Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
19
datasets available to search
ShareScore release 0.9.0
Dataset results
19 results for “Task state”
fMRI: resting state and arithmetic task
Open the record for dataset details and reuse information.
Model-based aversive learning in humans is supported by preferential task state reactivation
Open the record for dataset details and reuse information.
Inference based decisions in a hidden state foraging task: differential contributions of prefrontal cortical areas
<p>Tabular dataset of behavioral data in the hidden state foraging task. The data is stored as a unique table, with one row per "attempt", i.e. a poke for mice and a tap for humans.</p> <p>The table includes four distinct experiments, encoded in the Experiment column. Experiment "Learning" refers to Fig. 2, experiment "VaryingParameters" refers to Fig. 2h and Fig.3. Experiment "LearningAndVaryingParameters" refers to Fig. 4. Experiment "OptogeneticInactivation" refers to Fig. 5.</p> <p>The column "TestingSession" defines whether those sessions were used for the analysis. It is used to exclude adaptation sessions to a new protocol for rodents in the "VaryingParameters" and "OptogeneticInactivation" experiments.</p> <p>In the human case, after an incorrect transition the subject receives an error cue and does not tap. This is considered a "trial" (and also a "streak") but not an attempt. This causes the StreakNumber and PokeNumber columns to increase by 2 after an error cue.</p>
EEG recordings during resting-state and the maintenance periods of a spatial working memory task in humans
<p>Scripts used to analyze data for the manuscript submitted for publication in EJN</p> <p><strong>Script_Curve_Fitting_HBM.rtf</strong></p> <p>Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com </p> <p><span>We therefore considered this continuous change in power as an extraneous variable </span><em><span>y<sub>k</sub>(x)</span></em><span> impacting the measured power spectrum </span><em><span>Pow(E<sub>k</sub>)</span></em><span>, and modeled it with a binomial equation that best fit the data, where the coefficients in <em>p<sub>i</sub></em> are in descending powers, and the length of <em>p</em> is <em>(n+1), k </em>is trial number (<em>k = 1 to 10</em>):</span></p> <p><strong><em><span>y<sub>k</sub>(x) = p<sub><span>1 </span></sub>. x<sup><span>2</span></sup><span><span> </span></span>+ p<sub><span>2 </span></sub>. x<span> </span>+ p<sub><span>3</span></sub></span></em></strong></p> <p><span>In order to statistically compare the topographies between the trials with perfect recall and the trials with failed recall, we subtracted this variable from the mean spectral topographies of each subject and for each electrode by first producing the mean spectral curves of each maintenance trial in the theta and alpha frequency bands, taking into account the IAF, and then calculating the coefficients (</span><em><span>p<sub>1</sub></span></em><span>, </span><em><span>p<sub>2</sub></span></em><span> and </span><em><span>p<sub>3</sub></span></em><span>) of the binomial equation using the Matlab function <em>polyfit.m.</em> Once the coefficients were determined, this estimate was subtracted from each power spectrum matrix using the following formula:</span></p> <p><strong><em><span>PowFit(E<sub><span>k</span></sub>) = Pow (E<sub><span>k</span></sub>) – </span></em></strong><strong><em><span>y<sub>k</sub>(x)</span></em></strong></p> <p> </p> <p><strong>Script_Perf_Fail_EEG_Power_Spec_HBM.rtf</strong></p> <p>Dr. Hadj Meziane: hbmeziane@gmail.com<br>This script calculates EEG power spectra then compares perf and fail conditions, then plots brain topographies with statical results</p> <p> </p> <p><strong>Script_Perf_Fail_EEG_Sources_Spec_HBM.rtf</strong></p> <p>Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com<br>This script compares EEG source spectra then compares Perf vs. Fail conditions then plot statistical results (significant voxels) on MRI volume</p>
Figure 19 Caddisfly fauna from Bahia state, Brazil. A in LEAq - Laboratório de Entomologia Aquática "Prof. Claudio Gilberto Froehlich" and the task of facing the biodiversity knowledge deficits on Caddisflies (Trichoptera), Bahia, Brazil
Figure 19 Caddisfly fauna from Bahia state, Brazil. A, Map of Bahia state, caddisfly records before 2009; B, Map of Bahia with caddisfly records in 2023; C, Caddisfly knowledge along time, description year of species which occur in state (gray), and first distributional records in state (black).
