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2,305 results for “text”
Ground-Truthed Data Set of Zenon Papyri for Handwritten Text Recognition
<p>Diplomatic transcription of papyri found in the Zenon archive [see <a href="https://en.wikipedia.org/wiki/Zenon_of_Kaunos">en.wikipedia.org/wiki/Zenon_of_Kaunos</a>]</p> <p>Manually prepared as PageXML with Transkribus within <a href="http://d-scribes.philhist.unibas.ch/">D-Scribes</a> project.</p> <p> </p>
TCAB: Text Classification Attack Benchmark Dataset
<p>TCAB is a large collection of successful adversarial attacks on state-of-the-art text classification models trained on multiple sentiment and abuse domain datasets.</p> <p>The dataset is broken up into 2 files: <em>train.csv and</em> <em>val.csv</em>. The training set contains 1,448,751 instances (552,364 are "clean" unperturbed instances) and the validation set contains 482,914 instances (178,607 are "clean"). Each instance contains the following attributes:</p> <p><strong>scenario</strong>: Domain, either <em>abuse</em> or <em>sentiment</em>.</p> <p><strong>target_model_dataset</strong>: Dataset being attacked.</p> <p><strong>target_model_train_dataset</strong>: Dataset the target model trained on.</p> <p><strong>target_model</strong>: Type of victim model (e.g., <em>bert</em>, <em>roberta</em>, <em>xlnet</em>).</p> <p><strong>attack_toolchain</strong>: Open-source attack toolchain, either TextAttack or OpenAttack.</p> <p><strong>attack_name</strong>: Name of the attack method.</p> <p><strong>original_text</strong>: Original input text.</p> <p><strong>original_output</strong>: Prediction probabilities of the target model on the original text.</p> <p><strong>ground_truth</strong>: Encoded label for the original task of the domain dataset. 1 and 0 means toxic and toxic for abuse datasets, respectively. 1 and 0 means positive and negative sentiment for sentiment datasets. If there is a neutral sentiment, then 2, 1, 0 means positive, neutral, and negative sentiment.</p> <p><strong>status</strong>: Unperturbed example if "clean"; successful adversarial attack if "success".</p> <p><strong>perturbed_text</strong>: Text after it has been perturbed by an attack.</p> <p><strong>perturbed_output</strong>: Prediction probabilities of the target model on the perturbed text.</p> <p><strong>attack_time</strong>: Time taken to execute the attack.</p> <p><strong>num_queries</strong>: Number of queries performed while attacking.</p> <p><strong>frac_words_changed</strong>: Fraction of words changed due to an attack.</p> <p><strong>test_index</strong>: Index of each unique source example (original instance) (LEGACY - necessary for backwards compatibility).</p> <p><strong>original_text_identifier</strong>: Index of each unique source example (original instance).</p> <p><strong>unique_src_instance_identifier</strong>: Primary key to uniquely identify to every source instance; comprised of (<em>target_model_dataset</em>, <em>test_index</em>, <em>original_text_identifier</em>).</p> <p><strong>pk</strong>: Primary key to uniquely identify every attack instance; comprised of (<em>attack_name</em>, <em>attack_toolchain</em>, <em>original_text_identifier</em>, <em>scenario</em>, <em>target_model</em>, <em>target_model_dataset</em>, <em>test_index).</em></p>
EconBiz Images for Text Extraction from Scholarly Figures
<p>Scholarly figures are data visualizations like bar charts, pie charts, line graphs, maps, scatter plots or similar figures. Text extraction from scholarly figures is useful in many application scenarios, since text in scholarly figures often contains information that is not present in the surrounding text. This dataset is a corpus of 121 scholarly figures from the economics domain evaluating text extraction tools. We randomly extracted these figures from a corpus of 288,000 open access publications from <a href="https://www.econbiz.de/">EconBiz</a>. The dataset resembles a wide variety of scholarly figures from bar charts to maps. We manually labeled the figures to create the gold standard.</p> <p>We adjusted the provided gold standard to have a uniform format for all datasets. Each figure is accompanied by a TSV file (tab-separated values) where each entry corresponds to a text line which has the following structure:</p> <ul> <li>X-coordinate of the center of the bounding box in pixel</li> <li>Y-coordinate of the center of the bounding box in pixel</li> <li>Width of the bounding box in pixel</li> <li>Height of the bounding box in pixel</li> <li>Rotation angle around its center in degree</li> <li>Text inside the bounding box</li> </ul> <p>In addition we provide the ground truth in JSON format. A schema file is included in each dataset as well. The dataset is accompanied with a ReadMe file with further information about the figures and their origin.</p> <p>If you use this dataset in your own work, please cite one of the papers in the references.</p>
