cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4515695
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2009 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2009. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.4515694
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2009 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2009. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.6084/m9.figshare.13727806
title: TEST

description: AAAGF
DOI: 10.6084/m9.figshare.13727806.v4
title: TEST

description: AAAGF
DOI: 10.6084/m9.figshare.13727806.v3
title: TEST

description: AAAGF
DOI: 10.5281/zenodo.4516040
title: Recurrent aphthous stomatitis: proteomics database (2020)

description: <strong>Supplementary Materials:</strong> Ethics, Database, Supplementary methods, Table S1: Clinical characteristics of participants, Table S2: List of diseases and conditions for exclusion, Table S3: Showing detailed pools.
DOI: 10.5281/zenodo.4482898
title: Recurrent aphthous stomatitis: proteomics database (2020)

description: <strong>Supplementary Materials:</strong> Ethics, Database, Supplementary methods, Table S1: Clinical characteristics of participants, Table S2: List of diseases and conditions for exclusion, Table S3: Showing detailed pools.
DOI: 10.5281/zenodo.4338556
title: Data for 'Ultrafast control of magnetic interactions via light-driven phonons'

description: This folder contains all the raw data required to generate the figures in the work 'Ultrafast control of magnetic interactions via light-driven phonons'. <strong>Contents</strong> <em>Figure 1:</em> (c) Time resolved polarization data for the AFM phase: 85_meV_trace.txt and 165_meV_trace.txt. The corresponding Fourier spectra shown in the inset: 85_meV_Fourier_inset.txt and 165_meV_Fourier_inset.txt (d) Time resolved polarization data for the WFM phase: 85_meV_trace.txt and 165_meV_trace.txt. The corresponding Fourier spectra shown in the inset: 85_meV_Fourier_inset.txt and 165_meV_Fourier_inset.txt (e) Frequency as function of temperature for the AFM and WFM phase: AFM points.txt and WFM_points.txt <em>Figure 2:</em> (a) Transient polarization rotation data after excitation: WFM_polarization_rotation_vs_time.txt (b) Frequency time-evolution in the WFM and AFM phase (main panel): WFM_frequency_points.txt and AFM_frequency_points.txt. Inset: inset_equilibrium_magnon_decay_time.txt and inset_frequency_decay_times.txt <em>Figure 3</em> (a) temperature dependent frequencies at 85 meV excitation, 165 meV excitation and reference data: temperature_dependence_85meV_excitation.txt, temperature_dependence_165meV_excitation, temperature_dependence_Refdata <em>Figure 4</em> (a) Time-resolved traces for different fluences: Time-resolved_fluence_dependence.txt (b) Fluence dependent magnetization: fluence_dependence.txt (c) Temperature dependent magnetization for I&gt;I_c (above Ic) and I&lt;I_c (below Ic): Above_Ic.txt, Below_Ic.txt (d) Time-resolved magnetization dynamics after different excitation energies: 85meV_trace.txt and 2.3eV_trace.txt
DOI: 10.5281/zenodo.4338555
title: Data for 'Ultrafast control of magnetic interactions via light-driven phonons'

description: This folder contains all the raw data required to generate the figures in the work 'Ultrafast control of magnetic interactions via light-driven phonons'. <strong>Contents</strong> <em>Figure 1:</em> (c) Time resolved polarization data for the AFM phase: 85_meV_trace.txt and 165_meV_trace.txt. The corresponding Fourier spectra shown in the inset: 85_meV_Fourier_inset.txt and 165_meV_Fourier_inset.txt (d) Time resolved polarization data for the WFM phase: 85_meV_trace.txt and 165_meV_trace.txt. The corresponding Fourier spectra shown in the inset: 85_meV_Fourier_inset.txt and 165_meV_Fourier_inset.txt (e) Frequency as function of temperature for the AFM and WFM phase: AFM points.txt and WFM_points.txt <em>Figure 2:</em> (a) Transient polarization rotation data after excitation: WFM_polarization_rotation_vs_time.txt (b) Frequency time-evolution in the WFM and AFM phase (main panel): WFM_frequency_points.txt and AFM_frequency_points.txt. Inset: inset_equilibrium_magnon_decay_time.txt and inset_frequency_decay_times.txt <em>Figure 3</em> (a) temperature dependent frequencies at 85 meV excitation, 165 meV excitation and reference data: temperature_dependence_85meV_excitation.txt, temperature_dependence_165meV_excitation, temperature_dependence_Refdata <em>Figure 4</em> (a) Time-resolved traces for different fluences: Time-resolved_fluence_dependence.txt (b) Fluence dependent magnetization: fluence_dependence.txt (c) Temperature dependent magnetization for I&gt;I_c (above Ic) and I&lt;I_c (below Ic): Above_Ic.txt, Below_Ic.txt (d) Time-resolved magnetization dynamics after different excitation energies: 85meV_trace.txt and 2.3eV_trace.txt
DOI: 10.6084/m9.figshare.13725622.v2
title: Woodiness

