Alain de Cheveigné

Primary affiliation: Senior Scientist, National Centre for Scientific Research (CNRS), Paris, France

Title: Virtual electrode or virtual scalpel? A cancellation-based approach to data analysis and source localization

Abstract: The "virtual electrode" is an appealing metaphor as it suggests that we can - with the right tools and skills - use macroscopic recording techniques such as EEG or MEG to place an electrode anywhere in the brain, and record from it as if it were a real, microscopic electrode. Like every good metaphor, it can sometimes lead us astray. In this talk I'll suggest that a more accurate metaphor - if less appealing - is that of a "virtual scalpel" by which activity from a source within the brain is suppressed within the macroscopic recording. With J channels, up to J-1 sources can be suppressed while still retaining some information about other sources. Interestingly, with appropriate source and forward models, it is possible to accurately locate any source within source space, as the intersection of the null sets of all the spatial filters that suppress it. A source separation method such as ICA offers J-1 such filters per source, potentially a very strong constraint on source location.

Bio: Alain de Cheveigné was trained in Mathematics and Physics at Paris University and is currently Senior Scientist emeritus at CNRS and the École normale supérieure in Paris. He is active in psychophysics and modeling of auditory perception, audio signal processing, and data analysis methods for electrophysiological and brain imaging data.

Christophe Grova

Primary affiliation: Associate Professor, Physics / PERFORM Centre, Concordia University, Montreal Canada; Adjunct Professor, Biomedical Engineering Dpt, McGill University, Montreal Canada

Title: EEG/MEG source imaging applications in sleep and epilepsy

Abstract: Accurate delineation of the epileptogeneic zone (EZ) during presurgical workup of focal drug-resistant epilepsy patients is challenging. Stereo-electroencephalography (SEEG) recordings, considered as the gold-standard for the localization of the EZ, is essential when mapping the seizure-onset zone (SOZ) and determining surgical candidacy. However, a successful investigation requires a strong pre-implantation hypothesis on the localization of the EZ, which can be derived from non-invasive investigations such as EEG or Magnetoencephalography (MEG) source imaging. The purpose of this talk is to introduce the Maximum Entropy on the Mean (MEM) source imaging framework, as a Bayesian approach to solve the ill-posed inverse problem of localizing the generators of EEG and MEG signals along the cortical surface. I will first review the time-domain version of MEM, which is sensitive to the spatial extent of the underlying generators, notably when localizing transient epileptic discharges. The localization accuracy of the MEM method and its ability to recover the spatial extent of the generators was quantitatively validated using SEEG (Abdallah Neurology 2022). I will then introduce the time-frequency wavelet-based extension of MEM (wavelet MEM) as a source image method of interest to localize transient oscillations, such as ictal oscillations localizing the seizure onset zone, transient high frequency oscillations and ongoing resting state oscillations. The accuracy of wavelet-based MEM to recover oscillatory power spectra or connectivity features from resting state MEG data was validated using the MNI SEEG atlas of normal brain activity (Afnan Neuroimage 2023) as ground truth and we reported interesting MEG resting state patterns able to predict postsurgical outcome in epilepsy (Aydin J. Neural Engineering 2020) . Finally, investigating sleep characteristics during prolonged recordings using high-density EEG or MEG is also an important topic, where the generators of sleep specific discharges such as spindles or slow waves are spatially extended. MEM-based approaches are particularly appealing for spindle localization (Zerouali Front. In Neuroscience 2014) or when investigating interactions between sleep and epilepsy (Avigdor Ann Clin Transl Neurol. 2024).

Bio: Christophe Grova is Associate Professor affiliated to the Department of Physics of Concordia University and a research member of PERFORM center since July 2014, while remaining adjunct Professor affiliated to Biomedical Engineering Dpt and Neurology and Neurosurgery Dpt at McGill Faculty of Medicine. He received his Engineering and Master degrees in biomedical engineering at the University of Technology of Compiègne (France) in 1998, followed by a Ph.D. in multimodal image registration at University of Rennes (France). From 2003 to 2008, his postdoctoral studies at the Montreal Neurological Institute were focussed on EEG source imaging of epileptic discharges and the correspondence with EEG/fMRI results. Dr Grova has been assistant Professor at McGill from July 2008 to July 2014 before joining Concordia University. His areas of expertise are EEG/MEG source localization, multimodal data fusion involving EEG/MEG, fMRI and functional Near InfraRed Spectroscopy (fNIRS), for application in epilepsy and sleep research. His team is also handling the development and validation of two software packages: MEM in Brainstorm for EEG/MEG source localization and NIRSTORM for fNIRS data analysis.

