Overcoming the underdetermination of specimens

Philosophers of science are well aware that theories are underdetermined by data. But what about the data? Scientific data are selected and processed representations or pieces of nature. What is useless context and what is valuable specimen, as well as how specimens are processed for study, are not obvious or predetermined givens. Instead, they are decisions made by scientists and other research workers, such as technicians, that produce different outcomes for the data. Vertebrate fossils provide a revealing case of this data-processing, because they are embedded in rock that often matches the fossils’ color and texture, requiring an expert eye to judge where the fossil/context interface is. Fossil preparators then permanently define this interface by chiseling away the material they identify as rock. As a result, fossil specimens can emerge in multiple possible forms depending on the preparator’s judgment, skill, and chosen tools. A prepared fossil then is not yet data but potential data, following Leonelli’s (2015) relational framework in which data are defined as evidence that scientists have used to support a proposed theory. This paper draws on ethnographic evidence to assess how scientists overcome this underdetermination of specimens, as potential data, in addition to the underdetermination of theories and of data, to successfully construct specimen-based knowledge. Among other strategies, paleontology maintains a division of labor between data-makers and theory-makers. This distinction serves to justify the omission of preparators’ nonstandard, individualized techniques from scientific publications. This separation has benefits for both scientists and technicians; however, it restricts knowledge production by preventing scientists from understanding how the pieces of nature they study were processed into researchable specimens

Overcoming the underdetermination of specimens

Philosophers of science are well aware that theories are underdetermined by data. But what about the data? Scientific data are selected and processed representations or pieces of nature. What is useless context and what is valuable specimen, as well as how specimens are processed for study, are not obvious or predetermined givens. Instead, they are decisions made by scientists and other research workers, such as technicians, that produce different outcomes for the data. Vertebrate fossils provide a revealing case of this data-processing, because they are embedded in rock that often matches the fossils’ color and texture, requiring an expert eye to judge where the fossil/context interface is. Fossil preparators then permanently define this interface by chiseling away the material they identify as rock. As a result, fossil specimens can emerge in multiple possible forms depending on the preparator’s judgment, skill, and chosen tools. A prepared fossil then is not yet data but potential data, following Leonelli’s (2015) relational framework in which data are defined as evidence that scientists have used to support a proposed theory. This paper draws on ethnographic evidence to assess how scientists overcome this underdetermination of specimens, as potential data, in addition to the underdetermination of theories and of data, to successfully construct specimen-based knowledge. Among other strategies, paleontology maintains a division of labor between data-makers and theory-makers. This distinction serves to justify the omission of preparators’ nonstandard, individualized techniques from scientific publications. This separation has benefits for both scientists and technicians; however, it restricts knowledge production by preventing scientists from understanding how the pieces of nature they study were processed into researchable specimens

Introduction: Caring for Equitable Relations in Interdisciplinary Collaborations

Collaborative research between scholars of science and technology studies (STS)and scholars of science, technology, engineering, and math (STEM) is a growing trend. The papers assembled in thisSpecial Section offer both embodied and empirical knowledge on how ethnographers negotiate our roles in integrative research when constrained by what our technoscientific collaborators value, what funders demand, what our home institutions expect, what we want to learn from the worlds we study, and the social transformations we envision in science and society. We grapple with how we as ethnographers can best balance caring for the communities we study, the ones we serve, and the ones we identify with. We take care that knowledge making is political. Race, gender, class, and ability status of scholars intersect with the organizational, institutional, and cultural contexts in which we practice science to shape and be shaped by entrenched power relations.Through a feminist politics of care, this collection transforms tensions in interdisciplinary collaborations into resources that enlarge our understandings of what these collaborations are like for STS ethnographers, make visible certain labors within them and, crucially, enrich our vision for what we want these collaborations to be

Vulnerable newborn types: analysis of subnational, population‐based birth cohorts for 541 285 live births in 23 countries, 2000–2021

Setting: Subnational, population-based birth cohort studies (n = 45) in 23 low-and middle-income countries (LMICs) spanning 2000–2021. Population: Liveborn infants. Methods: Subnational, population-based studies with high-quality birth outcome data from LMICs were invited to join the Vulnerable Newborn Measurement Collaboration. We defined distinct newborn types using gestational age (preterm [PT], term [T]), birthweight for gestational age using INTERGROWTH-21st standards (small for gestational age [SGA], appropriate for gestational age [AGA] or large for gestational age [LGA]), and birthweight (low birthweight, LBW [<2500 g], non- LBW) as ten types (using all three outcomes), six types (by excluding the birthweight categorisation), and four types (by collapsing the AGA and LGA categories). We defined small types as those with at least one classification of LBW, PT or SGA. We presented study characteristics, participant characteristics, data missingness, and prevalence of newborn types by region and study. Results: Among 541 285 live births, 476 939 (88.1%) had non-missing and plausible values for gestational age, birthweight and sex required to construct the newborn types. The median prevalences of ten types across studies were T+AGA+nonLBW (58.0%), T+LGA+nonLBW (3.3%), T+AGA+LBW (0.5%), T+SGA+nonLBW (14.2%), T+SGA+LBW (7.1%), PT+LGA+nonLBW (1.6%), PT+LGA+LBW (0.2%), PT+AGA+nonLBW (3.7%), PT+AGA+LBW (3.6%) and PT+SGA+LBW (1.0%). The median prevalence of small types (six types, 37.6%) varied across studies and within regions and was higher in Southern Asia (52.4%) than in Sub-Saharan Africa (34.9%). Conclusions: Further investigation is needed to describe the mortality risks associated with newborn types and understand the implications of this framework for local targeting of interventions to prevent adverse pregnancy outcomes in LMICs

IgM N-glycosylation correlates with COVID-19 severity and rate of complement deposition

The glycosylation of IgG plays a critical role during human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, activating immune cells and inducing cytokine production. However, the role of IgM N-glycosylation has not been studied during human acute viral infection. The analysis of IgM N-glycosylation from healthy controls and hospitalized coronavirus disease 2019 (COVID-19) patients reveals increased high-mannose and sialylation that correlates with COVID-19 severity. These trends are confirmed within SARS-CoV-2-specific immunoglobulin N-glycan profiles. Moreover, the degree of total IgM mannosylation and sialylation correlate significantly with markers of disease severity. We link the changes of IgM N-glycosylation with the expression of Golgi glycosyltransferases. Lastly, we observe antigen-specific IgM antibody-dependent complement deposition is elevated in severe COVID-19 patients and modulated by exoglycosidase digestion. Taken together, this work links the IgM N-glycosylation with COVID-19 severity and highlights the need to understand IgM glycosylation and downstream immune function during human disease