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942 results for “Scenario”
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Mediterranean Sea
<p>The ensemble provides future projections of key marine variables under climate change for the Mediterranean region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the North Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p> <p> </p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Baltic Sea
<p>The ensemble provides future projections of key marine variables under climate change for the Baltci Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
Scenarios simulations of Prague (TURBAN-D05)
<h3>Basic information</h3> <p><span>This dataset contains simulation results for the so-called Holešovi</span><span>č</span><span>ky domain, an area in </span><span>the city of </span><span>Prague, Czech Republic, expected to undergo major traffic infrastructure changes in the near future. </span><span>Three scenarios were modelled: current infrastructure with traffic intensity projections for 2023 (C1), future outlook with a finished part of city inner ring-road in 2030 (C2) and effect of finishing the northern part of the Prague outer ring-road (C3), which will decrease heavy traffic in the domain. Note that all scenarios have slightly different landcover (trees, buildings, bridges, tunnels etc.), so there could be small areas containing NA values in the maps and GIS files. </span><span>All times are in UTC (local time, CEST is UTC +02:00)</span><span>.</span></p> <p><span>For more detailed description of the experiments see the TURBAN project website at <a title="TURBAN" href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>.</span></p> <h3>General organisation</h3> <p><span>Each scenario has two folders; post-processed results from the PALM model as averaged ASCII files that can be viewed in many GIS applications (output-gis) and maps in the PNG format (output-png). Each variable was averaged from original 10min values to 1, 3 and 24-hour averages. The C1 scenario was used as a baseline. In addition to that, also differences for all variables were calculated for the scenarios C2 and C3. In total, the C1 scenario has 3 subfolders with absolute values (prefix abs), the scenarios C2 and C3 have 6 (3 with absolute values and 3 with differences; prefix diff).</span></p> <h3>Modelled variables</h3> <p><span>Each subfolder includes 7 subfolders with variables. Variable <em>bio_mrt</em> is the Mean Radiant Temperature (MRT), <em>bio_pet</em> is the Physiological Equivalent Temperature (PET), <em>bio_utci</em> is the Universal Thermal Climate Index (UTCI), <em>kc_PM10_02m</em> is the concentration of PM10 at 2m above ground, <em>theta_2m</em> is the potential temperature at 2m above ground, <em>tsurf</em> is the surface temperature and <em>wspeed_10m</em> is the wind speed at 10m above ground.</span></p> <h3>File nomenclature</h3> <p><span>Each file (PRJ or ASC, PNG) has the same nomenclature. An example (bio_utci_abs-01h_20190724_1200-1300.png) could be parsed as: variable name (bio_utci), processed output (abs-01h), date (20190724) and averaged period (1200-1300). </span><span>So,</span><span> the result is a map with hourly averaged UTCI for 24 Jul 2019 between 12:00 and 13:00 UTC.</span></p> <h3>Important note</h3> <p><span>During the processing phase a few potentially important problems were identified and need to be analysed in detail. One of them are extremely overestimated concentrations due to stable conditions from boundary condition inputs. In certain situations it can happen that the best regional meteorological model can provide inappropriate input conditions for some episode. This needs to be checked in detail before any following interpretation.</span></p> <h3>Acknowledgements</h3> <p><span>The PALM simulations, and pre- and postprocessing were performed partially on the HPC infrastructure of the Institute of Computer Science of the Czech Academy of Sciences (ICS), supported by the long-term strategic development financing of the ICS (RVO:67985807) and partially on the IT4I HPC infrastructure supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254). The work was performed within the project TURBAN (TO01000219; TURBAN – Turbulent-resolving urban modelling of air quality and thermal comfort) supported by Norway Grants and Technology Agency of the Czech Republic.</span></p>
