Research Funding

Funding Opportunity Announcements

Browse below for Data Science Funding Opportunity Announcements.

This page last reviewed on September 14, 2018

Feed last updated: April 04 2019 9:10 am
Title FOA Number Organization Release Date Expiration Date Purpose Search Terms
The KUH Predoctoral to Postdoctoral Fellow Transition Award (F99/K00 Clinical Trial Not Allowed)

RFA-DK-18-024

NIDDK

Jan 28 2019

Nov 21 2019

The purpose of the KUH/NIDDK Predoctoral to Postdoctoral Fellow Transition Award (F99/K00) is to recruit exceptional graduate students who are recognized by their institutions for their high potential and to incentivize them to pursue a Kidney, Urologic or Hematologic (K, U, or H) postdoctoral position that ultimately positions them to become independent K, U, or H researchers. This two-phase award will facilitate completion of the doctoral dissertation and stablize the transition of highly talented Ph.D candidates from a variety of fields, including, but not limited to, engineering, statistics, data science, imaging, biochemistry and genetics into strong postdoctoral appointments that are focused on K, U or H research. It is anticipated that successful completion of this phased award will make the individual highly competitive for a subsequent NIDDK award (e.g., K99/R00, R01). Opportunities for career development activities relevant to their long-term career goals of becoming independent researchers will be provided. Graduate students who are already involved in K, U, or H research are encouraged to apply for the NIDDK F31 (PA-18-671).
This Funding Opportunity Announcement (FOA) does not allow applicants to propose to lead an independent clinical trial, but does allow applicants to propose research experience in a clinical trial led by a sponsor or co-sponsor

data science
Single Cell Opioid Responses in the Context of HIV (SCORCH) Program: Data Coordination, Analysis, and Scientific Outreach (UM1 Clinical Trial Not All

RFA-DA-19-038

NIDA

Apr 01 2019

May 09 2019

Along with RFA-DA-19-037, which will support the generation of single cell datasets for one or more brain regions relevant to opioid use disorder and persistent HIV infection, a data coordination/analysis/outreach center (RFA-DA-19-038) will be established to make NIDA-funded single cell data and other molecular HIV/SUD data FAIR (Findable, Accessible, Interoperable, and Reusable). Harmonized single cell HIV/SUD data sets will enable immediate data mining by the scientific community for HIV and/or SUD biomarkers and potential pathways for therapeutic intervention. It will also enable future mining of these data sets as new and improved data science and information technology approaches are developed, maximizing NIDAs original investment in the data generating activities.

data science, data integration, common data, artificial intelligence, machine learning
Research Career Enhancement Award to Advance Therapy Development for Alzheimer's (K18)

PAR-17-052

NIA

Nov 07 2016

Jan 08 2020

This NIA Research Career Enhancement Award (K18) program invites applications from qualified researchers to acquire training and career development experiences that close expertise gaps in data science and in drug discovery. The goal of the program is to allow Alzheimer's Disease (AD) researchers to expand their expertise to become more effective in leading cross-disciplinary, translational, team-science projects in AD or AD-related dementias (ADRD). This award will also allow data scientists to redirect their expertise toward the study of AD and ADRD.

informatics, computational
RESCINDED - Notice of Intent to Publish a Funding Opportunity Announcement for Rare Disease Cohorts in Heart, Lung, Blood and Sleep Disorders (UG3/UH3

