cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15161/oar.it/73840
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.4915923
title: Supplementary data to publication "Excretion of Histomonas meleagridis following experimental co-infection of distinct chicken lines with Heterakis gallinarum and Ascaridia galli"

description: Supplementary data to publication "Excretion of Histomonas meleagridis following experimental co-infection of distinct chicken lines with Heterakis gallinarum and Ascaridia galli" in Parasites & Vectors; https://doi.org/10.1186/s13071-021-04823-1
description: This work is an outcome of the Integhof Project supported by the German Government's Special Purpose Fund held at Landwirtschaftliche Rentenbank (Grant No. 28RZ3-72.051).
DOI: 10.5281/zenodo.4915922
title: Supplementary data to publication "Excretion of Histomonas meleagridis following experimental co-infection of distinct chicken lines with Heterakis gallinarum and Ascaridia galli"

description: Supplementary data to publication "Excretion of Histomonas meleagridis following experimental co-infection of distinct chicken lines with Heterakis gallinarum and Ascaridia galli" in Parasites & Vectors; https://doi.org/10.1186/s13071-021-04823-1
description: This work is an outcome of the Integhof Project supported by the German Government's Special Purpose Fund held at Landwirtschaftliche Rentenbank (Grant No. 28RZ3-72.051).
DOI: 10.5281/zenodo.4922828
title: Data and models for: Entropy based active learning of graph neural networks for materials properties

description: <strong>Re-creating the Paper</strong> To recreate the experiments performed in the paper you will first need to clone the code in the GitHub repository to your local machine: https://github.com/keeeto/gp-net Next, set up the right python environment - there is a gp-megnet.yml file in the repository that can be used to create the right environment using conda. Then take the paper-experiments.tgz from this repository in the top directory of the repository that you cloned from GitHub. From the directory experiments you can then run the the experiments with the following commands: <pre><code>python gp-net.py -data formation_energy_per_atom_data.pkl -ndims 1 -q 3000 -frac 0.4 -epochs 0 -prev -cycle 1 3000 -maxiters 100 -stop 0.99 -include</code></pre> <pre><code>python gp-net-skip-activation.py -data formation_energy_per_atom_data.pkl -ndims 1 -q 3000 -frac 0.4 -epochs 0 -prev -cycle 1 3000 -maxiters 0 -stop 0.99 -include -samp random -amp 0.4281 -length 2.3997</code></pre> <strong>Plots</strong> Details on the data analysis and plotting are included in the .ipynb files. <strong>Details</strong> These are the data and ML models needed to recreate the results in: Entropy based active learning of graph neural networks for materials properties. * The hdf5 files contain the saved parameters for the trained Megnet models used in this study. * gp_mae_{random/entropy}.npy contain the results of the active learning procedure with either entropy based or random sampling. * gp_mean and gp_stdev contain the mean and standard deviation of the fully trained model on the test set data. * yvals contains the true lablel for the same data as in gp_mean and gp_stedev. * samp_indices contains the sample indices for yvals and can be used to work out which material corresponds to which value. * formation_energy_per_atom.pkl is the training data from the Materials Project used in this study. * latent_test.npy contains the latent space vectors in 2D. * yvals_latent,npy contains the formation energies corresponding to the the latent space points in latent_test.npy The data in this folder can be plotted using notebooks available in the <em>publication_notebooks</em> folder of the github repo for the active learning code: https://github.com/keeeto/gp-net
DOI: 10.5281/zenodo.4665867
title: Data and models for: Entropy based active learning of graph neural networks for materials properties

