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4,325 results for “real”
Source data for manuscript "Real-time microscopy of the relaxation of a glass".
<p>Source data for manuscript "Real-time microscopy of the relaxation of a glass" (DOI: 10.1038/s41567-023-02125-0), including:</p> <p>- AFM source images</p> <p>- data points for all plots in the manuscript</p>
CTU-SME-11: a labeled dataset with real benign and malicious network traffic mimicking a small medium-size enterprise environment
<p>As technology advances, the number and complexity of cyber-attacks increase, forcing defense techniques to be updated and improved. To help develop effective tools for detecting security threats it is essential to have reliable and representative security datasets. Many existing security datasets have limitations that make them unsuitable for research, including lack of labels, unbalanced traffic, and outdated threats.</p> <p>CTU-SME-11 is a labeled network dataset designed to address the limitations of previous datasets. The dataset was captured in a real network that mimics a small-medium enterprise setting. Raw network traffic (packets) was captured from 11 devices using tcpdump for a duration of 7 days, from 20th to 26th of February, 2023 in Prague, Czech Republic. The devices were chosen based on the enterprise setting and consists of IoT, desktop and mobile devices, both bare metal and virtualized. The devices were infected with malware or exposed to Internet attacks, and factory reset to restore benign behavior. </p> <p>The raw data was processed to generate network flows (Zeek logs) which were analyzed and labeled. The dataset contains two types of levels, a high level label and a descriptive label, which were put by experts. The former can take three values, benign, malicious or background. The latter contains detailed information about the specific behavior observed in the network flows. The dataset contains 99 million labeled network flows. The overall compressed size of the dataset is 80GB and the uncompressed size is 170GB.</p>
Real Power Consumption Traces
<p>We collected 5 real power consumption traces and we used one to generate the training dataset for the LSTM. The other four real power consumption traces were used for the testing dataset.</p> <p>Each real power consumption trace consists of a pair of lists:</p> <ul> <li>traces: a list of samples, which are the measurements of power consumption</li> <li>cycle: a list of element pairs, where each pair consists of: <ul> <li>a numeric tag identifying the field operation performed (Addition, Subtraction, Multiplication, Square and other routine operations not of interest to us):</li> <li>the clock ticks counter when the field operation starts</li> </ul> </li> </ul>
Urban Traffic Simulation Data from Real Fusion Estimates
<p>The datasets contain vehicle data from the simulation of the city center of Guimarães. The simulation was created using data collected from real sensor data, thus, providing an accurate view of the traffic flows. The data contains route information, fuel consumption, emissions, driving distance, and the amount of time each vehicle is stopped.</p>
Supplementary Movies and Source Data for: Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation
<p>Supplementary Movies and raw data for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation":</p> <p>Source_Data.zip: Supplementary Code, Supplementary Data and Weka Analysis</p> <p>Lan_supplementary_movies_AVI.zip: Supplementary movies as AVI</p> <p>Lan_supplementary_movies_MP4.zip: Supplementary movies as MP4</p> <p>Lan_raw_movies.zip: Raw TIFF stacks of the movies.</p> <p>Lan_supplementary_movies.zip: Old version of the movies.</p>
Data of the publication "Real-time broadening of bath-induced density profiles from closed-system correlation functions"
<p>The Lindblad master equation is one of the main approaches to open quantum systems. While it has been<br> widely applied in the context of condensed matter systems to study properties of steady states in the limit<br> of long times, the actual route to such steady states has attracted less attention yet. Here, we investigate the<br> nonequilibrium dynamics of spin chains with a local coupling to a single Lindblad bath and analyze the transport<br> properties of the induced magnetization. Combining typicality and equilibration arguments with stochastic<br> unraveling, we unveil for the case of weak driving that the dynamics in the open system can be constructed<br> on the basis of correlation functions in the closed system, which establishes a connection between the Lindblad<br> approach and linear response theory at finite times. In this way, we provide a particular example where closed and<br> open approaches to quantum transport agree strictly. We demonstrate this fact numerically for the spin-1/2 XXZ<br> chain at the isotropic point and in the easy-axis regime, where superdiffusive and diffusive scaling is observed,<br> respectively.</p>
