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1,630 results for “Stepping”
FIGURE 1 in One more step in solving the Chydorus puzzle-a morphological comparison between Chydorus tilhoi Rey & Saint-Jeans, 1969 and Chydorus sphaericus (O.F. Müller, 1776) (Crustacea: Cladocera)
FIGURE 1. Chydorus sphaericus (O.F. Müller, 1776) from Bjørndalsvatn Lake Sveindal-Hornnes, Norway, parthenogenetic female. A, habitus; B, dorsal view; C, ventral view; D, ventral margin of carapace, anterior portion; E, ventral margin of carapace, posterior portion; F, head pores; G, labral keel; H, idem, frontal view; I, antenna I; J, antenna II; K, idem, distal segment of endopodite; L, idem, distal segment of exopodite; M, postabdomen. Scale bars = 0.1 mm.
Data for A weak coupling mechanism for the early steps of the recovery stroke of myosin VI: a free energy simulation and string method analysis
<p>Representative frames, topology for MD simulation with NAMD and GROMACS, data and notebook to reproduce analyses in the paper "A weak coupling mechanism for the early steps of the recovery stroke of myosin VI: a free energy simulation and string method analysis" (Blanc, Houdusse, Cecchini, PLOS Computational Biology 2024).</p>
Blueprint for modelling time effects in a stepped-wedge, cluster-randomised trial
<div><strong># Project title</strong></div> <div>Blueprint for modelling time effects in a stepped-wedge, cluster-randomised trial</div> <div> </div> <div><strong># Brief project overview</strong></div> <div>This is Data coming out of the IPOS-Dem project, a Stepped-wedge Cluster Randomised Trial (SW-CRT). Please make shure to check out the SW-CRT protocol for more details at <a href="https://www.doi.org/10.1111/jan.14953">https://www.doi.org/10.1111/jan.14953</a>. </div> <div> </div> <div>We are currently working on our main study (SW-CRT) analysis. While we remain dedicated to open science, our priority is to ensure the utmost integrity of our ongoing research.</div> <div> </div> <div><strong># Contributors</strong></div> <div>Frank Spichiger, Andrea Koppitz, André Meichtry</div> <div> </div> <div><strong>## Repository overview</strong></div> <div> </div> <div>|-- readme.md</div> <div>|-- documentation</div> <div> |--readme.md</div> <div> |--Datadictionary.html</div> <div>|-- data</div> <div> |--readme.md</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_Qualidem.feather</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_locf_Qualidem.feather</div> <div> |--csv</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_Qualidem.csv</div> <div> |--IPOS-Dem_CH-SW-CRT_PLD_locf_Qualidem.csv</div> <div>|-- analysis</div> <div> |--readme.md</div> <div> |--240229_Analysis_Main.html</div> <div> |--240229_Analysis_Main.qmd</div> <div> |--grateful-refs.bib</div> <div> |--output</div> <div> |--models-REML.csv</div> <div> |--models.csv</div> <div> |--figures</div> <div> |--Conversions for plotting - 1.png</div> <div> |--Define Model 5-1.png</div> <div> |--Defining Model 4-1.png</div> <div> |--m1predictions.eps</div> <div> |--m1predictions.png</div> <div> |--m2predictions.eps</div> <div> |--m2predictions.png</div> <div> |--m3predictions.eps</div> <div> |--m3predictions.png</div> <div> |--m4predictions.eps</div> <div> |--m4predictions.png</div> <div> |--m5predictions.eps</div> <div> |--m5predictions.png</div> <div> |--Model 1 plot-1.png</div> <div> |--Model 1 plot-2.png</div> <div> |--Model 2 plot-1.png</div> <div> |--Model 2 plot-2.png</div> <div> |--Model 3 plot-1.png</div> <div> |--More plots for Model 1-1.png</div> <div> |--More plots for Model 1-2.png</div> <div> |--More plots for Model 4-1.png</div> <div> |--More plots for Model 5-1.png</div> <div> |--Plots for Model 4-1.png</div> <div> |--Plots for Model 5-1.png</div> <div> |--Plots for the other two models-1.png</div> <div> |--Plots for the other two models-2.png</div> <div> </div> <div> </div> <div># Applicable instructions</div> <div>The R markdown files were generated using R 4.2.2 in RStudio 2023.12.1 for MacOS X you can run them using free software but will need to install R, Rstudio and the packages cited at the end of the quarto or the rendered html we used.