Oxy-fuel Cutting Task State Image Dataset
<p><strong>Associated Paper: </strong>CNN-based Task State Estimation for Safer Automation of Oxy-fuel Metal Cutting<br><strong>Paper Status:</strong> Published (IEEE CASE 2023, doi: <a href="https://doi.org/10.1109/CASE56687.2023.10260647" target="_blank" rel="noopener">10.1109/CASE56687.2023.10260647</a>)</p> <p><strong>PAPER ABSTRACT:</strong></p> <p>The industrial operation of oxy-fuel metal cutting via gas torches involves tasks such as ignition, preheating, and combustion along the target surface. Automated oxy-fuel cutting systems are exposed to risks and anomalies that can lead to incorrect actions and safety hazards. In this paper, we develop a classifier for online task state estimation to assess the cutting robot’s actions, detect anomalies, and reduce the risk of hazards. Using representative footage from our robotic cutting experiments, we curate an image dataset labeled with four types of cutting task states. Using deep learning methods, we design and train a convolutional neural network model for classifying the cutting task state from input images. The classifier architecture is optimized for rapid inferences during online estimation. After evaluation, our classifier achieves an overall accuracy of 93.8% with high inference speeds on two types of representative hardware. Our ‘Oxy-fuel Cutting Task State’ (OCTS) dataset is available at <a href="https://doi.org/10.5281/zenodo.7734951">doi.org/10.5281/zenodo.7734951</a>.</p> <p><strong>DATASET DESCRIPTION:</strong></p> <p>The Oxy-fuel Cutting Task State (OCTS) dataset contains image data from footage recorded during a series of robotic oxy-fuel metal cutting experiments labeled with one of four cutting task states, identified using their prominent feature:</p> <ul> <li>Torch flame (<strong>TF</strong>): Associated with the vision system calibration task.</li> <li>Preheating pool (<strong>PP</strong>): Associated with the surface conditioning task.</li> <li>Combustion pool (<strong>CP</strong>): Associated with the combustion control task.</li> <li>Not applicable (<strong>NA</strong>): Associated with halting operations since none of the previous elements are identified; this is an anomaly.</li> </ul> <p>The dataset files consist of:</p> <ul> <li><strong>Data: </strong>Available as a ZIP archive split into 5 volumes (~2.8 GB each).</li> <li><strong>Labels:</strong> Available in JSON format.</li> <li><strong>Metadata:</strong> Available in CSV and PDF formats, contains the individual experiment set IDs, their dates and times, their total frame counts, and their label-wise frame counts.</li> </ul> <p><strong>DATASET LICENSE:</strong></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a> (CC BY-NC 4.0).</p> <p><strong>DATA INSTRUCTIONS:</strong></p> <p>To extract the ZIP archive, download all five ZIP volumes into a common directory and extract the file <strong>dataset.zip.001</strong>. After extraction, 50 directories are obtained ('S01', 'S02', …, 'S50'). These contains the raw data (image frames) of each of the 50 individual cutting experiments. The image filenames are their frame number ('000000.jpeg', '000001.jpeg', …). All images are in JPEG format. All image filenames are their 6-digit frame number (includes leading zeroes such as in '004021.jpeg') for a particular experiment. Essentially, this is the sequential image data from the footage of each experiment.</p> <p><strong>JSON INSTRUCTIONS:</strong></p> <p>The JSON file partitions the images of each experiment set into the four labels. The first JSON level contains the experiment set ID as a string ('S01', 'S02', …, 'S50'). The second JSON level contains the four labels ('TF', 'PP', 'CP', 'NA') for each set ID. The third JSON level contains arrays of strings containing the frame number (filename without extension) of each image (e.g., ['000000', '000001', , …]). Usage of the JSON file is illustrated in the following Python code snippet:</p> <pre><code>import json with open ("labels.json") as json_file: labels = json.load(json_file) # `labels` is a dictionary. # Get all image filenames (frame numbers) from experiment `S01` in the `NA` label. labels['S01']['NA'] # returns list of image filenames (strings) #output: ['003843', '003844', …, '003978', '003979']</code></pre> <p>Thus, the labels are retrieved for each of the images in each of the set IDs.</p> <p><strong>METADATA INSTRUCTIONS:</strong></p> <p>The metadata associates the set ID of each experiment to its recording date and time. In addition, it lists the total frames of each experiment and the frame count in each of the four labels ('TF', 'PP', 'CP', 'NA'). This is available in PDF format for convenient viewing but also in CSV format.</p>
