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3,688 results for “computer”
Helical dinuclear 3d metal complexes with bis(bidentate) [S,N] ligands: synthesis, structural and computational studies
<h1>Raw data for the publication entitled:</h1> <h2>Helical dinuclear 3d metal complexes with bis(bidentate)<br>[S,N] ligands: synthesis, structural and computational<br>studies</h2> <p><em>Dalton Transactions</em>, <strong>2024</strong>, DOI: 10.1039/D4DT02395A</p> <p>Authors:<br>Jamie Allen, Jörg Saßmannshausen, Kuldip Singh, Alexander F. R. Kilpatrick*</p> <p>These folders contain the raw data which were used to prepare the above publication.</p> <h1>Information regarding the raw files of the DFT calculations.</h1> <p>The zip-files in this section containing the raw-data of the DFT calculations leading to the Zn, Co and Fe calculated structures. As filenames are notoriously bad in handling special characters, the names of the folder appear different from what is being used in the final publication. We try to provide as much information as possible to facilitate the usage of these results.</p> <p>Thus:</p> <table> <tbody> <tr> <th>Abbreviation publication</th> <th>Abbreviation folder</th> <th>Abbreviation filename</th> </tr> </tbody> <tbody> <tr> <td>[Zn(<strong>3</strong>)<sub>2</sub>]</td> <td>Zn3-2</td> <td>SNdipp2Zn</td> </tr> <tr> <td>[Co(<strong>3</strong>) <sub>2</sub>]</td> <td>Co3-2</td> <td>SNdipp2Co</td> </tr> <tr> <td>[Fe(<strong>3</strong>) <sub>2</sub>]</td> <td>Fe3-2</td> <td>SNdipp2Fe</td> </tr> <tr> <td>[Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Zn2-2</td> <td>zn2</td> </tr> <tr> <td>[Co<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Co2-2</td> <td>co2</td> </tr> <tr> <td>[Fe<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>]</td> <td>Fe2-2</td> <td>fe2</td> </tr> </tbody> </table> <p>Some test calculations were performed as well utilizing Gaussian-09. They can be found in a folders with the suffix <em>-G09</em> or <em>-g09</em>.</p> <p>The closed shell compound [Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>] was investigated further. In order to look into the influence of the used Grimme dispersion correction, we re-calculated the final result without that correction. These files are in the Zn2-2-pbe0 folder. Furthermore, we used [Zn<sub>2</sub>(μ-<strong>2</strong>)<sub>2</sub>] and removed one of the Zn atoms and replaced the dangling bonds with H. We then fully optimized that structure. The results are in the Zn2-2-cut folder.</p> <h1> </h1> <h1>Information regarding the raw characterisation data</h1> <p>The raw characterisation data files for all nuclear magnetic resonance (NMR) spectroscopy, infrared (IR) spectroscopy, cyclic voltammetry (CV), single crystal X-ray diffraction (XRD) and solution magnetometry studies are enclosed in separate .zip files.</p>
Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 Dataset
<p>High-throughput computational screening of metal-organic frameworks rely on the availability of<strong><em> </em></strong>atomic coordinate files which can be used as input to simulation software packages. CoRE MOF Datasets are derived from Cambridge Structural Database (CSD) and also from the World Wide Web.</p> <p><strong>Nomenclatures:</strong></p> <p>LCD (Largest Cavity Diameter), PLD (pore limiting diameter), LFPD (Largest Sphere along the Free Path), ASA (Accessible Surface Area), NASA (Non-accessible surface area), AV_VF (Void Fraction, 0 - 1), NAV (Non Accessible Volume)</p> <p><strong>Dataset Directory Organization</strong></p> <p>CoREMOF2019_public_v2.zip: dataset with CR and NCR classifications</p> <p>1. CR dataset: computaion-ready (<em>N</em> = 10,367)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 6,603)</li> <li> FSR: free solvent removed (<em>N</em> = 3,764)</li> </ul> <p>2. NCR: not computaion-ready (<em>N</em> = 8,714)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 5,417) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 2,597)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 958)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,859)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> <li> FSR: free solvent removed (<em>N</em> = 3,297) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 1,646)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 463)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,185)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> </ul> <p>2. NCR_detail.xlsx: details of all structures by mofchecker and Chen_Manz for each NCR cases</p> <p><strong>November, 24 2024</strong></p> <ul> <li>Re-ordering of folders such that top level directory is based on computation-ready and not-computation ready classification.