</div> <div> </div> <div># Additional resources</div> <div>- Main study protocol: <a href="https://www.doi.org/10.1111/jan.14953">https://www.doi.org/10.1111/jan.14953</a></div> <div>- QUALIDEM measure used: <a href="https://doi.org/10.1186/1477-7525-11-91">https://doi.org/10.1186/1477-7525-11-91</a> </div> <div>- Pos-Pal consortium with more information on the measure: <a href="https://www.pos-pal.org">https://www.pos-pal.org</a></div> <div>- IPOS-Dem translation and adaption: <a href="https://www.doi.org/10.1186/s41687-022-00420-7">https://www.doi.org/10.1186/s41687-022-00420-7</a></div> <div>- IPOS-Dem inter-rating reliability: <a href="https://doi.org/10.1371/journal.pone.0286557">https://doi.org/10.1371/journal.pone.0286557 </a></div>
Data: relation between soft tissue energy dissipation and leg stiffness in running at different step frequencies
<p>Data set - paper: <span>Relation between soft tissue energy dissipation and leg stiffness in running at different step frequencies </span></p>
Cumulative Step Size Adaptation for Adaptive SEMO in Integer Space (Code and Dataset)
<p>This is the code and the dataset for our paper published at EMO 2025, <a href="https://emo2025.org/">https://emo2025.org</a></p> <p>Title: "Cumulative Step Size Adaptation for Adaptive SEMO in Integer Space"<br>Abstract: Parameter control involves dynamically adjusting the parameter values of the evolutionary algorithm throughout the optimization process, including parameters like mutation rate and operator selection. <br>Self-adaptation can improve the performance and robustness of the algorithm, however, parameter control mechanisms themselves need to be designed and configured carefully. <br>In this article, we review the cumulative step size adaptation method originally proposed for single-objective optimization over continuous variables and recast it for deployment in multiobjective optimization over unbounded integer space. <br>We contribute a systematic investigation of its hyperparameters which shows that while (1) the very best configurations remain problem-specific, (2) the performance of the algorithm is largely independent of the self-adaptation scheme's parameterization and initial configuration.</p>
Establishing Technical Debt Management - A Five-Step Workshop Approach and an Action Research Study
<div> <div>The additional material for the paper "Establishing Technical Debt Management - A Five-Step Workshop Approach and an Action Research Study" comprises the following documents:</div> <div> </div> <div>* Guideline_to_Use_TD-SAGAT.docx (.pdf)</div> <div> A short guideline on how to use the TD-SAGAT method.</div> <div>* SAGAT_for_TD_from_Goals_to_Queries.pdf</div> <div> Determination of survey questions by following the SAGAT approach of following the hierarchy of goals, subgoals, decisions, requirements, and queries </div> <div>* WIKI page on technical debt.pdf</div> <div> The teams' wiki page on TD management written by the TD manager</div> <div>* AzureTDTickets_Template.pdf</div> <div> Screenshot of the backlog's TD item template as presented in the paper for better readability.</div> <div>* OverviewOfActionCycles_ExtendedTable5.xlsx</div> <div> Table 5 (Overview of actions cycles with workshop topics, TD activities, actions taken, and learning) as an Excel sheet with additional information on actions and learning, which we did not put into the paper</div> <div> </div> <div>* Workshops/workshop_slides/TDMGuide_<x>_Workshop.pptx</div> <div> Slides for each of the six workshops </div> <div>* Workshops/workshop_slides/TDMGuide_0_InitTalk_35Min_EN.pptx</div> <div> The initial one-hour introduction into the TD topic (~40 min. talk + 20 min. for a short discussion) </div> <div> </div> <div>* Workshops/workshop_flipcharts/WS<No.