity equivalent to or better than the current CDC RT-PCR Lab based methodology.", "keywords": [ "COVID-19 detection", "Centers for Disease Control and Prevention (U.S.)", "Devices", "Home", "Influenza A virus", "Influenza B Virus", "Methodology", "RADx", "Reverse Transcriptase Polymerase Chain Reaction", "Sensitivity and Specificity", "base", "programs" ], "approved": true } }, { "type": "Grant", "id": "11362", "attributes": { "award_id": "1S10AI174104-01", "title": "PPE Request", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 6245, "first_name": "Philip O.", "last_name": "Renzullo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-05-02", "end_date": "2024-04-30", "award_amount": 1438731, "principal_investigator": { "id": 6247, "first_name": "Lawrence", "last_name": "Corey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 757, "ror": "", "name": "FRED HUTCHINSON CANCER RESEARCH CENTER", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 758, "ror": "https://ror.org/007ps6h72", "name": "Fred Hutchinson Cancer Center", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "On-going US government sponsored COVID-19 vaccine trials require screening, enrolling, and following over 150,000 volunteer study participants over 24-36 months. The trial participants are selected based on their risk of COVID-19 infection, with trial participants coming from settings with high rates of community transmission, groups with higher risk of progression to symptomatic disease, or both. Study staff working on COVID-19 vaccine trials interact with trial participants during scheduled trial assessments and unscheduled sick visits, which includes high risk procedures such as nasal swabbing and performing physical examinations in often poorly ventilated environments. As a result, a regular supply of personal protective equipment (PPE), including surgical masks, face shields or goggles, gowns, and disposable gloves, is critical for their own protection and for prevention of transmission within the clinic. This project will provide on-going PPE support to COVID-19 Prevention Network (CoVPN) trials across three continents, completing work anticipated through 2023. 1", "keywords": [ "COVID-19 Prevention Network", "COVID-19 risk", "COVID-19 vaccine", "Clinic", "Disease", "Enrollment", "Environment", "Face", "Goggles", "Government", "Participant", "Physical Examination", "Prevention", "Procedures", "SARS-CoV-2 infection", "Schedule", "Visit", "Work", "community transmission", "high risk", "nasal swab", "personal protective equipment", "screening", "surgical mask", "transmission process", "vaccine trial", "ventilation", "volunteer" ], "approved": true } }, { "type": "Grant", "id": "13823", "attributes": { "award_id": "75N93022D00016-0-759302300001-1", "title": "EARLY PHASE CLINICAL TRIAL UNITS (EPCTU): PHASE I CLINICAL TRIAL TO EVALUATE A SARS-COV-2 THERAPEUTIC", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-05-15", "end_date": "2024-03-01", "award_amount": 1800783, "principal_investigator": { "id": 30168, "first_name": "CASEY", "last_name": "LONG", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2353, "ror": "", "name": "DYNPORT VACCINE COMPANY, LLC", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "The Early Phase Clinical Trial Units provide a vehicle for investigation of new agents in the treatment and prevention of infectious diseases, and can carry out all aspects of interventional clinical trial implementation.", "keywords": [ "COVID-19 therapeutics", "Clinical Trials Unit", "Communicable Diseases", "Intervention", "Investigation", "New Agents", "Phase I Clinical Trials", "Prevention", "clinical trial implementation", "coronavirus disease", "early phase clinical trial" ], "approved": true } }, { "type": "Grant", "id": "9585", "attributes": { "award_id": "2200112", "title": "PIPP Phase I: Computational Theory of the Co-evolution of Pandemics, (Mis)information, and Human Mindsets and Behavior", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "PIPP-Pandemic Prevention" ], "program_reference_codes": [], "program_officials": [ { "id": 1030, "first_name": "Mitra", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-01", "end_date": "2024-01-31", "award_amount": 999955, "principal_investigator": { "id": 5929, "first_name": "Peter", "last_name": "Pirolli", "orcid": "https://orcid.org/0000-0002-9018-4880", "emails": "[email protected]", "private_emails": "", "keywords": "['computational cognitive theory', ' natural language processing', ' artificial intelligence', ' machine learning', ' online social networks']", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 735, "ror": "", "name": "Florida Institute for Human and Machine Cognition, Inc.", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 25333, "first_name": "Kathleen M", "last_name": "Carley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25334, "first_name": "Christian", "last_name": "Lebiere", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25335, "first_name": "Mark", "last_name": "Orr", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 