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Isometric exercise facilitates attention to salient events in women via the noradrenergic system
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Patellar Tendon Load Progression during Rehabilitation Exercises: Implications for the Treatment of Patellar Tendon Injuries
<h3><strong>Purpose </strong></h3><p>To evaluate patellar tendon loading profiles (loading index, based on loading peak, loading impulse, and loading rate) of rehabilitation exercises to develop clinical guidelines to incrementally increase the rate and magnitude of patellar tendon loading during rehabilitation.</p><h3><strong>Methods </strong></h3><p>Twenty healthy adults (10 females/10 males, 25.9 ± 5.7 years) performed 35 rehabilitation exercises, including different variations of squats, lunge, jumps, hops, landings, running, and sports specific tasks. Kinematic and kinetic data were collected and a patellar tendon loading index was determined for each exercise using a weighted sum of loading peak, loading rate, and cumulative loading impulse. Then, the exercises were ranked, according to the loading index, into tier 1 (loading index≤0.33), tier 2 (0.33 < loading index<0.66), and tier 3 (loading index≥0.66).</p><h3><strong>Results </strong></h3><p>The single-leg decline squat showed the highest loading index (0.747). Other tier 3 exercises included single-leg forward hop (0.666), single-leg countermovement jump (0.711), and running cut (0.725). The Spanish squat was categorized as a tier 2 exercise (0.563), as was running (0.612), double-leg countermovement jump (0.610), single-leg drop vertical jump (0.599), single-leg full squat (0.580), double-leg drop vertical jump (0.563), lunge (0.471), double-leg full squat (0.428), single-leg 60° squat (0.411), and the Bulgarian squat (0.406). Tier 1 exercises included 20 cm step up (0.187), 20 cm step down (0.288), 30 cm step up (0.321), and double-leg 60° squat (0.224).</p><h3><strong>Conclusions </strong></h3><p>Three patellar tendon loading tiers were established based on a combination of loading peak, loading impulse, and loading rate. Clinicians may use these loading tiers as a guide to progressively increase patellar tendon loading during the rehabilitation of patients with patellar tendon disorders and after anterior cruciate ligament reconstruction using the bone patellar tendon bone graft.</p>
The exercise paradox: Avoiding physical inactivity stimuli requires higher response inhibition
<p><strong>Dataset related to the paper on Response inhibition to physical inactivity stimuli using go/no-go tasks. </strong></p> <p>This dataset includes:</p> <p><strong>1) A codebook (including the name of the main variables)</strong></p> <p>--> "code_book_Go_noGo_Miller.xlsx"</p> <p><strong>2) Raw data of the behavioral outcomes (i.e., reaction times) of the affective go/no-go task</strong></p> <p>--> "corrected.behavioral.data.csv"</p> <p>--> "correct_Order.csv"</p> <p><strong>3) Self-reported data </strong></p> <p>--> "Self_report_data.csv"</p> <p><strong>3) EEG data </strong></p> <p>--> "gng_data"</p> <p><strong>5) R script for the data management (i.e., from the raw data to data ready to be analyzed)</strong></p> <p>--> "Data_management_Self_report_go_no_go_Miller.R" for the self-reported data (return the file: "Data_SR_final.RData")</p> <p>--> "Data_management_behav_go_no_go_Miller.R" for the behavioral outcomes (return the file: "Data_GNG_behav.RData")</p> <p>--> Data ready to be analyzed "Data_GNG_final_all.RData"</p> <p><strong>6) Eprime script for the affective go/no-go task ("Go_no_go_task.zip")</strong></p> <p>--> Images depicting physical activity and physical inactivity stimuli were kindly Share by Kullmann et al. (2014)</p> <p><strong>7) R script for the models tested</strong></p> <p><strong>--> "</strong>Models_GoNogo_Miller_VZenodo.R" for behavioral data</p> <p>--> "Models_EEG_GoNogo.R" for EEG data</p>
S47 | ECHAPLASTICS | A list from the Plastic Additives Initiative Mapping Exercise by ECHA
<p>This is the collection associated with list S47 ECHAPLASTICS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S47</p> <p>ECHAPLASTICS</p> <p><strong>A list from the Plastic Additives Initiative Mapping Exercise by ECHA</strong></p> <p>Merged ECHA Plastic Additives with Structures <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/ECHA_PlasticAdditivesInitiative_06032019.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/ECHA_PlasticAdditivesInitiative_06032019.csv">CSV</a> (06/03/2019)</p> <p>ECHA Plastic Additives <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/ECHA_Plastics_InChIKeys.txt">InChIKeys</a> (06/03/2019)</p> <p>List with several categories released on <a href="https://echa.europa.eu/mapping-exercise-plastic-additives-initiative">https://echa.europa.eu/mapping-exercise-plastic-additives-initiative</a> and mapped to structures by CAS and Name by E. Schymanski. </p>
