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.
101
datasets available to search
ShareScore release 0.9.0
Dataset results
101 results for “workload”
External and Internal Workload Demands in Youth Basketball Players
<p>External and Internal Workload Demands in Youth Basketball Players</p>
Towards Social Fairness in Smart Policing: Leveraging Territorial, Racial, and Workload Fairness in the Police Districting Problem
<p>Data, results, and source code.</p>
Multi Modal Workload Data Capture Experiment 1
<p>EEG, Video, Game Video and IMU dataset 1st experiment</p>
Computational Artifacts for Performance Feedback Autoscaling Experiments with Workloads of Workflows in Apache Airflow
<p>These computational artifacts are related to the software artifacts DOI:10.5281/zenodo.2635571</p> <p><strong>The content of the computational artifacts:</strong></p> <ul> <li><strong>experiments.pdf</strong> contains the list of all the conducted experiments with the Airflow system. Experiment IDs are not sequential since some experiments required rerunning, etc., we report only successful results. The file lists different experiment configurations, e.g., the number of processed workflows, the name of the used workload, the user budgets, and PFA settings.</li> <li><strong>db.tar.gz</strong> contains directories with Airflow database snapshots and autoscaler logs. The names of the directories correspond to those listed in `experiments.pdf`. Each experiment directory contains an autoscaler log and a full copy of a PostgreSQL database directory just after each experiment finished. The database name is `airflow`, the user name is `ailyushk`. Within each database, most of the paper-related data are stored in the `stat_log` table. The scripts for extracting data from these databases are available as software artifacts in `tools/analysis`.</li> <li><strong>gurobi.tar.gz</strong> contains the results obtained from the Gurobi solver when solving the MIP model.</li> <li><strong>pdf.tar.gz</strong> contains all the figures in pdf format, also those that were not included neither in the paper nor in the technical report. The scripts for creating this plots are delivered as software artifacts.</li> <li><strong>csv.tar.gz</strong> contains the analysis results extracted from Airflow database snapshots. These files are used to create the plots in the `pdf` directory. The scripts for doing this are delivered as software artifacts.</li> <li><strong>wl1.tar.gz</strong> is the first synthetic realistic workload (WL I) with three subsets of 200 workflows each (`1_0`, `1_1`, `1_2`). Each directory contains the file with interarrivals `interarrivals.txt`, and the file with workflow IDs `workload.txt` in the subset. The `dags` directory contains Python-based Airflow descriptors and CSV files that summarise the same descriptors in CSV format for simpler analysis. The scripts for extracting workload statistics from these CSV files are available in the software artifacts in `tools/analysis`. The `inputs` directory contains initial input files for each worfklow. The `dax` contains original DAX files obtained from the generator: <a href="https://github.com/pegasus-isi/WorkflowGenerator/tree/master/bharathi/src/simulation/generator">https://github.com/pegasus-isi/WorkflowGenerator/tree/master/bharathi/src/simulation/generator</a></li> <li><strong>wl2.tar.gz</strong> is the second synthetic realistic workload (WL II) with three subsets of 200 workflows each (`4_0`, `4_1`, `4_2`). Has similar structure as `wl1.tar.gz`, except that `dax` directory is omitted, as WL II uses the same DAX structures as WL I.</li> <li><strong>wl3.tar.gz</strong> is the small synthetic workload based on WL I for the experiment with the MIP solver, contains three subsets with 5 workflows in each, all in the `3_0` directory (thus, the structure differs from the WL I and WL II). The input data files are empty. The identifiers of workflows forming each subset are stored in the `workload_1.txt`, `workload_2.txt`, and `workload_3.txt` files.</li> </ul>
Impact of Workload and Fatigue on Sleep in OR Nurses
ClinicalTrials.gov study NCT06285344. IPD Sharing: NO. Countries: 1. Publications: 0.
Mizzou Nurse Workload and Well-Being Study
ClinicalTrials.gov study NCT07247708. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Task-dependent workload adjustment of female breeders in a cooperatively breeding fish
Open the record for dataset details and reuse information.
Reduced cardiac antioxidant defenses mediate increased susceptibility to workload-induced myocardial injury in males with genetic cardiomyopathy.
GEO Series GSE249946. Mus musculus. 17 samples. Type: Expression profiling by high throughput sequencing.
