cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17073558
title: Real-Time Consumer Attention Shifts in Crisis: Digital Evidence from Ukraine's Sustainable Fashion Market

description: This dataset supports the research article "Sustainable Fashion as Post-War Strategy: Positioning Ukraine's Textile Industry for Competitive Recovery" submitted to Sustainability journal. The dataset provides comprehensive quantitative analysis of sustainable fashion discourse evolution and its potential role in Ukraine's post-war economic recovery strategy, with specific focus on textile industry competitiveness.

Contents:



Academic Research Trends: Annual keyword frequencies from Google Scholar, ScienceDirect, and Dimension AI tracking sustainable fashion, circular economy, and post-war recovery literature

Social Discourse Analysis: Google Search and Google Trends data (Ukraine and global) measuring public interest in sustainable fashion and recovery strategies

Correlation Matrices: Cross-correlations between sustainability keywords, war recovery terms, and textile industry competitiveness indicators

Hypothesis Testing Results: Statistical validation of relationships between sustainable fashion adoption and post-war recovery potential


Research Context:Data spans 2014–2025 capturing pre-war baseline, wartime transformation, and projected recovery phases. Focus on positioning sustainable fashion practices as a strategic advantage for Ukraine's textile industry reconstruction and international competitiveness.

File Format: Microsoft Excel (.xlsx) with organized sheets by data source and analysis type.

Intended Use: Academic research on post-war economic recovery strategies, sustainable fashion industry development, and crisis-driven innovation in textile sectors.

Related Publication: [To be updated with DOI upon acceptance]
DOI: 10.5281/zenodo.17073557
title: Real-Time Consumer Attention Shifts in Crisis: Digital Evidence from Ukraine's Sustainable Fashion Market

description: This dataset supports the research article "Sustainable Fashion as Post-War Strategy: Positioning Ukraine's Textile Industry for Competitive Recovery" submitted to Sustainability journal. The dataset provides comprehensive quantitative analysis of sustainable fashion discourse evolution and its potential role in Ukraine's post-war economic recovery strategy, with specific focus on textile industry competitiveness.

Contents:



Academic Research Trends: Annual keyword frequencies from Google Scholar, ScienceDirect, and Dimension AI tracking sustainable fashion, circular economy, and post-war recovery literature

Social Discourse Analysis: Google Search and Google Trends data (Ukraine and global) measuring public interest in sustainable fashion and recovery strategies

Correlation Matrices: Cross-correlations between sustainability keywords, war recovery terms, and textile industry competitiveness indicators

Hypothesis Testing Results: Statistical validation of relationships between sustainable fashion adoption and post-war recovery potential


Research Context:Data spans 2014–2025 capturing pre-war baseline, wartime transformation, and projected recovery phases. Focus on positioning sustainable fashion practices as a strategic advantage for Ukraine's textile industry reconstruction and international competitiveness.

File Format: Microsoft Excel (.xlsx) with organized sheets by data source and analysis type.

Intended Use: Academic research on post-war economic recovery strategies, sustainable fashion industry development, and crisis-driven innovation in textile sectors.

Related Publication: [To be updated with DOI upon acceptance]
DOI: 10.13140/rg.2.2.15281.85600
title: Supplementary Material for the Paper: A Fundamental Solution of the Hubble Tension
DOI: 10.6084/m9.figshare.29986933
title: Landslide susceptibility mapping using Frequency Ratio (FR) and Random Forest (RF) model in the Bandarban Hill District, Bangladesh

description: The research aims to investigate the connections between landslide inventory and causative factors for Landslide Susceptibility Mapping (LSM) in the Bandarban Hill District. The investigation used 14 causative factors (resampling with 30 m resolution) drawn from prior literature and data availability to implement Frequency Ratio (FR), Random Forest (RF), and ensemble FR-RF approaches for generating LSM. An inventory map of 237 landslide sites with equal numbers of non-landslide incidents was randomly split into model training (75%) and validation (25%), utilizing historical landslide data, Google Earth, and a field survey. Also, the study evaluated the performance of three LSM maps using statistical metrics like accuracy, recall, precision, F1 score, and ROC curve, revealing a high accuracy rate exceeding 85%. The ensemble FR-RF approach map indicates five susceptible zones: very low (16.74%), low (23.97%), moderate (27.44%), high (11.97%), and very high (19.88%). Regional analysis reveals that the Bandarban Sadar and Lama Upazilas are very highly susceptible to landslides, which is significantly influenced by factors like NDVI, geology, road distance, rainfall, and slope. The study aids local authorities and stakeholders in land use planning, evaluates potential loss and damage, and devises prevention and mitigation strategies through enhanced drainage systems, slope stabilization, and structural fortifications.
DOI: 10.6084/m9.figshare.29986933.v1
title: Landslide susceptibility mapping using Frequency Ratio (FR) and Random Forest (RF) model in the Bandarban Hill District, Bangladesh

