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858 results for “Energy Data”
Data files of the paper "Energy conversion by magnetic reconnection in multiple ion temperature plasmas"
<p>Reconnection rate and energy budget files for the simulations used in for the paper "Energy conversion by magnetic reconnection in multiple ion temperature plasmas". The paper has two simulations. The first one, without cold ions, has his reconnection rate data stored in "rate_147.dat" and the energy budget data in "Ebudget_symm_nocold_Xframe.h5". The second one, with cold ions, has his reconnection rate data stored in "rate.dat" and the energy budget data in "Ebudget_symm_cold_Xframe.h5".</p> <p>On top of that is a datafile from simulation 1 at time 144.5 (corresponding to the time of the picture A in figure 1) with all the output fields from the simulation at this given time.</p>
Optical data for Cu2O, CuO and Cu in the 1 eV to ~ 100 eV energy range.
<p>Our optical data in (J. Phys.: Condensed Matter.24 (2012) 175002; DOI10.1088/0953-8984/24/17/175002) for Cu<sub>2</sub>O, CuO, and Cu in the 1 eV to ~ 100 eV energy range has received a strong and continued interest and we have had requests to share the data in digital form.</p> <p>Here we therefore make the data available for n and k in Fig. 8 of the paper in two Excel files.</p>
Supporting data set for: On the challenge of obtaining an accurate solvation energy estimate in simulations of electrocatalysis
<p>The data set generated for the article: "On the challenge of obtaining an accurate solvation energy estimate in<br> simulations of electrocatalysis".</p> <p>Consists of subfolders for various sets of calculations. The data analysis procedure is shown in detail on <a href="https://bjk24.gitlab.io/bg-solvation/intro.html">this website</a>. If you want to peform the data analysis yourself, follow the instructions on the <a href="https://bjk24.gitlab.io/bg-solvation/docs/setup.html">setup page</a> of the website to download the repository, insert this data set into it, and run the Jupyter book.</p>
U.S. building energy efficiency and flexibility as an electric grid resource (Data and Code)
<p><strong>* New in Version 2.1 *</strong></p> <ul> <li> <p>All residential measure savings shapes data (<strong>Latest_Res_Shapes.zip</strong> and residential measures in <strong>Latest_BM_Shapes.zip</strong>) were updated to correct post-processing errors present in version 2.</p> </li> <li> <p>The raw baseline-case data that are used in Scout to estimate sector-level baseline hourly loads (file <a href="https://github.com/trynthink/scout/blob/master/supporting_data/tsv_data/tsv_load.gz">tsv_load</a>) are now included in this data resource (see files <strong>Latest_Res_Baselines.zip</strong> and <strong>Latest_Com_Baselines.zip</strong>).</p> </li> <li> <p>Additional residential measure run documentation is available (<a href="https://github.com/NREL/resstock/blob/e2a98b7345d5c453ba35341b70af2f8859dd22fe/GEB_Potential.yml">here</a> for all except water heating efficiency plus flexibility (EE+DF) measure and <a href="https://github.com/NREL/resstock/blob/9611d92388e1e23466c9dc451e115c21321b4012/GEB_Potential_v2.5.0_appl_ee_dr.yml">here</a> for the water heating EE+DF measure).</p> </li> <li>A guide to reading and/or preparing savings shapes CSVs is available <a href="https://scout-bto.readthedocs.io/_/downloads/en/latest/pdf/">in the Scout documentation</a>, p. 36. The documentation also summarizes the net system load conditions that measures with flexibility (DF) characteristics respond to (Table 1, p. 37).</li> </ul> <p><strong>* New in Version 2 *</strong></p> <p>All hourly savings shapes CSV files that support the original <a href="https://doi.org/10.1016/j.joule.2021.06.002">analysis</a> have been updated to reflect the following improvements:</p> <ul> <li> <p>Generate residential data using ResStock v2.5.0 and commercial data using DOE Commercial Prototypes generated with OpenStudio v3.3.0.</p> </li> <li> <p>Residential and commercial measures with flexibility (DF) features respond to updated grid conditions (net peak/low load periods) that are consistent with projections from the EIA 2022 Annual Energy Outlook (AEO) “Low renewables cost” <a href="https://www.eia.gov/outlooks/aeo/tables_side_xls.php">side case</a>.</p> </li> <li> <p>Residential baseline loads and load savings are now distinguished by three building types (single family, multi family, and mobile homes).</p> </li> </ul> <p>Updated savings shape CSVs are organized into three ZIP files that may be separately downloaded depending on user interests:</p> <p><strong>Latest_BM_Shapes.zip</strong> includes only the subset of savings shape CSVs needed to execute the <a href="https://doi.org/10.5281/zenodo.3158929">Scout Benchmark Scenarios</a>.