Jens Haueisen

Primary affiliation: tba

Title: tba

Abstract: tba

Stefan Haufe

Primary affiliation: Technische Universität Berlin, Germany

Title: Estimating across-site PAC, delays, and complex network properties under source mixing

Abstract: Magneto- and electroencephalography (M/EEG) offer high temporal resolution, making it possible to study directed and frequency-resolved interactions including different types of cross-frequency coupling. However, inevitable source mixing can cause spurious FC if not properly accounted for. In this talk, I will consider the problems of detecting and quantifying Granger causality, phase-amplitude coupling, signal transmission delays and complex network properties from M/EEG data in the presence of source mixing. I will present methods and simulation studies highlighting the capabilities and limitations of M/EEG based FC estimation. Finally, I will show normative functional M/EEG connectomes derived from large pediatric and adult cohorts and their dependance on age and gender. These normative connectomes are expected to become instrumental for characterizing pathological brain communication patterns.

Bio: Stefan Haufe is joint Associate Professor of Uncertainty, Inverse Modeling and Machine Learning at Technische Universität Berlin and Physikalisch-Technische Bundesanstalt Berlin as well as a Group Leader at Charité - Universitätsmedizin Berlin. His group develops signal processing, inverse modeling and machine learning methods primarily for analyzing neuroimaging data. He is also interested in model interpretation and "explainable artificial intelligence".

Laura Marzetti

Primary affiliation: Associate Professor in Applied Physics at the University of Chieti-Pescara, Chieti, Italy

Title: tba

Abstract: tba

Bio: Laura Marzetti is Associate Professor in Applied Physics and head of the Methods and Models for Brain Oscillations (MAMBO) lab at the University of Chieti-Pescara. She began her research career in Germany at the University of Ulm and completed a PhD in Functional Neuroimaging at the University of Chieti-Pescara in collaboration with the Fraunhofer FIRST Institute Berlin on the development of methods for MEG/EEG functional connectivity analysis. Her current research focuses on the development of methods and tools to exploit the characteristics of functional brain networks in a multimodal framework and in real time. Her major research lines are multidimensional functional connectivity methods and real time estimation of functional connectivity for brain-state-dependent neurostimulation.

Pieter van Mierlo (represented by Jolan Heyse)

Primary affiliation: Associate Professor at Ghent University, Belgium

Title: Advanced EEG analysis in epilepsy: localization and diagnosis

Abstract: The first part of the presentation will focus on automated EEG source localization to localize the epileptogenic focus using patient-specific head models. The second part of the presentation will cover the diagnosis of epilepsy after a first seizure using Bayesian machine learning.

Bio: Pieter van Mierlo is an associate professor at Ghent University. He works in the domain of neuro-engineering. His main expertise is in advanced EEG analysis for clinical applications. Pieter is also the co-founder and chief scientific officer at Clouds of Care NV, a spin-off company of Ghent University that provides EEG analysis as a service for clinical applications and trials.

Srikantan Nagarajan

Primary affiliation: Professor at the University of California, San Francisco (UCSF)

Title: Structure-Function Integration with MEG - Spectral Graph Modeling of Neural Oscillations

Abstract: In this talk, I will present recent work combining MEG imaging data with structural connectivity information obtained from diffusion MRI. We have developed a modeling framework from graph spectral theory and Bayesian inference algorithms for estimating model parameters that fit MEG imaging data. I will present initial results from spectral graph modeling and its application to MEG imaging data in patients with Alzheimer's disease.

Bio: Srikantan Nagarajan is a Professor at the University of California, San Francisco (UCSF) in the Department of Radiology and Biomedical Imaging. His research interests are in the development of machine learning algorithms for multimodal structural and functional brain imaging and neuromodulation with applications to speech neuroscience and brain plasticity. He has led several basic and clinical neuroscience studies in a variety of diseases including dementia, brain tumors, epilepsy, schizophrenia, neurodevelopmental disorders, and tinnitus. He is a Fellow of the IEEE and the AIMBE.

