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514 results for “principles”
Dataset for "Reproducibility and FAIR Principles: The Case of a Segment Polarity Network Model"
<p>Results of random sampling the segment polarity network with the simulator COPASI. These results correspond to Fig. 2 and Table 2 of von Dassow et. al (2000) (doi:10.1038/35018085). The random sampling was carried out with file vonDassow2000_1x4_alt.cps with COPASI version 4.39 selecting the appropriate parameter set named (1-7) and setting the number of repeats in the parameter scan task to the desired number. Full results of sampling are in files prefixed with the row number of Table 2 of von Dassow et. al (2000) and extension .tsv. Results with scores below 0.2 are in corresponding files with the word "-hits" in the filename. Includes also results from a time course simulation of this model using four different simulators (COPASI, Tellurium, Amici, and VCell). Finally also contains a study on multistability carried out by random sampling of parameters and initial conditions (run with COPASI). Markdown file README.md contains more detailed explanation. See also https://github.com/pmendes/models/tree/main/vonDassow2000</p>
Supplementary Material for "General Principles for Yield Optimization of Nucleoside Phosphorylase-Catalyzed Transglycosylations"
<p>This is the supplementary material for our publication "General Principles for Yield Optimization of Nucleoside Phosphorylase-Catalyzed Transglycosylations".</p> <p>The .pdf file contains the supplementary information: Author Contributions, Conflict of Interest, Sample analysis by HPLC, Table S1 and S1, Figure S1 and a suggested workflow for NPase-catalyzed nucleoside synthesis.</p> <p>The .xlsx file contains an implementation of the simplified formula for yield prediction (equation (4) ), using previously reported thermodynamic data (10.5281/zenodo.3459298).</p> <p>The Python code and all data calculated from numerical solutions of the system of equilibrium constraints is available elsewhere (10.5281/zenodo.3522588).</p>
Molecular dynamics trajectories for "Structure and chemistry of graphene oxide in liquid water from first principles"
<p>This dataset contains molecular dynamics (MD) trajectories from the paper <a href="https://doi.org/10.1038/s41467-020-15381-y">“Structure and chemistry of graphene oxide in liquid water from first principles”, F. Mouhat, F.-X. Coudert and M.-L. Bocquet, <em>Nature Commun.</em>, <strong>2020</strong>, <em>11</em>, 1566, 10.1038/s41467-020-15381-y</a></p> <p> </p>
Data for "First Principle Calculation on Pressure Dependent Yielding in Solute Strengthened Aluminium Alloys"
<p>The dataset contains the DFT results which is the basis for the results and discussions in the related article, "First principle calculations of pressure dependent yielding in solute strengthened aluminium alloys". The details of the DFT calculations are written in the article.</p> <p>The two different file-name conventions are explained below.</p> <p>OUTCAR_Al_R1_HSP<br> OUTCAR_X_HSP_POS</p> <p>Al-files represent the pure aluminium models, showing the dislocation energies at various hydrostatic pressures. X (Cu, Si, Mg) represents the solute specie in the full model showing the configurational energy. R1 is the radius of the relaxed region. HSP represents the superimposed hydrostatic pressure, which is given by (-560+HSP*80) MPa. POS is the atomic index in the OUTCAR files, where the solute is substituted.</p>
Dataset of the publication: Magnon Straintronics in the 2D van der Waals Ferromagnet CrSBr from First-Principles
<p>Dataset of the publication: Magnon Straintronics in the 2D van der Waals Ferromagnet CrSBr from First-Principles</p> <p>DOI: 10.1021/acs.nanolett.2c02863</p> <p>D. L. Esteras, A. Rybakov, A. M. Ruiz, J. J. Baldoví</p> <p>Nano Lett. 2022, 22, 21, 8771–8778</p>
First-Principles Core Spectroscopy of LiCoO2 and CoO2
<p>Input and results data for the paper "First-Principles Core Spectroscopy of LiCoO2 and CoO2"</p>
Supplementary information: The nuclear-spin-forbidden rovibrational transitions of water from first principles
