Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
397
datasets available to search
ShareScore release 0.7.1
Dataset results
397 results for “NLP”
Relations in the Biographical Dictionary of Republican China - Raw NLP Output
<p>This file provides the data on relations as extracted from the Biographical Dictionary of Republican China (BDRC) with CoreNLP. This is the raw output before any form of processing and cleaning.</p>
Counting Words That Count: NLP for exploring Romanian Parliament Transcripts
<p>The data is obtained by scraping the cdep.ro website and contains 500k+ instances of speech from the parliament podium from 1996 to 2019. (Up to 2001 only the Chamber of Deputies published transcripts, after jan. 2001 Senate data is also included.) <br> <br> Columns: </p> <p>'index' - incremented integer as row number in order of scraping</p> <p>'title', - title of the scraped page, usually contains the name of the chamber and the exact data</p> <p>'name', - the name of the speaker, preappended with Mr. or Mrs. </p> <p>'speech', - the content of the speech, </p> <p>'gender', - the gender of the speaker</p> <p>'url' - the url to the profile of the speaker (useful for extending the data)</p> <p> </p> <p>CDEPs2.csv - Contains all transcripts, prone to parsing errors. 100% of data.</p> <p>validated-1.csv - Consists of 99% of original data. Less than 1% dropped for convenience. Ready to use.</p>
Qugu Qiang texts and wordlists for NLP
<p>This is a collection of Qugu Qiang (Glottolog nort2722, ISO cng) vocabulary and texts for use in NLP. These materials are presented in the Qiang orthography: Rrmea Lehhrr. </p> <p>The wordlist forms are from Zhou's 2010 dictionary of Qugu. Including example sentences, the dictionary contains approximately 26,000 entries. Of these entries, there were approximately 360 forms that were deemed to have typographical errors. These have been removed in the document titled 'Zhou_2010_cleaned.csv'. The full forms exactly as they were printed in Zhou 2010 are given in the file 'Zhou_2010_original.csv'. The text 'Zhou_2010_text' is a short introduction to the dictionary project written in Rrmea Lehhrr.</p> <p>This collection also includes a set of traditional texts in Rrmea Lehhrr. The texts 'Huang_&_Zhou_2006' are from Huang & Zhou's 2006 descriptive grammar of Qugu with a set of annotated texts. These texts were converted from the international phonetic alphabet into the Qiang orthography.</p> <p>Additional texts come from the Chinese Qiang History and Culture volumes published in 2021, which contain some text in Rrmea Lehhrr. This is listed as 'Qiang_2021_text.txt' Sentences with greetings were taken from Huang, Zhou & Zhang 2014. This is a publication called '366 daily Qiang sentences'. Lastly, some sentences were taken from the Baidu Wiki page on Rrmea Lehhrr. https://baike.baidu.com/item/%E7%BE%8C%E6%96%87/830740 </p>
Helsinki-NLP/WMT16-test-enfi: WMT16 test set for Finnish-English
<p>This package provides the test set for the news translation task at WMT 2016 for Finnish-English and the machine-translated submissions by the University of Helsinki.</p>
IBM Watson's NLP model for annotating potato literature.
