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18 results for “style change”
Data from: Density-dependent changes in neophobia and stress-coping styles in the world's oldest farmed fish
Farmed fish are typically reared at densities much higher than those observed in the wild, but to what extent crowding results in abnormal behaviours that can impact welfare and stress coping styles is subject to debate. Neophobia (i.e. fear of the 'new') is thought to be adaptive under natural conditions by limiting risks, but it is potentially maladapted in captivity, where there are no predators or novel foods. We reared juvenile Nile tilapia (Oreochromis niloticus) for six weeks at either high (50g/L) or low density (14g/L), assessed the extent of skin and eye darkening (two proxies of chronic stress), and exposed them to a novel object in an open-test arena, with and without cover, to assess the effects of density on neophobia and stress coping styles. Fish reared at high density were darker, more neophobic, less aggressive, less mobile and less likely to take risks than those reared at low density, and these effects were exacerbated when no cover was available. Thus, the reactive coping style shown by fish at high density was very different from the proactive coping style shown by fish at low density. Our findings provide novel insights into the plasticity of fish behaviour and the effects of aquaculture intensification on one of the world's oldest farmed and most invasive fish, and highlight the importance of considering context. Crowding could have a positive effect on the welfare of tilapia by reducing aggressive behaviour, but it can also make fish chronically stressed and more fearful, which could make them less invasive.
Do Change in Life Style Improve Work Ability?
ClinicalTrials.gov study NCT03286374. IPD Sharing: NO. Countries: 1. Publications: 2.
Life-style Changes in Obstructive Sleep Apnea
ClinicalTrials.gov study NCT01102920. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Change of Adipose Tissues and Triglyceride After Bariatric Surgery or Life-style Intervention
ClinicalTrials.gov study NCT03875625. IPD Sharing: NO. Countries: 1. Publications: 29.
GOAL Life-Style Change Intervention to Prevent Type 2 Diabetes
ClinicalTrials.gov study NCT00398060. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Changes in Body Composition and Metabolic Risk Parameters by Life Style Intervention.
ClinicalTrials.gov study NCT00356785. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Density-dependent changes in neophobia and stress-coping styles in the world’s oldest farmed fish
Open the record for dataset details and reuse information.
Data from: Increased novel single nucleotide polymorphisms in weedy rice populations associated with the change of farming styles: Implications in adaptive mutation and evolution
Substantial genetic variation is found in weedy rice (Oryza sativa f. spontanea Roshev.) populations from different rice-planting regions with the change of farming styles. To determine the association of such genetic variation with rice farming changes is critical for understanding the adaptive evolution of weedy rice. We studied weedy-rice specific novel SNPs by genome-wide comparison between DNA sequences of weedy and cultivated rice, in addition to PCR fingerprinting at 22 selected novel SNP loci in weedy rice populations. A great number of novel SNPs were identified across the weedy rice genome. High frequencies of the novel SNPs were determined at the 22 selected loci, although with considerable variation among weedy rice populations in different rice-planting regions. The highest frequency (~57%) of novel SNPs was identified in weedy rice populations from Jiangsu that experienced the most dramatic changes in rice farming styles, including the shift from transplanting to direct seeding, and from indica to japonica varieties. The lowest frequency (~29%) was detected in weedy rice populations from Northeast China where rice farming has a relatively less change. The association between frequencies of novel SNPs in weedy rice populations and the extent of changes in rice farming styles suggests the critical role of adaptive mutation and accumulation of the mutation influenced by human activities in the rapid evolution of weedy rice.
PAN22 Authorship Analysis: Style Change Detection
<p>This is the dataset for the <a href="https://pan.webis.de/clef22/pan22-web/style-change-detection.html">Style Change Detection</a> task of PAN 2022.</p> <p><strong>Task</strong></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. Hence, a fundamental question is the following: If multiple authors have written a text together, can we find evidence for this fact; i.e., do we have a means to detect variations in the writing style? Answering this question belongs to the most difficult and most interesting challenges in author identification: Style change detection is the only means to detect plagiarism in a document if no comparison texts are given; likewise, style change detection can help to uncover gift authorships, to verify a claimed authorship, or to develop new technology for writing support.</p> <p>Previous editions of the Style Change Detection task aim at e.g., detecting whether a document is single- or multi-authored (<a href="https://pan.webis.de/clef18/pan18-web/style-change-detection.html">2018</a>), the actual number of authors within a document (<a href="https://pan.webis.de/clef19/pan19-web/style-change-detection.html">2019</a>), whether there was a style change between two consecutive paragraphs (<a href="https://pan.webis.de/clef20/pan20-web/style-change-detection.html">2020</a>, <a href="https://pan.webis.de/clef21/pan21-web/style-change-detection.html">2021</a>) and where the actual style changes were located (<a href="https://pan.webis.de/clef21/pan21-web/style-change-detection.html">2021</a>). Based on the progress made towards this goal in previous years, we again extend the set of challenges to likewise entice novices and experts:</p> <p>Given a document, we ask participants to solve the following three tasks:</p> <ul> <li><strong>[Task1] Style Change Basic:</strong> for a text written by two authors that contains a single style change only, find the position of this change (i.e., cut the text into the two authors’ texts on the paragraph-level),</li> <li><strong>[Task2] Style Change Advanced:</strong> for 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 assumed for the multi-author document)</li> <li><strong>[Task3] Style Change Real-World:</strong> for a text written by two or more authors, find all positions of writing style change, where style changes now not only occur between paragraphs, but at the sentence level.