cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23614494.v2
title: The Marginal Ice Zone as a dominant source region of atmospheric mercury during central Arctic summertime

description: This dataset was used in the manuscript of "Marginal Ice Zone as a dominant source region of atmospheric mercury during central Arctic summertime".This dataset includes hourly gaseous elemental mercury (GEM) concentrations that measured during  the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, and the corresponding meteorological, atmospheric and marine chemical data.
DOI: 10.6084/m9.figshare.23614494.v1
title: The Marginal Ice Zone as a dominant source region of atmospheric mercury during central Arctic summertime

description: This dataset was used in the manuscript of "Marginal Ice Zone as a dominant source region of atmospheric mercury during central Arctic summertime".This dataset includes hourly gaseous elemental mercury (GEM) concentrations that measured during  the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, and the corresponding meteorological, atmospheric and marine chemical data.
DOI: 10.5517/ccdc.csd.cc2p7j7q
title: CCDC 2479883: Experimental Crystal Structure Determination

description: Related Article: Jiangtao Jia|2025|CSD Communication|||
description: Related Article: Li Ping, Tan Geng, Huang Xin, Sun Fuxing, Jia Jiangtao, Zhu Guangshan, Liu Jia, Li Jiyang|2025|Wuji Huaxue Xuebao|41|2063|doi:10.11862/CJIC.20250020
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.85p4a7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((100.494 1.592182, 100.42371 1.586614, 100.355152 1.570047, 100.290015 1.542889, 100.229903 1.50581, 100.176297 1.459722, 100.130516 1.405761, 100.093689 1.345257, 100.06672 1.279699, 100.050274 1.210702, 100.044754 1.139965, 100.050296 1.069229, 100.066761 1.000237, 100.093746 0.934687, 100.130583 0.874192, 100.176367 0.820242, 100.22997 0.774165, 100.290072 0.7370949999999999, 100.355193 0.709945, 100.423732 0.693383, 100.494 0.687816, 100.564268 0.693383, 100.632807 0.709945, 100.697928 0.7370949999999999, 100.75803 0.774165, 100.811633 0.820242, 100.857417 0.874192, 100.894254 0.934687, 100.921239 1.000237, 100.937704 1.069229, 100.943246 1.139965, 100.937726 1.210702, 100.92128 1.279699, 100.894311 1.345257, 100.857484 1.405761, 100.811703 1.459722, 100.758097 1.50581, 100.697985 1.542889, 100.632848 1.570047, 100.56429 1.586614, 100.494 1.592182))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002439-260120142942310/datasets/export for details.
DOI: 10.15468/dl.ez3u2w
title: Occurrence Download

description: A dataset containing 2637 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Germany",
 "OccurrenceStatus is Present",
 "TaxonKey is Canis lupus"
 ]
}

 The dataset includes 2637 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002437-260120142942310/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.28541272
title: Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques

description: Landslides threaten communities worldwide, resulting in financial, environmental, and human losses. Although some studies have employed machine learning (ML) algorithms and multi-criteria analysis (MCA) for landslide susceptibility mapping (LSM), comparative evaluations of these methods remain scarce, particularly regarding predictor importance, performance metrics, and hyperparameter optimization. This research addresses these gaps by comparing logistic regression (LR), random forest (RF), support vector machines (SVM), and MCA, focusing on landslide susceptibility in Petrópolis, Brazil. The ML models used 29 influencing factors, encompassing geographic, geological, climatic, and anthropogenic variables, where feature importance analysis and hyperparameter tuning were applied to identify the most significant predictors. RF achieved the highest performance, with an accuracy of 0.94, ROC AUC of 0.98, and F1 score of 0.94. SVM and LR also performed well, with ROC AUCs of 0.96 and 0.95 and F1 scores of 0.92 and 0.89, respectively. Conversely, MCA showed lower results, with an accuracy of 0.41, ROC AUC of 0.41, and F1 score of 0.55. We attribute RF’s robustness to its adaptability to diverse variable types, reduced overfitting risk, and high predictive accuracy. These findings underscore RF’s strength in LSM and highlight ML’s potential to support urban planning and mitigate risks in landslide-prone areas. Effective landslide susceptibility analysis is essential for anticipating and mitigating risks.MCA failed to identify non-landslide areas, highlighting its limitations.ML overcomes traditional MCA in landslide susceptibility mapping.RF achieved the highest prediction accuracy for landslide susceptibility, outperforming other methods.ML-based landslide susceptibility mapping ranks susceptibility factors more effectively. Effective landslide susceptibility analysis is essential for anticipating and mitigating risks. MCA failed to identify non-landslide areas, highlighting its limitations. ML overcomes traditional MCA in landslide susceptibility mapping. RF achieved the highest prediction accuracy for landslide susceptibility, outperforming other methods. ML-based landslide susceptibility mapping ranks susceptibility factors more effectively.
DOI: 10.6084/m9.figshare.28541272.v1
title: Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques

