Skip to main content
ConductScienceScienceDex
Powered by ShareScore

Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

4,230

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

4,230 results for “Energie”

Learn how ShareScore rates datasets ↗
zenodo40/100

Long-term Performance and Life Cycle Assessment of Energy Piles in three Different Climatic Conditions_Dataset

<p>In this file it is possible to find the dataset linked to the related pubblication. In the file each spreadsheet corresponf to a picture of the paper.</p>

opencc-by-4.0Jul 2019View details →
zenodo40/100

H2020 OPERA Project: Power Take-Off and Control Law testing in MARMOK-A-5 Wave Energy Converter at BiMEP

<p>Funded under European Union&#39;s Horizon 2020 Programme, <a href="http://opera-h2020.eu/">OPERA</a> project&rsquo;s main objective is to reduce the time to market of wave energy, by further advancing in 4 key innovations aiming to reduce up to 50% the Levelized Cost of Energy (LCOE) projections of a floating Oscillating Water Column (OWC) technology.</p> <p>As part of project activities, a series of Power Take-Off (PTO) and Control Law (CL)&nbsp;tests were carried out using IDOM&#39;s MARMOK-A-5 wave energy converter, while this was deployed in the&nbsp;Biscay Marine Energy Platform (BiMEP)&nbsp;from October 2018 to June 2019.</p> <p>The datasets herein contain a collection of PTO&nbsp;and CL&nbsp;testing results obtained during this extensive testing campaign, providing&nbsp;quantitative evidence of the performance of both innovations.&nbsp;</p>

opencc-by-nc-nd-4.0Jul 2019View details →
zenodo40/100

H2020 OPERA Project: Mooring System Experimental data from MARMOK-A-5 Wave Energy Converter at BiMEP

<p>Funded under European Union&#39;s Horizon 2020 Programme, <a href="http://opera-h2020.eu/">OPERA</a> project&rsquo;s main objective is to reduce the time to market of wave energy, by further advancing in 4 key innovations aiming to reduce up to 50% the Levelized Cost of Energy (LCOE) projections of a floating Oscillating Water Column (OWC) technology.</p> <p>As part of project activities, a condition monitoring system was deployed during the open-sea testing campaign of IDOM&#39;s MARMOK-A-5 wave energy converter, while this was deployed in the&nbsp;Biscay Marine Energy Platform (BiMEP) from October 2016 to June 2019.</p> <p>The dataset&nbsp;herein contains a collection of experimental&nbsp;results obtained during this extensive testing campaign, The campaign covers two deployment periods, where the first testing period includes polyesther tethers and the second testing campaing includes innovative elastomeric tethers as described in more detail in the project documentation. This experimental dataset aims to provide&nbsp;quantitative comparison data of the dynamic behavior of the system under these two different configurations.</p>

opencc-by-nc-nd-4.0Jul 2019View details →
zenodo40/100

Energy measurements for analyzing medical-implant-battery lifetime

<p>The dataset comprises of energy-consumption measurements pertaining to&nbsp;a modern&nbsp;ultra-low-power MCU (EFM32TG11). These numbers were used to evaluate the impact of using certain cryptographic primitives on the battery lifetime of implantable medical devices.</p> <p>Further details can be found in the relevant publication:</p> <p>Muhammad Ali Siddiqi and Christos Strydis. 2019. IMD security vs. energy: are we tilting at windmills?: POSTER. In&nbsp;<em>Proceedings of the 16th ACM International Conference on Computing Frontiers</em>&nbsp;(CF &#39;19). ACM, New York, NY, USA, 283-285. DOI: https://doi.org/10.1145/3310273.3323421</p>

opencc-by-4.0Aug 2019View details →
zenodo40/100

On the Relationship between Software Security and Energy Consumption - Dataset

<p>The dataset for estimating the&nbsp;Relationship between Software Security and Energy Consumption described in&nbsp;Siavvas, Miltiadis, Marantos, Charalampos, Papadopoulos, Lazaros, Kehagias, Dionysios, Soudris, Dimitrios, &amp; Tzovaras, Dimitrios. (2019). On the Relationship between Software Security and Energy Consumption (Version 1.0).</p>

opencc-by-4.0Jul 2019View details →
zenodo40/100

Machine Learning for LTE Energy DetectionPerformance Improvement

<p>LTE signal generated using matlab. LTE signal is transmitted through fading channel with shadowing. Energy Detection is performed on received LTE data. Energy Detection results are saved in the text files. Those results can be used in Python programs to test out k-Nearest Neighbors and Random Forest Machine Learning algorithms. Results can be then used in matlab programs to show on plots improvement in terms of probability of detection and false alarm.</p>

opencc-by-4.0Aug 2019View details →
zenodo40/100

Data set for the PLOS ONE paper Expecto transitio: Exploring non-experts' techno-economic expectations of the energy future