Backpain exercise therapy remodels human epigenetic profiles in buccal and human peripheral blood mononuclear cells: An exploratory study in young male participants
<pre><strong>###### Files description #####</strong><br> <strong>Notes</strong>. 1) "BT" refers to before therapy and "AT" to after therapy. 2) 0 refers to FALSE and 1 to TRUE for binary variables. The provided files have tab-separated columns except the .RDS which is and R output of the mixOmics DIABLO integration analysis. <strong># Questionnaire</strong> > participants_categories.tsv: per participant (rows), output of the clustering with the participant ("ID") category ("category") per class<br> ("class") > questionnaire_agility_metrics.tsv: questionnaire and agility metrics per participant (rows) for the participants ("ID") with at least one paired AT+BT data in one type of biological sample (indicated in the columns "swab", "PBMC", and "plasma") <strong># PTMs</strong> Samples´ names are encoded as PBMC_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch (we removed _batch column suffix for the <br>processed files). NA indicates an undetected intensity. > raw_PBMC_light_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_light_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavy_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_heavy_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavynormalized_intensities.tsv: raw intensity of light peptides normalized by heavy peptides intensity (row) by precursor per <br>sample (column) > raw_swab_heavynormalized_labelled_intensities.tsv: idem from buccal cells > processed_cleaned_PBMC_log2intensities.tsv: processed (heavy normalized, imputed, batch-corrected) intensity of peptides aggregated by modification (PTM, row) by precursor per sample (column) after log2-transformation. The relative abundances are computed from this file. Rows without me/ac suffix represents the amount of unmodified peptide for the considered site. > processed_cleaned_swab_log2intensities.tsv: idem from buccal cells > rel_abundance_PTM_PBMC.tsv: relative abundance computed per precursor, e.g. for a given sample, the H3_K4+H3_K4me1+H3_K4me2+H3_K4me3 <br>relative abundance values must sum to 100, with the relative abundance of H3_K4 representing the absence of modified K4. > rel_abundance_PTM_swab.tsv: idem from buccal cells > tests_from_rel_abundance_PTM_swab_PBMC.tsv: per type of samples ("Sample.origin", i.e.swab of PBMC) and per PTM (rows, "PTM"), report <br>the output of classic (p-values, adjusted with Benjamini-Hochberg (BH), or Benjamini-Yekutieli procedure (BY), from raw and arcsin square <br>root transformed percentage) and PLS-DA tests (VIP - Variable Importance score - and its 95% confidence interval). The percentage of change<br>of each PTM after therapy relative tobefore therapy is reported in "perc_change.AT.over.BT" column. The "is_candidate" indicates if the PTM has been considered as a hit in the swab or PBMC. <strong># Plasma</strong> Samples´ names are encoded as PLASMA_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch. NA indicates an undetected intensity. > raw_plasma_maxquant_log2ibaq_intensities.tsv: raw data from protein group MaxQuant file. The iBAQ columns are used in later steps. > processed_cleaned_plasma_log2intensities.tsv: processed (imputed, batch-corrected) intensity of protein groups after log2-transformation. > tests_from_intens_plasma.tsv: per protein group ("Proteins.ID"), report the output of classic (p-values, adjusted Benjamini-Hochberg (BH),<br>or Benjamini-Yekutieli procedure (BY), from log2-transformed intensities) and PLS-DA tests (VIP and its 95% confidence interval). The log2 <br>fold change after therapy relative to before therapy is reported in "log2FC.AT.over.BT" column. The "is_candidate" indicates if the protein group has been considered as a hit. <strong># Integration</strong> > circos_input: output of DIABLO analysis with correlation threshold set to 0.7. Use the readRDS R function to open.</pre> <p> </p>
Effects of Auditory Stimuli During Submaximal Exercise on Cerebral Oxygenation
<p>Manuscript, supplementay files, raw and proprocessed data, code, and materials for the associated registered report.</p>
A Systematic Review and Meta-Analysis of Mindfulness-Based (Baduanjin) Exercise for the Rehabilitation of Stroke Patients
<p><span>A Systematic Review and Meta-Analysis of Mindfulness-Based (Baduanjin) Exercise for the Rehabilitation of Stroke Patients</span></p>
Data set for the integrated Climate, Land, Energy and Water systems modelling exercise RCLEWs in OSeMOSYS
