cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.mssqfk
title: Occurrence Download

description: A dataset containing 17288 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-6.602075414629626 38.86799271436789, -6.604773102518364 38.87639392490905, -6.609516750127102 38.884222298187574, -6.616124667595259 38.891176654005, -6.62434319310842 38.896989319430155, -6.633856398517484 38.901436479435326, -6.644298244507817 38.904346844670776, -6.655266716106461 38.90560829310266, -6.666339384836143 38.90517222125272, -6.677089782215355 38.903055431334714, -6.687103933987934 38.89933947880905, -6.695996397445479 38.89416750646058, -6.703425165883323 38.88773869152563, -6.7091048533692605 38.880300527204895, -6.712817646842889 38.872139244986144, -6.714421607060021 38.86356875600668, -6.713856010078946 38.854918545404566, -6.711143541346369 38.8465209912837, -6.706389279352691 38.838698598497075, -6.69977652990321 38.83175163676826, -6.6915596904411485 38.82594665334584, -6.682054432433659 38.821506293742175, -6.671625585277807 38.8186008120068, -6.660673184991011 38.817341586704245, -6.649617213335551 38.81777688286101, -6.638881596810698 38.81989001635581, -6.628878059419247 38.823599988386114, -6.619990427927109 38.82876456664401, -6.612559973419491 38.8351856995769, -6.606872338521456 38.84261706349909, -6.603146546217161 38.85077346222119, -6.601526514704095 38.859341728073325, -6.602075414629626 38.86799271436789))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 17288 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005728-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.886hty
title: Occurrence Download

description: A dataset containing 19666 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Coordinate rounded, Geodetic datum invalid, Geodetic datum assumed WGS84, Footprint WKT mismatch, Country derived from coordinates, Continent coordinate mismatch, Continent derived from coordinates, Recorded date mismatch, Recorded date invalid, The scientific name ID was not found in the checklist, The taxon concept ID was not found in the checklist, The taxon ID was not found in the checklist, Modified date invalid, Identified date invalid, Multimedia URI invalid, References URI invalid, Individual count invalid, Occurrence status unparsable, Occurrence status inferred from individual count)",
 "OccurrenceStatus is Present",
 "TaxonKey is Brassica oleracea",
 "Year 1950-2025"
 ]
}

 The dataset includes 19666 records from 189 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005729-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/g2be-z746
title: "CourseraToC: Augmented Coursera Course Table of Contents Dataset for Educational Resource Organization"

description: "CourseraToC augments an existing Coursera course collection by supplementing table of contents metadata for approximately 5,000 English courses. The dataset preserves Coursera's original two-tier categorization system (L1, 11; L2, 45) with enhanced hierarchical ToC structure, excluding specialization courses to maintain document independence."
DOI: 10.17632/57j6fyskw8
title: Behavioral Risk Preference as a Catalyst for Environmental Efficiency

description: This datasets and code (python) are associated with the article "Behavioral Risk Preference as a Catalyst for Environmental Efficiency".
DOI: 10.17632/57j6fyskw8.1
title: Behavioral Risk Preference as a Catalyst for Environmental Efficiency

description: This datasets and code (python) are associated with the article "Behavioral Risk Preference as a Catalyst for Environmental Efficiency".
DOI: 10.15468/dl.a7rz4x
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-6.758470917786134 38.91675868583059, -6.759287617098508 38.91927806974784, -6.760717882964936 38.92162457295247, -6.762706805370819 38.92370798992855, -6.76517797572352 38.92544821820987, -6.768036419440768 38.92677834244648, -6.77117224711864 38.92764721191738, -6.774464883763487 38.92802141152323, -6.777787712610347 38.92788654970802, -6.781012953515682 38.92724781323598, -6.784016586532184 38.92612976724244, -6.786683129464709 38.924575408337994, -6.7889100840495304 38.922644507592025, -6.790611878633911 38.920411307787305, -6.791723155274539 38.91796166431952, -6.792201275144904 38.91538974052738, -6.792027946926741 38.91279438525693, -6.7912099171621 38.91027533244409, -6.7897786979517365 38.907929369024004, -6.787789344448231 38.90584661833526, -6.785318330874956 38.904107081407936, -6.782460607945963 38.90277756833955, -6.7793259553317196 38.90190913680632, -6.776034769133086 38.90153513523549, -6.772713445297697 38.901669925034014, -6.769489534844715 38.902308330401645, -6.766486855172119 38.90342583661062, -6.763820743337088 38.90497952922768, -6.761593631972694 38.90690973861969, -6.759891116599969 38.90914232725619, -6.758778664895744 38.911591532786375, -6.758299094579297 38.9141632585435, -6.758470917786134 38.91675868583059))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 6 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005733-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/75wpkvch9z
title: LIufangfang

