Find research datasets worth reusing

opencc-by-4.0Nov 2021View details →

Modern China Geospatial Database - Main Dataset

MCGD_Data_V2.2 contains all the data that we have collected on locations in modern China, plus a number of locations outside of China that we encounter frequently in historical sources on China. All further updates will appear under the name "MCGD_Data" with a time stamp (e.g., MCGD_Data2023-06-21) You can also have access to this dataset and all the datasets that the ENP-China makes available on GitLab: https://gitlab.com/enpchina/IndexesEnp Altogether there are 464,970 entries. The data include seven variables: - Name:  Place names and their variants in Chinese, pinyin, and any recorded transliteration - Prov_Zh: Chinese province names in Chinese characters (新疆, 江蘇, 河北, etc.) - Prov_Py: Chinese province names in pinyin - LAT: Latitude coordinates - LONG: Longitude coordinates - LocID: Location identifiers - NameID: Location name identifiers The Name IDs all start with H followed by seven digits. This is the internal ID system of MCGD. Locations IDs that start with "D" are data points extracted from China Historical GIS (Harvard University); those that start with "E" are locations extracted from the data points in Geonames or data points we have added from various map sources. One of the main features of the MCGD Main Dataset is the systematic collection and compilation of place names from non-Chinese language historical sources. Locations were designated in transliteration systems that are hardly comprehensible today, which makes it very difficult to find the actual locations they correspond to. This dataset allows for the conversion from these obsolete transliterations to the current names and geocoordinates. From June 2021 onward, we have adopted a different file naming system to keep track of versions. From MCGD_Data_V1 we have moved to MCGD_Data_V2. In June 2022, we introduced time stamps, which result in the following naming convention: MCGD_Data_YYYY.MM.DD.    UPDATES MCGD_Data2025_08_06 introduces a significant update with the addition of the ‘Code’ column. This column categorizes place names as follows: <ul> <li> A: Canonical Chinese name </li> <li> C: Alternative Chinese name </li> <li> P: Romanized name in pinyin </li> <li> W: Romanized name in another transliteration system </li> </ul> When the codes P or W are doubled (PP, WW), this indicates that the place name does not match any existing Chinese name in the dataset. These unmatched names will be reviewed and linked progressively, rather than through a systematic batch process, due to their high volume.The coding system is designed to facilitate name-matching operations between MCGD and place names extracted from historical sources using programming tools. It also enables filtering for more precise and efficient matching. The dataset contains a total of 472,749 entries. MCGD_Data2025_02_28 includes a major change with the duplication of all the locations listed under Beijing, Shanghai, Tianjin, and Chongqing (北京, 上海, 天津, 重慶) and their listing under the name of the provinces to which they belonge origially before the creation of the four special municipalities after 1949. This is meant to facilitate the matching of data from historical sources. Each location has a unique NameID. Altogether there are 472,818 entries MCGD_Data2025_02_27 inclues an update on locations extracted from  Minguo zhengfu ge yuanhui keyuan yishang zhiyuanlu 國民政府各院部會科員以上職員錄 (Directory of staff members and above in the ministries and committees of the National Government). Nanjing: Guomin zhengfu wenguanchu yinzhuju 國民政府文官處印鑄局國民政府文官處印鑄局, 1944). We also made corrections in the Prov_Py and Prov_Zh columns as there were some misalignments between the pinyin name and the name in Chines characters. The file now includes 465,128 entries. MCGD_Data2024_03_23 includes an update on locations in Taiwan from the Asia Directories. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown"). MCGD_Data2023.12.22 contains all the data that we have collected on locations in China, whatever the period. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown"). The dataset also includes locations outside of China for the purpose of matching such locations to the place names extracted from historical sources. For example, one may need to locate individuals born outside of China. Rather than maintaining two separate files, we made the decision to incorporate all the place names found in historical sources in the gazetteer. Such place names can easily be removed by selecting all the entries where the 'Province' data is missing.

