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1,899 results for “Running”
Building No.7 (RCMP Police Dog Kennel and Run)
This building was constructed by joining two smaller, existing buildings together and was used by the Police in their dog-breeding program during the 1940s through to the early 60s. The Kennels are not normally accessible to the public as plywood is left over the doors to the building during the operating season. The Dog Run was constructed as a fenced enclosure using driftwood logs as posts with wire mesh fencing between the posts. Much of the fencing is lying on the ground at the present time although the posts still define the Dog Run. Neither the Kennel nor the Run are currently used. They have been stabilized so that they remain as features within the landscape of the Park. Source: Objaverse 1.0 / Sketchfab
Models (atmospheric and atomic) for the P-CORONA code together with some sample runs.
<p>The dataset comprises some sample example runs with all necessary input parameters and corresponding outputs expected from P-CORONA, along with a few atomic and atmospheric models. Description of the files included is given in the README.txt file.</p> <p>The P-CORONA code can be obtained at <a href="https://gitlab.com/polmag/P-CORONA">https://gitlab.com/polmag/P-CORONA</a> and its documentation at <a href="https://polmag.gitlab.io/P-CORONA/">https://polmag.gitlab.io/P-CORONA/</a></p> <p>The version of P-CORONA used to generate this dataset (which corresponds to the commit #85d7552 in <a href="https://gitlab.com/polmag/P-CORONA">https://gitlab.com/polmag/P-CORONA</a>) can be found at: <a href="https://doi.org/10.5281/zenodo.15195460">https://doi.org/10.5281/zenodo.15195460</a></p>
TensorBoard runs for reinforcement learning on automated conjectured bounds on Laplacian spectral radius of graphs
<p>The zip archive contains separate folders with TensorBoard event files for the runs of our reinforcement learning implementation on the conjectured upper bounds for the Laplacian spectral radius, which are listed in Appendix B of the forthcoming paper: S. Al-Yakoob, M. Ghebleh, A. Kanso, D. Stevanović, Reinforcement learning for graph theory, I. Reimplementation of Wagner’s approach, Art Discrete Appl. Math. (2024).</p> <p>To view the contained graphs and the evolution of rewards, unzip the archive in a folder of your choice, and run in the terminal the command "tensorboard --logdir runs" from the parent folder of the unzipped "runs" folder.</p>
Examples for running TraceGroomer: format and normalise your labeled metabolomics data for DIMet analysis
<p>Examples to test our tool <a href="https://github.com/cbib/TraceGroomer">TraceGroomer</a>, to prepare your files for DIMet (Differential analysis of Isotope-labeled Metabolomics data).</p> <p>Each example represents one type of input supported by TraceGroomer. Please download the entire .zip and find inside the example that best suits your case.</p> <p>The new version v2 contains four types of input:</p> <ol> <li>IsoCor .tsv generated file: <em>example-isocor_data</em></li> <li>The rule of "three .tsv files" , i.e. sampleMetadata, variableMetadata and dataMatrix: <em>example-ruletsv_data</em></li> <li>custom or generic .xlsx file: <em>example-sheet_data</em></li> <li>VIB MEC .xlsx file: <em>example-vib_data</em></li> </ol> <p>Visit the <a href="https://github.com/cbib/TraceGroomer/wiki">TraceGroomer Wiki page</a> for further information and how to run the tool on the provided examples. </p> <p><strong>For users of the Galaxy </strong>version of Tracegroomer: please only use the 'data/' folder (ignore the 'groom_files/' folder)</p>
Graded Incremental Test Data (Cycling, Running, Kayaking, Rowing): an open access dataset
