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zenodo52/100

Database of Annotated Core Arguments: English, Lao and Russian

This database contains corpus examples of transitive clauses with annotated core arguments realized as syntactic subjects and objects (A and P) in English, Lao and Russian. The coding scheme was developed together with Alena Witzlack-Makarevich

opencc-by-4.0Oct 2020View details →

zenodo48/100

Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text

Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.

opencc-by-4.0May 2020View details →

zenodo48/100

A Sentiment Analysis Dataset for Code-Mixed Malayalam-English

There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff’s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.

opencc-by-4.0May 2020View details →

zenodo48/100

Lemmatized English Word2Vec data

# Lemmatized English Word2Vec data This is a version of the original GoogleNews-vectors-negative300 Word2Vec embeddings for English. In addition, we provide the following modified files: - converted to conventional CSV format (and gzipped) - subclassified:   for the most frequent 1.000.000 words:     subclassified according to WordNet parts of speech: ADJ, ADV, NOUN, VERB, OTHER     note that one embedding can be associated with multiple parts of speech   for the remaining words:     RARE: top 1.000.001 - 2.000.000 words     VERY_RARE: top 2.000.001 - 3.000.000 words - WordNet lemmatization (via NLTK) in separate files     (first lemma only) Note that this is not a product of original research, but a derived work, deposited here as a point of permanent reference and as a building stone of subsequent research. For such application, a publication independent from Google is necessary to guarantee stability against changes in their data releases. The original Word2vec code and data was published via https://code.google.com/archive/p/word2vec/ under an Apache License 2.0. We obtained the Word2vec data from  https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing on Jun 3, 2020. The Word2vec documentation included the following references:     [1] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR, 2013.     [2] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of NIPS, 2013.     [3] Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig. Linguistic Regularities in Continuous Space Word Representations. In Proceedings of NAACL HLT, 2013. The derived data is made available under the same license (Apache License 2.0). However, note that the content derived from WordNet (lemmas) are subject to the Princeton Wordnet license as stated in LICENSE.wordnet. Data provided by the Applied Computational Linguistics Lab of the Goethe University Frankfurt, Germany. Original data developed by Mikolov et al.

openapache2.0Jan 2021View details →

zenodo48/100

LaTeX formulae from English Wikipedia

Public dump of LaTeX (texvc) input used in English Wikipedia Initially appeared in the public in the following form https://archive.softwareheritage.org/swh:1:cnt:da76ae5a988839894a2ecdab29a5ab7c8df7dc80;origin=https://github.com/wikimedia/mediawiki-services-texvcjs;visit=swh:1:snp:1ea38f5e961b25c6d0adde0145f64791eb8fb67d;anchor=swh:1:rev:101925e2ab1712c70472ed144efaea34743e6700;path=/test/en-wiki-formulae.json with the current 2024-11-23 results of the normalized output. The JSON files store the data in the following form <pre><code>key=>Tex</code></pre>

opencc-by-4.0Nov 2014View details →

zenodo48/100

Multi-LEX: a database of multi-word frequencies (English files)

Written word frequency is a key variable used in many psycholinguistic studies and is central in explaining visual word recognition. Indeed, methodological advances on single word frequency estimates have helped to uncover novel language-related cognitive processes, fostering new ideas and studies. In an attempt to support and promote research on a related emerging topic, visual multi-word recognition, we extracted from the exhaustive Google Ngram datasets a selection of millions of multi-word sequences and computed their associated frequency estimate. Such sequences are presented with Part-of-Speech information for each individual word. An online behavioral investigation making use of the French 4-gram lexicon in a grammatical decision task was carried out. The results show an item-level frequency effect of word sequences. Moreover, the proposed datasets were found useful during the stimulus selection phase, allowing more precise control of the multi-word characteristics.

