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57 results for “time series models”
Antarctic time series of temperature, precipitation, and stable isotopes in precipitation from the ECHAM5/MPI-OM-wiso past1000 climate model simulation
<p>This data set contains time series of two-metre air temperature (tas), surface temperature (ts), total precipitation (pr), oxygen-18 isotopic composition in precipitation (oxy), and deuterium isotopic composition in precipitation (dtr) from the past-millennium (800-1999 CE) simulation of the fully coupled ECHAM5/MPI-OM-wiso atmosphere-ocean general circulation model equipped with stable isotope diagnostics (Sjolte et al., 2018, Werner et al., 2016) used in the publication of Münch et al. (2021).</p> <p>The data here are provided for the Antarctic region, i.e., all model grid cells south of 60° S. The model's atmospheric component was run with a T31 spectral resolution (3.75° x 3.75°) and with 19 vertical levels, resulting in a total of N = 768 model grid cells covered by this data set. Note, however, that all time series off the continent of Antarctica have been set to NA values, so that the effectively available number of model grid cells is N<sub>eff</sub> = 442.</p> <p>Time series are provided at the original monthly resolution of the model output and on annual resolution obtained from the monthly resolution data. At annual resolution, the temperature and isotopic composition data are available as normal time averages and as precipitation-weighted time averages. In addition to the time series, the spatial field of time-invariant means is supplied, also as normal and precipitation-weighted time averages.</p> <p>Data are available as netcdf files and as R data files. In addition, processing code (bash and R scripts) are provided to reproduce the processing from monthly to annnual and time-invariant resolution and to read the data into the R data format. To process the R data, you will need the CRAN packages "ncdf4" and "lubridate", and the package "pfields" available on GitHub (see References).</p>
UDP Synthetic Dataset for training ML time series models
<p>The dataset available has been produced by the "Next-Generation IoT solutions for the universal supply chain" (iNGENIOUS) project’s consortium under EC grant agreement 957216, made publicly available as part of the Horizon 2020 Open Research Data Pilot (<a href="https://www.openaire.eu/what-is-the-open-research-data-pilot">ORD pilot</a>).<br> The European Commission is not liable for any use that may be made of the information contained herein.</p> <p>The available dataset is in csv format and contains synthetic data of UDP packets received and sent by a single User Plane Function (UPF) covering a span of 6 weeks. The format of the datafile is:</p> <ul> <li>index</li> <li>timestamp </li> <li>UDP packets_rcvd - Total number of UDP packets received</li> <li>UDP packets sent - Total number of UDP packets sent</li> </ul> <p>The simulation was performed based on behavior of UPF and 5GC Network functions inferred from stress tests performed in the iNGENIOUS project's Automated Robots with Heterogeneous Networks Use Case, as well as patterns in urban mobility taken from available UE datasets [NCS+19].</p> <p>More information on the iNGENIOUS project can be found on the project’s website: <a href="https://ingenious-iot.eu/">https://ingenious-iot.eu/</a></p> <p>[NCS+19] Noussan M, Carioni G, Sanvito FD, Colombo E. Urban Mobility Demand Profiles:<br> Time Series for Cars and Bike-Sharing Use as a Resource for Transport and Energy<br> Modeling. Data. 2019; 4(3):108. https://doi.org/10.3390/data4030108</p>
Measured and modelled significant wave height time series at the Bothnian Sea Wave buoy in the Baltic Sea
<p>Significant wave height data at the location of FMI's wave buoy in the Bothnian Sea, Baltic Sea (61 degrees 8' N, 20 degrees 14' E). Contains 2011-2019 wave buoy observations, 1965-2005 SWAN modelled data (Björkqvist et al. 2018), and 1979-2013 WAM modelled data (Tuomi et al. 2019).</p>
Modelled time series of CO2 in the vicinity of a seep in the North Sea.
<p>The Bergen Ocean Model (BOM) is used to simulate dispersion of CO2 leaking from nine different locations in the North Sea, focusing on temporal and spatial variability of the CO2 concentration. </p> <p>For details see: Ali, A., Frøysa, H. G., Avlesen, H., & Alendal, G. (2016). Simulating spatial and temporal varying CO 2signals from sources at the seafloor to help designing risk-based monitoring programs. <em>Journal of Geophysical Research-Oceans</em>, <em>121</em>(1), 745–757. http://doi.org/10.1002/2015JC011198.</p> <p>The data files contains time series of excess CO2 concentration, velocity components and time, and position in longitude and latitude. One file for each grid position, a total of 53x51 grid points with the seep in location (I,j)=(28,27). </p>
Long time-series ecological niche modelling using archaeological settlement data.
