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.
4,230
datasets available to search
ShareScore release 0.9.0
Dataset results
4,230 results for “Energie”
Datasets for "Hydrodynamic and hydromagnetic energy spectra from large eddy simulations"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Hydrodynamic and hydromagnetic energy spectra from large eddy simulations" Haugen & Brandenburg. If anything turns our to be incomplete, please email brandenb@nordita.org.</pre>
Energy spectra and eigenvectors of the sine-Gordon and double sine-Gordon model
<p>This dataset contains low-energy spectra and eigenvectors of two (1+1)-dimensional Quantum Field Theory models, the sine-Gordon (SG) and the double sine-Gordon (DSG) model, for a representative choice of parameter values. The data were computed using the Truncated Conformal Space Approach (TCSA), which is a Hamiltonian truncation method.</p> <p> </p> <p><strong>Parameters:</strong></p> <ul> <li>Cosine frequencies: <em>β</em> = 2.49239 for SG and <em>β</em><sub>1</sub> = 1.01066 and <em>β</em><sub>2</sub> = 2.49239 for DSG</li> <li>Dimensionless (mass)⨉(system size) parameter (<em>m</em>: first SG breather mass, <em>L</em>: system size): <em>mL</em> = 0.01, 0.1, 1, 2, 5</li> </ul> <p><strong>TCSA details: </strong></p> <ul> <li>truncation basis: free massless boson CFT with Dirichlet boundary conditions, restricted to the ground state symmetry sector</li> <li>truncation cutoff (maximum CFT energy shell): 42 </li> <li>basis size: 85674</li> </ul> <p>The spectra correspond to the full list of eigenvalues of the truncated Hamiltonian matrices in increasing order, and the eigenvectors correspond to matrices of dimensions 5173⨉5173, corresponding to the components of the lowest 5173 energy levels in the lowest 5173 basis states (the best convergent part of the eigenvector matrix at the top left corner). Each eigenvector corresponds to a column of the above matrices, in the same order as the eigenvalues.</p> <p><strong>Format:</strong></p> <p>Python NumPy .npy files</p> <p>The filenames are of the form: <em>descriptor</em>_<em>model</em>_mL<em>x</em>.npy</p> <p>where:</p> <p><em>descriptor</em> = "Spectrum" or "Eigenvectors" </p> <p><em>model</em> = "SG" or "DSG"</p> <p><em>x</em> = 0.01, 0.1, 1, 2 or 5 (<em>mL</em> value)</p>
Viskari et al. (2019) The influence of canopy radiation parameter uncertainty on model projections of terrestrial carbon and energy cycling
<p>Zenodo DOI release for permanent archiving outside of GitHub</p>
The Open Energy Ontology - Survey
<p>These files document the first survey conducted on the Open Energy Ontology. The analysis covers consistency of terms as well as term coverage.</p>
Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel
<p>Raw data associated with a paper submission.<br> " Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel" submitted to Additive Manufacturing.</p> <p>Contained are all the raw images used in figures, as well as csv's of any data pltoted in graphs.</p> <p>Raw images captured during printing of various processing parameters<br> EBSD scans (.ctf) of all disucssed samples </p> <p>Wall definitions (EBSD compared to paper)<br> Wall 1 - Wall A1 300 W 2750 mm/s<br> Wall 2 - Wall D 500 W 2250 mm/s<br> Wall 3 - Wall B 300 W 2250 mm/s<br> Wall 4 - Wall C 500 W 2750 mm/s<br> Wall 5 - Wall A2 300 W 2750 mm/s</p>
Beyond cost reduction: Improving the value of energy storage.
