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.
18
datasets available to search
ShareScore release 0.9.0
Dataset results
18 results for “Public Groups”
Public WhatsApp groups from the Brazilian online repositories
<p>This repository contains two gzip files with public WhatsApp groups collected from two Brazilian repositories, they are:</p> <ul> <li><a href="https://gruposdezap.com/">Grupos de Zap</a> - db_grupos_whats.json.gz</li> <li><a href="https://gruposwhats.app/">Grupo de Whats</a> - db_grupos_zap.json.gz</li> </ul> <p>The groups were collected on 01/2022.</p> <p>The files contain five properties: </p> <ul> <li><strong>title</strong>: <em>group title</em></li> <li><strong>description</strong>: <em>description of the group informed by the administrator</em></li> <li><strong>created_date</strong>: <em>creation date when the group was registered in the repository</em></li> <li><strong>num_vizualization</strong><em>: times the group was seen on the site - only for the zap groups repository</em></li> <li><strong>category</strong>: <em>group category</em></li> </ul> <p>If you use this dataset cite your paper, please:</p> <ul> <li><a href="https://doi.org/10.1145/3539637.3557056"><em>"Click Here to Join": A Large-Scale Analysis of Topics Discussed by Brazilian Public Groups on WhatsApp</em> </a></li> </ul> <p><em>Daniel Kansaon, Philipe Melo, and Fabrício Benevenuto. 2022. “Click Here to Join”: A Large-Scale Analysis of Topics Discussed by Brazilian Public Groups on WhatsApp. In Brazilian Symposium on Multimedia and Web (WebMedia ’22), November 7–11, 2022, Curitiba, Brazil. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3539637.3557056</em></p>
Group size effects and critical mass in public goods games
<pre>This dataset accompanies the paper "Group size effects and critical mass in public goods games", https://doi.org/10.1038/s41598-019-41988-3 It records participant decisions in a set of binary one-shot Public Goods Games with curvilinear payoff function (see details in the paper). The explanation of the data fields is present also in the metadata of the file: # Cooperation: 0=defection, 1=cooperation, 99=the participant did not make the decision # Group: interacting group size (N) # Treatment: Critical Mass Nc # dropout: 1=the participant did not make the decision, 0=the participant made the decision # incompleted: 1=participant did not make all the decisions of the experiment; 0=participant did made all the decisions of the experiment # female: 1=female, 0=male </pre> <p> </p>
Data accompanying publication "High-Income Groups Disproportionately Contribute to Climate Extremes Worldwide."
<p>This dataset accompanies the publication "How High-Income Groups Disproportionately Contribute to Climate Extremes Worldwide." </p> <p>In our study, we combine income-based emission inequality data with an emulator-based modeling framework to thoroughly study the link between emissions of individual, wealthy emitter groups and climate extremes worldwide. Specifically, we assess individual contributions to current global temperature levels and systematically attribute changes in regional monthly heat and drought extremes across the globe.</p> <p>We focus on emissions of the top 10/1/0.1 wealthiest individuals globally and in the US, the EU27, India and China. The dataset contains results for 1-in-50/100/10'000 year extremes at grid-cell level and whenever imapcts are aggregated by region we refer to the regionmask AR6 regions. </p> <p>The file contents are the following:</p> <ol> <li>Attributed_GMT.csv: attributed global mean temperature levels by emitter group</li> <li>tas_frequency_hot.nc, spei_frequency_dry.nc, spi_frequency_dry.nc: attributed changes in the frequency of extreme events for extreme heat (tas), potential droughts (spei-3) and meteorological droughts (spi-3) on grid-cell level</li> <li>tas_intensity_hot.nc, spei_intensity_dry.nc, spi_intensity_dry.nc: attributed changes in the intensity of extreme events for extreme heat (tas), potential droughts (spei-3) and meteorological droughts (spi-3) on grid-cell level</li> <li>processed_extremes_frequency.csv: attributed changes in the frequency of extreme events aggregated to ar6 land regions </li> <li>processed_extremes_intensity.csv: attributed changes in the intensity of extreme events aggregated to ar6 land regions</li> </ol>
Dataset: Vodafone Group Public Limited Company (VOD) 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.
