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Supplementary Materials for "Exploration of User Privacy in 802.11 Probe Requests with MAC Address Randomization Using Temporal Pattern Analysis"
<p>Supplementary Materials for "Exploration of User Privacy in 802.11 Probe Requests with MAC Address Randomization Using Temporal Pattern Analysis"</p> <p>This package contains an anonymized packets of 802.11 probe requests captured in in December 2021 at Universitat Jaume I . The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p>
Datasets and results from: "Random Forest Classification and Solar Flares Data: Analysis and Validation"
<p><strong>Instructions for the data and code repository</strong></p> <p>Results, post-processing workflow, and datasets for the research paper titled "Random Forest Classification and Solar Flares Data: Analysis and Validation".</p> <p>The folder contains three .csv files: the complete dataset (dataset.csv), the balanced training dataset (train_dataset.csv), and the testing dataset (test_dataset.csv).</p> <p>The folder also contains the result files from the research (.csv output files with predictions and .html files with evaluation metrics, etc.) exported from the JASP software. The number in each file name corresponds to the number of trees utilized in Random Forest modelling.</p> <p>In addition, the Python script for the post-processing workflow is provided, with comments located in the script.</p> <p>The soft range X-ray irradiance and VLF amplitude data were obtained from:<br> National Centers for Environmental Information (NCEI) Available online: https://www.ncei.noaa.gov/. Accessed on: 24th June 2023. <br> Worldwide archive of low-frequency data and observations (WALDO) Available online: https://waldo.world/. Accessed on: 24th June 2023.</p>
Sensor and nutrient data associated with the article Harrison et al. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression
This document describes a dataset used to produce Random Forests Regression models of stream nitrogen and phosphorus concentrations from high-frequency sensor data, as reported in: Harrison, J.W., Lucius, M.A., Farrell, J.L., Eichler, L.W., and Relyea, R.A. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression. Science of the Total Environment: https://doi.org/10.1016/j.scitotenv.2020.143005. The dataset consists of paired values of stream nitrogen and phosphorus concentrations and various high-frequency sensor parameters (water temperature, specific conductance, pH, fluorescent dissolved organic matter, turbidity, hydrostatic pressure, soil moisture) collected during baseflow and storm events from 2018 to 2019 as part of routine monitoring of eleven tributaries of Lake George, New York. This dataset does not include raw data; two levels of processing were performed: (1) erroneous values (extreme or otherwise outlying values with no apparent environmental cause) were removed from the sensor data as part of the routine QA/QC process of the Jefferson Project, and (2) one-hour rolling medians of the raw sensor data were calculated at a 1-minute timestep to maximize pairing of sensor data with nutrient concentrations. The resultant dataset was used to train and test the models presented in Harrison et al. 2020.
Consequences of non-random tree species loss on litter mass loss, nutrient dynamics, carbon cycling, and decomposer communities across a terrestrial-aquatic interface at Coweeta Hydrologic Lab, Otto, NC
Although litter decomposition is a fundamental ecological process, most of our understanding comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss. The focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. Data were analysed using a statistical approach that first looks for additive identity effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and or composition.
A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods
<p>Non-targeted mass spectrometry (MS) has become an important method over the last years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of data sets nontargeted, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection (and refinement of it) is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of:</p> <ul> <li>51 converted mass spectral files in mzML format</li> <li>An .RData-file containing the extracted ion chromtograms (EICs)</li> <li>The randomly selected subset and the original output table of MZmine in .csv-format</li> <li>Example .xlsx files for the classification</li> <li>2 central classification tables</li> <li>Several tables with additional information about the sampling, chemical analysis and expert jugdement on EICs</li> </ul> <p>For a full description of the experiment and the data set, please read the related Data Descriptor with the title "A data set of 255000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods" in Metabolites (https://www.mdpi.com/journal/metabolites; DOI: https://doi.org/10.3390/metabo10040162).</p>
Standing Balance Experiment with Long Duration Random Pulses Perturbation
