cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17573723
title: Plate 6.6 Water Balance of River Basins in the 20th Century
title: Tafel 6.6 Wasserhaushalt grosser Einzugsgebiete im 20. Jahrhundert
title: Planche 6.6 Bilan hydrologique de grands bassins versants au 20e siècle
title: Tavola 6.6 Bilancio idrologico di grandi bacini imbriferi nel XX secolo

description: The climatic changes observed since the middle of the 19th century include a rise in temperature which has led to greater melting of glaciers and higher levels of evaporation. Precipitation patterns can also change. Changes in land-use such as more intensive farming or increased afforestation also result in increased evaporation.Long measurement series for the elements that make up the water balance thus show on the one hand the water resources available in a catchment and, on the other, how the water balance has possibly changed owing to the influences mentioned above.
description: Mit der schon seit Mitte des 19. Jahrhunderts beobachteten Klimaveränderung steigt die Lufttemperatur, was das Abschmelzen der Gletscher fördert und die Verdunstung erhöht. Auch das Niederschlagsgeschehen kann sich verändern. Landnutzungsänderungen wie z.B. die Intensivierung der Landwirtschaft oder die Zunahme der Waldflächen lassen die Verdunstung zusätzlich ansteigen.Lange Zeitreihen der Wasserhaushaltselemente zeigen also einerseits, welche Wasserressourcen in einem Einzugsgebiet zur Verfügung stehen und andererseits, wie sich der Wasserhaushalt aufgrund der erwähnten Einflussgrössen allenfalls verändert hat.
description: Sous l’effet des changements climatiques, que l’on observe depuis le milieu du 19e siècle déjà, la température de l’air augmente, ce qui favorise la fonte des glaciers et élève l’évaporation. Cela peut aussi se répercuter sur les précipitations. Les changements d’affectation des terres, résultant par exemple de l’intensification de l’agriculture ou de l’extension de la surface forestière, renforcent encore l’évaporation.De longues séries chronologiques des éléments du bilan hydrologique renseignent ainsi d’une part sur la disponibilité des ressources en eau dans un bassin versant et d’autre part sur les changements que les divers facteurs mentionnés ci-dessus auraient pu faire subir au bilan hydrologique.
description: A seguito del mutamento climatico osservato già nel XIX secolo la temperatura dell’aria è aumentata, favorendo lo scioglimento dei ghiacciai e l’evaporazione. Anche gli eventi meteorici possono subire alterazioni. Mutamenti nello sfruttamento del territorio, quali ad esempio l’intensificazione dell’agricoltura o l’incremento delle aree boschive, innalzano ulteriormente l’evaporazione. Le lunghe serie storiche inerenti le componenti del bilancio mostrano dunque da un lato quali risorse idriche siano disponibili in un bacino e, dall’altro, come il bilancio stesso si sia eventualmente alterato a causa dei citati fattori influenti.
DOI: 10.6084/m9.figshare.30609881
title: 1_Cooling_centers

description: Cooling Center Facilities DataThis dataset contains geospatial point data for cooling center facilities across multiple U.S. states. Each facility record includes location coordinates, facility names, addresses, operational type classifications (public, private, other, unknown), and detailed facility categories such as libraries, community centers, government offices, senior centers, recreation centers, and religious organizations. Facilities are primarily public institutions, with libraries being the dominant category. The data were collected from official state and local government websites, National Center for Healthy Housing databases, community resource directories, and validated using Google Places API. This dataset supports research on urban heat vulnerability, spatial accessibility of cooling resources, climate adaptation planning, and public health infrastructure analysis.<br><br>
DOI: 10.6084/m9.figshare.30609881.v1
title: 1_Cooling_centers