Real-Time Sensor Network of Detroit Green Infrastructure: Datasets and Code
<ol> <li><strong>MonitoredRainGardens_PaperTable.xlsx: </strong>Excel spreadsheet where the "Garden" sheet contains the 14 monitored green infrastructure (GI) sites with their design and physiographic features and the "Field Log" sheet contains the installation and field maintenance trips.</li> <li><strong>WaterWells.xlsx: </strong>an Excel file containing all of the water wells in the Detroit region uses for interpolating groundwater levels.</li> <li><strong>GI_GIS_Analysis.aparx:</strong> ArcGIS Pro project file which includes the 14 monitored GI sites and the GIS data for Detroit (percent imperviousness, elevation, slope, land use type, wells, interpolated groundwater levels, hydrologic soil group).</li> <li><strong>Code.zip: </strong>Zip folder containing another folder titled "Code" which holds: (1) a folder titled "SensorData" containing 16 csv files with the raw pressure transducer data for the 16 monitored GI sites during the measurement period (including the two excluded sites); (2) a csv file titled "MonitoredRainGardens.csv" containing the 14 monitored green infrastructure (GI) sites with their design and physiographic features used in the correlation analysis; (3) a csv file titled "storm_constants.csv" which contain the computed decay constants for every storm in every GI during the measurement period; (4) a csv file titled "GLWA_RainGaugesforStudy.csv" that contains rainfall from 9 rain gauges during the measurement period; (5) a Jupyter notebook titled "storm_constants_analysis.ipynb" which provides the code for calculating the decay constants for the monitored GI; (6) a Jupyter notebook titled "storm_constants.ipynb" which provides the code for analyzing the decay constants including the correlation analysis and surface plots; and (7) a Jupiter notebook titled "modeled_response.ipynb" which provides the code for plotting the drawdown curves based on the decay constant.</li> </ol>
Real data from PMUs
<p>Real data that are derived from two PMUs installed in different substations of a real system that includes timestamp, three phase voltage and current phasor measurements for two lines of the system, frequency measurements, and rate of change of frequency (ROCOF) measurements. </p>
Real and Synthetic Dataset for Active Shooter Situations
<p>This dataset includes annotations for shooters (both person and visible portion of the gun) and guns in a primarily indoor setting. The synthetic data was generated using Unreal Engine 4 and Unreal Engine 5 and contains both the default semi-realistic textures of the environments and segmentation masks as a form of domain randomization. The textured synthetic data was further augmented with camera sensor effects as another domain adaptation technique. The folders use the following notation: T denotes textured synthetic, M denotes masked synthetic, R denotes real, and the prefix A denotes augmented. We also include some annotated videos for evaluating tracking performance.</p>
Data set for: Real-space Imaging of Confined Magnetic Skyrmion Tubes
<p>This repository contains the scripts and notebooks to reproduce the figures, simulations and numerical data shown in <strong>Real-space Imaging of Confined Magnetic Skyrmion Tubes</strong> by <em>M. T. Birch, D. Cortés-Ortuño, L. A. Turnbull, M. N. Wilson, F. Groß, N. Träger, A. Laurenson, N. Bukin, S. H. Moody, M. Weigand, G. Schütz, H. Popescu, R. Fan, P. Steadman, J. A. T. Verezhak, G. Balakrishnan, J. C. Loudon, A. C. Twitchett-Harrison, O. Hovorka, H. Fangohr, F. Ogrin, J. Gräfe and P. D. Hatton.</em></p> <p>Both simulation and experimental data analysis are performed using Python with the Matplotlib, Jupyter, Scipy, Numpy and h5py libraries.</p> <p>Jupyter notebooks are provided to process the experimental data and reproduce the STXM, X-Ray Holography and LTEM images, which are shown as Figures 2, 3, 4 and 5 in the paper.</p> <p>Simulation scripts are based on the finite difference micromagnetic code OOMMF with the extension to simulate DMI for materials with symmetry class <em>T</em>: [oommf-extension-dmi-t](https://github.com/joommf/oommf-extension-dmi-t)</p> <p>The analysis of OOMMF's output files, which are in the `OMF` format, are processed using the [OOMMFPy](https://github.com/davidcortesortuno/oommfpy) library, which can calculate the topological charge in a 2D slice.</p> <p>Three-dimensional visualisations of the magnetic states are performed using Paraview. In order to get VTK files for visualisation, convert the `OMF` files into `.vtk` using the `OOMMFPy` library.</p> <p> </p> <p>Latest version of this Data Set can be found at the Github repository:</p> <p><a href="https://github.com/davidcortesortuno/paper-2020_real-space_imaging_of_confined_magnetic_skyrmion_tubes">https://github.com/davidcortesortuno/paper-2020_real-space_imaging_of_confined_magnetic_skyrmion_tubes</a></p>
