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475 results for “words”
Qabiao (Tiechang) audio word list
<p>On August 26, 2012, I interviewed Yang Guangyou 杨光佑 (71, female) in Xinminzhai 新民寨, Dongdu Village 董渡村, Tiechang Township 铁厂乡, Malipo County, Yunnan, China. Xinminzhai 新民寨 is adjacent to Pufeng 普峰, where more ethnic Qabiao reside. Yang had been an informant for Li Jinfang, and said that Li and his team had worked with her for about 2 weeks.</p> <p>Yang Guangyou was born in Pula 普腊 / Pho La, Ha Giang province, Vietnam, but moved to China when she married a Qabiao speaker in China, who had already passed away.</p> <p>I also interviewed a Qabiao semi-speaker, Gong Youlian 贡友联 (74, female) in the Tiechang Township center.</p> <p>Qabiao people are also reported living in Makun 玛昆 , Donggan Town 董干镇.</p>
Yi (Kunzhai) audio word list
<p>On April 3, 2013, I interviewed Wang Dechang 王得昌 (male; date of birth: December 3, 1952) of Gusu village 古苏村, Kunzhai Township 昆寨乡, Nayong County, Guizhou, China who spoke a Loloish language called Nusuphu (autonym: [nɯ55 su21 phu33]). All Yi people in the township were "Nusuphu." He has also heard of the Caijia, but reports that there are now completely Sinicized, with no ethnic language or customs remaining.</p>
Qabiao (Pufeng) audio word list
<p>On August 26, 2012, I interviewed Yang Guangyou 杨光佑 (71, female) in Xinminzhai 新民寨, Dongdu Village 董渡村, Tiechang Township 铁厂乡, Malipo County, Yunnan, China. Xinminzhai 新民寨 is adjacent to Pufeng 普峰, where more ethnic Qabiao reside. Yang had been an informant for Li Jinfang, and said that Li and his team had worked with her for about 2 weeks.</p> <p>Yang Guangyou was born in Pula 普腊 / Pho La, Ha Giang province, Vietnam, but moved to China when she married a Qabiao speaker in China, who had already passed away.</p> <p>I also interviewed a Qabiao semi-speaker, Gong Youlian 贡友联 (74, female) in the Tiechang Township center.</p> <p>Qabiao people are also reported living in Makun 玛昆 , Donggan Town 董干镇.</p>
Caijia (Niujiaojing) audio word list
<p>My Caijia informant in Niujiaojing 牛角井, Yangjie Town, Weining County, Guizhou, China was an illiterate elderly woman named Wang Jinzhi 王金之 (born 1940), whom I interviewed on April 7, 2013.<br> <br> Caijia locations reported by residents of Niujiaojing are Niujiaojing 牛角井村, Xinglongchang 兴隆场村, Niuchishui 牛吃水 (reportedly with very fluent Caijia speakers), and Fadi 发地. Like the Caijia of Puding County, the Caijia of Weining County (or at least Niujiaojing) are officially classified as ethnic Gelao. However, the Caijia (locally Caizu) there told me that they do not like that classification at all, and are a completely distinct ethnic group that should be officially recognized. These are all previously unknown locations of Caijia.<br> <br> On April 1, 2013, one of the Puding County government officials also said that there are ethnic Caijia in 贵州 织金县 猫场乡 新国村 (Xinguo, Maochang Township, Zhijin County), but they do not know whether Caijia is still spoken there. There are also Caijia in Puding County, but they no longer speak the language, and are classified as ethnic Gelao. This is because their customs were similar to that of the neighboring Gelao. However, none of the government officials had any idea where ethnic Caijia are located within Puding County.</p>
Red Gelao (Tianba) audio word list
<p>In 1997, a divergent Red Gelao variety known as va³⁵ntɯ³¹ was discovered by Jerold Edmondson in Na Khê, Yên Minh District, Hà Giang Province, Vietnam. Edmondson reported that Red Gelao was still being acquired by children in the village, which exchanged brides with the other Red Gelao-speaking village of Bìch Đich (also known as Bạch Đích) in order to maintain Red Gelao culture. Even though Edmondson reported that in Vietnam, Red Gelao is spoken only in the two villages of Na Khê and Bìch Đich, my informants just across the border in Malipo County suggest that there could be more Red Gelao speakers living in the neighboring village of Mậu Long.