cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15257800
title: Paper dataset: Using High-Resolution Satellite Imagery and Deep Learning to Map Artisanal Mining Spatial Extent in the Democratic Republic of the Congo

description: This dataset accompanies the following publication: Using High-Resolution Satellite Imagery and Deep Learning to Map Artisanal Mining Spatial Extent in the Democratic Republic of the Congo. Remote Sensing, 17(24), 4057. https://doi.org/10.3390/rs17244057

 

Overview

This dataset contains paired satellite image tiles and binary segmentation masks for Artisanal and Small-scale Mining (ASM) sites in the Democratic Republic of the Congo (DRC). It is intended to support training and evaluation of deep learning models for ASM mapping using optical and SAR imagery. Below you can see the structure of the dataset.

dataset/├── ps/                             # PlanetScope tiles (optical, 4-band)│   ├── images/                # RGB + NIR GeoTIFF tiles│   └── masks/                 # Binary ASM masks (0 = background, 1 = ASM)└── s1/                            # Sentinel-1 tiles (SAR, 2-band)    ├── images/                # VV and VH polarization GeoTIFF tiles    └── masks/                 # Binary ASM masks (0 = background, 1 = ASM)

 

Data Specifications




 
PlanetScope
Sentinel-1




Source
Planet-NICFI
ESA Copernicus


Bands
Blue, Green, Red, NIR
VV, VH


GSD
4.77 m
10 m


Tile size
256 × 256 px
128 × 128 px


Number of tiles
782
782


Format
GeoTIFF
GeoTIFF




 

The PlanetScope and Sentinel-1 tiles are spatially overlapping — corresponding tiles in ps/ and s1/ cover the same geographic area in the Eastern Democratic Republic of the Congo.

Masks are binary rasters at the same resolution and spatial extent as the corresponding image tiles:



0 = Non-ASM (background)

1 = Artisanal and Small-scale Mining (ASM)


Ground truth labels were derived from field survey data collected by [e.g. IPIS] and processed as described in the accompanying paper.

 

License

This dataset contains data from two sources with different licenses:

PlanetScope tiles (ps/ directory): derived from Planet-NICFI basemaps. Imagery © Planet Labs Inc. All use subject to the Planet NICFI Participant License Agreement. Use of these tiles is restricted to non-commercial purposes in support of the NICFI program's goals (tropical forest monitoring, climate change, biodiversity conservation, and sustainable development).

Sentinel-1 tiles and all masks (s1/ directory and all masks/ subdirectories): Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

 

Citation

If you use this dataset, please cite:

Pasanisi et al. (2025). Using High-Resolution Satellite Imagery and Deep Learning to Map Artisanal Mining Spatial Extent in the Democratic Republic of the Congo. Remote Sensing, 17(24), 4057. https://doi.org/10.3390/rs17244057
DOI: 10.5281/zenodo.18678571
title: Flexible MXenesponge piezoresistive array for multi-point hand-pressure monitoring in badminton forehand smashes
DOI: 10.15468/dl.adpa5x
title: Occurrence Download

description: A dataset containing 48303 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Year is greater than or equal to 1900",
 "TaxonKey is Setaria verticillata"
 ]
}

 The dataset includes 48303 records from 731 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026849-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18678570
title: Flexible MXenesponge piezoresistive array for multi-point hand-pressure monitoring in badminton forehand smashes
DOI: 10.15468/dl.pku4tg
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leptobrachella crocea"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026850-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60597/9p9h-7z21
title: ATUc CBPC - Autorisation Temporaire d’Utilisation (ATU) de cohorte en oncologie - cancer du poumon à petites cellules
DOI: 10.15468/dl.8fwyfe
title: Occurrence Download

description: A dataset containing 65681 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Centropyge",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 65681 records from 201 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026821-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18678573
title: Unpacking the Link Between Digital Addiction and Cognitive Flexibility: The Intervening Mechanism of Self-Control

description: This dataset contains the anonymized survey data used in the study titled "Unpacking the Link Between Digital Addiction and Cognitive Flexibility: The Intervening Mechanism of Self-Control". The data supports the findings regarding the relationships between digital addiction, self-control skills, and cognitive flexibility.
DOI: 10.5281/zenodo.18678572
title: Unpacking the Link Between Digital Addiction and Cognitive Flexibility: The Intervening Mechanism of Self-Control

description: This dataset contains the anonymized survey data used in the study titled "Unpacking the Link Between Digital Addiction and Cognitive Flexibility: The Intervening Mechanism of Self-Control". The data supports the findings regarding the relationships between digital addiction, self-control skills, and cognitive flexibility.
DOI: 10.15468/dl.547j4j
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leptobrachella brevicrus"
 ]
}

