Find research datasets worth reusing

opencc-by-4.0Oct 2017View details →

Data and R script for 'Early-life begging effort reduces adult body mass but strengthens behavioural defence of the rate of energy intake in European starlings (Sturnus vulgaris)'

Data files and R script for Dunn et al. "Early-life begging effort reduces adult body mass but strengthens behavioural defence of the rate of energy intake in European starlings (Sturnus vulgaris)" Includes a single R script that produces all the analyses in the paper. The script makes use of three different .csv data files.

opencc-by-4.0Jan 2018View details →

Supplementary Data: Full Results: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system

Supplementary Data <a href="https://arxiv.org/abs/1801.05290">Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</a> Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner <a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a> The files in this record contain the full output data from each of the scenarios considered in the above publication. They also include the post-processed input data, which might be useful if you want to rerun the scenarios with only small changes to the input data. The scripts to build the model, input data and result summaries can be found in a <a href="https://zenodo.org/record/1146665">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.) For each scenario, there is a <a href="https://github.com/PyPSA/PyPSA">PyPSA</a> network file in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> and a CSV of shadow prices. To read in a network file do: <pre><code class="language-python">import pypsa network = pypsa.Network("network_file_name.h5")</code></pre> All data is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).

opencc-by-4.0Jan 2024View details →

Data for "Sintering activation energies of anisotropic layered and particle alumina/zirconia-based composites and their mechanical response"

The dataset contains several folders that provide open data files for the manuscript "Sintering activation energies of anisotropic layered and particle alumina/zirconia-based composites and their mechanical response".

The fifth dimension, the source of energy and definition of time

It's a new approach to physics, this theory changed physics modern

Comparability and Reproducibility in HPC Applications' Energy Consumption Characterization

The computational power of HPC systems continues to grow, and improving their energy efficiency is a critical issue for the field in the face of climate change and energy crises. One major aspect of energy optimization lies in the applications run on the systems themselves. In this work, we are looking into comparing energy consumption between different systems using a characterization process based on the recent energy characterization paper as a reference and starting point for other data centers to assess their application’s energy patterns. We demonstrated that we could use the methods from the starting paper, replicate the findings, and extend the work to more applications and more systems. Our work acts as a proof of concept for a repository of HPC applications’ energy patterns in our future work. This is the collection of jobscripts, data, and python scripts used in the paper.

opencc-by-4.0Sep 2023View details →

Wind shadows from U.S. east coast offshore wind energy lease areas.

Georeferenced data layers describing whole wind farm wakes (wind shadows) for use in planning and development along the U.S. east coast based on WRF simulations performed using the accompanying namelist. Full details of the analysis are provided in: Pryor and Barthelmie: Wind shadows impact planning of large offshore wind farms   This work is supported by the U.S. Department of Energy (DoE) (DE-SC0016605). The research used computing resources from the National Science Foundation: Extreme Science and Engineering Discovery Environment (XSEDE) (allocation award to SCP is TG-ATM170024) and National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

Consider Social Acceptability of Contentious Low-Carbon Energy Technologies

SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU’s transition to carbon neutrality.  SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages –  including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events. This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU’s journey to a sustainable future. The documents uploaded here are part of WP2 whereby novel, interdisciplinary teams were provided funding to undertake activities to develop a policy recommendation related to EU Green Deal policy. Each of these policy recommendations, and the activities that inform them, will be written-up as a chapter in an edited book collection. Three books will make up this edited collection - one on climate, one on energy and one on mobility.   The present project proposes to conduct a research on social acceptability of three controversial technologies in France: nuclear fusion, agrivoltaics and offshore wind turbines. We review the literature  and develop a framework for studying the dynamics of acceptability of these technologies over the entire 2013-2023 period.  This file contains: <ul> <li>Appendix 1 : This output uploaded is the literary corpus from the EUROPRESSE database used to carry out the sentimental analysis and the topics modelling</li> <li>Appendix 2 : The scraping results of the corpus from the EUROPRESSE database</li> <li>Appendix 3 : The topic modeling results of the corpus from the EUROPRESSE database</li> <li>Appendix 4 : The sentimental analysis results of the corpus from the EUROPRESSE database</li> <li>Appendix 5 : This file  provides a comprehensive analysis of the factors shaping the social acceptability of low-carbon energy technologies in France</li> <li>Appendix 6 : This file sorted the 10 most positive and the 10 most negative articles associated with each low-carbon energy technology</li> </ul>

opencc-by-4.0Feb 2024View details →

First Principles Validation of Energy Barriers in Ni75Al25

The data from the paper - First Principles Validation of Energy Barriers in Ni75Al25 Read the read me for explanation of what is in each folder

Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition

<ul> <li>Name: Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition</li> <li>Summary: This dataset contains answers from a panel of experts to build a) a taxonomy of determinants that explain the investment decision making on assets related to the energy transition, b) the individual contributions when sorting the taxonomy of determinantes on the different stages of the transtheoretical model for different archetypes of persons and c) the causal diagrams agreed between the different groups of experts.</li> <li>License: cc-BY-SA</li> <li>Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li>Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li>Collection Date: 22/07/2022</li> <li>Publication Date: 01/06/2024</li> <li>DOI: 10.5281/zenodo.11234441</li> <li>Other repositories:</li> <li>Author: University of Deusto</li> <li>Objective of collection: This data was originally collected to build a set of causal diagrams of the .</li> <li>Description: <ul> <li>Scenarios: This dataset contains the description of 20 different scenarios used in this research activity. </li> <li>File 1 - individual reasons to be coded This dataset compiles the reasons given by experts of different panels of the Intrinsic and Extrinsic Determinants, and the Barriers and potential Rebound effects of citizens towards a set of 20 different scenarios. The file contains the following sheets: <ul> <li>Methodology: Methodology followed by the coders.</li> <li>Help: Short summary of the Social Cognitive Theor and Self Determination Theory used for coding. </li> <li>Glossary: Glossary of terms build by the experts coding the answers. </li> <li>Appliances/Flexibility/Buildings/Mobility: The contributions of each expert, the code provided by the two researchers and the consensus achived. </li> <li>Summary: Assesment of the results.</li> </ul> </li> <li>File 2 - individual microdata to sort determinants into causal threads from expertsThis dataset includes the individual sortings made by the experts of the taxonomy of determinantes into each one of the stages of the transtheoretical model. The file includes one sheet per expert where he/she has sort each determinant for each arquetype into the stage he/she thinks is more relevant to advance to the next step of the TTM. </li> <li>File 3 - collective microdata to sort determinants into causal threads from EU and LATAM experts This dataset compiles the results, stage by stage, of the consensus reached by each panel regarding the determining factors that make up each of the archetypes in the contexts of Europe (EU) and Latin America (LATAM). And in which stage of the change of the Transtheoretical Model (TTM) the factors should appears. <ul> <li> Stage 1: The panels reached a consensus on the factors that describe each of the archetypes in their context. In the case of Latin America, for the panels of some countries, the existence of all eight archetypes was not evident. The number of archetypes analysed by each panel is indicated in parentheses in the following list: <ul> <li> European panels: Group – F (8), Group–A (8). Group–FF (8), Group–M (4) </li> <li> Latin America panels: Group-MX (5), Group-CO (8), Group-CL (7), Group-SV (7) </li> </ul> </li> </ul> <ul> <li> Stage 2: For each of the eight archetypes, the results of the consensus for each panel are consolidated in the tabs indicated in the list below. The column on the far right shows the weights (percentage) of each factor in each stage of the TTM: Archetype-EarlyAdopter, Archetype-Uninterested, Archetype-HomoEconomicus, Archetype-Fearful, Archetype-Stubborn, Archetype-Influencer, Archetype-Careful and Archetype-Activist. </li> <li> Stage3: In the "Archetypes - Consensus Results" tab, the weights of the factors for each archetype are consolidated. The far-right column calculates the average weight of each factor at each stage of the TTM (Transtheoretical Model of Change). </li> <li> Stage 4. In the “EU vs Latam - split context” sheet, it is presented a comparative assessment between the European and Latin American results. The comparison has four tables: <ul> <li> Table (s): Difference and Agreements between both context: European & Latin American Archetypes.  The table highlights the regions of determinants that mark the differences between both contexts for each archetype. If a determinant is identified by both contexts (EU, Latam), it is considered an agreement and allocated to the early TTM stage. The remaining determinants highlight the differences between the two contexts. European (-1) & Latin American (1) Archetypes FINAL Consensus (0) on TTM Stages. </li> <li> Table (t): This table shows the difference (E, L) and agreements (X) between both context: European (E) & Latin American (L) Archetypes. </li> <li> Table (t.1): This table shows just the agreements (X) between both context: European & Latin American Archetypes. </li> <li> Table (t.2): Show the difference between both context: European (E) & Latin American Archetypes (L). </li> <li> Table (t.3): This table shows the differences (E, L) and agreements (X) between both contexts: European (E) & Latin American (L) archetypes. In this table, the main regions of factors for each archetype are coloured to highlight the set of factors that make the main differences. </li> </ul> </li> </ul> </li> </ul> </li> <li>5 star: ⭐⭐⭐</li> <li>Preprocessing steps: Data transcription from written documents and oral discussions.</li> <li>Reuse: NA</li> <li>Update policy: No more updates are planned.</li> <li>Ethics and legal aspects: Names of the persons involved have been removed. </li> <li>Technical aspects: </li> <li>Other:</li> </ul>

