Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,902
datasets available to search
ShareScore release 0.7.1
Dataset results
1,902 results for “consumption”
Mining API Interactions to Analyze SoftwareRevisions for the Evolution of Energy Consumption (MSR'2021 Dataset)
<p><strong>Motivation</strong></p> <p>This repository contains the data-set used as a basis for our MSR'2021 paper <em>Mining API Interactions to Analyze Software Revisions for the Evolution of Energy Consumption</em>.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset is stored in a file <em>msr_2021_dataset.csv</em> and contains the following data:</p> <ul> <li>id - an individual identifier</li> <li>sampleNr - a number identifying the group this sample relates to</li> <li>name - the name of the library examined</li> <li>className - the class name as an abbreviation</li> <li>method - the name of the executed method</li> <li>duration - duration of method execution</li> <li>durationAdjusted - duration after alignment between method trace and energy profile</li> <li>energyConsumption - computed energy consumption</li> <li>watts - recorded wattage</li> <li>`package-names` - per package uAPI profile</li> <li>uApi - the computed uAPI profile value</li> </ul> <p>The files <em>joule_anova_posthoc_result.csv</em> and <em>uAPI_anova_posthoc_result.csv</em> contain the results of the ANOVA and Tukey HSD posthoc analysis to determine accuracy and F1-score of the presented approach.</p> <p> </p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p>
Optimised household consumption profiles through a smart building energy mangement system TABEDE
<p>In the context of the TABEDE project (<a href="https://www.tabede.eu/">https://www.tabede.eu/</a>) several synthetic profiles simulating the consumption and generation of residential buildings, whose appliances were controlled by our proposed Energy Management System (i.e., the TABEDE solution), were simulated. Their construction process was characterised by the following:</p> <ul> <li>Consumption profiles were generated via a bottom-up approach capable of emulating the consumption of individual household appliances. These last ones correspond to the most used appliances in the UK, which were randomly distributed among the buildings based on their average utilisation rate and ownership observed in residential buildings in the country.</li> <li>The physics in terms of heat exchange between neighbouring buildings and the environment were considered, together with the size of the buildings and their physical characteristics. A total of 66 houses and apartments, according to 8 type or building archetypes were created.</li> <li>PV generation profiles were generated according to the meteorological condition of the simulated day.</li> </ul> <p>Together with this, the profiles feature how the TABEDE solution optimised the flexible part of the consumption (i.e., appliances that were controllable by the solution and whose consumption could be shifted in time without sacrificing user comfort) to minimize the electricity bill of the buildings.</p> <p>The information contained in the actual database features the following variables:</p> <ul> <li>TABEDE penetration: percentage of buildings owning the TABEDE solution. Buildings with TABEDE will observe their flexible consumption being optimised.</li> <li>PV penetration: percentage of buildings with a PV system installed on them.</li> <li>Simulation day: one day in summer (19/06/2019) featuring the highest solar radiation of the year, and a day in winter (19/12/2019) with the lowest.</li> <li>Batteries: whether the PV systems is installed alongside household batteries.</li> </ul> <p>Details on the formulation can be found in: <a href="https://urldefense.com/v3/__https:/www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/__;!!La4veWw!khYBEaeJY85mX5yQUrp0PwoXcg5U10dEdgZ296hONYGyBS5xg91Z8MoDUQy34a4f9Lo$">https://www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/</a></p>
Projected Global Fertilizers Consumption Datasets during 2020-2100 under SSP scenarios