XML Data for "Four Early Chan Texts from Dunhuang - A TEI-based Edition"
<p>This zip archive contains the XML data, schema, stylesheets, and associated material that are the basis for a print edition of 48 Dunhuang manuscript witnesses of four early Chan Buddhist texts. The print edition is:</p> <p>Marcus Bingenheimer 馬德偉, Chang Po-Yung 張伯雍 (eds.): <em>Four Early Chan Texts from Dunhuang – A TEI-based Edition</em> 早期禪宗文獻四部 —— 以TEI標記重訂敦煌寫卷:楞伽師資記,傳法寶紀,修心要論,觀心論. Taipei: Shin Wen Feng 新文豐, 2018.<br> (Vol. 1: Facsimiles and Diplomatic Transcription 摹寫版 (ISBN: 978-957-17-2274-0), Vol. 2: Parallel, Punctuated and Annotated Edition 對照與點注版 (ISBN: 978-957-17-2275-7), Vol. 3: Calligraphy Practice 抄經版 (ISBN: 978-957-17-2276-4).)</p> <p>See the included README.txt for further information.</p>
Frauen* im Fokus. Transcriptions and full texts of letters and works of women's rights activists
<p>In winter 2023/24 the Berlin State Library and Potsdam University (chair for Comparative Literature) organized the citizen science workshop <a href="https://lab.sbb.berlin/events/frauen-im-fokus/">"Frauen* im Fokus"</a> (women* in focus).</p> <p>Within the project 48 participants transcribed 85 letters and documents by 19th- and early 20th-century women's rights activists held in the collections of the State Library. The transcriptions provided by the participants were aligned with the digital images of the items using the <a href="https://ocr-bw.bib.uni-mannheim.de/escriptorium/" target="_blank" rel="noopener">eScriptorium</a> platform and software and checked for potential errors. From eScriptorium, the transcriptions were exported as ALTO and PAGE files. From the PAGE files, TEI/XML files were created with added metadata about the correspondence (where applicable).</p> <p>In addition to these MS sources, 55 printed works of the same activists were OCRed and are included in the dataset as PAGE and ALTO-files; these files were not manually checked for quality.</p> <p>We would like to thank our trainees Lilly Bucksteeg and Lilly Welz for their valuable contribution to the creation of the data set.</p> <p>The data set consists of five zip-files containing:</p> <ul> <li>the print sources in PAGE format</li> <li>the print sources in ALTO format</li> <li>the manuscript sources in PAGE format</li> <li>the manuscript sources in ALTO format</li> <li>the manuscript sources in TEI format</li> </ul> <p>------------------</p> <p>Im Wintersemester 2023/24 führten die Staatsbibliothek zu Berlin und die Universität Potsdam (Professur für Allgemeine und Vergleichende Literaturwissenschaft) das <a href="https://lab.sbb.berlin/events/frauen-im-fokus/">Projekt "Frauen* im Fokus"</a> durch.</p> <p>Im Rahmen des Projekts transkribierten 48 Personen 85 Briefe und andere Nachlassdokumente von Frauenrechtlerinnen des 19. und frühen 20. Jahrhunderts. Das Organisationsteam nahm auf der Plattform <a href="https://ocr-bw.bib.uni-mannheim.de/escriptorium/" target="_blank" rel="noopener">eScriptorium</a> eine teilweise automatisierte Layoutanalyse und Zeilensegmentierung der Digitalisate vor und fügte nach erfolgter inhaltlicher Qualitätskontrolle die von den Teilnehmenden erstellten Transkriptionen dort ein, um sie dann als PAGE- und ALTO-Dateien zu exportieren; zusätzlich wurden aus den PAGE-Dateien TEI-Dateien der einzelnen Dokumente generiert, die (wo passend) mit Brief-Metadaten zu Absendern, Empfängern und Orten angereichert wurden.</p> <p>Im Rahmen des Projekts wurden zudem 55 Druckwerke der Frauenrechtlerinnen aus dem Bestand der Staatsbibliothek als Volltexte erschlossen. Die Segmentierung und Volltexterkennung der Druckwerke erfolgte automatisch in eScriptorium ohne zusätzliche manuelle Qualitätskontrolle. Die Daten liegen exportiert in den Formaten PAGE und ALTO vor.</p> <p>Besonderer Dank gebührt Lilly Bucksteeg und Lilly Welz, die als Praktiantinnen im Projekt maßgeblich zur Erstellung des Datensets beigetragen haben.</p> <p>Das Datenset besteht aus fünf zip-Dateien die folgende Dateien enthalten:</p> <ul> <li>die gedruckten Werke im PAGE-Format</li> <li>die gedruckten Werke im ALTO-Format</li> <li>die Manuskript-Transkriptionen im PAGE-Format</li> <li>die Manuskript-Transkriptionen im ALTO-Format</li> <li>die Manuskript-Transkriptionen im TEI-Format</li> </ul>
