cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.9rvb2g
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.74936 24.405063, 88.67225000000001 24.399486, 88.597059 24.382894, 88.525655 24.355699, 88.459812 24.318578, 88.401158 24.272452, 88.351141 24.218466, 88.31099 24.157959, 88.281683 24.092428, 88.263929 24.023492, 88.258146 23.952854, 88.26445699999999 23.882254, 88.28268799999999 23.813429, 88.31237299999999 23.748068, 88.352767 23.687776, 88.402868 23.634029, 88.461438 23.588142, 88.527039 23.551236, 88.59806399999999 23.524212, 88.67277900000001 23.50773, 88.74936 23.50219, 88.825941 23.50773, 88.900656 23.524212, 88.971681 23.551236, 89.037282 23.588142, 89.09585199999999 23.634029, 89.14595300000001 23.687776, 89.186347 23.748068, 89.216032 23.813429, 89.234263 23.882254, 89.240574 23.952854, 89.234791 24.023492, 89.217037 24.092428, 89.18773 24.157959, 89.14757899999999 24.218466, 89.097562 24.272452, 89.03890800000001 24.318578, 88.97306500000001 24.355699, 88.901661 24.382894, 88.82647 24.399486, 88.74936 24.405063))"},{"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 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004410-260120142942310/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.28617028
title: A smile hampers encoding and memory for non-happy eyes in a face: temporal dynamics and importance of initial fixation

description: Blended facial expressions with a smiling mouth but non-happy eyes (neutral, sad, etc.) are often (incorrectly) judged as “happy”. We investigated the time course of this phenomenon, both forward and backward. To do this, we varied the <i>order of presentation</i> of a prime stimulus (upper half of a face) and a probe (lower half of a face) stimulus, and their <i>display durations</i>. The forward and the backward influence of the smile was assessed when the mouth was seen before or after the eyes. Participants categorised the eye expression when the mouth and the eyes were <i>congruent</i> or <i>incongruent</i>. Results showed that, as a forward prime, a smiling mouth biased the recognition of incongruent (non-happy) eyes as if they were happy. The effect started as early as 100 ms and dissipated by 1000 ms. As a backward prime, the smile also biased recognition of non-happy eye expressions as happy for at least the first 300 ms. These results suggest, respectively, that the presence of a smiling mouth impairs the accurate encoding and memory for non-happy eyes. Angry eyes are the least susceptible to this effect, probably due to their distinctiveness. An alternative response (rather than sensitivity) bias was partially ruled out.
DOI: 10.6084/m9.figshare.28617028.v1
title: A smile hampers encoding and memory for non-happy eyes in a face: temporal dynamics and importance of initial fixation

description: Blended facial expressions with a smiling mouth but non-happy eyes (neutral, sad, etc.) are often (incorrectly) judged as “happy”. We investigated the time course of this phenomenon, both forward and backward. To do this, we varied the <i>order of presentation</i> of a prime stimulus (upper half of a face) and a probe (lower half of a face) stimulus, and their <i>display durations</i>. The forward and the backward influence of the smile was assessed when the mouth was seen before or after the eyes. Participants categorised the eye expression when the mouth and the eyes were <i>congruent</i> or <i>incongruent</i>. Results showed that, as a forward prime, a smiling mouth biased the recognition of incongruent (non-happy) eyes as if they were happy. The effect started as early as 100 ms and dissipated by 1000 ms. As a backward prime, the smile also biased recognition of non-happy eye expressions as happy for at least the first 300 ms. These results suggest, respectively, that the presence of a smiling mouth impairs the accurate encoding and memory for non-happy eyes. Angry eyes are the least susceptible to this effect, probably due to their distinctiveness. An alternative response (rather than sensitivity) bias was partially ruled out.
DOI: 10.48544/ea7f193e-aa7d-4d37-a25e-0eb27cd12baf
title: Jongeren over MDT :  ervaringen en impact van Pak je Kans  in Almere :
DOI: 10.6084/m9.figshare.31122697
title: Supplementary Material for: Explainable Artificial Intelligence for the Mayo Endoscopic Score in Ulcerative Colitis

description: Introduction: The Mayo endoscopic score (MES) is used widely in ulcerative colitis (UC) for severity assessment and therapeutic decision-making. Deep learning (DL) models developed to determine MES currently lack explainability. We aimed to develop explainable models for the MES in patients with UC and examine the human-artificial intelligence interactions with the models.