Guiomar Niso

Primary affiliation: Head of the Neuroimaging Group at Cajal Institute, Consejo Superior de Investigaciones Científicas

Title: Beyond Neural Traces: Open and Reproducible M/EEG Research Throughout the Full Cycle

Abstract: Good scientific practice in magnetoencephalography (MEG) and electroencephalography (EEG) is essential for ensuring reliable knowledge. This talk will provide an overview of good scientific practice in M/EEG research, emphasizing collaboration, openness, and reproducibility. It will introduce community-developed resources and tools that support these practices throughout the entire research cycle, covering study inception and planning, data acquisition, research data management with an emphasis on standards, data processing and analysis, and research dissemination beyond publication. We will also reflect on more intangible aspects, including ethical implications a›nd the responsibility we scientists have in engaging with societal challenges. The aim is to encourage a commitment to collaborative, open, and reproducible M/EEG research, embracing good practices. Adhering to these principles can facilitate the generation of high-quality results, significantly contributing to advancing our understanding of neural traces and, ultimately, of the complex functioning of the human brain.

Bio: Dr. Guiomar Niso is the head of the Neuroimaging Group at Cajal Institute, CSIC, and an elected member of the Young Academy of Spain. She works in human electrophysiology and neuroimaging to characterise healthy and diseased brain states. Dr. Niso has contributed to multiple open science initiatives, pioneering open data repositories (the Open MEG Archives: OMEGA), open software (Brainlife, Brainstorm, Hermes) and open standards (the Brain Imaging Data Structure: BIDS), to foster reproducibility and transparency in neuroscience. See more at: https://guiomarniso.com

Vadim Nikulin

Primary affiliation: Principal Investigator, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig

Title: Unifying neural oscillations and evoked responses

Abstract: Neural oscillations and evoked responses represent two main modes of electrophysiological activity (EEG/MEG) in the human brain, yet the link between them remains rather illusive. Here we present a unifying view on these two types of activity where evoked responses are generated by the amplitude modulation of neural oscillations with non-zero mean. We show how somatosensory, visual, motor and cognitive responses can be generated or affected by this baseline-shift mechanism (BSM). Moreover, we show how phase-amplitude coupling can be conceptualized through BSM, which in turn has implications for interpreting neural activity/interactions in basic and clinical research.

Bio: By training, Vadim is a neurobiologist and obtained his PhD in Neuroscience in University of Helsinki while performing MEG/TMS research in BioMag Laboratory. Currently, he is a group leader at Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig. His research projects relate to the investigation of large scale spatio-temporal neuronaldynamics, particularly oscillatory activity in relation to sensorimotor tasks and cognition. His clinical research is focused on Parkinson’s disease and Stroke. In his research he uses EEG, MEG, LFP recordings as well as different types of non-invasive brain stimulation. He also develops multivariate algorithms for the analysis of multichannel electrophysiological recordings.

Ashwini Oswal

Primary affiliation: MRC Brain Network Dynamics Unit, University of Oxford

Title: Towards a network-based understanding of Parkinson's disease symptoms and therapies

Abstract: Parkinson's disease is a common neurological condition which results in progressive and incurable motor and non-motor impairments. The considerable heterogeneity in the presentation and progression of PD remains poorly understood. I will present data adopting a multimodal (empirical and theoretical) approach, to provide insights into the network mechanisms of specific PD phenotypes. I will focus on how this approach can be used to understand and to predict cortico-subcortical interactions leading to movement impairments and cognitive impairments - both of which characterise the parkinsonian state. Finally, I will discuss how these insights may be used to inform the next generation of neuromodulation therapies.

Kamalini Ranasinghe

Primary affiliation: Assistant Professor of Neurology at the University of California, San Francisco, USA

Title: Neuronal hyperexcitability and neurophysiological manifestations in AD

Abstract: Excitation-to-inhibition (E/I) imbalance is believed to be a key contributor to synaptic and network degeneration in Alzheimer’s disease (AD). While aberrant network activity remains a clear unifying link between AD pathophysiology and cognitive deficits, the mechanisms of E/I imbalance leading to disrupted networks and their relationships to Aβ and tau in humans remain poorly understood. In this talk I will present our work investigating the pathophysiological correlates of oscillatory signatures and E/I imbalance in clinical AD populations.

