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942 results for “scenario”
Supplementary Datasets for "Oceanic enrichment of ammonium and its impacts on phytoplankton community composition under a high-emissions scenario"
<p>These are the four supplementary datasets used in the analysis and work presented in the publication </p> <p><strong><span>Oceanic enrichment of ammonium and its impacts on phytoplankton community composition under a high-emissions scenario</span></strong></p> <p> </p>
Database of Subjective Driver Ratings for Brazilian Freeway Scenarios
<p><strong>Dataset Description</strong></p> <p>This dataset contains 10,387 subjective ratings of freeway driving experiences collected by means of a web survey completed (or partially completed) by 1,614 Brazilian drivers. Each participant watched up to 12 one-minute video clips, each depicting a freeway driving scenario from the driver viewpoint. A total of 418 video clips depicted 128 scenarios that were systematically varied across five key factors: number of lanes in the trip direction, speed limit, road grade, truck percentage, and traffic density, according to a fractional factorial design.</p> <p>Each rating, on a scale of 0 to 100, represents the perceived quality of the driving experience, with higher scores indicating a more positive perception (from "poor" to "excellent" on a continuous scale). Each line in the dataset represents a rating and includes the following variables:</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td><code>IDuser</code></td> <td>A unique identifier for each participant.</td> </tr> <tr> <td><code>IDvideo</code></td> <td>A identifier for each video clip watched by the participant.</td> </tr> <tr> <td><code>Rating</code></td> <td>The subjective rating of the driving experience, on a scale of 0 (poor) to 100 (excellent).</td> </tr> <tr> <td><code>TimeToRateVideo</code></td> <td>The time taken by the participant to rate the video clip, measured in seconds, <br>from the moment the video clip is presented on the screen to the moment the participant proceeds to the next video clip.</td> </tr> <tr> <td><code>Video_clip_scenario</code></td> <td>A string encoding the specific conditions of the freeway scenario depicted in each video clip watched by the participant.</td> </tr> </tbody> </table> <p> </p> <p><strong>Scenario Encoding</strong></p> <p>The <code>Video_clip_scenario</code> variable uses a specific encoding scheme to represent the different driving conditions (scenarios). Each factor is represented by up to 3 letters and a numeric value. For example, <code>NL3_G4_SL120_PT10_k132_veh1</code> indicates the first replication of scenario with 3 lanes, 4% uphill grade, a 120 km/h speed limit, 10% trucks, and a traffic density of 13.2 veh/km/lane. The factors and their numeric values are explained in the following table:</p> <table> <tbody> <tr> <td><strong>Factor</strong></td> <td><strong>Definition<br></strong></td> <td><strong>Number of levels</strong></td> <td><strong>Numeric values</strong></td> </tr> <tr> <td><code>NL</code>:</td> <td>Number of lanes in the driving direction</td> <td>2</td> <td>3 or 4</td> </tr> <tr> <td><code>G</code>:</td> <td>Grade (%)</td> <td>2</td> <td>1 (nearly level grade) or 4 (steep uphill grade)</td> </tr> <tr> <td><code>SL</code>:</td> <td>Posted speed limit (km/h)</td> <td>4</td> <td>90, 100, 110 or 120</td> </tr> <tr> <td><code>PT</code>:</td> <td>Percentage of trucks in the traffic stream (%)</td> <td>4</td> <td>0, 10, 20 or 30</td> </tr> <tr> <td><code>k</code>:</td> <td>Traffic density (in 0.1 veh/km/ln)</td> <td>16</td> <td>36, 48, 72, ..., 168, 180 or 192</td> </tr> <tr> <td><code>veh</code>:</td> <td>Scenario replication identifier </td> <td>N.A.</td> <td>integer > 0</td> </tr> </tbody> </table> <p> </p> <p>These ratings were obtained using a web survey, approved by the Brazilian Human Research Ethics Committee (CEP-EACH-USP, protocol number: 2034830). The <code>ratings.csv</code> file contains the unfiltered ratings obtained on that web survey.</p> <p>For additional information about the dataset, contact the project leader or the data curator. If you find this dataset useful for your research, please let the authors know. We appreciate your feedback and would be interested to hear how you use the data.</p>
Preliminary land use scenarios for Nature Future Framework
<p>This deliverable is the product of tasks 5.2, we run high-resolution (1km) spatial land-use models that quantify potential land-use change and land management changes consistent with developed NFF storylines (T5.1). The European spatial land-use model (CLUMondo) was parametrized using economic modelling results and European SSP scenarios as input.</p>
Biomass availability at NUTS3 level for modelling European energy system with 3 future scenario
