Research and Training Funding

Funding Opportunity Announcements

Browse below for Data Science Funding Opportunity Announcements.

This page last reviewed on August 7, 2019

Feed last updated: September 09 2019 5:10 pm
Title FOA Number Organization Release Date Expiration Date Purpose Search Terms
Request for Information (RFI) on the NIH Big Data to Knowledge (BD2K) Initiative Resources for Teaching and Learning Biomedical Big Data Management an NOT-LM-15-001 NLM Nov 04 2014 N/A Request Information RFI) the NIH Big Data Knowledge BD2K) Initiative Resources Teaching Learning Biomedical Big Data Management Data Science Notice Number: NOT-LM-15-001 Key Dates Release Date: November 4, 2014 Response Date: December 31, 2014 Related Announcements None Issued National Library Medicine NLM) Purpose National Institutes Health NIH) recognizes increasing demands placed biomedical researchers share data generate through federally-funded research projects. part its Big Data Knowledge BD2K) Initiative, NIH wishes help broader scientific community update knowledge skills the important areas the science, storage, management sharing biomedical big data. NIH wants identify array timely, high quality courses online learning materials already available data science data management topics biomedical big data. Background ability harvest wealth information contained biomedical big data the potential advance our understanding human health disease; however, enormity big data creates major organizational analytical impediments rapid translational impact. biomedical datasets become increasingly large, diverse, complex, tax conventional methods sharing, managing, analyzing data. Furthermore, researchers’ abilities capitalize biomedical big data science-based approaches limited poor data accessibility interoperability, lack appropriate tools, insufficient training. response the opportunities challenges presented the dawning era quot;Big Data" biomedical research, NIH launched Big Data Knowledge BD2K) initiative a trans-NIH initiative cultivate digital research enterprise within biomedicine, facilitate discovery support new knowledge, to maximize community engagement. BD2K addresses four major aims that, combination, meant enhance utility biomedical big data: 1) facilitate broad of biomedical digital assets making discoverable, accessible, citable; 2) conduct research develop methods, software, tools needed fully analyze biomedical big data; 3) enhance training the development use methods tools necessary biomedical big data science; 4) enable data ecosystem accelerates both basic translational discovery part a digital enterprise. Biomedical big data from sources, massive stand-alone datasets generated large collaborations the small datasets produced individual investigators. value all data be amplified through aggregation integration. BD2K initiative a community-enabled endeavor towards maximizing collective value current future biomedical digital assets better inform protect human health. BD2K part a larger ecosystem driven data policies shared infrastructure. the BD2K initiative, term quot;Biomedical Big Data" inclusive the diverse digital objects may impact basic, translational, clinical, social, behavioral, environmental, informatics research questions. Such data types include imaging, phenotypic, genotypic, molecular, clinical, behavioral, environmental, many types biological biomedical data. may also include data generated other purposes e.g., social media, search histories, economic, geographical, cell phone data). Finally, also encompass metadata, data standards, software tools involved data processing analysis. universities begin implement federal policies requiring to share research data have gathered the support public funds1, scientists, graduate students, librarians other professional administrative staff learning or refreshing they know data science the management biomedical research data. organizations universities already engaged providing courses other instruction staff, faculty student researchers help master new skills needed working biomedical big data. part the BD2K program, shared Biomedical Big Data resource soon become reality, providing public access shared biomedical research data research tools2. Curriculum training materials relating data science data management topics also become part the shared biomedical big data research resource, including those funded NIH through BD2K initiatives. Information Requested this Request Information RFI) Notice, NIH invites interested knowledgeable persons inform NIH existing learning resources covering Biomedical Big Data management data science topics such as, not limited to: Data Management Data capture storage Data mapping integration disparate types Data annotation curation, including metadata Version tracking multi-site data management Pipelines data processing analysis Statistics Modeling including methods data integration) Inference including large p, small N problems) Prediction including Machine Learning) Quantification uncertainty Big Data Computer Science Algorithms, algorithmic complexity Optimization Distributed storage processing Visualization Programming languages NIH interested collections aggregations) the topics listed above well resources focused individual topics. needed instructional resources training data management data science already available. Please identify resources materials interest characteristics such as, not limited to: Graduate-level short courses, tutorials workshops online, in-person hybrid) are open all; Graduate-level online tutorials modules; Massive Open Online Courses MOOCs; Curriculum plans resources including sample datasets data management plans used data management training; Evaluation approaches online data science data management courses. NIH interested collections aggregations) the above well individual topics. Materials self-guided learning must available online for download standard digital formats. each class learning resource, please provide information will help NIH identify locate resources, such as: name the course resource; URL the online resource a site describes offers resource; sponsor the resource, such organization instructor. Additional information, also welcome, including comments the course resource. to Submit Response responses must submitted electronically December 31, 2014, the form an email NLMEPINFO_BD2K@mail.nih.gov , using subject data management'. Responses this RFI Notice voluntary. submitted information be reviewed the NIH staff later available the public. Submitted information not considered confidential. not attach PPT files other curriculum materials themselves your response. Responses welcome associations professional organizations well individual stakeholders. request for information planning purposes should be construed a solicitation as obligation the Federal Government NIH. awards be based responses this Request Information. information submitted be analyzed may used reports presentations. Those respond advised NIH under obligation acknowledge receipt your comments, provide comments your submission. proprietary, classified, confidential and/or sensitive information should included your response. NIH the government reserve right use any non-proprietary technical information any future solicitation(s). Inquiries Please direct inquiries to: Valerie Florance, Ph.D. National Library Medicine Telephone: 301-496-4621 Email: NLMEPINFO_BD2K@mail.nih.gov http://grants.nih.gov/grants/guide/notice-files/NOT-OD-14-111.html http://bd2k.nih.gov/faqs.html data science, big data, informatics, data integration, machine learning, data standards, statistics
