This article considers the temporal dimension of data processing and use and the ways in which it affects the production and interpretation of knowledge claims. I start by distinguishing the time at which data collection, dissemination, and analysis occur (Data time, or Dt) from the time in which the phenomena for which data serve as evidence operate (Phenomena time, or Pt). Building on the analysis of two examples of data reuse from modeling and experimental practices in biology, I then argue that Dt affects how researchers (1) select and interpret data as evidence and (2) identify and understand phenomena.

This article investigates epistemological aspects of scientists’ reuse of empirical data over decades and centuries. Giving examples, I discuss three respects in which empirical data are historical entities and the implications for the notion of data reuse. First, any data reuse necessitates metadata, which specify the data’s circumstances of origin. Second, interpretation of historical data often requires the tools of humanities disciplines, which produce a further historicization of data. Finally, some qualitative social scientists hold that data are personal to the researcher who coconstructs them in the research process and are therefore skeptical about the prospects of reusing data.

The new field of meta-research investigates industry bias, publication bias, contradictions between studies, and other trends in medical research. I argue that its findings should be used as meta-evidence for evaluating therapies. ‘Meta-evidence’ is evidence about the support that direct ‘first-order evidence’ provides the hypothesis. I consider three objections to my proposal: the irrelevance objection, the screening-off objection, and the underdetermination objection. I argue that meta-research evidence works by rationally revising our confidence in first-order evidence and, consequently, in the hypothesis—typically, downward.

What must laypersons understand about science to allow them to make sound decisions on science-related issues? Relying on recent developments in social epistemology, this article argues that scientific education should have the goal not of bringing laypersons’ understanding of science closer to that of expert insiders but rather of cultivating the kind of competence characteristic of “competent outsiders.” Moreover, it argues that philosophers of science have an important role to play in attempts to promote this kind of understanding but that, to successfully fulfill this role, they will have to approach central questions in the field differently.

The ontic conception of explanation, according to which explanations are full-bodied things in the world, is fundamentally misguided. I argue instead for what I call the eikonic conception, according to which explanations are the product of an epistemic activity involving representations of the phenomena to be explained. What is explained in the first instance is a particular conceptualization of the explanandum phenomenon, contextualized within a given research program or explanatory project. I conclude that this eikonic conception has a number of benefits, including making better sense of scientific practice and allowing for the full range of normative constraints on explanation.

A pluralist about explanation posits many explanatory relevance relations, while an invariantist denies any substantial role for context in fixing genuine explanation. This article summarizes one approach to combining pluralism and invariantism that emphasizes the contrastive nature of explanation. If explanations always take contrasts as their objects and contrasts come in types, then the role for the context in which an explanation is given can be minimized. This approach is illustrated using a classic debate between natural theology and natural selection about the structure of bees’ honeycombs.

With the ever-growing quality of observational data in astronomy, the complexity of astrophysical models has been increasing in turn. This trend raises the question: Are there still reasons to prefer simpler models if the final goal is an actual model-target comparison? I argue for two aspects in which astrophysical research may favor models having reduced complexity: first, to address the problem of determining the values of adjustable parameters and, second, to pave the way for a validation of the model based on the modeler’s understanding of the scope of the model and the critical processes on the target’s side.

Modularity is a key concept in building and evaluating complex simulation models. My main claim is that in simulation modeling modularity tends to degenerate for reasons inherent to simulation methodology. The argument will proceed by analyzing the techniques of parameterization, tuning, and kludging. They are—to a certain extent—inevitable when building complex simulation models but erode modularity. As a result, the common account of validating simulations faces a major problem, namely, a problem of holism. In the conclusion, I will ask to what extent holism sets limits to validation.

A standard requirement on natural kinds is that they be mind independent. However, many kinds in the human and social sciences, even the natural sciences, depend on human thought. This article suggests that the mind independence requirement on natural kinds be replaced with the requirement that natural kind classifications be defeasible. The defeasibility requirement does not require that natural kinds be mind independent, so it does not exclude mind dependent scientific kinds from being natural kinds. Furthermore, the defeasibility requirement captures the idea that natural kind classifications are tools for investigating the empirical world.

