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.
2,820
datasets available to search
ShareScore release 0.9.0
Dataset results
2,820 results for “Physical Activities”
Long-term effect of regular physical activity and exercise habits in patients with early parkinson disease
Open the record for dataset details and reuse information.
Data from: Behaviour changes techniques that constitute effective planning interventions to improve physical activity and diet behaviour for people with chronic conditions: a systematic review
Open the record for dataset details and reuse information.
Data from: 8-year trends in physical activity, nutrition, TV viewing time, smoking, alcohol and BMI: a comparison of younger and older Queensland adults
Open the record for dataset details and reuse information.
Association of physical activity and APOE genotype with longitudinal cognitive change in early PD
Open the record for dataset details and reuse information.
Adverse childhood experiences, depressive symptoms, functional dependence, and physical activity: A moderated mediation model
<p>This release contains R scripts to analyze the links between adverse childhood experiences (ACEs), depression, functional dependence, and physical activity, using the SHARE panel data survey.</p>
Multimedia Promotion of Youth Physical Activity in a Nationally Representative Sample of U.S. Synagogues
<p>Raw data extracted from nationally representative sample of U.S. synagogue websites during spring 2019 in order to ascertain the types and specific physical activities mentioned in child and adolescent programming.</p>
Physical activities of players during the 2018 Soccer World Cup
<p>Physical activities of players during the 2018 Soccer World Cup</p>
Data from: Smart bracelet to assess physical activity after cardiac surgery: a prospective study
<p><strong>Objectives</strong>: Little is known about the physical activity of patients after cardiac surgery. This study was designed to assess this activity using a connected bracelet. </p> <p><strong>Methods</strong>: In this prospective, monocentric study, patients scheduled for cardiac surgery were offered to wear an electronic bracelet. The main objective was to measure the physical activity recovery. Secondary objectives were the predictors of the correct use of the monitoring system, of the physical recovery and, if any, the relationship between physical activity and out-of-hospital morbidity. </p> <p><strong>Results</strong>: One hundred patients were included. Most patients (86%) were interested in participating in the study. The compliance to the device and to the study protocol was good (94%). At discharge, the mean number of daily steps was 1454 ± 145 steps, increasing quite homogeneously, reaching 5801±1151 steps at Day 60. The best fit regression curve gave a maximum number of steps at 5897±119 (r²=0.97). The 85% level of activity was achieved at Day 30±3. No predictor of noncompliance was found. At discharge, age was independently associated with a lower number of daily steps (p <0.001). At Day 60, age, peripheral arterial disease and cardio-pulmonary bypass duration were independently associated with a lower number of daily steps (p=0.039, p=0.041 and p=0.033, respectively). </p> <p><strong>Conclusions</strong>: After cardiac surgery, wearing a smart bracelet recording daily steps is simple, well tolerated and suitable for measuring physical activity. Standard patients achieved around 6000 daily steps 2 months after discharge. 85% of this activity is reached in the first month. </p>
Data from: Validation of the Regicor short physical activity questionnaire for the adult population
Objective: To develop and validate a short questionnaire to estimate physical activity (PA) practice and sedentary behavior for the adult population. Methods: The short questionnaire was developed using data from a cross-sectional population-based survey (n=6352) that included the Minnesota leisure-time PA questionnaire. Activities that explained a significant proportion of the variability of population PA practice were identified. Validation of the short questionnaire included a cross-sectional component to assess validity with respect to the data collected by accelerometers and a longitudinal component to assess reliability and sensitivity to detect changes (n=114, aged 35 to 74 years). Results: Six types of activities that accounted for 87% of population variability in PA estimated with the Minnesota questionnaire were selected. The short questionnaire estimates energy expenditure in total PA and by intensity (light, moderate, vigorous), and includes 2 questions about sedentary behavior and a question about occupational PA. The short questionnaire showed high reliability, with intraclass correlation coefficients ranging between 0.79 to 0.95. The Spearman correlation coefficients between estimated energy expenditure obtained with the questionnaire and the number of steps detected by the accelerometer were as follows: 0.36 for total PA, 0.40 for moderate intensity, and 0.26 for vigorous intensity. The questionnaire was sensitive to detect changes in moderate and vigorous PA (correlation coefficients ranging from 0.26 to 0.34). Conclusion: The REGICOR short questionnaire is reliable, valid, and sensitive to detect changes in moderate and vigorous PA. This questionnaire could be used in daily clinical practice and epidemiological studies.
Data from: Endocrine and metabolic disorders in survivors of childhood cancers and health-related quality of life and physical activity
Context: Childhood cancer survivors experience chronic health conditions that impact health related quality of life (HRQOL) and participation in optimal physical activity. Objective: The study aimed to determine independent effects of endocrine and metabolic disorders on HRQOL and physical activity. Design, Setting, and Patients: Retrospective cohort with longitudinal follow-up of survivors of childhood cancer enrolled in the North American Childhood Cancer Survivor Study. Main Outcome Measures: Medical Outcomes Short Form-36 estimated HRQOL while participation in physical activity was dichotomized as meeting or not meeting recommendations from the Center for Disease Control and Prevention. Log binomial regression evaluated the association of each endocrine/metabolic disorder with HRQOL scales and physical activity. Results: Of 7,287 survivors, median age 32 years (18-54) at their last follow-up survey, 4,884 (67%) reported one or more endocrine/metabolic disorders. Survivors with either disorder were significantly more likely to be male, older, received radiation treatment, and experience other chronic health conditions. After controlling for covariates, survivors with any endocrine/metabolic disorder were more likely to report poor physical function risk ratio ([RR] 1.25; 95% confidence interval [CI] 1.05-1.48), increased bodily pain (RR 1.27; CI 1.12-1.44), poor general health (RR 1.49; CI 1.32-1.68) and lower vitality (RR 1.21; CI 1.09-1.34) compared to survivors without. The likelihood of meeting recommended physical activity was lower among survivors with growth disorders (RR 0.90; CI=0.83-0.97), osteoporosis (RR 0.87; CI=0.76-0.99), and overweight/obesity (RR 0.92; CI 0.88-0.96). Conclusion: Endocrine and metabolic disorders are independently associated with poor HRQOL and sub-optimal physical activity among childhood cancer survivors.
