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1,930 results for “Matrix”
Plant trait matrix for fire-prone Mediterranean chronosequences
<p>A database of 23 traits for 117 Mediterranean plant species. It includes three groups of traits: i) four whole-plant traits (bud height, sclerophylly, xerophily and resprouting ability), ii) ten above-ground traits (plant height, flowering time and span, leaf size, area, weight, thickness, specific leaf area, seed size and dispersal distance), and iii) nine below-ground traits (root length, depth, laterality, root depth:laterality ratio, root weight, specific root length, root C and N concentration and the root CN ratio).</p>
Q matrix data for the problems studied using Flexible-PEPS simulator.
<p>How to read the Q matrix:<br><br>Load the dictionary, say as "main_dictionary".</p> <p>Q_dictionary=main_dictionary['Q_dict']</p> <p>Q_dictionary['(x,y)']=value_xy<br><br>value_xy is the Q matrix strength between the vertices (x, y), here x, y are integers starting from 1, to N, where N is the number of qubits (number of variables) in the system.</p>
Aligned DNA sequence matrix for phylogenetic analyses of Scinax in the article "Advertisement calls and DNA sequences reveal a new species of Scinax (Anura: Hylidae) on the Pacific Lowlands of Ecuador"
<p>Aligned DNA sequence matrix for phylogenetic analyses of Scinax in the article "Advertisement calls and DNA sequences reveal a new species of Scinax (Anura: Hylidae) on the Pacific Lowlands of Ecuador". The matrix is in NEXUS format.</p> <p>Gene partition as arranged as follows (tRNAs are included as part of larger adjacent genes):</p> <p>12S RNA: 1-1033</p> <p>16S RNA: 1034-2832</p> <p>NADH dehydrogenase subunit 1: 2833-3793</p> <p>Cytochrome Oxidase sub-unit I: 3965-4654</p> <p>Cytochrome B: 4655-5447</p> <p> </p>
Identifier Refinery Conversion Matrixes
<p><strong>identifier-refinery</strong></p> <p>Tools and assets for easy and reproducable gene identifier conversion.</p> <p><strong>Methods</strong></p> <p>This repository is used to build matricies which can convert between different gene identifiers.</p> <p>These conversion matricies are built by:</p> <ul> <li>Randomly choosing raw CEL files from NCBI GEO for a given platform accession code (in <code>/cels</code>)</li> <li>Reading the CEL header and joining Brainarray (e.g., <code>hgu133plus2hsensgprobe</code>) and Bioconductor (e.g., <code>hgu133plus2.db</code>) (x, y) coordinates</li> <li>Finding intersecting probe identifiers</li> <li>Extracting supported identifiers and probe IDs from the Bioconductor package</li> <li>Filtering on probe IDs and Ensembl Gene IDs in Brainarray</li> <li>Writing the output to a conversion TSV file</li> <li>Check that all output conversion TSV files have a shared SHA1</li> </ul> <p><strong>Repository Contents</strong></p> <p><strong>Source Files</strong></p> <p>The <code>cels</code> directory contains raw CEL files taken from GEO. The list of supported platforms is in <code>supported_microarray_platforms.csv</code>. Source files can be acquired by running the <code>acquire_cels.py</code> script.</p> <p><strong>Docker Image</strong></p> <p>The conversion scripts are run on custom Docker images.</p> <p>Two Dockerfiles are provided in this repository - <code>base</code> Docker image, which is used to install the quire R dependancies, and the <code>pd</code> image, which is used to build the required databases for a given platform.</p> <p><strong>Conversion Scripts</strong></p> <p>A <code>build_and_convert.py</code> script is provided, which build a unique Docker image for each package, mount the downloaded CEL files as a volume, and then run the gene conversion script <code>R/gene_convert.R</code> inside the image and output the master conversion matrix. Output TSV files live in <code>cels/out/</code>.</p> <p><strong>Reproducing</strong></p> <p>The entire process can be reproduced by running the following command script from a fresh checkout of this repository. It will take some time:</p> <pre><code>$ ./generate_matricies_from_scratch.sh </code></pre> <p>You can also choose to only build a specific platform, ex.,:</p> <pre><code>$ ./generate_matricies_from_scratch.sh celegans </code></pre> <p><strong>Identifiers</strong></p> <p>Released assets in this repository are availble under the DOI, <code>xyz:1.2.3.4</code>, which can be seen on Zenodo <a href="https://link.todo">here</a>.