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125 results for “Structure learning”
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 3. Symbols definition for a graph-based test
<p>In this scenario, we intend to generate a random graph and compute a deep first-search node list. The first defined random symbol is n, namely the number of nodes in the graph as an integer from 5 to 9. The next symbol is named g and denotes the graph object created randomly using 3 parameters: the number of nodes, the minimum, and the maximum value for the weight. For the number of nodes, we used the previously computed value of n, whereas for the weights, we used two constants 0 and 1 since the graph is not weighted</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 2. Auto-generative Learning Object Model Definition
<p>In this section, we will present the structure of AGLOs in the context of our approach. The AGLO meta-model is structured in XML as in Figure 2,a refinement from Chirila, Ciocarlie, and Stoicu (2015). The AGLO definition contains several sections like name, scenario, theory, question, answers, and feedback (line 01). The name element contains the name of the AGLO, possibly a small description in the human language (line 02). The section of the scenario (line 03) contains a comment (line 04) followed by a set of symbol definitions. The comment should describe the imagined scenario in details and it has the same role as code comments. The symbol is the central element of the AGLO model. The symbol has a name and is very similar to programming language variables. Symbols may be called also parameters since they control the content of the AGLO content in the process of instantiation. </p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 1. The AGLO online assessment approach
<p>In Figure 1, we present the lifetime of AGLOs in the context of online student assessment following a set of steps. In the backend, the tutor develops an AGLO model respecting a predefined meta-model. The model is intuitive, it has a few sections where symbols are defined using formulas and random numbers and then used in a section of a presentation for the student. When such models are created they are stored in a storage facility like a database to be selected by the student through the web application frontend. In the frontend, the students access the web application using a web browser from a workstation, tablet or smartphone. In the assessment process, the student will access several AGLOs. At this step, the accessed AGLOs are instantiated with random numbers, formulas are evaluated to fulfill the designed learning or testing scenario and to create the presentation content for the student. Nevertheless, the instantiated symbols will be used for the automatic assessment of the answers correctness</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 4. Online test assessment example
<p>Thus, applying these restrictions the computed solution is C, E, G, J, L, H, I and is unique. Node C is the starting node since it is the first from the lexicographical point of view. The first step CE is the only choice coping with the restrictions from the [CE, CG, and CJ] edges. Next, EG is the first edge in the list of [EG, EJ]. The next step is GJ which is the only choice. Edge JL is another unique choice. Edge LH is the next step from the list [LH, LI]. Finally, the last edge is obtained by backtracking to node L and then taking edge LI. These restrictions allow us to drive the student to build only one solution from the possible set of solutions. This will determine an easier way of comparing the student’s answer with the answer of the computer. Another more general solution is to use validation functions which require implementation in domain libraries written in JavaScript. </p>
AbDb processed and pickled for use in deep learning CDR-H3 Structure prediction
<p>This is a pickle file, ready for training by the neural network described in "Improving CDR-H3 modelling in Antibodies" found at the following URL:</p> <p><a href="https://github.com/OniDaito/MRes">https://github.com/OniDaito/MRes</a></p> <p>The data is derived from the AbDb dataset found at:</p> <p><a href="http://www.bioinf.org.uk/abs/abdb/">http://www.bioinf.org.uk/abs/abdb/</a></p>
Gaussian16 data for "Dynamic electronic structure fluctuations in the de novo peptide ACC-dimer revealed by first-principles theory and machine learning"
<p>This is the Gaussian 16 input and corresponding output, which was used as input into the machine learning presented in the paper titled "Dynamic electronic structure fluctuations in the de novo peptide ACC-dimer revealed by first-principles theory and machine learning". This upload is required before submission of the paper.<br><br>The 1001 and 100 snapshots from different extractions are preserved in separated directories. Each snapshot directory <code>*_snapshot</code> has the initial GROMACS snapshot <code>test_*.pdb</code> , the geometry after truncating the solvation shell in various formats, the Gaussian16 input, qsub input and the output directory <code>*.1</code> with a JobID number assigned by qsub. The output directory has the standard output from Gaussian in a <code>.log</code> file and <code>grep</code>ed output from the <code>.fchk</code> file in <code>*.out</code> .</p>
WikiMorph: Learning to Decompose Words into Morphological Structures
