cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26187/deakin.28748648
title: Data and code for "Multiple regression, not ratios, for analyzing relative appendage size"

description: In recent years, research has increasingly identified changes in animal body shape occurring concomitantly with climate change. Shape changes, or ‘shape-shifting’, are believed to reflect a temporal extension of Allen’s rule, wherein appendages increase in size in response to warmer temperatures. Shape-shifting responses are considered in terms of increases in appendage size relative to body size, however, the statistical methods of analyzing relative appendage size differ between studies. There have been two primary statistical methods by which changes in relative appendage size have predominantly been investigated: 1) a multiple regression approach, wherein appendage size is made relative to body size by inclusion of body size as a covariate in the model, and 2) a ratio approach, wherein a ratio of appendage size to body size is calculated prior to modelling changes in the ratio over time. In this paper, we use simulated and real-world data to test both statistical approaches and how they impact assessments of shape-shifting. We demonstrate that the two approaches can yield different results across a range of body and appendage size change scenarios. We discuss the implications of this, and suggest that the multiple regression approach is most suitable for detecting changes in relative appendage size because it properly accounts for allometric scaling. We further suggest that the ratio approach does not adequately disentangle changes in ratio that are caused exclusively by variation in body size. We conclude that the multiple regression approach is more appropriate for investigations of shape-shifting, especially when other factors may also be changing through time. While we demonstrate these principles using examples and data of changes through time, the same would apply for Allen’s rule and appendage size changes over spatial scales.R code shows data simulation and testing of statistical approaches. Data is taken from McQueen et al. 2022 (Nat Comms).
DOI: 10.26187/deakin.28748648.v1
title: Data and code for "Multiple regression, not ratios, for analyzing relative appendage size"

description: In recent years, research has increasingly identified changes in animal body shape occurring concomitantly with climate change. Shape changes, or ‘shape-shifting’, are believed to reflect a temporal extension of Allen’s rule, wherein appendages increase in size in response to warmer temperatures. Shape-shifting responses are considered in terms of increases in appendage size relative to body size, however, the statistical methods of analyzing relative appendage size differ between studies. There have been two primary statistical methods by which changes in relative appendage size have predominantly been investigated: 1) a multiple regression approach, wherein appendage size is made relative to body size by inclusion of body size as a covariate in the model, and 2) a ratio approach, wherein a ratio of appendage size to body size is calculated prior to modelling changes in the ratio over time. In this paper, we use simulated and real-world data to test both statistical approaches and how they impact assessments of shape-shifting. We demonstrate that the two approaches can yield different results across a range of body and appendage size change scenarios. We discuss the implications of this, and suggest that the multiple regression approach is most suitable for detecting changes in relative appendage size because it properly accounts for allometric scaling. We further suggest that the ratio approach does not adequately disentangle changes in ratio that are caused exclusively by variation in body size. We conclude that the multiple regression approach is more appropriate for investigations of shape-shifting, especially when other factors may also be changing through time. While we demonstrate these principles using examples and data of changes through time, the same would apply for Allen’s rule and appendage size changes over spatial scales.R code shows data simulation and testing of statistical approaches. Data is taken from McQueen et al. 2022 (Nat Comms).
DOI: 10.15468/dl.w8v345
title: Occurrence Download

description: A dataset containing 156812 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Aedes albopictus (Skuse, 1894)"
 ]
}

 The dataset includes 156812 records from 171 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006511-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15461894
title: Gramin Vikas ma NGO ni Bhoomika (Gujarati)
DOI: 10.5281/zenodo.15461895
title: Gramin Vikas ma NGO ni Bhoomika (Gujarati)
DOI: 10.15468/dl.882z4p
title: Occurrence Download

description: A dataset containing 6798 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((120.89397 -2.97059,121.96678 -3.39428,121.92014 -2.86564,121.77632 -2.37588,120.63743 -2.24372,120.89397 -2.97059))",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 6798 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006521-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25405/data.ncl.28956770
title: Numerical simulation data for quantum droplet formation in a Bose Einstein Condensate.

