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12,393 results for “materiality”
BeMAGIC_Reduction of coercivity in magnetic materials upon voltage application
<p>BeMAGIC ITN (GA861145) Reduction of coercivity in magnetic materials upon voltage application. Results from UCAM, IFW/TUC and INRIM.</p>
Supplementary material for: "Assessment of linkage disequilibrium patterns between structural variants and single nucleotide polymorphisms in three commercial chicken populations"
<p>Supplementary material for the publication "Assessment of linkage disequilibrium patterns between structural variants and single nucleotide polymorphisms in three commercial chicken populations"</p> <p>The realated preprint can be found at Research Square (<a href="https://doi.org/10.21203/rs.3.rs-861830/v1">https://doi.org/10.21203/rs.3.rs-861830/v1</a>)</p> <p>Supplementary file 1: Supplementary results, tables and figures.</p> <p>Supplementary file 2: MultiQC report.</p> <p>Supplementary file 3: Observer concordance of the visual filtering step.</p> <p>Supplementary file 4: Snakemake workflow and scripts.</p> <p> </p>
Supplemental Material Surveillance Improves Outcomes for Carriers of SDHB Pathogenic Variants
<p><strong>Supplemental table for Surveillance improves outcomes for carriers of <em>SDHB</em> pathogenic variants: a multi-center study</strong></p>
Dataset: HRSTEM Images of Defective and Non-Defective Quasi-Periodic Materials
<p>This is the image dataset and model used to produce the results reported in the following publication: <br> Dennler, N., Foncubierta-Rodriguez, A., Neupert, T., Sousa, M. (2021). <em>Learning-based defect recognition for quasi-periodic HRSTEM images</em>. Micron, 146(July 2020), 103069. https://doi.org/10.1016/j.micron.2021.103069<br> <br> For questions, please correspond with N. Dennler (n.dennler2<strong> </strong>at<strong> </strong>herts.ac.uk) or with M. Sousa (sou at zurich.ibm.com).</p> <p><strong>hrstem_defects_dataset.zip:</strong> These are the images and labels used to develop and test the algorithm proposed in the above-mentioned publication. They correspond to high resolution scanning transmission electron microscopy images obtained for various III-V films, namely InP, GaAs, InGaAs and InAlGaAs using a JEOL ARM200F microscope. The raw images have been converted in .tif format with the GMS 3 program from Digital Micrograph. The labels have been created by a microscopy expert. Black: main crystal symmetry (non-defective). Gray: secondary crystal symmetry (symmetry defect). White: blurred (amorphous region or beam defect)<br> <br> <strong>vgg16.zip: </strong>The trained neural network model as well as a detailed description of the training/testing dataset that was used to achieve the results reported in the above-mentioned publication.</p>
The Influence of Data Storytelling on the Ability to Recall Information — Auxiliary materials
<p>This repository contains auxiliary materials for the CHIIR 2022 paper "The Influence of Data Storytelling on the Ability to Recall Information" by Dominyk Zdanovic, Tanja Julie Lembcke and Toine Bogers (= corresponding author)</p> <p>Published in: <em>Proceedings of the 2022 Conference on Human Information Interaction and Retrieval (CHIIR '22)</em>, March 14--18, 2022, Regensburg, Germany.</p> <p>The paper presents the results of an experimental comparison of influence data storytelling and traditional data visualizations on the ability to recall information contained in the visualizations. There are two types of auxiliary materials:</p> <ol> <li> <p>Questions posed to participants in the two condition, both post-task and post-exeriment along with the correct answers.</p> </li> <li> <p>PDF versions of the six visualizations used in the experiment</p> </li> </ol>
Supplementary Material 1: Original dataset collected during the tracking and mark-release-recapture study and R script used to analyse the data
<p>The original dataset collected in northern Serbia during butterfly behavioural study on two species, <em>Phengaris teleius</em> and <em>Polyommatus icarus</em>. The dataset is provided in two separate CSV files for mark-release-recapture study and for butterfly tracking study. In addition, R script used to preopare the dataset and fit the models is given.</p>
TransProteus, Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers from images
<p>We present TransProteus, a dataset, for predicting the 3D structure and properties of materials, liquids, and objects inside transparent vessels from a single image without prior knowledge of the image source and camera parameters. Manipulating materials in transparent containers is essential in many fields and depends heavily on vision. This work supplies a new procedurally generated dataset consisting of 50k images of liquids and solid objects inside transparent containers. The image annotations include 3D models and material properties (color/transparency/roughness...) for the vessel and its content. The synthetic (CGI) part of the dataset was procedurally generated using 13k different objects, 500 different environments (HDRI), and 1450 material textures (PBR) combined with simulated liquids and procedurally generated vessels. In addition, we supply 104 real-world images of objects inside transparent vessels with depth maps of both the vessel and its content.