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812 results for “Renewables”
Data for: Techno-economic and environmental assessment of converting mixed prairie to renewable natural gas with co-product hydroxycinnamic acid, Iowa, USA, 2022-2023.
This dataset compiles model outputs, parameter sets, and documentation supporting a techno‑economic analysis (TEA) and life‑cycle assessment (LCA) of co‑digesting beef cattle manure with pretreated mixed prairie biomass to produce renewable natural gas (RNG), with hydroxycinnamic acids (HCA) and digestate‑derived biochar co‑products. It accompanies the study by Katherine Wild, Elmin Rahic, Lisa A Schulte Moore, and Mark Mba Wright "Techno-economic and environmental assessment of converting mixed prairie to renewable natural gas with co-product hydroxycinnamic acid," in Biofuels, Bioproducts, & Biorefining, 2024 (https://doi.org/10.1002/bbb.2710). The integrated simulation and assessment framework quantifies process performance, economics, and greenhouse‑gas intensity across five scenarios representing combinations of alkaline‑ethanol pretreatment for HCA extraction, liquid recirculation fractions, and biochar addition. This data collection includes: stream‑level mass flow/composition tables for each scenario; RNG, biochar, and HCA annual production summaries; literature‑based methane/biogas yield benchmarks; equipment‑level capital costs; TEA assumptions; emission‑factor inventories and displacement credits; and full sensitivity/uncertainty matrices for MFSP and GWP.
Financing conditions of renewable energy projects – results from an EU wide survey
<p>The dataset contains data related to financing conditions and costs of capital for onshore wind, solar PV and offshore wind within the EU. It provides data on minimum, maximum and average country and technology-specific values on costs of debt, debt service coverage ratios, loan tenors, debt size, costs of equity and WACC values. The data was collected between September 2019 and April 2020.</p> <p>The data contains values for onshore wind in Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Italy, Latvia, Lithuania, Netherlands, Poland, Portugal, Romania, Spain and Sweden. Furthermore, it contains values for solar PV in Czech Republic, Estonia, France, Greece, Hungary, Latvia, Portugal, Romania, Slovakia and Spain. Finally, it also contains values for offshore wind in Belgium, France, Germany and UK. </p> <p>The PDF files are survey questionnaires that were used for the data collection. This includes 1) a survey questionnaire used in an exploratory research phase, in which we identified the most relevant research aspects related to the impacts of auctions on costs of capital and financing 2) a survey questionnaire used for the focus-group countries (Germany, Denmark, Spain, Portugal and Greece), which includes a list of more extensive qualitative questions and 3) a survey questionnaire used for the focus-group countries (all other EU member states) and which focused only on collecting the quantitative data. </p>
Historical and modelled renewable energy production for India
<p>This archive contains all the datasets produced for the paper:<br><br><span>Hunt, K. M. R.</span>, & <span>Bloomfield, H. C.</span> (<span>2024</span>). <span>Quantifying renewable energy potential and realized capacity in India: Opportunities and challenges</span>. <em>Meteorological Applications</em>, <span>31</span>(<span>3</span>), e2196. <a href="https://doi.org/10.1002/met.2196">https://doi.org/10.1002/met.2196</a></p> <p> </p> <table style="border-collapse: collapse; width: 99.9642%;"><colgroup><col style="width: 31.0476%;"><col style="width: 17.1785%;"><col style="width: 37.7477%;"><col style="width: 14.0133%;"></colgroup> <tbody> <tr> <td><strong>Data Description </strong></td> <td><strong>Figure/Table</strong></td> <td><strong> File Name</strong></td> <td><strong>Dates Valid</strong></td> </tr> <tr> <td>Installed capacity by type in each state</td> <td>Table 1</td> <td>installed-by-state-oct2022.csv</td> <td>Oct 