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Tick-borne diseases are increasing in North America. Knowledge of which tick species and associated human pathogens are present locally can inform the public and medical community about the acarological risk for tick bites and tick-borne infections. Citizen science (also called community-based monitoring, volunteer monitoring, or participatory science) is emerging as a potential approach to complement traditional tick record data gathering where all aspects of the work is done by researchers or public health professionals. One key question is how citizen science can best be used to generate high-quality data to fill knowledge gaps that are difficult to address using traditional data gathering approaches. Citizen science is particularly useful to generate information on human–tick encounters and may also contribute to geographical tick records to help define species distributions across large areas. Previous citizen science projects have utilized three distinct tick record data gathering methods including submission of: 1) physical tick specimens for identification by professional entomologists, 2) digital images of ticks for identification by professional entomologists, and 3) data where the tick species and life stage were identified by the citizen scientist. We explore the benefits and drawbacks of citizen science, relative to the traditional scientific approach, to generate data on tick records, with special emphasis on data quality for species identification and tick encounter locations. We recognize the value of citizen science to tick research but caution that the generated information must be interpreted cautiously with data quality limitations firmly in mind to avoid misleading conclusions.

Citizen science is fundamentally shifting the future of biodiversity research. But although citizen science observations are contributing an increasingly large proportion of biodiversity data, they only feature in a relatively small percentage of research papers on biodiversity. We provide our perspective on three frontiers of citizen science research, areas that we feel to date have had minimal scientific exploration but that we believe deserve greater attention as they present substantial opportunities for the future of biodiversity research: sampling the undersampled, capitalizing on citizen science’s unique ability to sample poorly sampled taxa and regions of the world, reducing taxonomic and spatial biases in global biodiversity data sets; estimating abundance and density in space and time, develop techniques to derive taxon-specific densities from presence or absence and presence-only data; and capitalizing on secondary data collection, moving beyond data on the occurrence of single species and gain further understanding of ecological interactions among species or habitats. The contribution of citizen science to understanding the important biodiversity questions of our time should be more fully realized.

We are currently in the midst of Earth's sixth extinction event, and measuring biodiversity trends in space and time is essential for prioritizing limited resources for conservation. At the same time, the scope of the necessary biodiversity monitoring is overwhelming funding for professional scientific monitoring. In response, scientists are increasingly using citizen science data to monitor biodiversity. But citizen science data are ‘noisy’, with redundancies and gaps arising from unstructured human behaviours in space and time. We ask whether the information content of these data can be maximized for the express purpose of trend estimation. We develop and execute a novel framework which assigns every citizen science sampling event a marginal value, derived from the importance of an observation to our understanding of overall population trends. We then make this framework predictive, estimating the expected marginal value of future biodiversity observations. We find that past observations are useful in forecasting where high-value observations will occur in the future. Interestingly, we find high value in both ‘hotspots’, which are frequently sampled locations, and ‘coldspots’, which are areas far from recent sampling, suggesting that an optimal sampling regime balances ‘hotspot’ sampling with a spread across the landscape.

\"The conservation and sustainable use of biodiversity in the environments where this diversity originated or is being used, are issues which are high on the policy agenda. This book is the first to set out a clear overview of community biodiversity management (CBM) as an approach to meet social, economic and environmental change\"-- Provided by publisher.

