cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31032458
title: Assessing food insecurity and its determinants among local communities inside and outside protected areas in northeastern Cambodia

description: Food insecurity is a major global concern. This study used the Household Food Insecurity Access Scale (HFIAS) to assess food insecurity and its drivers among communities living inside (Group 1) and outside (Group 2) protected areas in Cambodia. Ordinal logistic regression showed that households with higher education levels, non-Indigenous heads, residence outside protected areas, no wild food plant collection, and medium to high incomes were more food secure. Overall, only 11.40% of households were food secure, with Group 2 showing 11.55% higher food security than Group 1. Achieving zero hunger by 2030 in Cambodia will require urgent, targeted policy interventions, particularly for Indigenous people and communities within protected areas.
DOI: 10.6084/m9.figshare.31032458.v1
title: Assessing food insecurity and its determinants among local communities inside and outside protected areas in northeastern Cambodia

description: Food insecurity is a major global concern. This study used the Household Food Insecurity Access Scale (HFIAS) to assess food insecurity and its drivers among communities living inside (Group 1) and outside (Group 2) protected areas in Cambodia. Ordinal logistic regression showed that households with higher education levels, non-Indigenous heads, residence outside protected areas, no wild food plant collection, and medium to high incomes were more food secure. Overall, only 11.40% of households were food secure, with Group 2 showing 11.55% higher food security than Group 1. Achieving zero hunger by 2030 in Cambodia will require urgent, targeted policy interventions, particularly for Indigenous people and communities within protected areas.
DOI: 10.15468/dl.samrx8
title: Occurrence Download

description: A dataset containing 105 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((71.94226 22.691134, 71.86614299999999 22.685557, 71.79191899999999 22.668966, 71.72143 22.641772, 71.656426 22.604651, 71.59851500000001 22.558525, 71.549125 22.504537, 71.509471 22.444025, 71.480519 22.378486, 71.462969 22.309541, 71.457238 22.238889, 71.463449 22.168272, 71.481432 22.099427, 71.510728 22.034045, 71.550603 21.973732, 71.600069 21.919963, 71.657904 21.874057, 71.72268699999999 21.837134, 71.792832 21.810097, 71.866623 21.793607, 71.94226 21.788065, 72.017897 21.793607, 72.091688 21.810097, 72.161833 21.837134, 72.22661600000001 21.874057, 72.284451 21.919963, 72.333917 21.973732, 72.37379199999999 22.034045, 72.403088 22.099427, 72.421071 22.168272, 72.42728200000001 22.238889, 72.42155099999999 22.309541, 72.40400099999999 22.378486, 72.375049 22.444025, 72.33539500000001 22.504537, 72.286005 22.558525, 72.228094 22.604651, 72.16309 22.641772, 72.092601 22.668966, 72.018377 22.685557, 71.94226 22.691134))"},{"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 105 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082303-251120083545085/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.29862990
title: Naphthalen-1-yl 2,4,6-trimethyl benzenesulfonate enhances cadmium tolerance in <i>Zea mays</i> by boosting antioxidant defense and photosynthetic efficiency

description: Cadmium (Cd) contamination in agricultural soils is a serious environmental issue that reduces crop yield and threatens food safety and human health. While conventional chelators enhance Cd uptake, they often impair plant health. This study investigates whether naphthalene-1-yl 2,4,6-trimethyl benzenesulfonate (NTB) acts as a dual-functional agent by enhancing Cd uptake and reducing toxicity in maize. Maize seedlings pretreated with 0.25 mM NTB and exposed to 100 µM CdCl<sub>2</sub> showed a 69% increase in Cd accumulation. NTB reduced oxidative stress, lowering TBARS and H<sub>2</sub>O<sub>2</sub> levels by 37% and 34%, respectively. Proline accumulation rose by only 12%, and RWC increased by 9%, indicating improved stress tolerance. Antioxidant enzyme responses shifted: SOD activity declined by 71%, while CAT, APX, and GPX decreased by 24%, 19%, and 37%. NTB also elevated antioxidants, including gallic acid (61%), epicatechin (71%), vanillic acid (42%), and rosmarinic acid (64%). Total chlorophyll increased by 68%, and gas exchange parameters improved: <i>P<sub>n</sub></i> by 70%, <i>T<sub>r</sub></i> by 46%, <i>g<sub>s</sub></i> by 14%, and <i>Ci</i> by 72%. Fluorescence parameters also improved, with Fv/Fm and ΦPSII increasing by 19% and 14%, and NPQ decreasing by 14%. These results show that NTB enhances Cd uptake while maintaining physiological balance, offering a promising phytoremediation strategy. NTB is a structurally unique, dual-function phytoremediation agent that simultaneously enhances Cd uptake and protects plant physiology. Its combined chelating and antioxidant properties offer a safer, more effective alternative to conventional agents like EDTA, supporting both contaminant removal and crop health in Cd-polluted soils.
DOI: 10.6084/m9.figshare.29862990.v1
title: Naphthalen-1-yl 2,4,6-trimethyl benzenesulfonate enhances cadmium tolerance in <i>Zea mays</i> by boosting antioxidant defense and photosynthetic efficiency

