Group items tagged

Our Data-Driven Discovery Initiative seeks to advance the people and practices of data-intensive science, to take advantage of the increasing volume, velocity, and variety of scientific data to make new discoveries. Within this initiative, we're supporting data-driven discovery investigators - individuals who exemplify multidisciplinary, data-driven science, coalescing natural sciences with methods from statistics and computer science. These innovators are striking out in new directions and are willing to take risks with the potential of huge payoffs in some aspect of data-intensive science. Successful applicants must make a strong case for developments in the natural sciences (biology, physics, astronomy, etc.) or science enabling methodologies (statistics, machine learning, scalable algorithms, etc.), and applicants that credibly combine the two are especially encouraged. Note that the Science Program does not fund disease targeted research. It is anticipated that the DDD initiative will make about 15 awards at ~$1,500,000 each, at $200K-$300K/year for five years.

The purpose of this Funding Opportunity Announcement (FOA) is to support the development of advanced computational, bioinformatic and statistical tools to determine the functional relevance of genetic variants associated with mental disorders of complex etiologies identified through genome-wide association or sequencing studies. The overarching goal of this initiative is to support the development of innovative computational methods that facilitate the elucidation of the functionality of genetic variants associated with mental illness, taking into account the added complexities and nuances of brain diseases, and to ultimately inform the identification and validation of potential targets for novel treatment development. This FOA should be used when two or more sites are needed to complete the study. For a linked set of collaborative R01s, each site must have its own Program Director/Principal Investigator and the set of linked applications provide a mechanism for cross-site coordination, quality control, database management, statistical analysis, and reporting.

The BIGDATA program seeks novel approaches in computer science, statistics, computational science, and mathematics, along with innovative applications in domain science, including social and behavioral sciences, geosciences, education, biology, the physical sciences, and engineering that lead towards the further development of the interdisciplinary field of data science. The solicitation invites two types of proposals: "Foundations" (F): those developing or studying fundamental theories, techniques, methodologies, technologies of broad applicability to Big Data problems; and "Innovative Applications" (IA): those developing techniques, methodologies and technologies of key importance to a Big Data problem directly impacting at least one specific application. Therefore, projects in this category must be collaborative, involving researchers from domain disciplines and one or more methodological disciplines, e.g., computer science, statistics, mathematics, simulation and modeling, etc. While Innovative Applications (IA) proposals may address critical big data challenges within a specific domain, a high level of innovation is expected in all proposals and proposals should, in general, strive to provide solutions with potential for a broader impact on data science and its applications. IA proposals may focus on novel theoretical analysis and/or on experimental evaluation of techniques and methodologies within a specific domain. Proposals in all areas of sciences and engineering covered by participating directorates at NSF are welcome.

The purpose of this FOA is to fund a single coordinating center to formalize and centralize support for the HEAL Preventing Opioid Misuse and Opioid Use Disorder in Older Adolescents and Young Adults (ages 16-30) initiative administered by NIDA. The coordinating center will provide administrative and scientific, technical, and logistical support for coordination of common data elements, data harmonization, data sharing across projects; methodological and statistical support in the areas of study design, complex statistical analyses, economic analyses, implementation research; coordination of stakeholder and partnership engagement activities; developing and coordinating a HEAL prevention initiative publication and dissemination plan, providing logistical support for in-person meetings, conference calls, and webinars that include HEAL prevention grantees, funders and stakeholders. In addition, the coordinating center also will assist with collecting data on prevalence, incidence, risk and protective factors, and contextual factors in the identified populations and settings that will inform HEAL prevention research projects.

