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11,879 results for “participation”
Questionnaire of all LEA School Lab -participants
<p>Questionnaire from all LEA School Lab participants. How the school labs were carried out, etc</p>
Participant observation data from local, storytelling, multi-stakeholder energy policy workshops
<p>The SHAPE ENERGY (EU Horizon 2020) undertook 17 multi-stakeholder energy policy workshops over November 2017 – May 2018. These workshops were mostly in partnership with municipalities who posed local energy problems that needed to be ‘solved’. The workshops brought local stakeholders together through storytelling facilitation techniques. The included data herein are anonymised components of the completed participant observation templates that partners used at these workshops. In particular, the data predominately relates to the conversational exchanges in one very closely monitored session across all the workshops.</p>
A National Forum on Web Privacy and Web Analytics — Participant Survey Instrument
<p>This survey instrument was administered to participants of the <em>National Forum on Web Privacy and Web Analytics</em>. Results informed the Forum event and Forum deliverables.</p> <p>The <em>National Forum on Web Privacy and Web Analytics </em>was held September 2018 in Bozeman, Montana, where 40 librarians, technologists, and privacy researchers collaborated in producing a practical roadmap for enhancing our analytics practice in support of privacy.</p> <p>More information is available on our project site: <a href="https://osf.io/gnfpu/">https://osf.io/gnfpu/</a>. </p> <p>This project is made possible in part by the Institute of Museum and Library Services, through grant <a href="https://www.imls.gov/grants/awarded/lg-73-18-0100-18"># LG-73-18-0100-18</a>.</p>
Data of 14 participants in the RCT titled Acute effects of virtual reality exercise bike games on psychophysiological outcomes in college North-African adolescents with cerebral palsy: A randomized clinical trial
<p>It is an Excel file including the numerical data of the 14 participants included in the study titled: <a name="_Hlk151119211"></a><span>Acute effects of virtual reality exercise bike games on psychophysiological outcomes in college North-African adolescents with cerebral palsy: A randomized clinical trial</span></p>
Analysis of websites, social media pages and apps for the development of new strategies for increasing participation of women in clinical trials
<p>For T2.5 of the i-CONSENT project, an analysis was undertaken of the strategies used to effectively communicate with women on the topic of women’s health or women’s health research, considering aspects such as tone, format and audience interaction. A total of 42 websites, social media pages and apps were included in the analysis. The attached document presents the findings from the data generation stage of this analysis.</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Graph Files
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding graph files of the extracted Zooniverse networks described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The graph files are in <code>.gexf</code> (graph exchange XML format) and <code>.gml</code> (graph modeling language) formats which can be used by common graph/network-analysis and visualisation tools such as Gephi.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations of the network structure, involving additional (not yet analysed) features such as the content of the comments etc.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains distinct graph files for each of the analysed projects. For each graph file, there are <em>nodes </em>and <em>edges</em> and their associated attributes (i.e., each edge can have an attribute). For the edges, apart from source and target, we have as attributes:</p> <ul> <li><code>weight</code></li> <li><code>project_title</code></li> <li><code>body </code>(i.e., text)</li> <li><code>created_at</code></li> <li><code>userRoles</code></li> <li><code>discussion_title</code></li> <li><code>discussion_id</code></li> <li><code>user_id</code></li> <li><code>board_title</code></li> <li><code>relation</code></li> <li><code>target_role</code></li> </ul> <p>For the nodes, the attributes are:</p> <ul> <li><code>user_id</code></li> <li><code>userRoles</code></li> <li><code>degree_reply </code>(i.e., degree for the reply relation)</li> <li><code>in_degree_reply</code></li> <li><code>out_degree_reply</code></li> <li><code>degree_comment</code></li> <li><code>in_degree_comment</code></li> <li><code>out_degree_comment</code></li> <li><code>degree_total</code></li> <li><code>in_degree_total</code></li> <li><code>out_degree_total</code></li> <li><code>target_role</code></li> </ul> <p><strong>Grouping:</strong></p> <p>Each graph file represents all the comments for the respective project across its lifespan irrespective of any time slices. Edges represent the comments and users represent the nodes. While the different boards are still contained within the data, all boards occur in the data.</p> <p> </p>