<p>The here provided ".nc" files are data files from the paper "Beyond cost reduction: Improving the value of energy storage". The data files represent 3 scenarios from a European energy system model PyPSA-Eur. It can be used as input to the Jupyter notebook analysis and plotting scripts provided in <a href="https://github.com/pz-max/Beyond-cost-reduction-Improving-the-value-of-energy-storage">GitHub.</a></p>
Adaptation and energy demand systematic mapping of the literature dataset
<p>Those files form a database of all the informations extracted during the associated systematic review : <a href="https://doi.org/10.1088/1748-9326/abc044">When adaptation increases energy demand: a systematic map of the literature</a></p> <p>ReadMe.pdf provides a detailled notice of the dataset.</p> <p>This dataset contains 1 SQLite file and for convenience 9 CSV files which are the tables contained in the SQLite file. CSV files are given both in Windows and Linux format in separate folders.</p>
Underlying data - Results from the Open Call: How Citizens can participate in solar energy research?
<p>Underlying data to the "Results from the Open Call: How Citizens can participate in solar energy research?" @</p> <pre>https://zenodo.org/record/3554901#.YAgimxaCE2w</pre> <p>Answers to the online survey in "Call for ideas_answers online_survey.xlsx"</p> <p>Notes from the World Cafe and other meetings from the secretaries: "notes_MMLs_GRECO_2019.pdf</p> <p> </p>
US Department of Energy funded publications from Astrophysics Data System
<p>The John G. Wolbach Library, in collaboration with the NASA Astrophysics Data System (ADS), has compiled this bibliography of United States Department of Energy (DOE) funded publications. Grants were identified within the ADS by regular expression matching against article full text. There are over 70,000 articles in the bibliography, with bibliographic information for each publication. This bibliography is being released to explore its possible usage.</p> <p>Please note:</p> <p>Papers may include arXiv preprints (as non-refereed, with arXiv bibcode) and their subsequent journal articles (as refereed, with journal bibcode) as separate and unlinked records</p> <p>This compilation is a snapshot in time for DOE grants and ADS papers</p> <p>The method of data collection does not differentiate between grants credited with supporting projects and those supporting individuals contributing to projects.</p> <p>Results are based on available metadata which may be incomplete. This may include incomplete records of grant identifiers, due to limitations within the ADS. However, all articles in the bibliography acknowledge the support of the DOE in some way.</p> <p>Results have not been individually verified</p> <p>All abstracts and articles in the ADS are copyrighted by the publisher, and their use is free for personal use only. For more information, please read the Terms and Conditions regulating usage of resources.</p> <p>This dataset contains bibliographic information only, no information on the grants themselves is included. A potential application of the data could be to link the bibliographic information to grant information.</p> <p><br /> Links</p> <p>Harvard-Smithsonian Center for Astrophysics John G. Wolbach Library http://www.cfa.harvard.edu/lib/information/about.html</p> <p>NASA ADS http://labs.adsabs.harvard.edu/</p> <p>USA Spending: information on US government spending, including the Department of Energy: http://www.usaspending.gov</p> <p>Grants.gov, more information on US government grants: http://www.grants.gov</p> <p>Department of Energy: http://energy.gov/</p>
WiggleZ Dark Energy Survey Baryon Acoustic Oscillation Random Catalogues
