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89 results for “power plants”
Austrian Hydro Power Plants
<p>Dataset of major hydropower plants in Austria. Provides location, capacity, turbine technology, head, flow, and further data.</p>
Mapping of solar panels and Fukushima Daiichi Nuclear Power Plant Accident-associated radioactive waste storage in 2022 and 2023, Fukushima, Japan
<p>The policy of reconstruction after the Fukushima Daiichi Nuclear Plant accident has led to a radical transformation of the landscapes of Fukushima Prefecture especially in two aspects (Asanuma-Brice et al., 2023). The first is related to an extensive decontamination policy which resulted in the removal of more than 13 million m<sup>3</sup> of contaminated soil (MOEJ, 2021). The second is the widespread installation of solar panels, which demonstrate the transition decided by the Prefecture and the inhabitants in terms of energy policy.</p> <p>A systematic mapping of these features was carried out from the satellite imagery of Google Map (2023) within the boundaries of Fukushima Prefecture. The objective was to highlight the evolution of specific land use features that are captured imprecisely by automatic detection mapping. We focused on the main visible change in the landscape in terms of land use since Fukushima Daiichi nuclear accident: contaminated waste disposal areas and solar panel fields. These zones were delineated allowing a calculation of the corresponding surface areas (m<sup>2</sup>).<strong> The dataset is composed of 4 shapefile layers: contaminated waste deposits in 2022 and 2023, solar panels in 2022 and 2023. For the year 2022 the last update was conducted in July 2022 and for the year 2023 the last update took place in March 2023.</strong></p> <p>As the land use is in constant and rapid transition (Asanuma-Brice, 2021), we considered as contaminated waste deposits, the permanent storage centers as well as the sites where there are still bags of contaminated waste in varying numbers, knowing that they will be removed and stored on other dedicated sites (Evrard et al., 2019). This choice was made to potentially identify, when the map was updated, the future uses of the land where this waste was stored temporarily.</p> <p>This dataset is part of a larger project that aims to provide the community with an interactive tool (https://mitatelab.cnrs.fr/mitate-labs-map-of-solar-panel-and-contaminated-wasted-land/) that makes available various types of information essential to the analysis of the reconstruction, such as: the delineation of the evacuated zone (which evolved throughout time), the delineation of the municipality boundaries affected by the reconstruction policy, the main services found in these localities, the location of the memorials of the disaster in the zone, as well as the geo-localization of the soil/sediment samples collected by other Mitate lab researchers in order to investigate the redistribution of radionuclides in the environment (Evrard et al., 2021).</p>
GCNT-Plume: Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning
<p>This repository contains the relevant code and data for the paper <strong>Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning</strong><strong> </strong>(Wei et al, 2023, <em>Remote Sensing of Environment</em>).</p> <p>Specifically, the <strong>U-Net.zip</strong> file includes the associated codes for segmenting surface thermal plumes from nuclear power plants along the global coasts and the Great Lakes by using the U-Net model integrated with prior knowledge. The <strong>GCNT-Plume.zip</strong> file includes the occurrence footprints of core area plumes (the <strong>occurrence_all </strong>folder), raw water temperature increment (WST) images (the <strong>delta </strong>folder), mixed area plumes and annotations (the <strong>extractWithLocation </strong>folder), model-predicted core area plumes (the <strong>prediction*_*</strong> folders), the mixed/core area plumes and background areas in shapefile format (the <strong>sampleAnnotation* </strong>folders), and location information (the <strong>location.xlsx </strong>table). Please refer to the <strong>README.md </strong>file in the <strong>U-Net.zip</strong> file for more detailed information.</p>
Data from: Pitcher geometry facilitates extrinsically powered ‘springboard trapping’ in carnivorous Nepenthes gracilis pitcher plants
Open the record for dataset details and reuse information.