<p><strong>Supplementary material to the manuscript <em>"The nuclear-spin-forbidden rovibrational transitions of water from first principles"</em> by Andrey Yachmenev, Guang Yang, Emil Zak, Sergei Yurchenko, and Jochen Küpper, <em>J. Chem. Phys., submitted. </em></strong><a href="https://arxiv.org/abs/2203.07945"> arXiv:2203.07945</a></p> <p>The data set contains hyperfine (spin-rovibrational) energies and dipole transition spectrum of water molecule (H<sub>2</sub><sup>16</sup>O), calculated using variational approach <a href="https://github.com/Trovemaster/TROVE">TROVE</a> and <a href="https://github.com/CFEL-CMI/richmol">RichMol</a>, and included spin-rotational and spin-spin hyperfine interactions.</p> <p>In addition, the data set includes HDF5-type richmol database file <strong><em>h2o_p48_j40_rovib.h5</em></strong> (see <a href="https://github.com/CFEL-CMI/richmol">https://github.com/CFEL-CMI/richmol</a>) containing rovibrational energies, matrix elements of nuclear spin-rotation, nuclear spin-spin, electric dipole, and electric quadrupole tensor operators of H<sub>2</sub><sup>16</sup>O, calculated using variational approach TROVE.</p> <ul> <li><strong>h2o_exomol_F.states </strong>and<strong> h2o_exomol_F.trans</strong> - hyperfine linelist of water stored in the ExoMol format (see, e.g., <a href="https://doi.org/10.1016/j.jms.2016.05.002">J. Molec. Spectrosc., 327, 73-94 (2016)</a>). The two files contain a set of hyperfine states with assignments and a set of dipole transitions (Einstein A-coefficients), respectively. The states in <strong>h2o_exomol_F.states</strong> file are arranged by quantum number of total angular momentum F = I + J (spin + rotation) in ascending order.</li> <li><strong>h2o_exomol_J.states </strong>and<strong> h2o_exomol_J.trans</strong> - contain same data as <strong>h2o_exomol_F.states </strong>and<strong> h2o_exomol_F.trans</strong> files, except that the states in <strong>h2o_exomol_J.states</strong> file are arranged by rotational quantum number (J) in ascending order.</li> <li><strong>h2o_p48_j40_rovib.h5<em> - </em></strong>Richmol HDF5 database file for H<sub>2</sub><sup>16</sup>O containing rovibrational energies (in cm<sup>-1</sup>), matrix elements of nuclear spin-rotation (in kHz), spin-spin (in kHz), molecular electric dipole moment (in Debye), and molecular electric quadrupole moment (in a.u.) operators. For details on how to read this file, see <a href="https://github.com/CFEL-CMI/richmol">Richmol GitHub repository</a> and <a href="https://richmol.readthedocs.io/en/latest/">Richmol documentation</a> (<em>or contact Andrey Yachmenev at andrey.yachmenev@cfel.de</em>).</li> <li><strong>ortho_para_transitions.txt</strong> - table with strongest predicted ortho-para transitions in H<sub>2</sub><sup>16</sup>O at T = 296 K with the 10<sup>−36</sup> cm/molecule intensity cut-off.<br> <br> <strong><em>An example of hyperfine energies and hyperfine dipole spectrum calculation for water using h2o_p48_j40_rovib.h5 file from this repository may be found in the <a href="https://github.com/CFEL-CMI/richmol">Richmol GitHub repository's</a> examples folder: <a href="https://github.com/CFEL-CMI/richmol/tree/develop/examples/hyperfine">https://github.com/CFEL-CMI/richmol/tree/develop/examples/hyperfine</a></em></strong></li> </ul> <p>Structure of<strong> h2o_exomol_F.states </strong>and<strong> h2o_exomol_J.states </strong>files:</p> <table align="left"> <thead> <tr> <th scope="col">Column No.</th> <th scope="col">Kind </th> <th scope="col">Meaning </th> </tr> </thead> <tbody> <tr> <td>1</td> <td>int</td> <td>state ID number</td> </tr> <tr> <td>2</td> <td>float</td> <td>hyperfine state energy relative to the ZPE, in cm<sup>-1</sup></td> </tr> <tr> <td>3</td> <td>int</td> <td>state degeneracy</td> </tr> <tr> <td>4</td> <td>int</td> <td>value of F quantum number (total spin-rotational angular momentum)</td> </tr> <tr> <td>5</td> <td>str</td> <td>state symmetry in C<sub>2v</sub></td> </tr> <tr> <td>6</td> <td>int</td> <td>value of J quantum number (total rotational angular momentum)</td> </tr> <tr> <td>7</td> <td>str</td> <td>symmetry of state's rotational component in C<sub>2v</sub></td> </tr> <tr> <td>8</td> <td>int</td> <td>value