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28 results for “Arabic Dataset”
A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19
<p><strong>Overview</strong></p> <p>This dataset is the repository for the following paper submitted to <em>Data in Brief</em>:</p> <p>Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. <em>Data in Brief</em> (submitted: December 2023).</p> <p>The <em>Data in Brief</em> article contains the supplement information and is the related data paper to:</p> <p>Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. <em>Journal of Arid Environments</em> (revision submitted: December 2023).</p> <p><strong>Description/abstract</strong></p> <p>The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant. </p> <p><strong>Folder structure</strong></p> <p>The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files: </p> <p>“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.</p> <p>“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.</p> <p>“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).</p> <p>“yield_productivity” contains .csv files of yield information for all countries listed above.</p> <p>“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).</p> <p>“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.</p> <p>“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”. </p> <p><strong>Code structure</strong></p> <p>1_MODIS_NDVI_hdf_file_extraction.R </p> <p><br>This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.</p> <p><br>2_MERGE_MODIS_tiles.R</p> <p><br>In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory__MODIS/merged").</p> <p><br>3_CROP_MODIS_merged_tiles.R</p> <p><br>Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS. <br>The repository provides the already clipped and merged NDVI datasets.</p> <p><br>4_TREND_analysis_NDVI.R</p> <p><br>Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.<br>To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique. </p> <p><br>5_BUILT_UP_change_raster.R</p> <p><br>Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022. </p> <p><br>6_POPULATION_numbers_plot.R</p> <p><br>For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration. </p> <p><br>7_YIELD_plot.R</p> <p><br>In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R. </p> <p><br>8_GLDAS_read_extract_trend</p> <p><br>The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection). <br>Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.<br>From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).<br>From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables. </p> <p><br>(9_workflow_diagramme) this simple code can be used to plot a workflow diagram and is detached from the actual analysis.</p> <p>___</p> <p>Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, and Funding acquisition: Michael Kempf</p> <p>___</p> <p><strong>Acknowledgements</strong></p> <p><span><span><span><span>I would like to thank three </span></span></span></span><span><span><span><span><span>anonymous</span></span></span></span></span><span><span><span><span> reviewers for their constructive comments and suggestions that sharpened the paper in the Journal of Arid Environments. I am particularly grateful to the Swiss National Science Foundation (SNSF/SNF) to fund my research project </span></span></span></span><span><span><span><span><em><span>EXOCHAINS - Exploring Holocene Climate Change and Human Innovations across Eurasia</span></em></span></span></span></span><span><span><span><span> at the University of Basel under grant number </span></span></span></span><span><span><span><span>TMPFP2_217358.</span></span></span></span></p> <p> </p> <p><span><span><span><span>__</span></span></span></span></p> <p><br>All data underlying the results of this article are publicly available on the internet:</p> <p>GLDAS Noah Land Surface Model L4 data: NASA's Earth Science Data Systems (ESDS) Program, https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 09th December 2023); </p> <p><br>Country borders: https://www.geoboundaries.org (last accessed 7th of March 2023) and Natural Earth https://www.naturalearthdata.com/ (last accessed 5th of December 2023);</p> <p><br>FAOstats (Food and Agriculture Organisation of the United Nations: https://www.fao.org/faostat/en/#data/QCL (last accessed 7th of March 2023);</p> <p><br>Global Human Settlement Layer datasets (GHSL): https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 7th of March 2023);</p> <p><br>Population development: <br>FAO, https://www.fao.org/countryprofiles/index/en/?iso3=JOR (last accessed 4th of March 2023); <br>the Worldbank, https://www.worldbank.org/en/home (last accessed: 04th of March 2023); <br>Worlddata.info, https://www.worlddata.info/asia/palestine/populationgrowth.php (last accessed 4th of March 2023);</p> <p><br>Water demand and population numbers (Tab. 1): https://www.fao.org/faostat/en/#data/OA; https://databank.worldbank.org/reports.aspx?source=world-development-indicators# (last accessed 13th of December 2023);</p> <p><br>MODIS: Earthdata server of the United States Geological Survey (USGS), MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006, https://lpdaac.usgs.gov/products/mod13q1v061/ (last accessed 7th of March 2023).</p> <p><br>Competing interests statement:<br>The author declares no conflict of interest.<br>The author has no relevant financial or non-financial interests to disclose.<br>Data availability: All data underlying the analyses are freely available on the internet and where applicable, sources are cited in the text.<br>Ethical approval: This article does not contain any studies with human participants performed by any of the authors.<br>Informed consent: This article does not contain any studies with human participants performed by any of the authors.</p>
AraHealth: A Dataset for Arabic Health-Related Advice Directed to the General Public on Twitter During the Early Spread of COVID-19 [Dataset]
<p>Health-related advice directed to the general public on Twitter provides insight into the use of social media during health emergencies. This paper describes our data collection, sampling, and analysis of 24 million tweets in Arabic in March and early April 2020. We make reference to a parallel dataset and analysis of tweets in English during the same period. The contribution of this paper is a description of our dataset, our coding process to indiciate tweets with health related advice, and our analysis and comparisons of the characteristics of the tweets with and without health-related advice. These contributions provide the basis for future research on semi-automated classifiers for health-related advice and efforts to reduce the spread of harmful health advice.</p>
SDADDS-Guelma : A Multi-purpose Dataset for Synthetic Degraded Arabic Documents
<h1><strong>SDADDS-Guelma : A Multi-purpose Dataset for Synthetic Degraded Arabic Documents </strong></h1> <h2><strong>Description:</strong></h2> <p>This is a partial release of the SDADDS-Guelma dataset.</p> <p>SDADDS-Guelma (Synthetic Degraded Arabic Document DataSet of the University of Guelma) is a database of synthetic noisy or degraded Arabic document images. It was created by Dr. Abderrahmane Kefali and his team to support research on preprocessing, analysis, and recognition of degraded Arabic documents, where having a large set of images for training and testing is essential. This dataset is made publicly available to researchers in the field of document analysis and recognition, with the hope that it will be useful and contribute to their research endeavors.</p> <p>In this first release of the dataset, 84 handwritten images and 120 printed images have been used, along with 25 images of historical backgrounds, forming a total of 26316 synthetic images of degraded Arabic documents along with their corresponding ground-truth files.</p> <p>This release is separated into two parts to facilitate upload and use: one for the handwritten documents and the second for the printed documents.</p> <h2><strong>Composition of the dataset:</strong></h2> <p>Each of the parts of the SDADDS-Guelma dataset is organized into directories as follows:</p> <ul> <li>TXT_Files: Contains texts in UTF-8 format. </li> <li>IMG: Contains images of printed and handwritten Arabic text constructed from the text files. </li> <li>Bin_IMG: Contains binary images corresponding to the original images. </li> <li>BG_IMG: Contains images of empty old document backgrounds used for the generation of synthetic historical document images. </li> <li>GT_Files: Contains XML annotation files corresponding to the text images.