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.
204
datasets available to search
ShareScore release 0.9.0
Dataset results
204 results for “Energy levels”
Dataset of "Tuning the morphology and energy levels in organic solar cells with metal- organic framework nanosheets"
<p>Metal-organic framework nanosheets (MONs) have proved themselves to be useful<br>additives for enhancing the performance of a variety of thin film solar cell devices. However,<br>to date only isolated examples have been reported. In this work we take advantage of the<br>modular structure of MONs in order to resolve the effect of their different structural and<br>optoelectronic features on the performance of organic photovoltaic (OPV) devices. Three<br>different MONs were synthesized using different combinations of two porphyrin-based ligands<br>meso-tetracarboxyphenyl porphyrin (TCPP) or tetrapyridyl-porphyrin (TPyP) with either zinc<br>and/or copper ions and the effect of their addition to polythiophene-fullerene (P3HT-PCBM)<br>OPV devices was investigated. The power conversion efficiency (PCE) of devices was found to<br>approximately double with the addition of MONs of Zn2(ZnTCPP), but was unchanged with<br>the addition of Cu2(ZnTPyP) and halved upon the addition of Cu2(CuTCPP) compared to<br>devices without nanosheets. Our analysis indicates that there are three different mechanisms<br>by which MONs can influence the photoactive layer – light absorption, energy level alignment,<br>and morphological changes. Analysis of external quantum efficiency, UV-vis photoelectron<br>spectroscopy data found that MONs have similar effects on light absorption and energy level<br>alignment. However, atomic force and Raman microscopy studies revealed that the nanosheet<br>thickness and lateral size are crucial parameters in enabling the MONs to act as beneficial<br>additives resulting in an improvement of the OPV device performance. We anticipate this<br>study will aid in the design of MONs and other 2D materials for future use in other light<br>harvesting and emitting devices.</p>
Majadas de Tietar: Ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean tree-grass ecosystem
<p>This dataset contains a subset of measurements collected at the experimental site Majadas de Tietar. We collected ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean Savanna using the eddy covariance technique and a series of meteorological sensors for the time period December 2015 - February 2018. The dataset is used for the development of a series of R packages including 'bigleaf' (Knauer et al., 2018).</p> <p>The experimental site is collected in Majadas de Tietar (Casals et al., 2009) located in western Spain (39°56′25″N 5°46′29″W). The ecosystem is a typical “Iberic Dehesa”, which is characterized by an herbaceous stratum of native pasture and sparse trees, for the majority (~98%) Quercus ilex. The tree density is about 20–25 trees/ha, the fractional cover of trees is about 20%, mean DBH of 46 cm, and a canopy height of about 8 m. (El-Madany et al., 2018). The herbaceous layer is composed of native annual species of the three main functional plant forms (grasses, forbs and legumes), whose fractional cover varies seasonally and is characterized by important inter-annual variations in the seasonal dynamics related to the onset of the dry period.</p> <p>Fluxes were measured with the eddy covariance technique with two different systems, one at ecosystem scale to characterize the fluxes of the whole ecosystem (15.5 m above ground), and one at 1.65 m above ground in an open space to measure the fluxes of the well-established understory grass layer.</p> <p>The description of the set-up, equipment and processing used to calculate ecosystem scale fluxes are described in El-Madany et al., (2018), while for the understory tower can be found in Perez-Priego et al., (2017).</p> <p>The dataset is composed of two files: 'ESLMa_MainTower', which is the ecosystem eddy covariance system, and 'ESLMa_SubCanopy', which is the understory eddy covariance system. The dataset contains half-hourly, processed eddy covariance of the ecosystem and understory tower, as well as the main biometeorological data used in the big-leaf package (net radiation, soil heat fluxes, horizontal wind velocity, atmospheric pressure, precipitation, air temperature). All the processing was conducted with EddyPro software (version 5.2.0, LI-COR Biosciences Inc., Lincoln, NE, USA) and the ustar filtering, gap-filling and partitioning with the R package REddyProc (Wutzler et al., 2018). The variables and the units are described in the Readme.txt file released with the dataset.