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559 results for “Track data”
Pointing movements and eye-tracking data_Facilitated Communication Users
<p>The repository contains all the pre-sorted data used for the analysis described in the paper. Data are divided into three:</p> <ul> <li>in A, we report the movement data of each pointing gesture considered in the analysis.</li> <li>in B we report keystrokes' related data.</li> <li>in C we report the sorted eye-tracking data.</li> </ul> <h3>A. User Correct Movements:</h3> <p>Each participant's data is organized into a 1xN cell array in Matlab, where N represents the number of pointing gestures analyzed. Each cell contains an Nx8 column vector with the following information:</p> <ol> <li> <p><strong>Time Information (column 1)</strong>:</p> <ul> <li>Time associated with the pointing gesture (milliseconds).</li> </ul> </li> <li> <p><strong>Arm Coordinates (columns 2,3 and 4)</strong>:</p> <ul> <li>X-axis coordinates (millimetres).</li> <li>Y-axis coordinates (millimetres).</li> <li>Z-axis coordinates (millimetres).</li> </ul> </li> <li> <p><strong>EMG Deltoid Activation (Facilitator) (columns 5, and 6) </strong>:</p> <ul> <li>Rectified EMG deltoid activation.</li> <li>Envelope EMG deltoid activation.</li> </ul> </li> <li> <p><strong>EMG Deltoid Activation (User) (columns 7 and 8)</strong>:</p> <ul> <li>Rectified EMG deltoid activation.</li> <li>Envelope EMG deltoid activation.</li> </ul> </li> </ol> <h3>B. Users' Keys Pressed with Probability:</h3> <p>Each participant's data is organized into an Nx6 string array, where N represents the number of pointing gestures analyzed. Each array contains the following information:</p> <ol> <li> <p><strong>Key Press Time</strong>:</p> <ul> <li>Absolute time the key is pressed (milliseconds, as recorded by the key-logger).</li> </ul> </li> <li> <p><strong>Time Between Keystrokes</strong>:</p> <ul> <li>Difference in milliseconds between two consecutive keystrokes.</li> </ul> </li> <li> <p><strong>Key Pressed</strong>:</p> <ul> <li>The key that has been pressed.</li> </ul> </li> <li> <p><strong>Pointing Time</strong>:</p> <ul> <li>Time taken by the arm to complete the forward phase of the pointing gesture (seconds).</li> </ul> </li> <li> <p><strong>Character Position</strong>:</p> <ul> <li>Position of the pressed character within the word (spacebar hits are assigned the number 300).</li> </ul> </li> <li> <p><strong>Key Selection Probability</strong>:</p> <ul> <li>Probability (percentage) of the key being selected.</li> </ul> </li> </ol> <h3><strong>C. EyeTracking data sorted</strong></h3> <p>Each participant's data is organized into an N×6 cell array, where N represents the number of pointing gestures analyzed through eye-tracking. The contents of each row are as follows:</p> <ol> <li> <p><strong>Fixation Data (Nx5 vector)</strong>:</p> <ul> <li><strong>N</strong> is the number of fixations related to one pointing gesture.</li> <li>Each vector contains: <ul> <li> <p><strong>The standardized time </strong>is determined by synchronizing the eye fixation with the arm movement. Given the movement duration is scaled from 0 to 10, we identify the moment when the eye-fixation occurs.</p> <p>This standardized time refers to the duration of the fixation relative to the duration of the pointing gesture. In <strong>column 1</strong>, we report the gross time, averaging the beginning and end of the fixation. In<strong> column 4</strong>, we provide the exact standardization at the start, and in <strong>column 5</strong>, the exact standardization at the end of the movement. During analysis, these times are synchronized with the movement duration, and we use the data from column 4.</p> </li> <li> <p><strong>Euclidean distance</strong> between the fixated key and the target key (2nd column).</p> </li> <li><strong>Duration</strong> of each fixation (3rd column).</li> </ul> </li> </ul> </li> <li> <p><strong>Sequence of Fixated Keys</strong>:</p> <ul> <li>Contains the sequence of keys fixated by the user during each pointing gesture.</li> </ul> </li> <li> <p><strong>Arm Movement Information</strong>:</p> <ul> <li>Includes details on the arm movement (same as reported in<strong> file A</strong>) corresponding to the eye-tracking fixation sequence.</li> </ul> </li> <li> <p><strong>Target Key Pressed</strong>:</p> <ul> <li>Indicates the target key pressed by the participant.</li> </ul> </li> <li> <p><strong>Probability of Key Pressed</strong>:</p> <ul> <li>Reports the likelihood of each key being pressed,<strong> as detailed in file B.</strong></li> </ul> </li> <li> <p><strong>Euclidean Distance Between Consecutive Keys</strong>:</p> <ul> <li>Measures the Euclidean distance between two consecutively pressed keys using the keyboard as a reference (refer to the paper text for more details).</li> </ul> </li> </ol> <p> </p> <p> </p>