Data from an Experiment in farming real conditions
<p>This file presents data from an experiment conducted in 2013/2014 and 2014/2015 with farmers in their fields. The experiment aimed at identifying sustainable agricultural management practices.</p>
Data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks"
<p>This upload contains the data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks", (D.O.I: <a href="https://doi.org/10.3390/jsan9010012">10.3390/jsan9010012</a>) published in the the special issue on "Localization in Wireless Sensor Networks" of the <a href="https://www.mdpi.com/journal/jsan"><em>Journal of Sensor and Actuator Networks</em></a> (ISSN 2224-2708).</p> <p>The data and code included allows to replicate the results of the article.</p>
Dataset of Real Faults in Deep Learning Systems
<p>This is a replication package for the "Taxonomy of Real Faults in Deep Learning Systems" paper.</p> <p>The dataset contains information on all the issues and real faults gathered in the course of the study.</p> <p>The dataset consists of three main folders: Manual_Labelling, Interviews and Survey.</p> <p>Manual_Labelling</p> <p>In this folder we have placed all the files associated with our manual labelling process. It is divided in 3 subfolders: SO_init, GitHub_init and Analysed_Artifacts.</p> <p>GitHub_init</p> <p>The GitHub mining process is explained in detail in Section 3.1.1 of the paper. The list of initially mined GitHub projects for each framework is presented in the file {frameworkname}_init.csv. Each line in the file provides project name, the link to the project and its general information such as number of commits, issues, stars and etc. As part of our procedure, we removed projects that do not represent real software systems. For such projects we provide an explanation on why it should be excluded in the column "Comment". The files {frameworkname}_after_init_removal.csv present the list of remaining projects after the exclusion of such systems. The files {frameworkname}_top_100.csv list the top 100 projects we selected for our final analysis.</p> <p>As explained in Section 3.1.1, to identify relevant commits and issues we used a vocabulary of related terms. The complete list of 11,968 stemmed words is presented in the file vocabulary_init.csv. The final list of 105 relevant words obtained by the exclusion of the words that appear less than 10 times and the further manual analysis is provided in the file vocabulary_final.csv.</p> <p>SO_init</p> <p>The extraction procedure from StackOverflow is explained in detail in Section 3.1.2 of the paper. In the file query.rtf we provide the code of the query we have used to extract discussions related to each of the analysed frameworks (Keras, Torch, Tensorflow) from StackOverflow. As a result, we got a csv file that lists discussions for each of the frameworks (files keras.csv, torch.csv and tensorflow.csv).</p> <p>Analysed_Artifacts</p> <p>Our manual analysis was conducted in 6 rounds (Table 1 in the paper). We provide information about each round in a separate csv file round_{roundnumber}.csv. In each file we provide the following information: (1) <strong>Entity Type</strong> - whether it is a StackOverflow or GitHub artifact and which framework it corresponds to; (2) <strong>Link</strong> to the artifact; (3) <strong>Number of Evaluators</strong> - how many evaluators were assigned to this artifact; (4) <strong>Conflict</strong> - whether there was a conflict between evaluators when assigning tag to this artifact, has a value 0 for no and 1 for yes; (5) <strong>Evaluator1..4</strong> and <strong>Tag1..4</strong> - the ID of each evaluator and the tag provided by each of the assigned evaluators; (6) <strong>Final Tag</strong> - final tag assigned to the artifact; (7) <strong>Taxonomy Tag</strong> - the tag in the final taxonomy;</p> <p>The detailed information on the statistics of each round can be found in file stats.xlsx.