</p> <p>Note that there are two files here:</p> <p><a href="https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z">Transproteus_SimulatedLiquids2_New_No_Shift.7z</a></p> <p>and</p> <p><br> <a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TranProteus2.7z?versionId=f16e7126-8750-41f7-99e6-d35ca60399cc">TranProteus2.7z </a>, contain subset of the virtual CGI data set.</p> <p>https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z</p> <p><a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TransProteus_RealSense_RealPhotos.7z">TransProteus_RealSense_RealPhotos.7z </a>: Contain real-world photos scanned with real sense with depth map of both the vessel and its content</p> <p>See ReadMe file in side the downloaded files for more details</p> <p>The full dataset (>100gb) can be found here:</p> <p><a href="https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV">https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV</a></p> <p>https://<a href="http://icedrive.net/1/6cZbP5dkNG">icedrive.net/1/6cZbP5dkNG</a></p> <p>See: <a href="https://arxiv.org/pdf/2109.07577.pdf"> https://arxiv.org/pdf/2109.07577.pdf</a> for more details</p> <p><strong><a href="https://zenodo.org/record/4736111#.YVOAx3tE1H4">**This dataset is complementary to LabPics dataset with 8k real images of materials in vessels in chemistry labs, medical labs, and other settings. The LabPics dataset can be downloaded from here:</a></strong></p> <p><strong><a href="https://zenodo.org/record/4736111#.YVOAx3tE1H4">https://zenodo.org/record/4736111#.YVOAx3tE1H4</a></strong></p> <p> </p> <p><strong>************************************************************************************</strong></p> <p><a href="https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z">Transproteus_SimulatedLiquids2_New_No_Shift.7z </a>and <a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TranProteus2.7z?versionId=f16e7126-8750-41f7-99e6-d35ca60399cc">TranProteus2.7z</a></p> <p>The two folders contain relatively similar data styles.<br> The data in No_Shift contain images that were generated with no camera shift in the camera paramters. If you try to predict 3d model from an image as a depth map, this is easier to use (Otherwise, you need to adapt the image using the shift). For all other purposes, both folders are the same, and you can use either or both. In addition, a real image dataset for testing is given in the RealSense file.</p> <p> </p> <p> </p> <p> </p>
Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives (Supplemental material)
<p>This data upload provides a detailed account of indicators that can be used to measure RRI progress around the world against the UNESCO Recommendation for Science and Scientific Researchers at the level of individual researchers and public opinion. This data upload provides supplemental material for an article in Open Research Europe entitled, 'Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives'.</p> <p>Abstract for main article:</p> <p>As calls for more socially responsible research and innovation (RRI) policies and practices grow more insistent, the need for high quality indicators that can be used to evaluate progress is becoming increasingly important. Given the global nature of science, such indicators need to be relevant to countries across all world regions. Moreover, the methodological quality of indicators is critical to provide a strong foundation for long-term comparative measurement of the impacts of different kinds of policy intervention. There is a practical challenge in this effort relating to the uneven mechanisms for data collection and analysis available in different countries. There is also a geopolitical challenge in gaining buy-in from countries with very different, and sometimes competing, agendas. Here, the 2017 UNESCO-led Recommendation on Science and Scientific Researchers is highlighted as an existing vehicle that can enable cooperation on globally comparative measurement of socially responsible research and innovation. In particular, the quadrennial monitoring of the implementation of this wide-ranging global policy instrument that has been ratified by 195 countries affords a unique opportunity to add value for these countries by linking RRI to the 2017 Recommendation while establishing benchmark indicators for RRI more generally. As a practical and methodological contribution to the global community of science and innovation policymakers, researchers and research and innovation stakeholders committed to socially responsible research, this report contains specific, detailed survey questions and response options focusing on the public opinion and individual researchers’ level of measurement. It provides details of sources of benchmark survey data that have readily available open data that can be used to benchmark the development of socially responsible research and innovation over time from the vantage points of the public and researchers around the world. The aim of this kind of indicators framework is to enable evidence-based practice in socially responsible research and innovation. Robust and practical RRI measurement on a global scale can reduce the risk that well-intentioned but ill-conceived RRI policy and practice interventions create undetected, unchecked, and unreformed negative impacts in practice.</p>