<p><strong>Section 1: Introduction</strong></p> <p> </p> <p>Brief overview of dataset contents:</p> <ul> <li>Current database contains anonymised data collected during exercise testing services performed on male and female participants (cycling, rowing, kayaking and running) provided by the Human Performance Laboratory, School of Medicine, Trinity College Dublin, Dublin 2, Ireland. </li> <li>835 graded incremental exercise test files (285 cycling, 266 rowing / kayaking, 284 running)</li> <li>Description file with each row representing a test file - COLUMNS: file name (AXXX), sport (cycling, running, rowing or kayaking)</li> <li>Anthropometric data of participants by sport (age, gender, height, body mass, BMI, skinfold thickness,% body fat, lean body mass and haematological data; namely, haemoglobin concentration (Hb), haematocrit (Hct), red blood cell (RBC) count and white blood cell (WBC) count )</li> <li>Test data (HR, VO<sub>2</sub> and lactate data) at rest and across a range of exercise intensities</li> <li>Derived physiological indices quantifying each individual’s endurance profile</li> </ul> <p> </p> <p>Following a request from athletes seeking assessment by phone or e-mail the test protocol, risks, benefits and test and medical requirements, were explained verbally or by return e-mail. Subsequently, an appointment for an exercise assessment was arranged following the regulatory reflection period (7 days). Following this regulatory period each participant’s verbal consent was obtained pre-test, for participants under 18 years of age parent / guardian consent was obtained in writing. Ethics approval was obtained from the Faculty of Health Sciences ethics committee and all testing procedures were performed in compliance with Declaration of Helsinki guidelines.</p> <p> </p> <p>All consenting participants were required to attend the laboratory on one occasion in a rested, carbohydrate loaded and well-hydrated state, and for male participants’ clean shaven in the facial region. All participants underwent a pre-test medical examination, including assessment of resting blood pressure, pulmonary function testing and haematological (Coulter Counter Act Diff, Beckmann Coulter, CA,US) review performed by a qualified medical doctor prior to exercise testing. Any person presenting with any cardiac abnormalities, respiratory difficulties, symptoms of cold or influenza, musculoskeletal injury that could impair performance, diabetes, hypertension, metabolic disorders, or any other contra-indicatory symptoms were excluded. In addition, participants completed a medical questionnaire detailing training history, previous personal and family health abnormalities, recent illness or injury, menstrual status for female participants, as well as details of recent travel and current vaccination status, and current medications, supplements and allergies. Barefoot height in metre (Holtain, Crymych, UK), body mass (counter balanced scales) in kilogram (Seca, Hamburg, Germany) and skinfold thickness in millimetre using a Harpenden skinfold caliper (Bath International, West Sussex, UK) were recorded pre-exercise.</p> <p> </p> <p><strong>Section 2: Testing protocols </strong></p> <p> </p> <p><strong>2.1: Cycling</strong></p> <p> </p> <p>A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on an electromagnetically braked cycle ergometer (Lode Excalibur Sport, Groningen, The Netherlands). Participants initially identified a cycling position in which they were most comfortable by adjusting saddle height, saddle fore-aft position relative to the crank axis, saddle to handlebar distance and handlebar height. Participant’s feet were secured to the ergometer using their own cycling shoes with cleats and accompanying pedals. The protocol commenced with a 15-min warm-up at a workload of 120 Watt (W), followed by a 10-min rest. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a workload of 100 or 120 W for female and male participants, respectively, and subsequently increasing by a 20, 30 or 40 W incremental increase every 3-min depending on gender and current competition category. During assessment participants maintained a constant self-selected cadence chosen during their warm-up (permitted window was 5 rev.min<sup>−1 </sup>within a permitted absolute range of 75 to 95 rev.min<sup>−1</sup>) and the test was terminated when a participant was no longer able to maintain a constant cadence.