cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8299894
title: MOx sensor dataset: Odour pulses of various concentration levels

description: <strong>Metal-Oxide (MOx) electronic nose (e-nose) sensor recordings, for odour pulses of various concentration levels </strong> This data is a subset of a larger, yet unpublished dataset - collected by the following authors: Dennler N., Drix D., Warner T., Rastogi R., van Schaik A., Schaefer A., Schmuker M. The custom-made electronic nose used to collect the data is a successor of the device described in Drix & Dennler et al. [1]. It comprises four Metal Oxide (MOx) gas sensors operated at a constant heater voltage, and . The odour stimuli were provided by a multi-channel odour delivery device that is described in detail by Ackels et al. [2]. It offers a high temporal fidelity, which is achieved by combining high-speed gas valves, flow controllers, as well as short and narrow gas pathways. The data collection comprises of recordings where odourant headspace samples were embedded in constant airflow and presented to the e-nose. Data of four different odourants were used: Ethyl Butyrate (EB), Eucalyptol (Eu), Isoamyl Acetate (IA), and 2 - Heptanone (2H). EB, Eu and IA gases were diluted in mineral oil at a ratio of 1 : 5, and 2H was diluted in a ratio of 1:50. The delivered odour concentration was varied by modulating the valve shutter duty cycle, ensuring a linear relationship between five different concentration levels (E.g. C5 = 5 x C1). Fluctuations in flow during gas presentations were minimized by careful calibration. The filenames consist of the timestamp (in processor time), the gas identity, and a concentration level between 1 and 5. [1] D. Drix, N. Dennler and M. Schmuker, "Rapid Recognition of Olfactory Scenes with a Portable MOx Sensor System using Hotplate Modulation," <em>2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)</em>, Aveiro, Portugal, 2022, pp. 1-4, doi: 10.1109/ISOEN54820.2022.9789654. [2] Ackels, T., Erskine, A., Dasgupta, D. <em>et al.</em> Fast odour dynamics are encoded in the olfactory system and guide behaviour. <em>Nature</em> 593, 558–563 (2021). https://doi.org/10.1038/s41586-021-03514-2
DOI: 10.5281/zenodo.8303480
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Johann Martin Honigberger, http://www.wikidata.org/entity/Q1101110. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q1101110
DOI: 10.5281/zenodo.8303479
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Johann Martin Honigberger, http://www.wikidata.org/entity/Q1101110. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q1101110
DOI: 10.5281/zenodo.8303484
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Gustav Hopke, http://www.wikidata.org/entity/Q116872123. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q116872123
DOI: 10.5281/zenodo.8303483
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Gustav Hopke, http://www.wikidata.org/entity/Q116872123. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q116872123
DOI: 10.5281/zenodo.8303486
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Brenda M. Howitt, http://www.wikidata.org/entity/Q99654389. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q99654389
DOI: 10.5281/zenodo.8303485
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Brenda M. Howitt, http://www.wikidata.org/entity/Q99654389. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q99654389
DOI: 10.6084/m9.figshare.24062226
title: HERT Side Penetration Results (Geant4 v11.1.2)

description: These are the results from the side penetration simulation setup for Geant4 with the particle sourced aimed at the middle of the detector stack. Each incident energy is a separate text file, and each simulation event (i.e. particle simulated) is separated by a header line of "Edep (MeV)):" followed by nine lines showing where energy was deposited in each detector in the stack. The first line being the first detector, the second line the second detector, and so forth. The raw text files from Geant4 have been processed by removing simulations where no energy was deposited. These amount of simulations can be found at the bottom of each text file.
DOI: 10.6084/m9.figshare.24062226.v1
title: HERT Side Penetration Results (Geant4 v11.1.2)

description: These are the results from the side penetration simulation setup for Geant4 with the particle sourced aimed at the middle of the detector stack. Each incident energy is a separate text file, and each simulation event (i.e. particle simulated) is separated by a header line of "Edep (MeV)):" followed by nine lines showing where energy was deposited in each detector in the stack. The first line being the first detector, the second line the second detector, and so forth. The raw text files from Geant4 have been processed by removing simulations where no energy was deposited. These amount of simulations can be found at the bottom of each text file.
DOI: 10.5281/zenodo.8187288
title: RemFX: Evaluation Datasets

