We characterize a type of functional explanation that addresses why a homologous trait originating deep in the evolutionary history of a group remains widespread and largely unchanged across the group’s lineages. We argue that biologists regularly provide this type of explanation when they attribute conserved functions to phenotypic and genetic traits. The concept of conserved function applies broadly to many biological domains, and we illustrate its importance using examples of molecular sequence alignments at the intersection of evolution and cell biology. (...) We use these examples to show how the study of conserved functions can integrate knowledge of a trait’s causal effects on fitness and its history of natural selection without invoking adaptation. We also show how conserved function provides a novel basis for addressing objections against evolutionary functions raised by Robert Cummins. (shrink)

To identify and conceptualize research problems in science, philosophers and often scientists rely on classical accounts of problems that focus on intellectual problems defined in relation to theories. Recently, philosophers have begun to study the structures and functions of research problems not defined in relation to theories. Furthermore, scientists have long pursued research problems often labeled as practical or applied. As yet, no account of problems specifies the description of both so-called intellectual problems and so-called applied problems. This article proposes (...) a new conceptual framework of problems that accounts both for intellectual aspects of problems and for their practical or applied aspects. I illustrate the account with an example of a recent research project from evolutionary biology, and I indicate further routes by that to develop the account, especially in connection to empirical studies of science. (shrink)

Biodiversity science is in a pivotal period when diverse groups of actors—including researchers, businesses, national governments, and Indigenous Peoples—are negotiating wide-ranging norms for governing and managing biodiversity data in digital repositories. These repositories, often called biodiversity data portals, are a type of organization for which governance can address or perpetuate the colonial history of biodiversity science and current inequities. Researchers and Indigenous Peoples are developing and implementing new strategies to examine and change assumptions about which agents should count as salient (...) participants in scientific projects, especially in projects that build and manage large digital data portals. Two notable efforts are the FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective benefit, Authority, Responsibility, Ethics) Principles for scientific data management and governance. To characterize how these principles influence the governance of biodiversity data portals, we develop an account of fit-for-use data that makes explicit its social as well as technical conditions in relation to agents and purposes. The FAIR Principles, already widely adopted by biodiversity researchers, prioritize machine agents and efficient computation, while the CARE Principles prioritize Indigenous Peoples and their data sovereignty. Both illustrate the potency of an emerging general strategy by which groups of actors craft and implement governance principles for data fitness-of-use to change assumptions about who are salient participants in data science. (shrink)

THIS IS AN EARLY DRAFT OF MY PAPER "RESEARCH PROBLEMS" PUBLISHED IN BJPS IN 2021. PLEASE REFER TO THAT PAPER INSTEAD OF THIS ONE. -/- Philosophers increasingly study how scientists conduct actual scientific projects and the goals they pursue. But as of yet, there are few accounts of goals that can be used to identify different kinds, and specific instances, of goals pursued by scientists. I propose that there are at least four distinct kinds of goals pursued by scientists: ameliorating (...) problems, addressing questions, satisfying values, and achieving epistemic aims. I focus on the first two kinds, and I provide tools to help conceptualize, distinguish, and identify the problems and questions pursued by scientists. This paper illustrates the use of those tools with two examples. (shrink)

This paper shows how institutional values influence the design and evaluation of arguments in funding proposals for research. We characterize a general argument made within proposals and several kinds of subarguments that contribute to it. We indicate that funders’ values inform the kinds of proposal documents funders require and their relative weighting of them. We illustrate these points by showing how a program office in the U.S. federal agency NOAA uses its public service mission to require and heavily weigh arguments (...) to transition new knowledge to NOAA service providers. We suggest conceptual questions raised by the use of transition knowledge. (shrink)

Researchers often pursue proof of concept research, but criteria for evaluating such research remain poorly specified. This article proposes a general framework for proof of concept research that k...

