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Dataset results
3 results for “multi-author”
PAN23 Multi-Author Writing Style Analysis
<p>This is the dataset for the shared task on <a href="https://pan.webis.de/clef23/pan23-web/style-change-detection.html">Multi-Author Writing Style Analysis PAN@CLEF2023</a>. Please consult the task's page for further details on the format, the dataset's creation, and links to baselines and utility code.</p> <p><strong>Task: </strong>We ask participants to solve the following intrinsic style change detection task: <strong>for a given text, find all positions of writing style change on the paragraph-level</strong> (i.e., for each pair of consecutive paragraphs, assess whether there was a style change). The simultaneous change of authorship and topic will be carefully controlled and we will provide participants with datasets of three difficulty levels:</p> <ol> <li><strong>Easy:</strong> The paragraphs of a document cover a variety of topics, allowing approaches to make use of topic information to detect authorship changes.</li> <li><strong>Medium:</strong> The topical variety in a document is small (though still present) forcing the approaches to focus more on style to effectively solve the detection task.</li> <li><strong>Hard:</strong> All paragraphs in a document are on the same topic.</li> </ol> <p>All documents are provided in English and may contain an arbitrary number of style changes. However, style changes may only occur between paragraphs (i.e., a single paragraph is always authored by a single author and contains no style changes).</p> <p><strong>Data: </strong>To develop and then test your algorithms, three datasets including ground truth information are provided (<em>dataset1</em> for the easy task, <em>dataset2</em> for the medium task, and <em>dataset3</em> for the hard task).</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.</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>Versioning:</strong> </p> <ul> <li>1.0: initial upload</li> </ul>
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>
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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.