735, "ror": "", "name": "Florida Institute for Human and Machine Cognition, Inc.", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "Epidemiological models are used to predict the spread of highly contagious and lethal diseases such as COVID-19. Public health officials use such models to inform pandemic response policies and advisories. Yet these models require a rigorous scientific foundation about human psychology to better predict people’s responses to information and policies about pandemics. The recent COVID-19 pandemic illustrates the central role of human decision making and behavior in the spread of such a transmissible disease. People’s decisions regarding social isolation, social distancing, mask wearing, hand washing, and vaccination are correlated with the rate at which the COVID-19 virus spreads or the seriousness of getting infected. People have different individual mindsets, and these can vary across different regions and subgroups, so different groups of people respond differently to messaging and mandates and those responses change over time. There is also an ongoing scientific debate about the degree to which pandemic information or misinformation, or the perceived credibility of information sources, influences the degree to which people change their behavior. To address these scientific needs, this project involves activities to develop a multidisciplinary research core and agenda and to develop a strong plan for a cohesive research center for Predictive Intelligence for Pandemic Prevention. The activities include exploratory research on computational models of human psychology, information flow and influence, and resulting pandemic transmission. The project will also support the training and mentoring of graduate students who represent the next generation of researchers tackling these global challenges.\n\nThis project uses computational theories and models to examine the fundamental interdependent evolution of infection, behavior, and information at multiple levels and drawing upon multiple disciplines in order to support improved pandemic intelligence, prediction, explanation, and countermeasures. The project is organized into (1) interdisciplinary, strategic research thrusts to Accelerate Convergent Science towards the Grand Challenge, (2) three invitational meetings to draw in diverse researchers to address focal research topics and research questions, to fill in gaps in the Research Challenges, and develop a strong research and education agenda for a cohesive PIPP center, and (3) Pilot Studies to Demonstrate Feasibility of integrated computational models of information, human psychology, and pandemic transmission. For the pilot research, a multidisciplinary team combines empirical assessments with computational cognitive models in an agent-based modeling system. For data the investigators draw on vaccination discussions in mass media, Twitter, geolocated timeseries data on vaccination rates, infection, death and recovery rates, state and national mandates regarding COVID-19 policies about vaccination and mask wearing from February 2020 through December 2021 in the United States. These data will be segmented by state and major cities within those states. \n\nThis award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [ "covid-19", " pandemic", " human behavior", " decision-making", " cognition", " online social networks" ], "approved": true } }, { "type": "Grant", "id": "12097", "attributes": { "award_id": "1S10OD032441-01", "title": "High-throughput Full Spectrum Cell Sorter", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 11602, "first_name": "GUANGHU", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-09-01", "end_date": "2024-08-31", "award_amount": 752359, "principal_investigator": { "id": 27957, "first_name": "Vinh", "last_name": "Nguyen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 768, "ror": "https://ror.org/043mz5j54", "name": "University of California, San Francisco", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "/ Abstract The University of California, San Francisco (UCSF) Parnassus Flow Cytometry CoLab (PFCC) is requesting funds to purchase a next generation Full Spectrum Cell Sorter. We have chosen the ThermoFisher Invitrogen™ BigFoot full spectrum cell sorter as the instrument that best fulfills the needs of the unsatiable demand for high resolution multidimension single cell data by our highly esteemed NIH-funded faculty. The BigFoot will complement our current arsenal of high-parameter analysis equipment such as the Fluidigm CyTOFs, Cytek Aurora and Becton Dickson FACSymphony X50 while most importantly adding the highly desired cell sorting capability. The BigFoot maintains the standard cell processing workflow PFCC users are accustomed to in conventional bandpass-based flow cytometers as well with the Aurora Full Spectrum Analyzer at PFCC. We will take advantage