cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.e9fkh7
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Paspalum scrobiculatum",
 "Year 1960-2010"
 ]
}

 The dataset includes 44 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006507-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3y2nwa
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((89.03512000000001 24.418982, 88.95800199999999 24.413405, 88.882802 24.396813, 88.811391 24.369618, 88.74554000000001 24.332497, 88.68688 24.286371, 88.636858 24.232385, 88.59670300000001 24.171878, 88.567393 24.106347, 88.549637 24.037412, 88.543853 23.966774, 88.55016500000001 23.896174, 88.568399 23.827348, 88.59808700000001 23.761988, 88.638485 23.701696, 88.688591 23.64795, 88.747167 23.602063, 88.812775 23.565157, 88.883808 23.538133, 88.95853099999999 23.52165, 89.03512000000001 23.516111, 89.111709 23.52165, 89.186432 23.538133, 89.257465 23.565157, 89.32307299999999 23.602063, 89.381649 23.64795, 89.431755 23.701696, 89.47215300000001 23.761988, 89.501841 23.827348, 89.52007500000001 23.896174, 89.526387 23.966774, 89.52060299999999 24.037412, 89.502847 24.106347, 89.47353699999999 24.171878, 89.43338199999999 24.232385, 89.38336 24.286371, 89.32470000000001 24.332497, 89.258849 24.369618, 89.187438 24.396813, 89.112238 24.413405, 89.03512000000001 24.418982))"},{"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 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006508-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.epag9e
title: Occurrence Download

description: A dataset containing 62503512 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is India",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 1994-2026"
 ]
}

 The dataset includes 62503512 records from 168 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006423-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17044/scilifelab.29543516
title: Mapping resinwood in CT scans of Scots pine with a hybrid Gaussian Mixture Model image segmentation pipeline

description: The presence of resinwood in <i>Cronartium pini</i>-infected Scots pine stems represents a major quality concern, diminishing timber value and challenging processing workflows. Although X‑ray computed tomography (CT) provides a non‑destructive means of visualising internal density, robust and automated methods to separate resinwood from heartwood and sapwood in such scans remain underdeveloped. To address this, we present a hybrid image analysis pipeline that combines a slice‑adaptive Gaussian Mixture Model (GMM), this statistically characterises tissue‑specific density patterns in each CT slice. This dataset was created through X-ray computed tomography (CT) imaging for developing segmentation method that capable identifying internal resinwood. Infected pine tree samples were inspected and harvested by trained inventory specialist, then scanned by MicroTec Mito industrial-scale CT scanner.The scanner produced data using a cone beam and two angled flat detectors on a helical scanning trajectory. The spatial resolution and resulting voxel dimensions were uniform at 0.5 × 0.5 × 0.5 mm³. The resolution of each image was 750 x 750 pixels on 2D. The resulting 3D images comprised 16-bit greyscale values representing density in kg/m³ at each location, and helical 1PI Katsevich was used for image reconstruction.
DOI: 10.17044/scilifelab.29543516.v1
title: Mapping resinwood in CT scans of Scots pine with a hybrid Gaussian Mixture Model image segmentation pipeline

description: The presence of resinwood in <i>Cronartium pini</i>-infected Scots pine stems represents a major quality concern, diminishing timber value and challenging processing workflows. Although X‑ray computed tomography (CT) provides a non‑destructive means of visualising internal density, robust and automated methods to separate resinwood from heartwood and sapwood in such scans remain underdeveloped. To address this, we present a hybrid image analysis pipeline that combines a slice‑adaptive Gaussian Mixture Model (GMM), this statistically characterises tissue‑specific density patterns in each CT slice. This dataset was created through X-ray computed tomography (CT) imaging for developing segmentation method that capable identifying internal resinwood. Infected pine tree samples were inspected and harvested by trained inventory specialist, then scanned by MicroTec Mito industrial-scale CT scanner.The scanner produced data using a cone beam and two angled flat detectors on a helical scanning trajectory. The spatial resolution and resulting voxel dimensions were uniform at 0.5 × 0.5 × 0.5 mm³. The resolution of each image was 750 x 750 pixels on 2D. The resulting 3D images comprised 16-bit greyscale values representing density in kg/m³ at each location, and helical 1PI Katsevich was used for image reconstruction.
DOI: 10.15468/dl.7zyj3e
title: Occurrence Download

description: A dataset containing 217 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Euphausia lucens",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLatitude is less than 10",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 217 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006509-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14590693
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.18346615
title: 202512 - Vessel detections from Sentinel 2

description: Vessel detections with Sentinel-2 satellite imagery from 2019 to 14 days ago. Multiyear detection of vessels with respective length, speed and orientation estimates based on deep-learning models applied to electro-optical imagery (RGB and near-infrared) at 10-m resolution covering most exclusive economic zones and marine protected areas across the ocean.