<p><strong>CR_settlement_niche_[N]_[Yr]_[BC/AD].tif</strong></p> <p>Ecological niche models in GeoTIFF format generated with the MaxEnt software based using prehistoric settlement evidence as training data and environmental layers (elevation, mean annual precipitation, mean annual temperature, landscape water balance, soil types) as background data. Raster values represent the probability of presence of a settlement.<br> <strong>N</strong> - chronological ordering<br> <strong>Yr, BC/AD</strong> - calendar years BC or AD</p> <p> </p> <p><strong>CR_settlement_niche_combined.tif</strong></p> <p>All models combined by averaging.</p> <p> </p> <p><strong>CR_settlement_archeo.zip</strong></p> <p>Archaeological data used to train the MaxEnt models in ESRI SHP format with the following fields:</p> <p><strong>Site_Type:</strong> Cemetery or Settlement</p> <p><strong>Archeo_Dat:</strong> Archaeological dating (culture or period)</p> <p><strong>Source:</strong> Source dataset (AMCR or LONGWOOD)</p> <p>AMCR: Archeologická mapa České republiky – Archaeological Map of the Czech Republic. Retrieved from https://digiarchiv.aiscr.cz/.</p> <p>LONGWOOD: Kolář, J., Tkáč, P., Macek, M., & Szabó, P. (2016). Archaeology and Historical Ecology: the Archaeological Database of the LONGWOOD ERC Project. Archäologisches Korrespondenzblatt 46/4, 539-554.</p> <p><strong>Yrs_BP_Avg:</strong> Average dating in calendar years BP (based on the archaeological dating)</p> <p><strong>Yrs_BP_Unc:</strong> Temporal uncertainty of the dating (half of the culture or period's duration)</p> <p><strong>Loc_Accur:</strong> Spatial accuracy derived from the recorded degree of the accuracy of location (radius in meters around the center point)</p>
NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series and the regionalisation of the ΔSNOW model
<p>Time series of daily Snow Water Equivalent (SWE) and Snow Density over the Northern Hemisphere, based on in-situ station observations of snow depth converted to SWE using the ΔSNOW model (Winkler et al., 2021) and regionalised parameters. </p> <p>An extensive description of the dataset and the method to generate it can be found in the data descriptor manuscript published in the journal Earth System Science Data: <a href="https://essd.copernicus.org/preprints/essd-2023-31/">https://essd.copernicus.org/articles/15/2577/2023/essd-15-2577-2023</a> </p> <p><strong>Dataset:</strong> A total of 11,0071 time series of modelled SWE and estimated snow density at the point scale, spanning 1950-2022, at daily resolution.<em> "NH-SWE_dataset_MAP.png"</em> shows a Northern Hemisphere map with the location of all stations in the NH-SWE dataset and their elevation in meters. </p> <p><strong>Files: </strong>The dataset is provided in two different formats:</p> <ol> <li>Individual <em>.csv</em> files for each station in the NH-SWE dataset at <em>"NH_SWE_dataset_vector_files.zip"</em></li> <li>Full-dataset <em>.csv </em>matrices with dates as rows and NH-SWE stations as columns at <em>"NH_SWE_dataset_matrix_files.zip"</em></li> </ol> <p><strong>Metadata:<em> </em></strong><em>"NH_SWE_METADATA.csv"</em> Includes information on NH-SWE stations location (ID, country, station name, coordinates, elevation), data source, length of time series, model parameters and the climate variables used to estimate them, and average snow climatology such as average maximum snow depth, average peak SWE and average maximum snow cover duration. More details and units in the <em>"README_fileformats.txt"</em> file. </p> <p><strong>ΔSNOW model parameter regionalisation: </strong>The code to obtain the ΔSNOW model parameters based on climate variables for all the stations in the NH-SWE dataset is shared in<em><strong> </strong>"DeltaSNOW_parameter_regionalisation.zip"</em>. The method is extensively described in the data descriptor manuscript by Fontrodona-Bach et al., (2023) submitted to Earth System Science Data. More details in the <em>"README_regionalisation.txt"</em> file. </p> <p><strong>Data use: </strong>Free, provided adequate citation of both the data descriptor manuscript and the zenodo record. See <em>"README_datausage.txt"</em></p> <p><strong>Version history:</strong><br>v1: Initial upload. The ΔSNOW model regionalisation was missing.