description: Figure and table captions 
Fig. 1. SEM images of OIDFs modified by different methods. 
Fig. 2. Characterization using (A) Fourier transform infrared spectroscopy (FTIR) and 
(B) X-ray diffraction (XRD). 
Fig. 3. Interfacial tension of OIDFs before and after modification. 
Fig. 4. Stability analysis of emulsions under varying environmental stresses: (A) pH, 
(B) temperature and (C) salt ion. 
Fig. 5. Rheological properties of N/UE-OIDFs suspensions (4 w/v): (A) viscosity and 
(B) modulus; and N/UE-OIDF emulsions (4 w/v): (C) viscosity and (D) modulus. 
Fig. 6. Microstructure of N/UE-OIDFs emulsion (4 w/v) stained with Nile red (oil in 
red) and calcofluor white (OIDF in blue). 
Fig. 7. Appearance comparison and electronic nose analysis of full-fat control cheese sticks versus cheese sticks with varying levels of fat replacement. 
Table 1. Chemical composition and fiber content. 
Table 2. Physicochemical and emulsifying properties.
DOI: 10.15468/dl.scj8bm
title: Occurrence Download

description: A dataset containing 161 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Living Specimen)",
 "TaxonKey is Astrochelys radiata"
 ]
}

 The dataset includes 161 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005734-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/75wpkvch9z.1
title: LIufangfang

description: Figure and table captions 
Fig. 1. SEM images of OIDFs modified by different methods. 
Fig. 2. Characterization using (A) Fourier transform infrared spectroscopy (FTIR) and 
(B) X-ray diffraction (XRD). 
Fig. 3. Interfacial tension of OIDFs before and after modification. 
Fig. 4. Stability analysis of emulsions under varying environmental stresses: (A) pH, 
(B) temperature and (C) salt ion. 
Fig. 5. Rheological properties of N/UE-OIDFs suspensions (4 w/v): (A) viscosity and 
(B) modulus; and N/UE-OIDF emulsions (4 w/v): (C) viscosity and (D) modulus. 
Fig. 6. Microstructure of N/UE-OIDFs emulsion (4 w/v) stained with Nile red (oil in 
red) and calcofluor white (OIDF in blue). 
Fig. 7. Appearance comparison and electronic nose analysis of full-fat control cheese sticks versus cheese sticks with varying levels of fat replacement. 
Table 1. Chemical composition and fiber content. 
Table 2. Physicochemical and emulsifying properties.
DOI: 10.17632/thmx2jyb37
title: code: Analytical prediction of collaboration structure transitions in heterogeneous multi-agent systems

description: This repository contains the code accompanying the article Analytical Prediction of Collaboration Structure Transitions in Heterogeneous Multi-Agent Systems by Wu et al.
DOI: 10.17632/thmx2jyb37.1
title: code: Analytical prediction of collaboration structure transitions in heterogeneous multi-agent systems

description: This repository contains the code accompanying the article Analytical Prediction of Collaboration Structure Transitions in Heterogeneous Multi-Agent Systems by Wu et al.
DOI: 10.17632/x8cw84dtw7
title: Ocular sebaceous neoplasms

description: Mendeley supplementary figures to accompany manuscript: Mismatch repair deficiency in ocular sebaceous neoplasms: implications for Muir-Torre screening and classification.
DOI: 10.17632/x8cw84dtw7.2
title: Ocular sebaceous neoplasms

description: Mendeley supplementary figures to accompany manuscript: Mismatch repair deficiency in ocular sebaceous neoplasms: implications for Muir-Torre screening and classification.
DOI: 10.17632/gcd6c2xkkc
title: Chronic nutrient inputs alter root exudate Composition and Morphology：A case study in an alpine meadow of the Qinghai-Tibetan Plateau

