cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25919/5cb57852805b0
title: Parkes observations for project P834 semester 2018OCTS_08

description: Finding the pulsar born in SN 1987A would be of major significance. It would be by far the youngest pulsar known and would enable unique investigations of the early evolution and birth properties of a pulsar, as well as acting as a probe of the circumstellar and interstellar medium. We propose to use the new Ultra-Wideband Low (UWL) receiver to attempt confirmation of dispersed pulse candidates from previous P834 observations, to search for more isolated (giant) pulses and to continue the search for periodic emission from a pulsar associated with SN 1987A. The UWL receiver, with its much wider bandwidth and much lower system noise compared to the 10cm/50cm system will allow searches with much higher sensitivity.
DOI: 10.5281/zenodo.2642197
title: Dataset of "Gravity waves – Toward connecting space weather and atmosphere-ionosphere coupling"

description: The first file is the Ap file, describing the variation of the geomagnetic index used in the model simulations. The data files starting with sw are the model simulation files for the different model experiments. Each file is on a 2x15 (lat x lon) horizontal grid and on 66 pressure levels. There are 24 longitude and 91 latitudes. The files describe the simulations from 7th to 9th August 2018 in two hour time steps. The output variables in each file are zonal wind, geopotential height, and zonal gravity wave drag, in this given order. The files are formatted in the standard binary format for GraDS.
DOI: 10.5281/zenodo.2642198
title: Dataset of "Gravity waves – Toward connecting space weather and atmosphere-ionosphere coupling"

description: The first file is the Ap file, describing the variation of the geomagnetic index used in the model simulations. The data files starting with sw are the model simulation files for the different model experiments. Each file is on a 2x15 (lat x lon) horizontal grid and on 66 pressure levels. There are 24 longitude and 91 latitudes. The files describe the simulations from 7th to 9th August 2018 in two hour time steps. The output variables in each file are zonal wind, geopotential height, and zonal gravity wave drag, in this given order. The files are formatted in the standard binary format for GraDS.
DOI: 10.17632/57mptw7vsr.1
title: Early Introduction of Peanut Protein

description: The QI project objective was to increase the recommendation of new guidelines by providers during routine infant care and evaluate caregiver acceptance of early peanut protein introduction. Following provider educational sessions in 4 clinical sites providers and caregivers were questioned about instruction on the introduction of peanut protein. Pre and post educational intervention data were collected from providers using chart audit and caregivers’ report through the modified Promoting Healthy Development Survey (PHDS). The educational intervention was effective in increasing provider knowledge (p
DOI: 10.17632/fh78rckdn3.1
title: Acoustic Radiation Force Impulse (ARFI) imaging of pancreas in patients with early onset idiopathic recurrent acute pancreatitis

description: Primary data of ARFI study
DOI: 10.17632/hrdjjz9cvg.1
title: Transcriptomic responses of the clam Meretrix meretrix to the organophosphorus pesticide (dimethoate)

description: Transcriptomic responses of the clam Meretrix meretrix to the organophosphorus pesticide (dimethoate)
DOI: 10.17632/gs6d534pyp.1
title: Data for: Referents can shape their labels: Expectations about word length is affected by the frequency of referential objects
DOI: 10.17632/yvwzvzvh4w.1
title: Data for: Isoscapes reveal patterns of δ13C and δ15N of pelagic forage fish and squid in the Northwest Pacific Ocean

description: Appendix Collection year, month, positions, genus, species, body mass, δ13C, δ15N, organisms and vertical habitat in the North western Pacific for estimating the isoscapes.
DOI: 10.5519/qd.09l27d48
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2523 records)
DOI: 10.5281/zenodo.2642476
title: Matrices for well-conditioned biorthogonal spline wavelet bases on the interval

description: Refinement matrices for the wavelet bases described in Chapter 2 of the thesis "Adaptive tensor product wavelet methods for solving PDEs" (T.J. Dijkema, 2009).

