This data supports the paper entitled "Mapping the landscape of geospatial data citations". The dataset covers geospatial data-intensive research papers published between 2015-2018 retrieved using Scopus. The article's citations were assessed for data citation occurances, and coded using a data citation classification. Data were enhanced and linked to subject coverage and journal policy status information using Excel & SPSS. For more information about how the data were created and coded please review the 'Methodology' section of the paper. More information is provided below, including supplemental documentation and related publications. Abstract (paper) ABSTRACT Data citations, similar to article and other research citations, are important references to research data that underlie published research results. In support of open science directives, these citations must adhere to specific conventions in terms of consistency of both placement within an article, and the actual availability or access to research data. To better understand the level to which geospatial research data are currently cited, we undertook a study to analyse the rate of data citation within a set of data-intensive geospatial research articles. After analysing 1717 scholarly articles published between 2015 and 2018, we found that very few, or 78 (5%), meaningfully cited primary or secondary geospatial data sources in the cited references section of the article. Even fewer researchers, only 25 or 1.5%, were found to have cited data using a DOI. Given the relatively low data citation rate, a focus on contributing factors including barriers to citing geospatial data is needed. And while open sharing requirements for geospatial data may change over time, driving data citation as a result, understanding benchmarks for data citation for monitoring purposes is useful.

The data contains features and indicators for 224 parks (at least 2 ha in size) in the city of Berlin and overall scores (indices) for natural elements, built elements (infrastructure) and spatial context (e.g. distance to public transport). All data is supplement to linked online web map.

List of data and content

Park_Berlin_Indicators: vector files (*.shp, *.geojson)

Park_Berlin_Indicators: excel files (*.xlsx)

Spatial reference All data is projected in ETRS 1989 UTM Zone 33N (EPSG:25833)

Web-GIS View data and explore interactively using the online application.

Data sources and processing For details on underlying data sources (e.g. availabilty, spatial resolution, time reference) and on data processing please refer to the linked publication, incl. Appendix 1

Acknowledgments We thank the City of Berlin for providing data. We greatly acknowledge OpenStreetMap (OSM) and contributers for providing important parts of the used data. This work was supported by the research project “Environmental‐Health Interactions in Cities (GreenEquityHEALTH) ‐ Challenges for Human Well‐Being under Global Changes” (project duration 2017–2022), funded by the German Federal Ministry of Education and Research (BMBF; no.01LN1705A).

Based on related original publication Kraemer, R., & Kabisch, N. (2021). Parks in context: Advancing citywide spatial quality assessments of urban green spaces using fine-scaled indicators. Ecology and Society, 26(2). https://doi.org/10.5751/ES-12485-260245

This dataset provides indicators for 249 public green spaces (at least 0.25 ha) in the city of Leipzig, Germany, and total scores for natural elements, built elements (infrastructure) and context, as well as an total quality score on the individual park level representing the potential supply of recreational ecosystem services. The quality score is also depicted in regular hexagons of 0.25 square kilometers. All data is supplement to the original linked publication.

List of data and content

Parks_Indicators: vector files (*.shp, .geojson), field description (.csv)

Parks_Scores: vector files (*.shp, .geojson), field description (.csv)

Parks_Scores_RGB: raster file (.tif), readme file (.txt)

QualityScore_Hexagons: vector files (*.shp, .geojson), readme file (.txt)

Spatial reference All data is projected in ETRS 1989 UTM Zone 33N (EPSG:25833)

Web-GIS View data and explore interactively using this online application.

Data sources and processing For details on underlying data sources (e.g. availabilty, spatial resolution, time reference) and on data processing please refer to the linked publication, incl. Appendix 1

Acknowledgments We thank Terra Concordia and the City of Leipzig for providing data. We greatly acknowledge OpenStreetMap and contributers for providing important parts of the data. This work was supported by the research project “Environmental‐Health Interactions in Cities (GreenEquityHEALTH) ‐ Challenges for Human Well‐Being under Global Changes” (project duration 2017–2022), funded by the German Federal Ministry of Education and Research (BMBF; no.01LN1705A).