<p>The database is built over three main sources</p> <ul> <li>S2Biom database from where most of the numbers come from <a title="S2Biom" href="https://s2biom.wenr.wur.nl/home" target="_blank" rel="noopener">(S2Biom original repo)</a></li> <li>ENSPRESO database that we use for few energy sources that are not part of s2biom (<a title="JRC" href="https://data.jrc.ec.europa.eu/collection/id-00138" target="_blank" rel="noopener">ENSPRESO</a>)</li> <li>National data for Switzerland (<a href="https://www.envidat.ch/dataset/swiss-biomass-potentials" target="_blank" rel="noopener">FoReMA Forest Resources Management Insititute</a></li> </ul> <p>Data processing is done with Julia code that has short documentation and additional databasePipeline.pdf to understand how the dataset was built. To rebuild the dataset, refer to the github repository linked to this dataset.</p> <p>Data are available as a csv file and as a sqlite database. Data query methods are available from the Github repository linked to this dataset.</p> <p>The dataset includes biomass energy availability, expressed in PJ, at nuts 0-3 (NUTS 2013), and ENTSOE bidding zones aggregation. For each biomass source, the roadsidecost of each source is associated. While the biomass data is varied, large, and detailed following standards (ISO 17225-1:2021, ISO 17225-2:2021, ISO 17225-3:2021, ISO 17225-4:2021, ISO 17225-5:2021, ISO 17225-6:2021, ISO 17225-7:2021, ISO 18125:2017, EN 13556), biomass sources have been aggregated into three categories: Forestry, Agriculture, Organic waste. There are 3 bioenergy potential, low, medium, and high. These were based on the available data listed above. </p> <p>Note: Technical availability of biomass is often much higher than the current use. Check comparison_biofuel_amounts.xlsx to compare the potentials to actual use in Eurostat and IEA data. Full potential should often not be used, because of possible issues with biodiversity and land use emissions.</p> <p> </p>
TradeRES scenario database
<p>Dataset for TradeRES scenarios</p> <p>The TradeRES project created four central scenarios of the future European power system. “Scenario” within TradeRES refers to a structured input data collection that encapsulates certain properties of the underlying future energy system. The four scenarios, abbreviated S1, S2, S3 and S4, are carbon-free by assumption and vary in terms of the flexibility of the demand-side, the variability of the supply-side and the degree of sector-coupling. In addition, TradeRES created a scenario S0 to represent an intermediate energy system on the path to decarbonisation. This scenario S0 with a 60% non-thermal renewables penetration, is considered in some case studies as a departing scenario for reference. It considers exogenous electricity generation capacities based on 2030 national energy and climate plans.</p> <p>In addition, TradeRES created scenarios for additional analysis where, for example, the weighted average cost of capital (WACC) of technologies was varied.</p> <p>The common data on the scenarios are given in the supplementary data file D2.1_TradeRES_Scenario_data_v3.xlsx. The table below describes the data items that are included in the data file. The report D2.1_TradeRES_A-database-of-TradeRES-scenarios_Ed3.pdf describes in more detail the scenarios and data files.</p> <table> <tbody> <tr> <td> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p><strong>Emission factors</strong></p> </td> <td> <p>Kg of CO<sub>2</sub> equivalents emitted from burning 1 MWh of fuel</p> </td> </tr> <tr> <td> <p><strong>Commodity prices</strong></p> </td> <td> <p>Price assumptions for commodities</p> </td> </tr> <tr> <td> <p><strong>New technology data</strong></p> </td> <td> <p>Projected cost and technical parameters of new energy conversion (production, consumption, storage) technologies</p> </td> </tr> <tr> <td> <p><strong>Wind potential</strong></p> </td> <td> <p>Wind (offshore and onshore) potential in European countries</p> </td> </tr> <tr> <td> <p><strong>Solar potential</strong></p> </td> <td> <p>Solar (different PV categories and CSP) potential in European countries</p> </td> </tr> <tr> <td> <p><strong>Transmission capacities</strong></p> </td> <td> <p>Electricity and hydrogen transmission capacities in the scenarios</p> </td> </tr> <tr> <td> <p><strong>Initial technology capacities</strong></p> </td> <td> <p>“Initial” electricity generation capacities in the scenarios</p> </td> </tr> <tr> <td> <p><strong>Aggregated </strong><strong>demand</strong></p> </td> <td> <p>Assumptions on aggregated electricity demand by sector and bidding zone</p> </td> </tr> <tr> <td> <p><strong>Demand </strong><strong>time series</strong></p> </td> <td> <p>Load consumption time series</p> </td> </tr> <tr> <td> <p><strong>Renewable time series</strong></p> </td> <td> <p>Capacity factor time series, hydro inflow time series</p> </td> </tr> <tr> <td> <p><strong>Building data</strong></p> </td> <td> <p>Main assumptions used in the model for heating and cooling of buildings</p> </td> </tr> <tr> <td> <p><strong>EV data</strong></p> </td> <td> <p>Main assumptions for the private passenger vehicles and their electrification</p> </td> </tr> </tbody> </table> <p> </p>
