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72 results for “Energy source”
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>
The source of energy, the fifth dimension and definition of time
<p>**TITLE: ENERGY SOURCE, THE FIFTH DIMENSION, AND THE DEFINITION OF TIME.**</p><p> </p><p>**INTRODUCTION:** It is known that each object in our universe is defined by these coordinates x, y, z, and that time, as concluded by Albert Einstein, is referred to as the fourth dimension. I am confident that the very existence of each object in our universe, in addition to these four dimensions (x, y, z, t), the energy source is the fifth dimension.</p><p> </p><p>This can be defined as the distance from any object to its energy source, which divides based on the time needed for this energy to reach the object. On our Earth, it is the sun, and in our galaxy, it is the black hole at the center. Thus, as a first deduction, black holes ⚫️ are sources of energy.</p><p> </p><p>These four forces - strong force, weak force, electromagnetic force, gravitational force - have grounded humanity on Earth 🌎, and to leave, it requires a lot of energy and many risks. So, it takes a new approach to physics, starting with an essential element, which is time.</p><p> </p><p>**METHODOLOGY:** The cosmos and universes are eternal; nothing is lost, and nothing is created; everything transforms. Thus, the term time is the beginning of an event or phenomenon, and time does not flow the same way from one star to another, from one galaxy to another, or from one individual to another.</p><p> </p><p>For example, you and I are the same age but show different signs of aging. So, time is sequential and manifests through temporal energy power.</p><p> </p><p>**DEFINITION OF THE ENERGY SOURCE:** The energy source is the speed of any energy to reach the surface of an object while embracing the gravitational lines of that object. We can determine it by a simple physical concept, which is \(S_e = C/G^2\); the energy source is the FIFTH DIMENSION.</p><p> </p><p>**DEFINITION OF TIME:** We can start talking about the moment when temporal energy is just at the point of contact between light and gravitational curves. Light slips between gravitational curves to reach us on Earth 🌎, knowing that light and gravity travel at the same speed in the universe. Thus, the beginning of the term TIME ⌛️ can be expressed in this way:</p><p> </p><p>\[T = cĥ \times \frac{C}{G²}\]</p><p> </p><p>\[E_t = M \times \frac{C}{\pi G²}\]</p><p> </p><p>This new physics conception could one day allow us to leave our Earth 🌎 easily with less energy and fewer risks, making interstellar travel more accessible.</p><p> </p><p>**DISCUSSION:** Rainbows and auroras are visible events to the naked eye of this interaction between light and gravitational curves. In a black hole, gravity is so intense that light does not travel inside, and time stops ⌛️ completely.</p><p> </p><p>This definition of time \(T=cĥ \times \frac{C}{G²}\) summarizes everything. So, time + space + light form the temporal sphere and manifest as temporal energy, which can be called the 5th dimension.</p><p> </p><p>TIME + SPACE = SPACE-TIME = 4TH DIMENSION. TIME + SPACE + LIGHT = TIME SPHERE = 5TH DIMENSION.</p><p> </p><p>In the fifth dimension, light excites gravity (gravitons), and the latter contracts to bend space-time and objects, stars, galaxies, stars... etc. This gives us the ability to bring distances between point A and point B closer without affecting or modifying anything, and this will be the next mechanism for future spacecraft.</p><p> </p><p>**CONCLUSION:** The model I have just published will complete physics in its fifth dimension, allowing humanity to create very sophisticated devices that can shorten routes and interstellar travel by using a space characteristic never used before, which is the CURVATURE OF SPACE.</p><p> </p><p>**PREDICTION AND DEDUCTION:** Each galaxy has its own black hole at the center, and each black hole is a source of energy. Each black hole has two faces, one that gives life to stars, planets, stars, nebulae, etc.</p><p> </p><p>1. A dying world gives birth to another world; nothing is lost, nothing is created, everything transforms.</p><p>2. Black holes dictate the rotation speed of each star and its inclination.</p><p>3. Celestial bodies, stars, planets regularly come from the side of the black hole to take charge of destiny, time, and memory to start their journeys in the universe again.</p><p>4. Just at the exit of the black hole, physically speaking, it's the moment t=0, the beginning of all life.</p><p>5. In the universe, time runs in two directions, one-way and a return to the energy source.</p><p>6. A place in the universe devoid of black holes will see accumulations of galaxies and planets and large voids.</p><p>7. Black holes uniformly distribute matter and energy in all corners of the universe.</p><p>8. On Earth, mass generates energy, but in the universe, energy generates mass for a potential balance. This means that masses already have their own accelerations or are simply fueled by energy present everywhere in the universe. Of course, in contact with an energy source like our sun, all planets around our sun are powered by the energy released by our sun in contact with the dark energy of our universe.</p><p>9. End of the use of fossil fuels and the end of air travel by plane, making way for a practical and non-polluting technique to move in the universe by bending space and bringing distant points closer without altering the texture of space.</p><p>10. End of wars and fights for earthly wealth; each country will have its galaxies to supply essential materials.</p>
How do Google News' top 100 sources visually represent the data centres' energy footprint?
