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155 results for “task performance”
Experimental data for the motor learning study performed: "Promoting Motor Variability During Robotic Assistance Enhances Motor Learning of Dynamic Tasks"
<p>The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at University of Bern. The details of the study are described in [doi: 10.3389/fnins.2020.600059]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses are stored as a “csv” file. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.</p>
Experimental data for the motor learning study performed: "Towards functional robotic training: Motor learning of dynamic tasks is enhanced by haptic rendering but hampered by robotic assistance"
<p>The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. The details of the study are described in [doi: ]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses and population metrics are stored as “CSV” files. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.</p>
PsPM-SF: SCR, ECG, PPU and respiration measurements from a delay fear conditioning task with auditory CS (monophones/triads), performed during MRI scanning
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG), peripheral pulse unit (PPU) and respiration measurements for 20 healthy unmedicated participants (10 females and 10 males, age range: 19 - 35 years, mean age: 24.2 +/- 4.9) participating in a classical (Pavlovian) discriminant delay fear conditioning experiment with auditory CS, during MRI scanning. Also included are CS and US information, and ratings of CS after the experiment. Simple and complex CS were simple sine tones (4 s), and triads in root position or in first inversion, respectively. US was a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. After the fear conditioning task, participants were first asked to report their subjective estimate of how likely they were to receive a shock after a given CS in the future, on a visual analogue scale of 0-100. Then they were asked to rate pairs of CS sounds with respect to which of the two stimuli they liked less. NOTE: In Staib et al. 2015, this dataset is denoted as SC1F</p>
Dataset of behavioral and neurophysiological data of a virtual sailing task published in: "Providing task instructions during motor training enhances performance and modulates attentional brain networks"
<p>Dataset belonging to the behavioral and neurophysiological data of the publication: "Providing task instructions during motor training enhances performance and modulates attentional brain networks". The two uploaded Zip files contain kinematic and electroencephalographic data of 36 participants for the Obstacle and HorizonTask.</p>
Raw data for "Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task"
<p>Raw data for the simulation study " Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task" [1].</p> <p>[1] Josupeit, A., Schoenmaker, E., van de Par, S., & Hohmann, V. (2018). Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task. <em>European Journal of Neuroscience</em>, https://doi.org/10.1111/ejn.13981.</p>
Background music and cognitive task performance: systematic review dataset
<p>This repository contains the raw data used for a systematic review of the impact of background music on cognitive task performance (Cheah et al., 2022). Our intention is to facilitate future updates to this work.</p><p><strong>Contents description</strong></p><p>This repository contains eight Microsoft Excel files, each containing the synthesised data pertaining to each of the six cognitive domains analysed in the review, as well as task difficulty, and population characteristics:</p><ul><li><i>raw-data-attention</i></li><li><i>raw-data-inhibition</i></li><li><i>raw-data-language</i></li><li><i>raw-data-memory</i></li><li><i>raw-data-thinking</i></li><li><i>raw-data-processing-speed</i></li><li><i>raw-data-task-difficulty</i></li><li><i>raw-data--population</i></li></ul><p><strong>Files description</strong></p><p><i>Tabs organisation</i></p><p>The files pertaining to each cognitive domain include individual tabs for each cognitive task analysed (c.f. Figure 2 in the original paper for the list of cognitive tasks). The file with the population characteristics data also contains separate tabs for each characteristic (extraversion, music training, gender, and working memory capacity).