cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p1hzudqz
title: Aquifer framework datasets used to represent the Arbuckle-Simpson aquifer, Oklahoma

description: 
The Arbuckle-Simpson aquifer covers an area of about 800 square miles in the Arbuckle Mountains and Arbuckle Plains of South-Central Oklahoma. The aquifer is in the Central Lowland Physiographic Province and is composed of the Simpson and Arbuckle Groups of Ordovician and Cambrian age. The aquifer is as thick as 9,000 feet in some areas. The aquifer provides relatively small, but important, amounts of water depended on for public supply, agricultural, and industrial use (HA 730-E).
 
This product provides source data for the Arbuckle-Simpson aquifer framework, including: 
Georeferenced images: 
1. i_46ARBSMP_bot.tif: Digitized figure of depth contour lines below land surface representing the base of fresh water in the Arbuckle-Simpson aquifer. The base of fresh water is considered to be the bottom of the Arbuckle-Simpson aquifer. The original figure is from the "Reconnaissance of the water resources of the Ardmore and Sherman Quadrangles, southern Oklahoma" report, map HA-3, page 2, prepared by the Oklahoma Geological Survey in cooperation with the U.S. Geological Survey (HA3_P2). 
 
Extent shapefiles: 
1. p_46ABKSMP.shp: Polygon shapefile containing the areal extent of the Arbuckle-Simpson aquifer (Arbuckle-Simpson_AqExtent). The extent file contains no aquifer subunits.
 
Contour line shapefiles: 
1. c_46ABKSMP_bot.shp: Contour line dataset containing depth values, in feet below land surface, across the bottom of the Arbuckle-Simpson aquifer. This dataset is a digitized version of the map published in HA3_P2. This dataset was used to create the rd_46ABKSMP_bot.tif raster dataset. This map generalized depth values into zoned areas with associated ranges of depth. The edge of each zone was treated as the minimum value of the assigned range, thus creating the depth contour lines. This interpretation was favorable as it allowed for the creation of the resulting raster. This map was used because more detailed point or contour data for the area is unavailable.
 
Altitude raster files: 
1. ra_46ABKSMP_top.tif: Altitude raster dataset of the top of the Arbuckle-Simpson aquifer. The altitude values are in meters reference to North American Vertical Datum of 1988 (NAVD88). The top of the aquifer is assumed to be at land surface (NED, 100-meter) based on available data. This raster was interpolated from the Digital Elevation Model (DEM) dataset (NED, 100-meter).
2. ra_46ABKSMP_bot.tif: Altitude raster dataset of the bottom of the Arbuckle-Simpson aquifer. The altitude values are in meters referenced to NAVD88. 
 
Depth raster files: 
1. rd_46ABKSMP_top.tif: Depth raster dataset of the top of the Arbuckle-Simpson aquifer. The depth values are in meters below land surface (NED, 100-meter). The top of the aquifer is assumed to be at land surface (NED, 100-meter) based on available data.
2. rd_46ABKSMP_bot.tif: Depth raster dataset of the bottom of the Arbuckle-Simpson aquifer. The depth values are in meters below land surface (NED, 100-meter). This raster was interpolated from the contour line dataset c_46ABKSMP_bot.shp.
DOI: 10.5066/p1zcfced
title: Aquifer framework datasets used to represent the Ada-Vamoosa aquifer, Oklahoma

description: 
The Ada-Vamoosa aquifer resides in the Central Lowland Physiographic Province in east-central Oklahoma and ends at the Kansas State line. The aquifer underlies an area of about 2,300 square miles. The aquifer consists mainly of layers of fine to coarse-grained sandstone irregularly interbedded with shale and limestone. The rocks are in the Ada and the Vamoosa Groups are Pennsylvanian age. The maximum thickness of the aquifer is about 900 feet. The aquifer is productive where conditions are unconfined and acts as the primary water source for several towns in the area (HA 730-E).
  