 The dataset includes 9 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026851-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17877/tudodata-2025-manobadl
title: Supplementary Material of "Multivariate and Multiple Contrast Testing in General Covariate-adjusted Factorial Designs"

description: This is the Supplementary Material of the paper Baumeister, M., Thiel, K. E., Matits, L., Zimmermann, G., Pauly, M., & Sattler, P. (2026). Multivariate and multiple contrast testing in general covariate-adjusted factorial designs. Journal of Multivariate Analysis, 214, 105617. &lt;a href="https://doi.org/10.1016/j.jmva.2026.105617"&gt;https://doi.org/10.1016/j.jmva.2026.105617&lt;/a&gt;
The paper introduces multiple contrast test procedures (MCTPs) in a robust semiparametric MANCOVA model.

The R code for all simulation results and the data example and further supplementary material can be found here. For running the R files, the working directory need to be set to the path of this unpacked folder.
description: Added related publication
DOI: 10.25381/cput.30530321
title: Motivational model to enhance sustainable productivity of workers in the South African construction industry (SACI)

description: The SACI faces the serious challenge of low productivity, which threatens to compromise the long-term sustainability of the industry. In this regard, this study examines how motivational drivers could improve worker performance and sustain the industry's productivity. Precisely, a conceptual model was developed through consolidating findings from an extensive review of literature on key constructs of motivational incentives, motivational empowerment, psychological influence, organizational and governmental motivational policies, and sustainable productivity relevant to the SACI.These objectives were realized using a sequential mixed-method approach. Quantitative data were collected from an online questionnaire administered to randomly selected construction professionals and contractors in civil building and general building categories registered from Grades 1-9 with CIBD. Data analysis includes descriptive statistics, exploratory factor analysis (EFA), and structural equation modeling (SEM) using PLS software.The conceptual model was confirmed, and the research questions were further developed during the qualitative phase. Findings from the quantitative phase also informed the interview guide in this stage. The motivational constructs and productivity outcomes were integrated to develop and validate the Sustainable Productivity of Workers' Enhancement Model using SEM.This research makes a significant contribution to advancing sustainable productivity within the South African Construction Industry. It reveals that while financial incentives are vital in driving performance, lasting motivation emerges from a deeper integration of empowerment, psychological fulfilment, supportive organisational and government policies. By uniting these dimensions into a holistic motivational framework, the study redefines productivity as both an economic and human endeavour anchored in dignity, purpose, and well-being. It provides actionable insights for fostering engaged, resilient, and high-performing workforces, positioning the construction industry as a driver of sustainable growth and social transformation in South Africa.
DOI: 10.25381/cput.30530321.v1
title: Motivational model to enhance sustainable productivity of workers in the South African construction industry (SACI)

description: The SACI faces the serious challenge of low productivity, which threatens to compromise the long-term sustainability of the industry. In this regard, this study examines how motivational drivers could improve worker performance and sustain the industry's productivity. Precisely, a conceptual model was developed through consolidating findings from an extensive review of literature on key constructs of motivational incentives, motivational empowerment, psychological influence, organizational and governmental motivational policies, and sustainable productivity relevant to the SACI.These objectives were realized using a sequential mixed-method approach. Quantitative data were collected from an online questionnaire administered to randomly selected construction professionals and contractors in civil building and general building categories registered from Grades 1-9 with CIBD. Data analysis includes descriptive statistics, exploratory factor analysis (EFA), and structural equation modeling (SEM) using PLS software.The conceptual model was confirmed, and the research questions were further developed during the qualitative phase. Findings from the quantitative phase also informed the interview guide in this stage. The motivational constructs and productivity outcomes were integrated to develop and validate the Sustainable Productivity of Workers' Enhancement Model using SEM.This research makes a significant contribution to advancing sustainable productivity within the South African Construction Industry. It reveals that while financial incentives are vital in driving performance, lasting motivation emerges from a deeper integration of empowerment, psychological fulfilment, supportive organisational and government policies. By uniting these dimensions into a holistic motivational framework, the study redefines productivity as both an economic and human endeavour anchored in dignity, purpose, and well-being. It provides actionable insights for fostering engaged, resilient, and high-performing workforces, positioning the construction industry as a driver of sustainable growth and social transformation in South Africa.
DOI: 10.15468/dl.pk96ey
title: Occurrence Download