JRC Open Power Plants Database (JRC-PPDB-OPEN)
<p>In 2017 the Joint Research Centre developed a Power Plant Database for energy systems modelling (JRC-PPDB) in order to support the unit activities in energy systems modelling and knowledge management.</p> <p>As demand for open data is increasingly sought after, an open version (JRC-PPDB-OPEN), based on exclusively open data was designed. The JRC-PPDB-OPEN is primarily based on a collection of all the information published by ENTSO-E<a href="#_ftn1">[1]</a> on the European power plants at unit level. This information was extended, improved, and where possible corrected using information contained in open datasets published by WRI Powerwatch<a href="#_ftn2">[2]</a>, Global energy observatory<a href="#_ftn3">[3]</a>, FRESNA<a href="#_ftn4">[4]</a> and the EEA<a href="#_ftn5">[5]</a>.</p> <p>The JRC-PPDB-OPEN database is a first attempt towards a more detailed and coherent, albeit still incomplete, dataset of European power plants. Further work to expand and improve the information contained therein is called for. To this extend, and in order to facilitate the future involvement of third parties in such efforts, the associations between records in the different datasets (linkage) are included.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> <a href="https://transparency.entsoe.eu/">https://transparency.entsoe.eu/</a></p> <p><a href="#_ftnref2">[2]</a> <a href="http://datasets.wri.org/dataset/globalpowerplantdatabase">http://datasets.wri.org/dataset/globalpowerplantdatabase</a></p> <p><a href="#_ftnref3">[3]</a> <a href="http://globalenergyobservatory.org/">http://globalenergyobservatory.org/</a></p> <p><a href="#_ftnref4">[4]</a> <a href="https://github.com/FRESNA/powerplantmatching">https://github.com/FRESNA/powerplantmatching</a></p> <p><a href="#_ftnref5">[5]</a> <a href="https://prtr.eea.europa.eu/#/home">https://prtr.eea.europa.eu/#/home</a></p>
Figure 1 in Avifauna of the region of the Volta Grande Hydroelectric Power Plant in Southeast Brazil
Figure 1. Grande River Basin located at the border between the states of Minas Gerais and São Paulo in Southeast Brazil. The five study sites are shown as: 1 and 2 located in Minas Gerais, and 3, 4 and 5 located in São Paulo.
Figure 2. Bird species accumulation curve and estimated richness curve obtained from the Chao 1 in Avifauna of the region of the Volta Grande Hydroelectric Power Plant in Southeast Brazil
Figure 2. Bird species accumulation curve and estimated richness curve obtained from the Chao 1 index for the study area located throughout the reservoir of the Volta Grande Hydroelectric Power Plant in Southeast Brazil. Vertical bars represent the standard deviation of the estimate.
River dams and the stability of bird communities: A hierarchical Bayesian analysis in a tropical hydroelectric power plant
<ol> <li>The effects of anthropogenic disturbance upon the stability of wildlife communities depend on the heterogeneity and connectivity of habitat remnants on multiple scales. The number of hydroelectric dams in biodiversity hotspots (Africa, South America and Asia) is growing rapidly. To establish their environmental impact, it is essential to understand the dynamics of wildlife communities before and following the establishment of dams.</li> <li>We evaluated the impacts of the filling of the Serra do Facão hydroelectric reservoir in the São Marcos river, central Brazil, upon the bird community. Using data from 1,145 surveys across 20 sampling sites over eight years, two years before and six years after the filling of the reservoir, we assessed the resistance, i.e., maintenance close to an equilibrium state during the disturbance, and resilience, i.e., ability to return to the original state following the disturbance, of the bird community. We used spatiotemporal hierarchical Bayesian models to assess the effects of reservoir filling on five community parameters: abundance, richness, phylogenetic diversity, functional diversity and species composition.</li> <li>In the period subsequent to reservoir filling, there was (i) a marked reduction in bird abundance, richness, phylogenetic diversity and functional diversity, and (ii) a reduction in the proportion of forest species, coupled with an increase in the proportion of savanna species. Except for bird abundance, none of the other community attributes returned to their original levels, even after six years. Our findings indicate that Cerrado bird communities have both low resistance and low resilience to habitat loss associated with the establishment of hydroelectric reservoirs.