<br>v2: Manuscript submission version. Updated dataset and includes the ΔSNOW model regionalisation code.</p> <p><strong>Reported errors:</strong><br>The dataset accidentally contains one station from the Southern Hemisphere (NH-SWE ID 500001), located in Antarctica (Country code AY). <br>The longitude of a few stations exceeds +180 decimal degrees. To obtain the correct value within the [-180,180] decimal degree longitude bounds, the value exceeding +180 needs to be added to -180 degrees (e.g. +181.0 degrees is actually -179.0 degrees).<br>Swedish stations have two different country codes, SE for the ECA&D stations, and SW for the GHCNd stations. <br>Japan country code is "JA" in the metadata, although the official country code should be JP. </p>
Calcium time series of cortex in a rat model of cortical dysplasia
<p>In vitro Calcium time series of rat (P30) primary motor cortex, were recorder by a CCD camera coupled to stereo-fluoerscence<br> microscope, with a fs = 300ms, following the next sequence: <strong>Basal, <em>Stimulus</em>, Rest.</strong></p> <p>All data is included in a compressed file named <strong>calcium_timeseries.tar.gz.</strong></p> <p>There are two groups of rats: <strong>Control</strong> (control animals), and <strong>BCNU</strong> (experimental animals using the BCNU/carmustine model of cortical dysplasia [1]).</p> <p>Time series are stored in .<strong>csv</strong> files with file names as <strong>R?Pilo-KCl.csv</strong> (where <strong>?</strong> indicates the rat ID). Each of these files holds the two recordings, one for each <em>Stimulus</em>, the first being <em>pilocarpine</em>, followd by <em>KCl</em> used as a control of cellular activity. (pilocarpine, KCl). recording session: The first 150 seconds of these time series correspond to basal activity, followed by 30 s of pilocarpine stimulus, and the rest of spontaneous activity after stimulation, for a total of 15 minutes for each <em>Stimulus</em>. The number of cells recorded varied between animals, as indicated by the number of columns in these .csv files. All of these files have the same number of rows (6000), with each row indicating a frame in the time series. The file <strong>dataEx.png</strong> illustrates this organization.</p> <p>Files named <strong>R?-Coor.csv</strong> (<strong>?</strong> indicates rat ID) show the <em>x</em> and <em>y</em> coordinates of every recorded cell, one for each row, ordered as<br> they appear in the calcium activity recordings. </p> <p><br> Authors:</p> <ul> <li>Ana Aquiles anaaquiles@ciencias.unam.mx</li> <li>Tatiana Fiordelisio tfiorde@ciencias.unam.mx</li> <li>Hiram Luna-Munguía hiram_luna@inb.unam.mx</li> <li>Luis Concha lconcha@unam.mx</li> </ul> <p> </p> <p>1. Benardete, E. A., & Kriegstein, A. R. (2002). Increased excitability and decreased sensitivity to GABA in an animal model of dysplastic cortex. <em>Epilepsia</em>, <em>43</em>(9), 970-982.</p>
Pre-processed and modeled GNSS time-series after the 2011 Tohoku Earthquake
<p>The raw, pre-processed, and modeled GNSS time-series of the 213 GEONET sites in the Tohoku region, Japan, from Mar. 12, 2011 to Nov. 20, 2021, relative to the Okhotsk plate (Argus et al., 2011, <em><em>Geochemistry, Geophysics, Geosystems</em></em>).</p> <p>The original GNSS time-series are F5 solutions, which are distributed by Geospatial Information Authority of Japan (GSI, https://www.gsi.go.jp/). The details and availability of F5 solutions are written in Takamatsu et al. (2023, Earth, Planets, and Space) https://doi.org/10.1186/s40623-023-01787-7.</p> <p>The GNSS time-series processing was performed by Tomita (submitted), and the following signals were excluded from the raw time-series: seasonal variation, coseismic step, antenna maintenance offset, and common mode errors. Then, the pre-processed time-series were modeled by a trajectory modeling method considering postseismic deformation of the 2011 Tohoku earthquake, the Boso SSEs, and postseismic deformations due to aftershocks and L-ASE (long-term aseismic slip event) since late 2019.