The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most States are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, and municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four States (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their States. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The 2010 Census boundaries for counties and equivalent entities are as of January 1, 2010, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This contains shapefiles of mine footprint used for Hunter surface water and ground water modelling as well as identifying FUTURE (baseline/ACRD) and MINETYPE (open cut/underground). Note: The surface water modelling and groundwater modelling use different sets of mine footprints and even when the same mine has been used in both, the extent may be different for SW vs GW modelling. ## Dataset History For each of the surface water and groundwater modelling activities, polygons identified as baseline and ACRD were taken from the source data and merged and dissolved to create respective total baseline and ACRD layers for each unique mine name (TITLE). Some mine polygons had CRDP shapes rather than ACRD shapes. These had their resepetive baseline areas clipped out of the CRDP area to create an ACRD polygon. Thus there are no explicit CRDP polygons in this dataset. CRDP areas can be inferred by considering the baseline + ACRD area for each mine. ## Dataset Citation Bioregional Assessment Programme (2017) HUN SW GW Mine Footprints for IMIA 20170303 v03. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/1a3b09ab-4dcb-4ea0-bbbd-8c4d9176f51d. ## Dataset Ancestors * Derived From HUN Groundwater footprint polygons v01 * Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012 * Derived From HUN Historical Landsat Images Mine Foot Prints v01 * Derived From Historical Mining footprints DTIRIS HUN 20150707 * Derived From HUN Mine footprints for timeseries * Derived From HUN Groundwater footprint kmz files v01 * Derived From Climate model 0.05x0.05 cells and cell centroids * Derived From HUN Historical Landsat Derived Mine Foot Prints v01 * Derived From HUN Mine footprints for GW modelling v01 * Derived From HUN SW footprint shapefiles v01 * Derived From Mean Annual Climate Data of Australia 1981 to 2012

Annual Mean Temperature for Peninsular Malaysia 1970-2096 PRECIS Model KMZ and nc files Hydroclimate projection data simulated in 2013 using PRECIS model (United Kingdom Meteorological Office). .nc (NetCDF-Network Common Data Form) can be opened using NetCDF library and Climate Data Operator (CDO) via command prompt (CMD). netCDF Library and CDO can be downloaded online. No. of Views : 124

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most States are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, and municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four States (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their States. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The 2010 Census boundaries for counties and equivalent entities are as of January 1, 2010, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

This data includes the location of cycle stands (e.g. Sheffield Stands) which are generally on-street. All data comes from the Local Authorities. The dataset is available in MapInfo .tab, Google .kmz, and ESRI .shp file formats.

This cycle map data has been collated from a number of different sources by Transport for Greater Manchester and cannot be guaranteed to be fully correct.

Please acknowledge the source of this information using the following attribution statement:

Contains Transport for Greater Manchester data. Contains OS data © Crown copyright and database right 2018.

Abstract: The Lurin Basin, Andean Measurement Campaign and Dataset (LAMA) was obtained during the years 2017 until 2020. LAMA is part of the BMBF funded interdisciplinary project TRUST ("Trinkwasserversorgung in prosperierenden Wassermangelregionen nachhaltig, gerecht und ökologisch verträglich"). Hydrological data has been collected in the Lurin river basin for a time span over three years in a range of altitudes from 200 m up to 4500 m above mean sea level. LAMA includes high resolution continuous measurements of precipitation (5 min resolution), soil humidity and temperature (10 min), discharge (15min resolution) and climate variables (10 min resolution). Discharge was measured at two piezometric stations, precipitation at five stations, soil humidity at three stations and climate variables at one station. Details regarding measurement equipment and calibration can be found in the readme files of the archives. Acknowledgment: LAMA is published as part of the Trust project, which is funded by the German Federal Ministry of Education and Research (BMBF). We thank Julian Bocanegra, Roberto Vicencio and Lucas Alcamo for their help during the measurement campaign. TechnicalRemarks: file "Map.kmz": Google Earth .kmz file with locations of all measurement stations of LAMA. file "stations_meta_data.csv": .csv file with meta data about measurement stations of LAMA. folder "precipitation/": includes sub folder "data" with 5min resolution precipitation data. "README.txt" explains data structure. "Calibration_data.txt" shows calibration data for each station. folder "climate/": "atmos41_cullpe.csv" -> original data retrieved from Meter climate station. folder "soil_humidity/": includes sub folder "data" with 10min resolution soil humidity and temperature data. "README.txt" explains data structure. folder "discharge/": includes sub folder "data" with 15min resolution water level data. includes sub folder "rating_tables" with text files of discrete rating curves, derived from tracer measurements. "README.txt" explains data structure. "WQ_measured_manchay.txt" shows results of all (tracer) discharge measurements at Manchay station "WQ_measured_santarosa.txt" shows results of all (tracer) discharge measurements at SantaRosa station

