Find research datasets worth reusing

Simulation data for the office cell building energy model with the attached overhang

Simulation data for 729,000 variants of the office cell building model with the overhang attached over the window. The variants are determined by the overhang depth and height, location, presence of obstacles, orientation and cooling and heating set points. The office cell model is described in the manuscript "Predicting the shape of loads for an office cell with an overhang from a small number of building energy simulations".

Stability of neutrino oscillation parameters at low energy scale with the variations of SUSY breaking scale under Renormalisation Group Equations

<pre>We discuss the stability of the neutrino oscillation parameters at low energy scale including self-complementarity (SC) relations among mixing angles under radiative corrections with the variation of SUSY breaking scale ($m_s$) in both normal and inverted hierarchical cases. We observe that the neutrino oscillation parameters including the SC relation maintains stability at the electroweak scale within $1\sigma$ range of the latest global fit data. NH case maintains more stability than IH case. All the numerical values related to the absolute neutrino masses viz., $\Sigma |m_i|$, $m_{\beta}$ and $m_{ \beta \beta}$ are found to lie below the observational upper bound.</pre>

A case study for measuring the relativistic dipole of a galaxy cross-correlation with the Dark Energy Spectroscopic Instrument: Data Repository

This repository contains the synthetic catalogue for the DESI Bright Galaxy Survey produced wit the N-body code gevolution, which is analysed in the manuscript "<a href="https://arxiv.org/abs/2306.04213">A case study for measuring the relativistic dipole of a galaxy cross-correlation with the Dark Energy Spectroscopic Instrument</a>", as well as the raw data of the analysis results. The catalogue "catalogue.csv.bz2" is in the CSV format and can be directly read using the pandas library of python, for example. The columns in the catalogue contain the following information: 0. Column index 1. Comoving coordinate x (in units of Mpc/h) 2. Comoving coordinate y (in units of Mpc/h) 3. Comoving coordinate z (in units of Mpc/h) 4. Observed redshift 5. Cosine of the observed polar angle measured with respect to the axis pointing in the direction (1,1,1) along the box diagonal (the original comoving coordinate system has been rotated with an intrinsic z-y-z Euler rotation, first rotating along the z-axis with $\phi_1 = \pi/4$, then rotating along the new y axis with $\theta_2 = \mathrm{arccos}(1/\sqrt{3})$ and setting the final rotation angle to zero, $\phi_3 = 0$; hence to get the unperturbed mu and phi coordinates, one needs to rotate the comoving x, y and z coordinates with the corresponding inverse Euler rotation matrix) 6. Observed azimuthal angle phi measured with respect to axis pointing in the direction (1,1,1) along the box diagonal (the original comoving coordinate system has been rotated with an intrinsic z-y-z Euler rotation, first rotating along the z-axis with $\phi_1 = \pi/4$, then rotating along the new y axis with $\theta_2 = \mathrm{arccos}(1/\sqrt{3})$ and setting the final rotation angle to zero, $\phi_3 = 0$; hence to get the unperturbed mu and phi coordinates, one needs to rotate the comoving x, y and z coordinates with the corresponding inverse Euler rotation matrix) 7. Logarithm of the luminosity in units of solar luminosity $L_\odot$ 8. Observed flux (in units of $L_\odot/\mathrm{Mpc}^2$) 9. Number of particles in each object, plus a uniform noise between 0 and 1. This quantity is the proxy of the mass that was used to assign luminosity to the objects. 10. Flag that identifies the selected objects within each redshift bin. The Flag is 0 for objects not included in the catalogue, and equal to the mean redshift of the bins $\bar{z} = 0.25, 0.35, 0.45$ for the selected objects.  11. Flag that identifies the bright and faint objects for case 1 (50% bright, 50% faint, no flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects. 12. Flag that identifies the bright and faint objects for case 2 (90% bright, 10% faint, no flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects. 13. Flag that identifies the bright and faint objects for case 3 (50% bright, 50% faint, with flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects. 14. Flag that identifies the bright and faint objects for case 4 (90% bright, 10% faint, with flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects. The example script "example-script.ipynb" demonstrates how to query the catalogue to extract e.g. the redshift distribution of the objects for the different cases considered in Table 4 of the manuscript. Additionally, the measured dipole data vectors with the jackknife covariance matrices ($\mathrm{cov}^\mathrm{JK}_{ij}$), as well as the theoretical data vectors with the theoretical measurement covariance ($\mathrm{cov}^\mathrm{th}_{ij}$) and the theoretical prediction covariance ($\mathrm{cov}^\mathrm{pred}_{ij}$) are provided within this repository: <ul> <li>In the measurements.tar.gz archive, the measured data for the flux-limited case can be found in the /flux-limit subdirectory, while the data for the case without flux-limit is in /no-flux-limit. The data vectors are named "dipole_<redshift bin>_<% of bright galaxies>.txt. The first column in each of those files is the separation bin $d$ in $\mathrm{Mpc}/h$, the second column is the mean two-point correlation function dipole of the 100 jackknife subsamples, and the third column is the square root of the diagonal part of the jackknife covariance matrix ($\mathrm{cov}^\mathrm{JK}_{ij}$). The corresponding jackknife covariance matrices are named "cov_<redshift bin>_<% of bright galaxies>.txt.</li> <li>In the theory.tar.gz archive, the theoretical predictions are found in /flux-limit for the case with flux limit and in /no-flux-limit for the case without flux limit. The theoretical data vectors are named "dipole_<% of bright galaxies>B_z<redshift bin>_gevol.dat". The first column in each of those files is the separation bin $d$ in $\mathrm{Mpc}/h$, the second column the theoretical two-point correlation function dipole and the third column is the square root of the diagonal part of the theoretical prediction covariance matrix ($\mathrm{cov}^\mathrm{pred}_{ij}$) . The theoretical measurement covariance matrices are named "covariance_Lp6_<% of bright galaxies>B_z<redshift bin>_gevol.dat", and the theoretical prediction covariance matrices are named "covtheo_<% of bright galaxies>B_z<redshift bin>_gevol.dat". </li> </ul> The example script also demonstrates how to use these data files to reproduce plots of the dipole measurement vs the theoretical prediction like in Figures 6, 7, C1 and C2. The archives need to be unpacked before using the example script to access the data.