description: Cooling Center Facilities DataThis dataset contains geospatial point data for cooling center facilities across multiple U.S. states. Each facility record includes location coordinates, facility names, addresses, operational type classifications (public, private, other, unknown), and detailed facility categories such as libraries, community centers, government offices, senior centers, recreation centers, and religious organizations. Facilities are primarily public institutions, with libraries being the dominant category. The data were collected from official state and local government websites, National Center for Healthy Housing databases, community resource directories, and validated using Google Places API. This dataset supports research on urban heat vulnerability, spatial accessibility of cooling resources, climate adaptation planning, and public health infrastructure analysis.<br><br>
DOI: 10.6084/m9.figshare.30609884
title: 2_Socio-demographic_population

description: Socio-demographic Population DataThis dataset contains census block group-level demographic and socioeconomic data for multiple U.S. states. Each GeoPackage file includes geospatial polygon boundaries for census block groups along with population statistics such as total population, elderly and non-elderly population counts, racial composition (white and non-white), and poverty statistics (population in poverty and non-poverty populations). Geographic identifiers include state, county, tract, and block group codes (GEOID), as well as geographic coordinates. The data are provided in GeoPackage format with one file per state, enabling spatial analysis of demographic characteristics at fine geographic resolution for assessing social vulnerability and equity in access to cooling resources.<br><br>
DOI: 10.6084/m9.figshare.30609884.v1
title: 2_Socio-demographic_population

description: Socio-demographic Population DataThis dataset contains census block group-level demographic and socioeconomic data for multiple U.S. states. Each GeoPackage file includes geospatial polygon boundaries for census block groups along with population statistics such as total population, elderly and non-elderly population counts, racial composition (white and non-white), and poverty statistics (population in poverty and non-poverty populations). Geographic identifiers include state, county, tract, and block group codes (GEOID), as well as geographic coordinates. The data are provided in GeoPackage format with one file per state, enabling spatial analysis of demographic characteristics at fine geographic resolution for assessing social vulnerability and equity in access to cooling resources.<br><br>
DOI: 10.15468/dl.mhywru
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hylodes caete"
 ]
}

 The dataset includes 34 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031043-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30609995
title: 3_Catchment_area

description: Catchment Spatial Analysis Data<br>This dataset contains service catchment areas for cooling center facilities across multiple U.S. states. Each GeoPackage file includes geospatial polygon boundaries representing the geographic coverage area accessible from each cooling center facility. The catchment polygons are defined based on walkable distance thresholds, with attributes including facility names, catchment types, and minimum travel time. The data enable spatial analysis of cooling center service coverage, identification of gaps in accessibility, and assessment of population reach within walkable distances. Files are provided in GeoPackage format with one file per state.<br><br>
DOI: 10.6084/m9.figshare.30609995.v1
title: 3_Catchment_area

description: Catchment Spatial Analysis Data<br>This dataset contains service catchment areas for cooling center facilities across multiple U.S. states. Each GeoPackage file includes geospatial polygon boundaries representing the geographic coverage area accessible from each cooling center facility. The catchment polygons are defined based on walkable distance thresholds, with attributes including facility names, catchment types, and minimum travel time. The data enable spatial analysis of cooling center service coverage, identification of gaps in accessibility, and assessment of population reach within walkable distances. Files are provided in GeoPackage format with one file per state.<br><br>
DOI: 10.6084/m9.figshare.30610046
title: 4_Walkable_coverage_ratio

description: Walkable Population Ratio DataThis dataset contains cumulative population coverage analysis for cooling center facilities across multiple U.S. states. Each CSV file provides facility-level statistics on population coverage within walkable distances, including individual facility coverage (coverage_pop), cumulative population coverage as facilities are added sequentially (cumulative_pop), and cumulative coverage ratios relative to total state population. The analysis is based on a minimum walkable distance threshold (typically 15 minutes) and uses socio-demographic sub groups as the primary metric. Files are organized by state and include facility names, coverage statistics, and total population figures. This data enables assessment of cooling center network effectiveness, identification of coverage gaps, and evaluation of incremental improvements in population accessibility as additional facilities are considered.<br><br>
DOI: 10.6084/m9.figshare.30610046.v1
title: 4_Walkable_coverage_ratio

