cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.2pgbhy
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Benin",
 "TaxonKey is Marsilea minuta"
 ]
}

 The dataset includes 11 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045121-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qpctts
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Marphysa macintoshi",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045119-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eu5qcv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((122.276 -3.520835, 122.205605 -3.526399, 122.13694 -3.542954, 122.071695 -3.570093, 122.011473 -3.60715, 121.957757 -3.653211, 121.911868 -3.707146, 121.874937 -3.767626, 121.847875 -3.833164, 121.831348 -3.902147, 121.825768 -3.972877, 121.831273 -4.043613, 121.847731 -4.112613, 121.874739 -4.178178, 121.911635 -4.238691, 121.957512 -4.292663, 122.01124 -4.338762, 122.071496 -4.375852, 122.136796 -4.403018, 122.205529 -4.41959, 122.276 -4.42516, 122.346471 -4.41959, 122.415204 -4.403018, 122.480504 -4.375852, 122.54076 -4.338762, 122.594488 -4.292663, 122.640365 -4.238691, 122.677261 -4.178178, 122.704269 -4.112613, 122.720727 -4.043613, 122.726232 -3.972877, 122.720652 -3.902147, 122.704125 -3.833164, 122.677063 -3.767626, 122.640132 -3.707146, 122.594243 -3.653211, 122.540527 -3.60715, 122.480305 -3.570093, 122.41506 -3.542954, 122.346395 -3.526399, 122.276 -3.520835))"},{"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/0045122-251120083545085/datasets/export for details.
DOI: 10.6084/m9.figshare.30850399
title: AI-Guided Real-Time Detection of Flow Irregularities and Bottlenecks in Pharmaceutical Vial Filling Lines Using Vision-Based Models

description: Ensuring smooth and consistent vial flow on pharmaceutical filling line conveyors is essential for maintaining sterility, reducing downtime, and avoiding costly disruptions. However, irregular vial movement such as spacing gaps, clustering, and vial flipping is often seen, leading to filling delays, broken glass, machine stoppages, and extended batch times. Existing systems lack the ability to detect such irregularities in real time and provide actionable alerts. A lightweight, AI-guided framework was developed for detecting vial flow irregularities and classifying bottleneck risks using computer vision. A high-speed camera positioned above the conveyor captured video frames processed by YOLOv8 for real-time object detection. OpenCV was used to extract vial positions and calculate flow metrics (average spacing, standard deviation, inter-vial gap thresholds). A rule-based classification system assigned bottleneck risk levels: low, medium, or high. Operational conveyor footage from a pharmaceutical filling line was analyzed; to protect confidentiality, frames were de-identified and re-rendered as schematic visualizations that preserve vial geometry and spacing statistics. Across 15 operational frames, the classifier achieved accuracy 93.3%, F1-macro 95.21%. On throughput, the end-to-end pipeline processed frames at ∼18 FPS on CPU and ∼25 FPS on Jetson Nano. Detector evaluation yielded mAP@0.5 = 100.00%, mAP@[0.5:0.95] = 70.09%, precision = 98.65%, recall = 100.00%, F1 = 99.32% at confidence 0.50. This scalable, non-invasive solution can be integrated into existing pharmaceutical lines to improve operational efficiency and product integrity. The rule-based approach offers interpretability, making it suitable for GMP-regulated environments. Across the operational validation set, the risk classifier achieved macro average precision 95.24%, recall 95.83%, and F1 95.21% .
DOI: 10.6084/m9.figshare.30850399.v1
title: AI-Guided Real-Time Detection of Flow Irregularities and Bottlenecks in Pharmaceutical Vial Filling Lines Using Vision-Based Models

description: Ensuring smooth and consistent vial flow on pharmaceutical filling line conveyors is essential for maintaining sterility, reducing downtime, and avoiding costly disruptions. However, irregular vial movement such as spacing gaps, clustering, and vial flipping is often seen, leading to filling delays, broken glass, machine stoppages, and extended batch times. Existing systems lack the ability to detect such irregularities in real time and provide actionable alerts. A lightweight, AI-guided framework was developed for detecting vial flow irregularities and classifying bottleneck risks using computer vision. A high-speed camera positioned above the conveyor captured video frames processed by YOLOv8 for real-time object detection. OpenCV was used to extract vial positions and calculate flow metrics (average spacing, standard deviation, inter-vial gap thresholds). A rule-based classification system assigned bottleneck risk levels: low, medium, or high. Operational conveyor footage from a pharmaceutical filling line was analyzed; to protect confidentiality, frames were de-identified and re-rendered as schematic visualizations that preserve vial geometry and spacing statistics. Across 15 operational frames, the classifier achieved accuracy 93.3%, F1-macro 95.21%. On throughput, the end-to-end pipeline processed frames at ∼18 FPS on CPU and ∼25 FPS on Jetson Nano. Detector evaluation yielded mAP@0.5 = 100.00%, mAP@[0.5:0.95] = 70.09%, precision = 98.65%, recall = 100.00%, F1 = 99.32% at confidence 0.50. This scalable, non-invasive solution can be integrated into existing pharmaceutical lines to improve operational efficiency and product integrity. The rule-based approach offers interpretability, making it suitable for GMP-regulated environments. Across the operational validation set, the risk classifier achieved macro average precision 95.24%, recall 95.83%, and F1 95.21% .
DOI: 10.15468/dl.ym5s9j
title: Occurrence Download

description: A dataset containing 224 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Salvia setulosa"
 ]
}