Request for Information (RFI) on Science Drivers Requiring Capable Exascale High Performance Computing NOT-GM-15-122 NIGMS Sep 15 2015 N/A Request Information RFI) Science Drivers Requiring Capable Exascale High Performance Computing Notice Number: NOT-GM-15-122 Key Dates Release Date:   September 15, 2015 Related Announcements NOT-GM-15-123 Issued National Institute General Medical Sciences NIGMS) Purpose is multi-agency request information identify scientific research topics applications need High Performance Computing HPC) capabilities extend 100 times beyond today’s performance scientific applications. Currently, computational modeling, simulation, well data assimilation data analytics used an increasing number researchers answer complex multispatial, multiphysics scientific questions more realism. the scientific discovery horizon expands as advances high performance computing become central scientific workflows, sustained petascale application performance be insufficient meet needs. addition, HPC expanding traditional numerically oriented computation also include large-scale analytics e.g., Bayesian approaches model refinement, large-scale image analysis, machine learning, decision support, quantifying uncertainty multimodal multi spatial analyses). Architectures technologies used modeling simulation currently differ those used data integration analytics, are increasingly converging. extreme computing ecosystem must therefore accommodate broad spectrum growing data science activities. Background White House Executive Order, July 29, 2015, establishes National Strategic Computing Initiative NSCI) a whole-of-government effort designed create cohesive, multi-agency strategic vision Federal investment strategy, executed collaboration industry academia, maximize benefits HPC the United States. Department Energy DOE), National Science Foundation NSF), the Department Defense DOD) the lead agencies this effort support significantly advanced HPC ecosystem within next decade.   of objectives the initiative to deliver capable Exascale” computing capability delivers 100 times today's application performance. is request information NSF, DOE, NIH community input identifying scientific research would benefit a greatly enhanced new generation HPC far beyond can done using current technologies architectures. information be used assist agencies construct roadmap, build exascale-capable ecosystem required support scientific research, inform research, engineering development process. is likely a range advanced capabilities need be developed respond the varied computing needs across science disciplines. seek responses applications subfields life e.g., biological, social, health biomedical) sciences, mathematical physical sciences, geosciences, energy science, engineering research.  seek only traditional areas numerical intensity such simulations nuclear physics, biomolecular physics, weather climate modeling, materials science, also any areas rely deriving fundamental understanding large scale analytics would require 100-fold increase over today’s application performance. Information Requested respect your field expertise traditional non-traditional research areas applications HPC, agencies request input/feedback. comments include are limited the following areas concern: specific scientific research challenges would need projected 100-fold increase application performance over is possible today.  potential impact the research the scientific community, national economy, society.   specific limitations/barriers existing HPC systems must overcome perform studies this area.  comment also include level performance current architectures, the projected increase performance is needed future architectures. Any related research areas foresee would benefit this level augmented computational capability. Identification any barriers addition computational capability impact proposed research also considered. Important computational technical parameters the problem you expect to in 10 years 2025).  addition any specialized unique computational capabilities are required and/or need be scaled for addressing scientific problem, e.g., the areas computing architectures, systems software hardware, software applications, algorithm development, communications, networking. Alternative models deployment resource accessibility arising of exascale computing. Improvements scientific workflow well particular requirements may needed specific domains. Capabilities needed the end-to-end system, including data requirements such data analytics visualization tools, shared data capabilities, data services includes databases, portals data transfer tools/nodes. Foundational issues need be addressed such training, workforce development collaborative environments. areas relevance the Agencies consider. Submitting Response responses must submitted NIGMS_exascale@nigms.nih.gov October 16, 2015. comments must submitted via E-mail text as attached electronic document.  Microsoft documents preferred.   Please try limit response two pages total. RFI for planning purposes only should be construed a solicitation applications an obligation the part the government. government not pay the preparation any information submitted for government’s of information. agencies use information submitted response this RFI their discretion will provide comments any responder's submission. Responses the RFI be reflected future funding opportunity announcements. information provided be analyzed, appear reports, may shared publicly agency websites. Respondents advised the government under obligation acknowledge receipt the information provide feedback respondents respect any information submitted. proprietary, classified, confidential, sensitive information should included your response. government reserves right use any non-proprietary technical information any resultant solicitation(s), policies procedures. Inquiries Please direct inquiries to: Susan Gregurick, Ph.D. National Institute General Medical Sciences NIGMS) National Institutes Health Telephone: 301-451-6446 Email: susan.gregurick@nigms.nih.gov William L. Miller, Ph.D. Division Advanced Cyberinfrastructure Directorate Computer Information Science Engineering National Science Foundation Telephone: 703-292-7886 Email: WLMiller@nsf.gov Barbara Helland Advanced Scientific Computing Research ASCR) Department Energy Telephone: 301-903-9958 Email: barbara.helland@science.doe.gov data science, computational, data integration, machine learning
Request for Comments: Proposal to Update Data Management of Genomic Summary Results Under the NIH Genomic Data Sharing Policy NOT-OD-17-110 NIH Sep 20 2017 N/A Request Comments: Proposal Update Data Management Genomic Summary Results Under NIH Genomic Data Sharing Policy Notice Number: NOT-OD-17-110 Key Dates Release Date: September 20, 2017 Response Date: October 20, 2017 Related Announcements NOT-OD-18-104 NOT-OD-07-088 NOT-OD-12-136 NOT-OD-14-124 NOT-OD-17-044 NOT-OD-19-023 NOT-OD-19-011--> Issued National Institutes Health NIH) Purpose National Institutes Health NIH) seeking public comments regarding proposed update the access procedures genomic summary results under Genomic Data Sharing GDS) Policy.1 Genomic summary results, also known aggregate genomic data’2 genomic summary statistics’ 3, results primary analyses genomic research convey information relevant understanding genomic associations traits diseases across datasets rather data specific any individual research participant. goal this proposed data management update to provide access genomic summary results through methods proportional the risks benefits posed this type information. Background Overview NIH’s mission to seek fundamental knowledge the nature behavior living systems the application that knowledge enhance health, lengthen life, reduce illness disability.4 Broad responsible sharing genomic summary results generated through analysis NIH-supported research promotes maximum public benefit the federal research investment providing information crucial the interpretation application genomic data research clinical practice. Genomic summary results an analytic output derived a study’s primary genomic data are currently defined the agency include calculated summary statistics, such genotype counts frequencies, allele counts frequencies, effect size estimates standard errors, likelihoods, p-values. Genomic summary results facilitate interpretation genomic variants may may not) contribute a disease disorder interest. Public sharing genomic summary results large-scale genomic research become crucial advancing scientific clinical discovery.5 However, studies available through NIH-designated data repository6, access this information currently only available through controlled access below an explanation controlled