opencc-by-4.0Oct 2022View details →

zenodo48/100

Lexicon of English Verbal Polarity Shifters

We provide a complete lexicon of English verbal polarity shifters and their shifting scope. Our lexicon covers all verbs of WordNet v3.1 that are single word or particle verbs. Polarity shifter and scope labels are given for each lemma-synset pair (i.e. each word sense of a lemma). Data The data is presented in the following forms: <ol> <li>A complete lexicon of all verbal shifters and their shifting scopes.</li> <li>Two auxiliary lists: <ol> <li>A list of all lemmas with shifter labels</li> <li>A list of all word senses with shifter labels</li> </ol> </li> </ol> All files are in CSV (comma-separated value) format. 1. Main Lexicon File name: <code>shifter_lexicon.csv</code> The main lexicon lists all verbal shifters and their shifting scopes. Verbal shifters are modelled as lemma-sense pairs with one or more shifting scopes. Each line of the lexicon file contains a single lemma-sense-scope triple, using the format: <pre><code>LEMMA,SYNSET,SCOPE </code></pre> The elements are defined as follows: <ul> <li>LEMMA: The lemma form of the verb.</li> <li>SYNSET: The numeric identifier of the synset, commonly referred to as offset or database location. It consists of 8 digits, including leading zeroes (e.g. 00334568).</li> <li>SCOPE: The scope of the shifting: <ul> <li><code>subj</code>: The verbal shifter affects its subject.</li> <li><code>dobj</code>: The verbal shifter affects its direct object.</li> <li><code>pobj_*</code>: The verbal shifter affects objects within a prepositional phrase. The preposition in question is included in the annotation. For example a from-preposition scope receives the label <code>pobj_from</code> and a a for-preposition receives <code>pobj_for</code>.</li> <li><code>comp</code>: The verbal shifter affects a clausal complement, such as infinitive clauses or gerunds.</li> </ul> </li> </ul> The lexicon lists all lemma-sense pairs that are verbal shifters. Any lemma-sense pair not listed is not a verbal shifter. When a lemma-sense pair has more than one possible scope, a separate entry is made for each scope. 2. Auxiliary Lists The auxiliary files represent the same shifter information as the main lexicon, but for lemmas and synsets, respectively, instead of for lemma-sense pairs. Due to their nature, these lists are more coarse-grained than the main lexicon and contain no information on shifter scope. They are provided as a convenience for fast experimentation. 2.1. List of Lemmas File name: <code>shifter_lemma_lexicon.csv</code> List of all verb lemmas and whether they are shifters in at least one of their word senses. <pre><code>LEMMA,LABEL </code></pre> <ul> <li>LEMMA: The lemma form of the verb.</li> <li>LABEL: <code>shifter</code> if the verb is a shifter in at least one of its word senses, otherwise <code>nonshifter</code>.</li> </ul> Many verbal shifter lemmas only cause shifting in some of their word senses. This list is therefore considerably more coarse-grained than the main lexicon. 2.2. List of Synsets File name: <code>shifter_synset_lexicon.csv</code> List of all synsets and whether their lemmas are shifters in this specific word sense. <pre><code>SYNSET,LABEL </code></pre> <ul> <li>SYNSET: The numeric identifier of the synset, commonly referred to as offset or database location. It consists of 8 digits, including leading zeroes (e.g. 00334568).</li> <li>LABEL: <code>shifter</code> if the word sense causes shifting, otherwise <code>nonshifter</code>.</li> </ul> Shifting is shared among lemmas of the same word sense. This list, therefore, provides (almost) the same granularity for the shifter label as the main lexicon. However, in a few exceptions, synsets contained words with subtly different senses that did not all cause shifting. These senses are considered shifters in this list, analogous to the generalisation in the list of lemmas. Attribution This dataset was created as part of the following publication: Schulder, Marc and Wiegand, Michael and Ruppenhofer, Josef and Köser, Stephanie (2018). "Introducing a Lexicon of Verbal Polarity Shifters for English". Proceedings of the 11th Conference on Language Resources and Evaluation (LREC). Miyazaki, Japan, May 7-12, 2018. <a href="https://doi.org/10.5281/zenodo.3365683">DOI: 10.5281/zenodo.3365683</a>. If you use the data in your research or work, please cite the publication.