description: Data for Spatiotemporal patterns in the woodiness of flowering plants
DOI: 10.6084/m9.figshare.13725622
title: TEST

description: Data for Spatiotemporal patterns in the woodiness of flowering plants
DOI: 10.6084/m9.figshare.13725622.v4
title: TEST

description: Data for Spatiotemporal patterns in the woodiness of flowering plants
DOI: 10.6084/m9.figshare.13725622.v3
title: Data for Analysis

description: Data for Spatiotemporal patterns in the woodiness of flowering plants
DOI: 10.21227/bc9m-f507
title: C4L Image Dataset

description: This is a collection of 2D and 3D images used for grayscale image processing tests. It includes at least 8 images of each of the following sizes:256x256 - 65.5k pixels512x512 - 262k pixels1024x1024 - 1.05m pixels1920x1080 - 2.07m pixels - 1080p or 'HD'2560x1440 - 3.69m pixels - 1440p or '2K'2560x1920 - 4.92m pixels - 5 MP camera photo256x256x128 - 8.39m voxelsEach set contains a randomly generated image. Within each image size a variety of images are includes such as smooth backgrounds, busy backgrounds, and textures.
DOI: 10.5281/zenodo.4516267
title: Replication package for: Quasi-Experimental Shift-Share Research Designs

description: This package contains all public data underlying the analyses in Borusyak et al. (2021), instructions for how remaining data can be accessed, and code to reproduce all datasets and analyses. Full citation: Borusyak, Kirill, Peter Hull, and Xavier Jaravel (2021): Quasi-Experimental Shift-Share Research Designs. Review of Economic Studies
DOI: 10.5281/zenodo.4516419
title: ASEST+ quasi-experiment dataset

description: The data in this sheet provides the result of the quasi-experiment presented in the following paper in revision: Tamayo A. D, Van Petegem W., Snoeck M. Improving teamwork in agile software engineering education: the ASEST+ framework. Submitted to IEEE Transactions on Education. February, 2021
DOI: 10.5281/zenodo.4516418
title: ASEST+ quasi-experiment dataset

description: The data in this sheet provides the result of the quasi-experiment presented in the following paper in revision: Tamayo A. D, Van Petegem W., Snoeck M. Improving teamwork in agile software engineering education: the ASEST+ framework. Submitted to IEEE Transactions on Education. February, 2021
DOI: 10.5517/ccdc.csd.cc23g5h8
title: CCDC 1950039: Experimental Crystal Structure Determination

description: Related Article: Ryoichi Kanega, Naoya Onishi, Shinji Tanaka, Haruo Kishimoto, Yuichiro Himeda|2021|J.Am.Chem.Soc.|143|1570|doi:10.1021/jacs.0c11927
DOI: 10.5517/ccdc.csd.cc26byn5
title: CCDC 2036317: Experimental Crystal Structure Determination

description: Related Article: Artis Kons, Anatoly Mishnev, Timur A. Mukhametzyanov, Alexey V. Buzyurov, Semen E. Lapuk, Agris Be̅rziņš|2021|Cryst.Growth Des.|21|1190|doi:10.1021/acs.cgd.0c01508
DOI: 10.5517/ccdc.csd.cc26byp6
title: CCDC 2036318: Experimental Crystal Structure Determination

description: Related Article: Artis Kons, Anatoly Mishnev, Timur A. Mukhametzyanov, Alexey V. Buzyurov, Semen E. Lapuk, Agris Be̅rziņš|2021|Cryst.Growth Des.|21|1190|doi:10.1021/acs.cgd.0c01508
DOI: 10.5517/ccdc.csd.cc26byq7
title: CCDC 2036319: Experimental Crystal Structure Determination

