Using Multiple Case Studies to Analyse Open Source Software Business Sustainability in Sub-Saharan Africa

Understanding the detailed timing of crop phenology and their variability enhances grain yield and quality by providing precise scheduling of irrigation, fertilization, and crop protection mechanisms. Advances in information and communication technology (ICT) provide a unique opportunity to develop agriculture-related tools that enhance wall-to-wall upscaling of data outputs from point-location data to wide-area spatial scales. Because of the heterogeneity of the worldwide agro-ecological zones where crops are cultivated, it is unproductive to perform plant phenology research without providing means to upscale results to landscape-level while safeguarding field-scale relevance. This paper presents an advanced, reproducible, and open-source software for plant phenology prediction and mapping (PPMaP) that inputs data obtained from multi-location field experiments to derive models for any crop variety. This information can then be applied consecutively at a localized grid within a spatial framework to produce plant phenology predictions at the landscape level. This software runs on the ‘Windows’ platform and supports the development of process-oriented and temperature-driven plant phenology models by intuitively and interactively leading the user through a step-by-step progression to the production of spatial maps for any region of interest in sub-Saharan Africa. Maize (Zea mays L.) was used to demonstrate the robustness, versatility, and high computing efficiency of the resulting modeling outputs of the PPMaP. The framework was implemented in R, providing a flexible and easy-to-use GUI interface. Since this allows for appropriate scaling to the larger spatial domain, the software can effectively be used to determine the spatially explicit length of growing period (LGP) of any variety.

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MAIP: A Prediction Platform for Predicting Blood-Stage Malaria Inhibitors

10.21203/rs.3.rs-41814/v1 ◽

2020 ◽

Author(s):

Nicolas Bosc ◽

Eloy Felix ◽

Ricardo Arcila ◽

David Mendez ◽

Martin Saunders ◽

...

Keyword(s):

Open Source ◽

Open Source Software ◽

Large Scale ◽

Comprehensive Evaluation ◽

Sub Saharan Africa ◽

Data Set ◽

Practical Challenge ◽

Qsar Models ◽

Data Points ◽

Sub Saharan

Abstract Malaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in Sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify novel molecules that may have antimalarial properties. The performance of machine learning methods generally improves with the number of data points available for training. One practical challenge in building large training sets is that the data is often proprietary and cannot be straightforwardly integrated. Here, this was addressed by sharing QSAR models, each built on a private data set. We describe the development of an open-source software platform for creating such models, a comprehensive evaluation of methods to create a single consensus model and a web platform called MAIP available at https://www.ebi.ac.uk/chembl/maip/. MAIP is freely available for the wider community to make large-scale predictions of potential malaria inhibiting compounds. This project also highlights some of the practical challenges in reproducing published computational methods and the opportunities that open source software can offer to the community.

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MAIP: A web service for predicting blood-stage malaria inhibitors

10.21203/rs.3.rs-41814/v2 ◽

2020 ◽

Author(s):

Nicolas Bosc ◽

Eloy Felix ◽

Ricardo Arcila ◽

David Mendez ◽

Martin Saunders ◽

...

Keyword(s):

Open Source ◽

Open Source Software ◽

Large Scale ◽

Comprehensive Evaluation ◽

Sub Saharan Africa ◽

Data Set ◽

Practical Challenge ◽

Qsar Models ◽

Data Points ◽

Sub Saharan

Abstract Malaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in Sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify novel molecules that may have antimalarial properties. The performance of machine learning methods generally improves with the number of data points available for training. One practical challenge in building large training sets is that the data are often proprietary and cannot be straightforwardly integrated. Here, this was addressed by sharing QSAR models, each built on a private data set. We describe the development of an open-source software platform for creating such models, a comprehensive evaluation of methods to create a single consensus model and a web platform called MAIP available at https://www.ebi.ac.uk/chembl/maip/. MAIP is freely available for the wider community to make large-scale predictions of potential malaria inhibiting compounds. This project also highlights some of the practical challenges in reproducing published computational methods and the opportunities that open-source software can offer to the community.

