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Monthly and yearly forecasts of energy production, consumption, and price at the national level and by energy type. Monthly forecasts extend 18 months and yearly forecasts extend to 2040. International yearly projections by region extend to 2040.

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Congressional Districts are the 435 areas from which people are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the States based on census population counts, each State is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a State as practicable. The 114th Congress is seated from January 2015 to 2017. The TIGER/Line shapefiles for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts are provided to the Census Bureau through the Redistricting Data Program (RDP).

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The Floodplain Mapping/Redelineation study deliverables depict and quantify the flood risks for the study area. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The Floodplain Mapping/Redelineation flood risk boundaries are derived from the engineering information Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).

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The Defense Meteorological Satellite Program (DMSP) maintains a constellation of sun-synchronous, near-polar orbiting satellites. The orbital period is 101 minutes and inclination is 99 degrees. The space weather sensors collect insitu observations from the near-Earth space environment to measure ionospheric plasma fluxes, densities, temperatures and velocities. The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) presently receives the space weather data stream from the Air Force Research Lab (AFRL). The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) maintain a publicly accessible archive and derived extensions of these data records. The DMSP SSJ/4/5 data provide a complete energy spectrum of the low energy particles that cause the aurora and other high altitude phenomena. The Special Sensor Magnetometer (SSM) measures geomagnetic fluctuations associated with geophysical phenomena (i.e., ionospheric currents flowing at high latitudes). The Special Sensor Ion Electron Scintillation (SSIES) instrument observes the ambient plasma by measuring 1) electron density and temperature; 2) ion density, temperature and drift velocity; and 3) scintillation at the DMSP orbital altitude.

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Bathymetry for Cape Cod Bay was derived from fifteen surveys containing139,022 soundings. One entire overlapping, older, less accurate surveywas omitted, and the overlap from five older, less accurate surveys wasomitted before tinning. The average separation between soundings was 102meters. The fifteen surveys used dated from 1933 to 1971 with the most recentfalling in the north and northwest portions of the bay. The total range ofsounding data was 1.8 meters to -58.8 meters at mean low water. Mean highwater values between 2.7 and 3.0 meters were assigned to the shoreline.Seven points were found that were not consistent with the surrounding data.These were removed prior to tinning. DEM grid values outside the shoreline (on land) were assigned nullvalues (-32676).Cape Cod Bay has twenty-one 7.5 minute DEMs and three one degreeDEMs. The 1 degree DEMs were generated from the higher resolution 7.5minute DEMs which covered the estuary. A Digital Elevation Model(DEM) contains a series of elevations ordered from south to northwith the order of the columns from west to east. The DEM isformatted as one ASCII header record (A- record), followed by aseries of profile records (B- records) each of which include a shortB-record header followed by a series of ASCII integer elevations(typically in units of 1 centimeter) per each profile. The lastphysical record of the DEM is an accuracy record (C-record).The 7.5-minute DEM (30- by 30-m data spacing) is cast on theUniversal Transverse Mercator (UTM) projection. It provides coveragein 7.5- by 7.5-minute blocks. Each product provides the samecoverage as a standard USGS 7.5-minute quadrangle but the DEMcontains over edge data. Coverage is available for many estuaries ofthe contiguous United States but is not complete.

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The reports provided as part of this tool present information and varied looks at trends related to pipeline incidents over the past 20 years. Information includes: serious incidents (involving a fatality or injury requiring in-patient hospitalization), significant incidents (meet a number of predefined conditions, including inflation adjusted value of property damage, minimum threshold for volume spilled for hazardous liquid, fire and explosion for hazardous liquid), reported incidents (all pipeline incidents reported to PHMSA in accordance with reporting criterion which has changed over time), consequences (effects on both the general public and the pipeline industry), and state-based reports.

