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The Strategic Plan focuses on incorporating three overarching goals: Warfighter Support Enhancement; Stewardship Excellence; and Workforce Development. These three goals and their core strategies require warfighter-focused, globally responsive supply chain leadership. This is DLA's vision and pledge of support to the men and women we serve, our many mission partners, and our stakeholders.

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Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of sales by type of processor on a monthly basis.

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This dataset contains a nationwide inventory of all congressional districts and the counties or pieces of counties associated with each congressional district for the 110th and 111th Congress.

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Collect and aggregate information from state level reporting entities that can be used to measure the progress of 9 1-1 authorities across the country in enhancing their existing operations and migrating to more advanced - Internet-Protocol-enabled emergency networks. The data will be maintained in a "National 9-1-1 Profile Database." One of the objectives of the National 9-1-1 Program is to develop, collect, and disseminate information concerning practices, procedures, and technology used in the implementation of E9 1 1 services and to support 9-1-1 Public Safety Answering Points (PSAPs) and related state and local public safety agencies for 9 1 1 deployment and operations. The National 9-1-1 profile database can be used to follow the progress of 9-1-1 authorities in enhancing their existing systems and implementing next-generation networks for more advanced systems.

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This is a map service for the Coastal Flood Hazard Composite dataset. This dataset was created by combining hazard zones from the following datasets: FEMA V zones, FEMA A zones, FEMA shaded X zones, NOAA OCM Flood Frequency zones, NOAA OCM potential sea level rise inundation of 3 ft above current Mean Higher High Water (MHHW), and SLOSH MOMs for category 3 hurricanes. This dataset was created to support the National Oceanic and Atmospheric Administration Office for Coastal Management's Coastal Flood Exposure Mapper application. The purpose of the online mapping tool is to provide coastal managers, planners, and stakeholders a preliminary look at exposures to coastal flooding hazards. The Mapper is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help communities initiate resilience planning efforts. Currently the extent of this dataset covers U. S. coastal areas along the Gulf of Mexico and Atlantic coasts. This is a cached map service. Tiles have been cached down to Level ID 15 (1:18,055).

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EDFacts Safe and Drug-Free Schools 2011-12 (EDFacts SDFS:2011-12) 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 SDFS:2011-12 (ed.gov/about/inits/ed/edfacts) annually collects cross-sectional data from states about safety and violence in elementary and secondary educations to support oversight and reporting of the Safe and Drug-Free Schools and Communities Act (Title IV, Part A of the Elementary and Secondary Education Act [ESEA]) and the Gun-Free Schools Act (GFSA) at the school, LEA, and SEA levels. EDFacts SDFS:2011-12 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 SDFS:2011-12 are from 7 data groups with information on Discipline Incidents, Students Disciplined, Firearm Incidents, GFSA Reporting Status, Persistently Dangerous Status, Students Involved with Firearms, and Truants. 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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NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) for select U.S. coastal regions. These integrated bathymetric-topographic DEMs are used to support tsunami forecasting and warning efforts at the NOAA Center for Tsunami Research, Pacific Marine Environmental Laboratory (PMEL). The DEMs are part of the tsunami forecast system SIFT (Short-term Inundation Forecasting for Tsunamis) currently being developed by PMEL for the NOAA Tsunami Warning Centers, and are used in the MOST (Method of Splitting Tsunami) model developed by PMEL to simulate tsunami generation, propagation, and inundation. Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. National Ocean Service (NOS), the U.S. Geological Survey (USGS), the U.S. Army Corps of Engineers (USACE), the Federal Emergency Management Agency (FEMA), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to the vertical tidal datum of North American Vertical Datum of 1988 (NAVD 88) or Mean High Water (MHW) and horizontal datum of World Geodetic System 1984 (WGS84). Cell size for the DEMs ranges from 1/3 arc-second (~10 meters) to 3 arc-seconds (~90 meters).

