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SBIR/STTR Programs
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:22:21.000Z<p>The NASA SBIR and STTR programs fund the research, development, and demonstration of innovative technologies that fulfill NASA needs as described in the annual Solicitations and have significant potential for successful commercialization. If you are a small business concern (SBC) with 500 or fewer employees or a non-profit RI such as a university or a research laboratory with ties to an SBC, then NASA encourages you to learn more about the SBIR and STTR programs as a potential source of seed funding for the development of your innovations.</p><p><strong>The SBIR and STTR programs have 3 phases</strong>:</p><ul><li><strong>Phase I</strong> is the opportunity to establish the scientific, technical, and commercial feasibility of the proposed innovation in fulfillment of NASA needs.</li><li><strong>Phase II</strong> is focused on the development, demonstration and delivery of the proposed innovation.</li></ul><p>The SBIR and STTR Phase I contracts last for 6 months with a maximum funding of $125,000, and Phase II contracts last for 24 months with a maximum funding of $750,000 - $1.5 million.</p><ul><li><strong>Phase III</strong> is the commercialization of innovative technologies, products, and services resulting from either a Phase I or Phase II contract. Phase III contracts are funded from sources other than the SBIR and STTR programs and may be awarded without further competition.</li></ul><p><strong>Opportunity for Continued Technology Development Post-Phase II</strong>:</p><p>The NASA SBIR/STTR Program currently has in place two initiatives for supporting its small business partners past the basic Phase I and Phase II elements of the program that emphasize opportunities for commercialization. Specifically, the NASA SBIR/STTR Program has the Phase II Enhancement (Phase II-E) and Phase II eXpanded (Phase II-X) contract options.&nbsp;</p><p><strong>Please review the links below to obtain more information on the SBIR/STTR programs.</strong></p><ul><li><strong><a target="_blank" href="http://sbir.gsfc.nasa.gov/sites/default/files/ParticipationGuide.pdf">Participation Guide</a></strong></li></ul><p>Provides an overview of the SBIR and STTR programs as implemented by NASA</p><ul><li><strong><a href="http://sbir.gsfc.nasa.gov/solicitations">Program Solicitations</a></strong></li></ul><p>Provides access to the annual SBIR/STTR Solicitations containing detailed information on the program eligibility requirements, proposal instructions and research topics and subtopics</p><ul><li><strong><a href="http://sbir.gsfc.nasa.gov/prg_sched_anncmnt">Schedule and Awards</a></strong></li></ul><p>Schedule and links for the SBIR/STTR solicitations and selection announcements</p><ul><li><strong><a href="http://sbir.gsfc.nasa.gov/content/additional-sources-assistance">Sources of Assistance</a></strong></li></ul><p>Federal and non-Federal sources of assistance for small business</p><ul><li><strong><a href="http://sbir.gsfc.nasa.gov/abstract_archives">Awarded Abstracts</a></strong></li></ul><p>Search our complete archive of awarded project abstracts to learn about what NASA has funded</p><ul><li><strong><a href="http://sbir.gsfc.nasa.gov/content/frequently-asked-questions">Frequently Asked Questions</a></strong></li></ul><p>&nbsp;Still have questions? Visit the program FAQs</p>
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2008 Environmental Performance Index (EPI)
nasa-test-0.demo.socrata.com | Last Updated 2015-07-19T07:26:08.000ZThe 2008 Environmental Performance Index (EPI) centers on two broad environmental protection objectives: (1) reducing environmental stresses on human health, and (2) promoting ecosystem vitality and sound natural resource management. Derived from a careful review of the environmental literature, these twin goals mirror the priorities expressed by policymakers. Environmental health and ecosystem vitality are gauged using 25 indicators tracked in six well-established policy categories: Environmental Health (Environmental Burden of Disease, Water, and Air Pollution), Air Pollution (effects on ecosystems), Water (effects on ecosystems), Biodiversity and Habitat, Productive Natural Resources (Forestry, Fisheries, and Agriculture), and Climate Change. The 2008 EPI utilizes a proximity-to-target methodology in which performance on each indicator is rated on a 0 to 100 scale (100 represents at target). By identifying specific targets and measuring how close each country comes to them, the EPI provides a foundation for policy analysis and a context for evaluating performance. Issue-by-issue and aggregate rankings facilitate cross-country comparisons both globally and within relevant peer groups. The 2008 EPI is the result of collaboration among the Yale Center for Environmental Law and Policy (YCELP), Columbia University Center for International Earth Science Information Network (CIESIN), World Economic Forum (WEF), and the Joint Research Centre (JRC), European Commission.
