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.
434
datasets available to search
ShareScore release 0.9.0
Dataset results
434 results for “activity level”
Four-year blip emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>This repository holds the netcdf files for aerosol emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database ( <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 5 years after 2020 before returning to baseline. The details of these activity estimates runs in parallel to those described in <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>, except instead of a 2-year blip, we have done a 4-year blip. Note that it is one year after the blip has finished before things return to baseline.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> for aerosols emissions. We present only a single scenario (called 4-year blip, featuring a one year recovery after the end of the 4 years) compared to the baseline.</p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
NO2 levels inside vehicle cabins with pollen and activated carbon filters: A real world targeted intervention to estimate NO2 exposure reduction potential
<p>In-vehicle and on-road (ambient) NO<sub>2</sub> measurements in different car cabin from Birmingham, UK. This dataset was used for the publication NO2 levels inside vehicle cabins with pollen and activated carbon filters: A real world targeted intervention to estimate NO<sub>2</sub> exposure reduction potential, Science of The total environment,160395 <a href="https://doi.org/10.1016/j.scitotenv.2022.160395">https://doi.org/10.1016/j.scitotenv.2022.160395</a></p>
Daily aerosol emissions changes in 2020 due to Covid19: modified SSP2-4.5 to account for sector activity level
<p>Daily aerosol emissions estimates for 2020, modified by the country-specific impacts of COVID-19 lockdown. </p> <p>This repository holds the netcdf files for aerosol and precursor emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020, with observation-based data up until the 5th of July and a fixed estimate thereafter. This is the daily equivalent of <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a>. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p> <p>see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details.</p>
Minute-Level Human Activity and Particulate Matter Exposure Dataset from Ljubljana, Slovenia
<p>This dataset encompasses detailed measurements of human activities and particulate matter exposure at a minute-level resolution, collected in Ljubljana, Slovenia from September 24 to October 31, 2020. Data was gathered from 18 participants using a combination of devices: a personal particulate matter monitor (PPM), a Garmin Vivosmart 3 smart activity tracker (SAT), and the Clockify app. The PPM provided real-time measurements of particulate matter concentrations (PM1, PM2.5, and PM10), as well as environmental parameters like temperature, humidity, and altitude. The SAT tracker offered insights into personal health data, including average heart rate and metabolic equivalent of task (MET). Clockify app was utilized for detailed logging of various activities categorized with minute accuracy.</p> <p>Key variables included in the dataset are:</p> <ol> <li>Participant ID</li> <li>Date of data collection</li> <li>Time of data recording (minute accuracy)</li> <li>Specific task or activity performed</li> <li>Particulate matter - PM1 - concentrations</li> <li>Particulate matter - PM2.5 - concentrations</li> <li>Particulate matter - PM10 - concentrations</li> <li>Environmental temperature</li> <li>Relative humidity</li> <li>Altitude</li> <li>Speed</li> <li>Average heart rate</li> <li>Metabolic equivalent of task</li> </ol> <p>This dataset offers a resource for exploring the interplay between individual behaviors and air pollution exposure, with applications in environmental health research and the development of machine learning models for activity recognition.</p>
Text-fig. 6. Shallowing pattern during the Middle Miocene to Late Miocene/Pliocene due to increasing magmatic activity as an external parameter. a: palaeobathymetry map during the Middle Miocene to Pliocene; b: sea level change curve indicating a shallowing pattern; c: relative changes of sea level and magmatic activity curve (Haq et al. 1987, Soeria-Atmadja et al. 1998, Muljana 2012). in Lithofacies And Ichnofacies Of Turbidite Deposits, West Java, Indonesia
Text-fig. 6. Shallowing pattern during the Middle Miocene to Late Miocene/Pliocene due to increasing magmatic activity as an external parameter. a: palaeobathymetry map during the Middle Miocene to Pliocene; b: sea level change curve indicating a shallowing pattern; c: relative changes of sea level and magmatic activity curve (Haq et al. 1987, Soeria-Atmadja et al. 1998, Muljana 2012).
Group-level trait and individual performance: the impact of in-nest activity on food recruitment in ants
<p>Dataset, R and Python scripts corresponding to the results displayed in the article "Group-level trait and individual performance: the impact of in-nest activity on food recruitment in ants".</p> <p>R script works in pair with all three .csv files.</p> <p>.txt files are example of output generated by the Python script that analyses a worker's path inside the nest.</p> <p>3 videos from the experiment are also available. They allow visualization of the setup as well as testing of Python scripts.</p>
Figure 2. Flufenacet degradation 24 h in Flufenacet activity is affected by GST inhibitors in blackgrass (Alopecurus myosuroides) populations with reduced flufenacet sensitivity and higher expression levels of GSTs
Figure 2. Flufenacet degradation 24 h after treatment with different inhibitors in the sensitive Alopecurus myosuroides population Herbiseed-S (A) and population Kehdingen1 with reduced flufenacet efficacy (B). Different letters indicate significant differences in flufenacet degradation between treatments,and asterisks (*) indicate significant differences between the two populations for each treatment (P ≤ 0.05).