</li> </ul> <p><strong>November, 13 2024</strong></p> <ul> <li>Classification of Computation-Ready (CR) and Not Computation-Ready (NCR) Structures based on <a href="https://pubs.rsc.org/en/content/articlelanding/2020/ra/d0ra02498h">Chen & Manz</a> (RSC Adv., 2020,10, <a>26944-26951</a>) and <a href="https://github.com/kjappelbaum/mofchecker">MOFChecker </a>program by <a href="https://github.com/kjappelbaum">Kevin M. Jablonka</a>)</li> <li>ML-predicted DDEC6 partial atomic charges based on <a href="https://github.com/mtap-research/PACMAN-charge">PACMAN</a></li> </ul> <p><strong>Acknowledgements</strong></p> <ul> <li>This reserach is supported by the National Research Foundation of Korea (No. 2016R1D1A1B3934484, NRF-2020R1C1C1010373, RS-2024-00449431)</li> <li>This research is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362 (Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Electronic Structure Theory, Molecular Simulations, Machine Learning, and Experiment)</li> </ul>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 8-bit Sub-Volumes
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of each dataset is 500x1000x1000. Below is a summary of the pixel-sizes and associated datasets on Zenodo.</p> <blockquote> <p>Key:</p> <ul> <li>160695 = 0.3125 Micron = https://zenodo.org/records/13327692</li> <li>169066 = 0.8125 Micron = https://zenodo.org/records/13327682</li> <li>169067 = 1.625 Micron = https://zenodo.org/records/13327651</li> <li>169068 = 2.6 Micron = https://zenodo.org/records/12206815</li> </ul> </blockquote> <p>The purpose of this dataset is to provide an easy to download sub-volumes of the larger (>50GB) datasets in the above Zenodo entries.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p>
Subjective human thresholds over computer generated images
<p>Realistic image computation mimics the natural process of acquiring pictures by simulating the physical interactions of light between all the objects, lights and cameras lying within a modelled 3D scene. This process is known as global illumination and was formalised by Kajiya with the following rendering Equation:<br> <span class="math-tex">\(\begin{equation} \label{eq:rendering_equation} L_o(x, \omega_o) = {L_e(x, \omega_o)} + \int_{\Omega}^{} {L_i(x, \omega_i)} \cdot f_r(x, \omega_i \rightarrow \omega_o) \cdot \cos \theta_i d\omega_i \end{equation}\)</span></p> <p>where:</p> <ul> <li> <span class="math-tex">\(L_o(x, \omega_o)\)</span> is the luminance traveling from point <span class="math-tex">\(x\)</span> in direction <span class="math-tex">\(\omega_o\)</span>;</li> <li><span class="math-tex">\(L_e(x, \omega_o)\)</span> is point <span class="math-tex">\(x\)</span> emitted luminance (it is null if point x does not lie on a ligth source surface);</li> <li>the integral represents the set of luminances <span class="math-tex">\(L_i\)</span>incident in <span class="math-tex">\(x \)</span> from the hemisphere of the directions <span class="math-tex">\(\Omega\)</span> and reflected in the direction <span class="math-tex">\(\omega_o\)</span>. The reflected luminances are weighted by the materials reflecting properties (bidirectionnal reflectance function <span class="math-tex">\(f_r(x, \omega_i \rightarrow \omega_o)\)</span>) and the cosinus of the incident angle.</li> </ul> <p>This equation cannot be analytically solved and Monte Carlo approaches are generally used to estimate the value of the pixels of the final image.</p> <p>This proposed dataset is composed of 80 points of view of photo realistics images with different level of samples (following the Monte Carlo approach) for each. Each image is 800 x 800 pixels in size. The most noisy image is of 20 samples and the reference one (the most converged image obtained) is of 10000 samples. The <a href="https://www.pbrt.org/index.html">pbrt</a> rendering engine (version 3) was used to generate these images.