</p> <p><strong>References</strong></p> <p>Casals, P. et al., 2009. Soil CO2 efflux and extractable organic carbon fractions under simulated precipitation events in a Mediterranean Dehesa. Soil Biol. Biochem. 41, 1915–1922. <a href="https://doi.org/10.1016/j.soilbio.2009.06.015">https://doi.org/10.1016/j.soilbio.2009.06.015</a>.</p> <p>El-Madany, T.S.,et al., 2018. Drivers of spatio-temporal variability of carbon dioxide and energy fluxes in a Mediterranean savanna ecosystem 21. <a href="https://doi.org/10.1016/j.agrformet.2018.07.010">https://doi.org/10.1016/j.agrformet.2018.07.010</a></p> <p>Knauer, J., et al., 2018. bigleaf - An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data. PLOS ONE, doi:10.1371/journal.pone.0201114</p> <p>Perez-Priego O, et al., 2017. Evaluation of eddy covariance latent heat fluxes with independent lysimeter and sapflow estimates in a Mediterranean savannah ecosystem. Agricultural and Forest Meteorology. 236: 87-99. doi: 10.1016/j.agrformet.2017.01.009.</p> <p>Wutzler, T., et al., 2018. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences Discuss., p. 1-39.</p> <p> </p>
Projected fresh water use from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016
<p>The dataset contains projections of fresh water withdrawal and consumption from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016.</p> <p>The energy sector in this scope includes energy production (production of coal, oil and gas) and energy transformation in oil refineries and power plants (nuclear, solid fuels, oil, gas, biomass and geothermal).</p> <p>The information in provided on NUTS 2 level following the NUTS2 2013 definition.</p> <p>The dataset is explained in more detail in the report <a href="https://ec.europa.eu/jrc/en/publication/projected-fresh-water-use-european-energy-sector">Projected fresh water use from the European energy sector</a>.</p>
Energy Levels of ThII in the range of 7 to 10 eV
<p>Data is presented on 166 previously unknown energy levels of Th+ ions, observed using resonant two-step laser excitation of trapped ions. The levels are of even parity and within the energy range from 7.8 to 9.8 eV. Observed lines are listed.<br> The observed levels can be relevant for the excitation or decay of the Th-229m isomeric nuclear state which lies in this energy range.</p>
Energy metabolic pattern of China and EU28 between 2000-2016 from subsectors to average societal levels.
<p>This repository contains the data needed to reproduce the results in:</p> <p>Velasco-Fernández, R., Pérez-Sánchez, L., Chen, L., Giampietro, M., 2020. A becoming China and the assisted maturity of the EU: Assessing the factors determining their energy metabolic patterns. Energy Strategy Reviews. 32, 100562. https://doi.org/10.1016/j.esr.2020.100562</p>
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
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"
<p>The data set to paper: </p> <p>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</p> <p>Oleksandr Romanyuk1,*, Štěpán Stehlík1,2, Josef Zemek1, Kateřina Aubrechtová Dragounová1,3 and Alexander Kromka1</p> <p>1 Institute of Physics of the Czech Academy of Sciences, Cukrovarnická 10, 162 00 Prague, Czech Republic<br>2 New Technologies—Research Centre, University of West Bohemia, Univerzitní 8, 306 14 Pilsen, Czech Republic<br>3 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, 115 19 Prague, Czech Republic</p> <p>* corresponding author: romanyuk@fzu.cz</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of data collection: 1. 1. 2024 - 15. 03. 2024</p> <p>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. <br>The data are in the following formats: <br>Figure 1: tiff, csv<br>Figure 2: tiff, csv<br>Figure 3: tiff, csv<br>Figure 4: tiff, csv<br>Figure 5: tiff, csv</p> <p>Data acquistion and processing is provided in the Experimental part in the publication: DOI:10.3390/nano14070590</p>
Dataset for "An alternative to market-oriented energy models: nexus patterns across hierarchical levels"
