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30 results for “code mixing”
Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text
<p>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.</p>
A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
<p>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.</p>
Data and Code for Publication "Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3rd to 1st millennia BC Southwest Germany"
<p>Data and code for publication: H. Rathmann, S. Lismann, M. Francken, A. Spatzier, Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3<sup>rd</sup> to 1<sup>st</sup> millennia BC Southwest Germany. <em>Journal of Archaeological Science</em> 156: 105802. <a href="https://doi.org/10.1016/j.jas.2023.105802">https://doi.org/10.1016/j.jas.2023.105802</a></p> <p>The repository contains:</p> <ul> <li>“R code for FLEXDIST.txt”: R code for executing FLEXDIST, a tool to estimate inter-individual Mahalanobis-type distances, taking correlations among variables into account, applicable to multiple variable scales (nominal, ordinal, continuous, or any mixture thereof), accommodating missing values, and handling high-dimensional data. <strong>Please refer to the latest version of this repository for the most up-to-date R code</strong>.</li> <li>“data.csv”: Pre-processed dataset comprising 85 dental morphological features collected from 64 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany used for analysis.</li> <li>“complete dataset.xlsx”: Complete dataset comprising 199 dental morphological features collected from 144 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany.</li> </ul>
Supplementary data and codes for "Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures"
<p>This file contains the data and codes used in the paper</p> <p>“Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures”, S. Williams et al, 2022.</p> <p>It includes the data used in all the figures and supplementary figures, as well as the codes used for simulations and escape rate estimation. The data files are in .mat format. The codes are Matlab codes with the exception of the analytical estimate of the escape rate which is a Mathematica worksheet.</p>
Code-mixed Indonesian-Javanese-English Twitter Dataset
<p>This is a Twitter dataset for code-mixed language identification. The dataset contains mixed Indonesian, Javanese, and English words. </p>
MALAYALAM LANGUAGE (MIX CODE) Recipe channels Youtube Comments
<p>The dataset used for performing Text Classification is a combination of two different datasets scrapped from the comment section of two Youtube channels namely "Veen's Curryworld" and "Lekshmi Nair". The data is extracted using the YouTube API and contains two atrributes namely “text” and “label” where the former contains the comments and later contains the corresponding label. The comments are classified into 7 labels:</p> <p> </p> <p>- Label 1: Gratitude</p> <p>- Label 2: About the recipe</p> <p>- Label 3: About the video</p> <p>- Label 4: Praising</p> <p>- Label 5: Hybrid</p> <p>- Label 6: Undefined</p> <p>- Label 7: Suggestions and Queries</p> <p> </p> <p>The number of instances in each category is listed below:</p> <p> </p> <p>- Label 1: 484</p> <p>- Label 2: 396</p> <p>- Label 3: 300</p> <p>- Label 4: 362</p> <p>- Label 5: 249</p> <p>- Label 6: 2062</p> <p>- Label 7: 438</p>
Data and code for: Behavioral plasticity shapes participation in a mixed-species flocking community of birds
<p>Behavioral plasticity can modulate the costs and benefits of sociality, and thus may play a prominent role in mediating competition and facilitation during social interactions in mixed-species groups. However, investigations of assembly patterns of mixed-species groups typically treat species' behavioral attributes as static rather than dynamic features that can change in social contexts. We investigate four axes of behavioral plasticity that may modulate interaction within mixed-species groups: 1) species' selective preference for joining certain groups, 2) species' ability to flexibly change their behavior in response to groupmates' behavior, and 3) shifts and/or 4) expansions of species' niche occupancy when foraging with conspecifics versus when foraging with heterospecifics. We assess variation in these axes of behavioral plasticity in an Australian mixed-species avian community. All species had selective preferences for flocks of certain strata, and some flexibly matched their flockmates' foraging strata. Three species exhibited patterns of niche shift, and one species showed niche expansion. These findings suggest that species converge in strata in mixed-species flocks despite the potential for increased competition and emphasize that species can plastically react to changes in their social environment in numerous ways. Acknowledgment of such plasticity is likely integral to understanding the nuances of heterospecific interactions.</p>
Code to reproduce the figures in the paper 'Listener Preference for Different Reproduction Systems and Mixes in Popular Music'
<p>In this upload you find all the scripts and data you need in order to reproduce<br> the figure from the paper Wierstorf et al., "Listener Preference for Different<br> Reproduction Systems and Mixes in Popular Music" [1].</p> <p>Software Requirements<br> ---------------------</p> <p>For the statistic analysis you will need [python](https://www.python.org) and<br> [R](https://www.r-project.org). I have used python 3.5.2 and R 3.2.3 for<br> published analysis.</p> <p>Under R you need to install the [eba](https://cran.r-project.org/package=eba)<br> package, which implements the Bradley-Terry-Luce model. You can install it in R<br> by running `install.packages("eba")`.</p> <p>Under python you have to install pandas and numpy.</p> <p>Reproduce figures<br> -----------------</p> <p>All figures were plotted using gnuplot 5.0. Every figure folder has an<br> ``figXX.plt`` (replace ``XX`` by the figure number) file that you can execute<br> and you will get the resulting pdf file. For Fig. 5 up to Fig. 9, also a<br> ``figXX.sh`` file is provided, that will rerun the statistical analysis of the<br> data presented in the figures.</p> <p>References<br> ----------</p> <p>[1] H. Wierstorf, C. Hold, A. Raake, "Listener Preference for Different<br> Reproduction Systems and Mixes in Popular Music," J. Audio. Eng. Soc, submitted. <br> </p>
Data and code for: Behavioral plasticity shapes participation in a mixed-species flocking community of birds
Open the record for dataset details and reuse information.
