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Fuel loads monitoring dataset of Campos Amazônicos Fire Experiment (Amazonas, Brazil)
<p>This dataset presents fuel loads monitoring data related to the Campos Amazônicos Fire Experiment (CAFE) (Amazonas, Brazil) project. Located within a protected area within the largest enclave of tropical savanna in the Southern Amazon, CAFE comprises a careful and systematic experimental design that was conceived from the start to evaluate satellite observations and their potential and limitations in studying spatial and temporal fire dynamics in tropical savannas.</p> <p>More information about the experimental design can be found in the following publication:</p> <p>Alves, D. B.; Fidelis, A.; Pérez-Cabello, F.; Alvarado, S. T.; Conciani, D. E.; Cambraia, B. C.; Silveira, A. L. P.; Silva, T. S. F. Impact of Image Acquisition Lag-Time on Monitoring Short-Term Postfire Spectral Dynamics in Tropical Savannas: the Campos Amazônicos Fire Experiment. Journal of Applied Remote Sensing v. 16, n. 3 (2022) - <a href="https://doi.org/10.1117/1.JRS.16.034507">https://doi.org/10.1117/1.JRS.16.034507</a></p> <p>_____________________________________________________________________________</p> <p>August 08, 2022 - Version 1.0 includes data from 30 monitored experimental plots of 1 hectare each (100×100 m). Three experimental treatments were then established: 12 plots were burned in May (Early-Dry Season – EDS), further 12 plots were burned in August (Mid-Dry Season – MDS), and 6 plots were kept as control by ensuring fire exclusion throughout the duration of the experiment. Controlled burning occurred during two separate field campaigns in 2019, the first between May 19th and 25th, and the second between August 22nd and 26th.</p> <p>Measurements of fuel load were obtained from eight subplots of 0.5 × 0.5 m randomly distributed within each plot. Samples included graminoids, leaves and branches near the ground. Biomass was dried at 70°C for 48 hours, and weighed to determine total fuel load (kg. m<sup>-2</sup>) for each sample. Samples were taken before fire for all control, EDS and MDS burn plots during both field campaigns, and repeated sampling was carried out after fire for the burned plots. In 2020, all 30 monitored plots were sampled again in May and August.</p> <p>The files available include: i) a table (Fuel_load_measures.csv) that contains the measures of fuel loads for each plot; ii) a text file (List_of_variables.rtf), which details each variable available in the table.</p> <p>_____________________________________________________________________________</p> <p>We thank the management team of the Campos Amazônicos National Park, and in particular to its Fire Brigade (squad leaders José Furtado Neto, Genaldo Júnior, Ademilton Carvalho, Simei Limoeiro, José Alexandre Medeiros, Antonio Machado and Leandro Lacerda, and on behalf of them to all other members of the brigade), who ensure safe burning of all fire experiments (SISBIO license number 67210-5). This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, grant numbers 2019/07357-8; 2015/06743-0); the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq, grant numbers 154660/2018-3; 441968/2018-0; 303988/2018-5).</p>
Supplement to "Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks"
<p>This deposit contains the scripts and data used in the research article "Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks", which proposed a novel method for electricity demand forecasting in low voltage networks.</p> <p>The scripts are written in the form of R markdown and include additional commentary on the methodology. Both input data and the resulting forecast data and evaluation results are provided, though the latter two may be regenerated by running the scripts.</p>
2-D ultrasound videos of the distal biceps brachii myotendinous junction displacement over varying elbow angles and tendon loads
<p>This publication contains data which was recorded during an experimental measurement campaign and was used to train a model of the <em>biceps brachii</em> distal tendon to predict myotendinous junction displacement over varying joint angles and tendon forces.</p> <p>Further Information about the data set is contained within the README.pdf file.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work has been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, <a href="https://www.dfg.de/en/">https://www.dfg.de/en/</a>) - ref. no. SCHN 1339/3-1, by the Federal Ministry of Education and Research (BMBF) within the project ITS.ML - ID 01IS18041 A (AS) and by the research training Group "DataNinja" funded by the German federal state of North Rhine-Westphalia.</p>
