cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17158574
title: Spatial heterogeneity of soil organic matter and microbial community composition across ice-wedge polygons and soil layers in Arctic lowland tundra
DOI: 10.15468/dl.kx8bkp
title: Occurrence Download

description: A dataset containing 901 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is VectorNet data Luxembourg 2023-2024"
 ]
}

 The dataset includes 901 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018323-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uhpjsj
title: Occurrence Download

description: A dataset containing 1240 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Vanellus vanellus)",
 "Country is one of (Algeria, Angola, Benin, Botswana, British Indian Ocean Territory, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Congo, Democratic Republic of the, Côte d’Ivoire, Djibouti, Egypt, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, French Southern Territories, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mayotte, Morocco, Mozambique, Namibia, Niger, Nigeria, Réunion, Rwanda, Saint Helena, Ascension and Tristan da Cunha, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Tanzania, United Republic of, Togo, Tunisia, Uganda, Western Sahara, Zambia, Zimbabwe)"
 ]
}

 The dataset includes 1240 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018321-250914085247600/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.30132247
title: Energy-safety trade-offs differ between winter-adapted species of prey to drive distinct activity patterns

description: Energy acquisition and risk avoidance can drive prey activity, but mechanistic understanding of the process through which prey traits mediate this energy-safety trade-off is lacking. We analyzed daily activity in winter of two winter-adapted prey that differ in antipredator strategies and physiological constraints on activity: snowshoe hares (<i>Lepus americanus</i>) and North American porcupines (<i>Erethizon dorsatum</i>). First, we constructed an energetics-based model to predict daily activity time for each species based on temperature and corresponding mass-dependent thermoregulatory costs. We then examined what factors drove the deviations of observed activity from the model prediction based on energy balance, considering individual variation. Neither hares nor porcupines achieved energy balance when temperatures fell below the thermoneutral zone. Activity deviations of hares from energy balance were explained by environmental factors associated with risk and energetics, with nominal individual variation being exhibited. In contrast, activity deviations of porcupines were independent of the environmental factors, and porcupines exhibited pronounced individual variation in activity. Our findings highlight that antipredator strategies and physiological constraints on activity can mediate the behavioral energy-safety trade-off of prey. Notably, morphological protection and energy reserves facilitate behavioral flexibility by relaxing the energy-safety trade-off, whereas dependence on behavioral predator avoidance limits the behavioral flexibility of prey.
DOI: 10.6084/m9.figshare.30132247.v2
title: Energy-safety trade-offs differ between winter-adapted species of prey to drive distinct activity patterns

description: Energy acquisition and risk avoidance can drive prey activity, but mechanistic understanding of the process through which prey traits mediate this energy-safety trade-off is lacking. We analyzed daily activity in winter of two winter-adapted prey that differ in antipredator strategies and physiological constraints on activity: snowshoe hares (<i>Lepus americanus</i>) and North American porcupines (<i>Erethizon dorsatum</i>). First, we constructed an energetics-based model to predict daily activity time for each species based on temperature and corresponding mass-dependent thermoregulatory costs. We then examined what factors drove the deviations of observed activity from the model prediction based on energy balance, considering individual variation. Neither hares nor porcupines achieved energy balance when temperatures fell below the thermoneutral zone. Activity deviations of hares from energy balance were explained by environmental factors associated with risk and energetics, with nominal individual variation being exhibited. In contrast, activity deviations of porcupines were independent of the environmental factors, and porcupines exhibited pronounced individual variation in activity. Our findings highlight that antipredator strategies and physiological constraints on activity can mediate the behavioral energy-safety trade-off of prey. Notably, morphological protection and energy reserves facilitate behavioral flexibility by relaxing the energy-safety trade-off, whereas dependence on behavioral predator avoidance limits the behavioral flexibility of prey.
DOI: 10.5281/zenodo.15309728
title: Habitat use and activity budget in the Falkland Steamer Duck (Tachyeres brachypterus)

description: GPS location data used in paper on "Habitat use and activity budget in the Falkland Steamer Duck (Tachyeres brachypterus)"
DOI: 10.5281/zenodo.15309729
title: Habitat use and activity budget in the Falkland Steamer Duck (Tachyeres brachypterus)

description: GPS location data used in paper on "Habitat use and activity budget in the Falkland Steamer Duck (Tachyeres brachypterus)"
DOI: 10.6084/m9.figshare.30132247.v1
title: Energy-safety trade-offs differ between winter-adapted species of prey to drive distinct activity patterns