NOT-HL-18-620

NHLBI

Apr 13 2018

N/A

Notice Intent Publish Funding Opportunity Announcement Rare Disease Cohorts Heart, Lung, Blood Sleep Disorders UG3/UH3) Notice Number: NOT-HL-18-620 Key Dates Release Date: April 13, 2018 RESCINDED) Estimated Publication Date Funding Opportunity Announcement: 12/01/2018 First Estimated Application Due Date:02/01/2019 Earliest Estimated Award Date:10/31/2019 Earliest Estimated Start Date:12/31/2019 Related Announcements None Issued National Heart, Lung, Blood Institute NHLBI) Purpose National Heart, Lung Blood Institute intends promote new initiative establish cohorts rare heart, lung, blood, and/or sleep HLBS) diseases enables multidisciplinary teams conduct natural history mechanistic studies provide evidence base future clinical trials improved diagnostics.  studies help advance fundamental insights key molecular, genomic, clinical, environmental behavioral determinants rare HLBS diseases their outcomes leading diagnostic therapeutic strategies.   NHLBI interested applications will address questions relevant the NHLBI mission, address gaps the NHLBI?s portfolio rare disease cohorts that align the Institute ' s Strategic Vision https://www.nhlbi.nih.gov/about/documents/strategic-vision). NHLBI intends to utilize UG3/UH3 activity code, bi-phasic, milestone-driven cooperative agreement. Details the planned FOA provided below. Notice being provided allow potential applicants sufficient time develop responsive proposals.  FOA expected be published October 2018 anticipated application receipt dates February 2019 February 2020. Research Initiative Details planned initiative provide opportunities efficiently advance rare disease research using genetics deep phenotyping characterize disease to identify disease sub-types; use data science methods integrate clinical patient-reported outcomes PROs) laboratory, imaging omics data understand natural history disease;  generate data differentiate patients the same morphological phenotype different genetic mutations  severity outcomes; elucidate genotype-phenotype interactions multisystem phenotyping develop reliable valid predictive tools determine will respond which treatments when intervene; to encourage innovative methods such telemedicine reach subjects rare diseases located remote locations without access a major academic medical center. goal this initiative to encourage creative innovative research; such, applications should propose studies investigating hypotheses research questions currently addressed cohorts funding the NHLBI. RFA invite applications propose cohorts are created de novo or re-establish study populations enrolling patients previously-funded cohorts, clinical trials registries. Funding Information Estimated Total Funding 1,625,000 Total Costs) Expected Number Awards 4 Estimated Award Ceiling 500,000 Direct Costs) Primary CFDA Numbers 93.837, 93.838, 93.839, 93.840, 93.233 Anticipated Eligible Organizations Public/State Controlled Institution Higher Education Private Institution Higher Education Nonprofit 501(c)(3) IRS Status than Institution Higher Education) Nonprofit without 501(c)(3) IRS Status than Institution Higher Education) Small Business For-Profit Organization than Small Business) State Government Indian/Native American Tribal Government Federally Recognized) City township governments Indian/Native American Tribally Designated Organization Native American tribal organizations than Federally recognized tribal governments) Indian/Native American Tribal Government than Federally Recognized) Regional Organization  Applications not being solicited this time.   Inquiries Please direct inquiries to: Ellen M. Werner, PhD, MA  National Heart, Lung, Blood Institute  301-435-0065  wernere@nhlbi.nih.gov 

data science
Request for Information (RFI): Strengthening the Early Stages of the NIDA Training Pipeline through Massively Open Online Courses on the Biomedical In