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EDFacts Teacher Quality 2010-11 (EDFacts TQ:2010-11) is one of 17 'topics' identified in the EDFacts documentation (in this database, each 'topic' is entered as a separate study); program data is available since 2005 at . EDFacts TQ:2010-11 (ed.gov/about/inits/ed/edfacts) annually collects cross-sectional data from states about teacher quality based on the performance on programs and activities supported by the Elementary and Secondary Education Act (ESEA), as amended, determined by whether teachers are classified as highly qualified for their assignment according to state definition. Indicators include the number of elementary and secondary classes in core academic subjects and the designation of a school's poverty quartile. EDFacts TQ:2010-11 data were collected using the EDFacts Submission System (ESS), a centralized portal and their submission by states is mandatory and required for benefits. Not submitting the required reports by a state constitutes a failure to comply with law and may have consequences for federal funding to the state. Key statistics produced from EDFacts TQ:2010-11 are from 3 data groups with information on State Poverty Designation, Teacher Quality in Elementary Classes, and Teacher Quality in Secondary Classes. For the purposes of this system, data groups are referred to as 'variables', as a result of the structure and format of EDFacts' data.

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As of January 1, 2001, RFE version 2.0 has been implemented by NOAA?s Climate Prediction Center. Created by Ping-Ping Xie, this replaces RFE 1.0 the previous rainfall estimation algorithm that was operational from 1995 through 2000 (Herman et al., 1997). RFE 2.0 uses additional techniques to better estimate precipitation while continuing the use of cloud top temperature and station rainfall data that formed the basis of RFE 1.0. Meteosat geostationary satellite infrared data is acquired in 30-minute intervals, and areas depicting cloud top temperatures of less than 235K are used to estimate convective rainfall. WMO Global Telecommunication System (GTS) data taken from approx. 1000 stations provide accurate rainfall totals, and are assumed to be the true rainfall near each station. RFE 1.0 used an interpolation method to combine Meteosat and GTS data for daily precipitation estimates, and warm cloud information was included to obtain dekadal (10-day) estimates. The two new satellite rainfall estimation instruments that are incorporated into RFE 2.0 are the Special Sensor Microwave/Imager (SSM/I) on board Defense Meteorological Satellite Program satellites, and the Advanced Microwave Sounding Unit (AMSU). Both estimates are acquired at 6-hour intervals and have a resolution of 0.25 degrees. RFE 2.0 obtains the final daily rainfall estimation using a two part merging process, then sums daily totals to produce dekadal estimates. All satellite data is first combined using a maximum likelihood estimation method, and then GTS station data is used to remove bias. Warm cloud precipitation estimates are not included in RFE 2.0. RFE data is mainly staged for public download in binary format, but is also available in GeoTiff format.

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LiDAR data is a remotely sensed high resolution elevation data collected by an airborne platform. The LiDAR sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The LiDAR systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The work order required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 1.5 meter. The final products include first, last, and at least one intermediate return LAS, full classified LAS and one (1) meter pixel raster DEMs of the bare-earth surface delivered in Esri 10 ArcGrid format.

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The Large Truck* Crash Causation Study (LTCCS) is based on a three-year data collection project conducted by the Federal Motor Carrier Safety Administration (FMCSA) and the National Highway Traffic Safety Administration (NHTSA) of the U.S. Department of Transportation (DOT). LTCCS is the first-ever national study to attempt to determine the critical events and associated factors that contribute to serious large truck crashes allowing DOT and others to implement effective countermeasures to reduce the occurrence and severity of these crashes.

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This digitally compiled map includes geology, oil and gas fields, and geologic provinces of South America. The oil and gas map is part of a worldwide series released on CD-ROM by the World Energy Project of the U.S. Geological Survey. For data management purposes the world is divided into eight energy regions corresponding approximately to the economic regions of the world as defined by the U.S. Department of State. South America (Region 6) includes Argentina, Bolivia, Brazil, Chile, Columbia, Ecuador, Falkland Islands, French Guiana, Guyuna, Netherlands, Netherlands Antilles, Paraguay, Peru, Suriname, Trinidad and Tobago, Uruguay, and Venezuela.

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The U.S. Department of Housing and Urban Development (HUD) periodically receives custom tabulations of data from the U.S. Census Bureau that are largely not available through standard Census products. These data, known as the CHAS data (Comprehensive Housing Affordability Strategy), demonstrate the extent of housing problems and housing needs, particularly for low income households. The CHAS data are used by local governments to plan how to spend HUD funds, and may also be used by HUD to distribute grant funds

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This map layer shows Omernik's Level III ecoregions, derived from a 1:7,500,000 map created by J.M. Omernik in 1987 and from refinements of Omernik's framework that were made for other projects. Ecoregions describe areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. Omernik's ecoregions are based on the premise that a hierarchy of ecological regions can be identified through the analysis of the patterns and the composition of both living and nonliving phenomena, such as geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology, that affect or reflect differences in ecosystem quality and integrity. All the characteristics are considered when determining ecoregions, but the relative importance of each characteristic may vary from one ecoregion to another. Level III is the most detailed level available nationally for this system of ecoregions.