</p> <p>Interviews</p> <p>The background information about our interview participants is presented in the file interview_participant_info.xlsx. The interview guide we used to conduct the semi-structured interviews is in the file interview_guide.docx. In the subfolder "Transcriptions" we provide transcribed versions of all 20 interviews.</p> <p>We provide the details of open coding process for the interviews in the file interview_open_coding.xlsx. Each row corresponds to a part of the interview text to which at least one of the evaluators has assigned a tag. Therefore, each row contains the following information: (1) <strong>Interview Num</strong> - the interview number; (2) <strong>Evaluator 1</strong> - tag provided by the first evaluator; (3) <strong>Evaluator 2</strong> - tag provided by the second evaluator; (4) <strong>Moderator Tag</strong> - tag assigned by the moderator; (5) <strong>Tag</strong> - the final taxonomy tag; (6) <strong>Status</strong> - whether this tag has been added to the final taxonomy or not ("A" for yes and "R" for no).</p> <p>In the file Interview_Tags.xlsx we provide aggregated information about the tags obtained from interviews. The column "Number" shows the number of times the tag was extracted from the interviews. Similarly to the previous file, the column "Status" shows whether the tag became part of the final taxonomy or not.</p> <p>Survey</p> <p>We provide the survey form we have used for our validation study in the file survey_form.pdf. The file with the information about participants and their answers to the survey questions are in the file participant_info_and_responses.xlsx. The percentages reported in Table 2 in the paper are calculated in this file (last rows with a bold font).</p>
Station catalog in "Real-time earthquake location based on the Kalman filter formulation"
<p>Station catalog used to locate earthquakes in Parkfield, California, in the manuscript entitled "Real-time earthquake location based on the Kalman filter formulation" submitted to Geophysical Research Letters</p>
Hubei STEC Data through CORS stations for DOY 059 and 061 of the year 2018 which used in (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City, manuscript submitted to Earth and Space Science Journal AGU)
<p>Manuscript submitted to Earth and Space Science AGU entitled with <br> (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City)<br> by: Mohamed Freeshah, Xiaohong Zhang, Xiaodong Ren, Jun Chen, and Zhibo Zhao</p> <p>The STEC data inside two compressed folders named as stec059 and stec061, respectively.<br> The STEC file name has the CORS station name for the first forth letters and next three numbers epresent the Day of the year.<br> For example:<br> ES010590.18STEC<br> ES01 is the station name<br> 059 is the day of year (DOY), 2018</p>
Real-time measurement and source apportionment of elements in Delhi's atmosphere
<p>Here we present semi-continuous and real-time measurements of elemental composition for PM2.5 and PM10 aerosols in Delhi, India, at a time resolution of 30 min to 1 h during two consecutive winters in 2018 and 2019, to identify the prevailing sources. Nine different aerosol sources were identified during both winters using positive matrix factorization (PMF), including dust, brake wear, a S-rich factor, two solid fuel combustion (SFC) factors and four industrial/combustion factors related to plume events (Cr-30 Ni-Mn, Cu-Cd-Pb, Pb-Sn-Se and Cl-Br-Se). Most of these sources had the highest relative contributions during late night (22:00 local time (LT)) and early morning hours (between 03:00 to 08:00 LT), which is consistent with enhanced emissions into a shallow boundary layer. Modelling of airmass source geography revealed that the pollutants enter Delhi via three distinct air corridors during winters including Nepal and Uttar Pradesh (India) in the east, and Pakistan, Punjab (India) and Haryana (India) in the north-west, during winter, when the national capital’s air quality is at its worst.</p> <p>This repository has excel file corresponding to each figures presented in main text published version.</p>
CURE-OR: Challenging Unreal and Real Environments for Object Recognition
