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464 results for “style”
The Effect of Reproductive Violence Education Given to Women on Perception of Violence, Conflict Resolution Styles and Awareness of Violence: A Randomized Controlled Trial
ClinicalTrials.gov study NCT06951529. IPD Sharing: YES. Countries: 0. Publications: 0.
Shi-style Cervical Manipulations for Nonspecific Neck Pain: a Multi-center, Randomized, Controlled Clinical Trial
ClinicalTrials.gov study NCT05108610. IPD Sharing: NO. Countries: 0. Publications: 0.
Chronic Pain, Life Style Factors and Quality of Life
ClinicalTrials.gov study NCT03028051. IPD Sharing: NO. Countries: 0. Publications: 0.
The Effect of Laughter Yoga-Supported Strong Woman, Safe Life Training Program on Attitudes Towards Violence, Styles of Coping with Stress and Psychological Flexibility
ClinicalTrials.gov study NCT06838052. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Go For It: to Improve Levels of Daily Physical Activity and to Reduce the Sedentary Life Style of Adolescents With CP
ClinicalTrials.gov study NCT02978469. IPD Sharing: NO. Countries: 0. Publications: 0.
A western-style diet, with and without chronic androgen treatment, alters the number, structure and function of small antral follicles in ovaries of young adult monkeys.
GEO Series GSE69716. Macaca mulatta. 9 samples. Type: Expression profiling by array.
Long-Term Hyperandrogenemia and/or Western-Style Diet Treatment Impairs Rhesus Macaque Preimplantation Embryo Development
GEO Series GSE186969. Macaca mulatta. 25 samples. Type: Expression profiling by high throughput sequencing.
PAN17 Multi-Author Analysis: Style-Change-Detection
<p>All documents are provided in English and may contain zero up to arbitrarily many switches (style breaches). Thereby switches of authorships may only occur at the end of sentences, i.e., not within.</p> <p>More information: <a href="https://pan.webis.de/clef17/pan17-web/style-change-detection.html">Link</a></p>
Replication package of paper 'Study of IoT System Architectural Styles and their Quality Requirements'
<p>Dataset of studied papers.</p> <p>It contains an Excel sheet that includes papers selected in each step of our selection process, along with the extracted data in each tab.</p>
Configurations of Teaching Styles among Secondary School Teachers: The Role of Teachers' Personal and Sociodemographic Factors
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PAN21 Authorship Analysis: Style Change Detection
<p>This is the dataset for the <a href="https://pan.webis.de/clef21/pan21-web/style-change-detection.html">Style Change Detection</a> task of PAN 2021.</p> <p>The goal of the style change detection task is to identify text positions within a given multi-author document at which the author switches. </p> <p><strong>Tasks</strong></p> <p>Given a document, we ask participants to answer the following three questions:</p> <ul> <li><em>Single vs. Multiple.</em> Given a text, find out whether the text is written by a single author or by multiple authors (task 1).</li> <li><em>Style Change Basic.</em> Given a text written by two or more authors and that contains a number of style changes, find the position of the changes (task 2).</li> <li><em>Style Change Real-World.</em> Given a text written by two or more authors, find all positions of writing style change, i.e., assign all paragraphs of the text uniquely to some author out of the number of authors you assume for the multi-author document (task 3).</li> </ul> <p>All documents are provided in English and may contain an arbitrary number of style changes, resulting from at most five different authors. However, style changes may only occur between paragraphs (i.e., a single paragraph is always authored by a single author and does not contain any style changes).</p> <p><strong>Data</strong></p> <p>The dataset is split into three parts:</p> <ol> <li><em>training set:</em> Contains 70% of the whole data set and includes ground truth data. Use this set to develop and train your models.</li> <li><em>validation set:</em> Contains 15% of the whole data set and includes ground truth data. Use this set to evaluate and optimize your models.</li> <li><em>test set:</em> Contains 15% of the whole data set. For the documents on the test set, you are not given ground truth data. This set is used for evaluation.</li> </ol> <p>The dataset is based on user posts from various sites of the StackExchange network, covering different topics. We refer to each input problem (i.e., the document for which to detect style changes) by an ID, which is subsequently also used to identify the submitted solution to this input problem. We provide one folder for train, validation, and test data.</p> <p>For each problem instance <code>X</code> (i.e., each input document), two files are provided:</p> <ol> <li><code>problem-X.txt</code> contains the actual text, where paragraphs are denoted by <code>\n\n</code>.