cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.d5837n
title: Occurrence Download

description: A dataset listing the 156 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-71.7574 19.70495,-71.75964 19.69884,-71.76264 19.6939,-71.7569 19.68779,-71.75615 19.67862,-71.74516 19.67674,-71.72769 19.67557,-71.71096 19.67745,-71.70596 19.68074,-71.70596 19.69155,-71.70946 19.70284,-71.71221 19.71083,-71.71795 19.71483,-71.72419 19.71882,-71.73468 19.71953,-71.74791 19.72047,-71.7554 19.71459,-71.7574 19.70495))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset's 156 records were derived from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008527-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mrk5cz
title: Occurrence Download

description: A dataset containing 254 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Atelopus pastuso Coloma, Duellman, Almendáriz, Ron, Terán-Valdez & Guayasamin, 2010"
 ]
}

 The dataset includes 254 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008540-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qb25z8
title: Occurrence Download

description: A dataset containing 2134 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Panicum repens L.",
 "Continent is Africa"
 ]
}

 The dataset includes 2134 records from 97 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008541-241007104925546/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.27214173.v1
title: random_hairpins

description: RNA hairpins randomly generated by "make_a_hairpin.py"
DOI: 10.6084/m9.figshare.27214173
title: random_hairpins

description: RNA hairpins randomly generated by "make_a_hairpin.py"
DOI: 10.5281/zenodo.13769543
title: WaivOps EDM-HSE: Open Audio Resources for Machine Learning in Music

description: EDM-HSE Dataset

EDM-HSE is an open audio dataset containing a collection of code-generated drum recordings in the style of modern electronic house music. It includes 8,000 audio loops recorded in uncompressed stereo WAV format, created using custom audio samples and a MIDI drum dataset. The dataset also comes with paired JSON files containing MIDI note numbers (pitch) and tempo data, intended for supervised training of generative AI audio models.

Overview

The EDM-HSE Dataset was developed using an algorithmic framework to generate probable drum notations commonly played by EDM music producers. For supervised training with labeled data, a variational mixing technique was applied to the rendered audio files. This method systematically includes or excludes drum notes, assisting the model in recognizing patterns and relationships between drum instruments, thereby enhancing its generalization capabilities.

The primary purpose of this dataset is to provide accessible content for machine learning applications in music and audio. Potential use cases include generative music, feature extraction, tempo detection, audio classification, rhythm analysis, drum synthesis, music information retrieval (MIR), sound design and signal processing.

Specifications



8,000 audio loops (approximately 17 hours)

16-bit WAV format

Tempo range: 120–130 BPM

Paired label data (WAV + JSON)

Variational drum patterns

Subgenre styles (Big room, electro, minimal, classic)


A JSON file is provided for referencing and converting MIDI note numbers to text labels. You can update the text labels to suit your preferences.

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-HSE dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Additional Info

Please note that this dataset has not been fully reviewed and may contain minor notational errors or audio defects.

For audio examples or more information about this dataset, please refer to the GitHub repository.
DOI: 10.5281/zenodo.13769544
title: WaivOps EDM-HSE: Open Audio Resources for Machine Learning in Music

description: EDM-HSE Dataset

EDM-HSE is an open audio dataset containing a collection of code-generated drum recordings in the style of modern electronic house music. It includes 8,000 audio loops recorded in uncompressed stereo WAV format, created using custom audio samples and a MIDI drum dataset. The dataset also comes with paired JSON files containing MIDI note numbers (pitch) and tempo data, intended for supervised training of generative AI audio models.

Overview

The EDM-HSE Dataset was developed using an algorithmic framework to generate probable drum notations commonly played by EDM music producers. For supervised training with labeled data, a variational mixing technique was applied to the rendered audio files. This method systematically includes or excludes drum notes, assisting the model in recognizing patterns and relationships between drum instruments, thereby enhancing its generalization capabilities.

The primary purpose of this dataset is to provide accessible content for machine learning applications in music and audio. Potential use cases include generative music, feature extraction, tempo detection, audio classification, rhythm analysis, drum synthesis, music information retrieval (MIR), sound design and signal processing.

Specifications



8,000 audio loops (approximately 17 hours)

16-bit WAV format

Tempo range: 120–130 BPM

Paired label data (WAV + JSON)

Variational drum patterns

Subgenre styles (Big room, electro, minimal, classic)


A JSON file is provided for referencing and converting MIDI note numbers to text labels. You can update the text labels to suit your preferences.