opencc-by-4.0Nov 2021View details →

Modern China Geospatial Database - Republican China Dataset

MCGD_Rep is a sample of spatial data for China in the first half of twentieth century (1900-1949). The data was extracted from the MCGD Main Dataset. It is based mostly on the list of xian (county) seats in 1931 [Source: Zang, Lihe  臧励龢, ed. Zhongguo gujin diming da cidian 中国古今地名大辞典. Shanghai 上海: Commercial Press, 1931], with the addition of some external data [Source: Crow Newspaper Directories]. By and large, it presents a list of the major locations in China between 1900 and 1949. It contains 1,977 entries with the following variables: name in Chinese, name in pinyin; name of the province in Chinese and in pinyin; latitude and longitude, and Name ID and Location ID.

opencc-by-4.0Apr 2018View details →

Geospatial Modelling of Australia's National Electricity Market - Dataset

This dataset contains information relating to the topology of Australia's largest electricity transmission network, along with details pertaining to the technical and economic characteristics of generators operating within this grid. Information has been compiled from publicly available datasets released by the Australian Energy Market Operator (AEMO) [1, 2] and Geoscience Australia (GA) [3, 4, 5]. Potential applications include the development of economic dispatch, power-flow, and unit commitment models. The network is comprised of 912 nodes, 1406 AC edges, and three HVDC links. Information regarding forward and reverse power-flow limits for two AC interconnectors is also provided. Latitude and longitude coordinates are given for each node, with the network based off of geospatial datasets obtained from GA [3, 4, 5]. Signals for electricity demand at each node were derived using regional load profiles in combination with population data obtained from the Australian Bureau of Statistics (ABS) [6]. Allocation methods outlined in [7, 8] were used to disaggregate regional load profiles according to the geospatial distribution of Australia's population. Construction of the generator dataset involved compiling information obtained from AEMO's Market Management System Data Model (MMSDM) [1] and National Transmission Network Development Plan (NTNDP) [2] datasets. Historic generator dispatch signals were also obtained from AEMO [1], allowing the output of market models to be compared with realised outcomes. For further information regarding the contents of each csv file please refer to <code>dataset_summary.pdf</code>. Jupyter Notebooks at [9] contain the Python code necessary to reproduce these datasets. Version history: v1.3 - Documentation update: <ul> <li>The document summarising datasets, <code>dataset_summary.pdf</code>, has been updated.</li> </ul> v1.2 - Startup cost correction: <ul> <li>Startup cost column labels in <code>generators.csv</code> were mistakenly switched (specifically, SU_COST_WARM and SU_COST_HOT). This has now been corrected.</li> </ul> v1.1 - Transmission line parameters and demand allocation update <ul> <li>Transmission lines: Line resistance and shunt susceptance values have been updated. Transmission line lengths and voltages have also been added to <code>network_edges.csv</code>. AC interconnector information is split over two files to better capture aggregate flow limits defined over the New South Wales - Victoria interconnector. Connection points for these interconnectors are described in <code>network_ac_interconnector_links.csv</code>, while <code>network_ac_interconnector_flow_limits.csv</code> contains aggregate forward and reverse power flow limits.</li> <li>Demand allocation: The algorithm used to approximate demand at different nodes has been updated. The new method constructs a Voronoi tessellation based on network nodes. These cells are then overlapped with geospatial ABS population data, which are used to estimate the number of people served by each node.</li> </ul> v1.0 - First release

opencc-by-4.0Feb 2024View details →

A high-resolution 4D geospatial laser scan dataset of the beach at Mariakerke Bad, Belgium

This dataset contains a high resolution (in both time and space) laser scan data set of a 1-year measurement campaign in 2017 and 2018 in the seaside resort of Mariakerke Bad in Belgium. The measurements consist of 8417 hourly laserscans of a 400 meter stretch of beach. The measurement campained was performed to study variations in shoreward sand transport at urbanized beaches.  Laserscan data is stored in local coordinates. Time dependent corrections per laserscan epoch are provided next to a global transformation matrix to transform the local coordinates to the Belgium Lambert 2008 coordinate system. This data is provided as is and is licensed under the Creative Commons Attribution 4.0 International (CC-BY-4.0). See the provided PDF on more information about the CC-BY-4.0. Version 1 contained an error in the global transformation matrix. Version 2 corrects this.