<br> <br> On August 25, 2012, I found a rememberer of Red Gelao named Wang Qiongying 王琼英, a 47-year-old woman, in Tianba 田坝, Dongdu village 董渡村, Tiechang township 铁厂乡, Malipo County 麻栗坡县, Yunnan, China. Wang Qiongying was born in Hà Giang province, Vietnam, but moved to Tianba after marrying Yang Changxing 杨昌兴, an ethnic White Gelao who does not speak any Gelao. Wang gave her Red Gelao autonym as ua³⁵ntei³¹. As of 2012, Wang speaks only Southwestern Mandarin fluently, and could produce some words, phrases, and basic sentences in her native Red Gelao variety. Despite being born in Vietnam, she does not speak any Vietnamese. Wang has not conversed in Red Gelao with anyone since her move to China about 30 years ago, and does not remember many basic vocabulary items such as the colors (e.g., ‘black’, ‘white’).<br> <br> Wang moved to China at the age of 17 when she married Yang Changxin 杨昌兴 in Tianba 田坝. She is a rememberer of Red Gelao, and cannot say complete sentences in it. Wang has not conversed fluently in Red Gelao since she had moved to China.<br> <br> Neither Wang nor her husband was able to recognize the names of Na Khê and Bìch Đich. However, her husband Yang Changxing said that Wang was born in a location in Hà Giang province called “Moudan 某丹”, which I believe is most likely the village of Mậu Long, located just to the north of Na Khê in Yên Minh District, Vietnam. A visit to Mậu Long would be needed to determine if there are still any remaining Red Gelao speakers of the ua³⁵ntei³¹ variety. Unfortunately, there is no other Red Gelao speaker or rememberer in Tianba, even though Li Jinfang’s informant Yang Yucai 杨玉才 had reported that his mother was a Red Gelao from Tianba, of the autonym u³³wei⁵⁵ . Even though there were several Red Gelao households in Tianba, none of the local Red Gelao people recognized the autonym u³³wei⁵⁵.<br> <br> Tianba also has a few elderly fluent White Gelao speakers of the autonym te³⁵lɯ³¹ , including 78-year-old Yang Guangsheng 杨光升. Yang reported that his brother Yang Guangqing 杨光睛, residing in Fanpo 翻坡, Yangwan township 杨万乡, also speaks White Gelao fluently. Yang Guangsheng also informed me that there were more White Gelao speakers in the nearby village of Fengyan 峰岩, including Yang Guangfu 杨光甫.<br> <br> In the meantime, her husband and another White Gelao man were arguing about a Gelao bride who was going to be migrating to China, highlighting the tense issues that may result from cross-border migrations in the region.</p>
Luoji audio word list
<p>The luo31 dʑi33 are located in Shejie 蛇街村, Yangjie Town 羊街镇, Weining County, Guizhou, China. The neighboring ethnic groups call them the “Qixingmin” (七姓民), while they also call themselves the “Baizi” 白子 in Chinese, and luo31 dʑi33 in their own language (non-Chinese). The locals say they are officially classified as ethnic Bai. Another group officially classified as ethnic Bai are the “Caizu” (蔡族), who are a completely distinct group from the Qixingmin. Both have different languages and customs. My minivan driver did not know about the Caizu until today.<br> <br> Also, Yangjie separated from Tiejiang Township 铁匠乡 by “only a bridge,” according to my driver (who has worked in “mountain carving mines” in Jiangxi and Zhejiang). Intriguingly, he reported that there are also ethnic Shui in the area.<br> <br> On April 7, 2013, I interviewed an elderly man named Zhang Dexu 张得旭 (born December 15, 1938). Zhang claimed that the ancestors of the Qixingmin had come from Dali, and that their ancestors had spoken a “Bai language” (白话 or 白族话) completely different from the “Yi” (彝话) that their grandparents had spoken. He claimed that this “Bai language” had gone extinct hundreds of years ago, and guessed that it could have been back in the Song Dynasty. He reports that there are also Qixingmin speakers in Jinzhong Township 金钟镇 and Dajie Township 大街乡, and Caijia speakers in Kele Township 可乐乡, Hezhang County and Dajie Township 大街乡, Weining County.</p>
Hezhang Buyi (Dazhai) audio word list
<p>Hezhang Buyi is a highly endangered mixed Kra-Dai language with a Northern Tai (Buyi) superstratum and Kra substratum.</p> <p>On April 8, 2013, I interviewed Yang Zhenmei 杨珍妹 (born July 15, 1928), who was the mother of a local village official in Tiejiang village 铁匠村, Tiejiang township 铁匠乡, Hezhang County 赫章县, Guizhou, China.</p> <p>In Dazhai 大寨, Fuchu township 辅处乡, Hezhang County 赫章县, Guizhou, China, my informant was Gao Chunjie 高春姐 (female, illiterate; born May 5, 1928 in Yuguo village 雨果村, Weining county). She was the best native speaker of “Shui” in the village. There were also “few” other elderly fluent speakers in the village.</p> <p>Gao's middle-aged male relative said their ancestors had migrated from from Dashiban, Taihe County, Fuguang Prefecture, Jiangxi 江西福广府太河县大石板. He also reported other locations of Shui in Hezhang County as Zhuyuan, Kele Township 可乐乡竹园 (and one other village) and Tangbian, Zhuming Township 朱明乡塘边.</p> <p> </p>