</p> <p><strong>Related Projects</strong></p> <ul> <li><a href="https://github.com/AlexsLemonade/refinebio">AlexsLemonade/refinebio</a></li> </ul> <p><strong>Copyright</strong></p> <p><code>identifier-refinery</code> output assets are released under a <a href="https://creativecommons.org/publicdomain/zero/1.0/legalcode">CC0 1.0 Universal</a> license. All code is released under the BSD 3-clause license. Input assets are property of the original providers to NCBI GEO, but may be <a href="https://www.ncbi.nlm.nih.gov/geo/info/disclaimer.html">freely downloaded and redistributed</a> unless otherwise noted.</p> <p> </p> <p>https://github.com/AlexsLemonade/identifier-refinery</p>
New Azo-DMOF-1 MOF as a photo-responsive low-energy CO2 adsorbent and its exceptional CO2/N2 separation performance in mixed matrix membranes
<p>Data repository for manuscript, published in <em>ACS Applied Materials & Interfaces</em>, <strong>2018</strong>, <em>10</em> (40), pp 34291–34301, <a href="https://dx.doi.org/10.1021/acsami.8b12261">http://dx.doi.org/10.1021/acsami.8b12261</a></p> <p><strong>Abstract</strong></p> <p>A new generation-2 light-responsive metal–organic framework (MOF) has been successfully synthesized using Zn as the metal source and both 2-phenyldiazenyl terephthalic acid and 1,4-diazabicyclo[2.2.2]octane (DABCO) as the ligands. It was found that Zn-azo-dabco MOF (Azo-DMOF-1) exhibited a photoresponsive CO<sub>2</sub> adsorption both in static and dynamic condition because of the presence of azobenzene functionalities from the ligand. Further application of this MOF was evaluated by incorporating it as a filler in a mixed matrix membrane for CO<sub>2</sub>/N<sub>2</sub> gas separation. Matrimid and polymer of intrinsic microporosity-1 (PIM-1) were used as the polymer matrix. It was found that Azo-DMOF-1 could enhance both the CO<sub>2</sub> permeability and selectivity of the pristine polymer. In particular, the Azo-DMOF-1–PIM-1 composite membranes have shown a promising performance that surpassed the 2008 Robeson Upper Bound.</p>
Aligned DNA sequence matrix for phylogenetic analyses of the article "A bizarre new species of Lynchius (Amphibia, Anura, Strabomantidae) from the Andes of Ecuador and first report of Lynchius parkeri in Ecuador"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "A bizarre new species of <em>Lynchius</em> (Amphibia, Anura, Strabomantidae) from the Andes of Ecuador and first report of <em>Lynchius parkeri</em> in Ecuador"</p> <p>The matrix is in NEXUS format. Genes are arranged as follows:</p> <p>RAG1: 1-652<br> Tyrosinase: 653-1195<br> 12S RNA: 1196-2242<br> tRNA Val: 2243-2313<br> 16S RNA: 2314-3994<br> tRNA Leu = 3995-4065<br> ND1: 4066-5026<br> tRNA Ile: 5027-5144;</p>
Aligned DNA sequence matrix for phylogenetic analyses in the article "Systematics of Huicundomantis, a new subgenus of Pristimantis (Anura, Strabomantidae) with extraordinary cryptic diversity and eleven new species"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "Systematics of Huicundomantis, a new subgenus of Pristimantis (Anura, Strabomantidae) with extraordinary cryptic diversity and eleven new species"</p> <p>The matrix is in NEXUS format. Genes are arranged as follows:</p> <p>16S RNA, tRNA-Leu = 1–1408<br> ND1 codon position 1 = 1409–2369\3;<br> ND1 codon position 2 = 1410–2367\3;<br> ND1 codon position 3 = 1411–2368\3;<br> tRNA-Ile, tRNA-Gln, rRNA-Met = 2370–2544; <br> RAG1 codon position 3 = 2545–3199\3;<br> RAG1 codon position 1 = 2546–3197\3<br> RAG1 codon position 2 = 2547–3198\3;</p>
Helsinki Region Travel Time Matrix
<p>Helsinki Region Travel Time Matrix contains <strong>travel time and distance</strong> information for routes between all 250 m x 250 m grid cell centroids (n = 13231) in the Helsinki Region, Finland by <strong>walking</strong>, <strong>cycling,</strong> <strong>public transportation</strong> and <strong>car</strong>. The grid cells are compatible with the statistical grid cells used by Statistics Finland and the YKR (<em>yhdyskuntarakenteen seurantajärjestelmä) </em>data set. The Helsinki Region Travel Time Matrix is available for <strong>three different years</strong>:</p> <ul> <li>2018</li> <li>2015</li> <li>2013</li> </ul> <p>The data consists of travel time and distance information of the routes that have been calculated between all statistical grid cell centroids (n = 13231) by walking, cycling, public transportation and car.