cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28942187
title: Monitoring and analysis of land cover changes in Yala National Park, Sri Lanka using Landsat time series (2000∼2022)

description: Yala National Park (YNP), a biodiversity hotspot in South Asia, is increasingly threatened by human-induced landscape transformation. However, long-term assessments of land cover dynamics and landscape integrity remain limited. This study, grounded in a landscape ecology – remote sensing framework, leverages 23 years of Landsat time-series data on Google Earth Engine (GEE) to quantify habitat loss and analyze landscape changes in YNP. The results reveal the following over a 23-year period: (i) YNP shows mild fragmentation characteristics, primarily manifested as local forest loss and patchiness of land cover under increased edge effects (0.7% decline in Contagion Index); (ii) The buffer zone has experienced high-intensity land cover conversion (with agricultural land accounting for 29.9% in 2022) and fragmentation of landscape patterns (13.7% decline in Contagion Index), with agricultural expansion infiltrating towards the edges of YNP; (iii) The increase in cropland area shows a significant positive correlation with the number of fatalities caused by Human-elephant conflict (Pearson's, r = 0.92, <i>p</i> &lt; 0.01). Our study demonstrates the monitoring and assessment of long-term landscape changes, providing practical guidance for national parks facing similar human-wildlife conflict challenges.
DOI: 10.6084/m9.figshare.28942187.v1
title: Monitoring and analysis of land cover changes in Yala National Park, Sri Lanka using Landsat time series (2000∼2022)

description: Yala National Park (YNP), a biodiversity hotspot in South Asia, is increasingly threatened by human-induced landscape transformation. However, long-term assessments of land cover dynamics and landscape integrity remain limited. This study, grounded in a landscape ecology – remote sensing framework, leverages 23 years of Landsat time-series data on Google Earth Engine (GEE) to quantify habitat loss and analyze landscape changes in YNP. The results reveal the following over a 23-year period: (i) YNP shows mild fragmentation characteristics, primarily manifested as local forest loss and patchiness of land cover under increased edge effects (0.7% decline in Contagion Index); (ii) The buffer zone has experienced high-intensity land cover conversion (with agricultural land accounting for 29.9% in 2022) and fragmentation of landscape patterns (13.7% decline in Contagion Index), with agricultural expansion infiltrating towards the edges of YNP; (iii) The increase in cropland area shows a significant positive correlation with the number of fatalities caused by Human-elephant conflict (Pearson's, r = 0.92, <i>p</i> &lt; 0.01). Our study demonstrates the monitoring and assessment of long-term landscape changes, providing practical guidance for national parks facing similar human-wildlife conflict challenges.
DOI: 10.6084/m9.figshare.29423062
title: Object based Markov random field model for hierarchical semantic segmentation of remote sensing imagery

description: Capturing hierarchical relationships among land-cover classes is crucial for accurate semantic segmentation of remote sensing images. Traditional object-based methods face inherent limitations in modeling these complex relationships. To overcome these limitations, we proposed a novel object-based Markov random field (OMRF) model for hierarchical semantic segmentation. The objective of our model is to address two key challenges: (i) the representation of hierarchical semantic features, and (ii) the edge preservation of segmentation results. To address the first challenge, we developed hierarchical semantic representations of images for two distinct land-cover class sets and incorporated a transition probability matrix into OMRF to capture the interaction between these two semantic layers. For the second challenge, we devised an innovative spatial energy function that effectively enforces hierarchical predictions and dynamically regulates boundary smoothness by evaluating spectral dissimilarities among neighboring objects. Furthermore, a generative cross-layer inference strategy was introduced to iteratively exchange and update information across semantic layers for improved prediction. Experimental results on 11 remote sensing images demonstrate the robustness and accuracy of the proposed method, achieving an average Kappa coefficient exceeding 0.96. In comparison to 15 state-of-the-art methods, our model achieved optimal performance in 9 instances and suboptimal performance in 2 instances.
DOI: 10.6084/m9.figshare.29423062.v1
title: Object based Markov random field model for hierarchical semantic segmentation of remote sensing imagery

