Friday, October 14

10:00 am - 12:30 pm
Wollman Hall, Eugene Lang Building, 65 West 11th St., 5th floor

Lev Manovich (Software Studies Initiative, UC San Diego)

Data Literacy and Cultural Analytics

The joint availability of numerous large data sets on the web and free tools for data scraping, cleaning, analyzing and visualizing enable potentially anybody to become a citizen data miner. But how do we enable this in practice? What are the necessary elements of “data literacy”? How do we inspire students in traditionally non-quantitative fields (art history, film and media studies, literary studies, etc.) to start playing with big data?

One the limitations of the existing popular data analysis and visualization tools is that they are designed to work with numbers and texts – but not images and video. To close this gap, In 2007 we have established Software Studies Initiative (softwarestudies.com) at University of California, San Diego. The lab’s focus in on development of new visualization methods particularly suited for media teaching and research. In my presentation I will show a sample of our projects including visualization of art, film, animation, video games,magazines, comics, manga, and graphic design. Our image sets range from 4535 covers of Time magazine to 320,000 Flickr images from “ArtNow” and “Graphic Design” groups, and one million manga pages.

In September 2011 we released ImagePlot - free software tool that visualizes collections of images and video of any size. I will discuss how we use Image Plot in classes with both undergraduate and graduate students to create collaborative projects which reveal unexpected cultural trends and also make us question our existing concepts for understanding visual culture and media.

Saturday, October 15

1:30-4:30 pm: Progressive Digital Pedagogy: Remix, Collaboration, Crowdsourcing
Wollman Hall, Eugene Lang Building 65 West 11th St., 5th floor

Elizabeth Losh (Sixth College, UC San Diego)

In recent years progressive digital pedagogy has borrowed from five major aspects of the popular culture developing around computational media: 1) remix practice, 2) multimodality, 3) accelerated response, 4) crowd sourcing, and 5) narrowcasting. Yet for many years the conventional classroom pedagogy around teaching “current events” has remained unchanged: it still generally focuses on having learners mechanically cut out recent news stories produced by traditional print journalists with little attention to how the news is made, how it remixes sources, how it appeals to particular audiences, or how particular patterns of visual imagery and verbal rhetoric could be analyzed critically. This talk focuses on recent work by the Software Studies initiative at U.C. San Diego by the Cultural Analytics group and shows how media visualization and crowd sourcing could be used in educational contexts with large publically accessible libraries of digitized news and smaller archives of government public information videos.

DOWNLOAD IMAGEPLOT 0.9

ImagePlot was developed by the Software Studies Initiative (softwarestudies.com) with support from the National Endowment for Humanities (NEH), the California Institute for Telecommunications and Information Technology (Calit2), and the Center for Research in Computing and the Arts (CRCA).

Along with the program, we also distribute a number of articles by Lev Manovich, Jeremy Douglass and Tara Zepel that address methodologies for exploring large visual cultural data sets, and discuss our digital humanities projects which use ImagePlot. (The articles can be also downloaded directly from softwarestudies.com.)

Visualizations created with ImagePlot have been shown in science centers, art and design museums, and art galleries, including Graphic Design Museum (Breda, Netherlands), Gwangju Design Biennale (Korea), and The San Diego Museum of Contemporary Art.

ImagePlot software was developed as part of our Cultural Analytics research program.

ImagePlot works on Mac, Windows, and Lunix.

Max visualization resolution: 2.5 GB (2,684,354,560 grayscale pixels, or 671,088,640 RGB pixels).

(part 1 is here)

[august 4-14, 2011]

selected points (see complete text for details)

Many social and natural processes follow a familiar Bell curve (normal
distribution). What are the shapes of distributions of large cultural
data sets? Because humanists only recently started to work
with big data sets, it is too early to make any generalizations. However,
it would not be surprising if the distributions of features of
very large cultural sets do follow the Bell curve pattern:
a dense cluster containing most of the data, gradually falling off to the side,
and a large very sparse area.

