Sandra Braman (University of Wisconsin-Mulwaukee)
Why does it matter?
– Research success is mission critical for many institutions, which means you need researchers. The Ability to do work is central to researcher identity. Serious researchers are willing to trade salary or switch institutions to improve support. It’s clear to researchers that IT are collaborators, not just service providers.
How do we know what we know? Direct and indirect info from national reports, conversations with faculty, anecdotal & systematic reports from CIOs, scholarly publications on research trends and methods, and trade press reports.
Factors affecting research use of IT – Disciplinary cultures; professional development on computational techniques (more widespread at elite institutions); institutional and general incentive systems – there are many ways in which research trends (like collaboration or development of new algorithms) are being undermined by incentive systems; Efficiency – how do we evaluate the efficiency of research? Trade-offs will be made in how people use technology based on efficiency – if I have to add metadata to get data into an archive, it won’t likely happen; Relationship between research & teaching – the use of research computing among grad and undergrad students is growing; Diversity of research approaches – people use high performance computing in conjunction with other research methods.
Institutional issues: competition for resources; history of privileging certain faculty and units; emphasis on homogeneity – can generate problems for researchers; assumptions about activity in units – IT may not be engaged with units new to research computing, but the assumption that support is happening in the unit may not be correct; inertial regarding institutional motivations.
Research culture issues: the rapid spread of computationally-intense research across disciplnes, e.g. music and dance or databases of videos in humanities; speed and continuous nature of innovations in research methods (needs for training); generational issues.
Decision making about IT for research: Hiring commitments by deans etc without discussion with IT units; Resource allocation – faculty may want to participate; collaborative infrastructure development (Princeton’s new supercomputer purchase from funds shared across units); policy implementation – if faculty are in on development of policy they are more likely to abide by it.
Collaborative Decision making – multiple options not mutually exclusive. They are quite rare at present. MIT has multiple working groups that orient differently around research problems. Deans and chairs don’t often really have any idea what’s going on in the research areas in their units.
Computing needs – capacity; stability (and help with transitions); architectural flexibility – people who know about OS and research app architectures are usuall not talking to each other.
Training – there’s a gap between how you were trained in grad school and current practices. Inadequate reliance on current students – lots of issues with use of students in terms of knowledge transfer, security, etc. speed of innovation. Range of diffusion techniques – working groups for getting peers in communication. Institutional and inter-institutional synergies. Linkage with methods courses.
More about software – needs range from simple scripting to custom software creation; software specialization as national institutional niche; finding researcher-authored software (NSF is talking about funding software archives)
Data needs: Collection; storage; preparation; presentation
Storage & preparation – multiplicity of types of storage (project-specific repositories to long-lived data collections of global importance) – UCSD is offering up 100 years of data storage to researchers from any institution. Multiplicity of storage venues. Rising & complex preparation needs. Policy issues (access, control, raw vs. cooked, – feds are pressing for release of raw data to federally funded research; etc).
IT & Ontologies – NSF is funding lots of work in metadata and ontologies. Driven by effort to bring info architects and disciplinary scientists into system design. One approach to user-centered design.
The new big two issues – more support for learning, adatpting, and writing software specific to their research problems; Researchers need help managing their data as it enters worlds of public presentation & long-lived data archives. UMich has a lib school training work for disciplinary specialists to do ontological and metadata work. Peter Murray from UMBC quarrels with these two issues – the hot topic is sophisticated pre and post-award information systems. Faculty and PIs want to reduce the administrative burdens. Everybody’s dealing with compliance.
I didn’t see any mention in this presentation of what is probably the biggest issue for us, which is demand for housing research computing in a centrally run data center environment. That need is being driven by security and recoverability issues.
Greg Jackson makes the great point that this analysis doesn’t recognize that many of the shortcomings are the results of tradeoffs made as a result of incomplete analyses of cost, risk, and opportunities. We need to do the analysis of which services are actually worth spending scarce money on.
Oren Sreebny 