Data and code from: Mixed infection, risk projection and misdirection: Interactions among pathogens alter links between host resources and disease
<p>A growing body of literature links resources of hosts to their risk of infectious disease. Yet most hosts encounter multiple pathogens, and projections of disease risk based on resource availability could be fundamentally wrong if they do not account for interactions among pathogens within hosts. Here, we measured infection risk of grass hosts (<i>Avena sativa</i>) exposed to three naturally-co-occurring viruses either singly or jointly (barley and cereal yellow dwarf viruses [B/CYDVs]: CYDV-RPV, BYDV-PAV, and BYDV-SGV) along experimental gradients of nitrogen and phosphorus supply. We asked whether disease risk (i.e., infection prevalence) differed in single versus co-inoculations, and whether these differences varied with rates and ratios of nitrogen and phosphorus supply. In single inoculations, the viruses did not respond strongly to nitrogen or phosphorus. However, in co-inoculations, we detected illustrative cases of 1) resource-dependent antagonism (RPV with increasing N; possibly due to competition), 2) resource-dependent facilitation (SGV with decreasing N:P; possibly due to immunosuppression), and 3) weak or no interactions within hosts (for PAV). Together, these within-host interactions created emergent patterns for co-inoculated hosts, with both infection prevalence and viral richness increasing with the combination of low nitrogen and high phosphorus supply. We demonstrate that knowledge of multiple pathogens is essential for predicting disease risk from host resources, and that projections of risk that fail to acknowledge resource-dependent interactions within hosts could be qualitatively wrong. Expansions of theory from community ecology theory may help anticipate such relationships linking host resources to diverse pathogen communities.</p>
Annotated Dataset for Bilingual Code-Mixed English-Malay Sentiment Analysis and Sarcasm Detection in Public Security Domain
<p>Tweets from X, and post with comment from TikTok was acquired <span>from 11 September until 21 September 2022</span>. Data from both platforms was merged and selected. Three annotators manually labelling the selected data for sentiment and sarcasm. Sentiment labels are ‘positive’, ‘negative’, and ‘neutral’. Sarcasm label is ‘sarcastic’ and ‘not sarcastic’. Majority voting is considered for each label. Language identification label produced for each data. </p>
Numerical model code, input files and output data for publication "Rapid mixing and exchange of deep-ocean waters in an abyssal boundary current"
<p>Contains numerical model data (code, input files, selected output, matlab diagnostic routines) to supplement publication ``Rapid mixing and exchange of deep-ocean waters in an abyssal boundary current'', by Naveiro Garabato and co-authors. All numerical model data, including any errors, is the responsibility of Sonya Legg. This data set will allow reproduction of simulations, and reproduction of diagnostics shown in plots in the above-referenced paper.</p>
Data and Code For : "The Hierarchical Structure of Organic Mixed Ionic Electronic Conductors and Its Evolution in Water."