description: The research aims to investigate the connections between landslide inventory and causative factors for Landslide Susceptibility Mapping (LSM) in the Bandarban Hill District. The investigation used 14 causative factors (resampling with 30 m resolution) drawn from prior literature and data availability to implement Frequency Ratio (FR), Random Forest (RF), and ensemble FR-RF approaches for generating LSM. An inventory map of 237 landslide sites with equal numbers of non-landslide incidents was randomly split into model training (75%) and validation (25%), utilizing historical landslide data, Google Earth, and a field survey. Also, the study evaluated the performance of three LSM maps using statistical metrics like accuracy, recall, precision, F1 score, and ROC curve, revealing a high accuracy rate exceeding 85%. The ensemble FR-RF approach map indicates five susceptible zones: very low (16.74%), low (23.97%), moderate (27.44%), high (11.97%), and very high (19.88%). Regional analysis reveals that the Bandarban Sadar and Lama Upazilas are very highly susceptible to landslides, which is significantly influenced by factors like NDVI, geology, road distance, rainfall, and slope. The study aids local authorities and stakeholders in land use planning, evaluates potential loss and damage, and devises prevention and mitigation strategies through enhanced drainage systems, slope stabilization, and structural fortifications.
DOI: 10.15468/dl.qm5j25
title: Occurrence Download

description: A dataset containing 27191 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Dermacentor",
 "Year 2015-2026"
 ]
}

 The dataset includes 27191 records from 114 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004223-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vcq5eg
title: Occurrence Download

description: A dataset containing 286 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Hong Kong",
 "TaxonKey is Vespa soror"
 ]
}

 The dataset includes 286 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004224-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ngvudm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((97.908 5.025153, 97.837468 5.019583, 97.768677 5.003009, 97.703322 4.975841, 97.643016 4.93875, 97.58924500000001 4.892648, 97.543334 4.838675, 97.506412 4.77816, 97.479387 4.712594, 97.46292099999999 4.643594, 97.457418 4.572858, 97.463008 4.50213, 97.479553 4.433149, 97.506641 4.367614, 97.543603 4.307138, 97.589528 4.253207, 97.64328500000001 4.207149, 97.703551 4.170096, 97.768843 4.142959, 97.83755600000001 4.126405, 97.908 4.120841, 97.978444 4.126405, 98.047157 4.142959, 98.112449 4.170096, 98.172715 4.207149, 98.226472 4.253207, 98.272397 4.307138, 98.309359 4.367614, 98.33644700000001 4.433149, 98.352992 4.50213, 98.358582 4.572858, 98.35307899999999 4.643594, 98.336613 4.712594, 98.30958800000001 4.77816, 98.272666 4.838675, 98.226755 4.892648, 98.172984 4.93875, 98.112678 4.975841, 98.04732300000001 5.003009, 97.978532 5.019583, 97.908 5.025153))"},{"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/0004225-260120142942310/datasets/export for details.
DOI: 10.5281/zenodo.18333503
title: Data from manuscript: Drivers and consequences of size declines in unicells

description: Dataset including unicellular size, temperature, metabolic rate as well cell popuation density and whether or not the density was imputed or recorded directly from the literature. The species are also identified as autotrophs or not.
DOI: 10.5281/zenodo.18333504
title: Data from manuscript: Drivers and consequences of size declines in unicells

description: Dataset including unicellular size, temperature, metabolic rate as well cell popuation density and whether or not the density was imputed or recorded directly from the literature. The species are also identified as autotrophs or not.
DOI: 10.26036/cnp0008186
title: Transcriptome analysis of common wheat