NOT-DA-16-027

NIDA

Apr 12 2016

N/A

Request Information RFI): Strengthening Early Stages the NIDA Training Pipeline through Massively Open Online Courses the Biomedical Informatics Addiction Research Notice Number: NOT-DA-16-027 Key Dates Release Date:    April 12, 2016 Response Date: July 3, 2016 Related Announcements None     Issued National Institute Drug Abuse NIDA) Purpose Training next generation addiction researchers a critical priority the National Institute Drug Abuse NIDA).  need expertise biomedical informatics, statistics Big Data science increasing across of biomedical research, including addiction.  NIDA seeks create massive open online course MOOC) providing teaching materials begin training the next generation quantitative addiction researchers.  purpose this Request Information RFI) to seek broad public input the design the course, the best methods implementation. Background mission NIDA to lead nation bringing power science bear drug abuse addiction. Part fulfilling mission includes creating pipeline training opportunities ensure research workforce adequately skilled. need increased education statistics, informatics data science grown the development new technologies neuroscience biomedical research. MOOCs able reach audiences the thousands. are available online websites such Coursera EDx, offering huge advantage over local courses requiring travel.  are already MOOCs available focusing informatics neuroscience data analysis. However, are MOOCs biomedical informatics directly related addiction research.  specifically, NIDA interested strengthening training pipeline produce influx researchers trained these techniques providing MOOC-distributed training materials students and/or teachers. Information Requested RFI intended gather broad public input the biomedical informatics, statistics Big Data lecture topics deemed valuable training next generation addiction researchers the most appropriate methods distribute those topics.  Advice sought includes is limited the following items ranked priority NIDA, please provide feedback as as possible: Ideas the target audience:  NIDA considered advanced high school students have sophistication integrate materials related addiction research biomedical informatics.  example, includes ideas constraining audience those students considering experiences a research lab ideas the value broadly targeting students hopes exciting those considering experience a research lab. Ideas whether is appropriate constrain topic domain addiction, whether is valuable students early their training arc obtain broader biomedical informatics training spans disease areas. Ideas whether placing statistics biomedical informatics the context addiction research provides greater intuition. Ideas how MOOC materials fit existing curriculum:  example, a high school audience targeted, should MOOC integrated existing curriculum, used class a supplement such curriculum serve an outside activity such an after-school club. Ideas whether such course serve a virtual lab” students do have opportunity view analyze research data, are interested research related majors. Ideas whether such course serve a strength the resume students applying work labs the undergraduate level beyond, including community college/associate level. Ideas whether teachers should serve the primary viewers these online courses going through materials using to create customized curriculum their students. Ideas topic areas within biomedical informatics should included the MOOC. Ideas whether regression z-scores be suitable degree challenge high school students sought. Ideas whether multi-dimensional analysis machine learning suitable challenges pre-associates pre-baccalaureate levels targeted.  Ideas how lectures should MOOC comprised of, how long should lecture be: response or not provided consideration the above bullet lecture topics. Ideas whether funded research investigators are experts these areas should the presenters the MOOC lectures:  a high school audience sought, comment funded research investigators working high school teachers if the best to foster such collaborations. Ideas what media e.g. websites, technologies) should used disseminate materials. Insights format materials each individual lecture e.g. 1 video directed teachers, 1 video use students, data, handouts homework assignments). Insights the formatting the entire compiled course. NIDA collected following listing topics relevance advance biomedical informatics within addiction research.  Please comment these potential MOOC lecture topics what level education/aptitude should targeted towards.  Please also provide additional topics would prepare students early the training pipeline experiences the lab: Analysis voltammetry signaling; Longitudinal analysis calcium imaging microelectrode data over temporal course self-administration Analysis temporal geospatial data mHealth studies Construction correlation matrices during resting state fMRI tasks Epidemiological analysis national local drug Construction Manhattan plots genetics analysis Dimensionality reduction allowing visualization high-dimensional data; Single trial analyses other high-resolution investigations research data; Investigating individual variability self-administration behavioral data explore resilience vulnerability factors; Automated analysis machine learning classification big behavioral data," such multiple camera long-term video monitoring naturalistic behaviors e.g. the home cage setting), recording ultrasonic vocalizations other behavioral measures; Analysis electronic health record EHR) data identify patterns health care data could identify those risk developing substance misuse substance disorders those risk relapsing e.g. integration EHRs administrative data examine impact the design performance the service delivery system patient outcomes); Methods integrate analyze multiple sources health data i.e., EHR, mobile device, etc.) to Submit Response ensure consideration, responses must received July 3, 2016, should emailed vani.pariyadath@nih.gov. Respondents not receive individualized feedback. respondents encouraged sign for NIDA E-News updates http://www.drugabuse.gov/international/sign-up-e-news) receive information related Institute activities, including updates the development release the final Strategic Plan. Responses this RFI voluntary. Please not include any personally identifiable other information you not wish make public. Proprietary, classified, confidential, sensitive information should be included responses. Comments submitted be compiled discussion incorporated the NIDA Strategic Plan appropriate. Any personal identifiers personal names, email addresses, etc.) be removed responses compiled. RFI for informational planning purposes only should be construed a solicitation as obligation the part the Federal Government general, NIH, NIDA specifically. NIDA does intend make any awards based responses this RFI pay the preparation any information submitted for Government’s of such information. Inquiries Please direct inquiries to: Vani Pariyadath National Institute Drug Abuse NIDA) Telephone: 301-443-3209 Email: vani.pariyadath@nih.gov

data science, big data, informatics, machine learning
Request for Information (RFI): Soliciting Input for the National Institutes of Health (NIH) Strategic Plan for Data Science

