Archives des mots-clés : big science

Metascience as a scientific social movement

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Authors : David Peterson, Aaron Panofsky

Emerging out of the “reproducibility crisis” in science, metascientists have become central players in debates about research integrity, scholarly communication, and science policy. The goal of this article is to introduce metascience to STS scholars, detail the scientific ideology that is apparent in its articles, strategy statements, and research projects, and discuss its institutional and intellectual future.

Put simply, metascience is a scientific social movement that seeks to use the tools of science- especially, quantification and experimentation- to diagnose problems in research practice and improve efficiency.

It draws together data scientists, experimental and statistical methodologists, and open science activists into a project with both intellectual and policy dimensions. Metascientists have been remarkably successful at winning grants, motivating news coverage, and changing policies at science agencies, journals, and universities.

Moreover, metascience represents the apotheosis of several trends in research practice, scientific communication, and science governance including increased attention to methodological and statistical criticism of scientific practice, the promotion of “open science” by science funders and journals, the growing importance of both preprint and data repositories for scientific communication, and the new prominence of data scientists as research makes a turn toward Big Science.

URL : Metascience as a scientific social movement

DOI : https://doi.org/10.31235/osf.io/4dsqa

Data Management and Preservation Planning for Big Science

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‘Big Science’ – that is, science which involves large collaborations with dedicated facilities, and involving large data volumes and multinational investments – is often seen as different when it comes to data management and preservation planning.

Big Science handles its data differently from other disciplines and has data management problems that are qualitatively different from other disciplines. In part, these differences arise from the quantities of data involved, but possibly more importantly from the cultural, organisational and technical distinctiveness of these academic cultures.

Consequently, the data management systems are typically and rationally bespoke, but this means that the planning for data management and preservation (DMP) must also be bespoke.

These differences are such that ‘just read and implement the OAIS specification’ is reasonable Data Management and Preservation (DMP) advice, but this bald prescription can and should be usefully supported by a methodological ‘toolkit’, including overviews, case-studies and costing models to provide guidance on developing best practice in DMP policy and infrastructure for these projects, as well as considering OAIS validation, audit and cost modelling.

In this paper, we build on previous work with the LIGO collaboration to consider the role of DMP planning within these big science scenarios, and discuss how to apply current best practice.

We discuss the result of the MaRDI-Gross project (Managing Research Data Infrastructures – Big Science), which has been developing a toolkit to provide guidelines on the application of best practice in DMP planning within big science projects.

This is targeted primarily at projects’ engineering managers, but intending also to help funders collaborate on DMP plans which satisfy the requirements imposed on them.

URL : http://www.ijdc.net/index.php/ijdc/article/view/8.1.29