cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/qh5trg
title: Spider occurrences extracted from the '[Spider assemblages of the Usolsky District of the Perm Krai] = Население пауков Усольского района Пермского края'

description: The dataset contains spider occurrences extracted from the following article/book: [Lapaeva M.V., Esyunin S.L. 2013. [Spider assemblages of the Usolsky District of the Perm Krai] = Население пауков Усольского района Пермского края // Н.И. Литвиненко (гл. ред.), С.А. Овеснов (отв. ред.). Фундаментальные и прикладные исследования в биологии и экологии. Матер. регион. студ. конф. (15–20 апр. 2013г.). Пермь: Пермск. гос. нац. исслед. ун-т. С.126–128 (рус.)] <b>Attention! As far as these were published in print, we strongly recommend to cite the original work along with this GBIF dataset!</b>Data were digitized within the Faunistics International project for the occurrence data extraction from the scientific literature: faunistic, taxonomical, ecological and other kinds. Full text is available here. Data are available here as well.
DOI: 10.5281/zenodo.18430387
title: Socioeconomic Factors and Students' Environmental Values and Attitudes

description: This dataset contains a collection data collected from Czech primary school students aged 11-15. The table includes information on the occupation of the students' parents as well as the results of the students' participation in a standardised Pupil Environmental Literacy Questionnaire. The goal of this dataset was to determine which socioeconomic factors most strongly affect the students' environmental attitudes.

The dataset contains two tabs, one containing the data and the other containing terminology used.

V2 (Data-Enviro-Zaci-v2.csv): Contains an expanded version of the questionnaire with additional data.
DOI: 10.5281/zenodo.17365445
title: Socioeconomic Factors and Students' Environmental Values and Attitudes

description: This dataset contains a collection data collected from Czech primary school students aged 11-15. The table includes information on the occupation of the students' parents as well as the results of the students' participation in a standardised Pupil Environmental Literacy Questionnaire. The goal of this dataset was to determine which socioeconomic factors most strongly affect the students' environmental attitudes.

The dataset contains two tabs, one containing the data and the other containing terminology used.

V2 (Data-Enviro-Zaci-v2.csv): Contains an expanded version of the questionnaire with additional data.
DOI: 10.15468/dl.4f9z7e
title: Occurrence Download

description: A dataset containing 19465 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Australia",
 "OccurrenceStatus is Present",
 "TaxonKey is Maratus"
 ]
}

 The dataset includes 19465 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001848-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5v2as3
title: Occurrence Download

description: A dataset containing 279 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ceriops australis",
 "HasCoordinate is true",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2010",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "not" : {
 "HasGeospatialIssue" : [
 "is true"
 ]
 }
 }
 ]
}

 The dataset includes 279 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001849-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25824/redu/fkzxr5
title: Computação de reservatório e dinâmica não-linear para análise de séries temporais: uma aplicação em mercado financeiro

description: Este pacote de dados contém séries temporais, gráficos de barras e e a representação esquemática do modelo de computação de reservatório que foram utilizadas nos experimentos da presente tese, que combina aprendizado de máquina com medidas da dinâmica não linear na previsão de séries temporais financeiras. Contém também tabelas de resultados das métricas de avaliação que mediram a performance do modelo em comparação com outros modelos já existentes no estado da arte.
DOI: 10.15468/7ab3nm
title: Spider occurrences extracted from the 'Composition and structure of herpetobiont arthropod communities of mountain summits of the Southern Urals = Состав и структура сообществ герпетобионтных членистоногих горных вершин Южного Урала'

description: The dataset contains spider occurrences extracted from the following article/book: [Mikhailov Yu.E., Ermakov A.I. 2016. Composition and structure of herpetobiont arthropod communities of mountain summits of the Southern Urals = Состав и структура сообществ герпетобионтных членистоногих горных вершин Южного Урала // Фауна Урала и Сибири. № 1. С.61–74 (рус., рез. англ.)] <b>Attention! As far as these were published in print, we strongly recommend to cite the original work along with this GBIF dataset!</b>Data were digitized within the Faunistics International project for the occurrence data extraction from the scientific literature: faunistic, taxonomical, ecological and other kinds. Full text is available here. Data are available here as well.
DOI: 10.15468/dl.dht3jh
title: Occurrence Download

