cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/5lf-406-kae
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/j2r-m3r-9qc
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/9yq-j1g-jgc
title: †Astrononion perfossum (Clodius, 1922)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/bkl-gnc-y7j
title: Porifera Grant, 1836

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/5lf-pqk-fe9
title: Flemingites

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/sk4-a7b-8m5
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/agq-p4n-yzf
title: Terebratula Müller, 1776

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.5281/zenodo.18706837
title: A Dataset of University Students' Stress and Anxiety Levels based on Questionnaires and Wearable Sensors

description: Mental health issues such as stress and anxiety are highly prevalent among university students, often affecting their academicperformance and overall well-being. Understanding these conditions through objective, real-world data is essential fordeveloping effective monitoring and intervention strategies. We present a multimodal dataset that captures students’ dailystress and anxiety levels through self-reports and wearable sensor data. The dataset was collected during one academicsemester (February–July 2025) from undergraduate volunteers at two Mexican universities. Participants provided daily ratings ofstress and anxiety using a mobile application, while Fitbit Inspire 3 devices continuously recorded physiological and behavioraldata including heart rate variability, sleep quality, oxygen saturation, stress score, physical activity, and step count. The datasetfeatures over 80% questionnaire compliance and validated Fitbit measurements. This dataset addresses the scarcity of public,ecologically valid datasets on student mental health and enables reproducible research and analyses in affective computing,wearable sensing, and machine learning for stress and anxiety monitoring.

 

This work and all authors received support from SECTEI, IPN, and ITESM through the SMIEAE project (4618c24).
DOI: 10.5281/zenodo.18706836
title: A Dataset of University Students' Stress and Anxiety Levels based on Questionnaires and Wearable Sensors

description: Mental health issues such as stress and anxiety are highly prevalent among university students, often affecting their academicperformance and overall well-being. Understanding these conditions through objective, real-world data is essential fordeveloping effective monitoring and intervention strategies. We present a multimodal dataset that captures students’ dailystress and anxiety levels through self-reports and wearable sensor data. The dataset was collected during one academicsemester (February–July 2025) from undergraduate volunteers at two Mexican universities. Participants provided daily ratings ofstress and anxiety using a mobile application, while Fitbit Inspire 3 devices continuously recorded physiological and behavioraldata including heart rate variability, sleep quality, oxygen saturation, stress score, physical activity, and step count. The datasetfeatures over 80% questionnaire compliance and validated Fitbit measurements. This dataset addresses the scarcity of public,ecologically valid datasets on student mental health and enables reproducible research and analyses in affective computing,wearable sensing, and machine learning for stress and anxiety monitoring.

 

This work and all authors received support from SECTEI, IPN, and ITESM through the SMIEAE project (4618c24).
DOI: 10.5281/zenodo.18715137
title: Cannabis results - annotations (GNPS-SIRIUS-MS2Query-DreaMS-MS2LDA)

description: These are the results of positive and some negative files generated in our workflow from the cannabis flowers under different light treatments:



Final annotations table

Ms2query results

Ms2lda results

DreaMS results

graphml GNPS positive/negative

cys file for Cytoscape

SIRIUS results


The raw/preprocessed files can be found with the ID at MASSIVE: MSV000100919
DOI: 10.5281/zenodo.18625750
title: OPEN DATASET - Canopy height models of SFB ALS acquisitions in Amazonian forest concessions (2017–2025) - Altamira National Forest, Pará

description: OPEN DATASET - Canopy height models of Brazilian Forest Service (SFB) airborne laser scanning (ALS) acquisitions in Amazonian forest concessions (2017–2025) - Altamira National Forest

The Brazilian Forest Service (SFB) has acquired high-precision Light Detection and Ranging (LiDAR) data within forest management units of federal concessions in the Brazilian Amazon. Airborne laser scanning (ALS) is used by SFB to produce reliable, high-detail information on topography and forest canopy structure. These ALS surveys in forest concessions support: (1) improved forest inventories; (2) topographic mapping for planning, including slope, water bodies, and flood-prone areas; and (3) assessment of management impacts on biodiversity, such as effects from roads, skid trails, canopy openings from tree removal, and log decks.