description: Landslides threaten communities worldwide, resulting in financial, environmental, and human losses. Although some studies have employed machine learning (ML) algorithms and multi-criteria analysis (MCA) for landslide susceptibility mapping (LSM), comparative evaluations of these methods remain scarce, particularly regarding predictor importance, performance metrics, and hyperparameter optimization. This research addresses these gaps by comparing logistic regression (LR), random forest (RF), support vector machines (SVM), and MCA, focusing on landslide susceptibility in Petrópolis, Brazil. The ML models used 29 influencing factors, encompassing geographic, geological, climatic, and anthropogenic variables, where feature importance analysis and hyperparameter tuning were applied to identify the most significant predictors. RF achieved the highest performance, with an accuracy of 0.94, ROC AUC of 0.98, and F1 score of 0.94. SVM and LR also performed well, with ROC AUCs of 0.96 and 0.95 and F1 scores of 0.92 and 0.89, respectively. Conversely, MCA showed lower results, with an accuracy of 0.41, ROC AUC of 0.41, and F1 score of 0.55. We attribute RF’s robustness to its adaptability to diverse variable types, reduced overfitting risk, and high predictive accuracy. These findings underscore RF’s strength in LSM and highlight ML’s potential to support urban planning and mitigate risks in landslide-prone areas. Effective landslide susceptibility analysis is essential for anticipating and mitigating risks.MCA failed to identify non-landslide areas, highlighting its limitations.ML overcomes traditional MCA in landslide susceptibility mapping.RF achieved the highest prediction accuracy for landslide susceptibility, outperforming other methods.ML-based landslide susceptibility mapping ranks susceptibility factors more effectively. Effective landslide susceptibility analysis is essential for anticipating and mitigating risks. MCA failed to identify non-landslide areas, highlighting its limitations. ML overcomes traditional MCA in landslide susceptibility mapping. RF achieved the highest prediction accuracy for landslide susceptibility, outperforming other methods. ML-based landslide susceptibility mapping ranks susceptibility factors more effectively.
DOI: 10.15468/dl.4wdbz3
title: Occurrence Download

description: A dataset containing 349565 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Observation)",
 "Issue is one of (Geodetic datum assumed WGS84, Coordinate reprojection suspicious, Coordinate precision/uncertainty mismatch, Footprint WKT mismatch, Country derived from coordinates, Continent derived from country, Continent derived from coordinates, Taxon match fuzzy, Taxon match higher rank, Taxon match aggregate, The scientific name ID was not used in backbone matching, The taxon concept ID was not used in backbone matching, Depth not metric, Depth unlikely, Depth min max swapped, Depth non numeric, Elevation min max swapped, Elevation not metric, Elevation non numeric, Multimedia date invalid, Individual count conflicts with occurrence status, Occurrence status inferred from individual count, Occurrence status inferred from basis of record, Ambiguous institution, Ambiguous collection, Institution match none, Collection match none)",
 "OccurrenceStatus is Present",
 "TaxonKey is Acrocephalus dumetorum",
 "Year 2000-2025"
 ]
}

 The dataset includes 349565 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002374-260120142942310/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.27028286
title: Effect of hormone therapy on personality traits: preliminary evidence in older men and women

description: Findings concerning the effects of hormone therapy (HT) on cognition and dementia are mixed, with some trials suggesting increased harm at older ages. Personality, like cognition, changes with dementia, but no clinical trials to date have examined the effects of HT on personality traits. This study aimed to determine the effects of HT on personality traits in older men and women. Secondary data analysis was performed from randomized, double-blind, placebo-controlled cross-over studies of menopausal HT in women and testosterone therapy (TT) in men. Participants were community-dwelling cognitively normal adults (mean age = 75.2 years), including 29 men and 22 women. Three months of hormone intervention (for women, 0.625 mg/day conjugated equine estrogen with or without 2.5 mg/day medroxyprogesterone acetate; for men, 200 mg intramuscular testosterone enanthate every 2 weeks) were crossed over with 3 months of identical placebo with a 3-month washout between intervention phases. The main outcome measure was neuroticism and conscientiousness personality domains and facets assessed with the Revised NEO Personality Inventory (NEO-PI-R) after the active and placebo intervention phases. In linear mixed-effect models, HT in women decreased conscientiousness (<i>p</i> &lt; 0.01) and the conscientiousness facet of achievement striving (<i>p</i> &lt; 0.01), and increased vulnerability, a facet of neuroticism (<i>p</i> &lt; 0.05). Testosterone in men decreased conscientiousness (<i>p</i> &lt; 0.05) and the conscientiousness facet of dutifulness (<i>p</i> &lt; 0.05), and increased vulnerability (<i>p</i> &lt; 0.05). In a preliminary study of healthy older adults, HT and TT formulations produced adverse changes in vulnerability and conscientiousness facets that parallel personality changes in dementia.
DOI: 10.6084/m9.figshare.27028286.v1
title: Effect of hormone therapy on personality traits: preliminary evidence in older men and women