Simulated application load on a Kubernetes system based on Human traffic pattern
<p>Contained here is the dataset generated using the tool based on the report 'A Dynamic Kubernetes Load Generation Solution Mimicking Human Traffic Pattern'. The tool was created to fill in the gap of having to generate artificial load within Kubernetes infrastructure. This tool could then be used as a stable testing framework to determine whether the Autoscaling solutions in place can handle the expected load pattern or not. It can also be used as a standard benchmarking tool to test various algorithms that aim to improve the already existing autoscaling solutions. The tool is primarily for IaaS (Infrastructure As A Service) providers, who have to deal with applications as a black box and are unaware of the scaling needs of the application. Some of the data generated in this dataset were made in reference to existing datasets obtained from Google Dataset of different containers. The reference dataset will also be uploaded in the near future. </p> <p>The dataset contains CSV files, which have the name of the pod, average CPU usage, average memory usage, and the timestamp of when the data was collected. These measurements were taken directly from the Prometheus service which uses using Kubernetes metrics server to scrape the data from the pods. <br>The `avg_cpu_usage` is the average CPU percent of the total CPU available the application utilized over a 1-minute interval window. While the `avg_memory_usage` is the average memory utilized in bytes over 1 1-minute interval window. The CSV file names also mention the period of time from when the measurements were taken. Any date and time within the dataset are in the CEST timezone. All of the CSV files in the dataset have the same layout. </p> <p>The datasets were collected from a Kubernetes node in a VM, with the following specifications,<br>- Intel core (Broadwell, no TSX, IBRS), 2 cores @ 2.594 GHzkb<br>- 4GiB of memory<br>- Ubuntu 22.04.4 LTS x86_64<br>- Kernel: 5.15.0-105-generic</p> <p>The names of the file itself specify the type of application load generated. The file names can be divided into segments separated by `__`. The last two segments signify the start time of the measurement data, and the end time of the measurement data respectively. The optional segment before the mentioned two segments signifies the container from the original input dataset that was used as a reference to produce the given data.</p> <p>Similarly, there is an optional tag of `CPU`, `mem`, or `test` at the beginning of the file name. This signifies the type of application load that was used to generate the data. `CPU` would mean the application was created by using options on the tool that used CPU stressing algorithms from `ng-stress`. This was used to better mimic a CPU-intensive application. Similarly, `mem` primarily makes use of a test algorithm that stresses memory. `test` is a special option making use of a combination of tests to have a balanced outcome. </p> <p>In the real world, the applications are never static, and between each run, with the same parameters, the application will always show some variations. To account for these variations, we added some inherent randomness in the number of concurrent connections and the amount of time a virtual user waits to make a request. There are some measurements where these measurements were frozen, which makes the class,<br>- fixed-load_fixed-sleep <br>- random-load_fixed-sleep<br>- fixed-load_random-sleep</p> <p>measurements.<br>The default class of (random-load_random-sleep) is every other measurement not marked by these tags in their name.</p> <p>The dataset itself was collected over a period of 3 days, from 25-03-2024 to 28-03-2024. Each dataset generally contains 34 min of simulation data, with the first and last 2 minutes without any traffic being directed to stabilize the system. The actual 30 min of data corresponds to the daily behavioral pattern as shown in a 30-day period.</p>
Heat load maps at 100m resolution (Linz)
