cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0203738.10fu
title: SH0203738.10FU

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: 10.15468/dl.tzftfn
title: Occurrence Download

description: A dataset containing 67 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhododendron williamsianum Rehder & E.H.Wilson"
 ]
}

 The dataset includes 67 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004385-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wwgbmg
title: Occurrence Download

description: A dataset containing 18363 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ageratina adenophora (Spreng.) R.M.King & H.Rob."
 ]
}

 The dataset includes 18363 records from 242 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004384-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.naduqv
title: Occurrence Download

description: A dataset containing 2740 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Occurrence evidence, Human Observation)",
 "Continent is Asia",
 "Country is one of (China, India, Bangladesh, Chinese Taipei)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Caryota urens L."
 ]
}

 The dataset includes 2740 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004386-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14886850
title: Underwater Object Detection Dataset

description: Description:







This dataset is designed for advanced Underwater Object Detection Dataset and classification. It provides a comprehensive collection of images featuring underwater objects, each precisely annotated with bounding boxes. The dataset aims to support a wide range of research applications, from environmental monitoring to underwater robotics.







Download Dataset









Classes:



Fish (individual and grouped)

Crab

Human Diver

Trash (marine pollution)

Jellyfish

Coral Reef

Sea Turtle

Starfish


Dataset Structure:



Training Set (70%): A robust sample for building detection models.

Validation Set (10%): Used to fine-tune model performance.

Test Set (20%): A carefully selected set of images for evaluating model accuracy.


Pre-processing Techniques:



Auto-Orientation: Ensures all images are correctly aligned.

Resizing: Images are scaled to 640×640 pixels for uniformity.

Brightness Normalization: Corrects for underwater lighting conditions.

Contrast Stretching: Enhances visibility for objects in murky or low-contrast scenes.


New Annotation Techniques:



Polygonal Segmentation: Introduces more precise segmentation for irregular shapes such as coral reefs.

3D Depth Mapping: For enhanced understanding of object placement in underwater space.


Dataset Use Cases:



Marine Ecology: Assessing species diversity and tracking the impact of environmental changes.

Pollution Analysis: Detecting and classifying marine trash, aiding in cleanup efforts.

Underwater Robotics: Training AUVs to recognize and navigate around complex underwater structures like coral reefs or large groups of fish.


Conclusion:

The expanded Underwater Object Detection provides a rich resource for researchers, environmentalists, and engineers working on underwater object detection and classification. Its enhanced classes, precise annotations, and preprocessing techniques make it a valuable asset for developing robust models in marine exploration and conservation.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14886856
title: Raw Data GC-FID-ALS volatile liquid samples (methanol, methyl acetate, acetaldehyde, ethanol, ethyl propionate, acetone, formaldehyde, eugenol, cresol, isopropanol, phenol)

description: The uploaded data files contain Gas Chromatography-Flame Ionization Detector (GC-FID) with Autosampler (ALS) analysis results for liquid samples obtained from electrochemical decarboxylation experiments. These experiments were conducted as part of Work Package 2 (WP2) of the EBIO project, focusing on electrochemical transformations of carboxylated compounds.

The GC-FID technique was employed to analyze the composition of reaction products, identifying and quantifying key volatile organic compounds formed during the electrochemical decarboxylation process. The use of an autosampler (ALS) ensured consistent and automated sample injection, improving reproducibility and efficiency in data collection.
DOI: 10.5281/zenodo.14886857
title: Raw Data GC-FID-ALS volatile liquid samples (methanol, methyl acetate, acetaldehyde, ethanol, ethyl propionate, acetone, formaldehyde, eugenol, cresol, isopropanol, phenol)

description: The uploaded data files contain Gas Chromatography-Flame Ionization Detector (GC-FID) with Autosampler (ALS) analysis results for liquid samples obtained from electrochemical decarboxylation experiments. These experiments were conducted as part of Work Package 2 (WP2) of the EBIO project, focusing on electrochemical transformations of carboxylated compounds.

The GC-FID technique was employed to analyze the composition of reaction products, identifying and quantifying key volatile organic compounds formed during the electrochemical decarboxylation process. The use of an autosampler (ALS) ensured consistent and automated sample injection, improving reproducibility and efficiency in data collection.
DOI: 10.57702/s5n6gb0n
title: Political Blogs dataset

description: The Political Blogs dataset contains a list of political blogs from the 2004 US Election classified as liberal or conservative, and links between blogs.
DOI: 10.5281/zenodo.14886808
title: WP 2 GC-FID-TCD_HS Data files for insitu gas samples analysis (methane, Ethane, Ethylene, Propane, Propylene, Butane, Butene, Hydrogen, Oxygen, Carbon mono oxide, Carbon di oxide)

