cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25386/genetics.16689217.v1
title: Supplemental Material for Cao et al., 2021

description: The supplemental material data set includes Table S1, Figure S1, Table S2, Figure S2, Movie S1, Movie S2, and legend of supplemental figures and movies. These data are in support of manuscript GENETICS-2021-304674 for Revision 1.
DOI: 10.25386/genetics.16689217
title: Supplemental Material for Cao et al., 2021

description: The supplemental material data set includes Table S1, Figure S1, Table S2, Figure S2, Movie S1, Movie S2, and legend of supplemental figures and movies. These data are in support of manuscript GENETICS-2021-304674 for Revision 1.
DOI: 10.6096/kascade.16
title: 30 m flux tower, turbulence raw data – LA Toulouse – M30, Cadarache

description: Raw measured data from the three sonic anemometers, split into files of 30-min. Hence, from these files only the CSAT can be used to calculate fluxes. For the Young 1 and Young 2 we recommend to download the de-spiked data which can be found in the "Turbulence M30 raw data (despiked)" part on this website.
DOI: 10.5066/p9mb2qb8
title: Simulated rangewide big sagebrush regeneration estimates and relationships with abiotic variables as function of soils under historical and future climate projections

description: These NetCDF data were compiled to investigate how two complementary models can contribute to our understanding of contemporary and future big sagebrush regeneration across the historical and potential future sagebrush region. Objective of our study was to apply both models to address three specific objectives: (i) examine the geographic patterns of big sagebrush regeneration probabilities that the two different models project under historical conditions and future climate scenarios; (ii) quantify the robustness of model projections, e.g., the consistency among climate models in projected changes in regeneration for future time periods; and (iii) identify how model predictions for regeneration potential relate to environmental site characteristics like climate, soil moisture, and soils. Big sagebrush regeneration was modeled based on daily meteorological and ecohydrological variables across the historical and potential future geographic range of big sagebrush distribution in the western United States. These data represent the simulated potential of big sagebrush regeneration representing (i) range-wide big sagebrush regeneration responses in natural vegetation (process-based model, Schlaepfer et al. 2014) and (ii) big sagebrush restoration seeding outcomes following fire in the Great Basin and the Snake River Plains (regression-based model, Shriver et al. 2018) as well as soil moisture and climatic variables for recent climate 1980-2010, and for future projected climate represented by all available climate models under two representative concentration pathways (RCP4.5 and RCP8.5) at two time periods during the 21st century (2020-2050 and 2070-2099) at 10-km resolution based on a simulation experiment described in Bradford et al. 2019 using the SOILWAT2 ecosystem water balance model (Schlaepfer et al. 2021). These data were created by a collaborative research project between the U.S. Geological Survey and Yale University.
DOI: 10.5523/bris.2g0fu4uyv5tft28qglaf23b2ti
title: Quantum Enhanced Precision Estimation of Transmission with Bright Squeezed Light

description: Experimental data relating to the manuscript “Quantum Enhanced Precision Estimation of Transmission with Bright Squeezed Light”
DOI: 10.5281/zenodo.5552762
title: Data Observasi Hasil Analisis Izin Akses Aplikasi Olahraga

description: Dataset ini berisikan data hasil analisis sebagai pendukung penulisan artikel jurnal berjudul <strong>Analisis Kepatuhan Keamanan pada Aplikasi Olahraga</strong>. Data yang disajikan adalah: Data kategorisasi izin akses data pengguna pada sampel aplikasi olahraga. Data yang ditampilkan adalah <strong>nama aplikasi,</strong> <strong>jenis akses</strong>, <strong>dan permintaan</strong><strong> izin akses </strong>yang diminta pada aplikasi olahraga.
DOI: 10.5281/zenodo.5552761
title: Data Observasi Hasil Analisis Izin Akses Aplikasi Olahraga

description: Dataset ini berisikan data hasil analisis sebagai pendukung penulisan artikel jurnal berjudul <strong>Analisis Kepatuhan Keamanan pada Aplikasi Olahraga</strong>. Data yang disajikan adalah: Data kategorisasi izin akses data pengguna pada sampel aplikasi olahraga. Data yang ditampilkan adalah <strong>nama aplikasi,</strong> <strong>jenis akses</strong>, <strong>dan permintaan</strong><strong> izin akses </strong>yang diminta pada aplikasi olahraga.
DOI: 10.5281/zenodo.5552404
title: Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy

description: scRNA-seq read count matrices from patient-derived PDAC organoids.
DOI: 10.5281/zenodo.5552403
title: Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy

description: scRNA-seq read count matrices from patient-derived PDAC organoids.
DOI: 10.34943/24dfde40-3ff5-4a25-b3ae-8fbd77b9ff14
title: Tofino Accelerometer Deployed 2018-03-22

description: The Nanometrics Titan SMA (S/N 000642) was deployed on 2018-03-22 at Tofino. Tofino is a community on west coast of Vancouver Island. This device is a Accelerometer. Accelerometers are instruments that measure accelerations. Acceleration can be static such as gravity pulling objects toward the earth, or dynamic as caused by oscillatory movements of the instrument. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 2.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.6096/kascade.10
title: GBA - CEA Cadarache - GBA, Cadarache

description: Permanently installed measurement tower (basic atmospheric parameters) Data is provided every 10-min from 12:10 13 december 2012 until 23:30 16 march 2013. The suffix .ts stands for "time series" but the file has a format similar to a .dat file. The variables from the 110 m GBA mast are 10-min averages named: FF = wind speed at 110 m agl (m/s) F1 = wind speed recomputed at 10 m agl assuming 1 m aerodynamic rugosity (m/s) HR = relative humidity (%) PP = pressure at station (hPa) T2 = temperature at 2 m agl (°C) DT = temperature difference betwen 110 m and 2 m (°C) DD = wind direction (degree) Q2 = mixing ratio (g/kg) PS = pressure at sea level (hPa) TD = dew point temperature (°C) RR = rainfall (mm)
DOI: 10.6096/kascade.14
title: Radiosondes – LA Toulouse – M30, Cadarache

description: Measurements obtained by radio-soundings with balloons of the recovery type. The "CRA2013monthdayhour.fil" file in each RS subfolder contains the following 19 variables: TempF = GPS time (s) AltitudF = geopotential altitude (m) LatitudeF = latitude (rad) LongitudeF = longitude (rad) VEstF = east component of probe velocity (m/s) VNordF = north component of probe velocity (m/s) VVertF = vertical component of probe velocity (m/s) VHorF = horizontal wind velocity (m/s) VDirF = wind direction (deg) TaNCalF = air temperature, uncalibrated (°C) TaCalF = air temperature, calibrated (°C) TaRadF = air temperature, calibrated and corrected for radiative effect (°C) -&gt; recommended use UNCaF = relative humidity, uncalibrated (%) UCalF = relative humidity, calibrated (%) -&gt; recommended use PressF = pressure (Pa) GPSAltF = raw GPS altitude (m) AD_OzoneF = not used T_OzoneF = not used AD_AuxF = not used
DOI: 10.6084/m9.figshare.16727488
title: AirwayPT Data Original 100121.csv

description: Results of change in apnea hypopnea index (AHI) before and after intervention of breathing against negative airway pressure in 15 subjects with a history of obstructive sleep apnea. The AHI was measured during an attended sleep study (observed by a sleep technician), and various aspects of AHI measurement are included such as during REM and NREM sleep and by position (supine, right, left). Additional data includes age, gender, and weight, BMI, neck circumference, and nadir oxygen saturation before and after intervention. The intervention is noted as the numerical value of the negative airway pressure used for breathing repetitions and sets three times a week for a month - so the pressure values each month are also collected.
DOI: 10.6084/m9.figshare.16727488.v1
title: AirwayPT Data Original 100121.csv

description: Results of change in apnea hypopnea index (AHI) before and after intervention of breathing against negative airway pressure in 15 subjects with a history of obstructive sleep apnea. The AHI was measured during an attended sleep study (observed by a sleep technician), and various aspects of AHI measurement are included such as during REM and NREM sleep and by position (supine, right, left). Additional data includes age, gender, and weight, BMI, neck circumference, and nadir oxygen saturation before and after intervention. The intervention is noted as the numerical value of the negative airway pressure used for breathing repetitions and sets three times a week for a month - so the pressure values each month are also collected.
DOI: 10.6096/kascade.13
title: Tethersonde profiles – LA Toulouse – M30, Cadarache

