cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15068187
title: The Role of AI Experience and Satisfaction in Reducing Technostress in the Workplace: Managing the Digital Dilemma among Indonesian Employees
DOI: 10.5281/zenodo.15068188
title: The Role of AI Experience and Satisfaction in Reducing Technostress in the Workplace: Managing the Digital Dilemma among Indonesian Employees
DOI: 10.5281/zenodo.15068178
title: VSAI Dataset

description: Description:

The VSAI (Vehicle Surveillance via Aerial Imagery) Dataset is a highly versatile and expansive collection of high-resolution aerial images gathered from multiple drone platforms. Covering a diverse geographical expanse, this dataset is specifically curated to support vehicle detection tasks in various complex real-world environments. It offers a multi-view perspective of different urban and rural areas, captured under a wide range of conditions.

This unique dataset is designed for advancing object detection in aerial imagery by addressing numerous real-world challenges, such as varying camera angles, diverse flight altitudes, changing weather conditions, and fluctuating light intensities. VSAI captures imagery from different times of the day and seasons, making it an essential resource for building more robust vehicle detection models.

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Dataset Composition

The VSAI dataset contains an impressive 49,712 vehicle instances meticulously annotated using oriented bounding boxes and arbitrary quadrilateral bounding boxes. This level of precision in annotation accommodates varying object shapes and positions in aerial views, enhancing the dataset's utility for training advanced object detection algorithms. It includes annotations for:



Small Vehicles: 47,519 instances

Large Vehicles: 2,193 instances


To enhance model robustness, the dataset also includes detailed annotations of occlusion rates, helping algorithms generalize better under challenging scenarios like partial visibility caused by environmental factors or other objects.

Key Features



Diverse Capture Conditions: The images in VSAI are captured across multiple drone platforms, which vary in altitude, camera angles, and flight paths.

Complex Scenarios: The dataset spans a wide range of environments, including urban centers, rural landscapes, industrial areas, and highways, representing real-world complexities.

Varied Temporal and Weather Conditions: Images have been captured during different times of the day and under diverse weather conditions, including clear skies, overcast, rainy, and foggy environments.

High Annotation Accuracy: Vehicle instances are annotated with highly precise bounding boxes, including both oriented and arbitrary quadrilateral shapes, to reflect the true contours of vehicles as seen from an aerial perspective.

Occlusion Annotation: Objects in the dataset are also labeled with their occlusion levels, providing additional challenges for model training and improving detection performance in scenarios with partially visible vehicles.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.15068179
title: VSAI Dataset

description: Description:

The VSAI (Vehicle Surveillance via Aerial Imagery) Dataset is a highly versatile and expansive collection of high-resolution aerial images gathered from multiple drone platforms. Covering a diverse geographical expanse, this dataset is specifically curated to support vehicle detection tasks in various complex real-world environments. It offers a multi-view perspective of different urban and rural areas, captured under a wide range of conditions.

This unique dataset is designed for advancing object detection in aerial imagery by addressing numerous real-world challenges, such as varying camera angles, diverse flight altitudes, changing weather conditions, and fluctuating light intensities. VSAI captures imagery from different times of the day and seasons, making it an essential resource for building more robust vehicle detection models.

Download Dataset

Dataset Composition

The VSAI dataset contains an impressive 49,712 vehicle instances meticulously annotated using oriented bounding boxes and arbitrary quadrilateral bounding boxes. This level of precision in annotation accommodates varying object shapes and positions in aerial views, enhancing the dataset's utility for training advanced object detection algorithms. It includes annotations for:



Small Vehicles: 47,519 instances

Large Vehicles: 2,193 instances


To enhance model robustness, the dataset also includes detailed annotations of occlusion rates, helping algorithms generalize better under challenging scenarios like partial visibility caused by environmental factors or other objects.

Key Features



Diverse Capture Conditions: The images in VSAI are captured across multiple drone platforms, which vary in altitude, camera angles, and flight paths.

