cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7523434
title: WaivOps HH-LFBB: Open Audio Resources for Machine Learning in Music

description: WaivOps HH-LFBB Dataset

HH-LFBB is an open audio dataset composed of a series of drum recordings in the style of lofi hip-hop music. The dataset contains 3332 audio loops recorded in uncompressed stereo WAV format, produced with custom drum samples and MIDI-programmed rhythms at various tempo rates.

Dataset

The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design, and signal processing.

Specifications



3332 audio loops (19.3 hours)

24-bit WAV format

BPM labeled

Tempo range: 60-96bpm

Expressive drum swings

Lofi and boom bap style rhythms


License

This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.

The HH-LFBB dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Additional Info

For audio examples or more information about this dataset, please refer to the GitHub repository.
DOI: 10.5281/zenodo.7523435
title: WaivOps HH-LFBB: Open Audio Resources for Machine Learning in Music

description: WaivOps HH-LFBB Dataset

HH-LFBB is an open audio dataset composed of a series of drum recordings in the style of lofi hip-hop music. The dataset contains 3332 audio loops recorded in uncompressed stereo WAV format, produced with custom drum samples and MIDI-programmed rhythms at various tempo rates.

Dataset

The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design, and signal processing.

Specifications



3332 audio loops (19.3 hours)

24-bit WAV format

BPM labeled

Tempo range: 60-96bpm

Expressive drum swings

Lofi and boom bap style rhythms


License

This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.

The HH-LFBB dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Additional Info

For audio examples or more information about this dataset, please refer to the GitHub repository.
DOI: 10.5281/zenodo.8327649
title: WaivOps RTRO-DRM: Open Audio Resources for Machine Learning in Music

description: WaivOps RTRO-DRM Dataset

RTRO-DRM is an open audio dataset composed of a series of drum recordings in the style of 1980s electronic music. The dataset comprises 2138 raw, unedited audio clips recorded in uncompressed stereo WAV format. These recordings were curated using an internal drum sample dataset and MIDI files sourced from a code-based music generation system, along with a MIDI transformer model trained on more than 30,000 MIDI files. The files primarily consist of recordings that may not meet conventional audio quality standards but can still be valuable for a range of applications and research projects.

Dataset

The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design, and signal processing.

Specifications



2138 audio loops (4.3 hours)

24-bit WAV format

BPM labeled

Tempo range: 100-145bpm

Variational drum patterns

Electronic drum machine sound (circa 1980s)


License

This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.

The RTRO-DRM dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Additional Info

For audio examples or more information about this dataset, please refer to the GitHub repository.
DOI: 10.5281/zenodo.8327650
title: WaivOps RTRO-DRM: Open Audio Resources for Machine Learning in Music

description: WaivOps RTRO-DRM Dataset

RTRO-DRM is an open audio dataset composed of a series of drum recordings in the style of 1980s electronic music. The dataset comprises 2138 raw, unedited audio clips recorded in uncompressed stereo WAV format. These recordings were curated using an internal drum sample dataset and MIDI files sourced from a code-based music generation system, along with a MIDI transformer model trained on more than 30,000 MIDI files. The files primarily consist of recordings that may not meet conventional audio quality standards but can still be valuable for a range of applications and research projects.

Dataset

The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design, and signal processing.

Specifications



2138 audio loops (4.3 hours)

24-bit WAV format

BPM labeled

Tempo range: 100-145bpm

Variational drum patterns

Electronic drum machine sound (circa 1980s)


License

This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.

The RTRO-DRM dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Additional Info

For audio examples or more information about this dataset, please refer to the GitHub repository.
DOI: 10.5281/zenodo.10278065
title: WaivOps EDM-TR9: Open Audio Resources for Machine Learning in Music

description: EDM-TR9 Dataset

EDM-TR9 is an open audio dataset composed of a series of drum recordings in the style of electronic dance music (EDM). This dataset primarily focuses on the distinctive sounds and rhythm patterns of the Roland TR-909 drum machine within the subgenres of dance, house and techno music. The dataset contains 3780 audio loops recorded in uncompressed stereo WAV format, produced with custom drum samples and MIDI-programmed rhythms at various tempo rates.

Dataset

The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design and signal processing.

Specifications



3780 audio loops (approximately 8 hours)

24-bit WAV format

BPM labeled

Tempo range: 120–140bpm

Variational drum patterns

EDM drum rhythms


License

This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.

The EDM-TR9 dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Additional Info

For audio examples or more information about this dataset, please refer to the GitHub repository.
DOI: 10.5281/zenodo.10278066
title: WaivOps EDM-TR9: Open Audio Resources for Machine Learning in Music

description: EDM-TR9 Dataset

EDM-TR9 is an open audio dataset composed of a series of drum recordings in the style of electronic dance music (EDM). This dataset primarily focuses on the distinctive sounds and rhythm patterns of the Roland TR-909 drum machine within the subgenres of dance, house and techno music. The dataset contains 3780 audio loops recorded in uncompressed stereo WAV format, produced with custom drum samples and MIDI-programmed rhythms at various tempo rates.