description: Measurements made with a tethered balloon from LA. The dataset contains 6-m vertically averaged variables of temperature, potential temperature, relative humidity, humidity mixing ratio, wind components (U, V, speed and direction). These components have been calibrated.
DOI: 10.6096/kascade.18
title: Tethersonde raw scans – LA Toulouse – M30, Cadarache

description: Observations obtained by the tethered balloon soundings, separated in scans and after calibration (see Technical Note for correction procedures). The data are not corrected for height yet. This data form can be used to construct averaged profiles over every certain height available, however, a minimum average of 6 m is recommended. These averages are provided in the data link "Tethered balloon profiles (averaged and calibrated)".
DOI: 10.6096/kascade.11
title: VER – CEA Cadarache – VER, Cadarache

description: Measurement site with permanently installed sensors (basic atmospheric parameters) Data is provided every 10min from 12:20 13 December 2012 until 23:30 17 March 2013. The suffix .ts stands for "time series" but the file has a similar format as a .dat file. The variables from the 15m XARIA mast at VER are 10min averages named: FF = wind speed at 15m agl (m/s) F1 = wind speed recomputed at 10m agl assuming 1m aerodynamic rugosity (m/s) HR = relative humidity (%) PP = pressure at station (hPa) T2 = temperature at 2m agl (°C) DT = temperature difference betwen 15m and 2m (°C) [information not available, zero value] DD = wind direction (degree) Q2 = mixing ration (g/kg) PS = pressure at sea level (hPa) TD = dew point temperature (°C) RR = rainfall (mm)
DOI: 10.5281/zenodo.5552288
title: Music Streams Labelled with Listening Situation - [User/Track/Device/Situation] Dataset

description: This is a contextual music dataset labeled with the listening situation associated with each stream. Each stream is composed of the user, track, and device data labelled with a situation. The dataset is collected from Deezer for the period of August 2019 from France and Brazil. The dataset is composed of 3 subsets of situations corresponding to 4, 8, and 12 different situations. The situations are extracted based on keyword matching with the associated playlist title in the Deezer catalog. The full set of situational tags are: "<strong>work, gym, party, sleep | morning, run, night, dance | car, train, relax, club"</strong>. Each instance contains the track/user/deviice triplets, and a situational tag indicating that this user listens to the track in the associated situation wth the corresponding data recieved from the device. The device data contain: "l<strong>inear-time, linear-day, circular-time X, circular-time Y,circular-day X, circular-day Y, device-type, network-type</strong>". The users are represented as <strong>embeddings</strong> based on their listening history computed through the matrix factorization of the user/track matrix. Additionally, the users are also represented with their demographic data of : "<strong>age, country, gender</strong>". The creation of the dataset and our experimental results are described in the paper: Karim M. Ibrahim, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "Audio Autotagging as Proxy for Contextual MusicRecommendation" [Under Revision]. The source code of the paper is available here: https://github.com/KarimMibrahim/Situational_Session_Generator.git The dataset is composed of the media_id which is the ID of the track in the Deezer catalog. The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: https://developers.deezer.com/api. Each user is represented with an <strong>anonymized</strong> <strong>user_id</strong> which is associated with the user embedding available in the user_embeddings.npy file. Note: The index of the embeddings in the user_embeddings arrary corresponds to the user_id, i.e. user_id = 100 have its embeddings at user_embeddings[100]. Finally, the dataset also contains the splits used in our experiments. Our splits were conditioned by one of three conditions: <em>ColdTrack</em> (no overlap of tracks between the splits), <em>ColdUser</em> (no overlap of users between the splits), and <em>WarmCase</em> (overlaps allowed). Each condition is split into 4 subsets for cross-validation marked with a "<strong>fold</strong>" number in each condition.
DOI: 10.5281/zenodo.5552287
title: Music Streams Labelled with Listening Situation - [User/Track/Device/Situation] Dataset

