cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30631292.v1
title: Bangladesh Road Traffic Accident Dataset (2007–2024): Multi-Source Integration.

description: This dataset presents a comprehensive compilation of 49,566 road traffic accident records from Bangladesh, meticulously consolidated from four authoritative institutional sources: the Accident Research Institute (ARI) of BUET, the Bangladesh Road Transport Authority (BRTA), the Dhaka Metropolitan Police (DMP), and the Military Police (MP) Unit, along with a primary dataset collected directly through structured reporting forms. Spanning a 17-year period (2007–2024), it integrates diverse data streams into a unified, structured, and research-ready resource for traffic safety and accident analysis. Specifically, ARI contributed historical accident records from 2007–2021, BRTA provided official national accident statistics for 2024, DMP supplied detailed urban traffic incident logs from 2020–2024, and the MP Unit furnished verified accident reports covering the same recent period. Additionally, the primary dataset collected via forms captures incident details directly from field reports, ensuring high granularity and accuracy in recorded attributes.The dataset captures a rich variety of attributes, including:Temporal Information: Exact date, time, and year of each recorded accident.Geospatial Data: Detailed location descriptions with latitude and longitude coordinates for spatial analysis.Incident Narratives: Text-based summaries describing the circumstances of each accident.Vehicle and Victim Data: Vehicle types, collision categories, number of vehicles involved, casualty counts, injury details, and victim classifications.Environmental Conditions: Light levels, weather conditions, and road surface status at the time of the incident.Road and Infrastructure Attributes: Road classification, nature, connection type, surface condition, junction types, and traffic control measures.Accident Severity Indicators: Death and injury counts, as well as accident intensity classifications. Preliminary statistical and correlation analyses conducted on the dataset reveal generally weak linear relationships between predictive features and accident severity, suggesting that traditional linear modeling techniques may have limited predictive capability. Consequently, advanced ensemble-based machine learning approaches, which can capture non-linear interactions, mitigate overfitting, and improve predictive robustness, are recommended for modeling and forecasting purposes.This dataset offers a robust empirical foundation for diverse research and policy applications, including traffic safety assessments, accident hotspot mapping, predictive risk modeling, urban transport infrastructure planning, and evidence-based road safety policy formulation. The inclusion of both institutional records and primary field-collected data ensures a level of depth, reliability, and coverage that makes this dataset an invaluable resource for academics, policymakers, urban planners, and data scientists addressing road safety challenges in Bangladesh.
DOI: 10.5281/zenodo.17719146
title: Landsat LST gap-filling experiments by an integrated machine learning approach

description: Regions and dates of the example data are provided in the titles.
DOI: 10.5281/zenodo.17719147
title: Landsat LST gap-filling experiments by an integrated machine learning approach

description: Regions and dates of the example data are provided in the titles.
DOI: 10.15468/dl.hxtskf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phyllachora maydis",
 "GadmGid is one of (USA.6_1, USA.17_1, USA.27_1, USA.28_1, USA.32_1, USA.35_1, USA.37_1, USA.42_1, USA.44_1, USA.51_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Human Observation, Machine Observation)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009082-251120083545085/datasets/export for details.
DOI: 10.57783/0hb2-nw96
title: Iowa HCUP
DOI: 10.15468/dl.adgqt3
title: Occurrence Download

description: A dataset containing 50358 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is one of (Asia, North America)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Magnolia"
 ]
}

 The dataset includes 50358 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009055-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25820/data.008131
title: Loss of MRAP2 in MC4R Neurons Protect from Obesity-Associated Autonomic and Cardiovascular Dysfunctions