2022</td> </tr> <tr> <td>All-India installed capacity by type</td> <td>Figure 2</td> <td>tabulated-installed-by-date.csv</td> <td>2017–2023</td> </tr> <tr> <td>Hourly wind capacity factor</td> <td>Figure 4</td> <td>wind capacity factor.zip</td> <td>1979–2022</td> </tr> <tr> <td>Hourly solar capacity factor</td> <td>Figure 6</td> <td>solar capacity factor.zip </td> <td>1979–2022</td> </tr> <tr> <td>Present-day installation locations</td> <td>Figure 11</td> <td>OSM[hydropower,wind_turbine,solar]_ installations.geojson</td> <td>Mar 2022</td> </tr> <tr> <td>Gridded 1°×1° estimate of installed wind/solar capacity</td> <td>Figure 12a/13a</td> <td>CEA_1x1_gridded_installed_[wind,solar]_cap.nc</td> <td>May 2021</td> </tr> <tr> <td>Gridded 1°×1° estimate of installed wind capacity</td> <td>Figure 12b</td> <td>TWP_1x1_gridded_installed_wind_cap.nc</td> <td>May 2021</td> </tr> <tr> <td>Gridded 1°×1° estimate of installed solar capacity</td> <td>Figure 13b</td> <td>K21_1x1_gridded installed solar cap.nc</td> <td>Sep 2018</td> </tr> <tr> <td>Reported daily wind/solar/hydro production</td> <td>Figure 14/S3</td> <td>POSOCO_reported_[wind,solar,hydro]_MU_ daily.csv</td> <td>2012–2023</td> </tr> <tr> <td>Modelled ‘historical’ production</td> <td>Figure 14/16/S4a/b</td> <td>modelled-historical-[daily,hourly]-renewable output.nc</td> <td>1979–2022</td> </tr> <tr> <td>Recommended locations for new wind/solar installations</td> <td>Figure 17</td> <td>areas-for-exploration.nc</td> <td>--</td> </tr> </tbody> </table> <p> </p> <p> </p>
Hybridization of Fossil- and CO2-Based Routes for Ethylene Production using Renewable Energy
<p>Dataset associated with the publication "Hybridization of Fossil- and CO<sub>2</sub>-Based Routes for Ethylene Production using Renewable Energy" by Iasonas Ioannou, Sebastiano C. D'Angelo, Antonio J. Martín, Javier Pérez-Ramírez, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1002/cssc.202001312">https://doi.org/10.1002/cssc.202001312</a>. The dataset includes the numeric data associated with most of the scenarios described in the main manuscript and in the Supporting Information (SI), as well as the tables presented in the main manuscript and in the SI converted in a machine-readable format.</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>MS-Results</strong>: numerical values associated with the economic and environmental results included in both the main manuscript and the SI, for all the considered scenarios. The results include the total price for the assessed scenarios, with and without externalities, with uncertainty ranges, as well as the environmental results for human health, ecosystems, resources, and global warming potential (GWP).</li> <li><strong>MS-Tables</strong>: table reported in the main manuscript associated with the price and breakeven point of four assessed scenarios dependent on different CO<sub>2</sub> source assumptions.</li> <li><strong>SI-Tables-Economics</strong>: tables reported in the SI associated with the economic assessment of all the scenarios.</li> <li><strong>SI-Tables-LCI</strong>: tables reported in the SI associated with the environmental assessment of all the scenarios.</li> <li><strong>SI-Tables-AdditionalResults</strong>: tables reported in the SI associated with additional results presented in the work.</li> </ul>
Absolute environmental sustainability assessment of renewable dimethyl ether fuelled heavy-duty trucks
<p>Dataset associated with the publication "Absolute environmental sustainability assessment of renewable dimethyl ether fuelled heavy-duty trucks" by Margarita A. Charalambous, Victor Tulus, Morten W. Ryberg, Javier Pérez-Ramírez, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1039/D2SE01409B">https://doi.org/10.1039/D2SE01409B</a>. The dataset includes all the LCA inventories and numeric data required to plot all the figures embedded in the main manuscript.