>_<explanation>.jpg</div> <div> Six anonymized flipchart photos to give an impression of the workshops, particularly of the kick-off workshop (other workshops made less use of flipcharts).</div> <div> Translations are directly included in the photos, except for ...</div> <div>* Workshops/workshop_flipcharts/WS1_Topic Cache - Translation.txt</div> <div> Translation of the German topic cache in an extra file, as there were too many topics to translate in the photo</div> <div> </div> <div>* Workshops/Workshop Agendas.pdf</div> <div>Agendas for the kick-off workshop and a general agenda for further workshops, each with a detailed agenda for workshop moderators and a rough agenda for participants.</div> <div> </div> <div>* Workshops/ TD_Energy_Matrix.xlsx</div> <div> Matrix where participants mark their own, the teams, and the companies' (assumed) level of willingness to invest in a TDM versus their (assumed) evaluation of TDM's importance. Used in kickoff workshop</div> <div> </div> <div> </div> <div>* Evaluations/Backlog_Evaluations_and_Figures.xlsx</div> <div>- Backlog data from the 5th workshop (for 6th workshop, we used the PowerBI Visualization instead)</div> <div>- Evaluation of this data comprising </div> <div>- priority calculations based on priority risk and </div> <div>- priority calculations based on priority risk based on ROI</div> <div> </div> <div>* Evaluations/Meetings_Overview_Observations_Evaluation_Figures.xlsx</div> <div>- Overview of all meetings plus information on length, participants, whether a video exists and was used for evaluation</div> <div>- Filled out observation protocols from different meetings</div> <div>- Summation, evaluations, and resulting figures</div> <div> </div> <div>* Evaluations/TD-SAGAT_Survey_and_SurveyResults_Figures.xlsx</div> <div>- Survey questions for TD-SAGAT plus results</div> <div>- Evaluation plus figures</div> <div> </div> <div>* Evaluations/Workshop_Survey_and_SurveyResults_Figures.xlsx</div> <div>- Survey questions for Workshops plus results</div> <div>- Evaluation plus figures</div> <div> </div> <div>* Evaluations/Kickoff_Survey_and_SurveyResults.xlsx</div> <div>- Survey questions for Kickoff Workshops (incl. questions on current TDM process) plus results</div> <div>- Evaluation in Workshop_Survey_and_SurveyResults_Figures.xslx</div> <div> </div> <div> </div> <div>* Visualizations/V<No.>_<adopted|mockup>_<explanation>.png</div> <div>- "adopted" visualizations are similarly presented in the paper in Fig. 2.</div> <div>- "mockup" visualizations were presented during the workshops but not adopted in this form. </div> <div>If a respective "adopted" visualization for this No. exists, the visualisation was replaced by a more suitable one. </div> <div>If no respective "adopted" visualization for this No. exists, the visualisation was discarded.</div> <div> </div> <div> </div> <div>* Visualizations/Dashboard <Manager|Team>.png</div> <div>- shows the dashboard mockups for the manager and the team, respectively. </div> <div>Visualizations presented in the dashboards that were not adopted by the team were removed or replaced by the improved visualizations in the final dashboard versions.</div> <div> </div> <div> </div> <div> </div> <div> </div> </div>
Shortening the runtime using larger time steps for the simulation of marine ecosystem models
<p><strong>Abstract:</strong></p> <p>The reduction of computational costs for marine ecosystem models is important for the investigation and detection of the relevant biogeochemical processes because such models are computationally expensive. In order to lower these computational costs by means of larger time steps we investigated the accuracy of steady annual cycles (i.e., an annual periodic solution) calculated with different time steps. We compared the accuracy for a hierarchy of biogeochemical models showing an increasing complexity and computed the steady annual cycles with offline simulations that are based on the transport matrix approach. For each of these biogeochemical models, we obtained practically the same solution even though larger time steps. This indicates that larger time steps shortened the runtime with an acceptable loss of accuracy.</p> <p><strong>Content:</strong></p> <ul> <li>SQLite database including the obtained results</li> <li>Tracer concentrations obtained with different time steps for all used models and parameter vectors</li> </ul>