</p> <p> </p> <p>Heart rate (HR) data were recorded continuously by radio-telemetry using a Cosmed HR monitor (Cosmed, Rome, Italy). During the test, blood samples were collected from the middle finger of the right hand at the end of the second minute of each 3-min interval. The fingertip was cleaned to remove any sweat or blood and lanced using a long point sterile lancet (Braun, Melsungen, Germany). The blood sample was collected into a heparinised capillary tube (Brand, Wertheim, Germany) by holding the tube horizontal to the droplet and allowing transfer by capillary action. Subsequently, a 25μL aliquot of whole blood was drawn from the capillary tube using a YSI syringepet (YSI, OH, USA) and added into the chamber of a YSI 1500 Sport lactate analyser<strong> </strong>(YSI, OH, USA) for determination of non-lysed [Lac] in mmol.L<sup>−1</sup>. The lactate analyser was calibrated to the manufacturer’s requirements (± 0.05 mmol.L<sup>−1</sup>) before each test using a standard solution (YSI, OH, USA) of known concentration (5 mmol.L<sup>−1</sup>) and analyser linearity was confirmed using either a 15 or 30 mmol.L<sup>-1</sup> standard solution (YSI, OH, USA).</p> <p> </p> <p>Gas exchange variables including respiration rate (Rf in breaths.min<sup>-1</sup>), minute ventilation (VE in L.min<sup>-1</sup>), oxygen consumption (VO<sub>2 </sub>in L.min<sup>-1</sup> and in mL.kg<sup>-1</sup>.min<sup>-1</sup>) and carbon dioxide production (VCO<sub>2 </sub>in L.min<sup>-1</sup>), were measured on a breath-by-breath basis throughout the test, using a cardiopulmonary exercise testing unit (CPET) and an associated software package (Cosmed<strong>,</strong> Rome, Italy). Participants wore a face mask (Hans Rudolf, KA, USA) which was connected to the CPET unit. The metabolic unit was calibrated prior to each test using ambient air and an alpha certified gas mixture containing 16% O<sub>2</sub>, 5% CO<sub>2</sub> and 79% N<sub>2</sub> (Cosmed, Rome, Italy). Volume calibration was performed using a 3L gas calibration syringe (Cosmed, Rome, Italy). Barometric pressure recorded by the CPET was confirmed by recording barometric pressure using a laboratory grade barometer.</p> <p> </p> <p>Following testing mean HR and mean VO<sub>2</sub> data at rest and during each exercise increment were computed and tabulated over the final minute of each 3-min interval. A graphical plot of [Lac], mean VO<sub>2</sub> and mean HR versus cycling workload was constructed and analysed to quantify physiological endurance indices, see Data Analysis section. Data for VO<sub>2</sub> peak in L.min<sup>-1</sup> (absolute) and in mL.kg<sup>-1</sup>.min<sup>-1</sup> (relative) and VE peak in L.min<sup>-1</sup> were reported as the peak data recorded over any 10 consecutive breaths recorded during the last minute of the final exercise increment.</p> <p> </p> <p><strong>2.2: Running protocol</strong></p> <p> </p> <p>A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a motorised treadmill (Powerjog, Birmingham, UK). The running protocol, performed at a gradient of 0%, commenced with a 15-min warm-up at a velocity (km.h<sup>-1</sup>) which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity. Subsequently, the warm-up was followed by a 10 minute rest / dynamic stretching phase. From a safety perspective during all running GxT participants wore a suspended lightweight safety harness to minimise any potential falls risk. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal running velocity which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity, and subsequently increased by ≥ 1 km.h<sup>-1</sup> every 3-min depending on gender and current competition category. The test was terminated when a participant was no longer able to maintain the imposed treadmill.</p> <p> </p> <p>Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.</p> <p> </p> <p><strong>2.3: Rowing / kayaking protocol</strong></p> <p> </p> <p>A discontinuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a Concept 2C rowing ergometer (Concept, VA, US) in rowers or a Dansprint kayak ergometer (Dansprint, Hvidovre, Denmark) in flat-water kayakers. The protocol commenced with a 15-min low-intensity warm-up at a workload (W) dependent on gender, sport and competition category, followed by a 10-min rest. For rowing the flywheel damping (120, 125 or 130W) was set dependent on gender and competition category. For kayaking the bungee cord tension was adjusted by individual participants to suit their requirements. A discontinuous protocol of 3-min exercise at a targeted load followed by a 1-min rest phase to facilitate stationary earlobe capillary blood sample collection and resetting of ergometer display (Dansprint ergometer) was used. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal load 80 to 120 W for rowing, 50 to 90 W for kayaking and subsequently increased by 20,30 or 40 W every 3-min depending on gender, sport and current competition category. The test was terminated when a participant was no longer able to maintain the targeted workload. </p> <p>Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.</p> <p> </p> <p><strong>3.1: Data analysis</strong></p> <p> </p> <p>Constructed graphical plots (HR, VO<sub>2</sub> and [Lac] versus load / velocity) were analysed to quantify the following; load / velocity at T<sub>Lac</sub>, HR at T<sub>Lac</sub>, [Lac] at T<sub>Lac</sub>, % of VO<sub>2</sub> peak at T<sub>Lac</sub>, % of HRmax at T<sub>Lac</sub>, load / velocity and HR at a nominal [Lac] of 2 mmol.L<sup>-1</sup>, load / velocity, VO<sub>2</sub> and [Lac} at a nominal HR of 160 beats.min<sup>-1</sup>. Load at T<sub>Lac</sub> was determined using segmental regression analysis. Two linear segments were plotted that minimised the squared sum of the residuals between the plotted points and best fit lines. The intersection of these the two linear segments was defined as the relevant breakpoint or threshold, (Raleigh <em>et al. </em>2018<em>. Int J Exerc Sci</em>, <strong>11</strong>, 391-403.</p> <p> </p> <p><strong>4.1: Terms of Use</strong></p> <p> </p> <p>The attached database is provided as a research or educational asset / tool for coach, athlete and exercise science / exercise medicine education and usage only.</p>
The data for "Search for gravitational-lensing signatures in the full third observing run of the LIGO–Virgo network"
<p>This material is part of several data products associated with the publication "Search for gravitational-lensing signatures in the full third observing run of the LIGO–Virgo network" from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://arxiv.org/abs/2304.08393">arXiv</a>) and the related material linked from this page.</p><p><strong>Data release</strong></p><p>This data release contains plotting scripts, jupyter notebooks and datasets for the figures / tables in the aforementioned paper. Each script/notebook have detailed instructions and comments.</p><p><strong>How to download all files from this page</strong></p><p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p><p>pip install zenodo_get zenodo-get RECORD_ID_OR_DOI</p><p>where the record ID for the most recent version of this page is v5 and IDs for other versions can be found in the Versions section at the side of this page.</p>
Baseline model runs along with detailed result files presented in "pepMTL"
<p>Within Article "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model", there exists a collection of executable files and detailed records of the operation results pertaining to the pepMTL model along with several benchmark models in the context of Retention Time (RT), Collision Cross Section (CCS), and Tandem Mass Spectrometry (MS/MS) methodologies. This compendium encompasses the source code for both the benchmark models and the pepMTL model itself—altered as necessary to accommodate the benchmark datasets—the specific datasets designed to execute these models effectively, the raw output files resulting from three parallel runs of each model, and corresponding log files meticulously documenting the execution history. Readers can garner extensive raw data concerning the direct comparisons between the pepMTL model and the array of benchmark models across the RT, CCS, and MS/MS domains as presented in the article. Furthermore, they have the ability to retrain these benchmark models on the designated datasets using the optimized code provided by the authors.<br>这是文章 "pepMTL: a synchronous multi-properties predictor for peptides enabled by multi-task framework and pre-trained protein language model"中,pepMTL模型以及RT、CCS、MS/MS方面各个基准模型的运行文件以及详细运行结果的记录文件。该文件中包含了各个基准模型以及pepMTL模型运行的代码(有必要的话会经过作者的修改以适应基准数据集)、适应模型运行的基准数据集、模型平行运行三次后产生的原始结果文件以及运行日志记录文件。读者可以从这些数据中获取文章中关于pepMTL在RT、CCS、MS/MS方面与各个基准模型进行对比的详细原始信息,并可以通过运行作者优化后的代码将这些基准模型在基准数据集上进行模型训练。<br><br></p> <p> </p>
NetCDF input files to run Ichthyop examples