description: <strong>Re-creating the Paper</strong> To recreate the experiments performed in the paper you will first need to clone the code in the GitHub repository to your local machine: https://github.com/keeeto/gp-net Next, set up the right python environment - there is a gp-megnet.yml file in the repository that can be used to create the right environment using conda. Then take the paper-experiments.tgz from this repository in the top directory of the repository that you cloned from GitHub. From the directory experiments you can then run the the experiments with the following commands: <pre><code>python gp-net.py -data formation_energy_per_atom_data.pkl -ndims 1 -q 3000 -frac 0.4 -epochs 0 -prev -cycle 1 3000 -maxiters 100 -stop 0.99 -include</code></pre> <pre><code>python gp-net-skip-activation.py -data formation_energy_per_atom_data.pkl -ndims 1 -q 3000 -frac 0.4 -epochs 0 -prev -cycle 1 3000 -maxiters 0 -stop 0.99 -include -samp random -amp 0.4281 -length 2.3997</code></pre> <strong>Plots</strong> Details on the data analysis and plotting are included in the .ipynb files. <strong>Details</strong> These are the data and ML models needed to recreate the results in: Entropy based active learning of graph neural networks for materials properties. * The hdf5 files contain the saved parameters for the trained Megnet models used in this study. * gp_mae_{random/entropy}.npy contain the results of the active learning procedure with either entropy based or random sampling. * gp_mean and gp_stdev contain the mean and standard deviation of the fully trained model on the test set data. * yvals contains the true lablel for the same data as in gp_mean and gp_stedev. * samp_indices contains the sample indices for yvals and can be used to work out which material corresponds to which value. * formation_energy_per_atom.pkl is the training data from the Materials Project used in this study. * latent_test.npy contains the latent space vectors in 2D. * yvals_latent,npy contains the formation energies corresponding to the the latent space points in latent_test.npy The data in this folder can be plotted using notebooks available in the <em>publication_notebooks</em> folder of the github repo for the active learning code: https://github.com/keeeto/gp-net
DOI: 10.5281/zenodo.4922849
title: Synthetic Distribution Grid Model for Eindhoven

description: <strong>SYNTHETIC DISTRIBUTION GRID MDOEL FOR EINDHOVEN</strong><br> Authors: Antoine Bidel & Tom Schelo, 2021 Use with the software package FLEDGE:<br> doi: 10.5281/zenodo.4896047<br> https://github.com/TUMCREATE-ESTL/fledge/tree/v0.4.1 INSTRUCTIONS:<br> - paste the folder "eindhoven" to your FLEDGE input folder<br> - make sure to add proper scenario data in "scenario.csv"<br> - add appropriate models in "der_model_name" in "electric_grid_ders.csv" for both non-residential and residential loads The study area includes portions of the neighborhoods<br> - Schouwbroek (1530 inhabitants),<br> - Philipsdorp (3095 inhabitants),<br> - Eliasterrein-Vonderkwartier (3165 inhabitants)<br> - Engelsbergen (635 inhabitants) and<br> - Hagenkamp (1180 inhabitants)<br> of the Dutch city of Eindhoven. The municipality is located in the province of Noord-Brabant and fed by the distribution<br> network operated by Enexis. 'POLYGON((5.45014 51.43969, 5.45142 51.44019, 5.45272 51.44086, ' \<br> '5.45442 51.44131, 5.45644 51.44175, 5.45743 51.44189, 5.45832 51.44236, ' \<br> '5.45961 51.44281, 5.46166 51.44287, 5.46336 51.44280, 5.46394 51.44257, ' \<br> '5.46517 51.44196, 5.46713 51.44056, 5.46758 51.44001, 5.46774 51.43873, ' \<br> '5.46841 51.43830, 5.46848 51.43740, 5.46784 51.43557, 5.46759 51.43541, ' \<br> '5.46744 51.43358, 5.46518 51.43259, 5.46346 51.43118, 5.46044 51.42868, ' \<br> '5.45638 51.43049, 5.45420 51.43184, 5.45125 51.43729, 5.45014 51.43969))'
DOI: 10.5281/zenodo.4922848
title: Synthetic Distribution Grid Model for Eindhoven

