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985 results for “Classics”
Cadenza Challenge (CAD2): databases for rebalancing classical music task
<h1>Cadenza</h1> <p>Please, cite CadenzaWoodwind as</p> <blockquote> <p><strong>Gerardo Roa-Dabike , Trevor J. Cox , Alex J. Miller , Bruno M. Fazenda , Simone Graetzer , Rebecca R. Vos , Michael A. Akeroyd , Jennifer Firth , William M. Whitmer , Scott Bannister , Alinka Greasley , Jon P. Barker , The Cadenza Woodwind Dataset: Synthesised Quartets for Music Information Retrieval and Machine Learning, Data in Brief (2024), doi: https://doi.org/10.1016/j.dib.2024.111199</strong></p> </blockquote> <p>This is the training and validation data for the rebalancing classic music task from the <a href="https://cadenzachallenge.org/">Second Cadenza Machine Learning Challenge (CAD2).</a></p> <p>The Cadenza Challenges are improving music production and processing for people with a hearing loss. According to The World Health Organization, 430 million people worldwide have a disabling hearing loss. Hearing aid users report several issues when listening to music, including distortion in the bass, difficulties in perceiving the full range of the music, especially high-frequency pitches, and a tendency to miss the impact of quieter parts of compositions [1]. In a pilot study, we found giving listeners sliders to allow them to rebalance different instruments in a classical music ensemble was desirable.</p> <p>Overview of files:</p> <ol> <li>CadenzaWoodwind. Synthesized dataset of small ensembles of woodwind instruments for training and validation.</li> <li>EnsembleSet_Mix_1. A subset of the synthesised <a href="../records/6519024">EnsembleSet [7]</a> for training and validation (Mix_1 render).</li> <li>Real Data for Tuning: <a href="../api/records/12664932/draft/files/Stereo_Reverb_Real_Data_For_Tuning.zip/content" target="_blank" rel="noopener noreferrer">Stereo_Reverb_Real_Data_For_Tuning.zip</a>.</li> <li>metadata.zip contains audiograms, scene details, target gains and compressor settings.</li> </ol> <p>The audio files are in FLAC format in the .zip archives. The json files contain metadata.</p> <p>More details below.</p> <p> </p>
DV-QKD in coexistence with classical channels in multicore fiber
<p>The simulation results demonstrate the possibility of simultaneous transmission of the classical and quantum channels in the same multicore fiber using space division multiplexing. Low crosstalk between the fiber cores helps to isolate the T12 DV-QKD channel (that propagates in one of the fiber cores) from the detrimental effect of the spontaneous Raman scattering of the classical channels propagating in both directions in each other core. The simulation illustrates how QBER and secret key rate degrade with an increase of the classical channel power for the specified crosstalk level between the fiber cores. The simulations examine the system over a wide range of classical channel powers (which may exceed typical values) to find an upper bound under which secure communication is still possible.</p>
PsPM-DoxMem2: Pupil, SCR, ECG, EMG and respiration measurement in a classical pavlovian discriminant delay fear conditioning task, reminder under doxycycline/placebo, retention and re-learning
<p>This dataset includes eyetracker, skin conductance response (SCR), electrocardiogram (ECG), respiration and electromyogram (EMG, only relevant for retention phase) measurements. Also included are CS and US information, keypress responses and keypress response times for 79 healthy participants (40 males and 39 females aged 24.8+/-4.9 years). Participants underwent a classical (Pavlovian) discriminant delay fear conditioning task with 1 CS- and 2 CS+ (50% reinforcement), were reminded of one CS+ one week later under either doxycycline or placebo, and were tested in a retention/extinction and re-learning task another week later. CS were isoluminant coloured triangles. US consisted of 0.5 s square electric pulses with 0.2 ms duration and 500 Hz frequency. SOA between the CS onset and US was 3.5 s. CS and US co-terminated. Before the fear conditioning task, participants completed several questionnaires. During the retention/extinction phase, an auditory startle probe (ST) and no US was delivered 3.5 s after CS onset via headphones (102 dB, 40 ms duration with 2 ms on- and offset ramp). In an immediately following re-learning phase, the ST was omitted and the CS reinforced with the same schedule as during acquisition. The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p> <p> </p>