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GPM, DPR, GMI Level 3 Combined Precipitation V03
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:03:54.000ZThere are uncertainties in the interpretation of data from any one of the instruments (KuPR, KaPR, and GMI). By using data from multiple instruments, further constraints on the solution of precipitation structure improve the final product.The purpose of 3CMB is to give a daily and monthly accumulation of the 2BCMB precipitation product. The 3CMB product is a daily and monthly accumulation of the 2BCMB orbital combined product at two grid sizes, 5 x 5 degrees (G1) and 0.25 x 0.25 degrees (G2). Grid G1 contains the following physical measurements of general interest, among others. Grid G2 contains the same groups, but it is on the ltH x lnH grid and does not have the surface type (st) dimension or the histograms (see dimension definitions below). Below, conditional products represent means based upon precipitating areas only; unconditional products represent means for raining and non-raining areas combined. Probabilities represent the number of raining observations divided by the total number of raining and non-raining observations. precipTotRate (Group in G1)- Conditional mean rate for all precipitation phases (ice, liquid, mixed-phase). * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqRate (Group in G1) - Conditional mean rate for liquid precipitation. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotWaterContent (Group in G1) - Conditional mean water content for all precipitation phases. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqWaterContent (Group in G1) - Conditional mean liquid water content. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotDm (Group in G1) - Conditional mass-weighted mean particle diameter. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotRateDiurnal (Group in G1) - Conditional mean total surface precipitation rate indexed by local time. * count (4-byte integer, array size: ltL x lnL x ns x st x tim): Count. * mean (4-byte float, array size: ltL x lnL x ns x st x tim): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x st x tim): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. surfPrecipTotRateDiurnalAllObs (4-byte integer, array size: ltL x lnL x ns x st x tim): Number of total observa...
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Global Fire Emissions Database, Version 3.1
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:06:48.000ZThis data set provides monthly burned area, and monthly, and annual fire emissions data from July 1996 to February 2012. Emissions data are available for carbon (C), dry matter (DM), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), hydrogen (H2), nitrous oxide (N2O), nitrogen oxides (NOx), non-methane hydrocarbons (NMHC), organic carbon (OC), black carbon (BC), particulate matter 2.5 micron (PM2p5), total particulate matter (TPM), and sulfur dioxide (SO2). The C4 fraction of carbon emissions is also provided. The annual C emissions estimates were derived by combining burned area data with a biogeochemical model, CASA-Global Fire Emissions Database (CASA-GFED), that estimates fuel loads and combustion completeness for each monthly time step. The fuel loads were based on satellite derived information on vegetation characteristics and productivity to estimate carbon input and carbon outputs through heterotrophic respiration, herbivory, and fires. Note that while most emissions estimates included data for 32 variables (trace gases, aerosols, and carbon), not all data are available for all years, and not all variables (emission species) are included in each data product.Additional information may be obtained from the Global Fire Data website: http://www.globalfiredata.org/index.html. Data products include:- 0.5 degree x 0.5 degree gridded monthly burned area data (ha) for 1996 to 2012 provided as text files and as GeoTIFF files for 1996 to 2012.- 3-Hourly emssions (fraction) for 2003 to 2010 in NetCDF (.nc) format.- Daily emssions (fraction) for 2003 to 2010, in NetCDF (.nc) format.- Monthly emissions for 32 variables from 1997 to 2011, in text and GeoTIFF format.- Monthly emissions for 31 variables from specific sources (grassland and savanna, woodland, deforestation & degradation, forest, agricultural waste burning, and peat fires), both as absolute and relative emissions. The time period is for 2007 to 2011, and the files are provided in text and GeoTIFF format.- Global emission totals of C and other species from all sources, and from each individual source (forest fires, peat fires, agricultural waste burning, etc).- Annual emissions of carbon and other trace gases for all countries, for the period 1997 to 2010, provided as text files. These files are for indicative use only; they are not suitable for official reporting due to large uncertainties and potential for missing key regional aspects in the global approach used.- Ancillary data for monthly biosphere fluxes. The CASA-GFED biosphere flux sources include Net Primary Production (NPP), Heterotrophic respiration (Rh), and fires (biomass burning). These files are for the time period 1997 to 2009 and are provided as text files and in GeoTIFF format.