Estimates of Global Coastal Losses Under Multiple Sea Level Rise Scenarios
<p>Results from the Python Coastal Impacts and Adaptation Model (pyCIAM), the inputs and source code necessary to replicate these outputs, and the results presented in Depsky et al. 2023.</p> <p>All zipped Zarr stores can be downloaded and accessed locally or can be directly accessed via code similar to the following:</p> <pre><code>from fsspec.implementations.zip import ZipFileSystem import xarray as xr xr.open_zarr(ZipFileSystem(url_of_file_in_record}}).get_mapper())</code></pre> <p><strong>File Inventory</strong></p> <p><em>Products</em></p> <ul> <li><strong>pyCIAM_outputs.zarr.zip</strong>: Outputs of the pyCIAM model, using the <a href="https://doi.org/10.5281/zenodo.6449230">SLIIDERS</a> dataset to define socioeconomic and extreme sea level characteristics of coastal regions and the 17th, 50th, and 83rd quantiles of local sea level rise as projected by various modeling frameworks (<a href="https://doi.org/10.5281/zenodo.593357">LocalizeSL</a> and <a href="https://doi.org/10.5281/zenodo.6419953">FACTS</a>) and for multiple emissions scenarios and ice sheet models.</li> <li><strong>pyCIAM_outputs_{case}.nc:</strong> A NetCDF version of <code>pyCIAM_outputs</code>, in which the netcdf files are divided up by adaptation "case" to reduce file size.</li> <li><strong>diaz2016_outputs.zarr.zip</strong>: A replication of the results from <a href="https://link.springer.com/article/10.1007/s10584-016-1675-4">Diaz 2016</a> - the model upon which pyCIAM was built, using an identical configuration to that of the original model.</li> <li><strong>suboptimal_capital_by_movefactor.zarr.zip</strong>: An analysis of the observed present-day allocation of capital compared to a "rational" allocation, as a function of the magnitude of non-market costs of relocation assumed in the model. See Depsky et al. 2023 for further details.</li> </ul> <p><em>Inputs</em></p> <ul> <li><strong>ar5-msl-rel-2005-quantiles.zarr.zip</strong>: Quantiles of projected local sea level rise as projected from the LocalizeSL model, using a variety of temperature scenarios and ice sheet models developed in <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2014EF000239">Kopp 2014</a>, <a href="https://www.pnas.org/doi/pdf/10.1073/pnas.1817205116">Bamber 2019</a>, <a href="https://www.geo.umass.edu/climate/papers2/Deconto_Nature_2021.pdf">DeConto 2021</a>, <a href="https://www.ipcc.ch/srocc/">IPCC SROCC</a>. The results contained in <em>pyCIAM_outputs.zarr.zip</em> cover a broader (and newer) range of SLR projections from a more recent projection framework (FACTS); however, these data are more easily obtained from the appropriate Zenodo records and thus are not hosted in this one.</li> <li><strong>diaz2016_inputs_raw.zarr.zip</strong>: The coastal inputs used in <a href="https://link.springer.com/article/10.1007/s10584-016-1675-4">Diaz 2016</a>, obtained from <a href="https://github.com/delavane/CIAM">GitHub</a> and formatted for use in the Python-based pyCIAM. These are based on the <a href="http://diva.globalclimateforum.org">Dynamic Integrated Vulnerability Assessment (DIVA)</a> dataset.</li> <li><strong>surge-lookup-seg(_adm).zarr.zip</strong>: Pre-computed lookup tables estimating average annual losses from extreme sea levels due to mortality and capital stock damage. This is an intermediate output of pyCIAM and is not necessary to replicate the model results. However, it is more time consuming to produce than the rest of the model and is provided for users who may wish to start from the pre-computed dataset. Two versions are provided - the first contains estimates for each unique intersection of ~50km coastal segment and state/province-level administrative unit (admin-1). This is derived from the characteristics in SLIIDERS. The second is simply estimated on a version of SLIIDERS collapsed over administrative units to vary only over coastal segments. Both are used in the process of running pyCIAM.</li> <li><strong>ypk_2000_2100.zarr.zip</strong>: An intermediate output in creating SLIIDERS that contains country-level projections of GDP, capital stock, and population, based on the Shared Socioeconomic Pathways (SSPs). This is only used in normalizing costs estimated in pyCIAM by country and global GDP to report in Depsky et al. 2023. It is not used in the execution of pyCIAM but is provided to replicate results reported in the manuscript.