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10 results for “semantic networks”
The POPREBEL semantic social network data
<p>The <a href="https://populism-europe.com/poprebel/">POPREBEL project</a> explores the phenomenon of populism in Europe. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. It consists of coded interviews, realized between spring 2021 and spring 2022, to Internet users in Czechia, Germany and Poland, who used social media to gather information about the COVID-19 pandemic. The dataset is pseudonymized. POPREBEL is supported by the European Union's Horizon 2020 programme, grant n. 822682.</p> <ul> <li><a href="https://zenodo.org/record/7494327">Final ethnographic report.</a> Section 1.2 contains a detailed description of how and why data were collected.</li> <li><a href="https://wellbeing.edgeryders.eu">Funnel website</a> of the project.</li> <li><a href="https://hal.archives-ouvertes.fr/hal-02478720/document">About semantic social networks</a>.</li> <li><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data)</li> </ul>
The TREASURE semantic social network data on the circular economy aspect of automotive manufacturing
<p>The <a href="https://www.treasureproject.eu/">TREASURE</a> project looks at industrial innovation to address the problem of making onboard electronics in the automotive industry easier to recycle, increasing the industry's contribution to the circular economy. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. interviews conducted between January 2022 and June 2023 with car owners and enthusiasts at car industry events. The interviews focus on experiences with car electronics and perspectives on sustainability and the circular economy. The dataset is pseudonymized. TREASURE is supported by the European Union's Horizon 2020 programme, grant n. 101003587.</p>
Semantic Segmentation of Time Series Imagery Using Deep Convolutional Neural Networks: A Case Study of Sandbars in Grand Canyon
<p>This dataset contains imagery used to train and test Deep Convolutional Neural Networks for the purpose of binary semantic segmentation of a time series of oblique imagery capturing sandbar monitoring sites in The Grand Canyon. In addition the scripts needed for removing image distortion, registering, rectifying, and labeling imagery is present. </p>
Data for the Article: Cross-validation of a semantic segmentation network for natural history collection specimens
<p>This deposit contains six datasets which were used for testing and validating a semantic segmentation network. The purpose was to evaluate the suitability of the segmentation network for use in the processing of images from Natural History Collections.</p>
The NGI Forward semantic social network data
<p>The <a href="https://research.ngi.eu/">NGI Forward project</a> is part of the European Union's <a href="https://www.ngi.eu">Next Generation Internet Initiative</a>. It is meant to provide European institutions with policy advice for how to shape the future, human-centric Internet. As part of it, a team of ethnographers coded a specially convened online conversation, then arranged its results into a semantic social network. This dataset encodes that conversation, as well as the results of the coding exercise, in raw data form for further exploration and replication purposes. The dataset is pseudonymized.</p> <ul> <li><a href="https://exchange.ngi.eu/">Funnel website</a> of the project.</li> <li><a href="https://journals.sagepub.com/doi/10.1177/1525822X20908236">About semantic social networks</a>.</li> <li><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data)</li> </ul>
Exploring Korean adolescent stress on social media: A semantic network analysis
<p><strong>Korean Adolescent's Stress Semantic Network Analysis Project</strong></p> <p>Semantic Network Analysis for Korean Adolescent's Stress</p> <p>Input data file</p> <ul> <li>data_news.csv : News data collected from Naver(<a href="https://www.naver.com">https://www.naver.com</a>)</li> <li>data_blog.csv : Blog data collected from Naver(<a href="https://www.naver.com">https://www.naver.com</a>) and Daum(<a href="https://www.daum.net">https://www.daum.net</a>)</li> </ul> <p>Output files</p> <ul> <li>Frequency Table of Each word in Documents (<em><strong>freq_news.csv</strong></em>, <em><strong>freq_blog.csv</strong></em>)</li> <li>TF-IDF(Term Frequency-Inverse Document Frequency) Table of Each word in Documents (<em><strong>tfidf_news.csv</strong></em>, <em><strong>tf_idf_blog.csv</strong></em>)</li> <li>Frequency Table of 30 keywords in Documents (<em><strong>freq_news_30.csv</strong></em>, <em><strong>freq_blog_30.csv</strong></em>)</li> <li>DTM(Document Term Matrix) of 30 keywords in Documents (<em><strong>DTM_news_30.csv</strong></em>, <em><strong>DTM_blog_30.csv</strong></em>)</li> <li>COM(Co-Occurrence Matrix) of 30 keywords in Documents (<em><strong>COM_news_30.csv</strong></em>, <em><strong>COM_blog_30.csv</strong></em>)</li> <li>Binary COM of 30 keywords in Documents (<em><strong>BinaryCOM_news_30.csv</strong></em>, <em><strong>BinaryCOM_blog_30.csv</strong></em>)</li> <li>Centrality Table of 30 keywords in Documents (<em><strong>centrality_news_30.csv</strong></em>, <em><strong>centrality_blog_30.csv</strong></em>)</li> </ul>
Reproducibility and robustness of graph measures of the Associative-Semantic Network
<p>Matfiles and matlab scripts used to study the reproducibility and robustness of graph measures of the Associative-Semantic Network.</p>
Sundqvist et al. - The white matter module-hub network of semantics revealed by semantic dementia - Supplementary Figure
<p><strong>Supplementary Figure</strong> The correlation circle: correlations between the different MRI measures and semantic scores and the two first principal components via coordinates. The two first components sum up 68% of the total variance.</p> <p>Semantic composite (verbal, non-verbal) scores are measured in percentage. L indicates left hemisphere, R indicates right hemisphere. Mean Diffusivity (MD) for white matter tracts are: UNC-L, UNC-R, ILF-L, ILF-R, ATL-WVFA-L, ATL-FFA-R, ATL-LA-L, and ATL-LA-R. Mean cortical thickness of the regions of interests are: ATL-L, ATL-R, WVFA-L, FFA-R, LA-L, and LA-R.</p> <p>Link to publication: https://doi.org/10.1162/jocn_a_01549</p> <p> </p>
Semantic Networks in Alcohol Use Disorder Patients: Exploratory Study
ClinicalTrials.gov study NCT05636033. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Language Acquisition in the Brain and Algorithms: Towards Systematic Monitoring of the Evolution of Semantic Representations in Biological and Artificial Neural Networks
ClinicalTrials.gov study NCT05217043. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
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