opencc-by-4.0Apr 2021View details →

Geospatial Input Datasets for Urban Teleconnections Analysis

This dataset includes all of the necessary input layers for the `gamut` software package. Layers were downloaded from the sources in the references and were either directly used in the `gamut` software or processed prior to being used in the software. Processing and data information for each dataset is included in the metadata.

opencc-by-4.0Nov 2024View details →

Geospatial Dataset of GNSS Anomalies and Political Violence Events

Geospatial Dataset of GNSS Anomalies and Political Violence Events Overview The Geospatial Dataset of GNSS Anomalies and Political Violence Events is a collection of data that integrates aircraft flight information, GNSS (Global Navigation Satellite System) anomalies, and political violence events from the ACLED (Armed Conflict Location & Event Data Project) database. Dataset Files The dataset consists of three CSV files: <ol> <li>Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv</li> <ul> <li>Description: Contains all grids and dates that had aircraft traffic during 2023.</li> <li>Number of Records: 6,777,228</li> <li>Purpose: Provides a complete view of aircraft movements and associated data, including grids without any GNSS anomalies.</li> </ul> <li>Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv</li> <ul> <li>Description: A filtered version of V1, including only the grids and dates where GNSS anomalies (jumps or gaps) were reported.</li> <li>Number of Records: 718,237</li> <li>Purpose: Focuses on areas and times with GNSS anomalies for targeted analysis.</li> </ul> <li>Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv</li> <ul> <li>Description: Contains aggregated monthly data for each grid cell, combining GNSS anomalies and ACLED political violence events. Summarizes aircraft traffic, anomaly counts, and conflict activity at a monthly resolution.</li> <li>Number of Records: 25,770</li> <li>Purpose: Enables temporal trend analysis and spatial correlation studies between GNSS interference and political violence, using reduced data volume suitable for modeling and visualization.</li> </ul> </ol> Data Fields:    Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv and Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv <ol> <li>grid_id</li> <ul> <li>Description: Unique identifier for a grid cell on Earth measuring 0.5 degrees latitude by 0.5 degrees longitude.</li> <li>Format: String combining latitude and longitude (e.g., -10.0_-36.0).</li> </ul> <li>day</li> <ul> <li>Description: Date of the recorded data.</li> <li>Format: YYYY-MM-DD (e.g., 2023-03-28).</li> </ul> <li>geometry</li> <ul> <li>Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.</li> <li>Format: POLYGON((longitude latitude, ...)) (e.g., POLYGON((-36.0 -10.0, -35.5 -10.0, -35.5 -9.5, -36.0 -9.5, -36.0 -10.0))).</li> </ul> <li>flights</li> <ul> <li>Description: Number of aircraft flights that passed through the grid on that day.</li> <li>Format: Integer (e.g., 28).</li> </ul> <li>GPS_jumps</li> <ul> <li>Description: Number of reported GNSS "jump" anomalies (possible spoofing incidents) in the grid on that day.</li> <li>Format: Integer (e.g., 1).</li> </ul> <li>GPS_gaps</li> <ul> <li>Description: Number of reported GNSS "gap" anomalies, indicating gaps in aircraft routes, in the grid on that day.</li> <li>Format: Integer (e.g., 0).</li> </ul> <li>gaps_density</li> <ul> <li>Description: Density of GNSS gaps, calculated as the number of gaps divided by the number of flights.</li> <li>Format: Decimal (e.g., 0).</li> </ul> <li>jumps_density</li> <ul> <li>Description: Density of GNSS jumps, calculated as the number of jumps divided by the number of flights.</li> <li>Format: Decimal (e.g., 0.035714286).</li> </ul> <li>event_id_cnty</li> <ul> <li>Description: ACLED event ID corresponding to political violence events in the grid on that day.</li> <li>Format: String (e.g., BRA69267).</li> </ul> <li>disorder_type</li> <ul> <li>Description: Type of disorder as classified by ACLED (e.g., "Political violence").</li> <li>Format: String.</li> </ul> <li>event_type</li> <ul> <li>Description: General category of the event according to ACLED (e.g., "Violence against civilians").</li> <li>Format: String.</li> </ul> <li>sub_event_type</li> <ul> <li>Description: Specific subtype of the event as per ACLED classification (e.g., "Attack").</li> <li>Format: String.</li> </ul> <li>acled_count</li> <ul> <li>Description: Number of ACLED events in the grid on that day.</li> <li>Format: Integer (e.g., 1).