<p>Archived files(.zip) of the IBM Watson's domain specific supervised NLP model for annotating potato literature. Created with the help of <a href="https://www.ibm.com/watson/services/knowledge-studio/">Watson Knowledge Studio</a> </p>
Systematic Mapping Study in Legal NLP (Raw Data)
<p>Systematic Mapping Study in Legal NLP (Raw Data). It contains all papers processed, accepted, information extraction and all the steps taken, and which researchers reviewed what.</p>
Evaluation of a simple score-based Natural Language Processing (NLP) algorithm: Intermediary Result
<p>The intermediary result of the experiment "Evaluation of a simple score-based Natural Language Processing (NLP) algorithm".</p>
Evaluation of a simple score-based Natural Language Processing (NLP) algorithm: Category Confusion Matrix
<p>Resulting category confusion matrix for the experiment "Evaluation of a simple score-based Natural Language Processing (NLP) algorithm".</p>
Evaluation of a simple score-based Natural Language Processing (NLP) algorithm: Result
<p>The result for the experiment "Evaluation of a simple score-based Natural Language Processing (NLP) algorithm".</p>
A part-of-speech (POS) lexicon of Classical Tibetan for NLP
<p>This part-of-speech (POS) lexicon of Classical Tibetan was prepared in the course of the research project 'Tibetan in Digital Communication' (2012-2015) hosted at SOAS, University of London and funded by the UK's Arts and Humanities Research Council (grant code: AH/J00152X/1). The data for verbs comes from a digitized version of <em>A Lexicon of Tibetan Verb Stems as Reported by the Grammatical Tradition</em> (Munich: Bayerische Akademie der Wissenschaften, 2010) by Nathan W. Hill. Otherwise data comes from the manually part-of-speech tagged training data produced by the corpus and a few lexical items specifically added by hand to improve rule based tagging.</p>
NLP and machine learning to measure peace from news media
<p>"Hate speech" can mobilize violence and destruction. What are the characteristics of "peace speech" that reflect and support the social processes that maintain peace? In this study we used a data driven, machine learning approach to identify the words most associated with lower-peace versus higher-peace countries. Logistic regression and random forest classifiers were trained using five respected, traditional peace indices: Global Peace Index, Positive Peace Index, World Happiness Index, Fragile States Index, and Human Development Index. The feature inputs into the machine learning model were the word frequencies from the news media in each country and the output classifications were the level of peace in that country. The machine learning model was successful in properly classifying the level of peace from the news media in a country (both accuracy and F1: 96% - 100%). We also used that trained machine model to create a machine learning peace index that measured the level of peace in countries, including countries not in the training set, which correlated with the average of those five traditional peace indices (r-squared = 0.8349). Using the random forest feature importance method we found that the words in news media in lower-peace countries were characterized by words related to government, order, control and fear (such as government, state, law, security and court), while higher-peace countries were characterized by an increased prevalence of words related to optimism for the future and fun (such as time, like, home, believe and game).</p>
Decrypting cryptic crosswords: Semantically complex wordplay puzzles as a target for NLP
<p>Cryptic crosswords, the dominant crossword variety in the UK, are a promising target for advancing NLP systems that seek to process semantically complex, highly compositional language. Cryptic clues read like fluent natural language but are adversarially composed of two parts: a definition and a wordplay cipher requiring character-level manipulations. Expert humans use creative intelligence to solve cryptics, flexibly combining linguistic, world, and domain knowledge. In this paper, we make two main contributions. First, we present a dataset of cryptic clues as a challenging new benchmark for NLP systems that seek to process compositional language in more creative, human-like ways. After showing that three non-neural approaches and T5, a