Demographic information of individuals participating in educational research questionnaire
<p>Pre- and post-intervention participant demographic information of individuals participating in educational research questionnaire.</p>
Not so weak-PICO: Leveraging weak supervision for Participants, Interventions, and Outcomes recognition for systematic review automation
<p><strong>Objective: </strong>PICO (Participants, Interventions, Comparators, Outcomes) analysis is vital but time-consuming for conducting systematic reviews (SRs). Supervised machine learning can help fully automate it, but a lack of large annotated corpora limits the quality of automated PICO recognition systems. The largest currently available PICO corpus is manually annotated, which is an approach that is often too expensive for the scientific community to apply. Depending on the specific SR question, PICO criteria are extended to PICOC (C-Context), PICOT (T-timeframe), and PIBOSO (B-Background, S-Study design, O-Other) meaning the static hand-labelled corpora need to undergo costly re-annotation as per the downstream requirements. We aim to test the feasibility of designing a weak supervision system to extract these entities without hand-labelled data.</p> <p><strong>Methodology:</strong> We decompose PICO spans into its constituent entities and re-purpose multiple medical and non-medical ontologies and expert-generated rules to obtain multiple noisy labels for these entities. These labels obtained using several sources are then aggregated using simple majority voting and generative modelling approaches. The resulting programmatic labels are used as weak signals to train a weakly-supervised discriminative model and observe performance changes. We explore mistakes in the currently available PICO corpus that could have led to inaccurate evaluation of several automation methods.</p> <p><strong>Results: </strong>We present Weak-PICO, a weakly-supervised PICO entity recognition approach using medical and non-medical ontologies, dictionaries and expert-generated rules. Our approach does not use hand-labelled data.</p> <p><strong>Conclusion: </strong>Weak supervision using weak-PICO for PICO entity recognition has encouraging results, and the approach can potentially extend to more clinical entities readily. </p>
iDPP@CLEF 2022 - Participants' repositories for the Intelligent Disease Prediction Progression Challenge
<p><a href="https://brainteaser.health/open-evaluation-challenges/idpp-2022/">iDPP@CLEF 2022</a> (Intelligent Disease Progression Prediction at CLEF) is a challenge organised by the <a href="https://brainteaser.health/">BRAINTEASER</a> Horizon 2020 project and co-located with <a href="https://clef2022.clef-initiative.eu/">CLEF 2022</a> (Conference and Labs of the Evaluation Forum). </p> <p>BRAINTEASER is a data science project that seeks to exploit the value of big data, including those related to health, lifestyle habits, and environment, to support patients with amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) and their clinicians. Taking advantage of cost-efficient sensors and apps, BRAINTEASER will integrate large, clinical datasets that host both patient-generated and environmental data.</p> <p>The goal of iDPP@CLEF is to design and develop an evaluation infrastructure for AI algorithms able to:</p> <ul> <li> <p>Better describe<strong> disease mechanisms</strong>.</p> </li> <li> <p><strong>Stratify patients </strong>according to their phenotype assessed all over the disease evolution.</p> </li> <li> <p><strong>Predict disease progression</strong> in a probabilistic, time dependent fashion.</p> </li> </ul> <p>iDPP@CLEF 2022 offered the following tasks:</p> <ul> <li> <p><strong>Pilot Task 1 – Ranking Risk of Impairment</strong>: It focuses on ranking of patients based on the risk of impairment in specific domains. More in detail, we will use the ALSFRS-R scale to monitor speech, swallowing, handwriting, dressing/hygiene, walking and respiratory ability in time and will ask participants to rank patients based on time to event risk of experiencing impairment in each specific domain.