of the BigFoot's versatility and powerful spectral technology by enabling collection of fluorescent emission profiles through most of the visible spectra to near-infrared ranges making the number of parameters that can be investigated on this instrument approaching 35 to 40. The UCSF community has rapidly taken advantage of the availability of the full spectrum technology for their high-parameter research, the BigFoot will uniquely add cell sorting capabilities to aid the quest to understand the complex dynamics of changing cell population in disease progression, therapy and remission. Our Researchers have extensive experience in high-parameter data acquisition/analysis using Mass Cytometry resulting in 29 high impact publications over the last 4 years at UCSF, with at least 5 more in review. However, full spectrum flow cytometry is becoming a formidable technology to multiparameter analysis using mass cytometry; transition to the Aurora full spectrum analyzer has gained momentum due to the ease of sample preparations, simplicity of use, high-throughput (50 times faster) and especially the cost savings compared to mass cytometry. Within 1 year of installation, a peer review paper using the Aurora has been published. The high-throughput capabilities coupled with minimal sample loss during preparation and acquisition makes the technology amenable to samples with extremely low cell numbers. Hence, sorting on high-parameter panels of over 25 fluorescent markers has been in demand; however, no cell sorter is currently available at UCSF that allows panels of more than 18 fluorescent markers to be used. The BigFoot will augment the Full Spectrum Flow Cytometry portfolio at UCSF and the ability to simultaneously sort cell populations of interest which will be foundational in providing further information on cellular dynamics during multi-drug clinical research. Due to the COVID-19 lockdown, and limited accessibility at PFCC an ever- increasing number of processed clinical samples has been accumulating adding to the already pre-existing bank of samples ready for high-parameter analysis and sorting. At PFCC, the BigFoot will be a user-controlled device available 24/7, with minimal setup or daily maintenance requirements, and it will be a major high-throughput and high-performance tool in the Core. The BigFoot will help alleviate the bottleneck of machine availability continually experienced by our cell sorting and high-parameter researchers at UCSF.", "keywords": [ "COVID-19", "California", "Cell Count", "Cell Separation", "Cells", "Clinical", "Clinical Research", "Collection", "Communities", "Complement", "Complex", "Cost Savings", "Coupled", "Cytometry", "Data", "Devices", "Dimensions", "Disease Progression", "Disease remission", "Equipment", "Faculty", "Flow Cytometry", "Funding", "Maintenance", "Paper", "Peer Review", "Performance", "Pharmaceutical Preparations", "Population", "Preparation", "Process", "Publications", "Publishing", "Research", "Research Personnel", "Resolution", "Sampling", "San Francisco", "Sorting", "Technology", "United States National Institutes of Health", "Universities", "data acquisition", "experience", "instrument", "interest", "next generation", "tool" ], "approved": true } }, { "type": "Grant", "id": "9395", "attributes": { "award_id": "75N91021C00003-0-9999-1", "title": "CYTOF AND O-LINK ASSESSMENT IN THE `NCI COVID-19 IN CANCER PATIENTS STUDY' (NCCAPS)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Cancer Institute (NCI)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2020-12-23", "end_date": "2021-12-22", "award_amount": 2712342, "principal_investigator": { "id": 25132, "first_name": "SACHA", "last_name": "GNJATIC", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 625, "ror": "https://ror.org/04a9tmd77", "name": "Icahn School of Medicine at Mount Sinai", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 625, "ror": "https://ror.org/04a9tmd77", "name": "Icahn School of Medicine at Mount Sinai", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The primary objective of this project is to study the immune response to COVID-19 in patients with cancer by assessing the cellular compartment of the immune system using mass cytometry by CyTOF and cytokines by O-link profiling.", "keywords": [ "COVID-19", "Cancer Patient", "Cytometry", "Immune response", "Immune system", "Link", "Malignant Neoplasms", "Patients", "cytokine" ], "approved": true } }, { "type": "Grant", "id": "9460", "attributes": { "award_id": "75N91019D00024-P00008-759101900129-43", "title": "NCI OPERATIONAL TASK ORDERCOVID-19: INITIATE DCEG COVID-19 CASE STUDY: YT 20-144. PID # 600.129.20.01.018.001.0005COVID-19: INITIATE DCEG COVID-19", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Cancer Institute (NCI)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2019-08-31", "end_date": "2020-08-30", "award_amount": 2070472, "principal_investigator": { "id": 