five

Development and Investigation of Delivery Mode of a Multivalent Bacterial Fish Vaccine in Zambia Data

收藏
NIAID Data Ecosystem2026-05-02 收录
下载链接:
https://doi.org/10.7910/DVN/BEUL7J
下载链接
链接失效反馈
官方服务:
资源简介:
The study was initiated by identifying 11 farm owners where their units were segregated as farms. For this activity, the collected information involved the number of farms and fish samples collected. The data was collected from Lake Kariba in Siavonga district (16.5323°S, 28.7111°E) Southern Province of Zambia. The data elaborates the seasonal period of sampling with water parameters at each sampling. The main intention of collecting this data was to collect sick fish and document clinical signs of observed sick fish. The data was generated over a period of one year to cover the cold season (July to August), hot season (September to November) and rain season (December to February). Observable clinical signs of disease were used to segregate fish as sick from the identified farms and cages. Infected fish were thoroughly examined and gross lesions such as pale gills, exophthalmia, corneal opacity, abdominal distension, shallow ulcers, skin and fin hemorrhages, and fin erosion were used to target the fish for scooping using a net. The scooped fish were then subjected to analysis and collection of samples. The samples were subjected to bacteria isolation and bacteria identification. The data allowed us to understand the observed clinical signs and bacteria associated with disease. Data on pathological observations was also interlinked to disease and bacteria isolated. The isolated bacteria were also subjected to antibiotic susceptibility tests in order to determine the antibiogram profiles of the bacteria found in fish for possible food safety concerns. The limitations with this data would arise from not growing or culturing some bacteria associated with disease as they may not grow on the media used in our study.
创建时间:
2024-09-13
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作