Bio: Kamalini Ranasinghe, is an Assistant Professor at the University of California, San Francisco (UCSF) Memory and Aging Center. As a physician-neuroscientist, she is passionate about understanding the neurons and neural networks that support human cognition and how their function gets disrupted with accumulation of proteins in neurodegenerative diseases, like Alzheimer’s disease. Her program of research uses electrophysiology in combination with molecular biomarkers to investigate the dysfunctional neural circuits and their mechanistic relationships with proteinopathies, and she currently leads the electrophysiological studies in patients with Alzheimer’s disease at the UCSF Memory and Aging Center.

Myriam C. Sander

Primary affiliation: Research Group Leader at the Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany

Title: Oscillatory traces of age differences in episodic memory

Abstract: Episodic memory decline is a hallmark of cognitive aging. I will show data suggesting that the neural traces of this memory decline are reduced alpha/beta desynchronization indicating reduced opportunities of information processing and imprecise coupling between theta phase and gamma power indicating reduced binding of information. Thus, a decline in the concerted interaction of oscillatory mechanisms during memory encoding and retrieval seems to be at the core of this prominent age-related memory decline.

Bio: Myriam is a Research Group Leader at the Max Planck Institute for Human Development in Berlin. As a developmental psychologist she is interested in cognitive and neural mechanisms that underlie memory formation, consolidation, and retrieval in different age groups. The core question of the group’s research is how age-related decline in memory performance can be explained by differences in the way memory content is represented in the neural code.

Tilmann Sander-Thömmes

Primary affiliation: Senior Researcher at the Physikalisch-Technische Bundesanstalt, Berlin, Germany

Title: MEG with OPM sensors: Opportunities for signal processing and modeling

Abstract: Optically pumped magnetometers (OPM) are rapidly gaining popularity as an alternative to SQUIDs for building sensor arrays for MEG. They are not a direct functional replacement for SQUIDs, but haver their own intrinsic challenges and advantages. In this talk I will explore the consequences of OPM characteristics for established signal processing methods in MEG.

Bio: Tilmann Sander-Thömmes is a senior researcher at the Physikalisch-Technische Bundesanstalt in the division of Biosignals. He holds a Diplom and PhD in physics.

Natalie Schaworonkow

Primary affiliation: Postdoctoral fellow, Ernst Strüngmann Institute for Neuroscience, Frankfurt

Title: Not just frequency: neural oscillations and their characteristic waveform shape

Abstract: Analysis of MEG/EEG/LFP data typically examines frequency and amplitude of neural oscillations. However, beyond frequency, these oscillations also often exhibit a distinct waveform shape. This presentation will cover the quantification of waveform shape to explore neural dynamics in the time domain. I will use examples taken from resting-state alpha- and mu-rhythms as well as oscillations in bat LFP. The objective is to improve our understanding of the physiological basis of cortical oscillations, partly challenging conventional spectral analyses that rely solely on canonical frequency bands.

Bio: Natalie Schaworonkow studied cognitive and computational neuroscience, and is currently a postdoctoral fellow at the Ernst Strüngmann Institute for Neuroscience in Frankfurt. Natalie uses EEG, MEG and iEEG recordings to investigate neural oscillations and works on spatiotemporal analysis techniques to enhance our understanding on the role of rhythms in cognition.

Rachel Kae Spooner

Primary affiliation: Alexander von Humboldt Postdoctoral Research Fellow, Heinrich-Heine University Düsseldorf

Title: Magnetoencephalography for the Investigation of Distant Deep Brain Stimulation Effects in Parkinson's Disease

Abstract: While the advent of subthalamic deep brain stimulation (STN-DBS) has been highly effective for temporarily alleviating motor symptoms in people with Parkinson's disease (PwP), some individuals are left without experiencing optimal clinical benefits. Some proposed parameters that may augment the efficacy of STN-DBS are the directionality and magnitude of current administered throughout the device, albeit the precise neurophysiological mechanisms underlying clinically-effective DBS parameter settings are not well understood. Herein, PwP implanted with STN-DBS completed low-frequency monopolar stimulation paradigms of the left STN during MEG. Data were evaluated in the time-frequency domain and imaged using minimum norm estimation. Peak vertex time series data were then extracted from ipsilateral sensorimotor regions to interrogate the directional specificity and magnitude of STN-DBS current administration on evoked and induced cortical responses and quantitative motor outcomes using linear mixed-effects models. Our results demonstrated temporally- (evoked) and spectrally-specific (oscillatory) neurophysiological modulations based on the clinical efficacy of DBS programming strategies, with more effective settings such as clinically-useful contacts and stimulation amplitudes eliciting increases in sensorimotor evoked potentials, reductions in cortical beta synchrony, and better behavioral performance compared to suboptimal settings. Taken together, our data suggest that the brain-behavior dynamics pertinent to STN-DBS protocols as measured herein using simultaneous low-frequency DBS-MEG recordings, may serve as effective targets for guiding DBS parameter selection for optimized motor symptom improvement in PwP in the future.

