Étiquette : open science

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A framework for assessing the trustworthiness of scientific research findings

Authors : Brian A. Nosek, David B. Allison, Kathleen Hall Jamieson, Marcia McNutt, A. Beau Nielsen, Susan M. Wolf

Vigorous debate has erupted over the trustworthiness of scientific research findings in a number of domains. The question “what makes research findings trustworthy?” elicits different answers depending on whether the emphasis is on research integrity and ethics, research methods, transparency, inclusion, assessment and peer review, or scholarly communication. Each provides partial insight.

We offer a systems approach that focuses on whether the research is accountable, evaluable, well-formulated, has been evaluated, controls for bias, reduces error, and whether the claims are warranted by the evidence. We tie each of these components to measurable indicators of trustworthiness for evaluating the research itself, the researchers conducting the research, and the organizations supporting the research.

Our goals are to offer a framework that can be applied across methods, approaches, and disciplines and to foster innovation in development of trustworthiness indicators. Developing valid indicators will improve the conduct and assessment of research and, ultimately, public understanding and trust.

URL : A framework for assessing the trustworthiness of scientific research findings

DOI : https://doi.org/10.1073/pnas.2536736123

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Data stewardship through the lens of Open Science Career Assessment Matrix

Authors : Antti M. Rousi, Xiaolong Li, Lara Ejtehadia, Richard Dars, Pedro E.S. Silv, Udayanto Dwi Atmoj

Data stewardship is a key expertise needed for the transformation towards more open and transparent science. This is particularly relevant in research institutions, where data stewards play a direct role in supporting research under open science requirements.

However, the absence of established frameworks and merits for assessing this expertise has hindered recognition, professional development, and the integration of data stewardship into institutional practices.

This work aims to examine how multidisciplinary data stewardship work transpires through the Open Science Career Assessment Matrix (OS-CAM); a tool designed to assess open science contributions across various dimensions. Using a case study approach, we report findings from a workshop where a multidisciplinary team of experts engaged in data stewardship described their work in relation to OS-CAM.

This work presents a summary of the CV narratives and suggested merits for data stewardship developed in the workshop. Assessing data stewardship through OS-CAM provides a structured framework for evaluating, recognising, and rewarding these contributions, thereby increasing their visibility in academic and professional evaluations.

However, our study also reveals notable gaps in OS-CAM’s coverage of data stewardship, particularly the underrepresentation of infrastructure-related activities such as the management of data repositories.

It is important to note that while OS-CAM may offer value in academic research settings, it is less applicable for data stewardship roles that extend beyond research or open science.

Therefore, we recommend further research to include diverse institutions and participants, combined with other complementary frameworks, for a more comprehensive understanding of data stewardship’s contribution to science and its recognition in or beyond academic communities.

URL : Data stewardship through the lens of Open Science Career Assessment Matrix

DOI : https://doi.org/10.2218/ijdc.v19i1.1088

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To Be FAIR: Theory Specification Needs an Update

Authors : Caspar J. Van Lissa, Aaron Peikert, Maximilian S. Ernst, Noah N. N. van Dongen, Felix D. Schönbrod, Andreas M. Brandmaier

Open science innovations have focused on rigorous theory testing, yet methods for specifying, sharing, and iteratively improving theories remain underdeveloped. To address this limitation, we introduce FAIR theory, a standard for specifying theories as findable, accessible, interoperable, and reusable digital objects.

FAIR theories are findable in well-established archives; accessible in terms of their availability and ability to be understood; interoperable for specific purposes, such as selecting control variables; and reusable in that they can be iteratively and collaboratively improved on.

This article adapts the FAIR principles for theory; reflects on current FAIR practices in relation to psychological theory; and discusses FAIR theories’ potential impact in terms of reducing research waste, enabling metaresearch on theories’ structure and development, and incorporating theory into reproducible research workflows—from hypothesis generation to simulation studies.

We present a conceptual workflow for FAIRifying theory that builds on existing open science principles and infrastructures. More detailed tutorials, worked examples, and convenience functions to automate this workflow are available in the theorytools R package.

FAIR theory constitutes a structured protocol for archiving, communicating about, and iteratively improving theory, addressing a critical gap in open scholarly practices and potentially increasing the efficiency of cumulative knowledge acquisition in psychology and beyond.

URL : To Be FAIR: Theory Specification Needs an Update

DOI : https://doi.org/10.1177/17456916251401850

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The Economics of Open Science and Ukraine’s Prospective Place in It

Author : Ganna Kharlamova

This article presents the evaluation of the factors influencing the adoption of Open Access (OA) within the Open Science (OS) paradigm, utilizing statistical dynamics of OA publications across EU countries from 2000 to 2022.

The study employs econometric modeling to test a set of hypotheses regarding the percentage of articles in OA, including: the proportion of freely accessible research outputs; the regulatory impact of OA declarations; state-driven OA publication; overall scientific development fostering collaboration; OA rates among top universities; young researchers engagement; and internet penetration as a facilitator of OA dissemination.