access’). Based input the research community over past few years below), NIH proposing allow broader access genomic summary results most studies subject the NIH GDS Policy. Institutions submitting genomic data NIH-designated data repositories be expected notify NIH any studies which are particular sensitivities i.e., studies including potentially stigmatizing traits, with identifiable isolated study populations). Access genomic summary results such datasets remain under controlled access. NIH committed safeguarding interests study participants maintaining public trust biomedical research. Therefore, NIH seeking public feedback this proposed data management update. History Access Genomic Summary Results 2007, NIH issued policy sharing data generated through NIH-supported genome wide association studies GWAS),7 launched Database Genotypes Phenotypes dbGaP).8 2014, GWAS Policy subsumed under NIH GDS Policy, applies all large-scale genomic data generated NIH-funded research. Under both policies, dbGaP other NIH-designated data repositories provided data through two-tiered system: 1) unrestricted access, includes descriptions available data, research protocols instruments used collect it, summary-level information the data; 2) controlled access, provides individual-level genomic phenotypic data appropriate research purposes under terms are consistent the informed consent under those data collected. Under NIH GWAS Policy, genomic summary results originally included among summary-level information available through unrestricted access. However, 2008 paper Homer et al. demonstrated statistical method the potential resolve individual’s inclusion a member a research group e.g., within disease group) using genomic summary results.9 Notably, method, well others have followed using genomic information types,10,11 requires independent access a known individual’s whole genome data order predict statistical matches’ information within genomic summary results. NIH responded this development moving genomic summary results controlled access portions NIH-designated data repositories. agency also stated intent further assess risks benefits associated unrestricted access this type information light the new methodology.12 basis the 2008 data management change that matching a known individual a disease case’) group within research study might reveal unknown health phenotype information obvious the independently acquired whole genome data.13   Although NIH maintains genomic summary results dbGaP under controlled-access model, research community since developed several highly utilized valuable public data resources share genomic summary results.14 addition, type information continues be publicly available an element published studies the scientific literature. Despite broad availability genomic summary results, NIH not aware any reported examples date individuals being matched participation a research study beyond research analyses designed demonstrate hypothetical possibility such unintended use.15    NIH Discussions Related Genomic Summary Results Access 2012, NIH held Workshop entitled Establishing Central Resource Data Genome Sequencing Projects”16 consider wide scope issues related aggregating genomic data. During discussions, workshop participants noted value genomic summary results scientific clinical discovery, recommended they publicly available—when appropriate. Also 2012, enable access genomic summary results General Research Use17 studies through single data access request, compilation genomic summary results appropriate studies made available through dbGaP.18,19 addition more efficient access this type information, creation the compilation study’ also it possible reduce unnecessary access individual-level genomic data since, that time, only to obtain access genomic summary results under GDS Policy in conjunction the full data set. 2016, NHGRI convened Workshop entitled Sharing Aggregate Genomic Data” explicitly re-consider risks benefits associated access and of genomic summary results.20  Workshop participants highlighted minimal risk associated public access genomic summary results supported open access model most studies.21  Participants note alternate access models should considered sensitive studies where may heightened concerns.   solicit broad input the risks benefits different access models genomic summary results, NIH included topic the February 2017 Request Information RFI) Processes dbGaP Data Submission, Access, Management.”22 Public comments received suggest support broader access genomic summary results, especially under scenarios include additional risk mitigation strategies genomic summary results sensitive studies.23 Proposed Update Genomic Summary Results Access maximize public benefit genomic information generated through NIH-supported research a manner consistent current scientific ethical considerations,24,25,26,27 NIH promote broad sharing genomic summary results most research studies data held an NIH-designated data repository through new rapid access” tier. Rapid access enable access appropriate genomic summary results after interested users affirm agreement a statement regarding responsible of information below). proposed update the GDS Policy’s data management practices support NIH’s goals promote scientific advances protect research participants’ privacy interests reducing need users request controlled access individual-level genomic data, unless is necessary address specific research questions. addition, proposed data management change establishes access model genomic summary results is proportional the distinct risks associated access this type information relative the risks associated access individual-level genomic data.28 Genomic summary results be available include those provided a study’s investigator, any, well summary statistics computed the relevant NIH-designated data repository across non-sensitive studies data included that repository below). Genomic summary results provided include systematically computed statistics such as, not limited to:  genotype counts frequencies; allele counts frequencies; effect size estimates standard errors; likelihoods; p-values. values be defined calculated using scientifically relevant subsets research participants included within study populations e.g., disease, trait-based, control populations). Information methods computing any summary statistics provided an NIH-designated data repository be available through repository’s website.      is possible privacy risks related broad access genomic summary results be heightened study populations isolated geographic regions with rare traits. is also possible certain study populations be vulnerable group harm due potential stigma related traits being studied other participant protection concerns. addition, studies include data potentially stigmatizing traits, outcomes any privacy breach conceivably cause greater harm research participants is likely under most circumstances. address types sensitivities, institutions submitting datasets NIH-designated data repositories indicate the data sharing plan the Institutional Certification29 genomic summary results such studies should provided only through controlled access.   