state-of-the-art neural language model, do not achieve good performance, we make our second main contribution: a novel curriculum approach, in which the model is first fine-tuned on related tasks such as unscrambling words. We also introduce a challenging data split, examine the meta-linguistic capabilities of subword-tokenized models, and investigate model systematicity by perturbing the wordplay part of clues, showing that T5 exhibits behavior partially consistent with human solving strategies. Although our curricular approach considerably improves on the T5 baseline, our best-performing model still fails to generalize to the extent that humans can. Thus, cryptic crosswords remain an unsolved challenge for NLP systems and a potential source of future innovation.</p>
VC1309 nlp-8(ok1799)I | 2010-12-16T16:05:45+00:00
<blockquote> <p>This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org</p> </blockquote> <ul> <li><b>preview link</b> : https://www.youtube.com/watch?v=ozKAnKvh_aw</li> <li><b>strain</b> : VC1309</li> <li><b>timestamp</b> : 2010-12-16T16:05:45+00:00</li> <li><b>gene</b> : nlp-8</li> <li><b>chromosome</b> : I</li> <li><b>allele</b> : ok1799</li> <li><b>strain_description</b> : nlp-8(ok1799)I</li> <li><b>sex</b> : hermaphrodite</li> <li><b>stage</b> : adult</li> <li><b>ventral_side</b> : clockwise</li> <li><b>media</b> : NGM agar low peptone</li> <li><b>arena</b> : <ul> <li><b>style</b> : petri</li> <li><b>size</b> : 35</li> <li><b>orientation</b> : away</li> </ul> </li> <li><b>food</b> : OP50</li> <li><b>habituation</b> : 30m wait</li> <li><b>who</b> : Laura Grundy</li> <li><b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.</li> <li><b>lab</b> : <ul> <li><b>name</b> : William R Schafer</li> <li><b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK</li> </ul> </li> <li><b>software</b> : <ul> <li><b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)</li> <li><b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219</li> <li><b>featureID</b> : @OMG</li> </ul> </li> <li><b>base_name</b> : nlp-8 (ok1799)I on food L_2010_12_16__16_05_45___1___8</li> <li><b>total time (s)</b> : 899.0</li> <li><b>frames per second</b> : 30.03</li> <li><b>video micrometers per pixel</b> : 4.36943</li> <li><b>number of segmented skeletons</b> : 26998</li> </ul>
RB2498 nlp-17(ok3461)IV | 2010-03-18T11:35:44+00:00
<blockquote> <p>This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org</p> </blockquote> <ul> <li><b>preview link</b> : https://www.youtube.com/watch?v=3l5iDKQyxK4</li> <li><b>strain</b> : RB2498</li> <li><b>timestamp</b> : 2010-03-18T11:35:44+00:00</li> <li><b>gene</b> : nlp-17</li> <li><b>chromosome</b> : IV</li> <li><b>allele</b> : ok3461</li> <li><b>strain_description</b> : nlp-17(ok3461)IV</li> <li><b>sex</b> : hermaphrodite</li> <li><b>stage</b> : adult</li> <li><b>ventral_side</b> : anticlockwise</li> <li><b>media</b> : NGM agar low peptone</li> <li><b>arena</b> : <ul> <li><b>style</b> : petri</li> <li><b>size</b> : 35</li> <li><b>orientation</b> : away</li> </ul> </li> <li><b>food</b> : OP50</li> <li><b>habituation</b> : 30m wait</li> <li><b>who</b> : Laura Grundy</li> <li><b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.</li> <li><b>lab</b> : <ul> <li><b>name</b> : William R Schafer</li> <li><b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK</li> </ul> </li> <li><b>software</b> : <ul> <li><b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)</li> <li><b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219</li> <li><b>featureID</b> : @OMG</li> </ul> </li> <li><b>base_name</b> : nlp-17 (ok3461)IV on food R_2010_03_18__11_35_44___8___5</li> <li><b>total time (s)</b> : 898.145</li> <li><b>frames per second</b> : 25.9067</li> <li><b>video micrometers per pixel</b> : 4.20853</li> <li><b>number of segmented skeletons</b> : 19644</li> </ul>
VC1309 nlp-8(ok1799)I | 2010-03-18T10:56:34+00:00