This product provides source data for the Ada-Vamoosa aquifer framework, including: 
Georeferenced images: 
1. i_28ADAVMS_top.tif: Digitized figure of altitude contour lines representing the top of the Ada-Vamoosa aquifer. The original figure was from Circular 87, Plate 3.
2. i_28ADAVMS_bot.tif: Digitized figure of altitude contour lines representing the bottom of the Ada-Vamoosa aquifer. The original figure was from Circular 87, Plate 3.
 
Extent shapefiles: 
1. p_28ADAVMS.shp: Polygon shapefile containing the areal extent of the Ada-Vamoosa aquifer (Ada-Vamoosa_AqExtent). The extent file contains no aquifer subunits.
 
Contour line shapefiles: 
1. c_28ADAVMS_top.shp: Contour line dataset containing altitude values, in feet reference to National Geodetic Vertical Datum of 1929 (NGVD29), across the top of the Ada-Vamoosa aquifer. These data were used to create the ra_28ADAVMS_top.tif raster dataset.
2. c_28ADAVMS_bot.shp: Contour line dataset containing altitude values, in feet reference to NGVD29, across the bottom of the Ada-Vamoosa aquifer. These data were used to create the ra_28ADAVMS_bot.tif raster dataset.
 
Altitude raster files: 
1. ra_28ADAVMS_top.tif: Altitude raster dataset of the top of the Ada-Vamoosa aquifer.  The altitude values are in meters reference to North American Vertical Datum of 1988 (NAVD88) vertical datum. This raster was interpolated from the c_28ADAVMS_top.shp dataset.
2. ra_28ADAVMS_bot.tif: Altitude raster dataset of the bottom of the Ada-Vamoosa aquifer.  The altitude values are in meters reference to NAVD88 vertical datum. This raster was interpolated from the c_28ADAVMS_bot.shp dataset.
 
Depth raster files: 
1. rd_28ADAVMS_top.tif: Depth raster dataset of the top of the Ada-Vamoosa aquifer.  The depth values are in meters below land surface (NED, 100-meter).
2. rd_28ADAVMS_bot.tif: Depth raster dataset of the bottom of the Ada-Vamoosa aquifer.  The depth values are in meters below land surface (NED, 100-meter).
DOI: 10.5066/p1f9cz6n
title: Aquifer framework datasets used to represent the Southern Nevada volcanic-rock aquifers, Nevada

description: 
The Southern Nevada volcanic-rock aquifers reside in southern-central Nevada. This group of aquifers can be categorized into welded tuff, bedded tuff, and lava flow aquifers. Each of these categories possess different physical characteristics and have varying degrees of welding and number of interconnected joints. The lithology of these aquifers is primarily dependent on mode of eruption and cooling and are highly variable. Because the lithology is so variable, so is groundwater transmission, storage, and yield (HA 730-B).
  
This product provides source data for the Southern Nevada volcanic-rock aquifers framework, including: 
Extent shapefiles: 
1. p_57SRNVDV.shp: Polygon shapefile containing the areal extent of the Southern Nevada volcanic-rock aquifers (SRNVDV_AqExtent). The extent file contains no aquifer subunits.
 
Altitude raster files:  
1. ra_57SRNVDV_top.tif: Altitude raster dataset of the top of the Southern Nevada volcanic-rock aquifers. The altitude values are in meters reference to North American Vertical Datum of 1988 (NAVD88). This data was sourced from Sweetkind and others, 2011. 
2. ra_57SRNVDV_bot.tif: Altitude raster dataset of the bottom of the Southern Nevada volcanic-rock aquifers. The altitude values are in meters reference to NAVD88. This data was sourced from Sweetkind and others, 2011. 
 