<p>Raw data file (SPSS and .cv versions) consisting of all data used for the Plos One publication.</p> <p>&nbsp;</p>

opencc-by-4.0Sep 2019View details →
zenodo40/100

TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"

<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time = 3672 ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; stations = 6 ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; name_strlen = 6 ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; depth = 3 ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; height = 201 ;<br> variables:<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double time(time) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:standard_name = &quot;time&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:long_name = &quot;time of measurement&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:units = &quot;hours since 2016-06-01 00:00:00&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:timezone = &quot;UTC&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:calendar = &quot;proleptic_gregorian&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double lat(stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lat:standard_name = &quot;latitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lat:long_name = &quot;station_latitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lat:units = &quot;degrees_north&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double lon(stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lon:standard_name = &quot;longitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lon:long_name = &quot;station_longitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lon:units = &quot;degrees_east&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double elev(stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; elev:standard_name = &quot;altitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; elev:long_name = &quot;station_altitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; elev:units = &quot;m ASL&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double height(height) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; height:standard_name = &quot;altitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; height:long_name = &quot;station_altitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; height:units = &quot;m ASL&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double depth(depth) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; depth:standard_name = &quot;soil_depth&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; depth:long_name = &quot;soil sensor depth&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; depth:units = &quot;cm&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; char station_name(name_strlen, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; station_name:long_name = &quot;station_name&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; station_name:cf_role = &quot;timeseries_id&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double T(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:standard_name = &quot;temperature&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:long_name = &quot;2m air temperature&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:units = &quot;degree_Celsius&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double Q(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:standard_name = &quot;mixing_ratio&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:long_name = &quot;2m mixing ratio&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:units = &quot;g kg-1&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double ET_i(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:standard_name = &quot;evapotranspiration_intensive&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:long_name = &quot;lysimeter evapotranspiration intensive management&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:units = &quot;g kg-1 h-1&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double ET_e(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:standard_name = &quot;evapotranspiration_extensive&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:long_name = &quot;lysimeter evapotranspiration extensive management&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:units = &quot;g kg-1 h-1&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double LvE_cor(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:standard_name = &quot;latent_heat_flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:long_name = &quot;energy balance corrected flux tower latent heat flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double HTs_cor(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:standard_name = &quot;sensible_heat_flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:long_name = &quot;energy balance corrected flux tower sensible heat flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double GHF(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:standard_name = &quot;ground_heat_flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:long_name = &quot;flux tower ground heat flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:positive = &quot;up&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double SW(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:standard_name = &quot;short_wave_radiation&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:long_name = &quot;downward short wave radiation&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double LW(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:standard_name = &quot;long_wave_radiation&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:long_name = &quot;downward long wave radiation&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double VWC_25(time, depth) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:standard_name = &quot;volumetric_water_content&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:long_name = &quot;DE-Fen SoilNet volumetric water content first quartile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:units = &quot;vol. %&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double VWC_50(time, depth) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:standard_name = &quot;volumetric_water_content&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:long_name = &quot;DE-Fen SoilNet volumetric water content second quartile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:units = &quot;vol. %&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double VWC_75(time, depth) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:standard_name = &quot;volumetric_water_content&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:long_name = &quot;DE-Fen SoilNet volumetric water content third quartile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:units = &quot;vol. %&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double T_prof(time, height) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:standard_name = &quot;temperature_profile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:long_name = &quot;DE-Fen HATPRO spline interpolated temperature profile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:units = &quot;K&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:source = &quot;scaleX campaign 2016&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double A_prof(time, height) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:standard_name = &quot;humidity_profile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:long_name = &quot;DE-Fen HATPRO spline interpolated absolute humidity profile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:units = &quot;kg m-3&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:source = &quot;scaleX campaign 2016&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double PRW(time) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:standard_name = &quot;precipitable_water&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:long_name = &quot;DE-Fen HATPRO column precipitable water&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:units = &quot;kg m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:source = &quot;scaleX campaign 2016&quot; ;</p> <p>// global attributes:<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :history = &quot;2019-09-12: File created.&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :institution = &quot;Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :Contact_person = &quot;Benjamin Fersch (benjamin.fersch@kit.edu)&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :Author = &quot;Benjamin Fersch (benjamin.fersch@kit.edu)&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :source = &quot;https://www.tereno.net, https://scalex.imk-ifu.kit.edu&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :Conventions = &quot;CF-1.6&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :License = &quot;Creative Commons Attribution Non Commercial Share Alike 4.0 International&quot; ;</p> <p>&nbsp;</p>

