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6,498 results for “Physicality”
Global physical input-output tables for iron and steel (2008-2017).
<p><strong>Dataset:</strong> Global physical input-output tables for iron and steel</p> <p><strong>Years:</strong> 2008-2017</p> <p><strong>Base classification:</strong> 32 regions, 39 processes and 30 flows</p> <p><strong>Associated journal article: </strong>The PIOLab - Building global physical input-output tables in a virtual laboratory (forthcoming, Journal for Industrial Ecology)</p> <p><strong>Associated GitHub repository</strong>: www.github.com/fineprint-global/PIOLab</p> <p><strong>Contact:</strong> hanspeter.wieland@wu.ac.at</p> <p>The folder <em>RawData</em> contains the unprocessed results of the reconciliation run in the PIOLab. These tables (in the Tvy format) form the basis for the R scripts that are available from the GitHub repository mentioned above. Please note the instructions on GitHub for further information and how i.e. where the content of <em>RawData</em> needs to be stored in your local repository.</p> <p>The folder <em>gPSUT</em> contains the processed physical supply-use tables, including final use matrices and boundary input and output blocks. The variable names are described in detail in the method section of the journal article.</p> <p>The folder <em>gPIOT</em> contains the process-by-process IO model, which was used for the calculation of the footprint indicators in the Journal article. Please read the information on the footprint calculus in the journal article.</p> <p>The folder<em> Diagnostics </em>contains, for all years of the time series, results from the analyses of the constraint realization. The journal article presents only the diagnostic test for the year 2008.</p>
Dataset of 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks'
<p>Dataset of the article 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks' (https://doi.org/10.1016/j.cma.2023.116652). The codes processing data here are on https://github.com/AlvaroMS90/Complete-flow-characterization-from-snapshot-PIV-fast-probes-and-physics-informed-neural-networks.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085) and by MCIN/AEI /10.13039/501100011033 and the European Union ‘NextGenerationEU/PRTR’ as part of the grant FJC2020-044342-I.</p>
Dataset of the scientific paper " Multimodal robotic system for upper-limb rehabilitation in physical environment" (Advances in Mechanical Engineering)
<p>There are eight files with the following information:<br> - pos_stateXX.bin, binary file with information of the end effector position of the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - target_stateXX.bin, binary file with information of the target position for the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - emg_channelXX.bin, binary file with information of channel 1 of the EMG sensor in mV during during the whole time of the experiment<br> - color_stateXX.bin, binary file with information of color filter information during state XX of the experiment. This information is the percentage of pixels with the correct color (yellow, cyan or magenta) inside the region of interest</p> <p> </p>
Physics-based parametrization of the surface impedance for radio frequency sheaths
<p>The accompanying files contain digital data for the figures in the article "Physics-based parametrization of the surface impedance for radio frequency sheaths" by J.R. Myra, Physics of Plasmas 24, 072507 (2017).</p> <p>Filenames correspond to the figures or parts thereof. All data is given as ascii text in csv format. The files may be open and plotted by common spreadsheet programs and may be easily read by procedural programs. The first row gives the column headers. These correspond to the labels on the x and y axes of the figures (as represented by ascii text). Most headers are self-explanatory. Exceptional cases are noted below.</p> <p>Fig. 2<br> w = frequency omega<br> yir20 = real part of ion impedance Re[yi] for Vrf = 20<br> yii20 = imaginary part of ion impedance Im[yi] for Vrf = 20<br> yir10 = real part of ion impedance Re[yi] for Vrf = 10<br> yii10 = imaginary part of ion impedance Im[yi] for Vrf = 10<br> yir5 = real part of ion impedance Re[yi] for Vrf = 5<br> yii5 = imaginary part of ion impedance Im[yi] for Vrf = 5<br> yir0 = real part of ion impedance Re[yi] for Vrf = 0<br> yii0 = imaginary part of ion impedance Im[yi] for Vrf = 0</p> <p>The diagonal blue lines in the published figures for Figs. 3 - 8 indicate the line y = x and are not explicilty tabulated here.