description: Findings concerning the effects of hormone therapy (HT) on cognition and dementia are mixed, with some trials suggesting increased harm at older ages. Personality, like cognition, changes with dementia, but no clinical trials to date have examined the effects of HT on personality traits. This study aimed to determine the effects of HT on personality traits in older men and women. Secondary data analysis was performed from randomized, double-blind, placebo-controlled cross-over studies of menopausal HT in women and testosterone therapy (TT) in men. Participants were community-dwelling cognitively normal adults (mean age = 75.2 years), including 29 men and 22 women. Three months of hormone intervention (for women, 0.625 mg/day conjugated equine estrogen with or without 2.5 mg/day medroxyprogesterone acetate; for men, 200 mg intramuscular testosterone enanthate every 2 weeks) were crossed over with 3 months of identical placebo with a 3-month washout between intervention phases. The main outcome measure was neuroticism and conscientiousness personality domains and facets assessed with the Revised NEO Personality Inventory (NEO-PI-R) after the active and placebo intervention phases. In linear mixed-effect models, HT in women decreased conscientiousness (<i>p</i> &lt; 0.01) and the conscientiousness facet of achievement striving (<i>p</i> &lt; 0.01), and increased vulnerability, a facet of neuroticism (<i>p</i> &lt; 0.05). Testosterone in men decreased conscientiousness (<i>p</i> &lt; 0.05) and the conscientiousness facet of dutifulness (<i>p</i> &lt; 0.05), and increased vulnerability (<i>p</i> &lt; 0.05). In a preliminary study of healthy older adults, HT and TT formulations produced adverse changes in vulnerability and conscientiousness facets that parallel personality changes in dementia.
DOI: 10.6084/m9.figshare.27223561.v1
title: Physician–patient alignment on menopause-associated symptom burden: real-world evidence from the USA and Europe

description: This study aimed to evaluate physician–patient alignment on menopausal symptom burden and impact for women experiencing natural vasomotor symptoms (nVMS) or VMS induced by endocrine therapy for breast cancer (iVMS). For this real-world, cross-sectional survey, physicians from the USA and five European countries provided data for consulting patients experiencing nVMS/iVMS; patients optionally self-reported their experiences. Alignment between physician and patient responses was assessed using weighted Cohen’s <i>κ</i> analysis. Physicians and patients completed 1029 pairs of surveys (846 nVMS; 183 iVMS). In 28.1% of cases for nVMS and 29.6% for iVMS, patients reported more severe vasomotor symptoms (VMS) than physicians; alignment of responses was slight (nVMS, <i>κ</i> = 0.1364, <i>p</i> ≤ 0.0001; iVMS, <i>κ</i> = 0.1014, <i>p</i> = 0.039). For the non-VMS symptoms surveyed, 18.5–34.9% of patients with nVMS and iVMS reported symptoms without a corresponding physician report; sleep disturbances, cognitive difficulties and mood changes were among the symptoms most under-reported by physicians. Alignment regarding the impact of nVMS and iVMS on sleep, mood and overall quality of life was moderate. Only slight to moderate physician–patient alignment was found across all areas surveyed. These findings suggest that physicians often underestimate the severity of VMS and the presence of other menopausal symptoms, highlighting a need to improve physician–patient communication.
DOI: 10.6084/m9.figshare.27223561
title: Physician–patient alignment on menopause-associated symptom burden: real-world evidence from the USA and Europe

