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Dataset: Analysis of timing variability in human movements by aligning parameter curves in time
<p>Supplementary Data for <em><strong>Analysis of timing variability in human movements by aligning parameter curves in time</strong></em> article</p> <p>Dataset associated with the following publication:<br> Maurer, L. K., Maurer, H., & Müller, H. (2017). Analysis of timing variability in human movements by aligning parameter curves in time.</p> <p>-------------------------------------------------------------------------------</p> <p>The data files are structured in the following way:<br> (1) Basic subject information (age, sex) can be found in the file subject_data.txt (tabulator separated text file).</p> <p>(2) The folder parameter_curves contains the angle trajectories of all trials structured in blocks of 50 trials (sometimes less than 50 because of data cleaning procedures deleating corrupted data and trials in which participants released accidentally [with zero velocity]). Each participant performed five practice days with four blocks of 50 trials, i.e. 20 blocks. File names contain subject (1,...,14), day (1,...,5), and block (1,...,4) information. Within the tabulator separated text files each column contains the angle trajectory of one trial consisting of 1000 values (sampled with 1000 Hz). Index 600 is the moment when participants released the virtual ball.</p>
lga BGC Domain alignment files for "Repeated horizontal acquisition of lagriamide-producing symbionts in Lagriinae beetles"
<p>Domains from all lga BGCs recovered from beetle metagenomes were aligned to identified conserved regions and determine if domains were potentially inactivated via mutation of deletion of conserved regions or residues. </p>
PubMLST allele profiles and sequence alignments of 382 carbapenem-resistant Pseudomonas aeruginosa isolates collected from Japanese hospitals in 2019-2020
<p>This dataset provides PubMLST allele profiles, sequence alignments, and Mash distance data used in the study of "Nationwide genome surveillance of carbapenem-resistant Pseudomonas aeruginosa in Japan". </p>
Supplementary material for: Calibrating coordinate system alignment in a scanning transmission electron microscope using a digital twin.
<h1>Calibrating coordinate system alignment in a scanning transmission electron microscope using a digital twin.</h1> <h2>Supplementary material</h2> <p>This deposition contains supplementary material for a paper on coordinate system calibration in 4D STEM. A preprint of the paper is available at <a href="https://arxiv.org/abs/2403.08538">https://arxiv.org/abs/2403.08538</a>.</p> <h2>Contents</h2> <div> <div><code>20221025_154811.zip</code>: Overfocused 4D STEM test dataset</div> <div> </div> <div><code>overfocus.sif</code>: Apptainer image with complete software stack. <code>apptainer run --writable overfocus.sif</code> to execute. It starts a Jupyterlab instance with two notebooks, one to genreate test data and the other to perform the interactive adjustment. This documents the software version that was used for the figures in the paper.</div> <div> </div> <div><code>requirements.txt</code>: Python package versions of dependencies in <code>overfocus.sif</code>. </div> <div> </div> <div><code>COM - Jupyter Notebook - Google Chrome 2023-01-25 12-40-07_processed.mp4</code>: Screen capture video with explanation of the first live calibration with an early prototype.</div> <div> </div> <div><code>video description.docx</code>: Explanation of the plots and adjustment process in the screen capture video.</div> <div> </div> <div><code>Microscope-Calibration.tar.gz</code>: Repository archive of the software and examples for calibration in the version used in the paper.</div> <div> </div> <div><code>TemGym.tar.gz</code>: Repository archive of TemGym Basic in the version used in the paper.</div> </div>
Alignment and Tree file from: Two novel species of tropical Morchella (Ascomycota, Pezizales, Morchellaceae) discovered in the UNESCO Rinjani Lombok Biosphere Reserve, Indonesia
<p>Alignment and Tree file from: Two novel species of tropical Morchella (Ascomycota, Pezizales, Morchellaceae) discovered in the UNESCO Rinjani Lombok Biosphere Reserve, Indonesia</p>
Pterula and Pterulicium concatenated sequence alignment
Open the record for dataset details and reuse information.
