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917 results for “Theorie”
Canonical Decision Diagrams Modulo Theories - Benchmarking
<p>This archive contains all data and results that were used to benchmark the approach Canonical Decision Diagrams Modulo Theories. The archive contains the following files: 3 subfolders, one for each dataset that was used in the benchmarking process, each one containing a "data" subfolder which contains the problems (in SMT/SMT2 format) and some output folders which contain JSON files describing in detail the results of each run on the problems.</p>
CATCH-EyoU: Exploiting European data and testing the integrated theory of youth active EU citizenship: PIDOP subset reanalysis
<p>This is a subset of the full PIDOP dataset. The derived subset contains cross-sectional survey results from the PIDOP questionnaire survey that were collected in 9 European countries (incl. Turkey) during a period of 16-26 year old in 2011. The data set includes 9060 individual cases. The questionnaire used in the survey is published in Barrett, M. & Zani, B. (Eds.) (2015). <em>Political and civic engagement: Multidisciplinary perspectives.</em> Hove: Routledge (p.519-534).</p>
On the mixing between flavor singlets in lattice gauge theories coupled to matter fields in multiple representations - data release
<p>This release contains all data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2405.05765"><em>On the mixing between flavor singlets in lattice gauge theories coupled to matter fields in multiple representations</em> [2405.05765].</a> </p> <p>If you encounter difficulties downloading the large files, we recommend using <a href="../records/11142962">zenodo-get</a>. This provides a command-line downloader for any Zenodo record. For unstable connections we recommend using it with the -w flag to generate a list all files in this Zenodo record. This can then be used with tools such as <a href="https://www.gnu.org/software/wget/">wget</a> to resume partial downloads as</p> <p><code>zenodo_get RECORD_ID_OR_DOI -w - | xargs wget -c<br>zenodo_get RECORD_ID_OR_DOI </code></p> <p>(The second line ensures that the downloads completed correctly, and that the md5 hashes match)<br><br>Further details are given in the file README.md.</p> <p>The work of EB and BL is supported in part by the EPSRC ExCALIBUR programme ExaTEPP (project EP/X017168/1). The work of EB, BL, MP, and FZ has been supported by the STFC Consolidated Grant No. ST/X000648. The work of EB has also been supported by the UKRI Science and Technology Facilities Council (STFC) Research Software Engineering Fellowship EP/V052489/1. The work of NF has been supported by the STFC Consolidated Grant No. ST/X508834/1. The work of DKH was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B06033701). The work of DKH was further supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A4A5031460). The work of JWL is supported by IBS under the project code, IBS-R018-D1. The work of HH and CJDL is supported by the Taiwanese MoST grant 109-2112-M-009-006-MY3 and NSTC grant 112-2112-M-A49-021-MY3. The work of CJDL is also supported by Grants No. 112-2639-M-002-006-ASP and No. 113-2119-M-007-013. The work of BL and MP has been further supported in part by the STFC Consolidated Grant No. ST/T000813/1.<br>BL and MP received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under Grant Agreement No.~813942. The work of DV is supported by STFC under Consolidated Grant No. ST/X000680/1.</p> <p>Numerical simulations have been performed on the DiRAC Extreme Scaling service at the University of Edinburgh, and on the DiRAC Data Intensive service at Leicester.<br>The DiRAC Extreme Scaling service is operated by the Edinburgh Parallel Computing Centre on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This equipment was funded by BEIS capital funding via STFC capital grant ST/R00238X/1 and STFC DiRAC Operations grant ST/R001006/1. DiRAC is part of the National e-Infrastructure</p>
SU(2) gauge theory with one and two adjoint fermions towards the continuum limit—data release