<blockquote> <p>This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org</p> </blockquote> <ul> <li><b>preview link</b> : https://www.youtube.com/watch?v=CCcV7SP1Hl4</li> <li><b>strain</b> : VC1309</li> <li><b>timestamp</b> : 2010-03-18T10:56:34+00:00</li> <li><b>gene</b> : nlp-8</li> <li><b>chromosome</b> : I</li> <li><b>allele</b> : ok1799</li> <li><b>strain_description</b> : nlp-8(ok1799)I</li> <li><b>sex</b> : hermaphrodite</li> <li><b>stage</b> : adult</li> <li><b>ventral_side</b> : anticlockwise</li> <li><b>media</b> : NGM agar low peptone</li> <li><b>arena</b> : <ul> <li><b>style</b> : petri</li> <li><b>size</b> : 35</li> <li><b>orientation</b> : away</li> </ul> </li> <li><b>food</b> : OP50</li> <li><b>habituation</b> : 30m wait</li> <li><b>who</b> : Laura Grundy</li> <li><b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.</li> <li><b>lab</b> : <ul> <li><b>name</b> : William R Schafer</li> <li><b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK</li> </ul> </li> <li><b>software</b> : <ul> <li><b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)</li> <li><b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219</li> <li><b>featureID</b> : @OMG</li> </ul> </li> <li><b>base_name</b> : nlp-8 (ok1799)I on food R_2010_03_18__10_56_34___8___3</li> <li><b>total time (s)</b> : 898.629</li> <li><b>frames per second</b> : 25.7069</li> <li><b>video micrometers per pixel</b> : 4.20853</li> <li><b>number of segmented skeletons</b> : 19593</li> </ul>
RB1396 nlp-20(ok1591)IV | 2010-03-18T11:55:39+00:00
<blockquote> <p>This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org</p> </blockquote> <ul> <li><b>preview link</b> : https://www.youtube.com/watch?v=Q5x4tecHpbM</li> <li><b>strain</b> : RB1396</li> <li><b>timestamp</b> : 2010-03-18T11:55:39+00:00</li> <li><b>gene</b> : nlp-20</li> <li><b>chromosome</b> : IV</li> <li><b>allele</b> : ok1591</li> <li><b>strain_description</b> : nlp-20(ok1591)IV</li> <li><b>sex</b> : hermaphrodite</li> <li><b>stage</b> : adult</li> <li><b>ventral_side</b> : clockwise</li> <li><b>media</b> : NGM agar low peptone</li> <li><b>arena</b> : <ul> <li><b>style</b> : petri</li> <li><b>size</b> : 35</li> <li><b>orientation</b> : away</li> </ul> </li> <li><b>food</b> : OP50</li> <li><b>habituation</b> : 30m wait</li> <li><b>who</b> : Laura Grundy</li> <li><b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.</li> <li><b>lab</b> : <ul> <li><b>name</b> : William R Schafer</li> <li><b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK</li> </ul> </li> <li><b>software</b> : <ul> <li><b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)</li> <li><b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219</li> <li><b>featureID</b> : @OMG</li> </ul> </li> <li><b>base_name</b> : nlp-20 (ok1591)IV on food L_2010_03_18__11_55_39___8___6</li> <li><b>total time (s)</b> : 898.647</li> <li><b>frames per second</b> : 25.9067</li> <li><b>video micrometers per pixel</b> : 4.20853</li> <li><b>number of segmented skeletons</b> : 19367</li> </ul>
FX1880 nlp-14(tm1880)X | 2010-03-18T13:14:07+00:00
<blockquote> <p>This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org</p> </blockquote> <ul> <li><b>preview link</b> : https://www.youtube.com/watch?v=EXTYCi5WbB0</li> <li><b>strain</b> : FX1880</li> <li><b>timestamp</b> : 2010-03-18T13:14:07+00:00</li> <li><b>gene</b> : nlp-14</li> <li><b>chromosome</b> : X</li> <li><b>allele</b> : tm1880</li> <li><b>strain_description</b> : nlp-14(tm1880)X</li> <li><b>sex</b> : hermaphrodite</li> <li><b>stage</b> : adult</li> <li><b>ventral_side</b> : anticlockwise</li> <li><b>media</b> : NGM agar low peptone</li> <li><b>arena</b> : <ul> <li><b>style</b> : petri</li> <li><b>size</b> : 35</li> <li><b>orientation</b> : away</li> </ul> </li> <li><b>food</b> : OP50</li> <li><b>habituation</b> : 30m wait</li> <li><b>who</b> : Laura Grundy</li> <li><b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.</li> <li><b>lab</b> : <ul> <li><b>name</b> : William R Schafer</li> <li><b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK</li> </ul> </li> <li><b>software</b> : <ul> <li><b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)</li> <li><b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219</li> <li><b>featureID</b> : @OMG</li> </ul> </li> <li><b>base_name</b> : nlp-14 (tm1880)X on food R_2010_03_18__13_14_07___8___10</li> <li><b>total time (s)</b> : 899.052</li> <li><b>frames per second</b> : 25.974</li> <li><b>video micrometers per pixel</b> : 4.20853</li> <li><b>number of segmented skeletons</b> : 18813</li> </ul>
RB1340 nlp-1(ok1469)X | 2010-03-18T12:16:55+00:00