Determining aligner-induced tooth movements in three dimensions using clinical data of two patients: datasets
<h2>Abstract</h2> <p>The effectiveness of a series of optically transparent aligners for orthodontic treatments depends on the anchoring of each tooth. In contrast with the roots, the crowns’ positions and orientations are measurable with intraoral scans, thus avoiding any X-ray dose. Exemplified by two patients, we demonstrate that three-dimensional crown movements could be determined with micrometer precision by registering weekly intraoral scans. The data show the movement and orientation changes in the individual crowns of the upper and lower jaws as a result of the forces generated by the series of aligners. During the first weeks, the canines and incisors were more affected than the premolars and molars. We detected overall tooth movement of up to about 1 mm during a nine-week active treatment. The data on these orthodontic treatments indicate the extent to which actual tooth movement lags behind the treatment plan, as represented by the aligner shapes. The proposed procedure can not only be used to quantify the clinical outcome of the therapy, but also to improve future planning of orthodontic treatments for each specific patient. This study should be treated with caution because only two cases were investigated, and the approach should be applied to a reasonably large cohort to reach strong conclusions regarding the efficiency and efficacy of this therapeutic approach.</p> <h2>Data</h2> <p>The repository contains the data of the intraoral scans and all data where manual interactions were performed to allow reproducing the results. </p> <p>The directory and file names are as follows:</p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Level, type<br></strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><em>p</em>_Clinical_Trial</td> <td>1st, directory</td> <td>Patient <em>p</em>: <em>p</em>=3485 stands for patient A, <em>p</em>=6457 stands for patient B</td> </tr> <tr> <td>Bottmedical</td> <td>2nd, directory</td> <td>Planning data</td> </tr> <tr> <td>Sirona</td> <td>2nd, directory</td> <td>Intraoral scan data</td> </tr> <tr> <td>T<em>n</em></td> <td>3rd, directory</td> <td>Time step <em>n</em> for <em>n</em>=0: before treatment, <em>n</em>=1-9: after 1-9 weeks of treatment; <em>n</em>=10: end of treatment</td> </tr> <tr> <td>Lower_Aligner_<em>n</em>_cut1.stl</td> <td>4th, file</td> <td>Manually cut surface mesh of planned data, lower jaw for time step <em>n</em></td> </tr> <tr> <td>Upper_Aligner_<em>n</em>_cut1.stl</td> <td>4th, file</td> <td>Manually cut surface mesh of planned data, upper jaw for time step <em>n</em></td> </tr> <tr> <td><em>p</em>_OnyxCeph3_Export_<em>j</em>_cut1.stl</td> <td>4th, file</td> <td>Manually cut surface mesh from intraoral scan, for lower (<em>j</em>=UK) or upper (<em>j</em>=OK) jaw</td> </tr> <tr> <td><em>p</em>_OnyxCeph3_Export_<em>j</em>.stl</td> <td>4th, file</td> <td>Original surface mesh from intraoral scan, for lower (<em>j</em>=UK) or upper (<em>j</em>=OK) jaw</td> </tr> <tr> <td>teethSeg</td> <td>4th, directory</td> <td>Segmented crowns via OnyxCeph3 TM</td> </tr> <tr> <td>teethSeg_noSnap_TolS</td> <td>4th, directory</td> <td>Transferred crown segmentations and occlusion plane points</td> </tr> <tr> <td><em>p</em>_Z<em>i</em>.stl</td> <td>5th, file</td> <td> <p>Surface mesh of crown segmentation for tooth number <em>i </em>of patient<em> p<br></em></p> </td> </tr> <tr> <td><em>p</em>_<em>j</em>.stl</td> <td>5th, file</td> <td> <p>Surface mesh of all crown segmentations for lower (<em>j</em>=UK) or upper (<em>j</em>=OK) jaw of patient <em>p</em></p> </td> </tr> <tr> <td><em>p</em>_<em>j</em>_occPlane.mat</td> <td>5th, file</td> <td> <p>Binary Matlab file of saved variable occPlane, which defines transferred occlusion plane points for lower (<em>j</em>=UK) or upper (<em>j</em>=OK) jaw of patient <em>p</em></p> </td> </tr> </tbody> </table> <p>File formats:</p> <p>stl files describe an unstructured triangulates surface by vertices and triangles. These files can be read by the open source software freecad or the MATLAB function stlread.</p> <p>mat files are MATLAB files and can be read via MATLAB function load.</p>
Concatenated alignment of 12S and 16S sequences of the genus Pristimantis.