<p>This dataset refers to the modelling exercise (version01_210616RCLEWs). The dataset contains the OSeMOSYS code used to run the modelling exercise, the model input data, the scenarios model data files, and the results. The code for the results visualization is available at https://github.com/KTH-dESA/teaching-CLEWs_visualization.</p> <p>This is an update of version 01_210827 available at: https://doi.org/10.5281/zenodo.5293834</p>
A database of physical therapy exercises with variability of execution collected by wearable sensors
<p>The PHYTMO database contains data from physical therapy exercises and gait variations recorded with magneto-inertial sensors, including information from an optical reference system. PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations commonly prescribed in physical therapies were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. Four magneto-inertial sensors were placed on the lower-or upper-limbs for the recording of the motions together with passive optical reflectors. The files include the specifications of the inertial sensors and the cameras. The database includes magneto-inertial data (linear acceleration, turn rate and magnetic field), together with a highly accurate location and orientation in the 3D space provided by the optical system (errors are lower than 1mm). The database files were stored in CSV format to ensure usability with common data processing software. The main aim of this dataset is the availability of inertial data for two main purposes: the analysis of different techniques for the identification and evaluation of exercises monitored with inertial wearable sensors and the validation of inertial sensor-based algorithms for human motion monitoring that obtains segments orientation in the 3D space. Furthermore, the database stores enough data to train and evaluate Machine Learning-based algorithms. The age range of the participants can be useful for establishing age-based metrics for the exercises evaluation or the study of differences in motions between different aged groups. Finally, the MATLAB function <em>features_extraction</em>, developed by the authors, is also given. This function splits signals using a sliding window, returning its segments, and extract signal features, in the time and frequency domains, based on prior studies of the literature.</p>
CMS Open Data 2012 datasets for dimuon exercises
<p>These datasets are a subset of the CMS Open data with 2021 data-taking conditions for education purposes.</p> <p>In this version, the data and simulation files are compressed into one big file for easy access. They are stored in two different formats (CSV and PKL) with the same content, therefore just use one of them.</p> <p>Once unzipped:</p> <p>- Data files, starting with output_data_CMS_Run2012B, correspond to 4429.37 /pb of data collected by the CMS Experiment. They are a subset of the dataset on reference [1].</p> <p>- Simulation files, starting with output_sim_CMS_MonteCarlo2012, are a subset of the dataset referenced on [2]. The number of generated events in this case is 30458871, and the cross section is 3503.71.</p> <p>All the files were processed with a modified version of the AOD2NanoAODOutreachTool [3]. The small modifications are related to the number of triggers stored, and some objects like taus were removed.</p> <p> </p> <p>--------------------------------------------------------</p> <p>[1] CMS collaboration (2017). DoubleMuParked primary dataset in AOD format from Run of 2012 (/DoubleMuParked/Run2012B-22Jan2013-v1/AOD). CERN Open Data Portal. DOI:<a href="http://doi.org/10.7483/OPENDATA.CMS.YLIC.86ZZ">10.7483/OPENDATA.CMS.YLIC.86ZZ</a></p> <p>[2] Wunsch, Stefan; (2019). DYJetsToLL dataset in reduced NanoAOD format for education and outreach. CERN Open Data Portal. DOI:<a href="http://doi.org/10.7483/OPENDATA.CMS.SRRA.2GON">10.7483/OPENDATA.CMS.SRRA.2GON</a></p> <p>[3] https://github.com/cms-opendata-analyses/AOD2NanoAODOutreachTool</p>
DEM Intercomparison eXercise (DEMIX) - Maps of completeness criteria scores for global DEMs
<h2>Introduction</h2> <p>This introduction gives a brief overview of the context in which the dataset has been produced. Readers curious about the detailed standards and procedures described in this section are encouraged to open the resources linked to this dataset.