Complex Scenarios: The dataset spans a wide range of environments, including urban centers, rural landscapes, industrial areas, and highways, representing real-world complexities.

Varied Temporal and Weather Conditions: Images have been captured during different times of the day and under diverse weather conditions, including clear skies, overcast, rainy, and foggy environments.

High Annotation Accuracy: Vehicle instances are annotated with highly precise bounding boxes, including both oriented and arbitrary quadrilateral shapes, to reflect the true contours of vehicles as seen from an aerial perspective.

Occlusion Annotation: Objects in the dataset are also labeled with their occlusion levels, providing additional challenges for model training and improving detection performance in scenarios with partially visible vehicles.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.11196870
title: Daily 1 km seamless Antarctic sea ice albedo product from VIIRS data

description: Summary: In the context of climate change, the sea ice albedo feedback mechanism makes the albedo of Antarctic sea ice a crucial element in polar environmental evolution and global climate models. Existing albedo products for Antarctic sea ice are limited, with a coarse spatiotemporal resolution, and numerous data gaps due to persistent cloud cover. This study uses the Multiband Reflectance Iteration (MBRI) algorithm, which retrieves the Antarctic sea ice albedo using reflectance data from the Visible Infrared Imaging Radiometer Suite (VIIRS). Moreover, the study reconstructs the albedo of cloudy-sky pixels by integrating spatiotemporal information and physical models. A new 1 km daily seamless albedo product for Antarctic sea ice has been developed, covering the period from 2012 to 2021. The results, validated against the automatic weather stations data from the Baseline Surface Radiation Network (BSRN), reveal that the proposed product has higher accuracy, finer spatiotemporal resolution, and superior spatial continuity compared to existing products. The algorithms used fully account for the anisotropy of the sea ice surface, and the high spatiotemporal resolution enables this dataset to enable quantitative analysis of both overall and localized Antarctic sea ice changes. This dataset is valuable for studying the radiation balance of Antarctic sea ice and conducting research in climate modeling. Uncertainty of the dataset is available at https://doi.org/10.5281/zenodo.15067607.

Spatial resolution: 1 km

Temporal resolution: daily (2012-2021)

Format: GeoTIFF

Projection: This dataset adopts Sinusoidal projection and is gridded using the MODIS Sinusoidal Tile Grid

How to name and use data files: The dataset has a longitude range of 180°W to 180°E and a latitude range of 50°S to 80°S, covering 53 tiles (v14: h06~h29; v15: 09~h26; V16: h11~h17, h21~h24). The GeoTIFF file contains a band that represents the shortwave sea ice albedo under clear-sky or cloudy-sky conditions, with 16-bit integer values of 0-10000 and a scale factor of 0.0001. The ocean water and Antarctic continent are set to a filling value of -1. The file name is "Antarctic_Sea_Ice_Albedo_ {yyyyddd}_ {hv}.tif ”, where yyyy represents year, ddd represents day of the year, and hv represents the number of the tile. For example, "Antarctic_Sea_Ice_Albedo_2014270_h18v15.tif" represents the sea ice albedo data of the h18v15 area on the 270th day of 2014. 

Contacts: Weifeng Hao (haowf@whu.edu.cn), Chao Ma (macwhu@whu.edu.cn)
DOI: 10.5281/zenodo.11196871
title: A daily 1 km seamless Antarctic sea ice albedo product from VIIRS data