description: The uploaded data files contain Gas Chromatography-Flame Ionization Detector (GC-FID-TCD) with Head space insitu gas analysis raw files for gaseous (hydrocarbons and light gases) samples obtained from electrochemical decarboxylation experiments. These experiments were conducted as part of Work Package 2 (WP2) of the EBIO project, focusing on electrochemical transformations of carboxylated compounds.
DOI: 10.5281/zenodo.14886807
title: WP 2 GC-FID-TCD_HS Data files for insitu gas samples analysis (methane, Ethane, Ethylene, Propane, Propylene, Butane, Butene, Hydrogen, Oxygen, Carbon mono oxide, Carbon di oxide)

description: The uploaded data files contain Gas Chromatography-Flame Ionization Detector (GC-FID-TCD) with Head space insitu gas analysis raw files for gaseous (hydrocarbons and light gases) samples obtained from electrochemical decarboxylation experiments. These experiments were conducted as part of Work Package 2 (WP2) of the EBIO project, focusing on electrochemical transformations of carboxylated compounds.
DOI: 10.57702/3jrfckdz
title: LiveJournal dataset

description: The LiveJournal dataset is a free online blogging social network of around 4 million users.
DOI: 10.57702/cazg9fcb
title: PosePrior

description: The dataset used for training and evaluation of the Fast-SNARF algorithm.
DOI: 10.5281/zenodo.14886849
title: Underwater Object Detection Dataset

description: Description:







This dataset is designed for advanced Underwater Object Detection Dataset and classification. It provides a comprehensive collection of images featuring underwater objects, each precisely annotated with bounding boxes. The dataset aims to support a wide range of research applications, from environmental monitoring to underwater robotics.







Download Dataset









Classes:



Fish (individual and grouped)

Crab

Human Diver

Trash (marine pollution)

Jellyfish

Coral Reef

Sea Turtle

Starfish


Dataset Structure:



Training Set (70%): A robust sample for building detection models.

Validation Set (10%): Used to fine-tune model performance.

Test Set (20%): A carefully selected set of images for evaluating model accuracy.


Pre-processing Techniques:



Auto-Orientation: Ensures all images are correctly aligned.

Resizing: Images are scaled to 640×640 pixels for uniformity.

Brightness Normalization: Corrects for underwater lighting conditions.

Contrast Stretching: Enhances visibility for objects in murky or low-contrast scenes.


New Annotation Techniques:



Polygonal Segmentation: Introduces more precise segmentation for irregular shapes such as coral reefs.

3D Depth Mapping: For enhanced understanding of object placement in underwater space.


Dataset Use Cases:



Marine Ecology: Assessing species diversity and tracking the impact of environmental changes.

Pollution Analysis: Detecting and classifying marine trash, aiding in cleanup efforts.

Underwater Robotics: Training AUVs to recognize and navigate around complex underwater structures like coral reefs or large groups of fish.


Conclusion:

The expanded Underwater Object Detection provides a rich resource for researchers, environmentalists, and engineers working on underwater object detection and classification. Its enhanced classes, precise annotations, and preprocessing techniques make it a valuable asset for developing robust models in marine exploration and conservation.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14886864
title: Phytoplankton Distribution and Influencing Factors in Typical Lakes of Inner Mongolia, China
DOI: 10.5281/zenodo.14886865
title: Phytoplankton Distribution and Influencing Factors in Typical Lakes of Inner Mongolia, China
DOI: 10.5281/zenodo.10843616
title: GeoEcoMar Macrozoobenthos DOORS September 2023

description: The dataset contains macrozoobenthos data collected during the MN248 Cruise, from 3 stations located in the Romanian Black Sea area, in 1-10 September 2023, in the framework H2020 DOORS Project.  The main goal of the cruise was to assess the sediment nutrients cycling capacity and the influence of faunal activity on benthic turnover rates of nutrient and oxygen through ex-situ sediment core incubation. The data acquisitions was focused on three main benthic habitats, as follows: coarse and mixed biogenic type of substrata suitable for thriving of mussel’s biogenic reefs and mud sediments under river plume influence. From each station, three replicates were collected with the Multicorer (the sampling size - 0.079 m2) and three with Van-Veen grab (the grasping area  -  0.125 m2). The sediment was preserved in buffered formalin 4% , then sieved through a 1 mm and 0.5 mm mesh sieve and subsequently stored in 70% ethanol. The database comprises taxonomic and quantitative data (density and wet weight biomass per square meter) for 69 macrozoobenthic species and 2 supraspecific groups.
DOI: 10.15468/dl.zzqtrc
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Occurrence evidence, Human Observation)",
 "Continent is Asia",
 "Country is one of (China, India, Bangladesh, Chinese Taipei)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ficus hirta Vahl"
 ]
}

