Grants

Dance Caliente

Dancing through the Decades

Visual Arts Research and Resource Center Relating to the Caribbean

Cultural After-School Adventure (CASA)

NYC Department of Cultural Affairs

Daniel's Music Foundation

Daniels Music Foundation

Daniels Music Foundation is an active grantmaking foundation based in New York, NY. Focus: arts. Contact the foundation directly for current funding opportunities.

NYC Department of Cultural Affairs

Danspace Project, Inc.

NIDDK - National Institute of Diabetes and Digestive and Kidney Diseases

PROJECT SUMMARY/ABSTRACT Lupus nephritis is a chronic, life-threatening autoimmune glomerulonephritis that disproportionately impacts young people with devastating consequences. Despite current standard treatment with glucocorticoids, immunosuppressives, antimalarials, and renin-angiotensin-aldosterone system blockade, lupus nephritis carries a 10-30% risk of end-stage kidney disease as well as increased risks of cardiovascular disease and premature morality. Proteinuria, a key feature of lupus nephritis, is a major risk factor for progressive glomerular filtration rate (GFR) decline and end-stage kidney disease. It is increasingly understood that lupus nephritis needs to be rapidly and aggressively treated with multiple agents during an early window of opportunity, before irreversible kidney damage has occurred. Sodium-glucose co-transporter-2 inhibitors (SGLT2i) are now proven to reduce chronic kidney disease progression among patients with diabetic and non-diabetic kidney disease via hemodynamic and metabolic mechanisms that reduce proteinuria, lower blood pressure, and improve metabolic syndrome. Observational studies indicate a possible role for SGLT2i to improve kidney and cardiovascular outcomes for patients with lupus. However, patients with lupus nephritis were largely excluded from randomized controlled trials (RCT) of SGLT2i and are excluded from the current FDA indications. Thus, it is unknown whether SGLT2i use will be safe, tolerated, and effective in immunosuppressed patients with early and active lupus nephritis, who are often subjected to complex medication regimens, but this is a key target population in the early window for the prevention of ongoing and irreversible kidney damage. A large, multicenter RCT trial is needed to determine the safety and efficacy of SGLT2i in patients with active lupus nephritis. To enable the planning, design, and success of this future trial, we propose a two-site, three-year pilot and feasibility trial of dapagliflozin as add-on therapy to standard-of-care for 30 patients with biopsy-proven Class III, IV and/or V lupus nephritis. This will be a concealed allocation, blinded RCT with 2:1 allocation ratio of dapagliflozin 10 mg or matched placebo for 12 weeks. The eligible population will include patients with active lupus nephritis with ongoing proteinuria on standard immunosuppression regimens. We will assess the feasibility of recruiting patients with active lupus nephritis, and pilot test study procedures to incorporate patient preferences for study visit formats, including virtual visits (Aim 1). We will pilot test data collection methods for proposed future trial outcomes, including changes in proteinuria and eGFR, SLE disease activity indices, and patient reported outcomes, and we will estimate changes in proteinuria over 12 weeks with dapagliflozin or placebo (Aim 2). We will detect safety signals, especially genitourinary infections, other infections, and hypovolemia, as well as drug tolerability and adherence to the study intervention (Aim 3). This pilot and feasibility trial will provide critical early insights into the use of SGLT2i for patients with active lupus nephritis and will guide the planning of a larger, definitive multicenter RCT to determine the safety and efficacy of SGLT2i in treatment of active lupus nephritis.