<p>As one of the research directions at <a href="https://ghassanalregib.info/">OLIVES Lab @ Georgia Tech</a>, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (<a href="https://github.com/olivesgatech/CURE-OR">CURE-OR</a>) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In <a href="https://github.com/olivesgatech/CURE-OR">CURE-OR</a> dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture, captured with multiple devices in different setups. The majority of images in the dataset were acquired with smartphones and tested with off-the-shelf applications to benchmark the recognition performance of devices and applications that are used in our daily lives. Please refer to our <a href="https://github.com/olivesgatech/CURE-OR">GitHub page</a> for code, papers, and more information. Some data specifications are provided below:</p> <p><strong>Image Name Format : </strong></p> <p>"backgroundID_deviceID_objectOrientationID_objectID_challengeType_challengeLevel.jpg"</p> <p><strong>Background ID: </strong></p> <p>1: White 2: Texture 1 - living room 3: Texture 2 - kitchen 4: 3D 1 - living room 5: 3D 2 – office</p> <p><strong>Object Orientation ID: </strong></p> <p>1: Front (0 º) 2: Left side (90 º) 3: Back (180 º) 4: Right side (270 º) 5: Top</p> <p><strong>Object ID:</strong></p> <p> 1-100</p> <p><strong>Challenge Type:</strong> </p> <p>No challenge 02: Resize 03: Underexposure 04: Overexposure 05: Gaussian blur 06: Contrast 07: Dirty lens 1 08: Dirty lens 2 09: Salt & pepper noise 10: Grayscale 11: Grayscale resize 12: Grayscale underexposure 13: Grayscale overexposure 14: Grayscale gaussian blur 15: Grayscale contrast 16: Grayscale dirty lens 1 17: Grayscale dirty lens 2 18: Grayscale salt & pepper noise</p> <p><strong>Challenge Level: </strong></p> <p>A number between [0, 5], where 0 indicates no challenge, 1 the least severe and 5 the most severe challenge. Challenge type 1 (no challenge) and 10 (grayscale) has a level of 0 only. Challenge types 2 (resize) and 11 (grayscale resize) has 4 levels (1 through 4). All other challenges have levels 1 to 5.</p>
RealVAD: A Real-world Dataset for Voice Activity Detection
<p><strong>RealVAD: A Real-world Dataset for Voice Activity Detection</strong></p> <p>The task of automatically detecting “Who is Speaking and When” is broadly named as Voice Activity Detection (VAD). Automatic VAD is a very important task and also the foundation of several domains, e.g., human-human, human-computer/ robot/ virtual-agent interaction analyses, and industrial applications.</p> <p>RealVAD dataset is constructed from a YouTube video composed of a panel discussion lasting approx. 83 minutes. The audio is available from a single channel. There is one static camera capturing all panelists, the moderator and audiences.</p> <p>Particular aspects of RealVAD dataset are:</p> <ul> <li> It is composed of panelists with different nationalities (British, Dutch, French, German, Italian, American, Mexican, Columbian, Thai). This aspect allows studying the effect of ethnic origin variety to the automatic VAD.</li> <li> There is a gender balance such that there are four female and five male panelists.</li> <li> The panelists are sitting in two rows and they can be gazing audience, other panelists, their laptop, the moderator or anywhere in the room while speaking or not-speaking. Therefore, they were captured not only from frontal-view but also from side-view varying based on their instant posture and head orientation.</li> <li> The panelists are moving freely and are doing various spontaneous actions (e.g., drinking water, checking their cell phone, using their laptop, etc.), resulting in different postures.</li> <li> The panelists’ body parts are sometimes partially occluded by their/other's body part or belongings (e.g., laptop).</li> <li> There are also natural changes of illumination and shadow rising on the wall behind the panelists in the back row.</li> <li> Especially, for the panelists sitting in the front row, there is sometimes background motion occurring when the person(s) behind them moves.</li> </ul> <p>The annotations includes:</p> <ul> <li> The upper body detection of nine panelists in bounding box form.</li> <li> Associated VAD ground-truth (speaking, not-speaking) for nine panelists.</li> <li> Acoustic features extracted from the video: MFCC and raw filterbank energies.</li> </ul> <p><em>All info regarding the annotations are given in the ReadMe.txt and Acoustic Features README.txt files.</em></p> <p><strong>When using this dataset for your research, please cite the following paper in your publication:</strong></p> <ol> <li>C. Beyan, M. Shahid and V. Murino, "RealVAD: A Real-world Dataset and A Method for Voice Activity Detection by Body Motion Analysis", in IEEE Transactions on Multimedia, 2020.</li> </ol>
Real and simulated cross-sectional and longitudinal images of hair