Depth raster files: 
1. rd_57SRNVDV_top.tif: Depth raster dataset of the top of the Southern Nevada volcanic-rock aquifers. The depth values are in meters below land surface (NED, 100-meter).
2. rd_57SRNVDV_bot.tif: Depth raster dataset of the bottom of the Southern Nevada volcanic-rock aquifers. The depth values are in meters below land surface (NED, 100-meter).
DOI: 10.5281/zenodo.17857621
title: PLAN DE MASSE
DOI: 10.5281/zenodo.17857620
title: PLAN DE MASSE
DOI: 10.5281/zenodo.17857748
title: Dataset and Analysis Code for Zooplankton diversity in urban ponds: the role of road salt is not reflected by watershed impervious cover

description: This dataset includes zooplankton abundance and environmental data, along with the associated R code for analyzing the effects of environmental stressors, such as salinity, on zooplankton diversity in urban stormwater management (SWM) ponds. Data were collected in the summer of 2022 from 50 SWM ponds in Brampton, Ontario. The dataset contains zooplankton abundance, fish presence/absence, and a range of environmental variables, including water quality, pond morphology, pond age, and watershed impervious cover. The accompanying R scripts perform statistical analyses and visualizations featured in the associated manuscript, 'Zooplankton diversity in urban ponds: The role of road salt is not reflected by watershed impervious cover'. This dataset is intended to support reproducibility and advance future research on the impacts of urbanization on aquatic ecosystems.
DOI: 10.5281/zenodo.15013222
title: Dataset and Analysis Code for Zooplankton diversity in urban ponds: the role of road salt is not reflected by watershed impervious cover

description: This dataset includes zooplankton abundance and environmental data, along with the associated R code for analyzing the effects of environmental stressors, such as salinity, on zooplankton diversity in urban stormwater management (SWM) ponds. Data were collected in the summer of 2022 from 50 SWM ponds in Brampton, Ontario. The dataset contains zooplankton abundance, fish presence/absence, and a range of environmental variables, including water quality, pond morphology, pond age, and watershed impervious cover. The accompanying R scripts perform statistical analyses and visualizations featured in the associated manuscript, 'Zooplankton diversity in urban ponds: The role of road salt is not reflected by watershed impervious cover'. This dataset is intended to support reproducibility and advance future research on the impacts of urbanization on aquatic ecosystems.
DOI: 10.15468/dl.wjp8j7
title: Occurrence Download

description: A dataset containing 37207 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "DecimalLatitude is greater than or equal to 39.085278",
 "DecimalLatitude is less than or equal to 44.085278",
 "DecimalLongitude is greater than or equal to 110.0",
 "DecimalLongitude is less than or equal to 115.0",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 37207 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038020-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h6bef3
title: Occurrence Download

description: A dataset containing 275 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-80.76958 8.43891,-77.89819 8.43891,-77.89819 9.58796,-80.76958 9.58796,-80.76958 8.43891))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bothrops asper"
 ]
}

 The dataset includes 275 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038019-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17857592
title: Dataset for PSASpotter: A Tool to Detect the Usage of Platform-Specific APIs in Python

description: This dataset was utilized in the research paper titled "PSASpotter: A Tool to Detect the Usage of Platform-Specific APIs in Python" for publication in  International Conference on Program Comprehension (ICPC). Herein, we present a concise abstract of the study.

A platform-specific API is implemented for a particular platform (e.g., operating system), thus, it may not work on other platforms than the target one. Detecting the usage of such APIs is important for supporting software maintenance, as it allows maintainers to be alerted about APIs that could pose potential risks. This paper proposes PSASpotter, a tool to detect the usage of platform-specific APIs in Python systems. PSASpotter also identifies whether the platform-specific APIs are used within a defensive code, such as try/except blocks or if blocks that check the current platform. PSASpotter can support the development of novel empirical studies about the usage of platformspecific APIs in the Python ecosystem. Moreover, the defensive code detected by PSASpotter may contain alternative solutions for unavailable APIs, which can provide insights for software development and testing across multiple platforms. PSASpotter is available at: https://github.com/ricardojob/PSASpotter. 

 



PSASpotter is an AST-based tool that detects API usage at the function/method level. Given a Git project, PSASpotter analyzes all Python files and exports details of platform-specific API usage. Specifically, for each usage, PSASpotter reports information about the analyzed project (name and commit), the used API (name and availability), and the usage location (filename, line, and GitHub link). In addition, PSASpotter also reports whether the usage happens within defensive code.


