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557 results for “demand”
EIA_Cleaned_Hourly_Electricity_Demand_Data: 2015 - 2024
<div> <p>Cleaned hourly electricity demand data for electric balancing authorities within the contiguous US. Raw data is based on the U.S. Energy Information Administration's collected data <a href="http://www.eia.gov/opendata/qb.php?category=2122628">here</a>.</p> <p>Cleaned hourly data spans July 2, 2015 - Dec 31, 2024 (9.5 continuous year).</p> <p> </p> <p>Please consider citing:</p> <p>Ruggles, T.H., Farnham, D.J., Tong, D. <em>et al.</em> Developing reliable hourly electricity demand data through screening and imputation. <em>Sci Data</em> <strong>7, </strong>155 (2020). <a href="https://doi.org/10.1038/s41597-020-0483-x">https://doi.org/10.1038/s41597-020-0483-x</a></p> </div> <p> </p> <p>Since the prior releases of this data, the EIA has limited the range of historical data available via their API. This release focuses on cleaning calendar years 2020 through 2024 and including the many balancing area subregions.</p> <p>Users can combine prior data releases with this latest release to have data extending from from 2 July 2015 through 31 Dec 2024.</p>
2021 electricity and heat demand data for a city district (Belgium)
<p>Dataset of electricity and heat demand in a city district in Belgium. For the time period of the data, the district was still under construction and no full inhabitation of the buildings was present. Electricity data include electricity demand for:</p> <ul> <li>Individual households</li> <li>EV charging stations</li> <li>Decentralised waste water treatment</li> <li>Heat pump</li> <li>District heating pumps</li> <li>Vacuum network pumps</li> <li>Miscellaneous</li> </ul> <p>'Total' in the electricity dataset (ElectricPower) refers to the sum of the separate time series. 'Total measured' is a measurement of the total electricity use (in W). Data is averaged out over 15 minutes and expressed in kiloWatt before June 6, in Watt after that date. The electricity demand for the individual households (ElectricPowerPrivateUnits) is expressed in Watt for the complete period.</p> <p>The heat demand data (HeatDemand) describes the heat demand of the complete district, i.e. all private living units as well as common areas, office buildings, sports hall... Like electricity demand data, heat demand data is averaged out over 15 minutes and expressed in kiloWatt before June 6, in Watt after that date.</p> <p> </p> <div> </div>
Data for the paper "Determining the value of preferred goods based on consumer demand in a home-cage based test for mice"
<p>All data related to the paper "Determining the value of preferred goods based on consumer demand in a home-cage based test for mice" will be made available to the scientific public here. The paper will be published soon in Behavioral Research Methods. </p>
Rebuilding green infrastructure in boreal production forest given future global wood demand
<p>Global policy for future biodiversity conservation is ultimately implemented at landscape and local scales. In parallel, green infrastructure (GI) planning needs to account for socio-economic dynamics at national and global scales. Progress towards policy goals must, in turn, be evaluated at the landscape scale. Evaluation tools are often environmental quality objectives (EQO) indicators.</p> <p>We present three management scenarios for a 100,000 hectare boreal forest landscape in Sweden in the coming 100 years. The scenarios optimize financial returns and account for downscaled projected global demand of wood given a middle-of-the road Shared Socioeconomic Pathway (SSP2). We contrast a <em>reference</em> scenario meeting the wood demand against an <em>economy</em> scenario with no upper harvest limit, and a <em>green infrastructure</em> (<em>GI</em>) scenario optimizing the levels of four EQO indicators (the area of old forest, the area of mature broadleaf-rich forest, the amount of deadwood and the density of large trees).</p> <p>EQO indicators generally reached the highest levels in the <em>GI</em> scenario and the lowest levels in the <em>economy</em> scenario. Most indicators increased further in set-asides. The financial profit was 14% lower in the <em>GI</em> and 2% higher in the <em>economy</em> than in the <em>reference</em> scenario.</p> <p>These scenarios were used in the associated publication to evaluate the future response of eleven model species from three different species groups with widely differing habitat requirements. The studied species were four bird species, six wood-decaying fungi and one lichen, all either of conservation concern or considered indicator species for forest of high conservation value. Models and data for the birds and fungi have been published previously. The model for the lichen <em>Lobaria pulmonaria</em> was created for this study; the underlying data is therefore presented here as well.