<p>A new species of Pristimantis (Amphibia, Anura, Strabomantidae) from a montane forest of the Pui Pui Protected Forest in central Peru</p>
Alignment files for coverage benchmarks: Illumina and Nanopore sequencing datasets
<ul> <li><strong>cpara-illumina-noseq.bam</strong> and <strong>cpara-ont-noseq.bam</strong>: BAM files produced aligning the raw reads produced respectively by Illumina NextSeq and ONT Nanopore sequencing of an isolate of <em>C. parapsilosis</em> to evaluate the coverage calculations using real datasets.*</li> <li><strong>HG00258.bam</strong>: Exome sequencing from the 1000 Genomes Project (Clarke et al 2016 <a href="https://doi.org/10.1093/nar/gkw829">https://doi.org/10.1093/nar/gkw829</a>).</li> <li><strong>panel_01.bam</strong>: targeted sequencing of a Human gene panel of 16 genes.*</li> </ul> <p>* Sequences and qualities have been removed</p>
Multi-modal dataset for music genre recognition based on six different modalities for LMD-aligned and SLAC datasets
<p>Multi-modal dataset for music genre recognition based on six different modalities for the LMD-aligned [1] and SLAC [2] datasets. Further details are provided in [3].</p> <p><strong>Descriptions of files</strong></p> <table> <thead> <tr> <th scope="col">Link</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td><a href="https://zenodo.org/record/5651429/files/LMD-aligned_Filelist.arff">LMD-aligned_Filelist.arff</a></td> <td>File list with 1575 music tracks selected from the LMD-aligned dataset [1] with tagtraum genre annotations [4] (only a subset of LMD-aligned is used, which includes only pieces for which all six modalities were accessible, and which includes only well-represented genres)</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/LMD-aligned_ExtractedFeatures.tar.gz">LMD-aligned_ExtractedFeatures.tar.gz</a></td> <td>Raw audio signal and model-based features extracted with AMUSE [5]</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/LMD-aligned_ProcessedFeatures.tar.gz">LMD-aligned_ProcessedFeatures.tar.gz</a></td> <td>Processed features: audio signal and model-based features aggregated for 4 s time frames with 2 s step size / all other features (see the table below) with the same values for all time frames</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/LMD-aligned_Datasets.tar.gz">LMD-aligned_Datasets.tar.gz</a></td> <td>Training, optimization, and test datasets for 3 splits for the recognition of 5 genres in [3]</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/SLAC_Filelist.arff">SLAC_Filelist.arff</a></td> <td>File list with 250 music tracks from the SLAC dataset [2] (genres and sub-genres are provided in the folder structure)</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/SLAC_ExtractedFeatures.tar.gz">SLAC_ExtractedFeatures.tar.gz</a></td> <td>Raw audio signal and model-based features extracted with AMUSE [5]</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/SLAC_ProcessedFeatures.tar.gz">SLAC_ProcessedFeatures.tar.gz</a></td> <td>Processed features: audio signal and model-based features aggregated for 4 s time frames with 2 s step size / all other features (see the table below) with the same values for all time frames</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/SLAC_Datasets.tar.gz">SLAC_Datasets.tar.gz</a></td> <td>Training, optimization, and test datasets for 3 splits for the recognition of 5 genres and 10 sub-genres in [3]</td> </tr> </tbody> </table> <p><strong>Modalities and feature sub-groups</strong></p> <table> <thead> <tr> <th scope="col">Modality</th> <th scope="col">Sub-group</th> <th scope="col"> <p>Dimensions in processed</p> <p>features of LMD-aligned</p> </th> <th scope="col"> <p>Dimensions in processed</p> <p>features of SLAC</p> </th> </tr> </thead> <tbody> <tr> <td>Audio signal</td> <td>Low-level</td> <td>1-524</td> <td>1-524</td> </tr> <tr> <td>Audio signal</td> <td>Semantic</td> <td>525-810</td> <td>525-810</td> </tr> <tr> <td>Audio signal</td> <td>Structural