</p> <p>By exploiting these levels of samples obtained and therefore of noise perceptible in the images, average subjective human thresholds were collected. For this purpose, the images were divided into 16 areas of 200 x 200 pixels in size for each point of view.</p> <p>The proposed image database is composed of the following files:</p> <ul> <li><strong>human-thresholds.csv</strong> : the set of human subjective thresholds obtained on 40 points of view. A line is composed of the name of the point of view followed by all the thresholds obtained for each of the 16 zones;</li> <li><strong>SIN3D_dataset.tar.gz</strong> : is an archive containing all the images from 20 to 10000 samples in steps of 20 samples for each point of view (i.e. 500 images per point of view). Each folder in the archive corresponds to a point of view.</li> </ul> <p><em>This image database has been exploited in order to propose an objective model for noise detection in photo-realistic computer-generated images (article referenced to this image database).</em></p> <p><strong>Note:</strong> Some of the proposed scenes come from:</p> <ul> <li><a href="https://pbrt.org/scenes-v3">https://pbrt.org/scenes-v3</a></li> <li><a href="https://benedikt-bitterli.me/resources/">https://benedikt-bitterli.me/resources/</a></li> </ul> <p><strong>Funding:</strong> This research was funded by ANR support: project ANR-17-CE38-0009.</p> <p> </p>
Resources to compute TF-IDF weightings on press articles and tweets
<p>These two datasets of features are used in order to compute TF-IDF weightings of documents. It is meant to be used with the <a href="https://pypi.org/project/compute-tf-idf-vectors/">compute-tf-idf-vectors</a> program written in Python and available on Pypi.org.</p> <p>- features_tweets.csv contains features (tokens, lemmas and entities) extracted from Tweets published by press agencies in french, german, spanish and english.</p> <p>- features_news.csv contains features (tokens, lemmas and entities) extracted from articles published by Deutsche Welle in the same languages.</p>
MAMEM Phase I Dataset - A dataset for multimodal human-computer interaction using biosignals and eye tracking information
<p>This dataset combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.</p>
Dataset for the comparison of two Computational Thinking (CT) test for upper primary school (grades 3-4) : the Beginners' CT test (BCTt) and the competent CT test (cCTt)
<p>This dataset contains quantitative student data acquired during the administration of two validated Computational Thinking (CT) assessments for upper primary school (grades 3 and 4): the Beginners' CT test (BCTt) [1] and the comptent CT test (cCTt) [2]</p> <p>To compare the psychometric properties of both instruments a comparative analysis was conducted with data acquired in schools in Portugal from the same school districts. More specifically, we analyse the results of: </p> <p>- the BCTt test administered in March 2020 to 374 students in grades 3-4,</p> <p>- the cCTt test administered in April 2021 to 201 different students in grades 3-4.</p> <p>These students had no prior experience in Computational Thinking, as this was not part of the national curriculum at the times of administration. </p> <p> </p> <p>The detailed psychometric comparison is published in Frontiers in Psychology - Educational Psychology [3] and provides indications regarding the use of both instruments for grades 3-4. </p> <p> </p> <p>A README is included and provides additional information regarding :</p> <p>- the requirements for re-use. </p> <p>- the specific content of the 2 csv files</p> <p> </p> <p>The BCTt is available upon request to maria.zapata@urjc.es and the cCTt items are available in [2] with an editable version being available upon request to laila.elhamamsy@epfl.ch. </p> <p>In case of other inquiries, please contact: laila.elhamamsy@epfl.ch, maria.zapata@urjc.es or pedro.marcelino@treetree2.org</p> <p> </p> <p><strong>References</strong></p> <p>[1] M. Zapata-Cáceres, E. Martín-Barroso and M. Román-González, "Computational Thinking Test for Beginners: Design and Content Validation," <em>2020 IEEE Global Engineering Education Conference (EDUCON)</em>, 2020, pp. 1905-1914, doi: 10.1109/EDUCON45650.2020.9125368.