</p> <p>Our study has shown that effects of global SSPs can be downscaled and accounted for in planning landscape-scale forest and conservation management. Accounting for EQO indicators in the management optimization was found to be an effective approach to reveal scenarios for reaching targets on both revenue and conservation. Rebuilding green infrastructure in the production forest is possible at a relatively minor economic cost and to the benefit of species of conservation concern.</p>
Global demand data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>resource file </strong>contains demand time-series generated by <a href="https://github.com/niclasmattsson/GlobalEnergyGIS/blob/b23206f8701acafdf7359f9cc952dfd4e7b819e5/src/downloaddatasets.jl">GEGIS</a> covering the world. The time series are produced for different socio-economic scenarios (SSP), weather years, and prediction years<strong>.</strong></p>
Data collection for article "Quantifying Local Ecosystem Service Outcomes by Modelling Their Supply, Demand and Flow in Myanmar's Forest Frontier Landscape"
<p>This dataset contains the nine ecosystem service models (in .neta format) underlying the publication "Quantifying Local Ecosystem Service Outcomes by Modelling Their Supply, Demand and Flow in Myanmar’s Forest Frontier Landscape". The ecosystem models were implemented using the commercial software Netica (version 6.05) for constructing and analysing Bayesian Networks.</p>
Climate Change and 2030 Cooling Demand in Ahmedabad, India: Opportunities for Expansion of Renewable Energy and Cool Roofs (Supplemental Information)
<p>Supplemental information and analysis files for article, "Climate change and 2030 cooling demand in Ahmedabad, India: opportunities for expansion of renewable energy and cool roofs" (Original article available at: https://doi.org/10.1007/s11027-022-10019-4)</p>
Bulk and critical material demand for selected 'Starter Kit' energy system models - dataset
<p>This repository contains the data related to the Data in Brief article titled: <strong>Bulk and critical material demand for selected ‘Starter Kit’ energy system models.</strong></p> <p>The data include the modeled mass of materials and their embodied emissions. A metadata file is also included to clarify the units, materials and scenario names.</p>
Data collection for article "Regional scale mapping of ecosystem services supply, demand, flow and mismatches in Southern Myanmar"
<p>This dataset contains supply, demand, and flow maps from nine ecosystem service models (as layer files in .tif format) underlying the publication "Regional scale mapping of ecosystem services supply, demand, flow and mismatches in Southern Myanmar". </p>
Hourly Water Demand of a Mixed District Recorded by Supplier
<p>This dataset contains time-resolved measurements of water consumption in cubic meter taken between January 2016 and March 2021 at a temporal resolution of 1 hour. Measurements of water consumption were collected at a pumping station in a regional water supply network for a mixture of residential, commercial and industrial consumers in Germany.</p> <p>The supply network consists of multiple containers, which are fed through a series of water pumps. Predicting the demand of water can help optimize the cost of water pump operation, ensure the required supply, and avoid storing superfluous amounts of water for extended time.</p> <p>This dataset was provided by Siemens AG for the purpose of a study at KIT (Karlsruhe Institute of Technology), which can be a found <a href="http://arxiv.org/abs/2405.03429" target="_blank" rel="noopener">here</a> (http://arxiv.org/abs/2405.03429).</p> <p><strong>Three variants are provided:</strong><br>1. The raw dataset<br>2. A NaN-free version where missing data has been fixed by interpolation and a large missing stretch in June 2017 has been filled with averages from previous years.<br>3. A NaN-free version with hours filled in or removed to compensate for time change and ensure all days have 24 hours.</p> <p><strong>The dataset columns are:</strong><br>Date: Date in format (M)M/(D)D/YYYY<br>Time: Hour of day in the format [start time]-[end time], with each time in the format HH:MM<br>Consumption: Water consumption in cubic meters<br>Temperature: Local temperature in centigrade<br>Rain: Local precipitation in arbitrary units<br>Sun: Local sunshine intensity in arbitrary units</p>