description: This is a contextual music dataset labeled with the listening situation associated with each stream. Each stream is composed of the user, track, and device data labelled with a situation. The dataset is collected from Deezer for the period of August 2019 from France and Brazil. The dataset is composed of 3 subsets of situations corresponding to 4, 8, and 12 different situations. The situations are extracted based on keyword matching with the associated playlist title in the Deezer catalog. The full set of situational tags are: "<strong>work, gym, party, sleep | morning, run, night, dance | car, train, relax, club"</strong>. Each instance contains the track/user/deviice triplets, and a situational tag indicating that this user listens to the track in the associated situation wth the corresponding data recieved from the device. The device data contain: "l<strong>inear-time, linear-day, circular-time X, circular-time Y,circular-day X, circular-day Y, device-type, network-type</strong>". The users are represented as <strong>embeddings</strong> based on their listening history computed through the matrix factorization of the user/track matrix. Additionally, the users are also represented with their demographic data of : "<strong>age, country, gender</strong>". The creation of the dataset and our experimental results are described in the paper: Karim M. Ibrahim, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "Audio Autotagging as Proxy for Contextual MusicRecommendation" [Under Revision]. The source code of the paper is available here: https://github.com/KarimMibrahim/Situational_Session_Generator.git The dataset is composed of the media_id which is the ID of the track in the Deezer catalog. The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: https://developers.deezer.com/api. Each user is represented with an <strong>anonymized</strong> <strong>user_id</strong> which is associated with the user embedding available in the user_embeddings.npy file. Note: The index of the embeddings in the user_embeddings arrary corresponds to the user_id, i.e. user_id = 100 have its embeddings at user_embeddings[100]. Finally, the dataset also contains the splits used in our experiments. Our splits were conditioned by one of three conditions: <em>ColdTrack</em> (no overlap of tracks between the splits), <em>ColdUser</em> (no overlap of users between the splits), and <em>WarmCase</em> (overlaps allowed). Each condition is split into 4 subsets for cross-validation marked with a "<strong>fold</strong>" number in each condition.
DOI: 10.15129/87efe3ab-c349-4354-9f76-aa9b4c21192f
title: Virtual Synchronous Machine (VSM) and Power Voltage Current Control + Inertia (PVCCI) Models and Initialisation Scripts

description: This dataset contains models and initialisation scripts for the modelling of control responses to frequency disturbance scenarios. The models and initialisation scripts should be implemented in matlab & simulink. The models are the .slx files named according to the control type, each of which uses a specific ...param.m initialisation script. The initialisation scripts are named according to the control model that they should be used for.
DOI: 10.15129/354fd220-c2b3-4984-bbab-fa87c9a64a3c
title: Results of inertia measurement procedures throughout varying wind and scripts to visualise

description: This dataset includes the matlab script FinalInertiaMeasurementPlotting.m and associated data arrays that exhibit the results of the application of inertia measurement methods to the output of a time domain wind turbine model throughout wind variations. The script includes a walkthrough of the data that accompanies it. The data and script simply need to be loaded into matlab before being able to reproduce the results from the related project
DOI: 10.23728/fmi-b2share.40a8802820bc4fa184c8737307a55ad0
title: Simulated ECHAM6 atmospheric model data from Karpechko et al. (2021) "Sensitivity of QBO teleconnection to model circulation biases", Quart. J. Roy. Meteor. Soc., v.147, 737, 2147-2159, https://doi.org/10.1002/qj.4014.

description: The data set includes monthly mean sea level pressure, geopotential height, zonal mean zonal winds and EP-fluxes from control and bias-corrected model simulations used in Karpechko et al. (2021). The data are for 100 year simulations. The upper level data are provided at standard pressure levels between 1000hPa and 1hPa.
DOI: 10.5517/ccdc.csd.cc23cx1g
title: CCDC 1947855: Experimental Crystal Structure Determination

description: Related Article: Jeremy L. Bourque, Robert A. Nanni, Mark C. Biesinger, Kim M. Baines|2021|Inorg.Chem.|60|14713|doi:10.1021/acs.inorgchem.1c01801
DOI: 10.5517/ccdc.csd.cc23cx2h
title: CCDC 1947856: Experimental Crystal Structure Determination

description: Related Article: Jeremy L. Bourque, Robert A. Nanni, Mark C. Biesinger, Kim M. Baines|2021|Inorg.Chem.|60|14713|doi:10.1021/acs.inorgchem.1c01801
DOI: 10.5517/ccdc.csd.cc23cx3j
title: CCDC 1947857: Experimental Crystal Structure Determination