opencc-by-4.0May 2018View details →

zenodo48/100

Bootstrapped Lexicon of English Verbal Polarity Shifters

<blockquote> An extended version of this dataset that also covers nominal and adjectival polarity shifters can be found at <a href="https://doi.org/10.5281/zenodo.3365601">doi:10.5281/zenodo.3365601</a>. </blockquote>   We provide a bootstrapped lexicon of English verbal polarity shifters. Our lexicon covers 3043 verbs of WordNet v3.1 (Miller et al., 1990) that are single word or particle verbs. Polarity shifter labels are given for each word lemma. Data The data consists of: <ol> <li>Two lists of WordNet verbs (Miller et al., 1990), annotated for whether they cause shifting. <ol> <li>The initial gold standard (§2) of 2000 randomly chosen verbs.</li> <li>The bootstrapped 1043 verbs (§5.3) that were labelled as shifters by our best classifier and then manually annotated.</li> </ol> </li> <li>Data set of verb phrases from the Amazon Product Review Data corpus (Jindal & Liu, 2008), annotated for polarity of phrase and polar noun.</li> </ol>   1. Verbal Shifters Files <ul> <li>The initial gold standard: <code>verbal_shifters.gold_standard.txt</code></li> <li>The bootstrapped verbs: <code>verbal_shifters.bootstrapping.txt</code></li> </ul> Format <ul> <li>Each line contains a verb and its label, separate by a whitespace.</li> <li>Multiword expressions are separated by an underscore (WORD_WORD).</li> <li>All labels were assigned by an expert annotator.</li> </ul>   2. Sentiment Verb Phrases Files <ul> <li>All annotated verb phrases: <code>sentiment_phrases.txt</code></li> </ul> Content The file starts with 400 phrases containing shifter verbs, followed by 2231 phrases containing non-shifter verbs. Format Every item consists of: <ul> <li>The sentence from which the VP and the polar noun were extracted.</li> <li>The VP, polar noun and the verb heading the VP.</li> <li>Constituency parse for the VP.</li> <li>Gold labels for VP and polar noun by a human annotator.</li> <li>Predicted labels for VP and polar noun by RNTN tagger (Socher et al., 2013) and <code>LEX_gold</code> approach.</li> <li>Items are separated by a line of asterisks (*)</li> </ul> Related Resources <ul> <li>Paper: <a href="https://aclweb.org/anthology/I17-1063">ACL Anthology</a> or <a href="https://doi.org/10.5281/zenodo.3365609">DOI: 10.5281/zenodo.3365609</a></li> <li>Presentation: <a href="https://www.aclweb.org/anthology/attachments/I17-1063.Presentation.pdf">ACL Anthology</a></li> <li>Word Embedding: <a href="https://doi.org/10.5281/zenodo.3370051">DOI: 10.5281/zenodo.3370051</a></li> </ul> Attribution This dataset was created as part of the following publication: Marc Schulder, Michael Wiegand, Josef Ruppenhofer and Benjamin Roth (2017). "Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features". Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP). Taipei, Taiwan, November 27 - December 3, 2017. <a href="https://doi.org/10.5281/zenodo.3365609">DOI: 10.5281/zenodo.3365609</a>. If you use the data in your research or work, please cite the publication.

opencc-by-4.0Nov 2017View details →

zenodo48/100

Two decades of body length measurements in size-structured larval and juvenile fish populations in English rivers.