description: Cadmium (Cd) contamination in agricultural soils is a serious environmental issue that reduces crop yield and threatens food safety and human health. While conventional chelators enhance Cd uptake, they often impair plant health. This study investigates whether naphthalene-1-yl 2,4,6-trimethyl benzenesulfonate (NTB) acts as a dual-functional agent by enhancing Cd uptake and reducing toxicity in maize. Maize seedlings pretreated with 0.25 mM NTB and exposed to 100 µM CdCl<sub>2</sub> showed a 69% increase in Cd accumulation. NTB reduced oxidative stress, lowering TBARS and H<sub>2</sub>O<sub>2</sub> levels by 37% and 34%, respectively. Proline accumulation rose by only 12%, and RWC increased by 9%, indicating improved stress tolerance. Antioxidant enzyme responses shifted: SOD activity declined by 71%, while CAT, APX, and GPX decreased by 24%, 19%, and 37%. NTB also elevated antioxidants, including gallic acid (61%), epicatechin (71%), vanillic acid (42%), and rosmarinic acid (64%). Total chlorophyll increased by 68%, and gas exchange parameters improved: <i>P<sub>n</sub></i> by 70%, <i>T<sub>r</sub></i> by 46%, <i>g<sub>s</sub></i> by 14%, and <i>Ci</i> by 72%. Fluorescence parameters also improved, with Fv/Fm and ΦPSII increasing by 19% and 14%, and NPQ decreasing by 14%. These results show that NTB enhances Cd uptake while maintaining physiological balance, offering a promising phytoremediation strategy. NTB is a structurally unique, dual-function phytoremediation agent that simultaneously enhances Cd uptake and protects plant physiology. Its combined chelating and antioxidant properties offer a safer, more effective alternative to conventional agents like EDTA, supporting both contaminant removal and crop health in Cd-polluted soils.
DOI: 10.6084/m9.figshare.31032461
title: Which factors influence rider injury severity in powered two-wheeler accidents? A systematic review

description: The high accident risk associated with Powered Two-Wheelers (PTWs) and the limited generalizability of conclusions due to regional data disparities necessitate a comprehensive global analysis. This systematic review aims to identify the influencing factors of injury severity in PTW accidents worldwide. This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We conducted a comprehensive review of scientific literature on PTW accidents, ultimately identifying 32 high-quality peer-reviewed papers. The findings of these studies were thoroughly examined and synthesized using a six-dimensional classification framework (encompassing road, collision, vehicle, time, human, and environmental factors). The results indicated a consensus that the involvement of heavy vehicles, nighttime, weekends/holidays, summer, young/elderly riders, novice riders, drunk driving, frontal collisions, and running-off-the-road collisions significantly increase the severity of PTW accidents. Conversely, factors such as road classification, road geometry, intersections, weather conditions, and rider gender remain controversial, with the discrepancies likely stemming from the complex interplay of multiple variables. This systematic review integrates findings from diverse countries, offering valuable insights for global policymakers aiming to mitigate PTW accident severity. Furthermore, we identified methodological gaps in existing research and proposed future directions, thereby providing multiple avenues for researchers engaged in road safety studies.
DOI: 10.6084/m9.figshare.31032461.v1
title: Which factors influence rider injury severity in powered two-wheeler accidents? A systematic review