</p> <p><strong>Latest_Res_Shapes.zip</strong> includes all residential savings shape CSVs.</p> <p><strong>Latest_Com_Shapes.zip</strong> includes all commercial savings shape CSVs.</p> <p>Baseline load shapes in Scout have also been updated based on the same versions of ResStock and the DOE Commercial Prototypes, and peak/take period impact calculations have been updated to reflect the 2022 AEO system conditions. These updated data are contained in <a href="https://github.com/trynthink/scout/releases/tag/v0.8">Scout v0.8</a> (see ./supporting_data/tsv_data).</p> <p><br> <strong>Summary of Original Data Files</strong></p> <p>These data underpin an analysis of the near- and long-term technical potential bulk power grid resource offered by best available U.S. building efficiency and flexibility measures. Using multiple openly-available modeling frameworks supported by the U.S. Department of Energy, including <a href="https://scout.energy.gov/">Scout</a>, <a href="https://resstock.nrel.gov/">ResStock</a>, and the <a href="https://www.energycodes.gov/development/commercial/prototype_models">Commercial Building Prototype Models</a>, we pair bottom-up simulations of measures' building-level impacts with regional representations of the building stock and its projected electricity use to estimate the impacts of multiple building efficiency and flexibility scenarios on hourly regional system loads across the contiguous U.S. in 2030 and 2050. We find that demand-side management via building efficiency and flexibility could avoid up to nearly ⅓ of annual fossil-fired generation and ½ of fossil-fired capacity additions after 2020.<strong> </strong>Results are reported at both the national and regional scales and are disaggregated by building type and end use, facilitating a quantitative understanding of the role that buildings as a whole and specific building technologies or operational approaches can play in the future evolution of the U.S. electricity system.</p> <p>The four ZIP files that make up this data record are interpreted as follows:</p> <p><strong>Measure_Data.zip: </strong>Includes the Scout energy conservation measure (ECM) JSON definitions that were used to generate the main baseline and efficient/flexible scenario results ("Baseline_Measures" and "Efficiency_Flexibility_Measures", respectively), as well as side cases that assess the sensitivity of results to higher levels of variable renewable penetration ("High_RE_Sensitivity_Analysis") and a high degree of building load electrification ("High_Electrification_Measures"). Each measure set includes supporting 8760 load savings shapes in the sub-folder "Savings_Shapes". Additional details about defining and interpreting Scout measures with time-sensitive analysis features are available <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#time-sensitive-valuation">here</a>.</p> <p><strong>Results_Data.zip: </strong>Includes the main and side case results data. Baseline-case outcomes, which are consistent with the <a href="https://www.eia.gov/outlooks/archive/aeo19/">EIA 2019 Annual Energy Outlook</a>, are stored in "Baseline_Loads". Efficient/flexible scenario results are stored in "Efficiency_Flexibility_Measure_Impacts_Individual" and "Efficiency_Flexibility_Measure_Impacts_Portfolio," respectively, where the former includes results for individual measures in our analysis without considering any interactions across measures, and the latter includes results for aggregations of energy efficiency (EE), demand flexibility (DF), and efficiency and flexibility (EE+DF) portfolios that do consider interactions across measures in each portfolio. Results for the high electrification side case are stored in the "High_Electrification" sub-folder in the EE+DF case only. Results for the high renewable sensitivity analysis are stored in "High_RE_Sensitivity_Analysis", and residential and commercial 8760 savings shape outcomes for each of the EE, DF, and EE+DF measure portfolios and five of the 2019 EIA Electricity Market Module (EMM) <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">regions</a> (p.6) of focus are stored in "Sector_Level_8760s".