cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.36973b
title: Occurrence Download

description: A dataset containing 1090846 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "OccurrenceStatus is Present",
 "StateProvince is one of (Meta Dept, MEta, Meta, Meta Department, San Martin, Meta Dept., Meta Intendencia, Meta Prov)"
 ]
}

 The dataset includes 1090846 records from 617 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025299-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/kg04om5s
title: ExpertMatcher dataset

description: The dataset used for the ExpertMatcher framework, which includes six datasets: STL-10, MNIST, HAR, Reuters, Non Line of Sight, and Diabetic Retinopathy.
DOI: 10.57702/m945rpmw
title: Sinusoidal Wave Dataset

description: The dataset is a sinusoidal wave with amplitudes ranging between 1 and 5 V, and a sampling frequency of 3 and 5.5 kHz.
DOI: 10.57702/4t7fhh44
title: Goodreads Book Reviews

description: The review corpus used here consists of a collection of Goodreads book reviews obtained from the Kaggle website. Originally, it consists of around 10,000 reviews written towards top 100 science fiction books (ranked based on Goodreads ratings). However, upon examination, we find that a significant number of reviews comprise various types of issues, including missing values for the review text and like count, as well as duplicate sentences. We remove duplicate sentences from the reviews and exclude problematic reviews with missing fields. After clean-up, the corpus consists of 8845 reviews.
DOI: 10.57702/hf75wixj
title: General Video Game AI Level Generation Competition

description: The General Video Game AI (GVGAI) Level Generation Competition dataset is a collection of levels generated using different algorithms and techniques.
DOI: 10.57702/rzea6fom
title: MuJoCo

description: The dataset used in the paper is a collection of simulated environments, including blockworld, canada, cart, cartpole, cartpoleBalance, cartpoleParallelDouble, cartpoleSerialDouble, cartpoleSerialTriple, cheetah, fixedReacher, fixedReacherDouble, fixedReacherSingle, gripper, gripperRandom, hardCheetah, hopper, hyq, hyqKick, movingGripper, movingGripperRandom, pendulum, reacher, reacher3daFixedTarget, reacher3daRandomTarget, reacherDouble, reacherObstacle, walker2d.
DOI: 10.57702/cvjlkorw
title: RESIDE SOTS

description: The dataset used for training and testing the pyramid fusion dark channel prior (PF-DCP) algorithm for single image dehazing.
DOI: 10.5281/zenodo.18157621
title: Quantum Computing for Biodiversity: Case Study Data, Code, and Results

description: Overview

Technology readiness assessment for quantum computing in biodiversity research by Science Live.

Main Finding: Quantum computing methods are technically feasible but do not yet provide practical advantages at current ecological dataset sizes.

What We Did



PRISMA scoping review found 283 papers at quantum + biodiversity intersection

Selected 2 papers for case study implementation

Used 1 paper (QOMIC) — the other was a review with no implementable method

Applied QOMIC to ecological food web (test dataset NOT from PRISMA)

Compared quantum (QAOA) vs classical methods


Papers Selected from PRISMA Review

 




Paper
DOI
Confidence
Used




QOMIC (Ngo et al. 2024)
10.1093/bioadv/vbae208
0.9
✅ Yes


Quantum Ecology Review
10.48550/arXiv.2504.03866
0.7
❌ No (review paper)




Test Dataset (NOT from PRISMA)



Serengeti Food Web - Baskerville et al. 2011 - DOI: 10.1371/journal.pcbi.1002321

Ecological network data (161 species, 592 links)


Files




File
Description




serengeti_species.csv
161 species list


serengeti_predation.csv
85 mammal predation links


run_quantum_bifans.py
QAOA quantum simulation


run_classical_analysis.py
Classical motif analysis


quantum_bifan_results.json
Quantum results


classical_motif_results.json
Classical results




Results



629 bifans in real network (p=0.023 vs random)

Quantum = Classical at 15 qubits (no advantage)

Estimated break-even: ~1000+ variables


Related



Study Assessment: https://w3id.org/np/RAlN5rGFTlXawYWAMdSDMm2SfTh8mfsN9Jhx-Oh7yXR-4

GitHub: https://github.com/ScienceLive/quantum-biodiversity


License



Code: MIT

Data: CC-BY 4.0


Contact

Anne Fouilloux - ORCID: 0000-0002-1784-2920
DOI: 10.5281/zenodo.18157620
title: Quantum Computing for Biodiversity: Case Study Data, Code, and Results

description: Overview

Technology readiness assessment for quantum computing in biodiversity research by Science Live.

Main Finding: Quantum computing methods are technically feasible but do not yet provide practical advantages at current ecological dataset sizes.