</p><p><i>Tabs contents</i></p><p>In all files and tabs, each row corresponds to the data of a test. The same article can have more than one row if it reports multiple tests. For instance, the study by Cassidy and MacDonald (2007; cf. <i>Memory.xlsx</i>, tab: <i>Memory-all</i>) contains two experiments (immediate and delayed free recall) each with multiple test (immediate free recall: tests 25 – 32; delayed free recall: tests 58 – 61). Each test (one per row), in this experiment, pertains to comparisons between conditions where the background music has different levels of arousal, between groups of participants with different extraversion levels, between different tasks material (words or paragraphs) and different combinations of the previous (e.g., high arousing music vs silence test among extraverts whilst completing an immediate free recall task involving paragraphs; cf. test 30).</p><p>The columns are organised as follows:</p><ul><li>"TESTS": the index of the test in a particular tab (for easy reference);</li><li>"ID": abbreviation of the cognitive tasks involved in a specific experiment (see glossary for meaning);</li><li>"REFERENCE": the article where the data was taken from (see main publications for list of articles);</li><li>"CONDITIONS": an abbreviated description of the music condition of a given test;</li><li>"MEANS (music)": the average performance across all participants in a given experiment with background music;</li><li>"MEANS (silence)": the average performance across all participants in a given experiment without background music.</li></ul><p>Then, in horizontal arrangement, we also include groups of two columns that breakdown specific comparisons related to each test (i.e., all tests comparing the same two types of condition, e.g., L-BgM vs I-BgM, will appear under the same set of columns). For each one, we indicate mean difference between the respective conditions ("MD" column) and the direction of effect ("Standard Metric" column). Each file also contains a "Glossary" tab that explains all the abbreviations used in each document.</p><p><strong>Bibliography</strong></p><p>Cheah, Y., Wong, H. K., Spitzer, M., & Coutinho, E. (2022). Background music and cognitive task performance: A systematic review of task, music and population impact. <i>Music & Science, 5</i>(1), 1-38. <a href="https://doi.org/10.1177/0305735699272005">https://doi.org/10.1177/20592043221134392</a></p>
Dataset from "Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition"
<p>Written by: Paolo Mistretta<br> Contact information: paolo.mistretta@phd.unipd.it<br> Date: 24/01/2020</p> <p><br> This document contains supplementary material for the article<br> “Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition”<br> (Authors: Paolo Mistretta, Cecilia Marchesini, Andrea Volpini, Luca Tagliapietra, Tommaso Sciarra, Aldo Lazich, Salvatore Forte, Mauro De Matteis, Emanuele Menegatti and Nicola Petrone)<br> presented at the 13th conference of the International Sports Engineering Association, Tokyo, Japan, 22-25 June 2020.</p> <p><br> Data are contained in the file: “database_ISEA2020.mat”</p>
PsPM-SC2F: SCR, ECG, PPU and respiration measurements from a delay fear conditioning task with auditory CS, performed during MRI scanning
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG), peripheral pulse unit (PPU) and respiration measurements for 18 healthy unmedicated participants (9 females and 9 males, age range: 18 - 34 years, mean age: 24.8 +/- 4.2) participating in a classical (Pavlovian) discriminant delay fear conditioning experiment with auditory CS, during MRI scanning. Also included are CS and US information, and ratings of CS after the experiment. Simple and complex CS were simple sine tone (4 s), and up or down frequency sweep sounds, respectively. US was a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. After the fear conditioning task, participants were first asked to report their subjective estimate of how likely they were to receive a shock after a given CS in the future, on a visual analogue scale of 0-100. Then they were asked to rate pairs of CS sounds with respect to which of the two stimuli they liked less.</p>
TERMINUS WP6: Prototype device for sorting and separation. Reprocessing of materials. TASK 6.2: Performances of packaging in use.