description: A dataset containing 189 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Strigosella cabulica",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 189 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001851-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g6sesp
title: Occurrence Download

description: A dataset containing 2185 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Neoeucirrhichthys maydelli, Canthophrys gongota, Botia almorhae, Syncrossus berdmorei, Botia birdi, Botia histrionica, Botia rostrata, Schistura cincticauda, Schistura rupecula, Haludaria fasciata, Devario shanensis, Pangio goaensis, Batasio travancoria, Mystus horai, Hemibagrus menoda, Hemibagrus peguensis, Mystus pulcher, Mystus rufescens, Mystus tengara, Chandramara chandramara, Rita chrysea, Rita kuturnee, Pterocryptis indicus, Ompok malabaricus, Pterocryptis berdmorei, Pterocryptis torrentis, Pterocryptis wynaadensis, Hemirhamphodon kuekenthali, Nomorhamphus brembachi, Ailiichthys punctata, Clupisoma bastari, Nomorhamphus hageni, Nomorhamphus liemi, Nomorhamphus ravnaki, Clupisoma montana, Nomorhamphus sanussii, Clupisoma prateri, Clupisoma naziri, Nomorhamphus towoetii, Zenarchopterus dux, Zenarchopterus ectuntio, Eutropiichthys goongwaree, Pachypterus khavalchor, Pseudeutropius mitchelli, Telmatherina antoniae, Telmatherina obscura, Telmatherina opudi, Telmatherina prognatha, Telmatherina sarasinorum, Telmatherina wahjui, Neostethus bicornis, Proeutropiichthys macropthalmos, Neostethus borneensis, Neostethus lankesteri, Phenacostethus posthon, Phenacostethus trewavasae, Leptosynanceia asteroblepa, Amblyceps mangois, Ambassis macracanthus, Gymnochanda filamentosa, Akysis pictus, Paradoxodacna piratica, Akysis prashadi, Parambassis apogonoides, Parambassis macrolepis, Lagusia micracanthus, Conta conta, Erethistes pusillus, Erethistoides montana, Erethistoides pipri, Rhodeus smithii, Pareuchiloglanis feae, Parachiloglanis hodgarti, Pareuchiloglanis macrotrema, Exostoma berdmorei, Exostoma labiatum, Exostoma stuarti, Pungitius sinensis, Gagata gagata, Gagata sexualis, Glyptosternon maculatum, Glyptosternon malaisei, Toxotes microlepis, Glyptothorax annandalei, Glyptothorax brevipinnis, Glyptothorax poonaensis, Glyptothorax garhwali, Glyptothorax gracilis, Glyptothorax housei, Glyptothorax indicus, Glyptothorax kashmirensis, Glyptothorax lonah, Glyptothorax madraspatanus, Glyptothorax naziri, Glyptothorax nelsoni, Glyptothorax pectinopterus, Glyptothorax prashadi, Glyptothorax punjabensis, Glyptothorax saisii, Glyptothorax sinensis)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2185 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001850-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18430339
title: Census of Decentralized Autonomous Organizations Across Platforms (2025)

description: This dataset provides a comprehensive, multi-platform overview of Decentralized Autonomous Organizations (DAOs) as of October 2025, covering DAO deployments, proposals, and voting activity across 6 major platforms: Aragon, DAOHaus, DAOstack, Realms, Snapshot and Tally. It is designed to support research in blockchain governance, decentralized finance, and community-driven decision-making.

The dataset is structured into three CSV files:





deployments.csv – Contains metadata for each DAO deployment, including a unique identifier (deployment_id), platform name (platform), DAO name (name), number of proposals (proposals_count), number of distinct voters (unique_voters), total votes cast (votes_count), and estimated voting power (estimated_vp).




proposals.csv – Contains detailed information on proposals submitted to each DAO, including a unique proposal ID (proposal_id), associated DAO deployment (deployment_id), creation date (date), number of votes received (votes_count), total voting power used (total_vp), and the platform (platform).




votes.csv – Contains individual vote records, including a unique vote ID (vote_id), associated proposal (proposal_id), corresponding DAO deployment (deployment_id), voter address (voter), timestamp of the vote (date), voting power used (weight), and platform (platform).



Data collection for this release was completed in October 2025. The full data acquisition and processing pipeline typically requires approximately two to three weeks, involving the retrieval, validation, and integration of governance data from each platform’s public APIs and subgraphs.