Bio: Rachel Spooner is a clinical translational neuroscientist who specializes in using a multidisciplinary integration of systems biology and neuroscience approaches to comprehensively characterize motor deficits across the lifespan. She received her PhD in Neuroscience under the direction of Dr. Tony Wilson from the University of Nebraska Medical Center and completed her postdoctoral training with Drs. Esther Florin and Alfons Schnitzler at Heinrich Heine University Düsseldorf. Her research primarily focuses on evaluating the neurobiological, neurophysiological and behavioral substrates of healthy and aberrant motor function using emerging MEG approaches, invasive and non-invasive neuromodulation, quantitative biological assays and comprehensive behavioral and clinical testing. The ultimate goal of her research is to use such data to improve diagnostic accuracy and treatment efficacy for age- and disease-related disturbances in human motor performance.

Sara Sommariva

Primary affiliation: Assistant Professor (RTDA) at University of Genova, Italy

Title: MEG inverse problem: a word of caution towards connectivity estimation

Abstract: Disruption in the functional connection between spatially distant brain areas have been found in different neurodegenerative dementias. The first step for an accurate estimation of functional connectivity from MEG data consists in the solution of the ill-posed MEG inverse problem, a step that requires different informed decisions by expert users. In this talk I will discuss the impact that some of these choices have on the final connectivity estimation, including for example the choice of the level of regularization within a minimum norm estimation framework (Vallarino et al, 2023) and the choice of the brain parcellation used for defining the brain areas (Brkić et al, 2023). Eventually, I will present a novel approach to tackle the problem. This approach consists in the solution of a novel inverse problem that will allow directly estimating the cross-power spectrum at source level from that computed by using the recorded MEG time series.

Bio: Sara Sommariva (PI) is assistant professor (RTDa) in Statistics at the Department of Mathematics of the Università di Genova, Genoa, Italy. She obtained a PhD in Mathematics and Applications in 2017 at the Università di Genova. After that, she has been a postdoctoral researcher in different national and international institutes, including the Department of Neuroscience and Biomedical Engineering at Aalto University, the Department of mathematics at Università di Genova and Consiglio Nazionale delle ricerche. Sara’s main research interests concern with the study of numerical and statistical methods for data analysis and model development in various biomedical applications, including statistical approaches for M/EEG data analysis and chemical reaction network models for cancer.

Peter Uhlhaas

Primary affiliation: Professor for Early Detection and Intervention of Mental Disorders at the Department of Child and Adolescent Psychiatry, Charité–Universitätsmedizin Berlin

Title: Gamma-Band Oscillations and Schizophrenia: A Translational and Developmental Perspective

Abstract: There is converging evidence that 40-Hz Auditory Steady-State Responses (ASSRs) are robustly impaired in schizophrenia and could constitute a potential biomarker for characterizing circuit dysfunctions as well as enable early detection and diagnosis. Here, I will summarize findings from electro- and magnetoencephalographic studies in participants at clinical high risk for psychosis, patients with first-episode psychosis as well as patients with 22q11.2 deletion syndrome to identify the pattern of deficits across illness stages, the relationship with clinical variables, and the prognostic potential. Finally, data on genetics and developmental modifications will be reviewed, highlighting the importance of late modifications of 40-Hz ASSRs during adolescence, which are closely related to the underlying changes in GABA (gamma-aminobutyric acid) interneurons. Together, our review suggests that 40-Hz ASSRs may constitute an informative electrophysiological approach to characterize circuit dysfunctions in psychosis that could be relevant for the development of mechanistic biomarkers.