Long term ecological datasets are valuable in providing context and understanding to complex ecological processes that occur over broad temporal scales, and provide a baseline for analysing change. Monitoring of fish populations in UK waterbodies and elsewhere is typically through measuring the length of individual fish caught in surveys. Through this method, the age structure of fish populations can be determined, as well as over winer survival rates and future recruitment success and cohort sizes can be predicted. The larval and juvenile period are when fish are considered most vulnerable to predation, competition, disease and environmental perturbations.  This study presents the first long-term larval and juvenile fish lengths dataset for 67 survey sites over two decades (1999-2018) from the rivers Ancholme, Warwickshire Avon, Don, Trent, and Yorkshire Ouse (including the Swale, Ure, Nidd and Wharfe) in the United Kingdom. These rivers represent a range of topographical and biotopical characteristics. For the majority of this study, surveys were conducted on a monthly or fortnightly basis making both annual and seasonal analyses of size structure, growth and body length possible. Although there is some variation in the sampling frequency and some locations varied throughout the study according to requirements. In total, more than 380,000 larval or juvenile fish of 30 species were measured, likely representing one of the most comprehensive datasets of its type. Surveys were conducted in river margins, where the velocity was slowest and larval and juvenile fish tend to aggregate. Fish were captured using a 25 x 3 m micromesh (3 mm mesh size) seine net that was set in a rectangle parallel to the bank. This net capture fish as small as 5 mm and is the most appropriate method of catching larvae and juvenile fish, although occasionally some larger adult fish may have also been captured and measured as part of this dataset for completeness. All fish were identified to species and measured to standard length (mm) and released at the point of capture. The exception was the smallest larvae, which were euthanised with an overdose of methanesulphonate (MS-222) and preserved in 4% formalin solution for microscopic examination. The dataset contains 384,090 rows and 13 columns. Each row corresponds to a single fish that was measured at each site and date. Associated site information (site name, location, area fished (m2) and survey date) is reported for each row. When only a fraction of the catch was processed, the sub-sample size was reflected in the Count column (e.g. when half the sample was processed, the numbers of fish measured or only counted were multiplied by two). This enables accurate densities to be calculated as the total number of both measured and unmeasured fish is recorded. Description of columns found in the dataset:   <table> <tbody> <tr> <td> Column heading </td> <td> Column description </td> <td> Data type </td> <td> Units </td> </tr> <tr> <td> Fish _Catchment </td> <td> The river catchment/basin location of each fish site </td> <td> Text </td> <td> n/a </td> </tr> <tr> <td> Fish_River </td> <td> The river/watercourse location of each fish site. </td> <td> Text </td> <td> n/a </td> </tr> <tr> <td> Fish_SiteName </td> <td> The name of each fish site </td> <td> Text </td> <td> n/a </td> </tr> <tr> <td> Fish_Latitude </td> <td> The latitude of each fish site (WGS 1984) </td> <td> Integer </td> <td> Decimal degrees </td> </tr> <tr> <td> Fish_Longitude </td> <td> The longitude of each fish site (WGS 1984) </td> <td> Integer </td> <td> Decimal degrees </td> </tr> <tr> <td> Fish_Area </td> <td> Area of fish site surveyed </td> <td> Integer </td> <td> m-2 </td> </tr> <tr> <td> Fish_SurveyDate </td> <td> Date fish survey was carried out </td> <td> Integer </td> <td> dd/mm/yyyy </td> </tr> <tr> <td> Fish_Year </td> <td> Year fish survey was carried out </td> <td> Integer </td> <td> yyyy </td> </tr> <tr> <td> Common_Name </td> <td> The common/vernacular name of each fish taxon recorded in the dataset. </td> <td> Text </td> <td> n/a </td> </tr> <tr> <td> Latin_Name </td> <td> The scientific name of each fish taxon recorded in the dataset </td> <td> Text </td> <td> n/a </td> </tr> <tr> <td> Net_Number </td> <td> The net number the fish in a given survey were caught on </td> <td> Integer </td> <td> n/a </td> </tr> <tr> <td> Length_mm </td> <td> Length of individual fish caught </td> <td> Integer </td> <td> mm </td> </tr> <tr> <td> Count </td> <td> Count of fish caught accounting for sub- sampling </td> <td> Integer </td> <td> Number of fish </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2024View details →

zenodo48/100

EPOCHAL (Effects of Pollen on Cardiorespiratory Health and Allergic symptoms): Daily questionnaire (English and German)

opencc-by-4.0Mar 2023View details →

zenodo48/100

EPOCHAL (Effects of Pollen on Cardiorespiratory Health and Allergic symptoms): Nurse home visit form (English and German)

