This dataset contains documentation on the 146 global regions used to organize responses to the ArchaeGLOBE land use questionnaire between May 18 and July 31, 2018. The regions were formed from modern administrative regions (Natural Earth 1:50m Admin1 - states and provinces, https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-1-states-provinces/). The boundaries of the polygons represent rough geographic areas that serve as analytical units useful in two respects - for the history of land use over the past 10,000 years (a moving target) and for the history of archaeological research. Some consideration was also given to creating regions that were relatively equal in size. The regionalization process went through several rounds of feedback and redrawing before arriving at the 146 regions used in the survey. No bounded regional system could ever truly reflect the complex spatial distribution of archaeological knowledge on past human land use, but operating at a regional scale was necessary to facilitate timely collaboration while achieving global coverage. Map in Google Earth Format: ArchaeGLOBE_Regions_kml.kmz Map in ArcGIS Shapefile Format: ArchaeGLOBE_Regions.zip (multiple files in zip file) The shapefile format is a digital vector file that stores geographic location and associated attribute information. It is actually a collection of several different file types: .shp — shape format: the feature geometry .shx — shape index format: a positional index of the feature geometry .dbf — attribute format: columnar attributes for each shape .prj — projection format: the coordinate system and projection information .sbn and .sbx — a spatial index of the features .shp.xml — geospatial metadata in XML format .cpg — specifies the code page for identifying character encoding Attributes: FID - a unique identifier for every object in a shapefile table (0-145) Shape - the type of object (polygon) World_ID - coded value assigned to each feature according to its division into one of seventeen ‘World Regions’ based on the geographic regions used by the Statistics Division of the United Nations (https://unstats.un.org/unsd/methodology/m49/), with small changes to better reflect archaeological scholarly communities. These large regions provide organizational structure, but are not analytical units for the study. World_RG - text description of each ‘World Region’ Archaeo_ID - unique identifier (1-146) corresponding to the region code used in the ArchaeoGLOBE land use questionnaire and all ArchaeoGLOBE datasets Archaeo_RG - text description of each region Total_Area - the total area, in square kilometers, of each region Land-Area - the total area minus the area of all lakes and reservoirs found within each region (source: https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-lakes/) PDF of Region Attribute Table: ArchaeoGLOBE Regions Attributes.pdf Excel file of Region Attribute Table: ArchaeoGLOBE Regions Attributes.xls Printed Maps in PDF Format: ArchaeoGLOBE Regions.pdf Documentation of the ArchaeoGLOBE Regional Map: ArchaeoGLOBE Regions README.doc

This boundary dataset complements 13 other datasets as part of a study that compared ancient settlement patterns with modern environmental conditions in the Jazira region of Syria.

This study examined settlement distribution and density patterns over the past five millennia using archaeological survey reports and French 1930s 1:200,000 scale maps to locate and map archaeological sites. An archaeological site dataset was created and compared to and modelled with soil, geology, terrain (contour), surface and subsurface hydrology and normal and dry year precipitation pattern datasets; there are also three spreadsheet datasets providing 1963 precipitation and temperature readings collected at three locations in the region. The environmental datasets were created to account for ancient and modern population subsistence activities, which comprise barley and wheat farming and livestock grazing.

These environmental datasets were subsequently modelled with the archaeological site dataset, as well as, land use and population density datasets for the Jazira region. Ancient trade routes were also mapped and factored into the model, and a comparison was made to ascertain if there was a correlation between ancient and modern settlement patterns and environmental conditions; the latter influencing subsistence activities.

This boundary dataset was generated to define the extent of the study area, which comprises the border between Syria and Turkey, Syria and Iraq, the River Tigris and the River Euphrates. All related data collected was confined within this boundary dataset with the exception of the archaeological dataset. Archaeological sites were identified and mapped along both banks of the River Euphrates. Also, the town of Dayr az-Zawr, where the 1963 precipitation and temperature monthly values were collected for one of the datasets, falls outside the Jazira Region.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species). These are natural areas in which at least one observation of the species has been carried out in the recent period, as well as natural regions where the species is highly suspected (i.e. experts) or has older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the method of calculation, refer to the Natural Regions Map Explanation Sheet. Natural regions identify areas in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even at a single location) provides a strong presumption of other favourable habitats elsewhere in the natural region. Any observations shall be taken into account: they can be implanted populations, but also erratic individuals. This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible. Refer to the card reading instructions as well as PDF cards for more information.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species). These are natural areas in which at least one observation of the species has been carried out in the recent period, as well as natural regions where the species is highly suspected (i.e. experts) or has older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the method of calculation, refer to the Natural Regions Map Explanation Sheet. Natural regions identify areas in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even at a single location) provides a strong presumption of other favourable habitats elsewhere in the natural region. Any observations shall be taken into account: they can be implanted populations, but also erratic individuals.

