Reed's Ruminations: A Blog by Dan Reed

Research Data Sustainability and Access

N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACM blog.

How recently have you mounted a 9-track open reel tape, hoping to access the irreplaceable data that was the foundation of your first research paper? At this point, you may not even remember if it was 800, 1600 or 6250 bits per inch (bpi), EBCDIC or ASCII, blocked or unblocked. You are not that old, you say? What about your 5.25" or 3.5" floppy disks or DAT archive? Odds are you haven't accessed the data because you can't without seeking the services of a conversion company that specializes in data retrieval from obsolete media.

Have you ever been involved in a research project, either individually or as part of a multi-institutional team, that produced data intended for broader community use? If so, then you probably placed it on the project web site, perhaps with the research software needed to decode and process the data. A decade later, is the data still accessible and does the software even compile or execute on current systems?

Personally, I still have some 9-track computer tapes, a punched card deck and a paper tape in an office desk drawer, saved for both reasons of pedagogy and nostalgia. I also have some data analytics tools originally designed for workstations now found in the Computer History Museum.

These examples may seem quaint, and perhaps they are, but each of us has some variant of this data obsolescence and inaccessibility experience. They are the analog (pun intended) of our previous consumer media experiences. After all, have you played any of your 45s, 8-track tapes, or cassettes lately?

These are the symptoms of three bigger issues: the rapid obsolesce of specific storage technologies, the explosive growth of research data across all scientific and engineering disciplines, and the even more difficult task of sustaining data access past the end of research projects.

The first is a natural consequence of technological change, one that we have collectively managed across the history of modern digital computing. In turn, "big data" is a hot topic of research and business innovation, with new tools and techniques appearing to extract insights from large volumes of unstructured or ill-structured data. However, the social and economic challenges around research data preservation are profound and not yet resolved.

In the U.S., the Office of Science and Technology Policy (OSTP) on behalf of the National Science and Technology Council (NSTC) recently issued a request for information (RFI) on Public Access to Digital Data Resulting from Federally Funded Scientific Research. In addition, the National Science Foundation has instituted a requirement that research proposals include a data management plan, which notes that "Investigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants."

Several issues are convolved in our desire and expectations for data sharing. One is advancing discovery and innovation via the free flow of information, replicating and expanding experiments and sharing data from increasingly expensive national and international scientific instrumentation. This is central to the scientific process, though it brings the cultural disparity that exists across disciplines to the fore – astronomy, biology and computing are culturally quite different.

The second is the need for multidisciplinary sharing and cross-domain fertilization. Increasingly, new insights emerge from fusing and analyzing data drawn from diverse sources. Such integration places a premium on metadata schemas, well documented data formats and service access protocols. In turn, these require standards and coordination, both within and across disciplines.

The third is the distinction between research, which produces data, and data preservation, documentation and dissemination. In my experience, the skills and expertise, as well as reward metrics, are distinctly different for the two activities. This is an oblique way of saying that researchers will generally optimize for research advancement over data preservation when forced to choose between the two. This is only natural, given our current reward structure.

Research and data preservation also differ markedly in their timescales, for data preservation and dissemination services often require decadal planning, with associated infrastructure and professional staffing, rather than the 3-5 year funding for principal investigators, graduate students and post-doctoral research associates that is typical of research grants and contracts.

Finally, data preservation and dissemination can be expensive, rivaling or exceeding that of the initial research investment. Quite clearly, not everything can and should be preserved in perpetuity, but predicting the future value of data is both difficult and perilous.

This suggests that we need economic and social processes that more rigorously access the present and future value of data. They might include combinations of commercial, fee-based models, where researchers and organizations vote with their funds for access to and retention of certain data (i.e., a cloud-based research data marketplace), government funded and managed repositories where key data is retained (e.g., NIH's GenBank), or distributed but interconnected archives funded by multiple agencies and governments (e.g., the Worldwide LHC Computing Grid).

What is clear is that the dramatic growth of research data, the collaborative and competitive nature of international science and engineering research, expectations for economic returns from research investments and disciplinary differences all make this a pressing and difficult problem. Our current, ad hoc approaches are inadequate and not sustainable.

Science: It’s About the Wide-Eyed Wonder

The end of year holidays are a time filled with travel, food and family. Like many of you, I spent time with family and friends, socializing, discussing life events and sharing stories. Among the most memorable of those experiences was the time spent with my nieces' children, all of whom are between the ages of one and four. Because I do not spend much time with small children these days, I was able to observe them and their behavior from a fresh perspective.

Yes, there was the frenetic pace and seemingly boundless energy of the young, followed immediately by exhaustion and serene sleep. There was also the complex and sometimes daunting logistics of feeding, travel and entertainment that all parents manage daily. Neither of those, though, was what I found most fascinating. Rather, it was the wide-eyed wonder with which the young greet new things.

A one-year old fascinated by musical tones, a two-year old enthralled by the tensile strength of a spring, or a four-year old pondering the statics and dynamics of a pile of blocks, these are the wonders of science experienced firsthand.