description: Capturing hierarchical relationships among land-cover classes is crucial for accurate semantic segmentation of remote sensing images. Traditional object-based methods face inherent limitations in modeling these complex relationships. To overcome these limitations, we proposed a novel object-based Markov random field (OMRF) model for hierarchical semantic segmentation. The objective of our model is to address two key challenges: (i) the representation of hierarchical semantic features, and (ii) the edge preservation of segmentation results. To address the first challenge, we developed hierarchical semantic representations of images for two distinct land-cover class sets and incorporated a transition probability matrix into OMRF to capture the interaction between these two semantic layers. For the second challenge, we devised an innovative spatial energy function that effectively enforces hierarchical predictions and dynamically regulates boundary smoothness by evaluating spectral dissimilarities among neighboring objects. Furthermore, a generative cross-layer inference strategy was introduced to iteratively exchange and update information across semantic layers for improved prediction. Experimental results on 11 remote sensing images demonstrate the robustness and accuracy of the proposed method, achieving an average Kappa coefficient exceeding 0.96. In comparison to 15 state-of-the-art methods, our model achieved optimal performance in 9 instances and suboptimal performance in 2 instances.
DOI: 10.6084/m9.figshare.28693041
title: Revealing the characteristics and propagation patterns of hydrological drought in Sichuan using GNSS-based runoff estimates

description: Understanding hydrological drought is vital for effective disaster mitigation and management. However, challenges like sparse hydrological monitoring stations and data acquisition present significant obstacles for studying hydrological drought in specific regions. This study developed a Global Navigation Satellite System (GNSS) -based runoff index to assess the hydrological drought in Sichuan. Regional equivalent water height (EWH) was estimated using the vertical displacement of continuous GNSS stations based on Green's function method. Combining this with high-precision precipitation and evapotranspiration data, the runoff in Sichuan using the water balance equation, resulting in the standardized runoff index (SRI-GNSS) was calculated. The findings show that Sichuan’s hydrological drought duration varies from one to nine months, and SRI-GNSS indicates a trend towards hydrological wetness. The drought’s intensity remained stable at around 1.15, but its severity accumulated over longer periods. The transit from meteorological to hydrological drought took approximately two months, with a 48.4% propagation probability. Influences like the El Niño-Southern Oscillation, Pacific Decadal Oscillation and North Atlantic Oscillation significantly influenced SRI-GNSS variability during 2016–2018. This study addresses gaps in hydrological drought assessments based on runoff in Sichuan Province and highlights the potential of GNSS technology for advancing hydrological research in regions with limited runoff data.
DOI: 10.6084/m9.figshare.28693041.v1
title: Revealing the characteristics and propagation patterns of hydrological drought in Sichuan using GNSS-based runoff estimates

description: Understanding hydrological drought is vital for effective disaster mitigation and management. However, challenges like sparse hydrological monitoring stations and data acquisition present significant obstacles for studying hydrological drought in specific regions. This study developed a Global Navigation Satellite System (GNSS) -based runoff index to assess the hydrological drought in Sichuan. Regional equivalent water height (EWH) was estimated using the vertical displacement of continuous GNSS stations based on Green's function method. Combining this with high-precision precipitation and evapotranspiration data, the runoff in Sichuan using the water balance equation, resulting in the standardized runoff index (SRI-GNSS) was calculated. The findings show that Sichuan’s hydrological drought duration varies from one to nine months, and SRI-GNSS indicates a trend towards hydrological wetness. The drought’s intensity remained stable at around 1.15, but its severity accumulated over longer periods. The transit from meteorological to hydrological drought took approximately two months, with a 48.4% propagation probability. Influences like the El Niño-Southern Oscillation, Pacific Decadal Oscillation and North Atlantic Oscillation significantly influenced SRI-GNSS variability during 2016–2018. This study addresses gaps in hydrological drought assessments based on runoff in Sichuan Province and highlights the potential of GNSS technology for advancing hydrological research in regions with limited runoff data.
DOI: 10.6084/m9.figshare.29151215
title: Road scene map for autonomous driving and modeling method

description: Constructing maps suitable for autonomous vehicles (AVs) is a critical research focus in autonomous driving and AI, extending cartography’s challenges. Building on cartographic principles, we propose the concept of a road scene map along with its modeling method that incorporates dynamic/static traffic elements with geometric/semantic features. Current limitations include unclear road scene graph relationships and a lack of integration among 3D traffic entity detection, map element detection, and scene relation extraction. To address these issues, we propose a method for constructing road scene maps: (1) A multi-task detection model identifies traffic entities and map elements directly in bird’s-eye-view (BEV) space, providing precise location, geometry, and attribute data; (2) A unified road scene relation pattern enables rule-based spatial/semantic relationship extraction. Experiments on nuScenes demonstrate improvements: the detection model achieves 1.5% and 1.9% accuracy gains in traffic entity and map element detection over state-of-the-art methods, while the relation extraction method covers broader perceptual ranges and more complex interactions. Results confirm the effective integration of 3D object detection, map element recognition, and scene relation extraction into a unified map. This integration delivers critical environmental information (locations, geometries, attributes, and spatial/semantic relationships) to AVs, significantly enhancing their perception and reasoning in dynamic road scenarios.
DOI: 10.6084/m9.figshare.29151215.v1
title: Road scene map for autonomous driving and modeling method