This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible.

Refer to the card reading instructions as well as PDF cards for more information.

This dataset supercedes a previous Water Demand Region dataset (also called Monash Demand Regions). The regions in the current dataset have been developed using 2011 ABS Statistical Area Level 2 (SA2) boundaries, aggregated to best match previous Water Demand Region boundaries. The current dataset has 24 regions, while the previous version had 20. This dataset was developed for use in a new water demand-supply model. This dataset was formerly known as Water Demand Region Boundaries (DOW-059)

my_dataset

Note: This is an AI-generated dataset, so its content may be inaccurate or false. Source of the data: The dataset was generated using Fastdata library and claude-3-haiku-20240307 with the following input:

  System Prompt

You are a helpful assistant.

  Prompt Template

Generate English and Spanish translations on the following topic:

  Sample Input

[{'topic': 'I am going to the beach this weekend'}, {'topic': 'I am going… See the full description on the dataset page: https://huggingface.co/datasets/Wauplin/my_dataset.

Regional regression equations were calculated in Puerto Rico with generalized least squares techniques to estimate flood frequency statistics at ungaged locations using drainage area as the only explanatory variable. The island was divided into 2 regions to minimize residuals. The region division that resulted in lower and more balanced residuals runs primarily north-south near the center of the island, mostly along an 8-digit hydrologic unit code (HUC8) boundary. The division line runs through a HUC8 polygon on the southern end of the island, but care was taken to include entire watersheds and consideration was given where hydrologic and physiographic properties differed. This data release includes geographic information system files that define the polygons for both regions.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species). These are natural areas in which at least one observation of the species has been carried out in the recent period, as well as natural regions where the species is highly suspected (i.e. experts) or has older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the method of calculation, refer to the Natural Regions Map Explanation Sheet. Natural regions identify areas in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even at a single location) provides a strong presumption of other favourable habitats elsewhere in the natural region. Any observations shall be taken into account: they can be implanted populations, but also erratic individuals. This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible. Refer to the card reading instructions as well as PDF cards for more information.

Sensitivity maps made by the ODONAT Grand Est network in 2018 - 2019. The distribution of the species is represented on the basis of recent occurrence data (1999 – 2018 or 2009 – 2018 depending on the species). These are natural regions where at least one observation of the species has been made in the recent period as well as natural regions where the species is strongly suspected (expert reports) or benefits from older data. In each of the natural regions with recent non-marginal observations, this presence is represented by calculating the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the calculation method, refer to the explanation sheet of the Natural Regions maps. Natural regions identify territories in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even at a single location) makes it possible to strongly presume the existence of other favourable habitats elsewhere in the natural region. Any comments shall be taken into account: they can be implanted populations, but also erratic individuals. This layer represents the state of knowledge at the time of its realization, it should not be considered exhaustive. The presence of the species outside the identified areas is possible. Refer to the card reading instructions as well as PDF cards for more information.

Description for i03_DAU_county_cnty2018 is as follows:

Detailed Analysis Unit-(DAU) Convergence via County Boundary cnty18_1 for Cal-Fire, (See metadata for CAL-FIRE cnty18_1), State of California.

The existing DAU boundaries were aligned with cnty18_1 feature class.

Originally a collaboration by Department of Water Resources, Region Office personnel, Michael L. Serna, NRO, Jason Harbaugh - NCRO, Cynthia Moffett - SCRO and Robert Fastenau - SRO with the final merge of all data into a cohesive feature class to create i03_DAU_COUNTY_cnty24k09 alignment which has been updated to create i03_DAU_COUNTY_cnty18_1.

This version was derived from a preexisting “dau_v2_105, 27, i03_DAU_COUNTY_cnty24k09” Detailed Analysis Unit feature class's and aligned with Cal-Fire's 2018 boundary.

Manmade structures such as piers and breakers, small islands and coastal rocks have been removed from this version. Inlets waters are listed on the coast only.

These features are reachable by County\DAU. This allows the county boundaries, the DAU boundaries and the State of California Boundary to match Cal-Fire cnty18_1.

DAU Background

The first investigation of California's water resources began in 1873 when President Ulysses S. Grant commissioned an investigation by Colonel B. S. Alexander of the U.S. Army Corps of Engineers. The state followed with its own study in 1878 when the State Engineer's office was created and filled by William Hammond Hall. The concept of a statewide water development project was first raised in 1919 by Lt. Robert B. Marshall of the U.S. Geological Survey.

In 1931, State Engineer Edward Hyatt introduced a report identifying the facilities required and the economic means to accomplish a north-to-south water transfer. Called the "State Water Plan", the report took nine years to prepare. To implement the plan, the Legislature passed the Central Valley Act of 1933, which authorized the project. Due to lack of funds, the federal government took over the CVP as a public works project to provide jobs and its construction began in 1935.