Industrial Two-phase Olive Pomace Slurry-Derived Hydrochar Fuel for Energy Applications

This dataset contains extended data files for the publication entitled "Industrial Two-phase Olive Pomace Slurry-Derived Hydrochar Fuel for Energy Applications." Data files includes:  - Dataset description.txt (provides abbreviations or codes of samples and their properties) - Extended data.xlsx (data files for the biochemical, proximate, ultimate, HHV, and mineral characterisitics of raw material (two-phase olive pomace slurry) and hydrochars - 13C-NMR data.zip (raw data files for 13C-solid nuclear magnetic resonance analysis of raw material and hydrochars) - TGA-DTA data.zip (raw data for thermal gravimetric analysis of raw material and hydrochars) - FTIR data.zip (raw data for fourier transform infrared analysis of raw material and hydrochars)  Checksum numbers for enclosed data files:  - MD5 Checksum number for file named Extended dataset v1.0 = 6c542debe0a8b4297b8730830e7d5b58 - MD5 Checksum number for file named 13C-NMR data = bb25d31771d281ce1ca6518c4dc1cb41   - MD5 Checksum number for file named FTIR data = 0df81ec3a25b4d130be09cef59736d30 - MD5 Checksum number for file named TGA-DTA data = 7205cc5a24daa94e60c82a43ae748fb2

Energy Security: Global analysis of Energy Matrix demand Mozambique case

Energy system modelling, energy security, energy transition, renewable  energy, climate change, OSeMOSYS.

opencc-by-4.0Jun 2024View details →

Pose Selector Workflow - Docking Poses, Absolute Binding Free Energy Estimates and Structure Input Files for Machine Learning

The Pose Selector (PS) workflow calculates absolute binding free energies (ABFEs) for binding poses of protein-ligand complexes. First, it converts the binding poses (both docking poses as well as experimentally observed ligand binding poses), which are provided as a combination of protein PDB file and ligand MOL2 file, into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks and repair steps. Next, the PS workflow post-processes and analyses the last frame of the resulting eight 100 ps trajectories per binding pose with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the ABFE 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 PS workflow was run on docking poses generated for the PDBbind 2020 dataset (http://www.pdbbind.org.cn/index.php), shared in dockingPosesPDBBind2020.tar.gz. This entry and its partner entry 10.5281/zenodo.11397486 also share the intial coordinates used in the MD simulations of >800,000 docking poses of 4022 protein-ligand complexes (structureFiles_dockingPoses1.tar.gz in this entry and structureFiles_dockingPoses2.tar.gz in 10.5281/zenodo.11397486) and of the experimental ligand binding pose of 4549 complexes (structureFiles_experimentalStructures.tar.gz) as well as the corresponding ABFE estimates (absoluteBindingFreeEnergyEstimates.tar.gz). The MD simulations were run on the LUMI and MeluXina supercomputers while the implicit-solvent calculations were carried out on Galileo (Cineca). The README file describes the structure of the shared data in more detail and points out how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates as well as how to use the data provided in this entry to train a machine-learning model predicting the ABFE of binding poses of protein-ligand complexes. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Pose-Selector-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/).