Supplemental Material S2 for "Development of a highly sensitive luciferase-based reporter system to study two-step protein secretion in cyanobacteria"
<p>Table S1. Densitometric analysis of NLuc immunoblot in Fig. 2A.</p> <p>Figure S1. PilA1 quantification in the sheared pili fractions of WT, Δ<em>hfq</em> and Δ<em>pilT1</em> <em>Synechocystis</em> mutants.</p>
Shapiro step measurements in (BiSb)2Te3 junctions
<p>Dataset and analysis code accompanying the manuscript "Fractional AC Josephson effect in a topological insulator proximitized by a self-formed superconductor," available at <a href="https://arxiv.org/abs/2110.01039">https://arxiv.org/abs/2110.01039</a>. This dataset includes measurements of Josephson junction devices with (BiSb)2Te3 weak links, whose Pd-Te superconducting leads are self-formed through deposition of Pd on the (BiSb)2Te3. In junctions with the prefix "Al", an Al layer is added to the superconducting leads following Pd deposition. The dataset includes transport measurements of Shapiro steps under radiofrequency excitation as well as DC electronic transport measurements. Analysis code is provided in the accompanying iPython notebooks.</p>
Experiment Results for the Paper: "Augur: A Step Towards Realistic Drift Detection in Production ML Systems"
<p>Contains the experiment results for the paper "Augur: A Step Towards Realistic Drift Detection in Production ML Systems". More specifically, it contains the inputs and outputs for the different experiments to run using the Predictor tool. More information about the base dataset used in the README.md file. We are going through the code marking and internal approvals process that is required to make the code open source. The process will be complete in time for camera-ready submission if the paper is accepted.</p>
Energy expenditure does not explain step length-width choices during walking
Healthy young adults have a most preferred walking speed, step length, and step width that are close to energetically optimal. However, people can choose to walk with a multitude of different step lengths and widths, which can vary in both energy expenditure and preference. Here we further investigate step length-width preferences and their relationship to energy expenditure. In line with a growing body of research, we hypothesized that people's preferred stepping patterns would not be fully explained by metabolic energy expenditure. To test this hypothesis we used a two-alternative forced-choice paradigm. Fifteen participants walked on an oversized treadmill. Each trial participants experienced two stepping patterns and then chose the pattern they preferred. Over time, we adapted the choices such that there was 50% chance of choosing one pattern over another (equally preferred). If people's preferences are based solely on metabolic energy expenditure, then these equally preferred stepping patterns should have equal energy expenditure. We found that energy expenditure differed across equally preferred step length-width patterns (p < 0.001). On average, longer steps with higher energy expenditures were preferred over shorter and wider steps with lower energy expenditures (p < 0.001). We also asked participants to rank a set of shorter, wider, and longer steps from most preferred to least preferred, and from most energy expended to least energy expended. Only 7/15 participants had the same rankings for their preferences and perceived energy expenditure. Our results suggest that energy expenditure is not the only factor influencing a person's conscious gait choices. --