NOT-OD-18-134

OD

Mar 05 2018

N/A

Request Information RFI): Soliciting Input the National Institutes Health NIH) Strategic Plan Data Science Notice Number: NOT-OD-18-134 Key Dates Release Date: March 5, 2018 Response Date: April 2, 2018 Related Announcements NOT-OD-19-014 NOT-OD-19-034 Issued National Institutes Health NIH) Purpose purpose this Request Information RFI) to invite comments suggestions the first National Institutes Health NIH) Strategic Plan Data Science. NIH publishing Notice solicit input topics under consideration the strategic plan its stakeholders, including members the scientific community, academic institutions, private sector, health professionals, professional societies, advocacy groups, patient communities, well other interested members the public. Background Data science an integral component modern biomedical research. is interdisciplinary field inquiry which quantitative analytical approaches, processes, systems developed used extract knowledge insights increasingly large and/or complex sets data. Data science increased importance biomedical research over past decade NIH expects trend continue. order capitalize the opportunities presented advances data science, overcome key challenges, NIH developing Strategic Plan Data Science. plan describes NIH’s overarching goals, strategic objectives, implementation tactics promoting modernization the NIH-funded biomedical data science ecosystem. complete draft plan available at: https://grants.nih.gov/grants/rfi/NIH-Strategic-Plan-for-Data-Science.pdf. Information Requested RFI seeks input stakeholders throughout scientific research community the general public regarding above draft NIH Strategic Plan Data Science. NIH seeks comments any the following topics: appropriateness the goals the plan of strategies implementation tactics proposed achieve them; Opportunities NIH partner achieving goals; Additional concepts should included the plan; Performance measures milestones could used gauge success elements the plan inform course corrections; Any topic respondent feels relevant NIH consider developing strategic plan. to Submit Response Responses this RFI must submitted electronically at: http://grants.nih.gov/grants/rfi/rfi.cfm?ID=73 .  Responses must received April 2, 2018. Responses this RFI voluntary. Do include any proprietary, classified, confidential, trade secret, sensitive information your response. The responses be reviewed NIH staff, individual feedback not provided any responder. Government use information submitted response this RFI its discretion. Government reserves right use any submitted information public NIH websites, reports, summaries the state the science, any possible resultant solicitation(s), grant(s), cooperative agreement(s), in development future funding opportunity announcements. RFI for information planning purposes only shall be construed a solicitation, grant, cooperative agreement, as obligation the part the Federal Government, NIH, individual NIH Institutes Centers provide support any ideas identified response it. Government not pay the preparation any information submitted for Government’s of such information. basis claims against U.S. Government shall arise a result a response this request information from Government’s of such information. NIH looks forward your input we hope you share RFI document your colleagues. Inquiries Please direct inquiries to: Cindy Caughman. M.P.H. Scientific Data Council Telephone: 301-496-8190 Email: DataScienceRFI@mail.nih.gov  

data science
Request for Information (RFI): Next-Generation Data Science Challenges in Health and Biomedicine