</p> </li> <li> <p><strong>Pilot Task 2 – Predicting Time of Impairment</strong>: It refines Task 1 asking participants to predict when specific impairments will occur (i.e. in the correct time-window). In this regard, we assess model calibration in terms of the ability of the proposed algorithms to estimate a probability of an event close to the true probability within a specified time-window.</p> </li> <li> <p><strong>Position Papers Task 3 – Explainability of AI algorithms</strong>: We call for proposals of different visualization frameworks able to show the multivariate nature of the data and the model predictions in an explainable, possibly interactive, way.</p> </li> </ul> <p> </p> <p>This dataset contains the repositories of the participants to iDPP@CLEF 2022. These repositories contain the output, i.e. the predictions, produced by the participating systems as well as the performance scores for those systems.</p> <p>For additional information about iDPP@CLEF 2022, please see:</p> <ul> <li> <p>Guazzo, A., Trescato, I., Longato, E., Hazizaj, E., Dosso, D., Faggioli, G., Di Nunzio, G. M., Silvello, G., Vettoretti, M., Tavazzi, E., Roversi, C., Fariselli, P., Madeira, S. C., de Carvalho, M., Gromicho, M., Chiò, A., Manera, U., Dagliati, A., Birolo, G., Aidos, H., Di Camillo, B., and Ferro, N. (2022). Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2022. In Barr ́on-Cedeno, A., Da San Martino, G., Degli Es- posti, M., Sebastiani, F., Macdonald, C., Pasi, G., Hanbury, A., Potthast, M., Faggioli, G., and Ferro, N., editors, <em>Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the Thirteenth International Conference of the CLEF Association (CLEF 2022)</em>, pages 395–422. Lecture Notes in Computer Science (LNCS) 13390, Springer, Heidelberg, Germany.</p> </li> <li> <p>Guazzo, A., Trescato, I., Longato, E., Hazizaj, E., Dosso, D., Faggioli, G., Di Nunzio, G. M., Silvello, G., Vettoretti, M., Tavazzi, E., Roversi, C., Fariselli, P., Madeira, S. C., de Carvalho, M., Gromicho, M., Chiò, A., Manera, U., Dagliati, A., Birolo, G., Aidos, H., Di Camillo, B., and Ferro, N. (2022). Overview of iDPP@CLEF 2022: The Intelligent Disease Progression Prediction Challenge. In Faggioli, G., Ferro, N., Hanbury, A., and Potthast, M., editors, <em>CLEF 2022 Working Notes</em>, pages 1130– 1210. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073. <a href="https://ceur-ws.org/Vol-3180/paper-88.pdf">http://ceur-ws.org/Vol-3180/</a>.</p> </li> </ul>
Small-angle Scattering Data Analysis Round Robin dataset: original for participants.
<p>These are four datasets that were made available to the participants of the Small-angle Scattering data analysis round robin. The intent was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset. </p> <p>In this repository, there are:<br> 1) a PDF document with more details for the study,<br> 2) the datasets for people to try and fit<br> 3) an Excel spreadsheet to document the results.</p> <p>Datasets 1 and 2 were modified from: Deumer, Jerome, & Gollwitzer, Christian. (2022). npSize_SAXS_data_PTB (Version 5) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5886834</p> <p> </p> <p>Datasets 3 and 4 were collected in-house on the MOUSE instrument, detailed <a href="https://dx.doi.org/10.1088/1748-0221/16/06/P06034">here</a></p>
Platforms & registries for sharing participant-level COVID-19 data
<p>This dataset presents an overview of platforms and registries that store, harmonize, and, in some cases, share COVID-19-related participant-level data, including clinical-epidemiological, human and pathogen OMICs, and high dimensional imaging data. The dataset provides an in-depth review of adherence to the <a href="https://www.go-fair.org/fair-principles/">FAIR principles</a>, governance, benefit sharing and other ethical concerns related to resources that share harmonized, participant-level data for the research response to COVID-19. Systematic searches were conducted between April 2020 and June 2021 to identify relevant platforms and registries. We applied natural language processing to the CORD-19 dataset in March of 2021 and consulted with COVID-19 focused researchers in Asia, Africa, and Latin America to identify non-English language COVID-19-related data sharing resources.</p>
Understanding levels of online participation in the UK museum sector
<p>This is the corresponding data set for the article "Understanding levels of online participation in the UK museum sector". </p> <p>This data set uses a representative sample of 315 UK museums to create a much-needed benchmark against which museum practitioners can evaluate and contextualise prior studies and their own experiences. It includes data from museum websites and five social media platforms, and is one of the largest data sets of its kind in the European museum sector and the first of such scale in the UK. </p>
