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.
29,897
datasets available to search
ShareScore release 0.9.0
Dataset results
29,897 results for “activism”
SINS database - Node 13 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 10 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 9 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 2 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 8 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
CATCH-EyoU Processes in Youth's Construction of Active EU Citizenship Cross-national Wave 1 Questionnaires Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia - EXTRACT
<p>The dataset was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538 <a href="http://www.catcheyou.eu/">http://www.catcheyou.eu/</a>. </p> <p>The data set consists of:</p> <ul> <li>1 data file saved in .sav format</li> </ul> <p>“CATCH-EyoU Processes in Youth’s Construction of Active EU Citizenship Cross-national Wave 1 Questionnaires Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia - EXTRACT.sav”</p> <ul> <li>1 README file</li> </ul> <p>The file was generated through IBM SPSS software. Discrete missing values: 88, 99. The .sav file (SPSS) can be processed using “R” (library “foreign”): <a href="https://cran.r-project.orgv/">https://cran.r-project.org</a></p> <p>This dataset relates to following paper: </p> <p>Ekaterina Enchikova, Tiago Neves, Sam Mejias, Veronika Kalmus, Elvira Cicognani, Pedro Ferreira (2019) Civic and Political Participation of European Youth: fair measurement in different cultural and social contexts. Frontiers in Education.</p> <p>Data Set Contact Person: Ekaterina Enchikova [UP-CIIE]; mail: enchicova@gmail.com </p> <p>Data Set License: this data set is distributed under a Creative Commons Attribution (CC-BY) <a href="http://creativecommons.org/licenses">http://creativecommons.org/licenses</a></p> <p> </p>
SINS database - Node 11 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
Experimental CCN properties of 6 pollenkitts and two mixtures with ammonium sulfate reported in the study "Cloud condensation nuclei activity of six pollenkitts and the influence of their surface activity" by Prisle et al. (2019)
<p>Critical dry particle size Dp<sub>50</sub> measured for supersaturations 0.1–1.4% with a DMT CCN Counter (CCN-100). Size and composition resolved, and size-averaged hygroscopicity values calculated using the method presented by Rose et al. (2010), Cloud condensation nuclei in polluted air and biomass burning smoke near the mega-city Guangzhou, China - Part 1: Size-resolved measurements and implications for the modeling of aerosol particle hygroscopicity and CCN activity, <em>Atmospheric Chemistry and Physics</em>, <em>10</em>, 3365–3383.</p>
Experimental surface tensions of aqueous poplar and ragweed pollenkitts reported in the study "Cloud condensation nuclei activity of six pollenkitts and the influence of their surface activity" by Prisle et al. (2019)
<p>Surface tensions measured with a ramé-hart goniometer (Model 250) for aqueous solutions of varying concentrations comprising poplar and ragweed pollenkitts and their mixtures with ammonium sulfate</p>
Critical droplet properties for particles containing 6 pollenkitts evaluated with 4 Köhler models for the study "Cloud condensation nuclei activity of six pollenkitts and the influence of their surface activity" by Prisle et al. (2019)
<p>Critical droplet supersaturation and surface tension, calculated with 3 thermodynamic Köhler models presented in Prisle et al., <em>Surfactants in cloud droplet activation: mixed organic-inorganic particles</em>, Atmospheric Chemistry and Physics, 10, 5663-5683, doi:10.5194/acp-10-5663-2010, and with the model presented in Prisle et al., <em>A simple representation of surface active organic aerosol in cloud droplet formation</em>, Atmospheric Chemistry and Physics, 11, 4073-4083, doi: 10.5194/acp-11-4073-2011, for particles of varying size comprising 6 pollenkitts and mixtures with ammonium sulfate.</p>
Can metal organic frameworks outperform adsorptive removal of harmful phenolic compound 2-chlorophenol by activated carbon?
