Open the Data Resource: https://gis.chesapeakebay.net/cross-git/overview/ This story map summarizes the data assembled and the scoring criteria recommended by the subject matter experts involved in the Chesapeake Bay Program's Cross-GIT Mapping Project. It also presents the composite results of the analyses. Access the Cross-GIT HUC-12 Conservation Composite: https://gis.chesapeakebay.net/ags/rest/services/InterGIT/HUC12_Cons_Composite/MapServer Access the Cross-GIT HUC-12 Restoration Composite: https://gis.chesapeakebay.net/ags/rest/services/InterGIT/HUC12_Rest_Composite/MapServer

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

This GitHub repo and Jupyter Notebook contain code for analyzing, visualizing, and manipulating data for the opioid crisis in Tennessee and the USA.All work in the GitHub and Jupyter Notebook were conducted by Dr. Qiusheng Wu with UTK Geography.Click this link to open the GitHub repo and Jupyter Notebook.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

This San Francisco Bay Shoreline Vulnerability Index (Index) is a measure of shoreline vulnerability to erosion and/or overtopping due to extreme tides, waves, storm surges, and sea level rise. The Index gives a comprehensive look at how different sections of the Bay respond to storm surge, erosion from waves, and sea level rise. It ranks each shoreline segment’s vulnerability to impacts such as erosion and overtopping relative to other types of shoreline by by scoring characteristics that affect shoreline vulnerability. The Shoreline Vulnerability Index (SVI) uses the following 6 characteristics to determine shoreline vulnerability for the primary shoreline protection, which is the first elevated shoreline from the Bay. These characteristics are weighted in their importance towards shoreline vulnerability to flooding. Shoreline Vulnerability Characteristics1. Vulnerability of shoreline type to flooding and sea level rise2. Adaptability to sea level rise by shoreline type3. Presence of fortification4. Presence of frontage and/or secondary shoreline protection5. Elevation6. Wave energyFor more information visit the following links:ArcGIS Story Map: https://bcdc.maps.arcgis.com/apps/Cascade/index.html?appid=a90eb7b4eb7249809505e8d940bb2419 Methodology Document: https://www.adaptingtorisingtides.org/wp-content/uploads/2021/07/ShorelineVulnerabilityIndex_Methodology_2021.pdf GitHub: https://github.com/BCDC-GIS/shoreline-vulnerability-indexFor more information, please contact GIS@bcdc.ca.gov.

This repository serves as a comprehensive data archive for GIS data utilized in the development of TXSELECT (tx.select.tamu.edu). Contents include raw, processed, and intermediate GIS datasets (watershed boundaries, land cover, soil type, census blocks etc.), used to create input files for TXSELECT using the code available at this site - https://github.com/shubhamjain15/TX-SELECT.

This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.This Tutorial consists of four tutorials that deal with integrating the statistical programming language R with ArcGIS for Desktop. Several concepts are covered which include configuring ArcGIS with R, writing basic R scripts, writing R scripts that work with ArcGIS data, and constructing R Tools for use within ArcGIS Pro. It is recommended that the tutorials are completed in sequential order. Each of the four tutorials (as well as a version of this document), can viewed directly from your Web browser by following the links below. However, you must obtain a complete copy of the tutorial files by downloading the latest release (or by cloning the tutorial repository on GitHub) if you wish to follow the tutorials interactively using ArcGIS and R software, along with pre-configured sample data.To download the tutorial documents and datasets, click the Open button to the top right. This will automatically download a ZIP file containing all files and data required.You can also clone the tutorial documents and datasets for this GitHub repo: https://github.com/highered-esricanada/r-arcgis-tutorials.gitSoftware & Solutions Used: ArcGIS Pro 3.4 Internet browser (e.g., Mozilla Firefox, Google Chrome, Safari) R Statistical Computing Language – version 4.3.3 R-ArcGIS Bindings – version 1.0.1.311RStudio Desktop – version 2024.09.0+375Time to Complete: 2.5 h (excludes installation time)File Size: 115 MBDate Created: November 2017Last Updated: December 2024