<p>This is the dataset containing simulated and real data used in the analyses for the paper "<em><a href="https://www.biorxiv.org/content/10.1101/2020.11.24.392191v1">High-throughput phenotyping methods for quantifying hair fiber morphology</a>" </em>and is part of the<em> <a href="https://tinalasisi.github.io/2020_HairPheno_manuscript/index.html">Hair Phenotyping Methods Project</a> </em>run by <a href="https://www.tinalasisi.com/">Tina Lasisi</a>. </p> <p>The data can be analyzed with the <em>fibermorph</em> Python package available on <a href="https://pypi.org/project/fibermorph/">PyPi</a> and <a href="https://github.com/tinalasisi/fibermorph">Github</a>. </p> <p>This repository has 2 datasets with 2 different types of data:</p> <ol> <li>Simulated hair data <ol> <li>Cross-sectional data (simulated ellipses)</li> <li>Curvature data (simulated arcs)</li> </ol> </li> <li>Real hair data <ol> <li>Cross-sectional data (micrographs of hair fiber cross-sections)</li> <li>Curvature data (longitudinal images of hair fiber fragments)</li> </ol> </li> </ol> <p>Visit the <a href="https://tinalasisi.github.io/2020_HairPheno_manuscript/index.html"><em>Hair Phenotyping Methods Project</em></a> website for the most up to date information about this project and any updates relevant to this dataset.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Details</strong></p> <p><strong>Simulated data</strong></p> <ol> <li>Cross-sectional data <ul> <li>These ellipses were simulated with a python script developed as part of <a href="https://github.com/tinalasisi/fibermorph/"><em>fibemorph</em></a>. A version of that code that doesn't require the original Python package has been made available with the dataset (sim_ellipse.py).</li> <li>The script simulates a single cross-section per image.</li> <li>Each image has a width of 5200px and a height of 3900 with a resolution set to 4.25 px/micron.</li> </ul> </li> <li>Curvature data <ul> <li>An R script used for curvature simulation, written by <a href="https://www.arslanzaidi.com/">Arslan Zaidi,</a> has also been made available with this dataset (sim_curvature.R). </li> <li>The script generates 25 arcs per image. We used a set length of 1.57.</li> <li>Each image has a resolution of 132 px/mm. </li> </ul> </li> </ol> <p>Please note that due to the use of random generations, it is not possible to recreate the exact same datasets that are saved here. </p> <p><strong>Real data</strong></p> <p>The real data images are very large files and have been split into multiple zip files. Please check the specific instructions for unzipping split zip files for your OS. </p> <p>The images are from hair samples collected by the <a href="https://shriverlab.psu.edu/">Shriver Lab</a> at Penn State. There were a total of 192 samples, although not all images made it past quality control so certain IDs may have cross-section images but not curvature images or vice versa. </p> <p>The images have been de-identified and the hair samples for these individuals were collected with informed consent and ethical approval by The Pennsylvania State University Institutional Review Board (#44929 and #45727).</p> <ol> <li>Cross-sectional data <ol> <li>We developed and used <a href="https://www.protocols.io/view/sample-preparation-protocol-for-cross-sectional-mi-bbwcipaw">this protocol</a> to embed, section, and image the hairs.</li> <li>We embedded 6 samples per person and took images of both sides of the sectioned sample (A and B). These should be mirror images of each other.</li> </ol> </li> <li>Curvature data <ol> <li>We developed and used <a href="https://www.protocols.io/view/sample-preparation-protocol-for-hair-fiber-curvatu-bbweipbe">this protocol</a> to cut, wash, and image the hairs.</li> <li>We used 3-5 hairs per person where available. A number of samples did not have enough hair for this, so the images contain fewer fragments. We have made these available for full transparency although we filtered them from our analyses downstream.</li> </ol> </li> </ol> <p>Please see the <a href="https://github.com/tinalasisi/2020_HairPheno_manuscript">GitHub repository</a> for additional related participant data we used in our analyses.</p> <p> </p> <p> </p>
The contradiction is exsiting objectively and real
<p>The <strong>contradiction</strong> is existing independently of any human mind and consciousness, <strong>objectively and real </strong>and is equally the foundation of changes of objective reality as such. <strong>Negation</strong>, following Spinoza, Hegel and other, is the linking process between something and its own other.</p> <p>However, even if this fact allows and promotes a new, classical logic and probability theory compatible <strong>multi-valued (dialectical) logic, </strong>the same fact does not imply the correctness of <strong>para-consistent logic</strong> at all.</p> <p> </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.