Breakout # 1 – Data Collecting and Making It Available Data definition “ Any information that [environmental] researchers need to accomplish their tasks” Characteristics (give some numbers) Dimension Format (analogue and digital) – video, physical, numerical Media types (text, numeric, images, audio, video) – print, samples Attributes Historical and contemporary data Availability Timeliness Quality Metadata - Describes the data Security Wed, Oct 30, 2002

Special Properties and Aspects of Environmental Sciences Complexity – data from multiple disciplines Time series - long term monitoring, historical data, endangered data Geospatially referenced (GIS not handle time series well) Scale – vast time (seconds to 4.6 B yrs) and space (microns to kilo km) scales Marriage of observation and computation Real time data collection, distribution, use, response Societal impacts Emergent properties Education Data accessibility – physically and intellectually available to scientists and broader audiences (public, educators, decisions makers) Mostly open and free access of data

Needs Categories – “4 A’s” (define roles and professions who should be engaged in the processes) Acquisition – sensors, legacy, transmission, connectivity Archive - storage, managing, cataloguing, preservation Application – manipulation, interpretation Accessibility – discovery and outreach (make data known, marketing) Documentation Standards for metadata information Integration/compatibility with existing initiatives – e.g. NSDI Content standards process, separate from IT standards Data and cooperation from multidisciplinary sources 4-D observation, analyses, visualization Metadata needs to be linked to data p. 1

Needs Toolboxes and tutorials data mining (including semantic web, web services) data subsetting, distribution, and extraction tools 4-D search engine and filtering Longevity of data Rewards system for database contributions Distribution services for data generators and providers – libraries, value-added (data providers are also data users) People and education infrastructure Ready access to IT support No one can do it all or by themselves Training to work in teams Process for repurposing, reusability of data p. 2

Needs System Bandwidth – locally sufficient except for ‘last mile’ Ability to limit resolution to deal with ‘last mile’ problem Global connectivity - address 3 rd world limitations High performance transmission Data Interoperability/interchange Ease of use of system, hardware, software Maturation of technology system Internet connectivity all the way to the sensor – common protocols Systems engineering approach Services “Ship time” computational resources allocated automatically for funded projects Educated students; cross-trained Content Adequate quality data in digital form – computational capability may outstrip data resources

Challenges Across the board support for archiving data and populating data bases Global interconnectivity and interoperability – rapidly evolving formats and standards Reward structure – tenure track, proposals, citations, investigators, ‘meta-citations’ hits Citations for continuously evolving data sets Establishing metrics for data use There are only limited models to apply in developing the system Role of federal agencies (NSF and others) in establishing, managing, maintaining, sensor networks and observing systems (what is the “exit strategy”?, unfunded mandates) End to end plan from federal agencies in acquiring, populating, managing, maintaining, archiving, and distributing data sets (“exit strategies” and unfunded mandates) p. 1

Challenges Conversion of IT to sustainable scientific infrastructure Stewardship of the CI longterm Prioritization and criteria for preservation of legacy data Technological limitations – e.g. high speed wireless, IT tools, terascale operations (but data is coming in that fast) Security – data-, system-, national- Scalability Middleware and operating systems maintenance and support – no incentive to modify original version Building community consensus p. 2

Principles: Creation of the Cyberinfrastructure should: Be process oriented Respond to changing/evolving needs Enable innovation Think broadly, don’t limit approaches Be open sourced Advance scientific research Facilitate integration of research and education System needs to evaluate its own utility (metrics) Feedback system from users