Each directory of this data set contains the following matrices (in Matrix Market format):


	<strong>m</strong>-<em>j</em>: two-scale matrix on level <em>j</em> (thesis p.12)
	<strong>mass</strong>-<em>j</em>: mass matrix on level <em>j</em>
	<strong>r</strong>-<em>j</em>, <strong>tr</strong>-<em>j</em>, <strong>trinit</strong>-<em>j</em>: transformation matrix R_<em>j</em>, \tilde{R}_<em>j</em>,  \tilde{R}_<em>j</em>^{init} (thesis p.24-25)


The directory names are {d}-{td}-{ml}-{mr}-{tml}-{tmr} where:


	<strong>d</strong>: Jackson estimate on primal side
	<strong>td</strong>: Jackson estimate on dual side
	<strong>ml</strong> / <strong>mr</strong>: number of vanishing moments on left/right boundary, primal side
	<strong>tml</strong> / <strong>tmr</strong>: number of vanishing moments on left/right boundary, dual side
description: {"references": ["Dijkema (2009):  Adaptive tensor product wavelet methods for solving PDEs"]}
DOI: 10.5281/zenodo.2642477
title: Matrices for well-conditioned biorthogonal spline wavelet bases on the interval

description: Refinement matrices for the wavelet bases described in Chapter 2 of the thesis "Adaptive tensor product wavelet methods for solving PDEs" (T.J. Dijkema, 2009).

Each directory of this data set contains the following matrices (in Matrix Market format):


	<strong>m</strong>-<em>j</em>: two-scale matrix on level <em>j</em> (thesis p.12)
	<strong>mass</strong>-<em>j</em>: mass matrix on level <em>j</em>
	<strong>r</strong>-<em>j</em>, <strong>tr</strong>-<em>j</em>, <strong>trinit</strong>-<em>j</em>: transformation matrix R_<em>j</em>, \tilde{R}_<em>j</em>,  \tilde{R}_<em>j</em>^{init} (thesis p.24-25)


The directory names are {d}-{td}-{ml}-{mr}-{tml}-{tmr} where:


	<strong>d</strong>: Jackson estimate on primal side
	<strong>td</strong>: Jackson estimate on dual side
	<strong>ml</strong> / <strong>mr</strong>: number of vanishing moments on left/right boundary, primal side
	<strong>tml</strong> / <strong>tmr</strong>: number of vanishing moments on left/right boundary, dual side
description: {"references": ["Dijkema (2009):  Adaptive tensor product wavelet methods for solving PDEs"]}
DOI: 10.15482/usda.adc/1503714
title: Data from: Global Meta-Analysis of Cotton Yield and Weed Suppression from Cover Crop
DOI: 10.5281/zenodo.2641954
title: Post-revision data and R Script for Murtagh et al. 'The scent of enrichment: Exploring the effect of odour and biological salience on behaviour during enrichment of kennelled dogs.'

description: Data and analysis file (written in R studio) for the paper Murtagh et al. 'The scent of enrichment: Exploring the effect of odour and biological salience on behaviour during enrichment of kennelled dogs.' Updated based on reviewer comments and additional analyses requested (master data and analysis filed labelled with 'rev' suffix).
DOI: 10.5281/zenodo.2641955
title: Data and R Script for Murtagh et al. 'The scent of enrichment: Exploring the effect of odour and biological salience on behaviour during enrichment of kennelled dogs.'

description: Data and analysis file (written in R studio) for the paper Murtagh et al. 'The scent of enrichment: Exploring the effect of odour and biological salience on behaviour during enrichment of kennelled dogs.'
DOI: 10.24381/cds.5ec9712b
title: ERA5 monthly mean data on pressure levels from 1979 to present
DOI: 10.5281/zenodo.2641948
title: Dataset "Reactivity for safe operation"

description: Dataset "Reactivity for safe operation" described in Deliverable D9.5 "Final Data Management Plan" of the AEROARMS project, funded by the Horizon 2020 research and innovation programme of the European Commission under grant agreement No 644271.
DOI: 10.5281/zenodo.2641949
title: Dataset "Reactivity for safe operation"

description: Dataset "Reactivity for safe operation" described in Deliverable D9.5 "Final Data Management Plan" of the AEROARMS project, funded by the Horizon 2020 research and innovation programme of the European Commission under grant agreement No 644271.
DOI: 10.15468/aem7pc
title: Private insect collection of Heino Õunap

description: Dried pinned Coleoptera, Heteroptera, Diptera, Hymenoptera, Odonata, Lepidoptera, Raphidioptera. Collected between 1972–2019, mostly from Estonia. About 21500 specimens.
DOI: 10.5519/qd.p1ioo6ib
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (0 records)
DOI: 10.5281/zenodo.2640970
title: Ondřejov Dataset

description: Ondřejov dataset contains 12936 labelled stellar spectra from Ondřejov CCD700 archive. The spectra were observed with Ondřejov Perek 2m Telescope.

