COVID-19 situation in Saudi Arabia collected from MOH daily reports https://twitter.com/SaudiMOH Explore the latest data on the COVID-19 situation and demographics in Saudi Arabia. This dataset provides valuable insights into the impact of the pandemic within the country. Follow data.kapsarc.org for timely data to advance energy economics research.

COVID-19 Saudi Arabia

This dataset contains Saudi Arabia Oil Database for 2002-2021. Data from Joint Organisations Data Initiative. Follow datasource.kapsarc.org for timely data to advance energy economics research.

Comprehensive GIS dataset covering roads, land use, hydrography and administrative boundaries in Saudi Arabia. Multiple shapefiles are available for download.

This dataset provides detailed information about the governorates of the Kingdom of Saudi Arabia, including administrative boundaries, geographical coordinates, and relevant metadata. It is intended for use in research, government planning, and policy-making.

The Gross Domestic Product (GDP) in Saudi Arabia was worth 1237.53 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Saudi Arabia represents 1.17 percent of the world economy. This dataset provides - Saudi Arabia GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Saudi Arabia water data from Food and Agricultural Organization of the United Nations

http://www.fao.org/nr/water/aquastat/data/query

There are too many variables in this dataset. So we have split this dataset into three files namely Demand Water Data, Supply Ground Water Data, Supply Surface Water Data.

Comprehensive dataset of 2 Open universities in Saudi Arabia as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset is about countries per year in Saudi Arabia. It has 64 rows. It features 4 columns: country, GDP, and population.

Introduction

Welcome to the Saudi Arabian Arabic General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Arabic speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Saudi Arabian Arabic communication.

Curated by FutureBeeAI, this 40 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Arabic speech models that understand and respond to authentic Saudi accents and dialects.

Speech Data

The dataset comprises 40 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Saudi Arabian Arabic. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Arabic speech and language AI applications:

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.6464 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.1355 and 0.015 (in million kms), corressponding to 20.9638% and 2.315% respectively of the total road length in the dataset region. 0.4959 million km or 76.7212% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0026 million km of information (corressponding to 0.5269% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

This dataset is about politicians in Saudi Arabia. It has 149 rows. It features 10 columns including birth date, death date, country, and gender.

This dataset is about exchanges in Saudi Arabia. It has 1 row. It features 9 columns including exchange symbol, number of stocks, city, and country.

The datasets include gravity measurements and anomalies and physical property measurements of the northern Harrat Rahat, Saudi Arabia.

Water Quantity in thousand Cubic Meters

This dataset is about athletes in Saudi Arabia. It has 10 rows. It features 2 columns including country.

This dataset is about countries per year in Saudi Arabia. It has 64 rows. It features 4 columns: country, region, and rural population.

Comprehensive dataset of 770 Public libraries in Saudi Arabia as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset contains Riyadh Air Quality for 2019 - 2020. Data from The General Authority of Meteorology & Environmental Protection. Follow datasource.kapsarc.org for timely data to advance energy economics research.

Saudi Arabia recorded a Government Budget deficit equal to 2.80 percent of the country's Gross Domestic Product in 2024. This dataset provides - Saudi Arabia Government Budget - actual values, historical data, forecast, chart, statistics, economic calendar and news.