</li> <li> <i>Synthesis and applications.</i> The environmental costs of hydroelectric dams are still underestimated or neglected in Brazil. A new paradigm in the assessment of their environmental impacts is warranted, incorporating (i) models of spatiotemporal variations based on long-term monitoring with surveys initiated before disturbances and (ii) measures of functional and phylogenetic diversity, such that society can understand the costs and benefits of the establishment of new hydroelectric dams and make informed decisions. Biodiversity loss could be minimized by ensuring the preservation and connectivity of alluvial habitats, capable of maintaining the supply of resources and the functional and phylogenetic attributes of bird communities associated with such habitats.</li> </ol>
Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants
<p><span><span>The selective catalytic reduction (SCR) de</span><span>-</span><span>NO<sub>x</sub> </span><span>process in coal-fired power plants not only displays nonlinearity, large inertia, and time variation but also a lag in NO<sub>x</sub> analysis; </span><span>hence,</span><span> it is difficult to obtain an accurate model </span><span>that </span><span>can be used to control NH<sub>3</sub> injection </span><span>during changes in the </span><span>operating state. </span><span>In this work,</span><span> a novel dynamic inferential model with delay estimation was proposed for NO<sub>x</sub> emission prediction. First, k-nearest neighbour mutual information (knnMI) was used to estimate the time-delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square (mwKPLS) was</span><span> used</span><span> to improve the prediction ability, </span><span>and this was followed by verification using </span><span>benchmark dataset experiments. Finally, the delay-time difference (DTD) method and feedback correction strategy </span><span>were </span><span>proposed to deal with the time variation of the SCR de</span><span>-</span><span>NO<sub>x</sub> process.</span> <span>Through the analysis of the </span><span>experimental field data </span><span>in the</span> <span>steady state, </span><span>the variable</span><span> state and </span><span>the </span>NO<sub>x</sub> analyser blowback process<span>, the results proved that</span><span> this dynamic model has </span><span>high prediction accuracy</span><span> during</span><span> state changes and can </span><span>realize</span><span> advance prediction of the NO<sub>x</sub> emission. </span></span></p>
CALLISTO-SPK: A Stochastic Point Kinetics Code for Performing Low Source Nuclear Power Plant Start-up and Power Ascension Calculations Data Repository
<p>This dataset provides data to accompany the submission named "CALLISTO-SPK: A Stochastic Point Kinetics Code for Performing Low Source Nuclear Power Plant Start-up and Power Ascension Calculations" which has been submitted to Annals of Nuclear Energy. Details of the file included may be found in the readme file.</p>
A Study for Dose Impact of MCR Operators in Nuclear Power Plants Under ICRP 30 and ICRP 60
<p>Impact evaluation on tritium in Korean heavy water reactor was performed. The purpose of this study is to find the dose suitability for Tritium, Iodine, Xe and Kr. Another purpose is to find the contribution of Tritium during NPP operator's dose in Main Control Room of Design Basic Accident of NPP.</p> <p>In this study, DCF (Dose Conversion Factor) is ICRP 30 and ICRP 60. From this condition, some sensitivity analysis is carried out by simulation code RADTRAD 3.03 and ARCON96. These codes are approved from US NRC for the safety analysis of Nuclear Power Plants.</p> <p>Analysis condition is under considering the core coolant pipe break of Heavy Water Reactor. In order to recover the NPP state, some operator operation is carried out by the signal of DBA.</p> <p>In this moment, the operators reside in MCR (Main Control Room) against the DBA to response the NPP States. RADTRAD 3.03 simulates the accident of NPP and ARCON96 simulates the diffusion of fission products. MCR operator's dose effects are different between ICRP 30 and ICRP 60.