Code used for generation of this dataset is in podondra/ondrejov-dataset GitHub repository.

The dataset was created to support the discovery of emission-line spectra in the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) survey. The main idea was to use Ondřejov dataset to train a machine learning algorithm and (in combination with domain adaption) find interesting objects in the large spectral archive.

The dataset is released as a CSV file containing the following columns for each spectrum:


	<em>id</em>: a unique identifier (FITS file name),
	<em>label</em>: assigned class,
	<em>object</em>: title of observation,
	<em>ra</em>: right ascension,
	<em>dec</em>: declination,
	<em>expval</em>: exposure value in photon counts [Mcounts],
	<em>gratang</em>: diffraction grating angle,
	<em>detector</em>: name of the detector,
	<em>chipid</em>: name of CCD chip,
	<em>specfilt</em>: spectral filter,
	<em>date-obs</em>: UTC date start of the observation,
	<em>dichmir</em>: dichroic mirror number,
	<em>fluxes</em>: 140 columns of fluxes sampled uniformly between 6519 and 6732 Ångströms.


Spectra are divided into 3 classes according to the profile of the H-alpha spectral line:


	<em>absorption</em>: 6102 spectra (47.17%),
	<em>emission</em>: 5301 spectra (40.98%),
	<em>double-peak</em>: 1533 spectra (11.85%),


where <em>double-peak</em> is a special type of emission with typical disk geometry common in Be stars.

Spectra from Ondřejov CCD700 archive are in air wavelengths but LAMOST spectra use vacuum wavelengths. Therefore, conversion of Ondřejov spectra was made according to formulas provided on Vienna Atomic Line Database Wiki.

LAMOST spectrograph spectral resolving power is between 500 and 1800 which is much smaller than spectral resolving power 13000 in H-alpha of Ondřejov spectrograph. To overcome this difference spectra from the dataset were blurred with Gaussian filter with a standard deviation of value 7.

Machine learning algorithms require their inputs to be a set of features. In order to have the same features for all spectra, they need to be resampled to get the measurement in the same wavelength across all spectra. Then it is easy to create a design matrix where each row is a spectrum and columns contain fluxes in specified wavelengths between 6519 and 6732 Ångströms.
DOI: 10.5281/zenodo.2640971
title: Ondřejov Dataset

description: Ondřejov dataset contains 12936 labelled stellar spectra from Ondřejov CCD700 archive. The spectra were observed with Ondřejov Perek 2m Telescope.

Code used for generation of this dataset is in podondra/ondrejov-dataset GitHub repository.

The dataset was created to support the discovery of emission-line spectra in the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) survey. The main idea was to use Ondřejov dataset to train a machine learning algorithm and (in combination with domain adaption) find interesting objects in the large spectral archive.

The dataset is released as a CSV file containing the following columns for each spectrum:


	<em>id</em>: a unique identifier (FITS file name),
	<em>label</em>: assigned class,
	<em>object</em>: title of observation,
	<em>ra</em>: right ascension,
	<em>dec</em>: declination,
	<em>expval</em>: exposure value in photon counts [Mcounts],
	<em>gratang</em>: diffraction grating angle,
	<em>detector</em>: name of the detector,
	<em>chipid</em>: name of CCD chip,
	<em>specfilt</em>: spectral filter,
	<em>date-obs</em>: UTC date start of the observation,
	<em>dichmir</em>: dichroic mirror number,
	<em>fluxes</em>: 140 columns of fluxes sampled uniformly between 6519 and 6732 Ångströms.


Spectra are divided into 3 classes according to the profile of the H-alpha spectral line:


	<em>absorption</em>: 6102 spectra (47.17%),
	<em>emission</em>: 5301 spectra (40.98%),
	<em>double-peak</em>: 1533 spectra (11.85%),


where <em>double-peak</em> is a special type of emission with typical disk geometry common in Be stars.