complexity</td> <td>811-908</td> <td>811-908</td> </tr> <tr> <td>Model-based</td> <td>Instruments</td> <td>909-1018</td> <td>909-1018</td> </tr> <tr> <td>Model-based</td> <td>Moods</td> <td>1019-1146</td> <td>1019-1146</td> </tr> <tr> <td>Model-based</td> <td>Various</td> <td>1147-1402</td> <td>1147-1402</td> </tr> <tr> <td>Playlists</td> <td>Genres</td> <td>1403-1973</td> <td>1403-1973</td> </tr> <tr> <td>Playlists</td> <td>Styles</td> <td>1974-1695</td> <td>1974-1695</td> </tr> <tr> <td>Symbolic</td> <td>Pitch</td> <td>1696-1757</td> <td>1696-1757</td> </tr> <tr> <td>Symbolic</td> <td>Melodic</td> <td>1758-1781</td> <td>1758-1781</td> </tr> <tr> <td>Symbolic</td> <td>Chords</td> <td>1782-1836</td> <td>1782-1836</td> </tr> <tr> <td>Symbolic</td> <td>Rhythm</td> <td>1837-1935</td> <td>1837-1935</td> </tr> <tr> <td>Symbolic</td> <td>Tempo</td> <td>1936-1963</td> <td>1936-1963</td> </tr> <tr> <td>Symbolic</td> <td>Instrument presence</td> <td>1964-2441</td> <td>1964-2441</td> </tr> <tr> <td>Symbolic</td> <td>Instruments</td> <td>2442-2456</td> <td>2442-2456</td> </tr> <tr> <td>Symbolic</td> <td>Texture</td> <td>2457-2480</td> <td>2457-2480</td> </tr> <tr> <td>Symbolic</td> <td>Dynamics</td> <td>2481-2484</td> <td>2481-2484</td> </tr> <tr> <td>Album covers</td> <td>SIFT</td> <td>2485-2584</td> <td>2485-2584</td> </tr> <tr> <td>Lyrics</td> <td>jLyrics descriptors</td> <td>2585-2603</td> <td>2585-2671</td> </tr> <tr> <td>Lyrics</td> <td>Bag-of-Words</td> <td>2604-2703</td> <td> </td> </tr> <tr> <td>Lyrics</td> <td>Doc2Vec</td> <td>2704-2803</td> <td> </td> </tr> </tbody> </table>
MC VIII SAMOIEDICA 2: JURAK-SAMOIEDICA 1: Line-aligned Ground Truth
<p>MC VIII SAMOIEDICA 2: JURAK-SAMOIEDICA 1: Line-aligned Ground Truth</p> <p>This dataset contains 172 microfilm scans Tundra Nenets materials, in which the text content is manually aligned line by line with the scanned images. This material has been created in collaboration between the Finno-Ugrian Society and the University of Innsbruck. It is intended specifically for handwritten text recognition experiments, training and benchmarking. For electronic materials and printed volumes that are intended to be used in linguistic, ethnographic and folkloric research, please refer to other publications in this Zenodo collection or [Manuscripta Castreaniana website](https://www.sgr.fi/manuscripta/).</p> <p>The materials were aligned in the University of Innsbruck with contributions by Günter Mühlberger and Günter Hackl. Other contributors are Karina Lukin and Niko Partanen. [Transkribus](https://readcoop.eu/transkribus/?sc=Transkribus) platform was extensively used in processing this dataset, and the file format is a direct Transkribus image and Page XML export.</p> <p> </p>
Data on: Impact of neurite alignment on organelle motion
<p><strong>Impact of neurite alignment on organelle motion.</strong></p> <p>Maria Mytiliniou, Joeri A. J. Wondergem, Thomas Schmidt, Doris Heinrich.<br>J. R. Soc. Interface <strong>19</strong>:20210617.<br>doi: https://doi.org/10.1098/rsif.2021.0617</p> <p><strong>Abstract</strong></p> <p>Intracellular transport is pivotal for cell growth and survival. Malfunctions in this process have been associated with devastating neurodegenerative diseases, posing a need for deeper understanding of the involved mechanisms. Here, we used an experimental methodology that lead neurites of differentiated PC12 cells in either of two configurations: an one-dimensional, where the neurites align along lines, or a two-dimensional configuration, where the neurites adopt a random orientation and shape on a flat substrate. We subsequently monitored the motion of functional organelles, the lysosomes, inside the neurites. Implementing a time-resolved analysis of the mean-squared displacement, we quantitatively characterized distinct motion modes of the lysosomes. Our results indicate that neurite alignment gives rise to faster diiffusive and super-diiffusive lysosomal motion in comparison to the situation where the neurites are randomly oriented. After inducing lysosome swelling through an osmotic challenge by sucrose, we confirmed the predicted slowdown in diffusive mobility. Surprisingly we found that the swelling-induced mobility change affected each of the (sub- /super-) diiffusive motion modes differently and depended on the alignment configuration of the neurites. Our findings imply that intracellular transport is significantly and robustly dependent on cell morphology, which might be in part controlled by the extracellular matrix.</p>