</p> <h3>The Digital Elevation Model Intercomparison eXercise (DEMIX)</h3> <p>This work is part of the Digital Elevation Model Intercomparison eXercise (DEMIX), initiated by the <a href="https://ceos.org/ourwork/workinggroups/wgcv/current-activites/#:~:text=DEMIX%3A%20Digital%20Elevation%20Model%20Intercomparison,elevation%20model%20for%20their%20application.">Committee on Earth Observation Satellites (CEOS)</a>. This initiative aims at "<a href="https://isprs-archives.copernicus.org/articles/XLIII-B4-2021/395/2021/">providing harmonised terminology and methods, as well as practical guidelines and results allowing the intercomparison of continental or global Digital Elevation Models (DEM)</a>" (Strobl et al., 2021). Several publications have defined the framework of DEMIX, from <a href="https://doi.org/10.3390/rs13183581">the terminology and definitions</a> (Guth et al., 2021) to the <a href="https://doi.org/10.1109/TGRS.2024.3368015">DEM ranking methods</a> (Bielski et al., 2024). An additional methodology paper has been publicated regarding the assessment of <a href="https://doi.org/10.3390/ijgi13030096">planimetric displacements between DEMs</a> (Riazanoff et al., 2024), which are a common source of biases in DEM comparisons.</p> <h3>The DEMIX grid</h3> <p>Studies performed within the DEMIX framework rely on the <a href="../records/7504791">DEMIX grid</a> (Guth et al., 2023), a geodetic grid (EPSG:4326) dividing the world in areas of approximately 10x10km. These standard areas are called DEMIX tiles, and can be precisely located thanks to their identifier.</p> <h3>Criteria and scores</h3> <p>Within DEMIX, several criteria have been defined to assess the quality of DEMs. These criteria take as input a DEM and a DEMIX tile, and provide as output the score of the DEM for this specific tile. Repeating this process over several DEMs and DEMIX tiles of interest allow for a comparison of scores, leading to a ranking of DEMs. <a href="https://doi.org/10.1109/TGRS.2024.3368015">DEMIX rankings are based on the Randomized Complete Block Design (RCBD)</a> (Bielski et al., 2024).</p> <h2>This dataset</h2> <p>This dataset is composed of global maps of one map per (DEM, criterion) tuple. Each GeoTIFF map can be superimposed with the <a href="../records/7504791">DEMIX grid</a> (Guth et al., 2023) in a GIS (tested in QGIS 3.16).</p> <h3>Completeness criteria</h3> <p>The completeness criteria have originally been defined by Peter Strobl. A brief description of each criterion is given in the next table. Please see the column "Original document" and files of this repository for the complete definitions.</p> <table> <tbody> <tr> <td><strong>Criterion</strong></td> <td><strong>Description</strong></td> <td><strong>Requirements</strong></td> <td><strong> Original document</strong></td> </tr> <tr> <td>A01 - Product fractional cover</td> <td>Fraction of a DEMIX tile <strong>covered</strong> by the DEM product</td> <td>None</td> <td>See document "DEMIX_CDD-A01_20211103.docx"</td> </tr> <tr> <td>A02 - Valid data fraction</td> <td>Fraction of a DEMIX tile <strong>covered</strong> by <strong>valid </strong>pixels of the DEM product</td> <td>"No data" or "void" value in metadata</td> <td>See document "DEMIX_CDD-A02_20211103.docx"</td> </tr> <tr> <td>A03 - Primary data fraction</td> <td>Fraction of a DEMIX tile <strong>covered </strong>by <strong>valid </strong>pixels generated from the <strong>main source of data</strong> of the DEM product</td> <td>"No data" or "void" value in metadata + source data/editing mask</td> <td>See document "DEMIX_CDD-A03_20211103.docx"</td> </tr> <tr> <td>A04 - Valid land fraction</td> <td>Fraction of a DEMIX tile <strong>covered </strong>by <strong>valid </strong>pixels of <strong>land </strong>of the DEM product</td> <td>"No data" or "void" value in metadata + water body mask</td> <td>See document "DEMIX_CDD-A04_20211103.docx"</td> </tr> <tr> <td>A05 - Primary land fraction</td> <td>Fraction of a DEMIX tile <strong>covered </strong>by <strong>valid </strong>pixels of <strong>land </strong>generated from the <strong>main source of data </strong>of the DEM product</td> <td>"No data" or "void" value in metadata + water body mask + source data/editing mask</td> <td>See document "DEMIX_CDD-A05_20211103.docx"</td> </tr> </tbody> </table> <h3>DEMs and ancillary data</h3> <p>The following DEM products and ancillary layers have been used to generate the dataset.</p> <table> <tbody> <tr> <td><strong>Identifier</strong></td> <td><strong>Used layers</strong></td> <td><strong>Data access</strong></td> </tr> <tr> <td> <p>ASTGTM v003</p> </td> <td>ASTER GDEM elevations (dem.tif) + editing / source masks (num.tif)</td> <td><a href="https://lpdaac.usgs.gov/products/astgtmv003/">https://lpdaac.usgs.gov/products/astgtmv003/</a></td> </tr> <tr> <td> <p>ASTWBD v001</p> </td> <td>ASTER GDEM water body mask (att.tif)</td> <td><a href="https://lpdaac.usgs.gov/products/astwbdv001/">https://lpdaac.usgs.gov/products/astwbdv001/</a></td> </tr> <tr> <td> <p>AW3D30 v2003</p> </td> <td>ALOS World 3D elevations (DSM.tif) + editing / source / water body masks (MSK.tif)</td> <td><a href="https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm">https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm</a></td> </tr> <tr> <td>COP-DEM_GLO-30-DGED v2019_1</td> <td>Copernicus DEM GLO-30 elevations (DEM.tif) + editing (EDM.tif) + source (SRC.tif) + water body (WBM.tif) masks</td> <td><a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model</a></td> </tr> <tr> <td>COP-DEM_GLO-90-DGED v2019_1</td> <td>Copernicus DEM GLO-90 elevations (DEM.tif) + editing (EDM.tif) + source (SRC.tif) + water body (WBM.tif) masks</td> <td><a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model</a></td> </tr> <tr> <td> <p>NASADEM_HGT v001</p> </td> <td>NASADEM elevations (.hgt) + editing / source (.num) + water body (.swb) masks</td> <td><a href="https://lpdaac.usgs.gov/products/nasadem_hgtv001/">https://lpdaac.usgs.gov/products/nasadem_hgtv001/</a></td> </tr> <tr> <td> <p>SRTMGL1 v003</p> </td> <td>SRTMGL1 elevations (.hgt)</td> <td><a href="https://lpdaac.usgs.gov/products/srtmgl1v003/">https://lpdaac.usgs.gov/products/srtmgl1v003/</a></td> </tr> <tr> <td> <p>SRTMGL1N v003</p> </td> <td>SRTMGL1 editing / source / water body masks (.num)</td> <td><a href="https://lpdaac.usgs.gov/products/srtmgl1nv003/">https://lpdaac.usgs.gov/products/srtmgl1nv003/</a></td> </tr> </tbody> </table> <h3>Computation of scores</h3> <p>For each DEMIX tile and DEM, each "fractional cover" has been computed using the following procedure:</p> <ol> <li><strong>Crop DEMIX tile layers</strong> - The tiles of each DEM layer (elevations, editing, sources and water bodies) are cropped to the extent of the DEMIX tile.</li> <li><strong>Compute standardized layers</strong><strong> </strong>- Given the cropped DEM layers, four standardized layers are produced, which are: <ul> <li>Heights layer - Containing the heights of the DEM</li> <li>Land/water mask layer - Indicating whether DEM pixels are land or water: <ul> <li>0 = NO_DATA</li> <li>1 = BACKGROUND</li> <li>2 = INVALID</li> <li>3 = WATER</li> <li>4 = LAND</li> </ul> </li> <li>Source mask layer - Indicating the source data of DEM heights (or "edited" value): <ul> <li>0 = NO_DATA</li> <li>1 = BACKGROUND</li> <li>2 = INVALID</li> <li>3 = PRIMARY_DATA</li> <li>4 = EXTERNAL_DATA</li> <li>5 = EDITED</li> </ul> </li> <li>Valid mask layer - Indicating if the DEM pixels are valid or not: <ul> <li>0 = NO_DATA</li> <li>1 = BACKGROUND</li> <li>2 = INVALID</li> <li>3 = VALID</li> </ul> </li> </ul> </li> <li><strong>Retrieve pixel number N</strong><em><strong> </strong>-<strong> </strong></em>The total pixel number N is computed for one of the layers (all layers have the same number of pixels).</li> <li><strong>Retrieve criterion pixel number C </strong>-<strong> </strong>The criterion pixel number C is computed based on the standard layers, more precisely: <ul> <li>A01 - Product fractional cover - Number of pixels of <strong>valid mask layer equal to 1, 2 or 3</strong></li> <li>A02 - Valid data fraction - Number of pixels of <strong>valid mask layer equal to 3</strong></li> <li>A03 - Primary data fraction - Number of pixels of <strong>source mask layer equal to 3</strong></li> <li>A04 - Valid land fraction - Number of pixels of <strong>land/water mask layer equal to 4</strong></li> <li>A05 - Primary land fraction - Number of pixels of <strong>source mask layer equal to 3</strong> and<strong> land/water mask layer equal to 4</strong></li> </ul> </li> <li><strong>Compute the final score S</strong><strong> </strong>- The final score S is expressed as the following percentage: <strong>S = ceil(C/N*100)</strong></li> </ol> <h2>Known issues</h2> <p>The "SRTMGL1N v003" is known to have "tile repeating issues", where part of the data is wrongly flagged as water. This issue has been reported with no particular response from the providers of the DEM (see <a href="https://forum.earthdata.nasa.gov/viewtopic.php?t=2752">https://forum.earthdata.nasa.gov/viewtopic.php?t=2752</a>).</p> <p><strong>References:</strong></p> <ul> <li>Guth, P.L.; Strobl, P.; Gross, K.; Riazanoff, S. <em>DEMIX 10k Tile Data Set (1.0)</em> [Data set]. Zenodo 2023. <a href="https://doi.org/10.5281/zenodo.7504791" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7504791</a></li> <li>Guth, P.L.; Van Niekerk, A.; Grohmann, C.H.; Muller, J.-P.; Hawker, L.; Florinsky, I.V.; Gesch, D.; Reuter, H.I.; Herrera-Cruz, V.; Riazanoff, S.; López-Vázquez, C.; Carabajal, C.C.; Albinet, C.; Strobl, P. <em>Digital Elevation Models: Terminology and Definitions</em>. Remote Sens. 2021, 13, 3581. <a href="https://doi.org/10.3390/rs13183581">https://doi.org/10.3390/rs13183581</a></li> <li>Riazanoff, S.; Corseaux, A.; Albinet, C.; Strobl, P.A.; López-Vázquez, C.; Guth, P.L.; Tadono, T. <em>Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-Pixel Accuracy: Application to Digital Elevation Models</em>. <em>ISPRS Int. J. Geo-Inf.