description: Evaluation datasets for paper General Purpose Audio Effect Removal. These datasets are initially sourced from VocalSet, GuitarSet, DSD100, and IDMT-SMT-Drums datasets before being processed in our dataset generation script. The datasets are named according to the number of effects applied (0-5). For example, 2-2.zip contains 2 effects applied to each input audio example. The targets are left untouched. The audio effects applied are from the set (Distortion, Delay, Dynamic Range Compressor, Phasor, Reverb) and randomly sampled without replacement for each example. Each example is stored in a folder (0-999) and contains four files input.wav (clean) target.wav (effected) wet_effects.pt (serialized PyTorch file containing a list of the present effects (order)) dry_effects.pt (unused for this dataset) The wet effects list is in the order of Reverb, Chorus, Delay, Distortion, Compressor More information can be found on the github repo and our audio examples.
DOI: 10.5281/zenodo.8179431
title: RemFX: Evaluation Datasets

description: Evaluation datasets for paper General Purpose Audio Effect Removal. These datasets are initially sourced from VocalSet, GuitarSet, DSD100, and IDMT-SMT-Drums datasets before being processed in our dataset generation script. The datasets are named according to the number of effects applied (0-5). For example, 2-2.zip contains 2 effects applied to each input audio example. The targets are left untouched. The audio effects applied are from the set (Distortion, Delay, Dynamic Range Compressor, Phasor, Reverb) and randomly sampled without replacement for each example. Each example is stored in a folder (0-999) and contains four files input.wav (clean) target.wav (effected) wet_effects.pt (serialized PyTorch file containing a list of the present effects (order)) dry_effects.pt (unused for this dataset) The wet effects list is in the order of Reverb, Chorus, Delay, Distortion, Compressor More information can be found on the github repo and our audio examples.
DOI: 10.5281/zenodo.8303488
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Gordon E. Hunter, http://www.wikidata.org/entity/Q117459001. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q117459001
DOI: 10.5281/zenodo.8303487
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Gordon E. Hunter, http://www.wikidata.org/entity/Q117459001. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q117459001
DOI: 10.34740/kaggle/dsv/6391714
title: BharatFakeNewsKosh
DOI: 10.5281/zenodo.7810913
title: Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms - Datasets

description: Datasets for reproducibility of the results found in Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms. This study utilized data from the following 4 journals: Lake, B.B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun 10, 2832 (2019). Liao, J., Yu, Z., Chen, Y. et al. Single-cell RNA sequencing of human kidney. Sci Data 7, 4 (2020). Menon, R. et al. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight 5, e133267 (2020). Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J Am Soc Nephrol 29: 2069–2080 (2018). Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).
DOI: 10.5281/zenodo.8303490
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Ludwig von Höhnel, http://www.wikidata.org/entity/Q87967. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q87967
DOI: 10.5281/zenodo.8303489
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Ludwig von Höhnel, http://www.wikidata.org/entity/Q87967. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q87967
DOI: 10.6084/m9.figshare.24062244
title: HERT Side Penetration Results: First Detector (Geant4 v11.1.2)

description: These are the results from the side penetration simulation setup for Geant4 with the particle sourced aimed at the 1st detector. Each incident energy is a separate text file, and each simulation event (i.e. particle simulated) is separated by a header line of "Edep (MeV)):" followed by nine lines showing where energy was deposited in each detector in the stack. The first line being the first detector, the second line the second detector, and so forth. The raw text files from Geant4 have been processed by removing simulations where no energy was deposited. These amount of simulations can be found at the bottom of each text file.
DOI: 10.6084/m9.figshare.24062244.v1
title: HERT Side Penetration Results: First Detector (Geant4 v11.1.2)

