Find research datasets worth reusing

Simulated industrial CT dataset for deep learning with dual-energy tomograms and ground truth material maps for copper and iron

We use this dataset for training and evaluation of a deep learning model to discriminate multi-material systems with X-ray CT. The dataset consists of: <ul> <li>inputs: dual-energy tomograms as binary files without a header (tensor shape for numpy: 2x128x128 @float32) <ul> <li>simulated spectra are 250kVp and 450kVp both prefiltered using 2mmCuSn</li> </ul> </li> <li>outputs: the material maps a.k.a. ground truths for the training (same shape as inputs) <ul> <li>sampled with a delaunay algorithm and randomly filled with iron and copper fractions</li> </ul> </li> </ul> The dataset is normalized to [0, 1], so you have to multiply by the mass densities of copper and iron to obtain effective fractions in g/cm^3.

Supporting Information (software and data) for: Client-side energy and GHGs assessment of advertising and tracking in the news websites

This is the open data and free/libre and open source software repository for the article "Client-side energy and GHGs assessment of advertising and tracking in the news websites" by Fabio Pesari, Giovanni Lagioia, Annarita Paiano.

Evolution of the price of electricity and the renewable energy production in Spain

The dataset contains the evolution of electricity prices in Spain for the period 2020-11-01 to 2022-10-31. The average energy prices (in MWh) as a function of the market, the produced energy (in MW), as well as the renewable energy produced (in MW) by type (wind, solar, hydroelectric, etc.) are provided with a granularity of hours for the period of time mentioned above. The data has been obtained from the webpage: <a href="https://www.esios.ree.es/es/">https://www.esios.ree.es/es/</a>. Disclaimer: Express consent was provided by Red Eléctrica de España (source and proprietary of the data) to gather the data using web scraping under the framework of a practicum from the Master in Data Science from the Universitat Oberta de Catalunya (UOC). Under no circumstance do they support the reuse of the data. We express our intention to use the data for the purpose of the activity and decline any commercial interest in the use of the data extracted. The data is published under a license: CC BY-NC-SA 4.0.

opencc-by-4.0Feb 2022View details →

Dataset: Energy services' access deprivation in Mexico: A geographic, climatic and social perspective

This dataset contains all the information at the municipal level from the publication "Energy services' access deprivation in Mexico: A geographic, climatic and social perspective" published in Energy Policy (DOI: <a href="https://doi.org/10.1016/j.enpol.2022.112822">10.1016/j.enpol.2022.112822</a>). The information contains key categorizations on energy services access at the municipal level in Mexico, classified per climatic zone. It is complemented with key information on population and households at the municipal level. The raw data sources used to produce this secondary data are listed below. A detailed methodological description is available in the primary article (DOI: <a href="https://doi.org/10.1016/j.enpol.2022.112822">10.1016/j.enpol.2022.112822</a>) and the article "Dataset of household energy services access and socioeconomic variables in Mexico" to be published in Data in Brief.  Raw data: <ul> <li>2015 Intercensal Survey: <a href="https://www.inegi.org.mx/programas/intercensal/2015/">https://www.inegi.org.mx/programas/intercensal/2015/</a></li> <li>Poverty Index by Municipality in Mexico 2015: <a href="https://www.coneval.org.mx/Medicion/Paginas/PobrezaInicio.aspx">https://www.coneval.org.mx/Medicion/Paginas/PobrezaInicio.aspx</a></li> <li>Raster Map of Climates: <a href="https://www.inegi.org.mx/temas/climatologia/#Mapa">https://www.inegi.org.mx/temas/climatologia/#Mapa</a></li> </ul> The dataset's geographic scope is as follows: <ul> <li>City/Town/Region: All municipalities</li> <li>Country: Mexico</li> </ul>

DATASETS FOR: A keystone avian predator faces elevated energy expenditure in a warming Arctic