Related publication Kraemer, R., & Kabisch, N. (2021). Parks in context: Advancing citywide spatial quality assessments of urban green spaces using fine-scaled indicators. Ecology and Society, 26(2). https://doi.org/10.5751/ES-12485-260245

Literature review dataset

This table lists the surveyed papers concerning the application of spatial analysis, GIS (Geographic Information Systems) as well as general geographic approaches and geostatistics, to the assessment of CoViD-19 dynamics. The period of survey is from January 1^st, 2020 to December 15^th, 2020. The first column lists the reference. The second lists the date of publication (preferably, the date of online publication). The third column lists the Country or the Countries and/or the subnational entities investigated. The fourth column lists the epidemiological data utilized in each paper. The fifth column lists other types of data utilized for the analysis. The sixth column lists the more traditionally statistically-based methods, if utilized. The seventh column lists the geo-statistical, GIS or geographic methods, if utilized. The eight column sums up the findings of each paper. The papers are also classified within seven thematic categories. The full references are available at the end of the table in alphabetical order.

This table was the basis for the realization of a comprehensive geographic literature review. It aims to be a useful tool to ease the "due-diligence" activity of all the researchers interested in the spatial analysis of the pandemic.

The reference to cite the related paper is the following:

Pranzo, A.M.R., Dai Prà, E. & Besana, A. Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year. GeoJournal (2022). https://doi.org/10.1007/s10708-022-10601-y

To read the manuscript please follow this link: https://doi.org/10.1007/s10708-022-10601-y

Access to those reports within the Historic England Research Reports series that relate to geographically defined sites such as a building where a building assessment or dendrochronology has been carried out, or a field covered by a geophysical survey. It important to note that there a lot of reports in the database that do not have a spatial location e.g. those that are thematic. These are still available via the Research Reports database. Spatial data relating to reports within the Historic England Research Reports series. This is point data for the grid reference given in the report, or where this was not available from other sources. The majority of reports relate to discrete features such as a building where a building assessment or dendrochronology has been carried out, or a field covered by a geophysical survey. However, there are also a small number of thematic reports that have contained detailed gazetteers allowing multiple links to the same report e.g. Police Stations, Jewish Cemeteries and Shropshire Inns. The most extreme example of this is the Gas Industry report where over 1500 points are used. There are also a number of large area projects covering extensive regions. Because this is a point layer, these are merely covered by a centre point for the feature. Data updated frequently.

Within the U.S. Geological Survey (USGS), three-dimensional (3D) geologic models are created as part of geologic framework studies, to support energy, minerals, or water resource assessments, and to inform geologic hazard assessments. Such models are often used within the organization as digital input into process and predictive models. 3D geological modeling typically supports research and project work within a specific part of the USGS – called Mission Areas – and as a result, 3D modeling activities are decentralized and model results are released on a project-by-project basis. This digital data release inventories and catalogs, for the first time, 3D geological models constructed by the USGS across all Mission Areas. This inventory assembles in catalog form the spatial locations and salient characteristics of previously published USGS 3D geological models. This inventory covers the time period from 2004, the date of the earliest published model through 2022. This digital dataset contains spatial extents of the 3D geologic models as polygon features that are attributed with unique identifiers that link the spatial data to nonspatial tables that define the data sources used and describe various aspects of each published model. The nonspatial DataSources table includes full citation and URL address for both published model reports and any digital model data released as a separate publication. The nonspatial ModelAttributes table classifies the type of model, using several classification schemes, identifies the model purpose and originating agency, and describes the spatial extent, depth, and number of layers included in each model. A tabular glossary defines terms used in the dataset. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables.

Abstract: In 2008 the International Cartographic Association (ICA) proposed a reference model to describe Spatial Data Infrastructures (SDI) based on the Reference Model of Open Distributed Processing (RM-ODP), which has been adapted and validated by several research projects. This paper details the experience of applying the extended ICA Model to an Academic SDI of environmental character and collaborative functions, such as the description of stakeholders' roles, functions, and responsibilities, called IDE-AMB (acronym in Portuguese). The intent is to transform the IDE-AMB into a database composed of information from several institutions and academic research. The stakeholders were described based on previously established literature and their needs for access, use, production, and sharing of geospatial data from different sources for the Integrated Management Center (NGI, acronym in Portuguese) ICMBio Antonina-Guaraqueçaba, located on the northern coast of the state of Paraná. We concluded that the ICA Model presents robustness; however, it still lacks conceptual reviews and needs to be adapted to the new realities and complexities of emerging SDI.