<p>This package contains all data generated in preparing the publication <a href="https://arxiv.org/abs/2408.00171">SU(2) gauge theory with one and two adjoint fermions towards the continuum limit</a>. It includes four classes of data:</p> <ol> <li>Raw data, as generated from the measurement code running on HPC, in their native formats (raw_data.zip).</li> <li>Metadata around the analysis of the ensembles, in YAML format (ensembles.yaml).</li> <li>Data obtained by analysing the above data and presented in <a href="https://arxiv.org/abs/2408.00171">arXiv:2408.00171</a>, for specific ensembles, in sqlite3 format (su2.sqlite).</li> <li>The above data in (3), and additional data obtained by further analysing them, in CSV format (ensemble_results.csv and gammastar_results.csv).</li> <li>For convenience, the data in (1) above, repackaged in HDF5 format (package.h5).</li> </ol> <p>Each of these is documented in more detail in the file README.md.</p> <p>Due to their size, raw gauge configurations are not included in this package.</p> <p> </p>
Data files for the manuscript "Extended kinetic theory applied to pressure-controlled shear flows of frictionless spheres between rigid, bumpy planes"
<p>This depository contains the data of all DEM simulations used in the manuscript titled "Extended kinetic theory applied to pressure-controlled shear flows of frictionless spheres between rigid, bumpy planes" submitted to Soft Matter in July 2024.</p> <p>The data in the excel file are the measurements obtained after the coarse graining procedure.</p>
Category Theory Framework for Variability Models with Non-functional Requirements @ CAiSE 21
<p><strong>Your can watch this video in my Youtube channel:</strong></p> <p><strong><a href="https://youtu.be/rX50Q3fpMZE">https://youtu.be/rX50Q3fpMZE</a></strong></p> <p><strong>This is a Live Conference Presentation, please access and cite the published version of the respective publication:</strong></p> <p><strong><a href="https://doi.org/10.1007/978-3-030-79382-1_24">https://doi.org/10.1007/978-3-030-79382-1_24</a></strong></p> <p>In Software Product Line (SPL) engineering one uses Variability Models (VMs) as input to automated reasoners to generate optimal products according to certain Quality Attributes (QAs). Variability models, however, and more specifically those including numerical features (i.e., NVMs), do not natively support QAs, and consequently, neither do automated reasoners commonly used for variability resolution. However, those satisfiability and optimisation problems have been covered and refined in other relational models such as databases. Category Theory (CT) is an abstract mathematical theory typically used to capture the common aspects of seemingly dissimilar algebraic structures. We propose a unified relational modelling framework subsuming the structured objects of VMs and QAs and their relationships into algebraic categories. This abstraction allows a combination of automated reasoners over different domains to analyse SPLs. The solutions’ optimisation can now be natively performed by a combination of automated theorem proving, hashing, balanced-trees and chasing algorithms. We validate this approach by means of the edge computing SPL tool HADAS.</p>
Constraining scalar-tensor theories by neutron star-balck hole gravitational wave events
<p>This data release corresponds to the paper "Constraining scalar-tensor theories by neutron star-balck hole gravitational wave events" (<a href="https://arxiv.org/abs/2105.13644">arXiv:2105.13644</a>). In this paper, we consider three specific models of scalar-tensor theories, including the Brans-Dicke theory (BD), the theory with scalarization phenomena proposed by Damour and Esposito-Far\`{e}se (DEF), and Screened Modified Gravity (SMG). From all 4 possible NSBH events so far, we use two of them to place the constraints. The other two are excluded in this work due to the possible unphysical deviations. Four equations of state (EoSs), <em>sly</em>, <em>alf2</em>, <em>H4</em> and <em>mpa1</em>, are used to derive the scalar charges of neutron stars for BD and DEF. The constraints are obtained by performing the full Bayesian inference with the help of the open source software <a href="https://git.ligo.org/lscsoft/bilby">Bilby</a>.</p> <p>This dataset contains all posterior samples of the runs discussed in the paper. The models and EoSs can be read form the filenames for the runs of BD and DEF. The files of "<em>*_half_dipole.json</em>" correspond to the runs for constraining the dipole radiation without considering specific model parameters. All files are JSON format which is the default output format of <a href="https://git.ligo.org/lscsoft/bilby">Bilby</a>. They are human readable and also can be processed or visualized by <a href="https://git.ligo.org/lscsoft/bilby">Bilby</a> or <a href="https://git.ligo.org/lscsoft/pesummary">PESummary</a> conveniently.</p>
Data sets for the publication "Repulsive interatomic potentials calculated at three levels of theory" by K. Nordlund, S. Lehtola and G. Hobler.