Data for "Revisiting the reanalysis-model discrepancy in Southern Hemisphere winter storm track trends"
<p>The dataset supporting the conclusion of the submitted paper is uploaded here.</p> <p>The data are labeled after each figure. The npz files include data required to reproduce our results in python arrays.</p>
E2E-differentiable Charged Particle Tracking Data
<div> <div>In this data repository, we extend an earlier dataset (https://zenodo.org/records/7426388) generated for the Bergen pCT DTC by various additional Monte Carlo (MC) simulations (generated using the Gate 9.2 simulation toolkit [1, 2] built upon Geant4 [3,4,5]) with different setups and phantom materials with particular focus on an E2E-differentiable tracking algorithm provided on <a href="https://github.com/SIVERT-pCT/e2e-tracking">GitHub</a>. The data repository is further extended by trained model checkpoints and results of addtional analysis required for running the code, without re-training and re-evaluation.</div> <br> <h3><strong>Training Files</strong></h3> <strong>Files</strong>: We provide multiple simulations for different phantom geometries and simulation setups, each generated with a mono-energetic pencil beam (230 MeV, 2 sigma). The supplied files include single beam spots for water phantoms of various thicknesses (100, 150 and 200 mm): <ul> <li>water_{100,150,200}_5k.npz (validation data)</li> </ul> <div> <ul> <li>water_{100,150,200}_10k.npz (test data, taken from https://zenodo.org/records/7426388)</li> </ul> </div> <div> <ul> <li>water_{100,150,200}_100k.npz (training data)</li> </ul> </div> <br> <div><strong>Columns</strong>: All above-mentioned simulation files contain MC simulated data of a single simulation run in tabular form, where each row represents a single particle hit inside the detector. Furthermore, each particle hit is parametrized by the following columns:</div> <ul> <li><strong>posX, posY, posZ</strong>: Measured x, y, z position (in millimeter) of the particle hit relative to the simulation origin defined by the center of the phantom.</li> </ul> <div> <ul> <li><strong>edep</strong>: Amount of energy (in MeV) deposited by a particle while interacting with the sensitive area of the detector.</li> </ul> </div> <div> <ul> <li><strong>eventID</strong>: Each primary is simulated in its own isolated "event" and gets an incremental ID. Everything that happens during the simulation of said primary is grouped under the same eventID. Events are simulated independent of each other. trackIDs are only unique within their respective event.</li> </ul> </div> <div> <ul> <li><strong>trackID</strong>: A track describes a single particle throughout its entire lifetime in the simulation. In any given event, the first track (trackID = 1) is always associated with the primary particle. Every subsequently produced secondary particle has an incremental trackID.</li> </ul> </div> <div> <ul> <li><strong>parentID</strong>: The parentID specifies the trackID in the current event that caused this track to exist. If the parentID is 0, the particle is a primary, i.e., generated by the particle beam. Otherwise, the row describes a secondary which was generated through interactions of a primary with the traversed matter.</li> </ul> </div> <div> <ul> <li><strong>volumeID[2]</strong>: Incremental numerical identifier of layer containing particle hit inside GATE volume 2 defined within the detector geometry. 0 for tracking layers, 1 for calorimeter layers.</li> </ul> </div> <div> <ul> <li><strong>volumeID[3]</strong>: Incremental numerical identifier of layer containing particle hit inside GATE volume 3 defined within the detector geometry. Unique identifiers (starting from zero) for tracking layer (0, 1) and calorimeter layer (0, 1, …, 40).