Dataset for the paper "Designs in finite classical polar spaces"
<div> <div><span>This repository contains the designs from the paper </span><span>"Designs in finite classical polar spaces" by Michael Kiermaier, Kai-Uwe Schmidt, and Alfred Wassermann, </span><span>in Designs, Codes, and Cryptography. </span></div> <div> </div> <div> <div> <div><span>All designs in this repository are simple designs.</span></div> <br> <div><span>The file format is JSON. Each file contains the designs in a fixed finite polar space </span><span>for a pair of parameters t and k. </span></div> <div> </div> <div><span>The file README.md contains more detailed information about the file format.</span></div> </div> </div> </div>
Discrimination of Classical and Atypical BSE by a Distinct Immunohistochemical PrPSc Profile
<p>Bovine spongiform encephalopathy (BSE) is a fatal neurodegenerative disease in cattle belonging to the group of transmissible spongiform encephalopathies. Hallmark of the disease is the accumulation of the pathological prion protein (PrPSc) in the brain. Classical BSE (C-type) and two atypical BSE forms (L- and H-type) are known, and can be discriminated by biochemical characteristics. The data presented here underline that immunohistochemistry can also be used to identify type-specific PrPSc profiles which can be used for discriminatory purposes. For this brain samples from 21 cattle, intracerebrally inoculated with C-, H-, and L-type BSE, were used as well as three orally C-type BSE infected animals. Using six brain regions distinct lesion (H&E staining) and PrPSc profiles were determined. While the neuroanatomical distribution of lesions and the PrPSc accumulation were highly consistent between the groups, the topographic and cellular PrPSc profile revealed characteristic pattern for the different BSE types.</p>
Hybrid quantum-classical machine learning for generative chemistry and drug design: Generated molecules
<p>Deep generative chemistry models emerge as powerful tools to expedite drug discovery. How- ever, the immense size and complexity of the structural space of all possible drug-like molecules pose significant obstacles, which could be overcome with hybrid architectures combining quantum computers with deep classical networks. As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer. The size of the proposed model was small enough to fit on a state-of-the-art D-Wave quantum annealer and allowed training on a subset of the ChEMBL dataset of biologically active compounds. Finally, we generated 2331 novel chemical structures with medicinal chemistry and synthetic accessibility properties in the ranges typical for molecules from ChEMBL. The pre- sented results demonstrate the feasibility of using already existing or soon-to-be-available quantum computing devices as testbeds for future drug discovery applications.</p>
Classical Syriac Genitive Constructions
<p>The data is sourced from the 92-page historical manuscript, <em>The Chronicle of Joshua the Stylite</em> [5th c. A.D] (Wright, 1882). A total of 116 tokens are included, collected via a method of random sampling.</p> <p>Each token corresponds to a full genitive construction: appositional (including compound nominals), periphrastic or double. Each token is transcribed in Syriac, transliterated, translated and glossed for word-class, noun features and genitive type.</p> <p>The dataset is not quality-checked; manuscript source pages for each token is provided to facilitate ease of validatation.</p>
Classical Philology Syndication Feed Collection