The dataset encompasses two components: (1) a GeoPackage file (referencePolygons.gpkg) designed for geographic information systems (GIS), containing the spatial distribution of the hexagon-shaped coastal sites, each with a unique identifier (id), and (2) a comma-separated values compressed file (oceanographicConnectivity.csv.zip) containing a matrix detailing the realized connections between pairs of sites. This matrix includes information on the source site (id), sink site (id), date of particle release (day, month, year), and the corresponding travel time expressed in days.Users have two primary options for utilizing the dataset: they can either work directly with the geospatial vector for GIS, paired with the corresponding matrix of realized connections, to retrieve pairwise connectivity estimates between coastal sites globally, or they can use the coastalNet R package for streamlined access and analysis.For users opting to work within a GIS environment, the geospatial vector file contains the spatial distribution of the coastal hexagons, each identified by a unique ID. By identifying the source and sink sites of interest within the spatial data, users can retrieve the corresponding hexagon IDs. These IDs can then be cross-referenced with the matrix of realized connectivity events, which includes information on particle release date, source ID, sink ID, and travel time. This allows users to easily extract the specific connectivity events between selected sites, providing a flexible and detailed approach to analyzing connectivity patterns directly within GIS.For users opting to work within R, the coastalNet package can facilitate the use of the provided oceanographic connectivity estimates. It offers a comprehensive suite of functions for accessing, analyzing, and visualizing connectivity data. Check https://github.com/jorgeassis/coastalNet for additional information.

This map represents emissions in the Metro Atlanta region from both transportation and residential outlets.

This city boundary shapefile was extracted from Esri Data and Maps for ArcGIS 2014 - U.S. Populated Place Areas. This shapefile can be joined to 500 Cities city-level Data (GIS Friendly Format) in a geographic information system (GIS) to make city-level maps.

The Hills of Governor's Island Dataset for GRASS GIS
This geospatial dataset contains raster and vector data for the Hills region of Governor's Island, New York City, USA. The top level directory governors_island_hills_for_grass is a GRASS GIS location for NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet in US Surveyor's Feet with EPSG code 2263. Inside the location there is the PERMANENT mapset, a license file, data record, readme file, workspace, color table, category rules, and scripts for data processing. This dataset was created for the course GIS for Designers.

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database
directory. If you are new to GRASS GIS read the first time users guide.

Data Sources

• https://orthos.dhses.ny.gov/
• https://data.cityofnewyork.us/

Maps

• Orthophotographs from 2012, 2014, 2016, 2018, and 2020
• Digital elevation model from 2017
• Digital surface models from 2014 and 2017
• Landcover from 2014

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

This dataset contains raw GIS data sourced from the BAG (Basisregistratie Adressen en Gebouwen; Registry of Addresses and Buildings). It provides comprehensive information on buildings, including advanced height data and administrative details. It also contains geographic divisions within The Hague. Additionally, the dataset incorporates energy label data, offering insights into the energy efficiency and performance of these buildings. This combined dataset serves as the backbone of a Master's thesis in Industrial Ecology, analysing residential and office cooling and its environmental impacts in The Hague, Netherlands. The codebase of this analysis can be found in this Github repository: https://github.com/simonvanlierde/msc-thesis-ie

The dataset includes a background research spreadsheet containing supporting calculations. It also presents geopackages with results from the cooling demand model (CDM) for various scenarios: Status quo (SQ), 2030, and 2050 scenarios (Low, Medium, and High)

Background research data

The background_research_data.xlsx spreadsheet contains comprehensive background research calculations supporting the shaping of input parameters used in the model. It contains several sheets:

• Cooling Technologies: Details the various cooling technologies examined in the study, summarizing their characteristics and the market penetration mixes used in the analysis.
• LCA Results of Ventilation Systems: Provides an overview of the ecoinvent processes serving as proxies for the life-cycle impacts of cooling equipment, along with calculations of the weight of cooling systems and contribution tables from the LCA-based assessment.
• Material Scarcity: A detailed examination of the critical raw material content in the material footprint of ecoinvent processes, representing cooling equipment.
• Heat Plans per Neighbourhood: Forecasts of future heating solutions for each neighbourhood in The Hague.
• Building Stock: Analysis of the projected growth trends in residential and office building stocks in The Hague. AC Market: Market analysis covering air conditioner sales in the Netherlands from 2002 to 2022.
• Climate Change: Computations of climate-related parameters based on KNMI climate scenarios.
• Electricity Mix Analysis: Analysis of future projections for the Dutch electricity grid and calculations of life-cycle carbon intensities of the grid.