<h1><strong>1. Background</strong></h1> <p>Accurate projections of future global fertilizer consumption are critical for advancing research in earth system modeling, agricultural sustainability, and fertilizer industry planning. However, existing datasets often lack long-term temporal coverage and high spatial resolution. To address this gap, we present the <strong>Projected Global Fertilizers Consumption Datasets (PGFCD)</strong>, which provide spatially explicit estimates of nitrogen (N), phosphorus (P), and potassium (K) fertilizer consumption from 2020 to 2100 under three Shared Socioeconomic Pathway (SSP) scenarios: <strong>SSP1</strong> (sustainable development), <strong>SSP2</strong> (intermediate development), and <strong>SSP3</strong> (regional rivalry), <strong>SSP4</strong> (unequal development), and <strong>SSP5</strong> (fossil-fueled development).</p> <p> </p> <h1><strong>2. Methodology</strong></h1> <h2><strong>2.1 Ensemble Machine Learning (EML) Framework</strong></h2> <ul> <li><strong>Algorithms</strong>: Integrated six machine learning models: <ul> <li>Multiple Linear Regression (MLR)</li> <li>Decision Trees (DT)</li> <li>Autoregressive Integrated Moving Average (ARIMA)</li> <li>Multi-Layer Perceptron (MLP)</li> <li>Radial Basis Function (RBF)</li> <li>Random Forests (RF)</li> </ul> </li> <li><strong>Training Data</strong>: Historical national/regional fertilizer consumption (FAOSTAT, 1961–2015).</li> <li><strong>Validation Metrics</strong>: <ul> <li>Nash-Sutcliffe Efficiency (NSE): <strong>0.93</strong></li> <li>Kling-Gupta Efficiency (KGE): <strong>0.89</strong></li> <li>Mean Absolute Percentage Error (MAPE): <strong>10.97%</strong></li> </ul> </li> </ul> <h2><strong>2.2 Spatial Downscaling</strong></h2> <ul> <li><strong>Baseline</strong>: FAO 2000 fertilizer data combined with gridded nutrient application maps for major crops in 2000.</li> <li><strong>Dynamic Projection</strong>: Annual change rates applied to 5′ × 5′ grids under SSP-specific socioeconomic drivers.</li> </ul> <p> </p> <h1><strong>3. Dataset Overview</strong></h1> <h2><strong>3.1 Key Features</strong></h2> <ul> <li><strong>Temporal Coverage</strong>: 2020–2100 (10-year intervals).</li> <li><strong>Spatial Resolution</strong>: 5-arcminute (≈10 km at the equator).</li> <li><strong>Scenarios</strong>: SSP1, SSP2, SSP3, SSP4, SSP5. </li> <li><strong>Variables</strong>: N, P, and K fertilizer consumption (tonnes/year).</li> </ul> <h2><strong>3.2 Dataset Structure</strong></h2> <p>The dataset is provided as a compressed archive (PGFCD_Ver6.0.rar), containing:</p> <p><strong>1. Fertilization_Consumption_GeoTiff</strong><strong>/</strong></p> <ul> <li><strong>Subfolders</strong>: <ul> <li>N_fer/: Nitrogen fertilizer projections</li> <li>P_fer/: Phosphorus fertilizer projections</li> <li>K_fer/: Potassium fertilizer projections</li> </ul> </li> <li><strong>File Format</strong>: GeoTIFF (135 files total). <ul> <li><strong>Naming Convention</strong>:<br>[FertilizerType]_fer_con_[SSP]_[Year].tif <ul> <li>Example: K_fer_con_SSP1_2020.tif</li> </ul> </li> </ul> </li> </ul> <p><strong>2. Country_region_based/</strong></p> <ul> <li><strong>Shapefiles</strong>: National/regional fertilizer consumption for 26 prediction units (2020–2100).</li> <li><strong>Excel File</strong>: Global Fertilizer Consumption (2020-2100).xlsx.</li> </ul> <p><strong>3. Technical Annex.docx</strong></p> <ul> <li>Detailed methodology, validation, and workflow documentation.</li> </ul> <p> </p> <h1><strong>4. Applications</strong></h1> <p>This dataset supports:</p> <ul> <li><strong>Earth System Modeling</strong>: Improved parameterization of fertilization impacts on biogeochemical cycles.</li> <li><strong>Agricultural Policy</strong>: Scenario-based planning for sustainable fertilizer use.</li> <li><strong>Industry Strategy</strong>: Long-term market analysis under diverse socioeconomic pathways.</li> </ul> <p> </p> <h1><strong>Note:</strong></h1> <p>Global aggregated totals of fertilizer consumption derived from the 26 prediction units (country/regional scale) may exhibit minor discrepancies compared to sums calculated from the 5-arcminute gridded data (≈10 km resolution). Such differences stem from variations in spatial aggregation methods, file formats (vector vs. raster), and underlying data processing frameworks. Users may select the dataset best aligned with their analytical objectives:</p> <ul> <li>The <strong>country/region-based data (26 units)</strong> is recommended for national-scale analyses or policy evaluations requiring administrative boundaries.