<p>This feed collection allows gathering information on recent publications, scientific books and journals, in the field of Classical Philology and related disciplines. The OPML file (https://en.wikipedia.org/wiki/OPML) can be imported in programs with news aggregation functionality, f.e. Thunderbird.</p>
Raw frequency data: Thoughts on "Reliable" Learner's Vocabularies for Classical and Literary Chinese
<p>This dataset includes the raw frequency counts (classical_chinese_learners_vocabularies_raw_frequencies.zip) used in the article Thoughts on “Reliable” Learner’s Vocabularies for Classical and Literary Chinese. </p> <p>Corpus I – Micheal Loewe (1993)’s <em>Early Chinese Texts</em><br> Corpus II – Official Histories (zhengshi 正史)<br> Corpus III Six Novels (xiaoshuo 小說), as defined in Hsia 1968</p> <p>The download includes one folder per corpus, structured as follows:</p> <ul> <li>xx_corpus.csv > list of texts and sources / used versions, token and type counts</li> <li>xx_freq_1-1.csv > unigram / character frequencies and counts</li> <li>xx_freq_1-4.csv > 1 to 4 character word frequencies and counts, "words" according to Hanyu da cidian 漢語大詞典 (Luo 1986–1994))</li> <li>xx_freq_2-4.csv > 2 to 4 character words</li> </ul> <p>Additionally, pca_zhengshi_vs_loewe_vs_xiaoshuo.html is an interactive version of the Principal Component Analysis (PCA) presented in the article, texts from the three corpora are represented using the 1.000 most frequent 1–4 character combinations from the dataset.</p>
Classical Tibetan Word Embeddings
<p>Classical Tibetan word embeddings trained with FastText based on the 2018 version of the BDRC corpus, a segmented version of which is available on Zenodo:</p> <p>Meelen, Marieke, & Roux, Élie. (2020). The Annotated Corpus of Classical Tibetan (ACTib) - Version 2.0 (Segmented & POS-tagged) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3951503</p> <p>This is the first version trained with default FastText settings (100D) for a pilot study on Chinese-Tibetan crosslinguistic Semantic Textual Similarity:</p> <p>Felbur, Rafal, Marieke Meelen & Paul Vierthaler (2022), 'Crosslinguistic Semantic Textual Similarity of Buddhist Chinese and Classical Tibetan' in <em>Journal of Open Humanities Data</em>.</p> <p>This research was done with generous funding from the Open Philology project. This project (running 2018–2022) is funded by the European Research Council (ERC) under the Horizon 2020 program (Advanced Grant agreement No 741884). It is based at the Leiden University Institute for Area Studies.</p>
CLASSIC and OE02 Cell Detections
<p>This dataset consists of cell detections, taken from digital whole slide images from two datasts. The method of obtaining these cell detections as well as the sources of the datasets are described in more detail below. </p> <p>For both datasets, the 2D coordinates of the nuclear centroid locations and cell features were extracted using the HeteroGenius MIM Cell-Analysis Add-On (HeteroGenius, Leeds, UK). The model used was a UNet-based cell detector and classifier trained on over 50,000 manually annotated HE-stained cells. 12 nuclear features were extracted. These consisted of length (micrometers), elongation, angle, and the probabiltiy of the clel being one of the following 9 cell types: tumour cell, lymphoycte, granulocyte, plasma cell, fibroblast, smooth muscle cell, endothelial cell, normal epithelium, or other. </p> <p>One dataset consisted of cell detections from 950 haematoxylin eosin (HE) stained 3mm tissue microarray (TMA) cores from the resection specimen of gastric cancer patients from the CLASSIC trial (Noh et al., 2014). Manual annotations were made of different tissue classes for the purpose of supervised node classification using a graph neural network. These ground truth tissue classes can be found in the column "class" within the csv files. The tissue classes identified were cancer, lymphocyte aggregates, muscle, and stroma. For cells without a class, these were labelled as "notype". Ony 260 TMA cores contained these tissue annotations. A list of these files can be found in the file 'annotated_classic_cores.csv'</p> <p>The second dataset consisted of cell detections from 45 HE-stained endoscopic biopsies from oesophageal cancer patients from the OE02 trial (Girling et al. 2002). The ground truth target classes in this dataset were "tumour" and "not tumour". Exact annotations of the tumour areas were available from a previous study (Hale et al., 2016) and non-tumour areas were annotated manuyally for a seperate study. </p> <p> </p> <p>Noh, S. H., Park, S. R., Yang, H.