This dataset is suitable for empirical research on DAO governance dynamics, voting behavior, and multi-platform analysis. Researchers can use this dataset to analyze trends in proposal creation, voter participation, and governance outcomes in decentralized communities.

The code for collecting and formatting the data is in the github repository linked. The instructions of use, requirements, the data loading and some example analyses are in the MSR submission folder of the github repository.

 

Acknowledgements

We would like to acknowledge the use of tools and support provided by DAO Analyzer dataset, GraphiQL, Tally API Reference, and AI-Powered RPC Infrastructure for dApps & Chains | dRPC for data extraction. These resources were indispensable for the successful completion of this project.
DOI: 10.5281/zenodo.17529116
title: Census of Decentralized Autonomous Organizations Across Platforms (2025)

description: This dataset provides a comprehensive, multi-platform overview of Decentralized Autonomous Organizations (DAOs) as of October 2025, covering DAO deployments, proposals, and voting activity across 6 major platforms: Aragon, DAOHaus, DAOstack, Realms, Snapshot and Tally. It is designed to support research in blockchain governance, decentralized finance, and community-driven decision-making.

The dataset is structured into three CSV files:





deployments.csv – Contains metadata for each DAO deployment, including a unique identifier (deployment_id), platform name (platform), DAO name (name), number of proposals (proposals_count), number of distinct voters (unique_voters), total votes cast (votes_count), and estimated voting power (estimated_vp).




proposals.csv – Contains detailed information on proposals submitted to each DAO, including a unique proposal ID (proposal_id), associated DAO deployment (deployment_id), creation date (date), number of votes received (votes_count), total voting power used (total_vp), and the platform (platform).




votes.csv – Contains individual vote records, including a unique vote ID (vote_id), associated proposal (proposal_id), corresponding DAO deployment (deployment_id), voter address (voter), timestamp of the vote (date), voting power used (weight), and platform (platform).



Data collection for this release was completed in October 2025. The full data acquisition and processing pipeline typically requires approximately two to three weeks, involving the retrieval, validation, and integration of governance data from each platform’s public APIs and subgraphs.

This dataset is suitable for empirical research on DAO governance dynamics, voting behavior, and multi-platform analysis. Researchers can use this dataset to analyze trends in proposal creation, voter participation, and governance outcomes in decentralized communities.

The code for collecting and formatting the data is in the github repository linked. The instructions of use, requirements, the data loading and some example analyses are in the MSR submission folder of the github repository.

 

Acknowledgements

We would like to acknowledge the use of tools and support provided by DAO Analyzer dataset, GraphiQL, Tally API Reference, and AI-Powered RPC Infrastructure for dApps & Chains | dRPC for data extraction. These resources were indispensable for the successful completion of this project.
DOI: 10.21950/hjzkqg
title: Effort Project. Child experimental data and associated survey data (multiple-imputed data, m = 10)

description: Child experimental data and associated survey data (multiple-imputed data, m = 10): An R list of ten dataframes that contain imputed values for the principal explanatory variables of the analysis conducted for Radl et al. (2025). Missing data exist for some students because surveys were occasionally returned incomplete or illegible. Multiple imputation was conducted using multilevel joint modeling using the jomo.smc wrapper function in the R package jomo (v.2.7.6) (Quartagno and Carpenter 2023). See Radl et al. (2025) for more specification on how the imputation was conducted.
description: Some files in this dataset are embargoed until: 01/03/2027

<strong>See map</strong>
DOI: 10.4121/d3bdf118-d126-4ce2-ab6a-e7b7a91b00f5
title: Methods and data for: Modeling of Nonideal Compressible Fluids with Physics-Informed Neural Networks

description: Scripts and data used to produce the results presented in E.Bunschoten, A.Cappiello, M.Pini: "Data-driven regression of thermodynamic models in entropic form using physics-informed machine learning" in <em>Computers and Fluids </em>306,106932, 2026. doi: https://doi.org/10.1016/j.compfluid.2025.106932 and in Chapter 3 of the doctoral dissertation of E.Bunschoten titled "Consistent data-driven equations of state for reacting and nonideal compressible fluids".
DOI: 10.4121/d3bdf118-d126-4ce2-ab6a-e7b7a91b00f5.v1
title: Methods and data for: Modeling of Nonideal Compressible Fluids with Physics-Informed Neural Networks

