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.
1,742
datasets available to search
ShareScore release 0.9.0
Dataset results
1,742 results for “activity data”
Slow motion in films and video clips: Music influences perceived duration and emotion, autonomic physiological activation and pupillary responses [data set]
<p>Data set for a study to be published by PLOS ONE.</p>
Data for article: Understanding the Visible-light Photocatalytic Activity of GaN:ZnO Solid Solution: the Role of Rh2-yCryO3 Cocatalyst and Charge Carrier Lifetimes Over Tens of Seconds
<p>Dataset underlying the publication 'Understanding the Visible-light Photocatalytic Activity of GaN:ZnO Solid Solution: the Role of Rh<sub>2-y</sub>Cr<sub>y</sub>O<sub>3</sub> Cocatalyst and Charge Carrier Lifetimes Over Tens of Seconds', published in Chemical Science (doi: 10.1039/C8SC02348D)</p>
Raw data of the manuscript "The Ice Nucleation Activity of Black and Brown Soot"
<p>Raw data of the manuscript "The Ice Nucleation Activity of Black and Brown Soot" to be published in the Journal of Geophysical Research 2018</p>
Supplemental Raw Data for Imboden et al., High-speed mechano-active multielectrode array for investigating rapid stretch effects on cardiac tissue
<p>The file contains the central raw data underlying Figure 5 and, in the supplementary material, Figure 13 of the article 'High-speed mechano-active multielectrode array for investigating rapid stretch effects on cardiac tissue'.</p>
NMR, HPLC and HRMS data - General Cyclopropane Assembly via Enantioselective Transfer of a Redox-Active Carbene to Aliphatic Olefins
<p>Raw data for the article with the same title.</p> <p>ChemRxiv pre-print (<a href="https://doi.org/10.26434/chemrxiv.7436795">https://doi.org/10.26434/chemrxiv.7436795</a>)</p>
Supplementary data for "Molecular-scale thermally activated fractures in methane hydrates: A molecular dynamics study"
<p>In this dataset you can find</p> <p>- a data sample that can be used to confirm the plots in the paper. This can be found in the folder "data_sample". Each folder inside "data_sample" is one simulation. It contains the thermodynamic output from the simulation (log.lammps), and the input script (mw_hydrate_pennycrack.in) and input data (s1_unit_cell_mw.data and water_methane_hydrate.sw) that enables rerunning the simulation using LAMMPS.</p> <p>- A custom LAMMPS region, region_ellipsoid. This has to be compiled into LAMMPS in order to create the systems that we simulate.</p> <p>- A python script, plot_data.py, that shows how to extract the relevant data from the lammps log files, which enables the partial reproduction of figure 3 in the paper. See instructions below for requirements to use this script.</p> <p> </p> <p>"region_ellipsoid" and "data_sample" are in zip containers. In order to use them, please unzip them and leave the resulting folders in the same directory as this README file.</p> <p> </p> <p>Installation instructions to make "plot_data.py" work (assuming you already have numpy and matplotlib):</p> <p>> pip3 install git+https://github.com/henriasv/regex-file-collector.git</p> <p>> pip3 install git+https://github.com/henriasv/lammps-logfile.git</p> <p> </p> <p>If this does not work, please contact Henrik Andersen Sveinsson, henriasv@fys.uio.no</p>
SHAL Demonstrator - Activity Data of an Individual
<p>This is an example research data dataset for the SHAL demonstrator within the "AEGIS - Advanced Big Data Value Chain for Public Safety and Personal Security" big data project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732189.</p> <p>The time series data has been collected refer to activity and health measurement data of an individual, collected through the SHAL demonstrator and mobile App, utilising sensors such as Apple Healthkit providing heart rate and activity data, Smart Blood Pressure devices, smart weighting scales, SPO2 smart sensors, etc.</p> <p> The data in this research dataset was collected between 2018-06-12 and 2019-07-03 , and contains in total 483,644 measurements.</p> <p> </p> <p> </p>
Enantioselective Assembly of Congested Cyclopropanes using Redox-Active Aryldiazoacetates - NMR, HRMS and X-ray Raw Data
<p>NMR, HRMS and single crystal X-ray diffraction raw data for the compounds in the manuscript ACS Catalysis 2019, DOI: <a href="https://doi.org/10.1021/acscatal.9b02615">https://doi.org/10.1021/acscatal.9b02615</a></p>
Robot trajectory data for "Biohybrid Fly-Robot Interface system performs active collision avoidance"
<p>This dataset includes the videos of the trajectories of the biohybrid robot (Fly-Robot Interface), performing collision avoidance at the patterned wall corners (90 and 60 degrees). A python script for the manual tracking is also attached, as well as the processed coordinates of each individual robot trajectory, where two tracking markers were chosen on the front-left and front-right corners of the robot.</p> <p>Serial Number <20: the videos at 90-degree wall corner</p> <p>Serial Number >20: the videos at 60-degree wall corner</p>