</p> <p><strong>Source_Code.zip: </strong>Includes the source code needed to translate the measure inputs provided in "Measures_Data.zip" into the outputs provided in "Results_Data.zip". The core set of files required to execute the main analysis results is stored in "Base_Code_Package", while variants to certain files in the core package needed to execute the high renewable sensitivity and high electrification side cases are stored in "Code_Variants". In general, the process of running an analysis is as described in the Scout <a href="https://scout-bto.readthedocs.io/en/latest/quick_start_guide.html">Quick Start Guide</a>; however, the file "ecm_prep_batch.py" should be substituted for "ecm_prep.py" and the file "run_batch.py" should be substituted for "run.py". These batch files execute multiple versions of "ecm_prep.py" and "run.py" that are tailored to generate individual measure and whole portfolio results for annual, net peak summer and winter, and net off-peak summer and winter metrics (individual measures: "ecm_prep.json," "ecm_prep_spa," "ecm_prep_wpa," "ecm_prep_sta," "ecm_prep_wta"; whole portfolio: "ecm_results.json," "ecm_results_spa.json," "ecm_results_wpa.json," and "ecm_results_sta.json," and "ecm_results_wta.json"). Results for the side cases are generated by replacing the versions of the "ecm_prep" and "run" files included in the "Base_Code_Package" folder with those in the "Code_Variants" folder. Sector-level 8760 shapes are generated using the "--sect_shapes" command line option as described <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#sector-level-hourly-energy-loads">here</a>. See Scout's <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#local-execution-tutorials">Local Execution Tutorials</a> for more details on how to develop Scout inputs and outputs.</p> <p><strong>Supporting_Data.zip: </strong>Includes supplemental data files provided by EIA that describe key inputs and outputs to the <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">Electricity Market Module</a> in the AEO 2019 run of the National Energy Modeling System ("EIA EMM Data (AEO 2019)"), as well as raw EnergyPlus outputs that were used to develop the baseline Scout hourly load shape file found in "./Source_Code/Base_Code_Package/supporting_data/tsv_data/tsv_load.json". </p>
Virtual sensors for wind energy applications benchmark study data - preliminary version
<p>Test version of the time series data for the wind energy virtual sensing benchmark study data.</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>
Raw output data for "Chemical design of electronic and magnetic energy scales of tetravalent praseodymium materials", DOI: 10.1038/s41467-023-38431-7
<p>This repository contains output data from the first-principles calculations reported in the following article:</p> <p><strong>Authors:</strong> Arun Ramanathan, Jensen Kaplan, Dumitru-Claudiu Sergentu, Jacob A. Branson, Mykhaylo Ozerov, Alexander I. Kolesnikov, Stefan G. Minasian, Jochen Autschbach, John W. Freeland, Zhigang Jiang, Martin Mourigal, Henry S. La Pierre</p> <p><strong>Title:</strong> Chemical Design of Electronic and Magnetic Energy Scales in Tetravalent Praseodymium</p> <p><strong>Published in</strong> Nature Communications at: https://doi.org/10.1038/s41467-023-38431-7</p> <p><strong>Preprint on</strong> arXiv at: https://doi.org/10.48550/arXiv.2212.10401</p> <p><strong>Description:</strong></p> <p>The archive contains raw output files generated from quantum chemistry calculations conducted using the OpenMolcas program. OpenMolcas is a freely available program that can be accessed at <a href="https://gitlab.com/Molcas/OpenMolcas">https://gitlab.com/Molcas/OpenMolcas</a>. For specific details about the content of the archive, please refer to the '00README' file included in the archive.</p> <p> </p> <p> </p>
Data from: Rainforest conversion to plantations fundamentally alters energy fluxes and functions in canopy arthropod food webs
<p><span>Tropical rainforests around the world are rapidly being converted into cash-crop agricultural systems. The associated massive losses of plant and animal species lead to changes in arthropod food webs and the energy fluxes therein. These changes are poorly understood, in particular in the extremely biodiverse canopies of tropical ecosystems. Using canopy fogging followed by stable isotope and energy flux analyses, we show that land-use conversion from rainforest to rubber and oil palm plantations not only causes a drastic reduction in energy fluxes of up to 75% but also shifts fluxes among trophic groups. While rainforests featured high levels of both herbivory and algae-microbiology, and a balanced ratio of herbivory to predation, relative fluxes were shifted towards predation in rubber and towards herbivory in oil palm plantations, indicating profound shifts in ecosystem functioning. Our results highlight that the ongoing loss of animal biodiversity and biomass in tropical canopies degrades animal-driven functions and restructures canopy food webs.</span></p>
