cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.c9bkjb
title: Occurrence Download

description: A dataset containing 2527246 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-8.00367 34.76012,-10.66673 33.31218,-21.95937 20.69322,-13.16779 20.27638,-9.1847 23.74794,-5.75374 28.68889,-1.68245 29.76029,-1.8253 32.63519,-1.8253 35.43867,-4.14665 36.2065,-6.48586 35.83152,-8.00367 34.76012))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 2527246 records from 1146 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0065684-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gtcndp
title: Occurrence Download

description: A dataset containing 3807 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is ALB",
 "PublishingOrg is c8d737e0-2ff8-42e8-b8fc-6b805d26fc5f",
 "TaxonKey is Lepidoptera"
 ]
}

 The dataset includes 3807 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018916-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14028846
title: Datasets and Code for the paper "Toward Futuristic Autonomous Experimentation—A Surprise-Reacting Sequential Experiment Policy"

description: This page has the data and code used in the paper "Toward Futuristic Autonomous Experimentation—A Surprise-Reacting Sequential Experiment Policy", to be published in IEEE Transactions on Automation Science and Engineering.
DOI: 10.5281/zenodo.14028847
title: Datasets and Code for the paper "Toward Futuristic Autonomous Experimentation—A Surprise-Reacting Sequential Experiment Policy"

description: This page has the data and code used in the paper "Toward Futuristic Autonomous Experimentation—A Surprise-Reacting Sequential Experiment Policy", to be published in IEEE Transactions on Automation Science and Engineering.
DOI: 10.15468/dl.x6e7ey
title: Occurrence Download

description: A dataset containing 827 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((97.84159 2.90906,99.34896 2.90906,99.34896 4.22012,97.84159 4.22012,97.84159 2.90906))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia",
 "Year 2024-2024"
 ]
}

 The dataset includes 827 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018917-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5rdemz
title: Occurrence Download

description: A dataset listing the 62520 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Kenya",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 62520 records were derived from 1343 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018914-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13997002
title: Computational Approach to Discovering Plastic Degradation Enzymes

description: With at least 150 million tons of plastic already in oceans and over 10 million tons of plastic entering oceans annually, plastic waste has become a major global problem. Current methods to address this problem such as incineration and landfills are unsustainable and environmentally harmful. More trending approaches such as the degradation of plastic using microbial enzymes are rarely efficient enough to be applied industrially. To fill this gap in our knowledge, we developed a computational method called IPDE (Identification of Plastic Degradation Enzymes) to systematically identify promising enzymes, enzyme combinations, and microbial species for effective plastic waste degradation. Using IPDE, we discovered 32 enzymes in ocean microbiomes, with at least 16 (50.0%) having a role in plastic degradation. Additionally, we identified 37 significant enzyme combinations, 8 (21.6%) of which contain enzymes that co-occur in the same metabolic pathways. Furthermore, we found 60 microbial species, 16 (26.6%) of which have been implied to be linked to plastic degradation in literature. The results from IPDE provide promising candidates for experimental validation, protein engineering, and industrial application to tackle this plastic waste problem. The IPDE tool is freely available at https://github.com/SophieL8/Plastic-degrading-enzymes.
DOI: 10.5281/zenodo.13997003
title: Computational Approach to Discovering Plastic Degradation Enzymes

description: With at least 150 million tons of plastic already in oceans and over 10 million tons of plastic entering oceans annually, plastic waste has become a major global problem. Current methods to address this problem such as incineration and landfills are unsustainable and environmentally harmful. More trending approaches such as the degradation of plastic using microbial enzymes are rarely efficient enough to be applied industrially. To fill this gap in our knowledge, we developed a computational method called IPDE (Identification of Plastic Degradation Enzymes) to systematically identify promising enzymes, enzyme combinations, and microbial species for effective plastic waste degradation. Using IPDE, we discovered 32 enzymes in ocean microbiomes, with at least 16 (50.0%) having a role in plastic degradation. Additionally, we identified 37 significant enzyme combinations, 8 (21.6%) of which contain enzymes that co-occur in the same metabolic pathways. Furthermore, we found 60 microbial species, 16 (26.6%) of which have been implied to be linked to plastic degradation in literature. The results from IPDE provide promising candidates for experimental validation, protein engineering, and industrial application to tackle this plastic waste problem. The IPDE tool is freely available at https://github.com/SophieL8/Plastic-degrading-enzymes.
DOI: 10.15468/dl.m3t5cm
title: Occurrence Download

description: A dataset containing 924 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 "TaxonKey is one of (Poecilia reticulata Peters, 1859, Poecilia reticulate Peters, 1859)"
 ]
}