Building Public Confidence in Constructed Wetlands for Wastewater Treatment and Reuse: Survey Data and Focus Group Transcripts
<p>Constructed wetlands have been proposed as a cost-effective wastewater treatment, storage, and reuse solution for communities that are considering alternative water supply options to meet essential demands. In 2016, we began exploring the idea of wastewater reuse and the construction of an experimental wetland in Sewanee, located in the southern U.S. state of Tennessee. As a major barrier to water reuse is often public resistance, we conducted a survey and focus groups to determine strategies to develop and initiate a community engagement campaign, aiming to empower residents to form reasoned opinions about local water supply options.</p> <p>This data set includes the survey that was distributed to Sewanee community members between November 2015 and February 2016, as well as protocols for three focus groups that were conducted with K12 teachers and community leaders on February 11 and 12, 2016. The survey results are summarized in a Microsoft Excel file. The three focus groups were transcribed, these transcripts are included here as PDF documents.</p>
Data from: Working groups, gender and publication impact of Canada’s ecology and evolution faculty
Open the record for dataset details and reuse information.
Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software.
<p><strong>If you use the data or the analysis pipeline, please refer to:</strong></p> <p>Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. <a href="https://doi.org/10.3389/fnins.2018.00006">https://doi.org/10.3389/fnins.2018.00006</a></p> <p><strong>and/or</strong></p> <p>Andersen, L.M., 2018. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12. <a href="https://doi.org/10.3389/fnins.2018.00261">https://doi.org/10.3389/fnins.2018.00261</a></p> <p><strong>IMPORTANT</strong><br> Version 2 only contains subjects 1, 18, 20 and a new version of the FreeSurfer folder. This is due to a (very) wrong co-registration for subject 1 and due to 18 and 20 having had their anatomy files mixed up. This has now been fixed. For all other subjects, please see version 1. Also, get the updated scripts from github instead at: <a href="https://github.com/ualsbombe/omission_frontiers.git">https://github.com/ualsbombe/omission_frontiers.git</a></p> <p><br> </p> <p>Dataset with tactile expectations to be analysed with pipelines for either <a href="https://mne.tools/stable/index.html">MNE-Python</a> or <a href="http://www.fieldtriptoolbox.org/">FieldTrip</a>, aiming to follow the MEG-BIDS structure</p> <p><br> <strong>Unzipping the data</strong></p> <p>Data is compressed into twenty-two different zip-files, one for each of the twenty subjects, one for the FreeSurfer data, one for the scripts files . The easiest way to uncompress and prepare the analysis directories is to create a directory in your home folder called "analyses", which has a sub-directory called "omission_frontiers_BIDS-FieldTrip", which has a sub-directory called "data".<br> Thus, as an example, in my case, I should have the path: /home/lau/analyses/omission_frontiers_BIDS-FieldTrip/data</p> <p><strong>Path:</strong><br> on a Linux system the path would be /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data<br> on a macOS system the path would be /Users/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data<br> on a Windows system the path would be C:\Users\your_name\analyses\omission_frontiers_BIDS-FieldTrip\data</p> <p><strong>Steps for unzipping:</strong></p> <p>1. Set up the folder above according to your operating system, following the examples above and substitute "your_name" for your user name.<br> 2. Unzip each of the subject folders into the data folder (sub-01 - sub-20) (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)<br> 3. Also unzip the FreeSurfer folder into the data folder (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)<br> 4. Finally, unzip the scripts folder into /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/</p> <p>Now you are ready to run the analyses.