The analysis reveals the growth trajectory in dynamics of OA. The EU model forecasts an increase in the percentage of OA articles from approximately 50% in 2022 to 70% by 2030, contingent on sustained investment and policy alignment.

These hypotheses form a model initially developed for EU countries, providing a framework to assess Ukraine’s academic publishing landscape and its evolving position within OS. A SWOT and PESTLE analysis is conducted to evaluate the financing of Ukrainian science, identifying the broader implications of OA implementation.

Prospects for Ukraine’s integration into the OS paradigm are outlined, emphasizing the necessity of overcoming unique challenges such as war-related disruptions.

URL : The Economics of Open Science and Ukraine’s Prospective Place in It

DOI : https://doi.org/10.15388/Ekon.2025.104.4.1

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Scientific production on data repositories and open science published in the Web of Science database: Methodi Ordinatio and content analysis

Authors : Sinval Adalberto Rodrigues-Junior, Marcelo Votto Texeira

The opening of scientific data proposed by the Open Science movement presupposes careful planning for data collection, organization, and treatment, aiming at their sharing, accessibility, and reuse. Data repositories have been conceived as structures necessary to enable open access to data.

This study aimed to analyze the influence of data repositories on the disclosure and sharing of scientific data proposed by the Open Science movement. The Methodi Ordinatio, developed to organize a portfolio of scientific publications, was adopted to analyze the subject of ‘Data Repositories’ and ‘Open Science’.

The studies were ranked using the InOrdinatio index, and the 15 best ranked studies were included and analyzed through Bardin’s content analysis. Most studies describe the structure involved in data repositories within the biological, chemical, and health areas.

Other studies addressed data reuse, data organization and analysis processes and tools, as well as data selection and classification algorithms. The units of analysis selected for the content analysis were categorized as open access, information technologies, data processing, and information retrieval.

Systems (processes and structures), metadata standards, ontologies, semantic web, data types, and their management were addressed by these studies. It is concluded that open data repositories are growing rapidly. Production with the greatest impact has occurred in the biological and biomedical/health areas, highlighting the structure involved in repositories within these fields.

Data repositories provide systems for depositing, managing, searching, accessing, and reusing data based on processes and technologies — often developed as open-source software — in alignment with the proposed Open Science model.

URL : Scientific production on data repositories and open science published in the Web of Science database: Methodi Ordinatio and content analysis

DOI : https://doi.org/10.1590/2318-0889202537e2513075

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National Repository Infrastructure and Open Access Challenges: The Croatian Perspective

Authors : Ivana Matijevi, Ivona Milovanović

Repositories are one of the key infrastructure components in achieving the goals of open science. In response to legal obligations, emerging trends, and challenges in open science, several Croatian institutions jointly established a national digital repository infrastructure in 2015 – the DABAR system (Digital Academic Archives and Repositories).

Its purpose is to provide a unified space for storing, preserving, and ensuring open access to the scholarly output of scientists and institutions within the Croatian science and higher education system.

After nearly a decade of operation, it is crucial to assess the role of this infrastructure today and evaluate whether it has successfully embodied the core principles of open science – openness, transparency, and visibility of scientific and Croatian scholarly output. This paper presents the Croatian national repository infrastructure as a case study, offering insights for comparison with similar national infrastructures.

The study employs a quantitative research approach, divided into two parts to provide a comprehensive overview of the current state and future development of repositories in Croatia. The first part analyses quantitative data and repository statistics. The DABAR infrastructure currently comprises 182 repositories and hosts over 249,000 digital objects, yet only slightly more than 50% of them are openly accessible.

To investigate the reasons behind the high percentage of restricted or closed-access objects, a survey was conducted among institutions that primarily deposit such items.

The findings of this research contribute to a broader discussion on open science practices and repository management at both European and international levels. The results will serve as a foundation for further improvements to the infrastructure, the promotion of open science principles, and the development of systematic support mechanisms to encourage greater accessibility and transparency in scholarly communication.

URL : National Repository Infrastructure and Open Access Challenges: The Croatian Perspective

DOI : https://doi.org/10.53377/lq.23061

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Research Data: A Public Good or a Private Asset?

Authors : Tadeu Fernando Nogueira, Trude Eikebrokk, Laila Økdal Aksetøy

This article is concerned with the issue of how Research Performing Organizations can balance the market and non-market values of the research data they hold. To address this issue, we adopt the lenses of the Resource Based View and Open Science and explore the interplay between them.

In doing so, this article addresses the question of whether it is possible to achieve a balance between research data as a public good and as a private asset and if so, how. Of particular interest are Research Performing Organizations in the institute sector that operate under both market and non-market logics, which have implications for how they govern their research data.

From the discussions undertaken in the article, one of the main conclusions is that Research Performing Organizations may benefit from adopting a research data governance model that captures both the economic and societal values of research data.

They could do so, for instance, by developing an integrative institutional policy and by actively using data management plans to evaluate the value of the data produced in research projects.

URL : Research Data: A Public Good or a Private Asset?

DOI : https://doi.org/10.53377/lq.22604