description: Walkable Population Ratio DataThis dataset contains cumulative population coverage analysis for cooling center facilities across multiple U.S. states. Each CSV file provides facility-level statistics on population coverage within walkable distances, including individual facility coverage (coverage_pop), cumulative population coverage as facilities are added sequentially (cumulative_pop), and cumulative coverage ratios relative to total state population. The analysis is based on a minimum walkable distance threshold (typically 15 minutes) and uses socio-demographic sub groups as the primary metric. Files are organized by state and include facility names, coverage statistics, and total population figures. This data enables assessment of cooling center network effectiveness, identification of coverage gaps, and evaluation of incremental improvements in population accessibility as additional facilities are considered.<br><br>
DOI: 10.6084/m9.figshare.30610166
title: 5_Codes_and_validation

description: Analysis Code and Validation Data<br>This directory contains Python scripts for data analysis and validation, along with validation results. The code files include:1. **foursquare_validation.py**: Validation script that uses Foursquare Places API to verify and improve cooling center facility classifications. The script performs external validation through API queries, calculates distance-based confidence levels, applies facility name pattern matching for classification refinement, and generates final validation results. Input: cooling center data (v9 format). Output: validated data (v16 format).<br>2. **state_name_walkable_main.py**: Main analysis script for calculating walkable population coverage ratios. This script processes catchment areas, block group demographic data, and cooling center locations to compute area-weighted population coverage within walkable distance thresholds (15, 30, 45, 60 minutes). It generates cumulative coverage statistics and marginal coverage curves for different population subgroups (total population, elderly, non-elderly, racial groups, poverty populations).<br>3. **state_name_map_plot.py**: Visualization script for generating coverage curve plots across multiple states. The script creates multi-panel figures showing cumulative population coverage as a function of the number of facilities, with configurable styling and output formats (PDF, PNG). Users must set file paths via environment variables (CCA_BASE_DIR, CCA_OUT_DIR) before running.<br>4. **validation_foursq_final.gpkg**: Final validation results dataset containing cooling center facilities with Foursquare API validation information, including confidence scores and improved classifications.All scripts require standard geospatial Python libraries (geopandas, pandas, matplotlib, shapely) and are designed to work with the provided data files in the parent directories.<br><br>
DOI: 10.6084/m9.figshare.30610166.v1
title: 5_Codes_and_validation

description: Analysis Code and Validation Data<br>This directory contains Python scripts for data analysis and validation, along with validation results. The code files include:1. **foursquare_validation.py**: Validation script that uses Foursquare Places API to verify and improve cooling center facility classifications. The script performs external validation through API queries, calculates distance-based confidence levels, applies facility name pattern matching for classification refinement, and generates final validation results. Input: cooling center data (v9 format). Output: validated data (v16 format).<br>2. **state_name_walkable_main.py**: Main analysis script for calculating walkable population coverage ratios. This script processes catchment areas, block group demographic data, and cooling center locations to compute area-weighted population coverage within walkable distance thresholds (15, 30, 45, 60 minutes). It generates cumulative coverage statistics and marginal coverage curves for different population subgroups (total population, elderly, non-elderly, racial groups, poverty populations).<br>3. **state_name_map_plot.py**: Visualization script for generating coverage curve plots across multiple states. The script creates multi-panel figures showing cumulative population coverage as a function of the number of facilities, with configurable styling and output formats (PDF, PNG). Users must set file paths via environment variables (CCA_BASE_DIR, CCA_OUT_DIR) before running.<br>4. **validation_foursq_final.gpkg**: Final validation results dataset containing cooling center facilities with Foursquare API validation information, including confidence scores and improved classifications.All scripts require standard geospatial Python libraries (geopandas, pandas, matplotlib, shapely) and are designed to work with the provided data files in the parent directories.<br><br>
DOI: 10.15468/dl.5jczv2
title: Occurrence Download

description: A dataset containing 399 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Meetnetten.be - Bat maternity roosts counts Flanders, Belgium"
 ]
}