</p> <p>The data have been calculated for two different times of the day: 1) midday and 2) rush hour.</p> <p>The data may be used freely (under Creative Commons 4.0 licence). We do not take any responsibility for any mistakes, errors or other deficiencies in the data.</p> <p><strong>Organization of data</strong></p> <p>The data have been divided into 13231 text files according to destinations of the routes. The data files have been organized into sub-folders that contain multiple (approx. 4-150) Travel Time Matrix result files. Individual folders consist of all the Travel Time Matrices that have same first four digits in their filename (e.g. 5785xxx).</p> <p>In order to visualize the data on a map, the result tables can be joined with the MetropAccess YKR-grid shapefile (attached here). The data can be joined by using the field <strong><em>‘from_id’</em></strong> in the text files and the field <strong><em>‘YKR_ID’</em></strong> in MetropAccess-YKR-grid shapefile as a common key.</p> <p><strong>Data structure</strong></p> <p>The data have been divided into 13231 text files according to destinations of the routes. One file includes the routes from all statistical grid cells to a particular destination grid cell. All files have been named according to the destination grid cell code and each file includes 13231 rows.</p> <p>NODATA values have been stored as value -1.</p> <p>Each file consists of 17 attribute fields: 1) from_id, 2) to_id, 3) walk_t, 4) walk_d, 5) bike_f_t, 6) bike_s_t, 7) bike_d, 8) pt_r_tt, 9) pt_r_t, 10) pt_r_d, 11) pt_m_tt, 12) pt_m_t, 13) pt_m_d, 14) car_r_t, 15) car_r_d, 16) car_m_t, 17) car_m_d, 18) car_sl_t</p> <p>The fields are separated by semicolon in the text files.</p> <p><strong>Attributes</strong></p> <ul> <li><strong>from_id: </strong>ID number of the origin grid cell</li> <li><strong>to_id</strong><strong>: </strong>ID number of the destination grid cell</li> <li><strong>walk_t</strong><strong>: </strong>Travel time in minutes from origin to destination by walking</li> <li><strong>walk_d</strong><strong>: </strong>Distance in meters of the walking route</li> <li><strong>bike_f_t: </strong>Total travel time in minutes from origin to destination by fast cycling; Includes extra time (1 min) that it takes to take/return bike</li> <li><strong>bike_s_t: </strong>Total travel time in minutes from origin to destination by slow cycling; Includes extra time (1 min) that it takes to take/return bike</li> <li><strong>bike_d:</strong>Distance in meters of the cycling route</li> <li><strong>pt_r_tt</strong><strong>: </strong>Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account including the waiting time at home</li> <li><strong>pt_r_t</strong><strong>: </strong>Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account excluding the waiting time at home</li> <li><strong>pt_r_d</strong><strong>: </strong>Distance in meters of the public transportation route in rush hour traffic</li> <li><strong>pt_m_tt</strong><strong>: </strong>Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account including the waiting time at home</li> <li><strong>pt_m_t</strong><strong>: </strong>Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account excluding the waiting time at home</li> <li><strong>pt_m_d</strong><strong>: </strong>Distance in meters of the public transportation route in midday traffic</li> <li><strong>car_r_t: </strong>Travel time in minutes from origin to destination by private car in rush hour traffic; the whole travel chain has been taken into account </li> <li><strong>car_r_d</strong><strong>: </strong>Distance in meters of the private car route in rush hour traffic</li> <li><strong>car_m_t</strong><strong>: </strong>Travel time in minutes from origin to destination by private car in midday traffic; the whole travel chain has been taken into account </li> <li><strong>car_m_d</strong><strong>: </strong>Distance in meters of the private car route in midday traffic</li> <li><strong>car_sl_t: </strong>Travel time from origin to destination by private car following speed limits without any additional impedances; the whole travel chain has been taken into account </li> </ul> <p><strong>METHODS</strong></p> <p>For detailed documentation and how to reproduce the data, see <a href="https://github.com/AccessibilityRG/HelsinkiRegionTravelTimeMatrix2018">HelsinkiRegionTravelTimeMatrix2018</a> GitHub repository.