<p><strong>By querying "data centres' energy footprint" on Google News in incognito mode, the candidate has selected and mapped the top 100 results according to the ranking on May 15, 2022. </strong></p>
Zambezi dataset to "WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus"
<p>This is the dataset used in the HESS publication "<a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus</a>"</p> <p>The dataset describes the water-energy-food nexus of the Zambezi River Basin used as input to the <a href="https://github.com/RaphaelPB/WHAT-IF">WHAT-IF model</a>.</p> <p>The file Data_Organization.pdf, summarizes the available data. For more info look at the <a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">publication</a> and/or <a href="https://github.com/RaphaelPB/WHAT-IF">Github</a>.</p>
Dataset: Import options for chemical energy carriers from renewable sources to Germany
<p>This dataset contains results and additional data related to the publication "Import options for chemical energy carriers from renewable sources to Germany".</p> <p>Files containing major results / important cost input data:</p> <ul> <li><strong>results.csv</strong>: Contains major model results for all scenarios as CSV file (seperator is ';', all fields are quotes using double quotation marks '"'). Can be explored using standard software like Excel/Libre Office or other tools.</li> <li><strong>costs.zip</strong>: Technology specific input cost assumption for 2030, 2040 and 2050.</li> </ul> <p>The dataset further contains the following archives related to the model structure as contained in the software repository (GitHub):</p> <ul> <li><strong>config.zip</strong>: File contents of the <em>config/</em> folder of the model directory. Configuration files for running the model used by the publication.</li> <li><strong>data.zip</strong>: File contents of the <em>data/</em> folder of the model directory. Includes distance specifications, conversion efficiencies, details on shipping transport. Also contains (with this version) the cost data (same as in <em>costs.zip</em>).</li> <li><strong>resources.zip</strong>: Some file contents of the <em>resources/</em> folder of the model directory. Most files in this folder are automatically recreated if the <em>Snakemake</em> workflow is executed. The files in this archive are the files created by GlobalEnergyGIS (RES supply time-series and demand data for investigated regions) which is difficult to setup and are thus provided here as an optional dataset for download.</li> <li><strong>results.zip</strong>: Optimised energy system models (<a href="https://pypsa.readthedocs.io/en/latest/">PyPSA</a> networks, for PyPSA version v0.19.3) for all scenarios (default 10% WACC, optimistic 5% WACC, scenarios for sensitivity analysis), energy supply chains (ESCs) and exporting countries. For each network an additional results.csv exists containing a number of key results extracted from each network. Also contains the combined <em>results.csv</em> file as <em>results/results.csv</em> for all scenario runs.</li> </ul>
Fig. 1 in Divergence in energy sources for Prochilodus lineatus (Characiformes: Prochilodontidae) in Neotropical floodplains
Fig. 1. Map of the floodplain of the Upper Paraná River, highlighting the areas sampled in this study. The subsystems sampled were A = Paraná River, B = Baía River and C = Ivinheima River. The numbers indicate sampled sites in each subsystem.