 The dataset includes 399 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031044-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kcmfkp
title: Occurrence Download

description: A dataset containing 9305 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Meetnetten.be - Bat winter roosts counts (totals per room/section) Flanders, Belgium"
 ]
}

 The dataset includes 9305 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031046-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pzk6u2
title: Occurrence Download

description: A dataset containing 16218 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (USA.41_1, USA.34_1, USA.11_1)",
 "TaxonKey is Odocoileus virginianus"
 ]
}

 The dataset includes 16218 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031047-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tpcmnr
title: Occurrence Download

description: A dataset containing 698 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phrynosoma asio Cope, 1864"
 ]
}

 The dataset includes 698 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009853-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uc4fsu
title: Occurrence Download

description: A dataset containing 12192 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Andropogon virginicus L.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 12192 records from 246 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010630-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15558484
title: LeafMachine2 Data: Cornaceae Leaf Outlines and ECT

description: Cornaceae - 317,500 leaves - 44.4 GB: This repository contains HDF5 (.h5) files with leaf morphological data for species in the Cornaceae family. Each file represents a single leaf specimen and contains leaf shape outlines, the 128x128 ECT matrix, and associated metadata.

File Contents

This dataset contains 317,500 leaves from taxa in the family Cornaceae. Some leaves may be partial, predated, broken, or incomplete. 

Each .h5 file contains the following datasets:



ECT_matrices/ - Euler Characteristic Transform (ECT) matrices capturing topological features of leaf shapes

shapes/shape_0 - Coordinate array (x,y) defining the leaf outline boundary. 

component_names - Original filename identifier (without extension)

group_labels - Taxonomic classification dictionary containing:



family: Taxonomic family name

genus: Genus classification

genus_species: Binomial species name

fullname: Complete taxonomic identifier




Data Format

Files are organized with standardized naming:  [Herbarium]_[ID]_[Family]_[Genus]_[Species]__[LeafID].h5

Shape coordinates are normalized to a unit circle centered at the origin (-0.5 to 0.5 range), vertically oriented.

Uncompressed Size:

44.4 GB

Usage

Code for reading, processing, and analyzing these files is available at: https://github.com/Gene-Weaver/LM2-Data-Tools

The repository includes functions for extracting data and generating visualizations.

Citation: Please cite the LeafMachine2 paper and this dataset.



Weaver, W. N., & Smith, S. A. (2023). From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2. Applications in Plant Sciences, 11(5), e11548. https://doi.org/10.1002/aps3.11548
DOI: 10.5281/zenodo.15558485
title: LeafMachine2 Data: Cornaceae Leaf Outlines and ECT

description: Cornaceae - 317,500 leaves - 44.4 GB: This repository contains HDF5 (.h5) files with leaf morphological data for species in the Cornaceae family. Each file represents a single leaf specimen and contains leaf shape outlines, the 128x128 ECT matrix, and associated metadata.

File Contents

This dataset contains 317,500 leaves from taxa in the family Cornaceae. Some leaves may be partial, predated, broken, or incomplete. 

Each .h5 file contains the following datasets:



ECT_matrices/ - Euler Characteristic Transform (ECT) matrices capturing topological features of leaf shapes

shapes/shape_0 - Coordinate array (x,y) defining the leaf outline boundary. 

component_names - Original filename identifier (without extension)

group_labels - Taxonomic classification dictionary containing:



family: Taxonomic family name

genus: Genus classification

genus_species: Binomial species name

fullname: Complete taxonomic identifier




Data Format

Files are organized with standardized naming:  [Herbarium]_[ID]_[Family]_[Genus]_[Species]__[LeafID].h5

Shape coordinates are normalized to a unit circle centered at the origin (-0.5 to 0.5 range), vertically oriented.