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-HSE dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Additional Info

Please note that this dataset has not been fully reviewed and may contain minor notational errors or audio defects.

For audio examples or more information about this dataset, please refer to the GitHub repository.
DOI: 10.15468/dl.sfbpu7
title: Occurrence Download

description: A dataset containing 230 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Panicum sadinii (Vanderyst) Renvoize",
 "Continent is Africa"
 ]
}

 The dataset includes 230 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008543-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/fd7e336a9493e780777d97bb08ff2352
title: Desert Fertilization Experiment: investigation of Sonoran desert ecosystem response to atmospheric deposition and experimental nutrient addition, ongoing since 2006

description: Launched in 2006 with support from the National Science Foundation (NSF) and leveraged by the CAP LTER, the Carbon and Nitrogen deposition (CNdep) project sought to answer the fundamental question of whether elemental cycles in urban ecosystems are qualitatively different from those in non-urban ecosystems. Ecosystem scientists, atmospheric chemists, and biogeochemists tested the hypothesis that distinct biogeochemical pathways result from elevated inorganic nitrogen and organic carbon deposition from the atmosphere to the land. To test the hypothesis, scientists examined the responsiveness of Sonoran desert ecosystems to nutrient enrichment by capitalizing on a gradient of atmospheric deposition in and around the greater Phoenix metropolitan area. Fifteen desert study sites were established, with five locations each west and east of the urban core, and in the urban core in desert preserves. In addition to the gradient of atmospheric deposition in and around the urban core, select study plots at each of the fifteen desert locations receive amendments of nitrogen, phosphorus, or nitrogen + phosphorus fertilizer. Measured variables include soil properties, perennial and annual plant growth, and atmospheric deposition of nitrogen. At the close of the initial grant period, the CAP LTER assumed responsibility for the project, renamed the Desert Fertilization Experiment, which provides a remarkable platform to study the long-term effects of nutrient enrichment on desert ecosystem properties.
DOI: 10.15468/dl.n588ur
title: Occurrence Download

description: A dataset containing 373 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Panicum schinzii Hack.",
 "Continent is Africa"
 ]
}

 The dataset includes 373 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008544-241007104925546/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.27214182
title: GLORYS ROMS data

description: The dataset contains a single time slice of GLORYS temperature and salinity data that has been regridded laterally onto a ROMS horizontal grid. The data still has a vertical coordinate of constant depth, and needs to be regridded vertically onto the ROMS vertical coordinate.
DOI: 10.6084/m9.figshare.27214182.v1
title: GLORYS ROMS data

description: The dataset contains a single time slice of GLORYS temperature and salinity data that has been regridded laterally onto a ROMS horizontal grid. The data still has a vertical coordinate of constant depth, and needs to be regridded vertically onto the ROMS vertical coordinate.
DOI: 10.15468/dl.krc3e8
title: Occurrence Download

description: A dataset containing 9314 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters 0.0-1000.0",
 "Geometry POLYGON((-169.08757 23.28268,-66.50547 23.28268,-66.50547 67.3123,-114.97263 68.11912,-169.08757 67.3123,-169.08757 23.28268))",
 "TaxonKey is Carduus nutans L."
 ]
}

 The dataset includes 9314 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008545-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gbeds6
title: Occurrence Download

