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942 results for “scenario”
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany
<p><strong>Average number of heatwave days per year versus socio economic data - base scenario (1986-2005)</strong></p> <p>Heat stress exposure maps for the city of Berlin representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modelled over the reference period 1986-2005 using the present land use / cover situation for the city.</p> <p><strong>Exposure mapping variable include the following:</strong></p> <p>Total population 2013</p> <p>Population density inhabitants per hectare 2013</p> <p>Number of inhabitants aged 0 to 17 years 2013</p> <p>Number of inhabitants aged 18 to 65 years 2013</p> <p>Number of inhabitants aged +65 years 2013</p> <p>Number of schools 2014</p> <p>Number of childcare centers 2014</p> <p>Number of hospitals 2014</p> <p>Number of elderly stay facilities 2014</p>
Experimental Data for Natural Disaster Mobility Model and Typhoon Haiyan Scenario
<p>The experimental data set for running the <em>Typhoon Haiyan</em> scenario with the <em>Natural Disaster Mobility Model</em> presented in the paper:</p> <p>Milan Stute, Max Maass, Tom Schons, and Matthias Hollick, “<strong>Reverse Engineering Human Mobility in Large-scale Natural Disasters</strong>,” to appear in <em>ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)</em>, November 2017, Miami Beach, USA.</p>
Scenario data, model source code and plotting routine for manuscript: Separating CO2 emission from removal targets comes with limited cost impacts
<p>This data archive contains REMIND model setup, results data and data analysis files for manuscript:<br><strong>Separating CO2 emission reduction from removal targets comes with limited cost impact.<br><br>plotting</strong>(directory) contains results data, manuscript specific data analysis and plotting routine scripts used to generate the figures of the manuscript.<br><strong>remind</strong>(directory) contains REMIND model source code and scenario set-up. Detailed scenario configurations are set in remind/config/scenario_config_SepMark.csv.<br><strong>remind2</strong>(directory) contains the slightly modified R-library package used for post-processing of REMIND output.<br><br>AMENDMENT<br><strong>Plots_SeparateMarkets_afterReviewProcess.Rmd</strong> After the review process, the new plotting script was added including the additional figures in the Supplementary Material. This file should replace the previous R-markdown file SepMark_essential/plotting/Plots_SeparateMarkets.Rmd.</p>
Dataset for Bukovsky et al. (2021): "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections"
<p>This dataset contains derived data and model data necessary for reproducing the results found in "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections" by Melissa S. Bukovsky, Jing Gao, Linda O. Mearns, and Brian C. O'Neill. This dataset contains data not otherwise available in other public archives, as noted in Bukovsky et al. (2021, Earth's Future; preprint available at https://doi.org/10.1002/essoar.10504141.2). That is, this dataset contains data from the land-use change simulations that are not part of NA-CORDEX (na-cordex.org), but which are complementary to those published in the NA-CORDEX archive.</p>
BBN designed to develop future land-use scenarios for the Sierra Nevada under different environmental and management conditions
<p>Land-use change (deforestation for crops and pastures, reforestation, firewood removal, etc.) constitutes one of the primary drivers of global change, since human activity is to a greater or lesser degree altering the vegetation cover of the planet. The combined effects of climate change and shifts in land use determine the distribution and structure of the vegetation of the Sierra Nevada, and the associated ecosystem services. The surface cover of tree formations in Sierra Nevada has expanded from 15% to 51.23% over the last 60 years. Similarly, a densification of the scattered tree cover and the natural forests and a decline in the surface area occupied by cultivated fields (from 17.8% to 4.72%) has occurred in the last six decades (Zamora, et al, 2016). Therefore, it is important to ascertain future land use change and its effects on the vegetation cover. </p><p>The main purpose of this model is to facilitate the land-use management of Protected Areas (PAs) based on ecosystem services (ES). A BBN is being designed to develop future land-use scenarios for the Sierra Nevada under different environmental and management conditions. Afterwards, we will implement these scenarios in other ES assessment models. The analysis of ES trade-offs in several scenarios will help managers to predict the state of ES and their relations in the future.</p>
Extended reference scenarios (ERS) file (including only the temperature and trace species climatology)