<p>Astrid E. Delorme, Tanja Radusin, Petri Myllytie, Vincent Verney and Haroutioun Askanian: Enhancement of Gas Barrier Properties and Durability of Poly(butylene succinate-co-butylene adipate)-Based Nanocomposites for Food Packaging Applications. Nanomaterials 2022, 12, 978. https://doi.org/10.3390/ nano12060978</p> <p> </p> <p><strong>Abstract</strong></p> <p>Poly(butylene succinate-co-butylene adipate), PBSA, based materials are receiving growing attention in the packaging industry for their promising biodegradability. However, poor gas barrier properties and low durability of biodegradable polymers, such as PBSA, have limited their wide-spread use in food packaging applications. Here we report a scalable solution to improve gas barrier properties and stabilize PBSA against photo-aging, with minimal modifications to the biodegradable polymer backbone by using a commercially available and biocompatible layered double hydroxide, LDH, filler. We investigate and compare the mechanical, gas barrier and photoaging properties of PBSA and PBSA-LDH nanocomposite films produced on pilot scale. An increase in rigidity in the nanocomposite was observed upon addition of LDH fillers to neat PBSA, which direct the application of neat PBSA and PBSA-LDH nanocomposite to different food packaging applications. The addition of LDH fillers into neat PBSA improves the oxygen and water vapour barriers for the PBSA based nanocomposites, which increases the attractiveness of PBSA material in food packaging applications. Through changes in the viscoelastic behaviour, we observe an improved photo-durability of photoaged PBSA-LDH nanocomposites compared to neat PBSA. It is clear from our studies that the presence of LDH enhances the lifetime durability and modulates the photodegradation rate of the elaborated biocomposites.</p> <p> </p> <p><strong>Dataset</strong></p> <p>This dataset contains all the rheological, UV-Vis, DSC, TGA, IR Tensile testing, Gas Barrier and DSC raw data used to generate graphs and discussions in the article “Enhancement of gas barrier properties and durability of Poly(butylene succinate-co-butylene adipate)-based nanocom-posites for food packaging applications” doi . Data are available in a compressed .zip file with 1 folder (Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.zip) containing 7 .xlsx files containing all the rheological, IR, UV-Vis, DSC, TGA, Tensile testing, Gas Barrier raw data, 4 .pdf files one describing the experimental methods and materials, a second .pdf file outlining the metadata and information (this document) and two are the material data sheets for SOBACID®911 and PBSA used in the study, and one .zip file containing the SEM images in .TIF and .JPG formats.</p> <p> </p> <ul> <li>The <em>DSC-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the data of the heating and cooling steps of the DSC experiments performed on PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>Gas-Barrier-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the Oxygen Transmission Rates (OTR) and Water Vapour Transmission Rates (WVTR) of PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>IR-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the FT-IR data used to create the IR-spectra of aged and unaged PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>Rheology-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the melt rheology data of unaged and aged PBSA and PBSA-LDH nanocomposite films used for Cole-Cole plots and extrapolation of zero shear viscosity.</li> <li>The <em>Tensile-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the tensile testing data of PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>TGA-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the TGA data of PBSA and PBSA-LDH nanocomposite films.</li> <li>The<em> UV-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the UV-Vis absorption data of aged and unaged PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>PBSA-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.pdf</em> is the product sheet of PBSA used in this study.</li> <li>The <em>SORBACID-911-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.pdf</em> is the product sheet of SORBACID<sup>®</sup> 911 used in this study.</li> <li>The <em>SEM-images-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.zip</em> file contains for SEM images (.jpg and .tif) corresponding to neat PBSA film and PBSA-LDH nanocomposite films with 2 wt%, 5 wt% and 8 wt% LDH loadings.</li> <li>The <em>Materials_and_experimental_method-D6-2-Packaging-performances-PBSA-LDH.pdf </em>file details the experimental method and conditions for the data acquisition presented in the Laccase-thermostability-DES_v1.0_TER_WP4_D4-2.xlsx. Guidance is also provided on how to use the data to calculate the laccase activity and thermostability.</li> <li>The <em>Metadata_information-D6-2- Packaging-performances-PBSA-LDH.pdf</em> file includes more detailed metadata information for the datasets represented in here.</li> </ul>
Dataset: TaskUs, Inc. (TASK) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 2. Energy-VAS subjective measures for the participants (n=12) under two conditions (control and exercise involved cognitive task).
