Find research datasets worth reusing

opencc-by-4.0Sep 2022View details →

HPC-JEEP: Energy Usage on ARCHER2 and the DiRAC COSMA HPC services dataset

This package contains the data and tools used to analyse the energy use on the ARCHER2 and DiRAC COSMA UK HPC facilities. This analysis was performed as part of the HPC-JEEP project. HPC-JEEP is funded by the UKRI DRI Net Zero Scoping project.

How much data about the data centres' energy footprint is currently available globally?

By scraping the open database "Datacenter.rs", the candidate analysed and mapped more than 6.000 data centres worldwide on April 9, 2022.

opencc-by-4.0Sep 2022View details →

How do Google News' top 100 sources visually represent the data centres' energy footprint?

By querying "data centres' energy footprint" on Google News in incognito mode, the candidate has selected and mapped the top 100 results according to the ranking on May 15, 2022.

A Systematic Review on Techniques and Approaches to Estimate Mobile Software Energy Consumption (SUSCOM Dataset)

Dataset and replication data for the systematic review entitled "A Systematic Review on Techniques and Approaches \\to Estimate Mobile Software Energy Consumption".

Hourly LC impacts - Primary Non-renewable energy - current mix and future scenarios, average demand

Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Primary Non-renewable Energy, average demand perspective. Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".

Estimating surface water availability in high mountain rock slopes using a numerical energy balance model

Model output, forcing data and physical parameters used to estimate water and energy balance. The model was calibrated with field measurements from a study site in the Mont-Blanc massif, at 3842 m a.s.l, at a slope of 55 deegrees and aspect azimut of 150 degrees (south-east). The different ModelOutput files are from simulations at  different elevastions (from 4800 m to 2700 m at steps of 300 m). We used the CryoGrid community model (version 1.0) toolbox (Westermann et al., 2022) to simulate the 1D ground thermal regime and ice/water balance, and estimate the availability of surface water and its potential for infiltration in rock fractures. The S2M-SAFRAN dataset combines output from a numerical weather prediction model and in situ observations, and was originally developed for operational needs to estimate avalanche hazard in mountainous areas (Durand et al., 1993). The S2M-SAFRAN dataset that we used is available for various mountain areas, at elevation steps of 300 m, and with an hourly resolution between the years 1958 to 2021 (Vernay et al., 2022). It includes most parameters that are required for modeling with CryoGrid: Relative humidity, air T, incoming long wavelength radiation, incoming short wavelength solar radiation, and wind speed. To complete the forcing data we used top of the atmosphere incident solar radiation from ERA5 global reanalysis dataset (Hersbach et al., 2020).

opencc-by-4.0Aug 2017View details →

Historical Annual Revenue of Energy Storage on European Electricity Markets

This dataset provides the optimized annual revenue for 96 generic storage technologies (12 efficiency x 8 discharge duration).  It covers 17 European electricity markets for up to 16 years (Austria, AT; Belgium, BE;  Switzerland, CH; Czech Republic, CZ; German, DE; Spain, ES; France, FR; Italy, IT; Ireland, IR; Netherlands, NL; Nordpool which includes Denmark, Estonia, Finland, Latvia, Lithuania, Norway, Sweden, NP; Poland, PL; Portugal, PT; Romania, RO; Slovakia, SK; United Kingdom, UK). The optimization is an adaptation of the model presented in Gaudard et al. [2013]. It assumes perfect foresight assumption and a stochastic algorithm. Therefore, the results are an approximation of the maximum rather than the absolute optimum. Further information is provided in "Gaudard L. and Madani K., Energy storage race: Has the monopoly of pumped-storage in Europe come to an end?, forthcoming".  The following information is provided: Country: Code of the specific market (also the filename) Currency: The currency in which the results are expressed Discharge duration [hours]: The time required to empty at full nominal power a device that is fully charged.  Efficiency: Ratio between the amount of discharged and charged energy during a full cycle. Year: From January 1st to December 31st. The given numbers are in euros or GBP per year and normalized to 1kWh of energy storage. This means that for a specific device, the given figures must be multiplied by the volume of energy storage (in terms of kWh). As an example, for an energy device with the efficiency of 0.95, discharge duration of 6h and volume of energy storage of 2000kWh, the revenues in 2003 in Austria would be 9.26 x 2000=18520 euros.  For any questions or further requirements, please feel free to get in touch with the authors.

