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Started in 2011, DataSpaceTime is the collaboration of Brooklyn-based artists Lisa Gwilliam and Ray Sweeten. DST works are primarily internet and data-derived projects. Currently the work focuses on using QR codes as a means of both storing data and presenting images and concepts in a post-browser setting.

DST emphasizes a viewer experience mediated by technology and devices. The work explores, via newly contextualized technology, how data is consumed and generated by viewers or “end users” of individual DST pieces. While DST engages the use of new and future technologies in both the production and experience of the work, they maintain a formalism in the final presentation. This includes careful consideration of form and content drawing upon past work in sound, video and painting.

Artist Statement: 

DataSpaceTime transcribes and reframes net-based technologies towards interactive mixed-media installations. Data, models for social behaviors, information systems and methods of standardization are the core materials for the work. Traditional materials (surface, sound, text, image) and formal constructs (portraits, figure-to-ground, the “remix”) are re-contextualized as data repositories, dynamic software, and tracking devices. Viewers are given the opportunity to explore and interact with the work at varying levels of accessibility, alluding to the structures of closed and open information systems. As we continue to parse these structures conceptually and technically, we hope to arrive at a presentation that illuminates the dense, evolving landscape of information technology and its impact on our lives.

Data Gathering (Search)

Search, how background data is collected, managed and disseminated, remains a large part of the pie for internet-based art, largely because it’s so accessible. Along with the Web, Search has also evolved. Data gathering and discovery could be seen as the underlying entity of the Internet with many end-points feeding back to it. When presenting Search-based work, it is important to recognize the implications of its evolution, as well as the many open source Application Programming Interfaces (API’s) that make large amounts of data relatively easy to access. Because this data is accessible at the coding level, the artist has many potential applications for Search-based work.


The standard search-box Search (Google, Youtube etc.) is a kind of predetermined curatorial process which is presented to viewers. While this method of search appears limitless, the inherent boundaries in this model call into question how people use or are allowed to interface with data on the internet. By subverting this search model we hope to make the viewer more conscious of the process by which they find themselves searching the net everyday. DataSpaceTime automates this common practice with code by utilizing an API rather than presenting the viewer with a user-facing search box. DST essentially pre-performs a Search and then allows the viewer to interact directly with the data. It is then up to viewers to discover the connections in the data that is presented in each particular context and recover for themselves the initial search parameters and possible relationships within the piece. By manipulating the experience of how data is accessed, making it more complex than the minimalism of the search box, it allows us to explore the implications of the Search experience.

Opting In

Viewers of DataSpaceTime pieces are invited to download the DST mobile app and “opt-in” to the full experience of the work. The mobile device retrieves the the data from each QR code and presents the results on screen. It provides a history feature so you can keep track of which codes you’ve scanned and see those results again without having to find and re-scan bar codes. Viewers must engage in an acquisitive relationship (install mobile software, approve data gathering permissions) in order to fully interact with the pieces – something most internet users do everyday online, but which we present in the post-browser context of the gallery experience.

Data Generation (the other Search)

Somewhat counter intuitively, every search query you run online very often generates more data than the actual information you are trying to gather, and all of that data is thoroughly analyzed. This is how search engines are able to provide your results faster and more efficiently. This primarily hidden aspect of Search is its generative nature and is known in the industry as analytics. It is most often used to predict additional search interests, product placement and demographics and is not usually exposed in real time to the user.

Similarly, as a user explores the current DataSpaceTime work by scanning QR Codes with our custom mobile app, they are generating additional information. Each viewers mobile device provides a unique and anonymous set of data which we are able to bind to every interaction with the work. This includes which codes were scanned, from which piece, as well as when and where they were scanned (GPS). As this information is generated over the life of the piece, it can be compiled and analyzed to provide us with a historical picture of how audiences interacted with the work. Our data is publicly available via a lightweight, REST-like API turning a closed, cyclical structure into an open, potentially re-generative system.

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