NOT-LM-17-006

NLM

Sep 26 2017

N/A

Request Information RFI): Next-Generation Data Science Challenges Health Biomedicine Notice Number: NOT-LM-17-006 Key Dates Release Date: September 26, 2017 Response Date:   November 1, 2017 Related Announcements NOT-LM-18-001 Issued National Library Medicine NLM) Purpose behalf the National Institutes Health NIH), National Library Medicine NLM) seeks community input new data science research initiatives could address key challenges currently faced researchers, clinicians, administrators, others, all areas biomedical, social/behavioral health-related research. field data science broad scope, encompassing approaches the generation, characterization, management, storage, analysis, visualization, integration use large, heterogeneous data sets have relevance health biomedicine. Data science undergirds broad interdependent objectives the NIH Strategic Plan https://www.nih.gov/about-nih/nih-wide-strategic-plan). Information data science research directions could lead breakthroughs any all NIH interest areas welcomed, whether applicable across wide swaths health biomedicine, focused particular research domains. Background   During past five years, in response a 2012 working group report data informatics issued the Advisory Committee the NIH Director ACD) https://acd.od.nih.gov/working-groups/diwg.html), NIH made substantial investments a data science infrastructure tools, resources workforce components a digital research ecosystem health biomedicine. https://commonfund.nih.gov/bd2k/). Building this foundation, landscape data science research evolved is rapidly changing, within beyond NIH, institutes, centers, offices.  2015, another ACD working group recommended NLM become programmatic administrative home data science NIH, complementing NIH’s efforts catalyze open science, data science research reproducibility https://acd.od.nih.gov/documents/reports/Report-NLM-06112015-ACD.pdf). Accordingly, help NIH continue strengthen expand scope its investments data science, NLM seeks information public private organizations e.g., universities industry) individuals promising data science research directions health biomedicine. Information Requested NLM requests information the three focal areas listed below: 1. Promising directions new data science research the context health biomedicine.  Input might address such topics Data Driven Discovery Data Driven Health Improvement. 2. Promising directions new initiatives relating open science research reproducibility. Input might address such topics Advanced Data Management Intelligent Learning Systems Health. 3. Promising directions workforce development new partnerships. Input might address such topics Workforce Development Diversity New Stakeholder Partnerships. Within general topic areas, others related data science health biomedicine, NLM invites researchers, clinicians, organizations, industry representatives other interested parties provide input on: Research areas could benefit most advanced data science methods approaches; Data science methods need updating, gap areas where new approaches needed;  Priorities new data science research; Appropriate partnerships settings expanded data science research. to Submit Response Response this RFI must submitted https://www.research.net/r/NLMDataSci November 1, 2017. Responses should provided a narrative form up 3 pages per topic, links pertinent supplemental information needed. attachments be accepted. proprietary, classified, confidential, sensitive information should included your response. Responses this RFI voluntary. RFI seeks input planning purposes only should be construed a solicitation applications an obligation the part the Federal Government, National Institutes Health, individual NIH Institutes Centers. information collected not considered confidential, identifiers names, institutions, emails, etc.) be removed responses compiled. Processed, anonymized results be shared internally with members health-related scientific work groups, may posted an NIH public website, appropriate.  NIH acknowledge receipt information submitted, will comment the content. Inquiries Please direct inquiries to: Valerie Florance, PhD National Library Medicine NLM) Telephone: 301-496-4621 Email: NLMEPInfo@mail.nih.gov

data science, informatics
Request for Information (RFI): Input on National Cancer Institute Metadata Repository and Services