</p> <p>[2] El-Hamamsy, L., Zapata-Cáceres, M., Barroso, E. M., Mondada, F., Zufferey, J. D., & Bruno, B. (2022). The Competent Computational Thinking Test: Development and Validation of an Unplugged Computational Thinking Test for Upper Primary School. <em>Journal of Educational Computing Research</em>, <em>60</em>(7), 1818–1866. <a href="https://doi.org/10.1177/07356331221081753">https://doi.org/10.1177/07356331221081753</a></p> <p>[3] <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=LailaEl-Hamamsy&UID=781667">Laila El-Hamamsy</a>* , <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=Mar%C3%ADaZapata-C%C3%A1ceres&UID=2073859">María Zapata-Cáceres</a>, Pedro Marcelino, Jessica Dehler Zufferey, <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=BarbaraBruno&UID=893934">Barbara Bruno</a>, <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=EstefaniaMart%C3%ADn&UID=2086979">Estefanía Martín-Barroso</a> and <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=MarcosRom%C3%A1n-Gonz%C3%A1lez&UID=760761">Marcos Román-González</a> (2022). <a href="http://www.frontiersin.org/Journal/Abstract.aspx?d=0&name=Educational_Psychology&ART_DOI=10.3389/fpsyg.2022.1082659">Comparing the psychometric properties of two primary school Computational Thinking (CT) assessments for grades 3 and 4: the Beginners' CT test (BCTt) and the competent CT test (cCTt)</a>. <em>Front. Psychol.</em> doi:10.3389/fpsyg.2022.1082659</p>
The OREGANO knowledge graph for computational drug repurposing
<p>The files here are data files from the OREGANO project, which consists of building a holistic knowledge graph on drugs, including natural compounds. Here is the list of files:</p><p> </p><p>- OREGANO_V2.tsv : The triplet file used for link prediction. 3 columns : Subjet ; Predicate ; Object</p><p>- oreganov2.1_metadata_complet.ttl : The OREGANO knowledge graph in turtle format with the names and cross-references of the various integrated entities.</p><p> </p><p>The following files contain the cross-references of OREGANO entities according to their type. They are all organised as follows: the external sources are the titles of the columns and each line begins with the identifier of the entity in OREGANO :</p><p>- TARGET.tsv: Cross-reference table of the 22,096 targets.<br>- PHENOTYPES.tsv: Cross-reference table of the 11,605 phenotypes.<br>- DISEASES.tsv: Cross-reference table of the 18,333 diseases.<br>- PATHWAYS.tsv: Cross-reference table of the 2,129 pathways.<br>- GENES.tsv: Cross-reference table of the 35,794 genes.<br>- COMPOUND.tsv: Cross-reference table of the 90,868 compounds.<br>- INDICATIONS.tsv: Cross-reference table of the 2,714 indications.<br>- SIDE_EFFECT.tsv: Cross-reference table of the 6,060 side-effects.<br>- ACTIVITY.tsv: Names of the 78 activities.<br>- EFFECT.tsv: Names of the 171 effects.</p><p>The OREGANO knowledge graph is composed of 11 types of nodes and 19 types of links. The current version of the graph contains 88,937 nodes and 824,231 links.</p><p>A SPARQL endpoint has been provided to enable users to retrieve and explore the knowledge graph at <a href="http://91.121.148.199:8889/bigdata/#query">OREGANO SPARQL endpoint</a> .</p><p> </p><p>The integration files and the knowledge graph are available on the GitHub of the OREGANO project in the Integration folder: <a href="https://gitub.u-bordeaux.fr/erias/oregano">Gitub repository</a> .</p>
A dataset recorded during development of an affective brain-computer music interface: calibration session
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A dataset recorded during development of an affective brain-computer music interface: testing session
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A dataset recorded during development of an affective brain-computer music interface: training sessions
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Computational Supporting Information for How Chemical Environment Activates Anthralin and Molecular Oxygen for Direct Reaction