description: Aims: The melanocortin receptor accessory protein 2 (MRAP2), which is abundantly expressed in the brain including the hypothalamus, has emerged as a key regulator of melanocortin-4 receptor (MC4R) activity. We sought to delineate the physiological significance of MRAP2 in MC4R neurons, with a particular focus on metabolic, autonomic and cardiovascular functions.
description: Methods and results: Selective deletion of MRAP2 in MC4R neurons causes obesity that was associated with hyperphagia and impairment in glucose homeostasis and insulin sensitivity. MC4R agonist Melatonan II (MTII)-induced anorectic effects were blunted in mice lacking MRAP2 in MC4R neurons, whereas Celastrol retained its efficacy in reducing food intake and body weight. MRAP2 deletion also reduced baseline sympathetic nerve activity (SNA), particularly the SNA subserving the kidney. This was associated with reduced innervation of the kidney. In addition, MTII-induced increases in renal and brown adipose tissue (BAT) SNA as well as hepatic vagal nerve activity were significantly attenuated in MC4R neuron MRAP2-deficient mice. Transynaptic tracing revealed that MC4R neurons projecting to BAT and kidneys were localized to specific brain nuclei including the paraventricular nucleus of the hypothalamus, providing anatomical substrate for MRAP2 regulation of sympathetic outflow. Although the loss of MRAP2 in MC4R neurons did not affect arterial pressure, it caused a significant decrease in heart rate and baroreflex sensitivity. Finally, MRAP2 deficiency in MC4R neurons attenuated MTII-induced increase in arterial pressure and heart rate.
description: Conclusion: These findings demonstrate that in addition to its role in energy balance and glucose homeostasis MRAP2 in MC4R neurons is crucial for cardiovascular autonomic regulation and is required for the development of obesity-associated hypertension and autonomic dysfunction.
DOI: 10.7910/dvn/khuvkb
title: PDB - 5VL3: CD22 d1-d3, random seed 1 (311K, 38°C,100 ns)

description: PDB - 5VL3: CD22 d1-d3, random seed 1 (311K, 38°C,100 ns).
DOI: 10.15468/dl.eb3tju
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "TaxonKey is Cirrhaea fuscolutea"
 ]
}

 The dataset includes 13 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009083-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.emdntp
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "Geometry POLYGON((34.43146 15.88227,147.56854 15.88227,147.56854 61.11773,34.43146 61.11773,34.43146 15.88227))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Salsola arbuscula"
 ]
}

 The dataset includes 38 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009084-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2f6k88
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Salsola paulsenii"
 ]
}

 The dataset includes 43 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009085-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9hgn82
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Salsola australis"
 ]
}

 The dataset includes 17 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009086-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3x2veq
title: Occurrence Download

description: A dataset containing 459 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phyllostachys aurea",
 "GadmGid is one of (USA.6_1, USA.17_1, USA.27_1, USA.28_1, USA.32_1, USA.35_1, USA.37_1, USA.42_1, USA.44_1, USA.51_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Human Observation, Machine Observation)"
 ]
}

 The dataset includes 459 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009087-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t2pv7r
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "Geometry POLYGON((20.94428 26.78680,127.05572 26.78680,127.05572 69.21320,20.94428 69.21320,20.94428 26.78680))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Suaeda microphylla"
 ]
}

 The dataset includes 16 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009088-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5cdw8r
title: Occurrence Download

description: A dataset containing 1342157 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ardea alba, Ardea herodias, Arenaria interpres, Asio flammeus, Asio otus)",
 "Country is Canada",
 "OccurrenceStatus is Present",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON ((-84.76571 41.22334, -58.93394 41.22334, -58.93394 49.85477, -84.76571 49.85477, -84.76571 41.22334))",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 1342157 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009058-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/dsfag6
title: PDB - 5VL3: CD22 d1-d3, random seed 1 (312K, 39°C,100 ns)

description: PDB - 5VL3: CD22 d1-d3, random seed 1 (312K, 39°C,100 ns)
DOI: 10.15468/dl.wtjgjy
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Salicornia europaea"
 ]
}

 The dataset includes 11 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009091-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wkrn98
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "Geometry POLYGON((35.13871 23.16548,151.86129 23.16548,151.86129 69.83452,35.13871 69.83452,35.13871 23.16548))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Petrosimonia sibirica"
 ]
}

 The dataset includes 25 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009093-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ugtser
title: Occurrence Download

description: A dataset containing 26171 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus",
 "GadmGid is one of (USA.6_1, USA.17_1, USA.27_1, USA.28_1, USA.32_1, USA.35_1, USA.37_1, USA.42_1, USA.44_1, USA.51_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Human Observation, Machine Observation)"
 ]
}

 The dataset includes 26171 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009092-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17707455
title: FIRMS Global Tuna Atlas

description: The FIRMS Global Tuna Atlas (GTA) builds on historical atlases focusing on one single ocean (Ardill 1995, Carocci and Majkowski 1996) and global experiences and initiatives such as the Atlas of Tuna and Billfish catches (Carocci et al. 2004), the Global Harmonized database for tuna fisheries (Nordtrom and Fonteneau 1996, Fonteneau 1997, 2009, 2010), and the BlueBridge Tuna Atlas (Taconet et al. 2016, 2017) which eventually, in 2019, converged into one coordinated initiative under governance responsibility of the Fisheries and Resources Monitoring System Partnership (FIRMS).

