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942 results for “Scenarios”
Energy Consumption Reduction in Historic Urban Residential Sectors: A Case Study of Kyoto City Using Bottom-Up Modeling and Future Climate Scenarios
<p>This dataset is a detailed simulation result of 21 scenarios in the paper. The results include:</p> <ol> <li>Annual daily energy consumption data divided by energy source and residential type.</li> <li>Photovoltaic power generation data, direct photovoltaic use, battery use, and photovoltaic power generation consumed by apartment houses and Kyomachiya through P2C systems. </li> <li>Annual daily net energy consumption data divided by energy source and residential type.</li> </ol>
Supplementary materials for paper "Stack Trace Deduplication: Faster, More Accurately, and in More Realistic Scenarios"
<p>Update: this was an anonymized version for the double-blind submission and it is no longer actual.</p> <p>This is a replication package for the paper "Stack Trace Deduplication: Faster, More Accurately, and in More Realistic Scenarios". </p> <p>It presents the code of our approach and an instruction for running the experiments.</p> <p>Since we cannot provide the newly collected industrial dataset due to anonymity restrictions, instead, we append the existing open-source NetBeans dataset in the same format for clarity (it can be found in the `NetBeans` directory, its format is described in the README). Upon acceptance, we will release the new dataset to the research community.</p> <p>Please find all the istructions in the README.</p>
Data for climate mitigation scenarios with persistent COVID-19 related energy demand changes
<p>This repository contains data for the main text figures plus some supplementary figures in the article:<br> Kikstra et al 2021 Nat. Energy. DOI: <a href="https://doi.org/10.1038/s41560-021-00904-8">10.1038/s41560-021-00904-8</a></p> <p>This dataset should be cited as: Kikstra et al. (2021). Data for climate mitigation scenarios with persistent COVID-19 related energy demand changes. DOI: <a href="https://doi.org/10.5281/zenodo.5211169">10.5281/zenodo.5211169</a></p> <p>In order to reproduce the figures, one needs to use the script that is available on GitHub at:<br> <a href="https://github.com/iiasa/covid-energy-demand-scenarios">https://github.com/iiasa/covid-energy-demand-scenarios</a></p> <p>The most accessible way of exploring the scenario data behind this article would be to go to <a href="https://data.ece.iiasa.ac.at/engage/#/workspaces/60">https://data.ece.iiasa.ac.at/engage/#/workspaces/60</a>.<br> This goes to a web tool hosted by the International Institute of Applied Systems Analysis (IIASA) which provides access to a database of these and more variables of interest, defined for each scenario on the detail of MESSAGE regions, with a few example workspaces available within the ENGAGE Scenario Explorer.<br> The Scenario Explorer is a versatile open access tool to browse, visualize and download data and results. Users can freely create a private workspace where customized plots can be saved and shared.<br> For tutorials on how to use the Scenario Explorer, please visit <a href="https://software.ece.iiasa.ac.at/ixmp-server/tutorials.html">https://software.ece.iiasa.ac.at/ixmp-server/tutorials.html</a>.</p> <p>The scenarios that were used for the IPCC Special Report on 1.5C warming (SR1.5) have been made available at <a href="https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/">https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/</a>.</p> <p>The data is available for download at the <a href="https://data.ece.iiasa.ac.at/engage/">ENGAGE Scenario Explorer</a>. The license permits use of the scenario ensemble for scientific research and science communication, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and <a href="https://data.ece.iiasa.ac.at/engage/#/license">legal code</a> for more information.</p>
A land use scenario of SSP1-Low emissions scenario for Scotland
<p>This dataset contains a land use change scenario (2050) for Scotland within the scope of a SSP1 - Low emissions scenario (Shared Socio-Economic Pathways). For achieving a low-emissions scenario, simulated land use change targeted woodland expansion (including silvo-arable and silvo-pastoral) and decreased grazing intensity, both land use changes also aimed at benefitting four aspects of ecosystem services: carbon storage through tree planting, emission reduction through deintensification, biodiversity enhancement through tree planting, and pollination to support food production. The 2019 baseline land use map at 100m resolution, was created by combining the Land Cover Map 2019 (Morton et al, 2020), estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). The land use scenario map was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). The attached land use scenario map for 2050 is not an optimised result, but it is only one possibility among others that meets all the constraints stipulated for the scenario.</p> <p>For a detailed description of the scenario refer to the following web storymap : https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</p> <p>This analysis was conducted as part of the Land use Transformations (https://landusetransformations.hutton.ac.uk/) project (JHI-C3-1) in the Scottish Government funded Strategic Research Programme 2022-27.</p> <p>The exact licence for this dataset is currently being finalised. When ready, the dataset will be uploaded as a new version.<br> </p>
Emissions scenario database of the European Scientific Advisory Board on Climate Change, hosted by IIASA