Microdosing is the practice of regularly using low doses of psychedelic drugs. Anecdotal reports suggest that microdosing enhances well-being and cognition; however, such accounts are potentially biased by the placebo effect. This study used a ‘self-blinding’ citizen science initiative, where participants were given online instructions on how to incorporate placebo control into their microdosing routine without clinical supervision. The study was completed by 191 participants, making it the largest placebo-controlled trial on psychedelics to-date. All psychological outcomes improved significantly from baseline to after the 4 weeks long dose period for the microdose group; however, the placebo group also improved and no significant between-groups differences were observed. Acute (emotional state, drug intensity, mood, energy, and creativity) and post-acute (anxiety) scales showed small, but significant microdose vs. placebo differences; however, these results can be explained by participants breaking blind. The findings suggest that anecdotal benefits of microdosing can be explained by the placebo effect. Psychedelic psychotherapy, therapy enhanced with psychedelic drugs such as LSD or psilocybin (the active ingredient of ‘magic mushrooms’), has been suggested to improve psychological well-being. For this reason, trials on psychedelic therapy for the treatment of depression, addiction and other conditions are ongoing. Recently, ‘microdosing’ – a way of administering psychedelics that involves taking about 10% of a recreational dose two or three times per week – has gained popularity. Unlike taking large doses of psychedelics, microdosing does not induce hallucinations, but anecdotal reports suggest that it yields similar benefits as psychedelic therapy. A key feature of modern medicine are ‘placebo control’ studies that compare two groups of patients: one that takes a drug and another that takes inactive pills, known as placebos. Crucially, neither group knows whether they are taking drug or placebo. This control ensures that observed effects are due to the drug itself and not to unrelated psychological causes. For example, in trials of mood medicines, participants often expect to feel happier, which in itself improves their mood even when taking a placebo. This is known as the placebo effect. Restrictive drug policies make placebo-controlled studies on psychedelics difficult and expensive, in particular for microdosing, which involves taking psychedelics over a longer time period. To overcome this problem, Szigeti et al. developed a new citizen-science approach, where microdosers implemented their own placebo control based on online instructions. The advantages are the low cost and the ability to recruit participants globally. The experiment was completed by 191 microdosers, making it the largest placebo-controlled study on psychedelics to-date, for a fraction of the cost of an equivalent clinical study. The trial examined whether psychedelic microdosing can improve cognitive function and psychological well-being. The team found that microdosing significantly increased a number of psychological measures, such as well-being and life satisfaction. However, participants taking placebo also improved: there were no significant differences between the two groups. The findings confirmed positive anecdotes about microdosing improving people’s moods, but at the same time show that taking empty capsules, knowing they might be microdoses, have the same benefits. This result suggests that the observed benefits are not caused by the microdose, but rather by psychological expectations. The study’s innovative ‘do-it-yourself’ approach to placebo control may serve as a template for future citizen science studies on other popular phenomena where positive expectations and social factors could play a role, such as cannabidiol (CBD) oils, nootropics and nutrition.

\"Building on unique data, this book analyses the efficacy of a prominent climate change mitigation strategy: voluntary programs for sustainable buildings and cities. It evaluates the performance of thirty-five voluntary programs from the global north and south, including certification programs, knowledge networks, and novel forms of financing. The author examines them through the lens of club theory, urban transformation theory, and diffusion of innovations theory. Using qualitative comparative analysis (QCA) the book points out the opportunities and constraints of voluntary programs for decarbonising the built environment, and argues for a transformation of their use in climate change mitigation. The book will appeal to readers interested in sustainable city planning, climate change mitigation, and voluntarism as an alternative governance mechanism for achieving socially and environmentally desirable outcomes. The wide diversity of cases from the global north and south generate new insights, and offers practical guidelines for designing effective programs\"-- Provided by publisher.

Citizen science is mainstream: millions of people contribute data to a growing array of citizen science projects annually, forming massive datasets that will drive research for years to come. Many citizen science projects implement a \"leaderboard\" framework, ranking the contributions based on number of records or species, encouraging further participation. But is every data point equally \"valuable?\" Citizen scientists collect data with distinct spatial and temporal biases, leading to unfortunate gaps and redundancies, which create statistical and informational problems for downstream analyses. Up to this point, the haphazard structure of the data has been seen as an unfortunate but unchangeable aspect of citizen science data. However, we argue here that this issue can actually be addressed: we provide a very simple, tractable framework that could be adapted by broadscale citizen science projects to allow citizen scientists to optimize the marginal value of their efforts, increasing the overall collective knowledge.