cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28582878
title: A 3-dimension urban growth analysis using local climate zone mapping

description: To comprehend urban evolution, it is necessary to investigate changes in urban land in megacities. However, volumetric expansion has not been well studied. This study proposes an approach to characterize a city’s volumetric expansion for exploring urban land changes based on Local Climate Zone (LCZ) mapping by using a new convolutional neural network (CNN) model termed as DenseNetLCZ. The approach identifies three processes of urban growth: new urbanization, intensified compactness, and intensified height. The method was applied to four megacities, Beijing, Moscow, Paris, and Houston, from 2000 to 2020. The results showed satisfactory overall accuracy, ranging between 80% and 90%. The expansion of new urbanization was found to be consistently faster than intensified compactness and intensified height in all cities. Analysis of Beijing revealed that during this period, new urbanization increased by 52.8%, while intensified compactness decreased by 25%, and intensified height increased by 87.5%. However, due to the initially small base area of intensified height, this growth was less significant in terms of overall land coverage compared to new urbanization. Additionally, the “diffusion to coalescence” pattern was found to be beneficial for urban intensification. Our research forecasts urban expansion and intensification in 2100 under different Shared Socioeconomic Pathways (SSPs), indicating that stricter sustainability policies may promote concentrated, vertical urban growth, while looser ones may lead to more dispersed expansion, underscoring the crucial role of these policies in shaping future urban development strategies.
DOI: 10.6084/m9.figshare.28582878.v1
title: A 3-dimension urban growth analysis using local climate zone mapping

description: To comprehend urban evolution, it is necessary to investigate changes in urban land in megacities. However, volumetric expansion has not been well studied. This study proposes an approach to characterize a city’s volumetric expansion for exploring urban land changes based on Local Climate Zone (LCZ) mapping by using a new convolutional neural network (CNN) model termed as DenseNetLCZ. The approach identifies three processes of urban growth: new urbanization, intensified compactness, and intensified height. The method was applied to four megacities, Beijing, Moscow, Paris, and Houston, from 2000 to 2020. The results showed satisfactory overall accuracy, ranging between 80% and 90%. The expansion of new urbanization was found to be consistently faster than intensified compactness and intensified height in all cities. Analysis of Beijing revealed that during this period, new urbanization increased by 52.8%, while intensified compactness decreased by 25%, and intensified height increased by 87.5%. However, due to the initially small base area of intensified height, this growth was less significant in terms of overall land coverage compared to new urbanization. Additionally, the “diffusion to coalescence” pattern was found to be beneficial for urban intensification. Our research forecasts urban expansion and intensification in 2100 under different Shared Socioeconomic Pathways (SSPs), indicating that stricter sustainability policies may promote concentrated, vertical urban growth, while looser ones may lead to more dispersed expansion, underscoring the crucial role of these policies in shaping future urban development strategies.
DOI: 10.6084/m9.figshare.28731504
title: Assessing methods in fusion and fitting for time series construction in remote sensing-based earth observations

description: This study evaluates the comparative performance of spatiotemporal fusion and time-series fitting methods for constructing high-spatiotemporal-resolution remote sensing time-series data. Due to in-class similarity of fusion methods and fitting methods, we employ the Fit-FC (Fitting, spatial Filtering, and residual Compensation) model as a representative fusion method and the linear harmonic fitting model as a representative fitting method. Both Fit-FC and the linear harmonic fitting are widely used for high-spatiotemporal-resolution time-series data construction, and we modify the original Fit-FC model to enable automatic time-series fusion. To ensure data representativeness, we use 3 years (2019–2021) of Harmonized Landsat and Sentinel-2 surface reflectance datasets and Terra MCD43A4 products. Eight experimental regions are selected worldwide to guarantee generalization of the comparative performance between fusion and fitting methods, covering diverse land-use types (cropland, developed land, forest, and grassland) and varying climatological conditions. Time-series of NDVI and surface reflectance are analyzed under both actual observations and simulated data-missing scenarios. The constructed time-series data reveals that (1) the modified Fit-FC and linear harmonic fitting model achieve excellent performance in constructing high-resolution time-series images; (2) the fusion method outperforms the fitting method in constructing time-series of NDVI and surface reflectance images in cropland-, forest-, and grassland-dominated regions; (3) both methods achieve comparable performance in developed-dominated regions; (4) the fusion method is more robust to missing data, and better captures abrupt phenological transitions under conditions of continuous missing data; (5) the fitting method is computationally more efficient, making it suitable for large-scale time-series image reconstruction. This study provides valuable insights for selecting optimal strategies to generate high-resolution time-series images across diverse application scenarios and lays a foundation for extensions to other vegetation indices or land surface variables.
DOI: 10.6084/m9.figshare.28731504.v1
title: Assessing methods in fusion and fitting for time series construction in remote sensing-based earth observations