Figure 4 in Flufenacet activity is affected by GST inhibitors in blackgrass (Alopecurus myosuroides) populations with reduced flufenacet sensitivity and higher expression levels of GSTs
Figure 4. Expression of three tau-class glutathione transferases (GST1, GST2,GST3), two phi-class GSTs (GST4, GST5), and a theta-class isoform (GST6) differentially expressed in the sensitive Alopecurus myosuroides populations Herbiseed-S and Appel-S and the populations Kehdingen1 and Kehdingen2 with reduced flufenacet efficacy. Different letters indicate significant differences between populations (false discovery rate ≤ 0.05). TMM, trimmed mean of M values.
Figure 1 in Flufenacet activity is affected by GST inhibitors in blackgrass (Alopecurus myosuroides) populations with reduced flufenacet sensitivity and higher expression levels of GSTs
Figure 1. Dose–response analysis of the fresh weight of four Alopecurus myosuroides populations treated with different dose rates of flufenacet estimated using a threeparameter log-logistic model (see Equation 1).
Dataset: ALPS ETF Trust - Level Four Large Cap Growth Active ETF (LGRO) 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.
Dataset used in "Free context smartphone based application for motor activity levels recognition"
<p>This is the data set used in the paper "Free context smartphone based application for motor activity levels recognition", 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), Bologna, 2016, pp1-4.</p> <p>The data refer to three subjects (i.e. subject1, subject2 and subject3). For each subject a folder is created. The folder contains data used for training and for test in all the conditions addressed by the reference paper.</p> <p>Activities are labeled by the last character of the filename as follows: 1-2 resting; 3-6 walking; 7-8 running; 9-12 climbing stairs</p>
Ecosystem-Level Determinants of Sustained Activity in Open-Source Projects: A Case Study of the PyPI Ecosystem
<pre><em>Replication pack, FSE2018 submission #164: </em><em>------------------------------------------ </em></pre> <pre><strong>**</strong>Working title:<strong>** </strong>Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem <strong>**</strong>Note:<strong>** </strong>link to data artifacts is already included in the paper. Link to the code will be included in the Camera Ready version as well. <em>Content description </em><em>=================== </em> <strong>- **</strong>ghd-0.1.0.zip<strong>** </strong>- the code archive. This code produces the dataset files described below <strong>- **</strong>settings.py<strong>** </strong>- settings template for the code archive. <strong>- **</strong>dataset_minimal_Jan_2018.zip<strong>** </strong>- the minimally sufficient version of the dataset. This dataset only includes stats aggregated by the ecosystem (PyPI) <strong>- **</strong>dataset_full_Jan_2018.tgz<strong>** </strong>- full version of the dataset, including project-level statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages themselves, which take around 2TB. <strong>- **</strong>build_model.r, helpers.r<strong>** </strong>- R files to process the survival data (`survival_data.csv` in <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>**</strong>, `common.cache/survival_data.pypi_2008_2017-12_6.csv` in <strong>**</strong>dataset_full_Jan_2018.tgz<strong>**</strong>) <strong>- **</strong>Interview protocol.pdf<strong>** </strong>- approximate protocol used for semistructured interviews. <strong>- </strong>LICENSE - text of GPL v3, under which this dataset is published <strong>- </strong>INSTALL.md - replication guide (~2 pages)</pre> <pre><em>Replication guide </em><em>================= </em> <em>Step 0 - prerequisites </em><em>---------------------- </em> <strong>- </strong>Unix-compatible OS (Linux or OS X) <strong>- </strong>Python interpreter (2.7 was used; Python 3 compatibility is highly likely) <strong>- </strong>R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible) Depending on detalization level (see Step 2 for more details): <strong>- </strong>up to 2Tb of disk space (see Step 2 detalization levels) <strong>- </strong>at least 16Gb of RAM (64 preferable) <strong>- </strong>few hours to few month of processing time <em>Step 1 - software </em><em>---------------- </em> <strong>- </strong>unpack <strong>**</strong>ghd-0.1.0.zip<strong>**</strong>, or clone from gitlab: git clone https://gitlab.com/user2589/ghd.git git checkout 0.1.0 `cd` into the extracted folder. All commands below assume it as a current directory. <strong>- </strong>copy `settings.py` into the extracted folder. Edit the file: <strong> * </strong>set `DATASET_PATH` to some newly created folder path <strong> * </strong>add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` <strong>- </strong>install