<p>As shown in Figure 2, it was found that there was a statistically significant interaction in the<br> percentage of mental fatigue between the condition type and time-on-task factor times (F(6, 22) =<br> 492.19, p < 0.001) as well as there was a significant main effect of time-on-task (time5 to time30)<br> (F(5, 22) = 463.794, p < 0.001). In addition, there was also a significant main effect in the condition type (F (1, 22) = 713.133, p < 0.001) which represented a large effect size. For the physical fatigue<br> subjective measure, there was a significant difference between the two experimental conditions (p <<br> 0.001), and within the subject test times (p < 0.001). However, there was no significant difference<br> between the means of the concentration visual analogue scale for these two experimental conditions<br> (p = 0.057) despite a significant difference (p < 0.001) in the time-on-task repeated measures.</p>
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 1. The 10-20 international system electrode placement showing the EEG electrode placement
<p>For the EEG analysis, the average power in the theta band (4 – 8 Hz), and alpha band (8 – 12<br> Hz) were computed at the frontal and parietal electrodes Fz and Pz respectively. Next, the ratio of<br> these two powers was determined, and named the ‘cognitive ratio’ as several researchers found that<br> the fronto-parietal network play important roles in cognitive activities.</p>
Companion for "Design, Implementation and Performance Analysis of a CFD task-based Application for Heterogeneous CPU/GPU Resources"
<p>This is the companion data for the VECPAR2018 submission paper entitled: Design, Implementation and Performance Analysis of a CFD task-based Application for Heterogeneous CPU/GPU Resources by Lucas Leandro Nesi, Lucas Mello Schnorr, and Philippe Olivier Alexandre Navaux. All the data, source code, and images generation scripts used in the paper are present here.</p>
Companion for Visual Performance Analysis of Memory Behavior in a Task-Based Runtime on Hybrid Platforms
<p>This is the companion data for the CCGRID2019 submission paper entitled: Visual Performance Analysis of Memory Behavior in a Task-Based Runtime on Hybrid Platforms by Lucas Leandro Nesi, Samuel Thibault, Luka Stanisic and Lucas Mello Schnorr. All the data, source code, and images generation scripts used in the paper are presented here.</p>
Task Scheduler Performance Survey Results
<p><strong>Task scheduler performance survey</strong></p> <p>This dataset contains results of task graph scheduler performance survey.<br> The results are stored in the following files, which correspond to simulations performed on <br> the `elementary`, `irw` and `pegasus` task graph datasets published at https://doi.org/10.5281/zenodo.2630384.</p> <ul> <li>elementary-result.zip</li> <li>irw-result.zip</li> <li>pegasus-result.zip</li> </ul> <p>The files contain compressed pandas dataframes in CSV format, it can be read with the following Python code:<br> ```python<br> import pandas as pd<br> frame = pd.read_csv("elementary-result.zip")<br> ```</p> <p>Each row in the frame corresponds to a single instance of a task graph that<br> was simulated with a specific configuration (network model, scheduler etc.).<br> The list below summarizes the meaning of the individual columns.</p> <ul> <li><strong>graph_name</strong> - name of the benchmarked task graph</li> <li><strong>graph_set</strong> - name of the task graph dataset from which the graph originates</li> <li><strong>graph_id</strong> - unique ID of the graph</li> <li><strong>cluster_name</strong> - type of cluster used in this instance the format is <number-of-workers>x<number-of-cores>; 32x16 means 32 workers, each with 16 cores</li> <li><strong>bandwidth</strong> - network bandwidth [MiB]</li> <li><strong>netmodel</strong> - network model (simple or maxmin)</li> <li><strong>scheduler_name</strong> - name of the scheduler</li> <li><strong>imode</strong> - information mode</li> <li><strong>min_sched_interval</strong> - minimal scheduling delay [s]</li> <li><strong>sched_time</strong> - duration of each scheduler invocation [s]</li> <li><strong>time</strong> - simulated makespan of the task graph execution [s]</li> <li><strong>execution_time </strong>- real duration of all scheduler invocations [s]</li> <li><strong>total_transfer</strong> - amount of data transferred amongst workers [MiB]</li> </ul> <p>The file `charts.zip` contains charts obtained by processing the datasets.<br> On the X axis there is always bandwidth in [MiB/s].