Selected word frequencies for 20 short stories by three authors: Arthur Conan Doyle, Ernest Hemingway, Jack London
<p>Selected word frequencies for 20 short stories by three authors: Arthur Conan Doyle, Ernest Hemingway, Jack London. </p>
Words in -ica from the GIGAFIDA corpus/Besede na -ica iz korpusa Gigafida
<p>Iz korpusa pisne standardne slovenščine Gigafida 2.0 (https://viri.cjvt.si/gigafida/, Logar Berginc idr. 2012) je bil pridobljen naključni vzorec besed, ki se končujejo na "ica". </p> <p>Iz vzorca so bile odstranjene besede, ki niso bile primerne za analizo, saj so bile i) ortografsko podobne besede, ki nimajo zvočne podobe na -ica, kot America, ii) dvozložne besede, katerih edino možno naglasno mesto je -íca, npr. žlica, iii) nadaljnje tvorjenke iz besed z zadnjo tvorbeno stopnjo s pripono -ica, npr. nepravica ali iv) nejasni primeri, kar je bilo preverjeno z intuicijami šestih naravnih govorcev slovenščine (vključno z obema avtorjema prispevka). Končni vzorec vsebuje 583 besed (frekvenca na milijon besed v korpusu: povprečje 10,4, mediana 3,2, modus: 1,8, standardni odklon: 26,1; najvišja frekvenca: 344,5/milijon, minimalna frekvenca: 0/milijon).<br>Vse besede so bile razdeljene na morfeme. Po tem koraku je ostalo 23 besed, ki jih avtorja nista mogla gotovo razdeliti. Oblikovala sva nalogo določanja morfemskih mej, ki je vsebovala 23 nejasnih besed in 23 besed iz vzorca, katerih morfemska zgradba je bila gotovo razvidna. Nalogo so rešili štirje naravni govorci slovenščine z izobrazbo na področju jezikoslovja ali slovenistike. Na podlagi rezultatov sva izključila 19 od 23 nejasnih besed, in sicer kadar se sodbe anketirancev niso skladale s sodbami avtorjev prispevka ali kadar so bile sodbe anketirancev različne. Velik del izključenih besed so bili toponimi, ki pogosto ohranjajo arhaična stanja; še en pogost vzorec so bile tvorjenke s pripono -ica, katerih osnova je prikrita tvorjenka (npr. cesar-ica). Za potrebe čim boljšega nadzora nad posameznimi dejavniki sva torej iz vzorca odstranila vse primere, ki so ostali dvoumni. <br>Vsem besedam, ki so ostale v vzorcu, sva označila mesto naglasa (1, če je naglasno mesto na priponi, sicer 0) in frekvenco.<br>Stolpci v tabeli vsebujejo naslednje informacije:</p> <p>Stolpec A "številka": številka besede.<br>Stolpec B "beseda": citatna oblike besede.<br>Stolpec C "frekvenca na milijon besed": frekvenca besede v korpusu (na milijon besed).<br>Stolpec D "naglas": 1, če je naglas na prvem zlogu niza -ica (npr. pravíca), 0, če je naglas drugje.<br>Stolpec E "predvidljiv pomen: manjšalnica": 1, če ima -ica v besedi predvidljivi pomen manjšalnice (npr. mizica), 0 v vseh drugih primerih.<br>Stolpec F "vidno kompleksna tvorjenka": 1, če beseda razen pripone -ica in korena vsebuje še druge morfeme (npr. pis-ar-n-ica), 0, če beseda vsebuje le koren in pipono -ica.<br>Stolpec G "predvidljiv pomen: ženska ustreznica": 1, če ima beseda predvidljiv pomen ženske ustreznice (npr. članica), 0 v vseh drugih primerih.<br>Stolpec H "koren ni prost": 1, če koren ni prost (npr. opica), 0 v vseh drugih primerih.<br>Stolpec I "predvidoma izradikalne": 1, če je beseda predvidoma izradikalna, 0 v vseh drugih primerih.<br>Stolpec J "skupine besed": Povzema 5 skupin besed iz stoplpca J z oštevilčenjem.<br>Stolpec K "skupine besed – opisno": Besede so razdeljene na 5 skupin: <br>1) enostavna tvorjenka z idiosinkratičnim pomenom, vezani koren,<br>2) enostavna tvorjenka z idiosinkratičnim pomenom, prosti koren,<br>3) manjšalnica,<br>4) ženska ustreznica,<br>5) vidno kompleksna tvorjenka.</p>
Results outputs for "Beyond the Hype: Identifying and Analyzing Math Word Problem-Solving Challenges for Large Language Models"
<p>The provided files contain outputs generated by various Large Language Models (LLMs) for solving problems in the SVAMP dataset. Additionally, they include tagged statements of problems that LLMs incorrectly resolved.</p> <p>This repository includes the following two files:</p> <ul> <li>all_data.json --> Contains the generated samples for the SVAMP dataset.</li> <li>df_combined.pkl --> Contains the tagged SVAMP statements of problems that CodeLlama failed to resolve.</li> </ul>