Supplementary material 1 from: Chua PYS, Carøe C, Crampton-Platt A, Reyes-Avila CS, Jones G, Streicker DG, Bohmann K (2022) A two-step metagenomics approach for the identification and mitochondrial DNA contig assembly of vertebrate prey from the blood meals of common vampire bats (Desmodus rotundus). Metabarcoding and Metagenomics 6: e78756. https://doi.org/10.3897/mbmg.6.78756
A two-step metagenomics approach for prey identification from the blood meals of common vampire bats (Desmodus rotundus)
Data from: Intrapopulation differences in polar bear movement and step selection patterns
<p><strong>Background</strong>: The spatial ecology of individuals often varies within a population or species. Identifying how individuals in different classes interact with their environment can lead to a better understanding of population responses to human activities and environmental change and improve population estimates. Most inferences about polar bear (<em>Ursus maritimus</em>) spatial ecology are based on data from adult females due to morphological constraints on applying satellite radio collars to other classes of bears. Recent studies, however, have provided limited movement data for adult males and sub-adults of both sexes using ear-mounted and glue-on tags. We evaluated class-specific movements and step selection patterns for polar bears in the Chukchi Sea subpopulation during spring.</p> <p><strong>Methods</strong>: We developed hierarchical Bayesian models to evaluate polar bear movement (i.e., step length and directional persistence) and step selection at the scale of 4-day step lengths. We assessed differences in movement and step selection parameters among the three classes of polar bears (i.e., adult males, sub-adults, and adult females without cubs-of-the-year).</p> <p><strong>Results</strong>: Adult males had larger step lengths and less directed movements than adult females. Sub-adult movement parameters did not differ from the other classes but point estimates were most similar to adult females. We did not detect differences among polar bear classes in step selection parameters and parameter estimates were consistent with previous studies.</p> <p><strong>Conclusions</strong>: Our findings support the use of estimated step selection patterns from adult females as a proxy for other classes of polar bears during spring. Conversely, movement analyses indicated that using data from adult females as a proxy for the movements of adult males is likely inappropriate. We recommend that researchers consider whether it is valid to extend inference derived from adult female movements to other classes, based on the questions being asked and the spatial and temporal scope of the data. Because our data were specific to spring, these findings highlight the need to evaluate differences in movement and step selection during other periods of the year, for which data from ear-mounted and glue-on tags are currently lacking.</p>
Simulation input files and analysis scripts for "Small ionic radii limit time step in Martini 3molecular dynamics simulations"
<p>Simulation input files and analysis scripts for "Small ionic radii limit time step in Martini 3molecular dynamics simulations".</p> <p>See <a href="https://aip.scitation.org/doi/10.1063/5.0095523">https://aip.scitation.org/doi/10.1063/5.0095523</a> for details.</p> <p>NOTE: some combinations of settings result in simulations that crash!<br> </p>
Data for 'Storm Surge Modeling as an Application of Local Time-stepping in MPAS-Ocean'
<p>Data for 'Storm Surge Modeling as an Application of Local Time-stepping in MPAS-Ocean', submitted to the Journal for Advances in Modeling Earth Systems (JAMES).</p> <p>The repository consists of three main parts:</p> <ol> <li>The `run` directory contains a pre-built run directory as an example.</li> <li>The `scripts` directory contains the scripts that were used to generate important plots.</li> <li>The `data` directory contains model output from performance and accuracy experiments.</li> </ol>
The NSSE protocol, Nine-Square Step and Nine-Square Dance.