A collection of text files containing configurations, and XSL rendering defaults for spatial data set, spatial feature types, spatial features, and feature cross-walks as part of the Spatial Identifier Reference Framework (SIRF) linked data platform Lineage: The configuration files and rendering defaults were created by the SIRF project team and describe a variety of spatial data sources that were collated for the SIRF project including the UN gazetteer, Indonesian place name list. Feature type lists from Geoscence Australia, Badan Informasi Geospasial (BIG), UN FAO are also contained in the collection

Description This dataset contains both tabular data and the geometry of 198 countries’ Government Restrictions Index (GRI) and Social Hostilities Index (SHI) from 2007 to 2017. GRI and SHI were developed by Pew Research Center to rate 198 countries' and self-governing territories' levels of restrictions on religions. Source GRI and SHI were collected from Pew Research Center’s reports and digitized. The geospatial features, including polygons and boundaries of regions, are sourced from Natural Earth, Admin 0 – Countries version 4.1.0 (Published on 21 May 2018). For metadata, such as data description and available methods for geospatial data processing, please read the readme.pdf. Terms of use This dataset features in a collection of geospatial data "Geo-mapping databases for the Belt and Road Initiative". To cite this work, available citation styles can be found here: https://doi.org/10.6084/m9.figshare.c.6076193

This data package, LAGOS-NE-GIS v1.0, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. Three of the data packages each contain different types of data for 51,101 lakes and reservoirs larger than 4 ha in 17 lake-rich U.S. states to support research on thousands of lakes. These three package are: (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes. (2) LAGOS-NE-GEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes. These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. And, (3) LAGOS-NE-LIMNO v1.087.1: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. The other two data packages contain supporting data for the LAGOS-NE database: (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NE-GEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of LAGOS-NE-LIMNO. The LAGOS-NE GIS v1.0 module includes GIS datasets for: lake polygons and their hydrologic classification; wetland polygons and their classification; streams as a line coverage and their classification by stream order; the zones used for this study (state and county; hydrologic units [at the 4, 8 and 12 scales]); and, lake watersheds (IWS). We also include boundaries of U.S. states and Canadian provinces for mapping.

      Citation for the full documentation of this database:


      Soranno, P.A., E.G. Bissell, K.S. Cheruvelil, S.T. Christel, S.M.
      Collins, C.E. Fergus, C.T. Filstrup, J.F. Lapierre, N.R. Lottig, S.K.
      Oliver, C.E. Scott, N.J. Smith, S. Stopyak, S. Yuan, M.T. Bremigan,
      J.A. Downing, C. Gries, E.N. Henry, N.K. Skaff, E.H. Stanley, C.A.
      Stow, P.-N. Tan, T. Wagner, K.E. Webster. 2015. Building a
      multi-scaled geospatial temporal ecology database from disparate data
      sources: Fostering open science and data reuse. GigaScience 4:28
      doi:10.1186/s13742-015-0067-4


      Citation for the data paper for this database:


      Soranno, P.A., L.C. Bacon, M. Beauchene, K.E. Bednar, E.G. Bissell,
      C.K. Boudreau, M.G. Boyer, M.T. Bremigan, S.R. Carpenter, J.W. Carr,
      K.S. Cheruvelil, S.T. Christel, M. Claucherty, S.M.Collins, J.D.
      Conroy, J.A. Downing, J. Dukett, C.E. Fergus, C.T. Filstrup, C. Funk,
      M.J. Gonzalez, L.T. Green, C. Gries, J.D. Halfman, S.K. Hamilton, P.C.
      Hanson, E.N. Henry, E.M. Herron, C. Hockings, J.R. Jackson, K.
      Jacobson-Hedin, L.L. Janus, W.W. Jones, J.R. Jones, C.M. Keson, K.B.S.
      King, S.A. Kishbaugh, J.F. Lapierre, B. Lathrop, J.A. Latimore, Y.
      Lee, N.R. Lottig, J.A. Lynch, L.J. Matthews, W.H. McDowell, K.E.B.
      Moore, B.P. Neff, S.J. Nelson, S.K. Oliver, M.L. Pace, D.C. Pierson,
      A.C. Poisson, A.I. Pollard, D.M. Post, P.O. Reyes, D.O. Rosenberry,
      K.M. Roy, L.G. Rudstam, O. Sarnelle, N.J. Schuldt, C.E. Scott, N.K.
      Skaff, N.J. Smith, N.R. Spinelli, J.J. Stachelek, E.H. Stanley, J.L.
      Stoddard, S.B. Stopyak, C.A. Stow, J.M. Tallant, P.-N. Tan, A.P.
      Thorpe, M.J. Vanni, T. Wagner, G. Watkins, K.C. Weathers, K.E.
      Webster, J.D. White, M.K. Wilmes, S. Yuan. In Review. LAGOS-NE: A
      multi-scaled geospatial and temporal database of lake ecological
      context and water quality for thousands of U.S. lakes. In Review at
      GigaScience. Submitted April 2017.