description: The high accident risk associated with Powered Two-Wheelers (PTWs) and the limited generalizability of conclusions due to regional data disparities necessitate a comprehensive global analysis. This systematic review aims to identify the influencing factors of injury severity in PTW accidents worldwide. This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We conducted a comprehensive review of scientific literature on PTW accidents, ultimately identifying 32 high-quality peer-reviewed papers. The findings of these studies were thoroughly examined and synthesized using a six-dimensional classification framework (encompassing road, collision, vehicle, time, human, and environmental factors). The results indicated a consensus that the involvement of heavy vehicles, nighttime, weekends/holidays, summer, young/elderly riders, novice riders, drunk driving, frontal collisions, and running-off-the-road collisions significantly increase the severity of PTW accidents. Conversely, factors such as road classification, road geometry, intersections, weather conditions, and rider gender remain controversial, with the discrepancies likely stemming from the complex interplay of multiple variables. This systematic review integrates findings from diverse countries, offering valuable insights for global policymakers aiming to mitigate PTW accident severity. Furthermore, we identified methodological gaps in existing research and proposed future directions, thereby providing multiple avenues for researchers engaged in road safety studies.
DOI: 10.15468/dl.dtdmaa
title: Occurrence Download

description: A dataset containing 21373 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Country is one of (Mexico, Guatemala, Belize, Honduras, El Salvador, Nicaragua, Costa Rica, Panama)",
 "OccurrenceStatus is Present",
 "TaxonKey is Asclepias",
 "Year 1980-2026"
 ]
}

 The dataset includes 21373 records from 198 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082302-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/fy2tbc
title: Replication Data for: You and Whose Economy?: Group-Based Retrospection in Economic Voting

description: When managing the economy, governments make decisions that influence not only overall growth but also its distribution. How do voters judge incumbents for this? I revisit the idea of &lt;em&gt;group-based retrospective voting&lt;/em&gt; and argue that voters assess the economic performance of their social in-groups relative to the national economy. By sanctioning the incumbent for in-group performance, voters can incentivize policy-making that better aligns with their interests. I test the theory, first, by estimating the relationship between in-group performance and incumbent support in panel data. This relationship is comparable in magnitude to sociotropic voting. I further conduct three experiments in Denmark and the United States, randomizing information about the performance of groups defined by geography, age, education, ethnicity and class. The findings suggest there are limits to sociotropic voting, as voters want their groups to follow or beat the national trend. This has important implications for electoral accountability and party competition.
description: This dataset underwent an independent verification process, complying with the AJPS Verification Policy updated June 2023, which replicated the tables and figures in the primary article. For the supplementary materials, verification was performed solely for the successful execution of the code. The verification process was carried out by the Cornell Center for Social Sciences at Cornell University. 
<br>
 
The associated article has been awarded the Open Materials Badge. Learn more about the Open Practice Badges from the Center for Open Science. 
<br>


<br>
Open Materials Badge
DOI: 10.15468/dl.549f6w
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bothrops pirajai"
 ]
}

 The dataset includes 15 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082304-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h3c35e
title: Occurrence Download

description: A dataset containing 122 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((71.87514899999999 22.826466, 71.798957 22.82089, 71.72466 22.804298, 71.65410199999999 22.777104, 71.589034 22.739983, 71.53106699999999 22.693857, 71.48163 22.639869, 71.44193799999999 22.579358, 71.412959 22.51382, 71.395394 22.444875, 71.38966000000001 22.374224, 71.395878 22.303608, 71.41388000000001 22.234765, 71.44320500000001 22.169385, 71.48312 22.109073, 71.532633 22.055306, 71.590524 22.009401, 71.65536899999999 21.97248, 71.72557999999999 21.945444, 71.799441 21.928954, 71.87514899999999 21.923413, 71.950857 21.928954, 72.02471799999999 21.945444, 72.09492899999999 21.97248, 72.159774 22.009401, 72.217665 22.055306, 72.267178 22.109073, 72.30709299999999 22.169385, 72.33641799999999 22.234765, 72.35442 22.303608, 72.36063799999999 22.374224, 72.354904 22.444875, 72.337339 22.51382, 72.30835999999999 22.579358, 72.26866800000001 22.639869, 72.21923099999999 22.693857, 72.161264 22.739983, 72.09619600000001 22.777104, 72.025638 22.804298, 71.951341 22.82089, 71.87514899999999 22.826466))"},{"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 122 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082305-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bqjmwv
title: Occurrence Download