<p>Data products associated with the WiggleZ Dark Energy survey measurement of the Baryon Acoustic Oscillations (BAO), as described in Blake et al. arXiv:1108.2635 (2011, MNRAS, 418, 1707)</p> <p>Data is given for 6 WiggleZ regions (01, 03, 09, 11, 15, 22 hrs) in 3 different overlapping redshift slices (z=0.2-0.6, 0.4-0.8, 0.6-1.0), matching the datasets analyzed in the final WiggleZ baryon acoustic peak paper Blake et al. (2011, MNRAS, 418, 1707). For each sub-region the following files are given:</p> <p><strong>Correlation function:</strong> xi_**hr*.dat - correlation function measurement and covariance matrix for each sub-region. Format of the file:</p> <ul> <li>1st line: nbin, ngalaxy</li> <li>nbin lines: mean separation [Mpc/h], xi(s), sqrt[Cov(i,i)]</li> <li>nbin x nbin lines: i, j, Cov(i,j)</li> </ul> <p>The covariance matrix is from lognormal realizations, not jack-knife regions.</p> <p><strong>Combined correlation function:</strong> xi_combined*.dat - correlation function measurement for each redshift range combining the measurements in each sub-region, in the same format as above.</p> <p><strong>Integral constant:</strong> wigglez_ic.dat - integral constraint correction which should be added to the measured correlation function, format of the file is:</p> <ul> <li>Region [hr],</li> <li>zmin,</li> <li>zmax,</li> <li>integral constraint delta-xi</li> </ul> <p>We are considering adding another 90 random catalogues in the near future. For now the random catalogues used in the previous analysis are available below.</p>
Supplementary underlying data for "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"
<p>This dataset includes additional underlying data for the publication "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"</p> <p>Contents:</p> <p>Tukey Honest Significant Difference (HSD) results for solutes 1-47 across all seven parameter sets, as *.txt. These are pairwise comparisons of results between all possible parameter sets. Significant differences are treated as p < 0.05.</p>
Energy Mining
<p><strong>Overview of Data</strong></p> <p>Excessive energy consumption in mobile apps can be a consequence of energy greedy hardware, bad programming practices, or particular API usage patterns. We present the largest to date quantitative and qualitative empirical investigation into the categories of API calls and usage patterns that—in the context of the Android development framework—exhibit particularly high energy consumption profiles. By using a hardware power monitor, we measure energy consumption of method calls when executing typical usage scenarios in 55 mobile apps from different domains. Based on the collected data, we mine and analyze energy-greedy APIs and usage patterns. We zoom in and discuss the cases where either the anomalous energy consumption is unavoidable or where it is due to suboptimal usage or choice of APIs. Finally, we synthesize our findings into actionable knowledge and recipes for developers on how to reduce energy consumption while using certain categories of Android APIs and patterns</p> <p><strong>Abstract</strong></p> <p>Energy consumption of mobile applications is nowadays a hot topic, given the widespread use of mobile devices. The high demand for features and improved user experience, given the available powerful hardware, tend to increase the apps’ energy consumption. However, excessive energy consumption in mobile apps could also be a consequence of energy greedy hardware, bad programming practices, or particular API usage patterns. We present the largest to date quantitative and qualitative empirical investigation into the categories of API calls and usage patterns that—in the context of the Android development framework—exhibit particularly high energy consumption profiles. By using a hardware power monitor, we measure energy consumption of method calls when executing typical usage scenarios in 55 mobile apps from different domains. Based on the collected data, we mine and analyze energy-greedy APIs and usage patterns. We zoom in and discuss the cases where either the anomalous energy consumption is unavoidable or where it is due to suboptimal usage or choice of APIs. Finally, we synthesize our findings into actionable knowledge and recipes for developers on how to reduce energy consumption while using certain categories of Android APIs and patterns.</p>
Convergence plot Crack Release Energy for Mode-I opening