<p>The updated version of the dataset contains all original computational results, including validation of the level of theory, molecular structures, and analysis spreadsheets that are in support of our experimental observations of spontaneous reactivity of anthralin/dithranol molecule with molecular oxygen without any catalyst or co-substrate.<br> The paper was published in Journal of Organic Chemistry, 2020, 85(2), 1315–1321 (DOI: 10.1021/acs.joc.9b03133).</p> <p>In the meantime, the science was also also presented at the 8th ELSI Symposium, Tokyo Institute of Technology, Tokyo (Japan); February 3-7, 2020 in the context of molecular catalysis and their role in the chemical evolution of the building blocks of life.</p> <p>This version also has an important update that is being exclusively published here on Zenodo. The selected level of theory (MN15 functional with triple-zeta quality basis set supplemented with BOTH diffuse and polarization basis functions) is further confirmed to be one of the most reasonable one among 98 commonly used functionals.</p>
Dataset-Gender bias in magazines oriented to men and women: a computational approach
<p>This is the dataset associated with the research article 'Gender bias in magazines oriented to men and women: a computational approach' https://arxiv.org/abs/2011.12096</p>
Dataset supporting the paper: Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks
<p>Dataset supporting the paper:</p> <p>Matthew England, Hassan Errami, Dima Grigoriev, Ovidiu Radulescu, Thomas Sturm, and Andreas Weber. Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks. In Proceedings of CASC ’17, Beijing, China, September 18-22 2017, 15 pages. Springer, 2017.</p> <p>The files whose name starts with "SamplePoints" are text files containing the data that produced the plots in the paper.</p> <p>The files whose name starts with "Sys" show the Maple computations used to produce the data. The mw files are to be run with the Maple Computer Algebra System (https://www.maplesoft.com/products/maple/). Pdf printouts of these have also been included for those who do not have access to Maple.</p> <p> </p>
The OREGANO knowledge graph for computational drug repurposing
<p>The files here are data files from the OREGANO project, which consists of building a holistic knowledge graph on drugs, including natural compounds. Here is the list of files:</p><p> </p><p>- OREGANO_V2.tsv : The triplet file used for link prediction. 3 columns : Subjet ; Predicate ; Object</p><p>- oreganov2.1_metadata_complet.ttl : The OREGANO knowledge graph in turtle format with the names and cross-references of the various integrated entities.</p><p> </p><p>The following files contain the cross-references of OREGANO entities according to their type. They are all organised as follows: the external sources are the titles of the columns and each line begins with the identifier of the entity in OREGANO :</p><p>- TARGET.tsv: Cross-reference table of the 22,096 targets.<br>- PHENOTYPES.tsv: Cross-reference table of the 11,605 phenotypes.<br>- DISEASES.tsv: Cross-reference table of the 18,333 diseases.<br>- PATHWAYS.tsv: Cross-reference table of the 2,129 pathways.<br>- GENES.tsv: Cross-reference table of the 35,794 genes.<br>- COMPOUND.tsv: Cross-reference table of the 90,868 compounds.<br>- INDICATIONS.tsv: Cross-reference table of the 2,714 indications.<br>- SIDE_EFFECT.tsv: Cross-reference table of the 6,060 side-effects.<br>- ACTIVITY.tsv: Names of the 78 activities.<br>- EFFECT.tsv: Names of the 171 effects.</p><p>The OREGANO knowledge graph is composed of 11 types of nodes and 19 types of links. The current version of the graph contains 88,937 nodes and 824,231 links.</p><p>A SPARQL endpoint has been provided to enable users to retrieve and explore the knowledge graph at <a href="http://91.121.148.199:8889/bigdata/#query">OREGANO SPARQL endpoint</a> .</p><p> </p><p>The integration files and the knowledge graph are available on the GitHub of the OREGANO project in the Integration folder: <a href="https://gitub.u-bordeaux.fr/erias/oregano">Gitub repository</a> .</p><p> </p>
Dataset: An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine
<p><i><strong>"An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine"</strong></i></p><p><i>CHILECON 2023 - </i><a href="https://site.ieee.org/chilesur/ieee-chilecon-2023/"><i>https://site.ieee.org/chilesur/ieee-chilecon-2023/</i></a><i> </i></p><p>---</p><p>En el marco del trabajo de referencia, los autores ponemos a disposición de los lectores la base de datos utilizada para el proceso de toma de decisión multicriterio para la selección del software y del MCU de una maquina CNC. </p><p>En el repositorio podrán encontrar los datos referentes a los criterios, subcriterios, indicadores, datos, fuentes de los datos extraídos, política de decisión, cálculos de las evaluaciones de los modelos AHP aplicados y el análisis de sensibilidad de estos. Además, podrán encontrar las gráficas utilizadas en el estudio en la mejor calidad posible. </p><p>El material fue puesto a disposición de todos los interesados para fines académicos y científicos. </p><p>Atte. </p><p>Los autores. </p><p>---</p>
Elasticity tensors of 10276 crystals from DFT computations
<h2>Paper introducing this dataset</h2> <p>Wen, M., Horton, M., Munro, J., Huck, P., & Persson, K. (2024). An equivariant graph neural network for the elasticity tensors of all seven crystal systems. <em>Digital Discovery</em>. DOI:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3DD00233K">https://doi.org/10.1039/D3DD00233K</a></p> <p> </p> <p>This dataset consists of three data files in the json format. Each file is explained below.</p> <h2>crystal_elasticity_tensor.json</h2> <p>DFT computed elastic tensors of 10276 crystals used for developing the MatTen model.</p> <p>structure: crystal structure of the material<br>formula_pretty: chemical formula<br>crystal_system: crystal system<br>elastic_tensor: full fourth-rank elastic tensor<br>elastic_tensor_voigt: 6x6 Voigt matrix of the elastic tensor<br>split: split of the data into train, validation, and test subsets for model development</p> <h2><br>max_directional_E.json</h2> <p>New crystals with large maximum directional Young's modulus.</p> <p>material_id: Materials Project identifier<br>formula_pretty: chemical formula</p> <p>structure_original: crystal structure from the Materials Project database<br>elastic_tensor_matten_original: MatTen predicted elastic tensor using `structure_original`<br>max_directional_E_matten_original: MatTen predicted maximum directional Young's modulus using `structure_original`</p> <p>structure: further DFT optimized structure with a tigher criterion<br>elastic_tensor: DFT elastic tensor corresponding to `structure`<br>max_directional_E: DFT maximum directional Young's modulus using `structure`<br>elastic_tensor_matten: MatTen predicted elastic tensor using `structure`<br>max_directional_E_matten: MatTen predicted maximum directional Young's modulus using `structure`</p> <h2><br>elemental_cubic_metal_max_E_along_100_direction.json</h2> <p>New crystals with its maximum directional Young's modulus along the [100] direction.</p> <p>material_id: Materials Project identifier<br>formula_pretty: chemical formula</p> <p>structure_original: crystal structure from the Materials Project database<br>elastic_tensor_matten: MatTen predicted elastic tensor using `structure_original`<br>Delta_S_matten: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor_matten`</p> <p>structure: further DFT optimized structure with a tigher criterion<br>elastic_tensor: DFT elastic tensor corresponding to `structure`<br>Delta_S: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor`</p>
Unlocking the power of computer modelling and simulation across the life sciences product lifecycle
<p><strong>Unlocking the Power of Computer Modelling and Simulation Across the Life Sciences Product Lifecycle</strong></p> <p>In an era where technology continuously reshapes the boundaries of research and development, the field of life sciences stands at the cusp of a transformative shift. The potent combination of computer modelling and simulation has begun to unlock unprecedented opportunities across the product lifecycle in life sciences, promising to revolutionize everything from medicinal product development to clinical research. Let's delve into how these technological advancements are paving the way for groundbreaking progress in medicine and healthcare.</p> <p><strong>The Fusion of Technology and Life Sciences</strong></p> <p><em>In Silico Methods: A New Frontier in Medicine</em></p> <p>The term 'in silico' refers to computer simulations used in the study of biological and chemical processes. The video highlights the growing importance of in silico methods in the life sciences sector, particularly in the United Kingdom. These methods allow for the virtual testing of new medicinal products, significantly reducing the need for costly and time-consuming physical trials.