description: <strong>SYNTHETIC DISTRIBUTION GRID MDOEL FOR EINDHOVEN</strong><br> Authors: Antoine Bidel & Tom Schelo, 2021 Use with the software package FLEDGE:<br> doi: 10.5281/zenodo.4896047<br> https://github.com/TUMCREATE-ESTL/fledge/tree/v0.4.1 INSTRUCTIONS:<br> - paste the folder "eindhoven" to your FLEDGE input folder<br> - make sure to add proper scenario data in "scenario.csv"<br> - add appropriate models in "der_model_name" in "electric_grid_ders.csv" for both non-residential and residential loads The study area includes portions of the neighborhoods<br> - Schouwbroek (1530 inhabitants),<br> - Philipsdorp (3095 inhabitants),<br> - Eliasterrein-Vonderkwartier (3165 inhabitants)<br> - Engelsbergen (635 inhabitants) and<br> - Hagenkamp (1180 inhabitants)<br> of the Dutch city of Eindhoven. The municipality is located in the province of Noord-Brabant and fed by the distribution<br> network operated by Enexis. 'POLYGON((5.45014 51.43969, 5.45142 51.44019, 5.45272 51.44086, ' \<br> '5.45442 51.44131, 5.45644 51.44175, 5.45743 51.44189, 5.45832 51.44236, ' \<br> '5.45961 51.44281, 5.46166 51.44287, 5.46336 51.44280, 5.46394 51.44257, ' \<br> '5.46517 51.44196, 5.46713 51.44056, 5.46758 51.44001, 5.46774 51.43873, ' \<br> '5.46841 51.43830, 5.46848 51.43740, 5.46784 51.43557, 5.46759 51.43541, ' \<br> '5.46744 51.43358, 5.46518 51.43259, 5.46346 51.43118, 5.46044 51.42868, ' \<br> '5.45638 51.43049, 5.45420 51.43184, 5.45125 51.43729, 5.45014 51.43969))'
DOI: 10.5281/zenodo.4920226
title: More efficient phosphorus use can avoid cropland expansion. Datasets for Figures

description: This repository contains the data required to generate the figures for the following Article: Mogollón, J. M., Bouwman, A. F., Beusen, A. H. W., Lassaletta, L., van Grinsven, H. J. M., Westhoek, H. More efficient phosphorus use can avoid cropland expansion. <em>Nat. Food</em> (2021) doi:10.1038/s43016-021-00303-y For further information on units and how to interpret the file names please see the readme file.
DOI: 10.5281/zenodo.4920225
title: More efficient phosphorus use can avoid cropland expansion. Datasets for Figures

description: This repository contains the data required to generate the figures for the following Article: Mogollón, J. M., Bouwman, A. F., Beusen, A. H. W., Lassaletta, L., van Grinsven, H. J. M., Westhoek, H. More efficient phosphorus use can avoid cropland expansion. <em>Nat. Food</em> (2021) doi:10.1038/s43016-021-00303-y For further information on units and how to interpret the file names please see the readme file.
DOI: 10.6084/m9.figshare.14761977
title: Final SPSS .sav

description: cohort study for quality of life research study involving diabetic type 2 children
DOI: 10.25625/xplprr/spelvz
title: learnedIE_reproduction.tar

description: Docker image
DOI: 10.25625/xplprr/17onzi
title: learned_ie_git-repo.tar.gz

description: Archive of git repository.
DOI: 10.25625/xplprr
title: Replication Data for: Learned infinite elements

description: This dataset contains reproduction material for the paper "Learned infinite elements" (Hohage, Lehrenfeld, Preuss). The main file learnedIE_reproduction.tar is a Docker image providing all data, software and instructions for reproducing the numerical experiments of the paper. This image was built based on the Github repository github.com/schruste/docker-learned_IE . The main repository containing the actual software and data is gitlab.gwdg.de/learned_infinite_elements/learned_ie/ at commit ed8d1d9f1a7fe1daea11a5323ea831eb34a17924. The tarball learned_ie_git-repo.tar.gz contains an archive of this repository. A README file is included in the repository as well as in learnedIE_reproduction.tar, which contains detailed instructions (which commands to execute and so on) on how to obtain the results given in the paper. The image can additionally be found on Docker Hub: schruste/learned_ie:20210517
DOI: 10.5281/zenodo.4922733
title: Seafloor output from the MEDUSA model

