ORCID Profile
0000-0002-1694-7069
Current Organisation
University of Oxford
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Publisher: Springer Science and Business Media LLC
Date: 11-11-2021
DOI: 10.1038/S41562-021-01211-8
Abstract: We argue that statistical practice in the social and behavioural sciences benefits from transparency, a fair acknowledgement of uncertainty and openness to alternative interpretations. Here, to promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data (2) quantifying inferential uncertainty (3) assessing data preprocessing choices (4) reporting multiple models (5) involving multiple analysts (6) interpreting results modestly and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in Merton's ethos of science as reflected in the norms of communalism, universalism, disinterestedness and organized scepticism. We believe that these ethical considerations-as well as their statistical consequences-establish common ground among data analysts, despite continuing disagreements about the foundations of statistical inference.
Publisher: Center for Open Science
Date: 05-01-2023
Abstract: Given that the choices made during recording, preprocessing and analysis of event-related potentials (ERP) data can affect study outcomes, it is critical that they are transparently reported to allow for reproducibility and replicability. Yet, systematic reviews of reporting practices in the field have shown that journal articles do not meet this goal and that guidelines for writing them better have not resulted in a sufficient improvement to reporting transparency.ARTEM-IS aims to address this issue by building dynamic, interactive web applications that support documenting information required by existing publication guidelines in the form of a standardised metadata template. Completing an ARTEM-IS form results in a human-reader-friendly PDF and a machine-readable JSON summary of methodological information, which allows for a level of reporting precision higher than what is typically found in journal articles. These can be used as supplements to a publication, as a memory aid when writing a paper, or as records that allow easier metadata extraction in comparison to verbal descriptions in papers.Here, we present the ARTEM-IS for ERP, which supports describing a typical ERP study, including most of its core methodological aspects (study description, experimental design, hardware, data acquisition, pre-processing, measurement, visualisation, additional comments). We discuss the current contents of the form, web application functionalities, current limitations, and potential directions for future developments. In addition, the process of building the form contents and the web application through a collaborative grassroots initiative is described. Finally, we argue that a wider adoption of ARTEM-IS can bring benefits to different stakeholders: researchers themselves or their collaborators, especially on large-scale projects, reviewers, readers of a paper, and the scientific community at large.
Publisher: Center for Open Science
Date: 17-06-2022
Abstract: This preregistration template guides researchers who wish to preregister their EEG projects, more specifically studies investigating event-related potentials (ERPs) in the sensor space.
Publisher: Center for Open Science
Date: 02-03-2021
Abstract: We argue that statistical practice in the social and behavioral sciences benefits from transparency, a fair acknowledgement of uncertainty, and openness to alternative interpretations. To promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data (2) quantifying inferential uncertainty (3) assessing data preprocessing choices (4) reporting multiple models (5) involving multiple analysts (6) interpreting results modestly and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in Merton's ethos of science as reflected in the norms of communalism, universalism, disinterestedness, and organized skepticism. We believe that these ethical considerations --and their statistical consequences-- establish common ground among data analysts, despite continuing disagreements about the foundations of statistical inference.
Location: Netherlands
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Johannes Algermissen.