This questionnaire was developed for the EPOCHAL study (Effects of Pollen on Cardiorespiratory Health and Allergic symptoms). The study was sponsored and led by Swiss TPH in Basel, Switzerland and approved by the local ethics committee (Ethikkomission Nordwest- und Zentralschweiz EKNZ, project ID 2021-00151). Written informed consent was obtained from every participant prior to study inclusion.  This is the "nurse home visit form" which was administered 6 times for each participant during weekly home visits by our study nurses during the pollen season. It includes questions about the following topics: 1) Potential for Covid-19 infection, changes in vaccination status 2) Overall health status 3) Allergic symptoms: nose, eyes, lungs 4) Sleep, mood and quality of life 5) Medication use 6) Time spent outdoors 7) Daily covariate information: coffee and alcohol intake, eating, smoking, vigorous exercise 8) Blood pressure measurements 9) Heart rate variability recording 10) Exhaled nitric oxide measurements 11) Pulmonary function testing (spirometry) 12) Comments The questionnaire is also available in German under the same DOI. Please note that this questionnaire was administered electronically on a tablet, and contains: <ul> <li>Form logic, which determines the relevance of some questions based on previous answers. Affected questions are typically shown in grey color.</li> <li>Instructions (in bold blue font) to the participant/study nurse to guide the process of data collection (e.g. “please hand over the tablet to the participant/nurse”).</li> <li>Warnings (in large red font) and directions (in large grey font) to warn nurses against performance of spirometry measurements if contraindications were present. For example, when the nurse entered high blood pressure in Topic #8, or when recent surgery was indicated in Topic #11. Warnings and directions also flag incidental findings (e.g., high blood pressure ≥160 mmHg (systolic) or ≥100 mmHg (diastolic) requiring urgent action.</li> </ul>

opencc-by-4.0Mar 2023View details →

zenodo48/100

Macroscopic, histological and stereological image dataset of the Striped red mullet (Mullus surmuletus) ovaries from the English Channel (ICES area 27.7.d) stock