BASE DE DATOS DE CULTURA MATERIAL CALCOLÍTICO MEDITERRÁNEO
<p>This database is one of the data packagaes used in the making of the PhD dissertation "Connecting with the past: social networks in recent Prehistory".</p> <p>It contains information of counts for all the archaeological artifacts collected during the passing of the PhD works. It contains archaeological information from a wide area (provinces of Valencia, Alicante, Castellón, Teruel, Almería y Murcia), related with the period 5300-3800 cal BP (Late Neolithic-Chalcolithic-Early Bronze Age).</p> <p>It was made using Filemaker format, and is linked to a database with all the Bell-Beaker vessels found for the same area.</p>
The Data Set for the Publication "Laboratory Testing of Small Scale Solar Facade Module with Phase Change Material and Adjustable Insulation Layer"
<p>In the scope of the ambitious EU goals of carbon neutrality in 2050, building energy efficiency is one of the crucial segments. To ensure a faster energy transition process, innovations are needed at various built environment-related sectors - starting from building components up to urban level energy management advancements.</p> <p>Phase change material (PCM) enriched building components allow to shift the existing paradigm - to make a switch from the static building components to dynamic ones able to take an active part in building energy balance by ensuring energy storage in the building thermal envelope.</p> <p>The data set presented here supports the paper "Laboratory Testing of Small Scale Solar Facade Module with Phase Change Material and Adjustable Insulation Layer". The design of the façade module, experimental setup, used equipment, the plan of the experiment, and obtained results are described in the paper. The provided data set provides heat-flux and average temperature (in PCM) measurements.</p>
Bemisia tabaci cryptic species: supplementary materials
<p>This archive contains supplementary materials relating to the investigation of morphological differences in Bemisia tabcai puparia as rescribed in <span>MacLeod, N., Canty, R.J., and Polazek, A., 2022, Morphology-based identification of Bemisia tabaci cryptic species puparia via embedded group-contrast convolution neural network analysis: Systematic Biology. The materials archived include (1) and inventory of all puparial images, (2) full sets of results files for the image-based t-SNE and embedded, group-contrasts CNN analyses, (3) Wolfram Mathematical™ scripting code lisitngs for the image processing and CNN software used in the analysis.</span></p>
Original datasets for : A computational homogenization framework with enhanced localization criterion for macroscopic cohesive failure in heterogeneous materials
<p>The original datasets from tests in the article: <strong> A computational homogenization framework with enhanced localization criterion for macroscopic cohesive failure in heterogeneous materials</strong>. The results are produced by the in-house fem codes of the Computational Mechanics group, CiTG, TU delft.</p>
Evaluating the Usability of Open Source Frameworks in Energy System Modelling (Supplementary Material)
<p>Dataset and source code for analysis of the Energy System Modelling Usability Testing (ESMUT) procedure applied in the open_MODEX project.</p> <p>This is supplementary material for the publication:</p> <pre>Berendes et al. (2022). Evaluating the Usability of Open Source Frameworks in Energy System Modelling. <em>Renewable and Sustainable Energy Reviews. DOI: </em><a href="https://doi.org/10.1016/j.rser.2022.112174">https://doi.org/10.1016/j.rser.2022.112174</a></pre> <p> </p> <p> </p>
Supplementary material for: Phylogeny and biogeography of the ancient spider family Filistatidae (Araneae) is consistent both with long-distance dispersal and vicariance following continental drift
<p>Raw data and input files for phylogenetic and biogeographic analysis of the article "<strong>Phylogeny and biogeography of the ancient spider family Filistatidae (Araneae) is consistent both with long-distance dispersal and vicariance following continental drift</strong>".</p> <p><strong>Supplementary material S1. </strong>Matrix of phenotypic characters in .ss format.</p> <p><strong>Supplementary material S2. </strong>Alignment of COI sequences in fasta format..</p> <p><strong>Supplementary material S3. </strong>Alignment of H3 sequences in fasta format.</p> <p><strong>Supplementary material S4. </strong>Alignment of 16S sequences in fasta format before trimming with gblocks.</p> <p><strong>Supplementary material S5. </strong>Alignment of 28S sequences in fasta format before trimming with gblocks.</p> <p><strong>Supplementary material S6. </strong>Input for running parsimony analysis using TNT (phenotypic data only).</p> <p><strong>Supplementary material S7. </strong>Input for running Bayesian inference using MrBayes (phenotypic data only).</p> <p><strong>Supplementary material S8. </strong>Input for running parsimony analysis using TNT (sequence data only).