description: The effects of nutrient supplementation treatments on root traits, root exudation rate and its’ stoichiometry were evaluated using one-way analysis of variance (ANOVA). SPSS 28.0 (IBM Corp. Armonk, NY, USA) was used for statistical analyses with a significance level of 0.05. For the standardized data after extraction of root exudates and metabolome pretreatment, the relative abundance of metabolites in the Super Class was calculated first, and then the content of metabolites in root exudates under N and P supplementation treatments were statistically analyzed using bar plots. The Shannon index was calculated based on the relative abundance of metabolites during data processing. Principal Coordinates Analysis (PCoA) was performed using the Bray-Curtis dissimilarity matrix through the R vegan package, followed by visualization using the R “ggplot2” package. Permutational Multivariate Analysis of Variance (PERMANOVA) implemented in the R “vegan” package was used to test for significant differences in root exudate profiles among different treatments. Partial Least Squares Discriminant Analysis (PLS-DA) was performed to obtain the variable importance in projection (VIP) values of each metabolite. The statistical significance of each metabolite between the CK and treatments groups was calculated based on the Steudent’s t-test as implemented in R v.4.1.2 (R Core Team, 2021), and the fold change (FC) of each metabolite between the two groups was calculated. Finally, metabolites with P &lt; 0.05, VIP &gt; 1, and either FC ≥ 1.5 or  ≤ 0.667 were identified as differential metabolites. Then OPLS-DA was used to analyze and filter orthogonal variables that were not related to categorical variables, and the projected importance of variables (VIP value) was calculated. After that, univariate statistical analysis combined with student’s t-test was used to verify the differences between different treatments to achieve the screening of differential metabolites, and the volcano plot of differential metabolites was drawn. Metabolites with VIP&gt;1 and P-value&lt;0.05 were selected as metabolites with significant differences to evaluate the effects of metabolite changes on organisms. The metabolome analysis process is completed based on the MetaboAnalyst R package of R software.
DOI: 10.17632/gcd6c2xkkc.1
title: Chronic nutrient inputs alter root exudate Composition and Morphology：A case study in an alpine meadow of the Qinghai-Tibetan Plateau

description: The effects of nutrient supplementation treatments on root traits, root exudation rate and its’ stoichiometry were evaluated using one-way analysis of variance (ANOVA). SPSS 28.0 (IBM Corp. Armonk, NY, USA) was used for statistical analyses with a significance level of 0.05. For the standardized data after extraction of root exudates and metabolome pretreatment, the relative abundance of metabolites in the Super Class was calculated first, and then the content of metabolites in root exudates under N and P supplementation treatments were statistically analyzed using bar plots. The Shannon index was calculated based on the relative abundance of metabolites during data processing. Principal Coordinates Analysis (PCoA) was performed using the Bray-Curtis dissimilarity matrix through the R vegan package, followed by visualization using the R “ggplot2” package. Permutational Multivariate Analysis of Variance (PERMANOVA) implemented in the R “vegan” package was used to test for significant differences in root exudate profiles among different treatments. Partial Least Squares Discriminant Analysis (PLS-DA) was performed to obtain the variable importance in projection (VIP) values of each metabolite. The statistical significance of each metabolite between the CK and treatments groups was calculated based on the Steudent’s t-test as implemented in R v.4.1.2 (R Core Team, 2021), and the fold change (FC) of each metabolite between the two groups was calculated. Finally, metabolites with P &lt; 0.05, VIP &gt; 1, and either FC ≥ 1.5 or  ≤ 0.667 were identified as differential metabolites. Then OPLS-DA was used to analyze and filter orthogonal variables that were not related to categorical variables, and the projected importance of variables (VIP value) was calculated. After that, univariate statistical analysis combined with student’s t-test was used to verify the differences between different treatments to achieve the screening of differential metabolites, and the volcano plot of differential metabolites was drawn. Metabolites with VIP&gt;1 and P-value&lt;0.05 were selected as metabolites with significant differences to evaluate the effects of metabolite changes on organisms. The metabolome analysis process is completed based on the MetaboAnalyst R package of R software.
DOI: 10.17632/hmxfhwgwyb
title: Test results

description: The plots of the uniaxial compression, triaxial compression and direct shear tests of the intact rock specimens.
DOI: 10.17632/hmxfhwgwyb.1
title: Test results

description: The plots of the uniaxial compression, triaxial compression and direct shear tests of the intact rock specimens.
DOI: 10.17632/h9sh67mxs8
title: Benchmarking Smart Port Performance: A Neutrosophic Entropy-Weighted TOPSIS Analysis of European Ports