A Brazilian Portuguese Dataset for Offline Handwritten Text Recognition (BRESSAY)
<p>The BRESSAY dataset comprises images of handwritten essays in Brazilian Portuguese, which present a series of challenges to optical recognition models. These images were sourced from multiple online platforms, limiting our ability to standardize the capture process. Due to these varied sources and the lack of a uniform collection method, the dataset provides a realistic reflection of real-world conditions. Each essay is unique, contributed by different writers, and addresses a specific content topic. Furthermore, the constraints placed on the writers often lead to various handwriting scenarios, including hard-to-read words, connected words, noise, overwriting, and struck-through texts.</p> <h3><strong>Technical Details</strong></h3> <p>The BRESSAY dataset represents a comprehensive collection of handwritten essays in Brazilian Portuguese, offering detailed insights into various handwriting scenarios. It covers a total of 1,000 pages, each contributed by a unique writer, resulting in 1,000 distinct handwriting styles. This aspect of the dataset adds a layer of diversity, which is further emphasized by the total of 4,214 paragraphs, 30,090 lines, and 416,826 words. Regarding unique tokens, we have 41,318 unique words, and 107 unique characters.</p> <h3><strong>Data Structure</strong></h3> <p>The dataset is organized as follows:</p> <ul> <li>data/: Main folder containing segmented essay images <ul> <li>lines/: Images of individual lines <ul> <li> PNG files: Line images</li> <li> TXT files: Transcriptions of lines</li> </ul> </li> <li>pages/: Full page essay images <ul> <li> PNG files: Page images</li> <li> TXT files: Transcriptions of pages</li> </ul> </li> <li>paragraphs/: Images of paragraphs <ul> <li> PNG files: Paragraph images</li> <li> TXT files: Transcriptions of paragraphs</li> </ul> </li> <li>words/: Images of individual words <ul> <li> PNG files: Word images</li> <li> TXT files: Transcriptions of words</li> </ul> </li> </ul> </li> <li>sets/: Contains partition files <ul> <li>test.txt: Names of images in the test set</li> <li>validation.txt: Names of images in the validation set</li> <li>training.txt: Names of images in the training set</li> </ul> </li> </ul> <h3><strong>Dataset Usage and Annotations</strong></h3> <p>Each name in test.txt, validation.txt and training.txt represents the name of the page and all its content (words, lines, paragraphs) must be in the respective partition.</p> <p>Annotations used in the dataset:</p> <ul> <li> <code>##@@???@@##</code>: Superscript text that has become unidentifiable and unreadable.</li> <li> <code>$$@@???@@$$</code>: Subscript text that has become unidentifiable and unreadable.</li> <li> <code>@@???@@</code>: Text that cannot be read or identified due to its illegibility.</li> <li> <code>##--xxx--##</code>: Text that has been added as a superscript and subsequently crossed out, rendering it illegible.</li> <li> <code>$$--xxx--$$</code>: Text that has been added as a subscript and subsequently crossed out, rendering it illegible.</li> <li> <code>--xxx--</code>: Text that has been crossed out in a way that makes it unreadable.</li> <li> <code>##--text--##</code>: Text that has been added as a superscript and subsequently crossed out, but remains legible.</li> <li> <code>$$--text--$$</code>: Text that has been added as a subscript and subsequently crossed out, but remains legible.</li> <li> <code>##text##</code>: Text added as a superscript in the line, typically as a correction or additional note.</li> <li> <code>$$text$$</code>: Text added as a subscript in the line, typically as a correction or additional note.</li> <li> <code>--text--</code>: Text that has been crossed out but remains readable.</li> </ul>
Text Datasets for DSI
<p>Text data used in an article Tatsuya Haga, Yohei Oseki, Tomoki Fukai, "A unified neural representation model for spatial and semantic computations" (preprint in biorxiv doi: https://doi.org/10.1101/2023.05.11.540307). Codes and usage of data are available at https://github.com/TatsuyaHaga/DSI_codes</p> <p>Main dataset (enwiki_processed_pickle): This file contains preprocessed text data of 100,000 articles randomly sampled from English Wikipedia dump taken on 22-May-2020 (https://dumps.wikimedia.org/enwiki/latest/).</p> <p>Additional dataset (wikitext103train_processed_pickle): This file contains preprocessed text data based on WikiText-103 dataset (Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016. Pointer Sentinel Mixture Models. http://arxiv.org/abs/1609.07843)</p> <p>Both text data have already been preprocessed: all characters were lowercased, punctuation characters were removed, and all words were tokenized. Data format is python pickle format. </p> <p>We publish data under CC-BY-SA following the license of original datasets.</p>