<p>This dataset contains the NetCDF input files to run the Ichthyop model. These files have been removed from the Ichthyop Git repository to save space.</p>
Automatic message sequence chart creation from simulation run of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm
<p><span>These videos show the creation of two message sequence charts from simulation runs of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm using the CPN tool with three constituting processes. </span></p> <p><strong><span>Message Sequence Chart of <span> </span>Parametric Model With 3 Processes via Automatic Simulation Run_SuppInfo.mp4</span></strong><span>: This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.0. The number of constituting processes is parametric in the model and was set to three. The video was generated using the authors' updated CPN tool extension server. The automatic simulation run of the model has been used to create this video. The CPN tool randomly selects the enabled transition at each step in an automatic simulation run.</span></p> <p><strong><span>Message Sequence Chart of Parametric Model With 3 Processes via Step-By-Step Simulation Run_SuppInfo.mp4:</span></strong><span> This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm in a step-by-step simulation run with our updated extension server of the CPN tools version 4.0.0. The number of constituting processes is parametric in the model and was set to three. We manually fired our selected enabled transition of the model to create this video. </span></p>
Plasticizer Content by TGA - Isothermal TGA runs at 250 °C and dynamic TGA runs on the residue of isothermal runs.
<p>Datasets obtained for the Plasticizer Content by TGA analysis, from the paper "An Integrated Characterization Strategy on Board for Recycling of poly(vinyl butyral) (PVB) from Laminated Glass Wastes"</p>
Magic running and standing wave optical traps for Rydberg atoms - Data and code for analysis
<p>Data, theory calculation and plotting scripts for the publication titled "Magic running and standing wave optical traps for Rydberg atoms" (<a href="https://arxiv.org/abs/2410.20901" target="_blank" rel="noopener">arXiv:2410.20901</a>).</p> <p> </p> <p><strong>File legend</strong></p> <ul> <li> <code>data_FIGx_yyy.mat</code> contains the calculated or measured data used in Figure x</li> <li> <code>calc_FIGx_yyy.py</code> is the script to calculate the theoretical data used in Figure x</li> <li> <code>plot_FIGx_yy.py</code> is the script to create the Figure x of the paper</li> <li> <code>simulation_class.py</code> is a class with theory functions</li> <li> <code>paperstyle.mplstyle</code> is a matplotlib style file</li> <li> <code>requirements.txt</code> lists all the required python packages</li> </ul> <p> </p> <p><strong>Abstract</strong></p> <p>Magic trapping of ground and Rydberg states, which equalizes the AC Stark shifts of these two levels, enables increased ground-to-Rydberg state coherence times. We measure via photon storage and retrieval how the ground-to-Rydberg state coherence depends on trap wavelength for two different traps and find different optimal wavelengths for a 1D optical lattice trap and a running wave optical dipole trap. Comparison to theory reveals that this is caused by the Rydberg electron sampling different potential landscapes. The observed difference increases for higher principal quantum numbers, where the extent of the Rydberg electron wave function becomes larger than the optical lattice period. Our analysis shows that optimal magic trapping conditions depend on the trap geometry, in particular for optical lattices and tweezers.</p> <p> </p> <p><strong>Theory calculation</strong></p> <p>We implemented the potential arising from the Hamiltonians described in the paper. The functions are shared here in the python class <code>simulation_class.py</code>. This class is used in the calculation scripts named <code>calc_FIGx_yyy.py</code> and saves the data as <code>data_FIGx_yyy.mat</code> for the respective Figure x.</p> <p>In case of questions to the code or calculations, please contact Chris Nill or Lukas Ahlheit.</p> <p> </p> <p><strong>Experimental data</strong></p> <p>The experimental data published here are photon storage and retrieval traces of 780 nm probe photons as function of storage duration. We recorded photon traces for different trap laser detunings and Rydberg states.</p> <p>In case of questions to the data, please contact Lukas Ahlheit or Sebastian Hofferberth.</p> <p> </p> <p><strong>Inkscape modification to specific figures</strong></p> <ul> <li>Figure 1: The plotted data is joined in Inkscape with schematic drawings</li> <li>Figure 2: The plot created by the python file is edited in Inkscape for readability</li> <li>Figure 5: We add two schematics into the figure created by the python file</li> </ul>