description: Constructing maps suitable for autonomous vehicles (AVs) is a critical research focus in autonomous driving and AI, extending cartography’s challenges. Building on cartographic principles, we propose the concept of a road scene map along with its modeling method that incorporates dynamic/static traffic elements with geometric/semantic features. Current limitations include unclear road scene graph relationships and a lack of integration among 3D traffic entity detection, map element detection, and scene relation extraction. To address these issues, we propose a method for constructing road scene maps: (1) A multi-task detection model identifies traffic entities and map elements directly in bird’s-eye-view (BEV) space, providing precise location, geometry, and attribute data; (2) A unified road scene relation pattern enables rule-based spatial/semantic relationship extraction. Experiments on nuScenes demonstrate improvements: the detection model achieves 1.5% and 1.9% accuracy gains in traffic entity and map element detection over state-of-the-art methods, while the relation extraction method covers broader perceptual ranges and more complex interactions. Results confirm the effective integration of 3D object detection, map element recognition, and scene relation extraction into a unified map. This integration delivers critical environmental information (locations, geometries, attributes, and spatial/semantic relationships) to AVs, significantly enhancing their perception and reasoning in dynamic road scenarios.
DOI: 10.6084/m9.figshare.28370252
title: City-level total and sub-category energy intensity estimation using machine learning

description: Energy intensity is an important indicator for measuring the level of energy utilization efficiency in a region. Due to the limitation of energy intensity statistics, current research mainly focuses on the national and provincial scales, making it challenging to develop detailed energy conservation strategies. This study proposes a city-level total and subcategories (coal, oil, gas) energy intensity estimation method based on multi-source remote sensing data and machine learning models. The performance of the machine learning models is validated using the four-fold cross-validation approach. By comparing the various machine learning models, the deep neural network (DNN) model is used to estimate the city-level total and subcategories (coal, oil, gas) energy intensity from 2005 to 2020. The results indicate that nearly two-thirds of the cities in China have experienced a decreasing trend in energy intensity, and the total and sub-categories (coal, oil, gas) energy intensity tends to be lower in the east and south and higher in the west and north. The proposed method extends energy intensity estimation to the city level and across subcategories, providing a scientific basis for cities to develop targeted energy conservation and emission reduction policies.
DOI: 10.6084/m9.figshare.28370252.v1
title: City-level total and sub-category energy intensity estimation using machine learning

description: Energy intensity is an important indicator for measuring the level of energy utilization efficiency in a region. Due to the limitation of energy intensity statistics, current research mainly focuses on the national and provincial scales, making it challenging to develop detailed energy conservation strategies. This study proposes a city-level total and subcategories (coal, oil, gas) energy intensity estimation method based on multi-source remote sensing data and machine learning models. The performance of the machine learning models is validated using the four-fold cross-validation approach. By comparing the various machine learning models, the deep neural network (DNN) model is used to estimate the city-level total and subcategories (coal, oil, gas) energy intensity from 2005 to 2020. The results indicate that nearly two-thirds of the cities in China have experienced a decreasing trend in energy intensity, and the total and sub-categories (coal, oil, gas) energy intensity tends to be lower in the east and south and higher in the west and north. The proposed method extends energy intensity estimation to the city level and across subcategories, providing a scientific basis for cities to develop targeted energy conservation and emission reduction policies.
DOI: 10.24403/jp.1492564
title: How Does It Feel? : Fear and Loss in Mohsin Hamid’s The Last White Man / von Max Rosenzweig
DOI: 10.15468/dl.7wt226
title: Occurrence Download

description: A dataset containing 2313 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lotus pedunculatus",
 "Country is New Zealand",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2313 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042269-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.39f4yy
title: Occurrence Download

description: A dataset containing 226488733 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "OccurrenceStatus is Present",
 "Year is 2023"
 ]
}