<p>The NSSE protocol, Nine-Square Step and Nine-Square Dance.</p>
Supporting dataset for drafted article Evaluation of Multi-step Forecasting Models
<p>Supporting dataset for drafted article Evaluation of Multi-step Forecasting Models</p>
Detailed steps to simulate the mirror adjustments
<p>This is part of work for the preprint entitled "1887 Michelson-Morley experiment: a new analysis with computer assistance".</p> <p>In this dataset, the detailed simulation works for the mirror adjustments are presented in the Excel forms and the CAD drawings grouped under the same filenames. The sequential steps for the mentioned trials are described in Section "Pre-description" of the mentioned preprint.</p>
Data for: Effects of vestibular stimulation on gait stability when walking at different step widths
<p><strong>Read me - Effects of vestibular stimulation on gait stability when walking at different step widths</strong></p> <p> </p> <p>In this folder you will find the data (folders named Data) and analysis (folder named software) belonging to the manuscript “Effects of vestibular stimulation on gait stability when walking at different step widths”(Magnani et al. 2021). To re-run the analysis, create a folder, and put the file subject_numbers.m, as well as the contents of data.zip and software.zip in that top level folder. Next, navigate to the “software” folder, and run scripts C_main_all_outcomes_paper2 and E_main_stats_paper2.</p> <p> </p> <p>The following subject numbers were included in the analysis: 4 5 6 8 12 14 16 17 18 19 20 21 22 23 (see also subject_numbers.m, which includes explanation of why some subjects were excluded). We had problems with dust due to reconstruction in nearby labs, so some of the kinematic data was simply not useable.</p> <p> </p> <p>The scripts were written for data from 12 conditions, being analyzed the conditions numbered 1 2 5 6 7 8 :</p> <ol> <li>Steady state walking under vestibular stimulation (control, EVS)</li> <li>Steady state walking (control, no-EVS)</li> <li>Stabilization frame walking under vestibular stimulation (frame, EVS)</li> <li>Stabilization frame walking (frame, no-EVS)</li> <li>Narrow-base walking under vestibular stimulation (narrow, EVS)</li> <li>Narrow-base walking (narrow, no-EVS)</li> <li>Wide-base walking under vestibular stimulation (wide, EVS)</li> <li>Wide-base walking (wide, no-EVS)</li> <li>Stabilization frame ML walking under vestibular stimulation looking to the left side (left_frame, EVS)</li> <li>Stabilization frame ML (left_frame, no-EVS)</li> <li>Stabilization frame AP/ML walking under vestibular stimulation looking to the left side (left_frame_AP, EVS)</li> <li>Stabilization frame AP/ML walking looking to the left side (left_frame_AP, no- EVS)</li> </ol> <p> </p> <p>Below we describe the most important contents:</p> <table> <tbody> <tr> <td> <p><strong>Folders</strong></p> </td> <td> <p><strong>Content</strong></p> </td> </tr> <tr> <td> <p><em>Data.zip</em></p> </td> <td> <p>Data of all participants. Each subject has a separate subfolder.</p> <p> </p> <p>These subfolders contain 1 excel files (used to match the conditions with the corresponding data files), as well as the pointer file corresponding to the pointer used during the measurement, needed to digitize the bony landmarks and the muscles names.</p> <p>Each subject subfolder has the following subfolders:</p> </td> </tr> <tr> <td> <p>OPTO</p> </td> <td> <p>Optotrak and force plate files</p> <ul> <li>.ndf (motion capture data)</li> <li>.afp (force plate data)</li> <li>.hs calculated heelstrikes, as done in B_main_heelstrikes.m</li> </ul> </td> </tr> <tr> <td> <p>EMG</p> </td> <td> <p>EMG data stored at .mat and .bin files (not used in current manuscript)</p> </td> </tr> <tr> <td> <p>Software</p> </td> <td> <p><em>=VU 3D model=</em></p> </td> <td> <p>Contains general functions for the analysis of our kinematic and force plate data.