description: Energy acquisition and risk avoidance can drive prey activity, but mechanistic understanding of the process through which prey traits mediate this energy-safety trade-off is lacking. We analyzed daily activity in winter of two winter-adapted prey that differ in antipredator strategies and physiological constraints on activity: snowshoe hares (<i>Lepus americanus</i>) and North American porcupines (<i>Erethizon dorsatum</i>). First, we constructed an energetics-based model to predict daily activity time for each species based on temperature and corresponding mass-dependent thermoregulatory costs. We then examined what factors drove the deviations of observed activity from the model prediction based on energy balance, considering individual variation. Neither hares nor porcupines achieved energy balance when temperatures fell below the thermoneutral zone. Activity deviations of hares from energy balance were explained by environmental factors associated with risk and energetics, with nominal individual variation being exhibited. In contrast, activity deviations of porcupines were independent of the environmental factors, and porcupines exhibited pronounced individual variation in activity. Our findings highlight that antipredator strategies and physiological constraints on activity can mediate the behavioral energy-safety trade-off of prey. Notably, morphological protection and energy reserves facilitate behavioral flexibility by relaxing the energy-safety trade-off, whereas dependence on behavioral predator avoidance limits the behavioral flexibility of prey.
DOI: 10.15468/dl.cs8k6d
title: Occurrence Download

description: A dataset containing 192 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mytilus platensis",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is one of (Absent)"
 ]
 }
 }
 ]
}

 The dataset includes 192 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018324-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kdvh2r
title: Occurrence Download

description: A dataset containing 951 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Material sample, Material citation, Specimen, Living Specimen, Occurrence evidence)",
 "Continent is Asia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cifuna locuples"
 ]
}

 The dataset includes 951 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018325-250914085247600/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.30164509
title: scMAGCA-Horizontal

description: The horizontal datasets used for scMAGCA.
DOI: 10.6084/m9.figshare.30164509.v1
title: scMAGCA-Horizontal

description: The horizontal datasets used for scMAGCA.
DOI: 10.6084/m9.figshare.29949101
title: <b>AI-assisted data extraction helps uncover spatiotemporal patterns and socioeconomic drivers of wildlife crime involving sea turtles</b>

description: Crimes associated with an ever-increasing demand for wildlife products are one of the most notable threats to marine and freshwater ecosystems. To combat such crimes, it is crucial to identify their spatiotemporal patterns and hotspots, which have largely been overlooked in previous research. However, especially after the emergence of large language models (LLMs), this process has become more time-efficient and accurate. In this study, we analyzed spatiotemporal patterns and socioeconomic drivers of wildlife crime in sea turtles, using Deepseek to extract data from 247 court verdicts. DeepSeek data extraction reached an accuracy of over 99% in extracting 25 items from each verdict. We found that most individual sea turtles and products were seized in southeastern coastal cities of China and identified two main trafficking hotspots. First, nearly 73% (450/613) of the hawksbill turtles and 84% (325/386) of the green turtles were seized or originated from Hainan province, China. Second, nearly 98% (207/211) of the loggerhead turtles were seized from Zhoushan, Zhejiang province. Moreover, nearly all the manufactured products (over 99%, mainly made of tortoiseshell) were seized or originated from Hainan. Destinations of trafficking tended to be northern inland cities, with one main hotspot: 5.5% (68/1,236) of individuals and 30.3% (8,896/29,323) of the products were seized in Xuzhou, Jiangsu province, which originated from Hainan. Our study highlights how AI tools can boost biodiversity conservation research by leveraging large datasets. In doing so, we were able to identify major hotspots of wildlife crime, as well as main trafficking routes. These findings might be relevant for law enforcement efforts and help to enhance sea turtle conservation.
DOI: 10.6084/m9.figshare.29949101.v1
title: <b>AI-assisted data extraction helps uncover spatiotemporal patterns and socioeconomic drivers of wildlife crime involving sea turtles</b>

description: Crimes associated with an ever-increasing demand for wildlife products are one of the most notable threats to marine and freshwater ecosystems. To combat such crimes, it is crucial to identify their spatiotemporal patterns and hotspots, which have largely been overlooked in previous research. However, especially after the emergence of large language models (LLMs), this process has become more time-efficient and accurate. In this study, we analyzed spatiotemporal patterns and socioeconomic drivers of wildlife crime in sea turtles, using Deepseek to extract data from 247 court verdicts. DeepSeek data extraction reached an accuracy of over 99% in extracting 25 items from each verdict. We found that most individual sea turtles and products were seized in southeastern coastal cities of China and identified two main trafficking hotspots. First, nearly 73% (450/613) of the hawksbill turtles and 84% (325/386) of the green turtles were seized or originated from Hainan province, China. Second, nearly 98% (207/211) of the loggerhead turtles were seized from Zhoushan, Zhejiang province. Moreover, nearly all the manufactured products (over 99%, mainly made of tortoiseshell) were seized or originated from Hainan. Destinations of trafficking tended to be northern inland cities, with one main hotspot: 5.5% (68/1,236) of individuals and 30.3% (8,896/29,323) of the products were seized in Xuzhou, Jiangsu province, which originated from Hainan. Our study highlights how AI tools can boost biodiversity conservation research by leveraging large datasets. In doing so, we were able to identify major hotspots of wildlife crime, as well as main trafficking routes. These findings might be relevant for law enforcement efforts and help to enhance sea turtle conservation.
DOI: 10.5281/zenodo.17158677
title: Data from: A single-phonon directional coupler

