cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35111/qqer-qz15
title: Kasdi-Merbah (University) Emotional Database in Arabic Speech

description: Kasdi-Merbah Emotional Database in Arabic Speech (LDC2023S10) was developed by the University of Kasdi Merbah Ouargla. The corpus contains two hours of Modern Standard Arabic prompted speech from 500 speakers (254 female, 246 male) representing 5,000 utterances.

Speech data was collected in 2022. Each speaker read ten sentences, with two sentences each for five different emotions (sadness, fear, anger, happiness, neutral). The sentences are included in the README file.

The audio data is presented as flac compressed, single channel, 44 kHz, 16-bit linear PCM.
DOI: 10.25345/c5hq3s890
title: MassIVE MSV000093673 - Proteomic evaluation of decellularization of porcine ear cartilage
title: This dataset represents protein profiles of porcine ear cartilage before and after decellularization.
DOI: 10.17909/2f9b-ea80
title: Observations of TWA27 (2M1207)

description: HST/WFC3/UVIS2/F656N (PID: 12225, PI: A. Reiners) and JWST/NIRSpec/IFU (GTO 1270, PI: S. Birkmann) data of TWA 27. This data is used in Marleau, Aoyama, Hashimoto, and Zhou (2024) to estimate the accretion rate of TWA 27B.
DOI: 10.6084/m9.figshare.24843924
title: Subject log file description

description: Details for understanding contents of experiment log files.
DOI: 10.6084/m9.figshare.24843924.v1
title: Subject log file description

description: Details for understanding contents of experiment log files.
DOI: 10.5281/zenodo.10393213
title: OFF
DOI: 10.5281/zenodo.10393214
title: OFF
DOI: 10.22003/xfel.eu-data-900359-00
title: SHG commissioning 2023-II
DOI: 10.22003/xfel.eu-data-900379-00
title: LPD SuperModule Commissioning
DOI: 10.22003/xfel.eu-data-900391-00
title: LPD commissioning and calibration
DOI: 10.22003/xfel.eu-data-004456-00
title: Convergent-Beam Serial Crystallography
DOI: 10.25573/serc.21565671.v2
title: Database: Coastal Carbon Library (Version 1.1.0)

description: This data release documents the current version of the Coastal Carbon Data Library, an international database of soil carbon data from tidal wetlands synthesized by the Coastal Carbon Network. This database serves as an open source data tool designed to support soil Blue Carbon research and policy. This data can additionally be accessed through the Coastal Carbon Atlas, which is an interactive web application created to interface with the Data Library and allow for the exploration, filtering, and download of disaggregated soil core data.
DOI: 10.6084/m9.figshare.24843873.v1
title: Subject log archive

description: Subject log data evaluating limits of Talbot-Plateau law prediction of perceived brightness for a flashing stimulus.
DOI: 10.34740/kaggle/dsv/7211833
title: Forex Exchange Rates Since 2004 (Updated Daily)
DOI: 10.34740/kaggle/dsv/7211835
title: Top Spotify Songs in 73 Countries (Daily Updated)
DOI: 10.5281/zenodo.10392361
title: Developers' Information Seeking in Question & Answer Websites through a Gender Lens
DOI: 10.5281/zenodo.10392360
title: Developers' Information Seeking in Question & Answer Websites through a Gender Lens
DOI: 10.57830/2301742
title: Historic_Vegetation_Shapefiles.gdb

description: Between 2010 and 2016, PRNS moved forward with a historic vegetation mapping project that involved manual interpretation (“head’s-up digitizing”) and classification of broad, structurally based vegetation communities for black-and-white (B&W) aerial photograph sets obtained from the early 1940’s (40’s) and the early 1960’s (60’s). Mapping was also already available for the late 1990’s (90’s) from an earlier 1994 vegetation map (CDFG/AIS) of PRNS that had been revised to reflect changes following the 1995 Vision Fire as part of a dissertation project (Forrestel et al. 2011). PRNS staff had converted the revised 90’s map to the classification system used for the earlier mapping periods, but these GIS files had never been fully reviewed for accuracy and finalized. In 2022, PRNS received funding from a NPS Functional Conditional Assessment (FCA) grant and from the Point Reyes National Seashore Association (PRNSA) to fund QA/QC and revision of the three (3) historic mapping periods (early 40’s, early 60’s, late 90’s) and to crosswalk and revise the most recent large-scale vegetation map from 2018 (’18) for Point Reyes (Tukman Geospatial Analysts/AIS).
DOI: 10.34740/kaggle/dsv/7211859
title: Trending Youtube Video Statistics (113 Countries)
DOI: 10.34740/kaggle/dsv/7211862
title: Full TMDB Movies Dataset 2023 (930K Movies)
DOI: 10.5281/zenodo.10392068
title: Resistive switching in benzylammonium-based Ruddlesden–Popper layered hybrid perovskites for non-volatile memory and neuromorphic computing