</li> </ul> <p><em>Source Code</em></p> <ul> <li><strong>pyCIAM.zip:</strong> Contains the python-CIAM package as well as a notebook-based workflow to replicate the results presented in Depsky et al. 2023. It also contains two master shell scripts (run_example.sh and run_full_replication.sh) to assist in executing a small sample of the pyCIAM model or in fully executing the workflow of Depsky et al. 2023, respectively. This code is consistent with release 1.2.0 in the <a href="https://github.com/ClimateImpactLab/pyCIAM">pyCIAM GitHub repository</a> and is available as version 1.2.0 of the python-CIAM package on PyPI.</li> </ul> <p> </p> <p><strong>Version history:</strong></p> <p><em><strong>1.2</strong></em></p> <ul> <li>Point `data-acquisition.ipynb` to updated Zenodo deposit that fixes the dtype of `subsets` variable in `diaz2016_inputs_raw.zarr.zip` to be bool rather than int8</li> <li>Variable name bugfix in `data-acquisition.ipynb`</li> <li>Add netcdf versions of SLIIDERS and the pyCIAM results to `upload-zenodo.ipynb`</li> <li>Update results in Zenodo record to use SLIIDERS v1.2</li> <li> </li> </ul> <p><em><strong>1.1.1</strong></em></p> <ul> <li>Bugfix to inputs/diaz2016_inputs_raw.zarr.zip to make the `subsets` variable bool instead of int8.</li> </ul> <p><em><strong>1.1.0</strong></em></p> <ul> <li>Version associated with publication of Depsky et al., 2023</li> </ul>
Comparing V2X and RADAR safety performance in NLOS scenarios
<p><strong>Scenario 1: </strong>Highway car following in road curve </p> <p>This scenario simulates a highway environment where two vehicles (HV and RV) communicate via V2X and HV is also equipped with radar sensor, while navigating a curved road. The leading remote vehicle (RV) is moving with constant speed and it is intially out of range of HV's radar sensor.</p> <p>Safety metrics such as Time-to-Collision (TTC) are evaluated to analyze the system's performance under the influence of NLOS situations and road curvature. <br><em>Dataset file: <code>Highway_road_curve_scenario.csv</code></em><br><br><strong>Scenario 2: </strong>Intersection scenario <br><br>This scenario involves two vehicles crossing each other paths and communicating via V2X at an intersection. Radar and V2X data are used to calculate safety indicators such as Time-to-Intersection (TTI), assessing the effectiveness of cooperative communication in mitigating collision risks. <br><em>Dataset file: <code>Intersection_scenario.csv</code></em></p>
3DO Dataset | On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios
<p><strong>On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios</strong></p> <p>This repository contains the <strong>3DO dataset</strong> proposed in <a href="https://doi.org/10.1007/978-3-031-78354-8_13">[1]</a>.</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the 3DO dataset is provided at: <a href="https://github.com/StrohmayerJ/3DO/tree/main">https://github.com/StrohmayerJ/3DO</a></p> <p><strong>Dataset Description</strong></p> <p>The 3DO dataset comprises 42 five-minute recordings (~1.25M WiFi packets) of three human activities performed by a single person, captured in a WiFi through-wall sensing scenario over three consecutive days. Each WiFi packet is annotated with a 3D trajectory label and a class label for the activities: no person/background (0), walking (1), sitting (2), and lying (3). (<strong>Note:</strong> The labels returned in our dataloader example are walking (0), sitting (1), and lying (2), because background sequences are not used.)</p> <p>The directories <code>3DO/d1/</code>, <code>3DO/d2/</code>, and <code>3DO/d3/</code> contain the sequences from days 1, 2, and 3, respectively. Furthermore, each sequence directory (e.g., <code>3DO/d1/w1/</code>) contains a <code>csiposreg.csv</code> file storing the raw WiFi packet time series and a <code>csiposreg_complex.npy</code> cache file, which stores the complex Channel State Information (CSI) of the WiFi packet time series. (If missing, <code>csiposreg_complex.npy</code> is automatically generated by the provided dataloader.)