Please note that the included metadata applies to the full set of data provided by the San Francisco Estuary Institute. This layer represents historical habitats and does not include historical creeks and distributaries. Please see https://www.sfei.org/content/northern-san-diego-county-lagoons-historical-ecology-gis-data#sthash.U9l5NJNt.SGa2tbKs.dpbs for additional information.Original metdata as provided by the San Francisco Estuary Institute:OverviewThis geodatabase contains several feature classes representing a reconstruction of the historical ecological conditons of six northern San Diego County lagoons (Buena Vista Lagoon, Agua Hedionda Lagoon, Batiquitos Lagoon, San Elijo Lagoon, San Dieguito Lagoon, and Los Peñasquitos Lagoon) prior to Euro-American modification. This dataset integrates many sources of data describing the historical features of the estuaries.Extensive supporting information, including bibliographic references, analyses, and research methods, can be found in the accompanying report:Beller EE, Baumgarten SA, Grossinger RM, Longcore TR, Stein ED, Dark SJ, Dusterhoff SR. 2014. Northern San Diego County Lagoons Historical Ecology Investigation: Regional Patterns, Local Diversity, and Landscape Trajectories. Prepared for the State Coastal Conservancy. SFEI Publication #722, San Francisco Estuary Institute, Richmond, CA.The report and GIS data area available at the project website: http://www.sfei.org/HE_San_Diego_Lagoons.A geographic information system was used to collect, catalog, and analyze the spatial components of the study area. Historical maps and aerial photography were georeferenced, allowing us to compare historical layers to each other and to contemporary aerial photography and maps. Additionally, the georeferenced maps were used as a means to geographically locate information gathered from surveyor notes, early explorers' journals, travelers' accounts, and newspaper articles. Using the various georeferenced maps and photographs combined with narrative sources we constructed a series of synthesis layers representing historical ecological conditions for the six estuaries. The polygon and line layers making up the historical habitat map include Historical_Habitats, Historical_Creeks, and Historical_Distributaries.Habitat types used in the Historical_Habitats layer include Salt Marsh, Salt Flat (Seasonally Flooded), Open Water / Mud Flat, Freshwater / Brackish Wetland, Beach, and Dune. See the Northern San Diego County Lagoons Historical Ecology Investigation for a detailed description of the historical habitat types and the methods that were used to map them.Historical creeks and their distributaries were mapped as polyline features in two distinct layers. Distributary channels mark the endpoints of historically discontinuous channels.--Historical_Habitats Attribute Table Fields:Habitat_Type: The historical habitat type classification.Interp_Cert: coded H (high): feature definitely present before Euro-American modification; M (medium): feature probably present before Euro-American modification; or L (low): feature possibly present before Euro-American modification. Shape_Cert: coded H (high): mapped feature expected to be 90%-110% of actual feature size; M (medium): expected to be 50%-200% of actual size; L (low): expected to be 25%-400% of actual size. Loc_Cert: coded H (high): expected maximum horizontal displacement less than 50 m; M (medium): less than 150 m; L (low): less than 500 m.Notes: Additional documentation about the feature.S_Digitize: Source data used to digitize a feature. S_Interp1: Interpretation Source 1 - Primary data used to interpret a mapped feature if other than the digitizing source – often the earliest historical documentation/evidence found.S_Interp2: Interpretation Source 2 - Data used to support mapping of a feature – additional documentation/evidence other than Interpretation Source 1.Name: The name of the lagoon/wetland complex.Source_Quotes: Excerpt(s) from historical textual data sources used to support mapping of a feature.Source_Quotes2: Excerpt(s) from historical textual data sources used to support mapping of a feature.Notes2: Additional documentation about the feature.Shape.area: Area of the feature in square meters.Shape.len: Length of the feature in meters.--Historical_Creeks Attribute Table Fields:Interp_Cert: coded H (high): feature definitely present before Euro-American modification; M (medium): feature probably present before Euro-American modification; or L (low): feature possibly present before Euro-American modification. Shape_Cert: coded H (high): mapped feature expected to be 90%-110% of actual feature size; M (medium): expected to be 50%-200% of actual size; L (low): expected to be 25%-400% of actual size. Loc_Cert: coded H (high): expected maximum horizontal displacement less than 50 m; M (medium): less than 150 m; L (low): less than 500 m.Notes: Additional documentation about the feature.S_Digitize: Source data used to digitize a feature. S_Interp1: Interpretation Source 1 - Primary data used to interpret a mapped feature if other than the digitizing source – often the earliest historical documentation/evidence found.S_Interp2: Interpretation Source 2 - Data used to support mapping of a feature – additional documentation/evidence other than Interpretation Source 1.Marsh_Comp: Lagoon/marsh complex into which the channel drains.SHAPE.len: Length of the channel feature in meters.Flow: Channel type (Perennial, Intermittent, Unknown).--(Attribute table information not provided for Historical_Distributary layer)--Additional Bibliographic Information:For a full list of works cited in this study, please consult the References section of the Northern San Diego County Lagoons Historical Ecology Investigation. Additional information about sources cited in the GIS layers is provided below:USGS Digital Raster Graphics (DRG) for the study area were created/revised between 1975 and 1983, and are cited as USGS 1975-1983.Historical aerial photographs are cited as San Diego County 1928. In some cases, the citation is followed by a number in parentheses specifying the particular image consulted.Abbreviated source institution names and accession numbers are provided for additional photographs cited in the GIS layers. Source institutions include:Carlsbad Pub Library = Carlsbad City Library Carlsbad History RoomScripps = Scripps Institution of Oceanography Archives, UC San DiegoSDHC = San Diego History CenterSpence Air Photos = Benjamin and Gladys Thomas Air Photo Archives, UCLA Department of GeographyAdditional sources not cited in the report include:Alexander WE. n.d. Plat book of San Diego County, California. Township 13 S., R. 3 W. Township 13 S., R. 4 W. Los Angeles, CA: Pacific Plat Book Company. Courtesy of The Bancroft Library, UC Berkeley.

Harvard CGA Geotweet Sentiment Archive is a subset of Harvard CGA Geotweet Archive v2.0 enriched with a sentiment score. It contains the tweet identification records along with a sentiment score based on tweet text for about 4.3 billion geo-tagged tweets since 2019. This sentiment score was calculated using Bidirectional Encoder Representations from Transformers. More information about this methodology can be found in our Nature Paper on Twitter Sentiment Geographical Index. This dataset is available to the academic community at large, unlike the Harvard CGA Geotweet Archive v2.0 which is under Twitter's redistribution policy restriction for public sharing. It could serve as cross-validation data for publications that used data from Harvard CGA Geotweet Archive v2.0 . If you are interested in accessing this archive, please fill out our Geotweet Request Form. Before requesting or receiving Tweet IDs, requestors must agree to Twitter's Terms of Service, Twitter's Privacy Policy, and Twitter's Developer Policy . Geotweets IDs data provided by CGA can only be used for not-for-profit research and academic purposes. Recipients may not share CGA provided Tweet IDs or content derived from them without written permission from the CGA. Citations: If you use the Geotweet Archive in your research please reference it: "Harvard CGA Geotweet IDs Archive". ======================================================== Schema of Geotweet Census Archive Field name_TYPE_Description message_id----TEXT----Tweet ID score ----FLOAT----BERT sentiment score

This resource contains the CAMELS-SPAT data set. CAMELS-SPAT provides data that can support hydrologic modeling and analysis for 1426 streamflow measurement stations located across the United States and Canada.

