Participation is today central to many kinds of research and design practice in information studies and beyond. From user-generated content to crowdsourcing to peer production to fan fiction to citizen science, the concept remains both unexamined and heterogeneous in its definition. Intuitions about participation are confirmed by some examples, but scandalized by others, and it is difficult to pinpoint why participation seems to be robust in some cases and partial in others. In this paper we offer an empirically based, comparative analysis of participation that demonstrates its multidimensionality and provides a framework that allows clear distinctions and better analyses of the role of participation. We derive 7 dimensions of participations from the literature on participation and exemplify those dimensions using a set of 102 cases of contemporary participation that include uses of the Internet and new media.
In this article we analyze 102 case studies of Internet or social media-enabled participatory projects, technologies, platforms and companies in operation between roughly 2005-2015. We assign each case a "signature" representing the degree of presence/absence of seven dimensions of participation and then cluster these signatures to look for patterns of the most common ways of "doing participation" today. Two main clusters become apparent: 1) a "radical-direct" mode that emphasizes direct individual autonomy and influence, commitment to having a voice and setting goals, and individual or collective control over resources thereby produced; and 2) an "experientialaffective" mode that emphasizes the experience of being or becoming part of a collective, and the affective, communicational, and educational features of that experience.
Big Data Infrastructure at the Crossroads 3 perceptions that much data is either derivative, low quality, or gathered from sources that are inappropriate for open sharing. ▪ Ethical Challenges. The ethical dimensions of big data research remain contested, and some researchers are uncertain about best practices for ethical research conduct. Although IRB guidance is valued, some researchers expressed concerns that IRB regulations are not well adapted to new or evolving research methods. ▪ Support and Training. Researchers tend to favor informal training methods, such as internet tutorials, over formal training in big data methods. While such methods work well for solving immediate problems, they are less well suited to acquiring foundational knowledge, leaving the potential for blind spots in academic research.
Plain text data consists of a sequence of encoded characters or “code points” from a given standard such as the Unicode Standard. Some of the most common file formats for digital data used in eScience (CSV, XML, and JSON, for example) are built atop plain text standards. Plain text representations of digital data are often preferred because plain text formats are relatively stable, and they facilitate reuse and interoperability. Despite its ubiquity, plain text is not as plain as it may seem. The set of standards used in modern text encoding (principally, the Unicode Character Set and the related encoding format, UTF-8) have complex architectures when compared to historical standards like ASCII. Further, while the Unicode standard has gained in prominence, text encoding problems are not uncommon in research data curation. This primer provides conceptual foundations for modern text encoding and guidance for common curation and preservation actions related to textual data.
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