description: Source data for Figures.
DOI: 10.5281/zenodo.17158678
title: Data from: A single-phonon directional coupler

description: Source data for Figures.
DOI: 10.25403/upresearchdata.30127474
title: Financial metrics for effective tax rate calculations in Johannesburg Stock Exchange (JSE) top 40 South African companies (2004-2024)

description: This dataset comprises longitudinal financial metrics extracted from the annual financial statements of 22 South African companies listed in the Johannesburg Stock Exchange (JSE) Top 40 Index, spanning 21 years from 2004 to 2024. The data were sourced primarily from the Iress financial database, a reputable platform providing standardised financial reporting for JSE-listed entities, and cross-verified against publicly available company annual financial statements (AFS) accessed via investor relations websites to ensure accuracy and address any gaps.The dataset focuses on key variables essential for calculating Effective Tax Rate (ETR) proxies to assess corporate tax avoidance levels. These include:Income Tax Expense: Total tax expense (current and deferred) as reported in the consolidated statement of comprehensive income, used in GAAP ETR and CFM A calculations.Profit Before Tax: Net profit before taxation from the income statement, serving as the denominator for GAAP ETR and Cash ETR.Taxation Paid: Actual cash outflows for taxes (including corporate income tax, royalties, and settlements) from the cash flow statement, applied in Cash ETR and CFM B.Cash Generated by Operations: Cash inflows from core business activities before interest and taxes, utilised as the denominator for CFM A and CFM B.The sample was purposefully selected from the JSE Top 40 Index, which represents over 80% of the JSE's market capitalisation and spans sectors such as mining (e.g., Anglo American Platinum), banking (e.g., Absa Group), retail (e.g., Shoprite Holdings), telecommunications (e.g., Vodacom Group), and others. Exclusions were applied for non-South African companies (16 excluded), those listed for fewer than 15 years (4 excluded), and sectors like insurance/REITs (2 excluded) to ensure relevance to South African tax jurisdiction and data completeness. Banking companies (6) were analysed separately for non-cash flow measures due to negative operating cash flows distorting CFM proxies. Data cleaning involved truncating ETR values to 0-100% to handle outliers from negative denominators or tax refunds, yielding approximately 400 company-year observations after processing.Data were collected via targeted Iress requests and manual AFS verification. ETR proxies were computed as follows:GAAP ETR: Income tax expense / Profit before tax.Cash ETR: Taxation paid / Profit before tax.CFM A: Income tax expense / Cash generated by operations.CFM B: Taxation paid / Cash generated by operations. Long-run versions were calculated over three-year rolling periods to mitigate short-term volatility. Analyses were performed using Microsoft Excel and IBM SPSS version 30 for descriptive and inferential statistics.<br>
DOI: 10.25403/upresearchdata.30127474.v1
title: Financial metrics for effective tax rate calculations in Johannesburg Stock Exchange (JSE) top 40 South African companies (2004-2024)

description: This dataset comprises longitudinal financial metrics extracted from the annual financial statements of 22 South African companies listed in the Johannesburg Stock Exchange (JSE) Top 40 Index, spanning 21 years from 2004 to 2024. The data were sourced primarily from the Iress financial database, a reputable platform providing standardised financial reporting for JSE-listed entities, and cross-verified against publicly available company annual financial statements (AFS) accessed via investor relations websites to ensure accuracy and address any gaps.The dataset focuses on key variables essential for calculating Effective Tax Rate (ETR) proxies to assess corporate tax avoidance levels. These include:Income Tax Expense: Total tax expense (current and deferred) as reported in the consolidated statement of comprehensive income, used in GAAP ETR and CFM A calculations.Profit Before Tax: Net profit before taxation from the income statement, serving as the denominator for GAAP ETR and Cash ETR.Taxation Paid: Actual cash outflows for taxes (including corporate income tax, royalties, and settlements) from the cash flow statement, applied in Cash ETR and CFM B.Cash Generated by Operations: Cash inflows from core business activities before interest and taxes, utilised as the denominator for CFM A and CFM B.The sample was purposefully selected from the JSE Top 40 Index, which represents over 80% of the JSE's market capitalisation and spans sectors such as mining (e.g., Anglo American Platinum), banking (e.g., Absa Group), retail (e.g., Shoprite Holdings), telecommunications (e.g., Vodacom Group), and others. Exclusions were applied for non-South African companies (16 excluded), those listed for fewer than 15 years (4 excluded), and sectors like insurance/REITs (2 excluded) to ensure relevance to South African tax jurisdiction and data completeness. Banking companies (6) were analysed separately for non-cash flow measures due to negative operating cash flows distorting CFM proxies. Data cleaning involved truncating ETR values to 0-100% to handle outliers from negative denominators or tax refunds, yielding approximately 400 company-year observations after processing.Data were collected via targeted Iress requests and manual AFS verification. ETR proxies were computed as follows:GAAP ETR: Income tax expense / Profit before tax.Cash ETR: Taxation paid / Profit before tax.CFM A: Income tax expense / Cash generated by operations.CFM B: Taxation paid / Cash generated by operations. Long-run versions were calculated over three-year rolling periods to mitigate short-term volatility. Analyses were performed using Microsoft Excel and IBM SPSS version 30 for descriptive and inferential statistics.<br>
DOI: 10.5281/zenodo.16895367
title: Waterun website