</li> <li>The <strong>5-arcminute gridded data</strong> is preferable for spatially explicit modeling or subnational assessments.</li> </ul> <p>Both datasets maintain equivalent quality and methodological rigor; the choice depends on the desired spatial granularity and application context.</p> <p>We are profoundly indebted to <strong>Dr. Andreas Gericke</strong> at Section II 2.3 Protection of the Seas and Polar Regions, German Environment Agency, and <strong>Dr. Veronika Schlosser</strong> at Chair of Sustainability Assessment of Food and Agricultural Systems, Technical University of Munich for their diligent review and insightful feedback on the previously submitted data. Their expertise has enabled us to thoroughly correct the identified inaccuracies, strengthening the integrity of our research.</p> <p>We hold <strong>Dr. Andreas Gericke</strong> and <strong>Dr. Veronika Schlosser</strong> in the highest esteem and sincerely apologize for any oversights that may have marred our work.</p>
Towards sustainable food consumption: reference list
<p>This dataset includes all the bibliographic references from the SAPEA evidence review report 'Towards a sustainable food system'.</p>
Figure: Occupational Fatality and Health Metrics within EU Consumption and Various Supply Chain Accounting Frameworks
<p><span><strong>Occupational Fatality and Health Metrics within EU Consumption and Supply Chain Contexts.</strong> Directly taken from (Koundouri et al., 2023) and reproduced with the authors' permission. </span><span>It displays in Figure A the</span><span> work-related fatal occupational injuries tied to goods finally consumed within the EU (Consumption Based Accounting -CBA- framework) and those within supply chains passing through the EU (Throughflow Based Accounting -TBA- framework) </span><span><span>(Beaufils et al., 2023) and</span></span><span> the results denote fatalities. It also displays in Figure B the D<span>isability-Adjusted Life Years</span><em><span> (</span></em></span><span>DALYs) associated with asbestos, asthmagen, and chromium-related occupational fatalities linked to European goods consumption (CBA) and traversing supply chains (TBA). Last, in Figure C, it provides</span><span> a comparative breakdown for each commodity from panels A and B, illustrating proportions by accounting framework (TBA vs. CBA).</span></p> <p><span>This figure put forward that despite potential barriers to target direct import intervention, optimized supply chain management can markedly reduce occupational fatalities related to global value chains passing through EU. As such, ILO frameworks such as Occupational Safety and Health Convention, 2006 (No. 187) </span><span><span>(International Labour Organization, 2006)</span></span><span>, and Occupational Safety and Health Convention, 1981 (No. 155) </span><span><span>(International Labour Organization, 1981)</span></span><span> offers a path to significant fatality reductions</span></p>
Dataset: The effects of class balance on the training energy consumption of logistic regression models
<p>Two synthetic datasets for binary classification, generated with the Random Radial Basis Function generator from WEKA. They are the same shape and size (104.952 instances, 185 attributes), but the "balanced" dataset has 52,13% of its instances belonging to class c0, while the "unbalanced" one only has 4,04% of its instances belonging to class c0. Therefore, this set of datasets is primarily meant to study how class balance influences the behaviour of a machine learning model.</p>
Dataset for: Oxygen isotope fractionation of O2 consumption through abiotic photochemical singlet oxygen formation pathways
<p>Dataset containing raw and treated isotope-ratio mass spectrometry data as well as O2 concentration data, accompanying the manuscript "Oxygen isotope fractionation of O2 consumption through abiotic photochemical singlet oxygen formation pathways".</p>
CafeteriaFCD corpus: Food consumption data annotated with regard to different food semantic resources