<p>This file system package contains data sets for the publication "Repulsive interatomic potentials calculated at three levels of theory" by K. Nordlund, S. Lehtola and G. Hobler. It presents three quantum chemically calculated data sets ("MP2", "DMol", and "ZBL pair-specific") for diatomic interatomic potentials in the repulsive region, where the separation of the atoms is so short that the potential energy is >> 10 eV. The set also contains the fitted parameters for analytical NLH repulsive potentials that consist of a Coulomb term multiplied by a three-exponential screening function.</p> <p>The version from Oct 9, 2025 has updated NLH parameters for the pair Na-O (Z1=8, Z2=11). Otherwise the data is identical to before.</p> <p>The MP2 data sets contain potential data for all elements pairs Z1+Z2<=36, and the DMol, ZBL pair-specific and NLH potentials are proved for all elements pairs Z1, Z2 <=92.</p> <p>The data sets are arranged in the following directories:</p> <p>mp2/ : Data sets for the Hartree-Fock Moller-Plesset2 level calculations</p> <p>dmol/ : Data sets for the Density Functional Theory calculations with the DMOL code</p> <p>nlh/ : Coefficients for the fits of the Nordlund-Lehtola-Hobler potential to the DMol data sets</p> <p>zbl/ : Data sets for the pair-specific Ziegler-Biersack-Littmark potential calculations</p> <p>zbluniv/ : A directory with a Linux bash/awk script that generates the ZBL universal potential.</p> <p>zblspec/ : Coefficients for the pair-specific ZBL screening functions as contained in SRIM-2013.</p> <p>Each subdirectory has its own README.txt file giving additional details on the content of the directory and its subdirectories.</p>
Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.
<p>## Abstract</p> <p>from [1]:</p> <p>Mechanochemistry is a fast-developing field of interdisciplinary research with a growing number of applications. Therefore, many theoretical methods have been developed to quickly predict the outcome of mechanically induced reactions. Constrained geometries simulate External Force (CoGEF) is one of the earlier methods in this field. It is easily implemented and can be conducted with most DFT codes. However, recently, we observed totally different predictions for model systems of epoxy resins in different conformations and with different density functionals. To better understand the conformational and functional dependence in typical CoGEF calculations we present a systematic evaluation of the CoGEF method for different model systems covering homolytic and heterolytic bond cleavage reactions, electrocyclic ring opening reactions and scission of non-covalent interactions in hydrogen-bond complexes. From our calculations we observe that many mechanochemical descriptors strongly depend on the functional used, however, a systematic trend exists for the relative maximum Force. In general, we observe that the CoGEF procedure is forcing the system to high energetic regions on the molecular potential energy profiles, which can lead to unexpected and uncorrelated predictions of mechanochemical reactions. This is questioning the true predictive character of the method.</p> <p> </p> <p>## Contact</p> <p>Christian R. Wick</p> <p>Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Faculty of Science, Department of Physics, PULS Group, Interdisciplinary Center for Nanostructured Films (IZNF), Cauerstrasse 3, 91058, Germany</p> <p> </p> <p>## License</p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p>## Context</p> <p>Dataset to paper [1]</p> <p> </p> <p>## Contents</p> <ul> <li>All COGEF trajectories in xyz format.</li> <li>All CoGEF distances and DFT Energies in csv format.</li> </ul> <p>The following DFT levels of theory were investigated:</p> <ul> <li>B3LYP/6-31G(d)</li> <li>B3LYP-D3BJ/def2-SVP</li> <li>BP86-D3/def2-SVP</li> <li>PBE1PBE/def2-SVP</li> <li>M06-D3/def2-SVP</li> </ul> <p> </p> <p>## Folder structure</p> <ul> <li>- compound_X : data set for compound number X (numbering corresponds to the numbering scheme in [1]) <ul> <li>the xyz trajectories follow the following naming convention: "DFT_method"_"unrestricted/restricted".xyz</li> <li>the csv files follow the naming convention: "DFT_method"_"unrestricted/restricted".xyz.csv</li> </ul> </li> </ul> <p>## Software</p> <p>### COGEFF calculations: COGEF.py v1.8.0</p> <p>Zenodo release:</p> <p>https://doi.org/10.5281/zenodo.7079733</p> <p>### DFT calculations:</p> <p>Gaussian 16 Rev B [2]</p> <p> </p> <p>## Funding</p> <p>This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/1-2019 FRASCAL.</p> <p><br> ## References</p> <p>[1] C. R. Wick, E. Topraksal, D. M. Smith, A.-S. Smith, "Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.", Forces in Mechanics, 9, 100143; doi:10.1016/j.finmec.2022.100143</p> <p>[2] Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; et al. Gaussian 16 Rev. B.01, 2016.</p>