</p> <p>Noble gas is reduced in dose calculation and Tritium contribution is very small in dose effects. In the effect of tritium, it had an effect of less than 1% comparing with external dose and less than 2% comparing with internal dose. In this study, every calculation is carried out by assumption of no deposition of fission products on the wall surface, pipe and concrete. This conservation increase the TEDE of Tritium, Noble gas (Xe and Kr) and Iodine.</p> <p>Otherwise, in condition of deposition coefficient 0.01, RADTRAD's calculation results make 5~10% reduction of TEDE. In ICRP 60, I suggest that ICRP60's radio nuclides deposition should be studied in concrete material, metal pipe material, structure material and so on. Exact deposition coefficient allow the accurate and optimized TEDE dose calculations to be performed.</p>
Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery
<p><strong>Power Generation Data Set</strong></p> <p>This data set contains imaging data acquired by ESA's Sentinel-2<br> Earth-observing satellite constellation [1] for a sample of power stations that were picked using geographic coordinates <br> provided by the European Pollutant Release and Transfer Register [2]. The images<br> contain scenes of power stations, some of which are actively<br> emitting smoke plumes.</p> <p>This data set was created with the goal to automatically segment plumes, predict the type of fired fuel, predict the rate of power generation and estimate the amount of CO2 emissions, directly from remote sensing images.</p> <p><br> <strong>Description</strong><br> </p> <p>Each image is provided in the GeoTIFF file format, contains a total of 13 bands. Images have either a shape of 120x120 or 300x300 pixels (corresponding to a square area with an edge length of respectively 1.2 km and 3.0 km on the ground)<br> .</p> <p>This repository contains a total of 2131 images. This<br> repository contains a collection of JSON files that hold manual segmentation labels for plumes. Segmentation<br> labels were generated using label-studio [3]. Please note that polygon edge coordinates have to be scaled to fit the images.</p> <p><br> <strong>Content</strong></p> <p>The following files are contained in this repository:</p> <ul> <li>README.md - this file</li> <li>images.zip [2.0GB] - contains 2131 GeoTIFF images</li> <li>segmentation_labels.zip [1.5MB] - contains 2131 JSON files</li> <li>labels.csv [310KB] - contains additional labels for each image: <ul> <li>Generation output rate [4],[5]</li> <li>Country</li> <li>Type of fired fuel</li> <li>Latitude and longitude of the power plant</li> <li>Concurrent weather information (temperature, humidity and wind vector)</li> </ul> </li> </ul> <p> </p> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p> Hanna, J., Mommert, M., Scheibenreif, L., Borth, D.,<br> "Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery",<br> Tackling Climate Change with Machine Learning workshop at NeurIPS 2021.</p> <p>Please refer to this publication for additional information on the data set.</p> <p>The code used for this publication is available at https://github.com/HSG-AIML/RemoteSensingCO2Estimation.</p> <p> </p> <p><br> <strong>Author</strong></p> <p>Joëlle Hanna</p> <p>University of St. Gallen, AIML Lab, School of Computer Science joelle.hanna@unisg.ch</p> <p><br> <strong>References</strong><br> </p> <p>[1]: https://earth.esa.int/web/sentinel/missions/sentinel-2<br> [2]: https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial<br> [3]: https://labelstud.io/<br> [4]: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show<br> [5]: https://doi.org/10.5281/zenodo.3574566</p>
Results from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>This repository contains results from the paper, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied Statistics</em>. The storage of these results helps facilitate access to and replication of the analysis in the paper. The results include output from the:</p> <ol> <li>Sulfate analysis <ul> <li>tx-2016-100k-int-mx.RDS</li> </ul> </li> <li>Asthma analysis <ul> <li>asthma-pois-cut.RDS</li> <li>asthma-pois-plugin.RDS</li> <li>asthma-bart-cut.RDS</li> <li>asthma-bart-plugin.RDS</li> </ul> </li> <li>Medicare analysis <ul> <li>medicare-pois-cut.RDS</li> <li>medicare-pois-plugin.RDS</li> <li>medicare-bart-cut.RDS</li> <li>medicare-bart-plugin.RDS</li> </ul> </li> <li>Simulation study <ul> <li>simstudy-cm1-lm.RDS</li> <li>simstudy-cm1-bart.RDS</li> <li>simstudy-cm2-lm.RDS</li> <li>simstudy-cm2-bart.RDS</li> <li>simstudy-cm3-lm.RDS</li> <li>simstudy-cm3-bart.RDS</li> <li>simstudy-pm1-pois.RDS</li> <li>simstudy-pm1-bart.RDS</li> <li>simstudy-pm2-pois.RDS</li> <li>simstudy-pm2-bart.RDS</li> <li>simstudy-pm3-pois.RDS</li> <li>simstudy-pm3-bart.RDS</li> </ul> </li> <li>Log-linear BART sensitivity analysis <ul> <li>sensitivity-m100.RDS</li> <li>sensitivity-m200.RDS</li> <li>sensitivity-m300.RDS</li> <li>sensitivity-m400.RDS</li> <li>sensitivity-power05.RDS</li> <li>sensitivity-power1.RDS</li> <li>sensitivity-power15.RDS</li> <li>sensitivity-power2.RDS</li> <li>sensitivity-power25.RDS</li> <li>sensitivity-power3.RDS</li> <li>sensitivity-power4.RDS</li> <li>sensitivity-power5.RDS</li> </ul> </li> </ol> <p>A description of these results can be found at <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a>, along with the R code used to generate these (and other results, such as figures) found in the manuscript and supplementary material.</p>
Behaviour of dissolved inorganic salts in the cooling water of a nuclear power plant open recirculation system and formation of water discharge
<p>The main problem in the operation of nuclear power plants is the scale formation of mineral impurities in an open recirculating system. However, water discharge from an open recirculating system into water bodies can lead to changes in the chemical equilibrium of wastewater components and requires constant monitoring. The purpose of this study was to analyse the behaviour of dissolved inorganic salts in water in an open recirculating system during water treatment using the example of the Rivne Nuclear Power Plant. Moreover, the analysis impact of their discharge with return water in the Styr River. The dissolved inorganic salt concentration has a significant impact on the efficiency of the system and the environment of an open recirculating system power plant. Altogether, each dissolved inorganic salt component was analysed separately using standard measurement methods, using statistical methods of data processing, and correlation analysis. In addition, the annual discharge of the dissolved inorganic salts components was calculated and the amount of discharge was assessed for compliance with the maximum discharge limit. Thus, the influence of the formation of the dissolved inorganic salts and changes in their concentration value during the discharge of returned water into a natural water body was analysed.</p>
Supplementary data for "An initial assessment of the value of Allam Cycle power plants with liquid oxygen storage in future GB electricity system"
<p>The code for the Unit Commitment & Economic Dispatch model that was used in this work is available at: https://gist.github.com/vitali87/20688c161d7b5ad598b5d52b524f4585</p> <p>Sample output data can be found in the "Example Outputs.zip" file. This corresponds to the case outlined in the article that simulates a system with 5 Allam Cycle plants without Liquid Oxygen Storage, for the winter test week.</p> <p>To run the UCED model:</p> <ul> <li>Download "UC AIMMS Allam Cycle Model" code from the github and save as an AIMMS project file.</li> <li>Save the file in a folder that contains all the necessary input datasets, found in the "Universal Inputs for UCED Model.zip" file, and the example outputs, found in the "Example Outputs.zip" file, which are to be overwritten. Do not change the name of the input or output files.</li> <li>Open the project and execute the following procedures: <ul> <li>"Main Initialisation" - to initialise the problem</li> <li>"Read from Excell" - to read data from the input files</li> <li>"Main Execution" - to begin running the problem</li> </ul> </li> <li>Once the run is complete, execute "Run External Procedure" to overwrite the output files with the new data.</li> </ul> <p>To change the test week:</p> <ul> <li>Open "Demand Profiles" in 'sets' and change the set definition. Enter "C1" for the winter week and "C21" for the summer week. Another week can alternatively be selected. For example, entering "C45" would allow the model to run with the weather and demand data from the 45th week in the year 2010. </li> <li>Save and close the set.</li> </ul> <p>To change the number of plants in the system:</p> <ul> <li>Open "PCCSGenerators" in 'sets' and change the set definition. To run with 5 Post Combustion Capture plants, end the list of generators after plant number 5 by commenting the remaining plants. This is done by using "!" after the 5th plant name in the string. Then save and close the set.