Data analysis

The 36th Astronomical Data Analysis Software and Systems conference brings together developers and users of astronomical software, algorithms and data systems. ICRAR hosts the meeting with SKAO, CSIRO and AusSRC. The programme includes research presentations, tutorials and community discussions. General registration remains open until 15 October 2026, and poster abstracts close on 1 October; the early-bird period has ended. Online registration is also available.

Learn to conduct reproducible research data analysis in R and RStudio without prior R experience. Lectures, live coding and practical exercises introduce vectors, matrices, data frames, lists, packages and functions. Participants import and inspect data, handle missing values, subset and join datasets, calculate summaries and export results. Further sessions develop ggplot2 visualisations, R Markdown reports, project organisation, reusable functions and strategies for finding help and troubleshooting errors. Exercises can use supplied datasets or participants’ own research data. 2–6 November

…cell biology, imaging, pathology and data analysis in a multidisciplinary team. Three-year full-time fellowship; AU$103,000–113,000 annually plus 15.4% superannuation. Applications close 22 November 2026 at 23:00 AEDT.

…waves, detector technology and data analysis are included. Researchers from observatories, laboratories and universities will compare current results and approaches across these fields. This edition is hosted in Argentina, home to the Pierre Auger Observatory.

…the route from genomic discovery and data analysis to more individualized infectious-disease care. HIV and hepatitis C illustrate how human genetic variation can reveal disease mechanisms and guide translational research.

…of the outputs, a user analyzing the data must still interpret the findings with respect to the simulation setup, network connectivity, and parameters of the neuronal and synaptic models. This information is not immediately available

…focusing on platforms for open data sharing and open source tools for reproducible data analysis. I will also discuss the role of the Brain Imaging Data Structure (BIDS), a community standard for data organization, in enabling this open science ecosystem, and will outline the scientific impacts of these resources.

…microcontrollers, programming, data analysis, and machine learning by guiding students through the “hands on” construction of an increasingly capable robot. In parallel, related concepts in neuroscience are introduced as nature’s solution to the challenges students encounter while designing and building their own intelligent system.

In systems neuroscience, datasets are multimodal and include data-streams of various origins: multichannel electrophysiology, 1- or 2-p calcium imaging, behavior, etc. Often, the exact nature of data streams are unique to each lab, if not each project. Analyzing these datasets in an efficient and open way is crucial for collaboration and reproducibility. In this combined webinar and tutorial, Adrien Peyrache and Guillaume Viejo will present Pynapple, a Python-based data analysis pipeline for systems neuroscience. Designed for flexibility and versatility, Pynapple allows users to perform cross-

In systems neuroscience, datasets are multimodal and include data-streams of various origins: multichannel electrophysiology, 1- or 2-p calcium imaging, behavior, etc. Often, the exact nature of data streams are unique to each lab, if not each project. Analyzing these datasets in an efficient and open way is crucial for collaboration and reproducibility. In this combined webinar and tutorial, Adrien Peyrache and Guillaume Viejo will present Pynapple, a Python-based data analysis pipeline for systems neuroscience. Designed for flexibility and versatility, Pynapple allows users to perform cross-

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement

…The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minim

…Another example requiring large datasets - modern data analysis methods such as deep learning. These have been successfully applied to a number of biological and medical questions. In this talk we will describe an open-source platform allowing such experiments to be automated. The platform consists of an XY stage, perfusion sy

In this talk I will present recent developments in data sharing, organization, and analyses that allow to build fully reproducible workflows. First, I will present the Brain Imaging Data structure and discuss how this allows to build workflows, showing some new tools to read/import/create studies from EEG data structured that way. Second, I will present several newly developed tools for reproducible pre-processing and statistical analyses. Although it does take some extra effort, I will argue that it largely feasible to make most EEG data analysis fully reproducible.