 The dataset includes 924 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018918-241024112534372/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.25481587
title: Data of the manuscript "Elucidating Hillslope Creep under Tiny Disturbances through Granular Rheology"

description: Data of the manuscript "Elucidating Hillslope Creep under Tiny Disturbances through Granular Rheology"
DOI: 10.6084/m9.figshare.25481587.v2
title: Data of the manuscript "Elucidating Hillslope Creep under Tiny Disturbances through Granular Rheology"

description: Data of the manuscript "Elucidating Hillslope Creep under Tiny Disturbances through Granular Rheology"
DOI: 10.6084/m9.figshare.25481587.v1
title: Data of the manuscript "Tiny perturbations drive hillslope creep indefinitely"

description: Data of the manuscript "Tiny perturbations drive hillslope creep indefinitely".
DOI: 10.15468/dl.jedzfh
title: Occurrence Download

description: A dataset containing 657 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((8.4649 47.35618,8.57522 47.35618,8.57522 47.42,8.4649 47.42,8.4649 47.35618))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Nyctalus noctula (Schreber, 1774)",
 "Year 1984-2024"
 ]
}

 The dataset includes 657 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018919-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.856nnj
title: Occurrence Download

description: A dataset containing 12709936 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Animalia",
 "Year 2022-2024"
 ]
}

 The dataset includes 12709936 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018909-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14028844
title: A dataset for Emotion Recognition using EEG and MUsical Stimuli

description: # EREMUS: A Dataset for Emotion Recognition Using EEG and Musical Stimuli

## Description

EREMUS is a dataset designed for the study of emotion recognition using electroencephalography (EEG) data collected during musical stimuli exposure. The dataset includes EEG recordings from 34 young subjects in a controlled laboratory setting. Each subject participated in 16 trials, with a duration of approximately 90 seconds each. Eight trials featured songs from the subject's personal music playlist, while the other eight were randomly selected from the preferences of other participants. Following each trial, subjects self-assessed their emotional responses using the Geneva Emotion Wheel, which categorizes emotions into 20 families arranged in a circular format based on valence and dominance.

### Data Collection

EEG data was recorded using a 32-channel EPOC Flex EEG system with saline sensors, at a sampling rate of 128 Hz. Electrode placements adhered to the international 10-20 system.

### Training Dataset

The training set consists of 294 trials from 26 subjects, with each subject contributing approximately 12 labeled trials. Each trial includes the following metadata:

- **session_type**: Indicates whether the trial is from a *personal* or *other* session.- **subject_id**: Identifier for the subject.- **spotify_track_id**: Spotify identifier for the song.- **song_title**: Title of the song played.- **song_author**: List of song authors.- **emotion**: Self-assessed emotion based on the Geneva Emotion Wheel.- **label**: Emotion label in a Valence-Dominance space.- **id**: Trial identifier.

### Test Dataset

The test dataset is divided into two parts: held-out trials and held-out subjects, both released without emotion labels or subject identifiers.

- **Held-out Trials**: Comprises 104 trials from the 26 subjects included in the training dataset.- **Held-out Subjects**: Contains 122 trials from 8 subjects not present in the training dataset.

Additionally, the held-out trials dataset includes 44 extra trials for the subject identification task, which may contain or omit stimulation information.

### Preprocessing

The dataset is available in two versions:

1. **Raw EEG Data**: Unprocessed data.2. **Pruned Data**: Preprocessed using EEGLab, which includes a FIR filter applied between 0.5 and 40 Hz and artifact removal via Independent Component Analysis (ICA).