</p> <p><em>Bridging the Gap with Computational Modeling</em></p> <p>Computational modeling is another key aspect discussed in the presentation. It involves the use of computer algorithms and mathematical models to simulate real-world medical data. This approach enables researchers to predict how medicinal products will behave in various scenarios, including their interaction with different types of patient data. As a result, computational modeling is instrumental in enhancing the precision of clinical research and improving medical equitability by considering a broader range of patient profiles.</p> <p><strong>The Impact on Clinical Research and Patient Care</strong></p> <p><em>Enhancing Precision and Efficiency</em></p> <p>One of the most notable benefits of integrating computer modelling and simulation into the life sciences is the enhanced precision and efficiency it brings to clinical research. By leveraging real-world medical data, researchers can obtain more accurate predictions about the efficacy and safety of new medicinal products. This not only accelerates the development process but also ensures that treatments are more tailored to individual patient needs.</p> <p><em>Promoting Medical Equitability</em></p> <p>The video underscores the role of these technologies in promoting medical equitability. Through the use of patient data simulations, it becomes possible to account for a wider array of genetic, environmental, and lifestyle factors that influence health outcomes. This inclusive approach ensures that the benefits of medical advancements are accessible to a diverse population, addressing disparities in healthcare access and treatment efficacy.</p> <p><strong>Conclusion: The Future is Now</strong></p> <p>The integration of computer modelling and simulation in the life sciences heralds a new era of medical research and patient care. As we continue to explore the potential of these technologies, it's clear that they hold the key to unlocking more efficient, precise, and equitable healthcare solutions. The journey towards fully realizing this potential is just beginning, but the promise it holds is immense. As we stand on the brink of this technological revolution, one thing is certain: the future of medicine and healthcare is being shaped here and now, and it's brighter than ever.</p>
Rhyme analysis of Ukrainian Ballads: Towards a Computational Poetics
<p>This dataset is based on the folklore collection <em>Folk songs of Khmelnytsky region</em> (Iefremova & Dmytrenko, 2014). The text corpus includes 200 ballads in Ukrainian language. Ballads collected in the period between 1918 and 2010. There are 9125 ballad lines in the table; 40543 tokens.</p> <p>To analyze the rhyming and rhythmic elements of the text corpus of Khmelnytsky region ballads, the programming language R along with RStudio was used. Code written for text analysis in Estonian Literary Museum. </p> <p><br>This dataset consist of such files:</p> <ul> <li><strong>ballads_corpus_Khmelnytsky region</strong>: contains text data of Khmelnytsky region ballads in CSV;</li> <li><strong>stanza+syllables</strong>: R script to analyse the stanzas types and to calculate the number of syllables in each line;</li> <li><strong>finals in lines+POS analysis</strong>: R script to analyse the rhyme scheme by determine the final syllable in a line, and to do the PoS tags analysis of the rhyme;</li> <li><strong>rhyme_schemes:</strong> R script to analyze the distribution of rhyme schemes in Khmelnytsky region ballads;</li> <li><strong>ballads_corpus_POS:</strong> CSV file containing the text data of Khmelnytsky region ballads with part-of-speech (PoS) tags for the final word in each line;</li> <li><strong>rhyme_by_PoS:</strong> R script to analyze the distribution of rhymes by part of speech across the ballads;</li> <li><strong>ballads_fin_str</strong>: contains text data of Khmelnytsky region ballads with marked stress position in the last word in each line (in CSV);</li> <li><strong>rhyme_stressed_position</strong>: R script to analyse rhyme by stress position.</li> </ul>
BGC-Argo Satellite matchup to compute variability in the Chl:C ratio of phytoplankton.