<p>The FoodBase curated version which contains 1,000 manually evaluated recipes, annotated with the appropriate semantic tags from the Hansard Taxonomy, FoodON and SNOMED-CT.</p>
Demonstration Cases - Simulation data of energy consumption of residential building typologies
<p>The dataset is about the energy analysis for retrofit strategies of 5 building typologies and the EDEA project located in 3 climates zones in Europe: South (Madrid), Central (Berlin) and North (Helsinki).<br> The dataset includes:<br> (1) Open Document Spreadsheet (.ods) file with the results of Heating Consumption (kWh/m2·year) and Cooling Consumption (kWh/m2·year) for the five buildings, in three locations and for several scenarios:<br> - Locating external new insulation in walls and roof.<br> - Replacing Windows.<br> - Combination strategies: locating new insulation layers and replacing the existing windows.<br> - Installing solar protection devices.</p>
Dataset: energy consumption patterns in a science and technology park
<p>This dataset contains time series of energy consumption and external temperature for a group of buildings in a science and technology park, from 2018 to 2022, that is suitable for the development of algorithms to improve energy efficiency and for the early detection of energy consumption peaks based on night-time outdoor temperatures.</p> <p>Time series of power, energy consumption and external temperature for group of tertiary buildings, from 2018-01-01 to 2022-09-15.</p> <p>Peaks in electricity consumption are a major concern for building owners, especially during summer, when external temperatures are high, and users demand air conditioning. Owners may face high costs, observe increased risk of overheating in energy intensive equipment, and may exceed the power threshold set out in the electricity supply contract.</p> <p>Several effective "peak-shaving" strategies can be put in place, such as a higher temperature set-point (which implies a temporary reduction of comfort levels), switching off low-priority processes, and starting the cooling process earlier than usual.</p> <p>This dataset can be used to develop algorithms for early detection of peaks in energy consumption, based on external temperatures measured during the night.</p> <p> </p>
A Systematic Review on Techniques and Approaches to Estimate Mobile Software Energy Consumption (SUSCOM Dataset)
<p>Dataset and replication data for the systematic review entitled "A Systematic Review on Techniques and Approaches \\to Estimate Mobile Software Energy Consumption".</p>
Survey data on behaviours and attitudes towards green food consumption of participants of the SmartFood Urban Living Lab in Warsaw, Poland
<p>In this dataset, we present raw data of a survey on behaviours and attitudes towards green food consumption, conducted between June 2023 and April 2024 among a group of 21 households from Warsaw, participating in a SmartFood Urban Living Lab (ULL). The dataset is complemented with results collected from two control groups. The SmartFood Urban Living Lab was an intervention aimed at providing residents of urban blocks of flats with a novel technology for growing their own food. The ULL served as an experimental ground for testing and refining innovations such as hydroponic cabins, rainwater management systems, solar energy systems, and insect farming units. Residents actively participated in the lab, providing valuable insights into the practical challenges and benefits of urban farming, which helped refine and adapt the technologies for broader application. After each month of the intervention, a survey was conducted to check participants' behaviours and attitudes towards green food consumption</p>
21 coffee makers energy consumption dataset
<p>This dataset represents the use of 21 coffee machines over time, with each line representing an energy consumption event of a specific machine.</p>
Replication data for: "How does Docker affect energy consumption? Evaluating workloads in and out of Docker containers"