description: Structural, optoelectronic, and supplementary characterisation data for “Resistive Switching in Benzylammonium-Based Ruddlesden-Popper Layered Hybrid Perovskites for Non-Volatile Memory and Neuromorphic Computing ”, DOI:10.1039/d3ma00618b.
DOI: 10.5281/zenodo.10392067
title: Resistive switching in benzylammonium-based Ruddlesden–Popper layered hybrid perovskites for non-volatile memory and neuromorphic computing

description: Structural, optoelectronic, and supplementary characterisation data for “Resistive Switching in Benzylammonium-Based Ruddlesden-Popper Layered Hybrid Perovskites for Non-Volatile Memory and Neuromorphic Computing ”, DOI:10.1039/d3ma00618b.
DOI: 10.5281/zenodo.10393306
title: Lorient-1k

description: Created By Félix Gontier and Mathieu Lagrange, LS2N, CNRS, Ecole Centrale Nantes

Contact : mathieu.lagrange@cnrs.fr

If used for research, please refer to:

@article{gontier2021training,
  title={Polyphonic training set synthesis improves self-supervised urban sound classification},
  author={Félix Gontier and Vincent Lostanlen, and Mathieu Lagrange and Nicolas Fortin and Jean-Francois Petiot and Catherine Lavandier},
  journal={The Journal of the Acoustical Society of America},
  year={2021},
  publisher={Acoustical Society of America}
}


Lorient-1k contains 30 acoustic scenes of duration equal to 45 seconds.These scenes were recorded with Zoom H4n handheld devices at 10 different locations of Lorient (France).Four experts annotated the onset and offset times of three sources of interest: traffic, voice, and birds. Those annotations have been taken into account to produce a single annotations that is coherent with the notion of perceived time of presence. That is, the sum of activations per scene and per source is coherent with the perceived time of presence.

The total duration of the dataset is of the order of 1.35k seconds, i.e., 22.5 minutes.

The audio is provided as third-octave spectral data and mel spectrograms (as of YAMNET). The audio is made available as third octave spectral data, see demoTob.zip for an implementation of its computation from audio in Python.

 

From a python interpreter :

>> import numpy as np

>> s=np.load('Lorient-1k_spectralData.npy')

>> print(s.shape)

(30, 351, 29)

The three dimensions respectively corresponds to the sceneId, the frameId (time), and the spectralId (frequency).

>> a=np.load('Lorient-1k_presence.npy')

>> print(a.shape)

(30, 344, 3)

The third and fourth dimensions respectively corresponds to the sceneId, the frameId (time), the sourceId (traffic, voice, birds) and the annotatorId. Annotation is provided as a binary indicator of source presence for one second, that is 8 consecutive 125 ms frames with a hop of one frame.

>> a=np.load('Lorient-1k_time_of_presence.npy')

The time of presence is expressed in percents, per scene, and per source.

>> print(a.shape)

(30, 3)

The audio files are also available in the form of 16bits 44.1kHz wav files. Audio files are named in the same order as the first dimension of the .npy files : 00x.wav third-octaves and time of presence evaluation are accessed using s[x-1, :, : ] and a[x-1, :, : ]
DOI: 10.5281/zenodo.10393315
title: Lorient-1k

description: Created By Félix Gontier and Mathieu Lagrange, LS2N, CNRS, Ecole Centrale Nantes

Contact : mathieu.lagrange@cnrs.fr

If used for research, please refer to:

@article{gontier2021training,
  title={Polyphonic training set synthesis improves self-supervised urban sound classification},
  author={Félix Gontier and Vincent Lostanlen, and Mathieu Lagrange and Nicolas Fortin and Jean-Francois Petiot and Catherine Lavandier},
  journal={The Journal of the Acoustical Society of America},
  year={2021},
  publisher={Acoustical Society of America}
}


Lorient-1k contains 30 acoustic scenes of duration equal to 45 seconds.These scenes were recorded with Zoom H4n handheld devices at 10 different locations of Lorient (France).Four experts annotated the onset and offset times of three sources of interest: traffic, voice, and birds. Those annotations have been taken into account to produce a single annotations that is coherent with the notion of perceived time of presence. That is, the sum of activations per scene and per source is coherent with the perceived time of presence.

