By Peter Smid
Provides entire details on a variety of programming recommendations, from the fundamental components to dozens of complex ideas. comprises greater than 1,000 illustrations, tables, formulation, assistance, shortcuts and real-world examples. deals remarkable reference fabric helpful for abilities education in any respect degrees of CNC. offers an encyclopedic, logically equipped method of CNC programming, permitting the reader to appear up an issue of curiosity simply. makes use of pass references all through to lead the reader to the right kind solution or way to an issue.
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Additional resources for CNC Programming Handbook, 2nd Edition
And S. Pink (2012) ‘Social media ethnography: The digital researcher in a messy web’, Media International Australia Incorporating Culture and Policy: Quarterly Journal of Media Research and Resources, 145, 123–34. Rogers, R. (2013) Digital Methods. Cambridge, MA: MIT Press. Rogers, R. and N. Marres (2000) ‘Landscaping climate change: A mapping technique for understanding science and technology debates on the World Wide Web’, Public Understanding of Science, 9(2), 141–63. Salmons, J. (2009) Online Interviews in Real Time.
This shift can be observed in the transition from TwapperKeeper to yourTwapperKeeper. While the former offered data-gathering functionality as a web-based service, the latter simply provided an open-source Axel Bruns and Jean Burgess 25 version of TwapperKeeper functionality as a package which interested and sufﬁciently skilled researchers could install on their own servers and could use and even modify as required for their speciﬁc purposes. Unless steps are taken to speciﬁcally prevent such access, yourTwapperKeeper installations continue to make their archives of gathered data available for download to anybody – not just to the researchers who entered the search terms to be tracked.
276) 36 Big Data, Thick Data: Social Media Analysis Working from this position, several authors characterize social media data as a special kind of ‘ofﬂine’ social science data. For example, Hine (2006) argues that a key concern of social media analytics is to avoid a loss of quality in data. 4). 205); the representativeness and validity of the data more generally (Tufekci, 2014); and the fact that the production of data is not controlled and led by researchers but appears untamed ‘in the wild’ (Kitchin, 2014).