opencc-by-nc-sa-4.0Sep 2019View details →
zenodo40/100

Hourly U.S. Building Electricity Use, Cost, and Emissions Baselines to Support Time-Sensitive Analyses of Energy Efficiency and Flexibility Measures

<p>These data underpin an analysis of the time-sensitive impacts of energy efficiency and flexibility measures in the U.S. building sector using Scout (<a href="https://scout.energy.gov">scout.energy.gov</a>), a reproducible and granular model of U.S. building energy use&nbsp;developed by the U.S. national labs for the U.S. Department of Energy&#39;s Building Technologies Office.</p> <p>The analysis applies sub-annual adjustments to U.S. baseline building energy use, cost, and emissions in order to characterize how these metrics vary across hour of the day, season, and geographic region in the U.S. building sector. These adjustments are based on daily energy load, price, and emissions shapes from various data sources and are used to re-apportion baseline energy, cost, and emissions totals from <a href="https://www.eia.gov/outlooks/aeo/data/browser/%20/%20%7b%20/%20# \ }/?id=2-AEO2018 \ { \ &amp; \ }cases=r ef2018 \ { \ &amp; \ }sourcekey=0">EIA&#39;s Annual Energy Outlook (AEO) Reference Case projections</a> across all hours of a year. The resulting sub-annual baselines are specified by building sector, end use, region, and season and can be used in analyses of building efficiency and flexibility measures to quantify their time-sensitive impacts at the national scale. Analyses of these data demonstrate that energy efficiency measures continue to show strong value under a time-sensitive framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency.</p> <p>The data uploaded below include CSV files that show hourly energy use, cost, and emissions totals for the U.S. building sector as well as by end-use, region, and season. An additional CSV includes residential and commercial price intensities (USD/quad) for all hours of the day based on different time-of-use (TOU) rate data from the U.S. Utility Rate Database (URDB). Further detail on each of these CSVs is given below:</p> <ul> <li>&#39;TSV_baseline_totals.csv&#39;: this file shows hourly total energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030. It presents these estimates in Quads (source), Quads (site), and TWh (site). For the cost totals, it presents two estimates for each year and building sector, including one using the median TOU rate from the URDB and one using the average retail rate for the corresponding building sector. For converting source energy to site, total delivered electricity and electricity-related losses data for the residential and commercial sector are drawn from <a href="https://www.eia.gov/outlooks/aeo/data/browser/#/?id=2-AEO2018&amp;sourcekey=0">AEO Summary Table A2</a>.</li> <li>&#39;TSV_baseline_end-use.csv&#39;: this file shows hourly energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030 broken out by building end-use. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>&#39;TSV_baseline_region.csv&#39;: this file shows hourly energy, cost, and emissions estimates for commercial and residential space heating and cooling end uses in 2018 and 2030 for each <a href="https://www.eia.gov/consumption/residential/maps.php">American Institute of Architects (AIA) climate zone</a>. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>&#39;TSV_baseline_region_season.csv&#39;: this file shows a similar disaggregation of the data as &lsquo;TSV_baseline_region.csv&rsquo;, but it further disaggregates results by season. The seasonal definitions are as follows: &#39;intermediate&#39; (October to November; March to April), &#39;winter&#39; (November to February), and &#39;summer&#39; (May to September).</li> <li>&#39;TSV_annual_price_intensities.csv&#39;: this file presents annual hourly price intensities for the commercial and residential building sectors in 2018 and 2030 based on different TOU rate data from the URDB. Three different rate structures are included for each building sector, and these are the 5th, 50th, and 95th percentile of all existing commercial and residential TOU rates in the URDB in terms of their peak to off-peak price ratio.</li> </ul>

opencc-by-4.0Oct 2019View details →
zenodo40/100

Data and figures for Characterization of 30 ^{76}Ge enriched Broad Energy Ge detectors for GERDA Phase II