NYC Department of Cultural Affairs

Dare to Revitalize Education thru Arts & Mediation

Oregon Business Development Department

Darex LLC

City of Richmond

Little Mini Daycare

DEEP

Darling Pond Dam

DOT

Darling Road Bridge Rehabilitation

East Lyme

Darrow Pond Dam Area Clearing of Hazardous Trees

East Lyme

Darrow Pond Dam Area Clearing of Hazardous Trees

Buffalo Regional Innovation Clust 17-18

Darwin Martin House Buffalo Billions 16-17

Regional Council Capital Fund - RC3

Darwin Martin House Capital

FDA - Food and Drug Administration

1 Project Summary: Veterinary teaching hospitals are important tertiary care centers for high- 2 quality, cutting-edge care of animals. As veterinary medicine has evolved to resemble human 3 medicine in complexity, it has become apparent that complications historically observed in 4 human medicine are now of consequence in veterinary medicine and efforts are needed at the 5 hospital level to reduce harm. Antimicrobial resistance (AMR) is a critical human health 6 challenge and is reported more frequently in veterinary medicine. Not only do resistant bacterial 7 infections impact animal health and welfare, but they also have potential to cause negative 8 human health consequences through transmission of resistant bacteria or resistance genes 9 through animal contact or the shared environment. Little information is available to assess 10 impact of AMR bacterial infections in veterinary medicine, but presumably there is a significant 11 cost both in morbidity and mortality, economic output for treatment, lost productivity in animal 12 agriculture and emotional distress of animal owners and caregivers. In order to understand and 13 mitigate infections from antimicrobial resistant bacteria in a veterinary teaching hospital, the 14 collection of data related to patient outcomes, antimicrobial use and bacterial characterization, 15 including whole genome sequencing, must be performed. While obtaining data is a primary step, 16 data management, visualization and analysis is must be built to ensure data can be useful for 17 stakeholders in teaching hospitals, including faculty clinicians, trainees, students and staff. Data 18 dashboards provide this tool to tailor information that is accessible for users with varying skill in 19 data analytics. Dashboards can be configured to provide pertinent information on individual 20 providers, service and hospital wide antimicrobial use, rates of surgical site infections, 21 antimicrobial susceptibility test patterns, rates of resistant bacteria and results of environmental 22 surveillance of resistant bacteria. The purpose of this proposal is to create user-friendly data 23 dashboards for multiple metrics for the Washington State University College of Veterinary 24 Medicine Teaching Hospital for use in antimicrobial stewardship and infection prevention and 25 control efforts and education. Additionally, these dashboards could be shared with other 26 Veterinary Teaching Hospitals. 27 28

OD - NIH Office of the Director

ABSTRACT The exponential growth of biomedical and socio-behavioral data in the United States (US) presents new opportunities for understanding behavioral and social drivers of health. However, the fragmented nature of these data sources and the lack of specialized training limit their full potential in behavioral and social sciences research (BSSR). The Data Analytics Training and Professional Development (DATA-Pro) for behavioral and social sciences research (BSSR) program aims to bridge this gap by providing advanced training in big data methodologies. Hosted by the Medical University of South Carolina (MUSC), DATA-Pro will deliver structured e-learning modules on big datasets, research designs, traditional modeling, and novel machine learning and artificial intelligence approaches, covering responsible conduct of research and methods for enhancing reproducibility. Participants will gain hands-on experience through expert-guided research projects and develop essential skills in research dissemination. MUSC offers a conducive environment for training, myriad data resources, and robust teaching and research infrastructure, which is ideal for advanced training in data sciences. The program will prepare researchers to apply cutting-edge data analytics to answer complex social and behavioral research questions. By equipping researchers with the necessary skills to harness big data, DATA-Pro has the potential to accelerate discoveries, inform policy, and advance BSSR.

CDW Government LLC

Information Technology Broadcasting and Telecommunications

Fish and Wildlife Service

This is an announcement for issuing a single source financial assistance award, not to exceed five years, to the Merrimack Valley Planning Commission (MVPC, Cooperator). The intent of the award is to provide funding to expand an existing project funded through two Sandy resiliency projects: #43, "Restoring resiliency to the Great Marsh; Parker River NWR, MA" and NFWF's Hurricane Sandy Coastal Resiliency Competitive Grant Project "Coastal Resiliency Planning and Ecosystem Enhancement for northeastern Massachusetts". The previously awarded Sandy project provided $608,000 to develop a comprehensive hydrodynamic model for the Great Marsh ecosystem.