</p> <p><strong>Supplementary material S9. </strong>Input for running Bayesian inference using MrBayes (sequence data only).</p> <p><strong>Supplementary material S10. </strong>Input for running parsimony analysis using TNT (total evidence).</p> <p><strong>Supplementary material S11. </strong>Input for running Bayesian inference using MrBayes (total evidence).</p> <p><strong>Supplementary material S12. </strong>Input for running parsimony analysis using TNT (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S13. </strong>Input for running Bayesian inference using MrBayes (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S14. </strong>Input for running Bayesian inference using MrBayes (total evidence) and estimating node ages using tip-dating.</p> <p><strong>Supplementary material S15. </strong>Input for running Bayesian inference using Beast (sequence data only) and estimating node ages using node-dating.</p> <p><strong>Supplementary material S16. </strong>Raw geographic distances among areas in each time slice and dispersal probability matrices for each biogeographic model.</p> <p><strong>Supplementary material S17. </strong>Inputs for estimating ancestral ranges and performing biogeographic stochastic maps for our dataset.</p> <p><strong>Supplementary material S18. </strong>Consensus tree found with parsimony analysis using TNT (phenotypic data only).</p> <p><strong>Supplementary material S19. </strong>Consensus tree found with Bayesian inference using MrBayes (phenotypic data only).</p> <p><strong>Supplementary material S20. </strong>Consensus tree found with parsimony analysis using TNT (sequence data only).</p> <p><strong>Supplementary material S21. </strong>Consensus tree found with Bayesian inference using MrBayes (sequence data only).</p> <p><strong>Supplementary material S22. </strong>Consensus tree found with parsimony analysis using TNT (total evidence).</p> <p><strong>Supplementary material S23. </strong>Consensus tree found with Bayesian inference using MrBayes (total evidence).</p> <p><strong>Supplementary material S24. </strong>Consensus tree found with parsimony analysis using TNT (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S25. </strong>Consensus tree found with Bayesian inference using MrBayes (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S26. </strong>Consensus tree found with Bayesian inference using MrBayes (total evidence) and with node ages estimated using tip-dating.</p> <p><strong>Supplementary material S27. </strong>Maximum clade credibility tree found with Bayesian inference using Beast (sequence data only) and with node ages estimated using node-dating.</p>
Supplemental Material for Z. S. Cooper Dissertation Chapter 4 - Marinobacter Pangenomics
<p>This dataset contains main and supplemental data tables for a chapter of the doctoral dissertation by Zachary S. Cooper at the University of Washington School of Oceanography. The chapter is titled "Chapter 4: Evolutionary divergence of <em>Marinobacter </em>strains in cryopeg brines as revealed by pangenomics". Briefly, this chapter contains pangenomic analyses of novel genomes of the bacterial genus <em>Marinobacter</em> that are compared with representative species of the genus to discern evolutionary history and functional capabilities of this species in the extreme cryopeg environment. The main data tables include lists of genomes used in the analyses and their characteristics as described in the dissertation chapter. The supplemental data tables include gene cluster annotations with COGs (S1), functional frequencies across the pangenome (S2), functional enrichment analysis (S3), KEGG annotations (S4), KEGG pathway completion values (S5), average nucleotide identity values (S6), alignment coverage values (S7), and horizontal gene transfer analysis summaries (S8). The tables were produced from the combination of outputs of the bioinformatic programs Anvi'o, KEGG KofamKOALA, KEGG Decoder, PyANI, and HGTector2. Details on the methods and interpretations of this data are included in the dissertation.</p>
Topical Classification of Food Safety Publications with a Knowledge Base – materials
<p>Supplementary material for the article titled "<strong><a href="https://doi.org/10.1007/978-981-19-4364-5_48">Topical Classification of Food Safety Publications with a Knowledge Base</a></strong>".</p> <p><strong>Citation</strong></p> <p>If you use this data in research work, please cite this paper:</p> <p>Sowinski, P., Wasielewska-Michniewska, K., Ganzha, M., & Paprzycki, M. (2022). Topical Classification of Food Safety Publications with a Knowledge Base. In <em>Sustainable Technology and Advanced Computing in Electrical Engineering</em> (pp. 673-693). Springer, Singapore.</p> <p>BibTeX:</p> <pre><code>@incollection{sowinski2022topical, title={Topical Classification of Food Safety Publications with a Knowledge Base}, author={Sowinski, Piotr and Wasielewska-Michniewska, Katarzyna and Ganzha, Maria and Paprzycki, Marcin}, booktitle={Sustainable Technology and Advanced Computing in Electrical Engineering}, pages={673--693}, year={2022}, publisher={Springer}, doi={10.1007/978-981-19-4364-5_48} }</code></pre> <p> </p>
spectrapepper: A Python toolbox for advanced analysis of spectroscopic data for materials and devices.