the purposes this proposed data management update, examples potentially stigmatizing traits expected include, not limited to: illicit drug substance abuse; HIV/AIDS diagnosis; sexual attitudes, preferences, practices. Increased privacy risk heightened risk group harm anticipated stem study populations draw from, are limited to: rare disease communities; studies small sample sizes; isolated identifiable study populations, such indigenous populations underrepresented ethnic groups. Informational Resources support awareness the ethical responsibilities associated responsible of genomic information including genomic summary results), NIH develop informational resources be publicly available through relevant NIH-designated data repositories. Before genomic summary results made accessible through new rapid access’ tier, users affirm they reviewed informational resources provided below). Affirming Responsible of Genomic Summary Results promote responsible of genomic summary results available through rapid access mechanism, users affirm agreement advance science health through use the information. affirmation be achieved via click-through agreement’ users confirm intent use genomic summary results provided responsibly indicating they: 1. Reviewed informational resources available NIH-designated data repositories describing appropriate uses genomic data, including genomic summary results; 2. not attempt re-identify contact any individual group within study population, generate information could allow participants’ identities be readily ascertained; and, 3. promote scientific research health through any of genomic summary results. Informed Consent Consistent the expectations under NIH GDS Policy, NIH expects consent forms the informed consent process human genomic studies clearly articulate access plans data information generated through study, including genomic summary results. NIH expects consent processes other information available potential research participants be transparent participation an NIH-supported study infers acknowledgement investigators aggregate analyze data generated through study. NIH expects consent processes other information explain such analyses other summaries study information including genomic summary results) be shared the scientific literature through public scientific resources, such data sharing resources provide broad unrestricted access the information. Effective Date NIH expects proposed data management update access genomic summary results be effective upon final publication the update.  Proposed Implementation Plans After effective date this data management update, NIH-funded investigators performing research falls under scope the GDS Policy be expected indicate, their Genomic Data Sharing Plan, a study should designated sensitive the purposes access genomic summary results. determination should confirmed the Institutional Certification provided the NIH. datasets submitted to, for data already accessible through, NIH-designated data repositories prior the effective date this data management update, submitting institutions have six months indicate genomic summary results any study submitted one their investigators should maintained controlled access due concerns sensitivity study information. will possible request additional time complete assessment a particular dataset. such cases, genomic summary results that dataset remain controlled access until final determination received the funding Institute Center. a submitting institution confirms appropriateness broader access genomic summary results a particular study prior the end the six-month period, information be available through rapid access tier immediately Request Comments NIH seeking public feedback regarding proposed data management update accessing genomic summary results NIH-funded studies.  NIH encourages comments all stakeholders, is especially interested hearing members the general public, research participants, and/or broader patient community.  NIH seeking overall, general comments any aspect the proposed access model.  NIH also seeking feedback the following specific issues: 1. Risks benefits providing broad access genomic summary results most studies NIH-designated data repositories utilizing proposed rapid access mechanism associated click-through agreement. Risks benefits relate participant protection issues and/or scientific opportunity. 2. Risks benefits maintaining genomic summary results studies designated the submitting institution include sensitive information controlled access. Risks benefits relate participant protection issues and/or scientific opportunity. 3. Appropriateness the proposal institutions submitting study data under NIH GDS Policy indicate datasets should designated sensitive. 4. General comments any topic relevant unrestricted, rapid, controlled-access genomic summary results NIH-funded studies. NIH intends hold least public webinar the proposed data management update the GDS Policy, may also utilize opportunities answer questions receive feedback stakeholders they identified.  Submitting Response Comments the topic areas interest should submitted electronically the following webpage: https://osp.od.nih.gov/gsr-rfi/ mailed to: Office Science Policy OSP), National Institutes Health, 6705 Rockledge Drive, Suite 750, Bethesda, MD 20892, by fax to: 301-496-9839 October 20], 2017. Comments received, including any personal information, be posted without change after close the comment period the NIH GDS website https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/). Please not include any proprietary, classified, confidential, sensitive information your response. Please note the United States Government not pay the preparation any information submitted for use that information. NIH looks forward your input hope you share RFI document your colleagues. Updates this document, any, be noted. Government reserves right use any non-proprietary technical information summaries the state the science, any resultant solicitation(s). NIH use information gathered this RFI inform development modification data sharing databases, websites, policies practices, processes procedures, supporting documentation e.g., guidance, FAQs). References NIH Genomic Data Sharing Policy.  https://grants.nih.gov/grants/guide/notice-files/NOT-OD-14-124.html. 2 Aggregate data defined the NIH GDS Policy summary statistics compiled multiple sources individual-level data. 3 Summary statistics been defined calculated summary statistics, including genotype counts, allele frequencies, effect size estimates standard errors, p-values calculated a study sample. https://www.genome.gov/pages/policyethics/genomicdata/aggdatareport.pdf. 4 NIH Mission Goals. https://www.nih.gov/about-nih/what-we-do/mission-goals. 5 Lek, Monkol, et al. Analysis protein-coding genetic variation 60,706 humans." Nature 536, 285–291 18 August 2016). doi:10.1038/nature19057. 6 NIH-designated data repository any data repository maintained supported NIH either directly through collaboration. 7 Policy Sharing Data Obtained NIH Supported Conducted Genome-Wide Association Studies GWAS).  https://grants.nih.gov/grants/guide/notice-files/NOT-OD-07-088.html. 8 NIH Launches dbGaP, Database Genome Wide Association Studies. https://www.nih.gov/news-events/news-releases/nih-launches-dbgap-databas.... 9 Homer N, Szelinger S, Redman M, et al. Resolving Individuals Contributing Trace Amounts DNA Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays. Visscher PM, ed. PLoS Genetics. 2008;4(8):e1000167. doi:10.1371/journal.pgen.1000167. 10 Schadt, Eric E., Sangsoon Woo, Ke Hao. Bayesian method predict individual SNP genotypes gene expression data." Nature genetics 44.5 2012): 603-608. 11 Im, Hae Kyung, et al. sharing quantitative trait GWAS results an era multiple-omics data the limits genomic privacy." The American Journal Human Genetics 90.4 2012): 591-598. 12 https://osp.od.nih.gov/wp-content/uploads/Data_Sharing_Policy_Modificati.... 13 Zerhouni, E.A. Nabel, E.G. Protecting aggregate genomic data. Science. 2008 October 3; 322(5898): 44. Published online 2008 September 4. doi: 10.1126/science.1165490. 14 Genome Aggregation Database gnomAD); Exome Variant Server; Exome Aggregation Consortium ExAC); Type 2 Diabetes Knowledge Portal T2DKP); Michigan Imputation Server; AmbryShare; ClinVar, BRAVO Browse Variants Online, TOPMed’s WGS variant server). 15 Wendler DS, Rid A. Genetic Research Biospecimens Poses Minimal Risk. Trends genetics?: TIG. 2015;31(1):11-15. doi:10.1016/j.tig.2014.10.003. 16Workshop Establishing Central Resource Data Genome Sequencing Projects. 2012. National Institutes Health. https://www.genome.gov/27552142/workshop-on-establishing-a-central-resou.... 