</li> </ul> <li>acled_flag</li> <ul> <li>Description: Indicator of ACLED event presence in the grid on that day (0 for no events, 1 for one or more events).</li> <li>Format: Integer (0 or 1).</li> </ul> </ol>   Data Fields: Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv The file contains monthly aggregated GNSS anomaly and ACLED event data per grid cell. The structure and meaning of each field are detailed below: <ol> <li>grid_id</li> <ul> <li>Description: Unique identifier for a grid cell on Earth measuring 0.5° latitude by 0.5° longitude.</li> <li>Format: String combining latitude and longitude (e.g., -0.5_-79.0).</li> </ul> <li>year_month</li> <ul> <li>Description: Month and year of the aggregated data.</li> <li>Format: String in Mon-YY format (e.g., Jan-23).</li> </ul> <li>geometry</li> <ul> <li>Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.</li> <li>Format: POLYGON((longitude latitude, ...)) (e.g., POLYGON((-79.0 -0.5, -78.5 -0.5, -78.5 0.0, -79.0 0.0, -79.0 -0.5))).</li> </ul> <li>flights</li> <ul> <li>Description: Total number of aircraft flights that passed through the grid cell during the month.</li> <li>Format: Integer (e.g., 1230).</li> </ul> <li>GPS_jumps</li> <ul> <li>Description: Total number of GNSS "jump" anomalies (possible spoofing events) in the grid cell during the month.</li> <li>Format: Integer (e.g., 13).</li> </ul> <li>GPS_gaps</li> <ul> <li>Description: Total number of GNSS "gap" anomalies, indicating interruptions in aircraft routes, during the month.</li> <li>Format: Integer (e.g., 0).</li> </ul> <li>event_id_cnty</li> <ul> <li>Description: Semicolon-separated list of ACLED event IDs associated with the grid cell during the month.</li> <li>Format: String (e.g., ECU3151;ECU3158;ECU3150).</li> </ul> <li>disorder_type</li> <ul> <li>Description: Semicolon-separated list of disorder types (e.g., "Political violence", "Demonstrations") reported by ACLED in that grid cell during the month.</li> <li>Format: String.</li> </ul> <li>event_type</li> <ul> <li>Description: Semicolon-separated list of high-level ACLED event types (e.g., "Riots", "Protests").</li> <li>Format: String.</li> </ul> <li>sub_event_type</li> </ol> <ul> <li>Description: Semicolon-separated list of detailed subtypes of ACLED events (e.g., "Mob violence", "Armed clash").</li> <li>Format: String.</li> </ul> <ol> <li>acled_count</li> </ol> <ul> <li>Description: Total number of ACLED conflict events in the grid cell during the month.</li> <li>Format: Integer (e.g., 2).</li> </ul> <ol> <li>acled_flag</li> </ol> <ul> <li>Description: Conflict presence indicator: 1 if any ACLED event occurred in the grid cell during the month, otherwise 0.</li> <li>Format: Integer (0 or 1).</li> </ul> <ol> <li>gaps_density</li> </ol> <ul> <li>Description: Monthly density of GNSS gaps, calculated as GPS_gaps / flights.</li> <li>Format: Decimal (e.g., 0.0).</li> </ul> <ol> <li>jumps_density</li> </ol> <ul> <li>Description: Monthly density of GNSS jumps, calculated as GPS_jumps / flights.</li> <li>Format: Decimal (e.g., 0.0106).</li> </ul>   Data Sources <ul> <li>GNSS Anomalies Data:</li> <ul> <li>Calculated from ADS-B (Automatic Dependent Surveillance-Broadcast) messages obtained via the OpenSky Network's Trino database.</li> <li>GNSS anomalies include "jumps" (potential spoofing incidents) and "gaps" (interruptions in aircraft route data).</li> </ul> <li>Political Violence Events Data:</li> <ul> <li>Sourced from the ACLED database, which provides detailed information on political violence and protest events worldwide.</li> </ul> </ul> Temporal and Spatial Coverage <ul> <li>Temporal Coverage:</li> <ul> <li>From January 1, 2023, to December 31, 2023.</li> <li>Daily records provide temporal granularity for time-series analysis.</li> </ul> <li>Spatial Coverage:</li> <ul> <li>Global coverage with grid cells measuring 0.5 degrees latitude by 0.5 degrees longitude.</li> <li>Each grid cell represents an area on Earth's surface, facilitating spatial analysis.</li> </ul> </ul> Usage and Applications <ul> <li>Security Analysis:</li> <ul> <li>Assess potential correlations between GNSS anomalies and political violence events.</li> <li>Identify regions with increased risk of GNSS spoofing or signal disruption.</li> </ul> <li>Research and Development:</li> <ul> <li>Develop models to predict socio-political events based on GNSS anomalies.</li> <li>Study the impact of political instability on aviation safety.</li> </ul> <li>Policy and Decision Making:</li> <ul> <li>Inform aviation authorities and policymakers about regions requiring enhanced navigation security measures.</li> <li>Support conflict analysis and monitoring efforts.</li> </ul> </ul>