<p>Data and figures for Characterization of 30 <sup>76</sup>Ge enriched Broad Energy Ge detectors for GERDA Phase II</p>

opencc-by-4.0Oct 2019View details →
zenodo40/100

Psychological experiment in Spain of the ECHOES project (psychological determinants of investment in renewable energy)

<p>Psychological experiment in Spain of the ECHOES project (psychological determinants of investment in renewable energy). &nbsp;</p> <p>Data file is available in four formats: dta, rdata, sav and xlsx.&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Oct 2019View details →
zenodo40/100

Energy company survey of the ECHOES project

<p>Survey on psychological factors at the basis of energy choices at work among employees of a large scale European energy company.</p> <p>Data file is available in four formats: dta, rdata, sav and xls.&nbsp;</p>

opencc-by-4.0Oct 2019View details →
zenodo40/100

Finite element mesh of fusion energy heat exchange component: hybrid CAD/IBSim model including a graphite foam interlayer

<p>Image-Based Simulation (IBSim) mesh:<br> A finite element mesh of a conceptual design for a fusion energy heat exchange component (monoblock). The mesh is a hybrid from a computer aided design (CAD) drawing for the pipe and armour and IBSim for the interlayer. The IBSim interlayer is generated directly from a 3D volumetric image of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). Conversion of the data to FE mesh was achieved using ScanIP, part of the Simpleware suite of programmes, version 7 (Synopsys Inc., Mountain View, CA, USA).</p> <p>The FE mesh data uses the EnSight Gold file format and may be visualised using Paraview (<a href="https://www.paraview.org">https://www.<strong>paraview</strong>.org</a>).</p> <p>The CT data used for the mesh is available as a separate dataset:</p> <p>This data was used originally for the following publications (please cite if re-using the data):<br> Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, &ldquo;Image based in silico characterisation of the effective thermal properties of a graphite foam&rdquo;, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, &ldquo;Improving modelling of complex geometries in novel materials using 3D imaging&rdquo;, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. <a href="https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf">https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf</a></p>

opencc-by-4.0Oct 2019View details →
zenodo40/100

X-ray tomography (CT) image data of tungsten fusion energy heat exchange components

<p>X-ray tomography (CT) image data of tungsten fusion energy heat exchange components.</p> <p>The dataset includes images of four samples:</p> <ul> <li>CCFE_MB_ROI (Culham Centre for Fusion Energy thermal break concept monoblock, region of interest sample)</li> <li>IPP_Wf-Cu (Max-Planck-Institut f&uuml;r Plasmaphysik tungsten fibre / copper matrix coolant pipe)</li> <li>ITER_HHFT_ROI (ITER reference monoblock which has undergone high heat flux testing, region of interest sample)</li> <li>ITER_MB_ROI (ITER reference monoblock, region of interest sample)</li> </ul> <p>This data was used originally for the following publication (please cite if re-using the data) where further details on the data may be obtained:</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, &ldquo;Image based in silico characterisation of the effective thermal properties of a graphite foam&rdquo;, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Each of the sample directories include&nbsp;reconstructed slices in Tiff format. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads).&nbsp;CCFE_MB_ROI also includes raw radiographs; scan &amp; reconstruction parameter settings file.</p> <p>A Neutron CT version of this data is available for comparison: <a href="https://doi.org/10.5281/zenodo.3533418">https://doi.org/10.5281/zenodo.3533418</a></p> <p>Image-based simulation (IBSim) meshes were generated directly from these datasets: <a href="https://doi.org/10.5281/zenodo.3533422">https://doi.org/10.5281/zenodo.3533422</a></p>

opencc-by-4.0Nov 2019View details →
zenodo40/100

Neutron tomography (CT) image data of tungsten fusion energy heat exchange components