Energy Security: Global analysis of Energy Matrix demand Mozambique case
<p>Energy system modelling, energy security, energy transition, renewable <br>energy, climate change, OSeMOSYS. </p>
"Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", scripts and data
<p>This repository contains the data, scripts and results for the paper "Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", https://doi.org/10.1016/j.esr.2024.101329.</p> <p>Results in the paper are divided into three sections, corresponding to the numbers of the folders inside this dataset. They are described as follows:</p> <p>1 - EU policy impact: What is the impact on the Italian electricity of the european proposal of cutting power demand and shifting it during peak hours on gas consumption, system costs and emissions?</p> <p>2 - Gas cost sensitivity: Which would be Italy’s most convenient power system considering different gas prices?</p> <p>3 - DSM in mitigation: What could be the role of demand side measures in power systems with a high penetration of RES?</p>
DEMAND: a collection of multi-channel recordings of acoustic noise in diverse environments
<p><strong>DEMAND: Diverse Environments Multichannel Acoustic Noise Database</strong></p> <p>A database of 16-channel environmental noise recordings</p> <p><strong>Introduction</strong></p> <p>Microphone arrays, a (typically regular) arrangement of several microphones, allow for a number of interesting signal processing techniques. The correlation of audio signals from microphones that are located in close proximity with each other can, for example, be used to determine the spatial location of sound source relative to the array, or to isolate or enhance a signal based on the direction from which the sound reaches the array.</p> <p>Typically, experiments with microphone arrays that consider acoustic background noise use controlled environments or simulated environments. Such artificial setups will in general be sparse in terms of noise sources. Other pre-existing real-world noise databases (e.g. the <a href="http://catalog.elra.info/product_info.php?products_id=693">AURORA-2</a> corpus, the <a href="http://spandh.dcs.shef.ac.uk/projects/chime/PCC/datasets.html">CHiME</a> background noise data, or the <a href="http://www.speech.cs.cmu.edu/comp.speech/Section1/Data/noisex.html">NOISEX-92</a> database) tend to provide only a very limited variety of environments and are limited to at most 2 channels.</p> <p>The DEMAND (Diverse Environments Multichannel Acoustic Noise Database) presented here provides a set of recordings that allow testing of algorithms using real-world noise in a variety of settings. This version provides 15 recordings. All recordings are made with a 16-channel array, with the smallest distance between microphones being 5 cm and the largest being 21.8 cm.</p> <p><strong>License</strong></p> <p>This work, the audio data and the document describing it, is licensed under a <a href="http://creativecommons.org/licenses/by-sa/3.0/deed.en_CA">Creative Commons Attribution-ShareAlike 3.0 Unported License</a>.</p> <p><strong>The data</strong></p> <p>A description of the data and the recording equipment is provided in the file <strong>DEMAND.pdf</strong>. All recordings are available as 16 single-channel WAV files in one directory at both 48 kHz and 16 kHz sampling rates. All files are compressed into "zip" files.</p> <p><strong>Other information</strong></p> <p>The MATLAB scripts listed in the documentation can be found in the file <strong>scripts.zip</strong>.</p> <p><strong>The Authors</strong></p> <p>This work was created by Joachim Thiemann (IRISA-CNRS), Nobutaka Ito (University of Tokyo), and Emmanuel Vincent (Inria Rennes - Bretagne Atlantique). It was supported by Inria under the Associate Team Program <a href="http://versamus.inria.fr">VERSAMUS</a>.</p>
New Alternatives to the Flexibility of Electric Demand
<p>Over the past decades, the role of electric consumers has been increasingly active in terms of their connection with the profile of electricity demand they have. This change in approach has also been supported by the integration of renewable generation sources, which add a certain level of uncertainty to the system.</p>
Fig. 1 in Rapid endangerment of the lizard Podarcis pityusensis by an invasive snake demands an immediate conservation response
Fig. 1. Basic elements of the system. (A) Map showing the location of Ibiza island in relation to the nearest mainland areas in the Iberian Peninsula and northern Africa. (B) The endemic lacertid lizard, Podarcis pityusensis. (C) The invasive colubrid snake, Hemorrhois hippocrepis. Photos by: Javier Pleguezuelos (A), Juan M. Pleguezuelos (B), and Federico Rey (C).
