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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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Robust Optimal Fragmentation and Dispersion of Near-Earth Objects Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:31:30.000Z<p> During the past 2 decades, various concepts for mitigating the impact threats from NEOs have been proposed, but many of these concepts were impractical and not technically credible. In particular, all non-nuclear techniques require mission lead times larger than 10 years. However, for the most probable impact threat with a warning time less than 10 years, the use of high-energy nuclear explosives in space becomes inevitable for proper fragmentation and dispersion of an NEO in a collision course with Earth. However, the existing nuclear subsurface penetrator technology limits the impact velocity to less than 300m/s because higher impact velocities destroy prematurely the detonation electronic equipment. Thus, an innovative space system architecture utilizing high-energy nuclear explosives must be developed for a worst-case intercept mission resulting in relative closing velocities as high as 5-30km/s. An advanced system concept is proposed for nuclear subsurface explosion missions. The concept blends a hypervelocity kinetic-energy impactor with nuclear subsurface explosion, and exploits a 2-body space vehicle consisting of a fore body and an aft body. These 2 spacecraft bodies may be connected by a deployable boom. The fore body provides proper kinetic impact crater conditions for an aft body carrying nuclear explosives to make a deeper penetration into an asteroid body. For such a complex mission architecture design study, non-traditional, multidisciplinary research efforts in the areas of hypervelocity impact dynamics, nuclear explosion modeling, high-temperature thermal shielding, shock-resistant electronic systems, and advanced space system technologies are required. Expanding upon the current research activities, the Iowa State Asteroid Deflection Research Center will develop an innovative, advanced space system architecture that provides the planetary defense capabilities needed to enable a future real space mission more efficient, affordable, and reliable.</p>
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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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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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CODE STEM - Moon, Mars, and Beyond; DLESE-Powered On-Line Classroom Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:28:30.000Z"CODE (COrps DEvelopment) STEM (Science, Technology, Engineering, and Math) ? Moon Mars and Beyond; DLESE-Powered On-Line Classroom" shares the excitement of President Bush's vision of space exploration with 8th grade students. Program fulfills standards-based 8th grade curriculum and will be implemented in two diverse Escondido schools (Grant, Rincon). San Diego County Office of Education will ensure relevance and integration into formal curriculum. Through thematic approach to learning and multi-disciplinary exhibit-centered final project, Program strives to engage broad cross-section of students and teachers. Focus is on developing a theme-specific DLESE (Digital Library for Earth System Education) collection and portal. On-line learning communities and inquiry based learning protocols (San Diego State University) will enhance DLESE effectiveness. Implementation culminates with multi-disciplinary student exhibit at participating schools and San Diego Aerospace Museum, sharing excitement of space exploration with general public. In Phase II DLESE collection will be expanded to support additional grade levels (3rd, 5th, 8th, high school), there will be broad implementation in San Diego and Nashville (through Fisk University), and teacher training.
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GPM, DPR, GMI Level 3 Combined Monthly Precipitation V03
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:03:55.000ZThere 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. ABSTRACTThe 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 ...
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GPM, DPR, GMI Level 3 Combined Monthly Precipitation V03
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:03:56.000ZThere 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. ABSTRACTThe 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 ...
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High Interactivity Visualization Software for Large Computational Data Sets Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:17:34.000ZExisting scientific visualization tools have specific limitations for large scale scientific data sets. Of these four limitations can be seen as paramount: (i) memory management, (ii) remote visualization, (iii) interactivity, and (iv) specificity. In Phase I, we proposed and successfully developed a prototype of a collection of computer tools and libraries called SciViz that overcome these limitations and enable researchers to visualize large scale data sets (greater than 200 gigabytes) on HPC resources remotely from their workstations at interactive rates. A key element of our technology is the stack oriented rather than a framework driven approach which allows it to interoperate with common existing scientific visualization software thereby eliminating the need for the user to switch and learn new software. The result is a versatile 3D visualization capability that will significantly decrease the time to knowledge discovery from large, complex data sets. Typical visualization activity can be organized into a simple stack of steps that leads to the visualization result. These steps can broadly be classified into data retrieval, data analysis, visual representation, and rendering. Our approach will be to continue with the technique selected in Phase I of utilizing existing visualization tools at each point in the visualization stack and to develop specific tools that address the core limitations identified and seamlessly integrate them into the visualization stack. Specifically, we intend to complete technical objectives in four areas that will complete the development of visualization tools for interactive visualization of very large data sets in each layer of the visualization stack. These four areas are: Feature Objectives, C++ Conversion and Optimization, Testing Objectives, and Domain Specifics and Integration. The technology will be developed and tested at NASA and the San Diego Supercomputer Center.