opencc-by-4.0Jan 2024View details →

A geospatial dataset of lichen key attributes in the Earth's three poles

To develop the geospatial dataset, we initially defined two lichen attributes: color type and growth form.  These attributes were chosen due to their significant correlation with lichen physiological and biochemical characteristics, as well as their association with reflection spectra. Each record of this geospatial dataset consists of information such as scientific name, longitude, latitude, ecoregion name, biome name, color type, growth form, and the occurranceID belongs to the GBIF original dataset. This dataset serves as a foundational resource for extensive investigations into the intricate interplay between lichen physiology and the environment, addressing a significant knowledge gap in the field. Furthermore, our dataset holds the potential to address challenges associated with remote sensing monitoring of lichens, a longstanding issue in vegetation remote sensing. Precise in situ observation records, as provided by our dataset, can facilitate the development of remote sensing techniques tailored for lichen monitoring. "Program" is the code used in the process of establishing the geospatial dataset of lichen key attributes in the Earth’s three poles.

opencc-by-4.0Sep 2022View details →

Nationwide geospatial dataset of environmental covariates at 1km resolution in Mexico

Package of 39 covariates, a combination of topographic, climatic, and vegetation derived variables with pixel sizes of 1000 m for the period of 2009 to 2014 from google earth engine (GEE) to assemble a nationwide geospatial dataset of Mexico. Datasets included WorldClim V1; a set of bioclimatic variables derived from the monthly temperature and rainfall <a href="https://www.zotero.org/google-docs/?tmZL4M">(Hijmans, 2005)</a>; time-series analysis of Landsat images from the Hansen Global Forest Change v1.8 (2000-2020) dataset <a href="https://www.zotero.org/google-docs/?jGj5Rn">(Hansen et al., 2013)</a>; 4-day composite dataset from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors with fraction of photosynthetic active radiation and leaf area index at 500-m resolution <a href="https://www.zotero.org/google-docs/?t45qy3">(Myneni,  Ranga et al., 2015)</a> and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Database (2000-2008) <a href="https://www.zotero.org/google-docs/?8bVVc9">(Hulley et al., 2009, 2012, 2015; Hulley & Hook, 2008, 2009, 2011; NASA JPL, 2014)</a>. All covariates were resampled to 1000 m. The resampling was done with conventional bilinear interpolation as implemented in GEE.

opencc-by-4.0Jul 2024View details →

The first geospatial dataset of irrigated fields (2020-2024) in Vojvodina (Serbia)

