Subdivision of the country in biogeoclimatic areas according to the ecological classification method based on climate, constitution of the mother rock and the ground: 18 ecological sectors. Data harmonized according to the Bio-geographical Regions INSPIRE theme data specification. Description copied from catalog.inspire.geoportail.lu.

The worldwide data center cooling market by investment was valued at USD 8.73 billion in 2022 and is expected to reach USD 12.64 billion by 2028, growing at a CAGR of 6.36%.

DescriptionThe 15 TPRs include the 5 MPOs and 10 Rural TPRs - Pikes Peak, Denver Metro, North Front Range, Pueblo Area, Grand Valley, Eastern, Southeast, San Luis Valley, Gunnison Valley, Southwest, Intermountain, Northwest, Upper Front Range, Central Front Range and South Central.The State of Colorado has been divided into 15 Transportation Planning Regions as set forth in CRS 43-1-1102 (8)(a), 43-1-1103 (5) (C.R.S.) and the Rules and Regulations for The Statewide Transportation Planning Process and Transportation Planning Regions (The Rules). Five of these are the Metropolitan Planning Areas. The remaining 10 rural Transportation Planning Regions are grouped in geographic contiguous areas with transportation commonalities comprised of Counties and all Municipalities within the counties of these given boundaries.Adopted TPR boundaries as of 12/31/2015. This includes TPR boundaries affected by PPACG MPO and GV MPO boundary changes in 2015.

    Last Update
    2015


    Update FrequencyAs needed


    Data Owner
    Division of Transportation Development


    Data Contact
    GIS Support Unit


    Collection Method



    Projection
    NAD83 / UTM zone 13N


    Coverage Area
    Statewide


    Temporal



    Disclaimer/Limitations
    There are no restrictions and legal prerequisites for using the data set. The State of Colorado assumes no liability relating to the completeness, correctness, or fitness for use of this data.

mammoth666/cars-make-model-year-chunk-43 dataset hosted on Hugging Face and contributed by the HF Datasets community

Two categories of vegetation used in the analysis and their constituent Landfire biophysical settings and Ecological Systems.

See full Data Guide here. Regional Drainage Basin Set:

Connecticut Regional Drainage Basins is 1:24,000-scale, polygon and line feature data that define Regional drainage basin areas in Connecticut. These large basins mostly range from 40 to 400 square miles in size and make up the even larger major drainage basin areas. Connecticut Regional Drainage Basins includes drainage areas for all Connecticut rivers, streams, brooks, lakes, reservoirs and ponds published on 1:24,000-scale 7.5 minute topographic quadrangle maps prepared by the USGS between 1969 and 1984. Data is compiled at 1:24,000 scale (1 inch = 2,000 feet). This information is not updated. Polygon and line features represent drainage basin areas and boundaries, respectively. Each basin area (polygon) feature is outlined by one or more major and regional basin boundary (line) feature. These data include 85 regional basin area (polygon) features and 529 regional basin boundary (line) features. Regional Basin area (polygon) attributes include major and regional basin number, and feature size in acres and square miles. The regional basin number (RBAS_NO) uniquely identifies individual basins and is 2 characters in length. There are 44 unique regional basin numbers. Examples include 43, 60 and 61. The first digit (column 1) designates the major basin and the first two digits (columns 1-2) designate the regional basin. Note, there are slightly more regional basin polygon features (85) than unique regional basin numbers (44) due to coastal regional basins defined by series of polygon features located along the Connecticut shoreline. Regional basin boundary (line) attributes include a drainage divide type attribute (DIVIDE) used to cartographically represent the hierarchical drainage basin system. This divide type attribute is used to assign different line symbology to individual major and regional drainage basin divides. For example, major basin drainage divides are more pronounced and shown with a wider line symbol than regional basin drainage divides. Connecticut Regional Drainage Basin polygon and line feature data are derived from the geometry and attributes of the Connecticut Drainage Basins data. Purpose: The polygon features define the contributing drainage area for individual reservoirs, lakes, ponds and river and stream reaches in Connecticut. These are hydrologic land units where precipitation is collected. Rain falling in a basin may take two courses. It may both run over the land and quickly enter surface watercourses, or it may soak into the ground moving through the earth until it surfaces at a wetland or stream. In an undisturbed natural drainage basin, the surface and ground water arrive as precipitation and leave either by evaporation or as surface runoff at the basin's outlet. A basin is a self-contained hydrologic system, with a clearly defined water budget and cycle. The amount of water that flows into the basins equals the amount that leaves. A drainage divide is the topographic barrier along a ridge or line of hilltops separating adjacent drainage basins. For example, rain or snow melt draining down one side of a hill generally will flow into a different basin and stream than water draining down the other side of the hill. These hillsides are separated by a drainage divided that follows nearby hilltops and ridge lines. Use these basin data to identify where rainfall flows over land and downstream to a particular watercourse. Use these data to categorize and tabulate information according to drainage basin by identifying the local basin number for individual reservoir, lake, pond, stream reach, or location of interest. Due to the hierarchical nature of the basin numbering system, a database that records the 2-digit regional basin number for individual geographic locations of interest will support tabulations by major and regional basin as well as document the unique 2-digit regional basin identification number. To identify either all upstream basins draining to a particular location or all downstream basins flowing from a particular location, refer to the Gazetteer of Drainage Basin Areas of Connecticut, Nosal, 1977, CT DEP Water Resources Bulletin 45, for the hydrologic sequence, headwater to outfall, of drainage basins available at http://cteco.uconn.edu/docs/wrb/wrb45_gazetteer_of_drainage_areas_of_connecticut.pdf Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.). Not intended for analysis with other digital data compiled at scales greater than or more detailed than 1:24,000 scale. Use these data with 1:24,000-scale hydrography data also available from the State of Connecticut, Department of Environmental Protection.