</p> <p><strong>THE ROUTE BY CAR</strong> have been calculated with a dedicated open source tool called <strong>DORA</strong> (DOor-to-door Routing Analyst) developed for this project. DORA uses PostgreSQL database with PostGIS extension and is based on the pgRouting toolkit. MetropAccess-Digiroad (modified from the original Digiroad data provided by Finnish Transport Agency) has been used as a street network in which the travel times of the road segments are made more realistic by adding crossroad impedances for different road classes.</p> <p>The calculations have been repeated for two times of the day using 1) the “midday impedance” (i.e. travel times outside rush hour) and 2) the “rush hour impendance” as impedance in the calculations. Moreover, there is 3) the “speed limit impedance” calculated in the matrix (i.e. using speed limit without any additional impedances).</p> <p>The whole travel chain (“door-to-door approach”) is taken into account in the calculations:<br> 1) walking time from the real origin to the nearest network location (based on Euclidean distance),<br> 2) average walking time from the origin to the parking lot,<br> 3) travel time from parking lot to destination,<br> 4) average time for searching a parking lot,<br> 5) walking time from parking lot to nearest network location of the destination and<br> 6) walking time from network location to the real destination (based on Euclidean distance).</p> <p><strong>THE ROUTES BY PUBLIC TRANSPORTATION</strong> have been calculated by using the MetropAccess-Reititin tool which also takes into account the whole travel chains from the origin to the destination:<br> 1) possible waiting at home before leaving,<br> 2) walking from home to the transit stop,<br> 3) waiting at the transit stop,<br> 4) travel time to next transit stop,<br> 5) transport mode change,<br> 6) travel time to next transit stop and<br> 7) walking to the destination.</p> <p>Travel times by public transportation have been optimized using 10 different departure times within the calculation hour using so called Golomb ruler. The fastest route from these calculations are selected for the final travel time matrix.</p> <p><strong>THE ROUTES BY CYCLING</strong> are also calculated using the DORA tool. The network dataset underneath is MetropAccess-CyclingNetwork, which is a modified version from the original Digiroad data provided by Finnish Transport Agency. In the dataset the travel times for the road segments have been modified to be more realistic based on Strava sports application data from the Helsinki region from 2016 and the bike sharing system data from Helsinki from 2017.</p> <p>For each road segment a separate speed value was calculated for slow and fast cycling. The value for fast cycling is based on a percentual difference between segment specific Strava speed value and the average speed value for the whole Strava data. This same percentual difference has been applied to calculate the slower speed value for each road segment. The speed value is then the average speed value of bike sharing system users multiplied by the percentual difference value.</p> <p>The reference value for faster cycling has been 19km/h, which is based on the average speed of Strava sports application users in the Helsinki region. The reference value for slower cycling has been 12km/, which has been the average travel speed of bike sharing system users in Helsinki. Additional 1 minute have been added to the travel time to consider the time for taking (30s) and returning (30s) bike on the origin/destination.</p> <p>More information of the Strava dataset that was used can be found from the Cycling routes and fluency report, which was published by us and the city of Helsinki.</p> <p><strong>THE ROUTES BY WALKING</strong> were also calculated using the MetropAccess-Reititin by disabling all motorized transport modesin the calculation. Thus, all routes are based on the Open Street Map geometry.</p> <p>The walking speed has been adjusted to 70 meters per minute, which is the default speed in the HSL Journey Planner (also in the calculations by public transportation).</p> <p>All calculations were done using the computing resources of CSC-IT Center for Science (<a href="https://www.csc.fi/home">https://www.csc.fi/home</a>).</p>
genotyping matrix for common bean GWAS.