description: This study evaluates the comparative performance of spatiotemporal fusion and time-series fitting methods for constructing high-spatiotemporal-resolution remote sensing time-series data. Due to in-class similarity of fusion methods and fitting methods, we employ the Fit-FC (Fitting, spatial Filtering, and residual Compensation) model as a representative fusion method and the linear harmonic fitting model as a representative fitting method. Both Fit-FC and the linear harmonic fitting are widely used for high-spatiotemporal-resolution time-series data construction, and we modify the original Fit-FC model to enable automatic time-series fusion. To ensure data representativeness, we use 3 years (2019–2021) of Harmonized Landsat and Sentinel-2 surface reflectance datasets and Terra MCD43A4 products. Eight experimental regions are selected worldwide to guarantee generalization of the comparative performance between fusion and fitting methods, covering diverse land-use types (cropland, developed land, forest, and grassland) and varying climatological conditions. Time-series of NDVI and surface reflectance are analyzed under both actual observations and simulated data-missing scenarios. The constructed time-series data reveals that (1) the modified Fit-FC and linear harmonic fitting model achieve excellent performance in constructing high-resolution time-series images; (2) the fusion method outperforms the fitting method in constructing time-series of NDVI and surface reflectance images in cropland-, forest-, and grassland-dominated regions; (3) both methods achieve comparable performance in developed-dominated regions; (4) the fusion method is more robust to missing data, and better captures abrupt phenological transitions under conditions of continuous missing data; (5) the fitting method is computationally more efficient, making it suitable for large-scale time-series image reconstruction. This study provides valuable insights for selecting optimal strategies to generate high-resolution time-series images across diverse application scenarios and lays a foundation for extensions to other vegetation indices or land surface variables.
DOI: 10.6084/m9.figshare.29652767
title: Monitoring active fires in Borneo from Sentinel-2 MSI images

description: A detailed, spatially explicit fire inventory is essential for improving our understanding of biomass burning and for supporting the formulation of targeted fire mitigation policies. However, such fire inventories remain limited, especially in tropical regions. Existing active fire (AF) products derived from low-resolution sensors (e.g. MODIS and VIIRS) generally have high omission errors (OE), especially when detecting small and relatively colder temperatures fires. While moderate-resolution sensors offer unprecedented opportunities for detecting small and subtle fires, they face the dilemma of high commission errors (CE). To address this problem, we propose an object-oriented method to effectively detect AFs from Sentinel-2 MSI images, which focuses on suppressing the interference of various CEs through object-level inter-spectral criteria cloud filtering, seamline exclusion based on granule footprints, and false positive refinement based on random forest classification model. Using more than 55,000 Sentinel-2 MSI images acquired during 2016–2021, we have compiled a novel 20 m fire inventory covering forests and peatlands in Borneo. Initial assessment of the fire inventory suggests a CE of approximately 7.2% and an OE of 11.5%. Analysis of the Borneo fire inventory revealed the following: (<i>i</i>) A significant concentration of AFs was observed in Kalimantan, with Central Kalimantan accounting for approximately 55.9% of all detected peatland fires in Borneo, and West Kalimantan contributing 33.7% of forest fires. (<i>ii</i>) Peatland fires dominated widespread fires in Borneo in 2019, with 1.4 to 2.6 times the size and 3.1 to 16 times the number compared to other years in 2016–2021. (<i>iii</i>) The MSI AF detections show slight differences in spatiotemporal patterns compared to MODIS and VIIRS AF products, which is attributed to variations in sensitivity to small fires. Our study clarifies the spatial dynamic distribution of AFs in Borneo, providing fundamental support for local fire monitoring, fire regime, and carbon emission research.
DOI: 10.6084/m9.figshare.29652767.v1
title: Monitoring active fires in Borneo from Sentinel-2 MSI images