In 1945, the California Legislature authorized an investigation of statewide water resources and in 1947, the California Legislature requested that an investigation be conducted of the water resources as well as present and future water needs for all hydrologic regions in the State. Accordingly, DWR and its predecessor agencies began to collect the urban and agricultural land use and water use data that serve as the basis for the computations of current and projected water uses.

The work, conducted by the Division of Water Resources (DWR’s predecessor) under the Department of Public Works, led to the publication of three important bulletins: Bulletin 1 (1951), "Water Resources of California," a collection of data on precipitation, unimpaired stream flows, flood flows and frequency, and water quality statewide; Bulletin 2 (1955), "Water Utilization and Requirements of California," estimates of water uses and forecasts of "ultimate" water needs; and Bulletin 3 (1957), "The California Water Plan," plans for full practical development of California’s water resources, both by local projects and a major State project to meet the State's ultimate needs. (See brief addendum below* “The Development of Boundaries for Hydrologic Studies for the Sacramento Valley Region”)

DWR subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR), corresponding to the State’s major drainage basins. The next levels of delineation are the Planning Areas (PA), which in turn are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so are the smallest study areas used by DWR.

The DAU/counties are used for estimating water demand by agricultural crops and other surfaces for water resources planning. Under current guidelines, each DAU/County has multiple crop and land-use categories. Many planning studies begin at the DAU or PA level, and the results are aggregated into hydrologic regions for presentation.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species). These are natural areas in which at least one observation of the species has been carried out in the recent period, as well as natural regions where the species is highly suspected (i.e. experts) or has older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the method of calculation, refer to the Natural Regions Map Explanation Sheet. Natural regions identify areas in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even at a single location) provides a strong presumption of other favourable habitats elsewhere in the natural region. Any observations shall be taken into account: they can be implanted populations, but also erratic individuals. This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible. Refer to the card reading instructions as well as PDF cards for more information.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species). These are natural areas in which at least one observation of the species has been carried out in the recent period, as well as natural regions where the species is highly suspected (i.e. experts) or has older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the method of calculation, refer to the Natural Regions Map Explanation Sheet. Natural regions identify areas in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even at a single location) provides a strong presumption of other favourable habitats elsewhere in the natural region. Any observations shall be taken into account: they can be implanted populations, but also erratic individuals.

This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible.

Refer to the card reading instructions as well as PDF cards for more information.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species). These are natural areas in which at least one observation of the species has been carried out in the recent period, as well as natural regions where the species is highly suspected (i.e. experts) or has older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the method of calculation, refer to the Natural Regions Map Explanation Sheet. Natural regions identify areas in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even at a single location) provides a strong presumption of other favourable habitats elsewhere in the natural region. Any observations shall be taken into account: they can be implanted populations, but also erratic individuals. This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible. Refer to the card reading instructions as well as PDF cards for more information.