Contents: This dataset can be completed with the : Macroscopic, histological and stereological image dataset of the Striped red mullet (Mullus surmuletus) ovaries from the Bay of Biscay (ICES area 27.7.g,j & 27.8.a-c) stock, which can also be found on the Zenodo repository. This dataset contains the macroscopic and histological images of the ovaries of 214 Striped red mullet (female, Mullus surmuletus, Linnaeus 1758) collected from the English Channel stock (ICES area 27.7.d) in February 2021 (n=20), March 2021 (n=13), April 2021 (n=12), May 2021 (n=15), August 2021 (n=15), September 2021 (n=15), October 2021 (n=41), November 2021 (n=10), December 2021 (n=14), January 2022 (n=30), February 2022 (n=15) and August 2022 (n=14).   Images: <ul> <li>Macroscopic_pictures.zip: archive in zip format of 621 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 211 female Striped red mullets dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder et al., 2022) with : <ul> <li>one picture of the entire fish with its abdominal cavity open with the ovaries in view</li> <li>one picture of the whole fish with the ovaries outside of the abdominal cavity</li> <li>one picture of the ovaries</li> <li>the name of the picture is the same as the fish’s ID number.</li> </ul> </li> </ul> <ul> <li>Histology_slides.zip : archive in zip format containing the ovarian histological slides digitized using an Olympus V120 slide scanner, x20 lens. The pictures (.vsi from the OlympusVSI format) are of the 484 histological slides acquired during this study.</li> <li>Data was split for smaller size downloads : <ul> <li>Histology_slides_1of5 : histological sections for individuals numbered 001 to 045</li> <li>Histology_slides_2of5 : histological sections for individuals numbered 046 to 138</li> <li>Histology_slides_3of5 : histological sections for individuals numbered 154 to 180</li> <li>Histology_slides_4of5 : histological sections for individuals numbered 196 to 270</li> <li>Histology_slides_5of5 : histological sections for individuals numbered 271 to 334</li> </ul> </li> </ul>   Data: <ul> <li>Readings.zip : archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available. <ul> <li>Calibration : Reading results of 4 different agents, with the first and last readings, as well as the Qupath scripts used.</li> <li>Homogeneity : Reading results for 96 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 96 slides belong to 16 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.</li> <li>Total : Reading results for 214 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.</li> </ul> </li> </ul> <ul> <li>Macro_MULL_read_me.txt : a text file (.txt) listing the acronyms used in the Macro_MULL.xlsx file, as well as their meaning.</li> <li>Macro_MULL.xlsx : Excel file (.xlsx) containing measurements of macroscopic parameters for all 214 fish sampled during this study. The information contained in this table is as follows: <ul> <li>Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (Macroscopic_pictures_Data)</li> <li>ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27</li> <li>ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27</li> <li>Date: date the fish was caught (dd/mm/yyyy)</li> <li>Total_fish_length: total length of the fish (cm)</li> <li>Ungutted_fish_weight: total weight of the fish (g)</li> <li>Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode et al., 2022) software used by IFREMER</li> <li>Parasite: presence (Y) or absence (N) of parasite in or on the fish</li> <li>age: age (in years) of the fish after analysis of the fish’s otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis</li> <li>Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish’s gonad with the naked eye, following the WKASMSF (ICES, 2018) scale</li> <li>Liver_weight: liver weight (g)</li> <li>Droite_gonad_weight : gonad weight (g) of right ovary</li> <li>Gauche_gonad_weight : gonad weight (g) of left ovary</li> <li>Sections: number of cross sections sampled for the individual</li> </ul> </li> </ul> <ul> <li>Stereo_MULL_read_me.txt : a text file (.txt) listing the acronyms used in the Stereo_MULL.csv file, as well as their meaning.</li> <li>Stereo_MULL.csv : a text data file (.csv) of the stereology count results of 294 slides read during this study. Among these slides, 96 were read to test the homogeneity distribution of different cell types found throughout each ovary (16 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see Calibration folder for reading results of the 4 agents). Finally, 214 median histological ovarian slides were read. The information contained in this table is as follows: <ul> <li>cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin et al. 2023)</li> <li>idpt: identification number of the sampling point</li> <li>id: unique complex identification number of the sampling point generated by combining the x and y coordinates</li> <li>x: x coordinate of the sampling point</li> <li>y: y coordinate of the sampling point</li> <li>reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase</li> <li>slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with Fish_id</li> </ul> </li> </ul>

opencc-by-4.0Jun 2023View details →

zenodo44/100

Yearly pageviews of English Wikipedia articles with potential links to green open access scholarly articles

Number of visits in 2019 for a sample of 23462 English Wikipedia articles which contain references to academic sources which have a green open access copy available but not yet used. The consultation statistics were retrieved from the Wikimedia pageviews API using the Python client (script also included). The sample was selected among articles which in April 2020 had at least one citation of an academic paper (using the "cite journal" template) for which OAbot (through Unpaywall data) had found a green open access URL to add (gratis open access, not necessarily libre open access). Data shows that the top 1 % most visited articles received 30 % of the visits: over 500 million in the year, corresponding to 1 million potential citation link clicks to distribute across all references assuming a 0.2 % click-through rate per Piccardi et al. (2020).

opencc-zeroMay 2020View details →

zenodo44/100

ICF english codebook for Atlas.ti

<pre>This codebook was created simultaneously with the Portuguese version, previously published, to encourage the use of the ICF in qualitative analyses using the software Atlas.ti. Data was cited from ICF Documentation form, with the last updated from May, 2020.</pre> <pre> </pre>

opencc-by-4.0May 2020View details →

zenodo44/100

Ontolex-lemon and TIAD versions of Apertium English-Spanish dictionary

OntoLex-lemon and TSV conversion of Apertium Bidix. For more details, see <a href="https://www.aclweb.org/anthology/2020.lrec-1.401/">https://www.aclweb.org/anthology/2020.lrec-1.401/</a> Authors of the original data: (c) 2008, Universitat d'Alacant (Transducens group) (c) 2007, Generalitat de Catalunya (dades anglés) (c) 2007, Universitat Pompeu Fabra (IULA) (c) 2005, Universitat Politècnica de Catalunya (c) 2009, Jimmy O'Regan (c) 2009, Paul "greenbreen" Breen