Stability of neutrino oscillation parameters at low energy scale with the variations of SUSY breaking scale under Renormalisation Group Equations

Stability of neutrino oscillation parameters at low energy scale with the variations of SUSY breaking scale under Renormalisation Group Equations

opencc-zeroJul 2023View details →

Dataset of avian samples collected and analyzed in: Genetic identification of avian samples recovered from solar energy installations

Renewable energy production and development will drastically affect how we meet global energy demands, while simultaneously reducing the impact of climate change. Although the possible effects of renewable energy production (mainly from solar- and wind-energy facilities) on wildlife have been explored, knowledge gaps still remain, and collecting data from wildlife remains (when negative interactions occur) at energy installations can act as a first step regarding the study of species and communities interacting with facilities. In the case of avian species, samples can be collected relatively easily (as compared to other sampling methods), but may only be able to be identified when morphological characteristics are diagnostic for a species. Therefore, many samples that appear as partial remains, or "feather spots" – known to be of avian origin but not readily assignable to species via morphology – may remain unidentified, reducing the efficiency of sample collection and the accuracy of patterns observed. To obtain data from these samples and ensure their identification and inclusion in subsequent analyses, we applied, for the first time, a DNA barcoding approach that uses mitochondrial genetic data to identify unknown avian samples collected at solar facilities to species. We also verified and compared identifications obtained by our genetic method to traditional morphological identifications using a blind test, and discuss discrepancies observed. Our results suggest that this genetic tool can be used to verify, correct, and supplement identifications made in the field and can produce data that allow accurate comparisons of avian interactions across facilities, locations, or technology types. We recommend implementing this genetic approach to ensure that unknown samples collected are efficiently identified and contribute to a better understanding of wildlife impacts at renewable energy projects.

opencc-by-4.0Oct 2022View details →

Energy of shallow tremors and moment of shallow very low frequency earthquakes in Hyuga-nada, southwest Japan