Effect of unattended distributional training on phoneme category discrimination in English-Mandarin bilingual adult participants in Singapore (Main Study Data)
<p>Datasets from Main study of <strong>Effect of unattended distributional training on phoneme category discrimination in English-Mandarin bilingual adult participants in Singapore</strong> (project linked here: https://doi.org/10.17605/OSF.IO/S6VDN).</p>
Effect of unattended distributional training on phoneme category discrimination in English-Mandarin bilingual adult participants in Singapore (Pilot Study Data)
<p>Datasets from Pilot study of <strong>Effect of unattended distributional training on phoneme category discrimination in English-Mandarin bilingual adult participants in Singapore</strong> (project linked here: https://doi.org/10.17605/OSF.IO/S6VDN).</p>
Historical institutional affiliations of participants in Poland's 1989 Round Table
<p>Data collected by Dr. Domber from:</p> <ul> <li> <p>Okrągły stół . Kto jest kim opozycja (1989)</p> </li> <li> <p>Encyclopedia Solidarności (ESC and IPN)</p> </li> <li> <p>Kto jest Kim w Polsce (1989 and 1993)</p> </li> <li> <p>Opozycja w Polsce. Slownik Biograficzny 1956-1989</p> </li> <li> <p>Mołdawa, Ludzie Władza 1944-1991</p> </li> <li> <p>Kto jest Kim w Polsce (1989 and 1993)</p> </li> <li> <p>AAN PZPR CC Centralna Kartoteka</p> </li> <li> <p>IPN Biuletyn Informacji Publicznej</p> </li> <li> <p>Internet Searches</p> </li> </ul> <p> </p> <p>Data cleaned by Dr. Bodwin</p>
Online Panel Participation SPSS Dataset
<p>The provided dataset consists of 454 cases collected for the research of participation in non-probability online research panels. The dataset includes the following variables:</p> <p>Demographics variables</p> <p>The following demographic variables are included: participants' age, gender, level of education, and working status.</p> <p>Trust Scale</p> <p>The six-item General Trust Scale (Yamagishi, T., & Yamagishi, M., 1994) is used to examine the general level of trust of the respondents.</p> <p>Big Five Scale</p> <p>BFI-S (15 items) measuring the Big Five personality characteristics of the participants</p> <p>Online panel participation</p> <p>Online panel participation is measured with one categorical variable.</p> <p>Social media use</p> <p>Social media use is measured with one categorical variable exploring the duration of daily time spent on social media.</p>
WP3 Deliverable 3.1. Literature Review - The Making and Breaking of Rules in crisis situations: The Rule of Law and Democratic Participation
<p>The Covid-19 pandemic was a stress test to democracies. To date, literature lacks a comprehensive assessment of the political legitimacy of governments’ crisis responses in the context of multi-level political decision-making systems. LEGITIMULT aims to fill this gap in literature by analyzing the relationship between multilevel governance and political legitimacy from different angles. Work package 3 focuses on the role of democratic participation and the rule of law in a politically legitimate crisis governance. This working paper aims to develop a model of a democratic crisis governance and its empirical indicators based on the example of the Covid19 crisis. Thereby, it draws on the insights from a review of the extant literature on the democratic quality of the Covid-19 crisis governance. In addition, it presents an overview of the available datasets which assess the democratic quality and political legitimacy of governments’ Covid-19 crisis responses. Finally, it formulates first ideas on case selection for more in-depth analyses. Thus, this first deliverable provides the conceptual foundations of work package 3. Based on the suggested conceptualization and indicators of a democratic crisis governance, we will map the democratic quality of the Covid-19 crisis governance in 31 countries. To study the patterns of the results of this mapping, we will select a set of countries for an in-depth analysis of the impact of multi-level governance on the political legitimacy of Covid-19 crisis governance.</p>
Opération fourmis: le matériel du parfait collecteur, avec un protocole, des tubes et une fiche de données. Ce kit était envoyé gratuitement à tous les participants. in SOCIÉTÉ VAUDOISE D'ENTOMOLOGIE (SVE)
Opération fourmis: le matériel du parfait collecteur, avec un protocole, des tubes et une fiche de données. Ce kit était envoyé gratuitement à tous les participants.