Irrigation is a cornerstone of global food security, enabling sustainable agricultural production and helping to ensure that food is available for people around the world, now and in the future. Mapping irrigated fields provides valuable information for sustainable water management, agricultural development, and environmental conservation efforts. However, the collection of high-quality training data, which is necessary for accurate irrigation mapping remains costly and labour-intensive. To address this, we created a georeferenced regional dataset consisting of location, crop type, and occurrence of the irrigation equipment which are essential information for mapping irrigated fields. Four main irrigated crops were considered: maize, soybean, sugar beet, and wheat. The dataset, consisting of a total of 1256 parcels, is created for Vojvodina, the main agricultural area in Serbia, spanning the period of five years (2020 - 2024). This study’s goal is to give accessibility to our dataset which further can be explored and used for building or fine-tuning machine learning and deep learning models for the automatic detection of irrigated fields using satellite imagery.

opencc-by-4.0Jul 2021View details →

GRIDCERF: Geospatial Raster Input Datasets for Capacity Expansion Regional Feasibility

Geospatial Raster Input Datasets for Capacity Expansion Regional Feasibility (GRIDCERF) is a data package containing all the necessary input layers for the <a href="https://github.com/IMMM-SFA/cerf">Capacity Expansion Regional Feasibility (CERF) model</a>. The CERF model uses these layers to find feasible power plant siting locations at a 1 kilometer scale across the conterminous United States for renewable and non-renewable electricity production technologies. This package encompasses a wide variety of geospatial layers pertaining to restrictions, regulations, and challenges that come with siting new electricity production facilities.

opencc-by-4.0Nov 2024View details →

Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing

We produced a spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing from 2001-2024. GDP and building surface area are yearly data. PDSI is monthly data.

opencc-by-4.0Mar 2022View details →

A Geospatial Source Selector for Federated GeoSPARQL Querying - Datasets

This dataset comprises of a set of individual RDF files. It contains 3 data layers that cover Austria (namely Administrative data, Snow cover data, and Crop-type data), and each layer is also divided geospatially. Thus, each RDF file contains only one thematic layer and refers to a specific area. This data can be used in the context of experimenting with federated query processors. Thus, if we deploy each RDF file in a separate GeoSPARQL endpoint, we will have a resulting federation of 34 GeoSPARQL source endpoints. The objective of this scenario is to evaluate the effectiveness of a source selection mechanism of a federation engine, and, in particular, if the source selector is aware of the geospatial nature of the source endpoints.

opencc-by-4.0Sep 2024View details →

Extended 1.0 Dataset of "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary"

Introduction We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file Short_Description_of_Data_Analysis.pdf and Statistical_formulas.pdf  The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed. The dataset was expanded on 23rd September 2024 to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software. Short Description of Data Analysis and Attached Files (datasets): Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in. Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: <a href="https://www.ksh.hu/stadat_files/nep/hu/nep0035.html">https://www.ksh.hu/stadat_files/nep/hu/nep0035.html</a>, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the Data_of_demography.xlsx file. In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission. The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): <a href="https://www.nnk.gov.hu/index.php/efi">https://www.nnk.gov.hu/index.php/efi</a> (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: <a href="https://semmelweis.hu/egeszsegfejlesztes/en/">https://semmelweis.hu/egeszsegfejlesztes/en/</a> (n=1). (accessed 05 Dec. 2023.) Geocoordinates were determined using Google Maps (N=108): <a href="https://www.google.com/maps">https://www.google.com/maps</a>. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file. The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from: <a href="https://qgis.org/en/site/forusers/download.html">https://qgis.org/en/site/forusers/download.html</a>. (accessed 04 Jan. 2024.) The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file, imported via .csv file. The OpenStreetMap tileset is directly accessible from <a href="http://www.openstreetmap.org">www.openstreetmap.org</a> in QGIS. (accessed 04 Jan. 2024.) The Hungarian county administrative boundaries were downloaded from the following website: <a href="https://data2.openstreetmap.hu/hatarok/index.php?admin=6" target="_new">https://data2.openstreetmap.hu/hatarok/index.php?admin=6</a> (accessed 04 Jan. 2024.) HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km. Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation). A brief description of the statistical formulas applied is included in the Statistical_formulas.pdf. Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format. <ul> <li>Aggregated number of HDOs by county: Number_of_HDOs.xlsx</li> <li>Standardised data (Number of HDOs per 100,000 residents): Standardized_data.xlsx</li> <li>Calculation of the Lorenz curve: Lorenz_curve.xlsx</li> <li>Calculation of the Gini index: Gini_Index.xlsx</li> <li>Calculation of the LQ index: LQ_Index.xlsx</li> <li>Calculation of the Herfindahl-Hirschman Index: Herfindahl_Hirschman_Index.xlsx</li> <li>Calculation of the Entropy index: Entropy_Index.xlsx</li> <li>Regression and correlation analysis calculation: Regression_correlation.xlsx</li> </ul> Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav: <ul> <li>Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.</li> <li>Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv</li> <li>Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv</li> <li>Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv</li> <li>Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv</li> </ul> For easier readability, the files have been provided in both SPV and PDF formats. The translation of these supplementary files into English was completed on 23rd Sept. 2024.   If you have any further questions regarding the dataset, please contact the corresponding author: <a target="_new">domjan.peter@phd.semmelweis.hu</a>