Connecticut Regional Drainage Basins is 1:24,000-scale, polygon and line feature data that define Regional drainage basin areas in Connecticut. These large basins mostly range from 40 to 400 square miles in size and make up the even larger major drainage basin areas. Connecticut Regional Drainage Basins includes drainage areas for all Connecticut rivers, streams, brooks, lakes, reservoirs and ponds published on 1:24,000-scale 7.5 minute topographic quadrangle maps prepared by the USGS between 1969 and 1984. Data is compiled at 1:24,000 scale (1 inch = 2,000 feet). This information is not updated. Polygon and line features represent drainage basin areas and boundaries, respectively. Each basin area (polygon) feature is outlined by one or more major and regional basin boundary (line) feature. These data include 85 regional basin area (polygon) features and 529 regional basin boundary (line) features. Regional Basin area (polygon) attributes include major and regional basin number, and feature size in acres and square miles. The regional basin number (RBAS_NO) uniquely identifies individual basins and is 2 characters in length. There are 44 unique regional basin numbers. Examples include 43, 60 and 61. The first digit (column 1) designates the major basin and the first two digits (columns 1-2) designate the regional basin. Note, there are slightly more regional basin polygon features (85) than unique regional basin numbers (44) due to coastal regional basins defined by series of polygon features located along the Connecticut shoreline. Regional basin boundary (line) attributes include a drainage divide type attribute (DIVIDE) used to cartographically represent the hierarchical drainage basin system. This divide type attribute is used to assign different line symbology to individual major and regional drainage basin divides. For example, major basin drainage divides are more pronounced and shown with a wider line symbol than regional basin drainage divides. Connecticut Regional Drainage Basin polygon and line feature data are derived from the geometry and attributes of the Connecticut Drainage Basins data.

pranay-43/HAM10000 dataset hosted on Hugging Face and contributed by the HF Datasets community

Analysis regions are in Fig 2. All burn areas are corrected for missing small fires by dividing initial estimates by 0.95.1 Trends significant at α < 0.05 are starred (*), trends that are close to significant (p < 0.06) have a dark square (▀). The p-values are from the Mann-Kendall trend test after the Benjamini-Hochberg correction for n = 88 trend tests.2 Differences between recent or projected high-severity fire rotation and the range of historical high-severity fire rotations are categorized as: (1) In range, if recent or projected high-severity fire rotation was within the range of available historical estimates, (2) Too short, if recent or projected high-severity fire rotation was outside and shorter than the range of historical estimates, and (3) Too long, if recent or projected high-severity fire rotation was outside and longer than the range of historical estimates. “Y” indicates there was a significant upward trend in area burned at high severity, and “N” indicates there was not.3 The ratio of future area burned to recent area burned from the low and high projections by Yue et al. [42]4 The total excludes 68,530 ha of dry mixed-conifer forests not in the 20 analysis regions and not included in the analysis5 This is the mean across the regions for which there is a projectionDry mixed-conifer forests, high-severity fire rotations (FR), trends, and differences between recent or projected high-severity fire rotation and the range of historical high-severity fire rotations.

The dataset contains information on the monitoring of station P015 on regional road 66 at kilometre 43.

Province: PISTOIA. Municipality: Pistoia. Number of lanes: 2. Kilometer: 43. Coordinates (Lon,Lat): 10.8748,43.9794. Sensors: TR,WB,ME.

Link to the dataset of regional road sr66 Link to the list of all regional roads

Description of resources (csv file)

Each resource contains location traffic data, broken down by flow: - ascending/descending, i.e. total flow in both directions of travel; - only ascending (increasing progressive mileage); - only descending (progressive decreasing kilometer).

Annual TGM Average annual daily traffic, broken down by vehicle type and total, by weekday, public holiday and pre-holiday.

