Stand in the Place Where Data Live: Data Breaches as Article III Injuries

Every day, another hacker gains unauthorized access to information, be it credit card data from grocery stores or fingerprint records from federal databases. Bad actors who orchestrate these data breaches, if they can be found, face clear criminal liability. Still, a hacker’s conviction may not be satisfying to victims whose data was accessed, and so victims may seek proper redress through lawsuits against compromised organizations. In those lawsuits, plaintiff-victims allege promising theories, including that the compromised organization negligently caused the data breach or broke an implied contract to protect customers’ personal information. However, many federal courts see a data breach as essentially harmless, or that data breach plaintiff-victims do not necessarily suffer cognizable legal injuries. In practice, this means that the plaintiffs do not have Article III standing, and courts do not reach merits determinations of fault. Instead, a data breach to these courts is only harmful to the extent that it leads to a subsequent injury, like identity theft or fraud. Therefore, data breach victims must suffer even more harm before they can bring a lawsuit. Other courts under this framework do nonetheless find that data breach plaintiff-victims have standing. However, even those courts still wrongfully check whether the plaintiffs suffered future identity theft, fraud, or other harm. Those courts simply find that such subsequent harm is readily apparent. This Note offers a proper approach to standing in data breach lawsuits. I argue that the moment a victims’ data is exposed without their authorization, they suffer a cognizable common law injury, regardless of whether that data exposure actually causes subsequent harm. Rather than thinking of data breaches as a means to future data misuse, courts should think of data breaches as injurious in and of themselves

Social Media Attention Increases Article Visits: An Investigation on Article-Level Referral Data of PeerJ

In order to better understand the effect of social media in the dissemination of scholarly articles, employing the daily updated referral data of 110 PeerJ articles collected over a period of 345 days, we analyze the relationship between social media attention and article visitors directed by social media. Our results show that social media presence of PeerJ articles is high. About 68.18% of the papers receive at least one tweet from Twitter accounts other than @PeerJ, the official account of the journal. Social media attention increases the dissemination of scholarly articles. Altmetrics could not only act as the complement of traditional citation measures but also play an important role in increasing the article downloads and promoting the impacts of scholarly articles. There also exists a significant correlation among the online attention from different social media platforms. Articles with more Facebook shares tend to get more tweets. The temporal trends show that social attention comes immediately following publication but does not last long, so do the social media directed article views

Article 10 of the Convention includes the right of access to data held by intelligence agency

Analysis and comment of recent case law ECtHR on access to informatio

A path to filled archives - how bumpy is it going to be?

The need for data capturing during the data creation process becomes increasingly important in regard to mandatory data submissions in addition to article submissio

Reinforcing quantitative skills with applied research on tombstone-weathering rates.

This Journal of Geoscience Education article describes a tombstone weathering exercise that reinforces quantitative skills with applied research. The article describes an exercise designed and carried out by students in a surficial processes course. Students measured the rates of tombstone weathering, compiled and analyzed data. The complexity of the data involved in this exercise helps students demonstrate for themselves that mathematical analysis can give meaning to data. Educational levels: Graduate or professional, Undergraduate lower division