description: Dataset supporting the study “Benchmarking Smart Port Performance: A Neutrosophic Entropy-Weighted TOPSIS Analysis of European Ports.” Includes decision matrices, entropy weights, TOPSIS scores, and sensitivity analyses for five European ports.
DOI: 10.17632/h9sh67mxs8.1
title: Benchmarking Smart Port Performance: A Neutrosophic Entropy-Weighted TOPSIS Analysis of European Ports

description: Dataset supporting the study “Benchmarking Smart Port Performance: A Neutrosophic Entropy-Weighted TOPSIS Analysis of European Ports.” Includes decision matrices, entropy weights, TOPSIS scores, and sensitivity analyses for five European ports.
DOI: 10.17632/mrnvkzrdsd.1
title: Data and code for: Sensorimotor strategies for rapid leader-follower role switching in flying echolocating bats

description: This dataset contains the underlying data and analysis code for the manuscript 'Sensorimotor strategies for rapid leader-follower role switching in flying echolocating bats'. It includes two primary MATLAB tables: 1) Echolocation_AnalysisTable.mat: contains different encounter types (Alone, Leads, Follows) and corresponding echolocation parameters- CallPeakPower, Duration, IPI, and CallMaxFreq. 2) Switch_AnalysisTable.mat: contains flight kinematic parameters (Turn Phase StartFrame, Turn Phase Speed, Pre-Turn Speed, Turn Phase Angular Velocity, Pre-Turn Angular Velocity for every switch and no-switch events for both the leading and following bat. The three MATLAB scripts (Switch_GLMcode.m, CallPeakPower_GLMMcode.m and CallMaxFreq_GLMMcode.m) can be used to reproduce the main figures and statistical analyses reported in the paper. Please note - Rearranging of Switch_AnalysisTable.mat might be required to directly use the script given above.
DOI: 10.17632/mpkvr7wwkt
title: Raw Data

description: The raw data of TGCA-BRCA, GSE58812, GSE20711 and GSE161529(seurat).
DOI: 10.17632/mpkvr7wwkt.1
title: Raw Data

description: The raw data of TGCA-BRCA, GSE58812, GSE20711 and GSE161529(seurat).
DOI: 10.15468/dl.g2yfdb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammalia",
 "HasCoordinate is true",
 "Geometry POLYGON ((-114.8998 35.39983, -114.8992 35.39979, -114.8992 35.40035, -114.9004 35.40036, -114.8998 35.39983))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005735-251120083545085/datasets/export for details.
DOI: 10.17632/j4s25jt8v7.3
title: Is Bikeability Cycle Training an Effective Active Travel Behaviour Change Intervention? Confirmatory Evidence from Classroom-Based Surveys of UK Schoolchildren

description: These files relate to the paper of the same title and comprise the following: raw data files downloaded from the JISC survey platform, for each of nine Bikeability training providers who collected data in their local authority areas; a PDF of the survey that participants completed to provide those data; an Excel file containing a cleaned dataset, from which 'Other' responses to the Year Group item have been removed; a PDF of the SPSS output file from the data analysis process; a PowerPoint file that illustrates the geographical spread and diversity of the data location sites; and the summaries of (incomplete) demographic information procured from schools by Bikeability training providers.
DOI: 10.17632/xhdkmnxmrd.4
title: Does Forthcomingness Matter

description: This data summarizes results of an experiment with regard to manager reporting credibility for different investor types. The results summarize whether short-horizon investors differ from long-horizon investors in their sensitivity to truthful disclosure regarding negative earnings news.
DOI: 10.17632/n7h88xc7s9
title: Research data

description: This study investigates whether cyanobacterial inoculation can accelerate biocrust formation and mitigate wind erosion on dried seasonal wetland beds, with emphasis on the synergistic effects of inoculum density (0, 1.5, 3, 6 g m⁻²) and hydrological regime, completely dry (134 d) vs. dry–dewatering (60 d dry + 60 d submerged + 14 d dry). Soil (silt-loam, highly erodible) was collected from Kobi Baba Ali Wetland (Iran), and native Nostoc sp. and Oscillatoria sp., non-pathogenic, EPS-producing strains, were cultivated, dried, and applied via hydroseeding.

Biocrust development was assessed via two key indicators:
Chlorophyll-a (Chl-a), a proxy for cyanobacterial biomass and photosynthetic activity; Extracellular polymeric substances (EPSs), secreted polysaccharides that bind soil particles.
Wind erosion was quantified using a wind tunnel at 70 km h⁻¹ over three 10-min intervals (total 30 min), measuring soil loss rate (g m⁻² min⁻¹).