Here, we provide two datasets from a study in which we used triaxial accelerometers (Axy 4, Technosmart, 3g) to collect detailed behavioral records from little auks (Alle alle) at Ukaleqarteq (UK), East Greenland (70°44′N, 21°35′W) and Hornsund (HS) (77°00′N, 15°33′E; Svalbard archipelago), during the chick rearing period. We used this data to compile time activity budgets, from which we estimated daily energy expenditure (DEE). Data spans five years (2017-2021) at UK and two years at HS (2020, 2021). We assessed whether variation in DEE was affected by variability in climate change-sensitive environmental variables that affect availability of the little auk’s resource base of cold water zooplankton, that is sea surface temperature (SST) and sea ice coverage (SIC). SIC was only used for UK, since there was no appreciable sea ice at HS, which experiences higher average SST than UK. We also obtained small ~0.2-0.5 ml blood samples from the brachial veins of focal individuals to measure contamination from a potent chemical contaminant, mercury (Hg). We assessed the hypothesis that DEE is forced upward by challenging foraging conditions, but may be limited at some point due to energetic thresholds. We also assessed whether Hg contamination levels modified patterns of energy expenditure.      In addition, to further examine the relationship that emerged between DEE and SST, we compiled a dataset of 12 site-year observations of average DEE of breeding little auks using data from Gabrielsen et al. (1991) (n = 13), Grémillet et al. (2012) (n = 70) and the present study. This dataset spanned 35 years (1986-2021) and 3 sites (UK, HS, and Kongsfjorden, KF). KF is another breeding colony of little auks on Svalbard that experiences even warmer SST than HS.

opencc-by-4.0Oct 2023View details →

Modeling the recent drought and thinning impacts on energy, water and carbon fluxes in a boreal forest

This dataset includes the data used for model calibration and validation, as well as the simulation files with accepted runs, which are available for the readers to re-generate the results of this work. The *.bin files are the data for driving the model and for calibration and validation. They are specifically in the format for the CoupModel. Therefore, to check the data the CoupModel software needs to be installed.  Additionally, we provide the software for CoupModel, which the readers could install on local computers to check the simulations. For detailed instructions on how to run CoupModel, please visit the CoupModel website www.coupmodel.com.

opencc-by-4.0Aug 2020View details →

Neural Networks for Structure-Informed Prediction of Formation Energy (employed in SIPFENN)

pySIPFENN Documentation: <a href="https://pysipfenn.org">pysipfenn.org</a> pySIPFENN GitHub: <a href="https://github.com/PhasesResearchLab/pySIPFENN">git.pysipfenn.org</a> Original SIPFENN Paper: <a href="https://doi.org/10.1016/j.commatsci.2022.111254">10.1016/j.commatsci.2022.111254</a>   Network Changelog: V 0.10 - All models moved to the open ONNX format for improved interchangeability; NN30 neural network similar to NN20 but accepting the new KS2022 feature vector; Python code migrated to public GitHub repository. V 0.9 - Python code updated to the release version; paper published V 0.8 - Python code (beta) to run models included V 0.7 - Original upload of development models    Selected works with SIPFENN alongside DFT and experiments: - <a href="https://doi.org/10.1016/j.actamat.2021.117448">10.1016/j.actamat.2021.117448</a> - <a href="https://doi.org/10.1038/s41598-021-03578-0">10.1038/s41598-021-03578-0</a>   SIPFENN Abstract (original publication, 2021): In recent years, numerous studies have employed machine learning (ML) techniques to enable orders of magnitude faster high-throughput materials discovery by augmentation of existing methods or as standalone tools. In this paper, we introduce a new neural network-based tool for the prediction of formation energies based on elemental and structural features of Voronoi-tessellated materials. We provide a self-contained overview of the ML techniques used. Of particular importance is the connection between the ML and the true material-property relationship, how to improve the generalization accuracy by reducing overfitting, and how new data can be incorporated into the model to tune it to a specific material system.          In the course of this work, over 30 novel neural network architectures were designed and tested. This lead to three final models optimized for (1) highest test accuracy on the Open Quantum Materials Database (OQMD), (2) performance in the discovery of new materials, and (3) performance at a low computational cost. On a test set of 21,800 compounds randomly selected from OQMD, they achieve mean average error (MAE) of 28, 40, and 42 meV/atom respectively. The second model provides better predictions on materials far from ones reported in OQMD, while the third reduces the computational cost by a factor of 8.          We collect our results in a new open-source tool called SIPFENN (Structure-Informed Prediction of Formation Energy using Neural Networks). SIPFENN not only improves the accuracy beyond existing models but also ships in a ready-to-use form with pre-trained neural networks and a user interface.    Contacts: - Adam Krajewski: ak@psu.edu - Prof. Zi-Kui Liu: zxl15@psu.edu