</li> </ul> </div> <br> <h3><strong>Model Checkpoints and Analysis Data</strong></h3> <strong>Files</strong>: Additionaly to the simulated track data, we provide the trained model checkpoints and additional analysis data, generated using the source code for end-to-end differentiable charged particle tracking published on <a href="https://github.com/SIVERT-pCT/e2e-tracking">GitHub</a>. The compressed directory contains the following directories:<br> <div> <ul> <li><strong>cka</strong>: Calculated CKA similarities [6] estimated for all trained model combinations .</li> </ul> </div> <div> <ul> <li><strong>mode</strong>: Minimum energy connecting curves (mode connectivity) [7] of Bezier splines optimized for all trained model combinations.</li> </ul> </div> <div> <ul> <li><strong>pat_lambda_{25,50,75}</strong>: All trained end-to-end differentiable tracking networks.</li> </ul> </div> <div> <ul> <li><strong>ptt</strong>: All trained two-step tracking networks.</li> </ul> </div> <br><br> <div>[1] S. Jan, G. Santin, D. Strul et al., “GATE -Geant4 Application for Tomographic Emission: a simulation toolkit for PET and SPECT,”Phys Med Biol. Phys Med Biol, vol. 49, no. 19, pp. 4543–4561, 2004.</div> <br> <div>[2] S. Jan, D. Benoit, E. Becheva et al., “GATE V6: A major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy, ”Physics in Medicine and Biology, vol. 56, no. 4,pp. 881–901, 2011.</div> <br> <div>[3] S. Agostinelli, J. Allison, K. Amako et al., “GEANT4 - A simulation toolkit, ”Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 506, no. 3, pp. 250–303, 2003.</div> <br> <div>[4] J. Allison, K. Amako, J. Apostolakis et al., “Geant4 developments and applications, ”IEEE Transactions on Nuclear Science, vol. 53, no. 1, pp. 270–278, 2006.</div> <br> <div>[5] J. Allison, K. Amako, J. Apostolakis et al., “Recent developments in geant4”, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 835, pp. 186–225, 201</div> <br> <div>[6] Kornblith, S., Norouzi, M., Lee, H., & Hinton, G. (2019). Similarity of Neural Network Representations Revisited. 36th International Conference on Machine Learning, pp 6156–6175, 2019.</div> <br> <div>[7] Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D., & Wilson, A. G. (2018). Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. Advances in Neural Information Processing Systems, pp. 8789–8798, 2018.</div> </div>
data and appendix for paper "From Analytical Purposes to Data Visualizations: A Decision Process Guided by a Conceptual Framework and Eye Tracking"
<p>the folder contains the following:</p> <p>1. Data from the pre-experiment questionnaire provided to the participants (pre-experiment_data.xlsx)</p> <p>2. Questions and answer data (answers_accuracy.xlsx)</p> <p>3. Online figures appendix (figures.pdf)</p>
data for preprint: MARGO (Massively Automated Real-time GUI for Object-tracking),a platform for high-throughput ethology by Werkhoven et al
<p>Data for preprint: MARGO (Massively Automated Real-time GUI for Object-tracking),a platform for high-throughput ethology by Werkhoven et al.<br> <br> index.html provides a read-me for the organization of this repository. For its links to work the two .zip files should be extracted.</p>
Nanoresolution real-time 3D orbital tracking for studying mitochondrial trafficking in vertebrate axons in vivo - Data Set 2
<p>The data set was analyzed in main figures 2, 3 and supplementary figure 5.</p>
Nanoresolution real-time 3D orbital tracking for studying mitochondrial trafficking in vertebrate axons in vivo - Data Set 1
<p>24.01.2014 fish 5 was analyzed in main figures 1,2 and supplementary figure 4</p> <p>21.03.2014 fish 1 was analyzed in main figures 2,3 and supplementary figure 4</p>
Supporting data (pre-processed) for: "Frequency-tagged visual evoked responses track syllable effects in visual word recognition"
<p>Pre-processed data. </p> <p>Data were re-referenced off-line to the average of left and right mastoid electrodes, bandpass filtered from 5 to 100 Hz (4th order Butterworth filter) and then segmented to include 200 ms before and 2000 ms after stimulus onset. Epoched data were normalized based on a prestimulus period of 200 ms, and then evaluated according to a sample-by-sample procedure to remove noisy sensors that were replaced using spherical splines. Additionally, EEG epochs that contained data samples exceeding threshold (100 uV) were excluded on a sensor-by-sensor basis, including horizontal and vertical eye channels</p> <p>The original data:<br> Montani, Veronica. (2019). Supporting data for: "Frequency-tagged visual evoked responses track syllable effects in visual word recognition" [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3260451</p>