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MAIP: a web service for predicting blood‐stage malaria inhibitors

Journal of Cheminformatics ◽

10.1186/s13321-021-00487-2 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Nicolas Bosc ◽

Eloy Felix ◽

Ricardo Arcila ◽

David Mendez ◽

Martin R. Saunders ◽

...

Keyword(s):

Open Source ◽

Open Source Software ◽

Large Scale ◽

Comprehensive Evaluation ◽

Sub Saharan Africa ◽

Data Set ◽

Practical Challenge ◽

Qsar Models ◽

Data Points ◽

Sub Saharan

AbstractMalaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify novel molecules that may have antimalarial properties. The performance of machine learning methods generally improves with the number of data points available for training. One practical challenge in building large training sets is that the data are often proprietary and cannot be straightforwardly integrated. Here, this was addressed by sharing QSAR models, each built on a private data set. We describe the development of an open-source software platform for creating such models, a comprehensive evaluation of methods to create a single consensus model and a web platform called MAIP available at https://www.ebi.ac.uk/chembl/maip/. MAIP is freely available for the wider community to make large-scale predictions of potential malaria inhibiting compounds. This project also highlights some of the practical challenges in reproducing published computational methods and the opportunities that open-source software can offer to the community.

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Mapping 20 Years of Urban Expansion in 45 Urban Areas of Sub-Saharan Africa

Remote Sensing ◽

10.3390/rs13030525 ◽

2021 ◽

Vol 13 (3) ◽

pp. 525

Author(s):

Yann Forget ◽

Michal Shimoni ◽

Marius Gilbert ◽

Catherine Linard

Keyword(s):

Case Studies ◽

Urban Areas ◽

Urban Expansion ◽

Local Population ◽

Sub Saharan Africa ◽

Annual Growth Rate ◽

Urban Dynamics ◽

Mapping Approach ◽

Sub Saharan ◽

The Impact

By 2050, half of the net increase in the world’s population is expected to reside in sub-Saharan Africa (SSA), driving high urbanization rates and drastic land cover changes. However, the data-scarce environment of SSA limits our understanding of the urban dynamics in the region. In this context, Earth Observation (EO) is an opportunity to gather accurate and up-to-date spatial information on urban extents. During the last decade, the adoption of open-access policies by major EO programs (CBERS, Landsat, Sentinel) has allowed the production of several global high resolution (10–30 m) maps of human settlements. However, mapping accuracies in SSA are usually lower, limited by the lack of reference datasets to support the training and the validation of the classification models. Here we propose a mapping approach based on multi-sensor satellite imagery (Landsat, Sentinel-1, Envisat, ERS) and volunteered geographic information (OpenStreetMap) to solve the challenges of urban remote sensing in SSA. The proposed mapping approach is assessed in 17 case studies for an average F1-score of 0.93, and applied in 45 urban areas of SSA to produce a dataset of urban expansion from 1995 to 2015. Across the case studies, built-up areas averaged a compound annual growth rate of 5.5% between 1995 and 2015. The comparison with local population dynamics reveals the heterogeneity of urban dynamics in SSA. Overall, population densities in built-up areas are decreasing. However, the impact of population growth on urban expansion differs depending on the size of the urban area and its income class.

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Competency-Industry Relatedness (C-IR) Framework for Sustained Business Growth in Startups during and beyond Pandemic: Myths and Lessons from Publicly Funded Innovative Startups

Sustainability ◽

10.3390/su13094632 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4632

Author(s):

Varun Gupta ◽

Luis Rubalcaba

Keyword(s):