<p>Convergence plot for the mode-I opening of the infinite body with planar crack. The exact analytical solution is compared with analysis from crack propagation module. </p>
A modular set of synthetic spectral energy distributions for young stellar objects - Robitaille (2017) - v1.1 [Hyperion files]
<p>These are the input and output files for the radiative transfer code (Hyperion) for the model sets presented in</p> <p><em>A modular set of synthetic spectral energy distributions for young stellar objects</em>, Robitaille (2017)</p> <p>Each model set is provided as a single tar file. Each tar file expands to <strong>grids-1.1/<set name></strong>, so if you expand all tar files in the same folder, you will end up with a single <strong>grids-1.1</strong> folder with 18 sub-folders, one for each model set.</p> <p>For a given model set, the files are as follows:</p> <ul> <li>grids-1.1/<set name>/input - input Hyperion files</li> <li>grids-1.1/<set name>/log - log files from Hyperion</li> <li>grids-1.1/<set name>/output - output Hyperion files</li> <li>grids-1.1/<set name>/par - parameters for each model</li> <li>grids-1.1/<set name>/ranges.conf - ranges of parameters varied in the model set</li> <li>grids-1.1/<set name>/parameters.hdf5 - table of parameters for all models</li> <li>grids-1.1/<set name>/d03_5.5_3.0_A_sub.hdf5 - dust file used for the models</li> </ul> <p>Given the large number of models for some of the model sets, the models are not all stored directly inside the par, input, output or log directories - instead these directories contain folders formed from the first two characters (forced to lowercase) of the names of the models they contain. For example, a3 contains all models whose name starts with a3 or A3. This was done to avoid having too many files in a single folder which can cause issues on certain file systems.</p> <p>For the Hyperion input and output files, in some cases an _sed file is present. In these cases, the output SEDs (and polarization spectra) should be read from the _sed file, not the original output file. This is the case for all models that are in a set for which the ambient medium was present, as described in §4.2.3 of Robitaille (2017). Furthermore, in some cases the SED file is called _sed_noscat to indicate that scattering was not included, as described in §5.1 of Robitaille (2017).</p> <p>To avoid taking up too much disk space, the Hyperion HDF5 input/output files use external links to refer to each other and to the dust file. To make sure the links work, you should do all operations with the input/output files from the directory containing <strong>grids-1.1</strong>. For example, to open a Hyperion output file, you would need to do (in Python):</p> <p> In [1]: from hyperion.model import ModelOutput</p> <p> In [2]: mo = ModelOutput('grids-1.1/s---s-i/output/a3/A3kQmQtj.rtout')</p> <p>A notebook with examples of reading in the output files can be found here:</p> <p>https://github.com/hyperion-rt/paper-2017-sed-models/blob/master/notebook_raw/reading_raw_files.ipynb</p> <p>More information on using Hyperion, including reading input/output files, can also be found at http://docs.hyperion-rt.org</p> <p>For <strong>announcements</strong> of new versions of these models, you can subscribe to the following mailing list:</p> <p>https://groups.google.com/forum/#!forum/protostars</p> <p>For <strong>questions or issues</strong> using these models, you can open a GitHub issue in the companion repository:</p> <p>https://github.com/hyperion-rt/paper-2017-sed-models/issues/new</p>
Hydro Energy Inflow for Power System Studies
<p>Energy inflow time series for hydro power on the European country level.</p> <p>Inflow was derived from reanalysis data using a potential energy approach.</p> <p>Dataset includes ten years (2003-2012) of data with daily resolution for 30 European countries.</p> <p>The dataset is described in more detail in</p> <p>A Kies, K Chattopadhyay, L von Bremen, E Lorenz, D Heinemann ,Simulation of renewable feed-in for power system studies, RESTORE 2050 project report</p> <p> </p>