We estimated energies of shallow tremors detected by Yamashita et al. (2015) and Yamashita et al. (2021), and moments of shallow very low frequency earthquakes (VLFEs) detected by Asano et al. (2015) and those temporally correlated with shallow tremors detected by Yamashita et al. (2015) and Yamashita et al. (2021) in Hyuga-nada. We also evaluated scaled energies of shallow slow earthquakes in Hyuga-nada by the ratio of energy rate of tremors to moment rate of accompanying VLFEs. The method of estimation of energies of tremors and moments of VLFEs is written in Baba et al. (2024  <a href="https://doi.org/10.1093/gji/ggae039">https://doi.org/10.1093/gji/ggae039</a>). The content of each column is written in the first line of each file. The time is written in JST (UTC+9). tremor_2013.txt: Energy of shallow tremors in 2013 detected by Yamashita et al. (2015) (https://doi.org/10.1126/science.aaa4242). tremor_2015.txt: Energy of shallow tremors in 2015 detected by Yamashita et al. (2021) (https://doi.org/10.1186/s40623-021-01533-x). VLFE_2010.txt: Moments of shallow VLFEs in 2010 detected by Asano et al. (2015) (https://doi.org/10.1002/2014GL062165). VLFE_2013.txt: Moments of shallow VLFEs correlated with shallow tremors in 2013 detected by Yamashita et al. (2015) (https://doi.org/10.1126/science.aaa4242) and scaled energy of slow earthquakes. VLFE_2015.txt: Moments of shallow VLFEs correlated with shallow tremors in 2015 detected by Yamashita et al. (2021) (https://doi.org/10.1186/s40623-021-01533-x) and scaled energy of slow earthquakes. (Version 2: site amplification factors of tremor energy estimation at the reference station N.TASF was changed to be set as 2)

EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 3: Actors and Organisations

This document is Part 3 of the EnergyPROSPECTS Factsheet Series. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe. EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.

EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 6: Aspects of ENCI II.: Frontrunners and late adopters, pragmatic and transformative ENCI

This document is Part 6 of the EnergyPROSPECTS Factsheet Series. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship (ENCI) between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe. EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.

EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 8: Aspects of ENCI III.: Towards environmental sustainability

This document is Part 8 of the EnergyPROSPECTS Factsheet Series. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship (ENCI) between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe. EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.

openother-openAug 2023View details →

Photometry of outer Solar System objects from the Dark Energy Survey I: photometric methods, light curve distributions and trans-Neptunian binaries - data release

This repository contains the full data release for the 814 outer Solar System objects found in the Dark Energy Survey. A full description of the object search is described in <a href="http://(https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022)</a>, and a full description of the photometric processing is described in<a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230403017B/abstract"> Bernardinelli et al (2023)</a>. If you use these files, we ask you to cite the corresponding papers. The FITS table `y6_des_tnos_color.fits` contains both the orbital elements and the colors for each object. The full description of the orbital element information is given in Table 3 of <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022</a>). In addition to these, the table also includes the mean absolute magnitudes in each band, as well as mean $(g-r, r-i, r-z)$ colors and their corresponding covariance matrix, and the 68% confidence interval for the lightcurve amplitude. Inside the `fluxes` directory, the complete photometric record for each object is included in a `hdf5` file (for each object), and the MCMC chains for their fluxes and LCAs. The three Jupyter Notebooks included in the `notebooks` directory explains the columns and how to use these files to reproduce the results of the paper. Inside this directory there are also additional files needed to reproduce the code. The `binary` directory has the MCMC chains for their mutual orbits (in `.npy` files), as well as the astrometric and photometric record for the binary measurements. Another `README` file is included in that directory with a detailed explanation.

Data Set Accompanying "Free Energy Decompositions Illuminate Synergistic Effects in Interfacial Binding Thermodynamics of Mixed Surfactant Systems"

This data set accompanies "Free Energy Decompositions Illuminate Synergistic Effects in Interfacial Binding Thermodynamics of Mixed Surfactant Systems" by Colin K. Egan and Ali Hassanali.  It includes example GROMACS input files for all simulations analyzed in the paper, as well as example data sets and analysis scripts.  See https://doi.org/10.26434/chemrxiv-2023-h11k5 for the preprint manuscript.

opencc-zeroAug 2023View details →

Dataset for: Utilizing high-resolution genetic markers to track population-level exposure of migratory birds to renewable energy development

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.

opencc-zeroAug 2023View details →

A meta-analysis investigating the effects of energy infrastructure proximity on grouse demography and space use