-K., Chung, H. C., Chung, I.-J., Kim, S.-W., Kim, H.-H., Choi, J.-H., Kim, H.-K., Yu, W., Lee, J. I., Shin, D. B., Ji, J., Chen, J.-S., Lim, Y., Ha, S., & Bang, Y.-J. (2014). Adjuvant capecitabine plus oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): 5-year follow-up of an open-label, randomised phase 3 trial. The Lancet Oncology, 15(12), 1389–1396. https://doi.org/10.1016/s1470-2045(14)70473-5</p> <p>Girling, D. J., Bancewicz, J., Clark, P. I., Smith, D. B., Donnelly, R. J., Fayers, P. M., Weeden, S., Girling, D. J., Hutchinson, T., Harvey, A., & Lyddiard, J. (2002). Surgical resection with or without preoperative chemotherapy in oesophageal cancer: A randomised controlled trial. Lancet, 359(9319), 1727–1733. https://doi.org/10.1016/S0140-6736(02)08651-8</p> <p>Hale, M. D., Nankivell, M., Hutchins, G. G., Stenning, S. P., Langley, R. E., Mueller, W., West, N. P., Wright, A. I., Treanor, D., Hewitt, L. C., Allum, W. H., Cunningham, D., Hayden, J. D., & Grabsch, H. I. (2016). Biopsy proportion of tumour predicts pathological tumour response and benefit from chemotherapy in resectable oesophageal carcinoma - Results from the UK MRC OE02 trial. Oncotarget, 7(47), 77565–77575. https://doi.org/10.18632/oncotarget.12723</p>
Hachidaishu Classical Japanese Poetic Vocabulary Dataset
<h1>Hachidaishu classical Japanese poetic vocabulary dataset</h1> <h2>Hilofumi Yamamoto, Ph.D. (Institute of Science Tokyo)</h2> <h2>Bor Hodošček, D.Engineering (The University of Osaka)</h2> <h3>Data offset</h3> <p>Example: # 1 Kokinshu</p> <pre><code>01:000001:0001 A00 BG-01-1630-01-0100 02 年 年 とし 年 とし 01:000001:0001 A10 BG-01-1911-03-1800 02 年 年 とし 年 とし 01:000001:0002 A00 BG-08-0061-07-0100 61 の の の の の 01:000001:0003 A00 BG-01-1770-01-0300 02 内 内 うち 内 うち 01:000001:0004 A00 BG-08-0061-05-0100 61 に に に に に 01:000001:0005 A00 BG-01-1624-02-0100 02 春 春 はる 春 はる 01:000001:0006 A00 BG-08-0065-07-0100 65 は は は は は 01:000001:0007 A00 BG-02-1527-01-0102 47 き 来 く 来 き 01:000001:0008 A00 BG-03-1200-02-0900 74 に ぬ ぬ に に 01:000001:0008 A10 BG-09-0010-01-0101 74 に ぬ ぬ に に 01:000001:0008 A20 BG-09-0010-03-0200 74 に ぬ ぬ に に 01:000001:0009 A00 BG-09-0010-04-0300 74 けり けり けり けり けり 01:000001:0010 B00 BG-01-1950-14-0100 02 一とせ 一年 ひととせ 一年 ひととせ 01:000001:0010 C00 BG-01-1950-01-0300 19 一 一 いち 一 いち 01:000001:0010 C01 BG-01-1630-01-0100 02 年 年 とし 年 とし 01:000001:0011 A00 BG-08-0061-10-0100 61 を を を を を 01:000001:0012 A00 BG-01-1642-02-0100 02 こそ 去年 こぞ 去年 こぞ 01:000001:0013 A00 BG-08-0061-04-0100 61 と と と と と 01:000001:0014 A00 BG-08-0065-14-0100 65 や や や や や 01:000001:0015 A00 BG-02-3120-01-0100 47 いは 言ふ いふ 言は いは 01:000001:0016 A00 BG-03-3012-03-2600 74 ん む む む む 01:000001:0016 A10 BG-09-0010-02-0102 74 ん む む む む 01:000001:0017 B00 BG-01-1641-02-0100 02 ことし 今年 ことし 今年 ことし 01:000001:0017 C00 BG-03-1000-01-0100 57 この この この この この 01:000001:0017 C01 BG-01-1630-01-0100 02 年 年 とし 年 とし 01:000001:0018 A00 BG-08-0061-04-0100 61 と と と と と 01:000001:0019 A00 BG-08-0065-14-0100 65 や や や や や 01:000001:0020 A00 BG-02-3120-01-0100 47 いは 言ふ いふ 言は いは 01:000001:0021 A00 BG-03-3012-03-2600 74 ん む む む む 01:000001:0021 A10 BG-09-0010-02-0102 74 ん む む む む </code></pre> <h3>A line consists of 7 columns separated by spaces.</h3> <pre><code>01:000001:0007 A00 BG-02-1527-01-0102 47 き 来 く 来 き </code></pre> <ul> <li>1st column "01:000001:0007" consists of 3 fields: 1) anthology, 2) number of poem, and 3) serial ID of the token. The anthology ID indicates respectively: 01..Kokinshu, 02..Gosenshu, 03..Shuishu, 04..Goshuishu, 05..Kin'yoshu, 06..Shikashu, 07..Senzaishu, and 08..Shinkokinshu.