<p>This scenario ensemble collects emissions pathways quantitative, model-based scenarios related to the mitigation of climate change.</p> <p>The ensemble was compiled from the energy and integrated-assessment modelling community in response to a call by the European Scientific Advisory Board on Climate Change, see <a href="https://www.eea.europa.eu/about-us/climate-advisory-board/call-for-scenario-data-contributions">https://www.eea.europa.eu/about-us/climate-advisory-board/call-for-scenario-data-contributions</a>.</p> <p>The scenario ensemble can be accessed via the <strong>EU Climate Advisory Board Scenario Explorer</strong> hosted by IIASA at <a href="https://data.ece.iiasa.ac.at/eu-climate-advisory-board">https://data.ece.iiasa.ac.at/eu-climate-advisory-board</a>. The data can be downloaded and re-used for analysis and data visualization, but re-publication of a substantial portion is prohibited. </p> <p>The reason for the restriction is that we anticipate updates/extensions and (possibly) error corrections of this scenario ensemble.<br> We want to avoid a situation where multiple inconsistent versions of the scenario database are in wide circulation, which can lead to confusion for users. Therefore, please refer to the IIASA Scenario Explorer for the most-up-to-date version of the database.</p> <p>Further guidance and the full license text is available at <a href="https://data.ece.iiasa.ac.at/eu-climate-advisory-board/#/license">https://data.ece.iiasa.ac.at/eu-climate-advisory-board/#/license</a>.</p>
SCALABLE - Social-ecological pathways and gender perspectives for future conservation of biocultural mountain agro-ecosystems - Deliverable 3.1: Future scenarios developed for the conservation of mountain agro-ecosystems
<p>This dataset contains the results from participatory mapping workshops with local communities in the municipality of Abrucena, in Almería, Spain. Participatory workshops were carried out divided by gender in different days. In addition, within each workshop, participants were dividing in two different groups. Participants were asked to: a) map future biocultural diversity elements in the region (i.e., practices, knowledge, and traditions); and b) identify strategies to reach the ideal future scenario for the conservation of biocultural diversity in the region.</p> <p>In this upload you can find 4 files:</p> <p>1- Database of future scenarios for biocultural diversity identified by women: this file contains the dataset with biocultural diversity elements identified and classified by three categories: practices, knowledge, traditions.</p> <p>2- Database of future strategies to achieve future scenarios for the conservation of biocultural diversity identified by women: first sheet contains the dataset with strategies identified, and actors involved in each strategy. Second sheet contains general observations of the strategies to facilitate the interpretation of the data.</p> <p>3- Database of future scenarios for biocultural diversity identified by men: this file contains the dataset with biocultural diversity elements identified and classified by three categories: practices, knowledge, traditions.</p> <p>4- Database of future strategies to achieve future scenarios for the conservation of biocultural diversity identified by men: first sheet contains the dataset with strategies identified, and actors involved in each strategy. Second sheet contains general observations of the strategies to facilitate the interpretation of the data.</p>
Supporting information for Article "Hydrothermal carbonization for sludge disposal in Germany: A comparative assessment for industrial-scale scenarios in 2030"
<p>These supporting information files provides details of all case and scenario assumptions, scenario factors, sensitivity assumptions and factors as well as calculation results (PDF file) as well as the detailed backgound calculations (ZIP files with MS Excel Sheets) referred to in the article "Hydrothermal carbonization for sludge disposal in Germany: A comparative assessment for industrial-scale scenarios in 2030" (Journal of Industrial Ecology).</p>
Geometric Brownian Motion of stock indexes and financial market uncertainty in the context of non-crisis and financial crisis scenarios
<p>Supplementary file for the manuscript submitted in the <em>Special Issue of <strong>Entropy</strong> “Statistical Physics and Social Sciences”.</em></p>
Disability and mortality in a cohort of MS patients: how the real-world scenario is changed
<p>Demographical (sex and age) and clinical findings (functional system involved at MS onset, MS type, MS disability evaluated via EDSS scale, time to MS progression and use of disease modifying treatment) collected in a cohort of 86 patients with MS and with first hospitalization at IRCCS Mondino, Pavia (italy) in 2005-2006 and followed until 2018. </p>
Audio data from Mobile scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the recorded audio data from the Mobile scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p> <p>This dataset also has a second part that contains the sensor data data recorded in this scenario, which is not access controlled. See <a href="http://dx.doi.org/10.5281/zenodo.2537703">this deposit</a> for more details.</p>
Preliminary Code for "Scenarios for the Decarbonization of the Electric Vehicle Battery Supply Chain"
<p>Preliminary Dataset and Code for a paper by Sofia Martinez, Costa Samaras, and Corey Harper</p>
Cycles agroecosystem model weather files under nuclear winter scenarios
Open the record for dataset details and reuse information.