<p>The repository contains the principal 4D-STEM datasets and code used in the paper:</p> <p>"The Hierarchical Structure of Organic Mixed Ionic Electronic Conductors and Its Evolution in Water."</p> <p> </p> <p>* The measured and analyzed material is p(g3T2).</p> <p>* The code can be adjusted and used for the analysis of other conjugated polymers.</p> <p>* It should be noted that newer py4DSTEM versions with additional capabilities were released since the paper was </p> <p>submitted. </p> <p> </p> <p><strong>Contents:</strong></p> <p><strong>1. 4D-STEM_DATA_OMIECs.zip : </strong></p> <p><strong>4D-STEM data : </strong></p> <ul> <li>Dry_CL_2p1.dm4. </li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 20, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li> Calibrant_Dry_CL_2p1.dm4</li> </ul> <p>scanned area [pixels]: 45x48, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 40, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Dry_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.7, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 20, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Dry_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 45x48, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 40, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Hydrated_Water_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 15 nm, CL: 2.7, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 25, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Hydrated_Water_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 50x50, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 46, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Hydrated_NaCl_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.7, c2 ca: 10 um, alpha 0.18 mrad, bin = 4,</p> <p>exposure time: 13 ms, spot size: 6, mono: 15, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Hydrated_NaCl_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 50x50, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.18 mrad, bin = 4,</p> <p>exposure time: 13 ms, spot size: 6, mono: 80, E(extraction voltage): 300 kV, temprature: LN.</p> <p> </p> <p><strong>2. Notebooks.zip : </strong></p> <p><strong>Jupyter Lab Notebooks : </strong></p> <ul> <li>1A_pg3T2_dry_LN_CL2p1.ipynb. </li> </ul> <p>Analysis of dry film using CL 2.1.</p> <p>Goes with datasets: Dry_CL_2p1.dm4 and Calibrant_Dry_CL_2p1.dm4. </p> <ul> <li>1A_pg3T2_dry_LN_CL2p7.ipynb</li> </ul> <p>Analysis of dry film using CL 2.7.</p> <p>Goes with datasets: Dry_CL_2p7.dm4 and Calibrant_Dry_CL_2p7.dm4. </p> <ul> <li>1B_pg3T2_water_LN_CL2p7.ipynb</li> </ul> <p>Analysis of hydrated in water film using CL 2.7.</p> <p>Goes with datasets: Hydrated_Water_CL_2p7.dm4 and Calibrant_Hydrated_Water_CL_2p7.dm4. </p> <ul> <li>1C_pg3T2_NaCl_LN_CL2p7. </li> </ul> <p>Analysis of hydrated in 0.1 M NaCl(aq) film using CL 2.7.</p> <p>Goes with datasets: Hydrated_NaCl_CL_2p7.dm4 and Calibrant_Hydrated_NaCl_CL_2p7.dm4.</p> <ul> <li>aux_func.py: </li> </ul> <p>Contains auxilary functions and required for running the other notebooks.</p>
Data and code from: Mixed infection, risk projection and misdirection: Interactions among pathogens alter links between host resources and disease
Open the record for dataset details and reuse information.
SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets
<p>There are 2 sub-tasks: sentiment analysis for Spanglish (Spanish-English) and for Hinglish (Hindi-English).</p> <p>The sentiment classes are Positive, negative, neutral. </p> <p>Hinglish dataset has 20k instances.</p> <p>Spanglish dataset has ~19k instances. </p> <p>Website: <a href="https://ritual-uh.github.io/sentimix2020/">https://ritual-uh.github.io/sentimix2020/</a></p>
Numerical model code, input files and output data for publication ``Mixing and Transformation in a Deep Western Boundary Current: a case study''
<p>Contains numerical model data (code, input files, selected output) to supplement publication ``Mixing and Transformation in a Deep Western Boundary current'', by Spingys and co-authors. All umerical model data, including any errors, is the responsibility of Sonya Legg. This data set will allow reproduction of simulations used in the above-referenced paper.</p>
The Available dataset of paper - A Holistic Approach to Automatic Mixed-Precision Code Generation and Tuning for Affine Programs
<p>Here are all publicly available datasets for the paper: A Holistic Approach to Automatic Mixed-Precision Code Generation and Tuning for Affine Programs.</p>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF), Convolutional Neural Network (CNN) and Spatial Transformer Networks (STN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Jaderberg et al., 2015; Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <p>[1] Jaderberg, M., Simonyan, K., Zisserman, A., et al. (2015). Spatial transformer networks. Advances in neural information processing systems, 28.</p> <p>[2] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</p> <div>[3] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
Code and data for "Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction" By Guan et al. Submitted to JGR Oceans.
<p>This repository contains the code and data for the machine learning analysis of "Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction”By Guan et al. Submitted to JGR Oceans.</p> <p>Specifically, this repository contains the following items:<br> (1) Codes for assessing the representation skill of the machine learning and linear regression (LR) methods. Three machine learning methods are considered: random forest (RF), back-propagation neural network (BP), and convolutional neural network (CNN).<br> (2) Codes for assessing the prediction skill of the machine learning and LR methods. <br> (3) Seasonal-mean and annual-mean input data to run these codes. <br> (4) The package needed to run the random forest code, i.e. the RF_MexStandalone-v0.02 program package from https://code.google.com/archive/p/randomforest-matlab/downloads .</p>
Code and data for: The evolution of light and vertical mixing across a phytoplankton ice-edge bloom
<p>This is a first release of the code (and the data and figures it produced) used by Randelhoff et al. for "The evolution of light and vertical mixing across a phytoplankton ice-edge bloom", Elementa: Science of the Anthropocene (2019).</p>
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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.