<p>WikiMorph is a JSON dataset that contains word breakdowns for English words. These word breakdowns primarily consist of morphological compounds (both from English and the word's etymology) along with each compound's associated definition. It also contains other fields that might be useful, such as syllables and parts-of-speech tags. The dataset contains entries for 355,782 unique words and 505,033 total entries. The data collection process for this dataset was described in the paper "<a href="https://link.springer.com/chapter/10.1007/978-3-030-78270-2_72">WikiMorph: Learning to Decompose Words into Morphological Structures</a>", with some additional updates after publication.</p> <p> </p> <pre><code class="language-json"> { "Word": "abduction", "PoS": "Noun", "Syllables": [ "ab", "duc", "tion" ], "Definition": "The act of abducing or abducting; a drawing apart; the movement which separates a limb or other part from the axis, or middle line, of the body.", "Morphemes": [ { "Affix": "abduct", "Language": "en", "PoS": "Verb", "Meaning": "To draw away, as a limb or other part, from the median axis of the body.", "Etymology Compounds": [ { "Affix": "ab", "Language": "la", "Decoded": "ab", "PoS": null, "Meaning": "away" }, { "Affix": "duco", "Language": "la", "Decoded": "duco", "PoS": null, "Meaning": "to lead" } ] }, { "Affix": "-ion", "Language": "en", "PoS": "Suffix", "Meaning": "an action or process, or the result of an action or process", "Etymology Compounds": [ { "Affix": "-iō", "Language": "la", "Decoded": "-io", "PoS": "Suffix", "Meaning": "Used to form abstract nouns from verbs." } ] } ] }</code></pre> <p> </p>
Data and scripts from "Unsupervised learning for structure detection in plastically deformed crystals"
<p>This documents contains the scripts and dataset used for the paper "Unsupervised learning for structure detection in plastically deformed crystals".</p> <p> </p> <p>More precisely it contains 4 folders :</p> <p><br> DumpForFigures : subfolder containing the atomic positions in .dump format (see lammps documentation) used for the article figures.</p> <p>DumpForTraining : subfolder containing the atomic position in .dump format (see lammps documentation) used for training the autoencoder.</p> <p>ScriptsToDetectStructuresFromDump : subfolder containing the script sused to detect the substructures of the system by combining autoencoder and clustering methods. This folder contains a readme with the details of the contents.</p> <p>ScriptToGenerateDump : subfolder containing the scripts used to generate the atomic data with molecular dynamics. These data are then used to train the autoencoder. This folder contains a readme with the details of the contents.</p> <p>REQUIREMENTS :</p> <p> </p> <p>Lammps</p> <p>Python3 with packages :</p> <p>-numpy</p> <p>-matplotlib</p> <p>-pyscal</p> <p>-sci-kit learn</p> <p>-pytorch</p> <p>-glob</p> <p> </p> <p> </p> <p> </p>
Data and analytical codes for: Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure
<p>Data and analytical codes for: Ishizawa et al. (2023) Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure, bioRxiv, 2023.07.04.546222</p> <p> https://www.biorxiv.org/content/10.1101/2023.07.04.546222v1</p> <p> </p>
Capturing functional relations in fluid-structure interaction via machine learning
Open the record for dataset details and reuse information.
Use of Deep Learning for structural analysis of CT-images of soil samples
Open the record for dataset details and reuse information.
Source code for StrVCTVRE: a supervised learning method to predict the pathogenicity of human genome structural variants
Open the record for dataset details and reuse information.
Molecular dynamics simulations data for "Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes".
<p>Molecular dynamics simulation data created and used in "Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes".</p> <p> </p>
Learning Naturalistic Structure: Processed fMRI dataset
<p>This data was obtained from OpenNeuro as ds001545. We would like to thank the authors for their generosity in sharing their data, and we point interested users towards their paper describing its acquisition:</p> <blockquote> <p>Aly M, Chen J, Turk-Browne NB, & Hasson U (2018). Learning naturalistic temporal structure in the posterior medial network. Journal of Cognitive Neuroscience, 30(9): 1345-1365.</p> </blockquote> <p><br> <strong>Experimental design</strong></p> <p>In this dataset, subjects were scanned while watching repeated presentations of intact and scrambled clips from Wes Anderson's 2014 film, <em>The Grand Budapest Hotel</em>. Scrambled clips were presented in either a 'fixed' (i.e., consistent scrambling from run to run) or 'random' (i.e., random scrambling from run to run) condition. An overview of the <a href="https://www.mitpressjournals.org/na101/home/literatum/publisher/mit/journals/content/jocn/2018/jocn.2018.30.issue-9/jocn_a_01308/20180730/images/large/01308f01c.jpeg">experimental design is shown in this figure</a> from Aly and colleagues (2018)</p> <p><strong>Preprocessing</strong></p> <p>After downloading from OpenNeuro using <a href="https://www.datalad.org/">DataLad</a>, data was preprocessed using <a href="https://fmriprep.readthedocs.io">fMRIPrep 1.5.0rc1</a>. A complete transcript of the fMRIPrep processing is available as a README file in the repository. Post-processing was performed using <a href="http://nilearn.github.io">Nilearn</a>. Briefly, functional files were masked with the fMRIPrep-derived brain mask and trimmed to discard non-steady state volumes. Please see the README for further details.</p>