docker. For Ubuntu Linux, the command is `sudo apt-get install docker-compose` <strong>- </strong>install libarchive and headers: `sudo apt-get install libarchive-dev` <strong>- </strong>(optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools` Without this dependency, you might get an error on the next step, but it's safe to ignore. <strong>- </strong>install Python libraries: `pip install --user -r requirements.txt` . <strong>- </strong>disable all APIs except GitHub (Bitbucket and Gitlab support were not yet implemented when this study was in progress): edit `scraper/init.py`, comment out everything except GitHub support in `PROVIDERS`. <em>Step 2 - obtaining the dataset </em><em>----------------------------- </em> The ultimate goal of this step is to get output of the Python function `common.utils.survival_data()` and save it into a CSV file: # copy and paste into a Python console from common import utils survival_data = utils.survival_data('pypi', '2008', smoothing=6) survival_data.to_csv('survival_data.csv') Since full replication will take several months, here are some ways to speedup the process: <em>####Option 2.a, difficulty level: easiest </em> Just use the precomputed data. Step 1 is not necessary under this scenario. <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>** </strong><strong>- </strong>get `survival_data.csv`, go to the next step <em>####Option 2.b, difficulty level: easy </em> Use precomputed longitudinal feature values to build the final table. The whole process will take 15..30 minutes. <strong>- </strong>create a folder `<DATASET_PATH>/common.cache`, where `<DATASET_PATH>` is the value of the variable `DATASET_PATH` in `settings.py` <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018<strong>** </strong>to the newly created folder <strong>- </strong>rename files: mv backporting.csv monthly_data.pypi_backporting.csv mv cc_degree.csv monthly_data.pypi_cc_degree.csv mv commercial.csv monthly_data.pypi_commercial.csv mv commits.csv monthly_data.pypi_commits.csv mv contributors.csv monthly_data.pypi_contributors.csv mv dc_katz.csv monthly_data.pypi_dc_katz.csv mv downstreams.csv monthly_data.pypi_downstreams.csv mv d_upstreams.csv monthly_data.pypi_d_upstreams.csv mv github_user_info.csv user_info.pypi.csv mv issues.csv monthly_data.pypi_issues.csv mv non_dev_issues.csv monthly_data.pypi_non_dev_issues.csv mv non_dev_submitters.csv monthly_data.pypi_non_dev_submitters mv package_urls.csv package_urls.pypi.csv mv q90.csv monthly_data.pypi_q90.csv # raw_dependencies.csv is not required # raw_packages_info.csv is not required # Feel free to read README.md for more details about the data mv submitters.csv monthly_data.pypi_submitters.csv # In this scenario we'll generate a new survival_data.csv mv university.csv monthly_data.pypi_university.csv mv upstreams.csv monthly_data.pypi_upstreams.csv <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` Then, use the Python code above to obtain `survival_data.csv`. <em>####Option 2.c, difficulty level: medium </em> Use pre-downloaded raw data to build longitudinal feature values, and then the dataset. Despite most of the data is cached, some functions will pull up updates which might take anywhere from days to couple weeks to run. <strong>- </strong>Download <strong>**</strong>dataset_full_Jan_2018.tgz<strong>** </strong>(5.4Gb compressed, 34Gb unpacked). <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` <strong>- </strong>extract the content of this archive into `<DATASET_PATH>`. <strong>- </strong>clean up `<DATASET_PATH>/common.cache` (otherwise you'll get Step 2.a. You can reproduce Step 2.b by deleting only `survival_data.pypi_2008_2017-12_6.csv`) Run the Python code above to obtain `survival_data.csv`. <em>####Option 2.d, difficulty level: hard </em> Build the dataset from scratch. Although most of the processing is parallelized, it will take at least couple months on a reasonably powerful server (32 cores, 512G of RAM, 2Tb+ of HDD space in our setup). <strong>- </strong>ensure the `<DATASET_PATH>` is empty <strong>- </strong>add more GitHub tokens (borrow from your coworkers) to `settings.py`. Run the Python code above to obtain `survival_data.csv`. <em>Step 3 - run the regression </em><em>--------------------------- </em> install R libraries: install.packages(c("htmlTable", "OIsurv", "survival", "car", "survminer", "ggplot2", "sqldf", "pscl", "texreg", "xtable")) Use `build_model.r` (e.g. in RStudio) and produced `survival_data.csv` to build the regressions used in the paper. This process takes at least 16Gb of RAM and takes few hours to run due to the gigantic size of the dataset. </pre>
Dead or alive; or does it really matter? Level of congruency between trophic modes in total and active fungal communities in High Arctic soil.