### Files Structure

The dataset is organized in a hierarchical structure according to data type (raw or pruned) and split type (training, held-out trials, held-out subjects):

```dataset├── splits_subject_identification.json├── splits_emotion_recognition.json├── README.md├── raw│   ├── train│   │   ├── 1135903657_eeg.fif│   │   └── ...│   ├── test_trial│   └── test_subject└── pruned    ├── train    │   ├── 1135903657_eeg.fif    │   └── ...    ├── test_trial    └── test_subject```

The IDs for the test sets are specified in two JSON files:- `splits_subject_identification.json` for Task 1.- `splits_emotion_recognition.json` for Task 2.
DOI: 10.5281/zenodo.14028845
title: A dataset for Emotion Recognition using EEG and MUsical Stimuli

description: # EREMUS: A Dataset for Emotion Recognition Using EEG and Musical Stimuli

## Description

EREMUS is a dataset designed for the study of emotion recognition using electroencephalography (EEG) data collected during musical stimuli exposure. The dataset includes EEG recordings from 34 young subjects in a controlled laboratory setting. Each subject participated in 16 trials, with a duration of approximately 90 seconds each. Eight trials featured songs from the subject's personal music playlist, while the other eight were randomly selected from the preferences of other participants. Following each trial, subjects self-assessed their emotional responses using the Geneva Emotion Wheel, which categorizes emotions into 20 families arranged in a circular format based on valence and dominance.

### Data Collection

EEG data was recorded using a 32-channel EPOC Flex EEG system with saline sensors, at a sampling rate of 128 Hz. Electrode placements adhered to the international 10-20 system.

### Training Dataset

The training set consists of 294 trials from 26 subjects, with each subject contributing approximately 12 labeled trials. Each trial includes the following metadata:

- **session_type**: Indicates whether the trial is from a *personal* or *other* session.- **subject_id**: Identifier for the subject.- **spotify_track_id**: Spotify identifier for the song.- **song_title**: Title of the song played.- **song_author**: List of song authors.- **emotion**: Self-assessed emotion based on the Geneva Emotion Wheel.- **label**: Emotion label in a Valence-Dominance space.- **id**: Trial identifier.

### Test Dataset

The test dataset is divided into two parts: held-out trials and held-out subjects, both released without emotion labels or subject identifiers.

- **Held-out Trials**: Comprises 104 trials from the 26 subjects included in the training dataset.- **Held-out Subjects**: Contains 122 trials from 8 subjects not present in the training dataset.

Additionally, the held-out trials dataset includes 44 extra trials for the subject identification task, which may contain or omit stimulation information.

### Preprocessing

The dataset is available in two versions:

1. **Raw EEG Data**: Unprocessed data.2. **Pruned Data**: Preprocessed using EEGLab, which includes a FIR filter applied between 0.5 and 40 Hz and artifact removal via Independent Component Analysis (ICA).

### Files Structure

The dataset is organized in a hierarchical structure according to data type (raw or pruned) and split type (training, held-out trials, held-out subjects):

```dataset├── splits_subject_identification.json├── splits_emotion_recognition.json├── README.md├── raw│   ├── train│   │   ├── 1135903657_eeg.fif│   │   └── ...│   ├── test_trial│   └── test_subject└── pruned    ├── train    │   ├── 1135903657_eeg.fif    │   └── ...    ├── test_trial    └── test_subject```

The IDs for the test sets are specified in two JSON files:- `splits_subject_identification.json` for Task 1.- `splits_emotion_recognition.json` for Task 2.
DOI: 10.15468/dl.bepaaa
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((9.23073 45.84470,9.65557 45.84470,9.65557 46.01464,9.23073 46.01464,9.23073 45.84470))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Facchinia grignensis (Rchb.) Dillenb. & Kadereit"
 ]
}

 The dataset includes 11 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018921-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/9789049
title: Mahanagar Yatayat Transport Dataset
DOI: 10.15468/dl.xnub4p
title: Occurrence Download

description: A dataset containing 126392 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Hylesia lineata Druce, 1886, Phoebis sennae (Linnaeus, 1758), Battus philenor (Linnaeus, 1771), Junonia coenia Hübner, Thorybes pylades (Scudder, 1870))"
 ]
}

 The dataset includes 126392 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018920-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bggym6
title: Occurrence Download

description: A dataset containing 1142 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is PHL.59_1",
 "TaxonKey is Dinopium everetti (Tweeddale, 1878)"
 ]
}

 The dataset includes 1142 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018922-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10420811
title: Numerical Results for Bazán Flores et al., 2024. (JGR Accepted)

description: Numerical results for the paper :

Bazán Flores, L. I., Kyriakopoulos, C., Oglesby, D. D., Meltzner, A. J., Rockwell, T. K., Fletcher, J. M., & Brothers, D. (2024). Exploring the dynamic interactions between the Southern San Andreas Fault and a normal fault under the Salton Sea. Journal of Geophysical Research: Solid Earth, 129, e2023JB028621. https://doi.org/10.1029/2023JB028621

 
description: This dataset includes:

 



Files to visualize the Fault Geometry in ParaView

Necessary files to run a dynamic rupture simulation in FaultMod

"vtu" output files for visualization in ParaView; Paper main Figures

ParaView state files to visualize Fault Total Slip, DCFS, Fault Slip Rate, Normal Stress, Shear Stress, and Fault Rake

"vtu" output files for visualization in ParaView; Paper Supplementary Figures

README file

Basic steps to load ParaView state files

Basic steps to run a dynamic rupture simulation for SSAF-NF and SSAF_EXT-NF geometries
DOI: 10.5281/zenodo.10420812
title: Numerical Results for Bazán et al., 2023. (JGR REVIEW STAGE)

description: Numerical results for the paper :

Exploring the Dynamic Interactions between the Southern San Andreas Fault and a Normal Fault under the Salton Sea.