Key findings:
Uninoculated soil under dry–dewatering showed 39% lower erosion than under completely dry conditions, supporting Hypothesis i: even one wet–dry cycle enhances physical cohesion.
Cyanobacterial inoculation significantly boosted both Chl-a (up to +98%) and EPSs (up to +75%) versus controls. EPS increased at all doses (≥1.5 g m⁻²), but Chl-a rose significantly only at ≥3 g m⁻², indicating a biomass threshold for active crust maturation.
Under completely dry conditions, erosion decreased by 39%, 96%, and 97% at 1.5, 3, and 6 g m⁻², respectively. Under dry–dewatering, reductions were 64%, 94%, and 95%—highlighting synergy: low-dose inoculation (1.5 g m⁻²) performed markedly better with dewatering (+25% extra erosion control).
3 g m⁻² emerged as optimal: equivalent to 6 g m⁻² in efficacy (&gt;95% erosion suppression) but more cost-effective.
Interpretation: Dewatering provides essential moisture pulses for cyanobacterial reactivation and EPS production, while inoculation jump-starts biocrust formation, normally slow (decades), within months. The physical (moisture-induced cohesion) + biological (filament entanglement + EPS gluing) mechanisms combine to form a resilient physical–biological crust.

Implications: For dried wetland management, timed inoculation (post-flooding) at 3 g m⁻² offers rapid, eco-friendly dust mitigation. Future work should address field scalability, long-term persistence, and ecological safety (e.g., bloom risks, non-target impacts).
DOI: 10.17632/n7h88xc7s9.1
title: Research data

description: This study investigates whether cyanobacterial inoculation can accelerate biocrust formation and mitigate wind erosion on dried seasonal wetland beds, with emphasis on the synergistic effects of inoculum density (0, 1.5, 3, 6 g m⁻²) and hydrological regime, completely dry (134 d) vs. dry–dewatering (60 d dry + 60 d submerged + 14 d dry). Soil (silt-loam, highly erodible) was collected from Kobi Baba Ali Wetland (Iran), and native Nostoc sp. and Oscillatoria sp., non-pathogenic, EPS-producing strains, were cultivated, dried, and applied via hydroseeding.

Biocrust development was assessed via two key indicators:
Chlorophyll-a (Chl-a), a proxy for cyanobacterial biomass and photosynthetic activity; Extracellular polymeric substances (EPSs), secreted polysaccharides that bind soil particles.
Wind erosion was quantified using a wind tunnel at 70 km h⁻¹ over three 10-min intervals (total 30 min), measuring soil loss rate (g m⁻² min⁻¹).

Key findings:
Uninoculated soil under dry–dewatering showed 39% lower erosion than under completely dry conditions, supporting Hypothesis i: even one wet–dry cycle enhances physical cohesion.
Cyanobacterial inoculation significantly boosted both Chl-a (up to +98%) and EPSs (up to +75%) versus controls. EPS increased at all doses (≥1.5 g m⁻²), but Chl-a rose significantly only at ≥3 g m⁻², indicating a biomass threshold for active crust maturation.
Under completely dry conditions, erosion decreased by 39%, 96%, and 97% at 1.5, 3, and 6 g m⁻², respectively. Under dry–dewatering, reductions were 64%, 94%, and 95%—highlighting synergy: low-dose inoculation (1.5 g m⁻²) performed markedly better with dewatering (+25% extra erosion control).
3 g m⁻² emerged as optimal: equivalent to 6 g m⁻² in efficacy (&gt;95% erosion suppression) but more cost-effective.
Interpretation: Dewatering provides essential moisture pulses for cyanobacterial reactivation and EPS production, while inoculation jump-starts biocrust formation, normally slow (decades), within months. The physical (moisture-induced cohesion) + biological (filament entanglement + EPS gluing) mechanisms combine to form a resilient physical–biological crust.

Implications: For dried wetland management, timed inoculation (post-flooding) at 3 g m⁻² offers rapid, eco-friendly dust mitigation. Future work should address field scalability, long-term persistence, and ecological safety (e.g., bloom risks, non-target impacts).
DOI: 10.17632/g3ymg66kr8
title: Modeling Holocene Glacial Isostatic Adjustment Stresses and decompression melting in Iceland Using Non-Linear Rheology

description: Abaqus® & COMSOL® model files, postprocessing and figure generation scripts, model misfit results, geodetic profiles extracted from Drouin & Sigmundsson’s 2019 InSAR near-vertical velocity field,