All currently available information is provided by the five tuna Regional Fishery Management Organizations:





Commission for the Conservation of Southern Bluefin Tuna ( CCSBT )




Inter-American Tropical Tuna Commission ( IATTC )




International Commission for the Conservation of Atlantic Tunas ( ICCAT )




Indian Ocean Tuna Commission ( IOTC )




Western & Central Pacific Fisheries Commission ( WCPFC ), in collaboration with the Secretariat of the Pacific Community (SPC)



which, under the coordination of the FIRMS Secretariat, agreed to harmonize the submissions of their public datasets and update the GTA on an annual basis.

In its current version, the GTA disseminates catch data on tuna and tuna-like species. Two of the key products of the GTA are its metadata catalogue and the interactive map viewer, which presents the global distribution of catches in the period from 1950 to 2023 at various levels of spatio-temporal resolutions and for several species of temperate and tropical tuna (including oceanic principal market tuna species and more coastal/neritic species), billfish, tuna-like species such as bonitos and mackerels, and some bycatch species including several species of pelagic sharks. The Global Tuna Atlas includes 4 datasets:





Global annual catches from tuna fisheries (1918-2023) (FIRMS level 0): https://doi.org/10.5281/zenodo.5745958




Global monthly catches from tuna fisheries by 1° and 5° grids (1950-2023) (FIRMS level 0): https://doi.org/10.5281/zenodo.5747174




Global monthly catches from tuna fisheries by 5° grid (1950-2023) (FIRMS level 0): https://doi.org/10.5281/zenodo.5746041




Global monthly catches from tuna surface fisheries by 1° grid (1958-2023) (FIRMS level 0): https://doi.org/10.5281/zenodo.5745986



More information at https://www.fao.org/fishery/en/collection/firms-tuna-atlas
DOI: 10.5281/zenodo.8034730
title: FIRMS Global Tuna Atlas

description: The FIRMS Global Tuna Atlas (GTA) builds on historical atlases focusing on one single ocean (Ardill 1995, Carocci and Majkowski 1996) and global experiences and initiatives such as the Atlas of Tuna and Billfish catches (Carocci et al. 2004), the Global Harmonized database for tuna fisheries (Nordtrom and Fonteneau 1996, Fonteneau 1997, 2009, 2010), and the BlueBridge Tuna Atlas (Taconet et al. 2016, 2017) which eventually, in 2019, converged into one coordinated initiative under governance responsibility of the Fisheries and Resources Monitoring System Partnership (FIRMS).

All currently available information is provided by the five tuna Regional Fishery Management Organizations:





Commission for the Conservation of Southern Bluefin Tuna ( CCSBT )




Inter-American Tropical Tuna Commission ( IATTC )




International Commission for the Conservation of Atlantic Tunas ( ICCAT )




Indian Ocean Tuna Commission ( IOTC )




Western & Central Pacific Fisheries Commission ( WCPFC ), in collaboration with the Secretariat of the Pacific Community (SPC)



which, under the coordination of the FIRMS Secretariat, agreed to harmonize the submissions of their public datasets and update the GTA on an annual basis.

In its current version, the GTA disseminates catch data on tuna and tuna-like species. Two of the key products of the GTA are its metadata catalogue and the interactive map viewer, which presents the global distribution of catches in the period from 1950 to 2023 at various levels of spatio-temporal resolutions and for several species of temperate and tropical tuna (including oceanic principal market tuna species and more coastal/neritic species), billfish, tuna-like species such as bonitos and mackerels, and some bycatch species including several species of pelagic sharks. The Global Tuna Atlas includes 4 datasets:





Global annual catches from tuna fisheries (1918-2023) (FIRMS level 0): https://doi.org/10.5281/zenodo.5745958




Global monthly catches from tuna fisheries by 1° and 5° grids (1950-2023) (FIRMS level 0): https://doi.org/10.5281/zenodo.5747174




Global monthly catches from tuna fisheries by 5° grid (1950-2023) (FIRMS level 0): https://doi.org/10.5281/zenodo.5746041