Bio: Peter Uhlhaas obtained a BSc and PhD in Psychology from the University of Stirling, Scotland. He was a visiting researcher at Weill Medical College, Cornell University, New York (2001-2002), before joining the Department of Neurophysiology (Head: Prof. Wolf Singer), Max-Planck Institute (MPI) for Brain Research in Frankfurt, Germany. At the MPI, he became a group leader in 2006, investigating the neurophysiology of cognitive dysfunctions in schizophrenia. Peter joined the Institute of Neuroscience and Psychology, University of Glasgow, in 2012 where he is a principal investigator at the Centre for Cognitive Neuroimaging. He has published 120 articles in internationally high-ranking journals (Nature Rev Neuroscience, Neuron, PNAS, JAMA Psychiatry). In 2019, he became Professor for Early Detection and Diagnosis of Mental Disorders at the Department of Child and Adolescent Pschiatry Charité-Universitätsmedizin Berlin. His work is supported by the Medical Research Council, Einstein Foundation, German Research Foundation and Wellcome Trust.

Marieke van Vugt

Primary affiliation: Assistant Professor in the Cognitive Modeling Group at the University of Groningen, The Netherlands

Title: From mind-wandering to depression: using spontaneous thinking to develop EEG biomarkers of rumination

Abstract: A key component of depression is rumination, the tendency to engage in negative, self-related, repetitive and uncontrollable thought. Since the mind-wandering literature concerns itself with spontaneous thought, we sought to investigate rumination through the lens of mind-wandering. In my talk, I will present our work on finding EEG biomarkers of mind-wandering, and our examination of how these change when the mind-wandering becomes sticky or rumination-like. We compare these biomarkers in groups of more and less depressed, as well as remitted individuals. Results show that occipital alpha oscillations play an important role.

Bio: Marieke van Vugt is an assistant professor in the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen. Her research aims to understand how, when, and why we mind-wander. She is also fascinated by how this mind-wandering process is adaptive--as in the case of creativity--and when it becomes maladaptive, as is the case for depressive rumination. She uses a multimodal approach that combines computational modeling, scalp and intracranial EEG, behavioral studies, and eye-tracking. In addition, she is interested in how meditation practice affects our cognitive system, and she investigates meditation in both Western practitioners and Tibetan monks.

Umesh Vivekananda

Primary affiliation: Consultant neurologist and honorary lecturer, National Hospital of Neurology and Neurosurgery and Insitute of Neurology, UCL

Title: The cognitive implications of epilepsy at a functional network and single neuronal level

Abstract: Patients with temporal lobe epilepsy (TLE), the most common form of refractory epilepsy, suffer especially from cognitive issues in memory retention, spatial memory, and verbal memory. However the causes of cognitive impairment in TLE are unclear, in particular the relative contribution of factors such as epilepsy duration, long term AED use, burden of epileptiform activity present, and predisposition to neurodegeneration. At a whole-brain level, grey matter loss and cortical thinning and changes in functional connectivity networks have been implicated. In this talk I will discuss how intraregional oscillatory activity may be affected during spatial and associative memory formation in epilepsy, how epileptiform features such as interictal spikes have an impact, and how future approaches such as neural stimulation may remediate memory processing.

Bio: Dr Umesh Vivekananda undertook his PhD at the Institute of Neurology, UCL on the subject of presynaptic channelopathies. He continued his speciality training as part of an academic clinical lectureship at the National Hospital for Neurology and Neurosurgery, his research focussing on developing novel neurophysiological methods to epilepsy. He has published and taught on advanced analytic approaches to Magnetoencephalography (MEG) and was the first to report the use of Optically Pumped MEG in epilepsy.

Adina Wagner

Primary affiliation: Research Associate at Psychoinformatics Lab, INM-7, Juelich Research Centre

Title: DataLad - Data Management for Open Science

Abstract: DataLad is a Python-based tool for the joint management of code, data, and their relationship, built on top of a versatile system for data logistics (git-annex) and the most popular distributed version control system (Git). It adapts principles of open-source software development and distribution to address the technical challenges of data management, data sharing, and digital provenance collection across the life cycle of digital objects. This talk introduces the tool and its main features for open science in brief.

Bio: Adina Wagner is a research associate at the Forschungszentrum Jülich. She is a software developer for the DataLad project, an open source data management tool built upon Git and git-annex, and a proponent of open science, open source, and reproducible research.