</p> <p>Dataset Structure:</p> <p>/3DO</p> <p>├── d1 <em><-- day 1 subdirectory</em></p> <p> └── w1 <em><-- sequence subdirectory</em></p> <p> └── csiposreg.csv <em><-- raw WiFi packet time series</em></p> <p> └── csiposreg_complex.npy <em><-- CSI time series cache</em></p> <p>├── d2 <-- day 2 subdirectory</p> <p>├── d3 <-- day 3 subdirectory</p> <p> </p> <p>In [1], we use the following training, validation, and test split:</p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Day</strong></td> <td><strong>Sequences </strong></td> </tr> <tr> <td>Train</td> <td>1</td> <td>w1, w2, w3, s1, s2, s3, l1, l2, l3</td> </tr> <tr> <td>Val</td> <td>1</td> <td>w4, s4, l4</td> </tr> <tr> <td>Test</td> <td>1</td> <td>w5 , s5, l5</td> </tr> <tr> <td>Test</td> <td>2</td> <td>w1, w2, w3, w4, w5, s1, s2, s3, s4, s5, l1, l2, l3, l4, l5</td> </tr> <tr> <td>Test</td> <td>3</td> <td>w1, w2, w4, w5, s1, s2, s3, s4, s5, l1, l2, l4</td> </tr> </tbody> </table> <p><em>w = walking, s = sitting and l= lying</em></p> <p><strong>Note: </strong>On each day, we additionally recorded three ten-minute background sequences (b1, b2, b3), which are provided as well.</p> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a>.</p> <p><a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a> Strohmayer, J., Kampel, M. (2025). On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios. In: Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15315. Springer, Cham. <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-78354-8_13</a></p> <p>BibTeX citation:</p> <pre>@inproceedings{strohmayerOn2025, author="Strohmayer, Julian and Kampel, Martin",<br> title="On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios",<br> booktitle="Pattern Recognition",<br> year="2025",<br> publisher="Springer Nature Switzerland",<br> address="Cham",<br> pages="194--211",<br> isbn="978-3-031-78354-8" }</pre>
Compiled database, code and raw data for the article "A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios for the Evaluation of Functional-Structural Models"
<p>This dataset results from an experiment on young oil palm plants (<em>Elaeis guineensis</em>) in the Ecotron facility from CNRS in Montpellier. Four plants were put in a microcosm one by one with varying climatic conditions to investigate the effect of climate on leaf temperature, CO2, and H2O fluxes at the plant scale. The conditions were defined based on typical daily conditions from a location where it is grown (Libo, Indonesia), <em>i.e.</em>, a day with no rainfall and near-average air temperature and humidity. This base condition was then modified by adding more CO2 (400, 600 and 800ppm), less radiation (typical cloudy sky), and more or less temperature and vapour pressure deficit (± 30%).</p> <p>Find more details from the <code>README.md</code> file in the repository or from the associated <a href="https://github.com/PalmStudio/Biophysics_database_palm" target="_blank" rel="noopener">Github repository</a>.</p>
Supplementary dataset for "Potential of Wastewater Reuse to Alleviate Water Scarcity under Future Warming Scenarios"
<p>The folder contains water gap data relative to the paper:<br>Kahn, M., Sangiorgio, M., and Rosa, L. (2025) Potential of wastewater reuse to alleviate water scarcity under future warming scenarios. Environmental Research Letters, 20, 034012<br>https://doi.org/10.1088/1748-9326/adb31d</p> <p>All water gaps data are in km3/yr.</p> <p><br>Gridded data(NetCDF at 0.5°)</p> <ul> <li>baseline (2001-2010) <ul> <li>Water_gap_baseline_no_wastewater_reuse: Water gap under baseline climate scenario with no wastewater reuse.</li> <li>Water_gap_baseline_treated_wastewater_reuse: Water gap under baseline climate scenario with treated wastewater reuse.</li> <li>Water_gap_baseline_full_wastewater_reuse: Water gap under baseline climate scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <ul> <li>1.5°C warming <ul> <li>Water_gap_15_no_wastewater_reuse: Water gap under 1.5°C warming scenario with no wastewater reuse.</li> <li>Water_gap_15_treated_wastewater_reuse: Water gap under 1.5°C warming scenario with treated wastewater reuse.</li> <li>Water_gap_15_full_wastewater_reuse: Water gap under 1.5°C warming scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <ul> <li>3°C warming (5 models + average) <ul> <li>Water_gap_3_no_wastewater_reuse: Water gap under 3°C warming scenario with no wastewater reuse.</li> <li>Water_gap_3_treated_wastewater_reuse: Water gap under 3°C warming scenario with treated wastewater reuse.</li> <li>Water_gap_3_full_wastewater_reuse: Water gap under 3°C warming scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <p>Aggregated data (.xlsx)</p> <ul> <li>country_level_water_gaps.xlsx: Water gap aggregated by country for all the considered scenarios.</li> <li>city_water_gaps.xlsx: 0.5° pixels with populations greater than 5,000,000 and non-zero water gaps corresponding to urban center.</li> <li>seasonal_variations.xlsx: Monthly water gaps of the 5 most water scarce countries.</li> </ul> <p><br>Note: the global water gap obtained by summing all the countries is not completely equivalent to the sum of all the pixels because some pixels' center is outside the polygon of the corresponding country.</p>