description: Related Article: Artis Kons, Anatoly Mishnev, Timur A. Mukhametzyanov, Alexey V. Buzyurov, Semen E. Lapuk, Agris Be̅rziņš|2021|Cryst.Growth Des.|21|1190|doi:10.1021/acs.cgd.0c01508
DOI: 10.5517/ccdc.csd.cc26byr8
title: CCDC 2036320: Experimental Crystal Structure Determination

description: Related Article: Artis Kons, Anatoly Mishnev, Timur A. Mukhametzyanov, Alexey V. Buzyurov, Semen E. Lapuk, Agris Be̅rziņš|2021|Cryst.Growth Des.|21|1190|doi:10.1021/acs.cgd.0c01508
DOI: 10.5517/ccdc.csd.cc26bys9
title: CCDC 2036321: Experimental Crystal Structure Determination

description: Related Article: Artis Kons, Anatoly Mishnev, Timur A. Mukhametzyanov, Alexey V. Buzyurov, Semen E. Lapuk, Agris Be̅rziņš|2021|Cryst.Growth Des.|21|1190|doi:10.1021/acs.cgd.0c01508
DOI: 10.5517/ccdc.csd.cc26bytb
title: CCDC 2036322: Experimental Crystal Structure Determination

description: Related Article: Artis Kons, Anatoly Mishnev, Timur A. Mukhametzyanov, Alexey V. Buzyurov, Semen E. Lapuk, Agris Be̅rziņš|2021|Cryst.Growth Des.|21|1190|doi:10.1021/acs.cgd.0c01508
DOI: 10.5517/ccdc.csd.cc26byvc
title: CCDC 2036323: Experimental Crystal Structure Determination

description: Related Article: Artis Kons, Anatoly Mishnev, Timur A. Mukhametzyanov, Alexey V. Buzyurov, Semen E. Lapuk, Agris Be̅rziņš|2021|Cryst.Growth Des.|21|1190|doi:10.1021/acs.cgd.0c01508
DOI: 10.5517/ccdc.csd.cc26bywd
title: CCDC 2036324: Experimental Crystal Structure Determination

description: Related Article: Artis Kons, Anatoly Mishnev, Timur A. Mukhametzyanov, Alexey V. Buzyurov, Semen E. Lapuk, Agris Be̅rziņš|2021|Cryst.Growth Des.|21|1190|doi:10.1021/acs.cgd.0c01508
DOI: 10.5281/zenodo.4516231
title: Dataset of Paper "Modeling of H2 Permeation through Electroless Pore-Plated Composite Pd Membranes Using Computational Fluid Dynamics"

description: Dataset of Paper "Modeling of H2 Permeation through Electroless Pore-Plated Composite Pd Membranes Using Computational Fluid Dynamics": Elemental composition of the membrane estimated by EDS at diverse stages of the fabrication process. Main permeation parameters at 375 °C and different H<sub>2</sub> feed molar fractions (X<sub>H2</sub>) obtained from experimental data: y-intercept and H<sub>2</sub> permeance (K). Comparison of the experimental and predicted values of permeate flow for pure N<sub>2</sub> streams for different operating conditions with the PSS support (in-out configuration). Experimental and predicted H<sub>2</sub> permeate fluxes at a constant temperature (<em>T</em> = 375 °C) but different pressures and H<sub>2</sub> molar fractions in the feed stream (in–out configuration). Experimental and predicted H<sub>2</sub> permeate fluxes at a constant temperature (<em>T</em> = 375 °C) but different pressures and H<sub>2</sub> molar fractions in the feed stream (out– in configuration). Two mesh files (.msh file) for the membrane permeator: Out–in configuration and in–out configuration.
DOI: 10.5281/zenodo.4516230
title: Dataset of Paper "Modeling of H2 Permeation through Electroless Pore-Plated Composite Pd Membranes Using Computational Fluid Dynamics"

description: Dataset of Paper "Modeling of H2 Permeation through Electroless Pore-Plated Composite Pd Membranes Using Computational Fluid Dynamics": Elemental composition of the membrane estimated by EDS at diverse stages of the fabrication process. Main permeation parameters at 375 °C and different H<sub>2</sub> feed molar fractions (X<sub>H2</sub>) obtained from experimental data: y-intercept and H<sub>2</sub> permeance (K). Comparison of the experimental and predicted values of permeate flow for pure N<sub>2</sub> streams for different operating conditions with the PSS support (in-out configuration). Experimental and predicted H<sub>2</sub> permeate fluxes at a constant temperature (<em>T</em> = 375 °C) but different pressures and H<sub>2</sub> molar fractions in the feed stream (in–out configuration). Experimental and predicted H<sub>2</sub> permeate fluxes at a constant temperature (<em>T</em> = 375 °C) but different pressures and H<sub>2</sub> molar fractions in the feed stream (out– in configuration). Two mesh files (.msh file) for the membrane permeator: Out–in configuration and in–out configuration.
DOI: 10.5281/zenodo.4516392
title: Source code and data for: Spatial cognitive ability is associated with speed of movement in the early exploration of an environment