Input data

Geographic divisions

• The outline of The Hague municipality through the Municipal boundaries (Gemeenten) layer, sourced from the Administrative boundaries (Bestuurlijke Gemeenten) dataset on the PDOK WFS service.
• District (Wijken) and Neighbourhood (Buurten) layers were downloaded from the PDOK WFS service (from the CBS Wijken en Buurten 2022 data package) and clipped to the outline of The Hague.
• The 4-digit postcodes layer was downloaded from PDOK WFS service (CBS Postcode4 statistieken 2020) and clipped to The Hague's outline. The postcodes within The Hague were subsequently stored in a csv file.
• The census block layer was downloaded from the PDOK WFS service (from the CBS Vierkantstatistieken 100m 2021 data package) and also clipped to the outline of The Hague.
• These layers have been combined in the GeographicDivisions_TheHague GeoPackage.

BAG data

• BAG data was acquired through the download of a BAG GeoPackage from the BAG ATOM download page.
• In the resulting GeoPackage, the Residences (Verblijfsobject) and Building (Pand) layers were clipped to match The Hague's outline.
• The resulting residence data can be found in the BAG_buildings_TheHague GeoPackage.

3D BAG

• Due to limitations imposed by the PDOK WFS service, which restricts the number of downloadable buildings to 10,000, it was necessary to acquire 145 individual GeoPackages for tiles covering The Hague from the 3D BAG website.
• These GeoPackages were merged using the ogr2ogr append function from the GDAL library in bash.
• Roof elevation data was extracted from the LoD 1.2 2D layer from the resulting GeoPackage.
• Ground elevation data was obtained from the Pand layer.
• Both of these layers were clipped to match The Hague's outline.
• Roof and ground elevation data from the LoD 1.2 2D and Pand layers were joined to the Pand layer in the BAG dataset using the BAG ID of each building.
• The resulting data can be found in the BAG_buildings_TheHague GeoPackage.

Energy labels

• Energy labels were downloaded from the Energy label registry (EP-online) and stored in energy_labels_TheNetherlands.csv.

UHI effect data

• A bitmap with the UHI effect intensity in The Hague was retrieved from the from the Dutch Natural Capital Atlas (Atlas Natuurlijk Kapitaal) and stored in UHI_effect_TheHague.tiff.

Output data

• The residence-level data joined to the building layer is contained in the BAG_buildings_with_residence_data_full GeoPackage.
• The results for each building, according to different scenarios, are compiled in the buildings_with_CDM_results_[scenario]_full GeoPackages. The scenarios are abbreviated as follows:
  • SQ: Status Quo, covering the 2018-2022 reference period.
  • 2030: An average scenario projected for the year 2030.
  • 2050_L: A low-impact, best-case scenario for 2050.
  • 2050_M: A medium-impact, moderate scenario for 2050.
  • 2050_H: A high-impact, worst-case scenario for 2050.

This GIS database supports the paper: Guth, P.L.; Trevisani, S.; Grohmann, C.H.; Lindsay, J.; Gesch, D.; Hawker, L.; Bielski, C. Ranking of 10 Global One-Arc-Second DEMs Reveals Limitations in Terrain Morphology Representation. Remote Sens. 2024, 16, 3273. https://doi.org/10.3390/rs16173273

It is a major upgrade to version 2 of the database (Guth, P. L., 2023. DEMIX GIS Database Version 2 (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8062008 ) with new criteria and an order of magnitude more test tiles.

