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174 results for “online data”
Educational data collected from students - regarding the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The student questionnaire was designed with 20 questions. It was completed by 1,088 respondents and focuses on students' experiences related to online education. The collected data provides a broad perspective on various aspects of this, including access to technology, experiences with different platforms, perceptions of the advantages and disadvantages of this form of education, as well as direct feedback from students regarding their experiences. The full questionnaire can be accessed at: <a href="https://forms.gle/fhgzCUx1SDnxbCfZ6" target="_new" rel="noopener"><strong>https://forms.gle/fhgzCUx1SDnxbCfZ6</strong></a></p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
Educational data collected from teachers - for the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The dataset comes from a questionnaire structured into 24 questions, which can be accessed at <a href="https://forms.gle/bUgYMfoNHh7r6ebs6" target="_new" rel="noopener">https://forms.gle/bUgYMfoNHh7r6ebs6</a>. This questionnaire was completed by 956 respondents and aims to analyze the online activities carried out during March - April 2020, being distributed to teachers.<br>Each question is designed to reveal different aspects of the experiences, skills, and perspectives of teaching staff regarding online teaching and learning.</p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
MangroveDB: A comprehensive online database for mangroves based on multi-omics data
<p><span>Mangroves are dominant flora of intertidal zones along tropical and subtropical coastline around the world that offer important ecological and economic value. Recently, the genomes of mangroves have been decoded, and massive omics data were generated and deposited in the public databases. Reanalysis of multi-omics data can provide new biological insights excluded in the original studies. However, the requirements for computational resource and lack of bioinformatics skill for experimental researchers limit the effective use of the original data. To fill this gap, we uniformly processed 942 transcriptome data, 386 whole-genome sequencing data, and provided 13 reference genomes and 40 reference transcriptomes for 53 mangroves. Finally, we built an interactive web-based database platform MangroveDB (https://github.com/Jasonxu0109/MangroveDB), which was designed to provide comprehensive gene expression datasets to </span><span>facilitate their exploration</span><span> and equipped with several online analysis tools, including principal components analysis, differential gene expression analysis, tissue-specific gene expression analysis, GO and KEGG enrichment analysis. MangroveDB not only provides query functions about genes annotation, but also supports some useful visualization functions for analysis results, such as volcano plot, heatmap, dotplot, PCA plot, bubble plot, population structure <em>etc</em>. In conclusion, MangroveDB is a valuable resource for the mangroves research community to efficiently use the massive public omics datasets.</span></p>
Data collection of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset contains the definition and name of the data used in the study. It also contains rows of data for all flood parameters applied to the creation of flood vulnerability maps, namely rainfall data, landsat-8 files, DEM, DSMW and drainage survey data.</p>
Data testing of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset explains validation testing in a study of the Samarinda Seberang flood vulnerability map. There are two test methods, namely the Kappa accuracy test and the 3D simulation visualization test. The Kappa accuracy test tab displays a table of Kappa calculation results, and the second tab contains a 3D simulation scenario image.</p>
Data for: Gender medicine teaching increases medical students' gender awareness: results of a quantitative online survey
<p><span><strong>Background</strong> </span></p> <p><span>Gender medical knowledge is insufficiently integrated into university teaching in Germany. Gender awareness represents a key competence to integrate this knowledge into one's medical practice. This study is the first survey of the gender awareness of medical students in Germany.</span></p> <p><strong><span>Methods</span> </strong></p> <p><span>From April to July 2021, a quantitative cross-sectional survey in an online format using the 'Nijmegen Gender Awareness in Medicine Scale' (2008) was conducted at four German universities (Charité Berlin, Friedrich-Schiller-University Jena, Ludwig-Maximilians-University Munich, and the University of Cologne) with a varied implementation of teaching gender medicine. Students indicated their agreement or disagreement with assumptions and knowledge about the influence of gender in everyday medical practice (gender sensitivity), as well as gender role stereotypes towards patients and physicians (gender role ideology). </span></p> <p><strong><span>Results </span></strong></p> <p><span>The 750 included participants showed a relatively high gender sensitivity and low gender role stereotyping towards patients and doctors. The curricular implementation of gender medicine of the universities showed to have a significant influence on the students' gender sensitivity, as well as on their gender role stereotyping towards patients. Students who reported having taken classes in gender medicine showed significantly higher level of gender sensitivity. Cis-males showed significantly lower gender sensitivity and significantly higher gender role stereotyping.