</li> <li><code>truth-problem-X.json</code> contains the ground truth, i.e., the correct solution in JSON format: <pre><code class="language-json">{ "authors": NUMBER_OF_AUTHORS, "site": SOURCE_SITE, "multi-author": RESULT_TASK1, "changes": RESULT_ARRAY_TASK2, "paragraph-authors": RESULT_ARRAY_TASK3 }</code></pre> The result for task 1 (key "multi-author") is a binary value (1 if the document is multi-authored, 0 if the document is single-authored). The result for task 2 (key "changes") is represented as an array, holding a binary for each pair of consecutive paragraphs within the document (0 if there was no style change, 1 if there was a style change). If the document is single-authored, the solution to task 2 is an array filled with 0s. For task 3 (key "paragraph-authors"), the result is the order of authors contained in the document (e.g., <code>[1, 2, 1]</code> for a two-author document), where the first author is "1", the second author appearing in the document is referred to as "2", etc. Furthermore, we provide the total number of authors and the Stackoverflow site the texts were extracted from (i.e., topic).<br> <br> An example of a multi-author document, where there was a style change between the third and fourth paragraph could look as follows (we only list the relevant key/value pairs here): <pre><code class="language-json">{ "multi-author": 1, "changes": [0,0,1,...], "paragraph-authors": [1,1,1,2,...] }</code></pre> A single-author document would have the following form (again, only listing the relevant key/value pairs): <pre><code class="language-json">{ "multi-author": 0, "changes": [0,0,0,...], "paragraph-authors": [1,1,1,...] }</code></pre> <p> </p> </li> </ol>
PAN20 Authorship Analysis: Style Change Detection
<p>This is the data set for the <a href="https://pan.webis.de/clef20/pan20-web/style-change-detection.html">Style Change Detection task</a> of PAN 2020.</p> <p>The goal of the style change detection task is to identify text positions within a given multi-author document at which the author switches. Detecting these positions is a crucial part of the authorship identification process, and for multi-author document analysis in general. Note that, for this task, we make the assumption that a change in writing style always signifies a change in author.</p> <p><strong>Tasks</strong></p> <p>Given a document, we ask participants to answer the following two questions:</p> <ul> <li>Was the given document written by multiple authors? (task 1)</li> <li>For each pair of consecutive paragraphs in the given document: is there a style change between these paragraphs? (task 2)</li> </ul> <p>In other words, the goal is to determine whether the given document contains style changes and if it indeed does, we aim to find the position of the change in the document (between paragraphs).</p> <p>All documents are provided in English and may contain zero up to ten style changes, resulting from at most three different authors. However, style changes may only occur between paragraphs (i.e., a single paragraph is always authored by a single author and does not contain any style changes).</p> <p><strong>Data</strong></p> <p>To develop and then test your algorithms, two data sets including ground truth information are provided. Those data sets differ in their topical breadth (i.e., the number of different topics that are covered in the documents contained). <em>dataset-narrow</em> contains texts from a relatively narrow set of subjects matters (all related to technology), whereas <em>dataset-wide</em> adds additional subject areas to that (travel, philosophy, economics, history, etc.).</p> <p>Both of those data sets are split into three parts:</p> <ol> <li><em>training set:</em> Contains 50% of the whole data set and includes ground truth data. Use this set to develop and train your models.</li> <li><em>validation set:</em> Contains 25% of the whole data set and includes ground truth data. Use this set to evaluate and optimize your models.</li> <li><em>test set:</em> Contains 25% of the whole data set. For the documents on the test set, you are not given ground truth data. This set is used for evaluation (see later).</li> </ol> <p> </p> <p><strong>Input Format</strong></p> <p>Both dataset-narrow and dataset-wide are based on user posts from various sites of the StackExchange network, covering different topics. We refer to each input problem (i.e., the document for which to detect style changes) by an ID, which is subsequently also used to identify the submitted solution to this input problem.</p> <p>The structure of the provided datasets is as follows:</p> <pre> <code> train/ dataset-narrow/ dataset-wide/ validation/ dataset-narrow/ dataset-wide/ test/ dataset-narrow/ dataset-wide/ </code></pre> <p>For each problem instance <code>X</code> (i.e., each input document), two files are provided:</p> <ol> <li><code>problem-X.txt</code> contains the actual text, where paragraphs are denoted by <code>\n\n</code>.