If we want to visually compare two or more image sets to each other in
relation to two visual properties, we can project them into a 2D space
defined by these visual properties as we did with Piet Mondrian’s and
Mark Rothko’s paintings in part 1. Using min and max values of the measured
properties of all images in out sets combined as the boundaries of the
visualization will allow us to use the visualization area most
efficiently. However, if we want to understand the footprint of each image set in
relation to the absolute mix and max - i.e. lowest and highest
possible values of visual features of all possible images - we need to
map our images differently.

A related idea is to render parts of an image set over the background showing
the complete set. This allows us to see the footprint of the these parts
in relation to the larger footprint of all images. For example, we can compare
pages of two manga titles from our complete set
of 883 titles comprising 1,074,790 pages.

Lev Manovich.
Style Space: How to compare image sets and follow their evolution (part 1)

[august 4-6, 2011]

selected points (see complete text for details)

A style space is a projection of quantified properties of a
set of cultural artifacts (or their parts) into a 2D place. X and Y
represent the properties (or their combinations). The position of
each artifact is determined by its values for these properties.

We are not claiming that such representations can capture all
aspects of a visual styke. A “style space” representation is
a tool for exploring image sets. It is particularly effective for
large sets.) It allows us compare all images in a set (or sets).
according to their visual values.

Separating a “style” into distinct visual dimensions and
organizing images according to their values on these dimensions
allows us to see more clearly how differences between the images
in a set. Visual differences are translated into spatial distances.
Images which are visually similar will be close; images which
are different will be further away.

keywords: search, Google, knowledge discovery, digital library, database, classification, folksonomy, information retrieval, HCI, interface, information visualization, digital humanities, cultural analytics, visual analytics, software studies, Manovich

Early 21st century humanities and media studies researchers have access to unprecedented amounts of media – more than they can possibly study, let alone simply watch or even search. (For examples of large media collections, see the list of repositories made available to the participants of Digging Into Data 2011 Competition, www.diggingintodata.org). The basic method of humanities and media studies which worked fine when the number of media objects were small – see all images or video, notice patterns, and interpret them – no longer works. For example, how do you study 167,00 images on Art Now Flickr gallery, 236,000 professional design portfolios on coroflot.com (both numbers as of 7/2011), or 176,000 Farm Security Administration/Office of War Information photographs taken between 1935 and 1944 digitized by Library of Congress (http://www.loc.gov/pictures/)?

Given the size of typical contemporary digital media collections, simply seeing what’s inside them is impossible.

Although it may appear that the reasons for this are the limitations of human vision and human information processing, I think that it is actually the fault of current interface designs and web technology. Standard interfaces for massive digital media collections such as list, gallery, grid, and slide do now allow us to see the contents of a whole collection. These interfaces usually they only display a few items at a time (regardless of whether you are in a browing mode, or in a search mode). This access method does not allow us to understand the “shape” of overall collection and notice interesting patters.

The popular media access technologies of the 19th and 20th century such as slide lanterns, film projectors, microfilm readers, Moviola and Steenbeck, record players, audio and video tape recorders, VCR, and DVD players were designed to access single media items at a time at a limited range of speeds. This went hand in hand with the media distribution mechanisms: record and video stores, libraries, television and radio would all only make available a few items at a time. For instance, you could not watch more than a few TV channels at the same time, or borrow more than a few videotapes from a library. At the same time, hierarchical classification systems used in library catalogs made it difficult to browse a collection or navigate it in orders not supported by catalogs. When you walked from shelf to shelf, you were typically following a classiffication based on subjects, with books organized by author names inside each category.

Together, these distribution and classification systems encouraged 20th century media researchers to decide before hand what media items to see, hear, or read. A researcher usually started with some subject in mind – films by a particular author, works by a particular photographer, or categories such as “1950s experimental American films” and “early 20th century Paris postcards.” It was impossible to imagine navigating through all films ever made or all postcards ever printed. (One of the the first media projects which organizes its narrative around navigation of a media archive is Jean-Luck Godard’s “Histoire(s) du cinéma” which draws samples from hundreds of films. ) The popular social science method for working with larger media sets in an objective manner – content analysis, i.e. tagging of semantics in a media collection by several people using a predefined vocabulary of terms also requires that a researcher decide before hand what information would be relevant to tag.