description: Summary: In the context of climate change, the sea ice albedo feedback mechanism makes the albedo of Antarctic sea ice a crucial element in polar environmental evolution and global climate models. Existing albedo products for Antarctic sea ice are limited, with a coarse spatiotemporal resolution, and numerous data gaps due to persistent cloud cover. This study uses the Multiband Reflectance Iteration (MBRI) algorithm, which retrieves the Antarctic sea ice albedo using reflectance data from the Visible Infrared Imaging Radiometer Suite (VIIRS). Moreover, the study reconstructs the albedo of cloudy-sky pixels by integrating spatiotemporal information and physical models. A new 1 km daily seamless albedo product for Antarctic sea ice has been developed, covering the period from 2012 to 2021. The results, validated against the automatic weather stations data from the Baseline Surface Radiation Network (BSRN), reveal that the proposed product has higher accuracy, finer spatiotemporal resolution, and superior spatial continuity compared to existing products. The algorithms used fully account for the anisotropy of the sea ice surface, and the high spatiotemporal resolution enables this dataset to enable quantitative analysis of both overall and localized Antarctic sea ice changes. This dataset is valuable for studying the radiation balance of Antarctic sea ice and conducting research in climate modeling.

Spatial resolution: 1 km

Temporal resolution: daily (2012-2021)

Format: GeoTIFF

Projection: This dataset adopts Sinusoidal projection and is gridded using the MODIS Sinusoidal Tile Grid

How to name and use data files: The file name is "Antarctic_Sea_Ice_Albedo_ {yyyyddd}_ {hv}_v01.tif ”, where yyyy represents year, ddd represents day of the year, hv represents the number of the tile, and v01 represents the version v1. The dataset has a longitude range of 180°W to 180°E and a latitude range of 50°S to 80°S, covering 53 tiles (v14: h06~h29; v15: 09~h26; V16: h11~h17, h21~h24). For example, "Antarctic_Sea_Ice_Albedo_2014270_h18v15_v01. tif" represents the sea ice albedo data of the h18v15 area on the 270th day of 2014. The ocean water and Antarctic continent and ice shelf are set to a filling value of -1.

Contacts: Weifeng Hao (haowf@whu.edu.cn), Chao Ma (macwhu@whu.edu.cn)
DOI: 10.5281/zenodo.11216156
title: Daily 1 km seamless Antarctic sea ice albedo product from VIIRS data

description: Summary: In the context of climate change, the sea ice albedo feedback mechanism makes the albedo of Antarctic sea ice a crucial element in polar environmental evolution and global climate models. Existing albedo products for Antarctic sea ice are limited, with a coarse spatiotemporal resolution, and numerous data gaps due to persistent cloud cover. This study uses the Multiband Reflectance Iteration (MBRI) algorithm, which retrieves the Antarctic sea ice albedo using reflectance data from the Visible Infrared Imaging Radiometer Suite (VIIRS). Moreover, the study reconstructs the albedo of cloudy-sky pixels by integrating spatiotemporal information and physical models. A new 1 km daily seamless albedo product for Antarctic sea ice has been developed, covering the period from 2012 to 2021. The results, validated against the automatic weather stations data from the Baseline Surface Radiation Network (BSRN), reveal that the proposed product has higher accuracy, finer spatiotemporal resolution, and superior spatial continuity compared to existing products. The algorithms used fully account for the anisotropy of the sea ice surface, and the high spatiotemporal resolution enables this dataset to enable quantitative analysis of both overall and localized Antarctic sea ice changes. This dataset is valuable for studying the radiation balance of Antarctic sea ice and conducting research in climate modeling. Uncertainty of the dataset is available at https://doi.org/10.5281/zenodo.15067607.

Spatial resolution: 1 km

Temporal resolution: daily (2012-2021)

Format: GeoTIFF

Projection: This dataset adopts Sinusoidal projection and is gridded using the MODIS Sinusoidal Tile Grid

How to name and use data files: The dataset has a longitude range of 180°W to 180°E and a latitude range of 50°S to 80°S, covering 53 tiles (v14: h06~h29; v15: 09~h26; V16: h11~h17, h21~h24). The GeoTIFF file contains a band that represents the shortwave sea ice albedo under clear-sky or cloudy-sky conditions, with 16-bit integer values of 0-10000 and a scale factor of 0.0001. The ocean water and Antarctic continent are set to a filling value of -1. The file name is "Antarctic_Sea_Ice_Albedo_ {yyyyddd}_ {hv}.tif ”, where yyyy represents year, ddd represents day of the year, and hv represents the number of the tile. For example, "Antarctic_Sea_Ice_Albedo_2014270_h18v15.tif" represents the sea ice albedo data of the h18v15 area on the 270th day of 2014. 