REMix model input data for the THG95/GHG95 scenario analysed within the MuSeKo project
<ul> <li>This file contains data used in the REMix energy system model in a scenario assessment for the years 2020, 2030, 2040, and 2050</li> <li>The dataset comprises techno-economic data, energy demand data, renewable energy potentials, fuel as well as emission prices, and energy infrastructure capacities</li> <li>This data is considered in the THG95/GHG95 (Treibhausgas / green house gas) scenario developed within the project MuSeKo</li> <li>This scenario comprises Germany, its neighbours as well as Italy, Norway and Sweden</li> <li>Further descriptions and data is available in the project report of MuSeKo (in German), which can be downloaded from <a href="https://elib.dlr.de/135971/">https://elib.dlr.de/135971/</a></li> </ul> <p>Version 2 provides a correction of biogas potentials in Germany</p>
COMMIT Scenario Explorer
<p><strong>Release Notes</strong></p> <p>Version 1.1</p> <ul> <li> Error correction in the data for the <strong>model</strong>: "IMAGE", <strong>scenario</strong>: "2Deg2020_V4", <strong>variables</strong>: "Price|Carbon" and "Policy Cost|Area under MAC Curve"</li> </ul> <p>Version 1.0</p> <ul> <li>Initial release of the COMMIT data set.</li> </ul> <p>This scenario explorer presents a set of scenarios developed in the <a href="https://themasites.pbl.nl/commit/">COMMIT</a> project. <a href="https://themasites.pbl.nl/commit/">COMMIT</a> stands for Climate pOlicy assessment and Mitigation Modeling to Integrate national and global Transition pathways. The COMMIT project’s main aim has been to improve modelling of national low-carbon emission pathways and analysis of country contributions to the global ambition of the Paris Agreement. The main motivation for this aim was that a common understanding and coherent message from the research community in different parts of the world is crucial to support the international negotiation process on climate policy. COMMIT consisted of a consortium of a large number of national teams, who regularly support domestic climate policy-making in their respective countries (Australia, Brazil, Canada, China, EU, India, Indonesia, Japan, Russia, South Korea, USA) and leading global integrated assessment modelling teams (PBL, PIK, IIASA, RFFCMCC EIEE). The consortium was led by Detlef van Vuuren and Heleen van Soest (PBL Netherlands Environmental Assessment Agency).</p> <p>The set of scenarios presented here revolves around the Bridge scenario, which was developed to study how the emissions gap between Nationally Determined Contributions (NDCs) and the global emissions levels needed to achieve the Paris Agreement's climate goals can be closed based on good practice policies (GPP). Next to these GPP and Bridge scenarios, the set contains different reference scenarios (a no new policies baseline, a current policies scenario, and an NDC scenario), as well as different scenarios limiting global warming to 2 degrees Celsius. The Bridge scenario builds upon the current policies scenario and assumes that specific good practice policies, which have shown to be effective in some countries, will be implemented globally from 2020 until 2030. After 2030, the bridge scenario transitions to a 2 °C scenario following a cost-effective pathway. A distinction is made between low/medium and high-income countries in terms of timing and stringency of good practice policy targets. The set of policies was defined in dialogue with national model teams, granting a more realistic scenario narrative.</p> <p>Another set of scenarios included here consists of the Reference (here: Baseline) and Low-carbon scenarios as presented by Fragkos et al. in Energy.</p> <p>The data is also available for download and interactive viewing at the <a href="https://data.ene.iiasa.ac.at/commit">COMMIT Scenario Explorer hosted by IIASA</a>. The advantage of getting the data through the scenario explorer is that by registering with your email you will receive updates anytime there is a new version of this data set.</p> <p>The scenario ensemble is licensed under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>. Please refer to the creative commons site for more information.</p>
Global and regional health and food security under strict conservation scenarios.