NGFS Climate Scenarios Data Set
<h1><strong>Notice to users of NGFS long-term climate scenarios</strong></h1> <p>The NGFS informs users that the academic paper underpinning the physical risk estimates in Phase V of its long-term scenarios, Kotz et al. (2024), has received critiques in a post-publication review at <em>Nature</em>. The authors have revised their analysis, with limited impacts on results. The updated paper still has to undergo peer-review.</p> <p>The NGFS closely monitors the academic process and will incorporate any necessary updates in future iterations of its long-term scenarios.</p> <p>Users are reminded that neither the NGFS, nor its member institutions, nor any person acting on their behalf, is responsible or liable for any reliance on, or for any use of the NGFS scenarios and/or supplementary documentation. This also applies to the use of the data produced under the scenarios – see section 5 in <a title="https://protect.checkpoint.com/v2/r02/___https://data.ene.iiasa.ac.at/ngfs/%23/license___.YzJlOmlpYXNhOmM6bzpkYjlkNzM0YWJiNzc3OTZkOWFhNjVkMzJlOGU5OWMxMDo3OmRlNWI6MDc1NDk3ZWUxNDE1MGNiM2I3ZThmOTQxZjAxNTY5MzUxNmQ1ZTUzMThiNWEzYzk4NGQ3NWEzNzlmM2IyMDhjNjpoOkY6Tg" href="https://protect.checkpoint.com/v2/r02/___https://data.ene.iiasa.ac.at/ngfs/%23/license___.YzJlOmlpYXNhOmM6bzpkYjlkNzM0YWJiNzc3OTZkOWFhNjVkMzJlOGU5OWMxMDo3OmRlNWI6MDc1NDk3ZWUxNDE1MGNiM2I3ZThmOTQxZjAxNTY5MzUxNmQ1ZTUzMThiNWEzYzk4NGQ3NWEzNzlmM2IyMDhjNjpoOkY6Tg" target="_blank" rel="noopener noreferrer">https://data.ene.iiasa.ac.at/ngfs/#/license</a>. Thus, while the NGFS climate scenarios are certainly a helpful tool, they do not alleviate the responsibility of users, including banks and other (financial) organisations, to design and implement their own risk management frameworks.</p> <h1><strong>Download information</strong></h1> <h2><strong>Please do not request a data download here.</strong></h2> <p>Rather, the data is available for download at the <a href="https://data.ece.iiasa.ac.at/ngfs">NGFS Scenario Explorer</a> under this <strong>download link:</strong> <a href="https://data.ece.iiasa.ac.at/ngfs/#/downloads">https://data.ece.iiasa.ac.at/ngfs/#/downloads</a>. In order to download click on <em>Guest login. </em>You will be forwarded to the downloads page. At the <strong>bottom</strong> of the <strong>downloads</strong> page you can download the data.</p> <p>The license permits use of the scenario ensemble for scientific research and commercial use, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and <a href="https://data.ece.iiasa.ac.at/ngfs/#/license">legal code</a> for more information.</p> <h2><strong>About NGFS</strong></h2> <p>The Network for Greening the Financial System (NGFS) is a group of 127 central banks and supervisors and 20 observers committed to sharing best practices, contributing to the development of climate– and environment–related risk management in the financial sector and mobilising mainstream finance to support the transition toward a sustainable economy.</p> <p>This Scenario Explorer is a web-based user interface for NGFS Scenarios. This provides intuitive visualizations & display of time series data and download of the data in multiple formats.</p> <p>NGFS scenarios were produced by NGFS Workstream on Scenarios Design and Analysis in partnership with an academic consortium from the Potsdam Institute for Climate Impact Research (PIK), International Institute for Applied Systems Analysis (IIASA), University of Maryland (UMD), Climate Analytics (CA), and the National Institute of Economic and Social Research (NIESR). This work was made possible by grants from Bloomberg Philanthropies and ClimateWorks Foundation.</p> <p>The bespoke scenarios developed in Phase 4 of this project are generated by state-of-the-art well-established integrated assessment models (IAMs), namely GCAM, MESSAGE-GLOBIOM and REMIND-MAgPIE, as well as the NiGEM macroeconomic model.</p>