description: A detailed, spatially explicit fire inventory is essential for improving our understanding of biomass burning and for supporting the formulation of targeted fire mitigation policies. However, such fire inventories remain limited, especially in tropical regions. Existing active fire (AF) products derived from low-resolution sensors (e.g. MODIS and VIIRS) generally have high omission errors (OE), especially when detecting small and relatively colder temperatures fires. While moderate-resolution sensors offer unprecedented opportunities for detecting small and subtle fires, they face the dilemma of high commission errors (CE). To address this problem, we propose an object-oriented method to effectively detect AFs from Sentinel-2 MSI images, which focuses on suppressing the interference of various CEs through object-level inter-spectral criteria cloud filtering, seamline exclusion based on granule footprints, and false positive refinement based on random forest classification model. Using more than 55,000 Sentinel-2 MSI images acquired during 2016–2021, we have compiled a novel 20 m fire inventory covering forests and peatlands in Borneo. Initial assessment of the fire inventory suggests a CE of approximately 7.2% and an OE of 11.5%. Analysis of the Borneo fire inventory revealed the following: (<i>i</i>) A significant concentration of AFs was observed in Kalimantan, with Central Kalimantan accounting for approximately 55.9% of all detected peatland fires in Borneo, and West Kalimantan contributing 33.7% of forest fires. (<i>ii</i>) Peatland fires dominated widespread fires in Borneo in 2019, with 1.4 to 2.6 times the size and 3.1 to 16 times the number compared to other years in 2016–2021. (<i>iii</i>) The MSI AF detections show slight differences in spatiotemporal patterns compared to MODIS and VIIRS AF products, which is attributed to variations in sensitivity to small fires. Our study clarifies the spatial dynamic distribution of AFs in Borneo, providing fundamental support for local fire monitoring, fire regime, and carbon emission research.
DOI: 10.15468/dl.mhvsbe
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Holcoglossum flavescens",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009907-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18615822
title: Evaluating Guest Experience and Usability of Biometric Smart Room Access in Hotels: An HCI Perspective

description: The integration of biometric authentication technologies into smart hospitality environments introduces new challenges related to usability, privacy, and trust. This study evaluates biometric room access systems from a Human–Computer Interaction (HCI) perspective, focusing on how perceived ease of use, perceived usefulness, perceived security, and perceived privacy influence guest experience through trust. A quantitative research design was employed, and data were collected from 150 hotel guests who had previously used biometric room access. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that the model explains 63.8% of the variance in guest experience and 59.1% of the variance in trust. Trust emerges as the strongest predictor of guest experience, while perceived privacy and perceived security significantly influence experience indirectly through trust mediation. In contrast, usability-related factors demonstrate comparatively smaller effects once baseline functionality is achieved. These findings suggest that biometric authentication in smart environments operates as a trust-sensitive socio-technical system, where perceived data governance and psychological assurance are critical determinants of experiential evaluation. The study contributes to intelligent systems research by demonstrating that authentication technologies embedded in physical access control contexts must integrate technical robustness with perceptual trust-building mechanisms to achieve sustainable user acceptance.
DOI: 10.5281/zenodo.18615821
title: Evaluating Guest Experience and Usability of Biometric Smart Room Access in Hotels: An HCI Perspective