<p>Dataset used for the publication "Di Felice, Louisa Jane, Maddalena Ripa, and Mario Giampietro. "An alternative to market-oriented energy models: Nexus patterns across hierarchical levels." <em>Energy Policy</em> 126 (2019): 431-443.". The dataset follows the distinction across hierarchical levels as specified in the publication.</p> <p>The same dataset was also used for a case study developed for the MAGIC project, available <a href="http://magic-nexus.eu/case_study/electric-grid-catalonia-illustrations-musiasem">here</a>. </p>
Biomass availability at NUTS3 level for modelling European energy system with 3 future scenario
<p>The database is built over three main sources</p> <ul> <li>S2Biom database from where most of the numbers come from <a title="S2Biom" href="https://s2biom.wenr.wur.nl/home" target="_blank" rel="noopener">(S2Biom original repo)</a></li> <li>ENSPRESO database that we use for few energy sources that are not part of s2biom (<a title="JRC" href="https://data.jrc.ec.europa.eu/collection/id-00138" target="_blank" rel="noopener">ENSPRESO</a>)</li> <li>National data for Switzerland (<a href="https://www.envidat.ch/dataset/swiss-biomass-potentials" target="_blank" rel="noopener">FoReMA Forest Resources Management Insititute</a></li> </ul> <p>Data processing is done with Julia code that has short documentation and additional databasePipeline.pdf to understand how the dataset was built. To rebuild the dataset, refer to the github repository linked to this dataset.</p> <p>Data are available as a csv file and as a sqlite database. Data query methods are available from the Github repository linked to this dataset.</p> <p>The dataset includes biomass energy availability, expressed in PJ, at nuts 0-3 (NUTS 2013), and ENTSOE bidding zones aggregation. For each biomass source, the roadsidecost of each source is associated. While the biomass data is varied, large, and detailed following standards (ISO 17225-1:2021, ISO 17225-2:2021, ISO 17225-3:2021, ISO 17225-4:2021, ISO 17225-5:2021, ISO 17225-6:2021, ISO 17225-7:2021, ISO 18125:2017, EN 13556), biomass sources have been aggregated into three categories: Forestry, Agriculture, Organic waste. There are 3 bioenergy potential, low, medium, and high. These were based on the available data listed above. </p> <p>Note: Technical availability of biomass is often much higher than the current use. Check comparison_biofuel_amounts.xlsx to compare the potentials to actual use in Eurostat and IEA data. Full potential should often not be used, because of possible issues with biodiversity and land use emissions.</p> <p> </p>
FIGURE 4. Associated fauna from levels A and B in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 4. Associated fauna from levels A and B of the Is Mesas deposit (Late Pleistocene). A, Patella caerulea. B, Diodora gibberula. C, Cerithium vulgatum. D, Hexaplex trunculus. E, Melarhaphe neritoides. F, Acanthocardia tuberculata. G, Irus irus. H, Arca noae. I, Cardita calyculata. J, Cladocora caespitosa. Scale bars equal 1 cm.
Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption
<p>The data used to generate the graphs in Figures 2 and 3 of the paper "Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption" at SCOPES'18. The data includes power consumption, code size, and running time, indexed by an ID that identifies the combination of compiler options. Data is stored in a CSV file that identifies the specific benchmark, organized into a zip file that identifies the chip architecture.</p> <p>See README.txt for details.</p>
Dataset related to the publication "Measurement and assignment of J = 5 to 9 rotational energy levels in the 9070-9370 cm-1 range of methane using optical frequency comb double-resonance spectroscopy"
<p>The files contain the normalized interleaved double-resonance spectra recorded with four different pump transitions, indicated in the file name. The first column is the wavenumber, the second column is the transmission intensity.</p>
Full electronic tables for 'Spectrum and energy levels of the high-lying singly excited configurations of Nd III'
<p>Full electronic versions of table extracts of the accepted version of the preprint at <a href="https://doi.org/10.48550/arXiv.2408.07830">https://doi.org/10.48550/arXiv.2408.07830</a></p>
A 2.5-year campus-level smart meter database with equipment data for energy analytics
Open the record for dataset details and reuse information.
