Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort.

Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study. Investing effort in user documentation and interacting with the user base can, in turn, help developers improve quality of their software. In this work, we detail and evaluate our multi-modal teaching approach to research data management in the DataLad ecosystem, both in general and with concrete software use. Spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of research data management tips in a knowledge base, our free and open source collection of training material has made research data management and software training available to various different stakeholders over the past five years.

Open and reproducible neuroimaging: From study inception to publication.

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 general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.

FAIRly big: A framework for computationally reproducible processing of large-scale data.

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 restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original computing infrastructure. We demonstrate the framework's performance using two showcases: one highlighting data sharing and transparency (using the studyforrest.org dataset) and another highlighting scalability (using the largest public brain imaging dataset available: the UK Biobank dataset).

In defense of decentralized research data management.

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 interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation.

REMoDNaV: robust eye-movement classification for dynamic stimulation.

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 dynamic stimuli such as video sequences. Here we present an event classification algorithm-built on an existing velocity-based approach-that is suitable for both static and dynamic stimulation, and is capable of classifying saccades, post-saccadic oscillations, fixations, and smooth pursuit events. We validated classification performance and robustness on three public datasets: 1) manually annotated, trial-based gaze trajectories for viewing static images, moving dots, and short video sequences, 2) lab-quality gaze recordings for a feature-length movie, and 3) gaze recordings acquired under suboptimal lighting conditions inside the bore of a magnetic resonance imaging (MRI) scanner for the same full-length movie. We found that the proposed algorithm performs on par or better compared to state-of-the-art alternatives for static stimulation. Moreover, it yields eye-movement events with biologically plausible characteristics on prolonged dynamic recordings. Lastly, algorithm performance is robust on data acquired under suboptimal conditions that exhibit a temporally varying noise level. These results indicate that the proposed algorithm is a robust tool with improved classification accuracy across a range of use cases. The algorithm is cross-platform compatible, implemented using the Python programming language, and readily available as free and open-source software from public sources.

Loneliness and Health: The Moderating Effect of Cross-Cultural Individualism/Collectivism.

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 (N = 40,797), as provided by the Survey of Health, Ageing, and Retirement in Europe data. Multilevel regression analyses were employed. Moderating effects were analyzed for multiple health outcomes: activities of daily living, instrumental activities of daily living, grip strength, life satisfaction, depression, memory performance, verbal fluency, and numeracy. Results: Cultural individualism significantly moderated the effect of loneliness on health regarding most health outcomes. In general, the effect of loneliness on health became stronger in less individualistic/more collectivistic countries. Discussion: Cultural individualism proved to be one important moderator of the loneliness-health relationship. As previous studies mostly used samples from highly individualistic countries, the current literature might severely underestimate the global public health burden of loneliness.

Loneliness, social isolation, their synergistic interaction, and mortality.

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 nationally representative sample of middle-aged and older adults in Germany (N = 4,838) with a follow-up period of up to 20 years. RESULTS: We found that the effects of loneliness and social isolation synergistically interact with each other: The higher the social isolation, the larger the effect of loneliness on mortality, and the higher the loneliness, the larger the effect of social isolation. CONCLUSIONS: Both constructs are important in predicting health. Researchers and practitioners should consider loneliness, social isolation, and their interaction whenever possible. (PsycINFO Database Record

Disentangling Loneliness: Differential Effects of Subjective Loneliness, Network Quality, Network Size, and Living Alone on Physical, Mental, and Cognitive Health.

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 affect, negative affect, depression, cognitive performance, physical functioning, and pulmonary function) via regression analyses. RESULTS: We found that subjective loneliness and network quality best predicted mental health; contrarily, network size and living alone best predicted physical and cognitive health. DISCUSSION: Different measures of social disconnectedness have differential effects on health. Therefore, using only global measures or one aspect of social disconnectedness might obfuscate potential health hazards. Researchers and practitioners should be mindful of differences between these measures and should include multiple aspects of social disconnectedness in their research and practice. Future studies should explore the causes why these measures and their effects differ.

Subscales of the Barratt Impulsiveness Scale differentially relate to the Big Five factors of personality.

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 impulsiveness. In three independent studies, we related the subscales of the Barratt Impulsiveness Scale (BIS) to the Big Five factors of personality. Study 1 investigated the associations between the BIS subscales and the Big Five factors as measured by the NEO Five-Factor Inventory (NEO-FFI) in a student sample (N = 113). Selective positive correlations emerged between motor impulsiveness and Extraversion and between attentional impulsiveness and Neuroticism. This pattern of results was replicated in Study 2 (N = 132) using a 10-item short version of the Big Five Inventory. In Study 3, we analyzed BIS and NEO-FFI data obtained from a sample of patients with pathological buying (N = 68). In these patients, the relationship between motor impulsiveness and Extraversion was significantly weakened when compared to the non-clinical samples. At the same time, the relationship between attentional impulsiveness and Neuroticism was substantially stronger in the clinical sample. Our studies highlight the utility of the BIS subscales for clarifying the relationship between impulsiveness and the Big Five personality factors. We conclude that impulsiveness might occupy multiple places in multidimensional personality frameworks, which need to be specified to improve the interpretability of impulsiveness scales.