cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.wsy8fs
title: Occurrence Download

description: A dataset containing 582 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Centaurea praecox"
 ]
}

 The dataset includes 582 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005651-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xrh5j8
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mysateles gundlachi"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005652-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n23qa5
title: Occurrence Download

description: A dataset containing 84 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Creagrutus barrigai"
 ]
}

 The dataset includes 84 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005653-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mu7zwu
title: Occurrence Download

description: A dataset containing 51497333 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Asteraceae"
 ]
}

 The dataset includes 51497333 records from 9434 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005495-250711103210423/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.28900472.v1
title: Galang_Informed Consent Form and Robotfoto

description: Consent Form and Robotfoto
DOI: 10.6084/m9.figshare.28900472
title: Galang_Informed Consent Form and Robotfoto

description: Consent Form and Robotfoto
DOI: 10.15468/dl.axxxxx
title: Occurrence Download

description: A dataset containing 2239 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Continent is Africa",
 "TaxonKey is Tetradenia Benth."
 ]
}

 The dataset includes 2239 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007061-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/8mpsnnndnd.1
title: Quasi-Experimental Mixed-Methods Dataset: Rural Teachers’ Digital Competence and System Usage Logs (MIPSTA), Soto Norte, Colombia

description: This dataset contains de-identified data collected during the implementation and evaluation of the Model for Integrative and Predictive Smart Teaching Adaptation (MIPSTA) in rural public schools in Soto Norte (Santander, Colombia). The study followed a mixed-methods, quasi-experimental pre–post design over one academic semester with 120 rural teachers (60 experimental, 60 control). The dataset includes: (1) pre-intervention and post-intervention digital competence scores on a 0–100 scale; (2) individual change scores computed as Post minus Pre; (3) questionnaire responses measured on a five-point Likert scale, organized into six subscales (S1–S6); and (4) aggregated system usage logs describing engagement patterns (for example weekly sessions, module access frequencies, session duration summaries, and offline synchronization events when applicable). Optional files may include qualitative code counts derived from semi-structured interviews and documentation of the coding scheme used for thematic analysis.
DOI: 10.17632/w9b5z7znst
title: PAM Recordings of Seismic Surveys - IBAMA Regulations in Brazil - part1

description: This dataset is the first installment of a twoo-part collection of Passive Acoustic Monitoring (PAM) recordings acquired during seismic surveys conducted in accordance with the environmental legislation established by IBAMA (Brazilian Institute of Environment and Renewable Natural Resources). It features underwater acoustic data from three distinct projects along the Brazilian coastline, spanning from the Northeast to the Southeast regions.

The recordings were captured using arrays of four hydrophones, following the specifications outlined in the Guia de Monitoramento da Biota Marinha (2018) by IBAMA, available here (https://www.ibama.gov.br/phocadownload/licenciamento/petroleo-e-gas/diretrizes/2018-11-01-ibama-guia_de_monitoramento_da_biota_marinha_outubro.pdf).
Each audio file is accompanied by a corresponding annotation text file, which provides essential metadata and acoustic event labeling.

This dataset was compiled and curated based on the research article:
"Automated Delphinid Click Identification in Seismic Acoustic Surveys for Environmental Impact Assessment"
by Flora Medeiros Sauerbronn, Ingridy Moara Severino, and Andrea Dalben.
It is the result of a collaboration between the Federal University of Santa Catarina (UFSC) and the environmental consulting company BIOSONAR.

In total, the dataset comprises 47 multi-channel audio recordings and their corresponding annotation files in .txt format. These resources are intended to support the development of automated tools for cetacean click detection and classification, and to contribute to broader efforts in marine mammal monitoring and impact mitigation in seismic survey operations.
DOI: 10.17632/w9b5z7znst.2
title: PAM Recordings of Seismic Surveys - IBAMA Regulations in Brazil - part1

description: This dataset is the first installment of a twoo-part collection of Passive Acoustic Monitoring (PAM) recordings acquired during seismic surveys conducted in accordance with the environmental legislation established by IBAMA (Brazilian Institute of Environment and Renewable Natural Resources). It features underwater acoustic data from three distinct projects along the Brazilian coastline, spanning from the Northeast to the Southeast regions.

The recordings were captured using arrays of four hydrophones, following the specifications outlined in the Guia de Monitoramento da Biota Marinha (2018) by IBAMA, available here (https://www.ibama.gov.br/phocadownload/licenciamento/petroleo-e-gas/diretrizes/2018-11-01-ibama-guia_de_monitoramento_da_biota_marinha_outubro.pdf).
Each audio file is accompanied by a corresponding annotation text file, which provides essential metadata and acoustic event labeling.