of k<sub>a</sub> quantum number (a-axis projection of rotational angular momentum)</td> </tr> <tr> <td>9</td> <td>int</td> <td>value of k<sub>c</sub> quantum number (c-axis projection of rotational angular momentum)</td> </tr> <tr> <td>10</td> <td>int</td> <td>value of v<sub>1</sub> vibrational quantum number</td> </tr> <tr> <td>11</td> <td>int</td> <td>value of v<sub>2</sub> vibrational quantum number</td> </tr> <tr> <td>12</td> <td>int</td> <td>value of v<sub>3</sub> vibrational quantum number</td> </tr> <tr> <td>13</td> <td>int</td> <td>value of I quantum number (total nuclear spin)</td> </tr> <tr> <td>14</td> <td>float</td> <td>reference rovibrational state energy (i.e., without hyperfine effects) relative to the ZPE, in cm<sup>-1</sup></td> </tr> </tbody> </table> <p>Structure of<strong> h2o_exomol_F.trans </strong>and<strong> h2o_exomol_J.trans </strong>files:</p> <table> <thead> <tr> <th scope="col">Column No.</th> <th scope="col">Kind</th> <th scope="col">Meaning</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>int</td> <td>ID number of final transition state (col. no. 1 in <strong>h2o_exomol_F.states </strong>or<strong> h2o_exomol_J.states </strong>file)</td> </tr> <tr> <td>2</td> <td>int</td> <td>ID number of initial transition state (col. no. 1 in <strong>h2o_exomol_F.states </strong>or<strong> h2o_exomol_J.states </strong>file)</td> </tr> <tr> <td>3</td> <td>float</td> <td>Einstein A-coefficient, in s<sup>-1</sup></td> </tr> <tr> <td>4</td> <td>float</td> <td>Transition wavenumber, in cm<sup>-1</sup></td> </tr> </tbody> </table> <p> </p>
Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models
<p>Supporting data for <a href="https://arxiv.org/abs/2111.13736">https://arxiv.org/abs/2111.13736</a>.</p> <p>ACEhamiltonians.jl code</p> <p>This is an archived copy of the ACEhamiltonians.jl code to accompany the paper <a href="https://arxiv.org/abs/2111.13736">arXiv:2111.13736</a>.</p> <p>See <a href="https://github.com/ACEsuit/ACEhamiltoniansExamples">https://github.com/ACEsuit/ACEhamiltoniansExamples</a> for examples of how to use this code.</p> <p>The code is written in <a href="https://julialang.org/">Julia</a> and requires v1.6 or later. To install the Julia depenendencies:</p> <pre><code><code>$ cd ACEhamiltonians.jl $ julia julia> import Pkg julia> Pkg.activate(".") julia> Pkg.instantiate() </code></code></pre> <p>The scripts <code>test/plots.jl</code>, <code>test/fcc-to-bcc.jl</code> and <code>test/vacancy.jl</code> which produce all the plots in the paper can then run as, e.g.</p> <pre><code><code>julia --project=. test/plots.jl </code></code></pre> <p>Training data</p> <p>The <code>training_data</code> folder contains the atomic structure, Hamiltonian and overlap matrices stored in HDF5 format with the following schema:</p> <ul> <li>Data Group : <strong>aitb/</strong></li> <li>Datasets : <ul> <li><strong>H</strong> : Real-space Hamiltonian Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns)</li> <li><strong>S</strong> : Real-space Overlap Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns)</li> <li><strong>energy</strong> : Energy. Unit: eV. Type: Float64. Shape: Scalar</li> <li><strong>freeenergy</strong> : Free Energy. Unit: eV. Shape: Scalar</li> <li><strong>unitcell</strong> : Unit cell vectors. Type: Float64. Shape: Matrix(3,3)</li> <li><strong>positions</strong> : Atom positions. Type: Float64. Shape: Array(3)</li> <li><strong>forces</strong> : (Optional, if available) Forces. Type: Float64. Shape: Array(3)</li> <li><strong>metadata</strong> : JSON String including dictionary of information of FHIaims calculation (k-points, basis sets), TB Cells, Cutoff, Orbital definitions.,</li> </ul> </li> </ul> <p>The molecular dynamics and FHI-aims parameters are described in the manuscript.</p> <p>On-site models</p> <p>The <code>onsite_models_ord2</code> folder contains our correlation order 2 models for the on site blocks of the Hamiltonian, in a JSON format readable by the <a href="https://github.com/acesuit/ACE.jl">ACE.jl</a> and <a href="https://github.com/ACEsuit/ACEhamiltonians.jl">ACEhamiltonians.jl</a> Julia packages. There are separate files for the Hamiltonian (<code>*_H.json</code>) and overlap (<code>*_S.json</code>) models. The JSON files also contain training and test sets and associated errors as plotted in Figure 3 in our manuscript.