description: The datasets were used to produce the figures in the paper "Universal scaling law for quantum droplet formation" arXiv:2504.21641 (See README file). Paper abstract:Given the right set of circumstances, ultracold quantum gases are able to change character and condense into a liquid state of quantum droplets. The size distribution of the droplets is determined dynamically in the condensation process. A semi-quantitative argument is presented which suggests that, at zero temperature, a multiple droplet system has is a preferred scale $\propto v^{-d}$, with $d\approx 0.5$, where $v$ is the rate of change of parameters at the time of droplet formation. Numerical simulations of two dimensional systems strongly supporta power law, but with an exponent $d\in(0.3,0.4)$.
DOI: 10.15468/dl.9pgaw3
title: Occurrence Download

description: A dataset containing 221 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.5_1",
 "TaxonKey is Lasionycteris noctivagans (Le Conte, 1831)"
 ]
}

 The dataset includes 221 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006522-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qtccnt
title: Occurrence Download

description: A dataset containing 139257 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Observation",
 "Geometry POLYGON((77.5 -11,141 -11,141 26,77.5 26,77.5 -11))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 139257 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006512-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3wj8qr
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Quercus shennongii C.C.Huang & S.H.Fu"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006523-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cbqtp5
title: Occurrence Download

description: A dataset containing 22366 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Xeno-canto - Bird sounds from around the world",
 "Country is Sweden"
 ]
}

 The dataset includes 22366 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006526-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ccrtun
title: Occurrence Download

description: A dataset containing 10607 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Vascular plant herbarium TRH, NTNU University Museum",
 "Country is Sweden"
 ]
}

 The dataset includes 10607 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006525-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u2wqn3
title: Occurrence Download

description: A dataset containing 35748 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Vascular Plant Herbarium, Oslo (O) UiO",
 "Country is Sweden"
 ]
}

 The dataset includes 35748 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006527-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t6sh5w
title: Occurrence Download

description: A dataset containing 3909 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "TaxonKey is one of (Oreolalax liangbeiensis Liu & Fei, 1979, Gloydius shedaoensis (Zhao, 1979), Euprepiophis perlaceus (Stejneger, 1929), Achalinus jinggangensis (Zong & Ma, 1983), Dibamus bogadeki Darevsky, 1992, Gekko reevesii (Gray, 1831), Caprolagus hispidus (Pearson, 1839), Budorcas taxicolor whitei Lydekker, 1907, Naemorhedus griseus (Milne-Edwards, 1871), Budorcas taxicolor tibetana Milne-Edwards, 1874, Saiga tatarica (Linnaeus, 1766), Procapra przewalskii (Büchner, 1891), Elaphurus davidianus Milne-Edwards, 1866, Muntiacus muntjak nigripes G.M.Allen, 1930, Prionailurus viverrinus (Bennett, 1833), Manis crassicaudata É.Geoffroy Saint-Hilaire, 1803, Odorrana wuchuanensis (Xu, 1983), Onychodactylus zhaoermii Che, Poyarkov & Yan, 2012, Hynobius maoershanensis Zhou, Jiang & Jiang, 2006, Hynobius amjiensis Gu, 1992, Sacalia bealei (Gray, 1831), Neophocaena phocaenoides (G.Cuvier, 1829), Neophocaena sunameri Pilleri & Gihr, 1975, Cuora Gray, 1856, Budorcas taxicolor taxicolor, Budorcas taxicolor bedfordi Thomas, 1911)"
 ]
}

 The dataset includes 3909 records from 120 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006530-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.en2xzn
title: Occurrence Download

description: A dataset containing 3289920 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Turdus merula Linnaeus, 1758",
 "HasCoordinate is true",
 "Year is greater than 1989",
 "Year is less than 2026",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is Absent"
 ]
 }
 },
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 3289920 records from 1306 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006514-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/skw8tjc82f
title: Multimodal_Cactaceae_Dataset_25

description: This data article presents a multimodal, non-invasive dataset documenting the physiology, growth, and phenological stages of Stenocereus queretaroensis (commonly known as pitayo), a columnar cactus of high agronomic, nutritional, and cultural importance in the arid and semi-arid regions of southern Zacatecas, Mexico. Although this species has traditionally been harvested from wild populations, initiatives for its controlled cultivation are emerging as part of regional efforts to improve fruit quality, extend shelf life, and ensure consistent production. However, controlled cultivation remains relatively rare and understudied, and there is a lack of systematic, publicly available data describing its developmental cycle and physiological responses under managed conditions.

