ľ�ϸ���Ӱ��

Rens van de Schoot, Professor of Statistics for Small Data Sets at Utrecht ľ�ϸ���Ӱ��, has published two articles in Nature sub-journals within a short period of time. The first was published in the brand-new Nature Reviews Methods Primers. The other article, which is about , was published in Nature Machine Intelligence on 1 February.

The new was issued for the first time in mid-January. The online journal is aimed at a broad, interdisciplinary audience. It strives to present an authoritative introductory overview of specific methods and techniques, which is also accessible for non-experts. In addition, Methods Primers hopes to support researchers by evaluating research methods and offering guidance on reproducibility and open science.

We-science

The in Nature Reviews Methods Primers was written by Van de Schoot and colleagues and is about Bayesian statistics. The Utrecht ľ�ϸ���Ӱ�� statistician of creating this type of primer as something totally different from the classic approach of publishing in a scientific journal. ‘What I really like about this process is the shift from “me science” to “we science”. The international team that writes a primer is invited by the editor, and consists of both senior scientists and researchers who are just starting their careers. Each author is responsible for their own sub-section, and we give each other feedback. This creates an extra layer of peer feedback, in addition to 10 reviewers and five rounds of very intense editorial feedback.’ Van de Schoot thinks that there isn’t a single sentence left from the first submission. He laughs and adds: ‘And there’s still a mistake in our article, in Figure 1.’

Our aim with this project is to create an open-science community.

ASReview

Another form of ‘we science’ resulted in published in Nature Machine Intelligence on 1 February. This article describes the open-source research project called ASReview, which a multidisciplinary team from Utrecht ľ�ϸ���Ӱ�� is working on. Van de Schoot: ‘Our aim with this project is to create an open-science community in which both subject-matter researchers and data scientists help to develop AI-driven systematic literature studies.’ Van de Schoot says that everyone is welcome to . ‘People can get involved by helping to improve the documentation, donating data sets, or adding new features to the code.’

The multidisciplinary team from Utrecht ľ�ϸ���Ӱ�� that is working on the ASReview open-source research project consists of scientists from the Applied Data Science focus area, research software engineers and UX experts from ITS, information specialists from the ľ�ϸ���Ӱ�� Library, experts from the Open Science Programme, and a number of (former) Research Master’s students.