The NIEHS and partnering Institutes and Centers are establishing an infrastructure, the Human Health Exposure Analysis Resource (HHEAR) as a continuation of the Children's Health Exposure Analysis Resource (CHEAR). The goal of this consortium is to provide the research community access to laboratory and statistical analyses to add or expand the inclusion of environmental exposures in their research and to make that data publicly available as a means to improve our knowledge of the comprehensive effects of environmental exposures on human health throughout the life course. This FOA solicits the Data Center. The Data Center will provide intellectual and logistical support for the maintenance, integration, analysis, interpretation, curation, and reuse of data generated by HHEAR in support of extramural research projects. The Data Center will build and manage an exposure data repository and associated data science resources, including a public access Data Portal, a Data Submission and Review Portal, and an ontology and associated standardized data structure. In addition, the Data Center will provide statistical, analytical, and informatic support for HHEAR components and studies using the HHEAR infrastructure. The ultimate goal is maximizing potential use and impact of exposure data in human health studies.

The Division of Mathematical Sciences (DMS) in the Directorate for Mathematical and Physical Sciences (MPS) at the National Science Foundation (NSF) and the National Library of Medicine (NLM)at the National Institutes of Health (NIH)plan to support the development of innovative and transformative mathematical and statistical approaches to address important data-driven biomedical and health challenges. The rationale for this interagency collaboration is that significant advances may be expected as the result of continued NSF investments in foundational research in mathematics and statistics as well as inter- and multi-disciplinary research and training at the intersection of the quantitative/computational sciences and domain sciences, while NIH benefits from the enhancement of biomedical data science with new approaches that strengthen the reproducibility of biomedical research and support open science.

This NOFO will support achievement of national targets for 2018 antiretroviral therapy (ART) scale-up towards the UNAIDS Fast Track Targets in Ukraine. The recipient will implement innovative and effective recruitment and case management models for persons who inject drugs (PWID) and men who have sex with men (MSM) at the community level. These innovative changes will increase uptake of HIV community based testing and increase ART initiation for these populations. The recipient will pilot risk network-based testing using point-of-care recency assays to link recently infected PWID to care. The project will focus on the six regions with the highest HIV burden (Dnipropetrovsk, Mykolayiv, Odesa, government controlled areas (GCA) of Donetsk, Kyiv City, and Kherson) and continue to work in six additional medium burden oblasts (Cherkasy, Poltava, Chernihiv, Zaporizhzhya, Kirovohrad, and Kyiv). The recipient will also increase the capacity of the Government of Ukraine’s Center for Public Health (CPH) and regional monitoring and evaluation (M&E) centers specialists to conduct data analysis using statistical software and build institutional capacity to conduct economic evaluations of HIV interventions. Illustrative strategic information (SI) activities include development of trainings to support the Ministry of Health (MOH) and the newly established CPH in using statistical software, routine analysis of surveillance data, study design, and research protocol development.

This NOFO will support achievement of national targets for 2018 antiretroviral therapy (ART) scale-up towards the UNAIDS Fast Track Targets in Ukraine. The recipient will implement innovative and effective recruitment and case management models for persons who inject drugs (PWID) and men who have sex with men (MSM) at the community level. These innovative changes will increase uptake of HIV community based testing and increase ART initiation for these populations. The recipient will pilot risk network-based testing using point-of-care recency assays to link recently infected PWID to care. The project will focus on the six regions with the highest HIV burden (Dnipropetrovsk, Mykolayiv, Odesa, government controlled areas (GCA) of Donetsk, Kyiv City, and Kherson) and continue to work in six additional medium burden oblasts (Cherkasy, Poltava, Chernihiv, Zaporizhzhya, Kirovohrad, and Kyiv). The recipient will also increase the capacity of the Government of Ukraine’s Center for Public Health (CPH) and regional monitoring and evaluation (M&E) centers specialists to conduct data analysis using statistical software and build institutional capacity to conduct economic evaluations of HIV interventions. Illustrative strategic information (SI) activities include development of trainings to support the Ministry of Health (MOH) and the newly established CPH in using statistical software, routine analysis of surveillance data, study design, and research protocol development.