</span></p> <p><span><strong>Conclusion</strong> </span></p> <p><span>Implementation of gender medicine in the medical curriculum, attending courses on gender education and one's gender have a significant impact on medical students' gender competencies. These results support the need for structural integration of gender in medical education and gender trainings at medical schools in Germany.</span></p>
Anonymized data for "The Impact of Argument Arrangement on Persuasiveness in Online Discussions"
<p>Anonymous upload of the data for the paper "The Impact of Argument Arrangement on Persuasiveness in Online Discussions" for blind review.</p>
Datasets for "Generic and robust root cause localization for multi-dimensional data in online service systems"
<p>For simulation datasets, the ground-truth root causes are in <code>injection_info.csv</code> in each subfolder.</p> <p>For injection datasets, each subdirectory contains monitoring data for one fault injection. Their ground-truth root causes are indicated by the subdirectory names.</p> <p><br> <a href="https://github.com/NetManAIOps/PSqueeze">NetManAIOps/PSqueeze (github.com)</a></p>
Water content data for calibration of online measurements
<p>Data for water content of test samples of wood chips. The data have been used for calibration at a CHP plant to allow of online measurements. The data are used for "Calibration techniques for Water Content Measurements in Solid Biofuels" (in prep.)</p> <p>www.biofmet.eu</p> <p> </p>
User study data - Summaries with personalized persuasive suggestions to mitigate confirmation bias during interaction with online debates
<p><strong>Description</strong></p> <p>This data was collected to test the effect of debate summaries and personalized persuasive suggestions to engage with them on participants argument recall after engaging with the debate. It contains interaction data and questionnaire results of 212 participants who interacted with one out of four versions of an online debate page.</p> <p><strong>Variables</strong></p> <p>(names/column headers, description, coding)</p> <ul> <li><strong>display_con</strong>: debate display condition, coding: 1: without summary, 2: with summary and neutral suggestion, 3: with summary and personalized persuasive suggestion, 4: with summary and random persuasive suggestion</li> <li><strong>correct_comp</strong>: proportion of correctly recalled arguments (10 arguments)</li> <li><strong>AO_correct_comp</strong>: proportion of correctly recalled attitude-opposing arguments (5 arguments)</li> <li><strong>AC_correct_comp</strong>: proportion of correctly recalled attitude-confirming arguments (5 arguments), coding</li> <li><strong>assigned_topic</strong>: debate topic participant was assigned to</li> <li><strong>clicked_contribute</strong>: indicates whether participant made a contribution to the debate, binary</li> <li><strong>att_strength</strong>: strength of prior attitude, coding: 3: strong, 2: moderate</li> <li><strong>time_debate</strong>: time spent on the debate page in seconds</li> <li><strong>clicked_showmore</strong>: indicates whether participant clicked on the show more button to reveal two additional items of the summary, binary</li> <li><strong>att_change</strong>: change of prior to post attitude, coding: negative values indicate a weakaning, positive a strengthening of the initial attitude (attitude was measured on a seven-point Likert scale)</li> <li><strong>stps_highest</strong>: highest scoring persuasion category (persuasion profile)</li> </ul>
Data from: An R package and online resource for macroevolutionary studies using the ray-finned fish tree of life
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Data for: Gender medicine teaching increases medical students' gender awareness: results of a quantitative online survey
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Data from: Special care dentistry perception among dentists in Jakarta: an online survey study
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Data from: Online prenatal trial in mindfulness sleep management (OPTIMISM)
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Data of "Implementation of stimuli with millisecond timing accuracy in online experiments"
<p>Data of "Implementation of stimuli with millisecond timing accuracy in online experiments"</p>
Data from: From trial to implementation, bringing team-based learning online – Duke-NUS Medical School's response to the COVID-19 pandemic
