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量化自我:大数据科学和生物发现的根本性颠覆

The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery.

机构信息

MS Futures Group , Palo Alto, California.

出版信息

Big Data. 2013 Jun;1(2):85-99. doi: 10.1089/big.2012.0002.

Abstract

A key contemporary trend emerging in big data science is the quantified self (QS)-individuals engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information as n=1 individuals or in groups. There are opportunities for big data scientists to develop new models to support QS data collection, integration, and analysis, and also to lead in defining open-access database resources and privacy standards for how personal data is used. Next-generation QS applications could include tools for rendering QS data meaningful in behavior change, establishing baselines and variability in objective metrics, applying new kinds of pattern recognition techniques, and aggregating multiple self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. The long-term vision of QS activity is that of a systemic monitoring approach where an individual's continuous personal information climate provides real-time performance optimization suggestions. There are some potential limitations related to QS activity-barriers to widespread adoption and a critique regarding scientific soundness-but these may be overcome. One interesting aspect of QS activity is that it is fundamentally a quantitative and qualitative phenomenon since it includes both the collection of objective metrics data and the subjective experience of the impact of these data. Some of this dynamic is being explored as the quantified self is becoming the qualified self in two new ways: by applying QS methods to the tracking of qualitative phenomena such as mood, and by understanding that QS data collection is just the first step in creating qualitative feedback loops for behavior change. In the long-term future, the quantified self may become additionally transformed into the extended exoself as data quantification and self-tracking enable the development of new sense capabilities that are not possible with ordinary senses. The individual body becomes a more knowable, calculable, and administrable object through QS activity, and individuals have an increasingly intimate relationship with data as it mediates the experience of reality.

摘要

当前大数据科学领域的一个主要趋势是量化自我(QS)——个人以 n=1 个体或群体的形式对任何类型的生物、物理、行为或环境信息进行自我追踪。大数据科学家有机会开发新模型来支持 QS 数据的收集、整合和分析,也有机会率先定义开放获取数据库资源和个人数据使用的隐私标准。下一代 QS 应用程序可能包括将 QS 数据转化为行为改变的有意义信息的工具,建立客观指标的基线和可变性,应用新型模式识别技术,以及从可穿戴电子设备、生物传感器、移动电话、基因组数据和基于云的服务中聚合多个自我追踪数据流。QS 活动的长期愿景是一种系统监测方法,其中个体的持续个人信息环境提供实时性能优化建议。QS 活动存在一些潜在限制——广泛采用的障碍和对科学合理性的批评——但这些限制可能会被克服。QS 活动的一个有趣方面是,它本质上是一种定量和定性现象,因为它既包括客观指标数据的收集,也包括这些数据对主观体验的影响。随着量化自我正在以两种新方式成为有定性的自我,这种动态的一部分正在被探索:应用 QS 方法来追踪情绪等定性现象,以及认识到 QS 数据收集只是为行为改变创建定性反馈循环的第一步。从长远来看,量化自我可能会进一步转变为扩展的外自我,因为数据量化和自我追踪使开发新的感知能力成为可能,而这些能力是普通感知无法实现的。通过 QS 活动,个体身体成为一个更可知、可计算和可管理的对象,而数据则在个体体验现实的过程中发挥着越来越亲密的中介作用。

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