</li> <li><code>truth-problem-X.json</code> contains the ground truth, i.e., the correct solution in JSON format: <pre><code>{ "authors": NUMBER_OF_AUTHORS, "structure": ORDER_OF_AUTHORS, "site": SOURCE_SITE, "multi-author": RESULT_TASK1, "changes": RESULT_ARRAY_TASK2 }</code></pre> <p>The result for task 1 (key "multi-author") is a binary value (1 if the document is multi-authored, 0 if the document is single-authored). The result for task 2 (key "changes") is represented as an array, holding a binary for each pair of consecutive paragraphs within the document (0 if there was no style change, 1 if there was a style change). If the document is single-authored, the solution to task 2 is an array filled with 0s. Furthermore, we provide the order of authors contained in the document (e.g., <code>[A1, A2, A1]</code> for a two-author document), the total number of authors and the Stackoverflow site the texts were extracted from (i.e., topic).</p> <p>An example of a multi-author document, where there was a style change between the third and fourth paragraph could look as follows (we only list the two relevant key/value pairs here): </p> <pre><code>{ "multi-author": 1, "changes": [0,0,1,...] }</code></pre> <p>A single-author document would have the following form (again, only listing the two relevant key/value pairs):</p> <pre><code>{ "multi-author": 0, "changes": [0,0,0,...] }</code></pre> </li> </ol> <p> </p>
Architectural interior styles Dataset
<p>The dataset contains around 11500 images depicting a particular interior style. The photos belong to one of eight classes: rustic, industrial, classic, vintage, modernist, art-deco, scandinavian, glamour.</p> <p>The source of the dataset is Houzz.com. The images were retrieved using appropriate queries and grouped into folders.</p>
RpoS mutations mediate Enterobacter endophytic life style switch from alkaline desert soil to acidic host apoplast
GEO Series GSE277260. Enterobacter sp. SA187. 12 samples. Type: Expression profiling by high throughput sequencing.
Keep a distance to be different: axillary bud initiating at a distance to the shoot apical meristem are crucial for the perennial life style of Arabis alpina
GEO Series GSE140316. Arabis alpina. 23 samples. Type: Expression profiling by high throughput sequencing.
Prediction of biomarkers for coping styles according to the Mainz Coping Inventory
GEO Series GSE59359. synthetic construct; Homo sapiens. 12 samples. Type: Expression profiling by array.
The Role of Communication Style and External Strategies in Predicting Vaccination Experiences and Intentions: An Experimental Vignette Study
<p>This vignette-based study (<em>N </em>= 15466; <em>M</em><sub>age</sub> = 51.65) examined how health care workers could foster reflection about and intentions to get vaccinated against COVID-19 by experimentally varying their communication style (i.e., autonomy-supportive vs. controlling) and the reference to external strategies (i.e., use of a monetary voucher or corona pass vs. the lack thereof). An autonomy-supportive, relative to a controlling, communication style was scored as more effective and came with more reflection about and intentions to get vaccinated due to enhanced autonomy need satisfaction. Vouchers were perceived as a less effective strategy than the corona pass, thereby even impairing reflection and vaccination intention relative to a control group by forestalling the need for autonomy. Especially hesitant citizens were prone to the negative effects of a controlling style and vouchers, while refusing individuals were less affected by any type of motivational attempt. The findings highlight the critical role of autonomy support in promoting reflection about and a self-endorsed decision to get vaccinated.</p>
Emotional Style Questionnaire (ESQ-24)in French Cultural Context
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Data for "Assessing cognitive styles through EEG-based bistable perception processing"
<p>This is the raw data collected for an ERP study on field<br> dependence-independence. Field dependence-independence (FD-I) is a widely<br> studied dimension of cognitive styles designed to measure an individual’s<br> ability to identify embedded parts of an organized visual field as entities<br> separated from that given field. The aim of the research is to determine<br> whether the brain activity features that have been found to be perceptual<br> switching indicators could serve as robust features, differentiating<br> field-dependent from field-independent participants. Previous research suggests<br> that various event related potential (ERP)-related and frequency features are<br> associated with the perceptual reversal occurring during the observation of<br> a bistable image. In this study we combined these features in the context of<br> a different experimental scheme using ambiguous and unambiguous stimuli during<br> participants’ perceptual observations.</p> <p> </p>
An Experimental Study of the Effects of Contextual Pressure and Performance Feedback on Coaching Styles and Athlete Experiences in Youth Basketball
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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.