opencc-by-4.0Mar 2024View details →

A data set on "Utilizing Constant Energy Difference between sp-Peak and C 1s Core Level in Photoelectron Spectra for Unambiguous Identification and Quantification of Diamond Phase in Nanodiamonds"

The data set to paper:  Utilizing Constant Energy Difference between sp-Peak and C 1s Core Level in Photoelectron Spectra for Unambiguous Identification and Quantification of Diamond Phase in Nanodiamonds Oleksandr Romanyuk1,*, &Scaron;těpán Stehlík1,2, Josef Zemek1, Kateřina Aubrechtová Dragounová1,3 and Alexander Kromka1 1 Institute of Physics of the Czech Academy of Sciences, Cukrovarnická 10, 162 00 Prague, Czech Republic 2 New Technologies—Research Centre, University of West Bohemia, Univerzitní 8, 306 14 Pilsen, Czech Republic 3 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, 115 19 Prague, Czech Republic * corresponding author: romanyuk@fzu.cz Data manager: Kristýna Dostálová: dostalovak@fzu.cz Date of data collection: 1. 1. 2024 - 15. 03. 2024 All the data showed in the pictures are provided in X-Y format with described sample. Always, the respective Figure to which the data belong is provided in high resolution.  The data are in the following formats:  Figure 1: tiff, csv Figure 2: tiff, csv Figure 3: tiff, csv Figure 4: tiff, csv Figure 5: tiff, csv Data acquistion and processing is provided in the Experimental part in the publication: DOI:10.3390/nano14070590

Result data related to Tröndle et al (2024): Rebuilding Ukraine's energy supply in a secure, economic, and decarbonised way

This dataset contains the result data of all the scenarios ran in the scientific article "Rebuilding Ukraine’s energy supply in a secure, economic, and decarbonised way". The results of the main five scenarios of the study are available as PyPSA result files: <ul> <li>nuclear-and-renewables-high.nc: A scenario with nuclear in the mix and high economic growth assumption.</li> <li>nuclear-and-renewables-low.nc: A scenario with nuclear in the mix and low economic growth assumption.</li> <li>only-renewables-high-low-bio.nc: A scenario with only renewables, high economic growth assumption, and only 10% of assumed biomass potential.</li> <li>only-renewables-high.nc: A scenario with only renewables and high economic growth assumption.</li> <li>only-renewables-low.nc: A scenario with only renewables and low economic growth assumption.</li> </ul> See the PyPSA documentation for more information: <a href="https://pypsa.readthedocs.io" target="_blank" rel="noopener">https://pypsa.readthedocs.io</a>. The results of the 330 global sensitivity analysis runs are available as summary files in CSV format: <ul> <li>gsa-capacities-energy-gwh.csv: The installed energy storage capacities for each scenario.</li> <li>gsa-capacities-power-gw.csv: The installed generation capacities for each scenario.</li> <li>gsa-lcoe.csv: The levelised cost of electricity for each scenario.</li> </ul>

Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation

This data archive provides simulated hourly heating and cooling building energy demand for current and future RCP85 climate for 8 representative cities for a single-family and small office building archetype. The data forms part of the following publication: Eggimann S.; Fiorentini M. (2024): Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2024.114348 Attributes <ul> <li>ID_origin: City ID of source city</li> <li>ID_destination: City ID of target city</li> <li>Signature_Cooling: Cooling demand determined by the signature approach</li> <li>Model_Cooling: Cooling demand determined by EnergyPlus</li> <li>Absolute_Diff: Absolute difference</li> <li>Percentage_Diff: Relative difference</li> <li>Daily_Tout: Average daily dry-bulb ambient temperature</li> </ul> Instruction To obtain the simulation and energy signature-based results, it is required to filter the dataset and set the source ID to the destination ID. The city IDs are provided in the file city_table_ID. Source The archetypes are provided by the Office of Energy Efficiency & Renewable Energy: https://www.energycodes.gov/prototype-building-models

1 million cMSSM parameter space points with low-energy predictions from SPheno and MicrOMEGAs

This dataset was produced and used in the paper <a href="https://arxiv.org/abs/2405.18471">Symbolically Regressing Beyond the Standard Model Physics</a>. The code used to generate and to analyse these data can be found <a href="https://gitlab.com/miguel.romao/symbolic-regression-bsm">here</a>. The dataset specifications: <ul> <li>Randomly sampled 1 million points of the cMSSM parameter space and respective low-energy observables.</li> <li>Low-energy observables computed using using `SPheno` and `MicrOMEGAs`. <ul> <li>Only points that produced `SPheno` output and neutral LSP are processed by `MicrOMEGAs`.</li> <li>The dataset includes all points, even if they are "unphysical", i.e. points without `SPheno` output or neutral LSP. In the paper, this was used to train a classifier to filter out "unphysical" points.</li> </ul> </li> <li>The columns are <ul> <li>'m0', 'm12', 'A0', 'tanb': the four physical parameters of the theory sampled in the priori <ul> <li>'m0': [0, 10] TeV</li> <li>'m12': [0, 10] TeV</li> <li>'A0': [-60,60] TeV</li> <li>'tanb': [1.5,50]</li> <li>The sign of the 'mu' parameter was fixed to positive (+1)</li> </ul> </li> <li>'idx': an utility identifier used during generation, can/should be ignored</li> <li>Flattened `SPheno` outputs. These are obtained by reading the resulting slha spectrum file outputted by SPheno and flatten the blocks. For example from the 'MINPAR' block, the key-value pairs are given by the columns  'MINPAR_1', 'MINPAR_2',  'MINPAR_3',  'MINPAR_4', 'MINPAR_5', and likewise for all blocks in the slha file.</li> <li>`MicrOMEGAs` outputs. These inlcude: 'dm_Omega', 'dm_spin', 'dm_candidate`, `mo_output`, `dm_c_{bino,wino,higgsino1,higgsino2}`, which are, respectively: dark matter relic density value, dark matter candidate spin, dark matter candidate, the whole `MicrOMEGAs` output, and the coefficient of  `{bino,wino,higgsino1,higgsino2}` components of the dark matter state.</li> </ul> </li> </ul> Versions: <ul> <li>SPheno 4.0.5, with a patch to output a warning when the LSP is charged. This version can be found <a href="https://gitlab.com/lip_ml/blackboxbsm">here</a>.</li> <li>MicrOMEGAs 5.3.41, with the MSSM model adapted for low-scale slha inputs.</li> </ul> The datasets are provided in <a href="https://parquet.apache.org/">Apache `parquet`</a> format. In order to read them using `pandas`, an installation with the optional flag `[parquet]` should be used. Alternatively, one can use <a href="https://arrow.apache.org/docs/python/index.html">`pyarrow`</a>.

opencc-by-nc-4.0May 2024View details →

Solar Asset Mapper: A continuously-updated global inventory of solar energy facilities built with satellite data and machine learning