Aligned gene sequences
<p>Aligned gene sequences used for the phylogenetic analysis</p>
Dataset for WWW2022 accepted paper "SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs"
<p>Datasets for WWW2022 accepted paper "SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs"</p> <p>The code repository is <a href="https://github.com/THUDM/SelfKG">here</a>, and our paper is <a href="https://arxiv.org/abs/2203.01044">here</a>.</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>
InpactorDB: A Plant classified lineage-level LTR retrotransposon reference library for free-alignment methods based on Machine Learning
<p>LTR retrotransposons are mobile elements that make up the major part of most plant genomes. Their identification and annotation via bioinformatics approaches represent a major challenge in the era of massive plant genome sequencing. In addition to their involvement in the variation in genome size, these elements are also associated in the function and structure of different chromosomal regions and in the alteration of the function of coding regions, among others. Several plant retrotransposon sequence databases of LTR retrotransposons are available with public access such as PGSB, RepetDB or restricted access such as Repbase. Although they are useful for approaches to identify LTR-RTs in new genomes by similarity, the elements of these databases are not classified down to the lineage/family level. with great depth. </p> <p>Here, we present InpactorDB a semi-curated dataset composed of 130,511 elements from 195 plant genomes (belonging to 108 plant species), classified down to the lineage level. This data set has been used to train two deep neural networks (one fully connected and one convolutional) for fast classification of elements. Used in lineage-level classification approaches, we obtain a score above 98% of F1-score, precision and recall. </p> <p>In order to classify elements of the ‘LTR_STRUC’ and ‘EDTA’ datasets, we used the methodology proposed by Inpactor, which uses homology-based strategy with known coding domains belonging to LTR-RTs. We utilized the RexDB domain library as reference. LTR-RTs were classified into superfamilies, Gypsy (RLG) or Copia (RLC) and sub-classified into lineages according to the similarities of five different amino acid reference domains (GAG, AP, RT, RNAseH, and INT domains). In addition, we applied filters to remove keep only intact elements:</p> <p>1) to remove predicted elements with domains from two different superfamilies (i.e. Gypsy and Copia),</p> <p>2) or elements with domains belonging to two or more different lineages,</p> <p>3) to remove elements with lengths different than those reported by Gypsy Database (GyDB) with a tolerance of 20%,</p> <p>4) to delete incomplete elements which has less than three identified domains, and</p> <p>5) to remove elements with insertions of TE class II (reported in Repbase). </p> <p>The final non-redundant version of InpactorDB consists of 67,305 LTR retrotransposons. Both redundant and non-redundant versions of InpactorDB are available in Fasta format in which sequences have identifiers with the following general Identification code:</p> <p>>Superfamily-Lineage-plant_family-specie-source-length-ID,</p> <p>Where Superfamily can is either RLC (for Copia) or RLG (for Gypsy), Lineage/family follows following the RexDB nomenclature, source (can be Repbase, RepetDB, PGSB, LTR_STRUC or EDTA datasets), length, and ID, is a unique number which identify each element inside the InpactorDB.</p>
An Aligned Orbit for the Young Planet V1298 Tau b