This dataset was compiled and curated based on the research article:
"Automated Delphinid Click Identification in Seismic Acoustic Surveys for Environmental Impact Assessment"
by Flora Medeiros Sauerbronn, Ingridy Moara Severino, and Andrea Dalben.
It is the result of a collaboration between the Federal University of Santa Catarina (UFSC) and the environmental consulting company BIOSONAR.

In total, the dataset comprises 47 multi-channel audio recordings and their corresponding annotation files in .txt format. These resources are intended to support the development of automated tools for cetacean click detection and classification, and to contribute to broader efforts in marine mammal monitoring and impact mitigation in seismic survey operations.
DOI: 10.17632/7yhwhv48hn
title: CSNN&VHCT

description: Quantitative data come from a cross-sectional survey of 1,594 respondents collected in 2024–2025 using a snowball sampling approach.
DOI: 10.17632/7yhwhv48hn.1
title: CSNN&VHCT

description: Quantitative data come from a cross-sectional survey of 1,594 respondents collected in 2024–2025 using a snowball sampling approach.
DOI: 10.5281/zenodo.18259171
title: Derived input data supporting the GBD Diet–NCD Model Emulator (restricted access)

description: This record contains a restricted-access package of derived and reformatted input data created by the authors to support reproducible analyses using a Python-based emulator of the Global Burden of Disease (GBD) 2017 Diet–Noncommunicable Disease (Diet–NCD) model.

The data were constructed using Global Burden of Disease estimates produced by the Institute for Health Metrics and Evaluation (IHME). All underlying GBD data remain subject to the IHME Free-of-Charge Non-Commercial User Agreement. This record does not redistribute primary IHME data and does not grant permission to access, use, or redistribute GBD data without prior authorization from IHME.

Access to the files is restricted and will only be granted to users who can demonstrate compliance with IHME’s data use terms. For access requests, please contact the authors using the email addresses provided in the README of the associated GitHub repository.

Validation against GBD estimates 

Version 1.1.0 includes additional code and datasets used to validate the emulator against attributable DALYs reported in the Global Burden of Disease (GBD) 2017 study. These files enable replication of validation analyses comparing emulator outputs with published GBD estimates. Validation outputs include discrepancy heatmaps, summary histograms, and associated .csv files. These materials are provided to support transparency and to allow users to assess emulator performance and reproduce validation results.

Data provenance and licensing:Underlying GBD estimates are produced by IHME and are licensed separately. Users are responsible for ensuring compliance with IHME’s licensing requirements.
DOI: 10.5281/zenodo.18658029
title: Derived input data supporting the GBD Diet–NCD Model Emulator (restricted access)

description: This record contains a restricted-access package of derived and reformatted input data created by the authors to support reproducible analyses using a Python-based emulator of the Global Burden of Disease (GBD) 2017 Diet–Noncommunicable Disease (Diet–NCD) model.

The data were constructed using Global Burden of Disease estimates produced by the Institute for Health Metrics and Evaluation (IHME). All underlying GBD data remain subject to the IHME Free-of-Charge Non-Commercial User Agreement. This record does not redistribute primary IHME data and does not grant permission to access, use, or redistribute GBD data without prior authorization from IHME.

Access to the files is restricted and will only be granted to users who can demonstrate compliance with IHME’s data use terms. For access requests, please contact the authors using the email addresses provided in the README of the associated GitHub repository.

Validation against GBD estimates 

Version 1.1.0 includes additional code and datasets used to validate the emulator against attributable DALYs reported in the Global Burden of Disease (GBD) 2017 study. These files enable replication of validation analyses comparing emulator outputs with published GBD estimates. Validation outputs include discrepancy heatmaps, summary histograms, and associated .csv files. These materials are provided to support transparency and to allow users to assess emulator performance and reproduce validation results.

Data provenance and licensing:Underlying GBD estimates are produced by IHME and are licensed separately. Users are responsible for ensuring compliance with IHME’s licensing requirements.
DOI: 10.15468/dl.4ketz5
title: Occurrence Download

description: A dataset containing 162676 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ambrosia artemisiifolia",
 "Year 1990-2026"
 ]
}

 The dataset includes 162676 records from 1176 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021813-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2x69gjbcz6
title: A synthetic outdoor waste image dataset with YOLO-format annotations for object detection

description: This dataset contains 6,000 synthetic RGB images for outdoor waste object detection, annotated in YOLO format. The images represent realistic outdoor environments such as streets, parks, roadsides, and public open spaces, designed to support research in environmental monitoring and intelligent waste management. The dataset includes 10 waste categories: plastic, paper, cardboard, metal, glass, organic waste, battery waste, e-waste, cloth, and other waste. All images are resized to a fixed resolution of 640 × 640 pixels and are accompanied by normalized YOLO annotation files (class_id, x_center, y_center, width, height). In total, the dataset contains 21,057 annotated bounding boxes, with an average of 3.51 objects per image.