description: A dataset containing 71292 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-114.55557 32.41665,-114.64084 32.44249,-114.64417 32.41627,-114.71205 32.39165,-114.72819 32.35709,-114.86648 32.34777,-114.92164 32.2727,-114.9305 32.14332,-114.61698 31.84778,-114.52713 31.70206,-114.27148 31.54341,-114.14767 31.4942,-114.02573 31.49275,-114.02784 31.52113,-113.96062 31.56313,-113.97432 31.63901,-113.87241 31.58843,-113.84293 31.59902,-113.63809 31.47733,-113.60475 31.34209,-113.63742 31.33928,-113.62237 31.32695,-113.31402 31.25509,-113.28838 31.29235,-113.12686 31.36727,-113.13138 31.41066,-112.96163 31.45836,-113.07386 31.35627,-113.0842 31.319,-113.04315 31.31474,-112.82159 31.50079,-112.76031 31.49468,-112.71939 31.47033,-112.73655 31.44974,-112.6997 31.40925,-112.73586 31.37836,-112.85559 31.36704,-112.89414 31.33238,-112.87991 31.15425,-112.81264 31.11627,-112.74141 31.11638,-112.73516 31.15284,-112.58426 31.18087,-112.52729 31.21959,-112.49433 31.19847,-112.50861 31.1547,-112.46054 30.99185,-112.49633 30.93279,-112.44256 30.86577,-112.54886 30.74329,-112.59221 30.65504,-112.57706 30.61201,-112.41535 30.51285,-112.39865 30.43808,-112.32355 30.32224,-112.19264 30.39841,-112.21924 30.45402,-112.19878 30.47318,-112.12873 30.4417,-112.00205 30.32178,-111.95451 30.31108,-111.94152 30.42147,-111.98879 30.54732,-111.9538 30.5201,-111.81106 30.64971,-111.78309 30.64191,-111.77119 30.69144,-111.81422 30.71364,-111.7676 30.80779,-111.71196 30.80239,-111.70692 30.84203,-111.77069 30.84696,-111.83043 30.81075,-111.84684 30.8518,-111.8838 30.86194,-111.88449 30.90862,-112.02144 30.91041,-112.05685 30.88747,-112.22825 30.94507,-112.2718 31.09929,-112.24778 31.16692,-112.33078 31.30491,-112.33497 31.36779,-112.41007 31.39781,-112.49807 31.38552,-112.5133 31.43657,-112.61157 31.51961,-112.63708 31.58343,-112.76978 31.64204,-112.83817 31.74801,-112.78398 31.86984,-114.55557 32.41665))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 71292 records from 359 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008542-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s8s2wc
title: Occurrence Download

description: A dataset containing 3478 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Erysiphe aquilegiae DC."
 ]
}

 The dataset includes 3478 records from 107 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008546-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mn7ez7
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Atelopus petersi Coloma, Lötters, Duellman & Miranda-Leiva, 2007"
 ]
}

 The dataset includes 64 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008547-241007104925546/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.27214194
title: viral_and_host_hairpins.tar.gz

description: Hairpin structures from RNA virus, DNA virus, and host mRNA
DOI: 10.6084/m9.figshare.27214194.v1
title: viral_and_host_hairpins.tar.gz

description: Hairpin structures from RNA virus, DNA virus, and host mRNA
DOI: 10.6084/m9.figshare.27198894
title: Data deposition RE: Turner et al. 2023. Journal of General Physiology.

description: Published Manuscript Citation:Turner KL, Morris HS, Awinda PO, Fitzsimons DP, Tanner BCW (2023) RLC phosphorylation amplifies Ca<sup>2+</sup>-sensitivity of force in myocardium from cMyBP-C knockout mice. <i>J Gen Physiol</i>. 155:e202213250. doi: 10.1085/jgp.202213250.PMID: <b>36715675</b>PMCID: PMC9930131DOI: 10.1085/jgp.202213250Data Files:Hydroxyproline Assay, where results = raw absorbance reading and (y-b)/m column below = hydroxyproline concentration as calculated by the standard curve which represents all data in Fig 1C. File: cMyPBC_KO_HOP_2022_03_14_KT.xlsx<br>Isometric force data, with 3-parameter Hill Fits, which represents all the data from Figs 2-3 and Table 1. File: Mouse_Temperature_Fiber_Tensions_with_Fits_2021_10_4.xlsx<br>Sinusoidal perturbation analysis data. Frequency, Em (=elastic modulus), Vm (=viscous modulus) which represents all the data from Figs 4-5. Files: Mouse_Cardiac_Em_Vm_XLD_MSN_WT2021_11_8.xlsx and Mouse_Cardiac_Em_Vm_XLD_MSN_CPRO2021_11_8.xlsx <br>Polynomial "dip" and "peak" fit data, where dip_f_fit = "dip" and peak_f_fit = "peak" for each fiber, and represent data in Fig 5. File: Viscous_freq_analysis_All_22_05_11.xlsx<br>Source data from Fig 2C, showing Pro-Q Diamond phosphostain and SYPRO Ruby total protein stain. File: SourceDataFS2.pdf
DOI: 10.3886/e112322
title: Replication data for: Matching and Sorting in Online Dating