Data and script for "Characterizing Wet Season Precipitation in the Central Amazon Using a Mesoscale Convective System Tracking Algorithm"
<p>This is a placeholder for Data and script for "<strong>Characterizing Wet Season Precipitation in the Central Amazon Using a Mesoscale Convective System Tracking Algorithm</strong>" submitted to JGR-atmosphere.</p>
GEFS tropical cyclone track data by year and 5-day forecast data
<p>The df_master.pkl file contains the processed 5-day TC track forecast data. All remaining files contain the TC track data from ATCF (https://ftp.nhc.noaa.gov/atcf/archive/) by year (2008-2022).</p>
TC track data for BAMS In-Box article
<p>This tarball contains TC best track and model forecasted track data in the ATCF format. </p> <p>b_deck:<br>Contains TC best track files (b_deck) in the North Atlantic basin from 2014 to 2023.</p> <p>a_deck:<br>Contains all model forecasted TC tracks from the GFDL TC tracker in the ATCF data format.<br>models and their index:<br> GFS = AVNO<br> IFS = EMX<br> HWRF = HWRF<br> HAFS-A = HFSA<br> HAFS-B = HFSB<br> 5-day CLIPER5/SHIFOR5 guidance = OCD5</p> <p> SHiELD 2019 = GF95<br> 2020 = FV20<br> 2021 = F206<br> 2022 = F226<br> 2023 = F226</p> <p> T-SHiELD 2020 = T20H<br> 2021 = T21D<br> 2022 = T22D<br> 2023 = T23D</p> <p>Please contact Jan-Huey.Chen@noaa.gov for any questions.</p>
Dataset and codes used in the manuscript entitled "A rainfall-tracking travel time distribution model to quantify mixing and storage release preference in a large shallow lake by two-year stable isotopic data"
<p>This contains the codes and dataset for the manuscript entitled "A rainfall-tracking travel time distribution model to quantify mixing and storage release preference in a large shallow lake by two-year stable isotopic data". Detailed information about the dataset is described in the Readme.txt file.</p>
Data from: Small-mammal isotope ecology tracks climate and vegetation gradients across western North America
Stable carbon, nitrogen, hydrogen and oxygen isotopes have been used to infer aspects of species ecology and environment in both modern ecosystems and the fossil record. Compared to large mammals, stable isotopic studies of small-mammal ecology are limited; however, high species and ecological diversity within small mammals presents several advantages for quantifying resource use and organism–environment interactions using stable isotopes over various spatial and temporal scales. We analyzed the isotopic composition of hair from two heteromyid rodent species, Dipodomys ordii and Perognathus parvus, from localities across western North America in order to characterize dietary variation in relation to vegetation and climatic gradients. Significant correlations between the carbon isotopic composition (δ13C) of these species and several climatic variables imply that seasonal temperature and precipitation control the composition and distribution of dietary resources (grass seeds). Our results also suggest a moisture influence on the nitrogen isotopic composition (δ15N) of heteromyid diets. Population- and species-level variation in δ13C and δ15N values record fine-scale habitat heterogeneity and significant differences in resource use between species. Using classification and regression-tree techniques, we modeled the geographic variation in heteromyid δ13Cdiet values based on 10 climatic variables and generated an isotope landscape model ('isoscape'). The isoscape predictions for δ13Cdiet differ from expectations based on observed C4 distributions and instead indicate that D. ordii and P. parvus record seasonally abundant grass resources, with additional model deviations potentially attributed to geographic variation in dietary selection. The oxygen and hydrogen isotopic composition of D. ordii is enriched relative to local meteoric water and suggests that individuals rely on highly evaporated water sources, such as seed moisture. Based on the climatic influences on vegetation and diet documented in this study, the isotopic composition of small mammals has high potential for recording ecological responses to environmental changes over short and long time scales.