Supported data for manuscript: 'Future hydrogen economies imply environmental trade-offs and a supply-demand mismatch' (DOI: 10.1038/s41467-024-51251-7)
<p>After unpacking the ZIP file, this repository contains the following files:</p> <p><strong>results_lcoh_ecoinvent_391_reference.tif:</strong> grid-specific hydrogen production cost (euro/kg H2) for the reference scenario.</p> <p><strong>results_lcoh_SSP2-Base_2050_base.tif:</strong> grid-specific hydrogen production cost (euro/kg H2) for the baseline scenario in 2050.</p> <p><strong>results_lcoh_SSP2-PkBudg1150_2050_base.tif:</strong> grid-specific hydrogen production cost (euro/kg H2) for the 2°C scenario in 2050.</p> <p><strong>results_lcoh_SSP2-PkBudg500_2050_base.tif:</strong> grid-specific hydrogen production cost (euro/kg H2) for the 1.5°C scenario in 2050.</p> <p><strong>results_ghg_kg_h2_ecoinvent_391_reference.tif:</strong> grid-specific GHG emissions (kg CO2-eq./kg H2) from hydrogen production for the reference scenario.</p> <p><strong>results_ghg_kg_h2_SSP2-Base_2050_base.tif:</strong> grid-specific GHG emissions (kg CO2-eq./kg H2) from hydrogen production for the baseline scenario in 2050.</p> <p><strong>results_ghg_kg_h2_SSP2-PkBudg1150_2050_base.tif:</strong> grid-specific GHG emissions (kg CO2-eq./kg H2) from hydrogen production for the 2°C scenario in 2050.</p> <p><strong>results_ghg_kg_h2_SSP2-PkBudg500_2050_base.tif:</strong> grid-specific GHG emissions (kg CO2-eq./kg H2) from hydrogen production for the 1.5°C scenario in 2050.</p> <p><strong>main_gen_figures.ipynb: </strong>Jupyter Notebook script used to generate the main figures of the manuscript.</p>
MACHINE LEARNING APPROACHES FOR DEMAND FORECASTING: THE IMPACT OF CUSTOMER SATISFACTION ON PREDICTION ACCURACY
<p><span>This study investigates the effectiveness of various machine learning models in predicting product demand based on customer satisfaction data. Four models—Linear Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM)—were evaluated using performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² score. The results indicate that Gradient Boosting achieved the highest accuracy, with an MAE of 2.56, MSE of 12.75, RMSE of 3.57, and R² score of 0.82, effectively capturing the complex, non-linear relationships inherent in customer satisfaction factors. Random Forest also demonstrated strong performance, while Linear Regression and SVM showed limitations in handling intricate datasets. These findings underscore the importance of utilizing advanced machine learning techniques for accurate demand forecasting, highlighting the critical role of customer satisfaction data in enhancing predictive capabilities. The insights gained from this research can guide organizations in optimizing inventory management and improving customer satisfaction in a rapidly evolving market.</span></p>
PECD consistent heat demand and COP time series from when2heat
<p>This dataset uses the when2heat package to compute heat demand and COP time series. The data covers the years 1979 to 2019 and the sub-national level down to a resolution grouping Europe into 96 clusters based on ERA5 reanalysis data. Thanks to this temporal scope and spatial resolution, the data is consistent with the Pan-European Climatic Database (PECD) and enables multi-year analysis of multi-sector energy systems that include the heating sector.</p> <p>The "main data" folder provides the time series with COPs for various heat pump technologies and heat demand. The heat demand data deviates from the immediate when2heat outputs in two ways. First, it aggregates the demand for space heating and warm water in commercial and residential buildings into a single time series. The compositionHeatDemand.xlsx in the "background info" folder provides the weights assumed for this aggregation. Since the composition of heat demand changes in the future, the data includes a different time series for every fifth year from 2015 to 2050. Second, we normalize each profile over the historical years from 1979 to 2019 to its maximum value. Accordingly, multiplying the time series with maximum heat demand will give an absolute demand profile.</p> <p>A subfolder in "background_info" provides shape files describing the different spatial resolutions used. The "mappingRegions.csv" specifies which region names in this dataset correspond to region names in the PECD data. The "heating_thresholds.csv" in the "background info" folder provides the assumed heating thresholds to compute the heating demand.</p>