description: A dataset containing 105 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((71.992183 22.701419, 71.916061 22.695843, 71.84183 22.679251, 71.771337 22.652057, 71.706328 22.614936, 71.64841199999999 22.56881, 71.599019 22.514822, 71.55936199999999 22.45431, 71.53040799999999 22.388772, 71.512857 22.319826, 71.507126 22.249174, 71.51333700000001 22.178557, 71.531322 22.109712, 71.56062 22.044331, 71.600498 21.984018, 71.649967 21.93025, 71.70780600000001 21.884343, 71.772595 21.847421, 71.842744 21.820384, 71.916541 21.803894, 71.992183 21.798352, 72.067825 21.803894, 72.141622 21.820384, 72.211771 21.847421, 72.27656 21.884343, 72.334399 21.93025, 72.38386800000001 21.984018, 72.42374599999999 22.044331, 72.45304400000001 22.109712, 72.471029 22.178557, 72.47723999999999 22.249174, 72.471509 22.319826, 72.453958 22.388772, 72.425004 22.45431, 72.385347 22.514822, 72.335954 22.56881, 72.278038 22.614936, 72.21302900000001 22.652057, 72.14253600000001 22.679251, 72.068305 22.695843, 71.992183 22.701419))"},{"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 105 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082306-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5apb3z
title: Occurrence Download

description: A dataset containing 5661 species occurrences available in GBIF matching the query:
{
 "InstitutionKey" : [
 "is a3cb6216-60ad-4ed7-9fa2-4f3b7362165a"
 ]
}

 The dataset includes 5661 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082307-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c4w6cb
title: Occurrence Download

description: A dataset containing 196532 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "Geometry POLYGON((-122.26 37.33,-122.19 37.33,-122.19 37.4,-122.26 37.4,-122.26 37.33))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 196532 records from 129 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082291-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wgucn4
title: Occurrence Download

description: A dataset containing 887427 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Betula pendula",
 "HasCoordinate is true"
 ]
}

 The dataset includes 887427 records from 2867 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082287-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6bbjg2
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Living Specimen, Occurrence evidence)",
 "Continent is North America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is EN",
 "TaxonKey is Odonata"
 ]
}

 The dataset includes 40 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025655-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f3buwx
title: Occurrence Download

description: A dataset containing 70733 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-77.02524 38.87308,-76.99092 38.87308,-76.99092 38.90194,-77.02524 38.90194,-77.02524 38.87308))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 70733 records from 96 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0123000-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5xza4z
title: Occurrence Download

description: A dataset containing 3269 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-86.8528388000000007 33.4916237999999993, -86.8528388000000007 33.4999840999999989, -86.8528388000000007 33.5052092900000034, -86.8528388000000007 33.5118278699999976, -86.8528388000000007 33.5174013999999971, -86.8528388000000007 33.5264584000000028, -86.8486580099999941 33.5264584000000028, -86.8413415200000003 33.5264584000000028, -86.8350702500000011 33.5264584000000028, -86.8249665299999975 33.5264584000000028, -86.8159080199999948 33.5264584000000028, -86.8089400000000069 33.5264584000000028, -86.8089400000000069 33.5208848600000024, -86.8089400000000069 33.5135695999999967, -86.8089400000000069 33.5090410999999975, -86.8089400000000069 33.5034675599999971, -86.8089400000000069 33.4961522999999985, -86.8089400000000069 33.4916237999999993, -86.8148628099999939 33.4916237999999993, -86.8221793000000019 33.4916237999999993, -86.8322830200000055 33.4916237999999993, -86.8382058899999976 33.4916237999999993, -86.8469159899999994 33.4916237999999993, -86.8528388000000007 33.4916237999999993))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1932",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Observation, Specimen, Machine Observation)"
 ]
}

 The dataset includes 3269 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073733-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bjsdxu
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Iberis latealata Porta & Rigo ex Porta"
 ]
}

 The dataset includes 7 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049452-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.whqvrd
title: Occurrence Download

description: A dataset containing 2358 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Syrphus ribesii (Linnaeus, 1758)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Country is Sweden",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 2358 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0276781-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.