<p>The restrictions imposed by the COVID-19 pandemic resulted in Duke-NUS Medical School moving all their lessons online. Duke-NUS employs a team-based learning (TBL) pedagogy, which depends heavily on student discussion. In 2015, our university had implemented an eLearning week where lessons were conducted online. Using the already present online assessment processes, the data, insights and student feedback allowed for swift implementation of an online TBL module for home-based learning in response to the pandemic in 2020. These protocols were modified over the weeks, guided by feedback from students and faculty. An analysis of this online TBL module is presented herein.</p>
ABOME: A Multi-platform Data Repository of Artificially Boosted Online Media Entities
<p><strong>Motivation</strong></p> <p>The rise of online media has enabled users to choose various unethical and artificial ways of gaining social growth to boost their credibility (number of followers/retweets/views/likes/subscriptions) within a short time period. In this work, we present ABOME, a novel data repository consisting of datasets collected from multiple platforms for the analysis of blackmarket-driven collusive activities, which are prevalent but often unnoticed in online media. ABOME contains data related to tweets and users on Twitter, YouTube videos, YouTube channels. We believe ABOME is a unique data repository that one can leverage to identify and analyze blackmarket based temporal fraudulent activities in online media as well as the network dynamics.</p> <p><strong>License</strong></p> <p>Creative Commons License.</p> <p><strong>Description of the dataset</strong></p> <p>In this work, we focused on collecting data from credit-based freemium services. We divide the datasets into two parts:</p> <p><strong>- Historical Data (</strong><strong>historical_anon.zip</strong><strong>)</strong></p> <p>This consists of all the data for Twitter and YouTube from blackmarket services gathered via sequential querying of the website’s URLs between the period March-June, 2019. We collected the metadata of each entity present in the historical data.</p> <p><strong>Twitter:</strong></p> <p>We collected the following fields for retweets and followers on Twitter:</p> <p><code>user_details</code>: A JSON object representing a Twitter user.</p> <p><code>tweet_details</code>: A JSON object representing a tweet.</p> <p><code>tweet_retweets</code>: A JSON list of tweet objects representing the most recent 100 retweets of a given tweet.</p> <ol> <li> <p><a href="https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object">https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object</a><a href="#fnref1">↩︎</a></p> </li> <li> <p><a href="https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object">https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object</a><a href="#fnref2">↩︎</a></p> </li> </ol> <p><strong>YouTube:</strong></p> <p>We collected the following fields for YouTube likes and comments:</p> <p><code>is_family_friendly:</code> Whether the video is marked as family friendly or not.</p> <p><code>genre:</code> Genre of the video.</p> <p><code>duration:</code> Duration of the video in ISO 8601 format (duration type). This format is generally used when the duration denotes the amount of intervening time in a time interval.</p> <p><code>description:</code> Description of the video.</p> <p><code>upload_date:</code> Date that the video was uploaded.</p> <p><code>is_paid:</code> Whether the video is paid or not.</p> <p><code>is_unlisted:</code> The privacy status of the video, i.e., whether the video is unlisted or not. Here, the flag <em>unlisted</em> indicates that the video can only be accessed by people who have a direct link to it.</p> <p><code>statistics:</code> A JSON object containing the number of dislikes, views and likes for the video.</p> <p><code>comments:</code> A list of comments for the video. Each element in the list is a JSON object of the text (<em>the comment text</em>) and time (<em>the time when the comment was posted</em>).</p> <p>We collected the following fields for YouTube channels:</p> <p><code>channel_description:</code> Description of the channel.</p> <p><code>hidden_subscriber_count:</code> Total number of hidden subscribers of the channel.</p> <p><code>published_at:</code> Time when the channel was created. The time is specified in ISO 8601 format (YYYY-MM-DDThh:mm:ss.sZ).</p> <p><code>video_count:</code> Total number of videos uploaded to the channel.</p> <p><code>subscriber_count:</code> Total number of subscribers of the channel.</p> <p><code>view_count:</code> The number of times the channel has been viewed.</p> <p><code>kind:</code> The API resource type (e.g., <em>youtube#channel</em> for YouTube channels).</p> <p><code>country:</code> The country the channel is associated with.</p> <p><code>comment_count:</code> Total number of comments the channel has received.</p> <p><code>etag:</code> The ETag of the channel which is an HTTP header used for web browser cache validation.</p> <p>The historical data is stored in five directories named according to the type of data inside it. Each directory contains JSON files corresponding to the data described above. <strong>'historical_sample.zip'</strong> contains a small sample of the historical dataset.