Computational and Communication Science and Engineering

Permanent URI for this collectionhttps://dspace.nm-aist.ac.tz/handle/123456789/3880

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    Fowlpox forecasting multimodal dataset from northern Tanzania
    (Zenodo, 2026-04-27) Almasi, Kabeya
    This dataset contains weather data, epidemiology data, vaccination data, and seasonality data for fowlpox incidence in northern Tanzania.
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    Dataset: Machine Learning Dataset for Poultry Diseases Diagnostics - PCR annotated
    (2023-12-23) Machuve, Dina; Nwankwo, Ezinne; Lyimo, Emmanuel; Maguo, Evarist; Munisi, Charles
    The dataset of poultry disease diagnostics was annotated using Polymerase Chain Reaction (PCR). Polymerase Chain Reaction (PCR) is a molecular biology technique for rapid diagnostics. We gathered both the fecal images and fecal samples from layers, cross and indigenous breeds of chicken from poultry farms in Arusha and Kilimanjaro regions in Tanzania between September 2020 and February 2021. Each fecal sample collected was coded to its corresponding image during data collection. PCR method is used for detection and identification of pathogens through amplification of DNA sequences unique to the pathogen. We used existing primers from literature to amplify the target DNA/RNA on the poultry fecal samples for PCR. The targets were Coccidiosis, Newcastle disease and Salmonella. We used the primers for PCR diagnostics at the molecular laboratory of the Nelson Mandela African Institution of Science and Technology (NM-AIST). The fecal samples were stored at -80 degrees celsius. The PCR diagnostics were conducted using reagents and kits from Zymo Research and the protocol is summarized in these five stages: 1. DNA sample loading 2. DNA extraction 3. Amplification; 4. Quantification and 5. Detection.
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    Dataset: A Labeled Dataset of Healthy and Diseased Maize from Tanzania
    (Zenodo, 2025-07-04) Mduma, Neema; Laizer, Hudson; Kiriba, Deodatus
    The maize images dataset was developed to support research in diagnosing major maize diseases and improving crop yields. Sponsored by GrowFurther and conducted by researchers at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in collaboration with the Tanzania Agriculture Research Institute (TARI), the project produced a labeled dataset featuring healthy maize, Maize Lethal Necrosis and Maize Streak Virus. The dataset is designed for image classification and object detection tasks.
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    Dataset: A Labeled Dataset of Healthy and Diseased Common Beans from Tanzania
    (Zenodo, 2025-07-01) Mduma, Neema; Laizer, Hudson; Kiriba, Deodatus
    The common bean images dataset was developed to advance research in diagnosing key bean diseases and boosting crop productivity. Sponsored by GrowFurther and carried out by researchers at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in collaboration with the Tanzania Agriculture Research Institute (TARI), the project produced a labeled dataset featuring leaf images of healthy beans, Bean Anthracnose and Bean Rust. The dataset supports both image classification and object detection tasks.
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    Dataset: Banana Imagery Dataset - Tanzania
    (Zenodo, 2023-02-23) Mduma, Neema; Elinisa, Christian
    The banana images dataset was created to contribute to the study of banana diseases diagnostics. The images target the diagnostics of Black Sigatoka and Fusarium Wilt Race 1 diseases. We are motivated in developing end to end tools to help farmers diagnose diseases and improve banana productivity. The dataset was created to facilitate image classification and object detection tasks.
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    Dataset: Optimizing LoRaWAN Throughput in Maritime Environments Through Adaptive Coding and Modulation in Rayleigh Fading Channels
    (Zenodo, 2025) Lyimo, Martine; Mgawe, Bonny; Leo, Judith; Dida, Mussa; Michael, Kisangiri
    This dataset supports the article “Optimizing LoRaWAN Throughput in Maritime Environments through Adaptive Coding and Modulation under Rayleigh Fading”. It includes simulation outputs and MATLAB source code for reproducing all figures and results in the study. Files include throughput, PER, energy efficiency, spectral efficiency, and an ACM algorithm function.