SapphireEnergyIABR: Data from the Sapphire Energy Integrated Algal BioRefinery
<p>This dataset includes previously unpublished information collected during the operation of the Sapphire Energy Integrated Algal BioRefinery.</p> <p><strong>Terms of Use:</strong> These data are provided as is, without any warranties of any kind. A bibliographic citation should be included in the References section of publications and other media to acknowledge the authors of this dataset. Proper citations include the authors, title, publisher, and Digital Object Identifier (DOI) and will allow the products to be discovered and re-used by others.</p>
Data and software: 'Survey Operations for the Dark Energy Spectroscopic Instrument'
<p>Supplementary material to the DESI publication "Survey Operations for the Dark Energy Spectroscopic Instrument".</p><p>The main "figures.py" script generates the figures in the paper from the included data files. The software relies on the DESI software stack available at github.com/desihub, and conveniently available at NERSC in the default DESI environment.</p><p>Some documentation on the code implementing the survey and for running the survey simulations is available here:</p><ul><li>https://desisurvey.readthedocs.io/en/latest/</li><li>https://surveysim.readthedocs.io/en/latest/</li></ul><p>Contents:</p><ul><li>Configuration files<ul><li>config-main-actual.yaml: a yaml file giving the configuration for the survey simulation software (https://github.com/desihub/surveysim/releases/tag/0.12.5), specifying things like interexposure times and downtime, using the actual long shutdowns we had.</li><li>config-main-nominal.yaml: a yaml file giving the configuration for the survey simulation software, using the nominal long shutdowns we planned for.</li><li>config-main.yaml: same as config-main-nominal.yaml, except with the default name looked for by the survey simulation software.</li><li>rules-main.yaml: configuration rules file controlling how the next tile selector selects files for observation, implementing our depth-first strategy.</li></ul></li><li>Software files<ul><li>figures.py: main routines for generating the plots in this publication. Running `python figures.py` will create all of the figures used in this paper.</li><li>precess.py: set of routines implementing the precession of the Earth's pole used by figures.py for accurate airmass calculations.</li><li>downtime.py: routines used by figures.py for computing statistics about DESI's time usage, in particular the downtime.</li><li>util_efs.py: utility routines, primarily for making plots used by figures.py</li><li>util_efs_c.pyx: cython routines used by util_efs.py</li><li>margincomputations.py: routines for computing survey margin; i.e., how well we did relative to how much time was available.</li></ul></li><li>Data files<ul><li>Survey<ul><li>exposures-daily-main-20220614.ecsv: list of main survey exposures taken through 2022-06-14, following processing by the offline pipeline.</li><li>exposures-20220614.ecsv: list of main survey exposures taken through 2022-06-14, before processing by the offline pipeline. This largely duplicates exposures-daily, but contains much less information and contains more records for rare cases when exposures cannot be processed by the offline pipeline.</li><li>tiles-4112-packing-20210405-decorated-fixed.fits: file giving the geometry of DESI tile centers on the sky, plus associated information, like the extinction and stellar density at those locations.</li><li>tiles-daily.csv</li></ul></li><li>Simulation<ul><li>ephem_2019-01-01_2027-12-31.fits: ephemerides for DESI site for 2019 through 2027, generated by the desisurvey software ephemerides module. https://github.com/desihub/desisurvey</li><li>desi-status-end-nominal.ecsv: tile file giving completion of each DESI tile, for a survey simulation using the nominal configuration file.</li><li>desi-status-end-actual-noslew.ecsv: tile file giving completion of each DESI tile, for a survey simulation using the actual configuration file, without slew optimization</li><li>desi-status-end-actual.ecsv: tile file giving the completion of each DESI tile, for a survey simulation using the actual configuration file.</li><li>exposures_actual-noslew.fits: exposures generated by survey simulation software using the 'actual' configuration, with slew optimization disabled.</li><li>exposures_actual.fits: exposures file generated by survey simulation software using the 'actual' configuration.</li><li>exposures_nominal.fits: exposures file generated by survey simulation software using the 'nominal' configuration.