Sentinel-2 detections are  based on electro-optical imagery at 10-m resolution. Compared with our published Sentinel-1 detections based on SAR imagery at 20-m resolution, Sentinel-2 allows detecting smaller vessels with all types of vessel materials, in contrast with the weakness of SAR imagery on revealing vessels made of wood and fiberglass. Sentinel-2 also images the wakes of moving vessels, which further increase the detectability of small vessels and allow us to infer the vessels’ speed and orientation.The rich information contained in the optical imagery can be used to map not only vessel presence, but also vessel activities such as vessel encounters and bottom trawling. Sentinel-2 also covers more area of the ocean than Sentinel-1, and the novel neural-net detection approach does not require the exclusion of near-shore regions (as opposed to the CFAR approach), allowing detection of vessels in highly-packed areas all the way to the shoreline where most human activity is concentrated. Overall, with Sentinel-2 imagery we are able to detect about 3 times more vessels, “see” a broader range of vessel lengths and types, and infer more information of vessel activities than in our previous mapping using Sentinel-1 imagery.
DOI: 10.21950/laeeac
title: Federico-Tena World Population Historical Database : Bechuanaland (Botswana)

description: Project developed by Giovanni Federico (New York University Abu Dhabi) and Antonio Tena Junguito (Universidad Carlos III de Madrid). Dataset: Bechuanaland (Botswana)
description: Microsoft Excel
description: Web site: <strong>https://www.uc3m.es/investigacion/federico-tena-population</strong>



<strong>SEE MAP</strong>
DOI: 10.15468/dl.5gd47q
title: Occurrence Download

description: A dataset listing the 97 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Occurrence of plant species along Loduare - Golini Road, Ngorongoro Conservation Area, Arusha, Tanzania"
 ]
}

 The dataset's 97 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006503-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qtw42n
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((89.918678 24.427527, 89.841555 24.42195, 89.76635 24.405358, 89.694934 24.378163, 89.629079 24.341041, 89.570415 24.294915, 89.52038899999999 24.24093, 89.480231 24.180422, 89.45092 24.114891, 89.43316299999999 24.045956, 89.427379 23.975319, 89.43369199999999 23.904719, 89.451926 23.835893, 89.481616 23.770533, 89.522018 23.710242, 89.57212699999999 23.656495, 89.630707 23.610608, 89.696319 23.573702, 89.76735600000001 23.546678, 89.842084 23.530196, 89.918678 23.524657, 89.995272 23.530196, 90.06999999999999 23.546678, 90.141037 23.573702, 90.206649 23.610608, 90.26522900000001 23.656495, 90.315338 23.710242, 90.35574 23.770533, 90.38543 23.835893, 90.40366400000001 23.904719, 90.409977 23.975319, 90.40419300000001 24.045956, 90.386436 24.114891, 90.357125 24.180422, 90.31696700000001 24.24093, 90.266941 24.294915, 90.208277 24.341041, 90.142422 24.378163, 90.071006 24.405358, 89.995801 24.42195, 89.918678 24.427527))"},{"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 41 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006511-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sngpws
title: Occurrence Download

description: A dataset containing 288 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Euphausia similis",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLatitude is less than 10",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 288 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006512-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xgejpk
title: Occurrence Download

description: A dataset containing 115 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Stygiomedusa gigantea",
 "Year 1980-2026"
 ]
}

 The dataset includes 115 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006513-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4pbgwp
title: Occurrence Download

description: A dataset containing 698 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Occurrence of plant species along Loduare - Golini Road, Ngorongoro Conservation Area, Arusha, Tanzania"
 ]
}

 The dataset includes 698 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006514-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wedhg9
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Phalaris arundinacea",
 "Year 1970-2000"
 ]
}

 The dataset includes 64 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006516-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.84ugu3
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Torreya fargesii yunnanensis"
 ]
}

 The dataset includes 37 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006517-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.de8baq
title: Occurrence Download

description: A dataset containing 106 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Himalacodon"
 ]
}

 The dataset includes 106 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006519-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.egqh7t
title: Occurrence Download

description: A dataset containing 206 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Austrobaileya scandens",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 206 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006520-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pjs7uj
title: Occurrence Download

description: A dataset containing 3404632 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinus"
 ]
}