<p>Data supporting Nature Sustainability paper 'Global and regional health and food security under strict conservation scenarios'. Zipped folder contains LandSyMM output used to generate results. The tif files are the biodiversity prioritisation areas used in the 50% and 30% strict protection scenarios generated as per the methods in Jung, M., Arnell, A., de Lamo, X. <em>et al.</em> Areas of global importance for conserving terrestrial biodiversity, carbon and water. <em>Nat Ecol Evol</em> <strong>5, </strong>1499–1509 (2021). https://doi.org/10.1038/s41559-021-01528-7. </p>
Life cycle-based environmental impacts of energy scenarios - additional data
<p>This data set documents additional results of the paper "Life cycle-based environmental impacts of energy system transformation strategies for Germany: Are climate and environmental protection conflicting goals?" (Tobias Naegler and co-authors, published in Energy Reports (2020), https://doi.org/10.1016/j.egyr.2022.03.143). It shows life cycle-based environmental impacts for 10 different transformation strategies for the German energy and transport system.</p>
Global inventory of potentially cultivable land and potentially available cropland under different scenarios and policies
<p><strong>Global inventory of potentially cultivable land and potentially available cropland under different scenarios and policies</strong></p> <p>To identify and investigate potential land-use conflicts and emerging trade-offs between different Sustainable Development Goals, such as food security, climate protection and biodiversity conservation, it is important to identify where land-use change and particularly the expansion of cropland could potentially take place in the future. Therefore, we provide a consistent global dataset of land potentially cultivable and potentially available for agricultural use for past and future time periods from 1980 until 2100. Based on the agricultural suitability of land for 23 globally important food, feed, fiber and first- and second-generation bioenergy crops, and high resolution land cover data, the potentially cultivable land is defined by its agricultural suitability and the (technical) feasibility of agriculture. The potentially available cropland additionally considers potential nature protection policies restricting agriculture in forests, wetlands and strictly protected areas, thereby reflecting key aims of the Sustainable Development goals and recent efforts to stop deforestation, protect the climate and preserve biodiversity.</p> <p>The spatially explicit global datasets of potentially cultivable land (pcl) and potentially available cropland (pac) are available for four different time periods (1980-2009, 2010-20,39, 2040-2069, 2070-2099) under RCP2.6 and RCP8.5. The impact of irrigation on the agricultural suitability is considered by referring to current irrigations patters. However, to enable different assumptions on the irrigation of land potentially cultivable or available for cropland use, all datasets are also available for rainfed and irrigated conditions separately. Moreover, we provide a subset-version of all dataset which excludes land that is solely suitable for second-generation bioenergy crops. All datasets are available at 30 arc-seconds and 30 arc-minutes spatial resolution and aggregated at country level to enable the application in models that use aggregated data.</p> <p>By serving as an input for land-use models, the data could improve the comparability of the models and their output, and increase the consistency within interdisciplinary research and integrated model coupling approaches that investigate land-use change.</p> <p> </p> <p><strong>Further information:</strong></p> <p>A detailed description on the methods and underlying data is available in:</p> <p>Schneider. J.M., Zabel, F., Mauser, W. (2022): Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data.<em> </em><a href="https://doi.org/10.1038/s41597-022-01632-8">https://doi.org/10.1038/s41597-022-01632-8</a></p> <p><strong>Contact:</strong></p> <p>Please contact: Julia M. Schneider (Schneider.ju@lmu.de)<br>Department of Geography, Ludwig-Maximilians-Universität München (LMU), Munich, Germany.</p>
Scenario data for article: Environmental impacts of key metals' supply and low-carbon technologies are likely to decrease in the future