 The dataset includes 226488733 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042209-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d7yaym
title: Occurrence Download

description: A dataset containing 312 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Xylocopa ruficornis"
 ]
}

 The dataset includes 312 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042268-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.arbp88
title: Occurrence Download

description: A dataset containing 181 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lotus pedunculatus",
 "Country is Norway",
 "HasCoordinate is true"
 ]
}

 The dataset includes 181 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042270-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kktsu8
title: Occurrence Download

description: A dataset containing 2173 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lotus pedunculatus",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2173 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042272-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3n5m58
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Gobionellus stomatus)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 24 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042271-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v5hepp
title: Occurrence Download

description: A dataset listing the 109 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Galagidae"
 ]
}

 The dataset's 109 records were derived from 127 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042253-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16941524
title: Global Artificial Womb Facility Market 2025 To 2034

description: Artificial Womb Facility Market Size, Trends and Insights By Technology (Bioreactors, Extrauter in Support Devices, Others), By Application (Prenatal Care, Neonatal Care), By End User (Super Specialty Hospitals, Research Centers, In-Vitro Fertilization Centers (IVF)), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025 – 2034.

Reports Description

As per the Artificial Womb Facility Market conducted by the CMI Team, the global Artificial Womb Facility Market is expected to record a CAGR of 12.31% from 2025 to 2034. In 2025, the market size is projected to reach a valuation of USD 348.98 Million. By 2034, the valuation is anticipated to reach USD 992.36 Million.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=72687
DOI: 10.5281/zenodo.16941525
title: Global Artificial Womb Facility Market 2025 To 2034

description: Artificial Womb Facility Market Size, Trends and Insights By Technology (Bioreactors, Extrauter in Support Devices, Others), By Application (Prenatal Care, Neonatal Care), By End User (Super Specialty Hospitals, Research Centers, In-Vitro Fertilization Centers (IVF)), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025 – 2034.

Reports Description

As per the Artificial Womb Facility Market conducted by the CMI Team, the global Artificial Womb Facility Market is expected to record a CAGR of 12.31% from 2025 to 2034. In 2025, the market size is projected to reach a valuation of USD 348.98 Million. By 2034, the valuation is anticipated to reach USD 992.36 Million.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=72687
DOI: 10.5281/zenodo.16929071
title: Signal peptide sequences employed for enzyme expression in yeast

description: Set of signal peptide sequences used for testing the heterologous expression of the enzymes unspecific peroxygenases (UPOs, EC 1.11.2.1) and laccases (EC 1.10.3.2) in the organisms Komagataella phaffii and Saccharomyces cerevisiae, respectively.
DOI: 10.5281/zenodo.16929072
title: Signal peptide sequences employed for enzyme expression in yeast

description: Set of signal peptide sequences used for testing the heterologous expression of the enzymes unspecific peroxygenases (UPOs, EC 1.11.2.1) and laccases (EC 1.10.3.2) in the organisms Komagataella phaffii and Saccharomyces cerevisiae, respectively.
DOI: 10.15468/dl.yjcp4j
title: Occurrence Download

description: A dataset containing 116582 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "DatasetKey is one of (Artportalen, Observation.org, Nature data from around the World, EOD – eBird Observation Dataset, DOF/BirdLife Denmark - Observations from DOFbasen, Macro-moth distribution records for the UK from Butterfly Conservation, up to 2019, iNaturalist Research-grade Observations, INPN - Données flore des CBN agrégées par la FCBN, NABU|naturgucker, Butterfly distribution records for the UK from Butterfly Conservation, up to 2019)",
 "Month is one of (July, June, May)",
 "TaxonKey is Mammalia",
 "Year 2025-2025"
 ]
}

 The dataset includes 116582 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042255-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.24403/jp.1492565
title: „Wir stehen mit verschränkten Armen parat.“ : Heike Geißlers Die Woche / von Janne Lilkendey
DOI: 10.5281/zenodo.16940364
title: Field and model data sets

description: All data from the publication "Quantifying hydrological impacts of compacted sandy subsoils using soil water flow simulations: the importance of vegetation parameterization" by Pinza et al. (2025). 

They contain the following-soil hydraulic parameter data-monitoring data during experimental period 2022-2024 (soil moisture, precipitation, potential evapotranspiration)-climate projection data for 2072-2100 (precipitation, potential evapotranspiration used in this research)-results from the model for experimental period (2022-2024) and future climate (2072-2100)