Citizen science and wildlife biology: Synergies and challenges

Citizen science (CS) has evolved over the past decades as a working method involving interested citizens in scientific research, for example by reporting observations, taking measurements or analysing data. In the past, research on animal behaviour has been benefitting from contributions of citizen scientists mainly in the field of ornithology but the full potential of CS in ecological and behavioural sciences is surely still untapped. Here, we present case studies that successfully applied CS to research projects in wildlife biology and discuss potentials and challenges experienced. Our case studies cover a broad range of opportunities: large‐scale CS projects with interactive online tools on bird song dialects, engagement of stakeholders as citizen scientists to reduce human–wildlife conflicts, involvement of students of primary and secondary schools in CS projects as well as collaboration with the media leading to successful recruitment of citizen scientists. Each case study provides a short overview of the scientific questions and how they were approached to showcase the potentials and challenges of CS in wildlife biology. Based on the experience of the case studies, we highlight how CS may support research in wildlife biology and emphasise the value of fostering communication in CS to improve recruitment of participants and to facilitate learning and mutual trust among different groups of interest (e.g., researchers, stakeholders, students). We further show how specific training for the participants may be needed to obtain reliable data. We consider CS as a suitable tool to enhance research in wildlife biology through the application of open science procedures (i.e., open access to articles and the data on publicly available repositories) to support transparency and sharing experiences.     

Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space

Abstract

The use of modern data science has recently emerged as a promising new path to tackling the complex challenges involved in the creation of next-generation chemistry and materials. However, despite the appeal of this potentially transformative development, the chemistry community has yet to incorporate it as a central tool in every-day work. Our research program is designed to enable and advance this emerging research approach. It is centred around the creation of a software ecosystem that brings together physics-based modelling, high-throughput in silico screening and data analytics (i.e. the use of machine learning and informatics for the validation, mining and modelling of chemical data). This cyberinfrastructure is devised to offer a comprehensive set of data science techniques and tools as well as a general-purpose scope to make it as versatile and widely applicable as possible. It also emphasises user-friendliness to make it accessible to the community at large. It thus provides the means for the large-scale exploration of chemical space and for a better understanding of the hidden mechanisms that determine the properties of complex chemical systems. Such insights can dramatically accelerate, streamline and ultimately transform the way chemical research is conducted. Aside from serving as a production-level tool, our cyberinfrastructure is also designed to facilitate and assess methodological innovation. Both the software and method development work are driven by concrete molecular design problems, which also allow us to assess the efficacy of the overall cyberinfrastructure.     

Climate,landscape, habitat,and woodland management associations with hazel dormouse Muscardinus avellanarius population status

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