<p>These files inculded twenty phenotypic characteristics were evaluated on 628 accessions. Of these, 15 were quantitative and two were binary traits. Yield-related traits that were evaluated across four locations for three different years.In addition, associations with qualitative traits and 3 datasets for diseases in the greenhouse and insect infestation in a seed chamber were also assessed.</p> <p>In additational, also included genotyping matrix for common bean GWAS.</p>
Performance Evolution Matrix: Visualizing Performance Variations along Software Versions
<p># Performance Evolution Matrix<br> This repository contains the artifacts needed to replicate our experiment in the paper "Performance Evolution Matrix".</p> <p># Video Demo<br> [download](https://github.com/jpsandoval/PerfEvoMatrix/blob/master/MatrixMovie.mp4)<br> <br> # XMLSupport and GraphET Examples</p> <p>To open the XMLSupport and GraphET Examples (which appears in the paper) execute the following commands in a Terminal.</p> <p>**MacOSX.** We do all the experiments in a Mac Book Pro. To open the Matrix execute the following command in the folder where this project was downloaded. </p> <p>```<br> ./Pharo-OSX/Pharo.app/Contents/MacOS/Pharo Matrix.image<br> ```</p> <p>**Windows.**<br> You may also run the experiment in Windows, but depending on the windows version you have installed it may be some some UI bugs.<br> ```<br> cd Pharo-Windows<br> Pharo.exe ../XMLSupportExample.image<br> ```</p> <p>**Open the Visualization.**<br> Please select the following code, then execute it using the green play button (at the top right of the window).<br> ```<br> ToadBuilder xmlSupportExample.<br> ```<br> or <br> ```<br> ToadBuilder graphETExample.<br> ```<br> **Note.** There are two buttons at the panel top left In (zoom in) and Out (zoom out). To move the visualization just drag the moves over the panel.</p> <p># Experiment<br> This subsection describe how to execute the tools, for replicating our experiment.</p> <p>## Baseline<br> The baseline contains the tools and the project-dataset to realize the tasks described in the paper (identifying and understanding performance variations).</p> <p>## Open the Baseline</p> <p>**MacOSX.** We do all the experiments in a Mac Book Pro. To open the Baseline execute the following command in the folder where this project was downloaded. </p> <p>```<br> ./Pharo-OSX/Pharo.app/Contents/MacOS/Pharo Baseline.image<br> ```</p> <p>**Windows.**<br> You may also run the experiment in Windows, but depending on the windows version you have installed it may be some some UI bugs.<br> ```<br> cd Pharo-Windows<br> Pharo.exe ../Baseline.image<br> ```</p> <p>## Open a Project</p> <p>There are three projects under study, depending on the project you wanna use for the task, you may execute one of the following scripts. For executing a script press Cmd-d or right-click and press do it.</p> <p>**Roassal**<br> ```<br> TProfileVersion openRoassal.<br> ```</p> <p>**XML**<br> ```<br> TProfileVersion openXML.<br> ```<br> **Grapher**<br> ```<br> TProfileVersion openGrapher.<br> ```</p> <p>## Baseline Options<br> For each project, we provide a UI which contains all the tools we use as a baseline. Each item in the list is a version of the selected project.</p> <p><img src="images/baseline.png" width="300"></p> <p>- Browse: open a standard window to inspect the code of the project in the selected version.<br> - Profile: open a window with a call context tree for the selected version.<br> - Source Diff: open a window with the code differences between the selected version and the previous one.<br> - Execution Diff: open a window with the merge call context tree gathered from the selected version and the previous one. </p> <p>**Note.** All these options require you select first a item in the list.</p> <p># Matrix</p> <p>## Open Matrix Image.</p> <p>**MacOSX.** We do all the experiments in a Mac Book Pro. To open the Matrix execute the following command in the folder where this project was downloaded. </p> <p>```<br> ./Pharo-OSX/Pharo.app/Contents/MacOS/Pharo Matrix.image<br> ```</p> <p>**Windows.**<br> You may also run the experiment in Windows, but depending on the windows version you have installed it may be some some UI bugs.<br> ```<br> cd Pharo-Windows<br> Pharo.exe ../Matrix.image<br> ```</p> <p>## Open a project</p> <p>There are three projects under study, depending on the project you wanna use for the task, you may execute one of the following scripts. For executing a script press Cmd-d or right-click and press do it.