NATCONSUMERS - main factors and attitudes behind energy consumption
<p><strong>NATCONSUMERS</strong>’ key aim is to develop an economically and technologically feasible, advanced and complex user-centred framework to help decrease domestic energy consumption.</p> <p>Within this framework, the project has conducted surveys to find the most relevant drivers behind energy saving attitude.</p> <p>Four countries were chosen for the survey: the UK, Hungary, Italy and Denmark. In each of the four countries, a sample of 1,000 individuals aged 18-65 were surveyed. This sample size was deemed the most cost effective to provide a nationally representative sample. People living in shared accommodation – those in student campuses, residential care homes, sheltered housing or military barracks – were excluded from the sample as these people have limited or no control over energy use in their residence. Anyone working for the advertising or marketing industry or within the energy industry was also excluded from the sample as this could provide a conflict of interests which would bias their responses. Data collection happened in March and April 2016, conducted by Ipsos in the United Kingdom, Denmark and Italy, and by NRC in Hungary.</p> <p> </p> <p>The project D3.3 deliverable summarizes the main findings from the survey.</p> <p>http://natconsumers.eu/?wpdmdl=1582</p> <p> </p> <p>The project D4.2 deliverable presents how this data could be used in NATCONSUMERS counselling.</p> <p>http://natconsumers.eu/?wpdmdl=1586</p> <p> </p> <p>The project final deliverable D7.4 gives a summary how these data sources need to be processed and applied in a user-centred energy advice system.</p> <p>http://natconsumers.eu/?wpdmdl=1709</p> <p> </p> <p> </p>
Indicator for the current and future socio-ecological burden caused by the expansion of wind energy in German districts
<p>The data table lists the calculated present burden (IST-Belastungsgrad) based on the year 2014 and the maximum possible burden (MAX-Belastungsgrad) on the population caused by the further expansion of wind energy. The so-called burden level is calculated accounting for the area occupied by wind turbines, the total area of a district and the population density. Additionally the table holds data on possible future burden levels based on two scenarios for the year 2050. Each district can be identified by its key, "Regionalschlüssel", and corresponding geo data (EPSG: 25832) of the administrative area provided by the Federal Agency for Cartography and Geodesy: © GeoBasis-DE / BKG 2014 (data was changed).</p> <p>The dataset was created in the context of the interdisciplinary research project VerNetzen and is described in detail in the final project report: VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., pp. 98-118, 143-145.</p> <p><strong><em>Deutsch:</em></strong></p> <p>Die Tabelle enthält u.a. den derzeitigen Belastungsgrad, festgestellt für das Jahr 2014, und den maximal möglichen Belastungsgrad je Landkreis. Der Belastungsgrad ist ein Indikator für die durch den Zubau von Windenergie betroffene Bevölkerung und berechnet sich aus der Gesamtfläche eines Landkreises, der für die Windenergie genutzten Fläche und der Bevölkerungsdichte. In der Tabelle sind ebenfalls mögliche zukünftige Belastungsgrade auf Grundlage zweier Projektszenarien für das Jahr 2050 enthalten. Die jeweiligen Landkreise können mit dem Regionalschlüssel oder den geographischen Daten (EPSG: 25832) des Bundesamtes für Kartographie und Geodäsie zugeordnet werden: © GeoBasis-DE / BKG 2014 (Daten verändert).</p> <p>Der Datensatz ist im Kontext des interdisziplinären Forschungsprojekts VerNetzen entstanden und ist ausführlich im Projektabschlussbericht beschrieben: VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., S.98-118, S.143-145.</p> <p> </p> <p> </p>
Indicator for the current and future socio-ecological burden caused by the expansion of wind energy in German districts - auxiliary values