<p>Dataset supporting publication. High resolution images, and full data set as produced in manuscript figures.</p> <p><strong>Preprint</strong>: <a href="https://doi.org/10.26434/chemrxiv.10320752.v1">https://doi.org/10.26434/chemrxiv.10320752.v1 </a></p> <p><strong>Published article:</strong> <a href="https://doi.org/10.1016/j.cherd.2020.03.017">https://doi.org/10.1016/j.cherd.2020.03.017 </a></p> <p><strong>Abstract:</strong> Removal of persistent organic compounds from aqueous solutions is generally achieved using adsorbent like activated carbon (AC) but it suffers from limited adsorption capacity due to low surface area. This paper describes a pioneering work on the adsorption of an organic pollutant, 2-chlorophenol (2-CP) by two MOFs with high surface area and water stability; MIL-101 and its amino-derivative, MIL-101-NH<sub>2</sub>. Although MOFs have higher surface area than AC, the latter was proven better having the highest equilibrium 2-CP uptake (345 mg.g<sup>-1</sup>), followed by MIL-101 (121 mg.g<sup>-1</sup>) and MIL-101-NH<sub>2</sub> (84 mg.g<sup>-1</sup>). Used MIL-101 could be easily regenerated multiple times by washing with ethanol and even showed improved adsorption capacity after each washing cycle. These results can open the doors to meticulous adsorbent selection for treating 2-CP-contaminated water. </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>
Activity – or lack thereof – of RuO2 based electrodes in the electrocatalytic reduction of CO2
<p>Raw data for the article</p> <p><em>Activity – or lack thereof – of RuO<sub>2</sub> based electrodes in the electrocatalytic reduction of CO<sub>2</sub></em></p> <p>Stefano Mezzavilla, Yu Katayama, Reshma R Rao, Jonathan Hwang, Anna Regoutz, Yang Shao-Horn, Ib Chorkendorff, Ifan Erfyl Lester Stephens</p> <p>DOI: 10.1021/acs.jpcc.9b01431</p> <p> </p> <p>The dataset includes:</p> <p>1) Experimental Methods</p> <p>2) File with raw data of figures in the main text and supplementary Information file (Excel file with multiple tabs)</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>
Automatic and feature-specific prediction related neural activity in the human auditory system
<p>Downsampled (to 100Hz) data of the paper "Automatic and feature specific prediction related neural activity in the human auditory system"</p>
Antiviral activity of HIV-1 integrase strand-transfer inhibitors against mutants with integrase resistance-associated mutations and their frequency in treatment-naïve individuals
<p>The development of resistance to human immunodeficiency virus 1 (HIV-1) integrase strand-transfer inhibitors (INSTI) has been documented; however, knowledge of the impact of pre-existing integrase (IN) mutations on INSTI resistance (INSTI-R) is still evolving. The frequency of HIV-1 IN mutations in 2177 treatment-naïve subjects was investigated, along with the INSTI susceptibility of site-directed mutant viruses containing major and minor INSTI-R mutations. Total 6 of 39 minor INSTI-R mutations (M50I, S119P/G/T/R, and E157Q) were found in >1% of IN-treatment-naïve subjects with no impact on INSTI susceptibility. When each combined with major INSTI-R mutation, M50I, S119P, and E157Q led to decreased susceptibility to elvitegravir but remained sensitive to dolutegravir and bictegravir.</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>
Link to dataset related to article "Sustained activation of detoxification pathways promotes liver carcinogenesis in response to chronic bile acid-mediated damage"
<p>This record contains link to raw data related to article "Sustained activation of detoxification pathways promotes liver carcinogenesis in response to chronic bile acid-mediated damage"</p> <p>Chronic inflammation promotes oncogenic transformation and tumor progression. Many inflammatory agents also generate a toxic microenvironment, implying that adaptive mechanisms must be deployed for cells to survive and undergo transformation in such unfavorable contexts. A paradigmatic case is represented by cancers occurring in pediatric patients with genetic defects of hepatocyte phosphatidylcholine transporters and in the corresponding mouse model (Mdr2-/- mice), in which impaired bile salt emulsification leads to chronic hepatocyte damage and inflammation, eventually resulting in oncogenic transformation. By combining genomics and metabolomics, we found that the transition from inflammation to cancer in Mdr2-/- mice was linked to the sustained transcriptional activation of metabolic detoxification systems and transporters by the Constitutive Androstane Receptor (CAR), a hepatocyte-specific nuclear receptor. Activation of CAR-dependent gene expression programs coincided with reduced content of toxic bile acids in cancer nodules relative to inflamed livers. Treatment of Mdr2-/- mice with a CAR inhibitor blocked cancer progression and caused a partial regression of existing tumors. These results indicate that the acquisition of resistance to endo- or xeno-biotic toxicity is critical for cancers that develop in toxic microenvironments.</p> <p>https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE80777 under the accession GSE80777, which comprises ChIP-seq data (GSE80775) and expression data (GSE80776).</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.