<p>spectrapepper is a Python package that makes advanced analysis of spectroscopic data easy and accessible through straightforward, simple, and intuitive code. This library contains functions for every stage of spectroscopic methodologies, including data acquisition, pre-processing, processing, and analysis. In particular, advanced and high statistic methods are intended to facilitate, namely combinatorial analysis and machine learning, allowing also fast and automated traditional methods. The following is a short list of some main procedures that spectrapepper package enables: i) Baseline removal functions, ii) Normalization methods, iii) Noise filters, trimming tools, and despiking methods, iv) Chemometric algorithms to find peaks, fit curves, and deconvolution of spectra, v) Combinatorial analysis tools, such as Spearman, Pearson, and n-dimensional correlation coefficients, vi) Tools for Machine Learning applications, such as data merging, randomization, and decision boundaries, and vii) Sample data and examples</p>
supplementary materials: Classifying Be star variability with TESS I: the southern ecliptic
<p>The archived files contain ascii light curves (LC_data.tgz) and plots (LC_plots.tgz) from TESS cycle 1 for the Be star sample of "Classifying Be star variability with TESS I: the southern ecliptic". The light curve files have columns: 1) TESS JD (JD - 2457000), 2) relative flux, 3) photometric error. </p>
Supplementary Material from: Suppression of Pituitary Hormone Genes in Subjects Who Died From COVID-19 Independently of Virus Detection in the Gland
<p>Supplementary Table S1A. List of target genes analysed by the Human Host Response.</p> <p>Supplementary Table S1B. List of target genes analysed by the Coronavirus Panel Plus.</p> <p>Supplementary Table S1C. List of target genes analysed by the custom panel.</p> <p>Supplementary Table S2A. Differential gene expression analysis. Virus-positive vs control adenohypophyses.</p> <p>Supplementary Table S2B. Differential gene expression analysis. Virus-negative vs control adenohypophyses.</p>
Supplementary material: Picosecond pulse-shaping for strong three-dimensional field-free alignment of generic asymmetric-top molecules
<p><strong>Supplementary material to the manuscript <em>"Picosecond pulse-shaping for strong three-dimensional field-free alignment of generic asymmetric-top molecules"</em> by Terry Mullins, Evangelos T. Karamatskos, Joss Wiese, Jolijn Onvlee, Arnaud Rouzée, Andrey Yachmenev, Sebastian Trippel, and Jochen Küpper, <em>Nat Commun</em> 13, 1431 (2022). <a href="https://doi.org/10.1038/s41467-022-28951-z">https://doi.org/10.1038/s41467-022-28951-z</a>, arXiv: <a href="https://arxiv.org/abs/2009.08157">2009.08157 </a></strong></p> <ul> <li> <em><strong>simulations_part.z*</strong> </em>is split zip archive containing simulations data for indole molecule, such as files with rotational probability density distributions computed at different times <span class="math-tex">\(t=0..1500\)</span> ps during the laser pulse and field-free evolution, and example python scripts for data retrieval.</li> <li><strong><em>rawdata_part.z*</em></strong> is split zip archive containing raw experimental data.</li> <li><strong><em>analysis_scripts.zip</em></strong> is zip archive containing experimental analysis codes.</li> </ul> <p><strong>The <em>simulations_part.zip</em> contains the following files and folders:</strong></p> <ul> <li><em><strong>prob_density_euler_angles</strong></em> contains files <em>rotdens_av_<time>.gz</em> with simulated state-averaged rotational probability density distributions in terms of Euler angles for different times <time>, ranging from the beginning of the alignment laser pulse at <span class="math-tex">\(t=0\)</span> up to <span class="math-tex">\(t=1500\)</span> ps with a time step of 1 ps.<br> Calculations of probability density distributions were done using <a href="https://github.com/CFEL-CMI/richmol">Richmol</a> program.<br> The gzipped ASCII data files <em>rotdens_av_<time>.gz</em> contain in columns the values of the Euler angles <span class="math-tex">\(\phi,\theta,\chi\)</span> followed by the normalized probability density value.