Case Studies ◽

Business Environment ◽

Learning Curves ◽

The European Union ◽

Business Growth ◽

Product Lines ◽

Business Decisions ◽

Multiple Case ◽

Multiple Case Studies ◽

Industry Relatedness

Context: The coronavirus disease 2019 (COVID-19) pandemic led to a turbulent business environment, resulting in market uncertainties, frustrations, and rumors. Wrongly held beliefs—or myths—can hinder startups from turning new market opportunities into their favor (for example, by failing at diversification decisions) or undertaking wrong business decisions, e.g., diversifying in industries that have products of no real market value). Objectives: The objective of the paper is to identify the beliefs that drive the business decisions of startups in a pandemic and to isolate those beliefs that are merely myths. Further, this paper proposes strategic guidelines in the form of a framework to help startups make sound decisions that can lead to market success. Method: The two-step research method involved multiple case studies with five startups based in India, France, Italy, and Switzerland, to identify perceptual beliefs that drove strategic business decisions, followed by a case study of 36 COVID-19-solution focused startups, funded by the European Union (EU). The findings were validated through a survey that involved 102 entrepreneurs. The comparative analysis of two multiple case studies helped identify beliefs that were merely “myths”; myths that drove irrational strategic decisions, resulting in business failures. Results: The results indicate that startups make decisions in pandemic situations that are driven by seven myths, pertaining to human, intellectual, and financial resources. The decision on whether to diversify or continue in the same business operation can be divided into four strategic options of the Competency-Industry Relatedness (C-IR) framework: ignore, delay, phase-in, and diversify. Diversification in the same (or different industry) is less risky for startups if they have the skills, as needed, to diversify in related industries. Diversification in related industries helps startups leverage their experiences and learning curves (those associated with existing product lines) to adapt their existing products in new markets, or utilize their technologies to solve new problems via new products. The desired outcome for these startups should be sustainable business growth—to meet sustainability goals by contributing to the society and the economy. Conclusion: The C-IR framework is a strategic guide for startups to make business decisions based on internal factors, rather than myths. Accurately assessing skill diversity and the nature of new industries (or markets) will help startups leverage their existing resources optimally, without the need for (pricey) external funding. This will foster sustained business growth resulting in a nation economic development. Knowledge transfer from the Innovation ecosystem will further strengthen the C-IR framework effectiveness.

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Service Oriented Architecture (SOA) Implementation

International Journal of Information Systems in the Service Sector ◽

10.4018/ijisss.2018040101 ◽

2018 ◽

Vol 10 (2) ◽

pp. 1-21 ◽

Cited By ~ 3

Author(s):

Tom Yoon ◽

Bong-Keun Jeong

Keyword(s):

Case Studies ◽

Service Oriented Architecture ◽

Successful Implementation ◽

Business Units ◽

Small Project ◽

Service Oriented ◽

Multiple Case ◽

Multiple Case Studies ◽

And Training

Using a multiple case studies and surveys, this article finds that factors essential to successful Service Oriented Architecture (SOA) implementations include establishing effective SOA governance, establishing SOA registries, starting with a small project, collaboration between business and IT units, strengthening trust among business units, and training. This article also explores business and IT motivations for SOA implementation and the benefits realized from this implementation. The findings from this article can provide a guidance for practitioners on the successful implementation of SOA.

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Software Effort Estimation Using ISBSG Dataset: Multiple Case Studies

10.1109/uyms54260.2021.9659655 ◽

2021 ◽

Author(s):

Huseyin Unlu ◽

Ali Gorkem Yalcin ◽

Dilek Ozturk ◽

Guliz Akkaya ◽

Mert Kalecik ◽

...

Keyword(s):

Case Studies ◽

Effort Estimation ◽

Software Effort Estimation ◽

Multiple Case ◽

Multiple Case Studies

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Marine Search-and-Recovery Operation of Scientific Balloons in Japan

Journal of Astronomical Instrumentation ◽

10.1142/s2251171717400050 ◽

2017 ◽

Vol 06 (02) ◽

pp. 1740005 ◽

Cited By ~ 1

Author(s):

Y. Shoji ◽

H. Fuke ◽

K. Hamada ◽

I. Iijima ◽

C. Ikeda ◽

...

Keyword(s):

Case Studies ◽

Safety Issues ◽

Multiple Case ◽

Multiple Case Studies ◽

Radical Solution

Stratospheric balloons have been used worldwide for more than half a century for various scientific missions. However such balloon operations are facing safety issues due to the reduction in appropriate sites for landing. Instead of landing on the ground, landing and recovering on the sea can be a radical solution to this problem. Marine search-and-recovery operations for balloons are not conducted commonly; however, such the operation has been uniquely developed in Japan for more than 40 years. This study describes the methodology for such search-and-recovery of balloons and gondolas through examination of multiple case studies.

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