</li> <li>Degraded_IMG: This directory contains synthetically generated degraded images, separated into sub-directories based on noise types such as Local_Noise, Show_through, Rotation, Curvature, Comb_IMG, etc.</li> </ul> <h2><strong>Ground-truth information:</strong></h2> <p>Ground truth information is essential for a document dataset, as it annotates documents and represents their essential characteristics. Our dataset is designed to be a large-scale and multipurpose dataset. As such, our methodology ensures that ground truth information is provided at three levels: text level (character codes), pixel level (binary and cleaned image), and document physical structure and other annotation information level.</p> <ul> <li>Textual Ground Truth: these are identical to the original texts. </li> <li>Pixel-level ground truth: presented in the form of binary images.</li> <li>Ground truth at the document structure level: the structure of each document image, alongside the textual transcription of the words and PAWs, is recorded in a corresponding XML annotation file. The XML format utilized resembles that employed in similar works with adjustments made according to the specific characteristics of Arabic texts, including the presence of PAWs. </li> </ul> <p>Consequently, each original text image in our dataset is associated to an XML file detailing the entire ground truth and associated metadata. </p> <h3><em><strong>Structure of XML file:</strong></em></h3> <p>Each XML annotation file contains metadata about the document image and text content within the image, including the language, number of lines, and font attributes. It also provides detailed information about each text line, word, and Part of Arabic Words (PAWs), including their bounding boxes and textual transcriptions.</p> <p>Thus, each ground truth file takes the following form:</p> <pre><code><DOCUMENT imageName="PR1Kufi_bin.png" height="2631" width="1860" nbTextLines="8" language="Arabic" fontName="Kufi" fontSize="34"> <TEXTLINE id="0" nbWords="3" boundingBox="215,481,355,1379"> <WORD id="0" nbPAWs="2" boundingBox="217,1065,341,1379" transcription="خصائص"> <PAW id="0" nbCCs="2" boundingBox="217,1206,322,1379" transcription="خصا"> <CC id="0" nbPixels="4110" pixels="(217,1206,1206);(218,1206,1207);(219,1206,1208);(220,1206,1208);(221,1206,1211);(222,1206,1211);..."> </CC> <CC id="1" nbPixels="80" pixels="(263,1330,1336);(264,1329,1336);(265,1328,1337);(266,1328,1337);(267,1328,1337);(268,1328,1337);(269,1328,1337);...."> </CC> </PAW> .... </WORD> <WORD id="1" nbPAWs="2" boundingBox="215,817,338,1044" transcription="التفسير"> <PAW id="0" nbCCs="1" boundingBox="215,1030,322,1044" transcription="ا"> <CC id="0" nbPixels="1037" pixels="(215,1030,1030);(216,1030,1030);(217,1030,1031);..."></CC> .... </PAW> .... </WORD> </TEXTLINE> .... </DOCUMENT></code></pre> <h1><strong>Contact:</strong></h1> <p>Name: Dr. Abderrahmane Kefali<br>Affiliation: University of 8 May 1945-Guelma, Algeria<br>Email: kefali.abderrahmane@univ-guelma.dz</p>
Dvoice : An open source dataset for Automatic Speech Recognition on Moroccan dialectal Arabic
<p>Dialectal Voice is a community project initiated by AIOX Labs to facilitate voice recognition by Intelligent Systems. Today, the need for AI systems capable of recognizing the human voice is increasingly expressed within communities. However, we note that for some languages such as Darija, there are not enough voice technology solutions. To meet this need, we then proposed to establish this program of iterative and interactive construction of a dialectal database open to all in order to help improve models of voice recognition and generation.</p>
Arab-Andalusian music lyrics dataset