description: These are the results from the side penetration simulation setup for Geant4 with the particle sourced aimed at the 1st detector. Each incident energy is a separate text file, and each simulation event (i.e. particle simulated) is separated by a header line of "Edep (MeV)):" followed by nine lines showing where energy was deposited in each detector in the stack. The first line being the first detector, the second line the second detector, and so forth. The raw text files from Geant4 have been processed by removing simulations where no energy was deposited. These amount of simulations can be found at the bottom of each text file.
DOI: 10.5281/zenodo.8303492
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Paul Alfred Hösemann, http://www.wikidata.org/entity/Q64830870. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q64830870
DOI: 10.5281/zenodo.8303491
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Paul Alfred Hösemann, http://www.wikidata.org/entity/Q64830870. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q64830870
DOI: 10.5281/zenodo.8303494
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Per Høst, http://www.wikidata.org/entity/Q7166714. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q7166714
DOI: 10.5281/zenodo.8303493
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Per Høst, http://www.wikidata.org/entity/Q7166714. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q7166714
DOI: 10.5281/zenodo.8303496
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Jens Carl Hørbye, http://www.wikidata.org/entity/Q109342224. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q109342224
DOI: 10.5281/zenodo.8303495
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Jens Carl Hørbye, http://www.wikidata.org/entity/Q109342224. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q109342224
DOI: 10.5281/zenodo.7810912
title: Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms - Datasets

description: Datasets for reproducibility of the results found in Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms. This study utilized data from the following 4 journals: Lake, B.B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun 10, 2832 (2019). Liao, J., Yu, Z., Chen, Y. et al. Single-cell RNA sequencing of human kidney. Sci Data 7, 4 (2020). Menon, R. et al. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight 5, e133267 (2020). Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J Am Soc Nephrol 29: 2069–2080 (2018). Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).
DOI: 10.5281/zenodo.8138166
title: The numerical data used for creating figures in manuscript "Diffused pore pressure in a poroelastic, layered medium with dynamic reservoir impoundment".

description: The file uploaded on is the numerical data used for creating figures in manuscript "Diffused pore pressure in a poroelastic, layered medium with dynamic reservoir impoundment". There are two subfiles inclued in it: (1) data used in figures creation, and (2) figures in manuscript and its appendix (both in pdf and jpg format).
DOI: 10.5281/zenodo.8141046
title: The numerical data used for creating figures in manuscript "Diffused pore pressure in a poroelastic, layered medium with dynamic reservoir impoundment".

description: The file uploaded on is the numerical data used for creating figures in manuscript "Diffused pore pressure in a poroelastic, layered medium with dynamic reservoir impoundment". There are two subfiles contanied in it: (1) data used in figures creation lined in figure1, 2 and so on , and (2) figures in manuscript and its appendix ( in pdf format). Because all numerical estimation in this study is conducted by private Fortran programs, no software file is uploaded.
DOI: 10.5281/zenodo.8297572
title: The numerical data used for creating figures in manuscript "Diffused pore pressure in a poroelastic, layered medium with dynamic reservoir impoundment".

description: The file uploaded on is the numerical data used for creating figures in manuscript "Diffused pore pressure in a poroelastic, layered medium with dynamic reservoir impoundment". There are two subfiles contanied in it: (1) data used in figures creation lined in figure1, 2 and so on , and (2) figures in manuscript and its appendix ( in pdf format). Because all numerical estimation in this study is conducted by private Fortran programs, no software file is uploaded.
DOI: 10.34740/kaggle/dsv/6391647
title: Pakistan Hospitals Dataset
DOI: 10.5281/zenodo.8303502
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Otto Jaekel, http://www.wikidata.org/entity/Q70028. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q70028
DOI: 10.15468/dl.89r49k
title: Occurrence Download

description: A dataset containing 240 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Sitophilus granarius (C.Linnaeus, 1758)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 240 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086805-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xeytch
title: Occurrence Download

description: A dataset containing 2147 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Phyllobius roboretanus Gredler, 1882",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 2147 records from 125 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086806-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vm4v2g
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Magnolia katiorum (Lozano) Govaerts)",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 100",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086807-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aj79kn
title: Occurrence Download

description: A dataset containing 240 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Sitophilus granarius (C.Linnaeus, 1758)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 240 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086809-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jaxa8k
title: Occurrence Download

description: A dataset containing 9280 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Phyllobius pomaceus Gyllenhal, 1834",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 9280 records from 239 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086810-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8zxf29
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Magnolia polyhypsophylla (Lozano) Govaerts)",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 100",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086811-240626123714530/datasets/export for details.