FloraNER: a Named Entity Recognition Dataset for Botanical French Text
<p>FloraNER is a Named-Entity Recognition (NER) dataset for botanical french literature. The dataset covers plant species names and plant morphological terms for both plant organs/characteristics and their descriptors. the descriptors are annotated both in a coarse-grained manner as the named entity type "DESCRIPTOR" and in a fine-grained manner, categorized into the following named-entity types: Form, Measure, Surface, Color, Position, Disposition, Structure, and Development. FloraNER is distantly annotated using a specialized botanical corpus. Consequently, it's important to note that not all named entities within the text are captured and annotated for the coarse-grained and fine-grained datasets.</p>
The Machine-Actionable Ancient Text (MAAT) Corpus
<p>The Machine-Actionable Ancient Text (MAAT) Corpus is a new resource providing training and evaluation data for restoring lacunae in ancient Greek, Latin, and Coptic texts. Current text restoration systems require large amounts of data for training and task-relevant means for evaluation. The MAAT Corpus addresses this need by converting texts available in EpiDoc XML format into a machine-actionable format that preserves the most textually salient aspects needed for machine learning: the text itself, unclear letters, restorations, and lacunae. Structured test cases are generated from the corpus that align with the actual text restoration task performed by papyrologists and epigraphist, enabling more realistic evaluation than the synthetic tasks used previously. The initial 1.0 beta release contains approximately 134,000 text editions, 178,000 text blocks, and 750,000 individual restorations, with Greek and Latin predominating. This corpus aims to facilitate the development of computational methods to assist scholars in accurately restoring ancient texts.</p>
Raw images (photographs) of urban text scenes for camera-based Thai text recognition
<p>Raw image collection of city scenes in Thailand with text content.<br> Text is photographed from diffeent angles. Also morning and evening<br> photographs were taken in order to capture different lighting<br> conditions. The material, 309 images, was photographed in 2013 by<br> Bowornrat Sriman and volunteers.</p> <p>Example EXIF:<br> JPEG image data, Exif standard: [TIFF image data, little-endian,<br> direntries=13, height=2448, manufacturer=SAMSUNG, model=GT-I9300,<br> orientation=upper-right, xresolution=220, yresolution=228,<br> resolutionunit=2, software=I9300XXEMA2, datetime=2013:03:14 18:17:49,<br> GPS-Data, width=3264], baseline, precision 8, 3264x2448, frames 3</p> <p>The images are not labeled. The orientation (landscape/portrait) is<br> not corrected yet. This material was used in preparation of the publication:</p> <p>Sriman, B. & Schomaker, L. (2015).<br> Object Attention Patches for Text Detection and Recognition in Scene Images using SIFT,<br> Proceedings of the International Conference on Pattern Recognition Applications and<br> Methods: ICPRAM 2015. De Marsico, M., Figueiredo, M. & Fred, A.<br> (Eds.). Lisbon, Portugal: SciTePress, Vol. 1, p. 304-311 8 p.</p> <p>Please cite this publication when using these data.</p>
InTeReC: In-text Reference Corpus - Single References Dataset
<p>This dataset contains a set of sentences extracted from articles published by the Public Library of Science (PLOS) up to September 2013. Information is given on the position of the sentences relative to the article and the section in which they appear, the section type with respect to the four main types of the IMRaD structure, as well as verb phrases that occur in the sentence. Each sentence contains one single in-text reference.</p> <p>The dataset is in the CSV format. Size: 314023 sentences.