 The dataset includes 224 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045123-251120083545085/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.30246878
title: Spatial Variation on Multiple Scales in Line Transect Data; the Case of Antarctic Fin Whales

description: Line transect sampling is a widely used survey method for estimating animal density or abundance. We present a novel model for such data that allows for spatial variation in animal density at two scales: a long scale, representing trends caused by for instance climatic or terrain gradients, and a short scale, representing abrupt shifts due to local effects such as prey patchiness. The long-range variation is modeled as a latent Gaussian random field, while the abrupt changes are modeled as a two-state continuous-time Markov process along the transect line. For species that form dense groups, a separate spatial model for group size is proposed. The model is implemented in the R package TMB, allowing for efficient and computationally feasible inference. We apply our approach to estimate the abundance of Antarctic fin whales in the Scotia Sea. The effect of the different spatial scales is discussed, and a comparison is made to another spatially explicit method for line transect data. We validate our approach with a simulation study using the fitted model, wherein we re-fit models on samples generated from our initial model. These re-fitted models effectively capture the true simulated abundance. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.30246878.v1
title: Spatial Variation on Multiple Scales in Line Transect Data; the Case of Antarctic Fin Whales

description: Line transect sampling is a widely used survey method for estimating animal density or abundance. We present a novel model for such data that allows for spatial variation in animal density at two scales: a long scale, representing trends caused by for instance climatic or terrain gradients, and a short scale, representing abrupt shifts due to local effects such as prey patchiness. The long-range variation is modeled as a latent Gaussian random field, while the abrupt changes are modeled as a two-state continuous-time Markov process along the transect line. For species that form dense groups, a separate spatial model for group size is proposed. The model is implemented in the R package TMB, allowing for efficient and computationally feasible inference. We apply our approach to estimate the abundance of Antarctic fin whales in the Scotia Sea. The effect of the different spatial scales is discussed, and a comparison is made to another spatially explicit method for line transect data. We validate our approach with a simulation study using the fitted model, wherein we re-fit models on samples generated from our initial model. These re-fitted models effectively capture the true simulated abundance.
DOI: 10.6084/m9.figshare.30246878.v2
title: Spatial Variation on Multiple Scales in Line Transect Data; the Case of Antarctic Fin Whales

description: Line transect sampling is a widely used survey method for estimating animal density or abundance. We present a novel model for such data that allows for spatial variation in animal density at two scales: a long scale, representing trends caused by for instance climatic or terrain gradients, and a short scale, representing abrupt shifts due to local effects such as prey patchiness. The long-range variation is modeled as a latent Gaussian random field, while the abrupt changes are modeled as a two-state continuous-time Markov process along the transect line. For species that form dense groups, a separate spatial model for group size is proposed. The model is implemented in the R package TMB, allowing for efficient and computationally feasible inference. We apply our approach to estimate the abundance of Antarctic fin whales in the Scotia Sea. The effect of the different spatial scales is discussed, and a comparison is made to another spatially explicit method for line transect data. We validate our approach with a simulation study using the fitted model, wherein we re-fit models on samples generated from our initial model. These re-fitted models effectively capture the true simulated abundance. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.25810/558f-c477.92
title: Australian Newspaper Coverage of Climate Change or Global Warming, 2000-2025 - November 2025

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.6084/m9.figshare.30850319
title: Regret research_dataset

description: This dataset pertains to the assessment of consumers’ regret using neuroscience-based measures in situations where their expectations are not met.
DOI: 10.6084/m9.figshare.30850319.v1
title: Regret research_dataset

description: This dataset pertains to the assessment of consumers’ regret using neuroscience-based measures in situations where their expectations are not met.
DOI: 10.15468/dl.g53b67
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhamphobrachium chuni",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045125-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zpgppu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((104.957 -3.496834, 104.886606 -3.502398, 104.817944 -3.518954, 104.7527 -3.546093, 104.69248 -3.58315, 104.638765 -3.629212, 104.592878 -3.683146, 104.555948 -3.743626, 104.528887 -3.809164, 104.512361 -3.878147, 104.506781 -3.948878, 104.512286 -4.019614, 104.528743 -4.088614, 104.555751 -4.154178, 104.592646 -4.214692, 104.638522 -4.268664, 104.692248 -4.314762, 104.752503 -4.351852, 104.8178 -4.379018, 104.886531 -4.395591, 104.957 -4.401161, 105.027469 -4.395591, 105.0962 -4.379018, 105.161497 -4.351852, 105.221752 -4.314762, 105.275478 -4.268664, 105.321354 -4.214692, 105.358249 -4.154178, 105.385257 -4.088614, 105.401714 -4.019614, 105.407219 -3.948878, 105.401639 -3.878147, 105.385113 -3.809164, 105.358052 -3.743626, 105.321122 -3.683146, 105.275235 -3.629212, 105.22152 -3.58315, 105.1613 -3.546093, 105.096056 -3.518954, 105.027394 -3.502398, 104.957 -3.496834))"},{"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/0045126-251120083545085/datasets/export for details.
DOI: 10.15468/dl.cy3cgy
title: Occurrence Download