description: This study aimed to evaluate physician–patient alignment on menopausal symptom burden and impact for women experiencing natural vasomotor symptoms (nVMS) or VMS induced by endocrine therapy for breast cancer (iVMS). For this real-world, cross-sectional survey, physicians from the USA and five European countries provided data for consulting patients experiencing nVMS/iVMS; patients optionally self-reported their experiences. Alignment between physician and patient responses was assessed using weighted Cohen’s <i>κ</i> analysis. Physicians and patients completed 1029 pairs of surveys (846 nVMS; 183 iVMS). In 28.1% of cases for nVMS and 29.6% for iVMS, patients reported more severe vasomotor symptoms (VMS) than physicians; alignment of responses was slight (nVMS, <i>κ</i> = 0.1364, <i>p</i> ≤ 0.0001; iVMS, <i>κ</i> = 0.1014, <i>p</i> = 0.039). For the non-VMS symptoms surveyed, 18.5–34.9% of patients with nVMS and iVMS reported symptoms without a corresponding physician report; sleep disturbances, cognitive difficulties and mood changes were among the symptoms most under-reported by physicians. Alignment regarding the impact of nVMS and iVMS on sleep, mood and overall quality of life was moderate. Only slight to moderate physician–patient alignment was found across all areas surveyed. These findings suggest that physicians often underestimate the severity of VMS and the presence of other menopausal symptoms, highlighting a need to improve physician–patient communication.
DOI: 10.15468/dl.n7whxk
title: Occurrence Download

description: A dataset containing 2474 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lembotropis nigricans"
 ]
}

 The dataset includes 2474 records from 99 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002441-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g7uwnh
title: Occurrence Download

description: A dataset containing 4652 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leucothoe grayana"
 ]
}

 The dataset includes 4652 records from 115 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002443-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b6n6hs
title: Occurrence Download

description: A dataset containing 189 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Ornithological Collection of the Museu Oceanográfico D. Carlos I"
 ]
}

 The dataset includes 189 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002442-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e244542
title: Millennium College of Nursing Sciences, Awka, Anambra State, 2026/2027 ADMISSION FORM is out call 09031383306 DR MR JAMES (+249031383306) for TRANSCRIPT Collection School Admin Line 09031383306 To apply Also 2026/2027 Nursing form

description: Millennium College of Nursing Sciences, Awka, Anambra State, 2026/2027 ADMISSION FORM is out call 09031383306 DR MR JAMES (+249031383306) for TRANSCRIPT Collection School Admin Line 09031383306 To apply Also 2026/2027 Nursing form
DOI: 10.5281/zenodo.17404682
title: This is an outdated and unnecessary supplementary document, as all the files are already included in the manuscript.
DOI: 10.15468/dl.4b6sdy
title: Occurrence Download

description: A dataset containing 963 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Material sample, Specimen)",
 "GadmGid is IND",
 "Geometry POLYGON((-180 25,180 25,180 90,-180 90,-180 25))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Fagales"
 ]
}

 The dataset includes 963 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002446-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c3gcby
title: Occurrence Download

description: A dataset containing 466 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-73.34099999999999 4.335161, -73.411463 4.329592, -73.480188 4.313019, -73.54548 4.285853, -73.605729 4.248764, -73.659451 4.202665, -73.70532300000001 4.148694, -73.742216 4.08818, -73.769222 4.022616, -73.785679 3.953616, -73.791184 3.88288, -73.785605 3.812149, -73.769081 3.743166, -73.74202200000001 3.677628, -73.705095 3.617147, -73.659212 3.563212, -73.605501 3.51715, -73.545286 3.480093, -73.480047 3.452953, -73.411389 3.436398, -73.34099999999999 3.430834, -73.270611 3.436398, -73.201953 3.452953, -73.136714 3.480093, -73.076499 3.51715, -73.02278800000001 3.563212, -72.976905 3.617147, -72.939978 3.677628, -72.912919 3.743166, -72.896395 3.812149, -72.890816 3.88288, -72.896321 3.953616, -72.912778 4.022616, -72.939784 4.08818, -72.976677 4.148694, -73.022549 4.202665, -73.07627100000001 4.248764, -73.13652 4.285853, -73.201812 4.313019, -73.270537 4.329592, -73.34099999999999 4.335161))"},{"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 466 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002444-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8924en
title: Occurrence Download

description: A dataset containing 105 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Carex nubigena",
 "Year 1970-2000"
 ]
}

 The dataset includes 105 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002445-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17404605
title: This is an outdated and unnecessary supplementary document, as all the files are already included in the manuscript.
DOI: 10.15468/dl.muveqd
title: Occurrence Download

description: A dataset containing 11347 records in GBIF matching the query:
SELECT
 '1' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND decimalLatitude &gt;= -51.21395264452012
 AND decimalLatitude &lt;= -46.479780028101516
 AND decimalLongitude &gt;= -80.17725619669127
 AND decimalLongitude &lt;= -69.23837900975829
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 11347 records were derived from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002419-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.