A Reputation Game Simulation: Emergent Social Phenomena from Information Theory
<p>Here, the data underlying the article "A Reputation Game Simulation: Emergent Social Phenomena from Information Theory" (<a href="https://doi.org/10.1002/andp.202100277">https://doi.org/10.1002/andp.202100277</a>) is provided.<br> <br> The data is structured according to the figures it has been used for. There are</p> <ul> <li>example simulations with basic communication strategies in the folder "single_simulations_3_agents" (Figures 4,5,8,D1)</li> <li>statistical simulations with 3 agents and special communication strategies in the folder "statistical_simulations_3_agents" (Figures 9-13, the upper panel of figure 15, figures 16-18, D2 and the left panels of figure D3)</li> <li>statistical simulations with 4 agents and special communication strategies in the folder "statistical_simulations_4_agents" (Figure 14, the middle panel of figure 15, the middle panels of figure D3 and the upper panels of figures D4, D5)</li> <li>statistical simulations with 5 agents and special communication strategies in the folder "statistical_simulations_5_agents" (The lower panel of figure 15, the right panels of figure D3 and the lower panels of figures D4,D5)</li> <li>propaganda simulations in the folder "propaganda_simulations" (Figure 7)</li> </ul> <p><br> Each simulation is represented by a .json file in which all events that happened during the simulation are collected. Generally, there are three types of events: communications, self-updates (information that the speaker gained about itself is processed) and updates (information that the receiver gained about the speaker and the topic is processed). Additionally, the first line specifies the parameters of each simulation, and the last few lines summarize the final status of the simulation. In the following all important abbreviations are explained:</p> <ul> <li>parameters <ul> <li>decpeting: whether or not agents in generally make dishonest statements</li> <li>listening: whether or not agents in listen to their communication partners</li> <li>disturbing: whether or not agents are particularly risk-taking when making dishonest statements</li> <li>x_est: intrinsic honesties of the agents</li> <li>RSeed: the used random seed</li> <li>NA: number of agents</li> <li>NR: number of rounds</li> </ul> </li> <li>communication <ul> <li>a: speaker</li> <li>b: receiver</li> <li>c: topic</li> <li>J: transmitted message in the form of</li> </ul> </li> <li>self_update <ul> <li>id: number of agent who is updating knowledge about itself</li> <li>Nl, Nt: number of dishonest/honest statements the agent has observed from itself so far</li> <li>I_<id>: knowledge that the agents has about itself after the update in the form of</li> </ul> </li> <li>update <ul> <li>id: number of agent who is updating its knowledge</li> <li>I_<id1>: knowledge that the updating agent has about agent <id1> in the form of</li> <li>Jothers_<id1>_<id2>: last statement that the updating agent heared agent <id1> make about agent <id2></li> <li>Iothers_<id1>_<id2>: what the updating agent believes that agent <id1> thinks about agent <id2> after the update</li> <li>Cothers_<id1>_<id2>: what the updating agent believes after the update that agent <id1> wants it to think about agent <id2></li> <li>new_friends/enemies: id of the agent, the updating agent after the update considers a friend/enemy</li> <li>new_K: normalized surprise the updating agent experienced in the last communication (used to calculate kappa)</li> <li>kappa: median of the last ten normalized surprises the updating agent experienced</li> </ul> </li> <li>final_status <ul> <li>id/name: number if the described agent</li> <li>x: the agent's honesty</li> <li>I: the agent's knowledge about all others</li> <li>Nc/Nt/Nl: total number of conversations/honest statements/dishonest statements the agent has made</li> <li>K: the last 10 normalized surprises the agent experienced</li> <li>kappa: the median of K</li> <li>friends/enemies: list of the agent's friends/enemies</li> <li>Jothers/Iothers/Cothers: same as above, now as full array, i.e. the combined information about all others</li> <li>openess/mind/decepting/strategic/egocentric/deceptive/flattering/aggressive/shameless/disturbing: the agent's character traits</li> </ul> </li> </ul>
Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions
<p>For reproducing the results presented in "<strong>Hashemi, A., Peljo, P., & Laasonen, K. (2022). Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions</strong>", this database provides the input files and CDFT-AIMD trajectory information. Please refer to the publication if you wish to use these data.</p> <p>---------------------------------------**************************************************************************-------------------------------------------------</p> <p><em>This study was financed by the Horizon 2020 Framework Programme CompBat with project number 875565. We also thank CSC-IT Center for Science Ltd. and Aalto Science-IT project for generous grants of computer time.</em><br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>The content of a directory is shown in a tree-like format:</strong><br> ├── 1DMDQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 2MeVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── mevi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 3OHVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── ohvi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ ├── b_to_a.tar.gz<br> │ │ ├── b_to_c<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 4dBR5<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 52HNQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── hnq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> └── 6_n_H2O_effect_mevi<br> ├── 08h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 10h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 20h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 40h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 97h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> └── fig3.png</p> <p>74 directories, 301 files<br> -------------------------------------------------------<br> There are 6 directories: 1DMDQ, 2MeVi, 3OHVi, 4dBR5, 52HNQ, 6_n_H2O_effect_mevi. Except for "6_n_H2O_effect_mevi", we see 3 subdirectories named 1_md, 2_cdftaimd, and 3_cdft_wH2O_sccs. The input files and AIMD trajectories can be found in 1_md. While 2_cdftaimd contains the CDFT-AIMD input files and trajectories. To reproduce snapshots and input files of 3_cdft_wH2O_sccs, follow the README files in the subdirectories.</p> <p>The directory "6_n_H2O_effect_mevi" contains the number of water effects (Figure 3 of the publication). Users are guided by README files once again. </p>
Dataset for "New Perspectives for Nonlinear Depth-inversion of the Nearshore Using Boussinesq Theory"
<pre>This dataset gathers all cross-spectral, spectral and bispectral data produced and used in the manuscript accepted for publication in Geophysical Research Letters: "New Perspectives for Nonlinear Depth-inversion of the Nearshore Using Boussinesq Theory", by K. Martins, P. Bonneton, O. de Viron, I. L. Turner, M. D. Harley and K. Splinter The sharing of the processed data is motivated by research reproductibility purposes and with the hope that it will foster efforts in improving the newly-proposed depth-inversion procedure. Three laboratory experiments are considered, namely the experiments reported in van Noorloos (2003), Michallet et al. (2011) and GLOBEX (e.g., see Ruessink et al., 2013). The processed data is organised in separate self-explanatory .mat files (generated with MATLAB software), gathering: - "cel_data" : wave phase velocities obtained from cross-spectral and cross-correlation analyses between adjacent wave gauges - "bulk_data" : range of bulk wave parameters computed across the different wave flumes - "bispectrum_data" : spectral and bispectral estimates computed across the different wave flumes The scripts for performing the depth-inversion as well as a for