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This paper describes an attempt to utilize paid citizen science in a research project that documented urban park usage during the early stages of the COVID-19 pandemic in two U.S. cities. Strategies used by the research team to recruit, pay, and evaluate the experiences of the 43 citizen scientists are discussed alongside key challenges in contemporary citizen science. A literature review suggests that successful citizen science projects foster diverse and inclusive participation; develop appropriate ways to compensate citizen scientists for their work; maximize opportunities for participant learning; and ensure high standards for data quality. In this case study, the selection process proved successful in employing economically vulnerable individuals, though the citizen scientist participants were disproportionately female, young, White, non-Hispanic, single, and college educated relative to the communities studied. The participants reported that the financial compensation provided by the study, similar in amount to the economic stimulus checks distributed simultaneously by the Federal government, were reasonable given the workload, and many used it to cover basic household needs. Though the study took place in a period of high economic risk, and more than 80% of the participants had never participated in a scientific study, the experience was rated overwhelmingly positive. Participants reported that the work provided stress relief, indicated they would consider participating in similar research in the future. Despite the vast majority never having engaged in most park stewardship activities, they expressed interest in learning more about park usage, mask usage in public spaces, and socio-economic trends in relation to COVID-19. Though there were some minor challenges in data collection, data quality was sufficient to publish the topical results in a peer-reviewed companion paper. Key insights on the logistical constraints faced by the research team are highlighted throughout the paper to advance the case for paid citizen science. 

Translational studies benefit from experimental designs where laboratory organisms use human-relevant behaviors. One such behavior is decision-making, however studying complex decision-making in rodents is labor-intensive and typically restricted to two levels of cost/reward. We design a fully automated, inexpensive, high-throughput framework to study decision-making across multiple levels of rewards and costs: the REward-COst in Rodent Decision-making (RECORD) system. RECORD integrates three components: 1) 3D-printed arenas, 2) custom electronic hardware, and 3) software. We validated four behavioral protocols without employing any food or water restriction, highlighting the versatility of our system. RECORD data exposes heterogeneity in decision-making both within and across individuals that is quantifiably constrained. Using oxycodone self-administration and alcohol-consumption as test cases, we reveal how analytic approaches that incorporate behavioral heterogeneity are sensitive to detecting perturbations in decision-making. RECORD is a powerful approach to studying decision-making in rodents, with features that facilitate translational studies of decision-making in psychiatric disorders. 

Urban parks and green spaces provide a wide range of ecosystem services, including social interaction and stress reduction. When COVID-19 closed schools and businesses and restricted social gatherings, parks became one of the few places that urban residents were permitted to visit outside their homes. With a focus on Philadelphia, PA and New York City, NY, this paper presents a snapshot of the park usage during the early phases of the pandemic. Forty-three Civic Scientists were employed by the research team to observe usage in 22 different parks selected to represent low and high social vulnerability, and low, medium, and high population density. Despite speculation that parks could contribute to the spread of COVID-19, no strong correlation was found between the number of confirmed COVID-19 cases in adjacent zip codes and the number of park users. High social vulnerability neighborhoods were associated with a significantly higher number of COVID-19 cases ([Formula: see text]). In addition, no significant difference in the number of park users was detected between parks in high and low vulnerability neighborhoods. The number of park users did significantly increase with population density in both cities ([Formula: see text]), though usage varied greatly by park. Males were more frequently observed than females in parks in both high vulnerability and high-density neighborhoods. Although high vulnerability neighborhoods reported higher COVID-19 cases, residents of Philadelphia and New York City appear to have been undeterred from visiting parks during this phase of the pandemic. This snapshot study provides no evidence to support closing parks during the pandemic. To the contrary, people continued to visit parks throughout the study, underscoring their evident value as respite for urban residents during the early phases of the pandemic. 

Abstract Data collected so far by the Pierre Auger Observatory have enabled major advances in ultra-high energy cosmic ray physics and demonstrated that improved determination of masses of primary cosmic-ray particles, preferably on an event-by-event basis, is necessary for understanding their origin and nature. Improvement in primary mass measurements was the main motivation for the upgrade of the Pierre Auger Observatory, called AugerPrime. As part of this upgrade, scintillator detectors are added to the existing water-Cherenkov surface detector stations. By making use of the differences in detector response to the electromagnetic particles and muons between scintillator and water-Cherenkov detectors, the electromagnetic and muonic components of cosmic-ray air showers can be disentangled. Since the muonic component is sensitive to the primary mass, such combination of detectors provides a powerful way to improve primary mass composition measurements over the original Auger surface detector design. In this paper, the so-called Scintillator Surface Detectors are discussed, including their design characteristics, production process, testing procedure and deployment in the field. 

Abstract A Large Ion Collider Experiment (ALICE) has been conceived and constructed as a heavy-ion experiment at the LHC. During LHC Runs 1 and 2, it has produced a wide range of physics results using all collision systems available at the LHC. In order to best exploit new physics opportunities opening up with the upgraded LHC and new detector technologies, the experiment has undergone a major upgrade during the LHC Long Shutdown 2 (2019–2022). This comprises the move to continuous readout, the complete overhaul of core detectors, as well as a new online event processing farm with a redesigned online-offline software framework. These improvements will allow to record Pb-Pb collisions at rates up to 50 kHz, while ensuring sensitivity for signals without a triggerable signature.