<p>This dataset contains the background scenarios for metal supply used for the publication <a href="https://doi.org/10.1111/jiec.13181">"Environmental impacts of key metals' supply and low-carbon technologies are likely to decrease in the future"</a> in the Journal of Industrial Ecology (2021).</p> <p><strong>Scenario description:</strong></p> <p>These background scenarios comprise five variables for the metals of copper, nickel, zinc, and lead for the time period of 2010-2050. These variables are:<br> V1: ore grade decline and energy requirements</p> <p>V2: market shares of primary production locations</p> <p>V3: energy efficiency improvements during smelting and refining</p> <p>V4: market shares of primary production routes</p> <p>V5: market shares of primary and secondary production.<br> <br> The associated <a href="http://doi.org/10.1111/jiec.13181">article</a> in the Journal of Industrial Ecology describes the modelling assumptions and data sources of the scenarios. It also conducts impact assessments for future metal supply and low-carbon technologies with these metal scenarios as well as additional electricity supply scenarios from the IAM of <a href="https://models.pbl.nl/image/index.php/Download#IMAGE_input_data_for_the_Prospective_Life_Cycle_Assessment_model">IMAGE</a> from <a href="https://doi.org/10.1111/jiec.12825">Mendoza Beltran et al. (2020)</a> in the background .</p> <p><strong>How to use this dataset:</strong></p> <p>The background scenarios are suitable for the life cycle inventory database of ecoinvent version 3.5 or 3.6 (allocation, cut-off by classification). They can be incorporated into ecoinvent either via the brightway-based module of <a href="https://github.com/PascalLesage/presamples">presamples</a> or using the <a href="https://github.com/LCA-ActivityBrowser">activity-browser</a> and its scenario-based calculation set-up. Thereby, they can be used as background scenarios for any other prospective LCA based on ecoinvent 3.5 or 3.6.</p> <p>Moreover, they can be combined with the electricity supply scenarios of the IAM of <a href="https://models.pbl.nl/image/index.php/Download#IMAGE_input_data_for_the_Prospective_Life_Cycle_Assessment_model">IMAGE</a> from <a href="https://doi.org/10.1111/jiec.12825">Mendoza Beltran et al. (2020)</a> using the <a href="https://github.com/LCA-ActivityBrowser/brightway-superstructure">superstructure approach</a> of the activity-browser (<a href="https://doi.org/10.1007/s11367-021-01974-2">de Koning & Steubing 2020</a>).</p> <p>Before using the dataset, please adjust the "database" columns to the name of your database, e.g. "ecoinvent3.5", and potentially also the "key" columns.</p> <p>Versions of the scenarios applicable to ecoinvent 3.7.1 or 3.8 may be added later.</p> <p><strong>License: </strong>The metal supply scenario data is licensed under the CC-BY 4.0 license.</p>
Summer Rainfall Scenarios and Climate Change Factor Projections over Wanzhou County, China
<p>This dataset consists of rainfall scenarios and ensemble projections of extreme daily rainfall and mean summer season rainfall over Wanzhou County, China.</p> <p><strong>Precipitation Reference Period (1979-2018)</strong></p> <p>The reference scenario rainfall covers the period of 1979-2018, and is derived from the China Meteorological Forcing Dataset (https://data.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49/). The extreme daily rainfall (in mm/day) is derived from Gumbel distributions fitted to monthly maximum daily rainfall covering the months of June to August. A spatial distribution of return periods from 2, 5, 10 20, 50 and 100 years for this scenario were derived and included in this dataset. The mean seasonal rainfall scenario covers the average daily rainfall (in mm/day) for the months of May to July to represent antecedent rainfall conditions of that could trigger shallow landslides during the summer season.</p> <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.1 degrees x 0.1 degrees</li> <li>Time period: 1979-2018</li> <li>Data Format: .csv files (.xyz file extensions)</li> <li>Variable: Rainfall (pr)</li> <li>Units: mm/day </li> </ul> <p><strong>Ensemble Projections and Climate Change Factors</strong></p> <p>The ensemble climate change projections cover two periods: Mid-21st Century (2021-2060) and Late-21st Century (2061-2100). The influence of climate change is assessed through climate change factors that represent a multiplicative factor of change between present and future climate model outputs. The ensemble projections are the mean climate change factor derived from four bias-corrected Regional Climate Model outputs. The ensemble consisted of the results REMO2015 and RegCM4 models that dynamically downscaled HadGEM2-ES, MPI-ESM-ML, and MPI-ESM-MR model outputs (https://esgf-data.dkrz.de/search/cordex-dkrz/). The bias correction was performed using the quantile delta method. An empirical transfer function for daily rainfall was used to derive the mean seasonal rainfall scenario, while a parametric (Gumbel distribution) transfer function was used to derive on the monthly maxima for the extreme daily rainfall scenarios.