<p>The dataset contains lyrics for the songs in the <a href="https://musicbrainz.org/collection/142ea0d7-7fdf-4ea5-9b04-219f68023d01">Arab-Anadalusian music collection curated within the CompMusic project</a>, that belong to the nawbas "Isbahan", "Maya”, “Raml Maya”, “Gharibat al-Husayn”, “Hijaz Kabir”, “Hijaz Msharqi”, “Istihlal”, “Rasd”, and ”Rasd Dayl”.</p> <p>Lyrics are stored in two formats: as Tab Separated Values (TSV) files and as JSON files.</p> <p>Each file is identified by its MusicBrainz recording ID (MBID).</p> <p>The lyrics are stored both in their original Arabic script (folder 'original') and a romanized/transliterated version (folder 'transliterated') using the American Library of Congress (ALA-LC standard).</p> <p>Corresponding audio files are available from <a href="https://zenodo.org/record/1291776#.WyeYnZ9fjCI">the Arab-Andalusian music corpus</a>, as well as the Internet Archive URL included in the metadata file ('metadata.csv').</p> <p>For more information about the exact format and contents of the dataset, please consult the README provided in the archive.</p> <p>For more information, please refer to <a href="http://compmusic.upf.edu/corpora">http://compmusic.upf.edu/corpora</a>.</p>
Baseline Detection in Arabic-script Manuscripts Dataset (BADAM)
<p>A freely licensed dataset of 400 annotated Arabic-script manuscript pages for baseline detection. Refer to the <a href="https://hal.archives-ouvertes.fr/hal-02167164">article</a> for more information.</p> <p> </p> <p> </p>
ArabicSL-Net: A Benchmark Video Dataset for Arabic Words Sign Language
<p>The data was captured by mobile camera in four main organization namely Bank , Cafe , Hospital , and Train station. The ArabicSL-Net initially consists of 307 words recorded in approximately 30,000 videos. For each organization, we capture the most representative words that are used in those places. For Bank data, we have a total of 76 of words, while Cafe data contains 54 words. For Hospital, we collects videos for 102 words, and collects videos for 71 words in Train station.</p>
Towards Generalization of Machine Learning Models: An Arabic Sentiment Analysis Dataset
<p>This data set consists of approximately 1.64 Million Arabic tweets (shared by their IDs) posted from 2009 to 2020, and their corresponding sentiment using a three-point classification system of Positive, Negative and Neutral/Mixed. No specific locations and/or keywords were specified throughout the data collection to obtain variation in the dialects and topics represented within the dataset. It is important to note that any biases in the proposed dataset in relation to the dialects and/or topics discussed were unintentional.</p> <p><strong>Please use the following citation if you use this data in a paper:</strong></p> <blockquote> <p>Abdaljalil, S., Hassanein, S., Mubarak, H., & Abdelali, A. (2023). Towards Generalization of Machine Learning Models: A Case Study of Arabic Sentiment Analysis. <strong>Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 971-980.</strong></p> </blockquote> <p> </p> <p> </p>
Arabic Handwritten Legal Amount (AHLA) Dataset
<p>The AHLA dataset is collected by distributing an advanced designed report with Arabic native speakers. Our dataset contains two kinds of Arabic handwritten : <br>(1) Arabic word-level images that express legal amounts of bank cheques, including the colloquial words used in writing Arabic numbers. <br>(2) Arabic legal amount sentence images.</p> <p>The primary objective of compiling this comprehensive dataset is to furnish a diverse range of Arabic language samples. These samples are intended for training and testing systems capable of autonomously recognizing and comprehending handwritten legal amounts on financial documents. Subsequently, the aim is to convert these semantic expressions into their respective numeric currency totals, facilitating digital processing and banking operations.</p>
Dataset of Israeli-Jews, Palestinians, Israeli-Arabs, Americans and Cypriot students who played Fact Finders and PeaceMaker (examining game performance)
<p><strong>Serious Games, Players' Characteristics and Game Performance</strong></p> <p>The study examines the impact of players' gender, religiosity, political orientation, and nationality on game performance, focusing on two games for social impact revolving around intractable conflicts: PeaceMaker (Israeli-Palestinian conflict) and Fact Finders (Cyprus Conflict). Using questionnaires, we conducted a case study with 168 undergraduates voluntarily playing the two games and reporting their final scores. The participants included Israeli-Jew, Israeli-Arab, Palestinian, American, and Cypriot undergraduate students, differentiating between direct parties and third parties to the conflicts.</p>