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The National Health Interview Survey (NHIS) is the principal source of information on the health of the civilian noninstitutionalized population of the United States and is one of the major data collection programs of the National Center for Health Statistics (NCHS) which is part of the Centers for Disease Control and Prevention (CDC). The National Health Survey Act of 1956 provided for a continuing survey and special studies to secure accurate and current statistical information on the amount, distribution, and effects of illness and disability in the United States and the services rendered for or because of such conditions. The survey referred to in the Act, now called the National Health Interview Survey, was initiated in July 1957. Since 1960, the survey has been conducted by NCHS, which was formed when the National Health Survey and the National Vital Statistics Division were combined. NHIS data are used widely throughout the Department of Health and Human Services (DHHS) to monitor trends in illness and disability and to track progress toward achieving national health objectives. The data are also used by the public health research community for epidemiologic and policy analysis of such timely issues as characterizing those with various health problems, determining barriers to accessing and using appropriate health care, and evaluating Federal health programs. The NHIS also has a central role in the ongoing integration of household surveys in DHHS. The designs of two major DHHS national household surveys have been or are linked to the NHIS. The National Survey of Family Growth used the NHIS sampling frame in its first five cycles and the Medical Expenditure Panel Survey currently uses half of the NHIS sampling frame. Other linkage includes linking NHIS data to death certificates in the National Death Index (NDI). While the NHIS has been conducted continuously since 1957, the content of the survey has been updated about every 10-15 years. In 1996, a substantially revised NHIS questionnaire began field testing. This revised questionnaire, described in detail below, was implemented in 1997 and has improved the ability of the NHIS to provide important health information.

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The Severe Weather Data Inventory (SWDI) is an integrated database of severe weather records for the United States. SWDI enables a user to search through a variety of source data sets in the NCDC archive in order to find records covering a particular time period and geographic region, and then to download the results of the search in a variety of formats. The formats currently supported are Shapefile (for GIS), KMZ (for Google Earth), CSV (comma-separated), and XML. The current data layers in SWDI are: Storm Cells from NEXRAD (Level-III Storm Structure Product); Hail Signatures from NEXRAD (Level-III Hail Product); Mesocyclone Signatures from NEXRAD (Level-III Meso Product); Digital Mesocyclone Detection Algorithm from NEXRAD (Level-III MDA Product); Tornado Signature from NEXRAD (Level-III TVS Product); Preliminary Local Storm Reports from the NOAA National Weather Service; Lightning Strikes from Vaisala NLDN.

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The Federal Housing Administration's HECM program is the only government-insured reverse mortgage program. The HECM program guarantees that the lender will meet its payment obligations to the homeowner, limits the borrower's loan origination costs, and insures full repayment of the loan balance to the lender up to the maximum claim amount. The loan amount is based on borrower age, home value, and current interest rates. The HECM data files provide loan-level records that will enable interested parties to explore issues regarding downpayment assistance provided to homebuyers utilizing HECM insured mortgage financing.

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This map layer shows elevation contour lines for the conterminous United States. The map layer was derived from the 100-meter resolution elevation data set which is published by the National Atlas of the United States, and is in the same Albers Equal-area Conic projection as that source data set. Contour intervals match the steps used in the National Atlas Color-Sliced Elevation data sets. Contours were adjusted so that they are in the proper relationship to National Atlas 1:1,000,000-scale lakes, reservoirs, and wide (double-line) streams.

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Deaths by Cause of Death, by Zip Code 1989-1998 (By Place of Residence): This dataset is for death of California residents, by cause of death, by Zip Code, for year 1989-1998. The death counts per cause of death, represents the deaths to California residents, regardless of the place of death occurrence. A non-California resident who die inside of California would not be included in the dataset.

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Rate and number of infant deaths per 1,000 live births by year. The total rate and number variables include all available races and are not limited to white and black races. Blank cells indicate that the data are not available.

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USGS Contract: G10PC00026, Task Order Number: G10PD02143 Task Order Numbers: G10PD01027 (ARRA) and G10PD02143 (non-ARRA) The LiDAR for the North East Project, funded in large part by the American Recovery and Reinvestment Act (ARRA) of 2009, as well as, other funding sources was designed to help stimulate the U.S. economy and provide for more accurate floodplain mapping in the North East, representing the start of a regional LiDAR collection program that served as a test case for a national elevation program. Lead by the United States Geological Survey's (USGS) National Geospatial Program Office and the State of Maine's Office of GIS with active collaboration and participation by other federal, state and local agencies resulted in LiDAR acquisition and processing of over 8,000 sq. miles of (LiDAR) data of a coastal zone spanning six North Eastern states, including Maine, New Hampshire, Massachusetts, Connecticut, Rhode Island, and New York. USGS's National Geospatial Technical Operations Center (USGS NGTOC) in Rolla, MO provided project management and quality control oversight for the project which consisted of two Task Orders issued to USGS contractor, GMR Aerial Surveys inc. d/b/a Photo Science (contractor), for task order execution through the use of USGS's Geospatial Products and Services Contract (USGS Contract: G10PC00026). Task Order specifications included state/area specific vertical accuracy, nominal post spacing and tide coordinated acquisition requirements. To see state/area specific information please see the individual project metadata links in the Supplemental_Information section below. NOAA OCM received the topographic LAS files from USGS and Maine Office of GIS. The data was minimally processed by OCM, however the classes were adjusted (individual points were not changed): NOAA OCM received a redelivery for 32 tiles in Maine to fill small voids in data. These redelivered tiles were incorporated on 1/23/2014. NOAA OCM Modified Classification Scheme: Class 1: Unclassified Class 2: Ground Class 7: Noise Class 9: Water Class 10: Breakline Proximity Class 14: Bare (Open) Water Class 15: Overlap Water Class 16: Overlap Bare (Open) Water Class 17: Overlap Default Class 18: Overlap Ground