POMFinder: Identifying polyoxometalate cluster structures from pair distribution function data using explainable machine learning
<p>Databases that were used to train the POMFinder ML model.</p>
Thermal conductivity analysis of polymer-derived nano-composite via image-base structure reconstruction, computational homogenization and machine learning
<p>This dataset includes supplementary data and utilities for validating simulation results and training machine learning models as outlined in the publication titled "Thermal Conductivity Analysis of Polymer-Derived Nanocomposite via Image-Based Structure Reconstruction, Computational Homogenization, and Machine Learning" (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>).</p> <p>This dataset containes the microstructure images (identified by particle diameters size \(D_1\) and \(D_2\) volume fraction \(V_\mathrm{f}\) and aspect ratio \(A_\mathrm{r}\)) (see Table 1) and their corresponding homogenized thermal conductivity. these images resemble the microstructure of the monolithic \(\mathrm{(Hf,Ta)C/SiC}\) ceramic following FAST sintering, the material system of this work (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>). White and black colors within the images represent distinct regions of the material system, respectively referring to former powder particles (FPPs) and sinter necks (SNs), which is explained in this work.</p> <p>Table 1. Parameterized descriptors extracted from the mesoscale SEM image analysis</p> <table> <tbody> <tr> <td>Param.</td> <td>Mean [unit]</td> <td>Std.</td> </tr> <tr> <td>\(D_{1}\)</td> <td>40, 50, 60 [μm]</td> <td>20%</td> </tr> <tr> <td>\(D_{2}\)</td> <td>20, 25, 26, 30, 33, 40 [μm]</td> <td>30%</td> </tr> <tr> <td>\(V_\mathrm{f}\)</td> <td>1.5, 2.0</td> <td>-</td> </tr> <tr> <td>\(A_\mathrm{r}\)</td> <td>35, 40, 45, 55, 60 [%]</td> <td>-</td> </tr> </tbody> </table> <p>This dataset contains:</p> <ul> <li><em>dataset.csv: </em>containing a summary of data including the names of microstructure images, their corresponding geometric details, as well as the first and third principal components of two-point statistics for all images, along with the effective thermal conductivity of the corresponding microstructures. Further details can be found in the associated publication.</li> <li><em>microstructures_images.zip</em>: containing binary cross-section images of the RVEs from synthetic microstructures。</li> <li><em>results.zip:</em> contains all the simulation results based on digitized diffuse-interface microstructures, which can be opened by the post-processing software, such as ParaView.</li> </ul>
Inverse Design of Octagonal Plasmonic Structure for Switching Using Deep Learning
<p>OctagonalRR_AOPS: Datasets for "<strong>Inverse Design of Octagonal Plasmonic Structure for Switching Using Deep Learning</strong>," (2024).</p>
Three-dimensional Unfolding and Unfaulting Dataset for Structural Interpretation Using Self-Supervised Learning
<p>This dataset accompanies the study "Three-dimensional unfolding and unfaulting for structural interpretation using self-supervised learning." It provides synthetic data used to develop and validate a novel 3-D lightweight neural network framework for restoring deformed geological structures to their flattened state. The data include highly deformed examples with complex faulting and folding, designed to test the model's ability to compute precise shifts that realign stratigraphic layers. </p>
Dataset for "A Comparative Review of Deep Learning Methods for RNA Tertiary Structure Prediction"
<p>Datasets used in "A Comparative Review of Deep Learning Methods for RNA Tertiary Structure Prediction".</p> <p>The provided zip file contains:</p> <ul> <li><strong>Datasets 1–3:</strong> For each dataset, directories include input sequences (FASTA), multiple sequence alignments (MSAs in AFA format), and normalized predicted structures from six deep learning tools. Dataset 3 also contains references - RNA chains extracted from complexes. These folders also include a CSV file with all metrics for all RNAs and tools.</li> <li><strong>Dataset 4:</strong> A text file listing the PDB IDs of RNAs included in this dataset, which is a subset of Dataset 3.</li> <li><strong>Dataset complexes:</strong> RNA chains extracted from predicted complexes, where predictions are made by AlphaFold 3 web server and, in some cases, RoseTTAFoldNA. There are also job files for the AlphaFold 3 web server used to obtain these predictions. Same as for previous datasets, this folder also includes a CSV file with all metrics for these RNA chains.</li> </ul> <p>The structure of the dataset and details of each folder are explained in the included README file.</p> <p> </p>
Dataset for: Anniés et al., "Accessing structural, electronic, transport and mesoscale properties of Li-GICs via a complete DFTB-model with machine-learned repulsion potential"
<p>GPrep training data, GPrep jupyter notebook, .skf files.</p> <p>The GPrep code is available at https://doi.org/10.5281/zenodo.3697913</p>
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.