Normalized profile of industrial demand response
<p>Normalized profile of industrial demand response based on an industrial facility in Austria in 2019</p>
PUDL US Hourly Electricity Demand by State
<p><strong>Hourly Electricity Demand by State</strong></p> <p>This archive contains the output of the <a href="https://catalyst.coop/pudl">Public Utility Data Liberation (PUDL) Project</a> state electricity demand allocation analysis, as of the v0.4.0 release of the PUDL Python package. <a href="https://github.com/catalyst-cooperative/pudl/blob/v0.4.0/src/pudl/analysis/state_demand.py">Here is the script</a> that produced this output. It was run using the Docker container and processed data that are included in <a href="https://doi.org/10.5281/zenodo.5214231">PUDL Data Release v2.0.0</a>.</p> <p>The analysis uses hourly electricity demand reported at the balancing authority and utility level in the FERC 714 (<a href="https://doi.org/10.5281/zenodo.4127101">data archive</a>), and service territories for utilities and balancing authorities inferred from the counties served by each utility, and the utilities that make up each balancing authority in the EIA 861 (<a href="https://doi.org/10.5281/zenodo.4127029">data archive</a>), to estimate the total hourly electricity demand for each US state.</p> <p>We used the total electricity sales by state reported in the EIA 861 as a scaling factor to ensure that the magnitude of electricity sales is roughly correct, and obtains the shape of the demand curve from the hourly planning area demand reported in the FERC 714. The scaling is necessary partly due to imperfections in the historical utility and balancing authority service territory maps which we have been able to reconstruct from the data reported in the EIA 861 Service Territories and Balancing Authority tables.</p> <p>The compilation of historical service territories based on the EIA 861 data is somewhat manual and could be improved, but overall the results seem reasonable. Additional predictive spatial variables will be required to obtain more granular electricity demand estimates (e.g. at the county level).</p> <p><strong>FERC 714 Respondents</strong></p> <p>The file <code>ferc714_respondents.csv</code> links FERC Form 714 respondents to what we believe to be their corresponding EIA utilities or balancing authorities.</p> <ul> <li><code>eia_code</code>: An integer ID reported in the FERC Form 714 corresponding to the respondent's EIA ID. In some cases this is a Utility ID, and in others it is a Balancing Authority ID, but which is not specified and so we have had to infer the type of entity which is responding. Note that in many cases the same company acts as both a utility and a balancing authority, and the integer ID associated with the company is often the same in both roles, but it does not need to be.</li> <li><code>respondent_type</code>: Either <code>balancing_authority</code> or <code>utility</code> depending on which type of entity we believe was responding to the FERC 714.</li> <li><code>respondent_id_ferc714</code>: The integer ID of the responding entity within the FERC 714.</li> <li><code>respondent_name_ferc714</code>: The name provided by the respondent in the FERC 714.</li> <li><code>balancing_authority_id_eia</code>: If the respondent was identified as a balancing authority, the EIA ID for that balancing authority, taken from the EIA Form 861.</li> <li><code>balancing_authority_code_eia</code>: If the respondent was identified as a balancing authority, the EIA short code used to identify the balancing authority, taken from the EIA Form 861.</li> <li><code>balancing_authority_name_eia</code>: If the respondent was identified as a balancing authority, the name of the balancing authority, taken from the EIA Form 861.</li> <li><code>utility_id_eia</code>: If the respondent was identified as a utility, the EIA utility ID, taken from the EIA Form 861.</li> <li><code>utility_name_eia</code>: If the respondent was identified as a utility, the name of the utility, taken from the EIA 861.</li> </ul> <p><strong>FERC 714 Respondent Service Territories</strong></p> <p>The file <code>ferc714_service_territories.csv</code> describes the historical service territories for FERC 714 respondents for the years 2006-2019. For each respondent and year, their service territory is composed of a collection of counties, identified by their 5-digit FIPS codes. The file contains the following columns, with each row associating a single county with a FERC 714 respondent in a particular year:</p> <ul> <li><code>respondent_id_ferc714</code>: The FERC Form 714 respondent ID, which is also found in <code>ferc714_respondents.csv</code></li> <li><code>report_date</code>: The first day of the year for which the service territory is being described.