description: The WATERUN project website(https://www.waterun.eu/) is the main online communication and dissemination platform of the consortium. It provides information about the project’s objectives, methodology, case studies, partners, and key results. The website hosts public deliverables, news and updates, as well as details on events and publications produced within the project. It is designed to ensure transparency, visibility, and outreach to stakeholders, policy makers, researchers, and the general public interested in water management, diffuse pollution, and innovative solutions for reducing pollutant runoff.

The website is regularly updated throughout the project lifetime to provide access to the latest outcomes and dissemination materials.
DOI: 10.5281/zenodo.16895368
title: Waterun website

description: The WATERUN project website(https://www.waterun.eu/) is the main online communication and dissemination platform of the consortium. It provides information about the project’s objectives, methodology, case studies, partners, and key results. The website hosts public deliverables, news and updates, as well as details on events and publications produced within the project. It is designed to ensure transparency, visibility, and outreach to stakeholders, policy makers, researchers, and the general public interested in water management, diffuse pollution, and innovative solutions for reducing pollutant runoff.

The website is regularly updated throughout the project lifetime to provide access to the latest outcomes and dissemination materials.
DOI: 10.15468/dl.vpkf38
title: Occurrence Download

description: A dataset containing 220767 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Muhlenbergia schreberi, Centaurea diluta, Cycloloma atriplicifolium, Persicaria nepalensis, Pueraria lobata, Araujia sericifera, Oenothera adriatica, Oenothera sesitensis, Cestrum parqui, Hypothenemus hampei, Melogale, Procyon, Tarebia granifera, Paspalum botterii, Flemingia strobilifera, Haematoxylum campechianum, Latrodectus geometricus, Coptotermes formosanus, Epilachna varivestis, Euscepes postfasciatus)",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation)",
 "HasCoordinate is true",
 "Year is greater than or equal to 1990"
 ]
}

 The dataset includes 220767 records from 635 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018309-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p8gqmb
title: Occurrence Download

description: A dataset listing the 2767 species recorded in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((-8.69112 41.17418,-8.68856 41.17003,-8.69145 41.16705,-8.6857 41.16364,-8.68674 41.15956,-8.68317 41.1579,-8.6811 41.15367,-8.6754 41.14752,-8.66669 41.14736,-8.66401 41.1481,-8.6583 41.14601,-8.65344 41.14789,-8.64778 41.14793,-8.63691 41.14822,-8.63 41.14447,-8.62595 41.14443,-8.62151 41.14245,-8.61407 41.14002,-8.60735 41.14088,-8.6005 41.14133,-8.59511 41.13982,-8.58958 41.14035,-8.58598 41.14311,-8.58262 41.14293,-8.57812 41.144,-8.56223 41.16436,-8.57171 41.18253,-8.59248 41.1846,-8.6151 41.18493,-8.64893 41.18126,-8.67074 41.1816,-8.69112 41.17418))"
 ]
}

 The dataset's 2767 records were derived from 140 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018317-250914085247600/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/13107527
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.16813019
title: CleanCityCover User Manual Version 1.0 (structural and functional connectivity of diffuse pollution)

description: User manual for the prototype of the identification tool and handbook on the assessment of structural and functional connectivity of diffuse pollution
DOI: 10.5281/zenodo.16813020
title: CleanCityCover User Manual Version 1.0 (structural and functional connectivity of diffuse pollution)

description: User manual for the prototype of the identification tool and handbook on the assessment of structural and functional connectivity of diffuse pollution
DOI: 10.5258/soton/d3660
title: Dataset for 'Challenging' educational journeys: how do children and young people experience education in prison and secure settings?'

description: Dataset supports thesis titled: ''Challenging' educational journeys: how do children and young people experience education in prison and secure settings?'

Thesis for the degree of Doctorate in Educational Psychology, Gallacher, Alana (2025).