<br> <br> "sitelist.txt" - Site information file<br> column 1: Full site ID<br> column 2: 4digits site ID<br> column 3: Longitude [deg]<br> column 4: Latitude [deg]<br> column 5: Height [m] <br> <br> "pre-process/xxxx.txt" - Time-series at xxxx (4digits site ID) site<br> column 1: days from Mar. 12, 2011 (1 corresponds to Mar. 12, 2011)<br> column 2: raw East-West displacement [m]<br> column 3: raw North-South displacement [m]<br> column 4: raw Up-down displacement [m]<br> column 5: pre-processed East-West displacement [m]<br> column 6: pre-processed North-South displacement [m]<br> column 7: pre-processed Up-down displacement [m]</p> <p>"model/xxxx/prediction_yy.txt" - Time-series for yy component (yy=EW, NS, UD) at xxxx (4digits site ID) site<br> column 1: days from Mar. 12, 2011 (1 corresponds to Mar. 12, 2011)<br> column 2: modeled displacement excluding the Boso SSEs [m]<br> column 3: modeled displacement excluding the Boso SSEs and postseismic deformation due to aftershocks caused one year after the 2011 Tohoku Eq. [m]<br> column 4: modeled displacement excluding the Boso SSEs, postseismic deformation due to aftershocks caused one year after the 2011 Tohoku Eq. and the 2019 L-ASE [m]</p> <p><br> The displacement on Mar. 12, 2011 was initially set to be zero before the pre-processing, but the removal of the above factors provided some deviation from zero.</p> <p>The raw time-series excluded outliers from the original F5 solutions, and the raw time-series were transformed into the Okhotsk plate reference.</p> <p>Following the above trajectory modeling, the fully-relaxed postseismic displacement fields due to 2015 Feb. 17 Sanriku-oki earthquake ("Table_displacement1.xlsx"), the 2015 May 13 Miyagi-oki earthquake ("Table_displacement2.xlsx"), and summation of the 2021 Feb. 13 Fukushima-oki, the 2021 Mar. 20 Miyagi-oki, and the 2021 May 1 earthquakes ("Table_displacement2.xlsx") were calculated. Moreover, the cumulative displacement field due to the 2019 L-ASE since Nov. 25, 2019 was also calculated. In those files, the estimation errors are also shown as 1σ standard deviation obtained from diagonal components of the model covariance matrices. </p> <p> </p> <p>The details of these data are introduced in the corresponding paper (Tomita, submitted).</p>
Observed and model postseismic time series at GPS sites due to the 2012 Craig and 2013 Haida Gwaii earthquakes
<p><strong>Files with the observed and model displacements, along with predicted model time series</strong>, which derive from the paper of '<em>Postseismic Deformation Due To the 2012 MW 7.8 Haida Gwaii and 2013 MW 7.5 Craig Earthquakes and Its Implications for regional rheological structure'</em>.</p> <p><strong>SITE.obs files:</strong> observed postseismic time series due to the 2012 Mw 7.8 Haida Gwaii and 2013 Mw 7.5 Craig earthquakes</p> <p><strong>SITE.mod files:</strong> Model postseismic displacements, along with predicted time series. Detailed explanations please see <strong>readme.txt</strong>.</p> <p><strong>GPS site names</strong> are the same with the study of Tian et al. (2021). </p> <p> </p>
Observed and model postseismic time series at GPS sites due to the 2012 Craig and 2013 Haida Gwaii earthquakes
<p><strong>Files with the observed and model displacements, along with predicted model time series</strong>, which derive from the paper of '<em>Postseismic Deformation Due To the 2012 MW 7.8 Haida Gwaii and 2013 MW 7.5 Craig Earthquakes and Its Implications for regional rheological structure' </em><strong>JGR: Soild Earth (2021), </strong><a href="https://doi.org/10.1029/2020JB020197">https://doi.org/10.1029/2020JB020197</a>.</p> <p><strong>SITE.obs files:</strong> observed postseismic time series due to the 2012 Mw 7.8 Haida Gwaii and 2013 Mw 7.5 Craig earthquakes</p> <p><strong>SITE.mod files:</strong> Model postseismic displacements, along with predicted time series. Detailed explanations please see <strong>readme.txt</strong>.</p> <p><strong>GPS site names</strong> are the same with the study of Tian et al. (2021). <a href="https://doi.org/10.1029/2020JB020197">https://doi.org/10.1029/2020JB020197</a>.</p>
TimeSpec4LULC: A Smart-Global Dataset of Multi-Spectral Time Series of MODIS Terra-Aqua from 2000 to 2021 for Training Machine Learning models to perform LULC Mapping