Datasets of DFT adsorption energies of H and for O and OH on different pure metals and binary intermetallic compounds considering the application of elastic strains and lists of candidates for screening

This resource contains two datasets and two lists of candidates for screening in JSON format. Also It contains ZIP folders with all Quantum Espresso Inputs and outputs from which the JSON datasets were obtained. All Quantum Espresso outputs will be later added to Catalysis Hub (https://www.catalysis-hub.org/). The file "QuantumEspresso_versions" is a text file contaning the information of the Quantum Espresso versions employed for obtaining the dataset. The datasets contain the adsorption energies for surface slabs of a large number of binary intermetallic compounds with different compositions and lattices (for instance, A3B fcc, A3B hpc, AB bcc, etc.). Adsorption energies were computed for different adsorbates (H, O, and OH) on distinct adsorption sites (e.g., fcc AAB, fcc AAA, hcp AAA, hcp AAB, on-top A, and on-top B) and minimum energy surfaces. In addition, different elastic strains (biaxial tension, biaxial compression) were applied to assess their effect on adsorption energies. All calculations were carried out using DFT approximations as implemented in the Open-source software Quantum Espresso. Besides the adsorption energies, the datasets also contain relevant geometric and electronic descriptors (PSI, cell volume, weighted atomic radius, generalized coordination number, weighted electronegativity, weighted first ionization energy, outer electrons, and biaxial strain)  calculated to feed them as features in the training of ML models. The datasets with the tag "scaled" on its name have the descriptors scaled following a MinMax scaling and are given in xlsx format. The lists for screening contain candidates not included in the dataset for which Random Forest predictions of the Eads were obtained. The lists contain the geometric and electronic descriptors of all screening candidates, as well as the predicted adsorption energy (Eads_RF). A GitHub repository is linked to this dataset (https://github.com/vvassilevg/HighHydrogenML). The repository contains two Python scripts: 1) Script for creating a dataset from QuantumEspresso outputs, where all relevant descriptors are computed. It outputs a pickle and json files that can be later converted to any other desired format (like xlsx). 2) Script for training a Random Forest model for the prediction of adsorption energies (the datasets with the "scaled" tag must be used for the script to work correctly).   The dataset, ML model and screening have been accepted for publication in Catalysis Science & Technology DOI: DOI:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D</a>. The accepted Manuscript and the Supplementary information are avilable within this repository.   If you use this dataset or any of the files within this repository, please cite the original publication (<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D)</a> in your work.

Fig. 4 in Angiostrongylus cantonensis induces energy imbalance and dyskinesia in mice by reducing the expression of melanin-concentrating hormone

Fig. 4 AC infection causes imbalance in GLU and lipid metabolism in mice. A Serum biochemical tests (n = 6), including GLU, CHO, TG, HDL, LDL. B, C GLU tolerance and AUC curve (n = 3). D, E Immunohistochemical images and statistics of UCP1 in mouse gWAT (n = 3). Data are presented as mean ± SD. Compared with the control group, statistical significance is indicated as *P <0.05, **P <0.01, ***P <0.001, ****P <0.0001. GLU glucose, TG triglycerides, LDL low-density lipoprotein, HDL high-density lipoprotein, TC total cholesterol, AUC area under the curve

Fig. 3 in Angiostrongylus cantonensis induces energy imbalance and dyskinesia in mice by reducing the expression of melanin-concentrating hormone