NOT-CA-15-019

NCI

Apr 03 2015

N/A

Request Information RFI): Input National Cancer Institute Metadata Repository Services Notice Number: NOT-CA-15-019 Key Dates Release Date: April 3, 2015 Response Date: 15, 2015  Related Announcements None     Issued National Cancer Institute NCI) Purpose National Cancer Institute NCI) Center Bioinformatics Information Technology CBIIT) seeking broad input feedback from sources of expertise interest semantic metadata management services. cornerstone these services the Cancer Data Standards Registry caDSR), repository data element descriptions form designs, semantics linked NCI’s controlled terminology. caDSR offers services the cancer research community create, access, maintain, use descriptions across application systems, files, databases. CBIIT wants understand to the NCI common data elements useful, accessible, easier integrate research care processes systems, better support community comment community curation these elements, linked semantics, metadata. CBIIT plans rebuild modernize services make easier discover consensus standards integrate elements linked data cancer research care workflows. intent to align NCI’s infrastructure emerging NIH, national, international metadata initiatives. Background CBIIT’s mission to provide advocate the appropriate of data science, informatics, information technology IT) support accelerate NCI Mission prevent cancer, treat cancer, improve cancer outcomes. important role the NCI Semantic Infrastructure SI) to support NCI research mission through community definition collection metadata. Data have well defined linked metadata improve use, interpretation, reuse data the extraction information knowledge these data. Supporting both human readable machine-readable definitions metadata been important driver the NCI Semantic Infrastructure. general metadata characteristics also among key principles data citation , are noted enable data access, verifiability discoverability. primary goals updating metadata services to: Simplify streamline community creation, curation, maintenance, discovery; Support content harmonization leveraging automated means identification overlapping content Support interoperability integration data elements, modules elements, semantics existing novel workflows; Support knowledge extraction. Information Requested stakeholders an interest improving cancer research through use well-described, discoverable, open descriptions data invited provide information. response mention membership affiliation within industry, government, academia. you choose, can identify area expertise by, not limited to, any the following: Metadata management services software; Formal community metadata standards, e.g., ISO/IEC 11179, Federal Government Open Data Metadata Schema, ISA-TAB, etc.; Semantics management, e.g., W3C semantic web technologies; Data Science bridging biomedical research health care. NCI seeking information includes is limited the following areas: Effective approaches, processes, capabilities augment/replace services currently available the caDSR; Identify requirement gaps provide related cases e.g., identification specific emerging fields technologies multiple existing metadata standards could benefit harmonization integration); Lessons learned existing metadata repository efforts, particularly examples field-tested processes infrastructure, examples failures metadata repository efforts; Common challenges metadata repository development interoperability e.g., methods community engagement, preventing redundant duplicate content, building interoperability related standards, supporting transformations between similar data); Simplifying use metadata computational human-based approaches support processes including data discovery, data analysis, data reuse; Effective approaches linking metadata data data catalogues e.g., use XML/JSON attributes link descriptive metadata associated the data). Submitting Response responses must submitted nbsp;SI_MDR_RFI@mail.nih.gov May 15, 2015. Please include Notice number the subject line. Response this RFI voluntary. Responders free address any all the categories listed above. submitted information be reviewed NIH staff. Submitted information be considered confidential. Please not include any proprietary, classified, confidential, sensitive information your response. NIH use information submitted response this RFI its discretion will provide comments any responder's submission. collected information be reviewed NIH staff, appear reports, may shared publicly an NIH website. 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 future funding opportunity announcements. RFI for information planning purposes only should be construed a solicitation as obligation the part the Federal Government, National Institutes Health NIH), individual NIH Institutes Centers. basis claims against U.S. Government shall arise a result a response this request information from Government’s of such information. Inquiries Please direct inquiries to: Denise Warzel National Cancer Institute NCI) Telephone: 301-480-6199 Email: warzeld@mail.nih.gov

data science, computational, informatics, bioinformatics, common data
Request for Information (RFI): Strategic Plan for the National Library of Medicine, National Institutes of Health