Spectra from Ondřejov CCD700 archive are in air wavelengths but LAMOST spectra use vacuum wavelengths. Therefore, conversion of Ondřejov spectra was made according to formulas provided on Vienna Atomic Line Database Wiki.

LAMOST spectrograph spectral resolving power is between 500 and 1800 which is much smaller than spectral resolving power 13000 in H-alpha of Ondřejov spectrograph. To overcome this difference spectra from the dataset were blurred with Gaussian filter with a standard deviation of value 7.

Machine learning algorithms require their inputs to be a set of features. In order to have the same features for all spectra, they need to be resampled to get the measurement in the same wavelength across all spectra. Then it is easy to create a design matrix where each row is a spectrum and columns contain fluxes in specified wavelengths between 6519 and 6732 Ångströms.
DOI: 10.5258/soton/d0883
title: Research Data: Near-Hashing-Bound Multiple-Rate Quantum Turbo Short-Block Codes

description: Datasets for plotting the performance figures of the paper entitled: Near-Hashing-Bound Multiple-Rate Quantum Turbo Short-Block Codes, accepted in IEEE Access.
DOI: 10.5281/zenodo.2641964
title: X-ray diffraction images for coenzyme F420H2 oxidase (FprA) from M. thermolithotrophicus.

description: Anomalous data collected at SOLEIL (Saint Aubin, France) using beamline PROXIMA-2. The crystal (Crystal form 1) was in the presence of the crystallophore Tb-Xo4.

 

Related Publication: Engilberge et al. (2019)

 

Partial XDS file:

 OSCILLATION_RANGE= 0.10000000149   

 STARTING_ANGLE= 0.0   

 STARTING_FRAME= 1   

 X-RAY_WAVELENGTH= 1.648507  

 DETECTOR_DISTANCE= 102.540  

 DETECTOR= EIGER    MINIMUM_VALID_PIXEL_VALUE= 0    OVERLOAD= 44419   

 DIRECTION_OF_DETECTOR_X-AXIS= 1.0 0.0 0.0   

 DIRECTION_OF_DETECTOR_Y-AXIS= 0.0 1.0 0.0   

 NX= 3110    NY= 3269    QX= 0.0750000035623    QY= 0.0750000035623   

 ORGX= 1509.33483887    ORGY= 1655.37805176   

 ROTATION_AXIS=  1.000000  0.000000  0.000000   

 INCIDENT_BEAM_DIRECTION= 0.0 0.0 1.0
description: Funding: Max Planck Society / ANR agency (program Ln23 ANR-13-BS07-0007-01)
DOI: 10.5281/zenodo.2641965
title: X-ray diffraction images for coenzyme F420H2 oxidase (FprA) from M. thermolithotrophicus.

description: Anomalous data collected at SOLEIL (Saint Aubin, France) using beamline PROXIMA-2. The crystal (Crystal form 1) was in the presence of the crystallophore Tb-Xo4.

 

Related Publication: Engilberge et al. (2019)

 

Partial XDS file:

 OSCILLATION_RANGE= 0.10000000149   

 STARTING_ANGLE= 0.0   

 STARTING_FRAME= 1   

 X-RAY_WAVELENGTH= 1.648507  

 DETECTOR_DISTANCE= 102.540  

 DETECTOR= EIGER    MINIMUM_VALID_PIXEL_VALUE= 0    OVERLOAD= 44419   

 DIRECTION_OF_DETECTOR_X-AXIS= 1.0 0.0 0.0   

 DIRECTION_OF_DETECTOR_Y-AXIS= 0.0 1.0 0.0   

 NX= 3110    NY= 3269    QX= 0.0750000035623    QY= 0.0750000035623   

 ORGX= 1509.33483887    ORGY= 1655.37805176   

 ROTATION_AXIS=  1.000000  0.000000  0.000000   

 INCIDENT_BEAM_DIRECTION= 0.0 0.0 1.0
description: Funding: Max Planck Society / ANR agency (program Ln23 ANR-13-BS07-0007-01)
DOI: 10.5281/zenodo.2642241
title: FluTE Synthetic Ecosystems for Los Angeles County

