Identifying the most important facilitators of open research data sharing and reuse in Epidemiology: A mixed-methods study.

To understand how open research data sharing and reuse can be further improved in the field of Epidemiology, this study explores the facilitating role that infrastructural and institutional arrangements play in this research discipline. It addresses two research questions: 1) What influence do infrastructural and institutional arrangements have on open research data sharing and reuse practices in the field of Epidemiology? And 2) how could infrastructural and institutional instruments used in Epidemiology potentially be useful to other research disciplines? First, based on a systematic literature review, a conceptual framework of infrastructural and institutional instruments for open research data facilitation is developed. Second, the conceptual framework is applied in interviews with Epidemiology researchers. The interviews show that two infrastructural and institutional instruments have a very high influence on open research data sharing and reuse practices in the field of Epidemiology, namely (a) access to a powerful search engine that meets open data search needs and (b) support by data stewards and data managers. Third, infrastructural and institutional instruments with a medium, high, or very high influence were discussed in a research workshop involving data stewards and research data officers from different research fields. This workshop suggests that none of the influential instruments identified in the interviews are specific to Epidemiology. Some of our findings thus seem to apply to multiple other disciplines. This study contributes to Science by identifying field-specific facilitators and challenges for open research data in Epidemiology, while at the same time revealing that none of the identified influential infrastructural and institutional instruments were specific to this field. Practically, this implies that open data infrastructure developers, policymakers, and research funding organizations may apply certain infrastructural and institutional arrangements to multiple research disciplines to facilitate and enhance open research data sharing and reuse.

Toward Linguistic Recognition of Generalized Anxiety Disorder.

Background: Generalized anxiety disorder (GAD) refers to extreme, uncontrollable, and persistent worry and anxiety. The disorder is known to affect the social functioning and well-being of millions of people, but despite its prevalence and burden to society, it has proven difficult to identify unique behavioral markers. Interestingly, the worrying behavior observed in GAD is argued to stem from a verbal linguistic process. Therefore, the aim of the present study was to investigate if GAD can be predicted from the language people use to put their anxious worries into words. Given the importance of avoidance sensitivity (a higher likelihood to respond anxiously to novel or unexpected triggers) in GAD, this study also explored if prediction accuracy increases when individual differences in behavioral avoidance and approach sensitivity are taken into account. Method: An expressive writing exercise was used to explore whether GAD can be predicted from linguistic characteristics of written narratives. Specifically, 144 undergraduate student participants were asked to recall an anxious experience during their university life, and describe this experience in written form. Clinically validated behavioral measures for GAD and self-reported sensitivity in behavioral avoidance/inhibition (BIS) and behavioral approach (BAS), were collected. A set of classification experiments was performed to evaluate GAD predictability based on linguistic features, BIS/BAS scores, and a concatenation of the two. Results: The classification results show that GAD can, indeed, be successfully predicted from anxiety-focused written narratives. Prediction accuracy increased when differences in BIS and BAS were included, which suggests that, under those conditions, negatively valenced emotion words and words relating to social processes could be sufficient for recognition of GAD. Conclusions: Undergraduate students with a high GAD score can be identified based on their written recollection of an anxious experience during university life. This insight is an important first step toward development of text-based digital health applications and technologies aimed at remote screening for GAD. Future work should investigate the extent to which these results uniquely apply to university campus populations or generalize to other demographics.

Introducing participatory fairness in emergency communication can support self-organization for survival.

Participatory resilience of disaster-struck communities requires reliable communication for self-organized rescue, as conventional communication infrastructure is damaged. Disasters often lead to blackouts preventing citizens from charging their phones, leading to disparity in battery charges and a digital divide in communication opportunities. We propose a value-based emergency communication system based on participatory fairness, ensuring equal communication opportunities for all, regardless of inequality in battery charge. The proposed infrastructure-less emergency communication network automatically and dynamically (i) assigns high-battery phones as hubs, (ii) adapts the topology to changing battery charges, and (iii) self-organizes to remain robust and reliable when links fail or phones leave the network. The novelty of the proposed mobile protocol compared to mesh communication networks is demonstrated by comparative agent-based simulations. An evaluation using the Gini coefficient demonstrates that our network design results in fairer participation of all devices and a longer network lifetime, benefiting the community and its participants.