<p>Standing balance experiment and the measured data-set are fundamental for identifying postural feedback controllers. As the generalized feedback controllers can only be identified from long duration balance data (under random external perturbations), a standing balance experiment is conducted and the long duration motion data was recorded. The data-set includes the perturbation reaction data from eight subjects. Each subject performed four experiment trials, including two quiet standing and two perturbed trials. Each trial lasted five minutes. A total of 80 minutes quiet standing and 80 minutes perturbed standing data are included in this data-set. Recorded information including three dimensional trajectories of thirty-two markers (27 on subjects' trunk and legs and 5 on the treadmill frame), six dimensional ground reaction forces, and nine Electromyography signals (EMGs, on subjects' right leg). In addition, joint angles and torques were calculated using a human body model and inverse dynamics. Basic statistical analysis of the data is also included.</p> <p>Measured raw data for each subject in each experimental trial includes three files:</p> <ol> <li>Mocapxxxx.txt: contains motion capture marker data, ground reaction force, and 76 analog channels. Data was recorded at 100 Hz sampling rate.</li> <li>Mocapxxxx_Motion Analysis_analog.txt: contains 76 high sampling rate (1000Hz) analog channels' data. Analog data is consisted of the analog singal from the froce sensor on the treadmill, EMG signals in the Delsys EMG sensors, and 3 axises acceeleration signals of the Delsys EMG sensors.</li> <li>Recordxxxx.txt: contains the sway motion data of treadmill and the three-axis acceleration data of two Xsens MTi-10 series sensors.</li> </ol> <p>Measured raw data also includes two files of the unloaded trial, which is used for the inertia compensation.</p> <ol> <li>Mocap0000.txt: contains motion capture marker data (5 markers on the treadmill frame) and ground reaction forces.</li> <li>Record0000.txt: contains the treadmill sway motion data and the acceleration data (three-axis) of two Xsens MTi-10 series sensors.</li> </ol> <p>Processed data of each subject in each experimental trial contains four files:</p> <ol> <li>Mocapxxxx.txt: contains the gap filled motion capture marker data and the inertia compensated ground reaction force data.</li> <li>Motionxxxx.txt: contains the calculated the trajectories of three joints' (hip, knee, and ankle) angles, angular velocities, moments, and joint contact forces.</li> <li>Data_infoxxxx.txt: contains the quality of recorded raw marker data (percentage and biggest duration of missing marker data), and the percentage of removed inertia artifacts in ground reaction forces</li> <li>MotionAnalysis.fig: shows the mean and standard deviation of three joints' trajectories in four experimental trials.</li> </ol> <p>There are two more plots in the processed data folder which shows the joint motion/moment and the raw/compensated ground reaction forces of one example experimental trial (subject 07 trial 03).</p> <p>The processed data was generated using the code in the 'Processing_Code' folder. The code was wrote using Matlab and the main function is "Data_Processing_Main.m"</p> <p>More details of the standing balance experiment can be found in the document 'Standing_Balance_Experiment_with_Long_Duration_Random_Pulses_Perturbation.pdf'</p>
Randomly generated dataset
<p>This dataset is randomly generated using the built-in function from python random.randint(). This csv file contains 2 columns, index and value. Index represents the unique row id and value represents the randomly generated value at each row.</p>
Random-Phase Approximation in Many-Body Noncovalent Systems: Methane in a Dodecahedral Water Cage
<p>Supplementary information and raw data for Random-Phase Approximation in Many-Body Noncovalent Systems: Methane in a Dodecahedral Water Cage. </p>
A novel educational approach for safe endodontic syringe irrigation: a randomized controlled study
<p><span>(1) Educational video emphasizing the fundamentals of safe irrigation practices, incorporating evidence-based guidelines on appropriate plunger forces and the required time for safe irrigant delivery. </span></p> <p><span>(2) Dataset comprising the measurement data collected and processed during this study.</span></p>
Collider Bias Correction for Multiple Covariates in GWAS Using Robust Multivariable Mendelian Randomization
<p>This repository contains the data underlying the figures in paper "Collider Bias Correction for Multiple Covariates in GWAS<br>Using Robust Multivariable Mendelian Randomization".</p> <p> </p> <p> </p> <p>The file names and sheet names in the xlsx file indicate the corresponding figures of data. </p> <p><br>The underlying data of manhattan plots and QQ plots are in text file. For other figures, the underlying data are in the spreadsheet.</p> <p>In each file, column names indicate the MVMR method used to obtain the result. </p> <p>For example: </p> <p>In text files:</p> <p>The abbreviation "mPC" refers to metabolomic principle components.</p> <p>beta_no_correction: the SNP effect estimate without bias correction.</p> <p>beta_cml or beta_MVMR_cml: the standard error of SNP effect estimate after the bias correction of MVMR-cML.</p> <p>SE_UVMR_cml: the standard error of SNP effect estimate after the bias correction of UVMR-cML.</p> <p>p_value_Egger or p_value_MVMR_Egger: the p-value of SNP effect estimate after the bias correction of MVMR-Egger regression.</p> <p><br>In the spreadsheet, column names follow the same style. </p> <p>The GWAS data is also available. The column names follows the plink output file. The detailed explanations are available at https://www.cog-genomics.org/plink/2.0/formats#glm_linear</p>
Transcutaneous Kilohertz High-Frequency Alternating Current at 10 kHz for Upper-Limb Tremor in People with Parkinson's Disease: A double-blind, randomized, crossover study.