Questionnaire on needs and requirements for energy performance gap reduction at the operational level of a building
<p>Questionnaires provided within the Hit2Gap H2020 project.</p> <p>Lsits needs and requirements of the different actors of the project; these actors are at different responsibilities of a building.</p>
Measuring frontier orbital energy levels of OLED materials using cyclic voltammetry in solution - accompanying data
<p>This dataset contains the data and the Matlab scripts to create the main figures in the published manuscript.</p>
Effects of increasing levels of whole Black Soldier Fly (Hermetia illucens) larvae in broiler rations on acceptance, nutrient and energy intakes and utilization, and growth performance of broilers
<p>Dataset for the experiments to evaluate feed and black soldier fly larvae (BSFL) eating rates, nutrient and energy intakes, and utilization and growth performance of broilers. Chicks received the whole BSFL at 10%, 20%, or 30% of the feed intake of control chickens that received no BSFL but only age-specific diets. <a href="https://doi.org/10.1016/j.psj.2022.102202">https://doi.org/10.1016/j.psj.2022.102202</a></p>
Expression levels and activities of energy-yielding ATPases in the oligohaline neritid snail Theodoxus fluviatilis under changing environmental salinities
<p>The aquatic gastropod Theodoxus fluviatilis occurs in Europe and adjacent areas of Asia. The snail species has formed two genetically closely related subgroups, the freshwater ecotype (FW) and the brackish water ecotype (BW). Other than individuals of the FW ecotype, those of the BW ecotype survive in salinities of up to 28‰. Coastal aquatic ecosystems may be affected by climate change due to salinization. Thus, we investigated how the two Theodoxus ecotypes adjust to changes in environmental salinity focussing on the question whether Na+ /K+ -ATPase or V-ATPase are regulated on the transcriptional, the translational or at the activity level under changing external salinities. Animals were gradually adjusted to extreme salinities in containers under long-day conditions and constant temperature. Whole body RNA- or protein extracts were prepared. Semi-quantitative PCR and Western Blot-analyses did not reveal major changes in transcript or protein abundances for the two transporters under low or high salinity conditions. No significant changes in ATPase activities in whole body extracts of animals adjusted to high or low salinity conditions were detected. We conclude that constitutive expression of ATPases is sufficient to support osmotic and ion regulation in this species under changing salinities given the high level of tolerance with respect to changes in body fluid volume.</p>
Data for: Ambient and substrate energy influence decomposer diversity differentially across trophic levels
<p><span>The species-energy hypothesis predicts increasing biodiversity with increasing energy in ecosystems. Proxies for energy availability are often grouped into ambient energy (i.e., solar radiation) and substrate energy (i.e., non-structural carbohydrates or nutritional content). The relative importance of substrate energy is thought to decrease with increasing trophic level from primary consumers to predators, with reciprocal effects of ambient energy. Yet, empirical tests are lacking. We compiled data on 332,557 deadwood-inhabiting beetles of 901 species reared from wood of 49 tree species across Europe. Using host-phylogeny-controlled models, we show that the relative importance of substrate energy versus ambient energy decreases with increasing trophic levels: the diversity of zoophagous and mycetophagous beetles was determined by ambient energy, while non-structural carbohydrate content in woody tissues determined that of xylophagous beetles. Our study thus overall supports the species-energy hypothesis and specifies that the relative importance of ambient temperature increases with increasing trophic level with opposite effects for substrate energy.</span></p>
Dataset for: Utilizing high-resolution genetic markers to track population-level exposure of migratory birds to renewable energy development
<p class="MsoNormal"><span>With new motivation to increase the proportion of energy demands met by zero-carbon sources, there is a greater focus on efforts to assess and mitigate the impacts of renewable energy development on sensitive ecosystems and wildlife, of which birds are of particular interest. One challenge for researchers, due in part to a lack of appropriate tools, has been estimating the effects from such development on individual breeding populations of migratory birds. To help address this, we utilize a newly developed, high-resolution genetic tagging method to rapidly identify the breeding population of origin of carcasses recovered from renewable energy facilities and combine them with maps of genetic variation across geographic space (called 'genoscapes') for five species of migratory birds known to be exposed to energy development, to assess the extent of population-level effects on migratory birds. We demonstrate that most avian remains collected were from the largest populations of a given species. In contrast, those remains from smaller, declining populations made up a smaller percentage of the total number of birds assayed. Results suggest that application of this genetic tagging method can successfully define population-level exposure to renewable energy development and may be a powerful tool to inform future siting and mitigation activities associated with renewable energy programs.</span></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.