The images were generated using a synthetic data generation pipeline, where annotated waste object instances were cropped from labeled source images and composited onto real outdoor background scenes sampled from the COCO dataset. To improve realism and diversity, object placement, scale, orientation, and photometric properties were randomly varied, while quality control checks ensured annotation correctness and realistic object visibility. This dataset is intended for training, validation, and benchmarking of object detection models, particularly for challenging outdoor waste detection scenarios involving varying object scales, cluttered backgrounds, and class diversity.
DOI: 10.17632/2x69gjbcz6.2
title: A synthetic outdoor waste image dataset with YOLO-format annotations for object detection

description: This dataset contains 6,000 synthetic RGB images for outdoor waste object detection, annotated in YOLO format. The images represent realistic outdoor environments such as streets, parks, roadsides, and public open spaces, designed to support research in environmental monitoring and intelligent waste management. The dataset includes 10 waste categories: plastic, paper, cardboard, metal, glass, organic waste, battery waste, e-waste, cloth, and other waste. All images are resized to a fixed resolution of 640 × 640 pixels and are accompanied by normalized YOLO annotation files (class_id, x_center, y_center, width, height). In total, the dataset contains 21,057 annotated bounding boxes, with an average of 3.51 objects per image.

The images were generated using a synthetic data generation pipeline, where annotated waste object instances were cropped from labeled source images and composited onto real outdoor background scenes sampled from the COCO dataset. To improve realism and diversity, object placement, scale, orientation, and photometric properties were randomly varied, while quality control checks ensured annotation correctness and realistic object visibility. This dataset is intended for training, validation, and benchmarking of object detection models, particularly for challenging outdoor waste detection scenarios involving varying object scales, cluttered backgrounds, and class diversity.
DOI: 10.17632/8srrb2rcw6
title: PAM Recordings of Seismic Surveys - IBAMA Regulations in Brazil - part2

description: This dataset is the second installment of a twoo-part collection of Passive Acoustic Monitoring (PAM) recordings acquired during seismic surveys conducted in accordance with the environmental legislation established by IBAMA (Brazilian Institute of Environment and Renewable Natural Resources). It features underwater acoustic data from three distinct projects along the Brazilian coastline, spanning from the Northeast to the Southeast regions.

The recordings were captured using arrays of four hydrophones, following the specifications outlined in the Guia de Monitoramento da Biota Marinha (2018) by IBAMA, available here (https://www.ibama.gov.br/phocadownload/licenciamento/petroleo-e-gas/diretrizes/2018-11-01-ibama-guia_de_monitoramento_da_biota_marinha_outubro.pdf).
Each audio file is accompanied by a corresponding annotation text file, which provides essential metadata and acoustic event labeling.

This dataset was compiled and curated based on the research article:
"Automated Delphinid Click Identification in Seismic Acoustic Surveys for Environmental Impact Assessment"
by Flora Medeiros Sauerbronn, Ingridy Moara Severino, and Andrea Dalben.
It is the result of a collaboration between the Federal University of Santa Catarina (UFSC) and the environmental consulting company BIOSONAR.

In total, the dataset comprises 47 multi-channel audio recordings and their corresponding annotation files in .txt format. These resources are intended to support the development of automated tools for cetacean click detection and classification, and to contribute to broader efforts in marine mammal monitoring and impact mitigation in seismic survey operations.
DOI: 10.17632/8srrb2rcw6.2
title: PAM Recordings of Seismic Surveys - IBAMA Regulations in Brazil - part2

description: This dataset is the second installment of a twoo-part collection of Passive Acoustic Monitoring (PAM) recordings acquired during seismic surveys conducted in accordance with the environmental legislation established by IBAMA (Brazilian Institute of Environment and Renewable Natural Resources). It features underwater acoustic data from three distinct projects along the Brazilian coastline, spanning from the Northeast to the Southeast regions.

The recordings were captured using arrays of four hydrophones, following the specifications outlined in the Guia de Monitoramento da Biota Marinha (2018) by IBAMA, available here (https://www.ibama.gov.br/phocadownload/licenciamento/petroleo-e-gas/diretrizes/2018-11-01-ibama-guia_de_monitoramento_da_biota_marinha_outubro.pdf).
Each audio file is accompanied by a corresponding annotation text file, which provides essential metadata and acoustic event labeling.

This dataset was compiled and curated based on the research article:
"Automated Delphinid Click Identification in Seismic Acoustic Surveys for Environmental Impact Assessment"
by Flora Medeiros Sauerbronn, Ingridy Moara Severino, and Andrea Dalben.
It is the result of a collaboration between the Federal University of Santa Catarina (UFSC) and the environmental consulting company BIOSONAR.