</li> <li>2nd column indicates type of token: A type is a single token; B type is a compound token; C type is a breakdown of B type. A00 indicates a single token; A01 indicates a single token and has another meaning; B00 indicates a compound token; B01 indicates a compound token which has another meaning; C00 indicates the first element of the B00/B01.. breakdown; C01 indicates the second element of the B00/B01.. breakdown.</li> <li>3rd column "BG-02-1527-01-0102": classification ID based on semantic categories according to Bunruigoihyo (Yamazaki et al. 2014).</li> <li>4th column indicates a Chasen POS number.</li> <li>5th column indicates surface form: a form appears in literary works.</li> <li>6th column indicates lemma in kanji writing.</li> <li>7th column indicates lemma in kana writing.</li> <li>8th column indicates conjugated form in kanji writing form.</li> <li>9th column indicates conjugated form in kana writing form.</li> </ul> <h2>Reference</h2> <ol> <li> <p>Yamamoto, Hilofumi (2007) Thesaurus of Japanese Poetic Vocabulary Based on the Semantic Classifications Chart, The 13th Annual Symposium for Database of the Humanities, 1-8, The Association for Database of the Humanities, Osaka.</p> </li> <li> <p>Yamamoto, Hilofumi (2009) Thesaurus for the Hachidaishu (ca. 905-1205) with the classification codes based on semantic principles, Nihongo no Kenkyu / Studies in the Japanese Language, 46-52, Society for Japanese Linguistics, 5, 1, ISSN1349-5119.</p> </li> <li> <p>Yamamoto, Hilofumi (2021) Hachidaishu vocabulary dataset, Zenodo, version 1.0.1, <a href="https://doi.org/10.5281/zenodo.4744170">https://doi.org/10.5281/zenodo.4744170</a></p> </li> <li> <p>Yamazaki, Makoto and Kashino, Wakako and Uchiyama, Kiyoko and Sunaoka, Kazuko, and Tajima, Ikudo and Yamamoto, Hilofumi and Han, Yoo-Sik and Seol, Geun-Su (2014) Bunruigoihyo zouhokaiteiban" e no anoteishion: kihongi no kettei (in Japanese), Keiryo Kokugo gakkai dai 58 kai taikai yokoshu, pp. 7--12.</p> </li> <li> <p><a href="http://kotenseki.nijl.ac.jp/biblio/200007092">国文学研究資料館二十一代集</a></p> </li> <li> <p><a href="http://codh.rois.ac.jp/pmjt/book/200007092/">二十一代集 DOI: 10.20730/200007092</a>: ROIS-DS人文学オープンデータ共同利用センター 新日本古典籍総合データベース(200007093)</p> </li> </ol> <p><!-- @dataset{yamamoto_hilofumi_2021_4735848, author = {Yamamoto, Hilofumi}, title = {Hachidaishu vocabulary dataset}, month = may, year = 2021, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.4735848}, url = {https://doi.org/10.5281/zenodo.4735848} } @Article{yamagen2009ae, author = {Yamamoto, Hilofumi}, title = {Thesaurus for the Hachidaishu (ca.\,905--1205) with the classification codes based on semantic principles}, journal = {Nihongo no Kenkyu / {S}tudies in the Japanese Language}, pages = {46--52}, OPTpublisher = {Society for Japanese Linguistics}, year = {2009}, volume = {5}, number = {1}, OPTedition = {ISSN1349-5119}, OPTmonth = {}, OPTnote = {}, OPTannote = {}, OPTlocation = {}, OPTmemo = {} } @InCollection{yamagen2007de, author = {Yamamoto, Hilofumi}, title = {Thesaurus of Japanese Poetic Vocabulary Based on the Semantic Classifications Chart}, year = {2007}, booktitle = {The 13th Annual Symposium for Database of the Humanities}, pages = {1--8}, publisher = {The Association for Database of the Humanities}, address = {Osaka}, OPTedition = {}, OPTmonth = {2007.12}, OPTmemo = {} }</p>
Greek toponyms collected from classical literature
<p>The toponyms in this dataset are derrived from Montanari, Franco. <em>The Brill Dictionary of Ancient Greek</em>. Boston, MA: Brill, 2015 and Kiesling, John Brady, and Aikaterini Laskaridis Founation. “ToposText.” Gazetteer. <em>ToposText Web Version 3.0</em>, 2019. <a href="https://topostext.org/">https://topostext.org/</a>.</p>
FIG. 7 in The use of animals in Northern Mesoamerica, between the Classic and the Conquest (200-1521 AD). An attempt at regional synthesis on central Mexico
FIG. 7. — Proportion of animals targeted by hunting (grey), garden-hunting (black) or both methods (white) in each sites.