Satellite-based shoreline detection: macrotidal high-energy coasts dataset

This dataset accompanies the article by Konstantinou et al. (2022) titled ‘Satellite-based shoreline detection: macrotidal high-energy coasts’. This study assesses the ability of existing satellite image analysis technology to capture shoreline position change at relevant magnitudes and timescales for two different coastal environments in the United Kingdom. It addresses the influence of tidal elevation and wave-induced water-level fluctuations at two sites representing end members of beach morphological type in a region of low satellite useability (high cloud cover combined with low image availability). The study uses 14 years of monthly topographic surveys at two macrotidal sites in the UK, combined with modelled wave data and harmonic tidal predictions to investigate the influence of tidal elevation and wave action on SDS accuracy. Description The dataset consists of two matlab files each containing the information listed below for the two test sites (Slapton Sands (SLSdata); Perranporth (PPTdata)): <ul> <li>dnum: date of satellite image capture in matlab datenum format</li> <li>sat: name of satellite (L5=Landsat 5; L7=Landsat 7; L8=Landsat 8; S2=Sentinel-2)</li> <li>transects: a structure containing the following variables:</li> </ul> <ul> <li>profName – the name of the survey profile</li> <li>startEast;   startNorth;   endEast;   endNorth: OSGB coordinates of the start (landward) and end (seaward) of the transect line</li> <li>startUTMlat;   startUTMlon;   endUTMlat;   endUTMlon: WGS84-UTM30 coordinates of the start (landward) and end (seaward) of the transect line</li> <li>orientation: profile orientation</li> <li>shoreOrient: shoreline orientation</li> <li>transAngle: profile angle to shore-normal.</li> </ul> <ul> <li>transTimeSeries: time series of the intersection of the SDW at each transect in three columns that include chainage (m), longitude, latitude.</li> <li>profData: a nested structure containing the survey data at organised by profile including survey date, OSGB and WGS84-UTM30 coordinates of surveyed points.</li> </ul>

opencc-by-4.0Dec 2022View details →

Urbach energy in CIGSe from PL

Data of the manuscript "On the origin of tail states and VOC losses in Cu(In,Ga)Se2".

opencc-by-4.0Dec 2022View details →

Ammonia Binding Energy distribution at Interstellar Icy Grains

Zip file containing all the structures and input obtained in our article ACS Earth Space Chem. 2022, 6, 6, 1514–1526, <a href="https://doi.org/10.1021/acsearthspacechem.2c00040">https://doi.org/10.1021/acsearthspacechem.2c00040</a> To easily handle all these structures an online interactive page is created: <a href="https://tinaccil.github.io/Jmol_BE_NH3_visualization/">https://tinaccil.github.io/Jmol_BE_NH3_visualization/</a>

opencc-by-4.0Sep 2022View details →

Data for: "Market Power and Price Exposure: Learning from Changes in Renewable Energy Regulation"

Given the key role of renewable energies in current and future electricity markets, it is important to understand how they affect firms' pricing incentives in these markets. In this paper, we study whether renewables depress electricity market prices, and how this effect depends on their degree of market price exposure. Our theoretical analysis shows that paying renewables with fixed prices, rather than with market-based prices, is relatively more effective at curbing market power when the dominant electricity firms own large shares of the renewable capacity, and vice-versa. To test this prediction, our empirical analysis leverages several short-lived changes to renewable energy pricing mechanisms in the Spanish electricity market. In this context, we find that the switch from full price exposure to fixed prices caused a 2-4% reduction in the average price-cost markup.