description: A dataset containing 7567672 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)"
 ]
 }
 },
 "Geometry POLYGON((38.000 85.000, -27.000 85.000, -27.000 56.000, 38.000 56.000, 38.000 85.000))",
 "Year 1970-1979"
 ]
}

 The dataset includes 7567672 records from 948 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006410-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k3nsj3
title: Occurrence Download

description: A dataset containing 185 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Andrena barbareae Panzer, 1805",
 "PhylumKey is Arthropoda",
 "Year is greater than or equal to 1992",
 "Year is less than or equal to 2012",
 "Geometry POLYGON((-26 30, 46 30, 46 68, -26 68, -26 30))"
 ]
}

 The dataset includes 185 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006430-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2r8zqe
title: Occurrence Download

description: A dataset containing 3273 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Material citation, Specimen, Occurrence evidence)",
 "Geometry POLYGON((11.06338 59.67482,-20.94241 40.49832,-5.32984 30.34703,26.67595 26.92208,43.06917 27.61594,61.41395 36.84288,61.80426 55.93556,21.60187 65.41326,11.06338 59.67482))",
 "HasGeospatialIssue is false",
 "TaxonKey is Hyla arborea (Linnaeus, 1758)"
 ]
}

 The dataset includes 3273 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006431-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.de3uaz
title: Occurrence Download

description: A dataset containing 5170 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Andrena bicolor Fabricius, 1775",
 "PhylumKey is Arthropoda",
 "Year is greater than or equal to 1992",
 "Year is less than or equal to 2012",
 "Geometry POLYGON((-26 30, 46 30, 46 68, -26 68, -26 30))"
 ]
}

 The dataset includes 5170 records from 120 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006432-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pysfzy
title: Occurrence Download

description: A dataset containing 43352 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is KUBI Ichthyology Collection",
 "PublishingOrg is b554c320-0560-11d8-b851-b8a03c50a862"
 ]
}

 The dataset includes 43352 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006433-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qcuxyv
title: Occurrence Download

description: A dataset containing 4072 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Andrena carantonica Pérez, 1902",
 "PhylumKey is Arthropoda",
 "Year is greater than or equal to 1992",
 "Year is less than or equal to 2012",
 "Geometry POLYGON((-26 30, 46 30, 46 68, -26 68, -26 30))"
 ]
}

 The dataset includes 4072 records from 102 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006436-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5ezmdw
title: Occurrence Download

description: A dataset containing 408 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-121.98883 39.44429,-122.28572 38.38168,-120.65283 36.56758,-119.6731 36.18513,-119.28714 37.2087,-121.15754 39.21464,-121.98883 39.44429))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mutillidae"
 ]
}

 The dataset includes 408 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006438-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r5vme3
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Andrena chrysopus Pérez, 1903",
 "PhylumKey is Arthropoda",
 "Year is greater than or equal to 1992",
 "Year is less than or equal to 2012",
 "Geometry POLYGON((-26 30, 46 30, 46 68, -26 68, -26 30))"
 ]
}

 The dataset includes 14 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006440-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wch7kc
title: Occurrence Download

description: A dataset containing 992 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-121.98883 39.44429,-122.28572 38.38168,-120.65283 36.56758,-119.6731 36.18513,-119.28714 37.2087,-121.15754 39.21464,-121.98883 39.44429))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chrysididae"
 ]
}

 The dataset includes 992 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006439-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9d9p5s
title: Occurrence Download

description: A dataset containing 1149 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-121.98883 39.44429,-122.28572 38.38168,-120.65283 36.56758,-119.6731 36.18513,-119.28714 37.2087,-121.15754 39.21464,-121.98883 39.44429))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sphecidae"
 ]
}

 The dataset includes 1149 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006441-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5em2we
title: Occurrence Download

description: A dataset containing 534 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Andrena coitana (Kirby, 1802)",
 "PhylumKey is Arthropoda",
 "Year is greater than or equal to 1992",
 "Year is less than or equal to 2012",
 "Geometry POLYGON((-26 30, 46 30, 46 68, -26 68, -26 30))"
 ]
}

 The dataset includes 534 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006442-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.