24928, "first_name": "LEONARD", "last_name": "FREEDMAN", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1610, "ror": "", "name": "LEIDOS BIOMEDICAL RESEARCH, INC.", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1610, "ror": "", "name": "LEIDOS BIOMEDICAL RESEARCH, INC.", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "COVID-19", "keywords": [ "COVID-19", "Case Study", "Division of Cancer Epidemiology and Genetics" ], "approved": true } }, { "type": "Grant", "id": "14380", "attributes": { "award_id": "316201200036W-P00003-759302200001-3", "title": "THE IMMUNOLOGY DATABASE AND ANALYSIS PORTAL (IMMPORT)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-30", "end_date": "2027-09-29", "award_amount": 822770, "principal_investigator": { "id": 30981, "first_name": "ATUL", "last_name": "BUTTE", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": null, "abstract": "As a NIH funded publicly accessible data sharing platform, ImmPort supports immunology research and clinical studies. ImmPort offers curated datasets and reference datasets that adhere to the FAIR Principles and is one of the few trustworthy biomedical data repositories certified by CoreTrustSeal. Within ImmPort, there are two specific datasets that are suitable for NAIRR pilots due to their high level of AI-readiness: the 10K Immunomes Project and the COVID-19 compendium. ImmPort make use of AWS though the NIH STRIDES program that can become a component of the open NAIRR program.", "keywords": [ "COVID-19", "Certification", "Clinical Research", "Data Set", "Databases", "Documentation", "FAIR principles", "Funding", "Immunology", "Infrastructure", "Process", "Readiness", "United States National Institutes of Health", "data access", "data integration", "data repository", "data resource", "data sharing", "programs", "research study", "sharing platform", "trustworthiness" ], "approved": true } }, { "type": "Grant", "id": "10472", "attributes": { "award_id": "75N95021D00028-0-759502200001-1", "title": "NCATS LINKAGE HONEST BROKER (LHB) - PATIENT-CENTERED OUTCOMES RESEARCH (PCOR) TRUST FUND ASPE NATIONAL COVID-19 LONGITUDINAL RESEARCH DATABASE LINKED", "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": "2022-03-01", "end_date": "2023-02-28", "award_amount": 1199625, "principal_investigator": { "id": 25071, "first_name": "UMBERTO", "last_name": "TACHINARDI", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1823, "ror": "", "name": "REGENSTRIEF INSTITUTE, INC.", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1823, "ror": "", "name": "REGENSTRIEF INSTITUTE, INC.", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "National COVID-19 Cohort Collaborative (N3C): The N3C Data Enclave is a secure platform storing harmonized clinical data provided by more than 50 contributing members. The Enclave hosts over 651 million clinical observations on over 6.5 million persons, including over 2.1 million COVID cases, amounting to more than 7.3 billion rows of data. Harmonization, anonymity, and security is accomplished through the N3C privacy-preserving record linkage (PPRL). The PPRL uses a de-identified, software-generated token applied by a data contributor to each patient record. A linkage honest broker holds the de-identified tokens and provides a service matching token generated across disparate data sets without knowledge of the identity of the patients. The de-identified tokens are held separately from data residing within the data enclave. As an illustration of the PPRL process, Regenstrief generates a series of de-identified tokens using software provided by Datavant, Inc. The tokens are provided to a data provider, such as Hospital Network Alpha (HNA). HNA strips identifying PII from its clinical records and replaces the PII with the tokens. HNA then uploads the de-identified clinical records to N3C. If researchers require additional information about those records, they provide the token to Regenstrief, who passes it on to HNA without any knowledge of the underlying clinical data. HNA then provides the appropriate response. The linkage honest broker function is critical to the PPRL, which in turn is critical to continued use of the N3C Data Enclave by researchers. The salient characteristic of this requirement is the vendor’s ability to provide uninterrupted linkage honest broker support (harmonization, anonymity, and security) for the N3C Data Enclave.", "keywords": [ "COVID-19", "Characteristics", "Clinical", "Clinical Data", "Computer software", "Data", "Data Set", "Databases", "Funding", "Hospitals", "Knowledge", "Link", "National Center for Advancing Translational Sciences", "Outcomes Research", "Patient-Focused Outcomes", "Patients", "Persons", "Process", "Provider", "Records", "Research", "Research Personnel", "Secure", "Security", "Series", "Services", "Translational Research", "Trust", "Vendor", "cohort", "coronavirus disease", "data enclave", "member", "privacy preservation", "response" ], "approved": true } } ], "meta": { "pagination": { "page": 1419, "pages": 1424, "count": 14236 } } }