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The Public Health Activities and Services (PHAS) data measures what public health does in the state and how much of it is done across all 35 local health agencies and the Department of Health in Washington State each year. Activities measured fall under the following broad categories: Access To Care Assessment Communicable Disease Communicable Disease: Immunization Emergency Preparedness Environmental Health Healthy Families Prevention and Wellness More PHAS data is available at https://fortress.wa.gov/doh/phip/PHIP/Home.mvc

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NNDSS - Table II. Varicella to West Nile virus disease - 2014. In this Table, all conditions with a 5-year average annual national total of more than or equals 1,000 cases but less than or equals 10,000 cases will be displayed (≥ 1,000 and ≤ 10,000). The Table includes total number of cases reported in the United States, by region and by states, in accordance with the current method of displaying MMWR data. Data on United States exclude counts from US territories. Note: These are provisional cases of selected national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables printed in the back of the Morbidity and Mortality Weekly Report (MMWR). Cases reported by state health departments to CDC for weekly publication are provisional because of ongoing revision of information and delayed reporting. Case counts in this table are presented as they were published in the MMWR issues. Therefore, numbers listed in later MMWR weeks may reflect changes made to these counts as additional information becomes available. Footnotes: C.N.M.I.: Commonwealth of Northern Mariana Islands. U: Unavailable. -: No reported cases. N: Not reportable. NN: Not Nationally Notifiable Cum: Cumulative year-to-date counts. Med: Median. Max: Maximum. * Case counts for reporting years 2013 and 2014 are provisional and subject to change. For further information on interpretation of these data, see http://wwwn.cdc.gov/nndss/document/ProvisionalNationaNotifiableDiseasesS.... Data for TB are displayed in Table IV, which appears quarterly. † Updated weekly from reports to the Division of Vector-Borne Infectious Diseases, National Center for Zoonotic, Vector-Borne, and Enteric Diseases (ArboNet Surveillance). Data for California serogroup, eastern equine, Powassan, St. Louis, and western equine diseases are available in Table I. § Not reportable in all states. Data from states where the condition is not reportable are excluded from this table, except starting in 2007 for the Arboviral diseases and influenza-associated pediatric mortality, and in 2003 for SARS-CoV. Reporting exceptions are available at http://wwwn.cdc.gov/nndss/document/SRCA_FINAL_REPORT_2006-2012_final.xlsx. More information on NNDSS is available at http://wwwn.cdc.gov/nndss/.

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Data for this study were collected as part of the 2007 National Inmate Survey (NIS), which is comprised of two questionnaires -- a survey of sexual victimization and a survey of past drug and alcohol use and abuse. The survey of sexual victimization resul

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The Sexually Transmitted Disease (STD) Morbidity online databases on CDC WONDER contain case reports reported from the 50 United States and D.C., Puerto Rico, Virgin Islands and Guam. The online databases report the number of cases and disease incidence rates by year, state, disease, age, gender of patient, type of STD, and area of report, since 1984. Data are updated annually. Data are produced by the U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention (CDC), National Center for HIV/AIDS, viral Hepatitis, STD and TB Prevention (NCHHSTP).

