Find research datasets worth reusing

opencc-by-4.0Mar 2023View details →

Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand)

A Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand).

CCG: Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System)

Seven clicSAND scenario files for Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System).  How to Visualise Results Online and Offline outline the steps required to re-run the scenarios on OSeMOSYS Cloud Scenario Short Note outlines the steps to replicate the analysis and rebuild the scenarios Annex - Input Data and Assumptions listing the data sources and assumptions in the scenarios

Theoretical water binding energy distribution and snowline in protoplanetary disks

Zip file containing all the structures and input obtained in our accepted article for publication in ApJ, 2023 To easily handle all these structures an online interactive page is created: <a href="https://tinaccil.github.io/Jmol_BE_H2O_visualization/">https://tinaccil.github.io/Jmol_BE_H2O_visualization/</a>

opencc-by-4.0Nov 2022View details →

A comprehensive open-access database of electron backscattering coefficients for energies ranging from 0.1 KeV to 15 MeV

The database provides measured values of electron backscattering coefficient for 50 elements and 19 compounds at electron energies from 0.1keV to 15MeV.

Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution (data)

Data for "Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution"

opencc-by-4.0Oct 2022View details →

Zinc(II) Complexes with Triplet Charge-Transfer Excited States Enabling Energy-Transfer Catalysis, Photoinduced Electron Transfer, and Upconversion

Raw data to the graphs of the publication

Pitch-angle and energy diffusion coefficients calculated for ions interacting with kinetic Alfven waves near the magnetopause

In each file: the first row (starting with the second column) contains pitch-angle grid in degrees the first column (starting with the second row) contains the energy grid in keV "x_to_L" value in the file name denotes the position in space along the normal to the magnetopause relative to the current sheet center (see description file). energy diffusion coefficients are measured in keV^2/s, and pitch-angle coefficients are measured in rad^2/s.

Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - sample untreated_162nm

RHEED intensity image dataset of sample untreated_162nm in work "Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."

Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - sample treated_81nm

RHEED intensity image dataset of sample t0.08 in work "Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."

Dataset for 'Heat and Humidity Exposure in Mega-cities - an Applied Tool for Energy and Water Harvesting Technologies'

This is the dataset for the article named 'Heat and Humidity Exposure in Mega-cities - an Applied Tool for Energy and Water Harvesting Technologies '

opencc-zeroApr 2023View details →

dryad36/100

Impacts of neonicotinoid insecticides on bumble bee energy metabolism are revealed under nectar starvation

Bumble bees are an important group of insects that provide essential pollination services as a byproduct of their foraging behaviors. These pollination services are driven, in part, by energetic exchanges between flowering plants and individual bees. Thus, it is important to examine bumble bee energy metabolism and explore how it might be influenced by external stressors, which contribute to declines in global pollinator populations. Two stressors that are commonly encountered by bees include insecticides and nutritional stress. Our study examines the effects of neonicotinoid insecticide exposure alone, and in combination with nutritional stress, on bumble bee metabolism using a novel metabolomic approach. We hypothesized that exposure to the insecticide imidacloprid would disrupt bumble bee energy metabolism, leading to changes in key metabolites involved in energy metabolism. We exposed Bombus impatiens workers to imidacloprid according to one of three exposure paradigms designed to explore how sustained versus more limited imidacloprid exposure influences energy metabolites. After bees were exposed to imidacloprid, they were subjected to artificial nectar starvation. Our results showed that the strongest effects of imidacloprid were observed when treated bees also experienced artificial nectar starvation, suggesting a combinatorial effect of neonicotinoids and nutritional stress on energy metabolism. Overall, this study provides important insights into the mechanisms underlying the impact of neonicotinoid insecticides on bumble bees and underscores the need for further investigation into the complex interactions between environmental stressors and bee metabolism.

openmit-licenseApr 2023View details →

An approach using performance models for supporting energy analysis of software systems

Replication package of the paper titled "An approach using performance models for supporting energy analysis of software systems". Usage instructions are contained in the README.md file.

dryad36/100

Lesser prairie-chicken habitat selection and survival relative to a wind energy facility located in a fragmented landscape