<blockquote> <p>This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org</p> </blockquote> <ul> <li><b>preview link</b> : https://www.youtube.com/watch?v=TFAtvjZxqqQ</li> <li><b>strain</b> : RB1340</li> <li><b>timestamp</b> : 2010-03-18T12:16:55+00:00</li> <li><b>gene</b> : nlp-1</li> <li><b>chromosome</b> : X</li> <li><b>allele</b> : ok1469</li> <li><b>strain_description</b> : nlp-1(ok1469)X</li> <li><b>sex</b> : hermaphrodite</li> <li><b>stage</b> : adult</li> <li><b>ventral_side</b> : anticlockwise</li> <li><b>media</b> : NGM agar low peptone</li> <li><b>arena</b> : <ul> <li><b>style</b> : petri</li> <li><b>size</b> : 35</li> <li><b>orientation</b> : away</li> </ul> </li> <li><b>food</b> : OP50</li> <li><b>habituation</b> : 30m wait</li> <li><b>who</b> : Laura Grundy</li> <li><b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.</li> <li><b>lab</b> : <ul> <li><b>name</b> : William R Schafer</li> <li><b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK</li> </ul> </li> <li><b>software</b> : <ul> <li><b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)</li> <li><b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219</li> <li><b>featureID</b> : @OMG</li> </ul> </li> <li><b>base_name</b> : nlp-1 (ok1469)X on food R_2010_03_18__12_16_55___8___7</li> <li><b>total time (s)</b> : 897.871</li> <li><b>frames per second</b> : 25.7732</li> <li><b>video micrometers per pixel</b> : 4.20853</li> <li><b>number of segmented skeletons</b> : 19250</li> </ul>
FX1908 nlp-2(tm1908)X | 2010-03-12T12:45:35+00:00
<blockquote> <p>This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org</p> </blockquote> <ul> <li><b>preview link</b> : https://www.youtube.com/watch?v=6QW7bBhlbpA</li> <li><b>strain</b> : FX1908</li> <li><b>timestamp</b> : 2010-03-12T12:45:35+00:00</li> <li><b>gene</b> : nlp-2</li> <li><b>chromosome</b> : X</li> <li><b>allele</b> : tm1908</li> <li><b>strain_description</b> : nlp-2(tm1908)X</li> <li><b>sex</b> : hermaphrodite</li> <li><b>stage</b> : adult</li> <li><b>ventral_side</b> : anticlockwise</li> <li><b>media</b> : NGM agar low peptone</li> <li><b>arena</b> : <ul> <li><b>style</b> : petri</li> <li><b>size</b> : 35</li> <li><b>orientation</b> : away</li> </ul> </li> <li><b>food</b> : OP50</li> <li><b>habituation</b> : 30m wait</li> <li><b>who</b> : Laura Grundy</li> <li><b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.</li> <li><b>lab</b> : <ul> <li><b>name</b> : William R Schafer</li> <li><b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK</li> </ul> </li> <li><b>software</b> : <ul> <li><b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)</li> <li><b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219</li> <li><b>featureID</b> : @OMG</li> </ul> </li> <li><b>base_name</b> : nlp-2 (tm1908)X on food R_2010_03_12__12_45_35___8___9</li> <li><b>total time (s)</b> : 899.34</li> <li><b>frames per second</b> : 25.641</li> <li><b>video micrometers per pixel</b> : 4.21045</li> <li><b>number of segmented skeletons</b> : 17774</li> </ul>
VC1063 nlp-15(ok1512)I | 2010-03-17T10:36:26+00:00
<blockquote> <p>This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org</p> </blockquote> <ul> <li><b>preview link</b> : https://www.youtube.com/watch?v=rWRDWOE7ipc</li> <li><b>strain</b> : VC1063</li> <li><b>timestamp</b> : 2010-03-17T10:36:26+00:00</li> <li><b>gene</b> : nlp-15</li> <li><b>chromosome</b> : I</li> <li><b>allele</b> : ok1512</li> <li><b>strain_description</b> : nlp-15(ok1512)I</li> <li><b>sex</b> : hermaphrodite</li> <li><b>stage</b> : adult</li> <li><b>ventral_side</b> : anticlockwise</li> <li><b>media</b> : NGM agar low peptone</li> <li><b>arena</b> : <ul> <li><b>style</b> : petri</li> <li><b>size</b> : 35</li> <li><b>orientation</b> : away</li> </ul> </li> <li><b>food</b> : OP50</li> <li><b>habituation</b> : 30m wait</li> <li><b>who</b> : Laura Grundy</li> <li><b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.</li> <li><b>lab</b> : <ul> <li><b>name</b> : William R Schafer</li> <li><b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK</li> </ul> </li> <li><b>software</b> : <ul> <li><b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)</li> <li><b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219</li> <li><b>featureID</b> : @OMG</li> </ul> </li> <li><b>base_name</b> : nlp-15 (ok1512) on food R_2010_03_17__10_36_26___8___2</li> <li><b>total time (s)</b> : 898.962</li> <li><b>frames per second</b> : 25.8398</li> <li><b>video micrometers per pixel</b> : 4.20853</li> <li><b>number of segmented skeletons</b> : 19146</li> </ul>
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.