To address this gap, we designed and executed a comprehensive data collection campaign between January and May 2025, spanning multiple phenological stages from vegetative growth through flowering and fruiting. A total of 109 healthy, fruit-bearing plants were selected according to inclusion criteria that prioritized structural integrity, absence of disease symptoms, and accessibility for imaging and in-field measurements. Data acquisition combined high-resolution multispectral drone imaging with ground-based spectrophotometry in the 400–700 nm range, enabling the capture of both canopy-level spatial information and precise point-based reflectance signatures.

Imaging was performed using a DJI Mavic 3M equipped with RGB and multispectral sensors. For each plant, one nadir RGB photograph and a corresponding set of multispectral captures were obtained. These data were processed to generate three complementary products: (i) original RGB images and baseline NDVI maps provided by the manufacturer’s software, (ii) spatially adjusted RGB frames cropped, scaled, and registered to NDVI outputs to ensure pixel-level alignment and allow region-of-interest (ROI) analysis, and (iii) recalculated NDVI images derived directly from NIR and Red bands to produce independent vegetation index maps. Spectral measurements were acquired with a YS45 field spectrophotometer using a standardized white reference for reflectance calibration. All spectral data were exported in .csv/.txt format and subsequently processed in MATLAB, where reflectance curves were stored as .mat files and plotted as PNG graphs for visual inspection.

Metadata for each sample include phenological stage, acquisition date and time, geographic coordinates, temperature, illumination conditions, and sensor configuration parameters. Together, these components form a hierarchically organized dataset that facilitates temporal tracking of individual plants and robust integration of image-derived features with field-collected spectra. Preprocessing steps included reflectance normalization, geometric registration of images, and standardization of file naming conventions, resulting in structured dataset ready for computational analysis.
DOI: 10.17632/skw8tjc82f.3
title: Multimodal_Cactaceae_Dataset_25

description: This data article presents a multimodal, non-invasive dataset documenting the physiology, growth, and phenological stages of Stenocereus queretaroensis (commonly known as pitayo), a columnar cactus of high agronomic, nutritional, and cultural importance in the arid and semi-arid regions of southern Zacatecas, Mexico. Although this species has traditionally been harvested from wild populations, initiatives for its controlled cultivation are emerging as part of regional efforts to improve fruit quality, extend shelf life, and ensure consistent production. However, controlled cultivation remains relatively rare and understudied, and there is a lack of systematic, publicly available data describing its developmental cycle and physiological responses under managed conditions.

To address this gap, we designed and executed a comprehensive data collection campaign between January and May 2025, spanning multiple phenological stages from vegetative growth through flowering and fruiting. A total of 109 healthy, fruit-bearing plants were selected according to inclusion criteria that prioritized structural integrity, absence of disease symptoms, and accessibility for imaging and in-field measurements. Data acquisition combined high-resolution multispectral drone imaging with ground-based spectrophotometry in the 400–700 nm range, enabling the capture of both canopy-level spatial information and precise point-based reflectance signatures.

Imaging was performed using a DJI Mavic 3M equipped with RGB and multispectral sensors. For each plant, one nadir RGB photograph and a corresponding set of multispectral captures were obtained. These data were processed to generate three complementary products: (i) original RGB images and baseline NDVI maps provided by the manufacturer’s software, (ii) spatially adjusted RGB frames cropped, scaled, and registered to NDVI outputs to ensure pixel-level alignment and allow region-of-interest (ROI) analysis, and (iii) recalculated NDVI images derived directly from NIR and Red bands to produce independent vegetation index maps. Spectral measurements were acquired with a YS45 field spectrophotometer using a standardized white reference for reflectance calibration. All spectral data were exported in .csv/.txt format and subsequently processed in MATLAB, where reflectance curves were stored as .mat files and plotted as PNG graphs for visual inspection.