<p>Climate indices (e.g. mean annual number of summer days, hot days, tropical nights) for 30-year historical/future climate periods. The calculation method is based on the cuboid method, a statistical-dynamical downscaling procedure that combines high-resolution (100m) urban climate simulations with long-term climate information from monitoring data/regional climate projections.</p> <p><strong>Climate indices for historical/current periods:</strong> - Background climate information: monitoring data from the airport station Linz Hoersching (1961-2010) - Background climate information: historical (bias-corrected) EURO-CORDEX simulations (1971-2000)</p> <p><strong>Climate indices for future periods:</strong> - Background climate information: bias-corrected EURO-CORDEX model simulations for different representative concentration pathways (2021-2100)</p>
WALOWA (WAve LOads on WAlls) - Large-scale Experiments in the Delta Flume on Overtopping Wave Loads on Vertical Walls
<p>Coasts of low lying countries are often comprised of a gentle foreshore and shallow waters, followed by a dike and a promenade. At the end of the promenade buildings or storm walls are constructed. This setting makes it possible for waves to overtop the dike and impact on the storm wall or building. Especially during storm season the overtopping waves induce large loads on these structures. New scenarios for climate change and sea level rise make it worthwhile to invest in research regarding overtopping wave loads.</p> <p>Within the European project 'Wave Loads on Walls' (WaLoWa) model tests in the Delta flume (The Netherlands) were conducted. It is the aim to study overtopping wave loads on storm walls and buildings. The project is coordinated by Ghent University (Belgium), in cooperation with TU Delft (The Netherlands), RWTH Aachen (Germany), University of Bari, University of L'Aquila, University of Calabria and University of Florence (Italy) and Flanders Hydraulics Research (Belgium). The project is financed by a grant by Hydralab+ in the framework of the EC Horizon 2020 program.</p> <p>A model geometry comprised of a sandy beach, a sloping dike, promenade and wall structure was built into the Delta flume. The beach alone consists of 1000m³ sand material and was an essential part of the structure, to obtain the broken wave conditions similar to reality. Waves representing a storm with a 1000 year return period and an additional water level to account for sea level rise result in the tested superstorm conditions.</p> <p>Measurements of the water surface elevation were taken close to the paddle, along the mildly sloping foreshore and at the dike toe location by resistance type wave gauges mounted to the flume side wall. The bathymetry of the sandy foreshore was measured by a mechanical profiler before and after the test. The overtopping flow properties thickness and velocity were measured by resistance type wave gauges, ultra-sonic distance sensors, paddle wheels and an electro-magnetic current meter installed along the promenade. Finally, the impact forces and pressures on the wall were measured by compression load cells and pressure sensors respectively. The data-set was complemented by a number of synoptic measurements, such as laser scan profiles, GoPro images, High-speed camera images, Digital camera images. Due to its large storage size, these data are provided on request.</p>
DATASET : Thresholds of fire response to moisture and fuel load differ between tropical savannas and grasslands across continents
<p><strong>Abstract </strong></p> <p><strong>Aim:</strong> An emerging framework for tropical ecosystems states that fire activity is either ‘<em>fuel build-up limited</em>’ or ‘<em>fuel moisture limited</em>’ i.e. as you move up along rainfall gradients, the major control on fire occurrence switches from being the amount of fuel, to the moisture content of the fuel. Here we used remotely sensed datasets to assess whether interannual variability of burned area is better explained by annual rainfall totals driving fuel build-up, or by dry season rainfall driving fuel moisture.</p> <p><strong>Location:</strong> Pantropical savannas and grasslands</p> <p><strong>Time period:</strong> 2002-2016</p> <p><strong>Methods:</strong> We explored the response of annual burned area to interannual variability in rainfall. We compared several linear models to understand how <em>fuel moisture </em>and <em>fuel build-up effect </em>(accumulated rainfall during 6 and 24 months prior to the end of the burning season respectively) determine the interannual variability of burned area and explore if tree cover, dry season duration and human activity modified these relationships.