In turn, this caused me to reflect on the nature of science, technology, engineering and math (STEM) education and the nature of scientific and engineering research. After all, the fundamental and unbridled curiosity of a child is the birthmother of all of science and engineering.

Science Education

I am no expert in educational pedagogy, far from it. Nevertheless, after spending over forty years in schools, colleges and universities, one does learn a few things. At the very least, I once carried the title of professor and was granted license to claim to know.

In reality, science and engineering, and even abstract mathematics, are about experiential learning – one gains insights and appreciation by doing rather than merely observing. It is why we conduct experiments to test hypotheses, build computational models to bring our theories to life, and build new devices that embody engineering knowledge. We want to see and experience, demonstrate and validate.

Yet so much of our K-12 science education remains driven by a dry recitation of facts that expunges all the experiential joy of science, leaving only dried husk of knowledge. Too frequently, we insist on conformity, when the practice of science and engineering has always been critically dependent on the iconoclasts who question the current orthodoxy. Indeed, that questioning is at the very foundation of the social and technical process we call science. I believe we must nurture that curiosity throughout the educational system, letting students see how they can ask and answer questions that make a difference in our world.

The questions are simple yet profound, and the shift from a child's question to our deepest unsolved queries is so very small. Mommy, why is the sky blue? (Answer: Raleigh scattering, though simple, not easily explained to a child.) How does a flower grow? What is gravity and why is there mass? (Answer: These are some of the deepest questions at the frontier of science.)

Discovery's Passion

All too often, the process, politics and bureaucracy of science and engineering consume us. Whether writing the research proposals and the inevitable progress reports, crafting the papers under deadlines, or traveling to conferences and symposia, our time and energy can be frittered away. All these are necessary, but they are not sufficient. In truth, they are the detritus of innovation and discovery, the artifacts of our modern scientific enterprise.

Periodically, it is worth taking a step back, pausing for a moment to remember why each of us became scientists. It was not the research grant (necessary though it is), nor was it the publication (essential to disseminating a discovery). It was the curiosity, the passion, the amazement and the wonder. Those things make it the love and the labor of a lifetime.

The next time you see a small child, staring in wide eyed, open mouthed wonder at some action or object, remember and savor the experience. It is why you are a scientist or an engineer, asking questions and staring in amazement at the answers the experiments reveal, still a child at heart.

Planting Seeds in the Field of Knowledge

In these challenging economic times, U.S. universities are under great stress – economically, politically and socially. Cash-strapped states are reducing appropriations for public universities, and those private universities fortunate enough to possess substantial endowments are managing them with a frugal eye on an uncertain economy. To compensate, tuition is rising rapidly, and the chase for already constrained Federal research funding is increasingly frenetic.

Unfortunately, increasing numbers of students are leaving (degreed or not) with substantial loan debt and – in this economy – with rather bleak employment prospects. The rising cost of higher education, coupled with uncertain employment options, is deeply troubling to parents and students, as well as faculty members and university leaders.

What does all this mean? Public and private institutions alike are now caught between their straitened economics and their historical roles. In a rapidly shifting knowledge economy, it has never been more important to educate a new generation of students, advance the frontiers of knowledge, foster economic development and facilitate debate on important societal issues.

Computing Responsibility

It is tempting for those of us in computing to ignore these issues. After all, our field is booming, with stunning advances in both computing theory and practice. We face a shortfall of qualified graduates, the competition for experienced and qualified practitioners is intense, and there are many interesting and well-remunerated jobs. (For detailed data, see the Computing Research Association (CRA) workforce data analysis.)

Yes, we should celebrate these happy facts, but I believe we must also consider our social obligation to help address today's university challenges, for we are the beneficiaries of past actions. In that spirit, it is worthwhile to consider another time when American society renegotiated its compact with higher education, triggered by social and economic upheaval.

The Morrill Act of 1862 granted each state 30,000 acres of public land. Proceeds from the land sales were to be invested in an endowment to support colleges of agriculture and mechanical arts in each of the states. For the first time, practical training became a major focus of U.S. higher education.

These "land grant" institutions brought modern engineering and agricultural methods to a still largely rural and agrarian, though rapidly industrializing society. Nor were they simply outposts of erudition, with agricultural experiment stations and extension services, they quite literally took new ideas and technology to the farmer's field.

Planting Seeds

What are the "agricultural and mechanical" arts of the 21^st century knowledge economy? What transformative force is driving economic and social change? The answer to both questions seems rather obvious – it is computing, both its enabling technology and its myriad applications.

Our universities are struggling to respond and adapt to a rapidly shifting environment, with new social demands and heightened economic pressures. How do we help them respond more nimbly to societal needs for education and skills, economic development and job creation? How can we best use computing to expand access to higher education? How can we reduce costs and provide just-in-time lifelong education when the inevitable technical dislocation and job losses occur? How can we accelerate discovery and innovation?

I do not know the answers, or even all the questions. However, like agriculture and engineering before us, computing has a social responsibility to be an active and enthusiastic partner in helping chart the nature of higher education and its evolving compact with society. It is, I believe, a responsibility we have not fully shouldered to this point.

We have the skills, insights and expertise. We can plant the seeds of the future.