National Institutes of Health

Data Coordinating Center for NCCIH Multi-Site Investigator-Initiated Clinical Trials of Mind and Body Interventions (Collaborative U24 Clinical Trial Required)

NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development

The Johns Hopkins University (JHU) seeks to strengthen the coordination of innovative research and practice efforts in maternal health through collaboration with the National Institutes of Health (NIH) and their Implementing a Maternal Health and Pregnancy Outcomes Vision for Everyone (IMPROVE) initiative grantees. The overarching goals of this project are to establish and maintain a Maternal Health Data Innovation and Coordination Hub to support Maternal Health Research Centers of Excellence, and to facilitate the reuse of the data they generate. The project will be implemented by a multidisciplinary team of maternal health experts and biostatisticians at JHU’s Bloomberg School of Public Health, and informatics and data science specialists at JHU’s School of Medicine, with support from an Experts’ Bureau comprised of subject matter experts in maternal health, bioethics, health economics, patient safety, patient and family engagement in research. Key project activities are to establish and maintain a secure, cloud-based coordination platform with controlled access, and a public-facing Data Hub website; develop common data elements using a modified Delphi approach; support the use of a common data model; provide data collection and analysis tools with integrated quality assurance workflows; provide support for statistical analyses using traditional and artificial intelligence/machine learning techniques; prepare and share data with NIH repositories; provide technical assistance and skills coaching, training, and professional development opportunities to Research Centers/IMPROVE grantees. Our proposal has technical and conceptual areas of innovation. Most notably, the proposed integration of the Data Hub with an existing research coordination platform with demonstrated feasibility -- JHU’s Precision Medicine Analytics Platform (PMAP). It utilizes the Observational Health Data Science and Informatics (OHDSI) open-source community and the Observational Medical Outcomes Partnership (OMOP), employed by large NIH-funded research. OMOP is based upon standard clinical terminologies; enables extraction, ingestion, collation of variables of interest into an observational research registry; and has the capability for data storage, security, analysis, and transfer among participating sites. Also innovative are the proposed training and career development opportunities, including tuition scholarships, data challenge awards, and a mentorship program. We anticipate that these activities will lead to short-term and intermediate outcomes (e.g. improved data science capabilities; generation of findable, accessible, interoperable, and reusable data), which, over the long-term, will advance research to improve maternal health outcomes. Process and outcomes evaluations will ascertain the extent to which our project is successfully supporting Research Centers. Data science methods and findings from research projects will be disseminated on the Data Hub website, through reports, peer-reviewed articles, and scientific presentations.

NSF

This award supports research in relativity and relativistic astrophysics, addressing the priority areas of NSF's "Windows on the Universe" Big Idea. Gravitational waves and black holes are among the most dramatic predictions of Einstein's theory of General Relativity. The NSF's Laser Interferometer Gravitational-wave Observatory (LIGO) has transformed our understanding of the universe by detecting gravitational waves from colliding black holes and neutron stars. As part of the International Gravitational-Wave Observatory Network (IGWN), LIGO provides an entirely new way to study the most extreme phenomena in the universe. The first direct observation of gravitational waves by LIGO in 2015 positioned the United States as a world leader in gravitational-wave science. Detecting weak gravitational-wave signals requires substantial computational infrastructure that spans astrophysical data analysis, detector and analysis middleware, software sustainability, and computational hardware support. This project addresses a critical challenge: sustaining and enhancing the computational infrastructure required to transform detector data into groundbreaking scientific discoveries. Gravitational-wave astronomy relies on the timely analysis of continuous data streams from the global network of detectors and the coordinated efforts of approximately 3,000 scientists. The computational needs are served by the IGWN Computing Grid, a distributed facility that provides the backbone enabling rapid discovery, real-time alerts to astronomers worldwide for follow-up observations, and the detailed studies that advance our understanding of black holes, neutron stars, and the fundamental nature of gravity itself. This project involves the development of cutting-edge signal processing and computational techniques with diverse applications beyond gravitational-wave science. It trains the next generation of scientists and engineers in advanced computational methods, directly supporting national STEM workforce development. The project also enhances international scientific cooperation while maintaining U.S. leadership in this transformative field. This award supports the skilled personnel who will continue to deliver the services, scalability, and reliability needed to sustain the scientific return from investments in the LIGO detectors. The primary goals of this project are: 1) to deliver the software infrastructure and services to support gravitational-wave astronomy with LIGO and its international partners; 2) to provide the cyber-infrastructure needed to bring gravitational-wave astronomy together with the broader astronomical community; and 3) to support open access to LIGO data for the scientific community and the public. This 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.