</li><li>stats_actual.fits: statistics file generated by survey simulation software using the 'actual' configuration, containing information about DESI time usage.</li><li>stats_actual-noslew.fits: same as stats_actual.fits, except with slew time optimization disabled.</li><li>stats_nominal.fits: same as stats_actual.fits, except with the nominal survey sim configuration file.</li><li>tiles-daily.csv: tile file with detailed information on all tiles completed by the survey</li><li>tiles-main-20220614.ecsv: tile file with tile completeness information on 20220614</li><li>tiles-main.ecsv: tile file with completeness information on tiles in the survey</li><li>performance_current.csv.gz: performance information detailing state of DESI instrument every second, used for tracking downtime statistics.</li></ul></li></ul></li></ul><p>Dependencies: The included software uses the 'surveysim,' 'desisurvey,' and 'desimodel' packages, available on github through the desihub organization. Otherwise it depends on the usual astronomy software stack: numpy scipy matplotlib astropy. Alternatively, people with access to NERSC can load the default DESI environment and pull in all needed dependencies.</p>
Characterizing the Folding Transition State Ensembles in the Energy Landscape of an RNA Tetraloop - available data.
<p>Trajectory file and scripts necessary to generate an ELViM [Oliveira, A. B.; Yang, H.; Whitford, P. C.; Leite, V. B. P. JCTC, 2019, 15, p.6482] projection of the conformational space for the GCAA tetraloop.</p>
Data for: Coupling dynamic energy budget and population dynamic models to inform stock enhancement in fisheries management
<p><span>Extensive applications of fishery stock enhancement worldwide bring up broad concerns about its negative effects, creating a pivotal need for science-based assessment and planning of enhancement strategies. However, the lack of mechanistic understanding of enhanced population dynamics, particularly the density-dependent processes, leads to compromise in model development and limits the capacity in predicting enhancement effects. Here, we developed an individual-based model based on dynamic energy budget theory and full life history processes, to understand the mechanism of density dependence in population dynamics that emerge from individual-level processes. We demonstrated the utility of the model framework by applying it </span><span>to an extensively enhanced species, Chinese prawn (<em>Fenneropenaeus chinensis</em></span><span>, Penaeidae</span><span>). The model could yield projections reflecting the observed trajectory of population biomass and yields. The model also delineated the key effects of density dependence on the vital rates of growth, fecundity, and starvation mortality. Regarding the manifold effects of stock enhancement, we demonstrated a dampened shape in population biomass and yields with increasing magnitude of enhancement, and trade-offs between the ecological and economic objectives, i.e., pursuing high benefit might compromise the wild population without proper management. Furthermore, we illustrated the possibility of combining stock enhancement and harvest regulation in promoting population recovery while maintaining fisheries yields. We highlight the potential of the proposed model for understanding density dependence in enhancement program, and for designing integrated management strategies. The approach developed herein may serve as a general approach to assess the population dynamics in stock enhancement and inform enhancement management.</span><span> </span></p>
Data Set For Efficient Calculation of Dispersion Energy for Multireference Systems with Cholesky Decomposition. Application to Excited-state Interactions
<p>Data Set to Accompany:</p> <p>"Efficient Calculation of Dispersion Energy for Multireference Systems with Cholesky Decomposition. Application to Excited-state Interactions"</p>
Scripts and datas for "Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance"