<p>The table contains the population and the size of the total area for each German district as of 2013. Furthermore it contains the size of those areas per district, that potentially could be used for wind energy.</p> <p>The data on the population is provided by the Federal Statistical Office and the statistical Offices of the Länder: © Federal Statistical Office and the statistical Offices of the Länder, Regionaldatenbank Deutschland, December 2014, Datenlizenz by-2-0 (https://www.govdata.de/dl-de/by-2-0) (data was changed). The total district area is derived from geo data provided by the Federal Agency for Cartography and Geodesy: © GeoBasis-DE / BKG 2014 (data was changed).</p> <p>For further information on potential areas see VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., pp. 105-109.</p> <p><em><strong>Deutsch:</strong></em></p> <p>Die Tabelle umfasst die Bevölkerungsanzahl und Flächengröße je deutschem Landkreis für das Jahr 2013. Außerdem ist die Größe jener Fläche angegeben, die potentiell für die Windenergie genutzt werden könnte.</p> <p>Die Bevölkerungszahlen werden von den Statistischen Ämtern des Bundes und der Länder zur Verfügung gestellt: © Statistische Ämter des Bundes und der Länder, Regionaldatenbank Deutschland, Dezember 2014, Datenlizenz by-2-0 (https://www.govdata.de/dl-de/by-2-0) (Daten geändert). Die Landkreisflächen werden auf Grundlage von Geodaten des Bundesamtes für Kartographie und Geodäsie berechnet: © GeoBasis-DE / BKG 2014 (Daten geändert).</p> <p>Für weitere Informationen bzgl. der Potentialflächen siehe VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., S. 105-109.</p>
Calculated state-of-the art results for solvation and ionization energies of thousands of organic molecules relevant to battery design
<p>This dataset presents molecular properties critical for battery electrolyte design, specifically solvation energies, ionization potentials, and electron affinities. The dataset is intended for use in machine learning model testing and algorithm validation. The properties calculated include solvation energies using the COSMO-RS method [1] and ionization potentials and electron affinities using various high-accuracy computational methods as implemented in MOLPRO [2]. Computational details can be found in Ref. [3], with scripts used to generate the data mostly uploaded to our github repository [4].</p> <p>Molecular Datasets Considered:</p> <ul> <li> <p>QM9 Dataset: Contains small organic molecules broadly relevant for quantum chemistry [5]</p> </li> <li> <p>Electrolyte Genome Project (EGP): Focuses on materials relevant to electrolytes.[6]</p> </li> <li> <p>GDB17 and ZINC databases: Offer a broad chemical diversity with potential application in battery technologies. [7, 8]</p> </li> </ul> <h2>Data structure</h2> <p>How to Load the Data:</p> <p>All files can be loaded with</p> <p><br><code>import json</code></p> <p><code>with open("file.json", "r") as f:</code><br><code> data_dict = json.load(f)</code></p> <p><br>and the filestructure can be explored with</p> <p><code>data_dict.keys()</code></p> <p>We have also added an example script in python that shows how to extract all data from the JSON files following this link</p> <p><a href="https://github.com/chemspacelab/VienUppDa/blob/main/SolQuest/BIG_MAP_DATA/load_db.py">How to extract the data</a></p> <p>Note the file structure of the the AMONS JSON files is slightly different as explained below!</p> <h3>Solvation energies</h3> <p>The data is stored in two types of JSON archives: files for full molecules of GDB17 and ZINC and files for amons of GDB17 and ZINC. They are structured differently as amon entries are sorted by the number of heavy atoms in the amon (e.g., all amons with 3 heavy atoms are stored in <code>ni3</code>). Because of the large number of amons with 6 or 7 heavy atoms,they are further split into <code>ni6_1</code>, <code>ni6_2</code>, and so on. A sub dictionary of an amon dictionary or a full molecule dictionary contains the following keys:</p> <p><code>ECFP</code> - ECFP4 representation vector</p> <p><code>SMILES</code> - SMILES string</p> <p><code>SYMBOLS</code> - atomic symbols</p> <p><code>COORDS</code> - atomic positions in Angstrom</p> <p><code>ATOMIZATION</code> - atomization energy in [kcal/mol]</p> <p><code>DIPOLE</code> - dipole moment in Debye</p> <p><code>ENERGY</code> - energy in Hartree</p> <p><code>SOLVATION</code> - solvation energy in [kcal/mol] for different solvents at 300 K.