description: A dataset containing 33306 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gadiculus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 33306 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026853-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/adbedn
title: Percept-Lens Evaluation Data - Conceptual Captions
title: Percept-Lens-Conceptual Captions

description: &lt;p&gt;&lt;strong&gt;Percept-Lens-Conceptual Captions Dataset Description&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Purpose.&lt;/strong&gt;&lt;br&gt;
The Percept-Lens-Conceptual Captions dataset is curated as part of the &lt;em&gt;Percept-Lens&lt;/em&gt; benchmark to evaluate the generalization capability of AI-generated image detectors under prompt-induced distribution shifts. It serves as an out-of-distribution (OOD) test set, designed to probe model robustness against semantically rich prompts not seen during training.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Nature.&lt;/strong&gt;&lt;br&gt;
This dataset contains only AI-generated images created from text prompts sourced from the Conceptual Captions Dataset. These prompts span abstract concepts, visual reasoning, compositional scenes, and imaginative queries. Images were generated using diverse diffusion models, including &lt;em&gt;Stable Diffusion variants&lt;/em&gt;, and &lt;em&gt;FLUX.variants&lt;/em&gt;, ensuring stylistic variation. No real images are included—each image is the synthetic output of a generative model responding to a high-level prompt.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Scope.&lt;/strong&gt;&lt;br&gt;
The dataset includes approximately 192,042 images, with each image linked to its original prompt. It is used exclusively for evaluation within the Percept-Lens benchmark and is not part of the training data. This subset is particularly useful for testing &lt;em&gt;semantic generalization&lt;/em&gt;, where linguistic complexity drives visual synthesis beyond familiar domains.
DOI: 10.15468/dl.hvft6p
title: Occurrence Download

description: A dataset containing 2711 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is NYSDEC aerial surveys",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 2711 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032711-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/pimbqm
title: Percept-Lens Evaluation Data - COCO Captions
title: Percept-Lens-COCO Captions

description: &lt;p&gt;&lt;strong&gt;Percept-Lens-COCO Captions Dataset Description&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Purpose.&lt;/strong&gt;&lt;br&gt;
The Percept-Lens-COCO Captions dataset is curated as part of the &lt;em&gt;Percept-Lens&lt;/em&gt; benchmark to evaluate the generalization capability of AI-generated image detectors under prompt-induced distribution shifts. It serves as an out-of-distribution (OOD) test set, designed to probe model robustness against semantically rich prompts not seen during training.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Nature.&lt;/strong&gt;&lt;br&gt;
This dataset contains only AI-generated images created from text prompts sourced from the COCO Captions Dataset. These prompts span abstract concepts, visual reasoning, compositional scenes, and imaginative queries. Images were generated using diverse diffusion models, including &lt;em&gt;Stable Diffusion variants&lt;/em&gt;, and &lt;em&gt;FLUX.variants&lt;/em&gt;, ensuring stylistic variation. No real images are included—each image is the synthetic output of a generative model responding to a high-level prompt.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Scope.&lt;/strong&gt;&lt;br&gt;
The dataset includes approximately 85,000 images, with each image linked to its original prompt. It is used exclusively for evaluation within the Percept-Lens benchmark and is not part of the training data. This subset is particularly useful for testing &lt;em&gt;semantic generalization&lt;/em&gt;, where linguistic complexity drives visual synthesis beyond familiar domains.
DOI: 10.6084/m9.figshare.29069453
title: Chromosome-level genome assembly of <i>Phlomoides rotata</i>