description: The integration of biometric authentication technologies into smart hospitality environments introduces new challenges related to usability, privacy, and trust. This study evaluates biometric room access systems from a Human–Computer Interaction (HCI) perspective, focusing on how perceived ease of use, perceived usefulness, perceived security, and perceived privacy influence guest experience through trust. A quantitative research design was employed, and data were collected from 150 hotel guests who had previously used biometric room access. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that the model explains 63.8% of the variance in guest experience and 59.1% of the variance in trust. Trust emerges as the strongest predictor of guest experience, while perceived privacy and perceived security significantly influence experience indirectly through trust mediation. In contrast, usability-related factors demonstrate comparatively smaller effects once baseline functionality is achieved. These findings suggest that biometric authentication in smart environments operates as a trust-sensitive socio-technical system, where perceived data governance and psychological assurance are critical determinants of experiential evaluation. The study contributes to intelligent systems research by demonstrating that authentication technologies embedded in physical access control contexts must integrate technical robustness with perceptual trust-building mechanisms to achieve sustainable user acceptance.
DOI: 10.15468/dl.x7dx9c
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Brachyglanis nocturnus",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 23 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009908-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.neuquq
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hypolytrum paraense",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 27 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009909-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t2mwwp
title: Occurrence Download

description: A dataset containing 489 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crotalus simus"
 ]
}

 The dataset includes 489 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009910-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m9y7gp
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ionopsis burchellii",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009911-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jpu7rv
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Brachyrhamdia marthae",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 32 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009912-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fgs2eu
title: Occurrence Download

description: A dataset containing 137 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ipomoea cavalcantei",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 137 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009913-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17909/m83w-8w86
title: Data for paper 'Cloudy mornings and clear evenings on a gas giant exoplanet'

description: TESS data products used in this study
DOI: 10.15468/dl.476gvz
title: Occurrence Download

description: A dataset containing 658 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Isochilus linearis",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 658 records from 118 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009914-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14797082
title: Tables S1–S2 for geochemical study on Menengai geothermal field in Kenya

description: For peer review
DOI: 10.5281/zenodo.14797081
title: Tables S1–S2 for geochemical study on Menengai geothermal field in Kenya

description: For peer review
DOI: 10.5281/zenodo.15249875
title: Tables S1–S2 for geochemical study on Menengai geothermal field in Kenya

description: For peer review
DOI: 10.5281/zenodo.15303600
title: Regarding the Bulk-RNA Seq matrix of the fluorescent mouse model with the Cγ1Cre mTmG system under immunization

description: By immunizing fluorescent mice with the c - γ -1CRE mTmG system, Bulk-RNA sequencing was performed on the corresponding immune cells at different sites to analyze the dynamic expression patterns of the corresponding genes.
DOI: 10.5281/zenodo.15303601
title: Regarding the Bulk-RNA Seq matrix of the fluorescent mouse model with the Cγ1Cre mTmG system under immunization

description: By immunizing fluorescent mice with the c - γ -1CRE mTmG system, Bulk-RNA sequencing was performed on the corresponding immune cells at different sites to analyze the dynamic expression patterns of the corresponding genes.
DOI: 10.5281/zenodo.15304043
title: The data supporting the time delay results of this article

description: The data supporting the key time delay results of the article
DOI: 10.5281/zenodo.15304044
title: The data supporting the time delay results of this article

description: The data supporting the key time delay results of the article
DOI: 10.15468/dl.dybgdh
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Andrena malacothricidis Thorp, 1969"
 ]
}

 The dataset includes 10 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005362-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aupwds
title: Occurrence Download

description: A dataset containing 376593 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((17.61278 55.63826,9.0762 52.36885,13.47689 46.86301,21.70194 49.05821,25.6669 47.0611,23.71142 45.17398,26.86413 42.48012,37.67551 50.84656,35.31733 54.8315,28.24276 53.96562,21.531 56.34842,17.61278 55.63826))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is June",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 1981-2010"
 ]
}

 The dataset includes 376593 records from 274 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005293-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e199665
title: Code for: Communicating Program Eligibility: A Supplemental Security Income (SSI) Field Experiment

description: We conducted a direct mail field experiment with 4,016,461 individuals to test several key hypotheses about why take-up of Supplemental Security Income among individuals age 65 and above is so low. Communicating likely eligibility in a basic letter generated substantial increases in take-up in relative terms. Adding behaviorally-informed statements increased the effectiveness of these communications. Yet, the application rate in our study sample during the full 24-month follow-up period remained no greater than 7%. Our results reveal a modest tradeoff between increasing applications and the conditional likelihood of award, as well as the award amount.