<p>Neutron tomography (CT) image data of tungsten fusion energy heat exchange components.</p> <p>The dataset includes three sets of images:</p> <ul> <li>ITER_171T-WA-0002_MB (ITER reference monoblock)</li> <li>CCFE_ThBr_MB&nbsp;(Culham Centre for Fusion Energy thermal break concept monoblock)</li> <li>ROIsamples_Stack (A stack of four region of interest samples*)</li> </ul> <p>The region of interest samples within the stack are as below:</p> <ul> <li>CCFE_ThBr_ROI (Culham Centre for Fusion Energy thermal break concept monoblock)</li> <li>IPP_Wf-Cu_p5_s1 (Max-Planck-Institut f&uuml;r Plasmaphysik tungsten fibre / copper matrix coolant pipe)</li> <li>ITER_HHFT_ROI (ITER reference monoblock which has undergone high heat flux testing)</li> <li>ITER_17IT-WA-0002_ROI&nbsp;(ITER reference monoblock)</li> </ul> <p>This data was used originally for the following publication (please cite if re-using the data) where further details on the data may be obtained:</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, &ldquo;Image based in silico characterisation of the effective thermal properties of a graphite foam&rdquo;, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Each of the sample directories include&nbsp;reconstructed slices in Tiff format. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads).</p> <p>CCFE_ThBr_ROI and ROIsamples_Stack include&nbsp;raw radiographs; dark and flat field images; scan &amp; reconstruction parameter settings file.</p> <p>ITER_171T-WA-0002_MB includes data relating to the modulation transfer function (MTF) measurement.</p> <p>An X-Ray CT version of the ROI data is available for comparison: <a href="https://doi.org/10.5281/zenodo.3533420">https://doi.org/10.5281/zenodo.3533420</a></p> <p>Image-based simulation (IBSim) meshes were generated directly from these datasets: <a href="https://doi.org/10.5281/zenodo.3533422">https://doi.org/10.5281/zenodo.3533422</a></p>

opencc-by-4.0Nov 2019View details →
zenodo40/100

JRC-EU-TIMES - JRC TIMES energy system model for the EU

<p>JRC-EU-TIMES is designed for analysing the role of energy technologies and their innovation for meeting Europe&#39;s energy and climate change related policy objectives. This database contains a synchronised model version of the full JRC-EU-TIMES.and all input Excel files for the JRC-EU-TIMES model, owned by JRC. The TIMES code is not part of this download; it is owned by ETSAP. The TIMES code is open for anyone that requests the code after signing a letter of agreement. Other third party software is needed:VEDA software for data and result handling and GAMS for the optimisation.</p>

opencc-by-4.0Sep 2019View details →
zenodo40/100

Fig. 15. Energy-dispersive X in On the Nature of Tintinnid Loricae (Ciliophora: Spirotricha: Tintinnina): a Histochemical, Enzymatic, EDX, and High-resolution TEM Study

Fig. 15. Energy-dispersive X-ray spectrometric (EDX) analysis in the scanning electron microscope, using uncoated material. The analysed area of the Eutintinnus angustatus lorica is marked by a white frame (~ 11 × 9 µm in size). Since this part of the lorica was freely suspended in the vacuum, elemental detection occurred without influence of the carbon substrate.

opencc-by-4.0Dec 2012View details →
zenodo40/100

Fig. 3. Energy dispersive x in Extraction and elemental composition of meconium in Polistes dominulus (Hymenoptera: Vespidae)

Fig. 3. Energy dispersive x-ray spectra of Polistes dominulus meconia samples according to locality number: (1) Hatay, Payas; (2) Hatay, Erzin, YeŞiltepe; (3) İÇel, Mezitli; (4) İÇel, Tarsus, Kaleburcu; (5) İÇel, Tarsus, Çamtepe; (6) Osmaniye, Kadirli; (7) Osmaniye, Toprakkale; (8) Adana, Yüreğir; (9) Adana, SarıÇam; (10) Adana, Kozan, Anavarza; (11) Adana, Ceyhan; (12) Adana, İmamoglu.

opencc-by-4.0Jul 2020View details →
zenodo40/100

Figure 2 in Exploring Energy Management on GPUs in Game Architectures

Figure 2. - Photographic evidence from New Caledonia. (A) Inter-specific depredation of a ~1.2 m TL grey reef shark C. amblyrhynchos (GRS) that was depredat- ed by a ~2.6 m TL bullshark C. leucas (BS), which was itself almost entirely consumed probably by a larger conspecific after (B) consuming the grey reef shark (Photos courtesy of B. Claude).

opencc-by-4.0Dec 2014View details →
zenodo40/100

Figure 1 - Photographic evidence from Seychelles. A in Exploring Energy Management on GPUs in Game Architectures

Figure 1 - Photographic evidence from Seychelles. A: Intra-specific depredation of a ~2 m TL tiger shark Galeocerdo cuvier (TS1) by a 4 m TL conspecific (TS2); B: Inter-specific depredation based on the catch of a ~1≈m TL grey reef shark Carcharhinus amblyrhynchos (GRS), that was almost entirely consumed by C: ~2 m TL bullshark C. leucas (BS), which was (D) itself depredated, probably by a large tiger shark (Photo courtesy of JBG).

opencc-by-4.0Dec 2014View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record