The area upstream of each station has been divided into various subbasins. The provided data include: (1) shapefiles outlining the location of each basin and its subbasins, (2) streamflow observations at daily and hourly resolution at the outlet of each basin, (3) meteorological data from 4 different data sets (RDRS, EM-Earth, ERA5, Daymet), at their native gridded resolution as well as averaged to the basin and subbasin level, (4) geospatial data from 11 different data at their native gridded resolution, and (5) statistical summaries (i.e. catchment attributes) calculated from the streamflow, meteorological and geospatial data at the basin and subbasin level.

Data set structure is described in the README found in this repository. Data set development is described in Knoben et al (under review; https://doi.org/10.5194/egusphere-2025-893). When using the CAMELS-SPAT data, please follow the attribution guidelines provided in Section 6 in this paper (briefly, individual attribution of any data set included in CAMELS-SPAT is requested if this data is used). BibTeX entries for the individual data sources aggregated in CAMELS-SPAT are provided in the citation.bib file found in this repository.

Temporary reference: Knoben, W. J. M., Keshavarz, K., Torres-Rojas, L., Thébault, C., Chaney, N. W., Pietroniro, A., and Clark, M. P.: Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): Streamflow observations, forcing data and geospatial data for hydrologic studies across North America, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-893, 2025

We have compiled a metadata base that includes >13,000 entries representing one or more nearshore resources (from physical attributes such as water and air, to birds and mammals). Each entry is viewable in three formats, 1) a geospatial explicit format (ArcView 3.3), 2) a spreadsheet format (Excel 2002 and within ArcView, and 3) A Procite database of references included in 1 and 2 above. The metadata base includes more than 1,100 independent references dating from 1896 to 2003 that are sorted into one or more of 15 ArcView themes, with associated areas of inference that can be sorted and displayed through any one of up to 24 fields that include location, author, metric, year, taxa, and method. The hierarchical GIS data base contains the following layers: 1) A base map of the GOA, 2) 15 files (ArcView themes) with resource specific references (e.g. algae, invertebrates, fishes, seabirds, and sea otters), and 3) buffer files for each resource that provides spatial reference to the area of inference for each reference. Attribute data (location, taxa, metrics, methods...) for each reference are accessible through the ArcView tables and the Excel spreadsheet. All references included in the metadata base, plus references such as review articles without geo-spatial reference can also be accessed through a Procite database. The metadata set and the ArcView themes will aid the EVOS Trustee Council members with their decision-making regarding the long-term monitoring and restoration plans for nearshore environments in the Gulf of Alaska. The metadata base and the ArcView themes will allow informed decisions regarding selection of species, methods, and locations for inclusion into the nearshore component of the EVOS GEM program. This component of Project 03687 provides documentation of the process and method used to create the metadata base, and provides instruction for accessing and using the databases.

Publications: Bodkin, J. L., and T. Dean. 2003. Alternative Sampling Designs for Nearshore Monitoring (Gulf Ecosystem Monitoring and Research Project G-030687), US Geological Survey, Alaska Science Center, Anchorage, Alaska.