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This dataset was developed by NREL's distributed energy systems integration group as part of a study on high penetrations of distributed solar PV [1]. It consists of hourly load data in CSV format for use with the PNNL taxonomy of distribution feeders [2]. These feeders were developed in the open source GridLAB-D modelling language [3]. In this dataset each of the load points in the taxonomy feeders is populated with hourly averaged load data from a utility in the feeder’s geographical region, scaled and randomized to emulate real load profiles. For more information on the scaling and randomization process, see [1]. The taxonomy feeders are statistically representative of the various types of distribution feeders found in five geographical regions of the U.S. Efforts are underway (possibly complete) to translate these feeders into the OpenDSS modelling language. This data set consists of one large CSV file for each feeder. Within each CSV, each column represents one load bus on the feeder. The header row lists the name of the load bus. The subsequent 8760 rows represent the loads for each hour of the year. The loads were scaled and randomized using a Python script, so each load series represents only one of many possible randomizations. In the header row, "rl" = residential load and "cl" = commercial load. Commercial loads are followed by a phase letter (A, B, or C). For regions 1-3, the data is from 2009. For regions 4-5, the data is from 2000. For use in GridLAB-D, each column will need to be separated into its own CSV file without a header. The load value goes in the second column, and corresponding datetime values go in the first column, as shown in the sample file, sample_individual_load_file.csv. Only the first value in the time column needs to written as an absolute time; subsequent times may be written in relative format (i.e. "+1h", as in the sample). The load should be written in P+Qj format, as seen in the sample CSV, in units of Watts (W) and Volt-amps reactive (VAr). This dataset was derived from metered load data and hence includes only real power; reactive power can be generated by assuming an appropriate power factor. These loads were used with GridLAB-D version 2.2. [Browse files](http://en.openei.org/datasets/files/968/pub/) in this dataset, accessible as individual files and as a single ZIP file. This dataset is approximately 242MB compressed or 475MB uncompressed. For questions about this dataset, contact andy.hoke@nrel.gov. If you find this dataset useful, please mention NREL and cite [1] in your work. References: [1] A. Hoke, R. Butler, J. Hambrick, and B. Kroposki, “Steady-State Analysis of Maximum Photovoltaic Penetration Levels on Typical Distribution Feeders,” IEEE Transactions on Sustainable Energy, April 2013, available at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6357275 . [2] K. Schneider, D. P. Chassin, R. Pratt, D. Engel, and S. Thompson, “Modern Grid Initiative Distribution Taxonomy Final Report”, PNNL, Nov. 2008. Accessed April 27, 2012: http://www.gridlabd.org/models/feeders/taxonomy of prototypical feeders.pdf [3] K. Schneider, D. Chassin, Y. Pratt, and J. C. Fuller, “Distribution power flow for smart grid technologies”, IEEE/PES Power Systems Conference and Exposition, Seattle, WA, Mar. 2009, pp. 1-7, 15-18.

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The geology data set for this map includes arcs, polygons, and labels that outline and describe the general geologic age and type of bedrock of Iran. The geologic provinces data set includes arcs, polygons, and labels of geologic and petroleum provinces interpreted and designated by R.M. Pollastro from a number of literature and map resources to assist in the assessment of oil and gas resources for the USGS World Energy Project. The oil and gas field centerpoints data set is a point coverage that marks the approximate centerpoints of oil and gas fields in Iran. Political boundaries are provided to show the general location of country and/or other reference 'political' boundaries.