Global monthly catches from tuna surface fisheries by 1° grid (1958-2023) (FIRMS level 0): https://doi.org/10.5281/zenodo.5745986



More information at https://www.fao.org/fishery/en/collection/firms-tuna-atlas
DOI: 10.25399/unisadata.30689105
title: Teacher Education Students' Use of Technology and AI

description: This exploratory study investigated the readiness and perspectives of first-year pre-service teachers regarding technology integration, with specific reference to AI, in their educational practices as students and future teachers. UNESCO’s AI Competency Framework for Teachers was used to frame this single case study. Within an interpretative paradigm, the study adopted a qualitative approach. Data were generated at an ODL institution in South Africa through open-ended questionnaires completed by 197 Bachelor of Education students. Deductive thematic analysis was used to analyse the data. The findings from the five themes revealed varying levels of readiness, shaped by disparities in access, confidence, and familiarity with AI tools. Many participants lacked foundational skills and called for structured training and ongoing professional development to build competencies in ethical, pedagogical and practical use of AI. The study concludes that effective integration of AI into sustainable teacher education must be inclusive and context-responsive, especially in under-resourced ODL environments. Limitations include a single case study with one instrument and self-reported data, which calls for follow-up research. The paper contributes valuable insights into how teacher education programmes can prepare future teachers to use AI responsibly and effectively in support of inclusive and quality education for all.
DOI: 10.25399/unisadata.30689105.v1
title: Teacher Education Students' Use of Technology and AI

description: This exploratory study investigated the readiness and perspectives of first-year pre-service teachers regarding technology integration, with specific reference to AI, in their educational practices as students and future teachers. UNESCO’s AI Competency Framework for Teachers was used to frame this single case study. Within an interpretative paradigm, the study adopted a qualitative approach. Data were generated at an ODL institution in South Africa through open-ended questionnaires completed by 197 Bachelor of Education students. Deductive thematic analysis was used to analyse the data. The findings from the five themes revealed varying levels of readiness, shaped by disparities in access, confidence, and familiarity with AI tools. Many participants lacked foundational skills and called for structured training and ongoing professional development to build competencies in ethical, pedagogical and practical use of AI. The study concludes that effective integration of AI into sustainable teacher education must be inclusive and context-responsive, especially in under-resourced ODL environments. Limitations include a single case study with one instrument and self-reported data, which calls for follow-up research. The paper contributes valuable insights into how teacher education programmes can prepare future teachers to use AI responsibly and effectively in support of inclusive and quality education for all.
DOI: 10.5281/zenodo.17591884
title: Immune and molecular aging responses in analog astronauts during the AMADEE-24 MARS analog mission: code and data for reproducing the results

description: This submission contains code and data for reproducing the results of the manuscript "Immune and molecular aging responses in analog astronauts during the AMADEE-24 MARS analog mission."
DOI: 10.5281/zenodo.17591883
title: Immune and molecular aging responses in analog astronauts during the AMADEE-24 MARS analog mission: code and data for reproducing the results

description: This submission contains code and data for reproducing the results of the manuscript "Immune and molecular aging responses in analog astronauts during the AMADEE-24 MARS analog mission."
DOI: 10.5281/zenodo.17707488
title: Ipseity Daily Data

description: At what rate do individuals say they are X?

Begining on 2025-07-08, I surveyed a small random sample of American adults on a daily cadence.  They responded to many items of the form: Does <signifier> describe you today?

The file ipseity-daily-aggregated.csv in this repository contains the aggregated results through 2025-07-28.  The columns are as follows:



signifier - a word, phrase or emoji

yes_count - the number of respondents who answered yes (aggregated all-time).

no_count - the number of respondents who answered no (aggregated all-time).

prevalence_per_10k - an estimate of the prevalence per 10,000.  If we were to survey 10,000 American adults, how many would we expect to respond yes to this signifier?  The number is calculated as yes_count / (yes_count + no_count) * 10,000.

earliest_obs_date - this date represents the first time this particular signifier was ever displayed to a respondent.  Signifiers are chosen with stochasticity from a long list; new signifiers are sometimes added, old signifiers are sometimes deleted, and a signifier may be moved higher or lower in priority for sampling.

latest_obs_date - the date closest to the present date on which signifier was most recently presented to a respondent.


Version created on 2025-11-25 — cumulative data.