Workshop on Cyberinfrastructure for Environmental Research and Education

1. Workshop on Cyberinfrastructure for Environmental Research and Education DISCIPLINARY PERSPECTIVE BIOLOGY/ECOLOGY November 1, 2002

2. Creating a Unique Beast

3. WHERE WE COME FROM: • Edu. Spatial ecology problems • Edu. Computer science • Com.Applied IS/information policy/ infrastructure devel • Edu.Science policy; natural resource assessments • Edu. Genomics/organismic ecology • Edu. Urban LTER – Data management • Edu. Economist/environmental engineering/industrial ecology • Edu.Org? – data management and modeling • Org – Infrastructure development • Edu.Org- synthesis and analysis

4. Explicit Example (short term) • Remote sensing satellite data – • hard to get and needs ground truthing • Share data that provide ground truthing – • Make both widely available • Requires huge amount of storage, infrastructure • Problems of scale and types of data collection – small scale science combined with large scale remote sensing • Add temporal comparison complexity If we had this, global change issue could be addressed more effectively to include atmo; geo; bio; human and social/economic parameters

5. Needs of this Integrated/Interdiscipliary Science • Relationship of other disciplines for cross ontological ties (once we work through our own metadata) • Digital libraries are working in this area (coordinate with them) • Need to make a commitment to involve social science researchers working in the b/e domain • Vocabularies don’t mesh

6. Develop a Culture of looking at data as a resource – beyond PI need – repurposing • Encourage data publishing policy • Share lessons learned – evolving • Work with “Publishing Community” to insist on georeferencing • Support Progress: Work with ESA -- new Journal – Ecological Archives • Be more explicit about documenting quality – meaningful for the purpose • Develop language for communicating accuracy/explicitness • Definition of digital object - static/dynamic/versioning • Composite, extensible metadata standard

7. Planning for life cycle and future uses of data • Data and metadata standards must remain as flexible as possible • NSF – in addition to final report should check off that data is deposited (Where? See below) • Need for a nationally recognized “repository” system/process – global in concept • Models: Genbank • Repository needs sustained support – need to contribute to the concept of a model (including economics) for long term access and preservation of data • NSF should provide advice and guidance to PIs

8. More Needs • Develop tool to help scientist develop experiment which includes database output in standard structure – make it easy to have scientists accomplish this • Need more of this kind of thing to make it easier to contribute to the infrastructure • Accessiblity to lots of cycles when needed for workbench applications (COTS like SAS, MatLab, Excel)

9. Explicit example (long term) Ecological forecasting • Species occurrence data –What data sets are needed to take current species distributions and project where they’ll be tomorrow • Invasives and spread; human or wildlife disease vectors • Communities and change • Pattern recognition • Truly spatial approaches to economics of resource management • Federal monitoring $650M/year --- make it possible to leverage this investment by developing tools to analyze it

10. NSF Needs to have initiative to strategically fill in gap between data of individual research questions and the bigger integrative questions • If looking for future predictive capability, need to understand what’s needed for this kind of data, and • Help scientists and others contribute useful data • How to build the data/knowledge grid so that the big questions can be answered by the sum of many distributed research activities (e.g. Heinz Report) • Need both bottom up and top down • Needs iterative relationship among research and policy and other communities • Better data practice and explicit commitment towards standardization

11. Ontologies of questions can lead to understanding among disciplines and user communities (e.g. policy) • If we know the policy question, can it be disambiguated so that metadata can be generated which can then make it transparent that data are available • Can help drive the more fundamental metadata question – how to anticipate what parameters may be needed • and how can metadata evolve even ex post facto to enrich data set for future other purposes. • Need to work with Social Sciences because the vocabularies are so different

12. Data Discovery Issues • Once identified, magnitude of task to actually make them usable for a new analysis • Can clarify this through formal metadata • Schema discovery, interpretation and translation • Covering all the parameters: geospatial scales; temporal; presevation, etc. • Explore the ultimate implications of how to capture metadata to allow for flexible future use • Need ontologies of the research question to ensure metadata can give the answer • Bring in socioeconomic context (Biocomplexity program tries)

13. NSF Program to do More Integrative Research • Interdisciplinary proposals which requires CS work teams to get at large scale ecological questions • ITR and BDEI have potential but needs more PR to get to the community • Needs focus on studies that push maturation of b/e science towards predictivity • At the same time, help small field stations (staff of 1.5) or Joe Q Citizen who wants to do data-collection “right” • Need large confederation process • Need collaborative tools to allow interactions to take place (DOE Access Grid as an example) • Human dimension component of Biocomplexity call needs additional emphasis

14. The impact Focus this NSF Initiative on New Ways Metadata can contribute to Semantic Interoperability This in turn will refocus researcher time – if the 70% of time they spend repurposing and interoperating data sets can be reduced by X% …. The increase in scientific productivity could be very great