The tool PSASpotter is publicly available at https://github.com/ricardojob/PSASpotter.
DOI: 10.5281/zenodo.17857593
title: Dataset for PSASpotter: A Tool to Detect the Usage of Platform-Specific APIs in Python

description: This dataset was utilized in the research paper titled "PSASpotter: A Tool to Detect the Usage of Platform-Specific APIs in Python" for publication in  International Conference on Program Comprehension (ICPC). Herein, we present a concise abstract of the study.

A platform-specific API is implemented for a particular platform (e.g., operating system), thus, it may not work on other platforms than the target one. Detecting the usage of such APIs is important for supporting software maintenance, as it allows maintainers to be alerted about APIs that could pose potential risks. This paper proposes PSASpotter, a tool to detect the usage of platform-specific APIs in Python systems. PSASpotter also identifies whether the platform-specific APIs are used within a defensive code, such as try/except blocks or if blocks that check the current platform. PSASpotter can support the development of novel empirical studies about the usage of platformspecific APIs in the Python ecosystem. Moreover, the defensive code detected by PSASpotter may contain alternative solutions for unavailable APIs, which can provide insights for software development and testing across multiple platforms. PSASpotter is available at: https://github.com/ricardojob/PSASpotter. 

 



PSASpotter is an AST-based tool that detects API usage at the function/method level. Given a Git project, PSASpotter analyzes all Python files and exports details of platform-specific API usage. Specifically, for each usage, PSASpotter reports information about the analyzed project (name and commit), the used API (name and availability), and the usage location (filename, line, and GitHub link). In addition, PSASpotter also reports whether the usage happens within defensive code.


The tool PSASpotter is publicly available at https://github.com/ricardojob/PSASpotter.
DOI: 10.15468/dl.hb64fu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((102.169 -0.003815, 102.098737 -0.009382, 102.030204 -0.025946, 101.965088 -0.053098, 101.904993 -0.090171, 101.851398 -0.136252, 101.805623 -0.190205, 101.768795 -0.250703, 101.74182 -0.316256, 101.725362 -0.38525, 101.719828 -0.455986, 101.725354 -0.5267230000000001, 101.741803 -0.595719, 101.768772 -0.661274, 101.805596 -0.721776, 101.85137 -0.775734, 101.904967 -0.821818, 101.965066 -0.858895, 102.030187 -0.886051, 102.098728 -0.902616, 102.169 -0.908184, 102.239272 -0.902616, 102.307813 -0.886051, 102.372934 -0.858895, 102.433033 -0.821818, 102.48663 -0.775734, 102.532404 -0.721776, 102.569228 -0.661274, 102.596197 -0.595719, 102.612646 -0.5267230000000001, 102.618172 -0.455986, 102.612638 -0.38525, 102.59618 -0.316256, 102.569205 -0.250703, 102.532377 -0.190205, 102.486602 -0.136252, 102.433007 -0.090171, 102.372912 -0.053098, 102.307796 -0.025946, 102.239263 -0.009382, 102.169 -0.003815))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038022-251120083545085/datasets/export for details.
DOI: 10.15468/dl.nbsf7e
title: Occurrence Download

description: A dataset containing 65565 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "DatasetKey is Field Museum of Natural History (Zoology) Mammal Collection",
 "OccurrenceStatus is Present",
 "TaxonKey is Chiroptera"
 ]
}

 The dataset includes 65565 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038012-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.np4u47
title: Occurrence Download

description: A dataset containing 22389 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Artemisia tridentata"
 ]
}

 The dataset includes 22389 records from 143 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038021-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/1kkj90
title: PAN-00130417 - coin/coin-related, Valentinianus I en opvolgers (364-392), aes III

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00130417&lt;/p&gt;
description: Modified: 2025-10-21T11:02:20+02:00
DOI: 10.6084/m9.figshare.30822857
title: Annotation of 739 studies included in the review

description: Annotation of 739 studies included in the review
DOI: 10.6084/m9.figshare.30822857.v1
title: Annotation of 739 studies included in the review

description: Annotation of 739 studies included in the review
DOI: 10.5281/zenodo.14029217
title: Dataset for PSASpotter: A Tool to Detect the Usage of Platform-Specific APIs in Python

description: This dataset was utilized in the research paper titled "PSASpotter: A Tool to Detect the Usage of Platform-Specific APIs in Python" for publication in  International Conference on Program Comprehension (ICPC). Herein, we present a concise abstract of the study.