</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.</p> <p><strong>Data</strong></p> <p>To develop and then test your algorithms, three datasets including ground truth information are provided (<em>dataset1</em> for task 1, <em>dataset2</em> for task 2, and <em>dataset3</em> for task 3).</p> <p>Each dataset is split into three parts:</p> <ol> <li><em>training set:</em> Contains 70% of the whole dataset 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 dataset 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 dataset, no ground truth data is given. This set is used for evaluation (see later).</li> </ol> <p>You are free to use additional external data for training your models. However, we ask you to make the additional data utilized freely available under a suitable license.</p> <p><strong>Input Format</strong></p> <p>The datasets 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. We provide one folder for train, validation, and test data for each dataset, respectively.</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</code> for tasks 1 and 2. For task 3, we provide one sentence per paragraph (again, split by <code>\n</code>).</li> <li><code>truth-problem-X.json</code> contains the ground truth, i.e., the correct solution in JSON format. An example file is listed in the following (note that we list keys for the three tasks here): <pre><code>{ "authors": NUMBER_OF_AUTHORS, "site": SOURCE_SITE, "changes": RESULT_ARRAY_TASK1 or RESULT_ARRAY_TASK3, "paragraph-authors": RESULT_ARRAY_TASK2 }</code></pre> <p>The result for task 1 (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). For task 2 (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). The result for task 3 (key "changes") is similarly structured as the results array for task 1. However, for task 3, the <code>changes</code> array holds a binary for each pair of consecutive <em>sentences</em> and they may be multiple style changes in the document.</p> <p>An example of a multi-author document with a style change between the third and fourth paragraph (or sentence for task 3) could be described as follows (we only list the relevant key/value pairs here):</p> <pre><code>{ "changes": [0,0,1,...], "paragraph-authors": [1,1,1,2,...] }</code></pre> <p> </p> </li> </ol> <p><strong>Output Format</strong></p> <p>To evaluate the solutions for the tasks, the results have to be stored in a single file for each of the input documents and each of the datasets. Please note that we require a solution file to be generated for each input problem for each dataset. The data structure during the evaluation phase will be similar to that in the training phase, with the exception that the ground truth files are missing.</p> <p>For each given problem <code>problem-X.txt</code>, your software should output the missing solution file <code>solution-problem-X.json</code>, containing a JSON object holding the solution to the respective task. The solution for tasks 1 and 3 is an array containing a binary value for each pair of consecutive paragraphs (task 1) or sentences (task 3). For task 2, the solution is an array containing the order of authors contained in the document (as in the truth files).</p> <p>An example solution file for tasks 1 and 3 is featured in the following (note again that for task 1, changes are captured on the paragraph level, whereas for task 3, changes are captured on the sentence level):</p> <pre><code>{ "changes": [0,0,1,0,0,...] }</code></pre> <p>For task 2, the solution file looks as follows:</p> <pre><code>{ "paragraph-authors": [1,1,2,2,3,2,...] }</code></pre> <p> </p>
Genetic Information as a Life Style Change Motivator
ClinicalTrials.gov study NCT03794141. IPD Sharing: NO. Countries: 0. Publications: 1.
Data from: Increased novel single nucleotide polymorphisms in weedy rice populations associated with the change of farming styles: Implications in adaptive mutation and evolution
Open the record for dataset details and reuse information.
Sperm DNA methylation changes after nut supplementation in healthy men consuming a Western-style diet
GEO Series GSE140004. Homo sapiens. 144 samples. Type: Methylation profiling by array.
Maternal Western-style high fat diet induces sex-specific physiological and molecular changes in two-week-old mouse offspring
GEO Series GSE46359. Mus musculus. 27 samples. Type: Expression profiling by array.
PAN18 Multi-Author Analysis: Style-Change-Detection
<p>Dataset for binary style change detection.</p> <p>More information about the task: <a href="https://pan.webis.de/clef18/pan18-web/style-change-detection.html">Link</a></p>
PAN19 Authorship Analysis: Style Change Detection
<p>This is the data set for the <a href="http://pan.webis.de/clef19/pan19-web/style-change-detection.html">Style Change Detection task</a> of <a href="http://pan.webis.de/clef19/pan19-web/">PAN@CLEF 2019</a>.</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). dataset-narrow contains texts from a relatively narrow set of subjects matters (all related to technology), whereas dataset-wide adds additional subject areas to that (travel, philosophy, economics, history, etc.).</p> <p>Both of those data sets are split into three parts:</p> <ul> <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.</li> </ul> <p><br> <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> <p> </p> <pre><code class="language-json">train/ dataset-narrow/ dataset-wide/ validation/ dataset-narrow/ dataset-wide/ test/ dataset-narrow/ dataset-wide/</code></pre> <p> </p> <p>For each problem instance X (i.e., each input document), two files are provided:</p> <p>problem-X.txt contains the actual text, where paragraphs are denoted by \n\n.<br> truth-problem-X.json contains the ground truth, i.e., the correct solution in JSON format:</p> <pre><code class="language-json">{ "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., [A1, A2, A1] 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 class="language-json">{ "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 class="language-json">{ "multi-author": 0, "changes": [0,0,0,...] }</code></pre> <p> </p>
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>
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>
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.