</em> 2024, <em>13</em>, 96. <a href="https://doi.org/10.3390/ijgi13030096">https://doi.org/10.3390/ijgi13030096</a></li> <li>Bielski, C.; López-Vázquez, C.; Grohmann, C.H.; Guth, P.L.; Hawker, L.; Gesch, D.; Trevisani, S.; Herrera-Cruz, V.; Riazanoff, S.; Corseaux, A.; Reuter, H.I.; Strobl, P.A.; <em>Novel Approach for Ranking DEMs: Copernicus DEM Improves One Arc Second Open Global Topography</em> in <em>IEEE Transactions on Geoscience and Remote Sensing</em>, vol. 62, pp. 1-22, 2024, Art no. 4503922. <a href="https://doi.org/10.1109/TGRS.2024.3368015">https://doi.org/10.1109/TGRS.2024.3368015</a></li> <li>Strobl, P.A.; Bielski, C.; Guth, P.L.; Grohmann, C.H.; Muller, J.P.; López-Vázquez, C.; Gesch, D.B.; Amatulli, G.; Riazanoff, S.; Carabajal, C. The Digital Elevation Model Intercomparison eXperiment DEMIX, a community based approach at global DEM benchmarking. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2021, XLIII-B4-2021, 395–400. <a href="https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021">https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021</a></li> </ul>
Locked Shields Partners Run 23 (LSPR23): A novel IDS dataset from the largest live-fire cybersecurity exercise
<p>IDS Dataset from the Largest Live Fire Cybersecurity Exercise Using Virtual Blue Team Network Traffic.<br><br></p> <ul> <li> <p>LSPR23 is derived from Locked Shields 2023, a major live-fire cyber defense exercise.</p> </li> <li> <p>LSPR23 includes ~16M network flows, of which ~1.6M are labeled malicious.</p> </li> </ul> <p> </p> <p>Please cite our research article:"LSPR23: A novel IDS dataset from the largest live-fire cybersecurity exercise" when using our dataset:<br>https://doi.org/10.1016/j.jisa.2024.103847<br><br><br></p>
REHAB24-6: A multi-modal dataset of physical rehabilitation exercises
<p>To enable the evaluation of HPE models and the development of exercise feedback systems, we produced a new rehabilitation dataset (REHAB24-6). The main focus is on a diverse range of exercises, views, body heights, lighting conditions, and exercise mistakes. With the publicly available RGB videos, skeleton sequences, repetition segmentation, and exercise correctness labels, this dataset offers the most comprehensive testbed for exercise-correctness-related tasks.</p> <h2>Contents</h2> <ul> <li>65 recordings (184,825 frames, 30 FPS): <ul> <li>RGB videos from two cameras (<code>videos.zip</code>, horizontal = Camera17, vertical = Camera18);</li> <li>3D and 2D projected positions of 41 motion capture marker (<code><2/3>d_markers.zip</code>, marker labels in <code>marker_names.txt</code>);</li> <li>3D and 2D projected positions of 26 skeleton joints (<code><2/3>d_joints.zip</code>, joint labels in <code>joint_names.txt</code>);</li> </ul> </li> <li>Annotation of 1,072 exercise repetitions (<code>Segmentation.csv</code>, indexed based <strong>only on</strong> 30 FPS data, described in <code>Segmentation.txt</code>): <ul> <li>Temporal segmentation (start/end frame, most between 2–5 seconds);</li> <li>Binary correctness label (around 90 from each category in each exercise, except Ex3 with around 50);</li> <li>Exercise direction (around 90 from each direction in each exercise);</li> <li>Lighting conditions label.</li> </ul> </li> </ul> <h2>Recording Conditions</h2> <p>Our laboratory setup included 18 synchronized sensors (2 RGB video cameras, 16 ultra-wide motion capture cameras) spread around an 8.2 × 7 m room. The RGB cameras were located in the corners of the room, one in a horizontal position (hor.), providing a larger field of view (FoV), and one in a vertical (ver.), resulting in a narrower FoV. Both types of cameras were synchronized with a sampling frequency of 30 frames per second (FPS).</p> <p>The subjects wore motion capture body suits with 41 markers attached to them, which were detected by optical cameras. The OptiTrack Motive 2.3.0 software inferred the 3D positions of the markers in virtual centimeters and converted them into a skeleton with 26 joints, forming our human pose 3D ground truth (GT).</p> <p>To acquire a 2D version of the ground truth in pixel coordinates, we applied a projection of the virtual coordinates into the camera using the simplified pinhole model. We estimated the parameters for this projection as follows. First, the virtual position of the cameras was estimated using measuring tape and knowledge of the virtual origin. Then, the orientation of the cameras was optimized by matching the virtual marker positions with their position in the videos.</p> <p>We also simulated changes in lighting conditions: a few videos were shot in the natural evening light, which resulted in worse visibility, while the rest were under artificial lighting.