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411 results for “Arabs”
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>
Arab-Andalusian metadata set
<p>This repository contains metadata of the <a href="https://zenodo.org/record/1291776#.WzOqSq2B28o">Arab-Andalusian corpus</a> collected in the CompMusic project composed of 164 concert recordings (overall playable time more than 125 hours).</p> <p>Metadata involves the following information: title, form, mizan, nawba and tab of all recordings of the corpus, saved in separate json files. The corresponding MusicBrainz collection is available at <a href="https://musicbrainz.org/collection/142ea0d7-7fdf-4ea5-9b04-219f68023d01">this link</a>.</p> <p>The lyrics for the recordings are available from <a href="https://zenodo.org/record/1291904#.Wyea5Bx9jCI">the Arab-Andalusian music lyrics dataset</a>.</p> <p>For more information, please refer to <a href="http://compmusic.upf.edu/corpora">http://compmusic.upf.edu/corpora</a></p> <p> </p>
National Checklists 2017: United Arab Emirates Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from United Arab Emirates collected using effechecka and geonames polygons
National Checklists 2019: United Arab Emirates Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from United Arab Emirates collected using effechecka and geonames polygons
ٱلْمَمْلَكَة ٱلْعَرَبِيَّة ٱلسُّعُوْدِيَّة.الحناكية. جبل العهين. Arabic inscription offering a confession and quoting Qur'ān 10.90. Photo mosaic.
<p>Kingdom of Saudi Arabia, al-Ḥanākīya, <a href="https://doi.org/10.5281/zenodo.5594399">Jabal al-'Hayn</a>. Arabic inscription offering the confession of Rāfi<sup>c</sup> ibn <sup>c</sup>Alī, quoting <a href="https://quran.com/10">Qur'ān 10</a>: <a href="https://quran.com/10:90?font=v1&translations=131%2C20">90</a> and paraphrasing <a href="https://quran.com/3">Qur'ān 3</a>: <a href="https://quran.com/3:67?font=v1&translations=131%2C20">67</a>. Photo mosaic.</p> <p><strong>Coordinates: </strong>24°59'21"N 40°38'27"E. <a href="https://en.wikipedia.org/wiki/Jabal_al-%CA%BFHayn">Wikipedia</a>.</p> <ul> <li><a href="https://maxvanberchem.org/fr/thesaurus-d-epigraphie-islamique">Thesaurus d'Epigraphie Islamique</a> : <a href="http://www.epigraphie-islamique.uliege.be/Thesaurus/User/EpigraphyDisplay.aspx?id=14070&pan=1&st=ARABIE-%u1e24an%u0101kiyya+(al-)*&sc=5">FICHE 14070</a>; Donner (<a href="https://doi.org/10.5281/zenodo.4575259">1984</a>): no. W1.</li> </ul>
Virome diversity of Hyalomma dromedarii ticks collected from camels in the United Arab Emirates
<p>Viruses are important components of the microbiome of ticks. Ticks are capable of transmitting several serious viral diseases to humans and animals. Hitherto, the composition of viral communities in <em>Hyalomma dromedarii</em> ticks associated with camels in the United Arab Emirates (UAE) remains unexplored. The purpose of this study was to characterize the RNA virome diversity in male and female <em>H. dromedarii</em> ticks collected from camels in Al Ain, UAE.<strong> </strong>We collected ticks, extracted and sequenced RNA, using Illumina (NovaSeq 6000) and Oxford Nanopore (MinION).<strong> </strong>From the total generated sequencing reads, 180,559 (~0.35 %) and 197,801 (~0.34 %) reads were identified as virus-related reads in male and female tick samples respectively. Taxonomic assignment of the viral sequencing reads was accomplished based on bioinformatic analyses. Further, viral reads were classified into 39 viral families. Poxiviridae, Phycodnaviridae, Phenuiviridae, Mimiviridae, and Polydnaviridae were the most abundant families in the tick viromes. Notably, we assembled the genomes of three RNA viruses, which were placed by phylogenetic analyses in clades that included the Bole tick virus.<strong> </strong>Overall, this study attempts to elucidate the RNA virome of ticks associated with camels in the UAE and the results obtained from this study improve the knowledge of the diversity of viruses in <em>H. dromedarii</em> ticks.</p>
Figure 4. Callytron monalisa. A in Increasing knowledge of the entomological fauna of the United Arab Emirates and the role of private collections
Figure 4. Callytron monalisa. A living specimen of Callytron monalisa, one of two specimens collected by B. Howarth in 2007 on Reem Island, Abu Dhabi. This rare species is known only from the Iranian side of the Arabian Gulf (photograph by D. Gardner).
Figure 2. The jewel beetle Pseudocastalia arabica. A in Increasing knowledge of the entomological fauna of the United Arab Emirates and the role of private collections
Figure 2. The jewel beetle Pseudocastalia arabica. A jewel beetle from the JAAENHG Insect Collection; this specimen was originally collected by J. N. B. Brown in Abu Dhabi and kept in the ENHG Collection there (photograph by B. Howarth).