Word frequencies and text data for the study of Statistical Laws in Complex Systems
<p>The files contain the data reported in the manuscript "Statistical Laws in Complex Systems" and in the repository <a href="https://github.com/edugalt/StatisticalLaws">https://github.com/edugalt/StatisticalLaws</a></p> <p>The files contain list of word frequencies and filtered word texts, computed from the data provided in <a href="https://gutenberg.org/">Project Gutenberg</a>, <a href="https://en.wikipedia.org/wiki/Wikipedia:Database_download">Wikipedia</a>, and the <a href="https://books.google.com/ngrams/">Google N-gram database.</a></p>
The School Leavers' Study Data Verification: A Cross Check for Typographical Errors, Omissions, and Dubious Word Interpretations
<p>This data set reflects a verification procedure to check for typographical errors, data omissions, and dubious word interpretations made by UK data curators. During the course of verification, the XML files contained in the UK Data Service repository were cross checked against the RTF files. In the course of verifying the documents, some changes were adopted to wording, as with essay 110, where the word "regretted" makes sense in the sentence as opposed to the suggested term "{regarted?}" noted in the XML file. Any changes during verification were performed by the close reading method, whereby contextual clues in the sentence guided the investigator to make a guess on the term intended by the original essay writer. When contexts were not sufficient to resolve meanings and spellings, original words and spellings were kept. Examples of former omissions within documents include essay number 148. The following statement was omitted from the XML file: "I retired not long ago after forty years with the same company. Life is very tedious now and I spend most of my time just sitting around thinking of the past." Essay 150 suffered the same problem: "life my first interview was nerve wracking.", exists in the RTF file, but was missing from the XML file. In cases where the RTF and XML files are in dispute over omissions, I have included the missing information from the RTF file into the data set. Place names have been edited so they read with standard British spellings. The data have been cleaned and standardized to American spellings, and this reflects the investigator's framework over the data. English turns of phrases were retained as much as possible. The investigator is aware that any transcription of historical data (e.g. be it from the curators' translations of writings on original printed pages to digital objects) includes adding personal perspective into the documents themselves. This is a limitation to the data cleaning process, and is here noted. Readers are invited to compare and contrast this verified data set with the original XML and RTF files.The second edition of the data deposited in the UK repository is cited as: </p> <p>Pahl, R.E. (2012). <em>School Leavers Study, 1978</em>. [data collection]. <em>2nd Edition. </em>UK Data Service. SN: 4867, <a href="http://doi.org/10.5255/UKDA-SN-4867-1">http://doi.org/10.5255/UKDA-SN-4867-1</a></p> <p>The original data are available under a Creative Commons-NonCommercial-ShareAlike 4.0 International license. The data may be accessed through the UK Data Service Qualibank.</p>
sentiment specific word embedding
<p>sentiment specific word embedding learned based on the approach described in the following paper:</p> <p>D. Tang, et al., Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification, ACL 2014</p> <p> </p>
CVL Database - An Off-line Database for Writer Retrieval, Writer Identification and Word Spotting
