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Representation in Scientific Practice, published by the MIT Press in 1990, helped coalesce a long-standing interest in scientific visualization among historians, philosophers, and sociologists of science and remains a touchstone for current investigations in science and technology studies. This volume revisits the topic, taking into account both the changing conceptual landscape of STS and the emergence of new imaging technologies in scientific practice. It offers cutting-edge research on a broad array of fields that study information as well as short reflections on the evolution of the field by leading scholars, including some of the contributors to the 1990 volume. The essays consider the ways in which viewing experiences are crafted in the digital era; the embodied nature of work with digital technologies; the constitutive role of materials and technologies -- from chalkboards to brain scans -- in the production of new scientific knowledge; the metaphors and images mobilized by communities of practice; and the status and significance of scientific imagery in professional and popular culture.ContributorsMorana Alac, Michael Barany, Anne Beaulieu, Annamaria Carusi, Catelijne Coopmans, Lorraine Daston, Sarah de Rijcke, Joseph Dumit, Emma Frow, Yann Giraud, Aud Sissel Hoel, Martin Kemp, Bruno Latour, John Law, Michael Lynch, Donald MacKenzie, Cyrus Mody, Natasha Myers, Rachel Prentice, Arie Rip, Martin Ruivenkamp, Lucy Suchman, Janet Vertesi, Steve Woolgar

The Strait of Dardanelles is a long and shallow strongly stratified strait connecting Aegean and Marmara Seas characterized by complicated time-varying oceanographic regime. The seasonal variability of exchange flows depends mainly on the temperature, salinity and elevation changes in the adjacent seas, whereas short-term changes in the atmospheric pressure and the wind characteristics are responsible for short-term variability. Despite that hydrodynamic conditions are critical for marine transport and hydraulic structures in the Strait, only a few observation and simulation studies were carried out. For this high resolution study, the model SCHISM based on unstructured grids and a vertical coordinate system LSC 2 was used. The numerical experiments were focused on the period September 2008-January 2009 when measurement data were available. The simulation agrees with observed profiles of velocity and discharges in upper and lower layers. Simulated and observed features include flow reversals in the Strait during strong storm events (November 2008), and high-frequency current and elevation variability in the Strait and the Marmara Sea. Simulations have shown the persistent hydraulic jump and complex flow structure in the narrowest Nara Passage. The results of simulation for locations of two pylons (“European” and “Anatolian”) of bridge “1915 Çanakkale” showed strong temporal variability of currents on daily scale caused by fluctuations of sea level in connecting basins, flow from the Bosphorus Strait, direct wind and tidal forcing.

Many animals, including humans, acquire valuable skills and knowledge by copying others. Scientists refer to this as social learning. It is one of the most exciting and rapidly developing areas of behavioral research and sits at the interface of many academic disciplines, including biology, experimental psychology, economics, and cognitive neuroscience.Social Learningprovides a comprehensive, practical guide to the research methods of this important emerging field. William Hoppitt and Kevin Laland define the mechanisms thought to underlie social learning and demonstrate how to distinguish them experimentally in the laboratory. They present techniques for detecting and quantifying social learning in nature, including statistical modeling of the spatial distribution of behavior traits. They also describe the latest theory and empirical findings on social learning strategies, and introduce readers to mathematical methods and models used in the study of cultural evolution. This book is an indispensable tool for researchers and an essential primer for students. Provides a comprehensive, practical guide to social learning researchCombines theoretical and empirical approachesDescribes techniques for the laboratory and the fieldCovers social learning mechanisms and strategies, statistical modeling techniques for field data, mathematical modeling of cultural evolution, and more

Biocultural and archaeological research on food, past and present, often relies on very specific, precise, methods for data collection and analysis. These are presented here in a broad-based review. Individual chapters provide opportunities to think through the adoption of methods by reviewing the history of their use along with a discussion of research conducted using those methods. A case study from the author's own work is included in each chapter to illustrate why the methods were adopted in that particular case along with abundant additional resources to further develop and explore those methods.

Behavioral scientists – including those in psychology, infant and child development, education, animal behavior, marketing and usability studies – use many methods to measure behavior. Systematic observation is used to study relatively natural, spontaneous behavior as it unfolds sequentially in time. This book emphasizes digital means to record and code such behavior; while observational methods do not require them, they work better with them. Key topics include devising coding schemes, training observers and assessing reliability, as well as recording, representing and analyzing observational data. In clear and straightforward language, this book provides a thorough grounding in observational methods along with considerable practical advice. It describes standard conventions for sequential data and details how to perform sequential analysis with a computer program developed by the authors. The book is rich with examples of coding schemes and different approaches to sequential analysis, including both statistical and graphical means.

Scholars of international relations increasingly use temporal dependence variables (polynomials or splines) to control for unmodeled duration dependence in nonlinear models (such as logit or probit) of events ranging from interstate conflict and civil war to sanctions imposition and trade agreements. I identify two inferential obstacles that are widespread to nonlinear models, and are exacerbated by the unique features of temporal dependence variables. First, compression causes the quantities of interest to be sensitive to the values in the counterfactual scenario (most notably, time). Second, presenting substantive effects calculated at one simulation scenario (such as an “average” scenario) grossly inflates the representativeness of that scenario and neglects the variability within the sample. The consequences of these problems range in severity from understating the magnitude of the substantive effects to deriving inferences that are wholly unrepresentative of the data. I offer a simple checklist. First, use the values observed in the data to generate in-sample quantities of interest. Second, plot those quantities of interest across the offending variable (for example, time) and interpret the relationship. Finally, provide a sense of the sample’s variability in quantities of interest through simple summary statistics (such as mean, standard deviation, and range). These simple fixes provide much-needed transparency and act as a shield against scholars who might otherwise present misleading results.

Objectives: Estimating gestational age is usually based on date of last menstrual period (LMP) or clinical estimation (CE); both approaches introduce potential bias. Differences in methods of estimation may lead to misclassification and inconsistencies in risk estimates, particularly if exposure assignment is also gestationdependent.This paper examines a 'what-if' scenario in which alternative methods are used and attempts to elucidate how method choice affects observed results. Methods: We constructed two 20-week gestational age cohorts of pregnancies between 2000 and 2005 (New Jersey, Pennsylvania, Ohio, USA) using live birth certificates: one defined preterm birth (PTB) status using CE and one using LMP. Within these, we estimated risk for 4 categories of preterm birth (PTBs per 10⁶ pregnancies) and risk differences (RD (95% CIs)) associated with exposure to particulate matter (PM2.5). Results: More births were classified preterm using LMP (16%) compared with CE (8%). RD divergences increased between cohorts as exposure period approached delivery. Among births between 28 and 31 weeks, week 7 PM2.5 exposure conveyed RDs of 44 (21 to 67) for CE and 50 (18 to 82) for LMP populations, while week 24 exposure conveyed RDs of 33 (11 to 56) and –20 (–50 to 10), respectively. Conclusions: Different results from analyses restricted to births with both CE and LMP are most likely due to differences in dating methods rather than selection issues. Results are sensitive to choice of gestational age estimation, though degree of sensitivity can vary by exposure timing. When both outcome and exposure depend on estimate of gestational age, awareness of nuances in the method used for estimation is critical.