Methods: This was a retrospective multicenter study conducted across four large tertiary institutions in China. A total of 2,600 white-light images were used for training. Two approaches were adopted: traditional blackbox or explainable AI (XAI). The trained models were evaluated with three external test datasets (#1 Changshu & Jintan hospitals, n = 100; #2 HyperKvasir, n = 100; #3 Yongding hospital, n = 260), and the performance was compared with endoscopists. The primary outcome was the performance of 4-way classification. For explainability, moreover, Grad-CAM was for computer vision, while local interpretation, variable importance, and partial dependence plots were for the classifier within XAI.

Results: In the test #1 dataset, a Xception-backboned XAI showed accuracy of 0.910, Matthew’s correlation coefficient 0.880 and Cohen’s kappa 0.960 [95% CI, 0.940 – 0.990]. The metrics were better than other models, as well as the two endoscopists. With the AI-assistance, the performance of endoscopists were improved (senior’s accuracy from 0.890 to 0.930 and junior’s accuracy from 0.810 to 0.880). Similar trend was observed in the test #2 and #3 datasets. 

Conclusion: The use of an explainable framework empowers AI models to achieve improved performance with transparency. XAI can also improve endoscopist performance in interpretation of MES in UC.
DOI: 10.6084/m9.figshare.31122697.v1
title: Supplementary Material for: Explainable Artificial Intelligence for the Mayo Endoscopic Score in Ulcerative Colitis

description: Introduction: The Mayo endoscopic score (MES) is used widely in ulcerative colitis (UC) for severity assessment and therapeutic decision-making. Deep learning (DL) models developed to determine MES currently lack explainability. We aimed to develop explainable models for the MES in patients with UC and examine the human-artificial intelligence interactions with the models.

Methods: This was a retrospective multicenter study conducted across four large tertiary institutions in China. A total of 2,600 white-light images were used for training. Two approaches were adopted: traditional blackbox or explainable AI (XAI). The trained models were evaluated with three external test datasets (#1 Changshu & Jintan hospitals, n = 100; #2 HyperKvasir, n = 100; #3 Yongding hospital, n = 260), and the performance was compared with endoscopists. The primary outcome was the performance of 4-way classification. For explainability, moreover, Grad-CAM was for computer vision, while local interpretation, variable importance, and partial dependence plots were for the classifier within XAI.

Results: In the test #1 dataset, a Xception-backboned XAI showed accuracy of 0.910, Matthew’s correlation coefficient 0.880 and Cohen’s kappa 0.960 [95% CI, 0.940 – 0.990]. The metrics were better than other models, as well as the two endoscopists. With the AI-assistance, the performance of endoscopists were improved (senior’s accuracy from 0.890 to 0.930 and junior’s accuracy from 0.810 to 0.880). Similar trend was observed in the test #2 and #3 datasets. 

Conclusion: The use of an explainable framework empowers AI models to achieve improved performance with transparency. XAI can also improve endoscopist performance in interpretation of MES in UC.
DOI: 10.6084/m9.figshare.31122700
title: Supplementary Material for: Biophysical Alterations of the Stratum Corneum Induced by Optical Clearing Agents: Implications for Efficiency and Safety Optimization

description: Introduction. Optical clearing (OC) of biological objects is a promising technique with broad potential in clinical and diagnostic applications, as it enhances imaging depth, spatial resolution, and contrast of acquired images or spectra. The aim of this study was to study the effects of the common optical clearing agents (OCAs) glycerol, fructose, dimethyl sulfoxide (DMSO) and iohexol (Omnipaque®) solutions at various concentrations and treatment durations on the stratum corneum (SC) of intact skin using confocal Raman microspectroscopy (CRM). 
Methods. The pig ear was selected for study. Various OCAs were applied topically on the skin, including water solutions of DMSO (100%), fructose (50%), Omnipaque® (90% and 100%), glycerol (40%, 60%, 80%, 90% and 100%). Ex vivo measurements were carried out on porcine ear skin using a Model 3510 SCA confocal Raman microscope (RiverD International B.V., Rotterdam, the Netherlands). 
Results. It was found that a 25%-25%-50% water-ethanol-fructose solution permeated the SC at all tested exposure times (5, 15, 30, and 45 min). Similarly, a mixture of 80% glycerol and 20% DMSO, applied for 45 min, also demonstrated effective penetration through the SC. Enhanced OC was achieved by combining OCAs with DMSO, ethanol, or distilled water. The highest OC efficiency was observed with a 25%-25%-50% water-ethanol-fructose solution and a mixture of 90% Omnipaque® with 10% DMSO, both applied for 5 minutes, resulting in a 4.1- and 4.2-fold increase, respectively, in signal intensity at a skin depth of 80 µm. It was shown that the optical properties of the skin can be controlled at a depth of approx. 80 μm. Almost all used OCAs, which are not occlusive from the conventional viewpoint, have an occlusion effect on the SC. 
Conclusion. The findings of this study contribute to the identification of optimal OCA combinations – namely, a 25%-25%-50% water-ethanol-fructose solution and a mixture of 90% Omnipaque® with 10% DMSO, each applied for 5 minutes – for potential in vivo applications. Moreover, the results provide a strong foundation for future multimodal investigations aimed at developing advanced methods for visualizing biological tissues and organs and analyzing their biophysical parameters, in conjunction with controlled OC.
DOI: 10.6084/m9.figshare.31122700.v1
title: Supplementary Material for: Biophysical Alterations of the Stratum Corneum Induced by Optical Clearing Agents: Implications for Efficiency and Safety Optimization