TransitionZero’s Solar Asset Mapper is a global, satellite-derived dataset of utility-scale solar farms generated with a combination of machine learning and human annotation. Our Q1 2024 dataset contains the location and shape of 63,616 assets, along with estimated capacities. We estimate the construction date for over 80% of these assets. The dataset contains over 19,100 square kilometres of solar farms across 183 countries, with a total estimated capacity of 711 GW. Download the dataset, read the explainer and explore our polygon browser UI at <a href="https://www.transitionzero.org/products/solar-asset-mapper" target="_blank" rel="noopener">TransitionZero.org.</a> <a href="https://blog.transitionzero.org/hubfs/Data%20Products/TZ-SAM/tz-sam-scientific-methodology-Q12024.pdf" target="_blank" rel="noopener">Download our methodology paper here </a> <h1>1. Dataset Description</h1> We publish six files. <ul> <li>analysis_polygons.gpkg: our “analysis-ready” dataset containing geometries, capacity estimates and construction date estimates.</li> <li>analysis_polygons.csv: a version of analysis_polygons.gpkg containing a central latitude and longitude in place of a geometry, to allow parsing without geospatial software.</li> <li>sources.csv: a table mapping the IDs of our analysis-ready dataset to the raw geometries that make them up.</li> <li>raw_polygons.gpkg: the raw geometries used to compose analysis_polygons.gpkg.</li> <li>TZ Solar Asset Mapper Q1 2024.xlsx: an Excel formatted version of the analysis_polygons.csv file.</li> <li>tz-sam_scientific_data.pdf: A pre-print aricle that explains the methodology in detail.</li> </ul> <h2>1.1 Analysis-level datasets</h2> Our analysis-level dataset comprises our most complete view of global asset-level solar installations, incorporating our own detections as well as known solar farm geometries from other datasets. The geospatial dataset contains the following fields: <ul> <li>id: unique ID for the asset</li> <li>geometry: Polygon or MultiPolygon defining the asset</li> <li>capacity_mw: estimated capacity of the asset in megawatts</li> <li>constructed_before: upper bound for construction date (estimated date of the image in which the solar plant was first seen in a constructed state)</li> <li>constructed_after: lower bound for construction date (estimated date of the image in which construction began for the solar plant)</li> </ul> The CSV version replaces the Geometry column with: <ul> <li>latitude: the latitude of the centroid of the asset</li> <li>longitude: the longitude of the centroid of the asset</li> <li>country: administrative country name</li> </ul> <h2>1.2 Raw datasets and sources</h2> The analysis-level datasets hide some complexity in the underlying data that we expose in the raw_polygons and sources file. <ul> <li>We produce new sets of polygons for each run. Often these overlap, sometimes in complicated ways.</li> <li>We cluster together overlapping and nearby geometries from both our detections and external sources. Currently these sources are:</li> <li>Large solar farms scraped from OpenStreetMap (OSM)</li> <li>Validated geometries from <a href="../records/5005868">Kruitwagen et. al., A global inventory of solar photovoltaic generating units</a>.</li> </ul> Each cluster comprises one row in the analysis-level dataset. In order to enable tracking raw detections from run to run, as well as to provide detailed sourcing information, we provide all of these raw polygons, along with a source file that lists all of the raw polygons contained in each analysis-level polygon. raw_polygons.gpkg contains the following fields: <ul> <li>id: ID of the raw source polygon</li> <li>geometry: Polygon or MultiPolygon defining the asset</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> Sources.csv contains the following fields: <ul> <li>cluster_id: ID of the corresponding item in the analysis-level dataset</li> <li>source_id: ID of the raw source polygon</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> <h2>1.3 Caveats and limitations</h2> <h3>1.3.1 Capacity Estimates</h3> While we have made every effort to remove false positives from the published dataset, some will remain due to the difficulty of manually validating detections in 10-metre satellite imagery. To estimate false positive prevalence throughout the data a subset of approximately 2000 detections were selected at random from our positively labelled solar assets. Each of these were validated through a higher degree of scrutiny utilising high-resolution imagery. This analysis yielded an expected rate of false positives of around 1%. <h3>1.3.2 Plant Shapes</h3> Our plant outlines are not perfect. They will occasionally be much smaller or larger than the underlying plant. Our tests show that on average, these effects average out. <h3>1.3.3 Capacity Updates</h3> Our capacity estimation model should produce relatively unbiased country-level aggregates, since it is trained to learn the typical ground coverage ratio of plants by country. The model has no way to distinguish between a very dense and a very sparse (e.g. dual-axis-tracking) plant in the same country. Plants with unusually high or low ground coverage ratios will not have accurate capacity estimates. <h3>1.3.4 Construction Date Estimates</h3> We are not able to directly estimate the construction date of a plant. We estimate an upper bound (the date of the image in which the plant was first seen in constructed state) and a lower bound (the date of the image in which the plant was last seen in an unconstructed state). For plants that were constructed before the launch date of Sentinel-2 in 2017, we produce only an upper bound. We leave it to consumers of the data to interpret these bounds and/or estimate likely grid connection dates. 2. Attribution TZ-SAM is made available under a Creative Commons Attribution Non-Commercial 4.0 International License (CC-BY-NC-4.0). Attribution to TransitionZero is required. You must also clearly indicate if you have made any changes to the TZ-SAM dataset and what these are. Please refer to the suggested citation formats: <ul> <li>“TransitionZero Solar Asset Mapper, TransitionZero, May 2024 release.”</li> <li>“TZ-SAM, TransitionZero, May 2024 release.”</li> <li>“TransitionZero (2024) Solar Asset Mapper.”</li> </ul>

PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing

The urban population continues to grow despite health risks associated with densely populated cities, such as traffic congestion and air pollution. At the same time cities are also further heating up due to climate change. Environmental monitoring is increasingly critical to react quickly to temporarily increased concentrations of, for example, carbon monoxide, nitrogen oxides, ozone, and particulate matter.  We introduce a significantly improved version of our PhytoNode, an energy-efficient sensor node designed for phytosensing, that is, using of plants as environmental sensors. We aim for a scalable and sustainable real-time monitoring solution following our vision of an `intelligent plant' as an inexpensive and accurate sensor node.  We measure electrical potentials and leaf temperatures of plants to assess their well-being and, in turn, environmental conditions.  The PhytoNode achieves long-term energy autonomy by harvesting energy via solar cells and shares data via Bluetooth Low Energy (BLE) communication. We process the gathered time series plant data onboard in real-time using methods of Machine Learning (ML) to analyze the plant's activity and to detect dangerous concentrations of gases. In a few showcasing experiments, we demonstrate the feasibility of both our hardware and software approach for continuous, long-term environmental monitoring based on phytosensing. By embedding engineered devices in living plants as a `plant wearable' that listens to plant responses, we hope to help pushing towards smarter future cities and healthier urban environments.    Data repository for our paper "PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing", submitted to the 8th Future of Information and Communication Conference 2025 (FICC 2025). Please refer to the paper for more information.

opencc-by-4.0Jun 2024View details →

Binding Affinity Prediction Workflow - Simulation Input Files and Absolute Binding Free Energies

The Binding Affinity Prediction (BAP) workflow calculates absolute binding free energies for protein-ligand complexes by taking their crystal structures, converting them into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks, and analysing the resulting trajectories with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the free-energy estimates. The workflow was designed for soluble proteins without post-translational modifications, co-factors and non-standard amino acids, and it has limited support for coordinated ions. For the dataset published here, the BAP workflow was run on the PDBbind 2020 (http://www.pdbbind.org.cn/index.php) refined set. This entry contains the MD simulation input files (BAPSimulationInputFiles.tar.gz) and the ABFE estimates (BAPBindingFreeEnergyEstimates.csv) obtained from four 250 ns trajectories for each complex. The MD simulations for more than 4000 complexes were run on the Leonardo supercomputer while the implicit-solvent calculations were carried out on Galileo, both operated by Cineca (Italy). The MD trajectories will be stored at Cineca for approx. 1 year after publication of this entry; contact Cineca's user support if you are interested in the trajectories. The README file describes how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Binding-Affinity-Prediction-workflow). The MD simulations were run with GROMACS 2023.2 (https://manual.gromacs.org/2023.2/index.html), and the implicit-solvent calculations were carried out with gmx_MMPBSA 1.6.1 (https://valdes-tresanco-ms.github.io/gmx_MMPBSA/v1.6.1/).