How to Keep Your AC Running Smoothly
<p>During hot summer months, there's nothing more frustrating than an air conditioner that doesn’t perform as expected. Whether it's struggling to cool your home or inexplicably giving up, these issues can be frustrating to deal with. The good news is that proper maintenance can often help you avoid these issues, and this guide will cover all of the steps you should take to keep your AC unit in top shape.</p> <h3><strong>Step #1 - Clean or Replace Air Filters Regularly</strong></h3> <p>Your air conditioner's filter plays a crucial role in its performance and efficiency. A dirty filter can block airflow, reducing the system's ability to cool your home and causing it to work harder than necessary. Over time, this strain can lead to higher energy bills and even system failure. To avoid these issues, aim to check your air filter every month, especially during periods of heavy use.</p> <p>If your filter looks dirty or clogged, it’s time for a new one. Many filters are simple to replace and can be found at your local hardware store. Keeping a clean filter will not only help your AC system run efficiently but it will also improve indoor air quality.</p> <h3><strong>Step #2 - Check and Clear Condensate Drain Line</strong></h3> <p>The condensate drain line is another vital component of your AC system. It removes the moisture your air conditioner extracts from the air. If this line becomes clogged, it can lead to water damage and increased humidity levels in your home. It's a good idea to check this line regularly, looking for any visible blockages.</p> <p>To do this, you'll need to locate the drain line, which is usually found outdoors near the condenser unit. Slowly pour a cup of water through the access point to see if it flows out freely. If it seems clogged, using a wet/dry vacuum to clear blockages can be a quick fix.</p> <h3><strong>Step #3 - Proper Thermostat Calibration</strong></h3> <p>Your thermostat is essentially the command center of your cooling system. If it's not calibrated correctly, it could cause your AC to work overtime without delivering the desired results. This can increase energy costs and decrease the lifespan of your unit. </p> <p>Checking the accuracy of your thermostat is simple to do. You can place a small thermometer next to it and see if the readings match. If the thermostat is off by more than a few degrees, it might need recalibrating, which can often be managed through the thermostat’s settings menu.</p> <h3><strong>Step #4 - Seal Gaps in Window and Door Frames</strong></h3> <p>Keeping your home cool is not only about the AC unit itself but also about maintaining an efficient environment. Drafts and leaks around windows and doors can let cool air escape, forcing your system to work much harder than necessary. </p> <p>Addressing these can lead to immediate improvements in your home’s comfort and energy use. Reducing the load on your unit can also help with its longevity.</p> <p>Inspect windows and doors for visible gaps or drafts. Weatherstripping or caulking can often seal these gaps effectively.</p> <h3><strong>Step #5 - Schedule Routine Professional Maintenance Annually</strong></h3> <p>Even with regular DIY checks, there are some aspects of AC maintenance that are best handled by professionals. Annual maintenance visits from a certified HVAC technician can diagnose issues that aren’t always obvious, like checking refrigerant levels and cleaning internal components.</p> <p>Scheduling a maintenance appointment with a <a href="https://www.mtnvalleyplumbing.com/loveland-cooling-ac-repair">Loveland air conditioning repair</a> professional helps prevent costly repairs down the road. An HVAC professional will also ensure your system is operating at peak efficiency and can offer tips for continuous performance. They can even help you assess whether you can stick with this unit for a few more years or if an upgrade would be more cost-effective.</p> <p> </p>
