cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.11583/dtu.19688983.v1
title: Onshore wind generation time series for technology SP199 HH200 at resource grade C (PECD 2021 update)

description: This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 199 W/m2 turbines at hub height (HH) of 200 m for the regions shown in the attached map. The analysed wind power plants are sited at the 50 % lowest mean wind speed locations in each region, i.e., in resource grade (RG) C. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00 is the aggregated onshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581
DOI: 10.11583/dtu.14229995
title: DTU Roadrunners Autonomous Ecocar Race Track Dataset

description: <b>DTU Roadrunners Dataset</b>This dataset contains data from a Velodyne VLP-16 Puck lidar, a ZED 2 stereo camera, a Swift Navigation Piksi Multi RTK GPS, and odometry, captured on the DTU Roadrunners Dynamo Ecocar.The data was captured at the race track at Roskilde Racing Center (RRC) in March - July 2020 and at the DTU Autonomous Vehicle Test Track in May 2020.For more information about the DTU Roadrunners project visit http://ecocar.dk/<br><b>Rosbag Content</b>/clockTime<br>/velodyne_packetsRaw LIDAR point cloud from Velodyne VLP-16 Puck LIDAR<br>/gyro_angleRaw angle from gyroscope used for odometry<br>/car_pose_estimateOdometry with fieldsmsg_data-&gt;data[0], // xmsg_data-&gt;data[1], // ymsg_data-&gt;data[2], // zmsg_data-&gt;data[3], // orientationmsg_data-&gt;data[4], // speedmsg_data-&gt;data[5] // accelerationmsg_data-&gt;data[6] // driven_distance<br><b>SVO File</b>Stereolabs ZED2 Stereo camera data with IMU.Images can be viewed using ZED Explorer, extracted with SVO Export, or published to ROS using the ROS wrapper.See https://www.stereolabs.com/docs/installation/ for more information.<br><b>GPS CSV File</b>Baseline, Position, and Velocity."Flags" column has following definition:0 Invalid1 Single Point Position (SPP)2 Differential GNSS (DGNSS)3 Float RTK4 Fixed RTK5 Undefined6 SBAS Position <br><br><b>Conversion of /velodyne_packets into sensor_msgs/PointCloud2</b>Install velodyne drivers$ sudo apt-get install ros-melodic-velodyneLaunch cloud nodelet$ roslaunch cloud_nodelet_conversion.launchPlay bag file$ rosbag play X.bag<br>
DOI: 10.11583/dtu.14229995.v1
title: DTU Roadrunners Autonomous Ecocar Race Track Dataset

description: <b>DTU Roadrunners Dataset</b>This dataset contains data from a Velodyne VLP-16 Puck lidar, a ZED 2 stereo camera, a Swift Navigation Piksi Multi RTK GPS, and odometry, captured on the DTU Roadrunners Dynamo Ecocar.The data was captured at the race track at Roskilde Racing Center (RRC) in March - July 2020 and at the DTU Autonomous Vehicle Test Track in May 2020.For more information about the DTU Roadrunners project visit http://ecocar.dk/<br><b>Rosbag Content</b>/clockTime<br>/velodyne_packetsRaw LIDAR point cloud from Velodyne VLP-16 Puck LIDAR<br>/gyro_angleRaw angle from gyroscope used for odometry<br>/car_pose_estimateOdometry with fieldsmsg_data-&gt;data[0], // xmsg_data-&gt;data[1], // ymsg_data-&gt;data[2], // zmsg_data-&gt;data[3], // orientationmsg_data-&gt;data[4], // speedmsg_data-&gt;data[5] // accelerationmsg_data-&gt;data[6] // driven_distance<br><b>SVO File</b>Stereolabs ZED2 Stereo camera data with IMU.Images can be viewed using ZED Explorer, extracted with SVO Export, or published to ROS using the ROS wrapper.See https://www.stereolabs.com/docs/installation/ for more information.<br><b>GPS CSV File</b>Baseline, Position, and Velocity."Flags" column has following definition:0 Invalid1 Single Point Position (SPP)2 Differential GNSS (DGNSS)3 Float RTK4 Fixed RTK5 Undefined6 SBAS Position <br><br><b>Conversion of /velodyne_packets into sensor_msgs/PointCloud2</b>Install velodyne drivers$ sudo apt-get install ros-melodic-velodyneLaunch cloud nodelet$ roslaunch cloud_nodelet_conversion.launchPlay bag file$ rosbag play X.bag<br>
DOI: 10.11583/dtu.21967985
title: Harwood Forest Edge Experiment