NOT-LM-17-002

NLM

Nov 08 2016

N/A

Request Information RFI): Strategic Plan the National Library Medicine, National Institutes Health Notice Number: NOT-LM-17-002 Key Dates Release Date: November 8, 2016 Response Date : New Date - January 23, 2017 per issuance NOT-LM-17-003 Related Announcements NOT-LM-17-003   Issued National Library Medicine NLM) Purpose National Library Medicine undertaking Strategic Planning Process is soliciting input its broad stakeholder community.   recognize many our stakeholders generously replied the 2015 RFI regarding future directions NLM.  Input provided 2015 already under consideration need be re-submitted.  2015 RFI issued NIH behalf the NLM Working Group the Advisory Committee the NIH Director ACD) obtain input their June 2015 report http://acd.od.nih.gov/reports/Report-NLM-06112015-ACD.pdf) a vision the future NLM the context NLM’s leadership transition emerging NIH data science priorities.  current RFI issued obtain public input goals priorities NLM’s next strategic plan. Background defined statute, purpose the NLM to assist advancement medical related sciences to aid dissemination exchange scientific other information important the progress medicine to public health.” the world’s largest biomedical library, NLM presents highly visible face NIH across United States around globe. Through information systems, biomedical informatics data science research portfolio, extensive training programs, many partnerships, NLM plays essential role furthering fundamental research; catalyzing supporting translation basic science new treatments, products, improved practice; providing useful decision support health professionals, public health emergency response workforce, patients. the ten years since development the NLM’s last long range plan, have significant advances biomedical informatics; major new initiatives the NIH data science, precision medicine, open access biomedical information; changes the environment infrastructure our country’s health systems.  NLM committed building data infrastructure will support future biomedical research. Planning Themes we undertake strategic planning process, NLM be considering priorities future directions around following four themes: 1)  Role NLM advancing data science, open science, biomedical informatics NLM serves the organizational leader a major sponsor research, development, training workforce development data science, information science, biomedical informatics, health sciences librarianship, of facilitate open science. Understanding trends data management, curation, knowledge representation, analysis technologies, communications infrastructure, the semantics importance new classes health-relevant data be essential the institution’s success these areas the future. 2)  Role NLM advancing biomedical discovery translational science NLM a global resource supports catalyzes health-related scientific discovery effective translation new knowledge practice.  Integrated retrieval analysis tools provide linkages promote discovery across wide variety databases containing biomedical literature, genomic information, other scientific clinical data. Novel translational resources such ClinicalTrials.gov accelerate accrual clinical research studies promote scientific integrity via publication study designs research results.  Researcher access new classes data, such electronic health records, supporting discovery science.  Both curiosity-driven translational science expected continue evolve rapidly over coming decade. 3)  Role NLM supporting public’s health: clinical systems, public health systems services, personal health NLM’s mission includes providing information promote health reduce burden suffering disease worldwide.  Healthcare organizations undergoing dramatic changes response the need demonstrate value, safety, effectiveness. its initiatives distribute promote adoption health data standards, NLM been influential enabling interoperability clinical systems meaningful of electronic health records. Factors such behavioral lifestyle characteristics, environmental exposures, biomarkers immune status becoming important.  New technologies communication tools enabling individuals reach goals health promotion disease prevention.  Novel validated models decision support demanded the expanded complexity knowledge all disciplines human health disease. 4)  Role NLM building collections support discovery health the 21st century NLM the world’s largest collection published biomedical literature, many items are unavailable anywhere else.  NLM collections already extend far beyond traditional publications, whether physical digital format, include unpublished manuscripts, images, video, sound recordings, web pages, and, especially, databases containing wide variety enormous quantities digital data. nature scholarly publication scientific discovery continue evolve rapidly, implications what data information NLM should collect the methods be used acquire, archive, disseminate new data, information knowledge relevant human health disease. Information Requested each the four themes described above data open science, discovery translational science, public’s health, collections), invite input the most audacious goals the most compelling questions could potentially drive innovation research information systems the next decade beyond. are seeking input our research community users our information services analysis tools what can imagine the greatest achievements biomedical informatics research biomedical information access use over next 10 years. seek input we endeavor push boundaries information science biomedical informatics, identify knowledge gaps, develop scientific expertise needed bridge them. Within four planning themes, invite comments the following areas.  1)  Identify you consider audacious goal your area interest – challenge may daunting would represent huge leap forward it be achieved.  Include input the barriers and benefits achieving goal. 2)  most important thing NLM does this area, your perspective. 3)  Research areas are most critical NLM conduct support. 4)  Healthcare systems public health arenas which NLM participation most critical. 5)  New data types data collections anticipated over next 10 years. 6)   comments, suggestions, considerations, keeping mind the aim to build NLM the future. to Submit Response respond this RFI, please to submission website. ensure consideration, responses must submitted January 9, 2017. do require to provide name the response. Responses this RFI voluntary. RFI for planning purposes only should be construed a solicitation an obligation the part the Federal Government, National Institutes Health, individual NIH Institutes Centers. NIH does intend make any type award based responses this RFI to pay either preparation information submitted the Government’s of such information. NIH use information submitted response this RFI its discretion will provide comments any responder's submission. However, responses the RFI be reflected future funding opportunity announcements. information provided be analyzed may shared publicly appear reports. Respondents advised the Government under obligation acknowledge receipt the information received 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). Inquiries Please direct inquiries to: Office Health Information Programs Development National Library Medicine NLM) Telephone:  301-496-2311 Email relevant this RFI: NLMStrategicPlan@nih.gov

data science, informatics
Request for Information (RFI): Second Draft Specification for Conformant Cloud Providers