</p> <p>**Roassal**<br> ```<br> ToadBuilder roassal.<br> ```</p> <p>**XML**<br> ```<br> ToadBuilder xml.<br> ```<br> **Grapher**<br> ```<br> ToadBuilder grapher.<br> ```</p> <p># Data Gathering</p> <p>Before each participant starts a task we execute the following script in Smalltalk. For executing a script press Cmd-d or right-click and press do it. It allows us to track the time that a user starts the experiment and how many mouse clicks, movements.<br> ```<br> UProfiler newSession.<br> UProfiler current start.<br> ```</p> <p>After finishing the task we executed the following script. It stop recording the mouse events and save the stops time.<br> ```<br> UProfiler current end.<br> ```</p> <p>The last script generates a file with the following information: start time, end time, number of clicks, number of mouse movements, and the number of mouse drags (we do not use this last one).<br> ```<br> 11:34:52.5205 am,11:34:56.38016 am,14,75,0</p> <p>```<br> # Quit<br> To close the artifact, just close the window or press click in any free space of the window and select quit.</p>
Aligned DNA sequence matrixes for the study of the divergent times of phytoplasmas
<p>Sequence alignments of 16S rRNA and methionine aminopeptidase (map) are provided in FASTA files “Cao_et_al_16S.fas” and “Cao_et_al_map.fas”, respectively. Detailed information of the data matrixes is as follows:</p> <p> </p> <p>File name: Cao_et_al_16S.fas</p> <p>Number of taxa: 220</p> <p>Number of characters: 1655</p> <p>Gap: -</p> <p> </p> <p>File name: Cao_et_al_map.fas</p> <p>Number of taxa: 83</p> <p>Number of characters: 564</p> <p>Gap: -</p>
Aligned DNA sequence matrixes for the study of the divergent times of phytoplasmas
<p>Sequence alignments of 16S rRNA and methionine aminopeptidase (map) are provided in FASTA files “Cao_et_al_16S.fas” and “Cao_et_al_map.fas”, respectively. Detailed information of the data matrixes is as follows:</p> <p> </p> <p>File name: Cao_et_al_16S.fas</p> <p>Number of taxa: 220</p> <p>Number of characters: 1655</p> <p>Gap: -</p> <p> </p> <p>File name: Cao_et_al_map.fas</p> <p>Number of taxa: 83</p> <p>Number of characters: 564</p> <p>Gap: -</p>
The Ubiquitin Ligase WWP1 Contributes to Shifts in Matrix Proteolytic Profiles and a Myocardial Ageing Phenotype with Diastolic Heart Failure
<p><strong><em>Aims</em></strong>. Ubiquitylation is a key event that regulates protein turnover, and induction of the ubiquitin ligase E3 WWP1 has been associated with age. Left ventricular hypertrophy (LVH) commonly occurs as a function of age and can cause heart failure with a preserved ejection fraction (EF; HFpEF). We hypothesized that overexpression (O/E) of WWP1 in the heart would cause LVH as well as functional and structural changes consistent with the ageing HFpEF phenotype.</p> <p><strong><em>Methods and Results.</em></strong> Global WWP1 O/E was achieved in mice (n=11) and echocardiography (40 MHz) performed to measure LV mass, EF, Doppler velocities (early-E, late/atrial-A), myocardial relaxation (E’), and isovolumetric relaxation time (IVRT) at 4, 6, and 8 weeks. Age matched wild type animals (n=15) were included as referent controls. LV EF was identical (60+1% vs 60+1%, p>0.90) with no difference in LV mass (67+3 vs 75+5, p>0.25) at 4 weeks. LVH and diastolic dysfunction occurred with WWP1 O/E wherein LV mass increased by over two-fold, E/A fell (impaired passive filling), and E/E’ was lower and IVRT prolonged (impaired LV relaxation). Collagen percent area increased by over two-fold with WWP1 O/E and increased expression of determinants of fibrosis and growth (qPCR) were also concomitantly increased.</p> <p><strong><em>Conclusion.</em></strong> Inducing WWP1 expression caused LVH with significant diastolic dysfunction, consistent with the HFpEF phenotype. Thus, targeting the WWP1 pathway may be a novel therapeutic target for this intractable form of HF associated with ageing</p>
Single-cell Hi-C matrix Nagano 2017
<p>Single-cell Hi-C interaction matrix in mcool format, provided in 10kb and 1Mb. Data based on Nagano 2017: Cell-cycle dynamics of chromosomal organization at single-cell resolution</p>
