Adina Svenja Wagner

Adina Svenja Wagner
Forschungszentrum Jülich · Institute of Neurosciences and Medicine (INM)

Master of Science

About

20
Publications
7,140
Reads
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246
Citations
Introduction
After graduating with a M.Sc in Psychology in 2019, I'm now a doctoral researcher at the Juelich Research Centre, INM-7, in the Psychoinformatics Lab. My research focus lies on open source software development to foster open and reproducible scientific practices and analyses in neuroscience. In previous employments and internships, I acquired experience in neuropsychological assessments, primarily with dementia patients, in experimental and comparative psychology, working with different species of corvids, and in teaching, as a teaching assistant for statistics.
Additional affiliations
April 2019 - present
Forschungszentrum Jülich
Position
  • PhD Student
October 2018 - February 2019
Dartmouth College
Position
  • visiting researcher
Description
  • Research stay at Jim Haxbys lab under supervision of Prof. Yaroslav Halchenko. Work on open source software development for neuroscience, fmri and eye-tracking analysis.
October 2017 - July 2018
Otto-von-Guericke-Universität Magdeburg
Position
  • Tutor (Graduate teaching assisstant)
Description
  • Statistics tutor in Stats1 & Stats2 for BSc Psychology
Education
October 2016 - February 2019
Otto-von-Guericke-Universität Magdeburg
Field of study
  • Clinical Neuroscience/ Psychology
October 2012 - April 2016
Technische Universität Braunschweig
Field of study
  • Psychology

Publications

Publications (20)
Article
Full-text available
Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictio...
Article
Full-text available
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 managemen...
Book
The DataLad Handbook is a comprehensive educational resource for data management with DataLad. Find it at handbook.datalad.org
Preprint
Full-text available
Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in...
Preprint
Full-text available
Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictio...
Article
Full-text available
As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of st...
Preprint
Full-text available
The move from in-person to online scientific conferences due to the global health crisis has been hailed as a positive step towards inclusivity in its attenuation of economic, physical and legal barriers. Yet pre-existing and new challenges to truly inclusive conference experiences remain unaddressed. While acknowledging the benefits of an online s...
Article
Full-text available
Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but inte...
Article
Full-text available
Objectives: The adverse health effects of loneliness are well documented, but less is known about cultural moderators of this relationship. Contributing to the literature, we examined whether cross-cultural differences in individualism moderate the effect of loneliness on health. Methods: We used population-based longitudinal data of 14 countries (...
Article
Full-text available
Tracking of eye movements is an established measurement for many types of experimental paradigms. More complex and more prolonged visual stimuli have made algorithmic approaches to eye-movement event classification the most pragmatic option. A recent analysis revealed that many current algorithms are lackluster when it comes to data from viewing dy...
Chapter
The simultaneous acquisition of functional magnetic resonance imaging (fMRI) with in-scanner eye tracking promises to combine the advantages of full-brain coverage of brain activity measurements with a fast and unobtrusive capture of eye movement behavior and attentional deployment. Despite its applicability to a wide variety of research questions,...
Preprint
Full-text available
Tracking of eye movements is an established measurement for many types of experimental paradigms. More complex and lengthier visual stimuli have made algorithmic approaches to eye movement event detection the most pragmatic option. A recent analysis revealed that many current algorithms are lackluster when it comes to data from viewing dynamic stim...
Article
Full-text available
Objective: Although loneliness and social isolation are distinct constructs, only few studies have examined the putative synergistic effects of loneliness and social isolation on health. The current study strives to fill this gap. We ask, "Do loneliness, social isolation, and their interaction predict mortality?" Methods: We used a large nationa...
Article
Die hohe Zahl Erkrankter macht die Demenzen mit Lewy-Körpern zu einem Thema von Relevanz. Für die Diagnose bleibt die klinische Symptomatik wichtigster Anhaltspunkt und noch immer kann die Erkrankung nur symptomatisch behandelt werden. Neue Möglichkeiten der Bildgebung und ein Umdenken hinsichtlich der Pathophysiologie bilden die Basis für interess...
Article
Full-text available
The place of impulsiveness in multidimensional personality frameworks is still unclear. In particular, no consensus has yet been reached with regard to the relation of impulsiveness to Neuroticism and Extraversion. We aim to contribute to a clearer understanding of these relationships by accounting for the multidimensional structure of impulsivenes...
Article
Full-text available
Objective: To examine whether different measures of social disconnectedness-subjective loneliness, network quality, network size, living alone-have differential effects on the health of older adults. Methods: We used a longitudinal sample of the German Aging Survey ( N = 4,184) and analyzed seven measures of health (life satisfaction, positive a...

Questions

Question (1)
Question
Hi,
I have some experience in working with eye tracking data, but during a paper review I received an advice/a request that gave me the impression that I have so far missed one core aspect of noise estimation.
So far, I was under the impression that precision estimation is commonly done in the form of root mean squared deviations of recorded samples against a known fixation target. In other words, prior to my participants viewing some sort of stimuli, I have them fixate a target. I'll calculate the root mean squared deviations (in visual angle) of the recorded fixations to this target in order to get an estimate on how good the precision of the eye tracking setup is. A high value is indicative of low precision and thus lower quality data.
In a paper that is currently under review, we provide a number of noise estimates, but no sample to sample RMS (mainly, because we used an open dataset, and some precision measurements were already included in this original publication). A reviewer now suggested that we calculate and add the S2S RMS for our data and referred to this paper as an example:
This paper details their precision estimation as follows:
"Precision was computed with a moving-window method applied to the entire signal. We computed the RMS deviation per window (31 samples, 103.33 ms), took the median RMS deviation per trial and averaged this over all trials." Trials in this case consisted of viewing static stimuli.
So far, I was unaware that precision is commonly computed on trial data, and the paper unfortunately doesn't go into further detail on their method or provides code. I would be delighted if someone could shed some light on the method described in this paper, elaborate on whether precision estimation is commonly done on trial data (the trials in the data we are using consist of free viewing of dynamic stimuli), or about other methods on how to do so.
Many thanks in advance!

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Projects

Projects (2)
Project
Teach and demonstrate state of the art (research) data management practices in a free and open source educational resource.
Project
An open source (Python) implementation of Dewhurst et al.'s (2012) MultiMatch toolbox for multidimensional comparison of scanpaths. Current status found here: https://github.com/AdinaWagner/multimatch