</p> <p>Models have a unique identifier (UUID) which is a hash of the input parameters and training data. The mapping from (order, max_degree) to UUID is as follows:</p> <pre><code><code>(2,4) - 13427527590286463256 (2,5) - 10538156191357510769 (2,6) - 1646489440533135164 (2,7) - 12130775482127724115 (2,8) - 12487060958610974041 (2,9) - 2653067664384673997 (2,10) - 1143382251563115664 (2,11) - 4564001820340015372 (2,12) - 9474261500251782658 </code></code></pre> <p>Off-site models</p> <p>The <code>offsite_models_ord1</code> and <code>offsite_models_ord2</code> folders contain our order 1 and order 2 offsite models for Hamiltonian and overlap matrices. The mapping from (H_order, H_max_degree) + (S_order, S_max_degree) to UUID is as follows:</p> <pre><code><code>(1,6) + (1,8) - 7014526518680934587 (1,7) + (1,9) - 8594416159488562244 (1,8) + (1,10) - 10204186688118368371 (1,9) + (1,11) - 13078304848585360574 (1,10)+ (1,12) - 14750835312950641338 (1,11)+ (1,13) - 9883802224093245794 (1,12)+ (1,14) - 3907899412408606585 (1,13)+ (1,15) - 201683837542179657 (1,14)+ (1,16) - 277744202775070779 (2,6) + (1,8) - 4699475053563592071 (2,7) + (1,9) - 489637409713831432 (2,8) + (1,10) - 18034631670613263469 (2,9) + (1,11) - 720654516759450160 (2,10)+ (1,12) - 15214900801060024044 (2,11)+ (1,13) - 13798832597295943078 (2,12)+ (1,14) - 13162803789413134473 </code></code></pre> <p>FCC only</p> <p>Onsite models:</p> <pre><code><code>2 6 5311732756869418284 2 7 13030014632886405308 2 8 5820099621734447846 2 9 10161014511878227635 2 10 11298425190201843107 2 11 9932031839231628354 2 12 9447261873515969583 </code></code></pre> <p>Optimised FCC model <code>16110190062237887798</code></p> <p>BCC only</p> <p>Onsite models:</p> <pre><code><code>2 6 8949023800586845770 2 7 8045797268444730200 2 8 6919809282139600809 2 9 9935027806122780319 2 10 6376963380608532713 2 11 5001375576268070883 2 12 9678585765722197901 </code></code></pre> <p>Optimised BCC model <code>10293566074413000591</code></p> <p>FCC+BCC optimised models</p> <p>Onsite models</p> <pre><code><code>2 6 2154760103892646619 2 7 6450474921309693835 2 8 14227277988574899288 2 9 476820595195218567 2 10 5364136683220082110 2 11 14619519825012606580 2 12 14181614899005838824 </code></code></pre> <p>Offsite FCC+BCC optimised model - <code>4570230078043807257</code></p> <p>Model errors</p> <p>The <code>model_errors</code> directory contains summarised model errors for the training and testing errors for the models listed above.</p> <p>Reference data</p> <p>Reference electronic structure data computed for the BCC and FCC crystals, along the Bain path and for the relaxed vacancy is stored in the <code>reference_data</code> folder. The Hamiltonian and overlap matrices are stored as compressed binary HDF5 files. The format and metadata can be viewed with the <code>h5dump</code> utility, or read in using the supplied Julia code (or indeed from other languages).</p> <p>Predicted data</p> <p>The <code>predicted_data/FCC</code> and <code>predicted_data/BCC</code> folders contain HDF5 files with the results of all model predictions shown in the manuscript on the FCC and BCC crystal structures. <code>predicted_data/FCC-to-BCC</code> contains the results of predictions along the Bain path with the optimized model described in the manuscript and <code>predicted_data/vacancy</code> contains the vacancy calculations.</p>
Design Principles for the Development of Gd(III) Polarizing Agents for Magic Angle Spinning Dynamic Nuclear Polarization
<p>This is the raw dataset for publication </p> <p>Design Principles for the Development of Gd(III) Polarizing Agents for Magic Angle Spinning Dynamic Nuclear Polarization. with the DOI of 10.1021/acs.jpcc.2c01721. It contains all NMR, EPR raw data and the MATLAB codes that are used in this paper.</p> <p>For details, please refer to the readme file.</p>
(DATASET) Functionalized boron–nitride nanotubes: First-principles calculations.