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This 1-kilometer raster (grid) dataset for the conterminous United States was created by interpolating base-flow index (BFI) values estimated at U.S. Geological Survey (USGS) streamgages. Base flow is the component of streamflow that can be attributed to ground-water discharge into streams.

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<p>In 2012, the NASA Game Changing Development Program (GCDP), residing in the NASA Space Technology Mission Directorate (STMD), commissioned a High Performance Spaceflight Computing (HPSC) formulation activity to determine the value of an investment in a next-generation flight computing system.  A multi-center NASA team was established to sharpen understanding of the gap between the current state of the practice in flight computing and the actual future flight computing needs of NASA missions.  This team posed the question: “What are the paradigm shifting NASA space-based applications that drive flight computing?”  A series of workshops was conducted with personnel from NASA Johnson Space Center (JSC), NASA Goddard Space Flight Center (GSFC) and the Jet Propulsion Laboratory (JPL).  These scientists, engineers and mission designers identified high priority functions, capabilities and mission scenarios from the NASA future mission set, and identified use cases that required or would take full advantage of a high performance spaceflight computing capability.  Both robotic and human spaceflight mission applications were examined.</p><p>To answer the related question: “What are the requirements imposed on flight computing by these applications?” NASA system engineers and mission personnel characterized for each use case: the nature of the computing, the environment, the criticality of the application, the system constraints, and the computing system and processor chip requirements.</p><p>Analysis of the set of use cases and derived requirements led to the insight that the drivers for future flight computing fall into three broad categories: 1) hard real-time calculations (such as vision-based algorithms for entry, descent and landing), 2) high throughput instrument data processing (such as for hyper-spectral and synthetic aperture radar – SAR), and 3) model-based reasoning techniques, such as for AI-based mission planning and fault management).  The takeaway was that there would not likely be a one-size-fits-all architectural solution.</p><p>The HPSC formulation team next evaluated extant and emerging computing architectures to determine which architectural concept would provide the most investment value to address the identified future NASA flight computing requirements.  Several architecture were examined, both COTS (commercial off-the-shelf) -based, and, where available, space-qualified or space-qualifiable versions:  Reconfigurable Computing, Graphic Processing Units (GPUs), Multi-Core, specialized processors (e.g., DSP – Digital Signal Procsssing), as well as hybrid approaches.  The study identified rad-hard general-purpose multi-core as the highest value architectural approach for NASA’s needs – not only addressing the range of flight computing requirements derived from the use cases, but providing certain architectural advantages in a flight computing system beyond the computational performance advance.</p><p>As NASA’s HPSC activity proceeded, the Air Force Research Lab (AFRL) and NASA identified significant requirements overlap and common interest in future flight computing.  An agency-level partnership emerged, which is manifesting through a joint investment in a Next Generation Space Processor (NGSP).</p><p>In 2013, under an initial risk-retirement phase, termed the Innovation Phase, AFRL and NASA issued awards via a joint solicitation to three companies:  BAE, Boeing and Honeywell.  These contractors are developing hardware architecture designs for the targeted future flight computing system based on a multi-core architecture.  NASA is also developing a set of benchmarks consistent with the broad set of NASA applications.  Some benchmarks will capture performance needs but others are for the purpose of evaluating certain system-level properties of the operational details of vendor-offered designs, such as support for energy management and fault tolerance.  These benchmarks are targeted for flight computing, and as such, are different than the more familiar computing benchmarks that are available in the ground-based supercomputing community.</p><p>The hardware architecture designs will be developed and evaluated in 2014.  Assuming a viable departure point has been achieved with sufficient risk retirement, in 2015 a three-year Development Phase will follow, to culminate in a board-level flight computing system product, incorporating a multi-core chip, with integrated real-time operating system, flight software development environment, and NASA-developed middleware to fully exploit multi-core architectural features for the benefit of the future spaceflight applications.  </p><p>NASA and AFRL intend to continue in partnership through the Development Phase, and are seeking additional programmatic partners for this national-level capability investment.</p>