<p><strong><span>Abstract: <span>Background/Objectives:</span></span></strong><span> Preclinical studies have evidenced a peripheral nerve blockade with kilohertz high-frequency alternating current (KHFAC) stimulation. It could have a potential effect on aberrant nerve hyperactivity, such as tremor in people with Parkinson’s disease (PwPD). The objective was to investigate the effects of transcutaneous KHFAC at 10 kHz compared with sham intervention on tremor modulation, upper limb motor function, and adverse events in PwPD. <strong>Methods:</strong> This randomized, double-blind, crossover trial included PwPD, who received transcutaneous KHFAC and sham interventions, within a 48h washout period. Measurements were taken pre-intervention, during, immediately after, and 10 minutes post-intervention. The main outcomes were rest, postural, and kinetic tremor acceleration. Secondary outcomes were handgrip strength, nine-hole peg test (NHPT), movement onset time, and adverse events.<strong> </strong></span></p>
Omnibus Poll Ukraine - August 2023 (Ilko Kucheriv Democratic Initiatives Foundation + Razumkov Centre) – Random-sample questionnaire-based representative poll
This data collection offers a representative omnibus survey of the Ukrainian population, living in territories controlled by the Ukrainian government without ongoing armed hostilities. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation together with the sociological service of the Razumkov Center from 09 to 15 August 2023. The survey was conducted using a stratified multi-stage sample. The structure of the sample reflects the demographic structure of the adult population of the surveyed territories as of the beginning of 2022 (by age, gender, type of settlement). 2019 respondents aged 18 and older were interviewed. The theoretical sampling error does not exceed 2.3%. At the same time, additional systematic sample deviations may be caused by the consequences of Russian aggression, in particular, the forced evacuation of millions of citizens. The survey covers five thematic fields: assessment of the current situation in the country, the Russian war of aggression, energy sector, corruption, volunteering. This data collection contains the original survey data. The SPSS file (.sav) is the original file provided by the Ilko Kucheriv Democratic Initiatives Foundation. It has been exported into an Excel file. The content of the respective xlsx-file should be identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation are also included in this data collection as separate pdf-file. Additionally, the data collection contains three files with "selected results" which document some major results of the survey in the form of analytical summaries and descriptive statistics: two in English, covering assessment of the current situation in the country + the Russian war of aggression as well as volunteering; one in Ukrainian covering corruption. New in version 1.1: The numbering of questions in the separate questionnaire (file "DIF_CR_0823-questionnaire-revised.pdf") has been adjusted to the numbering in the original data file ("DIF_CR_0823.sav"). A third file with "selected results" has been added. New in version 1.2: An English translation of the questionnaire has been added under "files".
Omnibus Poll Ukraine - July 2023 (Ilko Kucheriv Democratic Initiatives Foundation + Kyiv International Institute of Sociology) – Random-sample questionnaire-based representative poll
This data collection offers a representative omnibus survey of the Ukrainian population, living in territories controlled by the Ukrainian government without ongoing armed hostilities. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation together with the Kyiv International Institute of Sociology from 03 to 17 July 2023. A description of the methodology is given on p.2 of the "selected results" file, which is part of this data collection. The poll covers the following thematic fields: jobs + entrepreneurship, corruption, economic situation, healthcare sector, war, people under Russian occupation. This data collection contains the original survey data. The SPSS file (.sav) is the original file provided by the Ilko Kucheriv Democratic Initiatives Foundation. It has been exported into an Excel file. The content of the respective xlsx-file should be identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation are also included in this data collection as separate pdf-files. Additionally, the data collection contains one file with "selected results" which document some major results of the survey in the form of a analytical summaries and descriptive statistics and another file with a clarification concerning the interpretation of question 5.24 about the president's "personal responsibility" for corruption in the country. These files are in Ukrainian only. New in version 1.1: An English translation of the questionnaire has been added under "files".