Assessing Safe Locomotion with Exoskeletons in Realistic Scenarios (SALOEXO)
<p>This dataset includes data from various subjects walking over a treadmill (N-Mill from Motek) with a lower-limb exoskeleton (H3 from Technaid S.L.) with multiple sensors. A further description of the data will be uploaded. It is a result of the SALOEXO project, granted by the European project COVR (grant 779966).</p>
Data provided for the Preenacting Climate Change Scenarios project 2021
<p>CMIP6 model output data processed using the scripts provided here: https://github.com/lukasbrunner/preenact/</p>
India Transportation Scenarios
<p>The dataset contains modeling results of the transportation scenarios for India. Five teams modeled five policy scenarios – fuel efficiency, electrification, alternative fuels, modal shifts, and moderation in transport demand – to explore which policy brings the largest synergetic effects in reducing carbon dioxide (CO<sub>2</sub>) and particulate matter (PM<sub>2.5</sub>) emissions. The teams also modeled the comprehensive scenario which included policy measures from individual scenarios. The dataset contains data on passenger vehicles (two-wheelers (2W), three-wheelers (3W), passenger cars (LDV), and buses (Bus)) and freight transport (light heavy-duty trucks (LHDV), medium heavy-duty trucks (MHDV), and heavy heavy-duty trucks (HHDV)). It includes data on service, final energy consumption, service intensity, CO2 emissions, as well as population and GDP. </p>
Supplementary Materials for "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor"
<p>This work corresponds to the results described in paper "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor": <a href="https://www.mdpi.com/2306-5729/6/6/62">https://www.mdpi.com/2306-5729/6/6/62</a></p> <p>The provided open-access dataset consists of JavaScript Object Notation (JSON) records stored in Comma-Separated Values (CSV) files, and the data were gathered in a span of multiple hours during two days of measurements. Each JSON file contains parameters as described below. In addition to the payload itself, every record on the server also contains additional metadata. Metadata contains general information about the LoRaWAN message and the array of parameters that provide more detailed message reception information for each Gateway (GW) receiving the message separately. Notably, these names may differ between LoRaWAN service providers. In the case of Ceske Radiokomunikace (CRa), the metadata contains the following parameters:</p> <ul> <li> <p>cmd—Command (message type): Incoming (uplink) message from the ED via the GW to the server. This also contains metadata from receiving GWs.</p> </li> <li> <p>seqno—Sequence number: The sequence number of the message in the form of a 32-bit integer. The Network Server generates this number.</p> </li> <li> <p>EUI—Extended Unique Identifier: A global identifier (64-bit) of the terminal device, which the manufacturer or owner assigns. The Institute of Electrical and Electronics Engineers (IEEE) Registration Authority manages the assignment of identifier pools. It is given in hexadecimal format. This identifier is used similarly to the MAC address of the network interface.</p> </li> <li> <p>ts—Timestamp: The time of the received message recorded at the first receiving GW. The parameter indicates the number of milliseconds since the Unix epoch (1 January 1970).</p> </li> <li> <p>fcnt—Frame count: Sequential number of the message (16-bit integer) sent from the device. In the case of a device reset, the value of the counter starts from zero. The value of this parameter can be used to detect a failure to receive messages.