This dataset provides standardised canopy height and elevation products for point cloud data acquired by SFB between 2017 and 2025. We organized and processed all raw ALS data from SFB into Canopy Height Models (CHMs), acquired across Altamira, Amapá, Bom Futuro, Caxiuanã, Crepori, Jacundá, Jamari, and Saracá-Taquera forests. The ALS datasets were processed using the Processing pipeline for Airborne Laser Scanning (ALS) v1.0.4 by Fischer et al. 2025 (https://doi.org/10.1101/2025.08.31.673375). 

The raw data used includes .las point clouds and additional acquisition information such as reports, trajectory files and metadata. The raw data are owned by CGMAF-SFB and are publicly available through an online repository (SFB open data policy). A table summarizing the raw datasets is available.

This dataset will be included in future versions of the Global Canopy Atlas!

Special thanks to Fabian J. Fischer and to José Humberto Chaves for his availability and kindness to support the processing. We are also very grateful to Silke Kämmerer (CEO | LAStools LiDAR Processing – rapidlasso GmbH) for providing the necessary LAStools license.
DOI: 10.5281/zenodo.18625749
title: OPEN DATASET - Canopy height models of SFB ALS acquisitions in Amazonian forest concessions (2017–2025) - Altamira National Forest, Pará

description: OPEN DATASET - Canopy height models of Brazilian Forest Service (SFB) airborne laser scanning (ALS) acquisitions in Amazonian forest concessions (2017–2025) - Altamira National Forest

The Brazilian Forest Service (SFB) has acquired high-precision Light Detection and Ranging (LiDAR) data within forest management units of federal concessions in the Brazilian Amazon. Airborne laser scanning (ALS) is used by SFB to produce reliable, high-detail information on topography and forest canopy structure. These ALS surveys in forest concessions support: (1) improved forest inventories; (2) topographic mapping for planning, including slope, water bodies, and flood-prone areas; and (3) assessment of management impacts on biodiversity, such as effects from roads, skid trails, canopy openings from tree removal, and log decks.

This dataset provides standardised canopy height and elevation products for point cloud data acquired by SFB between 2017 and 2025. We organized and processed all raw ALS data from SFB into Canopy Height Models (CHMs), acquired across Altamira, Amapá, Bom Futuro, Caxiuanã, Crepori, Jacundá, Jamari, and Saracá-Taquera forests. The ALS datasets were processed using the Processing pipeline for Airborne Laser Scanning (ALS) v1.0.4 by Fischer et al. 2025 (https://doi.org/10.1101/2025.08.31.673375). 

The raw data used includes .las point clouds and additional acquisition information such as reports, trajectory files and metadata. The raw data are owned by CGMAF-SFB and are publicly available through an online repository (SFB open data policy). A table summarizing the raw datasets is available.

This dataset will be included in future versions of the Global Canopy Atlas!

Special thanks to Fabian J. Fischer and to José Humberto Chaves for his availability and kindness to support the processing. We are also very grateful to Silke Kämmerer (CEO | LAStools LiDAR Processing – rapidlasso GmbH) for providing the necessary LAStools license.
DOI: 10.15468/dl.q9rbvg
title: Occurrence Download

description: A dataset containing 46120 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Millepora",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 46120 records from 186 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032579-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bdhwef
title: Occurrence Download

description: A dataset containing 18651 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Abies concolor"
 ]
}

 The dataset includes 18651 records from 376 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032578-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18715095
title: Dataset and analysis for "Flexibility in task performance and division of labour in a cooperatively breeding cichlid"

description: Datasets and analysis files for the manuscript "Flexibility in task performance and division of labour in a cooperatively breeding cichlid"

There are three primary datasets:





rawdata_dol.csv – Contains the raw observational data collected using the BORIS event-logging software.




group_details.xlsx – Provides detailed information about each group of fish observed.




final_dataset.csv – Contains summarized data derived from rawdata_dol.csv, aggregating information across different tasks. The steps to generate this dataset are included in the accompanying code.