<p>TimeSpec4LULC is a smart open-source global dataset of multi-spectral time series for 29 Land Use and Land Cover (LULC) classes ready to train machine learning models. It was built based on the seven spectral bands of the MODIS sensors at 500 m resolution from 2000 to 2021 (262 observations in each time series). Then, was annotated using spatial-temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE).</p> <p>TimeSpec4LULC contains two datasets: the original dataset distributed over 6,076,531 pixels, and the balanced subset of the original dataset distributed over 29000 pixels.</p> <p>The original dataset contains 30 folders, namely "Metadata", and 29 folders corresponding to the 29 LULC classes. The folder "Metadata" holds 29 different CSV files describing the metadata of the 29 LULC classes. The remaining 29 folders contain the time series data for the 29 LULC classes. Each folder holds 262 CSV files corresponding to the 262 months. Inside each CSV file, we provide the seven values of the spectral bands as well as the coordinates for all the LULC class-related pixels.</p> <p>The balanced subset of the original dataset contains the metadata and the time series data for 1000 pixels per class representative of the globe. It holds 29 different JSON files following the names of the 29 LULC classes.</p> <p>The features of the dataset are:</p> <p>- ".geo": the geometry and coordinates (longitude and latitude) of the pixel center.</p> <p>- "ADM0_Code": the GAUL country code.</p> <p>- "ADM1_Code": the GAUL first-level administrative unit code.</p> <p>- GHM_Index": the average of the global human modification index.</p> <p>- "Products_Agreement_Percentage": the agreement percentage over the 15 global LULC products available in GEE.</p> <p>- "Temporal_Availability_Percentage": the percentage of non-missing values in each band.</p> <p>- "Pixel_TS": the time series values of the seven spectral bands.</p>
A tempοral Deep Convolutional Neural Network model on Sentinel-1 Image Time Series for pixel-wise Flood Classification (dataset)
<p>This is a dataset which has been designed to be used for flood time series classification. Each time series is annotated as flood or no-flood and represents a pixel-wise time series derived from stack of Sentinel-1 IW GRD images that have been pre-processed according to <a href="http://doi.org/10.5281/zenodo.6510223">https://doi.org/10.5281/zenodo.6510223</a>.</p>
Data associated with the manuscript "Simple statistical models can be sufficient for testing hypotheses with population time series data"
<p>This is a revised version of the archive of R code and data used in the manuscript, <em>Simple statistical models can be sufficient for testing hypotheses with population time series data. </em>The data are in three files. <em>etodata1.csv</em> and <em>etodata2.csv</em> contain two versions of the same data for shoal-dwelling fishes in the Etowah River and associated environmental covariates. <em>knz_dat</em> contains data for small mammals collected in the Konza Prairie Biological Station and associated environmental covariates. The R code consists of four primary files that call nine auxiliary files. CaseStudy1-main_code and CaseStudy2-main_code are the primary files for running the two case studies. Simulations1 and Simulations2 are the files for running the two batteries of simulations. We thank the Konza Prairie Biological Station and Konza Prairie Long-Term Ecological Research Program supported by the National Science Foundation (DEB-1440484) for collecting and providing access to mammal community data. More details are in the manuscript and supporting information. </p>
Time series of detonation velocity for Fickett's model for various values of activation energy
<p>This dataset contains several time series of detonation velocity for Fickett's model.</p> <p>Parameters are: q=4, resolution per unit lenth is 1280.</p> <p>Activation energies (theta) are 0.95, 1, 1.004, 1.055, 1.065, 1.089.</p>
Time Series Comparisons, Model Code, and a Demo Dataset for SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements
<p>Time series comparisons between SIBaR, Brantley, and Apte background signals for all 312 time series in the Houston mobile monitoring campaign. Additionally, a R script demo (DemoData.R) of the SIBaR partitioning step on the demo datatset (DemoData.csv).</p>
The dataset for the submitted paper " Time Series Analysis of Normal Mode Energetics for Rossby Wave Breaking and Saturation using a Simple Barotropic Model".