</p> </li> <li> <p>port—The port number is used to distinguish the type of application payload message. It is, therefore, not necessary to explicitly add it to the application payload. The Port parameter’s (8-bit integer) possible values range from 1 to 223 for the users. Other values are reserved.</p> </li> <li> <p>freq—Frequency: A value that corresponds to the frequency (expressed in Hertz) of the given LoRaWAN channel. Before transmitting each message, the ED pseudo-randomly selects from the range of available LoRaWAN channels on which it will transmit the message.</p> </li> <li> <p>toa—Time on Air: Message transmission time in milliseconds. This value is directly proportional to the data rate and message size.</p> </li> <li> <p>dr—Data Rate: The string parameter specifying the spreading factor, bandwidth, and coding rate. The spreading factor fundamentally affects the data rate and thus, the message time on-air. The value can be selected from the interval 7 to 12. Bandwidth values are only 125, 250, and 500 kHz. The larger the bandwidth, the higher the data rate.</p> </li> <li> <p>ack—Acknowledge: The parameter is of a Boolean type and indicates whether the ED requires confirmation of the sent message. The default is to avoid using acknowledgments to reduce network traffic.</p> </li> <li> <p>gws—Gateways: Contain an array of information objects from individual GWs, especially information about the parameters of the received signal, timestamp, identifier, and location of the GW.</p> <ul> <li> <p>rssi—Received Signal Strength Indicator: The received signal level on the GW, expressed in dBm. The threshold value of the Semtech SX1301 receiver is −142 dBm [<a href="https://www.mdpi.com/2306-5729/6/6/62/htm#B44-data-06-00062">44</a>].</p> </li> <li> <p>snr—Signal-to-Noise Ratio: This parameter gives the ratio between the received power signal and the noise floor power level in dB. If the SNR is greater than 0, the received signal level is higher than the noise level.</p> </li> <li> <p>ts—Timestamp: The time of the received message in milliseconds since the Unix era (1 January 1970).</p> </li> <li> <p>tmms—Time in ms: GPS time in milliseconds since 6 February 1980. The GW must have GPS connectivity.</p> </li> <li> <p>time—UTC of the received message, with microsecond precision in the ISO 8601 format.</p> </li> <li> <p>gweui—GW extended unique identifier: The 64-bit number in a hexadecimal format specific for each GW.</p> </li> <li> <p>lat—Latitude: GW GPS latitude parameter in decimal degrees. The GW must have GPS connectivity.</p> </li> <li> <p>lon—Longitude: GW GPS longitude parameter in decimal degrees. The GW must have GPS connectivity.</p> </li> </ul> </li> <li> <p>bat—Battery status of the ED 8-bit integer value (0—external power supply, 255—battery status is unknown, 1–254—correspond to battery status 0–100%).</p> </li> <li> <p>data—The field contains HEX data, which is unique for the LoRaWAN device in question. It consists of information related to temperature, position, battery level, etc. In the case of our device, it represents our unique data format, which is specifically designed for the purposes of our measurements.</p> </li> <li> <p>device_Lat—Latitude of the measurement point gathered from the GPS.</p> </li> <li> <p>device_Lon—Longitude of the measurement point gathered from the GPS.</p> </li> </ul> <p>The undeniable advantage of the JSON format is that it is in a human-readable form. Thus, without the need for complex parsing, necessary information can be read immediately.</p>
USM Dataset - A Dataset for Polyphonic Sound Event Tagging in Urban Sound Monitoring Scenarios
<p>This dataset includes 24,000 5-seconds-long polyphonic stereo soundscapes composed of sounds taken from the FSD50k dataset:</p> <p>- Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, Xavier Serra. FSD50K: an Open Dataset of Human-Labeled Sound Events (<a href="https://arxiv.org/abs/2010.00475">https://arxiv.org/abs/2010.00475</a>)</p> <p>FSD50k samples used in the USM dataset were selected to allow for commercial usage.</p> <p>Find more details about the USM dataset at <a href="https://github.com/jakobabesser/USM">https://github.com/jakobabesser/USM</a></p>
Land Use - Crop - Climate Scenarios Oudlandpolder