<p>Extended reference scenarios are store in a single NetCDF file providing the atmospheric state (pressure, temperature and composition) expected to be seen by CAIRT for different conditions (altitude, latitude, season, time, solar activity and volcanic activity). </p>
KNMI'23 national climate scenarios - background dataset
<p>Dataset associated with <strong>Van der Wiel et al. (202?): KNMI'23 climate scenarios for the Netherlands: storyline scenarios of regional climate change, Earth's Future</strong><i><strong>, in review</strong></i><strong>.</strong></p><p>This dataset contains the CMIP6 data and EC-Earth3/RACMO data that formed the basis for the KNMI'23 national climate scenarios for the Netherlands (<a href="http://www.knmi.nl/klimaatscenarios">www.knmi.nl/klimaatscenarios</a>). Full details on the methodology, including the origin of this dataset, can be found in the above referenced paper.</p><ul><li><strong>original_ensembles.tar.gz</strong> - Contains the original CMIP6, EC-Earth3 and RACMO-based time series for the NL and NL+RM regions, for all variables of interest (TAS, PR, PET, WBDEFICIT, RSDS, PR10DMAX). Additionally, for TAS also global-mean time series are provided. Data from the historical experiment and three SSP-scenario experiments (SSP1-2.6, SSP2-4.5, SSP5-8.5) are provided.</li><li><strong>resampled_ensembles.tar.gz</strong> - Contains resampled datasets of EC-Earth3 and RACMO data, for each scenario-time horizon combination (Ld,Ln,Md,Mn,Hd,Hn, and 2050,2100,2150). Again these are the NL and NL+RM regional mean time series, for the variables of interest.</li></ul><p>Final data products for the KNMI'23 national climate scenarios can be downloaded from <a href="https://klimaatscenarios-data.knmi.nl/">https://klimaatscenarios-data.knmi.nl/</a>.</p>
Energy simulation outputs for different mitigation scenarios
<p><span>Advanced urban heat mitigation technologies that involve the use of super cool materials combined with properly designed green infrastructure, lower the urban ambient and land surface temperatures and reduce the cooling consumption at the city scale. We present the </span>results of the <span>world's largest heat mitigation project in Riyadh, KSA. Daytime radiative coolers as well as cool materials combined with irrigated or non-irrigated greenery, have been used to design eight holistic heat mitigation scenarios. We assessed the climatic impact of the scenarios as well as the corresponding energy benefits </span>of <span>3,323 urban buildings. An impressive decrease of the peak ambient temperature, up to 4.5°C, is calculated, consisting of the highest reported urban ambient temperature reduction, while the annual sum of the differences of the ambient temperature against a standard temperature base, (cooling degree hours), in the city decrease by up to 26%. We found that innovative urban heat mitigation strategies contribute to </span>remarkable cooling energy conservation by up to 16%, while the combined implementation of heat mitigation and energy adaptation technologies decreases the cooling demand by up to 35%. <span>It is the first article investigating the large-scale energy benefits of modern heat mitigation technologies when they are implemented in cities. </span></p>
CAIRT/IASI-NG/CAIRT+IASI-NG FL2S results of Case Study Scenario 3 for limb-nadir application
<p>Results of the fast level-2 simulator (FL2S) for Case Study Scenarios 3 (only CO and PAN files) for limb-nadir application. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak') and additional application of noise ('_aknoi') for CAIRT alone, IASI-NG alone, and the combined product CAIRT+IASI-NG. </p>
Evaluating Effects of Climate Change, Restoration Scenarios, and Hatchery Effects on Chinook Salmon in the Stillaguamish River Basin with the HARP Model
<p>Model code (R) to accompany the 2023 NOAA report "<strong>Evaluating Effects of Climate Change, Restoration Scenarios, and Hatchery Effects on Chinook Salmon in the Stillaguamish River Basin with the HARP Model</strong>"</p>
NANCY SNS-JU Project - Fronthaul network of fixed topology Usage Scenario - Dataset 1
<p>In the context of the NANCY project (https://nancy-project.eu/), this Dataset provides input data for the development of the B-RAN and attacks models for the NANCY framework, to model training and model inference functions. The data collected plays the role of ML algorithm-specific data preparation. The dataset contains time-series, collected transmitting a video content through the Italtel "VTU - video streaming and transcoding application", that can convert audio and video streams from one format to another, at multiple encodings schemes, changing resolution, bitrate, and video parameters. The data collected are related to the observation of some of the resources involved in the Usage Scenario: “Fronthaul network of fixed topology – Direct Connectivity”. In the Italtel