SC20 HPC Workload Analysis Dataset
<p>This repository contains the job trace dataset and analysis scripts for a publication submitted to SC20.</p>
Data from: Identifying the effects of an upgraded 'fever clinic' on COVID-19 control and the workload of emergency department: a retrospective study in a tertiary hospital in China
<p>Objectives: COVID-19 started spreading widely in China. Outpatient fever clinics (FC),were upgraded to serve for COVID-19 screening and prevention of disease transmission in large tertiary hospitals in China. We aimed to evaluate the effect of upgrading the FC system on rates of nosocomial COVID-19 infection and ED patient attendance at PUMCH Methods: The FC of PUMCH was upgraded on January 20, 2020. We performed a retrospective study of patients presenting to the FC between December 12, 2019 and February 29, 2020. January 20, 2020 was the date when COVID-19 was declared an outbreak in Beijing. Two groups of data were collected and subsequently compared with each other: The first group of data was collected within 40 days before January 20, 2020; The second group of data was collected within 40 days after January 20, 2020. All necessary data, including patient baseline information, diagnosis, follow-up conditions, and the transfer records between the FC and ED were collected and analyzed. Results: 6,365 patients were screened in the FC, among whom 2,912 patients were screened before January 21, 2020, while 3,453 were screened afterward. Screening results showed that upper respiratory infection was the major disease associated with fever. After the outbreak of COVID-19, the number of patients who were transferred from the FC to ED decreased significantly [39.21% vs 15.75%, p<0.001], and patients generally spent more time in the FC [55 vs 203minutes, p, compared with before the outbreak. For critically ill patients waiting for their screening results, the total length of stay in the FC was 22minutes before the outbreak, compared with 442minutes after the outbreak Conclusion: The workload of the FC increased significantly after the COVID-19 outbreak. New protocols regarding the use of FC likely helped prevent the spread of COVID-19 l. The upgraded FC reduce the burden on the ED</p>
MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks
<p><strong>Description: </strong>This MOCAS is a multimodal dataset dedicated for human cognitive workload (CWL) assessment. In contrast to existing datasets based on virtual game stimuli, the data in MOCAS was collected from realistic closed-circuit television (CCTV) monitoring tasks, increasing its applicability for real-world scenarios. To build MOCAS, two off-the-shelf wearable sensors and one webcam were utilized to collect physiological signals and behavioral features from 21 human subjects. After each task, participants reported their CWL by completing the NASA-Task Load Index (NASA-TLX) and Instantaneous Self Assessment (ISA). Personal background (e.g., personality and prior experience) was surveyed using demographic and Big Five Factor personality questionnaires, and two domains of subjective emotion information (i.e., arousal and valence) were obtained from the Self-Assessment Manikin, which could serve as potential indicators for improving CWL recognition performance. Technical validation was conducted to demonstrate that target CWL levels were elicited during simultaneous CCTV monitoring tasks; its results support the high quality of the collected multimodal signals.</p> <p><strong>Data Access:</strong> In order to protect the sensitive data and privacy of human subjects (e.g., physiological signals and facial views), only authorized researchers who consent to the End User License Agreement (EULA) are allowed to download the MOCAS. The researchers who want to access the MOCAS should visit our website and download the ELUA document;<a href="https://drive.google.com/file/d/1XKbZIlKDYH9RG7QD_v2fB7mYF3pNeA9I/view?usp=sharing"> https://drive.google.com/file/d/1XKbZIlKDYH9RG7QD_v2fB7mYF3pNeA9I/view?usp=sharing</a> After reviewing and filling the document up, they should email the signed ELUA document to <a href="mailto:info@smart-laboratory.org">info@smart-laboratory.org</a> and <a href="mailto:wonsu0513@gmail.com">wonsu0513@gmail.com </a>with Zenodo account and Reqeust Access via this repository (see the below file section). Then, our research group will review and grant their access to our Zenodo repository having the downsampled MOCAS dataset, subjective information, and supplementary codes used in this paper. For sharing the raw dataset, we will sequentially invite their email address used in the Zenodo and the EULA document to access raw dataset uploaded on an additional repository (Purdue BOX, https://purdue.box.com/v/mocas-dataset), due to huge size of the raw dataset.</p> <p><strong><a title="BibTeX">BibTeX </a>Citation:</strong></p> <div>Jo, W., Wang, R., Cha, G. E., Sun, S., Senthilkumaran, R. K., Foti, D., & Min, B. C. (2024). MOCAS: A multimodal dataset for objective cognitive workload assessment on simultaneous tasks. <em>IEEE Transactions on Affective Computing</em>.</div> <pre>or<br><br>@ARTICLE{jo2024mocas, author={Jo, Wonse and Wang, Ruiqi and Cha, Go-Eum and Sun, Su and Senthilkumaran, Revanth Krishna and Foti, Daniel and Min, Byung-Cheol}, journal={IEEE Transactions on Affective Computing}, title={MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks}, year={2025}, volume={16}, number={1}, pages={116-132}, doi={10.1109/TAFFC.2024.3414330}}</pre>
Workload and Sleep Quality of Hospital Shift Nurses
ClinicalTrials.gov study NCT04953429. IPD Sharing: NO. Countries: 1. Publications: 0.
Anaerobic Exercises on Cardiac Workload, Peripheral Resistance and Lipid Index Among Grade 1 Hypertensive Young Adults
ClinicalTrials.gov study NCT04683523. IPD Sharing: NO. Countries: 1. Publications: 0.
Influence of Type 1 Diabetes (T1DM) Treatment Type on Mental Workload
ClinicalTrials.gov study NCT06165159. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Use of AI to Safely Reduce the Workload in Breast Cancer Screening With Mammography in Region Östergötland
ClinicalTrials.gov study NCT06187350. IPD Sharing: NO. Countries: 1. Publications: 0.
Workload in Anesthesiological Practice
ClinicalTrials.gov study NCT04050241. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Automated urIne Flow Detection to Reduce Errors and Nursing Workload
ClinicalTrials.gov study NCT03636113. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of Physical and Cognitive Industrial Workload
ClinicalTrials.gov study NCT03580304. IPD Sharing: NO. Countries: 1. Publications: 0.
Emergency Medical Staff Workload Analysis
ClinicalTrials.gov study NCT06302764. IPD Sharing: UNDECIDED. Countries: 3. Publications: 0.
The Effect of ScopeGuide on the Mental Workload of Endoscopist
ClinicalTrials.gov study NCT02092493. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.