Additionally, METADATA_fordatasets.txt provides comprehensive information on all columns across the datasets.

The DOL_analysis.R script is an annotated R script that:





Processes the raw data to produce the datasets used for analysis.




Performs the data analysis and generates the results and plots presented in the manuscript.
DOI: 10.5281/zenodo.18715094
title: Dataset and analysis for "Flexibility in task performance and division of labour in a cooperatively breeding cichlid"

description: Datasets and analysis files for the manuscript "Flexibility in task performance and division of labour in a cooperatively breeding cichlid"

There are three primary datasets:





rawdata_dol.csv – Contains the raw observational data collected using the BORIS event-logging software.




group_details.xlsx – Provides detailed information about each group of fish observed.




final_dataset.csv – Contains summarized data derived from rawdata_dol.csv, aggregating information across different tasks. The steps to generate this dataset are included in the accompanying code.



Additionally, METADATA_fordatasets.txt provides comprehensive information on all columns across the datasets.

The DOL_analysis.R script is an annotated R script that:





Processes the raw data to produce the datasets used for analysis.




Performs the data analysis and generates the results and plots presented in the manuscript.
DOI: 10.15468/dl.ejaet9
title: Occurrence Download

description: A dataset containing 387 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Abies durangensis"
 ]
}

 The dataset includes 387 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032580-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k5ryxn
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Terpides sooretamae",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032581-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2fljk4
title: CCDC 2252136: Experimental Crystal Structure Determination

description: Related Article: Xingliang Chen, Zhiqiang Jiang|2024|Inorg.Chim.Acta|561|121852|doi:10.1016/j.ica.2023.121852
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2jd4ps
title: CCDC 2335375: Experimental Crystal Structure Determination

description: Related Article: Cheng-Yang Zhang, Zhuang-Hua Liu, San-Tai Wang, Gang Han, Dong-Shuai Wu, Chao Zhao, Jian Zhang, Wei-Hui Fang|2025|Angew.Chem.,Int.Ed.|64|e202421484|doi:10.1002/anie.202421484
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2jd4rv
title: CCDC 2335377: Experimental Crystal Structure Determination

description: Related Article: Cheng-Yang Zhang, Zhuang-Hua Liu, San-Tai Wang, Gang Han, Dong-Shuai Wu, Chao Zhao, Jian Zhang, Wei-Hui Fang|2025|Angew.Chem.,Int.Ed.|64|e202421484|doi:10.1002/anie.202421484
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2jd4sw
title: CCDC 2335378: Experimental Crystal Structure Determination

description: Related Article: Cheng-Yang Zhang, Zhuang-Hua Liu, San-Tai Wang, Gang Han, Dong-Shuai Wu, Chao Zhao, Jian Zhang, Wei-Hui Fang|2025|Angew.Chem.,Int.Ed.|64|e202421484|doi:10.1002/anie.202421484
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2jd4tx
title: CCDC 2335379: Experimental Crystal Structure Determination

description: Related Article: Cheng-Yang Zhang, Zhuang-Hua Liu, San-Tai Wang, Gang Han, Dong-Shuai Wu, Chao Zhao, Jian Zhang, Wei-Hui Fang|2025|Angew.Chem.,Int.Ed.|64|e202421484|doi:10.1002/anie.202421484
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2jd4vy
title: CCDC 2335380: Experimental Crystal Structure Determination

description: Related Article: Cheng-Yang Zhang, Zhuang-Hua Liu, San-Tai Wang, Gang Han, Dong-Shuai Wu, Chao Zhao, Jian Zhang, Wei-Hui Fang|2025|Angew.Chem.,Int.Ed.|64|e202421484|doi:10.1002/anie.202421484
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2jd4wz
title: CCDC 2335381: Experimental Crystal Structure Determination

