The assessment of research performance is widely seen as a vital tool in upholding the highest standards of quality, with selection and competition believed to drive progress. Academic institutions need to take critical decisions on hiring and promotion, while facing external pressure by also being subject to research assessment^1–4. Here we present an outlook on research assessment for career progression with specific focus on promotion to full professorship, based on 314 policies from 190 academic institutions and 218 policies from 58 government agencies, covering 32 countries in the Global North and 89 countries in the Global South. We investigated how frequently various promotion criteria are mentioned and carried out a statistical analysis to infer commonalities and differences across policies. Although quantitative methods of assessment remain popular, in agreement with what is found in more geographically restricted studies^5–9, they are not omnipresent. We find differences between the Global North and the Global South as well as between institutional and national policies, but less so between disciplines. A preference for bibliometric indicators is more marked in upper-middle-income countries. Although we see some variation, many promotion policies are based on the assumption of specific career paths that become normative rather than embracing diversity. In turn, this restricts opportunities for researchers. These results challenge current practice and have strategic implications for researchers, research managers and national governments.

How can citizen science projects advance the achievement of transformative air quality-related Sustainable Development Goals (SDGs) in Germany and Niger? We investigate the promise of using citizen-generated data (CGD) as an input for official SDG monitoring and implementation in a multidisciplinary project, based on activities undertaken in Niger and Germany ranging from surveys, action research, policy and legislative analysis and environmental monitoring in Niamey and Leipzig, respectively. We critically describe and evaluate the great potential, but very limited actual use of CGD sources for these global goals in both contexts from technical and policy perspectives. Agenda 2030 provides an opportunity to tackle indoor and outdoor air quality in a more integrated and transformative perspective. However, we find this agenda to be remarkably absent in air quality policy and monitoring plans. Likewise, we find no meaningful links of existing citizen science initiatives to official air quality policy. We propose how SDGs-aligned citizen science initiatives could make major contributions to environmental and health monitoring and public debate, especially in the wake of the COVID-19 pandemic. This however requires researchers to more strategically link these initiatives to policymakers and policy frameworks, such as SDG indicators and the governance structures in which they are embedded.

We consider the problem of variable selection via penalized likelihood using nonconvex penalty functions. To maximize the non-differentiable and nonconcave objective function, an algorithm based on local linear approximation and which adopts a naturally sparse representation was recently proposed. However, although it has promising theoretical properties, it inherits some drawbacks of Lasso in high dimensional setting. To overcome these drawbacks, we propose an algorithm (MLLQA) for maximizing the penalized likelihood for a large class of nonconvex penalty functions. The convergence property of MLLQA and oracle property of one-step MLLQA estimator are established. Some simulations and application to a real data set are also presented.

Breast cancer is a malignant tumor that begins in the cells of the breast and it is the second leading cause of women’s death after lung cancer. Futhermore, the exponential increase of medical data is a resource for the creation of artificial intelligence systems (AIS). In this article, we have proposed an ensemble learning method which is a combination of three (3) statistical learning techniques specifically the support vector machine (SVM), the artificial neural network (ANN) and the decision tree (C4.5). The resulting model (TechCE) is created by using the University of WisconsinHospital breast cancer database. TechCE achieved a precision of 97%.