description: For any questions about the code please contact Christine at c.e.beardsworth@gmail.com To use any data contained in this repository contact Joah at j.r.madden@exeter.ac.uk for permission. <strong>Code:</strong> The data for the following /code sections are found in /data, apart from the full filtered movement dataset (used in `1_cleaning and run HMM.R`) as it is too large to be included here. Figures created using this code are stored in /figs 1_cleaning and run HMM.R: Some extra cleaning of pheasant movement data and subsetting so that only birds that have 7 days of data (of at least 6 hours) are present in the dataset. The beginning section R code is an example only as the data is not available on this repository. The data can be retrieved from Christine or Joah. However, the code shows how the subsequent files that are included here are created and how we ran Hidden Markov models. 2_Choose HMM and describe states and HRs.R: This code shows how we chose the best HMM to describe state transitions. We also use the same dataset to create HRs that are shown in Fig 2 of the manuscript. 3_Statistics.R: This code performs the statistics used in the manuscript as well as some figures. 4_ESM.R: This code produces the figures found in the supplementary material.<br>
DOI: 10.5281/zenodo.4516391
title: Source code and data for: Spatial cognitive ability is associated with speed of movement in the early exploration of an environment

description: For any questions about the code please contact Christine at c.e.beardsworth@gmail.com To use any data contained in this repository contact Joah at j.r.madden@exeter.ac.uk for permission. <strong>Code:</strong> The data for the following /code sections are found in /data, apart from the full filtered movement dataset (used in `1_cleaning and run HMM.R`) as it is too large to be included here. Figures created using this code are stored in /figs 1_cleaning and run HMM.R: Some extra cleaning of pheasant movement data and subsetting so that only birds that have 7 days of data (of at least 6 hours) are present in the dataset. The beginning section R code is an example only as the data is not available on this repository. The data can be retrieved from Christine or Joah. However, the code shows how the subsequent files that are included here are created and how we ran Hidden Markov models. 2_Choose HMM and describe states and HRs.R: This code shows how we chose the best HMM to describe state transitions. We also use the same dataset to create HRs that are shown in Fig 2 of the manuscript. 3_Statistics.R: This code performs the statistics used in the manuscript as well as some figures. 4_ESM.R: This code produces the figures found in the supplementary material.<br>
DOI: 10.6084/m9.figshare.13728061
title: Modelagem &gt; data

description: Pontos de ocorrência das espécies
DOI: 10.6084/m9.figshare.13728061.v1
title: Modelagem &gt; data

description: Pontos de ocorrência das espécies
DOI: 10.6084/m9.figshare.13489359
title: Maqam478: Qur'anic Recitations in 8 different Maqams

description: A dataset conatining <b>478 Qur'anic audio recitations</b> in <b>8 different maqams</b>. The dataset is intended to be used for the purpose of maqam classification. <br>
DOI: 10.5281/zenodo.3906530
title: NeuroPAL Data

description: <strong>NeuroPAL Datasets</strong> The following datasets are provided for the publication "NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in <em>C. elegans</em>". The paper is available here: https://www.cell.com/cell/fulltext/S0092-8674(20)31682-2 See the included README for more information.
DOI: 10.5281/zenodo.3906529
title: NeuroPAL Data

description: <strong>NeuroPAL Datasets</strong> The following datasets are provided for the publication "NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in <em>C. elegans</em>". The paper is available here: https://www.cell.com/cell/fulltext/S0092-8674(20)31682-2 See the included README for more information.
DOI: 10.6084/m9.figshare.13681399.v1
title: Final Tumor Classifier

description: Tumor classifier state dict
DOI: 10.6084/m9.figshare.13728088
title: MICCAI 2012

description: MICCAI 2012 dataset
DOI: 10.6084/m9.figshare.13728088.v1
title: MICCAI 2012