The total duration of the dataset is of the order of 1.35k seconds, i.e., 22.5 minutes.

The audio is provided as third-octave spectral data and mel spectrograms (as of YAMNET). The audio is made available as third octave spectral data, see demoTob.zip for an implementation of its computation from audio in Python.

 

From a python interpreter :

>> import numpy as np

>> s=np.load('Lorient-1k_spectralData.npy')

>> print(s.shape)

(30, 351, 29)

The three dimensions respectively corresponds to the sceneId, the frameId (time), and the spectralId (frequency).

>> a=np.load('Lorient-1k_presence.npy')

>> print(a.shape)

(30, 344, 3)

The third and fourth dimensions respectively corresponds to the sceneId, the frameId (time), the sourceId (traffic, voice, birds) and the annotatorId. Annotation is provided as a binary indicator of source presence for one second, that is 8 consecutive 125 ms frames with a hop of one frame.

>> a=np.load('Lorient-1k_time_of_presence.npy')

The time of presence is expressed in percents, per scene, and per source.

>> print(a.shape)

(30, 3)

The audio files are also available in the form of 16bits 44.1kHz wav files. Audio files are named in the same order as the first dimension of the .npy files : 00x.wav third-octaves and time of presence evaluation are accessed using s[x-1, :, : ] and a[x-1, :, : ]
DOI: 10.5281/zenodo.4687057
title: Lorient-1k

description: Created By Félix Gontier and Mathieu Lagrange, LS2N, CNRS, Ecole Centrale Nantes Contact : mathieu.lagrange@cnrs.fr If used for research, please refer to: <pre>@article{gontier2021training, title={Polyphonic training set synthesis improves self-supervised urban sound classification}, author={Félix Gontier and Vincent Lostanlen, and Mathieu Lagrange and Nicolas Fortin and Jean-Francois Petiot and Catherine Lavandier}, journal={The Journal of the Acoustical Society of America}, year={2021}, publisher={Acoustical Society of America} } </pre> Lorient-1k contains 30 acoustic scenes of duration equal to 45 seconds.<br> These scenes were recorded with Zoom H4n handheld devices at 10 different locations of Lorient (France).<br> Four experts annotated the onset and offset times of three sources of interest: traffic, voice, and birds. Those annotations have been taken into account to produce a single annotations that is coherent with the notion of perceived time of presence. That is, the sum of activations per scene and per source is coherent with the perceived time of presence. <br> The total duration of the dataset is of the order of 1.35k seconds, i.e., 22.5 minutes. The audio is provided as third-octave spectral data. The audio is made available as third octave spectral data, see demoTob.zip for an implementation of its computation from audio in Python. From a python interpreter : &gt;&gt; import numpy as np &gt;&gt; s=np.load('Lorient-1k_spectralData.npy') &gt;&gt; print(s.shape) (30, 351, 29) The three dimensions respectively corresponds to the sceneId, the frameId (time), and the spectralId (frequency). &gt;&gt; a=np.load('Lorient-1k_presence.npy') &gt;&gt; print(a.shape) (30, 344, 3) The third and fourth dimensions respectively corresponds to the sceneId, the frameId (time), the sourceId (traffic, voice, birds) and the annotatorId. Annotation is provided as a binary indicator of source presence for one second, that is 8 consecutive 125 ms frames with a hop of one frame. &gt;&gt; a=np.load('Lorient-1k_time_of_presence.npy') The time of presence is expressed in percents, per scene, and per source. &gt;&gt; print(a.shape) (30, 3)
DOI: 10.5281/zenodo.4687056
title: Lorient-1k

description: Created By Félix Gontier and Mathieu Lagrange, LS2N, CNRS, Ecole Centrale Nantes

Contact : mathieu.lagrange@cnrs.fr

If used for research, please refer to:

@article{gontier2021training,
  title={Polyphonic training set synthesis improves self-supervised urban sound classification},
  author={Félix Gontier and Vincent Lostanlen, and Mathieu Lagrange and Nicolas Fortin and Jean-Francois Petiot and Catherine Lavandier},
  journal={The Journal of the Acoustical Society of America},
  year={2021},
  publisher={Acoustical Society of America}
}


Lorient-1k contains 30 acoustic scenes of duration equal to 45 seconds.These scenes were recorded with Zoom H4n handheld devices at 10 different locations of Lorient (France).Four experts annotated the onset and offset times of three sources of interest: traffic, voice, and birds. Those annotations have been taken into account to produce a single annotations that is coherent with the notion of perceived time of presence. That is, the sum of activations per scene and per source is coherent with the perceived time of presence.