description: Scripts and data used to produce the results presented in E.Bunschoten, A.Cappiello, M.Pini: "Data-driven regression of thermodynamic models in entropic form using physics-informed machine learning" in <em>Computers and Fluids </em>306,106932, 2026. doi: https://doi.org/10.1016/j.compfluid.2025.106932 and in Chapter 3 of the doctoral dissertation of E.Bunschoten titled "Consistent data-driven equations of state for reacting and nonideal compressible fluids".
DOI: 10.6084/m9.figshare.31209598.v1
title: ConUT_Artifact

description: Artifact For ConUT.
DOI: 10.5281/zenodo.18429938
title: Image-based Orientation, Sex, and Anatomical Segmentation of Phoridae (Diptera)

description: This dataset contains 1,281 high-resolution images of Phoridae (scuttle fly) specimens used for the tasks of orientation classification, sex classification, and anatomical body part segmentation, as described in our paper:

Automated Specimen Triage for Dark Taxa: Deep Learning Enables Orientation, Sex Identification, and Anatomical Segmentation from Robotic Imaging

The images were captured using the DiversityScanner and Entomoscope imaging systems. Each image has been annotated by taxonomic experts for the three tasks.

Folder Structure- orientation_classification: Data for 4-class orientation classification (Dorsal, Ventral, Left_Lateral, Right_Lateral).- gender_classification: Data for 3-class gender classification (Male, Female, Undetermined).- body_part_segmentation: Data for 9-class (+ background) semantic segmentation.

Data Format

Classification TasksInside each classification folder (`orientation_classification`/ and `gender_classification`/), you will find:- images: A folder containing all the images.- labels.xlsx: A file mapping each image to its corresponding label and data split (train, validation, test).

Segmentation TaskInside the `body_part_segmentation`/ folder, you will find:- images: A folder containing all the images.- labels: A folder containing single-channel PNG masks. Each mask filename corresponds to an image filename.

The pixel values in the masks map to anatomical regions as follows:- 0: background- 1: Head- 2: Thorax- 3: Abdomen- 4: Palps + labella- 5: Antenna- 6: Legs- 7: Wings- 8: Halteres- 9: Genitalia

 

Trained Models We have added models.zip, which contains the pre-trained weights for the best-performing models described in the paper.

File Structure (models.zip):





orientation_yolov8.pt: Weights for the Orientation Classification model (YOLOv8x-cls).





Performance: Accuracy 0.94, F1-Score 0.92.




Training: Trained with Weighted Random Sampling to address class imbalance.






sex_beitv2.onnx: Weights for the Sex Identification model (BEiTv2-Large).





Performance: Accuracy 0.88, F1-Score 0.85.






segmentation_unet.hdf5: Weights for the Body Part Segmentation model (U-Net with EfficientNet-B0 backbone).





Performance: Mean IoU 0.78, Mean Dice 0.87.




insect_crop.onnx: Model used to crop and process images, ensuring they match the trained images.


Usage: These weights are designed to be used with the inference code provided in our official repository. Code Repository: https://gitlab.kit.edu/kit/iai/ber/dark-taxa-triage-dl
DOI: 10.5281/zenodo.17252297
title: Image-based Orientation, Sex, and Anatomical Segmentation of Phoridae (Diptera)

description: This dataset contains 1,281 high-resolution images of Phoridae (scuttle fly) specimens used for the tasks of orientation classification, sex classification, and anatomical body part segmentation, as described in our paper:

Automated Specimen Triage for Dark Taxa: Deep Learning Enables Orientation, Sex Identification, and Anatomical Segmentation from Robotic Imaging

The images were captured using the DiversityScanner and Entomoscope imaging systems. Each image has been annotated by taxonomic experts for the three tasks.

Folder Structure- orientation_classification: Data for 4-class orientation classification (Dorsal, Ventral, Left_Lateral, Right_Lateral).- gender_classification: Data for 3-class gender classification (Male, Female, Undetermined).- body_part_segmentation: Data for 9-class (+ background) semantic segmentation.

Data Format

Classification TasksInside each classification folder (`orientation_classification`/ and `gender_classification`/), you will find:- images: A folder containing all the images.- labels.xlsx: A file mapping each image to its corresponding label and data split (train, validation, test).

