Founded in the spirit of facilitating the transition from closed scientific enquiry to a more open model we aim to transcending barriers between disciplines, science and the society.
We foster research at crossroads between interdisciplinary life and health sciences, basic understanding of learning processes and novel education technology/methodology testing and implementation, and digital sciences.
Anirudh Krishnakumar - CRI
Keywords: mental health, psychiatry, citizen science, interdisciplinary collaborative frameworks, science by/with/for communities, open collaborative science, community digital tools, SDGs, student-driven research paradigms
Anirudh Krishnakumar is a PhD student at CRI who will be defending his thesis shortly. This is the chance to hear more about his work over the past years. Thesis abstract is below.
One in four people worldover will be affected by mental health disorders at some point in her/his life, with a staggering cost estimate that could rise to €5.5 trillion by 2030, making it a global issue needing urgent attention, all the more critical due to the quality and access of services being far lower compared to physical illnesses. The foundation of this thesis is that, despite related approaches, technologies and initiatives being on the rise, current research efforts have been scattered, siloed and lacking interoperability to the point of sometimes marginalizing the role of even those directly afflicted. Thus, citizen science driven by the mental health community has significant scope to reach higher levels of inclusion, achievement and application in the times to come.
The overall aim of the thesis is to develop and study how an inclusive, interdisciplinary framework with multiple stakeholders can drive open collaborative citizen science on mental health that spans geographic, cultural and disciplinary borders. Specifically, I have focused on furthering a framework that will enable people with mental health conditions, their families, teachers and caregivers, researchers, clinicians, therapists, technology developers, students and citizens at large to explore and improve assessment and monitoring methodologies adding to/creating effective and innovative interventions/projects/initiatives. Towards this goal, the thesis involves the following interrelated and complementary components:
Student-driven collaborative frameworks for mental health open science: I deployed a cross-continental, student peer-to-peer learning-through-research study program for university students in France and India. A flexible curriculum targeting research meta-skills and 21st century skills was designed and implemented. The results and feedback suggested that students contributed to each others’ learning through interdisciplinary group work and action-oriented projects in mental health, developed research and contemporary skills, learned new research and digital tools and acquired new skills and methodologies within and beyond their own discipline. This component explores themes along the lines of reversing ‘brain drain’ for ‘brain gain’, students mutually benefitting from each other even while learning themselves, and applying their learning with mental health communities.
Linked Open Mental Health Database (MHDB): I curated and mapped the MHDB knowledge base of mental health disorders and symptoms, questionnaires, cognitive tasks, technologies, research publications, projects and resource guides in a single database for everyone to use. A research study was conducted investigating the effects of adding multiple domains (frequency, duration, intensity, context and ability) to the same core questions used in psychiatric assessments. Interestingly, the results suggested that context is more predictive than frequency in measuring disability and should be more uniformly applied, and also that the ‘multi-domain’ approach to questionnaires may improve mental health inferences and predictions of functional outcomes. The value of constructing MHDB, its potential applications and future projects and considerations are also discussed.
MindLogger Data Collection Platform and App for use across disciplines and user-scenarios: I presented the motivations and significance behind constructing this tool to give individuals/groups the possibility to do their own research, and build and administer surveys, assessments and tasks to collect and share observations, insights, data and solutions. I also highlighted how MindLogger could involve facilitating the monitoring of symptoms and behaviours, replicability and reproducibility of results, ecological momentary assessments, trans-cultural data collection, the improvement of the convenience, consistency and efficiency of research and data efforts. This also contributed to the way MindLogger evolved further. This component also covers MindLogger’s upcoming implementation in the Child Mind Institute’s Healthy Brain Network Study to collect data on participants’ daily activities (such as sleep, mood, stress, physical activity etc., and future projects and considerations.
Nicolas Behr - CRI
Reporting on recent progress made throughout my CRI Short-Term fellowship, I will present algorithmic advances and a first software prototype for the study of organo- and biochemical reaction systems (joint work with J.-L. Andersen, W. Fontana, R. Heckel, D. Merkle and M. G. Saadat). After providing a non-technical introduction to the key theoretical concept of rewriting theory, also touching briefly upon possible use scenarios within social network theory, the main focus of the talk will be a live demonstration of the prototype of our open-source algorithm collection for analyzing rewriting systems. Intuitively, the notion of pathways in chemical reaction systems is given a precise theoretical counterpart in the structure of so-called tracelets, which permits the development of a new approach to the static analysis of such pathways. The key purpose of our software project consists in rendering these category-theoretical algorithms accessible to practitioners in the applied sciences, concretely via providing a high-level API in the form of a Python package (and that relies upon the Microsoft Z3 SMT solver as key part of its computational core). Feedback on the live demonstration of the software prototype would thus be highly appreciated!