</p> <p><strong>Results:</strong> Fuel and moisture controls on fire occurrence in tropical savannas varied across continents. Only 24% of South American savannas were <em>fuel build-up limited</em> against 61% of Australian savannas and 47% of African savannas. On average, South America switched from fuel limited to moisture limited at 500 mm yr<sup>-1</sup>, Africa at 800 mm yr<sup>-1</sup> and Australia at 1000 mm yr<sup>-1 </sup>of mean annual rainfall.</p> <p><strong>Main conclusions:</strong> In 42% of tropical savannas (accounting for 41% of current area burned) increased drought and higher temperatures will not increase fire, but there are savannas, particularly in South America, that are likely to become more flammable with increasing temperatures. These findings highlight that we cannot transfer knowledge of fire responses to global change across ecosystems/regions – local solutions to local fire management issues are required, and different tropical savanna regions may show contrasting responses to the same drivers of global change.</p>
Data on the material characterization of cast and additively manufactured IN939 subjected to room-temperature low-cycle fatigue load
<p>The original data to the research paper termed "Room-temperature low-cycle fatigue behaviour of cast and additively manufactured IN939 superalloy" are enclosed. Two specimen orientations of L-PBF IN939 - horizontal and vertical, and two thermodynamical states - without subsequent heat treatment (non-treated) and standard aged according to Delargy et al., 1986, were investigated. The paper concerns the low-cycle fatigue performance of cast and additively manufactured IN939 superalloy. It brings a comprehensive account on the damage and deformation behaviour of the tested alloy, combining the test analyses with high-resolution SEM and TEM observations.</p>
5359 industrial VEA load profiles
Open the record for dataset details and reuse information.
Dataset for Analysis of the Overhead Crane Energy Consumption Using Different Container Loading Strategies in Urban Logistics Hubs
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Kłodawski Michał, Jachimowski Roland, & Chamier-Gliszczyński Norbert, 2024. „Analysis of the Overhead Crane Energy Consumption Using Different Container Loading Strategies in Urban Logistics Hubs”. Energies 17: 1–24. https://doi.org/10.3390/en17050985 - published online: 2024-02-20, which discusses the application of simulation in solving the problem of the overhead crane energy consumption using different container loading strategies in Urban Logistics Hubs.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset.</li> <li>Data_Crane.xlsx: Contains the input data used in the model for estimating crane energy consumption.</li> <li>Results_01.csv: Contains output data - Simulation results of energy consumption, and total average energy recovery for each scenario.</li> <li>Results_02.csv: Contains output data - Simulation results - mean values from the results of all scenario replications.</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Dynamic load model for passenger trains based on dynamic train signature
<p>20 conventional load model trains based on dynamic signature envelope of 3,140 operating passenger trains in Europe</p> <ul> <li>axle distances in m and axle loads in kN for all model trains as txt-files</li> </ul>
Supplementary Material for "Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP"
<p>Supplementary material to accompany pre-print of "Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP" by Jethro Browell and Matteo Fasiolo available on on arXiv. This is version 3. The only changes from version 1 & 2 to forecast evaluation (significance testing and additional plots). Future releases are subject to change following revisions of this article.</p>
Parametric Study of the Radiative Load Distribution on the EU-DEMO First Wall Due to SPI-Mitigated Disruptions and in Steady-State (dataset)
<p>Database for reproducing the calculations presented in the publication "Parametric Study of the Radiative Load Distribution on the EU-DEMO First Wall Due to SPI-Mitigated Disruptions", submitted to <em>Fusion Engineering and Design</em>.</p> <p>Work carried out within the framework of the EUROfusion Consortium.</p>
Data from the parametric analysis of masonry pointed arches with limit analysis subjected to vertical self-weight plus a vertical concentrated live load