</li> <li><em><strong>prob_density_atoms_xyz</strong></em> contains files <em>monte_carlo_av_<time>.h5</em> with state-averaged rotational probability density distributions of all atoms in the indole molecule in terms of their Cartesian coordinates, for different times <time>, ranging from the beginning of the alignment pulse at <span class="math-tex">\(t=0\)</span> up to <span class="math-tex">\(t=1500\)</span> ps with a time step of 1 ps.<br> Structure of <em>monte_carlo_av_<time>.h5</em> HDF5 files:<br> Key Description<br> ----- ----------------<br> 'C10' - Cartesian coordinates of carbon atom no. 10<br> 'C11' - Cartesian coordinates of carbon atom no. 11<br> 'C12' - ...<br> 'C14' - ...<br> 'C3' - ...<br> 'C6' - ...<br> 'C7' - ...<br> 'C9' - ...<br> 'N4' - ...<br> 'H1-C3' - Cartesian coordinates of a vector pointing from carbon atom no. 3 to hydrogen atom no. 1<br> 'H13-C11' - ...<br> 'H15-C12' - ...<br> 'H16-C14' - ...<br> 'H2-N4' - ...<br> 'H5-C7' - ...<br> 'H8-C9' - ...<br> 'ref_vectors' - reference molecular-frame Cartesian coordinates of all atoms<br> 'x' - coordinates of the x-axis of Principal Axes of Inertia Frame<br> 'y' - coordinates of the y-axis of Principal Axes of Inertia Frame<br> 'z' - coordinates of the z-axis of Principal Axes of Inertia Frame<br> 'pol_x' - coordinates of the x-axis of Principal Axes of Polarizability Frame<br> 'pol_y' - coordinates of the y-axis of Principal Axes of Polarizability Frame<br> 'pol_z' - coordinates of the z-axis of Principal Axes of Polarizability Frame</li> <li><em><strong>indole_deflected_states.txt</strong></em> ASCII file contains initial populations of rotational states of indole in the deflected beam.<br> The following data is arranged in columns: <em>m, J</em>, <em>id</em>, <em>energy</em>, <em>normalized population</em>. The <em>J</em> and <em>m</em> are rotational quantum numbers of the total angular momentum and its <em>Z</em>-projection, the <em>id</em> number refers to the state's index in file <em>indole_energies_j0_j20.txt</em> listing rotational states of indole.</li> <li><em><strong>indole_data.py</strong></em> Python module provides basic functions to extract information from HDF5 data files <em>monte_carlo_av_<time>.h5</em>. It can also be used to compute alignment and orientation.</li> <li><em><strong>example_cos.py</strong></em> and <em><strong>example_dens.py</strong></em> Python scripts that demonstrate how to use <em>indole_data.py</em> module for computing and plotting alignment traces and a 2D projection of the probability density distribution, respectively.</li> <li><em><strong>monte_carlo.py</strong></em> Python script that was used to compute through Monte-Carlo sampling probability density distributions for Cartesian positions of atoms in indole (<em>monte_carlo_av_<time>.h5</em> files) using probability density distribution functions in Euler angles (outputs of Richmol program <em>rotdens_av_<time>.gz</em>).</li> </ul> <p><strong>The <em>analysis_scripts.zip</em> contains the following files and folders:</strong></p> <ul> <li><strong><em>H_Plus</em></strong> folder contains codes relevant for the analysis of H<sup>+</sup> ion data. <ul> <li><strong><em>analyse_full_alignment_scans.m</em></strong>: subtracts background and combines delay scan data sets together, takes account of errors.</li> <li><em><strong>calculate_resamped_df.m</strong></em>: called by <em>analyse_full_alignment_scans.m</em> to calculate the frequency sampling.</li> <li><em><strong>unique_mean.m</strong></em>: called by <em>analyse_full_alignment_scans.m</em> when combining data sets. Combines non-unique data points into a single data point.</li> </ul> </li> <li><em><strong>C_Plus2</strong></em> folder contains codes for the analysis of C<sup>2+</sup> ion data. The file descriptions are identical to those in the <em>H_Plus</em> directory.</li> <li><em><strong>intensity/calculate_intensity.m</strong></em>: calculates peak intensity of the laser pulse from measured parameters as well as statistical error.</li> <li><em><strong>intensity/compare_exp_sim.m</strong></em>: fits experimental and theoretical tomography and delay-dependent 2D projection values.</li> <li><em><strong>intensity/nir2hdf5_kHz.py</strong></em>: converts raw data files (from <em>rawdata_part.z*</em> archive<em>)</em> into hdf5 files.</li> </ul>
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.