International Science Council

A better approach is needed to assess potential impact and feasibility of proposals.

Mental health disorders remain a significant public health challenge in low- and middle-income countries (LMICs) such as Kenya and Uganda due to limited resources, stigma, and inadequate service integration. The INSPIRE Mental Health (MH) Project aims to address these challenges by developing an open-source data science platform that aligns with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. A Theory of Change (ToC) was developed to guide project implementation. A participatory approach was employed, involving targeted intrest holder engagements through workshops, focus group discussions, and key informant interviews with policymakers, healthcare providers, Village Health Team (VHT) members, data managers, researchers, and community representatives. All participants provided informed consent be- fore inclusion in the study. Collected data was transcribed, cleaned, and analyzed using Atlas.ti, employing thematic analysis to identify emerging themes. The insights gained in- formed the development of the ToC, which visually maps causal linkages between project activities, assumptions, and anticipated outcomes. Interest-holders identified key challenges in mental health service delivery, including fragmented services, inadequate funding, and limited data management capacity. The findings were categorized into five themes: (1) mental health program design, (2) bur- den assessment, (3) screening and assessment tools, (4) data reporting and utilization, (5) prospects for longitudinal studies. Participants emphasized the need for standardized data collection, improved reporting systems, and capacity building in data science techniques to support evidence-based decision-making. The ToC framework offers a strategic roadmap to enhance mental health service delivery and data utilization, fostering collaboration and sustainability. By promoting FAIR data principles and interest holder engagement, the project aims to support informed mental health interventions in Kenya and Uganda.

This case study provides a comprehensive understanding of the typology of consumption practices web 3 (NFT, DAO) can bring to customers and to wineries. Learners develop an appreciation of how NFTs' and DAO's adoption by wineries can create experience, play, integration and classification according to Holt's consumer typology of consumption practices. Holt's framework is applied to Chateau Edmus, a Bordeaux winery, led by Laurent David, a former tech manager from Apple. This case gives insight for the marketing management in order to understand which opportunity to prioritize in order to engage customers and foster customer brand relationship, affiliation, distinction or experience.

Source Agritrop Cirad (https://agritrop.cirad.fr/611678/)

Longitudinal studies are essential for understanding the progression of mental health disorders over time, but combining data collected through different methods to assess conditions like depression, anxiety, and psychosis presents significant challenges. This study presents a mapping technique allowing for the conversion of diverse longitudinal data into a standardized staging database, leveraging the Data Documentation Initiative (DDI) Lifecycle and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standards to ensure consistency and compatibility across datasets. The "INSPIRE" project integrates longitudinal data from African studies into a staging database using metadata documentation standards structured with a snowflake schema. This facilitates the development of Extraction, Transformation, and Loading (ETL) scripts for integrating data into OMOP CDM. The staging database schema is designed to capture the dynamic nature of longitudinal studies, including changes in research protocols and the use of different instruments across data collection waves. Utilizing this mapping method, we streamlined the data migration process to the staging database, enabling subsequent integration into the OMOP CDM. Adherence to metadata standards ensures data quality, promotes interoperability, and expands opportunities for data sharing in mental health research. The staging database serves as an innovative tool in managing longitudinal mental health data, going beyond simple data hosting to act as a comprehensive study descriptor. It provides detailed insights into each study stage and establishes a data science foundation for standardizing and integrating the data into OMOP CDM.

This chapter provides an in-depth examination of the entrepreneurial ecosystem in Lebanon, with a specific focus on the pivotal role played by women. Offering both a historical overview and a contemporary reality check, it establishes an overarching framework within which entrepreneurship in Lebanon is comprehensively described and explored. The narrative delves into the emergence and evolution of the ecosystem against the backdrop of a turbulent environment, shedding light on the progressively evolving role of women entrepreneurs. A detailed analysis of the current status of women in Lebanon highlights the challenges and barriers that female entrepreneurs encounter. This chapter also emphasizes the active engagement of women entrepreneurs in various sectors and concludes with an outlook on the future.

This case study delves into the achievements of Medhi, a disabled entrepreneur who has defied the odds to succeed. In 2022, driven by the desire to empower others, Medhi sought to comprehend the key factors contributing to his own success. This investigation primarily centres on the effectual decision-making processes employed by the disabled entrepreneur, as well as the development of his self-identity within the workplace. This case study highlights how a disabled entrepreneur can transform his disability into a competitive advantage through the shrewd management of his self-identity. By exhibiting flexibility in how his self-identity is perceived across varying contexts, Medhi adeptly navigates these dimensions. He strategically chooses when to disclose or conceal his disability, thus leveraging it to his advantage. The findings of this research offer practical and social implications for entrepreneurs who diverge from the conventional able-bodied archetype.

The COVID-19 pandemic has spurred the use of AI and DS innovations in data collection and aggregation. Extensive data on many aspects of the COVID-19 has been collected and used to optimize public health response to the pandemic and to manage the recovery of patients in Sub-Saharan Africa. However, there is no standard mechanism for collecting, documenting and disseminating COVID-19 related data or metadata, which makes the use and reuse a challenge. INSPIRE utilizes the Observational Medical Outcomes Partnership (OMOP) as the Common Data Model (CDM) implemented in the cloud as a Platform as a Service (PaaS) for COVID-19 data. The INSPIRE PaaS for COVID-19 data leverages the cloud gateway for both individual research organizations and for data networks. Individual research institutions may choose to use the PaaS to access the FAIR data management, data analysis and data sharing capabilities which come with the OMOP CDM. Network data hubs may be interested in harmonizing data across localities using the CDM conditioned by the data ownership and data sharing agreements available under OMOP's federated model. The INSPIRE platform for evaluation of COVID-19 Harmonized data (PEACH) harmonizes data from Kenya and Malawi. Data sharing platforms must remain trusted digital spaces that protect human rights and foster citizens' participation is vital in an era where information overload from the internet exists. The channel for sharing data between localities is included in the PaaS and is based on data sharing agreements provided by the data producer. This allows the data producers to retain control over how their data are used, which can be further protected through the use of the federated CDM. Federated regional OMOP-CDM are based on the PaaS instances and analysis workbenches in INSPIRE-PEACH with harmonized analysis powered by the AI technologies in OMOP. These AI technologies can be used to discover and evaluate pathways that COVID-19 cohorts take through public health interventions and treatments. By using both the data mapping and terminology mapping, we construct ETLs that populate the data and/or metadata elements of the CDM, making the hub both a central model and a distributed model.