opencc-by-4.0May 2023View details →

Nationwide geospatial dataset of environmental covariates at 1km resolution in Mexico (2015-2020)

Package of 39 covariates, a combination of topographic, climatic, and vegetation derived variables with pixel sizes of 1000 m for the period of 2015 to 2020 from google earth engine (GEE) to assemble a nationwide geospatial dataset of Mexico. Datasets included WorldClim V1; a set of bioclimatic variables derived from the monthly temperature and rainfall <a href="https://www.zotero.org/google-docs/?tmZL4M">(Hijmans, 2005)</a>; time-series analysis of Landsat images from the Hansen Global Forest Change v1.8 (2000-2020) dataset <a href="https://www.zotero.org/google-docs/?jGj5Rn">(Hansen et al., 2013)</a>; 4-day composite dataset from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors with fraction of photosynthetic active radiation and leaf area index at 500-m resolution <a href="https://www.zotero.org/google-docs/?t45qy3">(Myneni,  Ranga et al., 2015)</a> and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Database (2000-2008) <a href="https://www.zotero.org/google-docs/?8bVVc9">(Hulley et al., 2009, 2012, 2015; Hulley & Hook, 2008, 2009, 2011; NASA JPL, 2014)</a>. All covariates were resampled to 1000 m. The resampling was done with conventional bilinear interpolation as implemented in GEE.

opencc-by-4.0Jul 2017View details →

Geospatial dataset for Cumulative Impact assessment, Sea Use conflict analysis and Marine Ecosystem Services assessment in the Adriatic Ionian Region

Geospatial dataset for Cumulative Impact assessment, Sea Use conflict analysis and Marine Ecosystem Services assessment in the Adriatic Ionian Region (reference year 2014). The datasets are derived from ADRIPLAN Portal (http://data.adriplan.eu/).

opencc-by-4.0Sep 2024View details →

Geospatial and time series dataset for hydrologic analyses within South Asia (GHSA)

Summary of changes: <ul> <li>v2504, GHSA <ul> <li>1,702 stations from 5 countries in South Asia (Bhutan, China, India, Nepal and Pakistan);</li> <li>districts and basin states from 7 countries in South Asia (Afghanistan, Bangladesh, Bhutan, China, India, Nepal and Pakistan)</li> <li>land use change, vegetation index (NDVI, NDVI-crop), precipitation, evaporation (E-total, E-crop and E-irrigation), streamflow, surface and root zone soil moisture, terrestrial water storage change, snow water equivalent and snow cover fraction; 1950-2023</li> <li>publication:</li> </ul> </li> <li>v2409 <ul> <li>test version</li> </ul> </li> <li>v2301, GHI <ul> <li>645 stations, limited to Peninsular India</li> <li>precipitation, evaporation and streamflow; 1950-2020</li> <li>publication: Goteti (2023), Earth Syst. Sci. Data, https://doi.org/10.5194/essd-15-4389-2023</li> </ul> </li> </ul>

opencc-by-4.0Sep 2024View details →

Dataset for "Geospatial analysis of toponyms in geotagged social media posts"