</p> <p>The dataset includes 4 Excel files. The content of each dataset is here elucidated:</p> <ul> <li><strong>LCA_data.xlsx:</strong> Inventory datasets used for life cycle assessment. Includes the inventory for the production of methanol from CO<sub>2</sub> and H<sub>2</sub> sources investigated in this work, carbon dioxide from direct air capture (DAC), and point source coal power plant and natural gas power plant, as well as, the production of hydrogen from biomass with CCS and polymer electrolyte water electrolysis powered with Wind power and BECCS. DME production from each methanol activity, and lastly, truck transport activities used in the study are also included. </li> <li><strong>LCA_relative_impact.xlsx</strong>: numerical values associated with the total share of safe operating space for all the assessed control variables of the seven planetary boundaries quantified in the study, for all the considered scenarios. These values represent the data used to create Figure 2 of the main manuscript.</li> <li><strong>LCA_breakdown.xlsx</strong>: numerical values associated with the breakdown of the environmental impacts for the studied scenarios, for all the control variables of the planetary boundaries. Each sheet includes data for one control variable. These values represent the data used to create Figure 3 in the main manuscript and Figures S4 and S5.</li> <li><strong>LCA_costs.xlsx: </strong>numerical values associated with the cost breakdown for each considered scenario. These values represent the data used to create Figure 4 of the main manuscript. </li> </ul>
PROSEU Collective Renewable Energy Prosumers Stakeholders Database (Template)
<p>As part of work package nº2 of the H2020 PROSEU project, which aimed to establish a baseline review and characterisation of renewable energy sources (RES) prosumer (self-consumption) initiatives across Europe, databases identifying the diversity of collective forms of RES prosumers and related stakeholders were built by the project partners using the templates and respective variables presented here (English language). The databases served to create a stratified sample of RES prosumer initiatives for purposes of a survey, as well as distinguish them from other stakeholders in the field.</p>
Data for - The environmental footprint of transport by car using renewable energy
<p>Replacing fossil fuels in the transport sector by renewable energy will help combat climate change. However, lowering greenhouse gas emissions by switching to alternative fuels or electricity can come at the expense of land and water resources. To understand the scale of this possible tradeoff we compare and contrast carbon, land and water footprints per driven km in midsize cars utilizing conventional gasoline, biofuels, bioelectricity, solar electricity and solar-based hydrogen. Results show that solar-powered electric cars have the smallest environmental footprints per km, followed by solar-based hydrogen cars, and that biofuel-driven cars have the largest footprints.</p>
Techno-economic sustainability analysis methodology for conversion routes of renewable feedstock resources to bio-based products – case studies
<p>The dataset provides a set of sustainability principles, criteria and indicators for the evaluation of the conversion routes stage of a bio-based product. The selected case studies on the employment of alternative feedstocks and production of the bio-based products are implemented in order to evaluate the proposed methodology. Mass and energy balances for all case studies, estimated techno-economic metrics, cost of externalities and risk assessment results are provided</p>
Result data related to "Tröndle et al (2020) -- Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe"
<p>The dataset contains aggregated result data of our study. See `README.md` for more information.</p> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 1.2 (2020-09-28)</p> <p>* Add location name to scenario results.<br> * Add scenario results in CSV format, next to already existing NetCDF format.<br> * Remove capacity factors from scenario results.</p> <p>Version 1.1 (2020-07-17)</p> <p>* Remove macOS resource forks cluttering the zip file.</p> <p> </p>