Arabic Poetry Analysis Datasets
<p>These datasets are used in the context of metered Arabic poetry analysis. They contain a poetry corpus, poems patterns data, poems visualizations as images and the data used for clustering poetry compositions with related results.</p>
CLDF dataset derived from Ratcliffes's "Glottometrics of Arabic" from 2021
<p>Cite the source of the dataset as:</p> <blockquote> <p>Ratcliffe, Robert R. (2021): The glottometrics of Arabic. Language Dynamics and Change. 2021. DOI: 10.1163/22105832-01001100.</p> </blockquote>
TunSwitch: Code-Switched Tunisian Arabic Speech Dataset
<p>We developed a tool for collecting Tunisian dialect data, prompting users to record themselves reading provided phrases. We sourced sentences from Tunisiya. These sentences are consequently removed from the LM training corpus. 89 persons have participated leading to the collection of 2631 distinct phrases. This set will be called TunSwitch TO, ``TO" standing for Tunisian Only, as these sentences do not have non-Tunisian words. </p> <p>In response to the limited availability of paired Text-Speech Tunisian datasets with code-switching, we have built a corpus through meticulous manual annotation. Whenever encountered, French and English words are enclosed within "<>" tags, and left Tunisian words without any enclosing tags. While these tags have not been used in the proposed models, they allow to have language-usage statistics and may be useful for further approaches handling code-switching. The resulting set is released as TunSwitch CS, ``CS" standing for Code-Switched.</p> <p>The TunSwitch CS dataset samples come from a set of radio shows and podcasts, representing diverse topics and a large number of unique speakers. The audio are first segmented into chunks, prioritizing word integrity using the WebRTC-VAD algorithm for silence detection. Afterward, we used a Pyannote overlap detection model to remove overlapping speech sections. Then, a music detection model is employed to eliminate music-containing chunks that could disrupt ASR model accuracy. <br> </p> <p> </p>
TunSwitch: Code-Switched Tunisian Arabic Speech Dataset
<p>This folder contains the data used to develop and test the Tunisian Arabic Automatic Speech Recognition model developed in the following paper :</p> <p>A. A. Ben Abdallah*, A. Kabboudi, A. Kanoun, and S. Zaiem*, “Leveraging data collection and unsupervised learning for code-switched tunisian arabic automatic speech recognition”, Submitted to ICASSP 2024, vol. * : These two authors have contributed equally. 2023.</p> <p><br> It contains 4 zipped folders containing audio data :<br> - TunSwitchCS.zip : containing annotated code-switched data.<br> - TunSwitchTO.zip : containing annotated Tunisian-Only data.<br> - weakly_labeled_tn.zip : containing weakly-labeled (or unlabeled) audio data. Audios may contain code-switching, but the current weak labels do not.<br> - test_wavs.zip : contains annotated testing data, divided between a code-switched part and a tunisian-only part.</p> <p><br> It also contains textual data, used for language modelling, contained in TextData.zip. Finally it also contains a language-detailed annotation of TunSwitchCS in the language_annotation.zip file .</p> <p>More details about the data are available in the paper. The current table are in a SpeechBrain-friendly format, the column path is irrelevant and has to be changed according to your local setting. Please use the provided train-dev-test splits if you work with this dataset.</p> <p>Please cite the aforementioned paper if you use or refer to this dataset. You can find models trained and tested on this dataset <a href="https://huggingface.co/SalahZa">Here</a>. Space demos are also available. </p> <p>If you use or refer to this dataset, please cite : </p> <p>```</p> <p>@misc{abdallah2023leveraging,<br> title={Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition}, <br> author={Ahmed Amine Ben Abdallah and Ata Kabboudi and Amir Kanoun and Salah Zaiem},<br> year={2023},<br> eprint={2309.11327},<br> archivePrefix={arXiv},<br> primaryClass={eess.AS}<br> }</p> <p>```</p> <p><br> </p>
A benchmark Arabic dataset for question classification with AAFAQ taxonomy
Open the record for dataset details and reuse information.