<br> There are the following files:</p> <ul> <li>[DATASET]-schedulers-time - Absolute makespan produced by schedulers [seconds] </li> <li>[DATASET]-schedulers-score - The same as above but normalized with respect to the best schedule (shortest makespan) for the given configuration.</li> <li>[DATASET]-schedulers-transfer - Sums of transfers between all workers for a given configuration [MiB]</li> <li>[DATASET]-[CLUSTER]-netmodel-time - Comparison of netmodels, absolute times [seconds]</li> <li>[DATASET]-[CLUSTER]-netmodel-score - Comparison of netmodels, normalized to the average of model "simple"</li> <li>[DATASET]-[CLUSTER]-netmodel-transfer - Comparison of netmodels, sum of transfered data between all workers [MiB]</li> <li>[DATASET]-[CLUSTER]-schedtime-time - Comparison of MSD, absolute times [seconds]</li> <li>[DATASET]-[CLUSTER]-schedtime-score - Comparison of MSD, normalized to the average of "MSD=0.0" case</li> <li>[DATASET]-[CLUSTER]-imode-time - Comparison of Imodes, absolute times [seconds]</li> <li>[DATASET]-[CLUSTER]-imode-score - Comparison of Imodes, normalized to the average of "exact" imode</li> </ul> <p><strong>Reproducing the results</strong></p> <p><em>1. Download and install Estee (https://github.com/It4innovations/estee)</em></p> <p>$ git clone https://github.com/It4innovations/estee<br> $ cd estee<br> $ pip install .<br> <br> <em>2. Generate task graphs</em><br> You can either use the provided script `benchmarks/generate.py` to generate graphs<br> from three categories (elementary, irw and pegasus):</p> <p>$ cd benchmarks<br> $ python generate.py elementary.zip elementary<br> $ python generate.py irw.zip irw<br> $ python generate.py pegasus.zip pegasus<br> <br> or use our task graph dataset that is provided at https://doi.org/10.5281/zenodo.2630384.</p> <p><em>3. Run benchmarks</em><br> To run a benchmark suite, you should prepare a JSON file describing the benchmark.<br> The file that was used to run experiments from the paper is provided in<br> `benchmark.json`. Then you can run the benchmark using this command:<br> <br> $ python pbs.py compute benchmark.json</p> <p>The benchmark script can be interrupted at any time (for example using Ctrl+C).<br> When interrupted, it will store the computed results to the result file and restore<br> the computation when launched again.</p> <p><em>3. Visualizing results</em><br> <br> $ python view.py --all <result-file><br> <br> The resulting plots will appear in a folder called `outputs`.</p>
Companion for "OpenMP and StarPU Abreast: the Impact of Runtime in Task-Based Block QR Factorization Performance"
<p>This is the companion website for the paper entitled *OpenMP and StarPU Abreast: the Impact of Runtime in Task-Based Block QR Factorization Performance* by Marcelo Cogo Miletto and Lucas Mello Schnorr that has been submited to Simpósio de Sistemas<br> Computacionais de Alto Desempenho (WSCAD) - 2019. This repository provides all the data and the code snippets used to generate the figures that are discussed inside the article, in a way that you can use them to reproduce our steps.</p>
SUPER-G Benchmarking performance on commercial farms (task 3.1)
<p><span>The SUPER-<em>G </em>project aimed to co-develop sustainable Permanent Grassland (PG) systems and policies with farmers and policy makers to effectively optimise productivity whist supporting biodiversity and delivering other Ecosystem Services (ES). This was achieved through the completion of a series of Work Packages (WP). Work Package 3 aimed </span><span>to benchmark performance in terms of profitability and sustainability and the delivery of ES. This was achieved in part through </span><span>deliverable (D) D3.2, where an “</span><span>Overview of data, key gaps, and trends in PG management in the different biogeographic regions” was compiled. This overview included a survey of 352 farms across six biogeographic regions conducted in 2019, which explored on-farm management and differences between regions. A second, subsequent, survey was undertaken in 2023, which included 203 of the original farms from five biogeographic areas. Deliverable report 3.3. </span>describes the distribution of key indicators from farms surveyed in the first survey and describes any changes which were captured in the second survey. <span>A</span><span>gri-environment indicators and key sustainability indicators were identified in WP2 and include forage efficiency, grass utilisation (the proportion of grass dry matter grown that is consumed by livestock) and grass/clover dry matter (DM) productivity. Due to the limitations of how many questions can be asked in a survey and the time required to perform a survey, it was not possible to calculate all these metrics. This report examines the sustainability indicators of stocking density, grass production and output per hectare and environmental indicators of areas reserved for nature and biodiversity.</span></p> <p> </p> <p><span>The data files include the survey questions, responses from 2021 survey, responses from 2023 survey and a blank survey form. </span></p>