 The dataset includes 10 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004387-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wpg4ug
title: Occurrence Download

description: A dataset containing 503 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cinclodes aricomae (Carriker, 1932)"
 ]
}

 The dataset includes 503 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004390-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18154/rwth-2025-01319
title: Experimental dataset to the journal article: 'The long-term strength and creep behavior of fully saturated shaly Opalinus Clay'

description: doi:10.18154/RWTH-2025-01319
description: Long-term deformation in tunneling is typically associated with consolidation and creep, two time-dependent processes that may occur simultaneously and are superimposed. However, from tunnel convergence measurements these two processes cannot be distinguished. Thus, an accurate laboratory characterization of creep mechanisms under long-term in-situ conditions (i.e., fully saturated and drained) is required to improve numerical predictions for deep geological nuclear waste repositories. The laboratory study investigates the pure rheological creep behavior of shaly Opalinus Clay. After full re-saturation and consolidation of the specimens, a fully drained multi-stage creep test was performed. Time-dependent axial and radial deformations were monitored during creep stages of constant effective stress. The results show that the creep strain rates increase exponentially with increasing differential stress accompanied by a change in the dominant creep mechanism. Creep strain rates at low differential stresses up to 10 MPa are in the magnitudes of 10<sup>-11</sup>s<sup>-1</sup> and 10<sup>-10</sup>s<sup>-1</sup>, whereas creep rates in the magnitudes of 10<sup>-10</sup>s<sup>-1</sup> and 10<sup>-9</sup>s<sup>-1</sup> are observed at elevated differential stresses of more than 10 MPa, before initiation of tertiary creep, i.e., creep failure. Two thresholds for possible creep failure are presented, defining a stress-related long-term strength and a strain-related onset of tertiary creep.
DOI: 10.15468/dl.zvm6gg
title: Occurrence Download

description: A dataset containing 151 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gastrolobium racemosum (Turcz.) Crisp"
 ]
}

 The dataset includes 151 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004388-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.2908/tps00208
title: Life expectancy at birth by sex
title: Espérance de vie à la naissance, par sexe
title: Lebenserwartung bei der Geburt, nach Geschlecht

description: Nombre moyen d'années à vivre à une personne à la naissance, si elle restait sujette pour le reste de sa vie aux conditions actuelles de mortalité.
description: The mean number of years still to be lived by a person at birth, if subjected throughout the rest of his or her life to the current mortality conditions.
description: Anzahl der Jahre, die eine Person bei der Geburt im Durchschnitt noch zu leben hätte, wenn sie für den Rest ihres Lebens den aktuellen Sterblichkeitsbedingungen ausgesetzt wäre.
DOI: 10.34740/kaggle/dsv/10782205
title: World Weather Repository ( Daily Updating )
DOI: 10.15468/dl.p98kxx
title: Occurrence Download

description: A dataset containing 33759 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ailanthus altissima (Mill.) Swingle",
 "Country is France",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 33759 records from 723 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004389-250214102907787/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.28435610
title: Data for Aqueous phase paper

description: This study investigates the application of electrochemical hydrogenation (ECH) as an alternative to the conventional hydrogenation process to upgrade bio-oil derived from wheat straw and pinewood.
DOI: 10.6084/m9.figshare.28435610.v1
title: Data for Aqueous phase paper

description: This study investigates the application of electrochemical hydrogenation (ECH) as an alternative to the conventional hydrogenation process to upgrade bio-oil derived from wheat straw and pinewood.
DOI: 10.48612/msupe/ezg5-na5u-u6h2
title: База данных психофизиологических показателей вовлеченности в онлайн-обучение

description: База данных содержит показатели электродермальной активности, а также айтрекинговые данные респондентов, прошедших экспериментальное исследование вовлеченности в онлайн-обучение. Респонденты, учувствовавшие в исследовании, являются студентами бакалавриата и магистратуры томских госуниверситетов. Цель исследования – выявление психофизиологических особенностей восприятия образовательного контента с применением инструментальной диагностики. База данных может применяться в экспериментальных психологических исследованиях (фундаментальных и прикладных), психологами, педагогами, а также учеными, занимающимися исследованиями в области нейротехнологий и когнитивного маркетинга.
DOI: 10.6084/m9.figshare.28216067
title: Daily temperatures

description: Mean daily temperature from Košetice and Marianske Lazne climatic stations operated by Czech Hydrometeorological Institute.
DOI: 10.5281/zenodo.10848752
title: Meiobenthos GeoEcoMar DOORS 2023