Processed data for "Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses"
<p>tar.gz of processed data in the form of compressed R files (rds) of SingleCellExperiment (<a href="https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html">https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html</a>) objects and a metadata csv for the data in the publication <em>Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses </em>(O'Flanagan et al. 2019).</p>
Data for three-dimensional active nematic turbulence
<p>Selected data from numerical simulation of bulk three-dimensional active nematic turbulence. Data includes nematic Q-tensor and the velocity field. The outer layer of data points is used for allocation of periodic boundary conditions. Symmetric Q-tensor is written in the order (Q<sub>xx</sub>, Q<sub>yy</sub>, Q<sub>zz</sub>, Q<sub>xy</sub>, Q<sub>xz</sub>, Q<sub>yz</sub>). Data points are written with x as the fast coordinate. The data is obtained by numerically solving the Beris-Edwards model for nematic hydrodynamics, generalized by the active stress term [1,2]. The details of the data and the numerical model are available in publication "Spectral energy analysis of bulk three-dimensional active nematic turbulence" [3].</p> <p>Files:</p> <ul> <li>"active_turbulence-135x135x135-0.300.tar" — A timeline of active turbulence including Q-tensor and velocity profile in a box of size (135)<sup>3</sup> at activity 0.3 L/Δx<sup>2</sup>. The timeline includes 26000 steps, relevant fields are written every 50 steps.</li> <li>"active_turbulence-407x407x407-0.025_Q.raw.gz" — Q-tensor profile in a box of size (407)<sup>3</sup> at activity 0.025 L/Δx<sup>2</sup>.</li> <li>"active_turbulence-407x407x407-0.025_u.raw.gz" — velocity profile in a box of size (407)<sup>3</sup> at activity 0.025 L/Δx<sup>2</sup>.</li> <li>"active_turbulence-407x407x407-0.100_Q.raw.gz" — Q-tensor profile in a box of size (407)<sup>3</sup> at activity 0.1 L/Δx<sup>2</sup>.</li> <li>"active_turbulence-407x407x407-0.100_u.raw.gz" — velocity profile in a box of size (407)<sup>3</sup> at activity 0.1 L/Δx<sup>2</sup>.</li> <li>"active_turbulence-407x407x407-0.300_Q.raw.gz" — Q-tensor profile in a box of size (407)<sup>3</sup> at activity 0.3 L/Δx<sup>2</sup>.</li> <li>"active_turbulence-407x407x407-0.300_u.raw.gz" — velocity profile in a box of size (407)<sup>3</sup> at activity 0.3 L/Δx<sup>2</sup>.</li> </ul> <p> </p> <p> </p>
Data for: "Considering unique, shared, and dominant brain activation in the VWFA and LOC: A comparison of separate and combined word and picture naming"
<p>FMRI data underlying analyses for Experiments 1 and 2 in "Considering unique, shared, and dominant brain activation in the VWFA and LOC: A comparison of separate and combined word and picture naming".</p>
Extended data of the article "Lockbox enrichment facilitates manipulative and cognitive activities for mice": Supplementary Figure
<p><span>Figure and results of the distance traveled in the Free Exploratory Paradigm, Open Field Test, and Elevated Plus Maze Test during habituation are shown</span>.</p>
Neutron activation analysis data of pottery from Cerro Mayal and the Chicama Valley, Peru
<p>Neutron activation analysis data of pottery from Cerro Mayal and other sites in the Chicama Valley, La Libertad Department, Peru.</p>
Predation activity data in organic, permaculture and conventional horticultural farms of Central Hungary
<p>This dataset has been produced from the PhD research of Alfréd Szilágyi supervised by Csaba Centeri and Eszter Kovács Tormáné. The study compared permaculture, organic and conventional farming systems regarding their ecosystem-service provision potential and sustainability. Multiple ecological indicators were measured in the field during the field study in 2020, and the basic datasets (soil test results; photo gallery of the studied farms with soil core sample; soil resistance and moisture; decomposition; earthworms; nematodes; soil surface fauna; pollinators; agrobiodiversity and habitat types) are uploaded in Zenodo separately to provide scientific data on permaculture systems. In this way, we hope to contribute to international efforts to evaluate the performance of agroecological agriculture alternatives. These publications also serve as supplements to the PhD thesis. For the sake of further usability of the datasets short description of the used methods is described. For further information please contact the authors.</p>
Data for manuscript: The Conformational Space of the SARS-CoV-2 Main Protease Active Site Loops is Determined by Ligand Binding and Interprotomer Allostery
<div>The data is provided as a part of the manuscript "<strong>The Conformational Space of the SARS-CoV-2 Main Protease Active Site Loops is Determined by Ligand Binding and Interprotomer Allostery</strong>". This repository includes an archive with folders:</div> <div> </div> <div><strong>md_data </strong></div> <div> <ul> <li>a directory with MD data for all simulation systems considered in the manuscript. Initial and final conformations are provided.</li> </ul> </div> <div> </div> <div><strong>fig_data</strong></div> <div> <ul> <li>a directory with the data underlying all the main text in the manuscript. </li> </ul> </div> <div> </div> <div>Videos S1-S3 are also included.</div>