plan4res public dataset for case study 3 "Cost of RES integration and impact of climate change for the European Electricity System in a future world with high shares of renewable energy sources"
<p>The objective of the plan4res project is to provide a well-structured and highly modular modelling framework to enable consistent insights into the different needs of future energy system. Three case studies will highlight the potentials of this framework by dealing with different aspects of a future energy systems.<br> Case study 3 will focus on cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources. Ist overall objectives are to identify the Cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources will be the main focus of case study 3.<br> The present dataset contains all the public data built for this case study.</p> <p>The related documentation is included in plan4res deliverable D4.5 </p> <pre>https://doi.org/10.5281/zenodo.3785010</pre>
Data Repository - Distinct Roles of Direct and Indirect Electrification in Pathways to a Renewables-dominated European Energy System
<p>This is the data repository to reproduce the scenario analysis of the paper "<a href="https://www.cell.com/one-earth/fulltext/S2590-3322(24)00037-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS259033222400037X%3Fshowall%3Dtrue">Distinct roles of direct and indirect electrification in pathways to a renewables-dominated European energy system</a>".</p> <p>The source code for the REMIND version used in this study is available at <a href="https://github.com/fschreyer/remind/tree/ElecH2_prod">https://github.com/fschreyer/remind/tree/ElecH2_prod</a>. The scenario config file that was used to start the specific model runs of the paper and that inlucdes all scenario-specific model settings can be found in the repository under <a href="https://github.com/fschreyer/remind/blob/ElecH2_prod/config/21_regions_EU11/scenario_config_ElecH2.csv">./config/21_regions_EU11/scenario_config_ElecH2.csv</a>. The repository is a fork with slight changes relative to the main release version available at <a href="https://github.com/remindmodel/remind/tree/v3.2.1">https://github.com/remindmodel/remind/tree/v3.2.1</a> and <a href="https://doi.org/10.5281/zenodo.7852740">https://doi.org/10.5281/zenodo.7852740</a>. The model documentation can be found at <a href="https://rse.pik-potsdam.de/doc/remind/3.2.0">https://rse.pik-potsdam.de/doc/remind/3.2.0</a>. </p> <p>Model output data as well as other data that were used in the study are stored in data.zip. Moreover, we added a PlotsData.zip file, which contains the data shown in the figures of the paper. The R script to produce the figures and analysis of the paper can be found in ElecH2paper_Plots.Rmd. We publish a comprehensive dataset of our model output which includes more data than what is needed to reproduce the figures of the paper. Those data can be helpful to compare and contextualize our scenarios or use them for further analyses. However, due to the scope and complexity of our modeling framework, these data need to be used with care. The data used for the analysis of this study have been thoroughly validated. However, we cannot always perform such validation for the whole dataset and data need to treated with caution in particular at high regional or sectoral resolution and with respect to aspects that were not in the focus of the study as there maybe artefacts or limitations of our modeling approach. Please contact us in case you would like to use our scenarios for further analyses. We welcome open and constructive exchange on our data. </p> <p> </p> <p>Contact:<br>Felix Schreyer<br>Potsdam Institute for Climate Impact Research<br>felix.schreyer@pik-potsdam.de</p>
Research data supporting "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains"
<p>Research data supporting the peer-reviewed article "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains" by the same authors.</p>
Data for: Wind-induced hypolimnetic upwelling between the multi-depth basins of Lake Geneva during winter: An overlooked deepwater renewal mechanism?
