Search for: All records

Environmental challenges are rarely confined to national, disciplinary, or linguistic domains. Convergent solutions require international collaboration and equitable access to new technologies and practices. The ability of international, multidisciplinary and multilingual research teams to work effectively can be challenging. A major impediment to innovation in diverse teams often stems from different understandings of the terminology used. These can vary greatly according to the cultural and disciplinary backgrounds of the team members. In this paper we take an empirical approach to examine sources of terminological confusion and their effect in a technically innovative, multidisciplinary, multinational, and multilingual research project, adhering to Open Science principles. We use guided reflection of participant experience in two contrasting teams—one applying Deep Learning (Artificial Intelligence) techniques, the other developing guidance for Open Science practices—to identify and classify the terminological obstacles encountered and reflect on their impact. Several types of terminological incongruities were identified, including fuzziness in language, disciplinary differences and multiple terms for a single meaning. A novel or technical term did not always exist in all domains, or if known, was not fully understood or adopted. Practical matters of international data collection and comparison included an unanticipated need to incorporate different types of data labels from country to country, authority to authority. Sometimes these incongruities could be solved quickly, sometimes they stopped the workflow. Active collaboration and mutual trust across the team enhanced workflows, as incompatibilities were resolved more speedily than otherwise. Based on the research experience described in this paper, we make six recommendations accompanied by suggestions for their implementation to improve the success of similar multinational, multilingual and multidisciplinary projects. These recommendations are conceptual drawing on a singular experience and remain to be sources for discussion and testing by others embarking on their research journey. 

Measuring socioeconomic indices at the scale of regions or countries is required in various contexts, in particular to inform public policies. The use of Deep Learning (DL) and Earth Observation (EO) data is becoming increasingly common to estimate specific variables like societal wealth. This paper presents an end- to-end framework ‘DeepWealth’ that calculates such a wealth index using open-source EO data and DL. We use a multidisciplinary approach incorporating satellite imagery, socio-economic data, and DL models. We demonstrate the effectiveness and generalizability of DeepWealth by training it on 24 African countries and deploying it in Madagascar, Brazil and Japan. Our results show that DeepWealth provides accurate and stable wealth index estimates with an 𝑅2 of 0.69. It empowers computer-literate users skilled in Python and R to estimate and visualize well-being-related data. This open-source framework follows FAIR (Findable, Accessible, Interoperable, Reusable) principles, providing data, source code, metadata, and training checkpoints with its source code made available on Zenodo and GitHub. In this manner, we provide a DL framework that is reproducible and replicable.