<p>Code, data, and MCMC chains associated with the article "An Aligned Orbit for the Young Planet V1298 Tau b," by Johnson et al. 2022 (accepted to The Astronomical Journal). Pre-print available at <a href="https://arxiv.org/abs/2110.10707">arXiv</a>.</p> <p> </p>
Data from 'Disparate inventories of hypoxia gene sets across corals align with inferred environmental resilience'
<p>Aquatic deoxygenation has been flagged as an overlooked but key factor driving mass bleaching-induced coral mortality as oxygen supplies lower to concentrations that can elicit an aerobic metabolic crisis i.e., hypoxia. Surprisingly little is known of the fundamental hypoxia responsive gene set inventory corals possess to respond to deoxygenation. It is unclear whether variation in gene copy number across species exist that potentially affect gene expression with subsequent differences in the effectiveness of a given stress response. Here, we used an ortholog-based meta-analysis to investigate how hypoxia gene inventories differed amongst coral species to assess putative copy number variation (CNV) across 24 coral protein sets from species with a sequenced genome that span corals from the robust and complex clade. We found approximately a third of the investigated genes exhibited copy number differences, and these differences were species-specific rather than the robust-complex split.</p> <p>Zipped folders of OrthoFinder results:</p> <p>'Results_Feb16' contains results including all 24 coral species from 7 genera (<em>Acropora, Pocillopora, Stylophora, Montastrea, Montipora, Obricella, Porites</em>).</p> <p>'gene_sets_acropora_acuminata_only' contains results including just one species per genera with <em>Acropora acuminata</em>.</p> <p>'gene_sets_acropora_cytherea_only' contains results including just one species per genera with <em>Acropora cytherea</em>.</p> <p>'gene_sets_acropora_digitifera_only' contains results including just one species per genera with <em>Acropora digitifera</em>.</p> <p> </p> <p>Results and Interpretations from these analyses are published open access here: <a href="https://doi.org/10.3389/fmars.2022.834332">https://doi.org/10.3389/fmars.2022.834332</a></p> <p>Full citation: Alderdice R, Hume BCC, Kühl M, Pernice M, Suggett DJ, Voolstra CR. Disparate inventories of hypoxia gene sets across corals align with inferred environmental resilience. Front Mar Sci. 2022;9. doi:10.3389/fmars.2022.834332</p> <p>Scripts are available here: <a href="https://zenodo.org/record/6396671#.YoYpoS8RoZg">https://github.com/didillysquat/alderdice_2021</a></p>
The Dataset of Quantifying Alignment Deviations for Uniaxial Material Mechanical Testing via Automated Machine Learning
<p>The dataset consists of 4 alignment deviations of the uniaxial testing machine as well as 12 strain measurement points on cruciform specimens. A deep learning model is trained on the dataset to quantify 4 alignment deviations using 12 strain values on a thin plate specimen. The design of experiments includes Optimal Latin Hypercube, numerical modelling of Finite Element Methods. Using the Optimal Latin Hypercube, 12496 distinct groups of DOE simulation tests are constructed. Under the boundary conditions of 4 distinct deviations, 12 strain values at the required location on the cruciform specimen are obtained using Python scripts.</p> <p>The nine CSV files correspond to the nine analysis steps. The only difference among the nine analysis steps is the pretension force acting on RP1. Each CSV file contains 24 columns of data, and the corresponding contents of each column of data are as follows:</p> <ul> <li>Columns 1-6 are the freedoms of RP1 reference point, which are U1, U2, U3, ur1, UR2 and UR3 respectively;</li> <li>Columns 7-12 are the freedoms of RP2 reference points, which are U1, U2, U3, ur1, UR2 and UR3 respectively;</li> <li>Columns 13-24 are the strain values of the last 12 strain measurements of the thin plate rectangular specimen。</li> </ul>
Platynereis dumerilii - Aligned serial sections of the parapodium used for the 3D Model
<p>Aligned serial semi-thin sections (1µm) of a parapodium of <em>Platynereis dumerillii. </em></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.