<p>Combining field observations, 3D hydrodynamic modeling and particle tracking, we investigated wind-driven interbasin exchange, and in particular hypolimnetic upwelling, between the deep <em>Grand Lac</em> (max. depth 309 m) and shallow <em>Petit Lac</em> (max. depth 75 m) basins of Lake Geneva (Switzerland/France) during the weakly stratified fall/winter period 2018-2019.</p> <p><br> The data include measurements from moored Acoustic Doppler Current Profilers (ADCPs) and vertical thermistor lines along with the corresponding 3D modeling and particle tracking results.</p> <p><br> The three-dimensional model used in this study is based on the MIT General Circulation Model (MITgcm, http://mitgcm.org/, https://doi.org/10.1029/96JC02775).</p> <p><br> The particle tracking code is based on ctracker (https://doi.org/10.5281/zenodo.1034118)</p>
WaterGAP2.2d model derived Potential evapotranspiration and Renewable water resources variables with standard and modified PET calculation methods
<p>This data set is produced as a part of the ''Improving the quantification of climate change hazards by hydrological models: A simple ensemble approach for considering the uncertain effect of vegetation response to climate change on potential evapotranspiration" journal publication (in preparation). WaterGAP2.2d global hydrological model with two different settings; 1) with standard PET method Priestley-Taylor (PT) and 2) with modified approach (PT-MA) (please refer to the publication for more details on the method) used to derive the data set. The bias-adjusted GCM-derived (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) climate data under RCP2.6 and RCP8.5 emission scenarios were used as the input. The model-derived potential evapotranspiration and the renewable water resources variables are available from 1981 to 2099 on the monthly scale for each land grid cell (spatial resolution: 0.5 degrees x 0.5 degrees). The data files are in the netCDF format (.nc4). </p>
Hourly LC impacts - Primary Non-renewable energy - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Primary Non-renewable Energy, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Region-specific sourcing of lignocellulose residues as renewable feedstocks for a net-zero chemical industry
<p><span>data_1_lignocellulose_residue_grid_all_years_scenarios – datasets presenting the <em>theoretical</em>, <em>ecological</em>, and <em>available</em> potential of various lignocellulose residues on the GLOBIOM grid level (200 km </span><span>×</span><span> 200 km)</span></p> <p><span>data_2_lignocellulose_feedstock_potential_impacts_country_level_all_scenarios – datasets presenting the <em>theoretical</em>, <em>ecological</em>, and <em>available</em> potential of various lignocellulose residues on the country level and their corresponding climate-change impacts, water stress, and land-use-related biodiversity loss impacts</span></p> <p><span>LUID_CTY – shapefile for the global map with GLOBIOM grids</span></p> <p> </p>
Dataset for the paper: "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU."
<p>Dataset used for the development of scenarios in the publication "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU. Sustainability 2018, 10, 3685." and used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of other secondary data (from papers, reports) specified in the dataset (under tab "input codes")</p>
The global renewable power support policy dataset
<p>The global renewable power support policy dataset was compiled by Sarah Hafner (Anglia Ruskin University, United Kingdom) and Johan Lilliestam (Institute for Advanced Sustainability Studies (IASS), Germany) in February-July 2017 and completed during 2017. The work was led by Johan Lilliestam but each author gathered half of the data. The data was formatted and checked for internal consistency by Tim Tröndle, IASS.</p> <p>All non-commercial users are allowed to use and manipulate our data, but are required to give appropriate attribution. Hence, <strong>please cite this data as</strong>:</p> <p>Hafner, S. & Lilliestam, J. (2019): <em>The global renewable power support dataset</em>. Institute for Advanced Sustainability Studies (IASS) & Anglia Ruskin University, Potsdam & Cambridge. Doi: https://doi.org/ 10.5281/zenodo.3371375.</p> <p><strong>If you are interested in contributing</strong> to and further developing the dataset: please contact Johan Lilliestam (IASS Potsdam).</p> <p>The search was done in publically available sources, including but not limited to the IEA renewables policy database, res-legal.eu, Worldbank data, as well as data from the responsible national ministries.</p> <p>Our data holds information on 10 specific policy instruments explicitly dedicated to the support for expansion of renewable electricity generation 1990-2016; some instruments, including taxation of non-renewables or emission trading, affect other sectors than renewable power, but are mentioned in their original policy description to also be dedicated to increasing renewable power. Our data concerns national policy measures, but ignores policies enacted on higher (e.g. EU-level in Europe) or lower (e.g. state-level policies in Canada, USA) political levels. For example, the “no support” entry for the United Arab Emirates indicates that there were no national-level policies: all policies were, in this case, emirate-specific.</p> <p>The data exists in two versions: one version readable for humans (RE_policies_fullglobal.xlsx) and for each instrument type as .csv. The information in the two versions is identical and differs only in the way it is displayed.</p> <p><strong>Please refer to the metadata file for a detailed description of the dataset and the data categories.</strong></p>