Data from: Satellite tracking reveals novel migratory patterns and the importance of seamounts for endangered South Pacific humpback whales
The humpback whale population of New Caledonia appears to display a novel migratory pattern characterized by multiple directions, long migratory paths and frequent pauses over seamounts and other shallow geographical features. Using satellite-monitored radio tags, we tracked 34 whales for between 5 and 110 days, travelling between 270 and 8540 km on their southward migration from a breeding ground in southern New Caledonia. Mean migration speed was 3.53±2.22 km h−1, while movements within the breeding ground averaged 2.01±1.63 km h−1. The tag data demonstrate that seamounts play an important role as offshore habitats for this species. Whales displayed an intensive use of oceanic seamounts both in the breeding season and on migration. Seamounts probably serve multiple and important roles as breeding locations, resting areas, navigational landmarks or even supplemental feeding grounds for this species, which can be viewed as a transient component of the seamount communities. Satellite telemetry suggests that seamounts represent an overlooked cryptic habitat for the species. The frequent use by humpback whales of such remote locations has important implications for conservation and management.
Data from: Genomic differentiation tracks earth-historic isolation in an Indo-Australasian archipelagic pitta (Pittidae; Aves) complex
Background: Allopatric speciation has played a particularly important role in archipelagic settings where populations evolve in isolation after colonizing different islands. The Indo-Australasian island realm is an unparalleled natural laboratory of biotic diversification. Here we explore how the level of earth-historic isolation has influenced genetic differentiation across the region by investigating phylogeographic patterns in the Pitta sordida species complex. Results: We generated a de novo genome and compared population genomics of 29 individuals of Pitta sordida from the entire distributional range and we reconstructed phylogenetic relationship using mitogenomes, a multi-nuclear gene dataset and single nucleotide polymorphisms (SNPs). We found deep divergence between an eastern and a western group of taxa across Indo-Australasia. Within both groups we have identified major lineages that are geographically separated into Philippines, Borneo, western Sundaland, and New Guinea, respectively. Although these lineages are genetically well-differentiated, suggesting a long-term isolation, there are signatures of extensive gene flow within each lineage throughout the Pleistocene, despite the wide geographic range occupied by some of them. We found little evidence of hybridization or introgression among the studied taxa, but forsteni from Sulawesi makes an exception. This individual, belonging to the eastern clade, is genetically admixed between the western and eastern clades. Geographically this makes sense as Sulawesi is not far from Borneo that houses a population of hooded pittas that belongs to the western clade. Conclusions: We found that geological vicariance events cannot explain the current genetic differentiation in the Pitta sordida species complex. Instead, the glacial-interglacial cycles may have played a major role therein. During glacials the sea level could be up to 120 m lower than today and land bridges formed within both the Sunda Shelf and the Sahul Shelf permitting dispersal of floral and faunal elements. The geographic distribution of hooded pittas shows the importance of overwater, "stepping-stone" dispersals not only to deep-sea islands, but also from one shelf to the other. The most parsimonious hypothesis is an Asian ancestral home of the Pitta sordida species complex and a colonization from west to east, probably via Wallacea.
Data from: Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries
Recent technological developments facilitate the collection of location data from fishing vessels at an increasing rate. The development of low-cost electronic systems allows tracking of small-scale fishing vessels, a sector of fishing fleets typically characterised by many, relatively small vessels. The imminent production of large spatial datasets for this previously data-poor sector, creates a challenge in terms of data analysis. Several methods have been used to infer the spatial distribution of fishing activities from positional data. Here, we compare five approaches using either vessel speed, or speed and turning angle, to infer fishing activity in the Scottish inshore fleet. We assess the performance of each approach using observational records of true vessel activity. Although results are similar across methods, a trip-based Gaussian mixture model provides the best overall performance and highest computational efficiency for our use-case, allowing accurate estimation of the spatial distribution of active fishing (97% of true area captured). When vessel movement data can be validated, we recommend assessing the performance of different methods. These results illustrate the feasibility of designing a monitoring system to efficiently generate information on fishing grounds, fishing intensity, or monitoring of compliance to regulations at a nationwide scale in near-real time.