description: Data files

A set of 3 data files needs to be in the same directory as the<br>
executable to specify a population for FluTE. The file names consist of<br>
a prefix (e.g., "seattle") followed by a suffix.<br>
<br>
<br>
*-tracts.dat - Tract populations and locations, from<br>
http://www.census.gov/geo/www/cenpop/cntpop2k.html. The columns in<br>
this comma-delimited file are: state FIPS code, county FIPS code,<br>
tract FIPS code, tract population, tract longitude, and tract<br>
latitude.<br>
<br>
<br>
*-wf.dat - Tract-to-tract workerflow, extracted from stp64.us from<br>
Census 2000 Special Tabulation Product 64: Census tract of work by<br>
census tract of residence 2000. For the US-level data. Commutes over<br>
100 miles were eliminated from the data. The columns in this space-<br>
delimited file are: home state FIPS code, home county FIPS code, home<br>
tract FIPS code, work state FIPS code, work county FIPS code, work<br>
tract FIPS code, and number of workers.<br>
<br>
<br>
*-employment.dat - The number of employed working-age adults and the<br>
total number of working-age adults, from the Census Summary File 3<br>
(SF3, Table PCT35). The columns in this space-delimited file are:<br>
state FIPS code, county FIPS code, tract FIPS code, number of employed<br>
20-64 year olds, and the total number of working-age individuals<br>
(19-64 year olds).

This sample population is:

la - Los Angeles County. la-tracts.dat is not from the 2000 Census;<br>
It is based on 2007 PEPS estimates from Walter R. McDonald &<br>
Associates, Inc. (WRMA) and padded with an additional 776,000<br>
individuals to represent the estimated undocumented population<br>
[Flaming et al. Hopeful Workers, Marginal Jobs. 2005]. The other<br>
"la" data files are based on the 2000 US Census.
DOI: 10.5281/zenodo.2642242
title: FluTE Synthetic Ecosystems for Los Angeles County

description: Data files

A set of 3 data files needs to be in the same directory as the<br>
executable to specify a population for FluTE. The file names consist of<br>
a prefix (e.g., "seattle") followed by a suffix.<br>
<br>
<br>
*-tracts.dat - Tract populations and locations, from<br>
http://www.census.gov/geo/www/cenpop/cntpop2k.html. The columns in<br>
this comma-delimited file are: state FIPS code, county FIPS code,<br>
tract FIPS code, tract population, tract longitude, and tract<br>
latitude.<br>
<br>
<br>
*-wf.dat - Tract-to-tract workerflow, extracted from stp64.us from<br>
Census 2000 Special Tabulation Product 64: Census tract of work by<br>
census tract of residence 2000. For the US-level data. Commutes over<br>
100 miles were eliminated from the data. The columns in this space-<br>
delimited file are: home state FIPS code, home county FIPS code, home<br>
tract FIPS code, work state FIPS code, work county FIPS code, work<br>
tract FIPS code, and number of workers.<br>
<br>
<br>
*-employment.dat - The number of employed working-age adults and the<br>
total number of working-age adults, from the Census Summary File 3<br>
(SF3, Table PCT35). The columns in this space-delimited file are:<br>
state FIPS code, county FIPS code, tract FIPS code, number of employed<br>
20-64 year olds, and the total number of working-age individuals<br>
(19-64 year olds).

This sample population is:

la - Los Angeles County. la-tracts.dat is not from the 2000 Census;<br>
It is based on 2007 PEPS estimates from Walter R. McDonald &<br>
Associates, Inc. (WRMA) and padded with an additional 776,000<br>
individuals to represent the estimated undocumented population<br>
[Flaming et al. Hopeful Workers, Marginal Jobs. 2005]. The other<br>
"la" data files are based on the 2000 US Census.
DOI: 10.5281/zenodo.2641898
title: singing voice detection dataset ALL-Pub-SVD-In-One

description: singing voice detection datasets:

it contains the public dataset of Jamendo Dataset, and the RWC dataset , and the iKala dataset, and the MIR-1K dataset , and teh MedlyDB dataset.

Jamendo are same with the raw.