<p>These are the rDNA and rRNA fragments of Internal transcribed spacer 2 (ITS2) extracted from snow fence experiment in Adventdalen, Svalbard. </p> <p>This is a dataset described in Wutkowska et al., (2019), 'Dead or alive; or does it really matter? Level of congruency between trophic modes in total and active fungal communities in High Arctic soil.', published in Frontiers in Microbiology</p> <p>All other corresponding data (for splitting libraries, environmental parameters etc.) can be found here: https://github.com/magdawutkowska/Dead_or_alive</p> <p> </p>
Data for: Skin bacterial microbiome diversity predicts lower activity levels in female, but not male, guppies, Poecilia reticulata
<p>While the link between the gut microbiome and host behaviour is well established, how the microbiomes of other organs correlate with behaviour remains unclear. Additionally, behaviour–microbiome correlations are likely sex-specific because of sex differences in behaviour and physiology, but this is rarely tested. Here, we tested whether the skin microbiome of the Trinidadian guppy, Poecilia reticulata , predicts fish activity level and shoaling tendency in a sex-specific manner. High-throughput sequencing revealed that the bacterial community richness on the skin (Faith's phylogenetic diversity) was correlated with both behaviours differently between males and females. Females with richer skin-associated bacterial communities spent less time actively swimming. Activity level was significantly correlated with community membership (unweighted UniFrac), with the relative abundances of 16 bacterial taxa significantly negatively correlated with activity level. We found no association between skin microbiome and behaviours among male fish. This sex-specific relationship between the skin microbiome and host behaviour may indicate sex-specific physiological interactions with the skin microbiome. More broadly, sex specificity in host–microbiome interactions could give insight into the forces shaping the microbiome and its role in the evolutionary ecology of the host.</p>
Data for: Skin bacterial microbiome diversity predicts lower activity levels in female, but not male, guppies, Poecilia reticulata
Open the record for dataset details and reuse information.
Artificial light at night alters activity, body mass and corticosterone level in a tropical anuran
Open the record for dataset details and reuse information.
CO2 emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>Monthly CO2 emission projections, modified by the country-specific impacts of COVID-19 lockdown. </p> <p>This repository holds the netcdf files for CO2 emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database ( <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 3 years after 2020 before returning to baseline. The details of these activity estimates can be found in <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> for aerosols emissions, and version numbers used here are consistent with the data seen in that database. We present only a single scenario (called 2-year blip, featuring a one year recovery after the end of the 2 years) compared to the baseline. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
Weekly NOx aviation emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>Weekly NOx aviation emissions estimates for 2020 until 21/07/2020, modified by the country-specific impacts of COVID-19 lockdown. </p> <p>This repository holds the netcdf files for NOx emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020, with observation-based data up until the 5th of July and a fixed estimate thereafter. This is the weekly equivalent of the aviation file in <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a> for a shorter time period, although the version number is different since we have more information available and the normalisation process has been improved. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p> <p>see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details on the methodology.</p>
Movement patterns and activity levels are shaped by the neonatal environment in Antarctic fur seal pups
<p>All data for the article "Movement patterns and activity levels are shaped by the neonatal environment in Antarctic fur seal pups", published in Scientific Reports.</p> <p>Both the raw data (csv files labelled with individual ID) and the collated data (allData.csv, data5min.csv, furSeals_finalData.csv) are provided. Please see the “README.txt” file for more information on what each raw data file contains. The RMarkddown.pdf includes all code used in the manuscript. If you have any further questions, please don’t hesitate to contact me (Rebecca Nagel) at renagel2@gmail.com.</p>
Habitat complexity dampens selection on prey activity level
<p>Conspecific prey individuals often exhibit persistent differences in behavior (i.e., animal personality) and consequently vary in their susceptibility to predation. How this form of selection varies across environmental contexts is essential to predicting ecological and evolutionary dynamics, yet remains currently unresolved. Here, we use three separate predator–prey systems (sea star–snail, wolf spider–cricket, and jumping spider–cricket) to independently examine how habitat structural complexity influences the selection that predators impose on prey behavioral types. Prior to conducting staged predator–prey interaction encounters, we ran prey individuals through multiple behavioral assays to determine their average activity level. We then allowed individual predators to interact with groups of prey in either open or structurally complex habitats and recorded the number and individual identity of prey that were eaten. Habitat complexity had no effect on overall predation rates in any of the three predator–prey systems. Despite this, we detected a pervasive interaction between habitat structure and individual prey activity level in determining individual prey survival. In open habitats, all predators imposed strong selection on prey behavioral types: sea stars preferentially consumed sedentary snails, while spiders preferentially consumed active crickets. Habitat complexity dampened selection within all three systems, equalizing the predation risk that active and sedentary prey faced. These findings suggest a general effect of habitat complexity that reduces the importance of prey activity level in determining individual predation risk. We reason this occurs because activity level (i.e., movement) is paramount in determining risk within open environments, whereas in complex habitats, other behavioral traits (e.g., escape ability to a refuge) may take precedence.</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.