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The NCVS RESTful API is a web service that provides criminal victimization data obtained annually from a nationally representative sample of about 79,800 households and 143,210 persons interviewed each year. NCVS data describe the frequency, characteristi

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The Map-A-Planet website allows users to create and download custom image maps of planets and satellites from a variety of missions in an easy to use web interface

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The entire collection of GEOSAT ERM (Nov.'86 - Dec. '89) data over land and ice regions is held at the National Geophysical Data Center (NGDC). These data will yield reasonable elevation values for land and ice regions of gently varying elevation. This data collection should not be used in regions of highly variable terrain. This satellite altimeter data base contains precise geoid and gravity anomaly profiles which were constructed from the average of 66 repeat cycles of GEOSAT. The data were developed by Professor David T. Sandwell at the University of California in San Diego. The data are contained in two files: (1) geo66asc.bin (2,383,232records) contains the ascending profiles which run southeast to northwest between 72S and 72N, and (2) geo66des.bin (2,397,888 records) contains all of the descending profiles. The dataparameters in addition to time and location are geoid height, gravity anomaly, and uncertainty in gravity anomaly. GEOSAT 66 was updated in 1994 to include the 3rd and last year of data. Thus 66 repeat cycles of data are included in the AVERAGE profile calculation. This satellite altimeter data base was contributed by NOS/Geoscience Laboratory and contains data collected during the first 18 months of the original "Geodetic Mission" of the U.S. Navy Geodetic Satellite (GEOSAT). These digital data are in the form of geophysical data records (GDRs) which are described in NOAA Technical Memorandum NOS NGS-46. The data are observed over a tightly spaced (typically 2 or 3 km at 60 degrees latitude) ground track pattern, and are global in coverage. The Southern Ocean data contained in this subset of the original Geodetic Mission were declassified in 1990 and received at NGDC in mid 1991. GEOSAT GRAVITY ANOMALY GRID SOUTH OF 30 SOUTH K.M. Marks, DC McAdoo, and W.H.F. Smith The Geosciences Laboratory, ocean and Earth Sciences (NOAA), has produced a digital gravity anomaly grid computed from recently declassified Geosat Geodetic Mission data, combined with Exact Repeat Mission data, for the region between 30 S and 72 S latitudes. The grid spacing is 0.04 degrees in latitude, and 0.05 degrees in longitude. The grid file, g30_UNIX.BIN, is a binary file of two-byte signed integers, stored in raster scan line (bands of Latitude) order. There are 1051 scan lines with the first line at 30 S and the last at 72 S latitude. Each line has 7201 integers with the first element at 0 E longitude and the last element at 360 E longitude. Values equal to 32767 indicate land areas where Geosat gravity is unavailable; all other values should be multiplied by 0.01 to yield Free-Air Gravity anomalies in mGals. Data in g30__UNIX.BIN are in "normal" byte order (Sun, Mac, etc.); the equivalent file G30_DOS.DOS is in "swapped" byte order (DEC, PC, etc.). RAPP92: This data base was compiled by Dr. Richard H. Rapp, Ohio State University and was received in April, 1993. The data base consists of the following: One file containing a 0.125 degree grid of free-air gravity anomalies and their standard deviations between +/- 72 degrees latitude. The anomalies in the ocean areas have been derived from a combination of Geos-3, Seasat and Geosat altimeter data and the ETOP05U bathymetric data. Although gravity values are given for land areas they have been, primarily, computed from the OSU91A potential coefficient model that is complete to degree 360. One file containing a 0.125 degree gridded mean sea surface (in the mean tide system), in the same geographic region as the data given in the file above. One file containing 30-minute x 30-minute mean gravity anomalies and geoid undulations (in the tide free system), derived form OSU's 0.125 degree gridded point anomalies and geoid undulations. One file containing 1 degree x 1 degree mean gravity anomalies and geoid undulations (in the tide free system), as derived from the original gridded point values. Principal gravity parameters include mean gravity anomaly and mean geoid undulations. The gravity anomaly computation uses the Geodetic Reference System 1967 (GRS 67) Theoretical Formula. The data are global in coverage where data are available. SANDWELL: The high density Geosat/GM altimeter data south of 30 S have finally arrived. In addition, ERS-1 has completed more than 6 cycles of its 35-day repeat track. These data provide a dramatically improved view of the marine gravity field. The files in this directory contain global marine gravity anomalies gridded on a Mercator projection (see Sandwell and Smith, EOS Trans. AGU, v. 73, p. 133, Fall 1992 AGU meeting supplement). The grid was derived from the following data sources: Seasat - Used in areas north of 30 S latitude. Profiles within 10 km of a Geosat/ERM track were excluded. Geosat/ERM - Average of 62 Geosat Exact Repeat Mission profiles. Geosat/GM - Recently declassified Geosat Geodetic mission data south of 30 S. ERS-1 - Fast delivery IGDR's obtained from Bob Cheney at NOAA. Six, 35-day repeat cycles were used in the grid. All of these data were gridded using the method described in: Sandwell, D.T., Antarctic marine gravity field from high-density satellite altimetry, Geophys. J. Int., v. 109, p. 437-448, 1992. The method was adapted to accommodate multiple satellite inclinations (Sandwell, D.T., A detailed view of the South Pacific from satellite altimetry, J. Geophys. Res., v. 89, p. 1089-1104, 1984). The gridded data are stored in an integer*2 format without any header or record information. world_grav.image - A 4224 by 7800 grid of 2-byte integers = 65894400 bytes. The gravity anomaly units are 0.1 milligal. The Mercator projected image spans longitudes of 0 E to 390 E and latitudes from 72.006 N to -72.006 N. The center of the upper left grid cell (i.e. the first integer in the file) is located at 71.998 N, 0.025 E. Longitudes increase with a 1/20 degree spacing. The center of the last integer in the file is located at -71.997 N, 389.975 E.