The increased global demand for energy will require additional tools to help guide policy and management actions to conserve wildlife. Grouse (Tetraoninae) are adversely affected by infrastructure associated with energy development, but the magnitude of effects are difficult to quantify in a singular management prescription. Advancement in monitoring and analysis techniques have allowed researchers to evaluate complex questions surrounding the effects of infrastructure on grouse populations, rapidly increasing our knowledge. To better inform management decisions, especially with the emergence of renewable energy, a quantitative synthesis of previous research evaluating the effects of infrastructure on grouse populations is needed. We reviewed studies evaluating the effect of energy infrastructure on grouse, with the main objective to determine the magnitude of effect on grouse lek attendance, resource selection, and survival to help inform future conservation actions. We modeled slope coefficients for distance to energy infrastructure, standardized by scale, on various behaviors to determine overall effect sizes in a meta-analysis. We used 93 study-result combinations from 21 studies that directly evaluated resource selection, survival, or lek attendance relative to energy infrastructure. Trends in overall effect sizes suggest an adverse effect of distance to energy infrastructure on grouse behavior; however, the combination of non-significant pooled regression slopes and high among-study heterogeneity suggest the effect of distance to energy infrastructure is context dependent. While distance to infrastructure is a common metric used in many grouse management plans, our results suggest distance to infrastructure may not be a reliable predictor of grouse behavior and the effect is context dependent making management prescriptions based solely on distance to infrastructure in a one size fits all approach difficult. Our analysis points to numerous aspects that scientists can improve upon by evaluating density in conjunction with distance to energy infrastructure as well as reporting the necessary statistics for future meta-analyses.

openAug 2023View details →

UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2017

<div> <h3>UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2017</h3> Overview The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using Deep Forest machine learning models. It integrates satellite-based Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. The UFLUX project includes five core flux components: <ul> <li> Gross Primary Production (GPP) </li> <li> Ecosystem Respiration (RECO) </li> <li> Net Ecosystem Exchange (NEE) </li> <li> Sensible Heat Flux (H) </li> <li> Latent Energy Flux (LE) </li> </ul> Background and Methodology The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change. Key innovations of UFLUX include: <ol> <li> Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data. </li> <li> Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). </li> <li> Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. </li> <li> Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches. </li> <li> Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods. </li> <li> High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop. </li> <li> Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. </li> </ol> Applications UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements. Resources <ul> <li>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></li> <li> Code Repository: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a> </li> <li> Technical & Descriptive Publication: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a> </li> </ul> </div>

Data for A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers

This repository contains dataset for the paper entitled "A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers". The magnitude of inverse energy cascade flux is revised in version 2.0 according to Xia et al. (2009) (https://doi.org/10.1063/1.3275861).

Data used for modeling in Energy-water-land-CCUS nexus model: carbon dioxide opportunities based on optimized regional development

In this dataset, the data used for modeling technologies in an energy-water-land-CCUS nexus model in Khark Island in Iran, and the main sources for gathering them are presented.

Astrometric Calibration and Performance of the Dark Energy Spectroscopic Instrument Focal Plane

Supplementary material to DESI's publication Astrometric Calibration and Performance of the Dark Energy Spectroscopic Instrument Focal Plane. Figure 6: Astrometric trends   trend.dat Figure 7: Deformation residuals   gfa-south-west.dat Figure 10: Turbulence   e2c.dat   b2c.dat Figure 11: Turbulence Corrected   turbcorr.dat Figure 13: Quiver residuals   quiver-R.dat Figure 14: Dither offsets   dither20220518-R.dat Figure 15: Chromatic residuals   chromatic.dat Figure 16: Temperature dependence    tempscale.dat

Code: Decarbonization Employment and Energy Systems (DEERS) Model

The Decarbonization Employment and Energy Systems (DEERS) model is a data-driven framework for estimating labor market pathways of large-scale, low-carbon energy-supply infrastructure development. The DEERS model is designed as a tool to inform regional and national workforce and infrastructure planning and policy-making in the U.S. The model simulates the distribution of labor effects over time and across economic sectors, resource sectors, occupations, and geography for multi-decadal energy-supply system transition scenarios. The model is used to estimate employment demand and wages, as well as experience, education, and training requirements, across domestic energy supply chains. We also incorporate time-variant factors, such as labor productivity and wage inflation, which are especially important in the context of emerging labor markets and long-term transitions. The DEERS model is adaptable to different energy system contexts and readily coupled with regional and downscaled macro-energy system modeling outputs. It can also be used to explore modifiable workforce and infrastructure planning and policy decisions, such as high road labor policies, siting domestic manufacturing facilities, creating just transition funds, and changing fossil fuel exports over time.