opengpl-2.0-or-laterMar 2020View details →

zenodo44/100

Ontolex-lemon and TIAD versions of Apertium Esperanto-English dictionary

OntoLex-lemon and TSV conversion of Apertium Bidix. For more details, see <a href="https://www.aclweb.org/anthology/2020.lrec-1.401/">https://www.aclweb.org/anthology/2020.lrec-1.401/</a> Authors of the original data: (c) 2008--2009, Jacob Nordfalk (c) 2009, Hèctor Alòs i Font (c) 2005--2007, Universitat d'Alacant (Transducens group) -- English data (c) 2005--2007, Universitat Pompeu Fabra -- English data

opengpl-2.0-or-laterMar 2020View details →

zenodo44/100

The Dublin Language Garden Perceptual Dialectology of Irish English Collection

<blockquote> Recommended citation for this dataset: Garnett, Vicky, & Lucek, Stephen. (2020). The Dublin Language Garden Perceptual Dialectology of Irish English Collection (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4247829 </blockquote>   About this Dataset The field of Perceptual Dialectology is an area of sociolinguistic study that investigates how non-linguists view different varieties of language.  It often includes hand-drawn map exercises in which participants indicate where they believe various varieties are spoken, and their attitudes towards them.  In 2015, as part of a public linguistics outreach event (the Dublin Language Garden) held at Trinity College Dublin, the authors created an activity for members of the public and collected hand-drawn maps from them that gave responses to the following tasks: a. Indicate where you come from on the map (using a red dot sticker) b. Draw where you think the Dublin dialect occurs c. Draw the boundaries of any other dialects you believe occur in Ireland d. Tell us what you think are the features of those dialects e. Tell us what you think are the characteristics of the people who speak those dialects. Participants of all ages were encouraged to take part, but only data from those over 18 were retained after the event and used in this data collection.  Participants were all given information on how the data was to be anonymised, processed and published on a clearly displayed poster to read before they were given a map to complete the 5 tasks (listed above).  No additional information about the participants, aside from that acquired through Task a, was collected.   File List: <ul> <li>_READ_ME - Dublin Language Garden Perceptual Dialectology of Irish English data.txt Contains a detailed description of this dataset.  </li> <li>DLG_PDIE_KML_data_by_location.zip This zipped folder contains the .kml data of multiple hand-drawn maps organised into folders by their location  </li> <li>DLG_PDIE_KML_data_by_part.zip This zipped folder contains the .kml data of multiple hand-drawn maps organised into folders according to the participants.</li> </ul> These folders have been organised in this way in order to make discoverability easier between the data.  Users may wish to analyse the data only by the locations of the varieties identified by the participants.  Other users may only be interested in the data given by specific participants, and therefore the folder that organises the data in this way may be of better use to them.  Both folders, however, contain the same data, it is simply how they are organised. <ul> <li>Garnett and Lucek DLG_PD_IE Qualitative Data (Nov 2020).xlsx Spreadsheet featuring tabulated qualitative data taken from all maps  </li> <li>Sample Hand-drawn Maps.zip Folder containing 2 sample hand-drawn maps from the participants to help contextualise the data presented here.</li> </ul>   Any questions? Any enquiries regarding this dataset should be directed to either Vicky Garnett (<a href="mailto:garnetv@tcd.ie">garnetv@tcd.ie</a>) or Stephen Lucek (<a href="mailto:stephen.lucek@ucd.ie">stephen.lucek@ucd.ie</a>).

