0.30 As transparent as infrastructure: on the research of  (Page 13/13)

SEASR. The Software Environment for the Advancement of Scholarly Research (SEASR) is a more ambitious infrastructure model that builds on work around text-mining done at the NCSA. For more on SEASR or to download it, go to (External Link) . See also the MONK (Metadata Offer New Knowledge) project at (External Link) . MONK has built an interesting interface for users to run tools on text collections. SEASR has a visual programming environment where programmer-users can develop applications (or flows) that can then be deployed on robust hardware for use in projects. It thus reconciles flexibility (in that programmers can create new components and advanced users can develop new flows) and robust delivery (in that a useful flow can be deployed as infrastructure and integrated into other projects). This model moves the most into the realm of infrastructure to be developed by professional engineers and supported for humanists. Humanists are encouraged to use the components, and, if they are sophisticated, to program their own flows on an infrastructure which generally has to be run by a center as infrastructure. The return is scalability and reliability. Content publishers are also encouraged to develop sophisticated tools in SEASR that can then be integrated into collections or other tools like Zotero. One can imagine how SEASR could be scaled up to the cloud to provide a visual programming and tool delivery platform for humanists.

There is a rich history of modeling tools for interpretation

While I have no doubt done a disservice to all of the projects listed, this quick survey was designed to show how fluid are the boundaries between research and infrastructure when it comes to text analysis tools. Many of these projects didn’t even conceive of themselves as infrastructure projects, but would fit under later definitions. We have been reinventing our tools, but each time based on revisiting the model as to who develops the tool, how it is distributed, where it is run, how much control the researcher has, who is responsible for it, and whether it is research itself. I suspect we are going to keep on reinventing this wheel and experimenting with models as the community matures, but the time may have come for a “die-off” and rationalization that leaves us with fewer, but better maintained models.

Conclusions

Some might read this paper as critical of the turn to cyberinfrastructure as it is a standard move in the humanities to “problematize” some accepted truth as a way of undermining it. My intent was not to declare “gotcha,” but to draw attention to the defining conversation we have to have. Infrastructure is not as transparent as it seems. That the turn to infrastructure is political; that it involves redefining what is research; and that it has dangers doesn’t mean we shouldn’t do it. My point is that we should do it thoughtfully, cognizant of what we may lose and the costs to research. I would go so far as to say that negotiating what is and what isn’t infrastructure is a good way to define what should by supported by whom. That line will shift. It will also vary from one institution to another. I therefore conclude with some suggestions. Most of these are adapted to the humanities from Edwards et al., Understanding Infrastructure: Dynamics, Tensions, and Design, one of the wiser reports in the field.

• We need to learn from the history of sociology of infrastructure development (as Understanding Infrastructure did.) We have colleagues that have studied other infrastructural revolutions—let’s listen to them.
• We need to support infrastructure experiments. We need to support prototyping in order to test risky models before massive investment. Such experiments should also be designed so that they do not become commitments to infrastructure. Such experiments are and should continue to be legitimate and valued research in humanities computing and library and information science.
• We should turn to research infrastructure where the infrastructure is not an area of research, but where it is clearly useful for a wide community.
• We need to recognize the social dimension of infrastructure—it isn’t just stuff and it is rarely neutral. Large-scale investments almost always have opportunity costs and paths not taken. It is better, especially in the humanities where the need for cyberinfrastructure is far from obvious to our colleagues, to go slow and be inclusive than to impose from above.
• Given a funding climate where long-term investments are less likely, we need to look at collaborative and social models for developing and maintaining infrastructure. The SETI@home project should be our paradigm, not the National Library (or Library of Congress.)
• We need to imagine infrastructure not just for professional researchers at universities, but the amateur researchers in the community. If we want long-term political investment, we need to open it up the community.

Infrastructure turns your thinking away to new problems

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