Aligned DNA sequence matrix for phylogenetic analyses in the article "A new glassfrog of the genus Nymphargus (Anura: Centrolenidae) from Cordillera del Cóndor, Ecuador"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "<span>A new glassfrog of the genus Nymphargus</span> (Anura<span>:</span> Centrolenidae) <span>from Cordillera del Cóndor, Ecuador</span>"</p> <p>The matrix is in NEXUS format and has 6613 bp and 102 terminals.</p> <p>Partitions are as follows:</p> <div> <div>charset 12S = 1-944;</div> <div>charset 16S = 945-2093;</div> <div>charset BNDFcodonPos1 = 2096-2792\3;</div> <div>charset BNDFcodonPos2 = 2094-2793\3;</div> <div>charset BNDFcodonPos3 = 2095-2791\3;</div> <div>charset ND1codonPos1 = 2795-3749\3;</div> <div>charset ND1codonPos2 = 2796-3750\3;</div> <div>charset ND1codonPos3 = 2794-3751\3;</div> <div>charset CXCR4codonPos1 = 3753-4107\3;</div> <div>charset CXCR4codonPos2 = 3754-4105\3;</div> <div>charset CXCR4codonPos3 = 3752-4106\3;</div> <div>charset cmyccodonPos1 = 4108-4507\3;</div> <div>charset cmyccodonPos2 = 4109-4508\3;</div> <div>charset cmyccodonPos3 = 4110-4509\3;</div> <div>charset POMCcodonPos1 = 4511-5120\3;</div> <div>charset POMCcodonPos2 = 4512-5121\3;</div> <div>charset POMCcodonPos3 = 4510-5122\3;</div> <div>charset RAG1codonPos1 = 5123-5576\3;</div> <div>charset RAG1codonPos2 = 5124-5577\3;</div> <div>charset RAG1codonPos3 = 5125-5578\3;</div> <div>charset SLC8A1codonPos1 = 5580-6120\3;</div> <div>charset SLC8A1codonPos2 = 5581-6118\3;</div> <div>charset SLC8A1codonPos3 = 5579-6119\3;</div> <div>charset SLC8A3codonPos1 = 6122-6587\3;</div> <div>charset SLC8A3codonPos2 = 6123-6585\3;</div> <div>charset SLC8A3codonPos3 = 6121-6586\3;</div> </div>
Community Maturation Matrix (Illustration): Professionalising Community Management Roles in Interdisciplinary Research Projects
<p>Figure 1 (.jpg) file, editable version (.pptx) and supplementary 1 (.xlsx) for the research article: Professionalising Community Management Roles in Interdisciplinary Research Projects (July, 2024)</p> <p>Figure 1: Community Maturation Indicator: This is a framework through which maturation status for a community can be indicated at any given time as “Level of Community Participation” and “Stage of Community Building”. The Levels of Community Participation is shown in the x-axis (spanning 6 levels): Level 1: Inform Community, Level 2: Invite Feedback, Level 3: Engage and Involve, Level 4: Mobilise and Connect, Level 5: Empower Groups, and Level 6: Decentralise Power. The Stages of Community Building are shown as y-axis (spanning 6 stages): Stage 1: Initiation, Stage 2: Planning and Design, Stage 3: Implementation, Stage 4: Growth and Scaling, Stage 5: Monitoring and Evaluation, and Stage 6: Sustainability or Sunsetting. </p> <p>Supplementary 1: An example of how a community may progress its maturation levels. After the level has been identified, a CoP can start applying targeted approaches to community building, which occurs in stages. For example, at the community initiation stage of a new CoP, an RCM shares information and communicates project goals (Stage 1); at the planning and design stage, they build stakeholder awareness plans and help to prioritise specific objectives (Stage 2); at the implementation stage, they provide collaborative opportunities for specific stakeholders (Stage 3); at the growth stage, they facilitate knowledge exchange and skill building (Stage 4); and at the evaluation stage, it assesses the success of the CoP through feedback data to inform plans (Stage 5). These stages collectively affect how the project and its community can be sustained in the future or whether it should be paused (sunsetting) (Stage 6). RCMs build effective community strategies and implement creative approaches to support the development of a CoP, advancing a community from one stage to the next (Supplementary 1, Column 1 of the Community Maturation Indicators).</p> <p> </p>
Space-Fractional Truss Element - shape functions and stiffness matrix
<p>The following research data zipped to a .zip file is included.</p> <p>Data refers to formulas for shape functions (\(N_{i}\)) and stiffness matrix components (\(K_{ij}\)) for a non-local truss element formulated in terms of fractional calculus (space-Fractional Truss Element) and the finite element method.</p> <p>The data are described as follows:</p> <ul> <li>The .zip file contains information in the name about the number of nodes of the truss element (m).</li> <li>Formulas for shape functions written in different formats: <ul> <li>Display_Ni.svg shows the formula in a conveniently viewable form;</li> <li>LaTeX_Ni.txt contains a formula written in LaTeX syntax;</li> <li>Sym_Ni.txt contains a formula written in symbolic form, allowing loading into a calculation program (e.g., by loading a file and using the sympify() function);</li> <li>Plot_Ni.svg contains plotted shape functions.</li> </ul> </li> <li>Formulas for the elements of the stiffness matrix written in different formats:<br> <ul> <li>The formula for the elements of the stiffness matrix is as follows, <br>\(K_{ij}=EA\frac{L}{2}\int_{-1}^{1} B_{i}B_{j} d\xi. \)</li> <li>In the files, the formulas for \(B_{i} \) and the product of \(B_{i}B_{j} \) are written - in different formats, with naming analogous to that described for the shape functions.</li> </ul> </li> </ul>
