---
title: "Research transparency"
chunk: 4/6
source: "https://en.wikipedia.org/wiki/Research_transparency"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:50:11.648451+00:00"
instance: "kb-cron"
---

=== Research transparency and open science (2015–) ===
Since 2000, the open science movement has expanded beyond access to scientific outputs (publication, data or software) to encompass the entire process of scientific production. In 2018, Vicente-Saez and Martinez-Fuentes have attempted to map the common values shared by the standard definitions of open science in the English-speaking scientific literature indexed on Scopus and the Web of Science. Access is no longer the main dimension of open science, as it has been extended by more recent commitments toward transparency, collaborative work and social impact. Through this process, open science has been increasingly structured over a consisting set of ethical principles: "novel open science practices have developed in tandem with novel organising forms of conducting and sharing research through open repositories, open physical labs, and transdisciplinary research platforms. Together, these novel practices and organising forms are expanding the ethos of science at universities."
The global scale of the open science movement and its integration in a large variety of technical tools, standards and regulations makes it possible to overcome the "classic collective action problem" embodied by research transparency: there is a structural discrepancy between the stated objective of scientific institutions and the lack of incentives to implement them at an individual level.
The formalization of open science as a potential framework to ensure research transparency has been initially undertaken by institutional and communities initiatives. The TOP guidelines were elaborated in 2014 by a committee for Transparency and Openness Promotion that included "disciplinary leaders, journal editors, funding agency representatives, and disciplinary experts largely from the social and behavioral sciences". The guidelines rely on eight standards, with different levels of compliance. While the standards are modular, they also aim to articulate a consistent ethos of science as "they also complement each other, in that commitment to one standard may facilitate adoption of others."
After 2015, theses initiatives have partly influenced new regulations and code of ethics. The European Code of Conduct for Research Integrity from 2017 is strongly structured around open science and open data: it "pays data management almost an equal amount of attention as publishing and is also in this sense the most advanced of the four CoCs." First adopted in July 2020, the Hong Kong principles for assessing researchers acknowledge open science as one of the five pillars of scientific integrity: "It seems clear that the various modalities of open science need to be rewarded in the assessment of researchers because these behaviors strongly increase transparency, which is a core principle of research integrity."

== Forms ==
Research transparency has a large variety of forms depending on the disciplinary culture, the material condition of research and the interaction between scientists and other social circles (policy-makers, non-academic professionals, general audience). For Lyon, Jeng and Mattern, "the term 'transparency' has been applied in a range of contexts by diverse research stakeholders, who have articulated and framed the concept in a number of different ways." In 2020, Kevin Elliott introduced a taxonomy of eight dimensions of research transparency: purpose, audience, content, timeframe, actors, mechanism, venues and dangers. For Elliott not all forms of transparency are achievable and desirable, so that a proper terminology can help to make the more appropriate decisions: "While these are important objections, the taxonomy of transparency considered here suggests that the best response to them is typically not to abandon the goal of transparency entirely to consider what forms of transparency are best able to minimize them.".

=== Reproducibility ===

==== Method reproducibility ====
Goodman, Fanelli and Ioannidis define method reproducibility as "the provision of enough detail about study procedures and data so the same procedures could, in theory or in actuality, be exactly repeated." This acception is largely synonymous with replicability in a computational context or reproducibility in an experimental context. In the report of the National Academies of Science, that opted for an experimental terminology, the counterpart of method reproducibility was described as "obtaining consistent results using the same input data; computational steps, methods, and code; and conditions of analysis".
Method reproducibility is more attainable in computational sciences: as long as it behaves as expected, the same code should produce the same output. Open code, open data and more recently, research notebook are common recommendations to enhance method reproducibility. In principle, the wider availability of research output makes it possible to assess and audit the process of analysis. In practice, Roger Peng already underlined in 2011, that many projects require "computing power that may not be available to all researchers". This issue has worsened in some areas such as Artificial Intelligence or Computer vision, as the development of very large deep learning models makes it nearly impossible to recreate them (or at a prohibitive cost), even when the original code and data are open. Method reproducibility can also be affected by library dependency, as the open code can rely on external programs which may not always be available or compatible. Two studies in 2018 and 2019 have shown that a large share of research notebook hosted on GitHub are no longer usable, either due to the of required extensions no longer being available or issues in the code.
In experimental sciences, there is no commonly agreed criterium of method reproducibility: "in practice, the level of procedural detail needed to describe a study as "methodologically reproducible" does not have consensus."