This FOA solicits new theories, computational models, and statistical tools to derive understanding of brain function from complex neuroscience data. Proposed tools could include the creation of new theories, ideas, and conceptual frameworks to organize/unify data and infer general principles of brain function; new computational models to develop testable hypotheses and design/drive experiments; and new mathematical and statistical methods to support or refute a stated hypothesis about brain function, and/or assist in detecting dynamical features and patterns in complex brain data. It is expected that the tools developed under this FOA will be made widely available to the neuroscience research community for their use and modification. Investigative studies should be limited to validity testing of the tools being developed.

Genome-wide association studies (GWAS) identify statistical relationships between common single nucleotide variants (SNVs) across the genome and a trait of interest. Due to the correlated nature of nearby SNVs (i.e., linkage disequilibrium), GWAS implicate regions of the genome (loci) and do not necessarily pinpoint the causal variant(s), gene(s), isoform(s) or proximate molecular mechanism(s) underlying the trait association. Greater than 90% of genome-wide significant variants associated with traits fall within non-coding regions of the genome. A minority of these variants will be the actual causal variants and a majority will not affect the nearest genes. Moreover, functional annotation of the non-coding genome is still incomplete; the target genes of many genomic regulatory elements such as enhancers remain unknown. This presents a major challenge to mapping variants onto genes and genes onto traits. Thus, an immediate barrier to translating genetic associations into causal disease mechanisms is the uncertain relationship between statistically identified genetic variants and the resultant molecular changes influencing, directly or indirectly, a trait.

The Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH), the Division of Epidemiology, Statistics, and Prevention Research intends to solicit proposals to award an Indefinite Delivery Indefinite Quantity (IDIQ) contract to an organization to conduct various studies focusing on a spectrum of health effects in men, women, and children that rely on quantification of analytes in biological specimens such as blood, urine, adipose tissue, follicular fluids, and seminal fluids.  The Division needs a variety of banked specimens analyzed for concentrations of various hormone levels, dietary biomarkers, and indicators of metabolic function among other analytes.

The Mathematical Biology Program supports research in areas of applied and computationalmathematics with relevance to the biological sciences. Successful proposals are mathematically innovative and address challenging problems of interest to members of the biological community. Some projects of interest to the Mathematical Biology Program may include development of mathematical concepts and tools traditionally seen in other disciplinary programs within the Division of Mathematical Sciences, e.g., topology, probability, statistics, computational mathematics, etc.

The Mathematical Biology Program supports research in areas of applied and computationalmathematics with relevance to the biological sciences. Successful proposals are mathematically innovative and address challenging problems of interest to members of the biological community. Some projects of interest to the Mathematical Biology Program may include development of mathematical concepts and tools traditionally seen in other disciplinary programs within the Division of Mathematical Sciences, e.g., topology, probability, statistics, computational mathematics, etc. 

The HFC program seeks proposals for research to develop and test hybrid geopolitical forecasting systems. These systems will integrate human and machine forecasting components to create maximally accurate, flexible, and scalable forecasting capabilities. Human-generated forecasts may be subject to cognitive biases and/or scalability limits. Machine-generated (i.e., statistical, computational) forecasting approaches may be more scalable and data-driven, but are often ill-suited to render forecasts for idiosyncratic or newly emerging geopolitical issues. Hybrid approaches hold promise for combining the strengths of these two approaches while mitigating their individual weaknesses. Performers will develop systems that will integrate human and machine forecasting contributions in novel ways. These systems will compete in a multi-year competition to identify approaches that may enable the Intelligence Community (IC) to radically improve the accuracy and timeliness of geopolitical forecasts.