This map includes change areas for city and county boundaries filed in accordance with Government Code 54900. The initial dataset was first published on October 20, 2021, and was based on the State Board of Equalization's tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax jurisdictions. The boundaries are continuously being revised when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions and should not be used to determine precise city or county boundary line locations.The data is updated within 10 business days of the CDTFA receiving a copy of the Board of Equalization's acknowledgement letter.BOE_CityAnx Data Dictionary: COFILE = county number - assessment roll year - file number (see note*); CHANGE = affected city, unincorporated county, or boundary correction; EFFECTIVE = date the change was effective by resolution or ordinance (see note*); RECEIVED = date the change was received at the BOE; ACKNOWLEDGED = date the BOE accepted the filing for inclusion into the tax rate area system; NOTES = additional clarifying information about the action.*Note: A COFILE number ending in "000" is a boundary correction and the effective date used is the date the map was corrected.BOE_CityCounty Data Dictionary: COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the Board of Equalization's 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).

View these data interactively: http://dierauer.shinyapps.io/SnowDroughtRiskThese ten files are rasters and .kmz files of snow drought risk and susceptibility over the mountain and inter-mountain regions of western United States and southwestern Canada. The snow drought risk rasters (risk_dry.asc, risk_warm.asc, risk_warm_dry.asc) correspond to the dry snow drought risk, warm snow drought risk, and warm and dry snow drought risk. Risk is calculated as the mean severity (fraction below long-term [1951-2000] peak snow water equivalent [SWE] mean) multiplied by the frequency (fraction of total years [n = 63]). Thus, snow drought risk in each raster has units of fractional deficit per year and is equal to the expected annual deficit in peak SWE for each snow drought type.The susceptibility rasters (susceptibility.asc, susceptibility_plus2degC.asc) contain the categorical ranking of temperature-related snow drought susceptibility over the mountain and inter-mountain western United States and southwestern Canada. The susceptibility.asc file represents the historical susceptibility (1951-2000) and the susceptibility_plus2degC.asc file represents the susceptibility under 2 degrees of warming (relative to 1951-2000). Raster values correspond to susceptibility rankings as follows: 0 = negligible, 1 = low, 2 = medium, 3 = high.All rasters are in ESRI Ascii (.asc) format and were created with the "raster" package in R. Resolution is 1/16 degree. Extent: xmin = -125; xmax = -100; ymin = 30; ymax = 53.For easy viewing in Google Earth, .kmz versions of the raster files are also included in this dataset.For further details, see: Dierauer, J.R., Allen, D.M., & Whitfield, P.H. Snow drought risk and susceptibility in the western United States and southwestern Canada. Water Resources Research, 55, 3076-3091. https://doi.org/10.1029/2018WR023229

Anomaly data is a product generated in the Ocean Weather Lab using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite and American Seas (AMSEAS) model parameters to detect anomalous conditions in the Gulf of Mexico. This subset was presented at the SPIE conference in Anaheim, CA in 2017. Anomaly fields are created using the difference between the week of interest and the previous 8-week mean (with a 2 week lag). Masks of standard deviation are also provided to mute variability within chosen degrees of standard deviation. The data are supplied as zipped kmz files for use in Google Earth. Each kmz provided contains a kml (location file) and a png (image file). Similar data from June-August 2015 can be found in dataset R4.x260.000:0053.

ZS9 is a seismic source model for Italy to be used as an input for country-wide probabilistic seismic hazard assessment (PSHA) in the frame of the compilation of the national reference map. ZS9 is made out of 36 zones where earthquakes with Mw > = 5 are expected. It also assumes that earthquakes with Mw up to 5 may occur anywhere outside the seismogenic zones, although the associated probability is rather low. Special care was taken to ensure that each zone sampled a large enough number of earthquakes so that we could compute reliable earthquake production rates. Although it was drawn following criteria that are standard practice in PSHA, ZS9 is also innovative in that every zone is characterised also by its mean seismogenic depth (the depth of the crustal volume that will presumably release future earthquakes) and predominant focal mechanism (their most likely rupture mechanism). These properties were determined using instrumental data, and only in a limited number of cases we resorted to geologic constraints and expert judgment to cope with lack of data or conflicting indications. These attributes allow ZS9 to be used with more accurate regionalized depth-dependent attenuation relations, and are ultimately expected to increase significantly the reliability of seismic hazard estimates. Data e Risorse Questo dataset non ha dati terremoti