A platform-specific API is implemented for a particular platform (e.g., operating system), thus, it may not work on other platforms than the target one. Detecting the usage of such APIs is important for supporting software maintenance, as it allows maintainers to be alerted about APIs that could pose potential risks. This paper proposes PSASpotter, a tool to detect the usage of platform-specific APIs in Python systems. PSASpotter also identifies whether the platform-specific APIs are used within a defensive code, such as try/except blocks or if blocks that check the current platform. PSASpotter can support the development of novel empirical studies about the usage of platformspecific APIs in the Python ecosystem. Moreover, the defensive code detected by PSASpotter may contain alternative solutions for unavailable APIs, which can provide insights for software development and testing across multiple platforms. PSASpotter is available at: https://github.com/ricardojob/PSASpotter. 

 



PSASpotter is an AST-based tool that detects API usage at the function/method level. Given a Git project, PSASpotter analyzes all Python files and exports details of platform-specific API usage. Specifically, for each usage, PSASpotter reports information about the analyzed project (name and commit), the used API (name and availability), and the usage location (filename, line, and GitHub link). In addition, PSASpotter also reports whether the usage happens within defensive code.


The tool PSASpotter is publicly available at https://github.com/ricardojob/PSASpotter.
DOI: 10.5281/zenodo.14029218
title: Dataset for PSASpotter: A Tool to Detect the Usage of Platform-Specific APIs in Python

description: This dataset was utilized in the research paper titled "PSASpotter: A Tool to Detect the Usage of Platform-Specific APIs in Python" for publication in  International Conference on Program Comprehension (ICPC). Herein, we present a concise abstract of the study.

A platform-specific API is implemented for a particular platform (e.g., operating system), thus, it may not work on other platforms than the target one. Detecting the usage of such APIs is important for supporting software maintenance, as it allows maintainers to be alerted about APIs that could pose potential risks. This paper proposes PSASpotter, a tool to detect the usage of platform-specific APIs in Python systems. PSASpotter also identifies whether the platform-specific APIs are used within a defensive code, such as try/except blocks or if blocks that check the current platform. PSASpotter can support the development of novel empirical studies about the usage of platformspecific APIs in the Python ecosystem. Moreover, the defensive code detected by PSASpotter may contain alternative solutions for unavailable APIs, which can provide insights for software development and testing across multiple platforms. PSASpotter is available at: https://github.com/ricardojob/PSASpotter. 

 



PSASpotter is an AST-based tool that detects API usage at the function/method level. Given a Git project, PSASpotter analyzes all Python files and exports details of platform-specific API usage. Specifically, for each usage, PSASpotter reports information about the analyzed project (name and commit), the used API (name and availability), and the usage location (filename, line, and GitHub link). In addition, PSASpotter also reports whether the usage happens within defensive code.


The tool PSASpotter is publicly available at https://github.com/ricardojob/PSASpotter.
DOI: 10.5281/zenodo.17624319
title: Supplementary Data for High-Resolution Surface Upward Longwave Radiation Estimation in Urban Using a Physics-Constrained Neural Network
DOI: 10.15468/dl.f3fwhk
title: Occurrence Download

description: A dataset containing 370 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trachypithecus auratus",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 370 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038025-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ecwpdz
title: Occurrence Download

description: A dataset containing 4307 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Italy",
 "OccurrenceStatus is Present",
 "TaxonKey is Caretta caretta"
 ]
}

 The dataset includes 4307 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038024-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uf2kwt
title: Occurrence Download

description: A dataset containing 34246 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is New Zealand National Forestry Herbarium"
 ]
}

 The dataset includes 34246 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038026-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.