plotting the results are provided. The new Boussinesq depth-inversion procedure relies on the function "fun_compute_krms_terms.m", which is provided and originates from the bispectral analysis library accessible from the first author GitHub repository at https://github.com/ke-martins/bispectral-analysis. Acknowledgements: Kévin Martins greatly acknowledges the financial support from the European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie Grant Agreement 887867 (lidBathy). We warmly thank Ap van Dongeren and Hervé Michallet for providing the raw data for the experiments described in van Noorloos (2003) and Michallet et al. (2011), respectively. The raw data from GLOBEX used in this research can be accessed on Zenodo at https://zenodo.org/record/4009405 and can be used under the Creative Commons Attribution 4.0 International license. The GLOBEX project was supported by the European Community’s Seventh Framework Programme through the Hydralab IV project, EC Contract 261520. References: Michallet, H., Cienfuegos, R., Barthélemy, E., & Grasso, F. (2011). Kinematics of waves propagating and breaking on a barred beach. European Journal of Mechanics - B/Fluids, 30 (6), 624 – 634. doi: 10.1016/j.euromechflu.2010.12.004 Ruessink, G. B., Michallet, H., Bonneton, P., Mouazé, D., Lara, J. L., Silva, P. A., & Wellens, P. (2013). GLOBEX: Wave dynamics on a gently sloping laboratory beach. In Coastal Dynamics ’13: Proceedings of the Seventh Conference on Coastal Dynamics, Arcachon, France. van Noorloos, J. C. (2003). Energy transfer between short wave groups and bound long waves on a plane slope (Master’s thesis, Delft University of Technology, Delft, The Netherlands). Retrieved from http://resolver.tudelft.nl/uuid:13616ff0-407d-43de-9954-ba707cd40d27</pre>
Spectral response of disorder-free localized lattice gauge theories
<p>Raw data for all figures in the manuscript "Spectral response of disorder-free localized lattice gauge theories"</p>
Network data and script accompanying the paper "Operationalizing anthropological theory: four techniques to simplify networks of co-occurring ethnographic codes"
<p>This repository accompanies the paper<a href="https://rdcu.be/dbuhi"> "Operationalizing anthropological theory: four techniques to simplify networks of co-occurring ethnographic codes"</a>, by Cottica et al. It contains:</p> <ol> <li>A data file, containing networks of co-occurrence of ethnographic codes from three ethnographies. Data are pseudonymized (see the paper for details).</li> <li>A script that, when run on the data, produces simplified versions of each network. Simplifications follow four different techniques, described in the paper. Each technique relies on a tuning parameter, so that, for each network and each techniques, the script produces several simplified networks, each one associated with a unique value of the tuning parameter.</li> </ol> <p>The data file format is that of a Tulip perspective. To open, download Tulip (https://tulip.labri.fr), launch it and open the file from within the Tulip GUI.</p> <p>The script file is in Python. To run, open it from within the Tulip IDE first.</p> <p> </p>
Designing Atmospheres: Theory and Science Symposium
<p>This dataset is an output of the ‘Designing Atmospheres: Theory and Science’ Symposium (ATS), an Interfaces event of the Academy of Neuroscience for Architecture (ANFA), sponsored by the EU’s Horizon 2020 MSCA Program — RESONANCES Project, the Perkins Eastman Studio, and the KSTATE APDesign. The symposium was hosted in the College of Architecture, Planning and Design (APDesign), Kansas State University, Manhattan (Kansas, USA), on March 28, 2023. Speakers: Kory Beighle (Kansas State University), Elisabetta Canepa (University of Genoa | Kansas State University), Bob Condia (Kansas State University), Zakaria Djebbara (Aalborg University | TU Berlin), and Harry Francis Mallgrave (Illinois Institute of Technology).</p> <p> </p> <p>Recent advances in science confirm many of the architects’ deep-rooted intuitions, improving knowledge about the perception of space and the meaning of architectural and urban design. The symposium ‘Designing Atmospheres: Theory and Science‘ presented to an audience of students, educators, architects, and scientists a conversation about the experience of design and building, specifically speaking to the significance of atmospheres, affordances, and emotions.</p> <p> </p> <p>This dataset is made of seven files:<br> no. 1 dataset summary (.pdf)<br> no. 1 symposium poster (.pdf)<br> no. 5 videos containing speakers’ presentations (.mp4)</p> <p> </p> <p>Recorded videos of each lecture are also available on the RESONANCES project website (www.resonances-project.com/harvest) and its YouTube channel (@resonancesproject5777).</p>