</p> <p>Column list:</p> <ul> <li><em>journal</em>: journal title</li> <li><em>doi</em>: DOI of the article from which the sentence was extracted</li> <li><em>article-length</em>: size of the article, as number of sentences</li> <li><em>article-pos</em>: position of the sentence in the article, as number of sentences from the beginning of the article</li> <li><em>section-length</em>: size of the section, as number of sentences</li> <li><em>section-pos</em>: position of the sentence in the section, as number of sentences from the beginning of the section</li> <li><em>section-type</em>: section type (see below)</li> <li><em>sentence-text</em>: full text of the sentence</li> <li><em>verb-phrases</em>: a list of verb phrases that occur in the sentence, comma separated</li> </ul> <p>Possible section types are:</p> <ul> <li>I: Introduction</li> <li>M: Methods</li> <li>R: Results</li> <li>D: Discussion</li> <li>MR: Methods and Results</li> <li>RD: Results and Discussion</li> </ul> <p> </p> <p>Full description of the construction of the dataset is published in:</p> <p>Marc Bertin and Iana Atanassova (2018) InTeReC : an In-text Reference corpus for applying Natural Language Processing to Bibliometrics. Bibliometric-enhanced Information Retrieval: 7th International BIR workshop (7th BIR workshop) at the 40th European Conference on Information Retrieval (ECIR).</p>
A recent overview of the state-of-the-art elements of text classification - dataset
<p>The two available datasets were used to conduct the quantitative analysis of the text classification area. The set, such as:</p> <ol> <li>biblio.bib contains all articles that are grouped in categories</li> <li>biblio.csv contains processed records from biblio.bib, based on it were built the statistics presented in the article</li> </ol>
Duhumbi Procedural Texts - Sound files
<p>This data set contains all the original sound files of the procedural texts of the 'Grammar of Duhumbi' (Brill) published in 2019. A separate Zenodo DOI contains all the Toolbox-compatible .txt files and Transcriber .trs files with the transcribed, parsed, glossed, translated examples (DOI 10.5281/zenodo.1406154). The following list contains the sound file names, the shortcut code for the sentence names and the title of the text.</p> <ul> <li>[CHUK210512I1] / PHPT / Hunting for porcupine </li> <li>[CHUK230512A1A] / SBDC / Making fermented soybean</li> <li>[CHUK220413A1] / CTTT / Catching frogs </li> <li>[CHUK220413B1] / CWTT / Collecting beeswax </li> <li>[CHUK220413C1] / CHTT / Collecting hornets</li> <li>[CHUK240413A1] / SNAP / Collecting stinging nettle </li> <li>[CHUK240413B1] / LCYT / Leather craft </li> </ul> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Duhumbi Religious Texts and Song - Sound files
<p>This data set contains all the original sound files of the religious texts and the song in the 'Grammar of Duhumbi' (Brill) published in 2019. A separate Zenodo DOI contains all the Toolbox-compatible .txt files and Transcriber .trs files with the transcribed, parsed, glossed, translated examples (DOI 10.5281/zenodo.1406181). The following list contains the sound file names, the shortcut code for the sentence names and the title of the text.</p> <ul> <li>[CHUK110413A2A] / RELJ / Buddhist admonition</li> <li> [CHUK221212D2A] / JIK / Bonpo prediction text</li> <li> [CHUK260413A1] / MSK / Impromptu song</li> </ul> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Dataset for ICFHR2018 Competition on Automated Text Recognition on a READ Dataset
<p>The main idea of this dataset is to analyse the impact of training data. How many training data specific to the document, you are transcribing, is necessary? </p> <p><strong>general data: </strong>This is a collection of heterogeneous documents to train an initial system. For each text line there is an image file of that line, a file with the ground truth text and an information file containing an automatically generated surrounding polygon.</p> <p><strong>specific data: </strong>The specific data contains documents related to the test data. For the specific systems only the images of the train list may be used. The file are of the same type as the general data.</p> <p><strong>test data: </strong>The test data contains only the images and the information files.</p> <p>More Information, some published results and an evaluation procedure at https://scriptnet.iit.demokritos.gr/competitions/10/</p>
Ontology based text mining of gene-phenotype associations: application to candidate gene prediction