Unfortunately, the current standard in media access – computer search – does not take us out of this paradigm. Search interface is a blank frame waiting for you to type something. Before you click on search button, you have to decide what keywords and phrases to search for. So while the search brings a dramatic increase in speed of access, it assumes is that you know beforehand something about the collection worth exploring further.

We need the techniques for efficient browsing of content and discovery of patterns in massive media collections. Consider this defintion of “browse”: “To scan, to casually look through in order to find items of interest, especially without knowledge of what to look for beforehand” (“Browse”, Wiktionary). Consider also one of the meanings of the word “exploration”: “to travel somewhere in search of discovery” (“Exploration”, Wiktionary.) How can we discover interesting things in massive media collections? I.e., how can we browse through them efficiently and effectively, without a knowledge of what we want to find?

http://ucsd.academia.edu/LevManovich/Papers

For example, how do we call a person who is interacting with digital media?
User? No good.. “interactor”?

In 20rh century cultural theory dealt with viewers, readers, listeners, participants. In 21st century we don’t know who to call people we study. (game studies have “players” so at least they have this figured out

Thinking more about it, I realized that we can’t have a single good term to describe what we do with digital media for a reason.

In the 1960s-1970s digital media pioneers like Alan Kay systematically simulated most existing mediums in a computer. Computers, and various computing devices which followed (such as “smart” phones)came to support reading, viewing, participating, playing, remixing, collaborating.. and also many new functions.

This is why 20th century term s- reader, viewer, participant, publisher, player, user - all apply.

This multiplicity of media experiences is one of the defining characteristics of digital media - or, as Alan Kay called it, “the computer metamedium.”

The journal’s primary aim is to examine the ways in which software undergirds and formulates contemporary life. Computational processes and systems not only enable contemporary forms of work and play and the management of emotional life but also drive the unfolding of new events that constitute political, social and ontological domains. In order to understand digital objects such as corporate software, search engines, medical databases or to enquire into the use of mobile phones, social networks, dating, games, financial systems or political crises, a detailed analysis of software cannot be avoided.

A developing form of literacy is required that matches an understanding of computational processes with those traditionally bound within the arts, humanities, and social sciences but also in more informal or practical modes of knowledge such as hacking and art.

The journal welcomes contributions that address such topics and many others that may derive and mix methodologies from cultural studies, science and technology studies, philosophy of computing, metamathematics, computer science, critical theory, media art, human computer interaction, media theory, design, philosophy.

Computational Culture publishes peer-reviewed articles, special projects, interviews, and reviews of books, projects, events and software. The journal is also involved in developing a series of events and projects to generate special issues.

The Editorial Group
Matthew Fuller, Goldsmiths
Andrew Goffey, Middlesex
Olga Goriunova, London Metropolitan
Graham Harwood, Goldsmith
Adrian Mackenzie, Lancaster

http://computationalculture.net
For initial enquiries, please contact: m.fullerATgold.ac.uk

PRC | version for Kindle and other e-readers

(.pdf and .doc versions are available on softwarestudies.com/softbook )

This format is readable not only by Kindle and the recent Kindle Apps, but also by many other e-readers.

The transcoding was done by
Zoltán Dragon
Assistant Professor
Department of American Studies
Institute of English & American Studies
University of Szeged, Hungary

Many thanks, Zoltán!

This text is a pretty substantial update of the part of the introduction for Software Takes Command manuscript (2008).

Many things happen in software culture between 2008-2011 - for instance, the rize of mobile platform and the “long tail” of apps for these platforms. Dozens of books have been written about Google and other web giants.
The MIT Press published a few titles in our Software Studies series established in 2008.
New software studies journal prepares its first issuie.

I have updated the article to include these developments, and also reworked a number of theoretical points and terms to reflect the state of software culture today. The article also offers a possible taxonomy of types of “cultural software” - open for discussion, of course.