</p> <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.22 degrees x 0.22 degrees</li> <li>Time periods: Mid-21st Century (2021-2060) & Late-21st Century (2061-2100)</li> <li>Data Format: .csv files</li> <li>Variable: Climate Change Factor (ccf)</li> <li>Unit: Dimensionless</li> <li>Included ensemble projection statistics: <ul> <li>Standard deviation (sd)</li> <li>Coefficient of Variation (cv)</li> </ul> </li> </ul>
What energy infrastructure to support 1.5°C scenarios? - Scenario data
<p>This excel file summarizes the main assumptions considered for the analysis " What energy infrastructure to support 1.5°C scenarios?", prepared by Artelys on behalf of the European Climate Foundation in 2020. The report is available here: https://www.artelys.com/wp-content/uploads/2020/12/Artelys-2050EnergyInfrastructureNeeds.pdf</p>
Canadian fossil fuel production and greenhouse gas emissions compared to predictions following the 2.0°C scenario
<p>This spreadsheet shows the amounts of coal, oil and natural gas produced in Canada from 2010 to 2020 using governmental sources. McGlade and Ekins (2015) proposed quotas for the production of each type of fossil fuel in order to provide a 67% chance to limit warming to 2.0°C by 2100. The proportion of each quota that is already spent is calculated. Emissions targets from 21 scenarios originating from five effort-sharing studies are compared with Canadian 2020 emissions to evaluate the difference. Carbon budgets from 18 scenarios originating from seven studies are compared with Canadian cumulative emissions to evaluate the percentage of the budgets already emitted within the 2010-2050 period. Emissions from five database are used in the calculations.</p>
Scenario data for article: Effects of the energy transition on environmental impacts of cobalt supply: A prospective Life Cycle Assessment study on future supply of cobalt
<p>This dataset contains the background data for the paper '<a href="https://onlinelibrary.wiley.com/doi/10.1111/jiec.13258">Effects of the energy transition on environmental impacts of the cobalt supply: A prospective Life Cycle Assessment study on the future cobalt supply</a>' as published in the Journal of Industrial Ecology.</p> <p><strong>Please note that an easier to use version of this data for LCA is available through the Premise (<a href="https://www.sciencedirect.com/science/article/pii/S136403212200226X">Sacchi et al. 2022</a>) Community Scenarios <a href="https://github.com/premise-community-scenarios/cobalt-perspective-2050">here</a>.</strong> This version is slightly adapted to fit into the Premise architecture and is compatible with ecoinvent v3.8 cutoff.</p> <p>This repository contains:</p> <ul> <li>Python code + readme to model the variables, generate presamples packages and generate LCA results based on those. (code folder)</li> <li>Input and output data for Variables 1-3 (files 1&2)</li> <li>Presamples excel sheets for each variable/scenario combination (file 3)</li> <li>Summarized LCA results (the full results can be generated through running the code provided) (file 4)</li> <li>Full LCA results used for the contribution analysis (file 5)</li> <li>Underlying data for each of the figures (file 6)</li> </ul> <p>We refer to the paper (linked above) for more information on the study.<br> </p> <p><strong>License: </strong>The metal supply scenario data is licensed under the CC-BY 4.0 license.</p> <p><strong>Access: </strong>Open access</p> <p> </p> <p>[Changelog]</p> <p>2023-03-23 - 1.3.1: Add link to Premise Community scenario page.<br> 2022-05-18 - 1.3.0: Fix minor error in data files '4 - LCA results' and '6 - Figure data' in demand amounts for total impacts.<br> 2022-04-06 - 1.2.1: Included link to article after publication<br> 2022-03-30 - 1.2.0: Included underlying figure data<br> 2022-01-24 - 1.1.1: Opened repository after paper acceptance<br> 2021-11-26 - 1.1.0: Update of code to comply with peer-review<br> 2021-07-12 - 1.0.0: Set-up of repository</p>
Model output data and code for Zhang et al., Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality
<p>Model output data and code for "Zhang et al., Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality" in Nature Communications.</p>