What We Did



PRISMA scoping review found 283 papers at quantum + biodiversity intersection

Selected 2 papers for case study implementation

Used 1 paper (QOMIC) — the other was a review with no implementable method

Applied QOMIC to ecological food web (test dataset NOT from PRISMA)

Compared quantum (QAOA) vs classical methods


Papers Selected from PRISMA Review

 




Paper
DOI
Confidence
Used




QOMIC (Ngo et al. 2024)
10.1093/bioadv/vbae208
0.9
✅ Yes


Quantum Ecology Review
10.48550/arXiv.2504.03866
0.7
❌ No (review paper)




Test Dataset (NOT from PRISMA)



Serengeti Food Web - Baskerville et al. 2011 - DOI: 10.1371/journal.pcbi.1002321

Ecological network data (161 species, 592 links)


Files




File
Description




serengeti_species.csv
161 species list


serengeti_predation.csv
85 mammal predation links


run_quantum_bifans.py
QAOA quantum simulation


run_classical_analysis.py
Classical motif analysis


quantum_bifan_results.json
Quantum results


classical_motif_results.json
Classical results




Results



629 bifans in real network (p=0.023 vs random)

Quantum = Classical at 15 qubits (no advantage)

Estimated break-even: ~1000+ variables


Related



Study Assessment: https://w3id.org/np/RAlN5rGFTlXawYWAMdSDMm2SfTh8mfsN9Jhx-Oh7yXR-4

GitHub: https://github.com/ScienceLive/quantum-biodiversity


License



Code: MIT

Data: CC-BY 4.0


Contact

Anne Fouilloux - ORCID: 0000-0002-1784-2920
DOI: 10.57702/mwrfjfvk
title: Turbosquid 3D Model Repository

description: A dataset of 3D models and textures.
DOI: 10.15468/dl.2np4t8
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Formica reflexa",
 "Country is Canada",
 {
 "not" : {
 "InstitutionCode" : [
 "is one of (iNaturalist)"
 ]
 }
 }
 ]
}

 The dataset includes 12 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080075-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/j3gy9z5k
title: Nonparametric Data Analysis on the Space of Perceived Colors

description: The dataset is used for nonparametric data analysis on the space of perceived colors.
DOI: 10.57702/yjayxkma
title: TaiChi-HD

description: TaiChi-HD for video generation
DOI: 10.57702/4r60hv99
title: Twin-dom dataset

description: The dataset used in the paper is a multi-camera system with six synchronized RGB cameras.
DOI: 10.25505/fiz.icsd.cc2mjxfj
title: CSD 2429328: Experimental Crystal Structure Determination

description: Related Article: Yanina Mariella Dreer, Alexandra Elisa Frank, Rainer Niewa|2025|Z.Anorg.Allg.Chem.|651|e202500058|doi:10.1002/zaac.202500058
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.57702/wsmjjyv6
title: HHS emPOWER map

description: The emPOWER dataset, which estimates the number of people who rely on in-home medical devices at zip code level
DOI: 10.25505/fiz.icsd.cc2mjxgk
title: CSD 2429329: Experimental Crystal Structure Determination

description: Related Article: Yanina Mariella Dreer, Alexandra Elisa Frank, Rainer Niewa|2025|Z.Anorg.Allg.Chem.|651|e202500058|doi:10.1002/zaac.202500058
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.57702/oieecilt
title: JARVIS-ML database

description: The JARVIS-ML database contains machine learning models for predicting material properties.
DOI: 10.25505/fiz.icsd.cc2mjxhl
title: CSD 2429330: Experimental Crystal Structure Determination

description: Related Article: Yanina Mariella Dreer, Alexandra Elisa Frank, Rainer Niewa|2025|Z.Anorg.Allg.Chem.|651|e202500058|doi:10.1002/zaac.202500058
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2mjxjm
title: CSD 2429331: Experimental Crystal Structure Determination

description: Related Article: Yanina Mariella Dreer, Alexandra Elisa Frank, Rainer Niewa|2025|Z.Anorg.Allg.Chem.|651|e202500058|doi:10.1002/zaac.202500058
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2mjxkn
title: CSD 2429332: Experimental Crystal Structure Determination

description: Related Article: Yanina Mariella Dreer, Alexandra Elisa Frank, Rainer Niewa|2025|Z.Anorg.Allg.Chem.|651|e202500058|doi:10.1002/zaac.202500058
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.57702/k530pzrr
title: Minimal Filtering Algorithms for Convolutional Neural Networks

description: The dataset used in this paper is a set of convolutional neural networks with small length FIR filters.
DOI: 10.57702/2kcz6qj1
title: Open Vocabulary Multi-Label Video Classification

description: Open vocabulary multi-label video classification dataset
DOI: 10.15468/dl.sx6sp2
title: Occurrence Download

description: A dataset containing 5967 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Eleusine coracana",
 "Year 1000-2020"
 ]
}

 The dataset includes 5967 records from 158 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080660-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0155710.10fu
title: SH0155710.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0155711.10fu
title: SH0155711.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0155712.10fu
title: SH0155712.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0155713.10fu
title: SH0155713.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0155714.10fu
title: SH0155714.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0155715.10fu
title: SH0155715.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0155716.10fu
title: SH0155716.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0155717.10fu
title: SH0155717.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0155718.10fu
title: SH0155718.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0155719.10fu
title: SH0155719.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.