---
title: "Research transparency"
chunk: 5/6
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"
---

==== Result reproducibility ====
Goodman, Fanelli and Ioannidis define result reproducibility as "obtaining the same results from the conduct of an independent study whose procedures are as closely matched". Result reproducibility is comparable to replication in an experimental context and reproducibility in a computational context. The definition of replicability retained in the National Academies of Science, largely applies to it: "obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data.". The reproducibility crisis met in experimental disciplines like psychology or medicine is mostly a crisis of "result reproducibility", since it concerns research that cannot been simply re-executed, but involve the independent recreation of the experimental design. As such it is arguably the most debated form of research transparency in the recent years.
Result reproducibility is harder to achieve than other forms of research transparency.  It involve a variety of issues that may include computational reproducibility, accuracy of scientific measurement and diversity of methodological approaches. There are no universal standard to determine how close are the original procedures matched and criterium may vary depending on the disciplines or, even on the field of research. Consequently, meta-analysis of reproducibility have faced significant challenges. A 2015 study of 100 psychology papers conducted by Open Science Collaboration has been confronted with the "lack of a single accepted definition" which "opened the door to controversy about their methodological approach and conclusions" and made it necessary to fall back on "subjective assessments" of result reproducibility.

==== Observation reproducibility and verifiability ====
In 2018 Sabina Leonelli defines observation reproducibility as the "expectation being that any skilled researcher placed in the same time and place would pick out, if not the same data, at least similar patterns". This expectation recovers a large range scientific and scholarly practices in non-experimental disciplines: "A tremendous amount of research in the medical, historical and social sciences does not rest on experimentation, but rather on observational techniques such as surveys, descriptions and case reports documenting unique circumstances"
The development of open scientific infrastructure has radically transformed the status and the availability of scientific data and other primary sources.  Access to theses resources has been thoroughly transformed by digitization and the attribution of unique identifiers. Permanent digital object identifiers (or DOI) have been first allocated to dataset since the early 2000s  which solved a long-standing debate on the citability of scientific data.
Increased transparency of citations to primary sources or research materials has been framed by Andrew Moravcsik as a "revolution in qualitative research".  Access to theses resources has been thoroughly transformed by digitization and the attribution of unique identifiers. Permanent digital object identifiers (or DOI) have been first allocated to dataset since the early 2000s  which solved a long-standing debate on the citability of scientific data.

=== Value transparency ===
Transparency of research values has been a major focus of disciplines with strong involvements in policy-making such as environment studies or social sciences. In 2009, Heather Douglas underlined that the public discourse on science has been largely dominated by normative ideals of objective research: if the procedures have been correctly applied, science results should be "value-free". For Douglas, this ideal remains largely at loss with the effective process of research and scientific advising as pre-defined values may largely predate choices about the concepts, the protocols and the data used. Douglas argued instead in favor of a disclosure of the values held by researchers: "the values should be made as explicit as possible in this indirect role, whether in policy documents or in the research papers of scientists."
In the 2010s, several philosopher of sciences attempted to systematize value transparency in the context of open science. In 2017, Kevin Elliott emphasized three conditions for value transparency in research, the first one involved "being as transparent as possible about (…) data, methods, models and assumptions so that value influence can be scrutinized".

=== Review and editorial transparency ===
Until the 2010s, the editorial practices of scholarly publishing have remained largely unformal and little studied: "Despite 350 years of scholarly publishing (…) research on ItAs [Instruction to authors], and on their evolution and change, is scarce."
Editorial transparency has been recently acknowledged as a natural expansion of the debate over research reproducibility. Several principles laid in the 2015 TOP guidelines already implied the existence of explicit editorial standards. Unprecedented attention given to editorial transparency has also been motivated by the diversification and the complexification of the open science publishing landscape: "Triggered by a wide variety of expectations for journals' editorial processes, journals have started to experiment with new ways of organizing their editorial assessment and peer review systems (...) The arrival of these innovations in an already diverse set of practices of peer review and editorial selection means we can no longer assume that authors, readers, and reviewers simply know how editorial assessment operates."

== Transparent by design: developing open workflow ==
The TOPs Guidelines have set up an influential transdisciplinary standard to establish result reproducibility in an open science context. While experimental and computational disciplines remains a primary focus, the standards have strived to integrate concerns and formats more specific to other disciplinary practices (such as research materials).
Informal incentives like badges or indexes have been initially advocated as a way to support the adoption of harmonized policies in regard to research transparency. Due to the development of open science, regulation and standardized infrastructures or processes are increasingly favored.