description: Related Article: Cheng-Yang Zhang, Zhuang-Hua Liu, San-Tai Wang, Gang Han, Dong-Shuai Wu, Chao Zhao, Jian Zhang, Wei-Hui Fang|2025|Angew.Chem.,Int.Ed.|64|e202421484|doi:10.1002/anie.202421484
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2jvs4b
title: CCDC 2349432: Experimental Crystal Structure Determination

description: Related Article: Cheng-Yang Zhang, Zhuang-Hua Liu, San-Tai Wang, Gang Han, Dong-Shuai Wu, Chao Zhao, Jian Zhang, Wei-Hui Fang|2025|Angew.Chem.,Int.Ed.|64|e202421484|doi:10.1002/anie.202421484
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2h3txc
title: CCDC 2297593: Experimental Crystal Structure Determination

description: Related Article: Liangchen Liu, Yuluan Liao, Lianxiang Li, Huirong Ma, Huatian Shi, Weibin Yu|2025|Inorg.Chem.|64|4345|doi:10.1021/acs.inorgchem.4c04872
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2h3v1j
title: CCDC 2297597: Experimental Crystal Structure Determination

description: Related Article: Liangchen Liu, Yuluan Liao, Lianxiang Li, Huirong Ma, Huatian Shi, Weibin Yu|2025|Inorg.Chem.|64|4345|doi:10.1021/acs.inorgchem.4c04872
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2jd4x0
title: CCDC 2335382: Experimental Crystal Structure Determination

description: Related Article: Cheng-Yang Zhang, Zhuang-Hua Liu, San-Tai Wang, Gang Han, Dong-Shuai Wu, Chao Zhao, Jian Zhang, Wei-Hui Fang|2025|Angew.Chem.,Int.Ed.|64|e202421484|doi:10.1002/anie.202421484
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.ruazk3
title: Occurrence Download

description: A dataset containing 32550 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ascidia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 32550 records from 337 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032582-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qrz72d
title: Occurrence Download

description: A dataset containing 12611 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Thraulodes",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 12611 records from 125 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032583-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fpxymr
title: Occurrence Download

description: A dataset containing 29347 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Parvicardium",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 29347 records from 312 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032585-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18714003
title: Dataset for GNN-based Ionospheric Scintillation Prediction in the Brazilian Sector (2014).

description: "This repository contains the dataset and Graph Neural Network (GNN) code for predicting ionospheric scintillation in the Brazilian sector. The dataset includes processed VTEC, S$_4$ indices, and geomagnetic data for four stations (SLZ, CXP, STM, SMAR) during 2014. This work provides a benchmark comparison against XGBoost and traditional ANN models."
DOI: 10.5281/zenodo.18714002
title: Dataset for GNN-based Ionospheric Scintillation Prediction in the Brazilian Sector (2014).

description: "This repository contains the dataset and Graph Neural Network (GNN) code for predicting ionospheric scintillation in the Brazilian sector. The dataset includes processed VTEC, S$_4$ indices, and geomagnetic data for four stations (SLZ, CXP, STM, SMAR) during 2014. This work provides a benchmark comparison against XGBoost and traditional ANN models."
DOI: 10.15468/dl.rbwjdm
title: Occurrence Download

description: A dataset containing 1085 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Abies guatemalensis"
 ]
}

 The dataset includes 1085 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032586-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18703190
title: Solubilities and Fick Diffusion Coefficients in Blowing-Agent-Loaded Polypropylene Melts

description: This data publication is based on the metadata and datasets underlying the manuscript "Solubilities and Fick Diffusion Coefficients in Blowing-Agent-Loaded Polypropylene Melts" submitted to the journal Polymer. A description of the data included in this dataset is compiled in the "README.txt" as well as in the "Information and Measurement Data.xlsx" file.
description: This work was financially supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) via the Project-ID 510591037. The authors gratefully acknowledge funding of the Erlangen Graduate School in Advanced Optical Technologies (SAOT) by the Bavarian State Ministry for Science and Art.