Data described in the article "Nitrogen-Containing Flavonoids─Preparation and Biological Activity"
<p>The dataset includes supplementary data, i.e. the results of optimization of Ullmann reaction, cellular antioxidant and anti-inflammatory activity, cytotoxicity, antibacterial activity, and molecular docking, <sup>1</sup>H, <sup>13</sup>C{<sup>1</sup>H} NMR data, HPLC, and HRMS analyses of the study titled "Nitrogen-Containing Flavonoids─Preparation and Biological Activity" available here: <a href="https://doi.org/10.1021/acsomega.4c04627">https://doi.org/10.1021/acsomega.4c04627</a></p>
Educational data collected from parents - regarding the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The responses of the 784 parents were collected through the questionnaire available at: <a href="https://forms.gle/Km8WE5QamrYYgXJi7" target="_new" rel="noopener"><strong>https://forms.gle/Km8WE5QamrYYgXJi7</strong></a></p> <p>It was designed with various types of responses, including binomial (yes/no), polynomial (multiple options), and open-ended responses, to capture a comprehensive range of data. This combined approach allows for both quantitative analysis of fixed-response questions and qualitative insights from open-ended questions. Patterns, correlations, and differences between various demographic groups and their experiences and attitudes toward online education can be identified.</p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
Self-Annotated Wearable Activity Data
<p>Our dataset contains 2 weeks of approx. 8-9 hours of acceleration data per day from 11 participants wearing a <a href="https://shop.espruino.com/banglejs">Bangle.js Version 1</a> smartwatch with our <a href="https://github.com/kristofvl/BangleApps/tree/master/apps/activate_test">firmware</a> installed.</p> <p>The dataset contains annotations from 4 different commonly used annotation methods utilized in user studies that focus on in-the-wild data. These methods can be grouped in user-driven, in situ annotations - which are performed before or during the activity is recorded - and recall methods - where participants annotate their data in hindsight at the end of the day.</p> <p>The participants had the task to label their activities using (1) a button located on the smartwatch, (2) the activity tracking app <a href="https://www.strava.com/">Strava</a>, (3) a (hand)written diary and (4) a tool to visually inspect and label activity data, called <a href="https://github.com/mad-lab-fau/mad-gui">MAD-GUI</a>. Methods (1)-(3) are used in both weeks, however method (4) is introduced in the beginning of the second study week.</p> <p>The accelerometer data is recorded with 25 Hz, a sensitivity of ±8g and is stored in a csv format. Labels and raw data are not yet combined. You can either write your own script to label the data or follow the instructions in our corresponding <a href="https://github.com/ahoelzemann/annotationMatters">Github repository.</a></p> <p>The following unique classes are included in our dataset:</p> <p>laying, sitting, walking, running, cycling, bus_driving, car_driving, vacuum_cleaning, laundry, cooking, eating, shopping, showering, yoga, sport, playing_games, desk_work, guitar_playing, gardening, table_tennis, badminton, horse_riding.</p> <p>However, many activities are very participant specific and therefore only performed by one of the participants.</p> <p>The labels are also stored as a .csv file and have the following columns:</p> <p><strong>week_day, start, stop, activity, layer</strong></p> <p>Example:</p> <p>week2_day2,10:30:00,11:00:00,vacuum_cleaning,d</p> <p>The <em>layer</em> columns specifies which annotation method was used to set this label.</p> <p>The following identifiers can be found in the column:</p> <p>b: in situ <strong>button</strong></p> <p>a: in situ <strong>app</strong></p> <p>d: self-recall <strong>diary</strong></p> <p>g: time-series recall labelled with a the <strong>MAD-GUI</strong></p> <p> </p> <p>The corresponding publication is currently under review.</p>
Data accompanying "HSP70 inhibits CHIP E3 ligase activity to maintain germline function in Caenorhabditis elegans" article.
<p>This work was funded by the National Science Centre, Poland (grant PRELUDIUM number 2021/41/N/NZ1/03086) (to P.T.) and by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation) under Germany’s Excellence Strategy – EXC 2030 – 390661388 and – FOR 5504 – project number 496650118 (to T.H.). M.T.P. received support by the Cologne Graduate School of Aging Research. N.A.S., A.S., K.J., and M.N. were supported by the International Institute of Molecular and Cell Biology in Warsaw.</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.