description: This data item contains the raw data from the multi-mast forest edge experiment at the Harwood forest edge, as well as measured tree heights and other structural parameters. <br> The raw data and a description of their organization are in the .zip file, whereas the forest structure data in the file Harwood_Tree_Characteristics_csv <br> Key statistics from the raw data can be found in Table 4 in <br> Irvine et al (1997) The Evolution Of Turbulence Across A Forest Edge. <em>Boundary-Layer Meteorology</em> <strong>84</strong>, 467–496 (1997). https://doi.org/10.1023/A:1000453031036
DOI: 10.11583/dtu.21967985.v1
title: Harwood Forest Edge Experiment

description: This data item contains the raw data from the multi-mast forest edge experiment at the Harwood forest edge, as well as measured tree heights and other structural parameters. <br> The raw data and a description of their organization are in the .zip file, whereas the forest structure data in the file Harwood_Tree_Characteristics_csv <br> Key statistics from the raw data can be found in Table 4 in <br> Irvine et al (1997) The Evolution Of Turbulence Across A Forest Edge. <em>Boundary-Layer Meteorology</em> <strong>84</strong>, 467–496 (1997). https://doi.org/10.1023/A:1000453031036
DOI: 10.11583/dtu.11814732
title: Gravimetric prospecting: Searching for new hydrocarbon basins using global models

description: Thesis Appendix (PDF) & Supplemental Material (ZIP): we present a digital publication, consisting of a set of global gravimetric maps, in the form of gravity anomalies, gradients, and invariants, gridded at various ellipsoidal heights starting ten kilometres above GRS80, in 2014 and 2015 released versions resolved respectively at a one by one and a one-sixth by one-sixth of an arc degree, corresponding to the observed GOCE satellite gravity signal, the computed regional gravity signature of CRUST1 superimposed seismic-constrained geological layers at depth, and, resulting via stripping, the independent residual gravity signature of sedimentary basins. Support documentation (README) is included.<br>
DOI: 10.11583/dtu.11814732.v1
title: Gravimetric prospecting: Searching for new hydrocarbon basins using global models

description: Thesis Appendix (PDF) & Supplemental Material (ZIP): we present a digital publication, consisting of a set of global gravimetric maps, in the form of gravity anomalies, gradients, and invariants, gridded at various ellipsoidal heights starting ten kilometres above GRS80, in 2014 and 2015 released versions resolved respectively at a one by one and a one-sixth by one-sixth of an arc degree, corresponding to the observed GOCE satellite gravity signal, the computed regional gravity signature of CRUST1 superimposed seismic-constrained geological layers at depth, and, resulting via stripping, the independent residual gravity signature of sedimentary basins. Support documentation (README) is included.<br>
DOI: 10.5281/zenodo.8138107
title: Phytoplankton size-abundance spectra in a coastal embayment (Ría de Vigo, NW Iberia)

description: The data set include the phytoplankton cell abundance (cell mL-1) and size distribution of cells ranging from 0.5 µm to 158 µm in equivalent spherical diameter. The data were obtained from surface samples collected in a central station (42°14.09’N, 8°47.18’W) of Ría de Vigo (NW Iberian peninsula) during weekly/monthly samplings in two time series: July 2001 – July 2002 and April 2021 – May 2022.
description: This research was supported by the Spanish Ministry of Science and Education through the project 'Responses of marine phytoplankton to environmental variability across levels of biological organization' (POLARIS, Grant No. PGC2018-094553B-I00, PI: E. Marañón) and by the Spanish Ministry of Science and Technology through the project 'Size structure, production and respiration in the microbial plankton of Ría de Vigo' (Grant No. REN2000-1248, PI: E. Marañón).
DOI: 10.5281/zenodo.8138106
title: Phytoplankton size-abundance spectra in a coastal embayment (Ría de Vigo, NW Iberia)