</p> <p>Abstract:</p> <p>The properties of sheaths near conducting surfaces are studied for the case where both magnetized plasma and intense radio frequency (rf) waves coexist. The work is motivated primarily by the need to understand, predict and control ion cyclotron range of frequency (ICRF) interactions with tokamak scrape-off layer plasmas, and is expected to be useful in modeling rf sheath interactions in global ICRF codes. Employing a previously developed model for oblique angle magnetized rf sheaths [J. R. Myra and D. A. D’Ippolito, Phys. Plasmas 22, 062507 (2015)], an investigation of the four-dimensional parameter space governing these sheath is carried out. By combining numerical and analytical results, a parametrization of the surface impedance and voltage rectification for rf sheaths in the entire four-dimensional space is obtained.</p>
Data set for the physical, chemical and biochemical modelling of the primary sedimentation tanks at the WWTP of Eindhoven
<p>These files contain data about measurement campaigns on the primary sedimentation tanks of the WWTP of Eindhoven (The Netherlands) in 2013 and 2014 and the routinely collected data for 2011 till 2013.</p> <p>The data was processed in the PhD of Youri Amerlinck, entitled "Model refinements in view of wastewater treatment plant optimization: improving the balance in sub-model detail."</p> <p>http://www.biomath.ugent.be/biomath/publications/download/amerlinckyouri_phd.pdf</p> <p><br> WWTP of Eindhoven PST Routine Measurements 2011_2013.csv<br> January 5, 2011 - June 14, 2013: <br> Routine analysis for BOD5, COD, TKN, TP, PO4, TSS</p> <p>WWTP of Eindhoven PST Reduced Capacity 2013.csv<br> June 24, 2013 - July 23, 2013 - September 9, 2013<br> Evaluation of reducing the capacity of the PST (including dosing of chemicals) for CODT, CODS, TP, PO4 ,TSS </p> <p>WWTP of Eindhoven PST measurement campaign full ASM 20140506.csv<br> May 6, 2014: <br> Full ASM fractionation BOD5, CODT, CODS, TSS, VSS TP, PO4 ,TN, NH4, NO3, pH</p> <p>WWTP of Eindhoven PST measurement campaign full ASM and Cations 20140902.csv<br> September 2, 2014:<br> Full ASM fractionation (repetition) and cation analysis (BOD10, CODT, CODS, TSS, VSS, TP, PO4 ,TN, NH4, NO3, pH - Ca, Mg, Na, K, Fe)</p>
The LILY Database: Linking Lithology to IODP Physical, Chemical, and Magnetic Properties Data
<p>During each expedition of the International Ocean Discovery Program and its precursor, the Integrated Ocean Drilling Program (jointly referred to as IODP), vast arrays of data are collected from drill cores. These data, which are accessible from the IODP LIMS (Laboratory Information Management System) database, include physical, chemical, and magnetic properties collected semi-continuously along cores using automated track systems, as well as a variety of analyses conducted on discrete subsamples taken from the cores. In addition, the lithology of all cores is described based on visual characteristics of the surface of split cores, visual examination of smear slides and thin sections, and compositional or mineralogical information derived from geochemical analyses. We extract basic lithologic information from this complex array of descriptive information and then tie that information to all other measurements. This new database is referred to as <strong>LI</strong>MS with <strong>L</strong>itholog<strong>y</strong> (LILY). LILY currently contains over 34 million data from 89 km of core recovered on 42 expeditions conducted 2009-2019. Some uses of LILY include identifying the abundance of different lithologies, finding data from core intervals with a specific lithology, assessing the efficacy of coring systems in different lithologies, or characterizing and analyzing physical, chemical, and magnetic properties based on lithology. We illustrate the use of LILY by computing the grain density by lithology from over 24,000 moisture and density measurements and then use those grain densities, along with the large IODP bulk density dataset, to compute a new high-resolution porosity dataset with over 3.7 million new porosity estimates.</p> <h2>CONTENT DESCRIPTION:</h2> <p><strong>The main LILY database is stored in the files with the suffix DataLITH.csv.</strong> Each file contains IODP LIMS data with lithology and other metadata added. The file prefix gives the type of data. For example, AVS_DataLITH.csv contains the Automated Vane Shear (AVS) shear strength data paired with lithology and other metadata. A list of all data types is given in Supporting Information Table S1 of Childress et al. (2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>). There are a total of 23 DataLITH files.