</p> </td> </tr> <tr> <td> <p>Functions</p> </td> <td> <p>Functions written specifically for the analysis of this dataset, including some unused functions</p> </td> </tr> <tr> <td> <p>Local Dynamic Stability</p> </td> <td> <p>Functions written specifically for the local dynamic stability.</p> <p>(Bruijn 2017).</p> </td> </tr> <tr> <td> <p>Results</p> </td> <td> <p>Contains storage of some intermediate results.</p> <p>The figures folder includes all</p> <p> </p> </td> </tr> <tr> <td> <p>A_main_check_kin</p> </td> <td> <p>Scripy used to check the kinematics</p> </td> </tr> <tr> <td> <p>B_main_heelstrikes</p> </td> <td> <p>Script to identify gait events were identified from center of pressure data (Roerdink et al. 2008), results are stored in .hs files in subject data folder</p> </td> </tr> <tr> <td> <p>C_main_all_outcomes_paper2</p> </td> <td> <p>Script to calculate all outcomes</p> </td> </tr> <tr> <td> <p>E_main_stats_paper2</p> </td> <td> <p>Scripts of the statistical analysis, which also creates the figure files as seen in the manuscript.</p> </td> </tr> <tr> <td> <p>figure1.eps</p> </td> <td> <p>Mean values of stride width and stride width variability (represents figure 2 from paper)</p> </td> </tr> <tr> <td> <p>figure2.eps</p> </td> <td> <p>Mean values of step time and step time variability (represents figure 3 from paper)</p> </td> </tr> <tr> <td> <p>figure3.eps</p> </td> <td> <p>Mean values and standard deviation of local divergence exponent and center of mass variability (represents figure 4 from paper)</p> </td> </tr> <tr> <td> <p>figure4.eps</p> </td> <td> <p>Mean values of R<sup>2</sup> (percentage of explained variance in foot placement) and the residual variance in foot placement (represents figure 5 from paper)</p> </td> </tr> <tr> <td> <p>ANOVA.csv</p> </td> <td> <p>ANOVA results for each variable</p> </td> </tr> <tr> <td> <p>Posthocs_condition.csv</p> </td> <td> <p>Posthoc results of the condition effect</p> </td> </tr> <tr> <td> <p>Posthocs_interaction.csv</p> </td> <td> <p>Posthoc results of the interaction effect</p> </td> </tr> <tr> <td> <p>Settings_rina.xls</p> </td> <td> <p>The setting of pointer, markers, and segments. </p> </td> </tr> <tr> <td> <p>Stimulationfig.pdf</p> </td> <td> <p>Figure of the electrical stimulation signal (corresponds to figure 1 from paper)</p> </td> </tr> <tr> <td> <p>Svs_5mA_0-25Hz_2000Hz_120s.txt</p> </td> <td> <p>File containing the random electrical vestibular stimulation from the zero-mean low-pass filtered (25 Hz cutoff, zero lag, fourth-order Butterworth) white noise, the peak amplitude of 5mA, root mean square (RMS) of ~ 1.2 mA, range frequency between 0 to 25Hz, and intensity of 2000Hz for 120 seconds of duration.</p> </td> </tr> <tr> <td> <p>Svs_5mA_0-25Hz_2000Hz_480s.txt</p> </td> <td> <p>File containing the electrical vestibular stimulation from the zero-mean low-pass filtered (25 Hz cutoff, zero lag, fourth-order Butterworth) white noise, the peak amplitude of 5mA, root mean square (RMS) of ~ 1.2 mA, range frequency between 0 to 25Hz, and intensity of 2000Hz for 480 seconds of duration.</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p>Bruijn SM (2017) Local Dynamic Stability. https://zenodo.org/record/1181937#.Y1Fj7i0w354</p> <p>Magnani RM, van Dieen JH, Bruijn SM (2021) Effects of vestibular stimulation on gait stability when walking at different step widths. bioRxiv 459650:. https://doi.org/https://doi.org/10.1101/2021.09.09.459650</p> <p>Roerdink M, Coolen BH, Clairbois BHE, et al (2008) Online gait event detection using a large force platform embedded in a treadmill. J Biomech 41:2628–32. https://doi.org/10.1016/j.jbiomech.2008.06.023</p> <p> </p>
Gate with Steps .::RAWscan::.
Gate with Steps .::RAWscan::. Gate with Steps in Rocchette (13th century). Rocchette together with Rocchettine are two fortified settlements dating back to the XIII century and located in the municipality of Torri in Sabina (RI) Italy 3D scan by photogrammetry Source: Objaverse 1.0 / Sketchfab
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.