Metadata for each sample include phenological stage, acquisition date and time, geographic coordinates, temperature, illumination conditions, and sensor configuration parameters. Together, these components form a hierarchically organized dataset that facilitates temporal tracking of individual plants and robust integration of image-derived features with field-collected spectra. Preprocessing steps included reflectance normalization, geometric registration of images, and standardization of file naming conventions, resulting in structured dataset ready for computational analysis.
DOI: 10.17632/42df84bzf7
title: Can you spot the real one: Consumers’ ability to detect real vs. fraudulent tickets - Study 1

description: Study 1 investigates consumers’ ability to distinguish between authentic and fraudulent event tickets. In addition, the study examines whether individual differences—specifically risk-taking, impulsiveness, and analytical reasoning—are related to participants’ discrimination accuracy. Participants viewed a set of eight tickets (four authentic and four fraudulent) presented in random order across four event types, and subsequently completed standardized measures assessing risk-taking, impulsiveness, and analytical reasoning. The dataset reflects all materials and responses collected as part of this study and corresponds to the hypotheses outlined in the accompanying manuscript.
DOI: 10.15468/dl.7mj99y
title: Occurrence Download

description: A dataset containing 13652 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-103 -40,-31 -40,-31 17,-103 17,-103 -40))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Accipiter striatus",
 "Year 1000-2018"
 ]
}

 The dataset includes 13652 records from 94 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039462-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/42df84bzf7.1
title: Can you spot the real one: Consumers’ ability to detect real vs. fraudulent tickets - Study 1

description: Study 1 investigates consumers’ ability to distinguish between authentic and fraudulent event tickets. In addition, the study examines whether individual differences—specifically risk-taking, impulsiveness, and analytical reasoning—are related to participants’ discrimination accuracy. Participants viewed a set of eight tickets (four authentic and four fraudulent) presented in random order across four event types, and subsequently completed standardized measures assessing risk-taking, impulsiveness, and analytical reasoning. The dataset reflects all materials and responses collected as part of this study and corresponds to the hypotheses outlined in the accompanying manuscript.
DOI: 10.15468/dl.fbjswj
title: Occurrence Download

description: A dataset containing 407 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Human Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Euphlyctis hexadactylus"
 ]
}

 The dataset includes 407 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039463-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/c9dvc6pz2b
title: Can you spot the real one: Consumers’ ability to detect real vs. fraudulent tickets - Study 2

description: Study 2 was designed to extend the findings of Study 1 and examine how contextual factors influence consumers’ ability to differentiate between authentic and fraudulent event tickets. Specifically, the study tested whether time pressure and ticket scarcity further impair discrimination accuracy. Prior research demonstrates that time pressure increases purchasing behavior and reduces individuals’ ability to detect fraudulent online environments, while scarcity cues elevate perceived value, urgency, and fear of missing out. In this study, participants evaluated event tickets under conditions involving manipulated levels of time pressure and scarcity. The dataset includes all experimental materials, behavioral responses, and condition assignments associated with these manipulations, in accordance with the hypotheses developed in the accompanying manuscript.
DOI: 10.17632/c9dvc6pz2b.1
title: Can you spot the real one: Consumers’ ability to detect real vs. fraudulent tickets - Study 2

description: Study 2 was designed to extend the findings of Study 1 and examine how contextual factors influence consumers’ ability to differentiate between authentic and fraudulent event tickets. Specifically, the study tested whether time pressure and ticket scarcity further impair discrimination accuracy. Prior research demonstrates that time pressure increases purchasing behavior and reduces individuals’ ability to detect fraudulent online environments, while scarcity cues elevate perceived value, urgency, and fear of missing out. In this study, participants evaluated event tickets under conditions involving manipulated levels of time pressure and scarcity. The dataset includes all experimental materials, behavioral responses, and condition assignments associated with these manipulations, in accordance with the hypotheses developed in the accompanying manuscript.