description: Related Article: Jeremy L. Bourque, Robert A. Nanni, Mark C. Biesinger, Kim M. Baines|2021|Inorg.Chem.|60|14713|doi:10.1021/acs.inorgchem.1c01801
DOI: 10.5517/ccdc.csd.cc23cx4k
title: CCDC 1947858: Experimental Crystal Structure Determination

description: Related Article: Jeremy L. Bourque, Robert A. Nanni, Mark C. Biesinger, Kim M. Baines|2021|Inorg.Chem.|60|14713|doi:10.1021/acs.inorgchem.1c01801
DOI: 10.5517/ccdc.csd.cc23cx5l
title: CCDC 1947859: Experimental Crystal Structure Determination

description: Related Article: Jeremy L. Bourque, Robert A. Nanni, Mark C. Biesinger, Kim M. Baines|2021|Inorg.Chem.|60|14713|doi:10.1021/acs.inorgchem.1c01801
DOI: 10.5517/ccdc.csd.cc23cx6m
title: CCDC 1947860: Experimental Crystal Structure Determination

description: Related Article: Jeremy L. Bourque, Robert A. Nanni, Mark C. Biesinger, Kim M. Baines|2021|Inorg.Chem.|60|14713|doi:10.1021/acs.inorgchem.1c01801
DOI: 10.5517/ccdc.csd.cc26zqvs
title: CCDC 2055326: Experimental Crystal Structure Determination

description: Related Article: Igor I. Savelev, Irina E. Efremova, Lidiya V. Lapshina, Vladislav V. Gurzhiy, Alexander V. Belyakov |2021|Chemistry of Heterocyclic Compounds|57|861|doi:10.1007/s10593-021-02992-6
DOI: 10.15151/esrf-es-569440084
title: BAG for the Structural Biology Groups in Madrid

description: This proposal is a collaborative effort of the Structural Biology Groups of Madrid with the objective to form a strong core of scientist devoted to this area of research in Spain. It is composed of 18 groups distributed in 4 Institutes.
DOI: 10.5287/bodleian:am9akye72
title: RNA-seq transcriptional atlas for the rat
DOI: 10.5281/zenodo.5552329
title: Data Sources for the World Atlas of late Quaternary Foraminiferal Oxygen and Carbon Isotope Ratios 2021

description: A tabulated text file containing all data sources used for the World Atlas of late Quaternary Foraminiferal Oxygen and Carbon Isotope Ratios 2021 (WA_Foraminiferal_Isotopes_2021), https://doi.org/10.1594/PANGAEA.936747 (Mulitza et al. 2021)
DOI: 10.5281/zenodo.5552204
title: Extra-pair paternity explains cooperation among neighbours in a bird

description: In many social animals, females mate with multiple males, but the adaptive role of female extra-pair mating is not fully understood. This data set tests whether male pied flycatchers (<em>Ficedula hypoleuca</em>) engaging in extra-pair copulations with neighbouring females were more likely to assist their neighbours in predator mobbing. It shows that extra-pair sires joined mobs more often, approached predators more closely, and attacked predators more aggressively than males without extra-pair offspring in the neighbouring nest. Our results support theoretical predictions suggesting that extra-pair mating incentivizes males to assist in nest defences.
DOI: 10.5281/zenodo.5552172
title: Dataset - Radially resolved dynamic inflow pitch step experiment in wind tunnel

description: The relevant turbine data of MoWiTO 1.8 and the preprocessed data presented in the following accepted paper are uploaded: <br> Berger, F., Onnen, D., Schepers, J. G., and Kühn, M.: Experimental analysis of radially resolved dynamic inflow effects due to pitch steps, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-70, accepted, 2021. The dataset describes a pitch step experiment with a model wind turbine in a wind tunnel between a high rotor load and a low rotor load. Measurements of turbine loads, near wake flow and radius resolved induction measurements are available.
DOI: 10.5258/soton/d1818
title: Dataset for: Establishing an invertebrate Galleria mellonella Greater wax moth larval model of Neisseria gonorrhoeae infection

description: Dataset for a journal article; Virulence 10.1080/21505594.2021.1950269