Fig. 3 AC infection leads to extensive loss of adipose tissue in mice. A Macroscopic images. B, C, D Statistical chart of tissue weight proportion to body weight in different parts (n = 6). E Pathological sections of adipose tissue. F, G Statistical chart of the average single-cell area of iWAT and gWAT (n = 3). Data are presented as mean ± SD. Compared with the control group, statistical significance is denoted as *P <0.05, **P <0.01, ****P <0.0001. BAT brown adipose tissue, iWAT inguinal white adipose tissue, gWAT gonadal white adipose tissue

Fig. 8 in Angiostrongylus cantonensis induces energy imbalance and dyskinesia in mice by reducing the expression of melanin-concentrating hormone

Fig. 8 Effect of intranasal MCH on synapse-related proteins. A Transcription levels of Bcl2, Map2, PSD95, Syp in mouse cortex (n = 5). B The expression levels of MAP2, PSD95, and SYP were evaluated by western blotting. C Densitometrical quantification of the blots after normalizing with β-actin (n = 3). Data are presented as mean ± SD. Compared with the AC group, statistical significance is indicated as *P <0.05, **P <0.01. Bcl2 B cell leukemia/lymphoma 2, Map2 microtubule-associated protein 2, PSD95 postsynaptic density protein 95, Syp Synaptophysin, AC Angiostrongylus cantonensis, MCH melanin-concentrating hormone

Fig. 1 in Angiostrongylus cantonensis induces energy imbalance and dyskinesia in mice by reducing the expression of melanin-concentrating hormone

Fig. 1 AC infection reduces MCH expression in mice. A Differential volcano plot illustrating brain transcriptome changes in AC-infected mice. B RT– qPCR analysis of Pmch mRNA transcription levels in the whole brain (n = 3) and hypothalamus (n = 4). C, D Panoramic localization of MCH in coronal brain sections. Part D is an enlarged view of part C. E, F Representative images of MCH costained with NeuN and GFAP. G, H Representative images depicting MCH immunofluorescence in hypothalamic regions, along with fluorescence intensity statistics (n = 3). Data are presented as mean ± SD. Compared with the control group, statistical significance is denoted as *P <0.05, ****P <0.0001. NotSig not significant, Pmch pro-melanin-concentrating hormone, dpi days post infection, MCH melanin-concentrating hormone, DAPI 4′,6-diamidino-2-phenylindole, NeuN neuronal nuclei, GFAP glial fibrillary acidic protein, 3V 3rd ventricle, ARH hypothalamic arcuate nucleus, DMH dorsomedial hypothalamus, LHA lateral hypothalamic area, VMH ventromedial hypothalamic nucleus, ZI zona incerta

Fig. 5 in Angiostrongylus cantonensis induces energy imbalance and dyskinesia in mice by reducing the expression of melanin-concentrating hormone

Fig. 5 AC infection causes neurological impairment and dyskinesia in mice. A Neurological function score (n ≥ 4). B MWM trajectory. C, D, E, F Statistics of platform crossings, percentage time in target quadrant, distance, and velocity in MWM (n ≥ 4). G, H Changes in running wheel activity and statistics (n ≥ 5). Data are presented as mean ± SD. Compared with the control group, statistical significance is indicated as *P <0.05, **P <0.01, ***P <0.001, ****P <0.0001. dpi days post infection

Fig. 7 in Angiostrongylus cantonensis induces energy imbalance and dyskinesia in mice by reducing the expression of melanin-concentrating hormone

Fig. 7 MCH improves neurological function and dyskinesia in mice. A Y-maze movement trajectory. B, C, D Statistics of Y-maze free alternation rate, distance, and velocity (n ≥ 6). E NOR test trajectory. F, G, H Statistics of recognition index, mouse travel distance, and velocity in NOR test (n ≥ 5). I MWM movement trajectory. J, K, L, M Statistics of platform crossings, percentage time in target quadrant, distance, and velocity in MWM (n ≥ 5). N Statistics of the time spent in the pole test (n = 4). O Neurological function score (n = 6). Data are presented as mean ± SD. Compared with the AC group, statistical significance is indicated as *P <0.05, **P <0.01, ***P <0.001, ****P <0.0001. AC Angiostrongylus cantonensis, MCH melanin-concentrating hormone