Example data sets and input parameters for running various features in the Python code Dynpy
<p>This is a set of examples intended to be used with the Python code called Dynpy at <a href="https://zenodo.org/records/13241475">https://zenodo.org/records/13241475</a></p>
OSU-UVic hosing run with LGM initial conditions (LGM Full)
<p>This directory contains model code, input, output, and scripts from a hosing (freshwater forcing in the North Atlantic) simulation with the OSU-UVic climate model (version 2.9.10) to investigate the effect of changes in the Atlantic Meridional Overturning Circulation (AMOC) on carbon and carbon-13 components in the ocean as described in Schmittner and Boling (2025) and Schmittner (2025).</p> <p>Model code is in the code/ subdirectory.<br>Model input data is in the data/ subdirectory and in the control.in and mk.in files.<br>Model output data is in the tavg*nc and tsi*nc files.<br>Ferret scripts used to produce the figures are in the ferret/ subdirectory.</p> <p>A more detailed description about the OSU-UVic climate model is available at https://github.com/OSU-CEOAS-Schmittner/UVic2.9 and https://doi.org/10.5281/zenodo.11224826.</p> <p>Andreas Schmittner (andreas.schmittner@oregonstate.edu)</p> <p>References:<br>Schmittner, A. and M. Boling (2025) Impact of Atlantic Meridional Overturning Circulation Collapse on Carbon Components in the Ocean, Global Biogeochemical Cycles, submitted manuscript.<br>Schmittner, A. (2025) Impact of Atlantic Meridional Overturning Circulation Collapse on Carbon-13 Components in the Ocean, Global Biogeochemical Cycles, submitted manuscript.</p>
Amalia synthetic model runs
<p>ISSM model run outputs and code, as described in the paper "Atypical landslide induces speedup, advance, and long-term slowdown of a tidewater glacier".</p> <p>Please contact me (vanwy048@umn.edu) if you are trying to open these files, but are having trouble interpreting ISSM's file structure.</p>
Sample data and fixed files for running 'fv3aerorad' in GSI
<p>The tarball, "fv3aerorad.tar.gz", contains the sample data and fixed files to run the regression test, 'fv3aerorad', in community Gridpoint Statistical Interpolation (GSI; https://doi.org/10.5281/zenodo.5735601).</p>
Visual duration but not numerosity is distorted while running
<p>Each folder contains the data for a separate experiment.</p> <p>Each file contains a matrix called “MatTuttiDati”. Each row of the matrix “MatTuttiDati” is a trial.</p> <p>The columns contain the following information:</p> <p>- 1st: number of trial</p> <p>- 2nd: condition</p> <p>- 3rd: Reference Magnitude (Duration/Numerosity)</p> <p>- 4th: Test Magnitude (Duration/Numerosity)</p> <p>- 6th: Subject response (Only for Numerosity)</p> <p>- 7th: Subject response (Only for Duration)</p>
Nanopore MinION Run Metrics and genomic DNA fragment size analysis data from automated phenol-chloroform extractions (RBI LabDroid Maholo)
<p>Nanopore MinION run MinKNOW statistical metrics output, Agilent Femto Pulse and Tape Station gDNA fragment size analysis reports of genomic DNA isolated from automated RBI LabDroid Maholo organic extractions.</p>
TIGAR Tonga Run Output
<p>A barotropic version of the TIGAR (Transient Inertia Gravity and Rossby wave dynamics) model has been run at T170 horizontal resolution for the 2022 Tonga eruption. TIGAR solves primitive equations on the sphere using the Hough harmonics [Vasylkevych and Žagar, 2021] thereby providing the time evolution of Rossby and inertia-gravity waves.</p>
Amyloid-beta 16-22 peptide monomer simulation with the CHARMM-Drude force field and OpenMM (Run 1)
<p>Amyloid-beta 16-22 peptide (monomer) simulations with the CHARMM-Drude force field and OpenMM. This is the first independent simulation runs out of 3.</p> <p>Part 1-2 are 200 ns long, 3-8 are 100 ns each. Total trajectory length is 1 microseconds. Frame saving frequency is 10 ps.</p> <p>The system contains ~ 150 mM NaCl.</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.