<p>Boron nitride nanotubes (BNNTs), a type of nanomaterial that was first made in 1995, have gained attention in the last two and a half decades, having been characterized as promising inorganic nanoparticles for the application and development of devices in nanomedicine (such as drug delivery systems), and chemical sensors, among other applications. With the use of density functional theory (DFT) calculations, we study the effect of functionalizing the surface of a BNNT with hydroxyl (-OH) and carboxyl (-COOH) organic groups at the N and B sites. The results indicate that the -OH radical remains adsorbed in the B site whereas it is detached from the surface when added to the N site. Very differently, the -COOH remains attached for both adsorption sites. Both functionalizations induced a magnetic moment, produced spin-polarized electronic bands, created flat impurity-like bands, decreased the band gaps, and modified the electron density of the nanotubes. All these changes can be used to control and modify the interaction of BNNTs with other molecules of interest like drugs, heavy metals, and greenhouse gasses, among other substances.</p>
Figure 1. The Turing Test Principle
<p>As the facts so far outlined show, round 1970, researchers were very optimistic that<br> machines would soon (latest in one generation) reach human intelligence level. There already<br> existed the first programs passing the Turing test, which was the official test for proving computer<br> intelligence. Looking at statements of that time today and recognizing that approximately 40 years<br> have passed since then, the logical question that arises is why there are still no intelligent machines<br> among us.</p>
Figure 6. One chromosome from the population and the five chromosomes existing in the evaluation partition.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>For example comparing the<br> chromosome given in Figure 6 with the first chromosome in the evaluation partition, the<br> difference between the relation Med-Med and Med-High as a pair is 0.0 and the difference<br> between the relation High-High and High-Med as a pair is 0.1. Similarly the difference between<br> the relation Med-Cold and Med-Hot as a pair is 0.1 and the difference between the relation<br> High-Cold and High-Hot as a pair is 0.2. We sum all these differences to get the value of<br> compare(i,j), the sum value is 0+0.1+0.1+0.2 = 0.4. Using the same approach we compute the<br> compare function with the other four chromosomes and we get values 0.4, 0.5,0.4 and 0.6. Now<br> we sum the five values 0.4 + 0.4 + 0.5+ 0.4 +0.6 = 2.3. The fitness value is then 1/ 2.3 = 0.434.<br> The highest is the fitness value, the better is the performance of the chromosome.</p>
Figure 4. Incorrect crossover operation. The High-High and High-Med probability values summation should be 1.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>In this genetic operator, we choose two chromosomes at random and apply crossover between<br> them. Figure 3 shows the proposed crossover. We choose a crossing cut site at random. It is to be<br> noted that the crossing cut site should be even number. We should have two crossing cut sites. If<br> we make crossing cut site odd number, the resultant child will not have a correct value of<br> probability. The incorrect crossover is shown in Figure 4.</p>
Figure 5. Mutation process. This is happened by decreasing 0.2 from Med-Cold probability and adding 0.2 to Med- Hot.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>Figure 5 illustrates an example of mutation process. In Figure 5, Med-Cold:0.9 and Med-Hot:0.1<br> before mutation and become Med-Cold:0.7 and Med-Hot:0.3 after mutation. This is done by<br> decreasing 0.2 from Med-Cold probability and adding 0.2 to Med-Hot probability.</p>
Figure 3. The crossover operation between two HMM chromosomes Figure-Genetic Algorithms Principles Towards Hidden Markov Model
