According to the United Nations Educational, Scientific, and Cultural Organization (UNESCO), Open Science is the movement to make scientific research and data accessible to all. It has great potential for advancing science. At its core, it includes (but is not limited to) open access, open data, and open research. Some of the associated advantages are promoting collaboration, sharing and reproducibility in research, and preventing the reinvention of the wheel, thus saving resources. As research becomes more globalized and its output grows exponentially, especially in data, the need for open scientific research practices is more evident — the future of modern science. This has resulted in a concerted global interest in open science uptake. Even so, barriers still exist. The formal training curriculum in most, if not all, universities in Kenya does not equip students with the knowledge and tools to subsequently practice open science in their research. Therefore, to work openly and collaboratively, there is a need for awareness and training in the use of open science tools. These have been neglected, especially in most developing countries, and remain barriers to the cause. Moreover, there is scanty research on the state of affairs regarding the practice and/or adoption of open science. Thus, we developed, through the OpenScienceKE framework, a model to narrow the gap. A sensitize-train-hack-collaborate model was applied in Nairobi, the economic and administrative capital of Kenya. Using the model, we sensitized through seminars, trained on the use of tools through workshops, applied the skills learned in training through hackathons to collaboratively answer the question on the state of open science in Kenya. While the former parts of the model had 20–50 participants, the latter part mainly involved participants with a bioinformatics background, leveraging their advanced computational skills. This model resulted in an open resource that researchers can use to publish as open access cost-effectively. Moreover, we observed a growing interest in open science practices in Kenya through literature search and data mining and that lack of awareness and skills may still hinder the adoption and practice of open science. Furthermore, at the time of the analyses, we surprisingly found that out of the 20,069 papers downloaded from BioRXiv, only 18 had Kenyan authors, a majority of which are international (16) collaborations. This may suggest poor uptake of the use of preprints among Kenyan researchers. The findings in this study highlight the state of open science in Kenya and challenges facing its adoption and practice while bringing forth possible areas for primary consideration in the campaign toward open science. It also proposes a model (sensitize-train-hack-collaborate model) that may be adopted by researchers, funders and other proponents of open science to address some of the challenges faced in promoting its adoption in Kenya.

Open, collaborative, and reproducible research – Open Science – has great potential for advancing science. However, the training in our local universities does not equip students with the tools to practice open science. Additionally, to work in the open and collaborate, the collaborators should be trained to use shared tools. Therefore, the main barrier to working open is the lack of awareness of the collaboration tools and the skills required to utilize these tools. Thus, to fill the gap, through an open science community funded by a Mozilla Mini-grant, OpenScienceKE, we are promoting open science among bioinformatics students and researchers in the Nairobi area using this model: sensitize, train, hack and collaborate. This model first sensitizes on open science practices through seminars, trains on open science tools through workshops, facilitates the hands-on application of the tools through hackathons, and finally fosters a community of open science enthusiasts through meetups.OpenScienceKE sought to address the following problems: the lack of awareness of open science practices and tools within the Bioinformatics community in Kenya; the low adoption of open science practices in Bioinformatics; and the absence of research establishing the state of affairs in adopting open science in Kenya. Through the OpenScienceKE hackathon, we created an open resource that the students could use to figure out where they can cost-effectively publish open access. We observed a growing interest in open science practices in Kenya through literature search and data mining, but the lack of awareness and skills still hinders adoption. The use of preprints for research dissemination has not caught up in Kenya; out of the 20,069 papers downloaded from BioRXiv, only 18 have Kenyan authors, a majority of which are international (16) collaborations. We also observed a lack of incentives and policies in academic and research institutions to support open science. The fear of being scooped and the competitive spirit within the scientific community are also significant barriers to working in the open.The first iteration of the model, which focused on academic institutions, set the foundation for the next phase: promote open and reproducible science in research institutions. This model provides the framework for the adoption of open science practices within the institutions. As genomic research data generated in Africa grows, there is a need to adopt open science practices in data storage, reproducible pipelines, and collaborative research. We propose an approach, which develops the necessary infrastructure within research institutions, and builds human capacity through the model: sensitize, train, hack, and collaborate. The promotion of open science in Africa recognizes the future direction of research, and OpenScienceKE is growing the culture and practice in research.

Introduction: Control of tropical diseases has for decades depended on diagnosis by microscopy as the gold standard method of detection. It, however, faces the drawbacks of low sensitivity, operator reliance on user expertise and experience essential to make an accurate diagnosis leading to variable results. Objective: The purpose of this study was to explore a method that could help improve the process of microscopy via machine learning models for the detection of intra- and intercellular parasites.Methods: A digital tool known as ‘cell explorer’ was developed to help in the detection and annotation of microscopic slide images taken from blood smears containing Leishmania donovani, Plasmodium falciparum and Trypanosoma brucei rhodesiense parasites. Advanced statistical modelling techniques were used including Convolutional Neural Networks, open-source image processing algorithms and clustering algorithms to detect cellular morphology of the parasites. Using Simple Linear Interactive Clustering, the cell explorer also functioned as a cell counter.Results: The neural network was able to immediately detect cellular morphology and identify the Leishmania and Trypanosome parasites as well as different stages of the Plasmodium parasite with an average accuracy of ̴ 95% . It was also able to accurately quantify the number of cells presented within each slide image.Conclusion: The cell explorer presents a fast and accurate computer-aided microscopy tool with the ability to detect cellular morphology, successfully identifying Leishmania donovani, Plasmodium berghei and Trypanosoma brucei rhodesiense parasites. This work highlights the research potential of machine learning models as disease diagnostic applications effective in improving the microscopy process.