description: - Output from the MEDUSA model (Yool et al., GMD, 2013) - NEMO resolution 1/4-degree - REGRID versions are regridded from ORCA025 grid to a regular 0.25-degree grid - Simulation performed as part of the ROAM project (UK Ocean Acidification Research Programme) - CMIP5 Historical and RCP 8.5 extension (1975-2099 inclusive) - Simulation described in Yool et al., JGR, 2015 - Subset of output prepared for Mission Atlantic project by A. Yool in April 2021 - Seafloor fields of physical and biogeochemical properties for the periods 2016-2025 and 2090-2099 - Note that this is test output produced for a specific purpose
description: THIS IS TEST OUTOUT
DOI: 10.5281/zenodo.4922840
title: Bakta Annotation Examples

description: This data repository provides exemplary bacterial genome annotations conducted with Bakta of a broad taxonomical range of genomes comprising many pathogens (all ESKAPE), commensals and environmental species. Bakta is a tool for the rapid & standardized local annotation of bacterial genomes & plasmids. It provides <strong>dbxref</strong>-rich and <strong>sORF</strong>-including annotations in machine-readble <code>JSON</code> & bioinformatics standard file formats for automatic downstream analysis: https://github.com/oschwengers/bakta
DOI: 10.5281/zenodo.4922530
title: SciGRID_gas IGGIELGNC

description: The IGGIELGNC dataset is an outcome of the SciGRID_gas project
DOI: 10.21944/sciamachy-aerosol-direct-effect
title: SCIAMACHY aerosol direct radiative effect over clouds, version 1.0

description: The SCIAMACHY above-cloud aerosol Direct Radiative Effect (DRE) database is available for all cloud scenes in an area over the south-east Atlantic (20W - 10E, 20S - 10N) from 2006 to 2009, during the months June - September. This is a period of massive vegetation burning in southern Africa during the dry season and often smoke plumes can be observed drifting over the Atlantic ocean. The aerosol DRE from SCIAMACHY indicates the warming effect of the smoke when it overlies clouds due to absorption of sunlight. This is measured in Watts per square meter (W/m2). <br> The data are accompanied by images that show an overview of the warming effect on a particular day. The SCIAMACHY aerosol DRE is overplotted over MODIS RGB showing clouds. However, the MODIS instrument passes over this area in the afternoon (around 13.00-14.00 UTC), while SCIAMACHY passes over in the morning (around 10.30 UTC), so the image and the data were acquired about 3.5 hours apart. MODIS was used, to compare the data more easily to the OMI data here. The exact times are indicated in the image. <br> The database is described in the following paper: <br> M. de Graaf, L.G. Tilstra, P. Wang and P. Stammes, Retrieval of the aerosol direct radiative effect over clouds from space-borne spectrometry, J. Geophys. Res. 117 (D7), D07207, doi:10.1029/2011JD017160, 2012. <br> For more information, see http://www.temis.nl/climate/adre.html
DOI: 10.5281/zenodo.4922872
title: Gas and electricity networks and demand data for NPT case study

description: The following data are publicly available: Gas demand, gas supply and gas price timeseries for the GB high pressure transmission networks: https://mip-prd-web.azurewebsites.net/DataItemExplorer The GB high pressure gas transmission network data (pages 69 to 74): https://www.nationalgrid.com/uk/gas-transmission/document/128886/download The GB high voltage electricity network data: https://www.nationalgrideso.com/electricity-ten-year-statement-etys-archive Renewable generation and electricity demand in South Wales: https://www.westernpower.co.uk/live-data-feed-application-data-view?licenceArea=WA
DOI: 10.5281/zenodo.4922871
title: Gas and electricity networks and demand data for NPT case study