<p>Crossover<br> In this genetic operator, we choose two chromosomes at random and apply crossover between<br> them. Figure 3 shows the proposed crossover. We choose a crossing cut site at random. It is to be<br> noted that the crossing cut site should be even number. We should have two crossing cut sites. If<br> we make crossing cut site odd number, the resultant child will not have a correct value of<br> probability. The incorrect crossover is shown in Figure 4.</p>
Figure 2. The general structure of the proposed approach-Genetic Algorithms Principles Towards Hidden Markov Model
<p>The chromosome contains 8 genes, each is represented by the relation between two states<br> accompanied with a probability value. The genes should be formed in this way because this is<br> important in the crossover operation as to be explained later. The most important thing is that each<br> two genes has the probability summation of 1.0. For example Med-Med:02 and Med-High:08 have<br> the summation of 1.0. Similarly High-High:0.6 and High-Med:0.4 have the summation of 1.0. Each<br> two genes with summation of 1.0 should be neighbors.</p>
Figure 1. HMM to describe a relation between the states Med. and High with the observations (invisible states) cold and hot.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>Hewahi [4] presented a modified version of Censored Production Rule (CPR) called<br> Modified Censored Production Rules (MCPR). CPR is proposed by Michalski and Winston [6 ] to<br> capture real time situations. MCPR can fit with hidden Markov model and present a scheme to<br> compute the certainty values of the obtained conclusions out of the induced rules. To compute the<br> certainty values for the rule actions (conclusions), the approach exploited only the probability<br> values associated with the hidden Markov model without using any of the other well known<br> certainty computation approaches. Hewahi [3] also proposed an intelligent networking<br> management system based on the induced MCPRs extracted from a networking structure based on<br> HMM. The advantage of using this technique is that MCPRs are very useful in real time<br> applications and can be adapted over time based on the obtained experience of the networking<br> working process.<br> Let us consider the HMM presented in Figure 1.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 7. Function Principle of Neuro-Symbols
<p>In Figure 7, the basic function principle of neuro-symbols is illustrated. One characteristic of neuro-symbols is that they represent symbolic information. In the case of perception, this symbolic inforamtion are perceptual images like for instance a face or a voice (see Section 4.2.1.2 for more details). Furthermore, neuro-symbols show a number of analogies to biological neurons. They have an activation degree (AD), which indicates if the perceptual image that each neuro-symbol respresents is currently perceived in the environment. Each neuro-symbol has a certain number of inputs and one output. Via the inputs, information about the activation degree of other neurosymbols is collected. Like illustrated in the example of Figure 7, a neuro-symbol representing a face could for instance receive information from neuro-symbols representing a head, eyes, and a mouth.</p>
Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System-Figure 3. The principle of SVM
<p>Manual segmentation training was hard and very time consuming and rely to operator's<br> accuracy, so we developed a color calibration algorithm using SVM(Support Vector Machine). In<br> this subsection, we present a color recognition algorithm using the support vector machine<br> (SVM).SVM is one of the classification algorithms which it has high generality since it can<br> calculate a super plane that maximizes the margin of classes, Fig.3.[8] In our algorithm, the SVM is<br> trained by the H'SY values of the classes and the mean of the obtained image H'SY values. After<br> training, the obtained image is binarized by setting the maximum and minimum value in the<br> distribution of each class as a threshold.</p>
First Principles Validation of Energy Barriers in Ni75Al25
<p>The data from the paper - First Principles Validation of Energy Barriers in Ni<sub>75</sub>Al<sub>25</sub></p> <p>Read the read me for explanation of what is in each folder</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.