Editorial of the Special Issue on Quantified Self and Personal Informatics

Abstract

In recent years, we witnessed the spreading of a plethora of wearable and mobile technologies allowing for a continuous and "transparent" gathering of personal data. People are increasingly visualizing information about physical activity, health, everyday movements, and mood to monitor their routines, increase their performance, or change their behavior.Two decades ago, lifelogging research, which originated in research labs, attempted to make everyday recording effortless [1,2], aiming at creating a sort of "total" repository of an individual's life. In more recent years, however, the idea of endlessly hoarding personal information for archiving purposes made way for the ambition of using such information to increase users' self-awareness, highlighting the need to make data useful for their situated purposes. The so-called Quantified Self (QS) movement first foresaw a future when individuals could manage their own data to raise their self-knowledge. Quantified selfers enact practices of self-experimentation in which data are used to understanding the factors that may influence a (problematic) behavior or condition (e.g., a chronic diseases) [3].In this vein, personal informatics (PI) research attempted to explore novel forms for collecting and displaying personal information. PI researchers aimed to go beyond the quantified selfers' specialized practices, supporting the "use" of personal data in individuals' daily living. There are a variety of domains, such as health, sports, fitness, and transportation, that might benefit from the increased availability of personal information. By making such data actionable, PI research aims at supporting people in understanding the richness of their own digital traces, thus increasing the meaningfulness and value of data.However, a variety of challenges still need to be addressed in order to achieve such a goal. For example, despite the recent advancements in automation for recognizing and mining emotional, cognitive, and behavioral information, an active role of the user through self-reporting is still required. Data "curation" is burdensome demanding efforts and time to be accomplished. This task cannot be easily managed without strong motivation and compliance over time, so that many users fail to report their data, making the use of self-tracking tools quite useless [4]. It is paramount, therefore, to find new ways for engaging users in actively collecting their data, making this activity more enjoyable and sustainable in their daily living [5].Moreover, numbers, per se, are not meaningful, and data need to be "narrated" [6] and integrated in the users' personal histories and system of meanings in order to really develop their self-knowledge [7]. Although PI research strived for designing visualizations going beyond stats and analytical representations, for example by using natural language [8], glanceable displays [9], metaphoric depictions [10], and multiple visual cuts [11], there is room for the exploration of radically novel modalities for managing and feeding info...

Shared motivations, goals and values in the practice of personal science - A community perspective on self-tracking for empirical knowledge

“…Within the fields of personal informatics and Human Computer Interaction (HCI), there has been an increased interest in understanding user's tracking of personal data in areas such as health, sports, productivity and others (Jarrahi, Gafinowitz and Shin, 2018). This research expands on previous approaches such as lifelogging (Rapp, Cena and Marcengo, 2018), exploring how self-tracking tools can provide individuals with actionable self-insights to change behavior and improve their quality of life (Dijk et al, 2017). Epstein et al (2020) show that personal informatics has mainly focused on self-tracking for health and wellness, and on identifying potential user needs.…”

“…However, there has been limited personal informatics research into how to support individual interpretation and sense making to "transform numbers into meaning" (Rapp et al, 2018), while other studies consider how self-tracking tools strive to assist successful behavioral change (Rapp and Tirassa, 2017). On the other hand, HCI studies on self-tracking have rarely addressed its collaborative implications, except for some patient communities' knowledge sharing and do-it-yourself practices (Kaziunas et al, 2018).…”

A shelf stable Fmoc hydrazine resin for the synthesis of peptide hydrazides

“…[44][45][46][47][48] When using PEGylated trityl chloride resins (e.g., TentaGel ® and ChemMatrix ® ), the yields of synthetic peptides are often significantly lower than expected and decrease over time after resins are first opened. [49] In addition, while there has been a move toward increased automation and real-time monitoring of peptide synthesis and purification workflows, regenerating and hydrazine loading a trityl chloride resin prior to each synthesis represents a manual process that must be performed in a fume hood, and cannot be monitored other than by initiation of synthesis. We therefore sought to develop a procedure for regenerating and loading trityl chloride resins that, upon receipt from the manufacturer, would allow for immediate loading evaluation and long term stable storage in a state that is competent for automated synthesis of peptide α-hydrazides without any extraneous steps.…”