description: The following data are publicly available: Gas demand, gas supply and gas price timeseries for the GB high pressure transmission networks: https://mip-prd-web.azurewebsites.net/DataItemExplorer The GB high pressure gas transmission network data (pages 69 to 74): https://www.nationalgrid.com/uk/gas-transmission/document/128886/download The GB high voltage electricity network data: https://www.nationalgrideso.com/electricity-ten-year-statement-etys-archive Renewable generation and electricity demand in South Wales: https://www.westernpower.co.uk/live-data-feed-application-data-view?licenceArea=WA
DOI: 10.21944/tropomi-no2-lnox-usa
title: TROPOMI NO2 data for lightning NOx study over the USA

description: For the study described by Allen et al., 2021 (doi:10.1029/2020JD034174), a special NO2 processing was performed of selected TROPOMI orbits over the Eastern USA. Processed are orbit segments with nadir satellite latitudes between about 10 and 60 degrees North, each 14-15 minutes in duration, with a test version of the version 2.2 TROPOMI NO2 processor (deployed mid 2021), wherein processor settings for handling saturation and outliers in the fit residual are relaxed w.r.t. the official v2.2 processing. Output files contain some additional dataset added to ease the analysis in the lightning NOx study.
DOI: 10.21944/retina-amsterdam-2016
title: Datasets used in and produced by RETINA-Amsterdam, algorithm version 2.0, June-August 2016

description: Datasets used in the manuscript "High-resolution mapping of urban air quality with heterogeneous observations: a new methodology and its application to Amsterdam", Atmospheric Measurement Techniques, vol. 13, 4601-4617, 2020, doi: 10.5194/amt-13-4601-2020.
DOI: 10.5281/zenodo.4922516
title: SciGRID_gas Cons

description: The CONS dataset is an outcome of the SciGRID_gas project The data is being stored in both CSV and GeoJSON files.
DOI: 10.17034/571eab04-36ec-428f-9f94-c2bcc364a1fc
title: Photographs relating to premature infants and the parental experience

description: Multisensory data consisting of photographs, images, and objects brought by participants to semi-structured interviews as part of a qualitative study conducted as part of a clinical psychology doctorate. These interviews were conducted with 12 mothers of premature infants born between 28-32 weeks gestation. Interviews were conducted by the lead researcher between June 6 and November 7, 2019 with parents who met inclusion criteria and who consented to participate in the study. Details of the interview along with information leaflets and consent forms were sent to participants prior to the interview. Five interviews took place in participants’ homes and seven took place in university premises. Prior to audio-recording the interviews using a dictaphone, participants were asked to provide written informed consent. Participants understood that their participation was voluntary and that they could end their participation at any time without this affecting their care or involvement with TinyLife. Verbal and written consent was provided by 11 participants to include these images in print form. During the interviews, participants were asked to discuss the meaning of each photograph/image/object they brought. These images consist of premature infants at birth and toddler stage, parents, and objects that were significant to parents during the premature birth experience. Owing to GDPR and to preserve the privacy of subjects, faces have been removed or pixelated, and names have been pseudonymised. File is available in pdf format.
DOI: 10.5281/zenodo.4916730
title: TAS PSL PR Data from PRIMAVERA (EU H2020) paper: Coupled Climate Impacts of Atlantic Multidecadal Variability : model and resolution dependence

description: Contains data from the PRIMAVERA project (https://www.primavera-h2020.eu/). The fields here are those used in the paper Coupled Climate Impacts of Atlantic Multidecadal Variability : model and resolution dependence. All have been regridded to a common 1 degree x 1 degree grid. The full dataset can be access using www.jasmin.ac.uk via https://prima-dm1.jasmin.ac.uk Please contact me for further details. Dan d.l.r.hodson@reading.ac.uk
DOI: 10.5281/zenodo.4916729
title: TAS PSL PR Data from PRIMAVERA (EU H2020) paper: Coupled Climate Impacts of Atlantic Multidecadal Variability : model and resolution dependence

