Citizen science in environmental and ecological sciences

Citizen science is an increasingly acknowledged approach applied in many scientific domains, and particularly within the environmental and ecological sciences, in which non-professional participants contribute to data collection to advance scientific research. We present contributory citizen science as a valuable method to scientists and practitioners within the environmental and ecological sciences, focusing on the full life cycle of citizen science practice, from design to implementation, evaluation and data management. We highlight key issues in citizen science and how to address them, such as participant engagement and retention, data quality assurance and bias correction, as well as ethical considerations regarding data sharing. We also provide a range of examples to illustrate the diversity of applications, from biodiversity research and land cover assessment to forest health monitoring and marine pollution. The aspects of reproducibility and data sharing are considered, placing citizen science within an encompassing open science perspective. Finally, we discuss its limitations and challenges and present an outlook for the application of citizen science in multiple science domains

Dimension characterizations of complexity classes

We use derandomization to show that sequences of positive pspace-dimension – in fact, even positive ∆ p k-dimension for suitable k – have, for many purposes, the full power of random oracles. For example, we show that, if S is any binary sequence whose ∆ p 3-dimension is positive, then BPP ⊆ PS and, moreover, every BPP promise problem is PS-separable. We prove analogous results at higher levels of the polynomial-time hierarchy. The dimension-almost-class of a complexity class C, denoted by dimalmost-C, is the class consisting of all problems A such that A ∈ CS for all but a Hausdorff dimension 0 set of oracles S. Our results yield several characterizations of complexity classes, such as BPP = dimalmost-P, Promise-BPP = dimalmost-P-Sep, and AM = dimalmost-NP, that refine previously known results on almost-classes.