How should billions of species observations worldwide be shared and made reusable? Many biodiversity scientists assume the ideal solution is to standardize all datasets according to a single, universal classification and aggregate them into a centralized, global repository. This ideal has known practical and theoretical limitations, however, which justifies investigating alternatives. To support better community deliberation and normative evaluation, we develop a novel conceptual framework showing how different organizational models, regulative ideals and heuristic strategies are combined to form shared infrastructures (...) supporting data reuse. The framework is anchored in a general definition of data pooling as an activity of making a taxonomically standardized body of information available for community reuse via digital infrastructure. We describe and illustrate unified and pluralistic ideals for biodiversity data pooling and show how communities may advance toward these ideals using different heuristic strategies. We present evidence for the strengths and limitations of the unification and pluralistic ideals based on systemic relationships of power, responsibility and benefit they establish among stakeholders, and we conclude the pluralistic ideal is better suited for biodiversity data. (shrink)

With the powerful analyses and resources they enable, digital humanities tools have captivated researchers from many different fields who want to use them to study science. Digital tools, as well as funding agencies, research communities, and academic administrators, require researchers to think carefully about how they conceptualize, manage, and store data, and about what they plan to do with that data once a given project is over. The difficulties of developing strategies to address these problems can prevent new researchers from (...) sticking with digital tools and flummox even experienced researchers. To help overcome the data hurdle, we present five principles to help researchers, novice and experienced alike, conceptualize and plan for their data. We illustrate the use of those principles with two digital projects from the history of science, the Embryo Project and the Marine Biological Laboratory History Project, and their associated HPS Repository for data. The principles apply beyond the digital realm, so those who collect and manage data by more traditional means will also find them useful. (shrink)

Biodiversity scientists often describe their field as aiming to save life and humanity, but the field has yet to reckon with the history and contemporary practices of colonialism and systematic racism inherited from natural history. The online data portals scientists use to store and share biodiversity data are a growing class of organizations whose governance can address or perpetuate and further institutionalize the implicit assumptions and inequitable social impacts from this extensive history. In this context, researchers and Indigenous Peoples are (...) developing and implementing new strategies to examine and change assumptions about which agents should count as salient participants to scientific projects, especially projects that build and manage large digital data portals. Two notable efforts are the FAIR and CARE Principles for scientific data management and governance. To characterize how these influence the governance of biodiversity data portals, we develop an account of fitness-for-use that makes explicit its social as well as technical conditions in relation to agents and purposes. The FAIR Principles, already widely adopted by biodiversity data projects, prioritize machine agents and efficient computation, while the CARE Principles prioritize Indigenous Peoples and their data sovereignty. Both illustrate the potency of an emerging general strategy by which groups of actors craft and implement governance principles for data fitness-of-use to change assumptions about salient participants to data science. (shrink)

At the interface of developmental biology and evolutionary biology, the very criteria of scientific knowledge are up for grabs. A central issue is the status of evolutionary genetics models, which some argue cannot coherently be used with complex gene regulatory network (GRN) models to explain the same evolutionary phenomena. Despite those claims, many researchers use evolutionary genetics models jointly with GRN models to study evolutionary phenomena. This dissertation compares two recent research projects in which researchers jointly use the two kinds (...) of models. I use methods from the empirical social sciences, such as digital corpus analysis, content analysis, and structured case analysis. I infer three primary conclusions about projects of the kind studied. First, they employ an implicit concept of gene that enables the joint use of both kinds of models. Second, they pursue more epistemic aims besides mechanistic explanation of phenomena. Third, they don’t work to create and export broad synthesized theories. Rather, they focus on phenomena too complex to be understood by a common general theory, they distinguish parts of the phenomena, and they apply models from different theories to the different parts. For such projects, seemingly incompatible models are synthesized largely through mediated representations of complex phenomena. (shrink)

What should the best practices be for modeling zoonotic disease risks, e.g. to anticipate the next pandemic, when background assumptions are unsettled or evolving rapidly? This challenge runs deeper than one might expect, all the way into how we model the robustness of contemporary phylogenetic inference and taxonomic classifications. Different and legitimate taxonomic assumptions can destabilize the putative objectivity of zoonotic risk assessments, thus potentially supporting inconsistent and overconfident policy decisions.