Fig. 4 in Divergence in energy sources for Prochilodus lineatus (Characiformes: Prochilodontidae) in Neotropical floodplains
Fig. 4. The average percentage contribution of each carbon source in different subsystems. The width of arrows represents the strength of resource utilization in each environment studied (MB = microbial biomass).
Energy characteristics and the source of a ball lightning obtained by investigating its spectra
<p>This work, based on quantitative spectrometric analysis, is an exploratory research on the radiated power density and its evolution feature of a ball lightning. The radiated power density has shown a periodic pulse feature, like the spectral characteristics and temperature evolution of this BL. We proposed that this BL may be the discharge from the residual charge at the bottom of the previous cloud-to-ground (CG) lightning channel initiating it. Meanwhile, the electromagnetic field produced by the power line may be a potential outside energy source that supports the life of the BL. The optical radiation from soil constituent dominates the bright light of this BL.</p><p> </p>
Source data for "Halving the North Sea's offshore wind energy carbon footprint"
<p>This dataset provides source data for the paper "Halving the North Sea’s offshore wind energy carbon footprint". It contains basic geographical factors, including wind speed, water depth, and distance from shore, and environmental impact intensities, including steel, Cu, and Al use, climate change, marine ecotoxicity, and marine eutrophication impacts. For more details, please refer to https://pubs.acs.org/doi/full/10.1021/acs.est.2c02183 and https://www.sciencedirect.com/science/article/pii/S1364032122004993. </p>
Two Source Energy Balance Model Inputs and Outputs from Drone Surveys at Majadas de Tietar in May 2021
<p><strong>MONSOON PROJECT SURVEY DATA OUTPUTS: Majadas de Tietar Tree-Grass Savanna Ecosystem 05/05/2021-20/05/2021</strong></p> <p>Here we make available high resolution (0.82 cm) energy and water flux maps from unmanned aerial system (UAS) data collected using a Micasense Altum in May 2021. We use the Two Source Energy Balance Model (via pyTSEB) and include model inputs and outputs. We use the Priestley Taylor (TSEB hereafter) and Dual Time Difference (DTD hereafter) methods in pyTSEB, the details of which can be found here pyTSEB https://pytseb.readthedocs.io/en/latest/index.html. The data collection method largely follows https://www.mdpi.com/2072-4292/13/7/1286, however a new paper detailing these surveys in Majadas is under review (as of November 2021). </p> <p>This upload includes the following gridded datasets:</p> <p><strong>Model inputs</strong></p> <p>Zipfiles are named according to their collection date (<strong>DDMMYYYY.7z</strong>). Within each zipfile are the datasets corresponding to different flight times UTC +2 (<strong>hhmm_DDMMYY</strong>). Within each survey folder are rasters with descriptive filenames using the following format:</p> <p><em>Product type_Resolution_survey area_date_flight time.tif</em></p> <p>The following prefixes denote the Product types:</p> <ul> <li>CHM_... = Canopy Height Model (m)</li> <li>GFrac2_... = Green Fraction (0-1)</li> <li>MSpec_... = Raw multispectral dataset from Altum (Blue, Green, Red, NIR, Rededge, LWIR)</li> <li>TEmpK_... = Radiometric Surface Temperature (empirical calibration, K)</li> <li>TRawK_... = Radiometric Surface Temperature (no calibration, K)</li> <li>LST2_... = Radiometric Surface Temperature (calibrated using methods outlined here https://www.mdpi.com/2072-4292/12/7/1075, K)</li> <li>Grass_... = grass vegetation mask</li> <li>Tree_... = tree vegetation mask</li> </ul> <p>We also supply the config files used to generate TSEB and DTD. To run these you will need to edit the filepaths according to your own system. </p> <p><strong>Model Outputs</strong></p> <p><strong>Majadas_TSEB_EMP_outputs.7z</strong> = Two