Dataset and scripts for "Non-zero temperature study of spin 1/2 charmed baryons using lattice gauge theory"
<p><strong>charmJ12Scripts</strong></p> <p>A set of scripts and folders to reproduce the analysis and plots in the spin 1/2 charm baryon paper which can be found at <a href="https://doi.org/10.1140/epja/s10050-024-01261-2">EPJA</a></p> <p> </p> <p>This repository includes the raw correlator data, the scripts and software used to analyse them as well as a script which can be run in order to reproduce the entire analysis, and particularly the figures in the manuscript.</p> <p> </p> <p><strong>correlators</strong></p> <p>Correlators from openqcd-fastsum-hadspec are zipped in the correlators folder. These are unzipped automatically by the script. The correlators are plain text files.</p> <p> </p> <p><strong>output</strong></p> <p>Analysis output is placed here. You do not need to look here in order to see the figures in the paper</p> <p> </p> <p><strong>code</strong></p> <p>The python code and scripts that do the analysis. There is some modularity here with the libraries in the lib folder</p> <p> </p> <p><strong>paperPlots</strong></p> <p>The plots from the paper will be generated here. They are not supplied with this repo as they can be found in the paper</p> <p> </p> <p><strong>plotXYData</strong></p> <p>The x-y and y-error data of each plot in the paper. Only 'scatter' style data is included. This is generated by the run script, but also supplied herein. It will be overwritten by the runscript</p> <p> </p> <p><strong>run</strong></p> <p>The folder where the main script needed to run all the analysis is.</p> <p> </p> <p><strong>Conda Notes</strong></p> <p>Install your favourite conda solution, such as <a href="https://docs.conda.io/en/latest/miniconda.html">https://docs.conda.io/en/latest/miniconda.html</a></p> <p> </p> <p>Switch to a faster environment solver</p> <p>This is optional, but likely will solve the dependencies much much faster. See <a href="https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community">https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community</a> <code>conda update -n base conda</code> <code>conda install -n base conda-libmamba-solver</code> <code>conda config --set solver libmamba</code></p> <p> </p> <p>Install Environment</p> <p><code>conda env create -f environment.yml</code></p> <p> </p> <p>Activate/Use</p> <p><code>conda activate charm</code></p> <p> </p> <p>Update (w. new packages)</p> <ol> <li>Edit <code>environment.yml</code></li> <li>Deactivate conda environment with <code>conda deactivate</code></li> <li>Update conda environment with <code>conda env update -f=environment.yml</code></li> </ol>
A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
<p>The file <strong>Corpora.txt </strong>keeps the corpus used to train the model and the different instances of the classifier. It is basically a text file with one sentence per line from the original corpus called <strong>test.tsv</strong> available at <a href="https://github.com/google-research-datasets/wiki-split.git">https://github.com/google-research-datasets/wiki-split.git</a>. We eliminated punctuation marks and special characters from the original file putting each sentence per line.</p> <p><strong>Enju_Output.txt </strong>holds the outputs generated by Enju in -so mode (Output in stand-off format) using Corpora.txt as input. This file has basically a natural language English per-sentence parse with a wide-coverage probabilistic for HPSG grammar.</p> <p>The file <strong>Supervision.txt </strong>keeps the grammatical tags of the corpus. This file holds a tag per word and each tag is situated in a single line. Sentences are separated by one empty line while tags from words in the same sentence are located in adjacent lines.</p> <p>The file<strong> Word_Category.txt</strong> carries the coarse-grained word category information needed by the model and introduced in it by apical dendrites. Each word in the corpus has a word-category tag which provides additional constraints to those provided by lateral dendrites. This file contains a tag per word and each tag is situated in a single line. Sentences are separated by one empty line while tags from words in the same sentence are located in adjacent lines.</p> <p>The file <strong>SynSemTests.xlsx</strong> keeps all the grammar classification results as well as the statistical analysis in the classification tests.</p>
Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms (supplemental material: brain wave loops movies)
<p>This is a collection of videos supplementing the paper "Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms"</p> <p>Examples of wave trajectories and emergent persistent loop patterns for the spherical shell cortex model with<br> varying amounts of tensor anisotropy and inhomogeneous shell layer thickness.</p> <p><br> Examples of brain wave trajectories and emergent persistent loop patterns for cortical fold geometry with different<br> approaches used for estimation of inhomogeneity and anisotropy. Among those examples are several simple cases with variable inhomogeneity and fixed anisotropy (similar to the above spherical shell cortex model) as well as with more complex estimates of anisotropy based on multiple diffusion gradients MRI (dMRI) acquisitions.</p>
Cross-platform mentions of the QAnon conspiracy theory
<p>This dataset contains mentions of the QAnon conspiracy theory across the Web between 28 October 2017 and 1 November 2018. The following list details the data per platform and its collection process:</p> <ul> <li><strong>4chan: </strong>Posts and comments on 4chan/pol/ mentioning "Q" or "QAnon". The data is collected through 4CAT, a data capturing and analysis tool that hosts all posts and comments made on 4chan/pol/ since 2014.</li> <li><strong>8chan:</strong> Posts and comments on the /qresearch/ board and other smaller boards mentioning "Q" or "QAnon". The data is derived qanon.news, a grassroots archive. Considering its amateur nature, the dataset is likely not 100% complete, but still includes over 200,000 posts.</li> <li><strong>Reddit:</strong> Comments made on politically-oriented subreddits mentioning "Q" or "QAnon". The data is gathered through the Pushshift API.</li> <li><strong>YouTube:</strong> Videos mentioning QAnon or "Q" in the title or video decription. The data is collected via the YouTube v3 API using the search endpoint. Multiple keywords were queried ("qanon", "qanon 4chan", etc) to collect a large sample. False positives were then filtered out manually.</li> <li><strong>Breitbart:</strong> Disqus comments on Breitbart.com mentioning QAnon or "Q". The data was gathered by crawling all of Breitbart.com in the timeframe and using the Disqus API.</li> <li><strong>Online news media</strong>: Articles from English online news sources mentioning QAnon. The data is derived from Nexis Uni and ContextualWeb Search by searching for "QAnon". Irrelevant sources and false positives were filtered manually.</li> </ul> <p>The datasets include timestamps, text bodies, and platform-specific information like subreddits and channel titles. To collect data from 4chan, 8chan, Reddit, and Breitbart, we used the same SQL query, sampled 200 comments, and edited the query to so it would have sufficient number of true positives (> 94%). The YouTube and online news media datasets are filtered manually.</p> <p>For Breitbart and Reddit, the data is anonymised by omitting author information. The online news media article text is omitted because of copyright concerns.</p> <p>See <a href="https://journals.uic.edu/ojs/index.php/fm/article/view/10643/9998">the article on First Monday</a> for the full collection process.</p>
Figure 2. D in Neotypification of Drawida hattamimizu Hatai, 1930 (Annelida, Oligochaeta, Megadrili, Moniligastridae) as a model linking mtDNA (COI) sequences to an earthworm type, with a response to the 'Can of Worms' theory of cryptic species
Figure 2. D. hattamimizu unscaled habitus (from Watanabe, 2005, fig. 1 after Hatai's 1931 original).
ScienceDex guides
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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.