opencc-by-4.0Nov 2020View details →

zenodo44/100

ENGLISH-AKUAPEM TWI PARALLEL CORPUS

This dataset (verified_data.csv) is bilingual machine translation training corpus for English and Akuapem Twi of 25,421 sentence pairs.  A transformer-based machine translator was used to generate initial translations in Akuapem Twi, which were later verified and corrected where necessary by native speakers.  The main idea of a typical use case for the dataset is for further training of machine translation models in Akuapem Twi. The data can also be used for other downstream NLP tasks such as Named Entity Recognition and POS tagging, with appropriate additional annotations.  Another potential application is training unsupervised embeddings for the Akuapem Twi language. In addition a higher quality 697 crowdsourced sentences (crowdsourced_data.csv) are provided for use as an evaluation set for the tasks highlighted above. It is recommended as a testing dataset for machine translation English to Twi and Twi to English models. Acknowledgement: This project was supported by the <a href="https://www.k4all.org/project/language-dataset-fellowship/">AI4D language dataset fellowship</a> through K4all and Zindi Africa

opencc-by-4.0Jan 2021View details →

zenodo44/100

Structured citations in the English Wikipedia

This dataset contains the metadata of citations in the English Wikipedia that editors have input as citation templates (which use the Citation Style 1). It has been obtained from an XML dump of Wikipedia (2016-05-01), and was parsed with the wikiciteparser library. This library runs the Lua code used in Wikipedia to format such citations and generate structured metadata (such as COinS) from them. https://github.com/dissemin/wikiciteparser Sample: <blockquote> 716551092    12    2016-04-22T10:19:33Z    Anarchism    cite journal     {"PublisherName": "International Group of San Francisco", "Title": "Towards Anarchism", "URL": "http://www.marxists.org/archive/malatesta/1930s/xx/toanarchy.htm", "Authors": [{"link": "Errico Malatesta", "last": "Malatesta", "first": "Errico"}], "ID_list": {"OCLC": "3930443"}, "Periodical": "MAN!", "PublicationPlace": "Los Angeles"} 716551092    12    2016-04-22T10:19:33Z    Anarchism    cite journal     {"Date": "2007-05-14", "URL": "http://www.theglobeandmail.com/servlet/story/RTGAM.20070514.wxlanarchist14/BNStory/lifeWork/home/", "Title": "Working for The Man", "Periodical": "The Globe and Mail", "Authors": [{"last": "Agrell", "first": "Siri"}]} 716551092    12    2016-04-22T10:19:33Z    Anarchism    cite web     {"Date": "2006", "URL": "http://www.britannica.com/eb/article-9117285", "PublisherName": "Encyclop\u00e6dia Britannica Premium Service", "Periodical": "Encyclop\u00e6dia Britannica", "Title": "Anarchism"} 716551092    12    2016-04-22T10:19:33Z    Anarchism    cite journal     {"Date": "2005", "Pages": "14", "Periodical": "The Shorter Routledge Encyclopedia of Philosophy", "Title": "Anarchism"} </blockquote> Credit: these citations have been input by Wikipedia editors (this dataset is therefore distributed under a CC-BY-SA license). Acknowledgments: this extraction process was carried on one of OpenJournal's servers. See also: citation identifiers extracted from Wikipedia (so, not looking at specific templates, but using regular expressions): http://dx.doi.org/10.6084/m9.figshare.1299540

opencc-by-sa-4.0Jun 2016View details →

zenodo44/100

Children speech recording (English, spontaneous speech + pre-defined sentences)

The dataset contains audio recordings (lossless WAV) of 11 young children (age M=4.9 years old; 5 females, 6 males). Recordings include: <ul> <li>free speech (retelling a picture book, ‘Frog, Where Are You?’ by Mercer Mayer)</li> <li>repeating 5 pre-defined short sentences (like 'the horse is in the stable')</li> <li>telling the numbers from 1 to 10</li> </ul> The recordings are in English and the participants include both native and non-native speakers. Each sample is recorded from 3 sources: <ul> <li>A studio-grade microphone (Rode NT1-A)</li> <li>A portable microphone (Zoom H1)</li> <li>The two front microphones of the Aldebaran NAO robot</li> </ul> (note that, due to technical issues, a few (sample/microphone) combinations are missing). For the free-speech recording, a manual segmentation of the utterances is provided as well.

opencc-by-4.0Dec 2016View details →

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Last verified 2026-04-29Open record