RWC use the simply mode same with the lable in Jamendo just sing or nosing.

iKala and MIR-1K are full wavfiles with the generated lables from the PitchLables. 0 for nosing and other for sing.

MedlyDB use part wavfiles from the RAW dataset and also generated lables from the MELODY_2 lables with 0 for nosing and other for nosing.
DOI: 10.7910/dvn/9hdrso
title: Replication Data for: Polling illiteracy

description: Description to follow
DOI: 10.5519/qd.f9iv0zk7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4311 records)
DOI: 10.5281/zenodo.2641990
title: X-ray diffraction images for coenzyme F420H2 oxidase (FprA) from M. thermolithotrophicus.

description: Anomalous data collected at ESRF (Grenoble, France) using beamline ID23-1. The crystal (Crystal form 2) was in the presence of the crystallophore Tb-Xo4.

 

Related Publication: Engilberge et al. (2019)
description: Funding: Max Planck Society / ANR agency (program Ln23 ANR-13-BS07-0007-01
DOI: 10.5281/zenodo.2641991
title: X-ray diffraction images for coenzyme F420H2 oxidase (FprA) from M. thermolithotrophicus.

description: Anomalous data collected at ESRF (Grenoble, France) using beamline ID23-1. The crystal (Crystal form 2) was in the presence of the crystallophore Tb-Xo4.

 

Related Publication: Engilberge et al. (2019)
description: Funding: Max Planck Society / ANR agency (program Ln23 ANR-13-BS07-0007-01
DOI: 10.5281/zenodo.2641987
title: Simulation of a POPE bilayer at 310K with the CHARMM36 force field

description: A bilayer of 144 POPE lipids (72 per leaflet) simulated in water (40 molecules per lipid) for 500 ns using the CHARMM36 force field [1] using GROMACS v. 2018.6 [2]. Topologies are obtained from CHARMM-GUI [3].

[1] <strong>DOI: </strong>10.1021/jp101759q

[2] <strong>DOI: </strong>10.1016/j.softx.2015.06.001

[3] <strong>DOI: </strong>10.1021/acs.jctc.5b00935
DOI: 10.5281/zenodo.2641986
title: Simulation of a POPE bilayer at 310K with the CHARMM36 force field

description: A bilayer of 144 POPE lipids (72 per leaflet) simulated in water (40 molecules per lipid) for 500 ns using the CHARMM36 force field [1] using GROMACS v. 2018.6 [2]. Topologies are obtained from CHARMM-GUI [3].

[1] <strong>DOI: </strong>10.1021/jp101759q

[2] <strong>DOI: </strong>10.1016/j.softx.2015.06.001

[3] <strong>DOI: </strong>10.1021/acs.jctc.5b00935
DOI: 10.5281/zenodo.2642246
title: FluTE Synthetic Ecosystems for Seattle

description: Data files

A set of 3 data files needs to be in the same directory as the<br>
executable to specify a population for FluTE. The file names consist of<br>
a prefix (e.g., "seattle") followed by a suffix.<br>
<br>
<br>
*-tracts.dat - Tract populations and locations, from<br>
http://www.census.gov/geo/www/cenpop/cntpop2k.html. The columns in<br>
this comma-delimited file are: state FIPS code, county FIPS code,<br>
tract FIPS code, tract population, tract longitude, and tract<br>
latitude.<br>
<br>
<br>
*-wf.dat - Tract-to-tract workerflow, extracted from stp64.us from<br>
Census 2000 Special Tabulation Product 64: Census tract of work by<br>
census tract of residence 2000. For the US-level data. Commutes over<br>
100 miles were eliminated from the data. The columns in this space-<br>
delimited file are: home state FIPS code, home county FIPS code, home<br>
tract FIPS code, work state FIPS code, work county FIPS code, work<br>
tract FIPS code, and number of workers.<br>
<br>
<br>
*-employment.dat - The number of employed working-age adults and the<br>
total number of working-age adults, from the Census Summary File 3<br>
(SF3, Table PCT35). The columns in this space-delimited file are:<br>
state FIPS code, county FIPS code, tract FIPS code, number of employed<br>
20-64 year olds, and the total number of working-age individuals<br>
(19-64 year olds).

This sample population is:

seattle - metropolitan Seattle, based on the 2000 US Census.