description: The dataset contains the taxonomic and quantitative analysis of meiobenthic samples (free-living nematodes and harpacticoida groups), collected within the DOORS Leg 1 cruise carried out within 1-10 September 2023 in the framework of Horizon 2020 Project ‘Developing Optimal and Open Research Support for the Black Sea’ (DOORS). The samples have been collected with a Multiple Corer Mark II device. 3 out of the 6 samples that contain in their ID the word "inc" represent the incubated cores (for fluxes experiments), while the other 3 the "control" samples collected in the same station. The latter were washed through a 63 µm mesh sieve on board and preserved in buffered formaldehyde 4% for further laboratory analysis (Giere, 2009)
DOI: 10.5281/zenodo.10901356
title: ToneTwist AFx Dataset: Custom Dynamic Fuzz

description: Settings




Gain
Sensitivity
Attack
Release
Volume


5
10
1ms
2500ms
10




Dry with markers



Dry inputs are a selection of clean guitar and bass recordings from different sources:



IDMT-SMT-GUITAR - dataset 2 (7:23 min)

IDMT-SMT-GUITAR - dataset 4 - Career SG (6:08 min)

IDMT-SMT-GUITAR - dataset 4 - Ibanez 2820 (5:14 min)

IDMT-SMT-Bass-Single-Track - (5:58 min)

NAM: Neural Amp Modeler - (3:11 min)

Private Guitar Data - (5:19 min)

YouTube Bass Recordings - (10:09 min)


Pre-processing:



All:



synchronization markers (2 impulses) added at start and end of every file



IDMT-SMT-GUITAR - dataset 2:



peak normalized to -6dBFS



NAM:



no pre-processing



Others:



peak normalized to -0.1dBFS

signal multiplied by random number every 5 seconds (uniform distribution [0.1, 1.0] = [-20dB, 0dB])






Authors

Marco Comunità - Centre for Digital Music, Queen Mary University of London

Github

https://github.com/mcomunita/tonetwist-afx-dataset

Reference

If you make use of this dataset, please cite the following publications:

@inproceedings{comunita2023modelling,
  title={Modelling black-box audio effects with time-varying feature modulation},
  author={Comunit{\`a}, Marco and Steinmetz, Christian J and Phan, Huy and Reiss, Joshua D},
  booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1--5},
  year={2023},
  organization={IEEE}
}

 

@misc{comunità2025nablafxframeworkdifferentiableblackbox,
      title={NablAFx: A Framework for Differentiable Black-box and Gray-box Modeling of Audio Effects}, 
      author={Marco Comunità and Christian J. Steinmetz and Joshua D. Reiss},
      year={2025},
      eprint={2502.11668},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2502.11668}, 
}
DOI: 10.5281/zenodo.10901357
title: ToneTwist AFx Dataset: Custom Dynamic Fuzz

description: Settings




Gain
Sensitivity
Attack
Release
Volume


5
10
1ms
2500ms
10




Dry with markers



Dry inputs are a selection of clean guitar and bass recordings from different sources:



IDMT-SMT-GUITAR - dataset 2 (7:23 min)

IDMT-SMT-GUITAR - dataset 4 - Career SG (6:08 min)

IDMT-SMT-GUITAR - dataset 4 - Ibanez 2820 (5:14 min)

IDMT-SMT-Bass-Single-Track - (5:58 min)

NAM: Neural Amp Modeler - (3:11 min)

Private Guitar Data - (5:19 min)

YouTube Bass Recordings - (10:09 min)


Pre-processing:



All:



synchronization markers (2 impulses) added at start and end of every file



IDMT-SMT-GUITAR - dataset 2:



peak normalized to -6dBFS



NAM:



no pre-processing



Others:



peak normalized to -0.1dBFS

signal multiplied by random number every 5 seconds (uniform distribution [0.1, 1.0] = [-20dB, 0dB])






Authors

Marco Comunità - Centre for Digital Music, Queen Mary University of London

Github

https://github.com/mcomunita/tonetwist-afx-dataset

Reference

If you make use of this dataset, please cite the following publications:

@inproceedings{comunita2023modelling,
  title={Modelling black-box audio effects with time-varying feature modulation},
  author={Comunit{\`a}, Marco and Steinmetz, Christian J and Phan, Huy and Reiss, Joshua D},
  booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1--5},
  year={2023},
  organization={IEEE}
}

 

@misc{comunità2025nablafxframeworkdifferentiableblackbox,
      title={NablAFx: A Framework for Differentiable Black-box and Gray-box Modeling of Audio Effects}, 
      author={Marco Comunità and Christian J. Steinmetz and Joshua D. Reiss},
      year={2025},
      eprint={2502.11668},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2502.11668}, 
}