</li> <li>Repeat the above step for the "ACGenerators" and "AirSeparationUnits" sets, to change the number of Allam Cycle plants in the system.</li> </ul> <p>To add or remove oxygen storage capability from the Allam Cycle plants:</p> <ul> <li>Open the "Main Initialisation" procedure.</li> <li>To run the model without oxygen storage: <ul> <li>make sure the following command is stated: "AC_ASU_coupled := 0;"</li> <li>save and close the procedure</li> </ul> </li> <li>To run the model with oxygen storage: <ul> <li>make sure the following is command is stated: "AC_ASU_coupled := 1;"</li> <li>make sure that the number, 'X', of "map_AC_to_ASU('Gas_CCS_AC_X') := 'ASU_X';" commands that are active matches the number of active Allam Cycle plants in the model</li> <li>save and close the procedure.</li> </ul> </li> </ul>
Virtual Storage Plant (VSP) preliminary demonstration of active power control
<p>This the dataset of preliminary demostration of using distributed control with consensus algorithm for dispatching a virtual storage plant. The dataset contains raw data from 4 experiments (3, 4, 5 and 6 units in a VSP) for 5 scenarios: TC_5.02.01, TC_5.02.02, TC_5.02.03, TC_5.02.04, TC_5.02.05.</p> <p>The experiments were conducted at the University of Zagreb's Smart Grid Laboratory (SGLab).</p> <p> </p>
PUDL Raw EIA Form 923 -- Power Plant Operations Report
<p>The EIA Form 923 collects detailed monthly and annual electric power data on electricity generation, fuel consumption, fossil fuel stocks, and receipts at the power plant and prime mover level. Archived from <a href="https://www.eia.gov/electricity/data/eia923">https://www.eia.gov/electricity/data/eia923</a></p> <p>This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by <a href="https://catalyst.coop">Catalyst Cooperative</a>. It is organized into <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages</a>. For additional information about this data and PUDL, see the following resources: </p><ul> <li><a href="https://github.com/catalyst-cooperative/pudl">The PUDL Repository on GitHub</a></li> <li><a href="https://catalystcoop-pudl.readthedocs.io">PUDL Documentation</a></li> <li><a href="https://zenodo.org/communities/catalyst-cooperative/">Other Catalyst Cooperative data archives</a></li> </ul> <p></p>
POLYPHEM - The future of small-scale Concentrated Solar Power plants
<p>☀️ The POLYPHEM project aims to improve the performance of small-scale Concentrated Solar Power (CSP) plants and their flexibility to generate power on demand. To this end, POLYPHEM develops a new technology: an innovative, solar-driven Micro-gas turbine which would help meet the variable energy demand of a mini-grid.</p> <p>📺 Watch our video to discover more about the POLYPHEM project and the future of small-scale Concentrated Solar Power plants!</p> <p>Follow us on:<br> ► Website: www.polyphem-project.eu<br> ► Twitter: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbk5Bb3lCX0hKOVBhTHJPbWQ0Qm9iMkplaDQtZ3xBQ3Jtc0ttam12Rmk3MG5zR09BMkFpMGNWVndva1FIZU1sTU1Vdk1ENHpsTGRNT2h1cklIcVpuS0VvVU1DMGZ4OC1VQ3dVb0daam5mRGVOZ05YeUxSY01saWRJSkhxa3NyYkxQN0VrUTVHdDBhZkFjNE5NVjBlVQ&q=https%3A%2F%2Ftwitter.com%2FH2020CSP&v=Lw_JCO8S734">https://twitter.com/H2020CSP</a><br> ► LinkedIn: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqay0xZHJwT0tqNEZKSzBYUEdURkNEemFUY3dLZ3xBQ3Jtc0tsNy1GSlQwSjlhMmdUNThyc2c0ZFBmRlRvMDVpa3JCVHliMVJRTDVPM1lwWFJwWTd1bkRFaWVqUTZUM2l3b3FPM3h2TFkwSlFNazJZcVBpT2FIUkwyMmQ4TzdxMHI5MXFjeGJjS0J5WWZneFd0TTA2WQ&q=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fpolyphem&v=Lw_JCO8S734">https://www.linkedin.com/company/poly...</a></p> <p>🇪🇺 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 764048.</p> <p>EDIT: This is an updated version of the POLYPHEM video.</p>
Analyzing defense-in-depth properties of nuclear power plant instrumentation and control system architectures using ontologies
<p>A proof-of-concept OWL ontology for representing knowledge over nuclear overall instrumentation & control (I&C) system architectures, and two case studies built around a proposed US variant of the European Pressurized Water Reactor and the NuScale Small Modular Reactor.</p>
A decade of cumulative radiocesium testing data for foodstuffs throughout Japan after the 2011 Fukushima Daiichi Nuclear Power Plant accident
<p>Updated dataset.</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.