Uncompressed Size:

44.4 GB

Usage

Code for reading, processing, and analyzing these files is available at: https://github.com/Gene-Weaver/LM2-Data-Tools

The repository includes functions for extracting data and generating visualizations.

Citation: Please cite the LeafMachine2 paper and this dataset.



Weaver, W. N., & Smith, S. A. (2023). From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2. Applications in Plant Sciences, 11(5), e11548. https://doi.org/10.1002/aps3.11548
DOI: 10.17182/hepdata.78924.v1/t4
title: "Table 4" of "Systematic studies of correlations between different order flow harmonics in Pb-Pb collisions at $\sqrt{s_{\rm NN}}$ = 2.76 TeV"

description: Centrality dependence of observables NSC(5,2) in Pb-Pb collisions at 2.76 TeV.
description: CERN-LHC. Measurements of correlations between event-by-event fluctuations of amplitudes of anisotropic flow harmonics in nucleus-nucleus collisions at $\sqrt{s_{_{\rm NN}}}=2.76$ TeV. Data was recorded in November 2010 with the ALICE detector at the CERN Large Hadron Collider. The measurements are performed in the central pseudorapidity region (|$\eta$| &lt; 0.8) and transverse momentum range 0.2 &lt; $p_T$ &lt; 5.0 GeV/c. The data for SC(3,2), SC(4,2), NSC(3,2) and NSC(4,2) in Figure 1 can be found at http://dx.doi.org/10.17182/hepdata.74142. The lower order $v_n$ for n&lt;4 in Figure 3 can be found at http://dx.doi.org/10.17182/hepdata.72886.v2 (Table 3 and 4).
DOI: 10.17182/hepdata.78924.v1/t6
title: "Table 6" of "Systematic studies of correlations between different order flow harmonics in Pb-Pb collisions at $\sqrt{s_{\rm NN}}$ = 2.76 TeV"

description: Centrality dependence of observables NSC(4,3) in Pb-Pb collisions at 2.76 TeV.
description: CERN-LHC. Measurements of correlations between event-by-event fluctuations of amplitudes of anisotropic flow harmonics in nucleus-nucleus collisions at $\sqrt{s_{_{\rm NN}}}=2.76$ TeV. Data was recorded in November 2010 with the ALICE detector at the CERN Large Hadron Collider. The measurements are performed in the central pseudorapidity region (|$\eta$| &lt; 0.8) and transverse momentum range 0.2 &lt; $p_T$ &lt; 5.0 GeV/c. The data for SC(3,2), SC(4,2), NSC(3,2) and NSC(4,2) in Figure 1 can be found at http://dx.doi.org/10.17182/hepdata.74142. The lower order $v_n$ for n&lt;4 in Figure 3 can be found at http://dx.doi.org/10.17182/hepdata.72886.v2 (Table 3 and 4).
DOI: 10.17182/hepdata.78924.v1/t7
title: "Table 7" of "Systematic studies of correlations between different order flow harmonics in Pb-Pb collisions at $\sqrt{s_{\rm NN}}$ = 2.76 TeV"

description: Transeverse momemtum dependence of observables SC(3,2) in Pb-Pb collisions at 2.76 TeV.
description: CERN-LHC. Measurements of correlations between event-by-event fluctuations of amplitudes of anisotropic flow harmonics in nucleus-nucleus collisions at $\sqrt{s_{_{\rm NN}}}=2.76$ TeV. Data was recorded in November 2010 with the ALICE detector at the CERN Large Hadron Collider. The measurements are performed in the central pseudorapidity region (|$\eta$| &lt; 0.8) and transverse momentum range 0.2 &lt; $p_T$ &lt; 5.0 GeV/c. The data for SC(3,2), SC(4,2), NSC(3,2) and NSC(4,2) in Figure 1 can be found at http://dx.doi.org/10.17182/hepdata.74142. The lower order $v_n$ for n&lt;4 in Figure 3 can be found at http://dx.doi.org/10.17182/hepdata.72886.v2 (Table 3 and 4).