supplementary tables for physical activity and sedentary behavior on gut microbiota.
Open the record for dataset details and reuse information.
Knee Proprioception, Muscle Strength, and Stability in Type 2 Diabetes Mellitus: Unveiling the Moderating Effects of Physical Activity – A cross-sectional study.
Open the record for dataset details and reuse information.
ORGANIZATIONAL ACTIVITY OF THE DEPARTMENT OF PHYSICS, MATHEMATICS AND TECHNICAL SCIENCES OF THE UZBEKISTAN ACADEMY OF SCIENCES
Open the record for dataset details and reuse information.
Action Planning Makes Physical Activity More Automatic, Only If it Is Autonomously Regulated: A Moderated Mediation Analysis
<p>Dataset used for analysis. </p>
Attentional bias for physical activity: eye-tracking data
<p><strong>Dataset related to the paper entitled "Physically active individuals look for more: An eye tracking study of attentional bias". </strong></p> <p>This dataset includes:</p> <p>1) A workbook</p> <p>2) Raw data of the behavioral outcome (i.e., reaction times) of the visual dot probe task</p> <p>"dpt_sample1_27_05_2019.csv" for the sample 1.</p> <p>"dpt_sample2_27_05_2019.csv" for the sample 2.</p> <p>3) Raw data of the eye-tracking outcomes (i.e., Gaze Data)</p> <p>4) Self-reported data</p> <p>"data_all_SR.Rdata" for the sample 1</p> <p>"data_SR1_inhib.RData" for the sample 2</p> <p>4) R script for the data management of the raw RT</p> <p>"Data_management_DPT_RT.R". This script leads to the file "dpt_behavioral_both_sample.RData", which merges the behavioral data of both sample with the self-reported data.</p> <p>5) Raw data of the eye-tracking outcomes</p> <p>"Data_management_DPT_Gaze_13_06_2019". This script leads to the file "dpt_both_sample_EyeTrack.RData", which merge the gaze data of both sample with the self-reported data.</p> <p>6) R script for the models tested on the behavioral and eye-tracking outcomes</p> <p>"Models_zenodo"</p> <p>7) the visual dot probe task (e-prime script)</p> <p>"DPT_Eyetracker.7z". This file contains the e-prime script as well as the images used in the visual dot probe task</p>
Enhance physical activity by designing school based active transport interventions. A systematic review with meta-analysis.
Open the record for dataset details and reuse information.
Data from: Using hidden Markov models to improve quantifying physical activity in accelerometer data – a simulation study
Introduction The use of accelerometers to objectively measure physical activity (PA) has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM) are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA. Methods:1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois]), the generalized Poisson distribution (HMM[GenPois]) and the Gaussian distribution (HMM[Gauss]) with regard to misclassification rate (MCR), bout detection, detection of the number of activities performed during the day and runtime. Results:The cutpoint method had a misclassification rate (MCR) of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint), 2.0 minutes (HMM[Gauss]) and 14.2 minutes (HMM[GenPois]). Conclusions: Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.
Physical activity interventions and nutrition-based interventions for children and adolescents with type 1 diabetes mellitus
<p>1. Baseline characteristics of participants</p> <p>2. Description of studies</p> <p>3. Data extraction_Excel</p>
Physical literacy as a determinant of physical activity level among late adolescents
<p>Dataset related to article. </p>
Disconnect to reconnect. The role of physical activity in family digital detox, part II
<p>This research was funded in whole by National Science Centre, grant number: 2022/06/X/HS6/00121. </p> <p>This is the data containing the following additional documents created and used during the research project: filled-in questionaires.<br>All data are in doc/exe and odt/ods formats.</p> <p>You can use the files by ensuring full anonymity of respondents, whose data has been anonymized and whose identity in no way can be identified and made known, much less any sensitive data about the respondents.<br>The data can be used for other humanistic and social research, provided that the author of the database is stated.<br>The data were obtained in the course of diadic and individual interviews with adults, preceded by the completion of questionnaires about all family members surveyed.</p> <p>The full data set consists of 4 parts:</p> <p><a href="https://zenodo.org/record/8007244">https://zenodo.org/record/8007244</a> - transcriptions of interviews</p> <p><a href="https://zenodo.org/record/8007340">https://zenodo.org/record/8007340</a> - filled-in questionaires</p> <p><a href="https://zenodo.org/record/8007081">https://zenodo.org/record/8007081</a> - interviewers' reports</p> <p><a href="https://zenodo.org/record/8006974">https://zenodo.org/record/8006974</a> - interview script, questionnaire form, questionnaire annex, interviewer instructions, interviewer checklist</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.