<p>For each one of the simulations performed from the parametric analysis of masonry pointed arches with limit analysis, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry panel. Finally, the .png file presents the collapse mechanism obtained. </p>
Data from the parametric analysis of masonry pointed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load
<p>For each one of the simulations performed from the parametric analysis of masonry pointed arches with limit analysis, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry panel. Finally, the .png file presents the collapse mechanism obtained. </p>
Data and code for: Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats, Suricata suricatta
<p>Data and code to go with our publication "Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats, <em>Suricata suricatta", </em>Nature Communications (2021).</p> <p><strong>FILE DESCRIPTIONS</strong></p> <p><em>****** DATA ******</em></p> <p><strong>meerkat_16S_data.tar.gz</strong> # 16S V4 amplicon sequences sequenced on an Illumina MiSeq platform using primer pair 515F and 806R, including all faecal samples, controls, and sand samples. Sequence identifiers and basic metadata are in <strong>sequence_identifiers.csv.</strong></p> <p><strong>sequence_identifiers.csv </strong># Simple metadata and identifiers for all sequences/samples (what type of sample/sequencing run, etc), required for QIIME2 processing of the raw fasta.gz files contained in meerkat_16S_data.tar.gz. It contains a column for whether the sample was included in the final analysis. Does not include sample biological metadata as generating this data requires access to Kalahari Meerkat Project database. Biological metadata for samples included in the final analysis are instead provided in <strong>processed_data_phyloseq.RDS </strong>and can be accessed via <em>phyloseq::sample_data(processed_data_phyloseq)</em>.</p> <p><strong>processed_data_phyloseq.RDS</strong> # Phyloseq object containing the processed data used in the presented analysis. Contains data for 1109 samples, and includes the ASV table, the taxonomic classification, the phylogenetic tree, and the sample metadata used in the analysis.</p> <p><strong>technical_replicate_data_phyloseq.RDS</strong> # Phyloseq object containing data from the 16 technical replicates.</p> <p><strong>pilot_study_data_phyloseq.RDS</strong> # Phyloseq object containing data from the pilot study on captive meerkats.</p> <p><em>****** CODE ******</em></p> <p><strong>CODE1_QIIME_script.R</strong> # QIIME2 script to generate ASV table, taxonomy, and phylo tree from <strong>meerkat_16S_data.tar.gz. </strong>Requires a reference taxonomy (SILVA) and a reference phylogeny (SEPP) for taxonomic and phylogenetic placements.</p> <p><strong>CODE2_processing_QIIME_output.Rmd</strong> # R markdown script that processes the QIIME2 output generated by <strong>CODE1_QIIME_script.R</strong>. Does not generate meerkat metadata as this requires access to the Kalahari Meerkat Project database. This metadata is provided in <strong>processed_data_phyloseq.RDS.</strong></p> <p><strong>CODE3_data_analysis_script.Rmd </strong># R markdown script that generates data and figures presented in paper, using data from <strong>processed_data_phyloseq.RDS, technical_replicate_data_phyloseq.RDS, </strong>and<strong> pilot_study_data_phyloseq.RDS.</strong></p> <p><em>****** R MARKDOWN REPORTS ******</em></p> <p>The following reports are html files that show the code output for the two RMD files above.</p> <p><strong>RMARKDOWN_data_processing.html </strong># R markdown report for<strong> CODE2_processing_QIIME_output.Rmd</strong></p> <p><strong>RMARKDOWN_data_analysis.html </strong># R markdown report for <strong>CODE3_data_analysis_script.Rmd</strong></p> <p>*****************************</p> <p>For general queries, unexpected errors and/or inconsistencies, please contact riselya@gmail.com.</p> <p> </p>
Wind2Loads 9D load simulation database statistics