<p>Regionalized time series for 2020-2100 with a day resolution based on different combinations of 4 climate RCP (Representative Concentration Pathways) and 4 Shared Social-economic Pathways (SSP) scenarios for the Oudlandpolder region in Belgium. This coastal lowland (polder) faces challenges related to climate adaptation and mitigation to be addressed by, amongst others, a proper balance of water distribution for the two main land use categories: agriculture and nature. The following variables are included:</p> <ul> <li>land use cover (agriculture and nature) in ha related to SSP</li> <li>crop fractions for 13 crops related to SSP</li> <li>crop factors Kc (dimensionless)</li> <li>precipitation in mm/day related to RCP</li> <li>potential evapotranspiration in mm/day related to RCP</li> <li>sea level in mm wrt January 1, 2020 related to RCP </li> <li>Waste Water Treatment Plant (WWTP) effluence ratio wrt to 2020</li> </ul> <p>RCP scenarios: RCP 2.6; RCP 4.5; RCP 6.0; RCP 8.5</p> <p>SSP scenarios: SSP1; SSP2; SSP4; SSP5. These SSP scenarios are based on consistent projections for land use change (agriculture vs nature) and crop schemes. </p> <p>Land use cover change was modelled with the VITO spatial-dynamic RuimteModel (see references below). </p> <p>Uncertainty in the RCP scenarios has been addressed by working with an ensemble of the following 8 climate models:</p> <ul> <li>BNU-ESM</li> <li>CSIRO-Mk3-6-0</li> <li>GFDL-ESM2G</li> <li>GFDL-ESM2M</li> <li>IPSL-CM5A-MR</li> <li>MPI-ESM-LR</li> <li>MPI-ESM-MR</li> <li>NorESM1-M</li> </ul> <p>The scenarios point at potential seasonal water shortages for certain combinations of RCP and SSP scenarios, in particular during the dry summer season. Climate adaptation actions can also benefit to a limited extent from identifying the climate robust crop schemes. </p> <p>For further information see: </p> <ul> <li>https://h2020-coastal.eu</li> <li>https://h2020-coastal.eu/publications/flipbook/171</li> <li>https://www.vlm.be/nl/projecten/Paginas/Oudlandpolder.aspx</li> <li>https://vito.be/en/spatial-model-flanders-ruimtemodel-vlaanderen</li> </ul>
H2020 773782-COASTAL MAL03 Scenarios for the Norrström-Baltic region
<p>The scenarios are developed based on projected climate and socio-economic changes, following the representative concentration pathways (RCPs) and the shared socioeconomic pathways (SSPs) for the region.</p> <p>The Norrström-Baltic SD model analyzes possible future shifts in the annual average conditions of sectoral and natural water system interactions. Such shifts are evaluated based on recent annual averages reflecting the condition of system components. Parameters taken into account are, amongst others, sectoral water availability, water fluxes between sectors and the corresponding nutrient (nitrogen and phosphorus) exchanges, coastal runoff and nitrogen and phosphorous loads ending up in the Baltic Sea. </p> <p>An overview of the model input variables and the parameters that are identified as system external uncertainties that may affect the behavior of the model:</p> <p>- Precipitation: climate change</p> <p>- Agricultural land: Development policies and market forces, food security and trade regulations, population growth and corresponding food demand/diet changes</p> <p>- Built-up land: Development policies and market forces, population growth, regional urbanization level, tourism expansion level</p> <p>- Forest land: Mitigation policies on climate change (i.e. afforestation and/or reforestation to maintain/enhance carbon capture and storage capacity), socio-economic developments leading to sectoral land competition (i.e. deforestation)</p> <p>- Open lands and wetlands: Policies and market forces supporting social and economic development in the region</p> <p>A total of 5 scenarios were developed for the Norrström/Baltic Sea case. One of them represents the ‘Base case’ conditions, while the rest are rooted in the combination of a certain SSP with a climate scenario linked to a certain RCP. The following overview shows the combinations used during the scenario building process:<br> - Scenario 1: SSP1 + RCP 4.5<br> - Scenario 2: SSP2 + RCP 4.5<br> - Scenario 3: SSP4 + RCP 4.5<br> - Scenario 4: SSP5 + RCP 4.5<br> - Base Case scenario: Continuation into the future of the past-recent long-term average conditions in relation to hydro-climate and land use variables in the SD model.</p> <p>All the scenarios developed for the Norrström-Baltic region are linked to a climate scenario corresponding with RCP4.5, because projected patterns and changes for climate variables under this climate scenario were found to be more consistent with the observed changes in the region than other RCPs. The period 2010-2100 is compared with the normal mean for the period 1961-1990. Each year is compared separately with the long-term annual average precipitation.