Predicting the LTER Technology Future

1. Predicting the LTER Technology Future Randy Butler University of Illinois National Center for Supercomputing Applicatins r-butler@illinois.edu

2. Quick Background • I have an ecological background but I strayed……and ended up in computer science • The good news is I have been able to blend the two disciplines by working with researchers from many different science domains on their cyberinfrastructure needs, including the LTERNO, DataOne, OOI, SEEK, NEON, the and many more. • Working at the INHS in the early 80s I was translating data from text files, oddball DBs, and handwritten notes & tags.

3. The Challenge • Forecast what ecological science might look like 100 years from now and how LTER might prepare from a technological standpoint. • Yet it is next to impossible to predict technologies 10 years into the future. • So I believe we cannot worry about technology. • However I do have a vision for what technologically we must do.

4. Quote from Dan Reed & Dennis Gannon The Fourth Paradigm (Jim Gray) Simply put, we are moving from data paucity to a data plethora, which is leading to a relative poverty of human attention to any individual datum and is necessitating machine-assisted winnowing. This ready availability of diverse data is shifting scientific approaches from the traditional, hypothesis(experiment)-driven scientific method to science based on (data)exploration. Researchers no longer simply ask, “What experiment could I construct to test this hypothesis?” Increasingly, they ask, “What correlations can I glean from extant data?” More tellingly, one wishes to ask, “What insights could I glean if I could fuse data from multiple disciplines and domains?” The challenge is analyzing many petabytes of data on a time scale that is practical in human terms.

5. 100 Years Into the Future • Data is being produced at an exponential rate. • Imagine a world where everything is monitored • Can you imagine the wealth of LTER data 100 years from today? • What if it all was • Accessible • Searchable • Useable

6. To what extent should LTER today be preparing to support this kind data-driven investigation today, and in the far future? • It is more than just supporting hypothesis-driven investigations, it is exploration of existing data, sometimes composing questions that can be asked or simply looking for correlations hiding in the complexities of LTER’s diverse and vast data collections. • To what extent can LTER today truly support data-driven future studies, or can it only collect data that might be useful in a later hypothesis-driven study? • 8/10ths of the answer lies in how the data is managed, how searchable is it, how accessible is it, how understandable is it, how translatable is it, to researchers that did not collect it. • 2/10th lies in the technology methods that support discovery, search, access, and analysis of the data • How will LTER preserve it’s investment in data so it is accessible, searchable and useable?

7. What could LTER be doing differently today to better facilitate data-driven future analysis? Should ALL sites be doing this? • To the first order every site and every researcher on every project should be thinking about how to ensure that their data could be understood and potentially useful to other researchers. • Gone are the days when all your data exists at one site or in a single database. Supporting geographically, and organizationally distributed data is essential. Yet that does not mean there is not a place for centralized repositories. • Effort should continue to harmonize historical data through translation and or creation of metadata leveraging the EML standard. • Supporting use of data from other domains, and enabling other domains to utilize LTER’s data is essential in today’s multi-domain approach to scientific exploration.

8. Who are the partners for creating a usable data infrastructure who can help in terms of prototyping new technologies with LTER input? • DataOne IMO is the project leading the way. D1 is focused on solving the fundamental data access problems in a way that honors the data owners and stewards. • Unlike the other potential partners D1 does not “own” any data, they succeed only with broad community participation and they achieve that by offering mechanisms that facilitate data discovery and accessibility. • LTERNO is now rolling out PASTA – sharply focused on data preservation and accessibility. • Don’t wait! There may be more elegant textbook approaches promised, there are likely much more complicated approaches but PASTA and D1 are here today and are making great progress, embrace them and build upon them! Their approach to the data is what will be sustained, not necessarily the technology that serves it

9. The future will be one where there are sensors everywhere, and the ability to manipulate them from devices we all carry. What implications do these trends have for LTER? • As a security person I see a critical need to design the security for such systems right at the very beginning and to take very seriously the risks of enabling such capabilities. • From an IT perspective this further emphasizes the need to leverage commercial off-the-shelf solutions. The rate of technology change is much too rapid for the scientific software development teams to keep up with by building custom solutions. Think about what cell phone you were carrying 10 years ago, even 5 years ago and ask yourself if it could support the applications you have on your smart phone today? • There is another discussion we could have about handing this onslaught of sensed data.

10. END R-Butler@Illinois.edu