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
<p>We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning methods focused on solid-state materials with periodic crystal structures. Applying machine learning methods to solid-state materials is a nascent field with substantial fragmentation largely driven by the great variety of datasets used to develop machine learning models. This fragmentation makes comparing the performance and generalizability of different methods difficult, thereby hindering overall research progress in the field. Building on top of open-source datasets, including large-scale datasets like the OpenCatalyst Project, OQMD, NOMAD, the Carolina Materials Database, and Materials Project, the MatSci ML benchmark provides a diverse set of materials systems and properties data for model training and evaluation, including simulated energies, atomic forces, material bandgaps, as well as classification data for crystal symmetries via space groups. The diversity of properties in MatSci ML makes the implementation and evaluation of multi-task learning algorithms for solid-state materials possible, while the diversity of datasets facilitates the development of new, more generalized algorithms and methods across multiple datasets. In the multi-dataset learning setting, MatSci ML enables researchers to combine observations from multiple datasets to perform joint prediction of common properties, such as energy and forces. Using MatSci ML, we evaluate the performance of different graph neural networks and equivariant point cloud networks on several benchmark tasks spanning single task, multitask, and multi-data learning scenarios. Our open-source code is available at https://github.com/IntelLabs/matsciml.</p>
Three syntactic probing tasks for hidden states
<p>This is a dataset for syntactic probes, used in the article "What does Chinese BERT learn about syntactic knowledge?"</p>
Code and data for: A computational model for driver's cognitive state, visual perception and intermittent attention in a distracted car following task
<p>A source code and data dump for analyses of the article "A computational model for driver’s cognitive state, visual perception and intermittent attention in a distracted car following task"</p> <p>Code is under GNU AGPL-v3. Data under CC-BY-4.0</p> <p>Versioned code is available at https://gitlab.com/mulsimco/follow17 and https://gitlab.com/mulsimco/cfmodels</p> <p>See README.md in follow17 for usage.</p>
Assessment of Neural Oscillations in Adult Subjects With Down Syndrome and Typically Developing Subjects in Resting State and While Conducting Cognitive Tasks
ClinicalTrials.gov study NCT04791124. IPD Sharing: NO. Countries: 1. Publications: 0.
The Efficacy and Safety of Task-state-based Temporal Interference Stimulation (TI) in the Treatment of Patients With Depression
ClinicalTrials.gov study NCT06826469. IPD Sharing: NO. Countries: 1. Publications: 0.
Network Connectivity and Inhibitory Control Under Atomoxetin Challenge- A Pharmacological 'Resting State' and 'Inhibiton Task' Study in Patients With ADHD
ClinicalTrials.gov study NCT03661788. IPD Sharing: YES. Countries: 1. Publications: 0.
A Spinal Functional Magnetic Resonance Imagine (fMRI) Study of Resting-State, Motor Task and Acupoint Stimulation
ClinicalTrials.gov study NCT00629655. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Intranasal Oxytocin on Functional Brain Network in Resting-state and Tasks
ClinicalTrials.gov study NCT03428906. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison of Compact 3T, Conventional 3T, and 7T Scanners Using Task Based and Resting State fMRI
ClinicalTrials.gov study NCT04172428. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Impact of Insomnia Treatment on Brain Responses During Resting-state and Cognitive Tasks
ClinicalTrials.gov study NCT04024787. IPD Sharing: NO. Countries: 1. Publications: 0.
Task-State-Based Temporal Interference Stimulation (TI) to Improve Depression in Patients With Bipolar Disorder
ClinicalTrials.gov study NCT06516991. IPD Sharing: NO. Countries: 1. Publications: 0.
Dataset for The Effects of Rational Emotive Behavior Therapy, Progressive Muscle Relaxation, Distraction, and Rumination on State Anger Using the Autobiographical Essay Memory Task
Open the record for dataset details and reuse information.
Hidden state foraging task, interleaved barrier manipulation
<p>Behavioural dataset of hidden state foraging task</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.