NOT-LM-16-002

NLM

Dec 22 2015

N/A

Request Information RFI): Second Draft Specification Conformant Cloud Providers Notice Number: NOT-LM-16-002 Key Dates Release Date:   December 22, 2015 Response Date: January 22, 2016 Related Announcements None Issued National Library Medicine NLM) Purpose National Institutes Health NIH) funds basic applied research the biomedical health sciences, generating large amounts new research data every year. the scope scale research data increased, standard mechanisms which data found shared indexes, web portals, journal articles, scientific conferences, direct transmission data via electronic networks, name few) insufficient. Further, federal policies require data generated federal support shared, making broadly accessible, easily computable readily sharable. part its efforts resolve problem, NIH developing Commons1, shared, virtual space sharing data, software, digital learning resources, workflows other products research. Commons help ensure the digital artifacts publicly funded research Findable, Accessible, Interoperable Reusable FAIR). NIH supporting implementation the Commons concept via variety pilot activities within NIH Big Data Knowledge BD2K) program.  Computational infrastructure the Commons initially envisioned employing cloud computing services. ldquo;conformant provider” cloud services the Commons offer array options, such Infrastructure a Service IaaS), Platform a Service PaaS) Software a Service SaaS). conformant provider one meets NIH defined requirements business relationships, interfaces, capacity, networking connectivity, information assurance authentication authorization. Dec 23, 2014, NIH released Sources Sought Notice2, SS-NIHCOMMONS-2015, FedBizOps, requesting information an initial set proposed conformance requirements. Comments received the initial requirements revised.  NIH now requesting comments the revised version the conformance requirements, prior identifying conformant providers.     NIH intends finalize requirements based industry scientific feedback during January 2016, to open process providers cloud compute services become conformant soon practicable after requirements finalized. Although details the conformance certification process not finalized, NIH intends utilize reviewed self-certification process during pilot phase. Information Requested NIH requests input two major areas: 1. NIH wishes obtain feedback potential providers a) ability meet proposed conformance requirements, b) interest acting providers c) any requirement deemed the provider be sufficiently burdensome it prevent potential provider participating. 2. NIH wishes obtain feedback potential users conformant cloud services, is, the research community, to a) whether services described the proposed conformance requirements meet needs, b) additional capabilities required, types capabilities be necessary meet general requirements scientific computing c) barriers researchers experienced using cloud services host data sets. NIH particularly interested responses those already cloud services store data make widely available draft conformance requirements posted at: https://datascience.nih.gov/commons/technical_conformance.pdf. to Submit Response Responses this RFI must submitted electronically conformance_requirements@nih.gov later 30 days after publication this RFI. Responses this RFI voluntary. RFI for planning purposes only should be construed a solicitation as obligation the part the Federal Government, National Institutes Health, individual NIH institutes Centers. NIH does intend make any type award based responses this RFI to pay either preparation information submitted the Government’s of such information. NIH use information submitted response the RFI its discretion. Respondents advised the Government under obligation acknowledge receipt the information provided will provide feedback respondents. information submitted be analyzed may shared internally, incorporated future changes the NIH cloud conformance requirements, appropriate at Government’s discretion. Proprietary, classified, confidential, sensitive information should be included your response. Inquiries Please direct inquiries to: George A. Komatsoulis, Ph.D. National Library Medicine NLM) Telephone:  301-594-7875 Email:  komatsog@mail.nih.gov Vivien Bonazzi, Ph.D. Office the NIH Associate Director Data Science ADDS) Telephone:  301-451-8276 Email:  bonazziv@mail.nih.gov 1https://datascience.nih.gov/commons 2https://www.fbo.gov/index?s=opportunity&mode=form&id=09c44aa4c0877c150e7...

data science, big data, computational

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