<p>The CVL Database is a public database for writer retrieval, writer identification and word spotting. The database consists of 7 different handwritten texts (1 German and 6 Englisch Texts). In total 310 writers participated in the dataset. 27 of which wrote 7 texts and 283 writers had to write 5 texts. For each text a rgb color image (300 dpi) comprising the handwritten text and the printed text sample is available as well as a cropped version (only handwritten). An unique id identifies the writer, whereas the Bounding Boxes for each single word are stored in an XML file.</p> <p>The CVL-database consists of images with cursively handwritten german and english texts which has been choosen from literary works. All pages have a unique writer id and the text number (separated by a dash) at the upper right corner, followed by the printed sample text. The text is placed between two horizontal separatores. Beneath the printed text individuals have been asked to write the text using a ruled undersheet to prevent curled text lines. The layout follows the style of the IAM database. The database was updated on 12/09/2013 since one writer ID (265/266) was wrong. The version number was changed to 1.1.</p> <p>Samples of the following texts have been used:</p> <ul> <li>Edwin A. Abbot – Flatland: A Romance of Many Dimension (92 words).</li> <li>William Shakespeare – Mac Beth (49 words).</li> <li>Wikipedia – Mailüfterl (73 words, under CC Attribution-ShareALike License).</li> <li>Charles Darwin – Origin of Species (52 words).</li> <li>Johann Wolfgang von Goethe – Faust. Eine Tragödie (50 words).</li> <li>Oscar Wilde – The Picture of Dorian Gray (66 words).</li> <li>Edgar Allan Poe – The Fall of the House of Usher (78 words).</li> </ul> <p>This database may be used for non-commercial research purpose only. If you publish material based on this database, we request you to include a reference to:</p> <p>Florian Kleber, Stefan Fiel, Markus Diem and Robert Sablatnig, <em>CVL-Database: An Off-line Database for Writer Retrieval, Writer Identification and Word Spotting</em>, In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 560-564, 2013.</p>
Mental imagery data from words
<p>Full database with norms and collected data for a corpus of descriptors varying in familiarity and vividness of imagery. </p>
An Empirical Study of Word Embedding Dimensionality Reduction
<p>In order to analyze the impact on model quality while reducing the number of dimensions, strictly controlled trainings of word embedding are performed on Wikipedia corpora of 170 languages. The specially designed word embedding training tool makes use of processed corpus and intermediate results to accelerate the training, while keeping the consistency of negative sampling.</p> <p>Tests of semantic relatedness show that, except for some corpora of poor scale, the margin gain from extra dimensions significantly decreases above 200.</p>
Training word vectors on text from The Physics Teacher using Word2Vec
<p>This notebook and dataset allows one to play around with word vectors trained on text from articles in the journal <a href="https://pubs.aip.org/aapt/pte">The Physics Teacher</a> published between 1963 (the start of publication) and 2020, around 15000 articles in total.</p> <p>The primary datafile, "TPT_word2vec_words_bigrams_V1.pkl", is a list of cleaned text from these articles. It contains a list, within which each paper is a sub-list. Each sentence in that paper is yet another sub-list which contains the words in that sentence in order. However, in the data cleaning process we have removed “stop words” (like if, and, but, etc.), punctuation, symbols, and numbers, as well as lowercased all words and combined words that frequently go together into one (like “high” and "school” to “high_school”). Here is an example of 3 sentences taken from a random paper: <br> <br>[['magnet', 'spin', 'tape', 'magnetize', 'strongly', 'time', 'pole', 'approach'],<br>['magnet', 'place', 'center', 'counterweight', 'period', 'magnetize', 'pulse', 'twice', 'long'],<br>['trial', 'tape', 'examine', 'sprinkle_iron', 'filing', 'length'], ... ]</p> <p>In the notebook, we create a set of word vectors from these sentences using the Word2Vec technique, first published by Mikolov et al. (2013):</p> <p>Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space (No. arXiv:1301.3781). arXiv. https://doi.org/10.48550/arXiv.1301.3781</p> <p>The notebook includes code for both loading in word vectors from a trained model (also included, "TPT_word2vec.model") and creating the same model from the TPT text dataset. Note that word vectors are randomly initialized, so we include a random seed to make this training replicable. Changing the seed will alter some of the results (although the changes seem fairly minor).</p> <p>With a trained model, we demonstrate some applications of word vectors: adding and subtracting meanings <br>(for example "experiment" - "uncertainty" = "demonstration") and visualizing low-dimensional representations of word vectors.</p> <p>In order to install the required packages, you can use the requirements.txt file. If using pip, run "pip install requirements.txt". Or, if using Anaconda (recommended), you can use "conda install --file requirements.txt". You will also need the software to run jupyter notebooks, which can be installed with Anaconda or pip.</p>