description: The data set include the phytoplankton cell abundance (cell mL-1) and size distribution of cells ranging from 0.5 µm to 158 µm in equivalent spherical diameter. The data were obtained from surface samples collected in a central station (42°14.09’N, 8°47.18’W) of Ría de Vigo (NW Iberian peninsula) during weekly/monthly samplings in two time series: July 2001 – July 2002 and April 2021 – May 2022.
description: This research was supported by the Spanish Ministry of Science and Education through the project 'Responses of marine phytoplankton to environmental variability across levels of biological organization' (POLARIS, Grant No. PGC2018-094553B-I00, PI: E. Marañón) and by the Spanish Ministry of Science and Technology through the project 'Size structure, production and respiration in the microbial plankton of Ría de Vigo' (Grant No. REN2000-1248, PI: E. Marañón).
DOI: 10.6084/m9.figshare.23666850
title: Empirical record, geochronology and theoretical determinates of Mesozoic climate in the Junggar Basin, NW China, in relation to other basins in NE China

description: Supplementary Dataset 2: Sangonghe bed 104.
DOI: 10.6084/m9.figshare.23666850.v1
title: Empirical record, geochronology and theoretical determinates of Mesozoic climate in the Junggar Basin, NW China, in relation to other basins in NE China

description: Supplementary Dataset 2: Sangonghe bed 104.
DOI: 10.6084/m9.figshare.23666853
title: Empirical record, geochronology and theoretical determinates of Mesozoic climate in the Junggar Basin, NW China, in relation to other basins in NE China

description: Supplementary Dataset 1: Badaowan bed 65.
DOI: 10.5258/soton/d2683
title: Dataset supporting the University of Southampton Doctoral Thesis "Palaeoecological responses to environmental changes on South Pacific islands".

description: This dataset is supporting the University of Southampton Doctoral Thesis "Palaeoecological responses to environmental changes on South Pacific islands". The files are in CSV format and the R code should be accessed using RStudio software and contain: Feacal_biomarker_data.xlsx Biotic_homogenisation_main.zip Vanuatu_main.zip Tonga_main.zip This work was funded by the NERC PhD stipend NE/L002531/1
DOI: 10.5258/soton/d2672
title: Dataset supporting the publication "Visualizing laser ablation using plasma imaging and deep learning ".

description: This dataset is supporting the publication "Visualizing laser ablation using plasma imaging and deep learning " published in Optics Continuum. https://doi.org/10.1364/OPTCON.495923 The data is supporting the figures from the article: Figure_1.png Simplified diagram of the experimental setup used for recording surface images and plasma images before, during and after single pulse laser ablation. The plasma was imaged perpendicular to laser axis. The images in the figure are experimental data. Figure_2.png Concepts of (a) the first experiment where a random pulse energy ablated the surface then a sensing pulse was incident onto the surface to produce plasma and (b) the second experiment where random pulse positions ablated a surface then a sensing pulse was incident on the surface to produce plasma. All images in the figure are schematics. Figure_3.png (a) The capability of the neural network to predict pulse energy directly from plasma images and (b) activation maps at the dropout layer when feeding a 0.3 mJ pulse through different channels. Figure_3.txt Data for Figure_3(a). Actual pulse 1 energy and Predicted pulse 1 energy. Figure_4.png The capability of the neural network to generate images of the surface resulting from a pulse with a randomly chosen pulse energy, directly from an image of the plasma produced when a subsequent pulse was incident on the sample. Figure_5.png Images of the first channel of each numbered layer as the result of feeding an input image (layer 1) into each numbered layer. The figure shows the transition from a plasma image to the generated surface image. Figure_6.png The process for masking the experimentally recorded surface images, showing the position of the 1 mJ pulse (red dashed circle) and the masked region (blue dashed circle). Figure_7.png The capability for visualization of a randomly ablated sample directly from the image of the plasma created from a subsequent pulse with energy of 1 mJ. Figure_8.png Images of plasma emission (top row) and associated images of the ablated surfaces (bottom row) for (a) air, (b) cyanoacrylate and (c) carbon tape. Licence: CC-BY Related projects: EPSRC grant EP/P027644/1 EPSRC grant EP/T026197/1 EPSRC grant EP/W028786/1
DOI: 10.5281/zenodo.3267922
title: Statistical modeling, estimation, and remediation of sample index hopping in multiplexed droplet-based single-cell RNA-seq data.