Dataset

The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design and signal processing.

Specifications



3780 audio loops (approximately 8 hours)

24-bit WAV format

BPM labeled

Tempo range: 120–140bpm

Variational drum patterns

EDM drum rhythms


License

This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.

The EDM-TR9 dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Additional Info

For audio examples or more information about this dataset, please refer to the GitHub repository.
DOI: 10.5281/zenodo.8388265
title: WaivOps WRLD-LP: Open Audio Resources for Machine Learning in Music

description: WRLD-LP Dataset

WRLD-LP is an open audio dataset comprised of a series of symbolic drum recordings in the genres of world percussion music. The dataset includes 3,162 audio loops recorded in uncompressed stereo WAV format. The compositions were generated with an internal sample dataset played with note-dense MIDI drum files from a code-based music generation system, along with a MIDI transformer model trained on more than 30,000 MIDI files.

Dataset

The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design, and signal processing.

Specifications



3,162 audio loops (7.3 hours)

24-bit WAV format

BPM labeled

Tempo range: 100-130bpm

Expressive percussion drumming

Mixed rhythms of world music


License

This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.

The WRLD-LP dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Additional Info

For audio examples or more information about this dataset, please refer to the GitHub repository.
DOI: 10.5281/zenodo.8388266
title: WaivOps WRLD-LP: Open Audio Resources for Machine Learning in Music

description: WRLD-LP Dataset

WRLD-LP is an open audio dataset comprised of a series of symbolic drum recordings in the genres of world percussion music. The dataset includes 3,162 audio loops recorded in uncompressed stereo WAV format. The compositions were generated with an internal sample dataset played with note-dense MIDI drum files from a code-based music generation system, along with a MIDI transformer model trained on more than 30,000 MIDI files.

Dataset

The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design, and signal processing.

Specifications



3,162 audio loops (7.3 hours)

24-bit WAV format

BPM labeled

Tempo range: 100-130bpm

Expressive percussion drumming

Mixed rhythms of world music


License

This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.

The WRLD-LP dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Additional Info

For audio examples or more information about this dataset, please refer to the GitHub repository.
DOI: 10.6084/m9.figshare.13265471
title: PKU cephalometric dataset

description: This PKU cephalometric dataset containing 102 cephalograms.Licence is CC BY-NC-SA.Use this dataset, please cite following paper,<br>Minmin, Zeng, Zhenlei, Yan, Shuai Liu, Yanheng Zhou, Lixin Qiu, 2020. Cascaded convolutional networks for automatic cephalometric landmark detection. Medical Image Analysis , 101904.
DOI: 10.15468/dl.fzxxc7
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tripogon hookerianus Bor"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071672-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kstbej
title: Occurrence Download

description: A dataset containing 176 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Pavo muticus Linnaeus, 1766"
 ]
}

 The dataset includes 176 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071673-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d8bsy5
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((147.839493 -34.504988, 147.839493 -34.509479, 147.843985 -34.509479, 147.843985 -34.504988, 147.839493 -34.504988))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071674-240626123714530/datasets/export for details.
DOI: 10.15468/dl.9uxw6v
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhododendron ×planecostatum Sleumer"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071675-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5ffca7
title: Occurrence Download

description: A dataset containing 1076 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Antrophyum plantagineum (Cav.) Kaulf."
 ]
}

 The dataset includes 1076 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071676-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13334804
title: Caso de aplicación de CONVEXSIM

description: Caso de aplicación del algoritmo CONVEXSIM en un diseño de 6 componentes con restricciones de frontera superior iguales a 0.75
DOI: 10.5281/zenodo.13334803
title: Caso de aplicación de CONVEXSIM

description: Caso de aplicación del algoritmo CONVEXSIM en un diseño de 6 componentes con restricciones de frontera superior iguales a 0.75
DOI: 10.5281/zenodo.13334850
title: T2 spectrum of Shanghai muddy clay under freezing conditions at ultra-low temperature
DOI: 10.5281/zenodo.13334851
title: T2 spectrum of Shanghai muddy clay under freezing conditions at ultra-low temperature
DOI: 10.15468/dl.czp57j
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhododendron ×planetum Balf.fil."
 ]
}