Net irrigation requirement under different climate scenarios using AquaCrop over Europe
<p>This repository contains the setup and data related to the peer-reviewed article "Net irrigation requirement under different climate scenarios using AquaCrop over Europe" accepted for HESS (https://hess.copernicus.org/preprints/hess-2021-631/).</p> <p>The README.txt file contains all information about the repository. Please contact Louise Busschaert (louise.busschaert@kuleuven.be) or Gabrielle De Lannoy (gabrielle.delannoy@kuleuven.be) for any further questions.</p>
Socioeconomic and climate scenarios for the Mar Menor-Campo de Cartagena socioecosystem
<p>The five scenarios describe plausible changes in 15 external drivers of the socioecosystem of the Mar Menor and surrounding Campo de Cartagena between 1964 and 2070. These scenarios were developed for evaluation of their impacts on Key Performance Indicators of sustainability using a simulation model based on System Dynamics. This model, developed by Martínez-López et al (2022) can be consulted <a href="http://doi.org/10.5281/zenodo.7142764">here</a>.</p> <p>Historic data combined with the Shared Socioeconomic Pathways (SSPs) from the IPCC report ‘Global warming of 1.5°C’ and the Representative Concentration Pathways (RCPs), were used as starting point to develop the model-specific scenarios. The five scenarios are based on SSP 1, SSP2, SSP4 and SSP5 in combination with emission scenarios that will keep global temperature rise below 1.5ºC. The BAU scenario represents a combination of SSP2 without any climate change.</p> <p>The detailed documentation of SSPs from O’Neil et al (<a href="http://dx.doi.org/10.1016/j.gloenvcha.2015.01.004">http://dx.doi.org/10.1016/j.gloenvcha.2015.01.004</a>) and subsequent expert interviews and input received during stakeholder workshops organised in the framework of the COASTAL project were used to prepare the region-specific time-series of the 15 variables for the Mar Menor and surrounding Campo de Cartagena.</p> <p>The 15 external drivers are:</p> <ol> <li>Agricultural revenue per hectare</li> <li>Growth rate of agriculture</li> <li>Percentage of nutrients that are metabolized by the native lagoon ecosystem</li> <li>Average excess of fertilizer use</li> <li>Yearly effectiveness in nutrients reduction of nutrients, soil and water retention measures</li> <li>Electricity Price</li> <li>Mean number of hours per day of photovoltaic electricity production</li> <li>Photovoltaic energy facilities growth rate in Megawatts installed</li> <li>Growth rate of tourism</li> <li>Average percentage of groundwater desalinated</li> <li>Agricultural water demand per hectare</li> <li>Catchment water sources</li> <li>Urban wastewater treatment plant effluents</li> <li>Yearly average of sea water desalination</li> <li>Amount of water transferred from the Tagus river (RCP15ATS)</li> </ol>
Dataset: Global range dynamics of the Bearded Vulture (Gypaetus barbatus) from the Last Glacial Maxima to climate change scenarios
<p>This dataset consists of Bearded Vulture <em>Gypaetus barbatus </em>occurrence points which were used to develop a distribution model to study its suitable habitat of this species. Using these data, we modelled the current distribution of Bearded Vulture throughout its entire range and projected the Last Glacial Maxima (LGM), Mid-Holocene (MH) and future distribution under 2070s climate change scenarios. We compiled these data from the entire distribution range in Asia, Europe and Africa using different sources: freely accessible online resources including, eBird and GBIF repositories, published reports and grey literature and occurrence data collected by the authors in the field, mostly in Nepal.</p> <p> </p> <p> </p>
Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses
<p>This repository contains supplementary files for our study "Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses". The two files are:</p> <p>- ConversationClassification_FeatureTable.xlsx is an MS Excel file that contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals.</p> <p>- ConversationClassification_SynchronyCalculation.zip contains the MATLAB 2021b code used to calculate four physiological synchrony metrics: dynamic time warping, nonlinear interdependence, coherence, and cross-correlation. It also includes some open-source code from other authors that is required for our synchrony calculation code to work. As inputs, the synchrony calculation functions accept 4-minute signal vectors from both participants in the dyad.</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.