<p>Input and output datasets used for a global reconstruction of the eddy kinetic energy (EKE) dissipation rate in relation to a submitted work :</p> <p><strong>R. Torres, R. Waldman, J. Mak and R. Séférian </strong>: <em>Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance</em>.</p> <p>Inputs datas include a merge of 2 datasets from the World Ocean Atlas 2018 (WOA18, Garcia et al., 2019) and cover the 1995-2017 (95B7) period. Folder structure for the surface altimetry L4 datasets from the EU-Copernicus Marine Services (2021) is kept empty in order to limit the archive size. Datas can be download <a href="https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_L4_MY_008_047/services">here</a>.</p> <p>Optional datasets include CMEMS MDT product (<em>CMEMS/SEALEVEL_GLO_PHY_MDT_008_063/P20Y</em>) downloaded <a href="https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_MDT_008_063/">here</a> and ocean masks (<em>misc/basins/doi_10.5281</em>) from Martinez-Moreno et al. (2021).</p> <p>In addition, simulation outputs from the NEMO-OMIP2 model runned with the GEOMETRIC parameterization are processed (mainly time-averaged) and stored in <em>CNRM/runs/omip2_LR.Geom_Emin0-alpha01_1cyc-trd/post</em>. These files are used in the uncertainties and errors quantification.</p> <p>Outputs and published results are stored in each individual product post-processing folder while final EKE dissipation computation are located in the <em>EKE_dissipation_rate</em> folder since it results from a combination of multiple products.</p> <p>IPython notebooks for computing and plotting global maps are also provided :</p> <ul> <li><em>1-post_process_climato.ipynb</em> : compute from the climatology (e.g. WOA18 datas) the EKE dissipation timescales (units in days) and the surface modes with rough topography (LaCasce and Groeskamp, 2020).</li> <li><em>2-post_process_altimetry.ipynb</em> : compute from altimetry (CMEMS) datasets the EKE at surface and eventually coarsen the grid from 0.25 to 1 degree in order to match the climatology grid.</li> <li><em>3-compute_global_eke_dissipation.ipynb</em> : combine both outputs from the two above scripts to compute the global EKE dissipation. The script also plots new maps.</li> <li><em>0-plot_global_maps.ipynb</em> : plot the global maps for climatology and altimetry products.</li> <li><em>0-plot_lbekedis_ogcm.ipynb</em> : plot and analyse EKE timescale errors from the NEMO-OMIP2 simulation outputs.</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</p>
Data and code for "Revealing the free energy landscape of halide perovskites: Metastability and transition characters in CsPbBr3 and MAPbI3"
<p>This record contains a neuroevolution potential (NEP) model (<code>nep-MAPI-SCAN.txt</code> ) for MAPbI3 used in the linked publication. The model can be used in conjunction with the <a href="https://gpumd.org">GPUMD package</a>. The <a href="https://calorine.materialsmodeling.org">calorine package</a> provides a Python interface to GPUMD.<br> Several primitive structures in extended xyz format can be found in the <code>*.xyz</code> files. These structures have been relaxed using the NEP model included here. The <code>demo-for-using-structures-and-model.py</code> script illustrates how to access the structures and model.</p>
Data from: The impacts of climate change, energy policy, and traditional ecological practices on future firewood availability for Diné (Navajo) People
<p>These data are part of a data portal that accompanies the special issue 'Climate change adaptation needs a science of culture,' published in Philosophical Transactions of the Royal Society B in 2023. To access the data portal, please visit <a href="https://doi.org/10.5061/dryad.bnzs7h4h4"><strong>https://doi.org/10.5061/dryad.bnzs7h4h4</strong></a>.</p> <p>The files consist of the code of an agent-based model (ABM) in a NetLogo, detailed documentation of the ABM in a standard format, and a table of data exported from the simulation experiment reported on in the paper. By downloading the Netlogo file, one could not only rerun the experiment we report on and recreate the data table but toggle parameters or edit the model to explore other dynamics.</p>
Data for Potential feeding sites for seabirds and marine mammals reveal large conflicting areas with offshore wind energy development worldwide
<p>This dataset contains;</p> <p>1) The dataset (sample_point_data.Rdata) to develop the Structural Equation model </p> <p>2) The spatial tiff bivariate maps for small-ranged seabird and marine mammals and fish and zooplankton biomass respectively</p> <p>3) The Potential Feeding Sites likelihood map in a tiff format</p> <p>4) The global power density at 200m </p> <p>5) Dataset of risk category and corresponding values and coordinates for spatial representation</p>
Data from: Energy harvesting in a flow-induced vibrating flapper with biomimetic gaits
Open the record for dataset details and reuse information.
Data from: Rainforest conversion to plantations fundamentally alters energy fluxes and functions in canopy arthropod food webs
Open the record for dataset details and reuse information.
Data from: Fungal energy channeling sustains soil animal communities across forest types and regions
Open the record for dataset details and reuse information.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.