Italian in-lab testbed, a MEC assisted 5G network scenario with a video streaming application for generating traffic is provided. Two different scenarios were set-up, related to downstream and upstream video flows. The variety of collected features ranges from radio front-end metrics to physical server operating system and network function metrics. The dataset consists of raw network traffic and extracted flow-based data captured in separate files. Each file captured is associated to a 10min video streaming of the “Big Buck Bunny” video. This video was transmitted on two different bands, N3 and N78, with different resolutions, 480p, 720p, 1080p; both in uplink (UL) and in downlink (DL); the type of protocol monitored is “HTTP protocol”; in case of N78 band, data related to the resource usage were also captured, for a total of more that 100 data files.</p> <p>The collected dataset is representative resource-intensive video traffic that has the greatest impact on 5G/B5G network planning and provisioning. The video streaming dataset includes data directly measured while watching the video on the mobile devices and data directly measured while generating downstream video stream traversing the gNB (i.e., downstream scenario), and vice versa (i.e., upstream scenario). In each experiment, we fixed the location of the UE and the gNB.</p> <p>The NANCY project has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union's Horizon Europe research and innovation programme under Grant Agreement No 101096456.</p>
Plant invasion in Mediterranean Europe: current hotspots and future scenarios
<p>These are the raw data that can be used to reproduce results of the paper: "<strong>Plant invasion in Mediterranean Europe: current invasion hotspots and future scenarios</strong>". </p> <p>The Mediterranean Basin has historically been subject to alien plant invasions that threaten its unique biodiversity. This seasonally dry and densely populated region is undergoing severe climatic and socioeconomic changes, and it is unclear whether these changes will worsen or mitigate plant invasions. Predictions are often biased, as species may not be in equilibrium in the invaded environment, depending on their invasion stage and ecological characteristics. To address future predictions uncertainty, we identified invasion hotspots across multiple biased modelling scenarios and ecological characteristics of successful invaders.</p> <p>We selected 92 alien plant species widespread in Mediterranean Europe and compiled data on their distribution in the Mediterranean and worldwide. We combined these data with environmental and propagule pressure variables to model global and regional species niches and map their current and future habitat suitability. We identified invasion hotspots, examined their potential future shifts, and compared the results of different modelling strategies. Finally, we generalised our findings by using linear models to determine the traits and biogeographic features of invaders most likely to benefit from global change.</p> <p>Currently, invasion hotspots are found near ports and coastlines throughout Mediterranean Europe. However, many species occupy only a small portion of the environmental conditions to which they are preadapted, suggesting that their invasion is still an ongoing process. Future conditions will lead to declines in many currently widespread aliens, which will tend to move to higher elevations and latitudes. Our trait models indicate that future climates will generally favour species with conservative ecological strategies that can cope with reduced water availability, such as those with short stature and low specific leaf area. Taken together, our results suggest that in future environments, these conservative aliens will move farther from the introduction areas and upslope, threatening mountain ecosystems that have been spared from invasions so far.</p> <p>With these data (environmental variables, species presences and background points, and distance to ports cities and to the coast) and using the R software following the ODMAP protocol attached to the original paper all results meet the criteria of reproducible science.</p>
PPARδ dataset curated and enriched using the Enalos tools and Enalos KNIME nodes for machine learning analysis (SCENARIOS project)
<p><span>A curated and enriched dataset for PPAR</span>δ<span>, suitable for in silico model development, was obtained from PubChem BioAssay under the numeric identifier AID 469785 using Enalos tools and Enalos KNIME nodes. This dataset comprises 136 compounds that induce luciferase activity, serving as an indicator of agonist activity against the human PPAR</span>δ<span> ligand-binding domain. These molecules were tested in a human embryonic kidney cell line (293T), co-transfected with a chimeric plasmid containing the yeast GAL4 DNA-binding domain (DBD). All 136 oxazole-based compounds retrieved from the dataset are accompanied by their half-maximal effective concentration (EC50) and enriched with 777 molecular descriptors extracted from their 2D structure using EnalosMold2 KNIME nodes</span></p>