Investigating the Auditory Selective Attention Switch using Matrix Sentences in VR
<h2>General</h2> <p>The audio-visual Auditory Selective Attention VR - Long Stimuli (avASAlongStimuli) project serves to investigate the extensions by matrix sentences of a paradigm on the auditory selective attention switch in a close-to-real-life classroom setting. This dataset consists of a Unity project and Matlab code used to collect data on the voluntary switching of auditory selective attention in a virtual reality classroom scenario.</p> <p><strong>The dataset contains:</strong></p> <p> Unity project for audiovisual display and the experiment structure<br> Matlab code for experiment preparation and HpFT measurement<br> Data collected in the experiment (experiment performance, head tracking)</p> <h2>Experiment preparation using Matlab</h2> <p>The code and software used to prepare the experiment is provided in the folder "matlab_avASAlongStimuli".</p> <p>The Matlab code used to prepare the trials for each participant as well as to measure the HpTFs. For the HpTF measurements, the ITA Toolbox for Matlab was used and is provided (https://git.rwth-aachen.de/ita/toolbox). A developmental version of Virtual acoustics (VA) 2022a (https://www.virtualacoustics.org/VA/overview/) is provided.</p> <p><strong>Software requirements:</strong></p> <p> Matlab 2020a or higher<br> ITA Toolbox for Matlab installed</p> <h2><br>Experiment conduction in Unity</h2> <p>The Unity project is provided in the folder "unity_avASAlongStimuli".</p> <p>Therefore, a virtual classroom with some basic furniture is provided. The used models, prefabs and plugins can be found in the Assets folder.</p> <p>The acoustic stimuli for the task were created using Voicemaker (https://voicemaker.in/).</p> <p>This Unity project was intended for the use in virtual reality using an HMD and respective controllers for input. However, it can also be used on a desktop pc. The mode can be changed using the "VRMode" toggle as described below.<br>The audio reproduction is realized using the Unity plugin for Virtual Acoustics (VA, http://www.virtualacoustics.org/ and https://git.rwth-aachen.de/ita/vaunity_package).</p> <p><strong>Software requirements:</strong></p> <p> Unity 2019.4.21.f1.<br> SteamVR 1.19.7<br> Virtual Acoustics v2022a, VAUnity: https://git.rwth-aachen.de/ita/VAUnity</p> <h2><br>Data evaluation</h2> <p>The collected data is provided in the folder "dataEvaluation_avASAlongStimuli". This folder contains two types of data: the raw data collected in the experiment (reaction times and error rates), the head tracking data.</p>
Paper: "Sensitivity analysis for linear changes of the constraint matrix of a linear program" output
<p>Output generated from the experiments in the paper "Sensitivity analysis for linear changes of the constraint matrix of a linear program"</p>
Road distances and trip duration matrix for USA counties
<p>This dataset presents a matrix with road distance and travel duration estimates for all trip combinations among USA counties. More details about the methodology are available <a href="https://rfsaldanha.github.io/data-projects/usa_road_distances.html" target="_blank" rel="noopener">here</a>.</p> <p>This version was generated considering the road network at the OSRM project in October 2024.</p> <p><strong>Variable dictionary</strong></p> <ul> <li>orig: County FIPS code of origin</li> <li>dest: County FIPS code of destiny</li> <li>dist: Road distance of the shortest route, in meters</li> <li>dur: Travel time estimation, in minutes.</li> </ul> <p><strong>Files</strong></p> <ul> <li>dist_usa.rds : R serialized object</li> <li>dist_usa.parquet : Parquet format</li> <li>dist_usa.csv : CSV file. Colon (, ) field delimiter and point ( . ) as decimal separator</li> <li>uscounties.csv : CSV file with counties names, codes, and other information, from the <a href="https://simplemaps.com/data/us-counties" target="_blank" rel="noopener">Simple Maps database</a>.</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.