</p> <p>The xsls file is organized as follows. It comprises three sheets:</p> <ul> <li>Precipitation RCP with annual data of changes in annual precipitation (in percentage), precipitation (in million of m3/year and in mm/year);</li> <li>Land cover RCPs and SSPs with scenario data on land cover, annual change in land cover (in percentage), annual land cover areas for the Norrström water management district area, land dover area average for teh Norrström water management district area and average change in land cover compared to the long-term average (in percentage);</li> <li>Input data model with the four input variables (precipitation change rate in hydro-climate scenarios, urban growth rate in socioeconomic scenarios, forest land change rate in socioeconomic scenarios and agricultural land change rate in socioeconomic scenarios) and their change for each scenario (expressed in percentage).</li> </ul>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - B. Data for 2020 - 2026 - Covid scenario
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2020 to 2026 (<em>covid</em> scenario).<br> Code, method material and data for years 2016-2019 are stored in the following repository: <a href="http://doi.org/10.5281/zenodo.5713811">10.5281/zenodo.5713811</a><br> Data for the <em>counterfactual</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></p> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>The <em>covid</em> scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - C. Data for 2020 - 2026 - Counterfactual scenario
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2020 to 2026 (<em>counterfactual</em> scenario).<br> Code, method material and data for years 2016-2019 are stored in the following repository: <a href="http://doi.org/10.5281/zenodo.5713811">10.5281/zenodo.5713811</a><br> Data for the <em>covid</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></p> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>The<em> counterfactual</em> scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
A Practical Tool-Chain for the Development of Coordination Scenarios - Graphical Modeler, DSL, Code Generators and Automaton-Based Simulator
<p>The Peer Model is a modeling tool for coordination based on blackboard-based collaboration. </p> <p>The tool-chain consists of a modeler, translator and simulator.</p> <p>Its goal is to help developers of distributed and concurrent coordination software better understand algorithms and identify deficiencies from the beginning.</p> <p><br> </p>
Fit for 55 MIX H2 2030 scenario for PyPSA
<p>Country-level PyPSA model based on the Fit for 55 MIX-H2 scenario for the year 2030. The model includes both electricity and hydrogen. </p> <p>Following the methodology described here: https://data.jrc.ec.europa.eu/dataset/d4d59b89-89f7-4275-801a-45ea8957e973</p> <p>Each NetCDF contains a "solved" scenario with a different climate year that can be imported into PyPSA with `pypsa.Network.import_from_netcdf()`</p>
MAL04 Territorial development scenarios for the Charente River basin and its coastal zone (France)
<p>This dataset includes the territorial development scenarios developed by the H2020 COASTAL project’s MAL #4 for the Charente River basin and its coastal zone. These scenarios were co-designed with local stakeholders to depict possible futures of the territory. “Towards a desirable future” represents the implementation of the business roadmap also designed in collaboration with stakeholders to achieve a desirable and sustainable future. “Improving current trends” describes the expected evolution of the territory if current efforts are maintained without significant innovation. “Towards a fragmented territory” illustrates a negative development of the territory, exacerbating current issues and inequalities. Each scenario consists in a narrative and in a set of values attributed to the decision variables of the MAL #4 system dynamics model. These values are converted into time-series to simulate the scenarios (cf. data_scenarios.xlsx in <a href="https://doi.org/10.5281/zenodo.7075123">https://doi.org/10.5281/zenodo.7075123</a>).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.