Researchers' contribution needed! - New survey on computational reproducibility is open

Researchers of Springer Nature communities from different disciplines are invited to contribute to the important topic of science reproducibility by filling the survey (see link below) and by providing their views and insights

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"Science crisis" appeared a few years ago, when it was revealed that more than 70% of the research results cannot be repeated and are not transparent (Baker, 2016). Also, it was reported that half of the annual research investment in the USA was lost due to lack of reproducibility, resulting in an estimated loss of USD 28 billion (Simeon-Dubach et al., 2016; Ortega-Paíno & Tupasela 2019). Enhancing reliability and efficiency of scientific research, reproducibility of results is a crucial point (Munafo et al., 2017; Macleod, 2021).

At researchers’ side, the pressure to publish in quantity often outshines objectives for quality and transparency. Even proposed interventions to promote reproducibility have themselves been irreproducible.

Recognizing these challenges, the Horizon Europe project, entitled “Open Science to Increase Reproducibility In Science (OSIRIS)” was launched in 2023. It aims to provide evidence-based solutions to support reproducibility in the scientific process by the involvement of various actors of the process. Accordingly, a team of open science experts in close collaboration with stakeholders, investigates inherent drivers, elaborate and test new solutions, assess existing solutions and identify incentives that persuade researchers, publishers and funders to promote reproducibility in scientific research.

Computational reproducibility is an important aspect of the work. In line with the Forrt definition, it refers to the “ability to recreate the same results as the original study (including tables, figures, and quantitative findings), using the same input data, computational methods, and conditions of analysis”. Nowadays, researchers from quite different fields can exploit a wide range of possibilities in their work, offered by digital technologies and computers. This, of course raises several questions and challenges in terms of research reproducibility, and it would be difficult to mention a single field that is not affected by this question.

In the past years, a few survey-based studies were prepared about data management and sharing, and about general and field specific aspects of computational reproducibility. However, considering the rapid development of digitalization, an up-to-date survey can point out interesting details about recent practices, issues and challenges.

Accordingly, an online survey about computational reproducibility has launched to discover the insights of researchers.

As the OSIRIS team members of the Hungarian University of Agriculture and Life Sciences, together with other OSIRIS members, we compiled the survey to acquire information from researchers, covering different geographical and disciplinary background about their insights and perceptions regarding computational reproducibility.

The partners in OSIRIS project are predominantly from biomedical and agricultural sciences. Therefore, the survey is designed to solicit insights also from other disciplines, aiming to contribute to more comprehensive guidelines and to the development of possible interventions.

The survey is anonymous and it is composed of 6 main parts of 35 questions.  Details can be studied in the respective protocol (Gelsleichter et al, 2023).

The survey is disseminated through various social media platforms and through the websites of scientific communities and available for filling by the end of July 2024.

We are requesting researchers of Springer Nature communities from different disciplines to contribute to this important field by filling the survey and by providing their views and insights.


Baker, M. (2016). 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454

Gelsleichter, Y.A., Naudet, F., Banzi, R., Gopalakrishna, G., Stegeman, I, Van den Eynden, V., Onghena, P., Kozula, M., Fratelli, M., Dudda, L.A., Baranyai, L., Kertesz, I., De Vito, N., Vinatier, C., Varga, M. Survey on computational reproducibility covering quantitative research. Study protocol.

Macleod, M. (2021). Want research integrity? Stop the blame game. Nature, 599(7886), 533–533.

Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., Percie Du Sert, N., Simonsohn, U., Wagenmakers, E.-J., Ware, J. J., & Ioannidis, J. P. A. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1(1), 0021.

Ortega-Paíno, E., & Tupasela, A. (2019). Biobanks and biobank networks.

Simeon-Dubach, D., Zeisberger, S. M., & Hoerstrup, S. P. (2016). Quality Assurance in Biobanking for Pre-Clinical Research. Transfusion Medicine and Hemotherapy: Offizielles Organ Der Deutschen Gesellschaft Fur Transfusionsmedizin Und Immunhamatologie, 43(5), 353–357.



Previous, short version of this blogpost was originally shared on the OSIRIS webpage: read it here -

The OSIRIS project is funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101094725.

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