The overlap of renewable wind energy with the range of lesser prairie-chickens (Tympanuchus pallidicinctus) raises concern of population declines and habitat loss. Lesser prairie-chickens are adversely affected by landscape change, however, it is unclear how this species may respond to wind energy development. Therefore, managers and wind energy developers are currently tasked with making management or siting recommendations of future wind energy facilities based on lesser prairie-chicken behavioral responses to other forms of anthropogenic development or responses of other grouse species to wind energy development. The current strategy of siting wind turbines in cultivated cropland within lesser prairie-chicken range has not been evaluated for its effectiveness at minimizing potential adverse impacts. We captured 60 female and 66 male lesser prairie-chicken from leks located along a gradient from wind turbines in southern Kansas, USA, from 2017–2021. Over the study period, we collected lesser prairie-chicken location data and demographic information to evaluate resource selection, movements, and demography relative to environmental predictors and metrics associated with the wind energy facility. Lesser prairie-chickens used habitats in close proximity to wind turbines, provided that turbine density was low; however, avoidance associated with cultivated cropland appeared to be more predictive than the presence of wind turbines. We observed movement between turbines suggesting that wind turbines did not act as a barrier to local movements. We did not detect an influence of wind turbines on nest success or individual survival during breeding or non-breeding periods, a relationship that is consistent among multiple grouse species using habitats near wind energy infrastructure. Additional research is necessary to evaluate impacts associated with wind energy development in intact lesser prairie-chicken habitats, but placing wind turbines in cultivated croplands or other fragmented landscapes appears to be an important siting measure when considering wind energy facility siting across the lesser prairie-chicken range.

opencc-zeroMay 2023View details →

Highly Accurate Potential Energy Surface and Dipole Moment Surface for Nitrous Oxide and Ames-296K Infrared Line Lists for 14N216O and Minor Isotopologues