description: Introduction. Optical clearing (OC) of biological objects is a promising technique with broad potential in clinical and diagnostic applications, as it enhances imaging depth, spatial resolution, and contrast of acquired images or spectra. The aim of this study was to study the effects of the common optical clearing agents (OCAs) glycerol, fructose, dimethyl sulfoxide (DMSO) and iohexol (Omnipaque®) solutions at various concentrations and treatment durations on the stratum corneum (SC) of intact skin using confocal Raman microspectroscopy (CRM). 
Methods. The pig ear was selected for study. Various OCAs were applied topically on the skin, including water solutions of DMSO (100%), fructose (50%), Omnipaque® (90% and 100%), glycerol (40%, 60%, 80%, 90% and 100%). Ex vivo measurements were carried out on porcine ear skin using a Model 3510 SCA confocal Raman microscope (RiverD International B.V., Rotterdam, the Netherlands). 
Results. It was found that a 25%-25%-50% water-ethanol-fructose solution permeated the SC at all tested exposure times (5, 15, 30, and 45 min). Similarly, a mixture of 80% glycerol and 20% DMSO, applied for 45 min, also demonstrated effective penetration through the SC. Enhanced OC was achieved by combining OCAs with DMSO, ethanol, or distilled water. The highest OC efficiency was observed with a 25%-25%-50% water-ethanol-fructose solution and a mixture of 90% Omnipaque® with 10% DMSO, both applied for 5 minutes, resulting in a 4.1- and 4.2-fold increase, respectively, in signal intensity at a skin depth of 80 µm. It was shown that the optical properties of the skin can be controlled at a depth of approx. 80 μm. Almost all used OCAs, which are not occlusive from the conventional viewpoint, have an occlusion effect on the SC. 
Conclusion. The findings of this study contribute to the identification of optimal OCA combinations – namely, a 25%-25%-50% water-ethanol-fructose solution and a mixture of 90% Omnipaque® with 10% DMSO, each applied for 5 minutes – for potential in vivo applications. Moreover, the results provide a strong foundation for future multimodal investigations aimed at developing advanced methods for visualizing biological tissues and organs and analyzing their biophysical parameters, in conjunction with controlled OC.
DOI: 10.5281/zenodo.18334431
title: Dataset of Magneto-optical response of chromium trihalide monolayers: chemical trends

description: Input files for Quantum Espresso to reproduce the self-consistent calculations of the electronic density and the electronic band structure. It contains the necessary files to reproduce the Kerr angle calculations using the Yambo code.
DOI: 10.5281/zenodo.18334430
title: Dataset of Magneto-optical response of chromium trihalide monolayers: chemical trends

description: Input files for Quantum Espresso to reproduce the self-consistent calculations of the electronic density and the electronic band structure. It contains the necessary files to reproduce the Kerr angle calculations using the Yambo code.
DOI: 10.15468/dl.u7p8gs
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "Geometry POLYGON((58.54624 35.72183,97.45376 35.72183,97.45376 51.27817,58.54624 51.27817,58.54624 35.72183))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Centaurea turkestanica"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004413-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dd3jwp
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "Geometry POLYGON((59.35277 36.74421,95.64723 36.74421,95.64723 51.25579,59.35277 51.25579,59.35277 36.74421))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chesneya dshungarica"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004414-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fyk2vp
title: Occurrence Download

description: A dataset containing 4380 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "CoordinateUncertaintyInMeters 0.0-1000.0",
 "Country is Japan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Asteraceae",
 "Year 1970-2025"
 ]
}

 The dataset includes 4380 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004415-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.shkame
title: Occurrence Download

description: A dataset containing 50880 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Garrulax canorus"
 ]
}

 The dataset includes 50880 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004377-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18273109
title: Models fitted with level-2 EUNIS-ESy habitat category for "Sixty years of plant community change in Europe indicate a shift towards nutrient-richer and denser vegetation"

description: This repository contains the model outputs used in our study “Sixty years of plant community change in Europe indicate a shift towards nutrient-richer and denser vegetation” for models using level-2 EUNIS-ESy habitat categories as predictors. This repository is a companion repository reportin main model results and predictions (DOI: 10.5281/zenodo.18266902).