description: Contains data from the PRIMAVERA project (https://www.primavera-h2020.eu/). The fields here are those used in the paper Coupled Climate Impacts of Atlantic Multidecadal Variability : model and resolution dependence. All have been regridded to a common 1 degree x 1 degree grid. The full dataset can be access using www.jasmin.ac.uk via https://prima-dm1.jasmin.ac.uk Please contact me for further details. Dan d.l.r.hodson@reading.ac.uk
DOI: 10.5281/zenodo.4916742
title: ZG500 Data from PRIMAVERA (EU H2020) paper: Coupled Climate Impacts of Atlantic Multidecadal Variability : model and resolution dependence

description: Contains data from the PRIMAVERA project (https://www.primavera-h2020.eu/). The fields here are those used in the paper Coupled Climate Impacts of Atlantic Multidecadal Variability : model and resolution dependence. All have been regridded to a common 1 degree x 1 degree grid. The code/ subdirectory contains the code for computing the ANOVA across the ensemble as presented in the paper. The full dataset can be access using www.jasmin.ac.uk via https://prima-dm1.jasmin.ac.uk Please contact me for further details. Dan d.l.r.hodson@reading.ac.uk
DOI: 10.5281/zenodo.4916741
title: ZG500 Data from PRIMAVERA (EU H2020) paper: Coupled Climate Impacts of Atlantic Multidecadal Variability : model and resolution dependence

description: Contains data from the PRIMAVERA project (https://www.primavera-h2020.eu/). The fields here are those used in the paper Coupled Climate Impacts of Atlantic Multidecadal Variability : model and resolution dependence. All have been regridded to a common 1 degree x 1 degree grid. The code/ subdirectory contains the code for computing the ANOVA across the ensemble as presented in the paper. The full dataset can be access using www.jasmin.ac.uk via https://prima-dm1.jasmin.ac.uk Please contact me for further details. Dan d.l.r.hodson@reading.ac.uk
DOI: 10.5281/zenodo.4917260
title: Sequence alignment file of Psae for the bioinformatics tool SASpector

description: The file is a binary alignment file (BAM file) of the illumina sequencing data of sample <em>Psae</em> provided as test data for the publication of the tool SASpector.
DOI: 10.5281/zenodo.4917259
title: Sequence alignment file of Psae for the bioinformatics tool SASpector

description: The file is a binary alignment file (BAM file) of the illumina sequencing data of sample <em>Psae</em> provided as test data for the publication of the tool SASpector.
DOI: 10.5281/zenodo.4916645
title: Supplementary data for "Three-dimensional and real-scale modeling of flow regimes in dense snow avalanches"

description: We are releasing the supplementary data for our accepted manuscript to Landslides. Figure 5: Data of Fig. 5. Distribution of particle stress state at 10 s. N<sub>p</sub> and N<sub>p</sub><sup>t</sup> are the particle number at each interval of γ and the total particle number, respectively. The interval of γ is 1.8°. a case I; b case II; c case III; d case IV. Red and blue denote distributions of elastic and plastic particles, respectively. Purple shows the overlapped part of the distributions of elastic and plastic particles Figure 6: Data of Fig. 6. Cumulative probability of density ratio in a case I, b case II, c case III, and d case IV Figure 9a: Data of Fig. 9a. Front velocity evolution along the flow path denoted by the gray curve, on which the black dot shows the observed stopping point of the avalanche Figure 10a: Data of Fig. 10a. Density profile at the tip of the avalanche deposit, z is along the direction of negative gravity, and z = 0 corresponds to the bottom of the measured point