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The NOAA Seamless Raster Chart Server provides a seamless collarless mosaic of the NOAA Raster Navigational Charts (RNC). The RNC are a collection of approximately 2,100 charts and inset maps at varying scales and resolutions. Full information on the NOAA RNCs can be found here: http://www.nauticalcharts.noaa.gov/mcd/Raster/. The Seamless Raster Chart Server integrates all charts in a single map service and clips the charts to the extent of the chart, removing the map collar. The set of charts displayed automatically change based on the viewer's scale to provide the most useful chart scale within the view. The charts in the Seamless Raster Chart Server are updated once every month at the beginning of the month to provide the latest Notice to Mariners updates.

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DailyMed provides high quality information about marketed drugs. This information includes FDA labels (package inserts). This Web site provides health information providers and the public with a standard, comprehensive, up-to-date, look-up and download resource of medication content and labeling as found in medication package inserts. The National Library of Medicine (NLM) provides this as a public service and does not accept advertisements.

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The National Motor Vehicle Crash Causation Survey (NMVVCS) was a nationwide survey of crashes involving light passenger vehicles, with a focus on the factors related to pre-crash events.

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Basic information on the U.S. population from Census 2000. It includes data on people's age, sex, and race, their family and household groups, and whether their home is owned or rented. There are 286 Detailed Tables available in the SF 1 data product, many available for the nine major race groups. Data are published to the census block level or the census tract level.

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A nationwide survey that collects information such as age, race, income, commute time to work, home value, veteran status, and other data. Data from the American Community Survey and the Puerto Rico Community Survey were collected during calendar year 2008. Available for geographic areas with populations of 65,000 or more.

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This information is derived from inspections of restaurants and other food establishments in Chicago from January 1, 2010 to the present. Inspections are performed by staff from the Chicago Department of Public Health’s Food Protection Program using a standardized procedure. The results of the inspection are inputted into a database, then reviewed and approved by a State of Illinois Licensed Environmental Health Practitioner (LEHP). For descriptions of the data elements included in this set, go to http://bit.ly/tS9IE8 Disclaimer: Attempts have been made to minimize any and all duplicate inspection reports. However, the dataset may still contain such duplicates and the appropriate precautions should be exercised when viewing or analyzing these data. The result of the inspections (pass, pass with conditions or fail) as well as the violations noted are based on the findings identified and reported by the inspector at the time of the inspection, and may not reflect the findings noted at other times. For more information about Food Inspections, go to http://bit.ly/tD91Sb.

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The National Environmental Public Health Tracking Network is a system of integrated health, exposure, and hazard information and data from a variety of national, state, and city sources. On the Tracking Network, you can explore information and view maps, tables, and charts about health and environment across the country.