description: Validation data for inferring the sample index hopping rate. The <em>hiseq4000_joined_datatable_plexed_nonplexed.zip file contains read counts for four samples (two non-multiplexed and two multiplexed) joined by a cell-barcode, UMI, and gene-ID (CUG) key combination. The hiseq4000_inner_joined_with_labels.zip file contains only those CUGs that are observed in both the non-multiplexed and multiplexed samples.</em><em> </em>
DOI: 10.5281/zenodo.3336553
title: Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing

description: Supplementary data that are needed to rerun the reproducible notebooks from the first steps using Alevin output and configuration files. Intermediate R data object that can be used to rerun the reproducible notebooks after the filtering steps. Validation data for inferring the sample index hopping rate. The <em>hiseq4000_joined_datatable_plexed_nonplexed.zip file contains read counts for four samples (two non-multiplexed and two multiplexed) joined by a cell-barcode, UMI, and gene-ID (CUG) key combination. The hiseq4000_inner_joined_with_labels.zip file contains only those CUGs that are observed in both the non-multiplexed and multiplexed samples.</em><em> </em>
DOI: 10.5281/zenodo.3267921
title: Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing

description: Supplementary data that are needed to rerun the reproducible notebooks from the first steps using Alevin output and configuration files. Intermediate R data object that can be used to rerun the reproducible notebooks after the filtering steps. Validation data for inferring the sample index hopping rate. The <em>hiseq4000_joined_datatable_plexed_nonplexed.zip file contains read counts for four samples (two non-multiplexed and two multiplexed) joined by a cell-barcode, UMI, and gene-ID (CUG) key combination. The hiseq4000_inner_joined_with_labels.zip file contains only those CUGs that are observed in both the non-multiplexed and multiplexed samples.</em><em> </em>
DOI: 10.7479/ccya-71k5
title: Calls of <i>Cophyla</i> (Amphibia, Anura, Mantellidae)

description: Five sound recordings of calls of frogs of Genus Cophyla from Madagascar. Published in Rakotoarison A, Scherz MD, Bletz MC, Razafindraibe JH, Glaw F, Vences M (2019) Description of the lucky Cophyla (Microhylidae, Cophylinae), a new arboreal frog from Marojejy National Park in north-eastern Madagascar. Zootaxa.
DOI: 10.6084/m9.figshare.23666799.v3
title: Additional file 3: Comparative genomic analysis of Bemisia tabaci s.l. and other arthropods

description: Output from Orthofinder of whole genome comparative analysis of eight populations of <em>Bemisa tabaci s.l.</em> analysed with non-whitefly species within the order Hemiptera, Hexapoda and non-Hexapoda arthropod outgroups. <br> Contains the compressed Orthofinder output (trees, sequences and Orthofamily clusters).
DOI: 10.11583/dtu.13580759
title: Data used in "Validation of Bifacial Photovoltaic Simulation Software against Monitoring Data from Large-Scale Single-Axis Trackers and Fixed Tilt Systems in Denmark"

description: This workbook includes all necessary data to run the bifacial PV simulations performed in: <br> "Validation of Bifacial Photovoltaic Simulation Software Against Monitoring Data from Large-Scale Single-Axis Trackers and Fixed Tilt Systems in Denmark" <br> <em>Appl. Sci.</em> 2020, <em>10</em>(23), 8487; https://doi.org/10.3390/app10238487 <br> <strong>Revisions/Corrigendum:</strong> Version 2: We added the PlantPredict .ppp file used to perform the HSAT bifacial simulation. <br> Version 3: A ~5min lead was discovered in the data acquisition system responible for GPOA, DC Power, and TMOD measurements. The timestamps and hourly aggregation is now adjusted accordingly. The result is improved model to measurement agreement particularly in the morning and afternoon.<br> <br> <strong>The workbook tabs include:</strong> -Site Data -Shade Scene -Structural Dimensions -PV Module Specifications -Inverter Specifications -Albedo Data -Meteorological Data -Validation (monitoring) Data <br> If you use these data/resources in a published work, please cite the publication and the dataset.
DOI: 10.11583/dtu.13580759.v2
title: Data used in "Validation of Bifacial Photovoltaic Simulation Software against Monitoring Data from Large-Scale Single-Axis Trackers and Fixed Tilt Systems in Denmark"

description: This workbook includes all necessary data to run the bifacial PV simulations performed in:<br>"Validation of Bifacial Photovoltaic Simulation Software Against Monitoring Data from Large-Scale Single-Axis Trackers and Fixed Tilt Systems in Denmark"<br><em>Appl. Sci.</em> 2020, <em>10</em>(23), 8487; https://doi.org/10.3390/app10238487<br><br>The workbook tabs include:<br>-Site Data-Shade Scene-Structural Dimensions-PV Module Specifications-Inverter Specifications-Albedo Data-Meteorological Data-Validation (monitoring) Data<br>In Version 2 of this dataset, we added the PlantPredict .ppp file used to perform the HSAT bifacial simulation. <br>If you use these data/resources in a published work, please cite the publication and the dataset.
DOI: 10.11583/dtu.13580759.v3
title: Data used in "Validation of Bifacial Photovoltaic Simulation Software against Monitoring Data from Large-Scale Single-Axis Trackers and Fixed Tilt Systems in Denmark"