The international transfer of emission allowances scenario
<p>The model output data for the international transfer of emission allowances assessment.</p>
tRIBS Model Scenarios: Forest Treatment Effects on Watershed Responses under Warming in the Beaver Creek (2003-2018)
<p>This dataset contains the model simulation setup and scenario results of the individual and combined effects of forest thinning and warming on the forest hydrologic response in the Beaver Creek watershed of central Arizona. The simulations were conducted with the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS) model at a variable resolution of about 120 m, hourly resolution from 2003-2018 and aggregated daily values in this dataset. Raw model outputs at hourly resolution are available upon request from the authors, but not included here due to their excessive size. </p> <p>The model setup files are organized into the tar gzipped file: <strong>BCmodel.tar.gz</strong>. This contains the following files: (1) a series of input files (*.in) used for the model simulations, and (2) the ancillary data sets required for model execution (terrain model, soil map, land cover map, initialization file, data descriptor tables). </p> <p>The model rainfall forcing files are organized into the tar gzipped file: <strong>BCrain.tar.gz</strong>. This contains the following directories: (1) rainfall data from the bias-corrected NEXRAD product, and (2) rainfall data from the NLDAS-2 product.</p> <p>The model meteorological forcing files are organized into the tar gzipped file: <strong>BCweather.tar.gz</strong>. This contains the following data from bias-corrected, adjusted NLDAS-2: (1) atmospheric pressure, (2) wind speed, (3) air temperature, (4) incoming solar radiation, and (5) relative humidity. </p> <p>The daily values of the model outputs are stored in the file <strong>tRIBSModelScenarios_DailyOutputs.xlsx</strong>. This includes the scenarios (BC0, BC1, BC2, BC4, BC6, PT0, PT1, PT2, PT4, PT6) and the variables streamflow (Q), total evapotranspiration (ET), snowmelt (M), ground sublimation (Subg), and canopy sublimation (Subc). All values in mm/day over the period 10/01/2002 to 09/30/2018 (16 water years). A readme file is provided. </p> <p>More details can be found in the associated paper (this record will be updated when the paper is published):</p> <p>Cederstrom, C., Vivoni, E.R., Mascaro, G., and Svoma, B. 2024. Forest Treatment Effects on Watershed Responses under Warming.<em> Water Resources Research. (in revision)</em>.</p>
GCAM-USA Scenarios for GODEEEP
<h1>GCAM-USA Scenarios for GODEEEP</h1> <p>This dataset contains a set of twelve future (2020-2050) scenarios modeled by <a href="https://gcims.pnnl.gov/modeling/gcam-global-change-analysis-model">GCAM-USA</a> for the <a href="https://godeeep.pnnl.gov">GODEEEP</a> project for the purpose of studying the effects of climate, socioeconomic change, technology change, current decarbonization incentives, and longer-term decarbonization policies on the U.S. energy-economy, the electricity grid, human well-being, and the environment.</p> <p>GCAM-USA is a version of the Global Change Analysis Model (GCAM) with state-level detail in the United States. GCAM-USA simulates the supply/demand dynamics and interactions of four systems (energy, water, agriculture and land use, and the economy) in 32 geopolitical regions in the world, including the 50 states and the District of Columbia within the U.S. It can be configured to include climate impacts on energy demands, water availability, and crop yields. The GCAM-USA scenarios for GODEEEP include business-as-usual (BAU) as well as net-zero (NZ) greenhouse gas emissions by 2050 policy scenarios. All the NZ policy scenarios include a carbon-free electricity system by 2035, also referred to as a "clean grid." Net-zero greenhouse gas emissions by 2050 requires a combination of solutions including carbon sequestration, new fuels, long- and short-term energy storage, and new technologies such as direct air capture that have not previously been included in GCAM-USA. The GCAM-USA scenarios for GODEEEP represent alternative combinations of assumptions for climate impacts, decarbonization policies, decarbonization incentives, and carbon capture and sequestration technology availability.</p> <p>GCAM-USA outputs are provided as XML databases, which can be read by the <a href="https://github.com/JGCRI/modelinterface">GCAM Model Interface</a> or packages such as <a href="https://github.com/JGCRI/gcamreader">gcamreader</a> for Python or <a href="https://github.com/JGCRI/gcamextractor">gcamextractor</a> for R.