Data from: Prior associations affect bumblebees' generalization performance in a tool-selection task
<p>A small brain and short life allegedly limit cognitive abilities. Our view of invertebrate cognition may also be biased by the choice of experimental stimuli. Here, the stimuli (color) pairs in Match-To-Sample (MTS) tasks affected the performance of buff-tailed bumblebees (<em>Bombus terrestris</em>). We trained the bees to roll a tool, ball, to a goal that matched its color. Color-matching performance was slower with yellow-and-orange/red than with blue-and-yellow stimuli. When assessing the bees' concept learning in a transfer test with a novel color, the bees trained with blue-and-yellow (novel color: orange/red) were highly successful, the bees trained with blue-and-orange/red (novel color: yellow) did not differ from random, and those trained with yellow-and-orange/red (novel color: blue) failed the test. These results highlight that stimulus salience can affect the conclusions on test subjects' cognitive ability. Therefore, we encourage paying attention to stimulus salience (among other factors) when assessing invertebrate cognition.</p>
GWAS results of selected binarized neurocognitive task performances from Philadelphia Neurodevelopmental Cohort
<p>This archive contains analysis results associated with the publication:</p> <p>Shraddha Pai, Shirley Hui, Philipp Weber, Soumil Narayan, Owen Whitley, Peipei Li, Viviane Labrie, Jan Baumbach, Anne L Wheeler, Gary D Bader. (2023). Multi-scale systems genomics analysis predicts pathways, cell types and drug targets involved in normative variation in peri-adolescent human cognition. Cerebral Cortex.</p> <p>-----------------------------------------------------------<br> *loco.mlma files: Univariate GWAS summary statistics for binarized performance of computerized neurocognitive tests in the Philadelphia Neurodevelopmental Cohorts. GWAS was performed on individuals ascertained to be of European genetic ancestry. For methods, please refer to the publication above.</p> <p>Phenotype codes:<br> lnb_tp2 Working memory LNB: Number of Correct Responses to 2-Back Trials (TP)<br> pcpt_t_tp Attention PCPT: Total of Correct Responses to Number Trials (TP) and Letter Trials (TP)<br> peit_cr Emotion identification PEIT: Total Correct Responses for All Test Trials, by genus<br> pfmt_ifac_tot Face memory PFMT: Total Correct Responses for All Test Trials<br> plot_tc Spatial reasoning PLOT: Total Correct Responses for All Test Trials, by genus<br> pmat_pc Nonverbal reasoning PMAT: Percent of Correct Responses for All Test Trials, by genus<br> pvrt_cr Verbal reasoning PVRT: Total Correct Responses for All Test Trials, by genus<br> pwmt_kiwrd_tot Word memory PWMT: Total Correct Responses for All Test Trials<br> volt_svt Object memory VOLT: Total Correct Responses for All Test Trial</p>
Dataset for the article: Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment
<p>This open access repository houses the dataset utilized in the research article:</p><p>Pankiewicz, M. (2023). Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 1413-1418). Vienna, Austria: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primary/p/222666</p><p>The repository includes these files:</p><p>"submissions.csv": This data file captures the evaluation results of programming assignments. It is organized by the following columns:</p><p> </p><ul><li>"user_id": The unique identifier for each student who submitted the assignment.</li><li>"task_id": The unique identifier for each task that received submissions.</li><li>"submission_seconds": The number of seconds since the first user accessed the initial task's description within the system.</li><li>"correct": The outcome of the evaluation (1 denotes correct; 0 denotes incorrect).</li><li>"subject": The specific subject matter that the task addresses.</li></ul><p> </p><p>"subjects.csv": This data file comprises the roster of subjects for which tasks have been assigned within the system. It includes these columns:</p><p> </p><ul><li>"subject_id": The unique identifier for each subject.</li><li>"subject": The actual name of the subject.</li></ul><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.