The HFC program seeks proposals for research to develop and test hybrid geopolitical forecasting systems. These systems will integrate human and machine forecasting components to create maximally accurate, flexible, and scalable forecasting capabilities. Human-generated forecasts may be subject to cognitive biases and/or scalability limits. Machine-generated (i.e., statistical, computational) forecasting approaches may be more scalable and data-driven, but are often ill-suited to render forecasts for idiosyncratic or newly emerging geopolitical issues. Hybrid approaches hold promise for combining the strengths of these two approaches while mitigating their individual weaknesses. Performers will develop systems that will integrate human and machine forecasting contributions in novel ways. These systems will compete in a multi-year competition to identify approaches that may enable the Intelligence Community (IC) to radically improve the accuracy and timeliness of geopolitical forecasts.

The Division of Mathematical Sciences in the Directorate for Mathematical and Physical Sciences at the National Science Foundation and the National Institute of General Medical Sciences at the National Institutes of Health plan to support research in mathematics and statistics on questions in the biological and biomedical sciences. Both agencies recognize the need and urgency for promoting research at the interface between the mathematical sciences and the life sciences. This program is designed to encourage new collaborations, as well as to support existing ones.

The Division of Mathematical Sciences in the Directorate for Mathematical and Physical Sciences at the National Science Foundation and the National Institute of General Medical Sciences at the National Institutes of Health plan to support research in mathematics and statistics on questions in the biological and biomedical sciences. Both agencies recognize the need and urgency for promoting research at the interface between the mathematical sciences and the life sciences. This program is designed to encourage new collaborations, as well as to support existing ones.

The purpose of this Funding Opportunity Announcement (FOA) is to support the development of advanced computational, bioinformatic and statistical tools to determine the functional relevance of genetic variants associated with mental disorders of complex etiologies identified through genome-wide association or sequencing studies. The overarching goal of this initiative is to support the development of innovative computational methods that facilitate the elucidation of the functionality of genetic variants associated with mental illness, taking into account the added complexities and nuances of brain diseases, and to ultimately inform novel treatment development based on human biology.

The purpose of this Funding Opportunity Announcement (FOA) is to support a consortium of collaborative projects, with a minimum of two sites, that propose to use cutting edge technologies to generate and analyze whole genome sequencing (WGS) data from either case control or family samples in order to elucidate the full genetic architecture underlying susceptibility to severe mental disorders. Insights into the genetic architecture underlying susceptibility to severe mental disorders will be achieved through implementation of state of the art WGS assays and innovative/novel statistical methods.

The Division of Mathematical Sciences in the Directorate for Mathematical and Physical Sciences at the National Science Foundation and the National Institute of General Medical Sciences at the National Institutes of Health plan to support research in mathematics and statistics on questions in the biological and biomedical sciences. Both agencies recognize the need and urgency for promoting research at the interface between the mathematical sciences and the life sciences. This competition is designed to encourage new collaborations, as well as to support existing ones.

The purpose of this FOA is to invite applications for Genomics, Epigenomics, and Transcriptomics Chemical Analysis Sites to join the Molecular Transducers of Physical Activity Consortium. Awards made through this FOA will support the establishment of sites that will use appropriate technology to conduct genomics, epigenomics, and transcriptomics analysis of tissues collected from human participants and animals undergoing a physical activity intervention, contribute that data to a public consortium database, and participate in the initial statistical analysis to generate fingerprints of candidate molecular transducers of physical activity.

The purpose of this Funding Opportunity Announcement (FOA) is to stimulate the development of innovative statistical, data science, or other quantitative approaches to studying the health effects of complex chemical mixtures in environmental epidemiology. 

The Division of Environmental Biology (DEB) supports fundamental research on populations, species, communities, and ecosystems. Scientific emphases range across many evolutionary and ecological patterns and processes at all spatial and temporal scales. Areas of research include biodiversity, phylogenetic systematics, molecular evolution, life history evolution, natural selection, ecology, biogeography, ecosystem structure, function and services, conservation biology, global change, and biogeochemical cycles. Research on organismal origins, functions, relationships, interactions, and evolutionary history may incorporate field, laboratory, or collection-based approaches; observational or manipulative experiments; synthesis activities; as well as theoretical approaches involving analytical, statistical, or computational modeling.