 

The R code and additional data required to reproduce the analyses are available in the main GitHub repository: https://github.com/gmidolo/interpolated_EIV_change.

Please note: The original raw vegetation data from the European Vegetation Archive (EVA) and ReSurveyEurope are not directly deposited here. Access to these complete original datasets, and their use for additional publications, is permissible only following acceptance from the EVA Coordinating Board (more information). Importantly, models and predictions stored here uses original `plot_id` values from EVA and ReSurveyEurope data, while plot identifiers in GitHub are modified / anonymized.These files are stored in the data/models/ESy2models folder. You can load them in R with readRDS()






File name prefix



Description





ESy2_RF.last_fit_



Final fit on the entire training set, evaluated on the test set; for models trained on a subset of data using level-2 EUNIS-ESy habitat class as predictor (GitHub R code) 





ESy2_RF.tune_res_



Tuning results. Output from tune::tune_grid(); for models trained on a subset of data using level-2 EUNIS-ESy habitat class as predictor (GitHub R code)
DOI: 10.5281/zenodo.18273110
title: Models fitted with level-2 EUNIS-ESy habitat category for "Sixty years of plant community change in Europe indicate a shift towards nutrient-richer and denser vegetation"

description: This repository contains the model outputs used in our study “Sixty years of plant community change in Europe indicate a shift towards nutrient-richer and denser vegetation” for models using level-2 EUNIS-ESy habitat categories as predictors. This repository is a companion repository reportin main model results and predictions (DOI: 10.5281/zenodo.18266902).

 

The R code and additional data required to reproduce the analyses are available in the main GitHub repository: https://github.com/gmidolo/interpolated_EIV_change.

Please note: The original raw vegetation data from the European Vegetation Archive (EVA) and ReSurveyEurope are not directly deposited here. Access to these complete original datasets, and their use for additional publications, is permissible only following acceptance from the EVA Coordinating Board (more information). Importantly, models and predictions stored here uses original `plot_id` values from EVA and ReSurveyEurope data, while plot identifiers in GitHub are modified / anonymized.These files are stored in the data/models/ESy2models folder. You can load them in R with readRDS()






File name prefix



Description





ESy2_RF.last_fit_



Final fit on the entire training set, evaluated on the test set; for models trained on a subset of data using level-2 EUNIS-ESy habitat class as predictor (GitHub R code) 





ESy2_RF.tune_res_



Tuning results. Output from tune::tune_grid(); for models trained on a subset of data using level-2 EUNIS-ESy habitat class as predictor (GitHub R code)
DOI: 10.15468/dl.m5eyw5
title: Occurrence Download

description: A dataset containing 5978 records in GBIF matching the query:
SELECT
 '97' 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;= -16.25916876999866
 AND decimalLatitude &lt;= -12.166598047573723
 AND decimalLongitude &gt;= -40.929903860996625
 AND decimalLongitude &lt;= -35.41533826670671
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 5978 records were derived from 242 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004398-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.weqmq3
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.74936 24.405063, 88.67225000000001 24.399486, 88.597059 24.382894, 88.525655 24.355699, 88.459812 24.318578, 88.401158 24.272452, 88.351141 24.218466, 88.31099 24.157959, 88.281683 24.092428, 88.263929 24.023492, 88.258146 23.952854, 88.26445699999999 23.882254, 88.28268799999999 23.813429, 88.31237299999999 23.748068, 88.352767 23.687776, 88.402868 23.634029, 88.461438 23.588142, 88.527039 23.551236, 88.59806399999999 23.524212, 88.67277900000001 23.50773, 88.74936 23.50219, 88.825941 23.50773, 88.900656 23.524212, 88.971681 23.551236, 89.037282 23.588142, 89.09585199999999 23.634029, 89.14595300000001 23.687776, 89.186347 23.748068, 89.216032 23.813429, 89.234263 23.882254, 89.240574 23.952854, 89.234791 24.023492, 89.217037 24.092428, 89.18773 24.157959, 89.14757899999999 24.218466, 89.097562 24.272452, 89.03890800000001 24.318578, 88.97306500000001 24.355699, 88.901661 24.382894, 88.82647 24.399486, 88.74936 24.405063))"},{"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 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004417-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6jkchg
title: Occurrence Download

description: A dataset containing 97 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Arundo donax",
 "Year 1970-2000"
 ]
}

 The dataset includes 97 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004418-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.