Quelques participants lors de l'excursion printanière de Loèche (à gauche). Une petite surface avait été accidentellement incendiée l'année précédente et était déjà visitée par Pyrgus onopordi (à droite), une espèce très localisée et menacée en Suisse. (Photos Marina Rey et Yannick Chittaro) in Groupe Entomologique De La Murithienne (Valais)
Quelques participants lors de l'excursion printanière de Loèche (à gauche). Une petite surface avait été accidentellement incendiée l'année précédente et était déjà visitée par Pyrgus onopordi (à droite), une espèce très localisée et menacée en Suisse. (Photos Marina Rey et Yannick Chittaro)
Can we collect health-related quality of life information from anticoagulated atrial fibrillation participants who have recently experienced a bleed? An observational feasibility study in primary, and secondary care and through an online forum
<p>The purpose of the study was to evaluate the feasibility of recruiting participants diagnosed with atrial fibrillation (AF) taking oral anticoagulation therapies (OACs) and recently experiencing a bleed to collect health-related quality of life (HRQoL) information.</p> <p><strong>Design</strong></p> <p>Observational feasibility study. The study aimed to determine the feasibility of recruiting participants with minor and major bleeds, the most appropriate route for recruitment and the appropriateness of the Patient Reported Outcome Measures (PROMs) selected for collecting HRQoL information in AF patients, and the preferred format of the surveys.</p> <p><strong>Setting</strong></p> <p>Primary care, secondary care, and via an online patient forum.</p> <p><strong>Participants</strong></p> <p>The study population was adult patients (≥ 18) with Atrial Fibrillation (AF) taking oral anticoagulation therapies (OATs) who had experienced a recent major or minor bleed within the last four weeks.</p> <p><strong>Primary and Secondary outcome measures</strong></p> <p>Primary outcome:</p> <p>Patient reported outcome measures (PROMs): EuroQol 5 dimensions-5 levels (EQ-5D-5L); Perception of anticoagulant treatment questionnaire, part 2 only (PACT-Q, part 2); Atrial fibrillation effect on quality of life (AFEQT)</p> <p>Secondary outcomes:</p> <p>Location of bleed; bleed severity; current treatment; patient perceptions of HRQoLin relation to bleeding events.</p> <p><strong>Results</strong></p> <p>We received initial expressions of interest from 103 participants. We subsequently recruited 32 participants to the study- 14 from primary care and 18 through the AF forum. No participants were recruited through secondary care. Despite 32 participants consenting, only 26 initial surveys were completed. We received follow-up surveys from 11 participants (8 primary care and 3 AF forum). COVID-19 had a major impact on the study. </p> <p><strong>Conclusions</strong></p> <p>Primary care was the most successful route for recruitment. Most participants recruited to the study experienced a minor bleed. Further ways to recruit in secondary care should be explored, especially to capture more serious bleeds. </p> <p><strong>Registration</strong></p> <p>The study was adopted onto the NIHR Portfolio (I.D. #47771) and registered with www.ClinicalTrials.gov (#NCT04921176) in February 2021.</p> <p>Dataset contains all anonymised data for the participants who completed the survey including demographics, details of bleeds, co-morbidities and completed patient reported outcomes</p>
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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.