</p> <p> </p> <p>Files:</p> <p> </p> <p><strong><em><code>GDB17.json.zip</code> </em></strong>(unpack with unzip first with unzip <strong><em><code>GDB17.json.zip</code></em></strong>) - subset of GDB17 random molecules</p> <p><strong><em><code>AMONS_ZINC.json</code> </em></strong>-<strong><em> </em></strong>all<strong><em> </em></strong>amons of ZINC up to 7 heavy atoms</p> <p><strong><em><code>EGP.json</code> </em></strong>- EGP molecules</p> <p><code><strong><em>AMONS_GDB17.json</em></strong></code> - all amons of GDB17 up to 7 heavy atoms</p> <p><code><strong>QM9IPEA_raw_molpro_output</strong>.zip</code> - compressed folder with raw Molpro input and output files</p> <table> <tbody> <tr> <td><strong>File Name</strong></td> <td><strong>Description </strong></td> <td><strong>Molecules</strong></td> </tr> <tr> <td>AMONS_GDB17.json</td> <td>GDB17 amons</td> <td>37860</td> </tr> <tr> <td>AMONS_ZINC.json</td> <td>ZINC amons </td> <td>88771</td> </tr> <tr> <td>GDB17.json</td> <td>Subset of GDB17</td> <td>309468</td> </tr> <tr> <td>EGP.json </td> <td>EGP molecules </td> <td>18362</td> </tr> </tbody> </table> <p>Atomic energies $E_{at}$ at BP and def2-TZVPD level in Hartree [Ha]</p> <table> <tbody> <tr> <td><strong>Element</strong></td> <td><strong>H</strong></td> <td><strong>C</strong></td> <td><strong>N</strong></td> <td><strong>O</strong></td> <td><strong>F</strong></td> <td><strong>Br</strong></td> <td><strong>Cl</strong></td> <td><strong>S</strong></td> <td><strong>P</strong></td> </tr> <tr> <td>Eat [Ha]</td> <td>-0.5</td> <td> -37.85</td> <td> -54.60</td> <td> -75.09</td> <td>-99.77</td> <td>-2574.40</td> <td> -460.20</td> <td> -398.16</td> <td>-341.30</td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td><strong>B</strong></td> <td><strong>Si</strong></td> </tr> <tr> <td> -24.65</td> <td> -289.40</td> </tr> </tbody> </table> <p>We follow the convention of negative atomization energies for stablity compared to the isolated atoms:</p> <p>$E_{atomization} = E_{mol} - \sum_{i} E_{at,i}$</p> <p><br>Free energy of solvation at 300 K in [kcal/mol]:</p> <h3>Ionization potentials and electron affinities</h3> <p>The upload contains two JSON files, <strong><em>QM9IPEA.json</em></strong> and <strong><em>QM9IPEA_atom_ens.json</em></strong>. <strong><em>QM9IPEA.json </em></strong>summarizes MOLPRO calculation data grouping it along the following dictionary keys:</p> <p> </p> <p><strong>QM9IPEA.json</strong></p> <p><code>COORDS</code> atom coordinates in Angstroms<br><code>SYMBOLS</code> atom element symbols<br><code>ENERGY</code> total energies for each charge (0, -1, 1) and method considered<br><code>CPU_TIME</code> CPU times (in seconds) spent at each step of each part of the calculation<br><code>DISK_USAGE</code> highest total disk usage in GB<br><code>ATOMIZATION_ENERGY</code> atomization energy at charge 0 (all methods)<br><code>IONIZATION_ENERGY</code> ionization energy for all methods<br><code>ELECTRON_AFFINITY</code> electron affinity for all methods<br><code>HOMO_ENERGY</code> HOMO energy from DFHF calculations<br><code>LUMO_ENERGY</code> LUMO energy from DFHF calculations<br><code>QM9_ID</code> ID of the molecule in the QM9 dataset</p> <p><strong>QM9IPEA_atom_ens.json</strong></p> <p><code>SPINS</code> the spin assigned to elements during calculations of atomic energies<br><code>ENERGY</code> energies of atoms using different methods</p> <p> </p> <p> </p> <p>All energies are given in Hartrees with NaN indicating the calculation failed to converge. Ionization potentials and electron affinities can be recovered as energy differences between neutral and charged (+1 for ionization potentials, -1 for electron affinities) species.