The total duration of the dataset is of the order of 1.35k seconds, i.e., 22.5 minutes.

The audio is provided as third-octave spectral data and mel spectrograms (as of YAMNET). The audio is made available as third octave spectral data, see demoTob.zip for an implementation of its computation from audio in Python.

 

From a python interpreter :

>> import numpy as np

>> s=np.load('Lorient-1k_spectralData.npy')

>> print(s.shape)

(30, 351, 29)

The three dimensions respectively corresponds to the sceneId, the frameId (time), and the spectralId (frequency).

>> a=np.load('Lorient-1k_presence.npy')

>> print(a.shape)

(30, 344, 3)

The third and fourth dimensions respectively corresponds to the sceneId, the frameId (time), the sourceId (traffic, voice, birds) and the annotatorId. Annotation is provided as a binary indicator of source presence for one second, that is 8 consecutive 125 ms frames with a hop of one frame.

>> a=np.load('Lorient-1k_time_of_presence.npy')

The time of presence is expressed in percents, per scene, and per source.

>> print(a.shape)

(30, 3)

The audio files are also available in the form of 16bits 44.1kHz wav files. Audio files are named in the same order as the first dimension of the .npy files : 00x.wav third-octaves and time of presence evaluation are accessed using s[x-1, :, : ] and a[x-1, :, : ]
DOI: 10.5281/zenodo.5153616
title: Lorient-1k

description: Created By Félix Gontier and Mathieu Lagrange, LS2N, CNRS, Ecole Centrale Nantes Contact : mathieu.lagrange@cnrs.fr If used for research, please refer to: <pre>@article{gontier2021training, title={Polyphonic training set synthesis improves self-supervised urban sound classification}, author={Félix Gontier and Vincent Lostanlen, and Mathieu Lagrange and Nicolas Fortin and Jean-Francois Petiot and Catherine Lavandier}, journal={The Journal of the Acoustical Society of America}, year={2021}, publisher={Acoustical Society of America} } </pre> Lorient-1k contains 30 acoustic scenes of duration equal to 45 seconds.<br> These scenes were recorded with Zoom H4n handheld devices at 10 different locations of Lorient (France).<br> Four experts annotated the onset and offset times of three sources of interest: traffic, voice, and birds. Those annotations have been taken into account to produce a single annotations that is coherent with the notion of perceived time of presence. That is, the sum of activations per scene and per source is coherent with the perceived time of presence. <br> The total duration of the dataset is of the order of 1.35k seconds, i.e., 22.5 minutes. The audio is provided as third-octave spectral data and mel spectrograms (as of YAMNET). The audio is made available as third octave spectral data, see demoTob.zip for an implementation of its computation from audio in Python. From a python interpreter : &gt;&gt; import numpy as np &gt;&gt; s=np.load('Lorient-1k_spectralData.npy') &gt;&gt; print(s.shape) (30, 351, 29) The three dimensions respectively corresponds to the sceneId, the frameId (time), and the spectralId (frequency). &gt;&gt; a=np.load('Lorient-1k_presence.npy') &gt;&gt; print(a.shape) (30, 344, 3) The third and fourth dimensions respectively corresponds to the sceneId, the frameId (time), the sourceId (traffic, voice, birds) and the annotatorId. Annotation is provided as a binary indicator of source presence for one second, that is 8 consecutive 125 ms frames with a hop of one frame. &gt;&gt; a=np.load('Lorient-1k_time_of_presence.npy') The time of presence is expressed in percents, per scene, and per source. &gt;&gt; print(a.shape) (30, 3)
DOI: 10.14272/hikrjhfhgkzkri-uhfffaoysa-n.1
title: 2,4,6-trimethylbenzaldehyde
title: InChI=1S/C10H12O/c1-7-4-8(2)10(6-11)9(3)5-7/h4-6H,1-3H3

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367].
 The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]:
 InChI descriptor[cheminf_000113]: InChI=1S/C10H12O/c1-7-4-8(2)10(6-11)9(3)5-7/h4-6H,1-3H3, and canonical SMILES descriptor[cheminf_000007]: O=Cc1c(C)cc(cc1C)C, and by the IUPAC name[cheminf_000107]: 2,4,6-trimethylbenzaldehyde.
 