description: This workbook includes all necessary data to run the bifacial PV simulations performed in: <br> "Validation of Bifacial Photovoltaic Simulation Software Against Monitoring Data from Large-Scale Single-Axis Trackers and Fixed Tilt Systems in Denmark" <br> <em>Appl. Sci.</em> 2020, <em>10</em>(23), 8487; https://doi.org/10.3390/app10238487 <br> <strong>Revisions/Corrigendum:</strong> Version 2: We added the PlantPredict .ppp file used to perform the HSAT bifacial simulation. <br> Version 3: A ~5min lead was discovered in the data acquisition system responible for GPOA, DC Power, and TMOD measurements. The timestamps and hourly aggregation is now adjusted accordingly. The result is improved model to measurement agreement particularly in the morning and afternoon.<br> <br> <strong>The workbook tabs include:</strong> -Site Data -Shade Scene -Structural Dimensions -PV Module Specifications -Inverter Specifications -Albedo Data -Meteorological Data -Validation (monitoring) Data <br> If you use these data/resources in a published work, please cite the publication and the dataset.
DOI: 10.5258/soton/d2412
title: Dataset in support of the Southampton Doctoral Thesis 'Inflammatory responses of intestinal epithelial cells and their modulation by omega-3 polyunsaturated fatty acids.'

description: Dataset in support of the Southampton Doctoral Thesis 'Inflammatory responses of intestinal epithelial cells and their modulation by omega-3 polyunsaturated fatty acids.' The data reflects all the information collated to support my thesis and is separated into the following files in excel spreadsheet: Perm-TEER-FD4, TEER-CCvsPUFA, FD4-ForFigures, MIT, GC, GC-ForFigures, Luminex, Western, Western Ratios and MIT- Viability.
DOI: 10.6084/m9.figshare.23666832.v1
title: Additional file 6 - Ensembl genebuild annotation of B. argentifolii and B. tabaci s.s. genomes

description: Genomic reannotation of the published <em>B. argentifolii</em> (doi.org/10.1186/s12915-016-0321-y) and <em>B. tabaci s.s</em> (doi.org/10.1093/gigascience/gix018) genomes using the Ensembl genebuild annotation pipeline; see here: https://www.ensembl.org/info/genome/genebuild/index.html. <br>
DOI: 10.6084/m9.figshare.23666844.v1
title: Additional file 7 - Endosymbiont assemblies and annotations of Candidatus Portiera aleyrodidarum from six populations of Bemisia tabaci s.l.

description: Candidatus Portiera aleyrodidarum (six populations of <em>Bemisia tabaci s.l.</em>), complete annotations and chromosome sequences. <br> Includes custom script to help with Flye assembler output parsing.
DOI: 10.11583/dtu.14805741
title: Dataset for the article "Integrating process-based reactive transport modeling and machine learning for electrokinetic remediation of contaminated groundwater"

description: The dataset contains the data obtained with the process-based model used for training, validation and testing of the surrogate model in the article "Sprocati, R., & Rolle, M. (2021). Integrating process-based reactive transport modeling and machine learning for electrokinetic remediation of contaminated groundwater. Water Resources Research, 57, e2021WR029959"
DOI: 10.11583/dtu.14805741.v1
title: Dataset for the article "Integrating process-based reactive transport modeling and machine learning for electrokinetic remediation of contaminated groundwater"

description: The dataset contains the data obtained with the process-based model used for training, validation and testing of the surrogate model in the article "Sprocati, R., & Rolle, M. (2021). Integrating process-based reactive transport modeling and machine learning for electrokinetic remediation of contaminated groundwater. Water Resources Research, 57, e2021WR029959"
DOI: 10.6084/m9.figshare.23666799.v4
title: Additional file 5: Comparative genomic analysis of Bemisia tabaci s.l. and other arthropods

description: Output from Orthofinder of whole genome comparative analysis of eight populations of <em>Bemisa tabaci s.l.</em> analysed with non-whitefly species within the order Hemiptera, Hexapoda and non-Hexapoda arthropod outgroups. <br> Contains the compressed Orthofinder output (trees, sequences and Orthofamily clusters).