Source Energy Balance (pyTSEB) model outputs (using the Priestley-Taylor method), using radiometric temperature datasets calibrated empirically. </p> <p><strong>Majadas_DTD_EMP_outputs.7z</strong> = TSEB Dual Time Difference model outputs (from pyTSEB) using radiometric temperature datasets calibrated empirically. </p> <p><strong>DTD_ET.7z</strong> = Evapotranspiration rasters (calculated using DTD latent heat data) in g m<sup>-2</sup> s<sup>-1</sup></p> <p><strong>File names are descriptive</strong>:</p> <p><em>Model type_radiometric temperature method_vegetation type_survey area_date_flighttime.tif</em></p> <p>Model type = DTD or TSEB</p> <ul> <li>Radiometric temperature method = always empirical calibration here</li> <li>vegetation type = grass, tree, or merge (which is both tree and grass)</li> <li>Survey area = N (north, or Nitrogen fertiliser treatment), C (central, or Control fertiliser treatment), S (south, or Nitrogen and Phosphorus fertiliser treatment)</li> <li>date = in DDMMYY format</li> <li>flight time = takeoff time for the drone (hhmm) (UTC+2)</li> </ul> <p>To find the exact local time of survey times, please see the table in flight_data3.csv</p>
Evaluating the Usability of Open Source Frameworks in Energy System Modelling (Supplementary Material)
<p>Dataset and source code for analysis of the Energy System Modelling Usability Testing (ESMUT) procedure applied in the open_MODEX project.</p> <p>This is supplementary material for the publication:</p> <pre>Berendes et al. (2022). Evaluating the Usability of Open Source Frameworks in Energy System Modelling. <em>Renewable and Sustainable Energy Reviews. DOI: </em><a href="https://doi.org/10.1016/j.rser.2022.112174">https://doi.org/10.1016/j.rser.2022.112174</a></pre> <p> </p> <p> </p>
Data for FEgrow: An Open-Source Molecular Builder and Free Energy Preparation Workflow
<p>Data illustrating the use of de novo design in building and scoring protein-ligand complexes.</p> <p>This is relationship to the FEgrow publication with the intiial preprint here: <br> https://chemrxiv.org/engage/chemrxiv/article-details/6287bb98a42e9c78d34769f6<br> </p> <p>The FEgrow software snapshot used can be found here: https://zenodo.org/record/7105647#.YzFwINLMIUE</p>
The fifth dimension, the source of energy and definition of time
<p>It's a new approach to physics, this theory changed physics modern </p>
Open-source design files for harvesting energy from overhead power line cables
<p>OpenSource_CalculationFile_MEH.xlsx : this is an excel file that provides a calculation tool to select a magnetic core for harvesting energy from powerlines.</p> <p>OpenSource_Schematic_MEH.pdf : this is a schematic file that provides a detailed design of the charging circuit.</p>
Data Sources for Archetype-based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries
<p><strong>Dataset Name:</strong><br> <em>Literature Data and Archetype Parameter Sheets for the publication, named Archetype-based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries</em>.</p> <p><strong>Description:</strong><br> This dataset includes Excel sheets containing literature sources and archetypal data on GCC countries' residential dwelling typologies.</p> <p><strong>Files:</strong><br> The following files are included in the dataset:</p> <ul> <li> <em>[CountryName]_LiteratureSources.xlsx:</em> Excel sheet containing literature sources and references,</li> <li> <em>[CountryName]_ArchetypeParameters.xlsx</em>: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li> <em>[CountryName]_Schedules.xlsx:</em> Excel sheet containing the operation schedules for countries. The sheet is compiled from literature sources and reorganized by expert consensus and given in Designbuilder input format,</li> <li> <em>[CountryName]_Stock.xlsx:</em> Excel sheet containing additional data on the building stock,</li> <li><em> VacantHouses.xlsx</em>: Vacant house rates for the countries, the found articles on the web, literature sources, etc.,</li> </ul> <p><strong>Usage:</strong><br> The dataset is intended for researching and analyzing the GCC countries' residential buildings. The literature sources included in the [CountryName]_LiteratureSources.xlsx and [CountryName]_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings.</p> <p><strong>License:</strong><br> The dataset is licensed under Creative Commons Attribution 4.0 International.</p> <p><strong>Citation:</strong><br> If you use this dataset in your research, please cite it as follows and contact the corresponding author:</p> <p>Akin, Sahin, Chibuikem Chrysogonus Nwagwu, Niko Heeren, and Edgar Hertwich. 2023. “Archetype-Based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries.” Energy and Buildings 298: 113537. https://doi.org/https://doi.org/10.1016/j.enbuild.2023.113537.</p> <p><strong>Contact:</strong><br> The archetypes' energy models (DesignBuilder or IDF files) can be provided on request. If you have any questions or comments about the dataset, please contact <strong>sahin.akin@ntnu.no, the corresponding author.</strong></p>
Integration of Renewable Energy Sources into the Water-Energy-Food (WEF) Nexus – Modelling a Demand Side Management Approach and Application to a Microgrid Farm in Morocco Dataset
<p>Here you can find the official data used for the publication "Integration of Renewable Energy Sources into the Water-Energy-Food (WEF) Nexus – Modelling a Demand Side Management Approach and Application to a Microgrid Farm in Morocco"</p> <p>If you want to run the model, please update line 11 in the run.jl file, to select the dataset from the scenario you want to look at. It is also recommended to change the result path, to a directory that corresponts to the current model run in order to find the results files quicker.</p> <p> </p> <p>To create a new plot a file called newPlot.jl can be found, that already take care of most data handling, only lines 136 and 141 need to be changed, in order to read in the result files of the results you want to investigate.</p>
Data from: Animal-mediated organic matter transformation: aquatic insects as a source of microbially bioavailable organic nutrients and energy
1. Animal communities are essential drivers of energy and elemental flow in ecosystems. However, few studies have investigated the functional role of animals as sources of dissolved organic matter (DOM) and the subsequent utilization of that DOM by the microbial community. 2. In a small forested headwater stream, we tested the effects of taxonomy, feeding traits, and body size on the quality and quantity of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) excreted by aquatic insects. In addition, we conducted steady-state solute additions to estimate instream demand for labile C and compared it to the C excreted by invertebrates. 3. Individual excretion rates and excretion composition varied with body size, taxonomy, and feeding guild. The estimated average community excretion rate was 1.31 μg DOC· per mg insect dry weight (DW)-1·h-1 and 0.33 μg DON·mg DW-1·h-1 and individuals excreted DON at nearly twice the rate of 〖"NH" 〗_"4" ^"+" . This DOM was 2-5 times more bioavailable to microbial heterotrophs than ambient stream water DOM. 4. We estimated that the insect community, conservatively, excreted 1.62 mg of bioavailable DOC·m-2·h-1 and through steady-state additions measured an ambient labile C demand as 3.97±0.67 mg C·m-2·h-1. This suggests that insect-mediated transformation and excretion of labile DOC could satisfy a significant fraction (40±7%) of labile C demand in this small stream. 5. Collectively, our results suggest that animal excretion plays an essential functional role in transforming organic matter into microbially bioavailable forms and may satisfy a variable but significant portion of microbial demand for labile C and N.