</p> <ul> <li>AVS_DataLITH.csv: automated vane shear; shear strength measurements.</li> <li>CARB_DataLITH.csv: total carbon, hydrogen, nitrogen, and sulfur, inorganic carbon (carbonate), and organic carbon measured on discrete samples.</li> <li>GE_DataLITH.csv: gas elements from gas chromatography.</li> <li>GRA_DataLITH.csv: gamma ray attenuation bulk density from the Whole-Round Multisensor Logger (WRMSL).</li> <li>ICP_DataLITH.csv: Inductively-coupled plasma data.</li> <li>IW_DataLITH.csv: interstitial water chemistry.</li> <li>JR6A_DataLITH.csv: discrete magnetic measurements from the JR6A spinner magnetometer.</li> <li>KAPPA_DataLITH.csv: Kappabridge susceptibility meter measurements.</li> <li>MAD_DataLITH.csv: moisture and density from discrete samples.</li> <li>MS_DataLITH.csv: magnetic susceptibility from the WRMSL.</li> <li>MSP_DataLITH.csv: point magnetic susceptibility from the Section Half Multisensor Core Logger (SHMSL).</li> <li>NGR_DataLITH.csv: natural gamma radiation from the Natural Gamma Radiation Logger (NGRL).</li> <li>PEN_DataLITH.csv: pocket penetrometer compressional strength measurements.</li> <li>PWB_DataLITH.csv: P-wave velocity from the bayonet system.</li> <li>PWC_DataLITH.csv: P-wave velocity from the caliper system.</li> <li>PWL_DataLITH.csv: P-wave velocity from the WRMSL.</li> <li>RGB_DataLITH.csv: Red-Green-Blue color from the Section Half Imaging Logger (SHIL).</li> <li>RSC_DataLITH.csv: reflectance spectroscopy from the SHMSL.</li> <li>SRA_DataLITH.csv: source rock analyzer measurements.</li> <li>SRM_DataLITH.csv: Superconducting Rock Magnetometer (SRM) measurements of split-core sections.</li> <li>SRMD_DataLITH.csv: SRM measurements of discrete samples.</li> <li>TCON_DataLITH.csv: thermal conductivity measured with the Teka Berlin TK04 probe.</li> <li>TOR_DataLITH.csv: Torvane shear strength measurements.</li> </ul> <p>Other compressed data folders contain multiple files used in creating the LILY database:</p> <p>RawDESC.zip: Contains 7,940 .csv files derived from the raw text content of the DESClogik Excel worksheets that was extracted, converted to comma separated value (.csv) format, and put into files with a consistent naming convention, without applying any corrections or conversions to the original text. Each file is the direct extraction of a tab from the DESC workbooks, available at <a href="https://web.iodp.tamu.edu/DESCReport/">https://web.iodp.tamu.edu/DESCReport/</a></p> <p>CoreSUMM.zip: Contains one file with Core Summary information, which includes the expedition, site, hole, core, coring type, top and bottom depths drilled, advances and recoveries, time and date of recovery, and the number of sections. These data are further paired with additional metadata (expanded core type, latitude, longitude, and water depth). Coordinates and water depth for each hole are derived from LIMS (and the JANUS database at <a href="http://www-odp.tamu.edu/database/">http://www-odp.tamu.edu/database/</a> for older expeditions).</p> <p>RawDATA.zip: Contains the raw track/discrete dataset downloaded by expedition from IODP LIMS database and placed in folders for each type of data (AVS, CARB, SRM, etc.) </p> <p>RawLITH.zip: Contains 42 .csv files, with one file for each expedition. Each file contains all lithologic description (prefix, principal and suffix, etc.) information for an entire expedition, as it was originally described. These have been transformed to a consistent format and paired with consistent identification information and additional metadata. Headers are normalized across all expeditions and SampleID information is standardized.</p> <p>CleanLITH: Contains 42 .csv files. Each file contains all lithologic description (prefix, principal and suffix) information for an entire expedition. The lithologic descriptions have been standardized to a consistent nomenclature using the dictionary given in Support Information Table S4 of Childress et al. (2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>). These data are further paired with additional metadata (e.g., degree of consolidation, expanded core type, latitude, longitude, and water depth).</p> <h2>GitHub Repository:</h2> <ul> <li>Contains a few notebooks to demonstrate how to work with the LILY database</li> <li><a href="https://github.com/IODP/LILY">IODP LILY GitHub Repository</a></li> </ul>
