A standardised open science framework for sharing and re-analysing neural data acquired to continuous stimuli
Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in scenarios involving continuous sensory streams, such as speech and music. Over the past 10 years or so, novel analytic frameworks combined with the growing participation in data sharing has led to a surge of publicly available datasets involving continuous sensory experiments. However, open science efforts in this domain of research remain scattered, lacking a cohesive set of guidelines. This paper presents an end-to-end open science framework for the storage, analysis, sharing, and re-analysis of neural data recorded during continuous sensory experiments. We propose a data structure that builds on existing custom structures (Continuous-event Neural Data or CND), providing precise naming conventions and data types, as well as a workflow for storing and loading data in the general-purpose BIDS structure. The framework has been designed to interface with existing EEG/MEG analysis toolboxes, such as Eelbrain, NAPLib, MNE, and mTRF-Toolbox. We present guidelines by taking both the user view (rapidly re-analyse existing data) and the experimenter view (store, analyse, and share), making the process straightforward and accessible. Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis.
Code (0)
등록된 구현이 없습니다.
Tasks
EEGSimilar Papers 제목 키워드 기반
Seven simple steps for log analysis in AI systems
AI systems produce large volumes of logs as they interact with tools and users. Analysing these logs can help understand model capabilities, propensities, and behaviours, or assess whether an evaluation worked as intende…
Federated Data Science to Break Down Silos [Vision]
Similar to Open Data initiatives, data science as a community has launched initiatives for sharing not only data but entire pipelines, derivatives, artifacts, etc. (Open Data Science). However, the few efforts that exist…
Standardising the NLP Workflow: A Framework for Reproducible Linguistic Analysis
The introduction of large language models and other influential developments in AI-based language processing have led to an evolution in the methods available to quantitatively analyse language data. With the resultant g…
The Rise of Open Science: Tracking the Evolution and Perceived Value of Data and Methods Link-Sharing Practices
In recent years, funding agencies and journals increasingly advocate for open science practices (e.g. data and method sharing) to improve the transparency, access, and reproducibility of science. However, quantifying the…
ArticlesMathtext-classificationText ClassificationMapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice
The rapid proliferation of Large Language Models (LLMs) has raised significant trustworthiness and ethical concerns. Despite the widespread adoption of LLMs across domains, there is still no clear consensus on how to def…
EthicsFairnessRAGRetrieval-augmented Generation