description: Using data on user attributes and interactions from an online dating site, we estimate mate preferences, and use the Gale-Shapley algorithm to predict stable matches. The predicted matches are similar to the actual matches achieved by the dating site, and the actual matches are approximately efficient. Out-of- sample predictions of offline matches, i.e., marriages, exhibit assortative mating patterns similar to those observed in actual marriages. Thus, mate preferences, without resort to search frictions, can generate sorting in marriages. However, we underpredict some of the correlation patterns; search frictions may play a role in explaining the discrepancy. (JEL C78, J12)
DOI: 10.3886/e112322v1-157407
title: Replication data for: Matching and Sorting in Online Dating

description: Using data on user attributes and interactions from an online dating site, we
estimate mate preferences, and use the Gale-Shapley algorithm to predict stable
matches. The predicted matches are similar to the actual matches achieved
by the dating site, and the actual matches are approximately efficient. Out-of-
sample predictions of offline matches, i.e., marriages, exhibit assortative
mating patterns similar to those observed in actual marriages. Thus, mate preferences,
without resort to search frictions, can generate sorting in marriages.
However, we underpredict some of the correlation patterns; search frictions
may play a role in explaining the discrepancy. (JEL C78, J12)
DOI: 10.3886/e112322v1-157408
title: Replication data for: Matching and Sorting in Online Dating

description: Using data on user attributes and interactions from an online dating site, we
estimate mate preferences, and use the Gale-Shapley algorithm to predict stable
matches. The predicted matches are similar to the actual matches achieved
by the dating site, and the actual matches are approximately efficient. Out-of-
sample predictions of offline matches, i.e., marriages, exhibit assortative
mating patterns similar to those observed in actual marriages. Thus, mate preferences,
without resort to search frictions, can generate sorting in marriages.
However, we underpredict some of the correlation patterns; search frictions
may play a role in explaining the discrepancy. (JEL C78, J12)
DOI: 10.5281/zenodo.13257813
title: WaivOps EDM-TR8: Open Audio Resources for Machine Learning in Music

description: EDM-TR8 Dataset

EDM-TR8 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 iconic sounds of the Roland TR-808 drum machine with additional electro synth drums. The dataset contains 3,790 audio loops recorded in uncompressed stereo WAV format, generated with custom audio samples and a MIDI dataset used for training symbolic music models.

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



3790 audio loops (approximately 9 hours)

16-bit WAV format

BPM labeled

Tempo range: 95–130bpm

Variational drum patterns

Multi-genre rhythm styles


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-TR8 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.13257814
title: WaivOps EDM-TR8: Open Audio Resources for Machine Learning in Music

description: EDM-TR8 Dataset

EDM-TR8 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 iconic sounds of the Roland TR-808 drum machine with additional electro synth drums. The dataset contains 3,790 audio loops recorded in uncompressed stereo WAV format, generated with custom audio samples and a MIDI dataset used for training symbolic music models.

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



3790 audio loops (approximately 9 hours)

16-bit WAV format

BPM labeled

Tempo range: 95–130bpm

Variational drum patterns

Multi-genre rhythm styles


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-TR8 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: 10.15468/dl.4nt6q5
title: Occurrence Download

description: A dataset containing 12087 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is MEX.30.131_2",
 "Geometry POLYGON((-97.62054 19.89678,-96.90419 19.89678,-96.90419 20.51613,-97.62054 20.51613,-97.62054 19.89678))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 12087 records from 191 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008549-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.93exgp
title: Occurrence Download

description: A dataset containing 2807 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Human Observation"
 ]
 },
 {
 "or" : [
 "TaxonKey is Maytenus viscifolia Griseb.",
 "TaxonKey is Ephedra americana Humb. & Bonpl. ex Willd.",
 "TaxonKey is Ephedra triandra Tul.",
 "TaxonKey is Giliastrum castellanosii J.M.Porter",
 "TaxonKey is Parkinsonia praecox (Ruiz & Pav.) Hawkins"
 ]
 }
 ]
}

 The dataset includes 2807 records from 120 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0094430-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8kxdvv
title: Occurrence Download

description: A dataset containing 818 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Holothuria nobilis (Selenka, 1867)"
 ]
}

 The dataset includes 818 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025070-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8yet9y
title: Occurrence Download

description: A dataset containing 928 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Suivi des oiseaux de passage : programme ACT - Programme ACT : Répartition Pigeon ramier 1998 - 2002 Coordonnées GPS Lambert 93"
 ]
}

 The dataset includes 928 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019238-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.