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ATSDR Hazardous Waste Site Polygon Data with CIESIN Modifications, Version 2
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T04:34:05.000ZThe Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data with CIESIN Modifications, Version 2 is a database providing georeferenced data for 1,572 National Priorities List (NPL) Superfund sites. These were selected from the larger set of the ATSDR Hazardous Waste Site Polygon Data, Version 2 data set with polygons from May 26, 2010. The modified data set contains only sites that have been proposed, currently on, or deleted from the final NPL as of October 25, 2013. Of the 2,080 ATSDR polygons from 2010, 1,575 were NPL sites but three sites were excluded - 2 in the Virgin Islands and 1 in Guam. This data set is modified by the Columbia University Center for International Earth Science Information Network (CIESIN). The modified polygon database includes all the attributes for these NPL sites provided in the ATSDR GRASP Hazardous Waste Site Polygon database and selected attributes from the EPA List 9 Active CERCLIS sites and SCAP 12 NPL sites databases. These polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). The Geospatial Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention) has created site boundary data using the best available information for those sites where health assessments or consultations have been requested.
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Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data, 1996
nasa-test-0.demo.socrata.com | Last Updated 2015-07-19T07:26:48.000ZThe Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data, 1996 consists of 2042 polygons for selected hazardous waste sites that were compiled in January 1996. The Hazardous Waste Site ATSDR layer was created by linking HAZ_SITES_ATSDR_BASE with additional data. Most polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). Typical sites are either on the EPA National Priorities List (NPL) or are being considered for inclusion on the NPL. This dataset is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide a polygon dataset of hazardous waste sites in the United States, which can be used to identify nearby populations and assess their potential risk)
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Photonic antenna enhanced middle wave and longwave infrared focal plane array with low noise and high operating temperature Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:33:59.000ZPhotodetectors and focal plane arrays (FPAs) covering the middle-wave and longwave infrared (MWIR/LWIR) are of great importance in numerous NASA applications, including earth remote sensing for carbon-based trace gases, Lidar mapping for earth resource locating, and environment and atmosphere monitoring. Existing MWIR/LWIR photodetectors have a low operating temperature of below 77K. The requirement for cryogenic cooling systems adds cost, weight and reliability issues, making it unsuitable for satellite remote sensing applications. This STTR project aims to develop a new plasmonic photonic antenna coupled MWIR/LWIR photodetector and FPA with significantly enhanced performance and a high operating temperature. In Phase I, we developed a preliminary plasmonic photonic antenna enhanced MWIR/LWIR photodetector and demonstrated significant enhancement in photodetectivity and operating temperature. Antenna directivity is also tested and agrees with the simulation. The phase I results not only demonstrated the feasibility of achieving high performance MWIR/LWIR photodetector using the proposed innovation, but also show its promising potentials for high operating temperature FPA development. Motivated by the successful feasibility demonstration and the promising potentials, in this STTR Phase II project, we will develop a prototype of the plasmonic photonic antenna enhanced MWIR/LWIR FPA with a high operating temperature and demonstrate its earth remote sensing capability.