Segmentation TaskInside the `body_part_segmentation`/ folder, you will find:- images: A folder containing all the images.- labels: A folder containing single-channel PNG masks. Each mask filename corresponds to an image filename.

The pixel values in the masks map to anatomical regions as follows:- 0: background- 1: Head- 2: Thorax- 3: Abdomen- 4: Palps + labella- 5: Antenna- 6: Legs- 7: Wings- 8: Halteres- 9: Genitalia

 

Trained Models We have added models.zip, which contains the pre-trained weights for the best-performing models described in the paper.

File Structure (models.zip):





orientation_yolov8.pt: Weights for the Orientation Classification model (YOLOv8x-cls).





Performance: Accuracy 0.94, F1-Score 0.92.




Training: Trained with Weighted Random Sampling to address class imbalance.






sex_beitv2.onnx: Weights for the Sex Identification model (BEiTv2-Large).





Performance: Accuracy 0.88, F1-Score 0.85.






segmentation_unet.hdf5: Weights for the Body Part Segmentation model (U-Net with EfficientNet-B0 backbone).





Performance: Mean IoU 0.78, Mean Dice 0.87.




insect_crop.onnx: Model used to crop and process images, ensuring they match the trained images.


Usage: These weights are designed to be used with the inference code provided in our official repository. Code Repository: https://gitlab.kit.edu/kit/iai/ber/dark-taxa-triage-dl
DOI: 10.15468/dl.uh8xk2
title: Occurrence Download

description: A dataset containing 77945 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Roeboides dayi, Clarias nieuhofii, Ancistrus cirrhosus, Hypostomus robinii, Rhamdia quelen, Poecilia picta, Poecilia vivipara, Clarias teijsmanni, Encheloclarias tapeinopterus, Cryptarius truncatus, Cephalocassis melanochir, Hemiarius stormii, Cephalocassis borneensis, Ketengus typus, Crenicichla alta, Sundasalanx microps, Sicydium punctatum, Gobiesox cephalus, Nomorhamphus ebrardtii, Dermogenys montana, Dermogenys orientalis, Dermogenys sumatrana, Dermogenys vogti, Hemirhamphodon chrysopunctatus, Hemirhamphodon kapuasensis, Hemibrycon taeniurus, Charax stenopterus, Astyanax lineatus, Characidium rachovii, Serrapinnus piaba, Odontostilbe pequira, Oligosarcus jenynsii, Apareiodon affinis, Cyphocharax platanus, Steindachnerina bimaculata, Cyphocharax gilbert, Megaleporinus obtusidens, Schizodon fasciatus, Pimelodella laticeps, Pimelodus albicans, Pimelodus maculatus, Pseudobunocephalus iheringii, Loricaria cataphracta, Loricariichthys labialis, Loricariichthys maculatus, Hypostomus commersoni, Crenicichla semifasciata, Catathyridium jenynsii, Mesopristes cancellatus, Diapoma speculiferum, Cynopotamus argenteus, Acestrorhynchus falcatus, Acentrogobius janthinopterus, Luciobarbus bocagei, Luciobarbus sclateri, Luciobarbus steindachneri, Squalius pyrenaicus, Achondrostoma arcasii, Achondrostoma oligolepis, Pungitius tymensis, Carassius langsdorfii, Aphyocypris chinensis, Pseudaspius sachalinensis, Phoxinus steindachneri, Pseudaspius hakonensis, Sarcocheilichthys variegatus, Coreoperca kawamebari, Schismatogobius roxasi, Mugilogobius chulae, Mugilogobius tagala, Rhinogobius brunneus, Tridentiger brevispinis, Tridentiger kuroiwae, Glossogobius olivaceus, Acanthogobius lactipes, Luciogobius albus, Luciogobius pallidus, Smilosicyopus leprurus, Lentipes armatus, Sicyopterus japonicus, Hypomesus japonicus, Lamnostoma polyophthalmum, Cottus kazika, Cottus pollux, Cottus nozawae, Cottus amblystomopsis, Labeo angra, Cottus hangiongensis, Cottus reinii, Labeo boga, Bangana devdevi, Labeo dyocheilus, Bangana gedrosicus, Altigena lippa, Tariqilabeo macmahoni, Labeo nandina, Labeo nigripinnis, Labeo rajasthanicus, Labeo udaipurensis, Mystacoleucus argenteus)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 77945 records from 387 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001822-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.