An Arabic Dataset for Disease Named Entity Recognition with Multi-Annotation Schemes
<p>This is a novel data descriptor that provides the Arabic natural language processing community with a dataset dedicated to named entity recognition tasks for diseases. The dataset comprises more than 60 thousand words, which were annotated manually by two independent annotators using the inside-outside (IO) annotation scheme. To ensure the reliability of the annotation process, the inter-annotator agreements rate was calculated, and it scored 95.14\%. Due to the lack of research efforts in the literature dedicated for studying Arabic multi-annotation schemes, a distinguishing and a novel aspect of this dataset is the inclusion of six more annotation schemes that will bridge the gap by allowing the researchers to explore and compare the effects of these schemes on the performance of the Arabic named entity recognizers. These annotation schemes are IOE, IOB, BIES, IOBES, IE, and BI. Additionally, five linguistic features, including part-of-speech tags, stopwords, gazetteers, lexical markers, and the presence of the definite article, are provided for each record in the dataset.</p>
Additional Arabic Dataset for Jawi Writing
<p>This dataset consists of augmented Arabic characters, specifically curated and augmented for recognition in Jawi script, a variant of the Arabic script used historically in the Nusantara region. The augmentation process involved the creation of additional Arabic characters representing Jawi script, focusing on various forms and configurations to encompass the connected and disconnected shapes present in the Jawi script. These characters were augmented using digital image processing techniques and incorporated to enhance the available dataset for improved Jawi script recognition using deep learning methodologies. The dataset is an essential resource for advancing research in Jawi script recognition and furthering the understanding of this historical and culturally significant script. </p><p>Content: Additional Arabic characters augmented for Jawi script recognition. Images resized to 300x300 pixels for consistency. </p><p>Sources: </p><p>HMBD Arabic script dataset (Source: [1] H. M. Balaha, H. A. Ali, M. Saraya, and M. Badawy, "A new Arabic handwritten character recognition deep learning system (AHCR-DLS)," Neural Comput Appl, vol. 33, no. 11, pp. 6325–6367, Jun. 2021, doi: 10.1007/s00521-020-05397-2.) </p><p>AHAWP Arabic script dataset (Source: [2] M. A. Khan, "Arabic handwritten alphabets, words and paragraphs per user (AHAWP) dataset," Data Brief, vol. 41, p. 107947, Apr. 2022, doi: 10.1016/j.dib.2022.107947.) </p><p>HUCD Urdu script dataset (Source: [3] F. Mushtaq, M. M. Misgar, M. Kumar, and S. S. Khurana, "UrduDeepNet: offline handwritten Urdu character recognition using deep neural network," Neural Comput Appl, 2021, doi: 10.1007/s00521-021-06144-x and Source: [4] M. M. Misgar, F. Mushtaq, S. S. Khurana, and M. Kumar, "Recognition of offline handwritten Urdu characters using RNN and LSTM models," Multimed Tools Appl, vol. 82, no. 2, pp. 2053–2076, Jan. 2023, doi: 10.1007/s11042-022-13320-1.)</p>
GLARE: Google Apps Arabic Reviews Dataset
<p>This paper introduces GLARE an Arabic Apps Reviews dataset collected from Saudi Google PlayStore. It consists of 76M reviews, 69M of which are Arabic reviews of 9,980 Android Applications. We present the data collection methodology, along with a detailed Exploratory Data Analysis (EDA) and Feature Engineering on the gathered reviews. We also highlight possible use cases and benefits of the dataset.</p>
Arabic Fraudulent Online Advertisements Dataset
<p>The Arabic Fraudulent Online Advertisements Dataset (AFOAD) contains 914 images, of which 457 are used in fraudulent advertisements and 457 in legitimate advertisements. All images were collected manually, and the labelling process was carried out after verifying whether each advertisement was fraudulent or genuine. The images primarily contain text written in Modern Standard Arabic.</p>
Arabic words sign language video dataset
<p>The dataset consists of 3000 unaltered videos captured by five volunteers, with each volunteer performing 20 repetitions of 30 signs, resulting in approximately 600 videos per volunteer. These videos were recorded without stabilization tools, reflecting the prevalent use of smartphones with built-in cameras. They encompass diverse resolutions, locations, places, and backgrounds, providing a comprehensive representation of real-life scenarios.</p>
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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.