</p> <p><br> <strong>The MEG data</strong></p> <p>Raw fif files are contained in the data folder, ordered by subject (n=20)<br> There is one recording for each subject, MaxFiltered, called oddball_absence-tsss-mc_meg.fif. These are split into three files with -1 and -2 being the remainder of the recording</p> <p><strong>Processed MRI data </strong></p> <p>For the MRI, only the segmented data are provided. This is to sufficient to make the volume conduction model and the source model, while protecting the subjects' identity</p> <p>For Fieldtrip, there is an mri_segmented.mat for each subject, which is found in the meg (sic!) folder for each subject. This has been co-registered to the MEG data<br> For MNE-Python, the FreeSurfer directory should also be used, which contains a folder for each subject that contains surfaces (surf) and boundary element methods models (bem) that are used for source reconstruction in MNE-python. There is also a trans-file for each subject (oddball_absence_dense-trans.fif) in the meg folder specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis. Finally, the FreeSurfer folder also contains the labels for the cortical surface. This is not used in any of the analyses, but are supplied for interested users.</p> <p><br> <strong>Metadata</strong></p> <p>Each subject has a number of tsv-files:<br> *channel.tsv contain information about the channels in that recording<br> *events.tsv contain information about the events in that recording<br> removed_trial_indices.tsv contains information about which events were removed manually (NB! this is only used for the FieldTrip analysis)<br> ica_components.tsv contains information which independent component were removed manually (NB! this is only used for the FieldTrip analysis)<br> *scans_tsv contain information about the scans conducted</p> <p><br> <strong>Scripts </strong></p> <p>Please see Github for the updated scripts at: <a href="https://github.com/ualsbombe/omission_frontiers.git">https://github.com/ualsbombe/omission_frontiers.git</a></p>
Data for a publication "Polymer-metal bilayer with alkoxy groups for antibacterial improvement"
<p><strong>Abstract:</strong></p> <p>Many bio‐applicable materials, medical devices, and prosthetics combine both polymer and metal components to benefit from their complementary properties. This goal is normally achieved by their mechanical bonding or casting only. Here, we report an alternative easy method for the chemical grafting of a polymer on the surfaces of a metal or metal alloys using alkoxy amine salt as a coupling agent. The surface morphology of the created composites was studied by various<br>microscopy methods, and their surface area and porosity were determined by adsorption/desorption nitrogen isotherms. The surface chemical composition was also examined by various spectroscopy techniques and electrokinetic analysis. The distribution of elements on the surface was determined, and the successful bonding of the metal/alloys on one side with the polymer on the other by alkoxy amine was confirmed. The composites show significantly increased hydrophilicity, reliable chemical stability of the bonding, even interaction with solvent for thirty cycles, and up to 95% less bacterial adhesion for the modified samples in comparison with pristine samples, i.e., characteristics that are promising for their application in the biomedical field, such as for implants, prosthetics, etc.<br>All this uses universal, two-step procedures with minimal use of energy and the possibility of production on a mass scale.</p> <p> </p>
Repository of speech features from speakers with and without Parkinson's Disease. Neurovoz - Rasta PLP - V2 - Scientific Reports Publication: Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson's Disease
<p>This repository contains the Rasta-PLP features of six different speech recordings (sentences) from Neurovoz corpus (47 parkinsonian and 32 control speakers whose mother tongue is Spanish Castillian.)<br> Number of PLP coefficients: [6, 8, 10, 12, 14, 16, 18, 20].<br> Delta coefficients: Yes<br> Delta Delta coefficients: Yes<br> Sampling rate: 16 kHz<br> Frame size: 15 ms<br> Frame overlapping: 50%</p> <p>This subset of the Neurovoz corpus was recorded between 2015 and 2017 by Universidad Politécncia de Madrid and Hospital General Universitario Gregorio Marañón.</p> <p>This version includes the same files as the previous version and information about UPDRS, H&Y, years since diagnosis and age of each participant.