</p> <p>Summaries of each scenario are provided below. For additional discourse on the scenarios, see <a href="https://doi.org/10.1016/j.egycc.2023.100117">Ou et al 2023</a> and other upcoming papers to be announced on the <a href="https://godeeep.pnnl.gov">GODEEEP website</a>.</p> <h5>Abbreviations used in scenario names and descriptions</h5> <ul> <li><strong>BAU</strong>: Business-As-Usual. These scenarios represent the continuation of policies from the recent past and include major state-level clean energy policies but do not include any federal policies or incentives for a clean grid or a net-zero economy.</li> <li><strong>NZ</strong>: Net-Zero. These scenarios represent a <a href="https://www.whitehouse.gov/wp-content/uploads/2021/10/us-long-term-strategy.pdf">U.S. decarbonization goal</a> that requires a carbon-free electricity grid by 2035 and a net-zero greenhouse gas emissions economy by 2050.</li> <li><strong>IRA</strong>: Inflation Reduction Act. These scenarios include the <a href="https://www.whitehouse.gov/cleanenergy/inflation-reduction-act-guidebook/">IRA incentives</a> for energy efficiency as well as clean energy, transportation, and fuels between 2025 and 2035.</li> <li><strong>CCS</strong>: Carbon Capture and Sequestration. These scenarios assume that electricity generators equipped with CCS technology are available, whereas the other scenarios assume CCS technology is unavailable.</li> <li><strong>Climate</strong>. These scenarios include the dynamic effects of a climate pathway (<a href="https://www.nature.com/articles/s41597-023-02485-5">RCP8.5</a>) on heating and cooling degree days (HDD/CDD) in the period 2020-2050. Note that in scenarios without "climate" in their name, HDD/CDD are left static at their 2020 levels.</li> <li><strong>SSP2</strong>: <a href="https://doi.org/10.1016/j.gloenvcha.2015.01.004">Shared Socioeconomic Pathway 2</a>. A "middle of the road" socioeconomic pathway where population and economic growth trends follow historical patterns.</li> </ul> <h3>Scenario Descriptions</h3> <table> <tbody><tr> <th>#</th> <th>Name</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>1</td> <td>bau</td> <td> <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are unavailable.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>No climate impacts are considered.</li> </ul> </td> </tr> <tr> <td>2</td> <td>bau_climate</td> <td> <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are unavailable.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>This scenario includes future climate impacts on heating and cooling degree days based on an RCP8.5 pathway.</li> </ul> </td> </tr> <tr> <td>3</td> <td>bau_ccs</td> <td> <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>No climate impacts are considered.</li> </ul> </td> </tr> <tr> <td>4</td> <td>bau_ccs_climate</td> <td> <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>This scenario includes future climate impacts on heating and cooling degree days based on an RCP8.5 pathway.</li> </ul> </td> </tr> <tr> <td>5</td> <td>bau_ira_ccs</td> <td> <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>No climate impacts are considered.</li> </ul> </td> </tr> <tr> <td>6</td> <td>bau_ira_ccs_climate</td> <td> <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>This scenario includes future climate impacts on heating and cooling degree days based on an RCP8.5 pathway.</li> </ul> </td> </tr> <tr> <td>7</td> <td>nz</td> <td> <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are unavailable.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>No climate impacts are considered.</li> </ul> </td> </tr> <tr> <td>8</td> <td>nz_climate</td> <td> <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are unavailable.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>This scenario includes future climate impacts on heating and cooling degree days based on an RCP8.5 pathway.</li> </ul> </td> </tr> <tr> <td>9</td> <td>nz_ccs</td> <td> <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>No climate impacts are considered.</li> </ul> </td> </tr> <tr> <td>10</td> <td>nz_ccs_climate</td> <td> <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>This scenario includes future climate impacts on heating and cooling degree days based on an RCP8.5 pathway.</li> </ul> </td> </tr> <tr> <td>11</td> <td>nz_ira_ccs</td> <td> <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>No climate impacts are considered.</li> </ul> </td> </tr> <tr> <td>12</td> <td>nz_ira_ccs_climate</td> <td> <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>This scenario includes future climate impacts on heating and cooling degree days based on an RCP8.5 pathway.</li> </ul> </td> </tr> </tbody> </table> <h3>Acknowledgement</h3> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Model output from historical and future scenarios related to 'Carbon Dioxide Removal: Tradeoffs and Lags'
Open the record for dataset details and reuse information.