 The dataset includes 11 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071677-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ve8rhy
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((147.974241 -34.504988, 147.974241 -34.507234, 147.976487 -34.507234, 147.976487 -34.504988, 147.974241 -34.504988))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071678-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.13334877
title: Normal-Pothole-dataset

description: In this dataset of 5000 images, there are 2500 images of Normal road and 2500 images of potholes, It help to keep the public safe using machine learning and deep learning methods.
DOI: 10.5281/zenodo.13334878
title: Normal-Pothole-dataset

description: In this dataset of 5000 images, there are 2500 images of Normal road and 2500 images of potholes, It help to keep the public safe using machine learning and deep learning methods.
DOI: 10.6084/m9.figshare.26768509
title: Supplementary Material for: C4d, rather than C3d and C5b-9, is associated with graft loss in recurrent IgA deposition after kidney transplantation

description: Introduction: Recurrent IgA deposition is common after kidney transplantation. However, it is difficult to define whether IgA deposition is innocuous or contributes to organ damage. Next, although complement is known to be involved in the pathogenesis of IgA nephropathy (IgAN), its involvement has not been studied systematically in kidney transplant recipients (KTR). 
Methods: KTR with biopsy-proven native IgAN who underwent kidney biopsy after transplantation between 1995 and 2020 were included. Recurrent IgA deposition was defined as IgA deposit in the glomerulus. Staining of complement factors C4d, C3d, and C5b-9 were quantitatively evaluated using ImageScope.
Results: Sixty-seven KTR (85% male, 46±13 years old, 12 [6-24] months after transplantation, 58% with indication biopsy) were included in the analyses. Of them, 25 (37%) had recurrent IgA deposition. There were no clinical differences between KTR with and without recurrent IgA deposition. C3d and C5b-9 were always present in biopsies with IgA deposition, while C4d was present in 48% of the biopsies. During a median follow-up of 9.6 [4.8-14] years, 18 (27%) KTR developed death-censored graft failure. Recurrent IgA deposition was not associated with graft failure. Of the evaluated complement factors, only C4d staining was associated with graft failure in KTR with recurrent IgA deposition (Hazard ratio = 2.55, 95% confidence interval = 1.07-6.03, p = 0.034).
Conclusions: Recurrent IgA deposition was not associated with graft failure in itself. C4d, when present, is strongly associated with graft loss in KTR with recurrent IgA deposition, suggesting a pathogenic role for the lectin pathway in recurrent IgAN.
DOI: 10.15468/dl.35p3wy
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhododendron hirsutipetiolatum R.C.Fang & A.L.Zhang"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071679-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.82ejuj
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.158397 -34.504988, 148.158397 -34.507234, 148.162888 -34.507234, 148.162888 -34.504988, 148.158397 -34.504988))"
 ]
}

 The dataset includes 47 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071680-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13335001
title: Dataset of the Paper "Copilot-in-the-Loop: Fixing Code Smells in Copilot-Generated Python Code using Copilot"

description: This dataset contains a list of 102 code smells detected from Copilot-generated Python code, along with Python code files generated by Copilot from the Repositories and Code label, respectively. This dataset also includes Copilot Chat’s responses to fixing the 102 detected code smells. A brief description of each document and folder in the dataset is provided below:

1. files folder

contains 311 Python code files generated by Copilot. In the 311 Python files, 171 are retrieved under the Repositories label, indicating Python code files entirely generated by Copilot, and 140 are retrieved under the Code label, indicating Python code snippets generated by Copilot.

2. results of RQ1.xlsx

contains a list of 102 code smells detected from Copilot-generated Python code.

3. results of RQ2.xlsx

contains Copilot Chat’s responses to fixing the detected 102 code smells instructed by three prompts of varying detail levels.
DOI: 10.6084/m9.figshare.24240211
title: Experimental Warming and Drying Increase Older Carbon Contributions to Soil Respiration in Lowland Tropical Forests

description: 14C of soil CO2 efflux measured seasonally in 2019 at soil warming and throughfall exclusion experiments (SWELTR and PARCHED) in Panama.
DOI: 10.15468/dl.rparfx
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhododendron ×psammogenes Sleumer"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071681-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sk38fa
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.542428 -34.504988, 148.542428 -34.507234, 148.544674 -34.507234, 148.544674 -34.504988, 148.542428 -34.504988))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071682-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.13334990
title: Data

description: This dataset contains a list of 102 code smells detected from Copilot-generated Python code, along with Python code files generated by Copilot from the Repositories and Code label, respectively. This dataset also includes Copilot Chat’s responses to fixing the 102 detected code smells. A brief description of each document and folder in the dataset is provided below:

1. files folder

contains 311 Python code files generated by Copilot. In the 311 Python files, 171 are retrieved under the Repositories label, indicating Python code files entirely generated by Copilot, and 140 are retrieved under the Code label, indicating Python code snippets generated by Copilot.

2. results of RQ1.xlsx

contains a list of 102 code smells detected from Copilot-generated Python code.

3. results of RQ2.xlsx

contains Copilot Chat’s responses to fixing the detected 102 code smells instructed by three prompts of varying detail levels.
DOI: 10.6084/m9.figshare.25939660
title: DSS1 Restrains dsDNA Engagement of BRCA2 to Promote Homologous Recombination, Stabilize Replication Forks, and Suppress R-loop Accumulation

description: Source Data