17NIH describes data available general research GRU) data no limitations restrictions beyond limitations outlined the Data Certification Agreement. See: https://osp.od.nih.gov/wp-content/uploads/NIH_PTC_in_Developing_DUL_Stat... see: https://osp.od.nih.gov/wp-content/uploads/standard_data_use_limitations.pdf.  18 Notice New Process Requesting dbGaP Access Aggregate Genomic Data General Research Purposes. https://grants.nih.gov/grants/guide/notice-files/NOT-OD-12-136.html. 19 https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs.... 20 NHGRI Workshop Sharing Aggregate Genomic Data. https://www.genome.gov/27566089/workshop-on-sharing-aggregate-genomic-da...   21 Workshop Report Sharing Aggregate Genomic Data. https://www.genome.gov/pages/policyethics/genomicdata/aggdatareport.pdf. 22 Request Information Processes dbGaP Data Submission, Access, Management. https://grants.nih.gov/grants/guide/notice-files/NOT-od-17-044.html. 23 https://osp.od.nih.gov/wp-content/uploads/dbGaP_Public_Comments_Data_Sub.... 24Erlich Y, Narayanan A. Routes breaching protecting genetic privacy. Nature reviews Genetics. 2014;15(6):409-421. doi:10.1038/nrg3723. 25Wendler DS, Rid A. Genetic Research Biospecimens Poses Minimal Risk. Trends Genetics?: TIG. 2015;31(1):11-15. doi:10.1016/j.tig.2014.10.003. 26 Sanderson, Saskia C., et al. Public Attitudes toward Consent Data Sharing Biobank Research: Large Multi-site Experimental Survey the US." The American Journal Human Genetics 100.3 2017): 414-427. 27 Gutmann, A. W., et al. Privacy progress whole genome sequencing." Presidential Committee the Study Bioethical 2012 2012). 28 Craig, D. W., Goor, R., Wang, Z., Paschall, J., Ostell, J., Feolo, M., & Manolio, T. A. Privacy Summary Level Data’; Assessing Managing Risk Sharing Aggregate Genetic Variant Data. Nature Reviews Genetics. 2011;12(10):730-736. doi:10.1038/nrg3067. 29 https://osp.od.nih.gov/scientific-sharing/institutional-certifications/ Inquiries Please direct inquiries to: NIH Office Science Policy Telephone: 301-496-9838 Email: SciencePolicy@od.nih.gov   data science, statistics
Rare Disease Cohorts in Heart, Lung, Blood and Sleep Disorders (UG3/UH3 Clinical Trial Not Allowed) RFA-HL-20-014 NHLBI Apr 04 2019 Jun 18 2020 The purpose of this FOA is to fund research centers that will establish longitudinal cohorts in rare HLBS diseases to investigate unaddressed research questions using epidemiologic study designs and methods that are appropriate for conditions affecting fewer than 200,000 persons in the US. These observational cohort studies should be designed to provide an evidence base for future interventional studies, including clinical trials; for developing better diagnostics than those that are currently available; for answering early translational questions; or for broader implementation of guidelines for managing these diseases. This program will provide opportunities to advance rare disease research using genetics and deep phenotyping to characterize the disease and to identify disease sub-types; to use data science methods that integrate clinical and patient-reported outcomes (PROs) with laboratory, imaging, environmental and -omics data to understand the natural history of disease; to generate data that differentiate patients with the same morphological phenotype but different genetic mutations and severity of outcomes; to elucidate genotype-phenotype interactions and multisystem phenotyping to develop reliable and valid predictive tools to determine who will respond to which treatments and when to intervene; and to encourage innovative methods such as telemedicine to include participants with rare diseases located in remote locations. This initiative will allow applicants to study related rare diseases, disorders, conditions or syndromes together based on pathogenesis, affected biochemical, cellular or physiological features or organ system involvement. Studying related rare diseases within the same cohort could help understand the nuances, and knowledge gained from one disease and could accelerate the advances in related diseases. For example, investigators may propose to study hemoglobin disorders rather than only sickle cell anemia or thalassemia. data science
NSF-NIH Interagency Initiative: Core Techniques and Technologies for Advancing Big Data Science and Engineering (BIGDATA) NOT-GM-12-109 NIGMS Mar 29 2012 N/A NSF-NIH Interagency Initiative: Core Techniques Technologies Advancing Big Data Science Engineering BIGDATA) Notice Number: NOT-GM-12-109 Key Dates Release Date:  March 29, 2012          Issued National Institute General Medical Sciences NIGMS), http://www.nigms.nih.gov/) National Cancer Institute NCI), http://www.nci.nih.gov/) National Human Genome Research Institute, http://www.nhgri.nih.gov) National Institute Biomedical Imaging Bioengineering NIBIB), http://www.nibib.nih.gov/) National Institute Drug Abuse NIDA), http://www.nida.nih.gov/) National Institute Neurological Disorders Stroke NINDS), http://www.ninds.nih.gov/) National Library Medicine NLM), http://www.nlm.nih.gov/) Purpose Institutes Centers the National Institutes Health NIH) the National Science Foundation NSF) identified Big Data a program focus.  particular, present initiative covers needs core techniques technologies advancing big data science engineering. Full details the programmatic goals application process described the NSF Program Solicitation NSF-12-499). Description:  BIGDATA seeks applications develop evaluate core technologies tools take advantage available collections large data sets accelerate progress science, biomedical research, engineering. addition, applications must also include description how project build capacity.  Also, project choose focus science engineering big data project an area national priority, this optional. small project receive NIH support to 150,000 direct funds to 3 years, while mid-scale projects receive NIH support ranging 150,000 650,000 direct funds to 5 years.  all, participating NIH Institutes committed to 2.75M FY 2012 and/or 2013.   It anticipated NIH pay 4 6 small projects 1 2 mid-scale projects through program fiscal years 2012 and/or 2013, subject availability funds.  Application submission through National Science Foundation. Following jointly conducted initial peer review these applications, likely NIH awardees be asked reformat application resubmit application NIH processing. Detailed information this program be obtained the Internet www.nsf.gov/funding/pgm_summ.jsp?pims_id=504767 Inquiries Potential applicants send inquiries NIH Program Contacts well NSF contacts named the solicitation NSF-12-499). Karin Remington PhD Director, Division Biomedical Technology, Bioinformatics, Computational Biology National Institute General Medical Sciences  (301) 451-6446 remingka@nigms.nih.gov data science, big data, computational, bioinformatics, computational biology
Notice to Specify High-Priority Research Topic for PAR-19-070 and PAR-19-071 NOT-AG-18-053 NIA Dec 17 2018 N/A Notice Specify High-Priority Research Topic PAR-19-070 PAR-19-071 Notice Number: NOT-AG-18-053 Key Dates Release Date: December 17, 2018 Related Announcements PAR-19-070 PAR-19-071 Issued National Institute Aging NIA) Purpose Notice Information specifies high-priority topic interest PAR-19-070 Research Current Topics Alzheimer's Disease Its Related Dementias R01 Clinical Trial Optional)" PAR-19-071 ldquo;Research Current Topics Alzheimer's Disease Its Related Dementias R21 Clinical Trial Allowed)”. Note applications proposing exploratory developmental projects which are insufficient preliminary data well certain focused secondary analysis projects should consider using R21 FOA, whereas projects already sufficient preliminary data a very strong well-developed scientific premise should the R01 FOA. Major Opportunities Research Epidemiology Alzheimer's Disease Related Dementias Cognitive Resilience Background etiology Alzheimer’s disease related dementias AD/ADRD) proven be complex expected. Our existing cohort studies both AD/ADRD cognitive aging include social, behavioral, cognitive, neuroimaging, biomarker, genetic, epigenetic, other measures assessed longitudinally collected initially ever earlier the lifespan. Indeed, is becoming clear both risk protective factors include exposures experiences early mid-life, long before appearance any neuropathology notable cognitive decline. Continued progress the epidemiology both AD/ADRD cognitive resilience therefore likely require cohorts, diverse cohorts, participants, variables, more occasions measurement. Additionally, greater collaboration among diverse scientific disciplines be needed. Research Objectives high-priority topic encourages investigator-initiated research all aspects cognitive epidemiology relevant AD/ADRD cognitive