<p>This dataset provides matchups between BGC-Argo and MODIS satellites (both atmospheric and ocean color products). This dataset allows users to compare the variability of the Chlorophyll (Chl) to Phytoplankton Carbon ratio from BGC-Argo floats depending on the light in the mixed layer and link to information obtained from satellites about cloud coverage. </p> <p>Quality control previously performed on this dataset and matchup criteria are described in the associated publication.</p> <p>Here are some of the column headers detailed for clarity:</p> <p>Columns 1-25 represent data from the BGC-Argo floats:</p> <ul> <li>ID: Float WMO ID number</li> <li>dt: Datetime in datenum format.</li> <li>biomes: Biomes according to Fay & McKinley, 2014 (with West Mediterranean biome 18 and East Mediterranean biome 19)</li> <li>zenith: Sun zenith angle at time of surfacing.</li> <li>kd_490_Xing: Downwelling diffuse attenuation coefficient at 490nm from Xing et al.,2021 method. </li> <li>kd_PAR_Xing: Downwelling diffuse attenuation coefficient of PAR from Xing et al.,2021 method. </li> <li>chla: Median chlorophyll from fluorescence in the mixed layer (corrected for Non-Photochemical Quenching following Xing et al., 2012)</li> <li>F_indiv: Calibration factor for chla (chlorophyll from fluorescence) according to the method described in Xing et al., 2011. </li> <li>F_median: Median Correction factor (F) for all the floats in a biome</li> <li>F_median_season: Median Correction factor (F) for all the floats in a biome in a given season</li> <li>Chl_cor: Chla from floats corrected using the F_median factor according to Xing et al., 2011. </li> <li>PAR_0_Argo: PAR(0-) right below the surface also from Xing et al., 2021.</li> <li>Z_iso : Depth of the 0.415 mol/quanta/m-2/d-1 isolume. </li> <li>Zeu: Euphotic depth, 1% of surface light.</li> <li>Eg_Argo: Median light level in the mixed layer during a float's profile, bounded by the surface and the MLD (in mol quanta m^-2 h^-1).</li> <li>MLD: Mixed layer depth, determined using the 0.03 density criteria from de Boyer Montégut, et al.,2004.</li> <li>bbp_XXX: Backscattering at a specific wavelength</li> <li>Cphyto: Median Phytoplankton Carbon in the mixed layer computed from Bbp following Graff et al., 2015.</li> <li>Cphyto_B: Median Phytoplankton Carbon in the mixed layer computed from Bbp following Behrenfeld et al., 2005.</li> <li>Cphyto_M: Median Phytoplankton Carbon in the mixed layer computed from Bbp following Martinez-Vincente et al., 2013.</li> <li>ratio_cor: Chl_cor /Cphyto.</li> </ul> <p>Columns 26-45 have products from matchups with ocean-color MODIS files:</p> <ul> <li>sat_dt: Datetime of satellite overpass in datenum format. </li> <li>chlor_a: Satellite chl obtained from NASA's OBPG hybrid algorithm.</li> <li>sat_IPAR: Instantaneous PAR at time of overpass.</li> <li>sat_PAR: MODIS Daily PAR product above the surface.</li> <li>sat_Daily_PARminus: MODIS Daily PAR product propagated right below the surface (0-)</li> <li>sat_Daily_Eg: Daily median light in the mixed layer computed as sat_DailyPAR_minus * exp(-Kd_PAR*MLD/2) ( in mol quanta m^-2 d^-1).</li> </ul> <p>Columns 46-49 have products from matchups with atmospheric MODIS files: </p> <ul> <li>a_lat, a_lon, a_dt: Same as above but for the atmospheric file</li> <li>Confident Cloudy: Number of pixels (Out of 25) with the Confident Cloudy flag. </li> </ul> <p> </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.