First generation data product and IR line lists for Nitrous Oxide (N2O), including an isotopologue-independent ab initio PES of Nitrous Oxide refined with selected HITRAN energy levels below 7000 cm-1 and experimental GV at higher energies, an ab initio DMS fitted with CCSD(T)/aug-cc-pV(T,Q,5)Z dipoles computed up to 20,000 cm-1 above potential minimum and extrapolated to one-electron basis set limit, room temperature IR line lists for 12 N2O isotopologues of 14/15N and 16/17/18O, and a combination "natural" list with terrestrial abundances.  This project is funded by NASA Grant 18-APRA18-0013 through NASA/SETI Institute Co-operative Agreement 80NSSC20K1358.  See https://huang.seti.org/N2O/n2o.html for data format and abundance information. <ol> <li>Ames-0 and Ames-1 PES subroutine & coefficient files, and PES refinement related files including reference energy level list and refinement output.</li> <li>J=0-150 energy level lists of 14N216O and 11 minor isotopologues, computed on the Ames-1 PES. The .zip file contains 12 compressed .tgz files.</li> <li> Ames-1 DMS subroutine & coefficient files, and ab initio data;</li> <li> Ames-296K IR line lists for 14N216O and 11 minor isotopologues, each with 100% abundance. Computed using Ames-1 DMS and rovibrational wavefunctions for those energy levels acquired on Ames-1 PES; 12 .tgz files combined into one .zip file</li> <li> A "natural" Ames-296K IR line list for N2O, including transitions from all 12 isotopologues with their 296K intensities scaled by terrestrial abundances. Computed on the Ames-1 PES and DMS. </li> <li>ORIGIN project file for related analysis and figures. Use Origin Viewer to open on PC and MAC, <a href="https://www.originlab.com/viewer/dl.aspx">https://www.originlab.com/viewer/dl.aspx</a>  </li> </ol> Line List Data Format: (N2O is the 4th molecules in HITRAN, we use 40+iso#, e.g., 41 - 446; 42 - 456; 43 - 546; 44 - 448; 45 - 447; ...) <pre>iso wavenumber S(Ames-2021) A21(Ames-2021) E"(Ames-1) vtet_qn' vtet_qn" JPS' #root' JPS" #root" J' J" wang_symmetry 43 2540.050758 2.696686E-31 2.829145E+00 4329.863425 0 0 3 1 0 0 50 2 2 109 49 1 2 24 50 49 e e </pre> Table 1. Abundances and number of IR lines of 12 N2O isotopologues in the Ames-296K natural IR line list for N2O up to 15,000 cm-1 and intensity down to 10-31 cm/molecule.  Their wavenumber range fmax (in cm-1), intensity max S296Kmax, and intensity sum are also included for each isotopologue. Intensities are scaled by corresponding abundances, in cm-1/molecule.cm-2. <table align="center"> <tbody> <tr> <td> # </td> <td> Iso </td> <td> Abundance </td> <td> #lines </td> <td> fmax (cm-1) </td> <td> S296Kmax </td> <td> Intensity Sum </td> </tr> <tr> <td> 1 </td> <td> 446 </td> <td> 0.990333 </td> <td> 1387178 </td> <td> 15000 </td> <td> 1.0217E-18 </td> <td> 7.2848E-17 </td> </tr> <tr> <td> 2 </td> <td> 456 </td> <td> 3.64093E-3 </td> <td> 375607 </td> <td> 14896 </td> <td> 3.5696E-21 </td> <td> 2.5816E-19 </td> </tr> <tr> <td> 3 </td> <td> 546 </td> <td> 3.64093E-3 </td> <td> 411253 </td> <td> 14970 </td> <td> 3.7098E-21 </td> <td> 2.6639E-19 </td> </tr> <tr> <td> 4 </td> <td> 448 </td> <td> 1.98582E-3 </td> <td> 377008 </td> <td> 14875 </td> <td> 1.8990E-21 </td> <td> 1.4206E-19 </td> </tr> <tr> <td> 5 </td> <td> 447 </td> <td> 3.69280E-4 </td> <td> 238697 </td> <td> 13964 </td> <td> 3.6668E-22 </td> <td> 2.6767E-20 </td> </tr> <tr> <td> 6 </td> <td> 556 </td> <td> 1.33858E-5 </td> <td> 93754 </td> <td> 11640 </td> <td> 1.2867E-23 </td> <td> 9.3609E-22 </td> </tr> <tr> <td> 7 </td> <td> 548* </td> <td> 7.30080E-6 </td> <td> 93609 </td> <td> 10681 </td> <td> 6.8881E-24 </td> <td> 5.1939E-22 </td> </tr> <tr> <td> 8 </td> <td> 458* </td> <td> 7.30080E-6 </td> <td> 86397 </td> <td> 10578 </td> <td> 6.5998E-24 </td> <td> 4.9864E-22 </td> </tr> <tr> <td> 9 </td> <td> 547* </td> <td> 1.35765E-6 </td> <td> 55324 </td> <td> 9065 </td> <td> 1.3299E-24 </td> <td> 9.7874E-23 </td> </tr> <tr> <td> 10 </td> <td> 457* </td> <td> 1.35765E-6 </td> <td> 50539 </td> <td> 8804 </td> <td> 1.2718E-24 </td> <td> 9.4017E-23 </td> </tr> <tr> <td> 11 </td> <td> 558* </td> <td> 2.68412E-8 </td> <td> 15761 </td> <td> 6373 </td> <td> 2.3969E-26 </td> <td> 1.8219E-24 </td> </tr> <tr> <td> 12 </td> <td> 557* </td> <td> 4.99134E-9 </td> <td> 8498 </td> <td> 4964 </td> <td> 4.6171E-27 </td> <td> 3.4327E-25 </td> </tr> </tbody> </table>

European offshore wind farms and marine energy deployements

Three distinct dataset used to forecast the development of marine energy in Europe in the upcoming three decades: - European offshore wind farms - tidal energy converter deployements in Europe - wave energy converter deployements in Europe

Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - gaussian_fit_parameters - sample untreated_162nm

RHEED raw dataset and Gaussia fitting parameter dataset for sample "untreated_162nm" in work "Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."

Compressed Datasets for Work "Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction"

Compressed version of RHEED image datasets and Gaussia fitting parameter datasets for samples "treated_213nm", "treated_81nm" and "untreated_162nm" in the work "Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."

opencc-by-sa-4.0Dec 2022View details →

Energy balance Ruditapes decussatus

Growth and physiological performance in growth phenotypes of the carpet shell clam (Ruditapes decussatus) fed diets of variable lipid/carbohydrate ratios

opencc-zeroMay 2023View details →

Projecting Residential Energy Consumption across Multiple Income Groups under Decarbonization Scenarios using GCAM-USA

Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL). GCAM-USA operates within the Global Change Analysis Model, which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022). GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: <a href="https://jgcri.github.io/gcam-doc/toc.html">https://jgcri.github.io/gcam-doc/toc.html</a> The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of May 15, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP per capita) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author. Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2045 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the key output variables related to the residential building energy sector under the four scenarios, including: <ul> <li>income shares by consumer groups at each state over 2015-2045 (Casper et al. 2022)</li> <li>residential energy consumption per capita by service and fuel, by state and income group, 2015-2045</li> <li>residential energy service output (energy consumption * technology efficiency) per capita by service, fuel, and technology, by state and income group, 2015-2045</li> <li>estimated energy burden (Eq.1), by state and income group, 2015-2045</li> <li>residential heating service inequality (Eq.2), by state, 2015-2045</li> </ul>   Table 1 <table> <thead> <tr> <th scope="col">Scenarios</th> <th scope="col">Policies</th> <th scope="col">Climate Change Impacts</th> </tr> </thead> <tbody> <tr> <td>BAU (Business-as-usual)</td> <td>Existing state-level energy and emission policies</td> <td>Constant HDD/CDD (heating degree days / cooling degree days)</td> </tr> <tr> <td>BAU_climate</td> <td>Existing state-level energy and emission policies</td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> <tr> <td>NZnoCCS (Net-Zero by 2050 without CCS)</td> <td> Two national targets: <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Constant HDD/CDD</td> </tr> <tr> <td>NZnoCCS_climate</td> <td> Two national targets: <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> </tbody> </table>   Eq. 1 \(Energy\ burden_i = \dfrac{\sum_j (service\ output_{i,j} * service\ cost_j)}{GDP_i}\) for income group i and service j   Eq. 2 \(Residential\ heating\ service\ inequality = \dfrac{S_{d10}}{(S_{d1} +S_{d2} + S_{d3} + S_{d4})}\) where S is the residential heating service output per capita of the highest income group (d10) divided by the sum of that of the lowest four income groups (d1, d2, d3, and d4), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality.   Reference Casper, Kelly, Narayan, Kanishka B., O'Neill, Brian C., & Waldhoff, Stephanie. 2022. State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100) (latest version obtained from the authors on April 6, 2023) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7227128">https://doi.org/10.5281/zenodo.7227128</a> IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].   This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.

Field Application of a High-Power Density Electromagnetic Energy Harvester to Power Wireless Sensors in Transportation Infrastructures

Traffic-induced vibration of transportation infrastructures is a reliable source of kinetic energy, which can be harvested to power conventional monitoring sensors and peripherals installed on bridges, thereby reducing some dependence on non-renewable energy. The highway statistics shows that the average daily vehicles miles travelled in the US is more than 5 billion. This is a massive source of kinetic energy that lies unused in the national transportation network. This study focuses on the design and field testing of a high-power density electromagnetic energy harvester (EMEH) to convert such a kinetic energy into electrical energy for powering ubiquitous sensors installed on transportation infrastructures. The principal investigators have been investigating the design of the EMEH using analytical and finite element simulations, as well as, its laboratory prototype fabrication and testing in the first phase. The proposed EMEH utilizes the innovative concept of creating planar array of large number of small permanent magnets through certain optimization criteria to achieve strong and focused magnetic field in a particular orientation. The proposed EMEH has a compact design, such that it can be integrated into the power circuit of wireless sensor nodes (WSNs) and installed at suitable part of a transportation infrastructure without elaborate wiring. It is capable of continuously charging the rechargeable battery of a WSN, thereby extending the lifespan of the monitoring system, almost, indefinitely. For the next phase of this research, the principal investigators propose the development and field implementation of a larger scale and more compact version of the EMEH with a minimum of 500 mW output power to be installed on selected transportation infrastructures for the evaluation of its energy harvesting efficiency and capability to derive different types of monitoring sensors and peripherals. Three different highway bridges with different fundamental frequencies, ideally between 2Hz to 8Hz, will be selected for the field testing of the EMEH. An acceleration sensor will be used to record the traffic-induced vibration of each bridge during a normal daily traffic that after signal processing is used to measure the fundamental frequency of that bridge. The dynamic characteristics of the proposed EMEH (i.e. tip mass and spring stiffness) will be modified to put it into a resonant condition with the bridge by matching their natural frequencies. The output power will be monitored and used to continuously charge a rechargeable battery powering a wireless sensor. The focus is on the feasibility of the proposed EMEH to power sensors that are used to regularly monitor the structural integrity of materials and components of highway bridges such as acceleration and temperature sensors.