<p>These files are the data of the result in the submitted paper, titled "Time Series Analysis of Normal Mode Energetics for Rossby Wave Breaking and Saturation using a Simple Barotropic Model".</p> <ul> <li>File Description</li> </ul> <p>pv13.data : Exp. 1<br> pv17.data : Exp. 2</p> <p>The raw potential vorticity (PV) data for the Exp.1 and Exp.2, respectively, used in drawing the Fig.1, 2, and the supplemental movie 1 and 2.<br> These are the grid point value files, 72 levels for the zonal direction, 30 levels for meridional direction. More details are described in the next ctl files.</p> <p> </p> <p>pv13.ctl<br> pv17.ctl</p> <p>Description files for pv13.data and pv17.data. This will be called from grads_pv13.gs and grads_pv17.data, respectively.</p> <p>grads_pv13.gs<br> grads_pv17.gs</p> <p>GrADS script for mapping the PV.</p> <p> </p> <p>energy17.txt : Exp.2</p> <p>The time series table of energy values for exp.2.<br> One raw is identified by combination of the TIME in the experiment and zonal wave number N.</p>
Soil organic carbon models need independent time-series validation for reliable prediction
<p>Supplementary Data 1 to the paper: Soil organic carbon models need independent time-series validation for reliable prediction</p> <p>By: Le Noë, J., Manzoni, S., Abramoff, R.Z., Bölscher, T., Bruni, E., Cardinael, R., Ciais, P., Chenu, C., Clivot, H., Derrien, D., Ferchaud, F., Garnier, P., Goll, D., Lashermes, G., Martin, M.P., Rasse, D., Rees, F., Sainte-Marie, J., Salmon, E., Schiedung, M., Schimel, J., Wieder, W.R., Abiven, S., Barré, P., Cécillon, L., Guenet, B.</p>
plan4res - public dataset for case study 1 part MIM-1: time series used for multi-modal investment pathway modelling
<p>Public data set which is used within the plan4res project for performing case study 1 "Multi-modal European energy concept for achiving COP21" - Multi-modal Investment modelling (MIM) Part 1: Time series for the reference year 2015</p> <p>The related documentation is included in plan4res' deliverable D4.5 chapter 3.2 (see 10.5281/zenodo.3785010) </p> <p>The data set includes the following data:</p> <p>a) characteristic annual load profiles for large industrial heat demand for chemical, iron & steel, food & beverage and pulp & paper industries for the reference year 2015</p> <p>HOTMAPS__TD_OUT_D_CHEM__20200608T160653__20200422T120000Z__v01.csv <br> HOTMAPS__TD_OUT_D_FOOD__20200608T160724__20200422T120000Z__v01.csv <br> HOTMAPS__TD_OUT_D_IRON__20200608T160705__20200422T120000Z__v01.csv HOTMAPS__TD_OUT_D_PAPER__20200608T160715__20200422T120000Z__v01.csv </p> <p>b) characteristic demand profiles for road-side car passenger transport and availability of cars for charging while (home) parking for the reference year 2015 </p> <p>SIEMENS__TD_OUT_D_RoadCar__20200608T160627__20200401T120000Z__v01.csv SIEMENS__TD_CAP_CarPark__20200608T160637__20200401T120000Z__v01.csv </p> <p>c) load profiles for exogeneous demand of electricity for the reference year 2015. The exogenous demand includes all electricity consumptions not explicitly modeled within MIM modeling.</p> <p>HRE4__TD_OUT_ElectricityExo__20200608T160732__20200401T120000Z__v01.csv </p> <p>c) regionally resolved demand profiles for (individual) space heating and space cooling for the reference year 2015</p> <p>HRE4__TRD_CAP_Cool_2015__20200608T160051__20200401T120000Z__v01.csv <br> HRE4__TRD_CAP_HeatInd_2015__20200608T155849__20200401T120000Z__v01.csv</p> <p>d) regionally resolved generation profiles of electricity from photovoltaic, wind onshore, wind offshore, hydro run-of-river, and for heat generation from solar thermal for the reference year 2015</p> <p>NINJA__TRD_CAP_PV_2015__20200608T160440__20191104T120000Z__v01.csv <br> NINJA__TRD_CAP_WindOFF_2015__20200608T155422__20191104T120000Z__v01.csv <br> NINJA__TRD_CAP_WindON_2015__20200608T155251__20191104T120000Z__v01.csv HRE4__TRD_CAP_HydroRoR_2015__20200608T155550__20200401T120000Z__v01.csv <br> HRE4__TRD_CAP_SolarThermal_2015__20200608T155718__20200401T120000Z__v01.csv </p> <p>e) regionally resolved generation profile of electricity from wind offshore transformed in a way to represent potential capacity factors in future as stated by doi:10.2760/041705. Data based on reference year 2015</p> <p>SIEMENS__TRD_CAP_WindOFF_2040__20200608T155127__20200401T120000Z__v01.csv </p> <p>x) A list of geographical description of the zone hierarchy data used in MIM for the EU33 region set.:</p> <p>SIEMENS__ZoneHierarchy_MIM_EU33__20181231T120000Z___20200131T1200000Z__v001.csv </p> <p>Further info:</p> <p>Time series are based on historical data for the reference year 2015. </p> <p>Values are normalized over one reference year in a way that either the maximum = 1 (CAP) or the integral = 1 (OUT).</p> <p>All values are listed in arbitrary units. </p> <p>All country names are according to ISO 3166-1 alpha-2.</p>
The raw GNSS position time series (raw_time_series.rar) and TDEFNODE models (TDEFNODE_models.rar) related to the manuscript authored by Rui Xu, D. S. Stamps and C. A. Williams
<p>This repository saves the raw GNSS position time series (raw_time_series.rar) and TDEFNODE models (TDEFNODE_models.rar) related to the manuscript authored by Rui Xu, D. S. Stamps and C. A. Williams. For more details, please refer to the NOTES files in each .rar archive.</p>
Model parameter time series of the Business Roadmap for sustainable development of the Mar Menor and surrounding Campo de Cartagena
<p>The Business Roadmap and Policy Recommendations (BRM) represents 14 solutions for sustainable development of the Mar Menor and surrounding Campo de Cartagena that were developed during an extensive co-design process (workshops, expert interviews, online questionnaires) with representatives from all relevant sectors involved. A full description of the BRM, its co-development, and the modelled impacts on Key Performance Indicators of sustainability can be found in<a href="http://doi.org/10.5281/zenodo.7142764"> Martínez-López et al (2022)</a>.</p> <p>The model parameter time-series in this dataset indicate if and when a solution is turned on or off between 1964 and 2070 based on preferences indicated by stakeholders. These time-series represent input parameters for the System Dynamics model of the socio-ecosystem of the Mar Menor-campo de Cartagena developed in the COASTAL project and that is used to quantify impacts on key performance indicators of sustainability. This System Dynamics based model, developed by Martínez-López et al (2022) can be consulted <a href="http://doi.org/10.5281/zenodo.7142764">here</a>.</p> <p>The Business Road Map and Policy Recommendations (BRM) consists of the following 4 milestones and related 14 solutions:</p> <p>(I) <strong>Rural ecotourism</strong>:</p> <p>1. The promotion of rural ecotourism activities.</p> <p>(II) <strong>Coastal ecotourism</strong>:</p> <p>2. The promotion of coastal ecotourism activities.</p> <p>(III) <strong>Sustainable agriculture</strong>:</p> <p>3. Implementation of nutrients, soil, and water retention measures</p> <p>4. Reduction in fertilizer use</p> <p>5. Denitrification of brine wastes from groundwater treated for irrigation</p> <p>6. Decrease in agricultural water demand per hectare (i.e. 10% of decrease by default in the model)</p> <p>(IV) <strong>Integrated sustainable management</strong>:</p> <p>7. Control of the extension of irrigated areas</p> <p>8. Promotion of environmental education</p> <p>9. Control of the number of groundwater wells (i.e. maximum 500 wells by default in the model)</p> <p>10. Promotion of small (<10MW) (agro)photovoltaic facilities</p> <p>11. Surface water pumping from the Albujón ephemeral stream</p> <p>12. Control of other point sources of pollution to the lagoon</p> <p>13. Groundwater pumping and treatment</p> <p>14. Increase in sea water desalination amount (twice the BAU value)</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.