resilience identifies specific areas build current efforts supported NIA new recommendations the 2018 Alzheimer’s Disease Research Summit see: https://www.nia.nih.gov/research/administration/recommendations-nih-ad-r... for information). following areas of particular interest the NIA. Augmenting existing longitudinal cohort studies National Institutes Health NIH) supports broad range population studies address questions related the trajectory Alzheimer’s disease other aging phenotypes. collection analysis new phenotypic information, including not limited new biomarkers, neuroimaging, non-traditional data modalities such that wearable sensors, broaden impact existing studies. addition genetic data existing newly collected cohorts the light existing novel phenotypes allow analyses how specific genetic variants polygenic risk scores contribute the risk or protection against AD/ADRD the trajectory cognitive performance. emerging opportunities stem the wider availability electronic health records administrative data e.g., CMS Medicare claims) the ability collect phenotypic data online lower cost. Enabling precision medicine AD/ADRD through deep molecular phenotyping precision-medicine approach see: http://www.nih.gov/precisionmedicine/) presents new opportunities understanding molecular determinants AD/ADRD risk cognitive resilience diverse populations at level the individual. Notice invites applications will enhance potential community-based cohort studies enable precision medicine AD/ADRD by, example: expanding types cross-sectional longitudinal ante- post-mortem-biospecimen data-collection needed generate multiple layers ldquo;omics” data; incorporating dense molecular endophenotyping e.g., genomic, epigenomic, proteomic, metabolomic, microbiomic); collecting nontraditional data modalities using wearable sensors mobile-health technologies; embedding biomarkers environmental exposure geocodes. large-scale multidimensional data generated the above approaches serve the basis future systems biology gene-environment studies the development a new taxonomy AD/ADRD prevention. Enhancing power multiethnic cohort studies. multi-factorial etiology heterogeneity AD/ADRD reveal itself racial ethnic differences overall AD/ADRD risk in putative risk protective factors in progression neuropathology. Although multi-ethnic cohorts be very informative, well-powered cohort studies needed identify specific risk protective factors vary between sub)populations. cohorts also benefit the addition measures may better help us identify determinants both disease risk cognitive resilience. 2015 Alzheimer’s Disease Research Summit includes recommendation establish new cohorts intense endophenotyping are sufficiently powered analyze effects gender diverse populations. Exploring trends the risk AD/ADRD their explanation via putative risk protective factors. Recent research well-characterized cohorts suggests age-specific risk AD/ADRD be declining some populations increasing others. answer the trend question clear implications public health policy. Trend data also provide potentially powerful to test whether putative risk protective factors truly causal. example, educational attainment appears be protective against AD/ADRD whereas both cardiovascular disease CVD) female sex confer additional risk. the reasons these observed patterns not yet clear. risk posed female sex status, example, reflect sex differences affecting disease process, may include differences the trajectory hormonal changes age in sex chromosome, gender differences educational attainment all these. Comparisons between cohorts differing these factors over time be informative may require sophisticated analyses meta-analyses replication plans. Collecting sequencing DNA samples well-characterized cases controls. Research conducted investigators the Alzheimer’s Disease Sequencing Project ADSP; see: https://www.niagads.org/adsp/content/home) others demonstrated value whole-genome whole-exome sequencing the detection genetic variants may modify AD risk protection. sequencing more genomes well-characterized cases controls family based cohorts large multiply-affected families accelerate gene discovery target identification efforts to accelerate progress the drug development pipeline. Well-characterized subjects diversity sample sets especially needed augment statistical power. Applicants interested this line research should aware current emerging NIH guidance respect sharing genomic data see: http://gds.nih.gov/) are expected facilitate rapid data-sharing according existing ADSP NIA policies, include providing types data the ADSP NIAGADS/dbGaP database https://www.niagads.org/adsp/). Electronic archiving cohort studies Although NIH encourages broad inclusive data-sharing large studies, electronic archiving data many longitudinal cohorts either incomplete relies data infrastructure is vulnerable research-funding lapses. current NIH Strategic Plan Data Science see: https://datascience.nih.gov/strategicplan for additional information) focuses enhancing discoverability usability data sets developing appropriate analysis tools, providing special opportunities collaboration between epidemiologists survey scientists the hand computer data scientists the other. addition a wealth information relevant cognitive epidemiology is trapped non-digitized obsolete formats, are highly relevant data sets biospecimen collections have never publicly shared well surveys where greater availability paradata metadata benefit researchers. welcome applications will more data available use the research community expeditiously possible, likewise encourage dissemination efforts enhance discoverability these data. Harmonizing complex data sets relevant AD/ADRD Although have substantial efforts NIH develop brief, reliable measures e.g., PROMIS® the NIH Toolbox®; see: https://www.nihpromis.org for information) well recommendations the of off-the-shelf phenotypic measures e.g., PhenX) large epidemiological studies, has less work creating crosswalks between measures those have historically used cohort studies. need harmonization across platforms particularly acute studies include longitudinal clinical, neuroimaging, genetic genomic, biomarker data are costly obtain. Coordination harmonization data existing cohort studies the Alzheimer’s Disease Neuroimaging Initiative ADNI; see: http://www.adni-info.org/ more), Accelerating Medicines Partnership AMP) effort AD/ADRD see: http://www.nih.gov/science/amp/alzheimers.htm), the ADSP see: https://www.niagads.org/adsp/content/home) also welcome. Harmonizing dementia assessment enhance cross-national comparisons. important harmonization to study dementia trends the risk protective factors against dementia) differ between cohorts, work needed the harmonization dementia-assessment methods could inform cross-national comparisons. requires than simple translation instruments, since even best ones not operate equivalently developing countries where literacy rates levels educational attainment much lower. recent examples where work being done the 10/66 Dementia Research Group see: https://www.alz.co.uk/1066/default.php) lower income countries more recent work done within US-based Health Retirement Study HRS; see: http://hrsonline.isr.umich.edu/). Both examples a harmonized cognitive assessment protocol HCAP) can used compare dementia prevalence higher- lower-income countries. Applications use extend approaches develop new approaches harmonize dementia assessment suitable cross-national comparisons feasible both clinical field settings encouraged. Inquiries Please direct inquiries to: Dallas W. Anderson, Ph.D. Division Neuroscience  National Institute Aging NIA) Telephone: 301-496-1494  Email: dallas.anderson@nih.gov Jonathan W. King, Ph.D. Division Behavioral Social Research  National Institute Aging NIA)  Telephone: 301-402-4156  Email: kingjo@nia.nih.gov data science