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Unstable Crystallographic & Molecular Structures for Machine Learning of System Energies

<div> <div> <div> Extended QM9 (E-QM9) includes diverse sizes (i.e. number of atoms) and compositions of OoE molecules, through extending a subset of QM9 with OoE versions of 10k of its molecules. Periodic crystals (PC) allows learning regular bonding patterns that arise in periodic structures by repeating the base crystal lattice. We use the Face-Centred Cubic (fcc) Bravais lattice for aluminium (Al) and copper (Cu) crystals. Crystal Growth (CG) contains growing crystals of increasing size and complexity. Starting from a basic fcc crystal seed of 14 atoms, new systems are generated by iteratively placing atoms at a random location on the surface of the growing crystal following its lattice pattern, with sizes ranging from 15 to 114 atoms. We use 20 random seeds for each atom type, thus creating 40 varied Al and Cu crystal growths and 4,000 stable systems. As a result, for a given crystal size and composition (atom type), there are 20 samples with differently located atoms. CG enables experi- menting with large scale atomic interactions in non-regular sys- tems, and enables evaluation of an ML method’s ability to learn how each atom contributes to the final potential energy. In all datasets, OoE systems are obtained by compressing/dilating all interatomic distances (i.e. isometrically) at regular intervals within 90-150% of stable geometry, which we refer to as ‘scaling’. In other words, scaling is applied to the coordinates of all atoms within the system. At each geometry, the ground-truth potential energy is calculated using CP2K7’s DFT. </div> </div> </div>

opencc-by-4.0Dec 2021View details →

opencc-by-4.0Dec 2023View details →

AdsMT: Multi-modal Transformer for Predicting Global Minimum Adsorption Energy

We built three Global Minimum Adsorption Energy (GMAE) benchmark datasets named OCD-GMAE, Alloy-GMAE and FG-GMAE from OC20-Dense, Catalysis Hub, and `functional groups' (FG)-dataset datasets through strict data cleaning, and each data point represents a unique combination of catalyst surface and adsorbate. These new benchmark datasets can be beneficial for future ML study on GMAE prediction. In addition, a similar data cleaning procedure was employed on the OC20 dataset to create a new dataset named OC20-LMAE, which comprises surface/adsorbate pairings along with their local minimum adsorption energies (LMAE). The OC20-LMAE dataset contains 363,937 data points and serves as an effective resource for model pretraining.

opencc-by-4.0Jun 2024View details →

Dataset from "Natural capital accounting reveals ecosystems' role in water and energy security in Colombia's Sinú Basin"

The data archived here are associated with the publication titled "Natural capital accounting reveals ecosystems' role in water and energy security in Colombia's Sinú Basin", available at: <a href="https://doi.org/10.1038/s43247-025-02254-9">https://doi.org/10.1038/s43247-025-02254-9</a>. The files within "Sinu_SDR_inputs.zip" and "Sinu_SWY_inputs.zip" were prepared and run in <a href="http://releases.naturalcapitalproject.org/?prefix=invest/3.12.0/">InVEST version 3.12.0</a>. "SDR" refers to the InVEST Sedimnet Delivery Ratio (SDR) model and "SWY" refers to the InVEST Seasonal Water Yield (SWY) model. Results of these model runs are found within "Sinu_SDR_results.zip" and "Sinu_SWY_results.zip" for the SDR and SWY models, respectively. These models were calibrated using observed data on average monthly water flows (from 1959 to 1992) and average annual sediment loads (from 1972 to 1992) from gauge stations on Colombia's Sinú River. Those observed data were obtained from Colombia's Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) hydrometeorological monitoring network <a href="http://dhime.ideam.gov.co/atencionciudadano/">webportal</a> and are summarized in the files included here, "MeanMonthlyObservedFlowsXgaugeStation.csv" for monthly water flows and "annualObservedSedimentXgaugeStation.csv" for annual sediment loads. "EcosystemTypeTable.xlsx" is the table of ecosystem values. "Cuenta_Sinu_SankeyData_v2_paper.xlsx" contains the Sankey and accounts tables.