description: Genome assembly and annotation of <i>Phlomoides rotata</i>
DOI: 10.7910/dvn/glbluc
title: Percept-Lens Evaluation Data - PartiPrompts
title: Percept-Lens-PartiPrompts

description: &lt;p&gt;&lt;strong&gt;Percept-Lens-PartiPrompts Dataset Description&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Purpose.&lt;/strong&gt;&lt;br&gt;
The Percept-Lens-PartiPrompts dataset is curated as part of the &lt;em&gt;Percept-Lens&lt;/em&gt; benchmark to evaluate the generalization capability of AI-generated image detectors under prompt-induced distribution shifts. It serves as an out-of-distribution (OOD) test set, designed to probe model robustness against semantically rich prompts not seen during training.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Nature.&lt;/strong&gt;&lt;br&gt;
This dataset contains only AI-generated images created from text prompts sourced from the PartiPrompts (P2) Dataset. These prompts span abstract concepts, visual reasoning, compositional scenes, and imaginative queries. Images were generated using diverse diffusion models, including &lt;em&gt;Stable Diffusion variants&lt;/em&gt;, and &lt;em&gt;FLUX.variants&lt;/em&gt;, ensuring stylistic variation. No real images are included—each image is the synthetic output of a generative model responding to a high-level prompt.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Scope.&lt;/strong&gt;&lt;br&gt;
The dataset includes approximately 29,865 images, with each image linked to its original prompt. It is used exclusively for evaluation within the Percept-Lens benchmark and is not part of the training data. This subset is particularly useful for testing &lt;em&gt;semantic generalization&lt;/em&gt;, where linguistic complexity drives visual synthesis beyond familiar domains.
DOI: 10.7910/dvn/gw2er0
title: Percept-Lens Evaluation Data - DrawBench
title: Percept-Lens-DrawBench

description: &lt;p&gt;&lt;strong&gt;Percept-Lens-DrawBench Dataset Description&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Purpose.&lt;/strong&gt;&lt;br&gt;
The Percept-Lens-DrawBench dataset is curated as part of the &lt;em&gt;Percept-Lens&lt;/em&gt; benchmark to evaluate the generalization capability of AI-generated image detectors under prompt-induced distribution shifts. It serves as an out-of-distribution (OOD) test set, designed to probe model robustness against semantically rich prompts not seen during training.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Nature.&lt;/strong&gt;&lt;br&gt;
This dataset contains only AI-generated images created from text prompts sourced from the DrawBench Dataset. These prompts span abstract concepts, visual reasoning, compositional scenes, and imaginative queries. Images were generated using diverse diffusion models, including &lt;em&gt;Stable Diffusion variants&lt;/em&gt;, and &lt;em&gt;FLUX.variants&lt;/em&gt;, ensuring stylistic variation. No real images are included—each image is the synthetic output of a generative model responding to a high-level prompt.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Scope.&lt;/strong&gt;&lt;br&gt;
The dataset includes approximately 3,200 images, with each image linked to its original prompt. It is used exclusively for evaluation within the Percept-Lens benchmark and is not part of the training data. This subset is particularly useful for testing &lt;em&gt;semantic generalization&lt;/em&gt;, where linguistic complexity drives visual synthesis beyond familiar domains.
DOI: 10.15468/dl.u8hb5z
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002725-190307172214381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y82muy
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069962-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ykgqf7
title: Occurrence Download

description: A dataset containing 213 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Papaver rupifragum Boiss. & Reut."
 ]
}

 The dataset includes 213 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0105360-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.1f9en5
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012673-181003121212138/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zhtflp
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069968-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dj9wjz
title: Occurrence Download

description: A dataset containing 305511485 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 305511485 records from 6534 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0104087-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f9tj4z
title: Occurrence Download

description: A dataset containing 94 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Salix sepulcralis",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 94 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012070-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xcsgt6
title: Occurrence Download

description: A dataset containing 94 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Datura innoxia",
 "Country is China",
 "HasCoordinate is true"
 ]
}

 The dataset includes 94 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012071-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.