</li> <li><code>state</code>: Two letter abbreviation for the state containing the county, for human readability.</li> <li><code>county</code>: The name of the county, for human readability.</li> <li><code>state_id_fips</code>: The 2-digit <a href="https://en.wikipedia.org/wiki/Federal_Information_Processing_Standard_state_code">FIPS state code</a>.</li> <li><code>county_id_fips</code>: The 5-digit <a href="https://en.wikipedia.org/wiki/FIPS_county_code">FIPS county code</a> for use with other geospatial data resources, like the <a href="https://www.census.gov/geographies/mapping-files/2010/geo/tiger-data.html">US Census DP1 geodatabase</a>.</li> </ul> <p><strong>State Hourly Electricity Demand Estimates</strong></p> <p>The file <code>demand.csv</code> contains hourly electricity demand estimates for each US state from 2006-2019. It contains the following columns:</p> <ul> <li><code>state_id_fips</code>: The 2-digit FIPS state code.</li> <li><code>utc_datetime</code>: UTC time at hourly resolution.</li> <li><code>demand_mwh</code>: Electricity demand for that state and hour in MWh. This is an allocation of the electricity demand reported directly in the FERC Form 714.</li> <li><code>scaled_demand_mwh</code>: Estimated total electricity demand for that state and hour, in MWh. This is the reported FERC Form 714 hourly demand scaled up or down linearly such that the total annual electricity demand matches the total annual electricity sales reported at the state level in the EIA Form 861.</li> </ul> <p>A collection of plots are also included, comparing the original and scaled demand time series for each state.</p> <p><strong>Acknowledgements</strong></p> <p>This analysis was funded largely by <a href="https://gridlab.org/">GridLab</a>, and done in collaboration with researchers at the Lawrence Berkeley National Laboratory, including <a href="https://eta.lbl.gov/people/umed-paliwal-0">Umed Paliwal</a> and <a href="https://eta.lbl.gov/people/nikit-abhyankar">Nikit Abhyankar</a>.</p> <ul> <li><a href="https://scholar.google.com/citations?user=q_rBGYgAAAAJ&hl=en">Ethan Welty</a> wrote the final code and most of the algorithms.</li> <li><a href="https://www.linkedin.com/in/yash-kumar/">Yash Kumar</a> did initial data explorations and geospatial analyses.</li> </ul> <p>The data screening methods were originally designed to identify unrealistic data in the electricity demand timeseries reported to EIA on Form 930, and have been applied here to data form the FERC Form 714.</p> <p>They are adapted from code published and modified by:</p> <ul> <li><a href="mailto:truggles@carnegiescience.edu">Tyler Ruggles</a></li> <li><a href="mailto:greg@carbonimpact.co">Greg Schivley</a></li> </ul> <p>And described at:</p> <ul> <li><a href="https://doi.org/10.1038/s41597-020-0483-x">Developing reliable hourly electricity demand data through screening and imputation</a></li> <li><a href="https://doi.org/10.5281/zenodo.3737085">EIA Cleaned Hourly Electricity Demand Code</a> (Zenodo)</li> <li><a href="https://github.com/truggles/EIA_Cleaned_Hourly_Electricity_Demand_Code">EIA Cleaned Hourly Electricity Demand Code</a> (GitHub)</li> </ul> <p>The imputation methods were designed for multivariate time series forecasting.</p> <p>They are adapted from code published by:</p> <ul> <li>Xinyu Chen <a href="mailto:chenxy346@gmail.com">chenxy346@gmail.com</a></li> </ul> <p>And described at:</p> <ul> <li><a href="https://arxiv.org/abs/2006.10436">Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting</a></li> <li><a href="https://arxiv.org/abs/2008.03194">Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation</a></li> <li><a href="https://github.com/xinychen/tensor-learning">Tensor Learning (张量学习)</a></li> </ul> <p><strong>About PUDL & Catalyst Cooperative</strong></p> <p>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://readthedocs.org/projects/catalystcoop-pudl/">The PUDL Documentation</a></li> <li><a href="https://zenodo.org/communities/catalyst-cooperative/">Other Catalyst Cooperative data archives on Zenodo</a></li> </ul>
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.