</p> <p>The sentences were:</p> <p>BARBAS: "Cuando las barbas de tu vecino veas pelar, pon las tuyas a remojar"</p> <p>CALLE: "De la calle vendrá quien de tu casa te echará"</p> <p>DIABLO: " Cuando el diablo no sabe qué hacer, con el rabo mata moscas "</p> <p>PETACA BLANCA: " La petaca blanca es mía"</p> <p>PIDIO: "No pidas a quien pidió ni sirvas a quien sirvió"</p> <p>SOMBRA: " El que a buen árbol se arrima, buena sombra le cobija "</p> <p> </p> <p>How to cite:<br> [1] Moro-Velazquez, L., Gomez-Garcia, J. A., Godino-Llorente, J. I., Grandas-Perez, F., Shattuck-Hufnagel, S. Yagüe-Jimenez, V., and Dehak, N. (2019). Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson’s disease.Scientific reports 9, 19066.</p> <p><br> [2] Moro-Velazquez, L., Gomez-Garcia, J. A., Godino-Llorente, J. I., Villalba, J., Rusz, J., Shattuck-Hufnagel, S. and Dehak, N. (2019). A forced Gaussians based methodology for the differential evaluation of Parkinson's Disease by means of speech processing. Biomedical Signal Processing and Control, 48, 205-220.</p> <p>BibTeX:</p> <pre><code>@article{moro2019phonetic, title={Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson's Disease}, author={Moro-Velazquez, Laureano and Gomez-Garcia, Jorge A. and Godino-Llorente, Juan I. and Grandas-Perez, Francisco and Shattuck-Hufnagel, Stefanie and Yague-Jimenez, Virginia and Dehak, Najim}, journal={Scientific Reports}, volume={9}, pages={19066}, year={2019}, publisher={Nature Research Publishing} } @article{moro2019forced, title={A forced Gaussians based methodology for the differential evaluation of Parkinson's Disease by means of speech processing}, author={Moro-Velazquez, Laureano and Gomez-Garcia, Jorge Andres and Godino-Llorente, Juan Ignacio and Dehak, Najim}, journal={Biomedical Signal Processing and Control}, pages={205--220}, volume={48}, year={2019}, publisher={Elsevier} } </code></pre> <p> </p>
Figure S1. Tear break-up time value in hyaluronic acid (HA) group and non-HA group after excluding the source of publication bias
<p>Figure S1. Tear break-up time value in hyaluronic acid (HA) group and non-HA group after excluding the source of publication bias <br> </p>
X-ray scattering datasets associated with the publication "Molecular Mobility of Polynorbornenes with Trimethylsiloxysilyl side groups: Influence of the Polymerization Mechanism"
<p>X-ray scattering datasets for samples described in the 2022 publication "Molecular Mobility of Polynorbornenes with Trimethylsiloxysilyl side groups: Influence of the Polymerization Mechanism". This dataset includes both raw and processed X-ray scattering data for samples APTCN and MPTCN, alongside background measurement files (BKG).</p>
Liste, travaux et publications des Groupes thématiques numériques (2020-2023)
<p>Les groupes thématiques numériques (GTnum) sont coordonnés par la Direction du numérique pour l'éducation du Ministère de l'Education nationale et de la Jeunesse. Les GTnum ont pour objectif de mettre à disposition des équipes éducatives, de façon accessible et ouverte, un état de la recherche sur quelques grandes thématiques relatives au numérique dans l'éducation. Financeur du projet : « Ministère de l'Éducation nationale et de la Jeunesse ».</p> <p>Accès aux publications : https://edunumrech.hypotheses.org/ https://hal.science/GTNUM/browse/latest-publications </p>
Public Health Nurse-Peer Co-Led Group Cognitive Behavioral Therapy for Postpartum Depression
ClinicalTrials.gov study NCT06597448. IPD Sharing: NO. Countries: 1. Publications: 85.
Group CBT for PPD in the Public Health Setting
ClinicalTrials.gov study NCT03039530. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Liste, travaux et publications des Groupes thématiques numériques (2017-2023)
<p>Liste, travaux et publications des Groupes thématiques numériques sur la période 2017-2023.</p>
Data from: Group-size diversity in public goods games
Open the record for dataset details and reuse information.
An Online Large-group One-session Treatment for Public Speaking Anxiety
ClinicalTrials.gov study NCT04790864. IPD Sharing: YES. Countries: 1. Publications: 0.
Group Version of the UP-A in a Spanish Public Mental Health Setting.
ClinicalTrials.gov study NCT06894368. 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.