Notice to Extend the Expiration Date of PAR-16-204 "NLM Career Development Award in Biomedical Informatics and Data Science (K01)" and Disco NOT-LM-19-004 NLM Apr 15 2019 N/A Notice Extend Expiration Date PAR-16-204 NLM Career Development Award Biomedical Informatics Data Science K01)" Discontinue FOA Notice Number: NOT-LM-19-004 Key Dates Release Date: April 15, 2019 Related Announcements PAR-16-204 Issued National Library Medicine NLM) Purpose purpose this Notice to inform interested applicants the expiration date the Funding Opportunity Announcement FOA) PAR-16-204 "NLM Career Development Award Biomedical Informatics Data Science K01)" be extended one receipt cycle. FOA expires on September 8, 2019. NLM does plan reissue FOA. Applicants applying the first time the June 12, 2019, September 7, 2019, due date should aware they not an opportunity resubmit a successor FOA. FOA been modified follows: Key Dates: Currently reads: Expiration Date May 8, 2019 Modified read: Expiration Date September 8, 2019 other aspects the FOA remain unchanged. Inquiries Please direct inquiries to: Hua-Chuan Sim, MD National Library Medicine NLM) Telephone: 301-594-4882 Email: simh@mail.nih.gov Jane Ye, PhD National Library Medicine NLM) Telephone: 301-594-4882 Email: yej@mail.nih.gov Alan Vanbiervliet, PhD National Library Medicine NLM) Telephone: 301-594-4882 Email: alan.vanbiervliet@nih.gov data science, informatics
Notice to Add AIDS Application Due Dates to RFA-LM-16-002 BD2K Predoctoral Training in Biomedical Big Data Science (T32) NOT-LM-16-005 NLM May 24 2016 N/A Notice Add AIDS Application Due Dates RFA-LM-16-002 BD2K Predoctoral Training Biomedical Big Data Science T32)” Notice Number: NOT-LM-16-005 Key Dates Release Date: 24, 2016 Related Announcements RFA-LM-16-002    Issued National Library Medicine NLM) Purpose purpose this Notice add AIDS application due dates RFA-LM-16-002 ldquo;BD2K Predoctoral Training Biomedical Big Data  Science T32)”.  Part 1. Key Dates Current Language: Application Due Date(s) July 25, 2016, 5:00 PM local time applicant organization. types non-AIDS applications allowed this funding opportunity announcement due this date. Applicants encouraged apply early allow adequate time make any corrections errors found the application during submission process the due date. New Language: Application Due Date(s) July 25, 2016, 5:00 PM local time applicant organization. types applications allowed this funding opportunity announcement due this date. Applicants encouraged apply early allow adequate time make any corrections errors found the application during submission process the due date. AIDS Application Due Date(s) July 25, 2016, 5:00 PM local time applicant organization. types applications allowed this funding opportunity announcement due this date. Applicants encouraged apply early allow adequate time make any corrections errors found the application during submission process the due date. other aspects the funding opportunity announcement remain same. Inquiries Please direct inquiries to: Jane Ye, PhD National Library Medicine NLM) Telephone: 301-594-4882 Email: bd2k_training@mail.nih.gov   data science, big data
Notice of Updates to the IMAG Multiscale Modeling Initiative (PAR-15-085) NOT-EB-16-011 NIBIB Dec 22 2016 N/A Notice Updates the IMAG Multiscale Modeling Initiative PAR-15-085) Notice Number: NOT-EB-16-011 Key Dates Release Date: December 22, 2016   Related Announcements PAR-15-085 Issued National Institute Biomedical Imaging Bioengineering NIBIB) National Cancer Institute NCI) National Heart, Lung, Blood Institute NHLBI) National Human Genome Research Institute NHGRI) National Institute Aging NIA) National Institute Alcohol Abuse Alcoholism NIAAA) National Institute Arthritis Musculoskeletal Skin Diseases NIAMS) Eunice Kennedy Shriver National Institute Child Health Human Development NICHD) National Institute Drug Abuse NIDA) National Center Complementary Integrative Health NCCIH) National Institute Environmental Health Sciences NIEHS) Office Behavioral Social Sciences Research OBSSR) U.S. Army Research Office ARO) - Biomathematics Department Energy DOE) - Office Science, Biological Environmental Research Program (BER) U.S. Food Drug Administration (FDA) – Office In-Vitro Diagnostics Radiological Health OIR), CDRH U.S. Food Drug Administration FDA) – Office Device Evaluation ODE), CDRH U.S. Food Drug Administration FDA) – Office Science Engineering Laboratories OSEL), nbsp;CDRH National Science Foundation NSF) - Directorate Computer amp; Information Science amp; Engineering CISE) National Science Foundation NSF) - Directorate Engineering ENG) National Science Foundation NSF) - Directorate Mathematical Physical Sciences MPS) National Aeronautics Space Administration NASA) - Human Research Program HRP) Office Naval Research ONR) - Division 311 nbsp; Purpose purpose this Notice to clarify programmatic goals specific interests the interagency funding opportunity announcement FOA) PAR-15-085, Predictive Multiscale Models Biomedical, Biological, Behavioral, Environmental Clinical Research U01).  application submission process remain same.  Notice providing additional information to: 1) express particular interest applications proposing development non-standard mathematical, statistical computational modeling methods address multiscale modeling research challenges would benefit joint interagency funding.  2) update Specific Interests the 22 participating funding components the associated Scientific/Research contacts.  1) funding components the seven funding agencies very interested jointly funding integrated research efforts incorporating research non-standard multiscale modeling methods within single project.  Applicants strongly encouraged submit applications develop multiscale models are high risk, push boundaries novel multiscale modeling methodologies. Applications should emphasize addressing multiscale modeling methodological challenges, while using domain applications testbeds addressing challenges. 2) Specific Interests section below lists updated interest statements all 22 funding components.  Below, excerpted statements the six non-NIH funding components, describing examples non-standard multiscale modeling methods: ARO:  innovative modeling methods, especially traditionally quot;pure" areas mathematics such topology, differential geometry, algebra DOE:  new methods characterizing imaging molecular systems, to synthesize redesign biology processes FDA:  models predict whether proposed medical product design function properly safely NASA:  overall conceptual framework organizing principle which might better understand the organism a whole responds space flight NSF:  Advances methods tools predictive modeling, simulation, analysis emergent behavior complex multiscale systems of interest, including issues verification, validation, uncertainty quantification across scales. ONR:  Basic research modeling dynamical properties networks determining causal effects influences needed networks interconnected nodes such social biological/neural networks would helpful the applicants identify agencies may interest specific research efforts within potential research proposal. Applicants strongly encouraged consult the Scientific/Research contacts based the Specific Interests the participating funding components. of 22 funding components participating this FOA involved contributing the review funding process; reserves right fund not fund those efforts regardless decisions other agencies. nbsp;For those applications are selected potential joint funding non-NIH funding components, PD/PI be requested submit same application directly interested funding agencies after review completed.  Specific Interests: following section describes updated specific interests two the 22 participating funding components this FOA. interests examples are limited these cases. Applicants strongly encouraged see full listing Specific Interests the guidelines Section I) contact funding components. U.S. Army Research Office ARO), Biomathematics Program interested basic, high-risk, high-reward research uses, develops, analyzes mechanistic multiscale mathematical models uncover fundamental relationships a wide variety biological systems. models use mathematical techniques fields traditionally used modeling, such probability, dynamical systems, partial differential equations, innovative modeling methods especially traditionally quot;pure" areas mathematics such topology, differential geometry, algebra. particular interest currently projects use mathematical modeling find commonalities mechanism between different biological systems that express underlying principles mathematical terms, well research taking advantage recent advances neuroscience newly-available experimental data gain fundamental understanding brain physiology, cognition, neurological disease through multiscale modeling. Office Naval Research ONR) Mathematical Data Science MDS) Program concerned basic research mathematics, probability statistics, signal processing, machine learning, data engineering, information theory.  