<p>Database of raw power measurements and energy summaries for our Docker energy tests.</p> <p>Please cite us if you use this dataset.</p> <p>Schema</p> <pre><code>CREATE TABLE configuration( name TEXT PRIMARY KEY, description TEXT ); CREATE TABLE experiment( name TEXT PRIMARY KEY, description TEXT ); CREATE TABLE run( id PRIMARY KEY, configuration TEXT REFERENCES configuration(name) ON DELETE CASCADE ON UPDATE CASCADE, experiment TEXT REFERENCES experiment(name) ON DELETE CASCADE ON UPDATE CASCADE ); CREATE TABLE measurement( run REFERENCES run(id) ON DELETE CASCADE ON UPDATE CASCADE, timestamp REAL NOT NULL, -- Unix timestamp in milliseoncds power REAL NOT NULL ); CREATE TABLE energy( id PRIMARY KEY REFERENCES run(id), configuration TEXT REFERENCES configuration(name) ON DELETE CASCADE ON UPDATE CASCADE, experiment TEXT REFERENCES experiment(name) ON DELETE CASCADE ON UPDATE CASCADE, energy REAL NOT NULL, started REAL NOT NULL, ended REAL NOT NULL, elapsed_time REAL NOT NULL -- in milliseconds );</code></pre>
Electricity consumption and real vaue-added data for the Swiss and Genevan secondary and tertiary sectors.
<p>This file contains datasets of electricity consumption and real value added (base year 2000) in the secondary and tertiary sector for Switzerland and Geneva for the years between 2000 and 2015. The structure of the Swiss and Genevan datasets has been matched to ensure comparability of results from index decomposition analyses conducted on each region. It also contains heating and cooling degrees for both Switzerland and Geneva.</p>
Dataset on consumer acceptance of policy measures for sustainable food consumption
<p>This data was obtained from an online survey conducted with Swiss participants from the German-speaking parts of Switzerland. The survey was conducted in February 2023. Participants were recruited through a professional panel provider and quotas were used for age and gender. The final sample contained 453 respondents. In the first part of the survey, respondents provided basic sociodemographic information and reported how often they consumed meat and dairy products. In the second part, their knowledge regarding the environmental impact of food was assessed using an existing scale by Hartmann et al. (2021). Next, respondents indicated for 19 different policy measures how much they agreed with each of them. Finally, respondents answered several questions assessing their health consciousness and environmental attitudes using existing scales. The survey can be used and adapted to different contents, aiming to investigate public acceptance of policy measures for sustainable food consumption.</p>
Dataset related to the Journal Article 'A deep learning method for the prediction of ship fuel consumption in real operational conditions'
<p>This dataset contains the data used to plot the graphs and create tables corresponding to the figure/table number in the published version of the paper.<br>Paper DOI:https://doi.org/10.1016/j.engappai.2023.107425</p> <p> </p>
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>
Danish case study simulation results (consumption, offered & accepted flexibility, revenues & expenses)
<p>This file contains results of the simulations of the Danish case study generated during Horizon 2020 MAGNITUDE project. The case study considers the provision of domestic hot water from 350 heat pumps installed in multi-storey apartment buildings. Heat pumps receive energy from electrical grid and use heat from ultra-low temperature district heating (ULTDH) as heat source. The overall system is referred to as Multi-Energy System (MES). MES can provide flexibility to either intra-day (ID) market or redispatch (RE) services to local distribution system operator. Results show how much flexibility was offered and activated, and the resulting consumption and production profiles due to that. In addition, the revenues and expenses based on the historical prices for Denmark in 2018 had been calculated.</p>
Coffee Consumption per Capita and Covid-19 Mortality Rate
<p>There is a correlation between average of "Coffee Consumption per capita" and average of "Covid-19 Mortality Rate" for countries with high coffee consumption per capita (the countries that has more than 5.4 kg per capita per year consumption).</p> <p>The Details of computations and data are provided in an attached supplementary file (Excel File Format).</p> <p>Data gathered on 10 Aug 2021</p> <p> </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.