Comprehensive and visual assessments utilizing Geographic Information Systems (GIS) offer an empirical foundation for the planning, construction, and optimization of Urban Green Infrastructure (UGI), effectively promoting its sustainable development. A comprehensive review of this field clarifies the research methods, application scope, trends, and challenges associated with using GIS to advance UGI development. This study synthesizes research findings from the Science Citation Index (SCI) and Social Science Citation Index (SSCI) within the Web of Science (WOS) database, as well as from the Scopus database, for the period from January 1, 2020, to June 30, 2024. The initial dataset included 640 articles from WOS and 952 articles from Scopus. After removing 1,572 duplicates and irrelevant studies, the final selection consisted of 20 articles. The integration of both WOS and Scopus databases ensures a comprehensive capture of current trends and limitations in GIS-based UGI assessments. This study centers on the scope, data sources, theoretical models, analyses, and objectives of GIS-based UGI assessments. The research indicates that over the past five years, GIS-based UGI assessments have primarily focused on areas such as accessibility, ecosystem service potential, resilience, and environmental justice, in addition to non-ecological aspects such as social benefits and aesthetics. While the integration of diverse data and analytical indicators into GIS has enhanced assessment comprehensiveness, and AI technologies have deepened data analysis, field research with urban residents remains crucial, underscoring the importance of inclusiveness in the study. This study also reveals a significant increase in interdisciplinarity in GIS-based assessments of UGI. The integration of assessment methods from ecology, computer science, urban planning, sociology, aesthetics, and other disciplines demonstrates that research in this field has fully considered ecological, social, economic, and humanistic factors, thereby more comprehensively reflecting the integrated needs of sustainable urban development.

The Quebec building reference system consists of a continuous layer, created from a bird's eye view and presented in the form of vector polygons. This work is part of the effort to provide Quebec with a rich and comprehensive database that brings together information related to the built environment. It is the result of collaborative work between the Center for Research in Geospatial Data and Intelligence at Laval University, the municipal sector, Natural Resources Canada and Quebec ministries and agencies. This data set integrates geometries from various partnerships and produced using artificial intelligence and automated extraction algorithms. The geographic coverage corresponds to the information available when it was published and will be extended according to the availability of new data. For changes since the previous release or for more details on the production techniques and source data used, consult the ** attribute list **. ## #Caractéristiques levels of completeness The quality of the geometric data may vary depending on the level of completeness (NC)\ * reached and could differ from the reality on the ground, both in terms of representation and in terms of authenticity. This release incorporates NC-0 and NC-1 data corresponding to the following descriptions: * NC-0: Raw vector geometries, from various sources, without quality control performed; * NC-1: Geometries validated manually (adjustments, additions, deletions). \ * Each level of completeness (NC) characterizes the level of editing work, validation, and descriptive content of the dataset. Thematic information will be added to the data set. This third party metadata element was translated using an automated translation tool (Amazon Translate).

The KBEC GIS data layers are grouped and organized into a Data Inventory by "factors" that contribute to this Characterization (biological, geological, physical, human, etc.). The Data Inventory contains information about each spatial data theme, including a title/description of the data file, a link to the associated metadata record, and a sample thematic map. The "thematic map" for each image theme is simply a low-resolution version of the image, while the "thematic maps" for feature and grid themes generally display one particular attribute of the spatial data, keeping in mind that most themes have multiple attributes associated with them that are not displayed.

The KBEC GIS layers pertain to the physical environment, the estuarine environment, the terrestrial environment and also contains information about scores of plant and animal species that occur in the vicinity of Kachemak Bay to supplement multiple sections of the narrative. Additionally, layers cover social data including an historical perspectie of Kachemak Bay and a present-day perspective of human uses.

In general, these maps do not distinguish ubiquitous and/or wide-ranging species. Instead, the focus is on species with special habitat requirements provided by the areas in and around the Kachemak Bay Watershed. These maps are not comprehensive, but they do represent the current state of knowledge. They, therefore, help to illuminate the strengths and gaps in our collective knowledge base. Recall that detailed information on sources, coverage, etc. of the data layers can be found in the metadata.

Funders: The Kachemak Bay Ecological Characterization (KBEC) Project was a cooperative effort between the National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center, the Alaska Department of Fish and Game (ADF&G), and Kachemak Bay Research Reserve. Additional funding came from the Exxon Valdez Oil Spill Trustee Council and the National Spatial Data Infrastructure program. Please cite these data as follows: "Kachemak Bay Research Reserve and National Oceanic and Atmospheric Administration, Coastal Services Center. Kachemak Bay Ecological Characterization. CD-ROM. NOAA/CSC/20017-CD. Charleston, SC: NOAA Coastal Services Center. "

Data sources table.