Table of flows Daily traffic for each day of the year, broken down by vehicle type and total, average total hourly traffic between 6 a.m. and 8 p.m., peak hour and total peak hour traffic.
For days when station data is not available, the fields in the Flow Table are not valued.

IBM Cloud has data centers across several regions, with a total of ** availability zones (AZs). In 2024, North and South America ranked at the top with ** AZs. Europe was second with ** zones, closely followed up by Asia Pacific with ** AZs.

Immigrant mobility by age and sex, pre-admission experience, knowledge of official languages at admission, immigrant admission category, admission year and tax year, for Canada, provinces and ecomomic regions.

The dataset contains information on the monitoring of station P015 on regional road 66 at kilometre 43. Province: PISTOIA. Municipality: Pistoia. Number of lanes: 2. Kilometer: 43. Coordinates (Lon,Lat): 10.8748,43.9794. Sensors: TR,WB,ME. Link to the dataset of regional road sr66 Link to the list of all regional roads ###Description of resources (csv file) Each resource contains location traffic data, broken down by flow: - ascending/descending, i.e. total flow in both directions of travel; - only ascending (increasing progressive mileage); - only descending (progressive decreasing kilometer). Annual TGM Average annual daily traffic, broken down by vehicle type and total, by weekday, public holiday and pre-holiday. Table of flows Daily traffic for each day of the year, broken down by vehicle type and total, average total hourly traffic between 6 a.m. and 8 p.m., peak hour and total peak hour traffic. For days when station data is not available, the fields in the Flow Table are not valued.

VizieR Online Data Catalog: The 2nd MSFRs Omnibus X-ray Catalog (MOXC2)(Townsley L.K.+, 2018)

According to the Freedom House Freedom Index 2023, the share of countries ranked as free worldwide in 2022 was 43 percent, with 28 percent ranked as partly free and 29 percent as not free. Europe was the region with the highest share of free countries, whereas in Eurasia, no countries were classified as free.

The biogeographical regions dataset contains the official delineations used in the Habitats Directive (92/43/EEC) and for the EMERALD Network set up under the Convention on the Conservation of European Wildlife and Natural Habitats (Bern Convention).

The Pannonian region of Serbia was missing in previous versions and this has been corrected in the 2016 version. Some Arctic islands which do not belong to the European part of Russia and which were erroneously included in previous versions have been removed.

In 2019, Asia Pacific was the largest computer peripherals market region worldwide, having a market share of 43 percent, whilst the Americas accounted for 31 percent of the global market. The overall global market size stood at 245 billion U.S. dollars in that year.

Shallow-water zoogeographic areas analyzed, compared with MeoW provinces and regions [43].

Includes Biogeographical Regions of Cyprus in accordance with Directive 92/43 / EEC on the basis of which is preparing the 6-year report on the status of habitats and species.

Extended label set of BigEarthNet-43 for Hierarchical Multi-Label Classification

This dataset contains an extended version of the original label set of BigEarthNet-43 for Hierarchical Multi-Label Classification.

  Dataset creation

It was created based on the CORINE Land Cover database of the year 2018 (CLC 2018), which provides detailed information about the land cover classes at multiple levels of the hierarchy

  Loading the Dataset

To load the dataset into your… See the full description on the dataset page: https://huggingface.co/datasets/marjandl/BigEarthNet-43-HMLC.

IntroductionMild traumatic brain injury (mTBI) caused by repetitive low-intensity blast overpressure (relBOP) in military personnel exposed to breaching and heavy weapons is often unrecognized and is understudied. Exposure to relBOP poses the risk of developing abnormal behavioral and psychological changes such as altered cognitive function, anxiety, and depression, all of which can severely compromise the quality of the life of the affected individual. Due to the structural and anatomical heterogeneity of the brain, understanding the potentially varied effects of relBOP in different regions of the brain could lend insights into the risks from exposures.MethodsIn this study, using a rodent model of relBOP and western blotting for protein expression we showed the differential expression of various neuropathological proteins like TDP-43, tight junction proteins (claudin-5, occludin, and glial fibrillary acidic protein (GFAP)) and a mechanosensitive protein (piezo-2) in different regions of the brain at different intensities and frequency of blast.ResultsOur key results include (i) significant increase in claudin-5 after 1x blast of 6.5 psi in all three regions and no definitive pattern with higher number of blasts, (ii) significant increase in piezo-2 at 1x followed by significant decrease after multiple blasts in the cortex, (iii) significant increase in piezo-2 with increasing number of blasts in frontal cortex and mixed pattern of expression in hippocampus and (iv) mixed pattern of TDP-3 and GFAP expression in all the regions of brain.DiscussionThese results suggest that there are not definitive patterns of changes in these marker proteins with increase in intensity and/or frequency of blast exposure in any particular region; the changes in expression of these proteins are different among the regions. We also found that the orientation of blast exposure (e.g. front vs. side exposure) affects the altered expression of these proteins.