opencc-by-4.0Jul 2024View details →

Dataset of IoT-Based Energy and Environmental Parameters in a Smart Building Infrastructure

This dataset includes detailed measurements from IoT sensors deployed throughout the M5 building, capturing energy consumption from various devices like coffee machines, microwaves, etc., as well as environmental data such as temperature, humidity, and occupancy in key areas like the Interdisciplinary lab, kitchen, and mailroom. This release aims to provide researchers and practitioners with comprehensive data to facilitate research on energy efficiency and environmental monitoring within smart building infrastructures. The data are structured to support various types of analysis, from operational efficiency assessments to environmental impact studies. For detailed information on the dataset's structure and usage, please refer to the README.md file included in this repository.

opencc-by-4.0Jul 2024View details →

gEneSys Project - Systematic Literature on the Nexus between Gender and Energy Transition Database

The present Dataset containes the data collected for the gEneSys Systematic Literature Review on the nexus between gender and energy transition. Data have been collected from 152 papers published between 2000 and 2023. The publications have been identified through an hoc research query and retrieved from the Web of Science Database. The dataset inscludes the following variables: <ol> <li>Title of the publication, category of the categorization of Bell et al., 2020 (Political, Economic, Socio-Ecological, Technological).</li> <li>Cluster in which the publication has been included.</li> <li>Parts of the publication’s results about the nexus between gender and energy.</li> <li>Parts of the publication’s text about the gender gap assessed by the publication.</li> <li>Parts of the publication’s text about the gender gap identified to be bridged by future research.</li> <li>The type of the gender issue/s addressed by the publication. </li> <li>The type of the gender issue/s addressed by the publication. </li> <li>Technology/ies mentioned in the publication.</li> <li>The name of the country or countries studied by the publication.</li> <li>World Bank classification of the level of income of the country or countries studied by the publication.</li> <li>World Bank classification of the region of the country or countries studied by the publication.</li> <li>Spatial Context (e.g. international, national, inner-country, peri-urban, rural) of the country or countries studied by the publication.</li> <li>Research method employed in the publication (qualitative, quantitative, mixed).</li> <li>Specific qualitative, quantitative or mixed method or methods employed in the publication.</li> <li>Number of observations for the methods used.</li> <li>Parts of the publication’s text about the policy recommendations elaborated in the publication.</li> <li>If the publication mentions a pathway.</li> <li>Year of publication.</li> <li>Author/s surname and name initial. </li> <li>Author/s full surnames and names. </li> <li>Keywords chosen by the author/s. </li> <li>Abstract of the publication. </li> <li>Name of the source or journal. </li> <li>Type of publication. </li> <li>Category/ies identified by Web of Science. </li> <li>Publication’s language. </li> <li>Keywords identified by Web of Science. </li> <li>Number of references cited by the publication. </li> <li>Number of times the publication has been cited in Web of Science Core Database. </li> <li>Number of times the publication has been cited in Web of Science All Databases. </li> <li>Name of the publisher. </li> <li>Digital Object Identifier. </li> <li>Digital Object Identifier link. </li> <li>Publication’s number of pages. </li> <li>Web of Science citation index. </li> <li>Research area or areas of the publication. </li> <li>Web of Science Unique Identifier.</li> </ol>