Techno-economic dataset for long-term energy systems modelling in Viet Nam

Techno-economic data and assumptions for long-term energy systems modelling in Viet Nam. This includes data on electricity generation and consumption, electricity imports and exports, fuel prices, emissions, refineries, power transmission and distribution, electricity generation technologies, and renewable energy potential and reserves for the years 2015 to 2050.

opencc-by-4.0Aug 2022View details →

Dataset: Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System

The dataset provided here is intended for publication - Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System. Direct use of our provided datasets is available from Zenodo, and the source code to generate the datasets is published in <a href="https://gitlab.com/dlr-ve/esy/open-brazilian-energy-data">Gitlab</a>. We describe the data collection process in detail and open source the code for data processing and analysis in our publication. The assembled dataset includes the following subcategories, as detailed in the methods section of our publication: i) geospatial data for Brazil, ii) aggregated grid network topology, iii) vRES potentials --- profile and installable generation capacity, iv) geographically installable capacity of biomass thermal plants, v) hydropower plants inflow, vi) existing and planned power generators with their capacity, vii) electricity load profile, viii) scenarios of sectoral energy demand and ix) cross-border electricity exchanges. This dataset is resolved geographically by Brazilian federal states, and time series data are resolved by hours, spanning 2012-2020. The dataset can be used as input to popular open energy system models such as PyPSA and any other modelling framework. We encourage you to contribute to improving the datasets.

Video: Turkish National Advisory Group Meeting on Horizon Europe Solar Energy Call topics, 18 Nov. 2022

Video of a 1 day open meeting to catalyze and support Turkish participation in a cluster of upcoming Horizon Europe Calls on Solar Energy.

Dataset of the paper "Energy Efficiency Improvement with Reversible Substations for Electrified Transportation Systems"

The dataset refers to the measurement and simulations of the supply system and rolling stock of line 10 B of Metro de Madrid. Simulations have been performed by changing the position of the reversible substation and computing the current flowing in the braking rheostat of the simulated rolling stock. The data refer to the paper "Energy Efficiency Improvement with Reversible Substations for Electrified Transportation Systems" published in "The Open Transportation Journal".

Strong enhancement of electromagnetic shower development induced by high-energy photons in a thick oriented tungsten crystal

We have observed a significant enhancement in the energy deposition by 25-100 GeV photons in a 1 cm thick tungsten crystal oriented along its  <111> lattice axes (data of Fig. 2). At 100 GeV, this enhancement, with respect to the value observed without axial alignment, is more than twofold. This effect, together with the measured huge increase in secondary particle generation (data of Fig. 3) is ascribed to the acceleration of the electromagnetic shower development by the strong axial electric field. The experimental results have been critically compared with a newly developed Monte Carlo adapted for use with crystals of multi-X_0 thickness (data of Fig.2, 3 and 4). These results may prove to be of significant interest for the development of high-performance photon absorbers and highly compact electromagnetic calorimeters and beam dumps for use at the energy and intensity frontiers.

Towards a Diverse Next-Generation Energy Workforce: Teaching Artificial Photosynthesis and Electrochemistry in Elementary Schools through Active Learning

Artificial photosynthesis is a promising approach to generate important commodity chemicals using abundant chemical feedstocks and renewable energy sources. Despite its importance, affordable and effective hands-on classroom activities that demonstrate artificial photosynthesis and teach key concepts, especially for primary school students, is lacking. This will be a critical step in the development of the next-generation energy workforce, especially one that is diverse in race and gender. To aid in this effort, we present an artificial photosynthesis lesson plan based on active-learning techniques that uses safe and highly accessible materials (baking soda, tap water, plastic jars, Ni coil, alligator clips, and a solar cell) to perform solar-powered water splitting. The efficacy of the lesson plan in teaching basic concepts of artificial photosynthesis was evaluated with pre- and post-test data, which shows a statistically significant improvement in overall student understanding. Importantly, the data show that the lesson plan presented here is effective at narrowing the performance gap between minority students and overly represented groups. This study aids in the development and education of a demographically diverse energy workforce through an active learning-based lesson plan for primary school students.

Data analysis of Maamela et al. 2023 The effect of temperature and dietary energy content on female maturation and egg nutritional content in Atlantic salmon

This folder includes the data and R scripts used in the data analysis of the Maamela et al. 2023 paper in Journal of Fish Biology.

Open-source design files for harvesting energy from overhead power line cables

OpenSource_CalculationFile_MEH.xlsx  : this is an excel file that provides a calculation tool to select a magnetic core for harvesting energy from powerlines. OpenSource_Schematic_MEH.pdf  : this is a schematic file that provides a detailed design of the charging circuit.