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Rapid Electrochemical Detection and Identification of Microbiological and Chemical Contaminants for Manned Spaceflight Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:40:47.000Z<p>A great deal of effort has gone into the development of point-of-use methods to meet the challenge of rapid bacterial identification for both environmental monitoring and clinical applications.&nbsp; Unfortunately, most of the methods developed rely on Preliminary Chain Reaction (PCR) and face inherent limitations because of the requirement for enzymatic components and thermal control.&nbsp; Other methods based on surface plasmon resonance, quartz crystal microbalance, and fluorescence has been reported with good detection limits, but, these methods are immunological and cannot provide genetic-level information.&nbsp; Further, they require labeled markers, complicated fluid handling systems, and sensitive optics that drive up cost and complexity and preclude them from outside the laboratory.&nbsp; Recent work by a group at the University of Toronto has focused on developing an electrochemical platform that combines ultrasensitive detection, straightforward sample processing, and inexpensive components to create a cost-effective, user-friendly device for detection and identification of microorganisms.&nbsp; The platform combines an electrical cell lysis chamber, and electrochemical reporter system, and nanostructured microelectrodes (NMEs) to detect specific nucleic acid sequences.&nbsp; The nucleic acid sequences are unique to a given type of microorganism and can be used to identify the microorganisms present in a sample.</p><p>From the perspective of the anticipated prototype device &nbsp;(Lam, et al. 2012. <em>Polymerase Chain Reaction-Free, Sample-to-Answer Bacterial Detection in 30 Minutes with Integrated Cell Lysis</em>. Anal. Chem. <strong>84(1)</strong>: 21-5), detection of microbial contaminants will begin with a lysis chamber designed to release DNA and RNA from microorganisms present in the sample using ultrasonic or electrochemical technology.&nbsp; The DNA and RNA mixture is then passed into an analysis chamber containing an array of nanostructured microelectrodes (NMEs).&nbsp; The surface of the NMEs will be functionalized with probe molecules for DNA or RNA sequences specific to the bacteria being targeted.&nbsp; Binding of the DNA or RNA to the appropriate detection probe on the NME surface in the presence of an electrochemical reporter system will change the electrochemical properties of the NMEs.&nbsp; A potentiostat is used to measure the current at each individual electrode before and after addition of the DNA and RNA mixture.&nbsp; The difference in current before and after addition of the mixture to the NMEs is compared against a pre-determined threshold to check for the presence of target bacteria in the sample.&nbsp; The process for detection of chemical contaminants is very similar.&nbsp; The lysis chamber would be bypassed and the sample would flow directly into the analysis chamber.&nbsp; The NMEs will be functionalized with molecules to selectively bind the desired targets (analytes) and the change in the electrochemical response of each NME can again be used to detect and quantify the contaminants.&nbsp; Depending on the analyte of interest, it may be possible to directly measure analyte binding on the surface of the NMEs without the use of an electrochemical reporter system. The overall project will focus on optimization of the individual aspects of the detection platform in preparation for construction of a prototype for a flight experiment.&nbsp; The scope of the work in this proposal is limited to characterization and optimization of the lysis step/sample preparation, probe selection, and NME structure.&nbsp; Lysis conditions will be optimized by evaluating parameters associated with the oscillation frequency and lysis time for ultrasonic techniques and applied voltage for the electrochemical techniques.&nbsp; Cell viability, as determined by fluorescent detection of DNA or R
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GPM, DPR, GMI Level 3 Combined Precipitation V03
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:03:53.000ZThere 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...