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Tsunami is a Japanese word meaning harbor wave. It is a water wave or a series of waves generated by an impulsive vertical displacement of the surface of the ocean or other body of water. This slide set depicts advancing waves, harbor damage, and structural damage from seven tsunami events which have occurred since 1946 in the Pacific region. The set includes before-and-after views of Scotch Cap Lighthouse (the Aleutian Islands).Tsunami is a Japanese word meaning "harbor wave." It is a water wave or a series of waves generated by an impulsive vertical displacement of the surface of the ocean or other body of water. Tsunamis are commonly called seismic sea waves or incorrectly, tidal waves. The term "tidal wave" is frequently used in the older literature and in popular accounts, but is now considered incorrect. Tides are produced by the rotational attraction of the sun and moon and occur predictably, with twelve hour periods. The effects of a tsunami may be increased or decreased depending on the level of the tide, but otherwise the two phenomena are independent.Major tsunamis occur in the Pacific Ocean region only about once per decade. These major events, such as that in Prince William Sound, Alaska, in March 1964, and the tsunami generated off the coast of Chile in 1960, have been devastating over large distances. Tsunamis have been responsible for thousands of fatalities, especially in Japan and Indonesia.Most tsunamis are caused by a rapid vertical movement along a break in Earth's crust (i.e., their origin is tectonic). A tsunami is generated when a large mass of earth on the bottom of the ocean drops or rises, thereby displacing the column of water directly above it. This type of displacement commonly occurs in large subduction zones, where the collision of two tectonic plates causes the oceanic plate to dip beneath the continental plate to form deep ocean trenches. Shallow tsunamigenic earthquakes occur offshore in these trenches. Subduction occurs along most of the island arcs and coastal areas of the Pacific, except for the west coast of the United States and Canada. Such trench areas off continental coasts and island arcs are generating areas for major tsunamis affecting the entire Pacific Basin.Volcanic eruptions have also generated significant tsunamis, resulting in death tolls as high as 30,000 people from a single event, as in the Krakatoa eruption of 1883. Tsunamis effectively transmit energy to areas outside the reach of the volcanic eruption itself. The most efficient methods of tsunami generation by volcanoes include disruption of a body of water by the collapse of all or part of the volcanic edifice, subsidence, an explosion, a landslide, a glowing avalanche, and an earthquake accompanying or preceding the eruption. Roughly one-half of all volcanic tsunamis are generated at calderas or at cones within calderas. Submarine eruptions may also cause minor tsunamis.The largest tsunamis are caused by meteorite impact in ocean basins. While there have been none recorded in historical times, tsunamis from the Chixulub, Yucatan, crater (66 million years b.p.) were up to 5 km high. Tsunami deposits have been found in west Texas nearly 1,000 kilometers inland. Modelling shows that a moderate size meteor impact in the Atlantic Ocean would have devastating results along the entire Atlantic seaboard of the United States. Such impacts are expected on an average of once in about 15,000 years.Subaerial and submarine landslides into bays or lakes have generated locally destructive tsunamis. Other possible but less efficient methods of tsunami generation also exist. More than one mechanism commonly is involved in the generation of a tsunami including vertical movement of the crust by a seismic impulse (an earthquake) or a submarine landslide.Tsunamis have been reported since ancient times. They have been documented extensively, especially in Japan and the Mediterranean areas. One of the first recorded tsunamis may have occurred off the coast of Syria in 2000 B.C. Since 1900 (the beginning of instrumentally-located earthquakes), most tsunamis have been generated off the coast of Japan, Peru, and Chile. The only regions that have generated remote-source tsunamis affecting the entire Pacific Basin are the Kamchatka Peninsula, the Aleutian Islands, the Gulf of Alaska, and the coast of South America. Hawaii, because of its location in the center of the Pacific Basin, has experienced tsunamis generated in all parts of the Pacific.The Atlantic Ocean has less tsunamis than the Pacific. Most tsunamis in the Atlantic Ocean are generated by great earthquakes west of Portugal. These can be destructive on both sides of the Atlantic. In the Atlantic Ocean, there are no subduction zones at the edges of plate boundaries to spawn tsunamis except small subduction zones under the Caribbean and Scotia arcs. The Caribbean and Mediterranean Seas both have histories of locally destructive tsunamis. In the Indian Ocean, the Indo-Australian plate is being subducted beneath the Eurasian plate at its east margin. Most tsunamis generated in this area are propagated toward the southwest shores of Java and Sumatra, rather than into the Indian Ocean. However, only a few tsunamis have been recorded as originating in the Indian Ocean.Our knowledge of tsunamis is incomplete. The generation phenomenon has not been observed nor measured directly. Large tsunami events are somewhat rare and therefore difficult to capture on high quality slides or videos. However, photographs can contribute significantly to our understanding of tsunamis by recording permanently the heights and the damaging effects of the waves of past tsunami events.

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ABSTRACT: This 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.There are 12 compressed (*.zip) files with this data set. The data are in text, NetCDF (.nc), and GeoTIFF (.tiff) formats as described above.

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Following the death of a Veteran by suicide, BHAP receives post-mortem medical data and interviewee contact information from VHA suicide prevention coordinators. Data include relevant historical activities and related medical concerns as reviewed in the Veteran's medical record. Interviewees typically include a Veteran's family or close friends. Interviewee data includes behavioral information about the Veteran prior to their death. Data are collected at the VISN 2 Center of Excellence for Suicide Prevention and are cleaned, processed, and managed by statistical staff and program analysts on behalf of Mental Health Services.

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The dataset is a list of all the Title VIII fair housing cases filed by FHEO from 1/1/2007 - 12/31/2012 including the case number, case name, filing date, state and county of the alleged violation, and the bases for the alleged discriminatory acts. The Federal bases are race, color, national origin, religion, sex, disability, familial status and retaliation for filing a fair housing complaint. Some of the bases have been broken out into more detailed groupings (Race - Asian, etc.) A single case may have multiple bases. The data can be aggregated to the county or state level to show patterns in the quantity and bases of fair housing complaints filed.

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To facilitate the public health and research community's access to NNDSS data on Lyme disease, CDC has developed a public use dataset. Based on reports submitted to CDC, this dataset provides the number of confirmed cases by county for the years 1992–2011, in four 5–year intervals. County tabulation is by American National Standard Institute (ANSI) [formerly Federal Information Processing Standard (FIPS)] codes. County codes of "0" represent "unknown" county of residence within each state. More recent county-level case counts are not publicly available at this time.