It builds on the first DEMIX paper, (Bielski, C.; López-Vázquez, C.; Grohmann, C.H.; Guth. P.L.; Hawker, L.; Gesch, D.; Trevisani, S.; Herrera-Cruz, V.; Riazanoff, S.; Corseaux, A.; Reuter, H.; Strobl, P., 2024. Novel approach for ranking DEMs: Copernicus DEM improves one arc second open global topography. IEEE Transactions on Geoscience & Remote Sensing. vol. 62, pp. 1-22, 2024, Art no. 4503922, https://doi.org/10.1109/TGRS.2024.3368015 )

The DEMIX tiles used are described (Guth, Peter L., Peter Strobl, Kevin Gross, & Serge Riazanoff. (2023). DEMIX 10k Tile Data Set (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7504791)

The Open Source MICRODEM can create, manipulate, and visualize the database.

· Source code: https://github.com/prof-pguth/git_microdem

· Dowload EXE and help file: https://microdem.org/

This data set includes:

· Files used by MICRODEM to create and manipulate the database

· Tables created for the analysis

This repository includes input data to run the OnStove Nepal model presented in the paper "Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis" DOI: https://doi.org/10.1016/S2542-5196(24)00209-2.

The code and automated workflow to run the model can be found in the Github repository https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal. All result files and figures can be downloaded from the permanent repository https://doi.org/10.5281/zenodo.10643983.

The "GIS_input_data/" directory includes all the geospatial datasets needed to run the model. Each dataset folder contains a Source.md file describing the dataset, source, attribution, and license. To run the model extract the data inside your "1. Data" folder in your project.

The "Scenario_inputs/" directory includes the CSV files with the input socio- and techno-economic data for the different scenarios. Sources for the socio- and techno-economic data can be found in the supplementary material of the related publication in the link https://doi.org/10.1016/S2542-5196(24)00209-2. To run the model extract the scenario data inside your "2. Scenario inputs" folder in your project.

This dataset is of simplified geometries from COD live services deployed June 2019. Simplification methods applied from ESRI libraries using Python, Node.js and Mapshaper.js and based on adapted procedures for best outcomes preserving shape, topology and attributes. These data are not a substitute for the original COD data sets used in GIS applications. No warranties of any kind are made for any purpose and this dataset is offered as-is. Versions of topojson, kml and csv are also available. For a list of other simplified CODs see the address list: https://github.com/UGA-ITOSHumanitarianGIS/mapservicedoc/raw/master/Data/AWSDeploymentURLlist.xlsx

These are the datasets generated during the evaluation of the core concept transformation algebra. These are the results obtained when matching transformation graphs (automatically generated from workflows consisting of tools that have been annotated with expressions of our transformation algebra) to conceptual task descriptions (manually created to describe the underlying task). The tool annotations, workflows and task specifications can be found at https://github.com/quangis/cct. That repository also contains tools to reproduce these results.

The MBTA GTFS Post-rating Recap collection contains text files that describe MBTA schedule which includes any planned changes in service that became known after the rating began (weekend shuttle buses, change to reduced service schedule due to a snow day, etc.) for a specific season.Data dictionary:https://github.com/mbta/gtfs-documentation/blob/master/reference/gtfs.mdTo view all previously published GTFS files, please refer to the link below:https://github.com/mbta/gtfs-documentation/blob/master/reference/gtfs-archive.mdMassDOT/MBTA shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of the data, and relative and positional accuracy of the data. This data cannot be construed to be a legal document. Primary sources from which this data was compiled must be consulted for verification of information contained in this data.

This dataset tracks the updates made on the dataset "PLACES: County Data (GIS Friendly Format), 2020 release" as a repository for previous versions of the data and metadata.

4.0.1 is a minor release to correct a deployment problem from Github to Zenodo.org. Content is the same as the 4.0 release:

Pleiades gazetteer datasets

Please report problems and make feature requests via the main Pleiades Gazetteer Issue Tracker.

Content is governed by the copyrights of the individual contributors responsible for its creation. Some rights are reserved. All content is distributed under the terms of a Creative Commons Attribution license (cc-by).

In order to facilitate reproducibility and to comply with license terms, we encourage use and citation of numbered releases for scholarly work that will be published in static form.

Please share notices of data reuse with the Pleiades community via email to pleiades.admin@nyu.edu. These reports help us to justify continued funding and operation of the gazetteer and to prioritize updates and improvements.