This dataset was updated April, 2024.This ownership dataset was generated primarily from CPAD data, which already tracks the majority of ownership information in California. CPAD is utilized without any snapping or clipping to FRA/SRA/LRA. CPAD has some important data gaps, so additional data sources are used to supplement the CPAD data. Currently this includes the most currently available data from BIA, DOD, and FWS. Additional sources may be added in subsequent versions. Decision rules were developed to identify priority layers in areas of overlap.Starting in 2022, the ownership dataset was compiled using a new methodology. Previous versions attempted to match federal ownership boundaries to the FRA footprint, and used a manual process for checking and tracking Federal ownership changes within the FRA, with CPAD ownership information only being used for SRA and LRA lands. The manual portion of that process was proving difficult to maintain, and the new method (described below) was developed in order to decrease the manual workload, and increase accountability by using an automated process by which any final ownership designation could be traced back to a specific dataset.The current process for compiling the data sources includes: Clipping input datasets to the California boundary Filtering the FWS data on the Primary Interest field to exclude lands that are managed by but not owned by FWS (ex: Leases, Easements, etc) Supplementing the BIA Pacific Region Surface Trust lands data with the Western Region portion of the LAR dataset which extends into California. Filtering the BIA data on the Trust Status field to exclude areas that represent mineral rights only. Filtering the CPAD data on the Ownership Level field to exclude areas that are Privately owned (ex: HOAs) In the case of overlap, sources were prioritized as follows: FWS > BIA > CPAD > DOD As an exception to the above, DOD lands on FRA which overlapped with CPAD lands that were incorrectly coded as non-Federal were treated as an override, such that the DOD designation could win out over CPAD.In addition to this ownership dataset, a supplemental _source dataset is available which designates the source that was used to determine the ownership in this dataset.Data Sources: GreenInfo Network's California Protected Areas Database (CPAD2023a). https://www.calands.org/cpad/; https://www.calands.org/wp-content/uploads/2023/06/CPAD-2023a-Database-Manual.pdf US Fish and Wildlife Service FWSInterest dataset (updated December, 2023). https://gis-fws.opendata.arcgis.com/datasets/9c49bd03b8dc4b9188a8c84062792cff_0/explore Department of Defense Military Bases dataset (updated September 2023) https://catalog.data.gov/dataset/military-bases Bureau of Indian Affairs, Pacific Region, Surface Trust and Pacific Region Office (PRO) land boundaries data (2023) via John Mosley John.Mosley@bia.gov Bureau of Indian Affairs, Land Area Representations (LAR) and BIA Regions datasets (updated Oct 2019) https://biamaps.doi.gov/bogs/datadownload.htmlData Gaps & Changes:Known gaps include several BOR, ACE and Navy lands which were not included in CPAD nor the DOD MIRTA dataset. Our hope for future versions is to refine the process by pulling in additional data sources to fill in some of those data gaps. Additionally, any feedback received about missing or inaccurate data can be taken back to the appropriate source data where appropriate, so fixes can occur in the source data, instead of just in this dataset.24_1: Input datasets this year included numerous changes since the previous version, particularly the CPAD and DOD inputs. Of particular note was the re-addition of Camp Pendleton to the DOD input dataset, which is reflected in this version of the ownership dataset. We were unable to obtain an updated input for tribral data, so the previous inputs was used for this version.23_1: A few discrepancies were discovered between data changes that occurred in CPAD when compared with parcel data. These issues will be taken to CPAD for clarification for future updates, but for ownership23_1 it reflects the data as it was coded in CPAD at the time. In addition, there was a change in the DOD input data between last year and this year, with the removal of Camp Pendleton. An inquiry was sent for clarification on this change, but for ownership23_1 it reflects the data per the DOD input dataset.22_1 : represents an initial version of ownership with a new methodology which was developed under a short timeframe. A comparison with previous versions of ownership highlighted the some data gaps with the current version. Some of these known gaps include several BOR, ACE and Navy lands which were not included in CPAD nor the DOD MIRTA dataset. Our hope for future versions is to refine the process by pulling in additional data sources to fill in some of those data gaps. In addition, any topological errors (like overlaps or gaps) that exist in the input datasets may thus carry over to the ownership dataset. Ideally, any feedback received about missing or inaccurate data can be taken back to the relevant source data where appropriate, so fixes can occur in the source data, instead of just in this dataset.

📘 Dataset Summary

AM-Thinking-v1 and Qwen3-235B-A22B are two reasoning datasets distilled from state-of-the-art teacher models. Each dataset contains high-quality, automatically verified responses generated from a shared set of 1.89 million queries spanning a wide range of reasoning domains. The datasets share the same format and verification pipeline, allowing for direct comparison and seamless integration into downstream tasks. They are intended to support the development of… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-Qwen3-Distilled.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species). These are natural areas in which at least one observation of the species has been carried out in the recent period, as well as natural regions where the species is highly suspected (i.e. experts) or has older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the method of calculation, refer to the Natural Regions Map Explanation Sheet. Natural regions identify areas in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even at a single location) provides a strong presumption of other favourable habitats elsewhere in the natural region. Any observations shall be taken into account: they can be implanted populations, but also erratic individuals.

This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible.

Refer to the card reading instructions as well as PDF cards for more information.

Recognize Anything Dataset Card

  Dataset details

Dataset type: These annotation files come from the Recognize Anything Model (RAM). RAM propose an automatic data engine to generate substantial image tags from image-text pairs. Dataset date: Recognize Anything Dataset was collected in April 2023, by an automatic data engine proposed by RAM. Paper or resources for more information: https://github.com/xinyu1205/recognize-anything Where to send questions or comments about the… See the full description on the dataset page: https://huggingface.co/datasets/xinyu1205/recognize-anything-dataset.

MapPool - Bubbling up an extremely large corpus of maps for AI

MapPool is a dataset of 75 million potential maps and textual captions. It has been derived from CommonPool, a dataset consisting of 12 billion text-image pairs from the Internet. The images have been encoded by a vision transformer and classified into maps and non-maps by a support vector machine. This approach outperforms previous models and yields a validation accuracy of 98.5%. The MapPool dataset may help to train… See the full description on the dataset page: https://huggingface.co/datasets/sraimund/MapPool.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species). These are natural areas in which at least one observation of the species has been carried out in the recent period, as well as natural regions where the species is highly suspected (i.e. experts) or has older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the method of calculation, refer to the Natural Regions Map Explanation Sheet. Natural regions identify areas in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even at a single location) provides a strong presumption of other favourable habitats elsewhere in the natural region. Any observations shall be taken into account: they can be implanted populations, but also erratic individuals. This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible. Refer to the card reading instructions as well as PDF cards for more information.

giaosucan/my-dataset-name dataset hosted on Hugging Face and contributed by the HF Datasets community