</p> <p>"CPU_time" entries contain steps corresponding to individual method calculations, as well as steps corresponding to program operation: "INT" (calculating integrals over basis functions relevant for the calculation), "FILE" (dumping intermediate data to restart file), and "RESTART" (importing restart data). The latter two steps appeared since we reused relevant integrals calculated for neutral species in charged species' calculations; we also used restart functionality to use HF density matrix obtained for the neutral species as the initial density matrix guess for the SCF-HF calculation for charged species. NaN CPU time value means the step was not present or that the calculation is invalid. Note that the CPU times were measured while parallelizing on 12 cores and were not adjusted to single-core.</p> <p><strong> </strong></p> <p><strong><em>QM9IPEA_atom_ens.json</em></strong> contains atomic energies used to calculate atomization energies in <strong><em>QM9IPEA.json</em></strong>, the dictionary keys are:</p> <p><code>SPINS</code> - the spin assigned to elements during calculations of atomic energies.</p> <p><code>ENERGY</code> - energies of atoms using different methods.</p> <p> </p> <p>(Note that H has only one electron and thus does not require a level of theory beyond Hartree-Fock.)</p> <p>NOTE: Additional calculations were performed between publication of arXiv:2308.11196 and creation of this upload. For the version of the dataset used in the manuscript, please refer to DOI:10.5281/zenodo.8252498.</p> <h3>Acknowledgement</h3> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957189 (BIG-MAP) and No. 957213 (BATTERY 2030+). O.A.v.L. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772834). O.A.v.L. has received support as the Ed Clark Chair of Advanced Materials and as a Canada CIFAR AI Chair. O.A.v.L. acknowledges that this research is part of the University of Toronto’s Acceleration Consortium, which receives funding from the Canada First Research Excellence Fund (CFREF). Obtaining the presented computational results has been facilitated using the queueing system implemented at <a href="https://leruli.com">https://leruli.com</a>. The project has been supported by the Swedish Research Council (Vetenskapsrådet), and the Swedish National Strategic e-Science program eSSENCE as well as by computing resources from the Swedish National Infrastructure for Computing (SNIC/NAISS).</p> <p> </p> <h3>References</h3> <p>[1] Klamt, A.; Eckert, F. COSMO-RS: a novel and efficient method for the a priori prediction of thermophysical data of liquids. Fluid Phase Equilibria 2000, 172, 43–72</p> <p>[2] Werner, H.-J.; Knowles, P. J.; Knizia, G.; Manby, F. R.; Schutz, M. Molpro: a general-purpose quantum chemistry program package. WIREs Comput. Mol. Sci. 2012, 2, 242–253</p> <p>[3] arxiv link of draft</p> <p>[4] <a href="https://github.com/chemspacelab/ViennaUppDa">https://github.com/chemspacelab/ViennaUppDa</a></p> <p>[5] Ramakrishnan, R.; Dral, P. O.; Rupp, M.; von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 2014, 1, 140022</p> <p>[6] Qu, X.; Jain, A.; Rajput, N. N.; Cheng, L.; Zhang, Y.; Ong, S. P.; Brafman, M.; Mag- inn, E.; Curtiss, L. A.; Persson, K. A. The Electrolyte Genome Project: A big data approach in battery materials discovery. Comput. Mater. Sci. 2015, 103, 56–67</p> <p><strong> </strong>[7] Ruddigkeit, L.; van Deursen, R.; Blum, L. C.; Reymond, J.-L. Enu- meration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17. Journal of Chemical Information and Modeling 2012, 52, 2864–2875</p> <p>[8] Irwin, J. J.; Shoichet, B. K. ZINC A Free Database of Commercially Available Compounds for Virtual Screening. Journal of Chemical Information and Modeling 2005, 45, 177–182.</p>
Time-varying global energy budget since 1880 from a reconstruction of ocean warming
<p>This dataset was produced as part of the research presented in the paper "Time-varying global energy budget since 1880 from a reconstruction of ocean warming" published in PNAS. For detailed methodology, analysis, and interpretation of the data, please refer to the original publication: 10.1073/pnas.2408839122.</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.