<h1>Geotagged Twitter posts dataset</h1> Dataset used for the research presented in the following paper: Takayuki Hiraoka, Takashi Kirimura, Naoya Fujiwara (2024) "Geospatial analysis of toponyms in geo-tagged social media posts". We collected georeferenced Twitter posts tagged to coordinates inside the bounding box of Japan between 2012-2018. The present dataset represents the spatial distributions of all geotagged posts as well as posts containing in the text each of 24 domestic toponyms, 12 common nouns, and 6 foreign toponyms. The code used to analyze the data is available on <a href="https://github.com/takayukihir/geotagged-tweets" target="_blank" rel="noopener">GitHub</a>. <h2>Data description</h2> <ul> <li><code>preprocessed_mcntlt7_selected/</code>: Number of geotagged twitter posts in each grid cell. Each csv file under this directory associates each grid cell (spanning 30 seconds of latitude and 45 secoonds of longitude, which is approximately a 1km x 1km square, specified by an 8 digit code <code>m3code</code>) with the number of geotagged tweets tagged to the coordinates inside that cell (<code>tweetcount</code>). <code>file_names.json</code> relates each of the toponyms studied in this work to the corresponding datafile (<code>all</code> denotes the full data). Note that these data files are modified from the v1.0.0 to exclude posts that contain seven or more mentions.</li> <li><code>population/population_center_2020.xlsx</code>: Center of population of each municipality based on the 2020 census. Derived from data published by the Statistics Bureau of Japan on <a href="https://www.stat.go.jp/data/kokusei/topics/topi135.html" target="_blank" rel="noopener">their website</a> (Japanese)</li> <li><code>population/census2015mesh3_totalpop_setai_area.csv</code>: Resident population in each grid cell based on the 2015 census. Derived from data published by the Statistics Bureau of Japan on <a href="https://www.e-stat.go.jp/gis/statmap-search?page=1&type=1&toukeiCode=00200521&toukeiYear=2015&aggregateUnit=S&serveyId=S002005112015&statsId=T000846" target="_blank" rel="noopener">e-stat</a> (Japanese)</li> <li><code>population/economiccensus2016mesh3_jigyosyo_jugyosya_area.csv</code>: Employed population in each grid cell based on the 2016 Economic Census. Derived from data published by the Statistics Bureau of Japan on <a href="https://www.e-stat.go.jp/gis/statmap-search?page=1&type=1&toukeiCode=00200553&toukeiYear=2016&aggregateUnit=S&serveyId=S002005112016&statsId=T000917" target="_blank" rel="noopener">e-stat</a> (Japanese)</li> <li><code>japan_MetropolitanEmploymentArea2015map/</code>: Shape file for the boundaries of Metropolitan Employment Areas (MEA) in Japan. See <a href="https://www.csis.u-tokyo.ac.jp/UEA/index_e.htm" target="_blank" rel="noopener">this website</a> for details of MEA.</li> <li><code>ward_shapefiles/</code>: Shape files for the boundaries of wards in large cities, published by the Statistics Bureau of Japan on <a href="https://www.e-stat.go.jp/gis/statmap-search?page=1&type=2&aggregateUnitForBoundary=A&toukeiCode=00200553&toukeiYear=2016&serveyId=A002005532016&coordsys=1&format=shape&datum=2011" target="_blank" rel="noopener">e-stat</a></li> </ul>

opencc-by-4.0Nov 2021View details →

Modern China Geospatial Database - PRC Dataset

MCGD_PRC is a list of cities in today's People’s Republic of China. It includes 2,525 locations, with with the following variables: name in Chinese (both traditional and simplified Chinese), name in pinyin, name of the province in Chinese and in pinyin; latitude and longitude.