Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2
datasets available to search
ShareScore release 0.9.0
Dataset results
2 results for “Pre-trained words”
Deep Reference Mining from Scholarly Literature in the Arts and Humanities - Pre-trained word embeddings
<p>Pre-trained word vectors of dimensionality 100 and 300 for the publication: Deep Reference Mining from Scholarly Literature in the Arts and Humanities, submitted to Frontiers in Digital Humanities.</p> <p>The corpus of scholarly publications from which these vectors were trained is under copyright, therefore we publish these vectors for reproducibility. Please refer to the publication's repository for further details: <a href="https://github.com/dhlab-epfl/LinkedBooksDeepReferenceParsing">https://github.com/dhlab-epfl/LinkedBooksDeepReferenceParsing</a>.</p> <p>These vectors were trained using Gensim 3.1.0. The corpus was preprocessed as follows:</p> <ol> <li>word tokenization with NLTK word_punct tokenizer.</li> <li>digits were converted into the $NUM$ token</li> <li>words less frequent than 5 times, for every document, were converted to the $UNK$ token</li> <li>vectors were trained using the function: Word2Vec(window=5, min_count=5, sg=1)</li> </ol>
Twitter pre-trained word vectors
<p>Clean up of <strong>glove.twitter.27B.zip </strong><ODC Public Domain Dedication and Licence (PDDL) 1.0>.</p> <p>"2B tweets, 27B tokens, 1.2M vocab, uncased"</p> <p><strong>Changes from original</strong></p> <ul> <li>Headers added to allow loading by gensim. [Added via scripts.glove2word2vec]</li> <li>Recompressed as individual gzip files [Instead of a combined zip].</li> </ul> <p>These changes make the files easier to work with and increase compatibility.</p> <p><strong>Headers</strong></p> <p>Example of added header line</p> <blockquote> <p>1193513 200</p> </blockquote> <p>Header gives number of tokens and dimensions.</p> <p><strong>Statistics</strong></p> <ul> <li>Entries: 1,193,513</li> <li>Token length (characters). Min:1, Max:140, Avg:6.73</li> <li>Number of words per token. Min:0, Max:17, Avg:1.00669200921984</li> <li>Tokens with more than one word: 4874 (0.41%)</li> <li>Twitter data collection date: <em>Unknown.</em><strong><em> </em></strong></li> </ul> <p>History:</p> <ul> <li><strong>?? Aug 2014 </strong>— GloVe v.1.0 released</li> <li><strong>16 Aug 2014 </strong>— Files first appear as headerless .txt.gz files, some files have mislabeled linked (via <a href="https://web.archive.org/web/20140816165523/http://www-nlp.stanford.edu/projects/glove/">wayback machine)</a></li> <li><strong>?? Oct 2015</strong> — GloVe v.1.2 released</li> <li><strong>?? ??? ????</strong> — Files replaced with a .zip file</li> <li><strong>03 June 2019 </strong>— (These files) Repackaged like original as .txt.gz, plus added headers for increased compatibility</li> </ul> <p>Example of 17-word token:</p> <blockquote> <p> سكس_طيز_قحبه_عنيف_اغتصاب_سكسيه_فحل_زب_نيك_بنات_مكوه_شهوه_لحس_عنف_تومبوي_ليدي_سبورت</p> </blockquote> <p><strong>200d file:</strong></p> <ul> <li>Normalized: no</li> <li>Values: Min:-6.7986, Max:4.609, Avg:0.009065093</li> <li>Zero values (exactly zero): none</li> <li>Zero values (approx zero) per entry: Min:0 (0.00%), Max:2 of 200 (1.0%), Avg:0.00375865197949247 (0.00%)</li> </ul>
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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DANDI Archive for NWB datasets
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OpenNeuro
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