Hydrologic scenarios of the main Nile tributaries
<p>The water supply model considers four main tributaries to the Nile: Blue Nile, White Nile, Dinder-Rahad and Tekeze-Atbara.</p> <p>Starting from future time series created according to three RCPs scenarios (RCP2.6, RCP4.5 and RCP8.5), a large ensemble of uncertain streamflow is generated by altering the timing and magnitude of extremes (Quinn, J. D., et al. Exploring how changing monsoonal dynamics and human pressures challenge multireservoir management for flood protection, hydropower production, and agricultural water supply. Water Resources Research, 2018, 54.7: 4638-4662). Future streamflows are derived from IPCC 5<sup>th</sup> assessement’s projections of temperature and precipitation using the HBV model and altered by first fitting the monthly means in a Fourier decomposition and then applying perturbations on amplitudes and phases to strengthen the magnitude of extremes and/or their timing.</p> <p>For each combination of tributary and RCP, 100 different realizations of the monthly streamflow of a 94-year-long time series are generated.</p> <p>Data are divided based on tributaries and climatic scenarios. In each file, every row is one of the 100 different realizations of the monthly streamflow time series, while each column contains the monthly streamflow of the river reported in m<sup>3</sup>/s starting from January 2007 in the first column until December 2100 in the last.</p>
A Dataset for Inertial Measurements of Scoliotic Patients during Timed-Up and Go Tests in Unbraced and Braced Scenarios
<p>Repository composition:</p> <p>*** Dataset ***</p> <p>Anonymous data pertaining to each participant is stored in a single folder, named S##. </p> <p>Each of these folders contains the following:</p> <p>1) Raw IMU data from the G-Walk sensor (3-axis acceleration, 3-axis gyroscope, 3-axis magnetometer) recorded during the Timed-Up and Go tests in .txt files. Specifically, the 3 experimental conditions are "Unbraced", "Conventional" and "3DPrinted". Each condition was recorded three times (01, 02, 03).</p> <p>2) A .xlsx file named "TUG_Metrics" with the values of the TUG metrics for each condition.</p> <p>3) A .xlsx file named "Segmentation_Times" with the start and end timepoints of the TUG phases for each condition.</p> <p>*** Boxplot ***</p> <p>This is a folder containing boxplots in .png files for each TUG metric, comparing the three experimental conditions.</p> <p>*** Histogram ***</p> <p>This is a folder containing histograms in .png files for each TUG metric, comparing the three experimental conditions.</p> <p>*** QQ Plot ***</p> <p>This is a folder containing qq-plots in .png files for each TUG metric, comparing the three experimental conditions.</p>
AWESOME Demographic and Socio-economic Scenarios
<p>In this record, scenarios for population and economic drivers are provided, expected to impact the agricultural sector for countries in the Mediterranean and adjacent regions relevant for the AWESOME models according to the Shared Socio-economic Pathways (SSP) guidelines for the horizon 2020-2100. These scenarios were designed, modelled and generated during the the first deliverable of AWESOME WP2, named D2.1 - Demographic Scenarios.</p> <p>Population scenarios are based on a probabilistic approach employing the Bayesian hierarchical population model proposed by Raftery et al., appropriately modified to fit within the SSP scenarios, while the economic drivers scenarios are provided in terms of the global macroeconomic MaGE model. Information concerning the methodology adopted for the scenario generation, the nature and format of the produced data as well as a small but representative sample of the produced projections are presented in the relevant report.</p> <p>This record contains:</p> <p>- The deliverable's report where the model considerations for generating both population (probabilistic) and economic scenarios under the SSP setting are presented.</p> <p>- The propabilistic scenarios output for population under the SSP framework for the aggregate Mediterannean region and at national level for each country in the region (rda files in the compresed folders) (the country list is contained in the report).</p> <p>- The generated economic drivers scenarios output under the SSP framework for the aggregate Mediterannean region and at national level for each country in the region (xls file).</p> <p>- Description of the data (txt file)</p>
ScienceDex guides
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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.