</p> <h2>Exercises</h2> <p>10 subjects participated in our recording and consented to release the data publicly: 6 males and 4 females of different ages (from 25 to 50) and fitness levels. A physiotherapist instructed the subjects on how to perform the exercises so that at least five repetitions were done in what he deemed the correct way and five more incorrectly. The participants had a certain degree of freedom, e.g., in which leg they used in Ex4 and Ex5. Similarly, the physiotherapist suggested different exercise mistakes for each subject.</p> <ul> <li><strong>Ex1 = Arm abduction</strong>: sideway raising of the straightened right arm;</li> <li><strong>Ex2 = Arm VW</strong>: fluent transition of arms between V (arms straight up) and W (elbows down, hands up) shape;</li> <li><strong>Ex3 = Push-ups</strong>: push-ups with hands on a table;</li> <li><strong>Ex4 = Leg abduction</strong>: sideway raising of the straightened leg;</li> <li><strong>Ex5 = Leg lunge</strong>: pushing a knee of the back leg down while keeping a right angle on the front knee;</li> <li><strong>Ex6 = Squats</strong>.</li> </ul> <p>Every exercise was also executed in two directions, resulting in different views of the subject depending on the camera. Facing the horizontal camera resulted in a front view for that camera and a profile from the other. Facing the wall between the cameras shows the subject from half-profile in both cameras. A rare direction, only used for push-ups due to the use of the table, was facing the vertical camera, with the views being reversed compared to the first orientation.</p> <h2>Citation</h2> <p>Cite the related conference paper:</p> <p>Černek, A., Sedmidubsky, J., Budikova P.: REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods. 17th International Conference on Similarity Search and Applications (SISAP). Springer, 14 pages, 2024.</p> <h2>License</h2> <p>This dataset is for academic or non-profit organization noncomercial research use only. By using you agree to appropriately reference the paper above in any publication making of its use. For comercial purposes contact us at info@visioncraft.ai</p>
Preparing educational presentation about therapeutic exercises to resident physiatrists
<p>This study evaluated the effectiveness of ChatGPT-4o, a generative artificial intelligence (AI) platform, in preparing educational presentation on therapeutic exercises specifically designed for physiatry residents. Both a physiatry expert and ChatGPT-4 created PowerPoint slides for a presentation on therapeutic exercises using the same reputable sources. Two other physiatry experts, blinded to the origin of the presentation and each other's scores, independently assessed both presentations using four of the CLEAR criteria (completeness, lack of false information, appropriateness, and relevance).</p> <p>Statistical analyses confirmed the interrater reliability. The average scores of the expert-prepared slides were significantly higher than those of the AI-prepared slides. However, when assessing the presentations as a whole, no statistically significant difference emerged between the average AI and expert scores. The overall scores of the AI-prepared slides were rated between good and excellent, while the expert-prepared slides were rated as excellent. Similarly, when assessing the presentations as a whole, the AI performed good whereas expert performed excellent. Upon evaluating the variability within the model for the assessed criteria, it was found that the AI ranked highest in relevance, whereas the expert ranked highest in terms of lack of false information.</p> <p>These findings indicated that although ChatGPT-4o can produce effective educational content, the expert still outperformed AI. This underscores the importance of professional oversight in maintaining the educational quality of resident physician training. Furthermore, fostering collaboration between humans and AI can lead to enhanced educational outcomes in the field.</p>
Ocean drifters from oil-on-water exercise in North Sea (Frigg oil field) June 2019
<p>Ocean drifters from oil-on-water exercise in North Sea (Frigg oil field) June 2019. Described in more detail in Brekke, C., Espeseth, M. M., Dagestad, K.-F., Röhrs, J., Hole, L. R., & Reigber, A. (2021). Integrated analysis of multisensor datasets and oil drift simulations - a free-floating oil experiment in the open ocean. Journal of Geophysical Research: Oceans, 126, e2020JC016499. https://doi.org/10.1029/2020JC016499</p> <p>Work is funded by grant no. 237906 (CIRFA) of the Norwegian Research Council.</p>
Datasets from BUBBLES validation exercises
<p>This dataset contains the telemetry data sent by the drones during the validation exercises and the response from the BUBBLES Separation Management Environment Platform. The data was gathered during test flights, in which 14 drones performed different representative operations, including agricultural tasks, surveillance, deliveries and lifeguard operations.</p> <p>For more information about the test flights, see D5.1 Validation plan and D5.4 Validation report from BUBBLES project.</p>