Data, plotting scripts, and figures for "A physics-based ignition model with detailed chemical kinetics for live fuel burning studies"
<p>This repository contains the data, plotting scripts, and figures associated with the paper "A physics-based ignition model with detailed chemical<br>kinetics for live fuel burning studies" by Diba Behnoudfar and Kyle E. Niemeyer.</p> <p>See the README file for additional details.</p>
Datasets of synthetic workflows for evaluating a multi-objective and multi-constrained scheduling approach for cyber-physical applications
<p>These datasets of synthetic workflows (task graphs) were generated to evaluate the performance and scalability of a multi-objective and multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) based on the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors with different processing core failure rates and varied sensing/actuating or other specialized capabilities. Our objectives were the minimization of the overall latency, the minimization of the overall energy consumption, and the maximization of the overall reliability of the workflow application in the specific CPS, under deadline, reliability, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random task graphs with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability, reliability threshold) were included post-generation, using appropriate values. More details are provided in README.txt.<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.<br>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p>
Artificial fingerprints engraved through block-copolymers as nanoscale physical unclonable functions for authentication and identification - Dataset
<p>This is the dataset of "Artificial fingerprints engraved through block-copolymers as nanoscale physical unclonable functions for authentication and identification" by Irdi Murataj, Chiara Magosso, Stefano Carignano, Matteo Fretto, Federico Ferrarese Lupi, and Gianluca Milano, Nature Communications (2024), DOI: 10.1038/s41467-024-54492-8</p> <p>Part of this was funded by the project MEMQuD, code 20FUN06. The project has received funding from the EMPIR program co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program.</p> <p>Part of this work was supported by the European project OpMetBat, code 21GRD01. The project has received funding from the European Partnership on Metrology, cofinanced from the the European Union's Horizon Europe Research and Innovation Programme, and by Participating States.</p> <p>Part of this work was supported by the European Union - Next Generation EU under the National Recovery and Resilience Plan (NRRP), Mission 04 Component 2 Investment 3.1 | Project Code: IR0000027 - CUP: B33C22000710006 - iENTRANCE@ENL: Infrastructure for Energy TRAnsition aNd Circular Economy @EuroNanoLab.</p>
EUV-ML solar physics dataset from STEREO + SOHO, 2 solar cycles
<p>A unified ML-ready dataset of all SOHO EIT and STEREO EUVI EUV images, aligned and set to 512x512 pixels, are available via the NASA TOPS ODR in HelioCloud at s3://gov-nasa-hdrl-data1/contrib/euvml/ (inside of AWS, and with free egress via https). We also provide the software used to create it via a public github repository, and a sample Python Jupyter Notebook (in this Zenodo link, and in the HelioCloud sample tutorials) for accessing them via the 'cloudcatalog' Python client.We</p> <p>We created a full set of ML-ready EUV data from 1995 to present, accessible via the cloud, by bringing in historical restoration of the STEREO/SOHO era into a machine-learning (ML) -ready dataset. This work will enable research on events, evolution of solar irradiance, segmentation approaches, 360 degree maps of the sun, and other research topics as well as for use with space weather. The reduced dataset is 6TB in size. The data will be freely available to scientists both within the AWS cloud and for downloading for local use on their laptops. The creation of this dataset had several steps, starting with the mechnical ingest stage (get the raw data), an analysis of best approach to create the ML-ready set, bulk processing, uploading the cloud, and dissemination and promotion. The task also required determination best approaches for cadence matching, interpolation, and effective spans of contiguous data for ML applications.</p> <p>Work was funded under NASA Heliophysics 21-LWSTM21_2-0018, award number 80NSSC22K0643.</p>
Data-driven physics-based modeling of pedestrian dynamics - dataset: Pedestrian trajectories at Eindhoven train station