<p>1. An Excel file providing a list of the environmental conditions used to generate the load simulations. Column names correspond to the variables that are used as load simulation inputs. An "F" prefix in the variable means this is the cumulative distribution function (CDF) of the distribution used to generate the random sample. If there is no F-prefix, the column corresponds to the physical value of the variable. </p> <p>2. Files containing the 10-minute statistics of load simulations. Files are delimited with semicolon ";"</p> <p>- Filenames specify details about the simulation: "PointNo" corresponds to the sample number listed in the "conditions list" Excel file. "SetNo" corresponds to the random realization (there are 5-8 random realization at each sample point). Each sample realization corresponds to 1h simulations, but the simulation data are split in parts of 10min (so there are 6 "parts" in each realization) - the "part" in the filename indicates which part of the realization this is.</p> <p>- The columns of each stats file are organized as follows (first column considered to have index 1):</p> <p>Column 1: File name</p> <p>For load channel number j, j = 1: n_channels:</p> <p>Column 7*(j-1) + 2: mean value of channel j</p> <p>Column 7*(j-1) + 3: standard deviation of channel j</p> <p>Column 7*(j-1) + 4: minimum of channel j</p> <p>Column 7*(j-1) + 5: maximum of channel j</p> <p>Column 7*(j-1) + 6: DEL4 - fatigue damage-equivalent load (DEL) with S-N curve slope m = 4, for channel j</p> <p>Column 7*(j-1) + 7: DEL8 - fatigue damage-equivalent load (DEL) with S-N curve slope m = 8, for channel j</p> <p>Column 7*(j-1) + 8: DEL12 - fatigue damage-equivalent load (DEL) with S-N curve slope m = 12, for channel j</p>
Wind2Loads 6D load simulation database statistics
<p>1. An Excel file providing a list of the environmental conditions used to generate the load simulations. Column names correspond to the variables that are used as load simulation inputs. An "F" prefix in the variable means this is the cumulative distribution function (CDF) of the distribution used to generate the random sample. If there is no F-prefix, the column corresponds to the physical value of the variable. </p> <p>2. Files containing the 10-minute statistics of load simulations. Files are delimited with semicolon ";"</p> <p>- Filenames specify details about the simulation: "PointNo" corresponds to the sample number listed in the "conditions list" Excel file. "SetNo" corresponds to the random realization (there are 5-8 random realization at each sample point). Each sample realization corresponds to 1h simulations, but the simulation data are split in parts of 10min (so there are 6 "parts" in each realization) - the "part" in the filename indicates which part of the realization this is.</p> <p>- The columns of each stats file are organized as follows (first column considered to have index 1):</p> <p>Column 1: File name</p> <p>For load channel number j, j = 1: n_channels:</p> <p>Column 7*(j-1) + 2: mean value of channel j</p> <p>Column 7*(j-1) + 3: standard deviation of channel j</p> <p>Column 7*(j-1) + 4: minimum of channel j</p> <p>Column 7*(j-1) + 5: maximum of channel j</p> <p>Column 7*(j-1) + 6: DEL4 - fatigue damage-equivalent load (DEL) with S-N curve slope m = 4, for channel j</p> <p>Column 7*(j-1) + 7: DEL8 - fatigue damage-equivalent load (DEL) with S-N curve slope m = 8, for channel j</p> <p>Column 7*(j-1) + 8: DEL12 - fatigue damage-equivalent load (DEL) with S-N curve slope m = 12, for channel j</p> <p> </p>
FLEXPART 10.4 output for "Occurrence and backtracking of microplastic mass loads including tire wear particles in Northern Atlantic air"
<p>The dataset consists of three (3) files:</p> <p>-- track3h.txt shows the position of the research vessel in each of the seven (7) ship tracks/campaigns in 3-hour resolution in ascii format structured in columns as follows:</p> <p>YEAR, MONTH, DAY, HOUR, MINUTE, SECOND, LONGITUDE, LATITUDE, SHIP TRACK NUMBER</p> <p>-- FLEXPART_720x360_fine.tar.gz shows the footprint emission sensitivities for fine particles (as described in the paper) in a gridded netCDF format of 0.5 degrees resolution for 80 release points matching the coordinates and times in the track3h.txt file.</p> <p>-- FLEXPART_720x360_coarse.tar.gz shows the footprint emission sensitivities for coarse particles (as described in the paper) in a gridded netCDF format of 0.5 degrees resolution for 80 release points matching the coordinates and times in the track3h.txt file.</p>
Multi-level power load dataset
<p>This is the dataset used in the paper "Benchmarks and Custom Package for Electrical Load Forecasting" submitted to Neurips2023 datasets and Benchmark track. This dataset contains 11 independent datasets, including two levels of data (building level and greater than building level). Except for the ELF and UCI datasets, all other data have corresponding temperature data.</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.