Cessation of anti-diabetic medications by 'Daily 2-Only Meals-and- Exercise' lifestyle modification and remission of Type-2 Diabetes Mellitus
<p>This is the dataset describing details of the patient's age, gender, weight, waist circumference, HBA1C levels and Fasting Insulin levels from the date of enrolment in the study and subsequent changes at monthly intervals. </p>
OMCF questionnaires - Surveying volcanic crises exercises: From open-question questionnaires to a prototype checklist
<p>English / French / Italian / Spanish versions</p> <p>Volcanic crisis exercises are usually run to test response capabilities, communication protocols, and decision-making procedures by those agencies, such as volcano observatories and/or Civil Protection authorities, with responsibilities to cope with scenarios of volcanic unrest with inherent uncertainty. During the last decades, the use of questionnaires has been increased to evaluate people’s knowledge on volcanic hazards and their perception of risk, which could affect their preparedness to respond to emergency measures plans. In this paper, we show the study carried out within the EUROVOLC project in extracting information on the experience gained during volcanic-crisis exercises by the project’s participants and beyond. In particular, we first distributed an open-question questionnaire survey within the EUROVOLC-project community. Based on the results obtained, we developed a more user-friendly online multi-choice questionnaire that we submitted to different volcanological communities within and outside the project. From the answers to the on-line questionnaire, we extracted a prototype checklist for guiding those who design such exercises in the future. Here, we give details on the lessons learnt from this study, in particular about the need to increase training activities, to improve external (between players and general public/media) communication tools, equipment and protocols and to better define decision-makers needs. Our preliminary results confirm that this type of survey is a very useful tool for gathering information on participants' experience and knowledge, and to understand which data and information may be useful when designing exercises for scientists, emergency managers and others involved in a volcanic crisis.</p> <p> </p>
The NGC Catalog, an exercise of data collection for UOC
<p>This project aims to download the NGC object catalog using webcrapping techniques and save it as a CSV file. The accessed URL is</p> <p><a href="https://in-the-sky.org/data/catalogue.php?cat=NGC&const=1&obj1Type=0&sort=0&view=1&page=x">https://in-the-sky.org/data/catalogue.php?cat=NGC&const=1&obj1Type=0&sort=0&view=1&page=x</a></p> <p>with 1 to 79 pages. The downloaded contents for each item compund the catalog are: Name = Name in the catalog type = Object kind mag = Visual magnitude of the object dist = Distance from the earth (when available) constellation = Constellation where to find the object RA = RA coordinates of the object. DEC = DEC Coordinates of the object str_name = Other name from the object. object_image = The image of the object.</p>
Alterations in RNA editing in skeletal muscle following exercise training in individuals with Parkinson's disease
<p>Parkinson’s Disease (PD) is the second most common neurodegenerative disease behind Alzheimer’s Disease, currently affecting more than 10 million people worldwide. The progression of PD results in the loss of function due to neurodegeneration and neuroinflammation. The etiology of PD is multifactorial, including both genetic and environmental origins. We explored changes in RNA editing, specifically editing through the actions of the Adenosine Deaminases Acting on RNA (ADARs), in the progression of PD. Analysis of ADAR editing of skeletal muscle transcriptomes from PD patients and controls, including those that engaged in a rehabilitative exercise training program revealed significant differences in ADAR editing patterns based on age, disease status, and following rehabilitative exercise. Further, deleterious editing events in protein coding regions were identified in multiple genes with known associations to PD pathogenesis. Our findings of differential ADAR editing complement findings of changes in transcriptional network identified by a recent Lavin et al. 2020 (<a href="https://doi.org/10.3389/fphys.2020.00653">https://doi.org/10.3389/fphys.2020.00653)</a> study and offer insights into dynamic ADAR editing changes associated with PD pathogenesis. VCF files were generated using AIDD (Plonski et al., 2020) (<a href="https://doi.org/10.1186/s12859-020-03888-6">https://doi.org/10.1186/s12859-020-03888-6</a>).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.