Version 4.0 - 6 February 2025

41,200 place resources

Since release 3.2 of pleiades.datasets on 3 November 2023, the Pleiades gazetteer published 876 new and 9,555 updated place resources, reflecting the work of Johan Åhlfeldt, Ella Arnold, Jeffrey Becker, Gabriel Bodard, Sarah Bond, Catherine Bouras, Lucas Butler, Iulian Bîrzescu, Anne Chen, Birgit Christiansen, Niels Christofferson, James Cowey, Francis Deblauwe, Dan Diffendale, Anthony Durham, Denitsa Dzhigova, Tom Elliott, Jordy Didier Orellana Figueroa, Martina Filosa, Jonathan Fu, Ryosuke Furui, Maija Gierhart, Sean Gillies, Matthias Grawehr, Amelia Grissom, Maxime Guénette, Andrew Harris, Greta Hawes, Ryan M. Horne, Carolin Johansson, Daniel C. Browning Jr., Noah Kaye, Philip Kenrick, Brady Kiesling, Yaniv Korman, Mark Krier, Divya Kumar-Dumas, Thomas Landvatter, Chris de Lisle, Yuyao Liu, Stanisław Ludwiński, Sean Manning, Gabriel McKee, John Muccigrosso, Jamie Novotny, Philipp Pilhofer, Jonathan Prag, Adam Rabinowitz, Rune Rattenborg, María Jesús Redondo, Charlotte Roueché, Karen Rubinson, Thomas Seidler, Rosemary Selth, Jason M. Silverman, R. Scott Smith, Néhémie Strupler, Richard Talbert, Francis Tassaux, Clifflena Tiah, Georgios Tsolakis, Scott Vanderbilt, Athanasia Varveri and Valeria Vitale.

Highlights

• Updated gazetteer data in this release: see "Contents" below.
• Removed deprecated "legacy CSV" serialization. JSON or "CSV for GIS" are the recommended packages for most third-party reuse.
• Added new "indexes" dataset: Pleiades places that reference certain external resources.
• Improved serialization of vocabulary terms in "CSV for GIS" serialization and added the previously omitted "Time Periods" vocabulary.
• Added new "sidebar" dataset: assertions by external datasets of relationships to Pleiades places.

Overview

This is a package of data derived from the Pleiades gazetteer of ancient places. It is used for archival and redistribution purposes and is likely to be less up-to-date than the live data at https://pleiades.stoa.org.

Pleiades is a community-built gazetteer and graph of ancient places. It publishes authoritative information about ancient places and spaces, providing unique services for finding, displaying, and reusing that information under open license. It publishes not just for individual human users, but also for search engines and for the widening array of computational research and visualization tools that support humanities teaching and research.

Pleiades is a continuously published scholarly reference work for the 21st century. We embrace the new paradigm of citizen humanities, encouraging contributions from any knowledgeable person and doing so in a context of pervasive peer review. Pleiades welcomes your contribution, no matter how small, and we have a number of useful tasks suitable for volunteers of every interest.

Access and Archiving

The latest versions of this package can be had by fork or download from the main branch at https://github.com/isawnyu/pleiades-datasets. Numbered releases are created periodically at GitHub. These are archived at:

• zenodo.org using the DOI 10.5281/zenodo.1193921
• archive.nyu.edu using the Handle 2451/34305
• archive.org using the URI https://archive.org/details/pleiades.datasets-{version_number}

Credits

Pleiades is brought to you by:

• Our volunteer content contributors (see data/rdf/authors.ttl for complete list and associated identifiers or data).
• Pleiades has received significant, periodic support from the National Endowment for the Humanities since 2006. Grant numbers: HK-230973-15, PA-51873-06, PX-50003-08, and PW-50557-10. Any views, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect those of the National Endowment for the Humanities.
• Web hosting and additional support has been provided since 2008 by the Institute for the Study of the Ancient World at New York University.
• Additional support has been provided since 2000 by the Ancient World Mapping Center at the University of North Carolina at Chapel Hill.
• Development hosting and other project incubation support was provided between 2000 and 2008 by Ross Scaife and the Stoa Consortium.

This dataset tracks the updates made on the dataset "PLACES: Place Data (GIS Friendly Format), 2020 release" as a repository for previous versions of the data and metadata.