FIG. 6 in The use of animals in Northern Mesoamerica, between the Classic and the Conquest (200-1521 AD). An attempt at regional synthesis on central Mexico
FIG. 6. — Hierarchical clustering of the: A, taxa; and B, sites analysed in the Canonical Analysis. Abbreviations: Aq., Aquatic animals; Can., Canids; Oth., Miscelanaous taxa; Exo., exotic animals; Ov., white-tailed deer; Art., other artiodactyls; Fel., felids; Sc., small carnivores; Lag., lagomorpha; Com., commensal animals; Tur, turkey; Rap., prey birds; Tiz., Tizayuca; Calix., Calixtlahuaca; Bar-Clas., Barajas Classic/Early Postclassic occupation; Bar-PCR, Barajas Late Postclassic occupation; E.S., El Salitre; Ang., Angamuco.
FIG. 5 in The use of animals in Northern Mesoamerica, between the Classic and the Conquest (200-1521 AD). An attempt at regional synthesis on central Mexico
FIG. 5. — Distribution of CA scores on: A, C1xC2 axes; and B, C1xC3 axes. Taxa bubbles surfaces represent their actual inertia in each plan. Sites and sup- plemental individuals are normalized to 1. Abbreviations: Aq., Aquatic animals; Can., Canids; Com., commensal animals; Ov., white-tailed deer; Tur, turkey.
Classical Syriac annotated lexemes
<p>31,972 lexemes in Classical Syriac and their inflectional forms annotated according to Sylak-Glassman (2016).</p>
A part-of-speech (POS) tagged corpus of Classical Tibetan
<p>This part-of-speech (POS) tagged corpus 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). For a description of the tag set see Garrett et al. 2014. and Garrett et al. 2015. This corpus includes the <em>Mdzaṅs blun</em> (9th century, canonical), the <em>Bu ston chos ḥbyuṅ</em> (13th century, ecclesiastical history), the <em>Mi la ras paḥi rnam thar</em> and <em>Mar paḥi rnam thar</em> (15th century, biography).</p>
Eggshell colour differences in a classic example of coevolved eggshell mimicry
<p>Avian brood parasitism is a model system for understanding coevolutionary arms races, and the great reed warbler (<em>Acrocephalus arundinaceus</em>, hereafter 'warbler') and its parasite the common cuckoo (<em>Cuculus canorus</em>, hereafter 'cuckoo') are prime examples of this coevolutionary struggle. Here, warblers select for egg colour mimicry by rejecting poorly matched cuckoo eggs. Contrary to long-held assumptions, recent work showed that warblers tend to reject lighter and browner eggs but tended to accept darker and bluer eggs rather than basing rejection decisions solely on perceived colour differences (i.e., the degree of mimicry). This counter-intuitive, colour-biased rejection behaviour would select for bluer and darker cuckoo eggs, but would only be adaptive if cuckoos were consistently lighter and browner than warbler eggs. Therefore, we tested whether warbler eggs were consistently bluer and darker than the cuckoo eggs. To do so, we re-analysed eggshell reflectance spectra of warblers and the cuckoos that parasitized them in the Czech Republic. As expected, we found that warbler eggs were significantly bluer and darker than the cuckoo eggs at the population level. Thus, we demonstrate imperfect mimicry in a long-coevolved cuckoo host-race and provide insights for exploring the coevolutionary interactions among hosts and their brood parasites.</p>
A Global Sensitivity Analysis of Parameter Uncertainty in the CLASSIC Model
<p>Input scripts, datasets and outputs used for the GSA methods. Please read the README and workflow files.</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.