Related Articles
Scale-dependent cloud enhancement from land restoration in West African drylands
Land restoration projects, including reforestation and area protection, are being implemented across African drylands such as the Sahel. In addition to biodiversity, livelihood and carbon sequestration benefits, restoration can also affect the local climate through land-atmosphere interaction. Yet, it remains unknown to what extent dryland restoration can affect cloud cover development and, ultimately, precipitation. Here, we use twenty years of high-resolution data from the Meteosat Second Generation satellite to study the impact of land restoration on cloud development in West African drylands. Results show that cloud cover frequency and convective initiation are higher above vegetated areas, particularly during the start and end of the wet seasons. Furthermore, we find a more pronounced cloud cover enhancement over protected areas larger than 121 km2, suggesting a scale-dependent relationship between project size and cloud cover development.
Trust in scientists and their role in society across 68 countries
Science is crucial for evidence-based decision-making. Public trust in scientists can help decision makers act on the basis of the best available evidence, especially during crises. However, in recent years the epistemic authority of science has been challenged, causing concerns about low public trust in scientists. We interrogated these concerns with a preregistered 68-country survey of 71,922 respondents and found that in most countries, most people trust scientists and agree that scientists should engage more in society and policymaking. We found variations between and within countries, which we explain with individual- and country-level variables, including political orientation. While there is no widespread lack of trust in scientists, we cannot discount the concern that lack of trust in scientists by even a small minority may affect considerations of scientific evidence in policymaking. These findings have implications for scientists and policymakers seeking to maintain and increase trust in scientists.
Evolution and impact of the science of science: from theoretical analysis to digital-AI driven research
The Science of Science (SoS) examines the mechanisms driving the development and societal role of science, evolving from its sociological roots into a data-driven discipline. This paper traces the progression of SoS from its early focus on the social functions of science to the current era, characterized by large-scale quantitative analysis and AI-driven methodologies. Scientometrics, a key branch of SoS, has utilized statistical methods and citation analysis to understand scientific growth and knowledge diffusion. With the rise of big data and complex network theory, SoS has transitioned toward more refined analyses, leveraging artificial intelligence (AI) for predictive modeling, sentiment annotation, and entity extraction. This paper explores the application of AI in SoS, highlighting its role as a surrogate, quant, and arbiter in advancing data processing, data analysis and peer review. The integration of AI has ushered in a new paradigm for SoS, enhancing its predictive accuracy and providing deeper insights into the internal dynamics of science and its impact on society.
A manifesto for a globally diverse, equitable, and inclusive open science
The field of psychology has rapidly transformed its open science practices in recent years. Yet there has been limited progress in integrating principles of diversity, equity and inclusion. In this Perspective, we raise the spectre of Questionable Generalisability Practices and the issue of MASKing (Making Assumptions based on Skewed Knowledge), calling for more responsible practices in generalising study findings and co-authorship to promote global equity in knowledge production. To drive change, researchers must target all four key components of the research process: design, reporting, generalisation, and evaluation. Additionally, macro-level geopolitical factors must be considered to move towards a robust behavioural science that is truly inclusive, representing the voices and experiences of the majority world (i.e., low-and-middle-income countries).
Building collaborative infrastructures for an interdisciplinary higher education master’s program
This paper examines the practices and importance of building a collaborative infrastructure in interdisciplinary education, using the context of the master’s program developed by the Interdisciplinary Consortium for Applied Research in Ecology and Evolution (ICARE) as a case study. The study focuses on two levels of collaborative infrastructure: The project organization and project practice of the ICARE program and the specific use of CoNavigator, a physical tool for interdisciplinary teaching, learning, and collaboration. The analysis explores the educational aspects of the ICARE program and investigates how the training teams (each consisting of a master’s student, supervisors, and mentors) within the project organized themselves and developed their collaboration methods. By examining the challenges faced by ICARE and the implications for its Trainees and stakeholders, this paper emphasizes the significance of prioritizing and developing robust and explicit collaborative infrastructures both at the program and institutional level, as the challenges identified in ICARE mirror those at higher institutional levels, where interdisciplinary activities are not sustained unless they are fully embedded in the visible and physical structures. The findings provide valuable insights for future interdisciplinary study programs and underscore the necessity of proactive infrastructure planning and implementation to support successful interdisciplinary teaching and learning practices.
Responses