Figure 3 in Increasing knowledge of the entomological fauna of the United Arab Emirates and the role of private collections
Figure 3. The flat-headed borer jewel beetle, Capnodis excisa. This specimen was collected by J. N. B. Brown in the Madam area, UAE; originally in the Abu Dhabi Collection, it is now housed in the JAAEN- HG Insect Collection in Al Ain (photograph by B. Howarth).
Figure 6 in Increasing knowledge of the entomological fauna of the United Arab Emirates and the role of private collections
Figure 6. An unidentified fly from the family Mydidae (photographed by D. Gardner). Whilst Mydidae had been observed in neighbouring Oman, before 2006 no records were known from the UAE.
Figure 1. All 4.683 in Mapping the terrestrial reptile distributions in Oman and the United Arab Emirates
Figure 1. All 4.683 records of terrestrial reptiles in Oman and the UAE. Although the coverage of records remains patchy, there are sufficient records to provide useful distribution information.
Figure 6 in Mapping the terrestrial reptile distributions in Oman and the United Arab Emirates
Figure 6. Non-overlapping and contiguous distributions of two species of sand geckos Stenodactylus slevini and S. leptocosymbotes.
Figure 4 in Mapping the terrestrial reptile distributions in Oman and the United Arab Emirates
Figure 4. The distributions of two endemic lacertids in the genus Omanosaura. Both species are restricted to the Hajar mountains and their distributions overlap broadly.
Hassan #1 Arabic and French transcripts of description and comments on his recording in Darb al-Ahmar, Cairo (Egypt), 25-10-2011
<p>« Mics in the Ears » binaural experiment in Cairo (Egypt): Vincent Battesti & Nicolas Puig, social anthropologists, asked inhabitants of Cairo megapolis in Egypt to record the surrounding urban sounds during one of their daily journeys (without the researcher), equipped with binaural microphones and GPS device. Participants have recorded in different Cairo neighbourhoods, and are themselves from different generations, social and economic backgrounds, and different genders.</p> <p>These text files (pdf and Word) are the transcripts (original in Arabic and translation in French) of the audio file of the description and comments one of them gave us a posteriori when listening to his/her own route he/she recorded with binaural mics. See https://vbat.org/article831</p>
Jarāʾid: A chronology of Arabic periodicals (1800-1929)
<p>This is the first release of Project Jarāʾid's bibliographic data set for all periodicals published globally in Arabic (or Arabic plus additional languages) between 1800 and 1929. The data set is the basis for the website <a href="https://projectjaraid.github.io/">https://projectjaraid.github.io/</a>. All relevant descriptive information can be found in the README and the main TEI XML file.</p> <p>We thank Hala Auji, Philippe Chevrant, Marina Demetriadou, Lamia Eid, Stacy Fahrenthold, Till Grallert, Rana Issa, Nicole Khayat, Peter Magierski, Leyla von Mende, Adam Mestyan, Christian Meier, Daniel Newman, Geoffrey Roper, Sinai Rusinek, Philip Sadgrove, Ola Seif, and Rogier Visser for contributing data.</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>
Romanised Arabic Chat Data
<p>Chat room conversations in Romanised Arabic (44.83%), including code switching in English (4.68%) and French (9.44%). The Romanised Arabic form primarily denoted the Levantine dialect (Lebanese, Egyptian) among 10 participants. These conversations were recorded under natural observation on 10th June 2015 in intervals of 90 minutes. All instances of data collection occurred between midday-2pm, Lebanese local time; they are listed in consecutive order.</p> <p>Conversations were collected on the instant messaging website, www.icq.com, and specifically, the chat room server titled ‘#icq-lebanon’. As per the terms outlined in the website company policy on 10th June 2015, no consent was required to use the chat room entries or other information from the online users, who constitute the participants of this study.</p> <p>Any data which marked personally identifiable information has been excluded, unless considered relevant for the purpose of the linguistic analysis.</p>
Fig.4 in A new species of the genus Phytocoris (Heteroptera: Miridae) from the United Arab Emirates
Fig.4. Phytocoris arwa Linnavuori & Al-Safadi, 1993: A – male head and pronotum in dorsal view; B – male head in lateral view; C – pygophore in dorsal view; D – right paramere; E-F – left paramere in different views; G – aedeagus. After LINNAVUORI & AL-SAFADI (1993).
Fig. 5. Phytocoris semicrux Wagner, 1969 in A new species of the genus Phytocoris (Heteroptera: Miridae) from the United Arab Emirates
Fig. 5. Phytocoris semicrux Wagner, 1969: A – male head and pronotum in dorsal view; B – male head in lateral view; C – male antennal segment 1; D – pygophore in dorsal view; E – right paramere; F – spiculum; G – left paramere. After WAGNER (1969).
Fig. 1 in A new species of the genus Phytocoris (Heteroptera: Miridae) from the United Arab Emirates
Fig. 1. Phytocoris sweihanus sp. nov., female holotype. A – habitus; B – dorsal view of head and pronotum.
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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.