Supplementary material to the manuscript: Regionalised Heat Demand and Power-To-Heat Capacities in Germany - An Open Data Set for Assessing Renewable Energy Integration
<p>This is the supplementary material for the manuscript:</p> <p>"Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration"</p> <p>Article DOI: <a href="https://doi.org/10.1016/j.apenergy.2019.114161">https://doi.org/10.1016/j.apenergy.2019.114161</a></p> <p>Open access preprint: <a href="https://arxiv.org/abs/1912.03763">https://arxiv.org/abs/1912.03763</a></p> <p> </p> <p><strong>DESCRIPTION OF THE DATASET AND LICENSES:</strong></p> <p>The subdirectory "04_results" contains the regionalised heat demand an power-to-heat capacity data on administrative district level (NUTS-3) for Germany. The subdirectories "01_census_special_evaluation_data" and "02_other_input_data" contain the utilised input data. The subdirectory "03_code" contains the developed and applied source code.</p> <p>The data in this repository are provided under open source licenses. For license information and other general information on the supplementary material, refer to the LICENSE files and README files in the respective subdirectories.</p> <p>For a detailed description of the approach developed by the author, the input data used and the generated results, refer to the manuscript "Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration".</p> <p><strong>METADATA:</strong></p> <p>Sector: Residential Buildings – Space Heating and Domestic Hot Water</p> <p>Geographical scope: Germany</p> <p>Geographical resolution: Administrative districts (NUTS-3)</p> <p>Temporal scope: 2011, three scenarios for 2030</p> <p>Temporal resolution: 15min</p> <p> </p> <p><strong>UNITS:</strong></p> <p>In the final results folders (04_results/01_installed_heating_p2h_capacity; 04_results/02_daily_time_series; 04_results/03_yearly_time_series) the units of the data are indicated in the file names or the column names, e.g. by "in_MW". In case of unit indication in the file name, the unit refers to all columns in the file.</p> <p>In the intermediate results folder (04_results/00_sql_tables_exported_to_csv) all units referring to power are "kW" and all units referring to energy are "kWh".</p> <p><strong>NEWS AND CONTACT:</strong></p> <p>This dataset will be used as part of the <a href="https://wiki.openmod-initiative.org/wiki/Region4FLEX">region4FLEX model</a>. We are currently enhancing the data by temporally and spatially resolved COP time series and determining load shifting potentials. If you wish to receive news or have general questions please contact: wilko.heitkoetter@dlr.de. </p>
JOINT WEBINAR: SUSTAFUELS, Three European Solutions Working on Algal & Renewable Fuels
<p>On May 21, 2024, an informative webinar titled “SUSTAFUELS, Three European Solutions Working on Algal & Renewable Fuels” was held from 12:00 to 13:00 CET. This online event was a collaborative effort among three key projects—ALFAFUELS, COCPIT, and FUELGAE—aimed at advancing renewable fuel technologies. Attendees were introduced to the main concepts, ambitions, and methodologies behind these innovative European initiatives. The event was structured in six parts, including presentations on non-biological algal renewable fuels, detailed discussions on each project, and a Q&A session.</p> <p>The webinar was moderated by Pablo Morales Moya from Sustainable Innovations (SIE), and featured a presentation from Javier Sánchez López of CINEA, who discussed the agency’s role in supporting climate, infrastructure, and environmental initiatives. Following the introductory segments, the spotlight shifted to the project coordinators. Charis Xiros from RISE Research Institutes of Sweden presented the ALFAFUELS project, Sary Awad from IMT Atlantique showcased the COCPIT project, and Silvia Morales de la Rosa from CSIC presented the FUELGAE project. Each coordinator provided insights into their project’s objectives, impacts, and collaborative efforts.</p> <p>Participants had the opportunity to learn about groundbreaking renewable fuel solutions and their potential for carbon capture. The event underscored the importance of European collaboration in tackling environmental challenges through innovative research and development. Recordings of the session will be used for dissemination purposes, ensuring that the knowledge shared continues to benefit a wider audience interested in sustainable fuel technologies.</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.