A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories
<p>Containes input data for MD simulations of 3 HSP90- small compound complexes from the paper</p> <p>A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories" from Daria B. Kokh, Bernd Doser , Stefan Richter , Fabian Ormersbach , Xingyi Cheng, Rebecca C. Wade, publishe in J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <ul> <li>ref.pdb - structure of the complex in PDB format</li> <li>ref.prmtop - topology file in AMBER</li> <li>ref-equal-NTP.pdb - structure after NTP equilibration </li> <li>ref-equal-NTP.rst7 - coordinates after NTP equilibration</li> <li>ref-equal-NTP.crd - coordinates after NTP equilibration </li> <li>gromacs.gro - coordinates in Gromacs format (after NTP equalibration)</li> <li>gromacs.top - Gromacs topology </li> </ul> <p> </p>
Accurately modeling biased random walks on weighted networks using node2vec+ - Additional data
<p>Human gene interaction network data used to reproduce gene classification experiments https://github.com/krishnanlab/node2vecplus_benchmarks</p>
Data from randomized control trials released hatchery salmon treated with anti-parasitic treatment
<p>Data used in the article "<strong>Parasite spillback from fish farms reduce return rate of wild salmon"</strong></p> <p> </p> <p>Each release group has been used as a randomized control trials (RCT) of hatchery reared salmon smolts where half of the fish has been treated with an antiparasitic drug. Description of this method has been given in various other publications (Vollset et al. 2014, Vollset et al 2016, Skilbrei et al. 2013). The method involves rearing salmon eggs originating from the national Gene Bank to smolt size in hatchery facilities during one year, and then treating the salmon smolts with fish feed pellets coated with emamectin benzoate (SLICE®). These fish are then released into the river or transported in tanks or mobile net pens further out in the fjord before release. The fish are tagged with either coded-wire-tags (CWT; years 2000-2017) or Passive Integrated Transponders (PIT; 2015-2019) so that it is possible to identify them as they are recaptured or registered on an antenna upon their return as adults. In a few trials, another antiparasitic treatment (Substance EX) has been used, but in most cases the EB has been the only available treatment. Releases of hatchery reared salmon in freshwater have not been successful in this system, i.e. very few fish have returned from any group released in the river, lakes or estuary of Vosso. Since the release groups are also a part of a restoration effort of the Vosso salmon, some years fish have only been released in the fjord. There has been some variation in the release sites in the fjords, but for the purpose of this study we group the release groups in either group that has been released in the outer fjord (70-105 km from the river mouth) and the inner fjord (15-70 km from the river mouth), and freshwater (approx -10 to 15 km from the river mouth). The two most prevalent locations are at Manger (WGS84; 60.63918, 4.92149) and Arna (WGS84; 60.50812, 5.37777).</p> <p> </p> <p><em>Sea lice surveillance</em></p> <p> </p> <p>Sea lice surveillance on sea trout has been conducted at Herdla, the northern peninsula of the island Askøy (WGS84; 60.568972, 4.963010) since 2009. Here, trout have been caught using a trap net that has been developed specifically to capture and treat trout while minimizing sea lice loss during handling (Barlup et al. 2013). From an earlier study by Vollset et al. (2018), it has been shown that the lice numbers on sea trout on this site correlate with the infestation pressure of fish farms in the outer region of the fjord. This area is also one of the largest fish farm zones with coordinated production and fallowing in the outer fjord system where all the released salmon smolts must migrate (see Vollset et al. 2018). This is also the area where surface salinity layers permit salmon lice to overlap with out-migrating salmon smolts (Vollset et al. 2016).</p> <p> </p> <p>The number of trout caught during the monitoring season has varied with weather conditions, sampling intensity, and number of traps operated. The way that trout are handled is described in more detail in Vollset et al. (2018), but in brief, the trap chambers are checked daily, and individual trout are transferred from the trap using a hand held dip net and are either euthanized and placed in zip-lock bag or transported in a large bucket with aerated water to land. Euthanized samples are kept cold and frozen when at land, and later thawed and counted in the lab, while live samples are counted after being sedated with half dose (0.05 g/L) of MS222 and then assessed for salmon lice in a high-contrast bucket using a headlamp by trained personnel. Since 2015 the sea lice surveillance at Herdla is also operated as a part of the Norwegian national sea lice monitoring program.</p> <p>We aimed to use a standardized time period from which to assess sea lice numbers on sea trout that can be representative of the lice infestation pressure from when the tagged hatchery salmon smolts are released. When counting sea lice on sea trout, the most observable lice are large chalimus and mobile stages, while recently attached copepods are more likely to be missed. Therefore, we use total lice counts on sea trout from Julian day 135 to 165 as an assessment of the infestation pressure the salmon smolts must experience. This corresponds to approximately 15 May to 15 of June, and is based on a study on progression rate of salmon smolts from hatchery smolt in this area (Vollset et al. 2016). To account for the fact that larger fish will attract more parasites, we use parasites per gram fish per individual and average data to get one index per year. This method is expected to provide a fair index of interannual variation of the infestation pressure.