NSF

In the era of big data, researchers and analysts have unprecedented access to extensive datasets, opening up new opportunities for scientific discovery and predictive modeling. These datasets, often collected from different sources, contain various forms of heterogeneity, such as distribution heterogeneity, observation heterogeneity, and task heterogeneity. Effectively addressing these complexities is critical for maximizing their potential. This project tackles various sources of data integration in supervised learning by introducing a novel framework, the Representation Retrieval (R2) framework, which simultaneously addresses all three types of heterogeneity. The new framework combines advanced representation learning with sparse-induced machine learning algorithms to achieve its objectives. Additionally, the investigator develops a new integrative penalty designed to improve the integration and effectiveness of the learned representations. This project will lead to a new paradigm for extracting and integrating information from heterogeneous data sources, providing fundamental solutions and a rigorous framework to address these challenges. This project will have broad applications across various fields, including medical and health sciences, social sciences, political science, education, finance, marketing, and artificial intelligence. The investigator will integrate education with research by developing a new course on data integration. The new framework extracts a shared representation dictionary from multiple heterogeneous data sources, then selects multiple source-specific retrievers, and estimates source-specific learners. The representation dictionary, accessible to all data sources, contains a set of representers which can represent covariates in a low-dimensional latent space. Each data source employs its own retrievers to select informative representers which project their covariates into an appropriate latent space. Using these retrieved representations, source-specific learners can then be applied to predict responses. The project aims to make significant contributions to the field of data integration through the following advancements: (1) Formulating a General Data Integration Framework: Define a supervised learning problem that incorporates three types of heterogeneity. This formulation generalizes several well-studied problem setups as special cases. (2) Introducing the Representation Retrieval (R2) Framework: Develop a comprehensive framework to address all three types of heterogeneity and overcome common limitations in existing methods. (3) Addressing Distribution Heterogeneity: Leverage a “partially-sharing structure" to model distribution heterogeneity effectively, solve optimization problems with sparsity-induced penalties, and introduce a novel “Selective Integration Penalty" to encourage representers shared across multiple data sources. (4) Handling Observation Heterogeneity: Propose a non-imputation-based approach to manage observation heterogeneity, providing a robust alternative to conventional methods. This 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.

NIMHD - National Institute on Minority Health and Health Disparities

Tribal Nations often lack accurate, granular, and comprehensive information on the true impact of mortality indicators across American Indian/Alaska Native (AI/AN) population groups needed for different use cases. Misclassification error in state death certificates is disproportionately 30% higher for the AI/AN population compared to other demographic groups due to higher proportions of persons with a range of backgrounds within AI/AN populations. Additionally, US census reported AI/AN population statistics suffer from severe under-reporting at every geographic resolution due to small sample sizes, challenges in data collection, cost, and privacy algorithms. Importantly, official mortality and population statistics do not comprehensively or accurately enumerate distinct AI/AN populations defined by (1) Self- reported AI/AN based on U.S. Census defined classification (census category), (2) Tribal citizenship with a Tribal Nation (legal category), and (3) Residence on Tribal lands (geographic category). To address this data gap, our approach combines Bayesian hierarchical small area disease mapping with measurement error methodologies to produce small area mortality estimates across AI/AN groups for both rural and non-rural areas with improved accuracy. Within the comprehensive methodological framework, model components are customized to produce population-specific mortality estimates with increased accuracy across multiple populations. We apply our methodological approach to obtain county-year trends in AI/AN opioid mortality rates (OMRs) for the 77 counties in Oklahoma between years 2015-2024 across the distinct AI/AN population groups and county-specific rural status. In Phase 1, we estimate county-year OMRs for Tribal citizens by linking data from Oklahoma state death certificates with Cherokee Nation Tribal Registry data. In Phase 2, we estimate county-year OMRs for self-affiliated AI/AN populations using misclassification rates from Phase 1 and assess trends for total populations-at-risk in counties inside and outside Tribal boundaries. Of utmost importance to the study is the independent application and use of the proposed methodology by local health departments. We will develop a data science pipeline consisting of both educational and computational resources for local health analysts to adopt and implement the proposed methodology as a mortality self-monitoring resource.