Evidence Enriched

Boyd, Nora Mills

doi:10.1086/697747

Cited by 36 publications

(26 citation statements)

References 21 publications

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“…This is because the initial registration may have aligned a different set of features than those requiring alignment for the new project. This is resonant with current philosophical work discussing the difficulty of sharing scientific data across epistemic contexts (Leonelli [2016]; Boyd [2018]). To use Boyd's ([2018]) terminology, empirical results may be maladapted to a scientific theory, meaning that they cannot serve as a constraint on that theory because of how data were collected or processed.…”

Section: Registration Pluralism and The Study Of The Brainmentioning

confidence: 96%

“…This is resonant with current philosophical work discussing the difficulty of sharing scientific data across epistemic contexts (Leonelli [2016]; Boyd [2018]). To use Boyd's ([2018]) terminology, empirical results may be maladapted to a scientific theory, meaning that they cannot serve as a constraint on that theory because of how data were collected or processed. However, sometimes data can be repurposed and become well adapted to theories to which they were initially maladapted.…”

Section: Registration Pluralism and The Study Of The Brainmentioning

confidence: 96%

“…Information about the registration procedure initially used to aggregate neuroscientific data is a kind of workflow metadata that is important to preserve and deploy when we want to repurpose neuroscientific results. Just like in the sciences that Leonelli ([2016]) and Boyd ([2018]) discuss, there is danger in transferring neuroscientific data from one context to another without taking account of how they were produced.…”

Section: Registration Pluralism and The Study Of The Brainmentioning

confidence: 99%

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Registration Pluralism and the Cartographic Approach to Data Aggregation across Brains

Ward

2022

The British Journal for the Philosophy of Science

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Abstract Neuroscience has become increasingly reliant on multi-subject research in addition to studies of unusual single patients. This research has brought with it a challenge: how are data from different human brains to be combined? The dominant strategy for aggregating data across brains is what I call the ‘cartographic approach’, which involves mapping data from individuals to a spatial template. Here I characterize the cartographic approach and argue that one of its key steps, registration, should be carried out in a way that is sensitive to the target of investigation. Because registration aims to align homologous brain locations, but not all homologous locations can be simultaneously aligned, a multiplicity of registration methods is required to meet the needs of researchers investigating different phenomena. I call this position ‘registration pluralism’. Registration pluralism has potential implications for neuroscientific practice, three of which I discuss here. This work shows the importance of reflecting more carefully on data aggregation methods, especially in light of the substantial individual differences that exist between brains. 1Introduction: Data Aggregation in Neuroscience2The Contemporary Cartographic Approach to Aggregation across Brains3Registration Pluralism4Homology and the Goal of Registration5Organizational Variation and Failures of Simultaneous Alignment6The Scope of Registration Pluralism7Potential Methodological Implications of Registration Pluralism7.1Purpose-sensitive selection of registration methods7.2Functional registration7.3Standardized preprocessing pipelines8Registration Pluralism and the Study of the Brain9Conclusion

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Section: Registration Pluralism and The Study Of The Brainmentioning

confidence: 96%

Section: Registration Pluralism and The Study Of The Brainmentioning

confidence: 96%

Section: Registration Pluralism and The Study Of The Brainmentioning

confidence: 99%

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Registration Pluralism and the Cartographic Approach to Data Aggregation across Brains

Ward

2022

The British Journal for the Philosophy of Science

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“…A hierarchical view of the evidence considers that the evidence of a study will vary according to its inherent characteristics (e.g., observation, near-experiments, experiments, systematic reviews). On this issue, Boyd’s work ( 2018 ) provides some clarification regarding the role of context in the epistemic utility of the evidence. To accumulate, evidence must outlive its original context.…”

Section: Data-texts Transformationsmentioning

confidence: 99%

Data-Texts in the Sciences

2021

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Grounded within current reform recommendations and built upon Giere’s views (1986, 1999) on model-based science, we propose an alternative approach to science education which we refer to as the Evidence-Explanation (EE) Continuum. The approach addresses conceptual, epistemological, and social domains of knowledge, and places emphasis on the epistemological conversations about data acquisitions and transformations in the sciences. The steps of data transformation, which we refer to as data-texts, we argue, unfold the processes of using evidence during knowledge building and reveal the dynamics of scientific practices. Data-texts involve (a) obtaining observations/measurements to become data; (b) selecting and interpreting data to become evidence; (c) using evidence to ascertain patterns and develop models; and (d) utilizing the patterns and models to propose and refine explanations. Throughout the transformations of the EE continuum, there are stages of transition that foster the engagement of learners in negotiations of meaning and collective construction of knowledge. A focus on the EE continuum facilitates the emergence of further insights, both by questioning the nature of the data and its multiple possibilities for change and representations and by reflecting on the nature of the explanations. The shift of emphasis to the epistemics of science holds implications for the design of learning environments that support learners in developing contemporary understandings of the nature and processes of scientific practices.

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“… 30 Such metadata are important for creating what Nora Boyd ( 2018 ) calls “enriched evidence”, by which she means “evidence enriched by auxiliary information about how those lines were generated . .…”

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confidence: 99%

Data models, representation and adequacy-for-purpose

Bokulich

Parker

2021

Euro Jnl Phil Sci

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We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational (PR) view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides insight into the common but little-discussed practices of iteratively reusing and repurposing data, which result in many datasets’ having a phylogeny—an origin and complex evolutionary history—that is relevant to their evaluation and future use. We relate these insights to the open-data and data-rescue movements, and highlight several future avenues of research that build on the PR view of data.

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Evidence Enriched

Cited by 36 publications

References 21 publications

Registration Pluralism and the Cartographic Approach to Data Aggregation across Brains

Registration Pluralism and the Cartographic Approach to Data Aggregation across Brains

Data-Texts in the Sciences

Data models, representation and adequacy-for-purpose

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