program aims develop rigorous mathematical algorithmic answers questions are currently addressed using heuristics non-principled approaches. Recent advances technology led the era massive data sets are only larger, both terms sample size dimensionality the data, also complex. data be multi-modal, multi-relational gathered different sources. massive data sets (“Big Data”) introduce unique computational statistical challenges require development new theoretical principles can extend inference learning algorithms massive scales. outstanding question the MDS program addressing how balance tradeoff between computational accuracy computational resources analyzing large complex datasets.  of most challenging datasets include networks interconnected nodes such social biological/neural networks. Basic research modeling dynamical properties networks determining causal effects influences needed this area. In addition, program interested addressing challenges collaborative decision making developing crowdsourcing methods solving complex problems. application ONR should include full work the budget corresponding only tasks associated the interests the MDS program. Furthermore, is responsibility the applicants provide justification the interagency funding support. Specifically, applicant should explain why interagency support needed how the efforts are funded different agencies benefit effort is targeting MDS program. Proposers encouraged contact Program Officer discuss research interest prior the submission formal proposals. Prior Consultation Scientific/Research Staff Consultation relevant Scientific/Research staff strongly encouraged, later the Letter Intent due date. is the same the Letter Intent, should included a separate communication the Scientific/Research Contacts, Section VII.  requested the applicants, staff advise whether proposed project meets goals this FOA.  Staff not evaluate technical scientific merit the proposed project; technical scientific merit be determined during peer review using review criteria indicated this FOA. During consultation phase, the proposed project does meet programmatic needs this FOA, applicants be strongly encouraged consider Funding Opportunities.  Inquiries Please direct inquiries to: Grace Peng, Ph.D. National Institute Biomedical Imaging Bioengineering NIBIB) Telephone: 301-451-4778 Email: grace.peng@nih.gov Jennifer Couch, Ph.D. National Cancer Institute NCI) Telephone: 240-276-6210 Email: couchj@ctep.nci.nih.gov Wen Chen, Ph.D. National Center Complementary Integrative Health NCCIH) Telephone: 301-451-3989 Email: chenw@mail.nih.gov Pankaj Qasba, Ph.D. National Heart, Lung Blood Institute NHLBI) Telephone: 301-435-0050 Email: qasbap@nhlbi.nih.gov Michael J. Pazin, Ph.D. National Human Genome Research Institute NHGRI) Telephone: 301-496-7531 Email: pazinm@mail.nih.gov Coryse St. Hillaire-Clarke, Ph.D. National Institute Aging NIA) Telephone: 301-496-9350 Email: sthillaireclacn@mail.nih.gov Gregory Bloss, M.A., M.P.P. National Institute Alcohol Abuse Alcoholism NIAAA) Telephone: 301-443-3865 Email: Gregory.Bloss@nih.gov Gayle Lester, Ph.D. National Institute Arthritis Musculoskeletal Skin Diseases NIAMS) Division Musculoskeletal Diseases Telephone: 301-594-3511 Email: lester1@mail.nih.gov Hung Tseng, Ph.D. National Institute Arthritis Musculoskeletal Skin Diseases NIAMS) Division Skin Rheumatic Disease Telephone: 301-594-5032 Email: tsengh@mail.nih.gov Regina Bures, Ph.D. Eunice Kennedy Shriver National Institute Child Health Human Development NICHD) Email: buresrm@mail.nih.gov Susan Volman, Ph.D. National Institute Drug Abuse NIDA) Telephone: 301-435-1315 Email: svolman@nida.nih.gov David Balshaw, PhD National Institute Environmental Health Sciences NIEHS) Telephone: 919-541-2448 Email: balshaw@niehs.nih.gov Bill Riley, Ph.D. Office Behavioral Social Science Research OBSSR) Telephone: 301-402-1146 Email: william.riley@nih.gov Virginia B. Pasour, PhD U.S. Army Research Office ARO) Telephone: 919-549-4254 Email: virginia.b.pasour.civ@mail.mil Ramana Madupu, Ph.D. Department Energy DOE), Biological Environmental Research Telephone: 301-366-2916 Email: ramana.madupu@science.doe.gov Donna R. Lochner Food Drug Administration FDA) Telephone: 301-796-6309 Email: donna.lochner@fda.hhs.gov Mary Ann Horn, Ph.D. National Science Foundation, Directorate Mathematical Physical Sciences NSF-MPS) Telephone: 703-292-4879 Email: mhorn@nsf.gov Vipin Chaudhary, Ph.D. National Science Foundation, Directorate Computer amp; Information Science amp; Engineering NSF-CISE) Telephone: 703-292-2254 Email: vipchaud@nsf.gov Michele Grimm, Ph.D. National Science Foundation, Directorate Engineering NSF-ENG) Telephone: 703-292-4641 Email: mgrimm@nsf.gov Pedja Neskovic, Ph.D. Office Naval Research ONR) Telephone: 703-696-4304 Email: predrag.neskovic@navy.mil Jennifer Fogarty, Ph.D. National Aeronautics Space Administration NASA) Email: jennifer.fogarty-1@nasa.gov data science, big data, computational, machine learning, neural networks, statistics
Notice of the Office of Strategic Coordination (Common Fund) Participation in RFA-ES-16-003 "BD2K Mentored Career Development Award in Biomedical NOT-RM-16-024 Roadmap May 17 2016 N/A Notice the Office Strategic Coordination Common Fund) Participation RFA-ES-16-003 BD2K Mentored Career Development Award Biomedical Big Data Science Intramural Investigators K22)" Notice Number: NOT-RM-16-024 Key Dates Release Date: 17, 2016 Related Announcements RFA-ES-16-003   Issued Office Strategic Coordination Common Fund) Purpose purpose this Notice to inform potential applicants the Office Strategic Coordination Common Fund) participating, effective immediately, the following Funding Opportunity Announcement FOA): RFA-ES-16-003 quot;BD2K Mentored Career Development Award Biomedical Big Data Science Intramural Investigators K22)" RFA-ES-16-003 updated, accordingly, reflect Common Fund's participation this FOA. are changes the Funds Available Anticipated Number Awards due the funding structure this BD2K FOA. Part I. Overview Information Components Participating Organizations National Institute Environmental Health Sciences NIEHS) National Cancer Institute NCI) National Eye Institute NEI) National Human Genome Research Institute NHGRI) National Institute Aging NIA) National Institute Alcohol Abuse Alcoholism NIAAA) National Institute Allergy Infectious Diseases NIAID) National Institute Arthritis Musculoskeletal Skin Diseases NIAMS) Eunice Kennedy Shriver National Institute Child Health Human Development NICHD) National Institute Deafness Other Communication Disorders NIDCD) National Institute Dental Craniofacial Research NIDCR) National Institute Diabetes Digestive Kidney Diseases NIDDK) National Institute Drug Abuse NIDA) National Institute Mental Health NIMH) National Library Medicine NLM) National Center Complementary Integrative Health NCCIH) National Center Advancing Translational Sciences NCATS) Division Program Coordination, Planning Strategic Initiatives, Office Disease Prevention ODP) Division Program Coordination, Planning Strategic Initiatives, Office Research Infrastructure Programs ORIP) Office Behavioral Social Sciences Research OBSSR) Office Research Women’s Health ORWH) Office Strategic Coordination Common Fund) Catalog Federal Domestic Assistance CFDA) Number(s) 93.113; 93.398; 93.867; 93.172; 93.866; 93.273; 93.855; 93.856; 93.846; 93.865; 93.173; 93.121; 93.847; 93.279; 93.242; 93.879; 93.213; 93.350; 93.351; 93.313; 93.310 other sections this FOA remain same. Inquiries Please direct inquiries to: Leslie Derr, Ph.D. Office the Director OD) Telephone: 301 594-8174 Email: derrl@mail.nih.gov data science, big data

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