description: In this study, we generated transcriptome data for miR9772-OE (OE) and wild-type (WT) plants under control and salt treatment conditions. The OE has been previously shown to decreased salt tolerance in plants, but the underlying molecular mechanisms are poorly understood. By comparing the transcriptomes of OE and WT plants under these conditions, we aimed to identify differentially expressed genes and pathways that contribute to the decreased salt tolerance phenotype of the OE. Our findings provide insights into the molecular basis of salt tolerance and may have implications for the development of salt-tolerant crops.
DOI: 10.15468/dl.hjewjp
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.767172 24.235265, 88.69016499999999 24.229688, 88.615073 24.213096, 88.543764 24.185901, 88.47800700000001 24.14878, 88.41943000000001 24.102654, 88.369479 24.048668, 88.329379 23.98816, 88.30010900000001 23.922628, 88.282376 23.853692, 88.27659800000001 23.783053, 88.282899 23.712451, 88.30110500000001 23.643623, 88.330749 23.578261, 88.37109 23.517967, 88.42112400000001 23.464217, 88.479618 23.418328, 88.545135 23.381421, 88.616069 23.354395, 88.69068799999999 23.337912, 88.767172 23.332372, 88.843656 23.337912, 88.91827499999999 23.354395, 88.989209 23.381421, 89.054726 23.418328, 89.11322 23.464217, 89.16325399999999 23.517967, 89.20359500000001 23.578261, 89.233239 23.643623, 89.251445 23.712451, 89.257746 23.783053, 89.25196800000001 23.853692, 89.234235 23.922628, 89.204965 23.98816, 89.16486500000001 24.048668, 89.114914 24.102654, 89.056337 24.14878, 88.99057999999999 24.185901, 88.91927099999999 24.213096, 88.844179 24.229688, 88.767172 24.235265))"},{"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 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004226-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4c7n2k
title: Occurrence Download

description: A dataset containing 197 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Juncus bufonius",
 "Year 1970-2000"
 ]
}

 The dataset includes 197 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004228-260120142942310/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/14576820
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.6084/m9.figshare.25539870.v2
title: Long-term co-circulation of multiple influenza A viruses in pigs, Guangxi, China

description: Influenza A viruses (IAVs) pose a persistent potential threat to human health because of the spillover from avian and swine infections. Extensive surveillance was performed in 12 cities of Guangxi, China, during 2018 and 2023. A total of 2540 samples (including 2353 nasal swabs and 187 lung tissues) were collected from 18 pig farms with outbreaks of respiratory disease. From these, 192 IAV-positive samples and 19 genomic sequences were obtained. We found that the H1 and H3 swine influenza A viruses (swIAVs) of multiple lineages and genotypes have continued to co-circulate during that time in this region. Genomic analysis revealed the Eurasian avian-like H1N1 swIAVs (G4) still remained predominant in pig populations. Strikingly, the novel multiple H3N2 genotypes were found to have been generated through the repeated introduction of the early H3N2 North American triple reassortant viruses (TR H3N2 lineage) that emerged in USA and Canada in 1998 and 2005, respectively. Notably, when the matrix gene segment derived from the H9N2 avian influenza virus was introduced into endemic swIAVs, this produced a novel quadruple reassortant H1N2 swIAV that could pose a potential risk for zoonotic infection.
DOI: 10.15468/dl.3rypwh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((97.619 5.028153, 97.548468 5.022583, 97.479676 5.006009, 97.414321 4.978841, 97.354015 4.94175, 97.30024400000001 4.895648, 97.254333 4.841675, 97.21741 4.78116, 97.19038500000001 4.715594, 97.173919 4.646594, 97.16841599999999 4.575858, 97.174007 4.50513, 97.190551 4.436149, 97.21763900000001 4.370614, 97.25460099999999 4.310137, 97.300527 4.256207, 97.35428400000001 4.210149, 97.41455000000001 4.173096, 97.479842 4.145959, 97.54855499999999 4.129405, 97.619 4.123841, 97.68944500000001 4.129405, 97.75815799999999 4.145959, 97.82344999999999 4.173096, 97.88371600000001 4.210149, 97.937473 4.256207, 97.98339900000001 4.310137, 98.02036099999999 4.370614, 98.047449 4.436149, 98.063993 4.50513, 98.06958400000001 4.575858, 98.064081 4.646594, 98.04761499999999 4.715594, 98.02059 4.78116, 97.983667 4.841675, 97.93775599999999 4.895648, 97.883985 4.94175, 97.823679 4.978841, 97.758324 5.006009, 97.689532 5.022583, 97.619 5.028153))"},{"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/0004229-260120142942310/datasets/export for details.
DOI: 10.5281/zenodo.18332552
title: SEM Withered Plant (BSE, 2026-01-22) — Kikoh Matsuura (Art–Science Archive)