Contacts: Weifeng Hao (haowf@whu.edu.cn), Chao Ma (macwhu@whu.edu.cn)
DOI: 10.6084/m9.figshare.28601810
title: <b>Paleomagnetic constraints on the age of basalts in the Khartoum Basin, </b><b>the eastern branch of </b><b>the West and Central African Rift System, Sudan </b><b>(supplementary data)</b>

description: One of the rare occurrences of volcanism in the eastern branch of the West and Central African Rift System (WCARS) is the quarried basalt outcropping within the Khartoum Basin. However, its age and stratigraphic relationships remain poorly constrained. In this study, we present new paleomagnetic data from basalts at Jebel Et Toriya and Jebel Fiteihab to address this gap. Rock magnetic results suggest magnetite or titanomagnetite as the primary magnetic carriers. Alternating field and thermal demagnetization revealed characteristic remanent magnetizations with mean directions of <i>D</i> = 334.6°, <i>I </i>= -3.7° (<i>k</i> = 19.1, <i>α</i><sub><em>95</em></sub> = 11.4°), corresponding to a virtual geomagnetic pole (VGP) at 59.4°N/ 269.8°E (<i>K</i> = 33.5, <i>A</i><sub><em>95</em></sub> = 8.5°). Tilt correction yielded mean directions of <i>D</i> = 334.5°, <i>I</i> = -19.2° (<i>k</i> = 37.4, <i>α</i><sub><em>95</em></sub> = 8.0°), with a VGP at 54.2°N/ 258.3°E (<i>K</i> = 41.6, <i>A</i><sub><em>95</em></sub> = 7.6°). The VGPs align well with published African Cretaceous paleopoles derived from igneous units and the Mesozoic section of the African apparent polar wander path. The tilt-corrected VGP suggests an age of 138.5 ± 43.5 Ma for the basalt, which indicates a link with the Early Cretaceous (~145–120 Ma) subsurface volcanic rocks reported in the Khartoum Basin. This implies emplacement during the initial rifting phase, probably predating the deposition of the Albian–Cenomanian Omdurman Formation. This challenges the presumed “Tertiary” age for the outcropping volcanic rocks within the WCARS in Sudan and emphasize the need for radiometric dating and further paleomagnetic sampling of associated units to refine their emplacement history. Our results not only contribute to the limited number of paleomagnetic studies conducted in Sudan but also highlight the importance of revisiting conventional stratigraphic classifications in the region. Moreover, the study demonstrates the utility of apparent polar wander-based dating for igneous rocks when integrated with other geological constraints.
DOI: 10.15468/dl.7rne44
title: Occurrence Download

description: A dataset containing 1031 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Agrilus sinuatus (Olivier, 1790)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1031 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021819-250310093411724/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.28639862
title: Map of irrigated cropland in northern China at 30m resolution from 1990 to 2020