In recent years, industry-funded research has come under fire because of concerns that it can be biased in favor of the funders. This article suggests that efforts by philosophers of science to analyze the concept of objectivity can provide important lessons for those seeking to evaluate and improve industry-funded research. It identifies three particularly relevant criteria for objectivity: transparency, reproducibility, and effective criticism. On closer examination, the criteria of transparency and reproducibility turn out to have significant limitations in this context, but the criterion of effective criticism suggests some important strategies for improving the objectivity of industry-funded science.

“Female sexual dysfunction” is the type of contested disease that has sparked concern about the role of the pharmaceutical industry in medical science. Many policies have been proposed to manage industry influence without carefully evaluating whether the proposed policies would be successful. We consider a proposal for incorporating citizen stakeholders into scientific research and show, via a detailed case study of the pharmaceutical regulation of flibanserin (misleadingly marketed as the “female Viagra”), that such programs can be co-opted. In closing, we use Holman’s asymmetric arms race framework as a tool for evaluating policies in industry-funded science.

The article examines the process of niche standardization in medical research as an example of democratizing strategies implemented in industry-funded science. I argue that niche standardization can lead to undesirable epistemic and ethical consequences, if the various goals of research are not properly aligned. I examine two examples: the case of Sarafem, approved for the treatment of premenstrual dysphoric disorder in women, and the case of BiDil, approved for exclusive use in African Americans for the treatment of congestive heart failure. I conclude by cautioning against the unqualified support of democratizing strategies in industry-funded research.

This article examines the concept of wishful thinking in philosophical literature on science and values. It suggests that this term tends to be used in an overly broad manner that fails to distinguish between separate types of bias, mechanisms that generate biases, and general theories that might explain those mechanisms. I explain how confirmation bias is distinct from wishful thinking and why it is more useful for examining the relationship between cognitive bias and beliefs about the existence of injustices.

Thought experiments play an important epistemic, rhetorical, and didactic function in Galileo’s dialogues. In some cases, Salviati, Sagredo, and Simplicio agree about what would happen in an imaginary scenario and try to understand whether the predicted outcome is compatible with their respective theoretical assumptions. There are, however, also situations in which the predictions of the three interlocutors turn out to be theory laden. Salviati, Sagredo, and Simplicio not only disagree about what would happen, but they reject one another’s solutions as question begging and sometimes even dismiss one another’s thought experiments as misleading or nonsensical.

Galileo proposed what has been called a proto-inertial principle, according to which a body in horizontal motion will conserve its motion. This statement is only true in counterfactual circumstances where no impediments are present. This article analyzes how Galileo could have been justified in ascribing definite properties to this idealized motion. This analysis is then used to better understand the relation of Galileo’s proto-inertial principle to the classical inertial principle.

Philosophy of science is expanding via the introduction of new digital data and tools for their analysis. The data comprise digitized published books and journal articles, as well as heretofore unpublished material such as images, archival text, notebooks, meeting notes, and programs. The growth in available data is matched by the extensive development of automated analysis tools. The variety of data sources and tools can be overwhelming. In this article, we survey the state of digital work in the philosophy of science, showing what kinds of questions can be answered and how one can go about answering them.

In this article, we argue, first, that there are very different research projects that fall under the heading of “systems biology of cancer.” While they share some general features, they differ in their aims and theoretical commitments. Second, we argue that some explanations in systems biology of cancer are concerned with properties of signaling networks (such as robustness or fragility) and how they may play an important causal role in patterns of vulnerability to cancer. Further, some systems biological explanations are compelling illustrations of how “top-down” and “bottom-up” approaches to the same phenomena may be integrated.

I consider three explanatory strategies from recent systems biology that are driven by mathematics as much as mechanistic detail. Analysis of differential equations drives the first strategy; topological analysis of network motifs drives the second; mathematical theorems from control engineering drive the third. I also distinguish three abstraction types: aggregations, which simplify by condensing details; generalizations, which simplify by generalizing details; and structurations, which simplify by contextualizing details. Using a common explanandum as a reference point—namely, the robust perfect adaptation of chemotaxis in Escherichia coli—I argue that each strategy targets various abstraction types to different mechanistic details.