</p> <p>- <strong>Time-series Data (time_series_anon.zip)</strong></p> <p>This consists of time-series data (collected every 8 hours) of Twitter users and tweets collected from the blackmarket services between the period of March-June, 2019. We collect the following time-series data for retweets and followers on Twitter:</p> <p><code>user_timeline</code>: This is a JSON list of tweet objects in the user’s timeline, which consists of the tweets posted, retweeted and quoted by the user. The file created at each time interval contains the new tweets posted by the user during each time interval.</p> <p><code>user_followers</code>: This is a JSON file containing the user ids of all the followers of a user that were added or removed from the follower list during each time interval.</p> <p><code>user_followees</code>: This is a JSON file consisting of the user ids of all the users followed by a user, i.e., the followees of a user, that were added or removed from the followee list during each time interval.</p> <p><code>tweet_details</code>: This is a JSON object representing a given tweet, collected after every time interval.</p> <p><code>tweet_retweets</code>: This is a JSON list of tweet objects representing the most recent 100 retweets of a given tweet, collected after every time interval.</p> <p>The time-series data is stored in directories named according to the timestamp of the collection time. Each directory contains sub-directories corresponding to the data described above. <strong>'time_series_sample.zip'</strong> contains a small sample of the time series dataset.</p> <p><strong>Data Anonymization</strong></p> <p>The data is anonymized by removing all Personally Identifiable Information (PII) and generating pseud-IDs corresponding to the original IDs. A consistent mapping between the original and pseudo-IDs is maintained to maintain the integrity of the data.</p> <p> </p>
Data from: Two new species of Limbodessus diving beetles from New Guinea - short verbal descriptions flanked by online content (digital photography, μCT scans, drawings and DNA sequence data)
Background: To date only one species of Limbodessus diving beetles has been reported from the Island of New Guinea, L. compactus (Clark, 1862), which is widerspread in the Australian region. New information: We describe two new species of microendemic New Guinea Limbodessus and use a compact descriptive format flanked by enriched online content in wiki powered species pages. Limbodessus baliem sp.n. is described from ca. 1,600 m altitude in the Baliem Valley of Papua and Limbodessus alexanderi sp.n. from >3,000 m altitude north of Sugapa, Papua. Based on our analysis, we also transfer three species from other genera to Limbodessus Guignot, 1939, with the following changes: Limbodessus deflectus (Ordish, 1966), new combination; Limbodessus leveri (J. Balfour-Browne, 1944), new combination; and Limbodessus plicatus (Sharp, 1882), new combination.
Data from: Evolutionary online behaviour learning and adaptation in real robots
Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.
Data from: Comparison of Nottingham Prognostic Index and Adjuvant Online prognostic tools in young women with breast cancer: review of a single-institution experience
Objective: Accurately predicting the prognosis of young patients with breast cancer (<40 years) is uncertain since the literature suggests they have a higher mortality and that age is an independent risk factor. In this cohort study we considered two prognostic tools; Nottingham Prognostic Index and Adjuvant Online (Adjuvant!), in a group of young patients, comparing their predicted prognosis with their actual survival. Setting: North East England. Participants: Data was prospectively collected from the breast unit at a Hospital in Grimsby between January 1998 and December 2007. A cohort of 102 young patients with primary breast cancer was identified and actual survival data was recorded. The Nottingham Prognostic Index and Adjuvant! scores were calculated and used to estimate 10-year survival probabilities. Pearson's correlation coefficient was used to demonstrate the association between the Nottingham Prognostic Index and Adjuvant! scores. A constant yearly hazard rate was assumed to generate 10-year cumulative survival curves using the Nottingham Prognostic Index and Adjuvant! predictions. Results: Actual 10-year survival for the 92 patients who underwent potentially curative surgery for invasive cancer was 77.2% (CI 68.6% to 85.8%). There was no significant difference between the actual survival and the Nottingham Prognostic Index and Adjuvant! 10-year estimated survival, which was 77.3% (CI 74.4% to 80.2%) and 82.1% (CI 79.1% to 85.1%), respectively. The Nottingham Prognostic Index and Adjuvant! results demonstrated strong correlation and both predicted cumulative survival curves accurately reflected the actual survival in young patients. Conclusions: The Nottingham Prognostic Index and Adjuvant! are widely used to predict survival in patients with breast cancer. In this study no statistically significant difference was shown between the predicted prognosis and actual survival of a group of young patients with breast cancer.
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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.