Trappes, Rose (2023) Data Synthesis for Big Questions: From Animal Tracks to Ecological Models. [Preprint]

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Abstract

This paper addresses a relatively new mode of ecological research: data synthesis studies.
Data synthesis studies involve reusing data to create a general model as well as a reusable aggregated dataset. Using a case from movement ecology, I analyse the trade-offs and strategies involved in data synthesis. Like theoretical ecological modelling, I find that synthesis studies involve a modelling trade-off between generality, precision and realism; they deal with this trade-off by adopting a pragmatic kludging strategy. I also identify an additional trade-off, the synthesis trade-off, between making data easy to synthesise for a particular project, on the one hand, and facilitating data reuse for other projects, on the other. In response to this synthesis trade-off, researchers create flexible datasets that are relatively easy to use for particular projects and can be adjusted to suit some other purposes. The flexibility compromise is also found in broader open data efforts, making it a significant element in the future of data-intensive ecology.

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Item Type:	Preprint
Creators:	Creators Email ORCID Trappes, Rose r.g.trappes@exeter.ac.uk 0000-0002-6398-5404
Keywords:	open data; data-centric science; evidence synthesis; ecological model building; kludging; movement ecology; data journey
Subjects:	General Issues > Data Specific Sciences > Biology > Ecology/Conservation General Issues > Technology
Depositing User:	Dr Rose Trappes
Date Deposited:	09 Sep 2023 17:34
Last Modified:	09 Sep 2023 17:34
Item ID:	22524
Subjects:	General Issues > Data Specific Sciences > Biology > Ecology/Conservation General Issues > Technology
Date:	9 September 2023
URI:	https://philsci-archive.pitt.edu/id/eprint/22524

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• Data Synthesis for Big Questions: From Animal Tracks to Ecological Models. (deposited 09 Sep 2023 17:34) [Currently Displayed]
  • Data Synthesis for Big Questions: From Animal Tracks to Ecological Models. (deposited 27 Jun 2024 13:44)

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