Data from: Lake sediment multi-taxon DNA from North Greenland records early post-glacial appearance of vascular plants and accurately tracks environmental changes
High Arctic environments are particularly sensitive to climate changes, but retrieval of paleoecological data is challenging due to low productivity and biomass. At the same time, Arctic soils and sediments have proven exceptional for long-term DNA preservation due to their constantly low temperatures. Lake sediments contain DNA paleorecords of the surrounding ecosystems and can be used to retrieve a variety of organismal groups from a single sample. In this study, we analyzed vascular plant, bryophyte, algal (in particular diatom) and copepod DNA retrieved from a sediment core spanning the Holocene, taken from Bliss Lake on the northernmost coast of Greenland. A previous multi-proxy study including microscopic diatom analyses showed that this lake experienced changes between marine and lacustrine conditions. We inferred the same environmental changes from algal DNA preserved in the sediment core. Our DNA record was stratigraphically coherent, with no indication of leaching between layers, and our cross-taxon comparisons were in accordance with previously inferred local ecosystem changes. Authentic ancient plant DNA was retrieved from nearly all layers, both from the marine and the limnic phases, and distinct temporal changes in plant presence were recovered. The plant DNA was mostly in agreement with expected vegetation history, but very early occurrences of vascular plants, including the woody Empetrum nigrum, document terrestrial vegetation very shortly after glacial retreat. Our study shows that multi-taxon metabarcoding of sedimentary ancient DNA from lake cores is a valuable tool both for terrestrial and aquatic paleoecology, even in low-productivity ecosystems such as the High Arctic.
Data from: Creating a multi-track classical music performance dataset for multi-modal music analysis: challenges, insights, and applications
We introduce a dataset for facilitating audio-visual analysis of musical performances. The dataset comprises 44 simple multi-instrument classical music pieces assembled from coordinated but separately recorded performances of individual tracks. For each piece, we provide the musical score in MIDI format, the audio recordings of the individual tracks, the audio and video recording of the assembled mixture, and ground- truth annotation files including frame-level and note-level tran- scriptions. We describe our methodology for the creation of the dataset, particularly highlighting our approaches for addressing the challenges involved in maintaining synchronization and ex- pressiveness. We demonstrate the high quality of synchronization achieved with our proposed approach by comparing the dataset against existing widely-used music audio datasets. We anticipate that the dataset will be useful for the devel- opment and evaluation of existing music information retrieval (MIR) tasks, as well as for novel multi-modal tasks. We bench- mark two existing MIR tasks (multi-pitch analysis and score- informed source separation) on the dataset and compare against other existing music audio datasets. Additionally, we consider two novel multi-modal MIR tasks (visually informed multi-pitch analysis and polyphonic vibrato analysis) enabled by the dataset and provide evaluation measures and baseline systems for future comparisons (from our recent work). Finally, we propose several emerging research directions that the dataset enables.
Data from: Noninvasive neuroimaging enhances continuous neural tracking for robotic device control
Brain-computer interfaces (BCIs) using signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems greatly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly improve the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework using electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the "brain" and "computer" components by increasing, respectively, user engagement through a continuous pursuit task and associated training paradigm and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by more than 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Last, this framework was deployed in a physical task, demonstrating a near-seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications for the eventual development and implementation of neurorobotics by means of noninvasive BCI.
Evaluation Data for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks.
<p>Evaluation Data for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks.</p> <p> </p> <p>2.1 results of static analysis.zip<br> - Metadata collected when fuzzing (protobuf format)<br> - the related protobuf files are in https://github.com/ZHYfeng/Dependency/05-proto<br> - Information to support manual analysis (generated by metadata and static analysis)<br> - with name "0xffffffffaddress.txt", including every information about this uncovered address.</p> <p>2.2 results of manual analysis.zip<br> - Information of non-UD and UD for each module<br> - Overall statistic data, for example, coverage, prevalent of UD<br> - Sample cases and accuracy checking of sampled UD and its WS<br> - Root causes of sampled UDs</p> <p>2.3 example case.zip<br> - example cases of different root causes</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.