In total, the dataset comprises 47 multi-channel audio recordings and their corresponding annotation files in .txt format. These resources are intended to support the development of automated tools for cetacean click detection and classification, and to contribute to broader efforts in marine mammal monitoring and impact mitigation in seismic survey operations.
DOI: 10.17632/vw4z86h629
title: De brazzas occurence

description: This is dataset contains spatial data of the De brazzas' monkey collected for period of 6 months in western kenya specifically in Kisere forest, Saiwa Swamp national park, and Kitale museum. The research aim was to quantify the home range areas across the three study sites, compare the methods to determine the site offers the highest core area. Data were collected using the distance sampling methods including the line transect. The dataset includes the GPS coordinates of the monkey. Data contains coordinates of three sites that can be mapped to show distribution o monkey.
DOI: 10.17632/vw4z86h629.1
title: De brazzas occurence

description: This is dataset contains spatial data of the De brazzas' monkey collected for period of 6 months in western kenya specifically in Kisere forest, Saiwa Swamp national park, and Kitale museum. The research aim was to quantify the home range areas across the three study sites, compare the methods to determine the site offers the highest core area. Data were collected using the distance sampling methods including the line transect. The dataset includes the GPS coordinates of the monkey. Data contains coordinates of three sites that can be mapped to show distribution o monkey.
DOI: 10.15468/dl.fcfbku
title: Occurrence Download

description: A dataset containing 904904 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((39.21438 80,38.48589 77.8062,38.0737 76.34014,37.72199 75,37.12651 72.73148,36.81111 71.42133,36.53231 70,36.35646 68.97749,36.13333 67.52577,35.93161 66.15329,35.75637 65,35.36844 62.79854,34.95877 61.01228,34.66663 60,34.09546 58.39205,33.66615 56.7748,33.42018 56.15722,33.06158 55.57896,32.52483 55,31.92982 54.57176,31.40207 54.31611,30.57362 54.09396,29.22753 53.95412,28.34398 53.706,26.74752 53.11506,24.38828 52.42296,22.61982 51.90676,20.48339 51.30739,18.40686 50.79214,17.38303 50.57529,15.46612 50.25575,14.56147 50.17232,11.94163 50.04933,11.67844 50,10.30975 49.74346,9.03876 49.35687,7.75034 48.77529,6.739 48.0642,4.85974 46.38994,3.82545 45.29591,77.12385 45,81.74664 47.02502,83.35772 47.76059,85.61451 48.7567,86.86169 49.29219,88.53805 50,100.30327 55,109.81999 60,114.97683 63.4803,117.03501 65,118.18474 65.87304,119.67759 66.86662,120.20696 67.36303,120.49417 67.78504,120.81948 68.55436,121.00505 70,120.90564 71.46123,120.78183 72.39605,120.24544 73.87551,119.31555 75,115.94277 76.50446,113.56555 77.21964,110.95737 77.66249,105.98231 78.11787,102.35061 78.64006,99.46919 79.08415,96.65016 79.54279,93.98408 80,91.34777 80.47584,88.68608 80.98165,86.07165 81.50467,83.50194 82.04542,81.3139 82.56034,79.2744 83.08954,70.22748 85,41.94596 85,40.61181 83.04883,39.88708 81.64079,39.21438 80))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Charadriiformes",
 "Year 2015-2024"
 ]
}

 The dataset includes 904904 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021830-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/zhpkygk2f3
title: Metabolic Alterations in Cardiomyocytes

description: All data generated or analyzed during“Metabolic Alterations in Cardiomyocytes”
DOI: 10.17632/zhpkygk2f3.2
title: Metabolic Alterations in Cardiomyocytes

description: All data generated or analyzed during“Metabolic Alterations in Cardiomyocytes”
DOI: 10.17632/tg8wk2txpk.1
title: METHANE BIFOCAL MODELING

description: Bifocal modeling is an instructional and analytical approach that connects two complementary "views" of the same phenomenon: (1) directly measurable macroscopic behavior, and (2) a simplified mechanistic model that explains why that behavior occurs. In the methane greenhouse effect experiment, bifocal modeling links real-time environmental data from a small enclosed space with a conceptual model of infrared absorption and heat trapping by CH₄.

In this setup, an Arduino microcontroller serves as the data acquisition unit. Methane concentration is monitored using an MQ-4 gas sensor, while temperature is measured using a DHT22 sensor. The space is exposed to a controlled heat source (a lamp), and the Arduino continuously records the sensor readings at fixed intervals. The macroscopic "focus" view is represented by data showing methane levels (in ppm) along with temperature increases and approaches to steady state. This view allows the user to identify patterns such as faster warming rates, higher peak temperatures, or slower cooling as methane levels increase.