<p>Gene-phenotype associations play an important role in understanding<br> the disease mechanisms which is a requirement for treatment<br> development. A portion of gene-phenotype associations are observed<br> mainly experimentally and made publicly available through several<br> standard resources such as MGI. However, there is still a vast<br> amount of gene--phenotype associations buried in the biomedical<br> literature. Given the large amount of literature data, we need<br> automated text mining tools to alleviate the burden in manual<br> curation of gene-phenotype associations and to develop<br> comprehensive resources. We developed an ontology based<br> approach in combination with statistical methods to text mine<br> gene-phenotype associations from literature. Our method achieved<br> AUC values of 0.90 and 0.75 in recovering known gene-phenotype<br> associations from HPO and MGI respectively. We posit that candidate<br> genes and their relevant diseases should be expressed with similar<br> phenotypes in publications. Thus, we demonstrate the utility of our<br> approach by predicting disease candidate genes based on the semantic<br> similarities of phenotypes associated with genes and diseases. We evaluated our disease candidate prediction model on<br> the gene-disease associations from MGI. Our model achieved AUC<br> values of 0.90 and 0.87 on OMIM (human) and MGI (mouse) datasets of<br> gene-disease associations respectively. Our manual analysis on the<br> text mined data revealed that, our method can accurately extract<br> gene-phenotype associations which are not currently covered by the<br> existing public gene-phenotype resources. Overall, results indicate<br> that our method can precisely extract known as well as new<br> gene-phenotype associations from literature. This released dataset at Zenodo covers our gene-phenotype extracts from the literature. All the methods used to extract the data are available at https://github.com/bio-ontology-research-group/genepheno.</p>
Aššur and His Friends: A Statistical Analysis of Neo-Assyrian Texts
<p>This is the data used for and generated during our research for the article "Aššur and His Friends: A Statistical Analysis of Neo-Assyrian Texts", published in <em>Journal of Cuneiform Studies </em>71 (2019).</p>
E commerce text dataset
<p>This is the classification based E-commerce text dataset for 4 categories - "Electronics", "Household", "Books" and "Clothing & Accessories", which almost cover 80% of any E-commerce website. </p> <p>The dataset is in ".csv" format with two columns - the first column is the class name and the second one is the datapoint of that class. The data point is the product and description from the e-commerce website.</p> <p>The dataset has the following features :</p> <p><strong>Data Set Characteristics: </strong>Multivariate</p> <p><strong>Number of Instances: </strong>50425</p> <p><strong>Number of classes:</strong> 4</p> <p><strong>Area: </strong>Computer science<strong> </strong></p> <p><strong>Attribute Characteristics: </strong>Real</p> <p><strong>Number of Attributes: </strong>1</p> <p><strong>Associated Tasks:</strong> Classification</p> <p><strong>Missing Values? </strong>No</p> <p>The dataset has been scraped from Indian e-commerce platform.</p>
Time-resolved compound repositioning predictions on a text-mined knowledge network
<p><strong>gs_positives.csv</strong>: The re-processed version of DrugCentral indications, utilized as training and testing positives in the analysis.</p> <p><strong>top_5000_predictions.csv</strong>: The top 5000 drug-disease pairs, by probability, produced by this analysis pipeline.</p> <p><strong>file_info.txt</strong>: Information about the column headings in of the two files.</p> <p> </p>
Webis Wikipedia Text Reuse Corpus 2018 (Webis-Wikipedia-Text-Reuse-18)
<p>The Wikipedia Text Reuse Corpus 2018 (Webis-Wikipedia-Text-Reuse-18) containing text reuse cases extracted from within Wikipedia and in between Wikipedia and a sample of the Common Crawl.</p> <p>The corpus has following structure:</p> <ul> <li>wikipedia.jsonl.bz2: Each line, representing a Wikipedia article, contains a json array of article_id, article_title, and article_body</li> <li>within-wikipedia-tr-01.jsonl.bz2: Each line, representing a text reuse case, contains a json array of s_id (source article id), t_id (target article id), s_text (source text), t_text (target text)</li> <li>within-wikipedia-tr-02.jsonl.bz2: Each line, representing a text reuse case, contains a json array of s_id (source article id), t_id (target article id), s_text (source text), t_text (target text)</li> <li>preprocessed-web-sample.jsonl.xz: Each line, representing a web page, contains a json object of d_id, d_url, and content</li> <li>without-wikipedia-tr.jsonl.bz2: Each line, representing a text reuse case, contains a json array of s_id (Wikipedia article id), d_id (web page id), s_text (article text), d_content (web page content)</li> </ul> <p>The datasets were extracted in the work by Alshomary et al. 2018 that aimed to study the text reuse phenomena related to Wikipedia at scale. A pipeline for large scale text reuse extraction was developed and used on Wikipedia and the CommonCrawl.</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.