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Low Cost Variable Conductance Heat Pipe for Balloon Payload Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:08:14.000ZWhile continuously increasing in complexity, the payloads of terrestrial high altitude balloons need a thermal management system to reject their waste heat and to maintain a stable temperature as the air (sink) temperature swings from as cold as -90<SUP>o</SUP>C to as hot as +40<SUP>o</SUP>C. Currently, constant conductance, copper-methanol heat pipes are utilized on balloon payloads to remove the waste heat. It would be desirable to use a Variable Conductance Heat Pipe (VCHP) instead, to allow the thermal resistance to increase under cold operating or cold survival environment conditions, keeping the instrument section warm. In spacecraft, thermal management is achieved using axially-grooved aluminum-ammonia heat pipes and VCHPs, which are relatively expensive to manufacture and validate. Advanced Cooling Technologies, Inc. (ACT) is proposing a low-cost VCHP based on smooth-bore, thin-wall stainless steel tubing, with either methanol or pentane as working fluids, that is capable of passively maintaining a relatively constant evaporator (payload) temperature while the sink temperature varies between -90<SUP>o</SUP>C and +40<SUP>o</SUP>C. The thin wall will be much lighter and will provide much better temperature control due to its higher thermal resistance, while the combination of working fluid and envelope material result in a heat pipe that is much less expensive to manufacture than standard grooved aluminum heat pipes. Spacecraft VCHPs normally have the gas reservoir at the end of the condenser, and maintain its temperature with electrical heaters. The proposed VCHP moves the reservoir near the evaporator, eliminating the need for electrical power to control the temperature. Preliminary calculations show that either system, methanol based or pentane based, is capable of meeting the thermal performance requirements. For both the pentane and methanol systems, the evaporator (payload) temperature varies less than 6<SUP>o</SUP>C while the heat sink temperature varies from 90<SUP>o</SUP>C to +40<SUP>o</SUP>C.
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Electronic Correlated Noise Calibration Standard for Interferometric and Polarimetric Microwave Radiometers Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:30:10.000ZA new type of calibration standard is proposed which produces a pair of microwave noise signals to aid in the characterization and calibration of correlating radiometers. The proposed Correlated Noise Calibration Standard (CNCS) is able to generate pairs of broad bandwidth stochastic noise signals with a wide variety of statistical properties. The CNCS can be used with synthetic aperture interferometers to generate specific visibility functions. It can be used with fully polarimetric radiometers to generate specific 3rd and 4th Stokes parameters of brightness temperature. It can also be used with spectrometers to generate specific power spectra and autocorrelations. It is possible to combine these features and, for example, generate the pair of signals that would be measured by a fully polarimetric, spectrally resolving, synthetic aperture radiometer at a particular pair of polarizations and antenna baselines for a specified scene over a specified frequency band. The proposed CNCS will cover all the frequencies used for radiometric observations in the 1 to 40 GHz range. In specific, the Phase II project will develop the system prototypes for L and X bands. While intended for ground based characterization of radiometer systems, the technological approach is amenable to on-orbit calibration.
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Adaptive Distributed Environment for Procedure Training Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:32:41.000ZWith its constantly evolving portfolio of highly technical systems requiring human construction maintenance and operation, NASA has an extreme form of a common yet challenging training problem: how to ensure that personnel are qualified on the (often changing) procedures required to work on or with these systems. Simulation-based training that enables learning while doing is a proven approach, but dependence on hardware-based simulators and the requirement for human instructors to develop and supervise training scenarios raise costs and limit flexibility in delivering training and retraining. We propose to build a distributable intelligent tutoring system (ITS) exploiting a unified representation of human and robotic mission activities that can be used to (1) trace student activity to assess, prompt, and correct their actions, (2) simulate robotic activity, (3) control training scenario generation/selection, (4) cover both general and specific cases, (5) allow for varying degrees of detail in human and robotic activity, (6) support extended scenarios involving multiple procedures, and (7) track detailed re-training requirements resulting from changes in procedures. The innovative merger of general procedure descriptions with specific scenario scripts will facilitate more efficient authoring of consistent broad-coverage automated simulation-based training while retaining the ability to author specific scenarios when needed.