DWUG DE Resampled: Diachronic Word Usage Graphs for German
<p>This data collection contains diachronic Word Usage Graphs (WUGs) for German. Uses were sampled for the target words from the <a href="https://zenodo.org/doi/10.5281/zenodo.5543723">DWUG DE</a> dataset and from the same source corpora. DWUG DE Resampled can thus be seen as a small-scale replication of DWUG DE. Find a description of the data format, code to process the data and further datasets on the <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUGsite</a>.</p> <p>Please find more information on the provided data in the papers referenced below.</p> <h3>Reference</h3> <p>Dominik Schlechtweg, Pierluigi Cassotti, Bill Noble, David Alfter, Sabine Schulte im Walde, Nina Tahmasebi. <a href="https://aclanthology.org/2024.emnlp-main.796/">More DWUGs: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages</a>. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.</p> <p>Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, Barbara McGillivray. 2021. <a href="https://aclanthology.org/2021.emnlp-main.567/">DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages</a>. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.</p>
DiscoWUG: Discovered Diachronic Word Usage Graphs for German
<p>This data collection contains discovered diachronic Word Usage Graphs (WUGs) for German. Find a description of the data format, code to process the data and further datasets on the <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUGsite</a>.</p> <p>Note:</p> <ul> <li>The date given for each word use does not correspond to the exact date of the document from which the use was sampled but only to the midpoint of the respective time period (1800-1899, 1946-1990), as the exact date was not available in the SemEval corpora.</li> </ul> <p>Please find more information on the provided data in the papers referenced below.</p> <h3>Reference</h3> <p>Sinan Kurtyigit, Maike Park, Dominik Schlechtweg, Jonas Kuhn, Sabine Schulte im Walde. 2021. <a href="https://aclanthology.org/2021.acl-long.543/">Lexical Semantic Change Discovery</a>. Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing.</p> <p>Dominik Schlechtweg, Pierluigi Cassotti, Bill Noble, David Alfter, Sabine Schulte im Walde, Nina Tahmasebi. <a href="https://aclanthology.org/2024.emnlp-main.796/">More DWUGs: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages</a>. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.</p>
DWUG EN Resampled: Diachronic Word Usage Graphs for English
<p>This data collection contains diachronic Word Usage Graphs (WUGs) for English. Uses were sampled for the target words from the <a href="https://zenodo.org/doi/10.5281/zenodo.5544443">DWUG EN</a> dataset and from the same source corpora. DWUG EN Resampled can thus be seen as a small-scale replication of DWUG EN. Find a description of the data format, code to process the data and further datasets on the <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUGsite</a>.</p> <p>Please find more information on the provided data in the papers referenced below.</p> <h3>Reference</h3> <p>Dominik Schlechtweg, Pierluigi Cassotti, Bill Noble, David Alfter, Sabine Schulte im Walde, Nina Tahmasebi. <a href="https://aclanthology.org/2024.emnlp-main.796/">More DWUGs: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages</a>. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.</p> <p>Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, Barbara McGillivray. 2021. <a href="https://aclanthology.org/2021.emnlp-main.567/">DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages</a>. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.</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
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.