 The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]:
 
 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-37661

 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]:
 Melting point descriptor[CHEMINF_000256]: 
 Boiling point descriptor[CHEMINF_000257]: 
 Refractive index descriptor[CHEMINF_000253]:

 The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]:
 
 CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 CHMO:0000596 | distortionless enhancement with polarization transfer (DEPT)
 
 CHMO:0000596 | distortionless enhancement with polarization transfer (DEPT)
 
 CHMO:0000599 | correlation spectroscopy (COSY)
 
 CHMO:0000604 | heteronuclear single quantum coherence (HSQC)
 
 CHMO:0000601 | heteronuclear multiple bond coherence (HMBC)
 
 CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: 

 Used ontologies:
 CHEBI - Chemical Entities of Biological Interest
 CHEMINF - chemical information ontology (information entities about chemical entities)
 CHMO - Chemical Methods Ontology
 OBI - Ontology for Biomedical Investigations
DOI: 10.14272/hikrjhfhgkzkri-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (2,4,6-trimethylbenzaldehyde)
title: InChI=1S/C10H12O/c1-7-4-8(2)10(6-11)9(3)5-7/h4-6H,1-3H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 13C NMR (100 MHz, Chloroform-d [77.2 ppm], ppm) δ = 193.1 (CH), 143.9 (Cq), 141.6 (2C, Cq), 130.6 (2C, CH), 130.1 (Cq), 21.6 (CH3), 20.6 (2C, CH3).
DOI: 10.14272/hikrjhfhgkzkri-uhfffaoysa-n/chmo0000604
title: heteronuclear single quantum coherence (HSQC) (2,4,6-trimethylbenzaldehyde)
title: InChI=1S/C10H12O/c1-7-4-8(2)10(6-11)9(3)5-7/h4-6H,1-3H3

description: dataset for heteronuclear single quantum coherence (HSQC)
DOI: 10.14272/hikrjhfhgkzkri-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (2,4,6-trimethylbenzaldehyde)
title: InChI=1S/C10H12O/c1-7-4-8(2)10(6-11)9(3)5-7/h4-6H,1-3H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 1H NMR (400 MHz, Chloroform-d [7.26 ppm], ppm) δ = 10.56 (s, 1H), 6.90 (s, 2H), 2.58 (s, 6H), 2.32 (s, 3H).
DOI: 10.14272/hikrjhfhgkzkri-uhfffaoysa-n/chmo0000596.1
title: distortionless enhancement with polarization transfer (DEPT) (2,4,6-trimethylbenzaldehyde)
title: InChI=1S/C10H12O/c1-7-4-8(2)10(6-11)9(3)5-7/h4-6H,1-3H3

description: dataset for distortionless enhancement with polarization transfer (DEPT)
DOI: 10.14272/hikrjhfhgkzkri-uhfffaoysa-n/chmo0000599
title: correlation spectroscopy (COSY) (2,4,6-trimethylbenzaldehyde)
title: InChI=1S/C10H12O/c1-7-4-8(2)10(6-11)9(3)5-7/h4-6H,1-3H3

description: dataset for correlation spectroscopy (COSY)
DOI: 10.14272/hikrjhfhgkzkri-uhfffaoysa-n/chmo0000596
title: distortionless enhancement with polarization transfer (DEPT) (2,4,6-trimethylbenzaldehyde)
title: InChI=1S/C10H12O/c1-7-4-8(2)10(6-11)9(3)5-7/h4-6H,1-3H3

description: dataset for distortionless enhancement with polarization transfer (DEPT)
DOI: 10.14272/hikrjhfhgkzkri-uhfffaoysa-n/chmo0000601
title: heteronuclear multiple bond coherence (HMBC) (2,4,6-trimethylbenzaldehyde)
title: InChI=1S/C10H12O/c1-7-4-8(2)10(6-11)9(3)5-7/h4-6H,1-3H3

description: dataset for heteronuclear multiple bond coherence (HMBC)