description: High-resolution irrigated cropland maps are essential for optimizing agricultural productivity and managing freshwater resources. China has the world’s largest irrigated cropland area, but rapid irrigation expansion in recent decades has exacerbated regional water stress. We develops an automated machine learning framework to map irrigated croplands over the past four decades at 30 m resolution across northern China, a region that accounts for more than 70% of China’s irrigated cropland and is highly susceptible to water shortages. The main contributions of our framework include: (1) delineating irrigated areas by analyzing the time-series characteristics of irrigated crops by constructing a composite irrigation performance index based on greenness, humidity, and temperature, and refining the sample pool with two specific irrigated crop types; (2) incorporating synthetic phenological features and a 30 m surface temperature dataset to capture the cooling effect of irrigation; (3) leveraging 98,697 Landsat scenes to train 673 localized random forest models per time period to enhance classification accuracy across diverse regions. Validation results indicate that the overall accuracy of the generated irrigated cropland maps ranges from 0.78 to 0.85. Produced maps show strong agreement with statistical data and outperform three existing irrigation products. The 30m spatial resolution map of irrigated cropland also captures the loss of irrigated cropland around the urban, highlighting the increased competition for regional water resources. By minimizing reliance on statistical data and integrating irrigation-induced cooling effects and phenological variations, our approach improves mapping accuracy and provides a valuable resource for regional irrigation planning and water resource management.Coordinate system EPSG:4326Value 0: non-cropland; Value 1: non-irrigated; Value 2: irrigated
DOI: 10.15468/dl.8t5rnm
title: Occurrence Download

description: A dataset containing 804 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Phytolacca americana L."
 ]
}

 The dataset includes 804 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021821-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xg4hcc
title: Occurrence Download

description: A dataset containing 359 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Chinese Taipei)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Phytolacca japonica Makino"
 ]
}

 The dataset includes 359 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021823-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g8u6zd
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bassus dimidiator (Nees, 1834)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 20 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021824-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zytkf9
title: Occurrence Download

description: A dataset containing 91 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Chinese Taipei)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Phytolacca polyandra Batalin"
 ]
}

 The dataset includes 91 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021825-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8x8xpj
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Serbia",
 "Month is one of (January, February, March, April, May, June, July, August)",
 "TaxonKey is Acrocephalus melanopogon (Temminck, 1823)",
 "Year 2024-2024"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021826-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p6388n
title: Occurrence Download

description: A dataset listing the 16 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is one of (Europe, Asia)",
 "TaxonKey is Lacerta agilis Linnaeus, 1758"
 ]
}

 The dataset's 16 records were derived from 740 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021822-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9y46gs
title: Occurrence Download

description: A dataset containing 163758 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (Europe, Asia)",
 "TaxonKey is Lacerta agilis Linnaeus, 1758"
 ]
}

 The dataset includes 163758 records from 740 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021820-250310093411724/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.30466088
title: <b>Ecosystem engineering by primary consumers increases long-term benthic primary production in Lake Mývatn, Iceland</b>

description: Ecosystem engineers impact ecosystems by changing the physical environment in which species interact. In Lake Mývatn, Iceland, larvae of the midge <i>Tanytarsus gracilentus</i> function as ecosystem engineers by building tubes in the sediment that solidify the benthic surface and create a 3-dimensional structure on which microalgae can grow. To assess the ecosystem-scale effect of midge engineering, we took advantage of the natural high-amplitude fluctuations in <i>T. gracilentus</i> over a 13-year time series. We combined larval abundance data with benthic primary production under saturating light conditions (P<sub>max</sub>) and pelagic gross primary production (GPP) measured on average 8.4 times per summer. <i>T. gracilentus</i> abundance had a positive effect on P<sub>max</sub>, as expected from their ecosystem engineering, while pelagic GPP had a negative effect on P<sub>max</sub>, reflecting competition from phytoplankton likely through shading. A complementary analysis of five replicate sediment cores taken on each of 26 sampling days of the time series shows a quantitatively similar positive effect of local-scale variation in larval abundance on primary production in space. Finally, we reanalyzed data from an experiment manipulating light levels and the presence/absence of larvae to show that larval engineering increased benthic net ecosystem production (NEP) more at low than at high light levels. This indicates that the positive engineering effect of midges on benthic P<sub>max</sub> arises from amelioration of light limitation through self-shading, likely due to the structure created by midge tubes. These results show that Mývatn's high productivity is due not only to high inputs of nutrients but also to the ecosystem engineering of tube-forming midges.