description: MICCAI 2012 dataset
DOI: 10.6084/m9.figshare.13681399
title: Final Tumor Classifier (Training)

description: tumorclassifier.pt : Tumor classifier model state dict<br>tumorclassifierw.pt : Tumor classifier whole model
DOI: 10.6084/m9.figshare.13681399.v2
title: Final Tumor Classifier (Training)

description: tumorclassifier.pt : Tumor classifier model state dict<br>tumorclassifierw.pt : Tumor classifier whole model
DOI: 10.6084/m9.figshare.13528421
title: ubs_com_sala_vacina.rar

description: Geolocalização das unidades básicas de saúde do Brasil, referentes a dezembro de 2020 que contavam com sala de vacina
DOI: 10.6084/m9.figshare.13528577
title: Base de dados do manuscrito: Plano nacional de vacinação contra a COVID-19: Uso de inteligência artificial espacial para superação de desafios

description: Base de dados brutos e com os resultados da análises referente ao manuscrito: Plano nacional de vacinação contra a COVID-19: Uso de inteligência artificial espacial para superação de desafios
DOI: 10.6084/m9.figshare.13528430.v1
title: Areas de abrangencia de 4km em torno das UBS com sala de vacinas

description: Shapefile com as áreas de abragencia de 4km a pé em torno das unidades de saúde com sala de vacina no Brasil em dezembro de 2020
DOI: 10.6084/m9.figshare.13528577.v1
title: Base de dados do manuscrito: Plano nacional de vacinação contra a COVID-19: Uso de inteligência artificial espacial para superação de desafios

description: Base de dados brutos e com os resultados da análises referente ao manuscrito: Plano nacional de vacinação contra a COVID-19: Uso de inteligência artificial espacial para superação de desafios
DOI: 10.6084/m9.figshare.13528421.v1
title: ubs_com_sala_vacina.rar

description: Geolocalização das unidades básicas de saúde do Brasil, referentes a dezembro de 2020 que contavam com sala de vacina
DOI: 10.6084/m9.figshare.13528424
title: Estaçoes de radio base - internet móvel Brasil

description: Localização das estações de radio base de celular (internet móvel) existentes no Brasil em dezembro de 2020
DOI: 10.6084/m9.figshare.13528430
title: Areas de abrangencia de 4km em torno das UBS com sala de vacinas

description: Shapefile com as áreas de abragencia de 4km a pé em torno das unidades de saúde com sala de vacina no Brasil em dezembro de 2020
DOI: 10.6084/m9.figshare.13528424.v1
title: Estaçoes de radio base - internet móvel Brasil

description: Localização das estações de radio base de celular (internet móvel) existentes no Brasil em dezembro de 2020
DOI: 10.34740/kaggle/dsv/1919535
title: US counties COVID 19 dataset
DOI: 10.5281/zenodo.4516578
title: Semantics-aware dataset for the mono-label supervised classification of animals

description: This image dataset has been derived from Wikimedia Commons (https://commons.wikimedia.org), a large-scale free and collaborative media repository currently including over 92 million items including images, videos and recordings. The dataset includes images of several types of animals. The images are assigned to categories according to the Wikimedia Commons Category Graph and its direct links with the considered medias. As an output of the development of this image dataset, we created three ZIP Files that can be used for the evaluation of the effect of semantic features of considered labels on the efficiency of mono-label image classification algorithms : Class1 (The most general one): The labels are "Birds" and "mammals". Each category precisely includes 450 images. Class2: The labels are "Cat", "Cattle", "Columbidae", "Dog", "Phoenicopteridae", and "Psittacidae". Each category precisely includes 150 images. Class3 (The most granular one): The labels here are "Highland cattle", "‎Holstein Friesian cattle", "Charolais cattle‎", "American Pit Bull Terrier", "‎Belgian Shepherd Malinois", "‎Miniature Schnauzer‎", "Persian cats", "‎Chinchilla (cat)", "‎Bengal cats", "‎Phoeniconaias minor‎", "Phoenicopterus roseus‎", "Phoenicoparrus andinus", "‎Anodorhynchus hyacinthinus‎", "Ara ararauna‎", "Ara macao‎", "Ocyphaps lophotes", "Columbina talpacoti", and "Columba palumbus"‎. Each category precisely includes 50 images.
description: This dataset has been developed to support a forthcoming proceedings paper in KDD 2021. Image Credits: Wikimedia Commons, CC-BY-SA 4.0.