<p>Pedestrian trajectories measured at train station Eindhoven Centraal (the Netherlands) on platform 2 with acces to tracks 3 and 4.</p> <p>The dataset is partitioned in files containing 10 consecutive days each, recording 4 data fields:</p> <ul> <li><strong>time_ms:</strong> Passed time since start of the measurements. Unit: milliseconds.</li> <li><strong>object_identifier:</strong> unique id identifying an object.</li> <li><strong>x_position_mm: </strong>coordinates of the object along the x-axis at the given time. Unit: millimeters.</li> <li><strong>y_position_mm:</strong> coordinates of the object along the y-axis at the given time. Unit: millimeters.</li> </ul> <p>Each object resembles a pedestrian on the train platform recorded with 10 frames per second. We deliberately removed exact date and time information for privacy reasons (see additional note). The data set consists of 60 consecutive days starting at an unkown time between 00:00 AM and 01:00 AM of a random date between April 1st and May 1st 2022. An overhead image of the platform is included showing train track 3 in the bottom and train track 4 in the top of the image.</p> <p>The data set is supplemented to the paper <a title="Data-driven physics-based modeling of pedestrian dynamics" href="https://doi.org/10.48550/arXiv.2407.20794" target="_blank" rel="noopener">Data-driven physics-based modeling of pedestrian dynamics</a> and can be processed by the associated <a title="Software: Data-driven physics-based modeling of pedestrian dynamics" href="https://github.com/c-pouw/physics-based-pedestrian-modeling" target="_blank" rel="noopener">Python implementation</a> to create pedestrian models. </p>
Dataset on Physics-Based Indicators for Optimizing Phase Change Material Effectiveness in Building Design
<p>This research dataset includes the results as well as the EnrgyPlus models developed to investigate and validate newly proposed indicators to quantify the effectiveness of phase change materials in buildings.</p>
Data for Publication "Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1"
<p>The data are made available as part of the paper "Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1", submitted to Geoscientific Model Development. This data set incorporates selected postprocessed files needed to reproduce the results presented in the paper. </p> <p>The files including the monthly means of precipitation for domain 2 (5 km) are named following the same structure:</p> <p> RR-D02-EXPERIMENTNAME-25km3Doms-YYYY_monthly.nc</p> <p>These are the options available in each case:</p> <ul> <li>EXPERIMENTNAME: Europe, SouthAmerica, Kenya, Micro13 or NoCumulus. These are the names included in Table 1 <br> from the paper.</li> <li>YYYY: 2008 or 2012. This is only applicable to precipitation.</li> </ul> <p>The field means for the northeastern slopes follow this structure:</p> <p> VARIABLE-D02-ERA5-Peru-25-Present-EXPERIMENTNAME-2008.EastLow.fldmean.nc</p> <ul> <li>VARIABLE: CLOUDFRA, PW, RH2, RR, SMOIS or T2. This abbreviations represent the following variable from the model<br> respectively: cloud fraction, precipitable water, relative humidity at 2 meters, total precipitation, soil moisture<br> and temperature at 2 meters.</li> <li>EXPERIMENTNAME: Europe, SouthAmerica, Kenya, Micro13 or NoCumulus. These are the names included in Table 1<br> from the paper.</li> </ul> <p>These files contained hourly values so to obtain the monthly means or sums the user must perform the following command:</p> <p> cdo monmean/monsum in.nc out.nc</p> <p>Two .txt files including the information about the stations considered for the validation of the WRF experiments for year 2008 or 2012 are also included. The data is separated with white spaces, and the structure of the columns is the following one:</p> <p> STATION LATITUDE LONGITUDE ELEVATION COUNTRY PROVIDER REGION</p> <p>Finally, two .zip files are provided. Scripts.zip includes all the scripts used to read, process and plot the data from the model, and Namelist_files.zip includes all the namelist files used to run the WRF simulations.</p> <p> </p>
TULIPS: a Tool for Understanding the Lives, Interiors, and Physics of Stars
<p>This is a basic reproduction package for the paper "TULIPS: a Tool for Understanding the Lives, Interiors, and Physics of Stars".</p> <p>This package contains inlists for MESA, analysis scripts, animations created with TULIPS, and processed output from MESA.</p>
Datasets for Spiliotis et. al., Laser Physics 30 075602 (2020)
<p>Datasets supplementing published journal article with DOI: <a href="https://doi.org/10.1088/1555-6611/ab8d2e">https://doi.org/10.1088/1555-6611/ab8d2e</a></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>