1Luis Iván Bazán Flores, 1Christodoulos Kyriakopoulos, 2David Oglesby, 3,4Aron J. Meltzner, 5Thomas Rockwell, 6John Fletcher and 7Daniel Brothers.

1Center for Earthquake Research and Information (CERI), The University of Memphis, Memphis, Tennessee.

2University of California, Riverside (UCR), Riverside, California.

3Earth Observatory of Singapore, Nanyang Technological University (NTU), Singapore.

4Asian School of the Environment, Nanyang Technological University (NTU), Singapore.

5San Diego State University (SDSU), San Diego, California.

6Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Baja   California, Mexico.

 7United States Geological Survey (USGS), Santa Cruz, California.
DOI: 10.5281/zenodo.12210253
title: Numerical Results for Bazán Flores et al., 2024. (JGR REVIEW STAGE)

description: Numerical results for the paper :

Exploring the Dynamic Interactions between the Southern San Andreas Fault and a Normal Fault under the Salton Sea.

1Luis Iván Bazán Flores, 1Christodoulos Kyriakopoulos, 2David Oglesby, 3,4Aron J. Meltzner, 5Thomas Rockwell, 6John Fletcher and 7Daniel Brothers.

1Center for Earthquake Research and Information (CERI), The University of Memphis, Memphis, Tennessee.

2University of California, Riverside (UCR), Riverside, California.

3Earth Observatory of Singapore, Nanyang Technological University (NTU), Singapore.

4Asian School of the Environment, Nanyang Technological University (NTU), Singapore.

5San Diego State University (SDSU), San Diego, California.

6Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Baja   California, Mexico.

 7United States Geological Survey (USGS), Santa Cruz, California.
DOI: 10.5281/zenodo.13952792
title: Numerical Results for Bazán Flores et al., 2024. (JGR Accepted)

description: Numerical results for the paper :

Bazán Flores, L. I., Kyriakopoulos, C., Oglesby, D. D., Meltzner, A. J., Rockwell, T. K., Fletcher, J. M., & Brothers, D. (2024). Exploring the dynamic interactions between the Southern San Andreas Fault and a normal fault under the Salton Sea. Journal of Geophysical Research: Solid Earth, 129, e2023JB028621. https://doi.org/10.1029/2023JB028621

 
description: This dataset includes:

 



Files to visualize the Fault Geometry in ParaView

Necessary files to run a dynamic rupture simulation in FaultMod

"vtu" output files for visualization in ParaView; Paper main Figures

ParaView state files to visualize Fault Total Slip, DCFS, Fault Slip Rate, Normal Stress, Shear Stress, and Fault Rake

"vtu" output files for visualization in ParaView; Paper Supplementary Figures

README file

Basic steps to load ParaView state files

Basic steps to run a dynamic rupture simulation for SSAF-NF and SSAF_EXT-NF geometries
DOI: 10.15468/dl.v6fdxp
title: Occurrence Download

description: A dataset containing 23088 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Crangon crangon (Linnaeus, 1758)"
 ]
}

 The dataset includes 23088 records from 290 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018923-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nky36l
title: Occurrence Download

description: A dataset containing 1126 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhamnidium elaeocarpum Reissek"
 ]
}

 The dataset includes 1126 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009008-160623103540712/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sa5xgw
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Excoecaria aporusifolia P.T.Li"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050399-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bpexca
title: Occurrence Download

description: A dataset containing 297 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Diospyros gilletii De Wild."
 ]
}

 The dataset includes 297 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023045-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rfh33s
title: Occurrence Download

description: A dataset containing 75 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Diplotaxis cretacea Kotov",
 "HasCoordinate is true",
 "Continent is Europe",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000",
 "CoordinateUncertaintyInMeters is less than 10000"
 ]
}

 The dataset includes 75 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014771-240216155721649/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.