description: SEM Withered Plant (BSE, 2026-01-22) is a child dataset within the series SEM Withered Plant (BSE Series) by Kikoh Matsuura.

It contains raw, uncompressed BMP images (without embedded scale bars) and JPG preview images (with overlaid scale bars), acquired on 2026-01-22 with a personally owned TECHNEX TinySEM M3-i using the BSE detector under high-vacuum conditions.

This dataset constitutes the primary observation data underlying the art–science work Withered Plant.The images capture the hidden microstructures of a dried plant fragment, prepared with an Au sputter coating and observed at an accelerating voltage of 14 kV with a label magnification of 50×.

By preserving both raw scientific evidence and its artistic context, this record stands as a unique trace at the boundary between art and science.

It links to the parent series record via the “Related works” field (IsPartOf) and contributes to the long-term archival of observation as both scientific evidence and a cultural record.
DOI: 10.5281/zenodo.18332551
title: SEM Withered Plant (BSE, 2026-01-22) — Kikoh Matsuura (Art–Science Archive)

description: SEM Withered Plant (BSE, 2026-01-22) is a child dataset within the series SEM Withered Plant (BSE Series) by Kikoh Matsuura.

It contains raw, uncompressed BMP images (without embedded scale bars) and JPG preview images (with overlaid scale bars), acquired on 2026-01-22 with a personally owned TECHNEX TinySEM M3-i using the BSE detector under high-vacuum conditions.

This dataset constitutes the primary observation data underlying the art–science work Withered Plant.The images capture the hidden microstructures of a dried plant fragment, prepared with an Au sputter coating and observed at an accelerating voltage of 14 kV with a label magnification of 50×.

By preserving both raw scientific evidence and its artistic context, this record stands as a unique trace at the boundary between art and science.

It links to the parent series record via the “Related works” field (IsPartOf) and contributes to the long-term archival of observation as both scientific evidence and a cultural record.
DOI: 10.7288/v4/magic/20474
title: Palaeointensity of Australasian tektites from South China (Dataset)

description: Paleomagnetic, rock magnetic, or geomagnetic data found in the MagIC data repository from a paper titled: Palaeointensity of Australasian tektites from South China
DOI: 10.15468/dl.eh3jym
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.58131899999999 24.236927, 88.504311 24.23135, 88.42921800000001 24.214758, 88.35790799999999 24.187563, 88.292151 24.150442, 88.23357300000001 24.104316, 88.183621 24.05033, 88.14352 23.989822, 88.11425 23.92429, 88.09651599999999 23.855354, 88.090739 23.784715, 88.09704000000001 23.714113, 88.115246 23.645285, 88.144891 23.579923, 88.185232 23.519629, 88.23526699999999 23.465879, 88.293762 23.41999, 88.359279 23.383083, 88.43021400000001 23.356057, 88.504834 23.339574, 88.58131899999999 23.334035, 88.657804 23.339574, 88.73242399999999 23.356057, 88.803359 23.383083, 88.868876 23.41999, 88.92737099999999 23.465879, 88.977406 23.519629, 89.017747 23.579923, 89.047392 23.645285, 89.06559799999999 23.714113, 89.071899 23.784715, 89.06612199999999 23.855354, 89.048388 23.92429, 89.01911800000001 23.989822, 88.979017 24.05033, 88.92906499999999 24.104316, 88.870487 24.150442, 88.80473000000001 24.187563, 88.73342 24.214758, 88.658327 24.23135, 88.58131899999999 24.236927))"},{"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 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004230-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f63339
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
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 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004231-260120142942310/datasets/export for details.