Calibration of adaptive energy management strategies for fuel cell trucks using real-world driving scenarios
<p>Energy management strategies have a significant impact on the hydrogen economy of fuel cell trucks and the lifetime of battery and fuel cell systems. Usually, the strategy design and calibration involve a multi-objective optimization of performance indexes related to hydrogen consumption, fuel cell transients, battery thermal state, and battery charge control. This contribution presents the calibration of an energy management strategy that is adaptive to battery and ambient temperature. Indeed, fuel cell trucks face critical operating conditions due to high ambient temperatures or high loads on long uphill roads. However, the presented adaptive energy management strategy can effectively avoid thermal issues to preserve the battery life without hindering the hydrogen economy. The strategy calibration can be optimized using a simulation environment consisting of drivetrain components in AVL CRUISE M and vehicle control unit functions in MATLAB Simulink. Moreover, AVL CAMEO can systematically vary the adaptive strategy's curve parameters to find the best trade-off between the key performance indexes. The calibration considers real-world driving cycles of road freight vehicles, including measured speed, road elevation, and variable vehicle mass. The calibration is robust because the performance indexes are optimized over more than 9000 kilometers, covering an extensive range of real-world driving scenarios. Besides the benefits of optimal calibrations, this contribution also provides interesting insights into the energy and thermal management problem of fuel cell trucks.</p>
Consequential Carbon Accounting Scenarios: CCUS Mexico
<p>This dataset presents the models for a consequential carbon accounting method as a time series for evaluating a hypothetical Carbon Capture, Utilisation and Storage (CCUS) project in the Southeast Region of México. The CO2 source is the Dos Bocas Refinery; CO2 utilisation for EOR considers two injection strategies (Water Alternating Gas and Continous Gas Injection); two different scenarios for the energy mix for Mexico´s context were considered (National Strategy on Climate Change and Sustainability):</p> <p>Scenario 1: WAG+NSCC</p> <p>Scenario 2: CGI + Sustainability</p> <p>Scenario 3: WAG+Sustainability</p> <p>Scenario 4: CGI+NSCC</p> <p> </p>
Assessing Environmental Exposure to Viruses in Wastewater Treatment Plant and Swine Farm Scenarios with Next-generation Sequencing and Occupational Risk Approaches
<p>Target enrichment sequencing of aerosol and surface samples collected from wastewater treatment plant (WWTP) and swine farm scenarios. </p>
Ou&Iyer 2021 GCAM scenario database
<p>GCAM scenario database for Ou&Iyer Science, 2021 https://www.science.org/doi/10.1126/science.abl8976</p>
(Dead) Copilot CWE Scenarios Dataset
<p>The dataset and source code and result generation framework used in the paper 'Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions'. It includes 89 different scenarios.</p>
(Dead) Copilot CWE Scenarios Dataset
<p>The dataset and source code and result generation framework used in the paper 'Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions'. It includes 89 different scenarios.</p>
Environmental sustainability assessment of cleanrooms under changing climate and energy scenarios
<p>This is the data for <em>Environmental sustainability assessment of cleanrooms under changing climate and energy scenarios.</em></p>
ScienceDex guides
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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.