 The dataset includes 3404632 records from 3944 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006391-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/kpng7nnf29.1
title: Fourie et al Food quantity odour data

description: These data were used to test whether odour cues alone would enable a mammalian herbivore to make patch choice decisions based on forage quantity from afar. We used semi-domesticated African elephants (Loxodonta africana) as a model system due to their reliance on olfaction and their high foraging selectivity. Elephants reliably selected the patch with more food when the difference in quantity was &gt;600 g. Previous research suggests that 600 g corresponds to approximately 6–10 trunk loads of grass or 16–29 trunk loads of woody vegetation leaves. Our findings suggest that olfactory cues can facilitate long-distance foraging decisions in mammalian herbivores based on relatively fine-scale differences in food availability.
DOI: 10.57745/cgkkxc
title: 2009-EGG-LT-1402-Ruzgys.ogg

description: Interprétation en français, interprète/traducteur : Ruta Janusaite
DOI: 10.57745/besnkq
title: 2009-EGG-LT-1403-Ruzgys.ogg

description: Enregistrement audio en lituanien avec interprétation en français, interprète/traducteur : Ruta Janusaite
DOI: 10.57745/wrfjpn
title: 2009-EGG-LT-1401-Ruzgys.ogg

description: Enregistrement audio en version originale lituanien
DOI: 10.15468/dl.gu7rkr
title: Occurrence Download

description: A dataset containing 780 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Aristida purpurea Nutt.",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 780 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007250-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15029878
title: Towards Supporting Open Source Library Maintainers with Community-Based Analytics

description: Overview

This replication package contains data and scripts used in our study. Please refer to the latest version of the data set. The package is structured into four main components.

Folder Structure

1. Data





ProcessedData: Contains refined datasets that guide our research questions.




RawData: Contains raw scraped data about dependents from GitHub, selected for analysis.



2. RepoClonerDataAnalyser





The starting repository for the study.




Select the top 10 libraries and their dependents.




Clones repositories and analyzes all research questions.




Implemented in Python.



3. methodTypeResolutionJavaParser





A Java project used for method resolution.




After cloning repositories and filtering potential Java files using the RepoClonerDataAnalyzer project, this tool is used for parsing and resolving method types.



4. JacocoCoverageReporter





Converts raw JaCoCo HTML coverage reports into CSV format.


Implemented in Python 


Usage Instructions

Each project within this package has its own README file with detailed setup and execution instructions. Below is a high-level guide:





Data Collection: Use RepoClonerDataAnalyser to select, clone, and filter dependents.




Method Resolution: Run methodTypeResolutionJavaParser on filtered Java files.




Coverage Analysis: Use JacocoCoverageReporter to convert JaCoCo HTML reports into CSV format and then Use RepoClonerDataAnalyser for further analysis. Please refer to our paper for more details.




Data Analysis: Utilize the processed data in the Data folder for research insights.



Requirements





Python 3.x




Java 8+




Required dependencies (listed in individual project README files. In version 1, you might notice a random GitHub repository URL provided in individual README. It is intended solely for context and clarity. It does not lead to an accessible resource and results in a 404 error. We have removed it in version 2 to avoid any confusion. We recommend using the latest version of the dataset.)
DOI: 10.5281/zenodo.15066429
title: Towards Supporting Open Source Library Maintainers with Community-Based Analytics

description: Overview

This replication package contains data and scripts used in our study. The package is structured into four main components.

Folder Structure

1. Data





ProcessedData: Contains refined datasets that guide our research questions.




RawData: Contains raw scraped data about dependents from GitHub, selected for analysis.



2. RepoClonerDataAnalyser





The starting repository for the study.




Select the top 10 libraries and their dependents.




Clones repositories and analyzes all research questions.




Implemented in Python.



3. methodTypeResolutionJavaParser





A Java project used for method resolution.




This tool is used for parsing and resolving method types after cloning repositories and filtering potential Java files using the RepoClonerDataAnalyzer project.



4. JacocoCoverageReporter





Converts raw JaCoCo HTML coverage reports into CSV format.


Implemented in Python 


Usage Instructions

Each project within this package has its own README file with detailed setup and execution instructions. Below is a high-level guide:





Data Collection: Use RepoClonerDataAnalyser to select, clone, and filter dependents.




Method Resolution: Run methodTypeResolutionJavaParser on filtered Java files.




Coverage Analysis: Use JacocoCoverageReporter to convert JaCoCo HTML reports into CSV format and then Use RepoClonerDataAnalyser for further analysis. 




Data Analysis: Utilize the processed data in the Data folder for research insights.



Requirements





Python 3.x




Java 8+




Required dependencies (listed in individual project README files. In version 1, you might notice a random GitHub repository URL provided in individual README. It is intended solely for context and clarity. It does not lead to an accessible resource and results in a 404 error. We have removed it in version 2 to avoid any confusion.)