Paramecium Polycomb Repressive Complex 2 physically interacts with the small RNA binding PIWI protein to repress transposable elements
<p>Polycomb Repressive Complex 2 (PRC2) maintains transcriptionally silent genes in a repressed state via deposition of histone H3 K27 trimethyl (me3) marks. PRC2 has also been implicated in silencing transposable elements (TEs), yet how PRC2 is targeted to TEs remains unclear. To address this question, we identified proteins that physically interact with the <em>Paramecium</em> Enhancer-of-zeste Ezl1 enzyme, which catalyzes H3K9me3 and H3K27me3 deposition at TEs. We show that the <em>Paramecium</em> PRC2 core complex comprises four subunits, each required <em>in vivo</em> for catalytic activity. We also identify PRC2 cofactors, including the RNA interference (RNAi) effector Ptiwi09, which are necessary to target H3K9me3 and H3K27me3 to TEs. We find that the physical interaction between PRC2 and the RNAi pathway is mediated by a RING finger protein and that small RNA recruitment of PRC2 to TEs is analogous to the small RNA recruitment of H3K9 methylation SU(VAR)3-9 enzymes.</p>
Automatic affective reactions to physical effort
<p>Dataset for the study titled "automatic affective responses during physical effort: a virtual reality study".</p> <p>This dataset includes:</p> <p><strong>1) A codebook (including the name of the main variables)</strong></p> <p>--> "code_book_affect_effort.xlsx"</p> <p><strong>2) Behavioral data (raw)</strong></p> <p>--> in the folder "data_ps_VR". </p> <p>The raw data are added for transparency, but are not necessary to run the models. </p> <p><strong>3) Self-reported data (raw)</strong></p> <p>--> "20220112_VR_expe.xlsx"</p> <p>--> "20220112_VR_pilot.xlsx"</p> <p>The raw data are added for transparency, but are not necessary to run the models. </p> <p><strong>4) clean data ready to used for the statistical analyses</strong></p> <p>--> "data_VR_all_clean.csv".</p> <p>This clean data are produced by the R script. These data included the self-reported and the behavioral measures. </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_effort.R" to create the dataset (return the file: "data_VR_all_clean.RData")</p> <p><strong>6) R script for the models tested</strong></p> <p>--> "Data_mixed_effects_models.R" for the models tested in the paper</p> <p><strong>7) The video of the experiment</strong></p>
Estimating historical air-sea CO2 fluxes: Incorporating physical knowledge within a data-only approach
<p>Reconstructed surface ocean pCO2 and air-sea CO2 fluxes for 1990-2019 using the pCO2-Residual Approach (JAMES 2021MS002960, in review)</p> <p>Surface ocean pCO2 (spo2) and resulting estimates of the air-sea CO2 flux (fCO2) are included in the netcdf file at monthly temporal resolution and for 1x1 grid cell spatial resolution. SeaFlux (https://zenodo.org/record/5482547#.YlT72y-B0_U) variables are used to calculate the fluxes from surface ocean pCO2.</p>
ECHAM6 verification without top 10 layers synchronization and with less physics schemes
<p>Verification of ECHAM6 nudging module when ECHAM6 has synchronized to its own outputs.</p> <p>Some physics schemes are switched off as the following namelist was used.</p> <p> </p> <p>&physctl<br> LCOVER = .false.<br> lphys=.true.<br> lconv=.false.<br> lgwdrag=.false.<br> lrad=.true.<br> lsurf=.false.<br> lvdiff=.false.<br> lcond=.false.<br> /</p> <p> </p> <p>Files and description:</p> <ul> <li>ndg_197102.nc <ul> <li>ECHAM6 grb output converting to netcdf format as a reference data for ECHAM6 nudging module</li> </ul> </li> <li>diff_t_197102_gp_rmse.nc <ul> <li>Spatial root mean square errors on all levels and at all outputs (6 hourly).</li> <li>RMSE between ECHAM6 output (echam6_nudging_grb_T63_197102.01_echam) and reference data (ndg_197102.nc)</li> </ul> </li> <li>namelist.echam <ul> <li>namelist used to run ECHAM6 nudging case, in which nudging is expected to only apply to layer 11 to 47.</li> </ul> </li> <li>Four ECHAM6 outputs when nudging module is switched on <ul> <li>echam6_nudging_grb_T63_197102.01_echam</li> <li>echam6_nudging_grb_T63_197102.01_echam.codes</li> <li>echam6_nudging_grb_T63_197102.01_nudg</li> <li>echam6_nudging_grb_T63_197102.01_nudg.codes</li> </ul> </li> <li>Two log files generated by ECHAM6 executable <ul> <li>r_nudging_err</li> <li>r_nudging_log</li> </ul> </li> </ul>
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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.