</p> <p> </p> <p>Table 1 Description of column names in csv file</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>release_year</td> <td>Year of release as smolts</td> </tr> <tr> <td>release_place</td> <td>Name of release place location</td> </tr> <tr> <td>release_date</td> <td>Date of release as smolts</td> </tr> <tr> <td>Released</td> <td>Number of hatchery smolt released</td> </tr> <tr> <td>Recaptured</td> <td>Number of hatchery smolt recaptured as adults</td> </tr> <tr> <td>treat</td> <td>Treatment (either treatment or control)</td> </tr> <tr> <td>tag</td> <td>Tag type (either CWT or PIT)</td> </tr> <tr> <td>release_category</td> <td>Release place (either river, outer fjord or inner fjord)</td> </tr> <tr> <td>lpg</td> <td>Lice per gram fish on trout during surveillance from 15 of May to 15 of June the year of release</td> </tr> <tr> <td>pr</td> <td>Percent (%) recaptures as adults</td> </tr> </tbody> </table> <p> </p>
Random and systematic uncertainties for OMPS-LP ozone profiles
<p>This data set contains random and systematic uncertainties for ozone profiles retrieved at the University of Bremen from OMPS-LP observations. The uncertainties are expressed in relative values and are reported as vertical profiles (every 5 km) and as a function of latitude (5 bands, i.e., SH polar, SH mid-latitude, tropics, NH mid-latitudes, NH polar) and season. The user can associate to any OMPS-LP ozone profile the uncertainty corresponding to the appropriate latitude-season bin. The description of the uncertainties is provided in the AMT paper "Assessment of the error budget for stratospheric ozone profiles retrieved from OMPS limb scatter measurements", Arosio et al. 2022.</p>
Backscattering from randomly placed Dirichlet particles
<p><strong>Summary:</strong></p> <p>A simulated data set of backscattered waves, governed by the 2D wave equation, from randomly placed Dirichlet particles. The particles are circular with 0.2 < radius < 2.0 and 1% < volume fraction < 20%. The incident wave is of the form e<sup>i k (x - x<sub>R</sub> - t)</sup> where the particles are placed in the halfspace x>0 and the backscattering is emitted and received at (x<sub>R</sub>, 0) with x<sub>R</sub> = -10.0. The data named bunny and bunnytest is for incident waves with 0 < k < 1 (where the term e<sup>- i k t</sup> factors out). The data named train and test is for incident waves of the form e<sup>0.1</sup><sup>(x - xR - t)^2</sup>, with 9.5 < t < 98. </p> <p><strong>Format:</strong></p> <p>An example of how to use the data is given in [4]. To load the data requires the packages [1] and [2] in the language Julia v0.5> [3]. When using load(filename) the data will automatically be loaded as an array of type FrequencySimulation or of type TimeSimulation. </p> <p>[1] https://github.com/jondea/MultipleScattering.jl</p> <p>[2] https://github.com/JuliaIO/JLD.jl </p> <p>[3] https://julialang.org/</p> <p>[4] https://github.com/gowerrobert/MultipleScatteringLearnMoments/tree/master/examples</p>
Trypanosoma Epitope Dataset: Valid Epitopes and Randomly Generated Peptides with Biochemical Metrics and AI-Generated Scores
<p>This dataset contains information about valid linear B-Cell epitopes from the Trypanosoma genus, as well as randomly generated peptides. It includes biochemical metrics generated by the EpiBuilder-1.0 tool and scores generated by the BepiPred-3.0 software. The data was originally collected from the IEDB and UniProtKB platforms and has been processed and enhanced with these informations for researchers interested in understanding the molecular interactions between Trypanosoma protozoans and the immune system.</p>
Observation of a regular structure formation on the surface of vibrated ball beds started from random lose packing
<p>Near 4,000 2-mm diameter plastic balls were poured 88 times into plexiglass cylinder of internal diameter 26 mm. Then, such initially random loose-packing systems/beds were vibrated vertically with 100 Hz frequency using the vibration table Vibrax (Renfert GmbH, Germany) working in sinusoidal mode until a regular structure was observed on the cylinder surface. The power levels of the vibrations in the recorded ordering of balls were selected to represent all four levels (1, 2, 3 or 4) of vibrations available in the table, where number 1 means the weakest vibration and 4 means the strongest one.</p> <p>Locations of the balls on all sides of a vibrated cylindrical bed were simultaneously recorded on one video frame thanks to the use of two perpendicular mirrors, which enables observation of four images: one of the real cylinder and three of its mirror reflections. View of the table with the attached cylinder containing balls and two mirrors is presented in Fig. 1, while an explanation of the scene, as seen by the recording camera, is given in the scheme in Fig. 2. Video names were given in a standard form explained in the README.txt file.</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.