Large-Scale Traveling Ionospheric Disturbances over the European sector during the geomagnetic storm on March 23-24, 2023: energy deposition in the source regions and the propagation characteristics
<p>IMAGE 2D Ionospheric Equivalent Currents for 23 and 24 March 2023 (https://space.fmi.fi/image/). </p> <p><em>We thank the institutes who maintain the IMAGE Magnetometer Array (<a href="https://space.fmi.fi/image/">https://space.fmi.fi/image/</a>): Tromsø Geophysical Observatory of UiT the Arctic University of Norway (Norway), Finnish Meteorological Institute (Finland), Institute of Geophysics Polish Academy of Sciences (Poland), GFZ German Research Centre for Geosciences (Germany), Geological Survey of Sweden (Sweden), Swedish Institute of Space Physics (Sweden), Sodankylä Geophysical Observatory of the University of Oulu (Finland), DTU Technical University of Denmark (Denmark), and Science Institute of the University of Iceland (Iceland). The provisioning of data from AAL, GOT, HAS, NRA, VXJ, FKP, ROE, BFE, BOR, HOV, SCO, KUL, and NAQ is supported by the ESA contracts number 4000128139/19/D/CT as well as 4000138064/22/D/KS. The authors would like to thank Dr. Liisa Juusola for providing the IMAGE 2D Ionospheric Equivalent Currents data.</em></p>
Data from: Geographic source of bats killed at wind-energy facilities in the eastern United States
<p>Bats subject to high rates of fatalities at wind-energy facilities are of conservation concern, but the impact on broader bat populations is difficult to assess. One reason is the poor understanding of the geographic source of individual fatalities and whether they constitute local resident individuals or migrants. Here, we used stable hydrogen isotopes, trace elements and species distribution models to determine the summer geographic origins of three different bat species (<em>Lasiurus borealis</em>, <em>L. cinereus</em>, and <em>Lasionycteris noctivagans</em>) killed at wind-energy facilities in Ohio and Maryland in the eastern United States. In Ohio, 58.4%, 78.7%, and 97.8% of all individuals of <em>L. borealis</em>, <em>L. cinereus</em>, and <em>L. noctivagans</em>, respectively, lacked evidence of movement and were likely residents. In contrast, in Maryland 22.7%, 62.9% and 72.7% of these same species were classified as residents. Our results suggest that a substantial portion of bats killed at a given wind facility are likely derived from resident populations. Finally, there is variation in the proportion of residents killed between seasons for some species and evidence of philopatry to summer roosts. Overall, these results indicate that impact of wind-energy facilities on resident bat populations may be greater than previously appreciated, but this impact is likely to vary across species and sites. Similar studies should be conducted across a boarder geographic scale to understand the impacts on bat populations from wind-energy facilities.</p>
Source molecular simulation data for calculating energy and friction profiles and permeability coefficients through model lipid membranes
<p>Energy files from GROMACS molecular dynamics simulations with enhanced free energy sampling contain time-dependent evolution of the free energy profiles and friction profiles (and other energies and simulation properties) that were used for calculating permeability coefficients in the publication https://www.biorxiv.org/content/10.1101/2021.07.16.452599v1</p> <p>Simulation system contains a lipid POPC or DPPC bilayer with a varying amount of cholesterol (specified as mol% in the file name). Hydrophobic level of the permeating particle is specified as "level-I", "level-II" etc. When unspecified in the file name, the particle is hydrophobic level "III". Lipids D-C14-PC denote PC lipids with both tails monounsaturated of length 14 carbon atoms. DOPC is equivalent to D-C18-PC. (Detailed description in the publication)</p> <p>Adaptive Weighted Histogram (AWH) method was used to sample the free energy profile of translocating small molecule through the lipid bilayer.</p> <p>GROMACS tool `gmx awh` reads the files and provides the described profiles.</p> <p>Files were generated by GROMACS `mdrun` simulation engine version 2019.3.</p> <p> </p> <p>Coarse-grained MARTINI 3.0 model was used for modeling the biomolecular interactions.</p> <p>Scripts to perform the simulations and the files with initial configurations and simulation settings are stored in a public GitHub repository depozited on Zenodo.org: <a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p> <p> </p> <p>Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).</p> <p>Lindahl, V., Lidmar, J. & Hess, B. Accelerated weight histogram method for exploring free energy landscapes. J. Chem. Phys. 141, 044110 (2014).</p> <p>Souza, P. C. T. et al. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat. Methods 18, 382–388 (2021).</p> <p>Melcr, J. Git repository with analysis scripts for MD simulations of permeability through lipid membranes. (2021) doi:<a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</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.