BG 5+6 How do we get to the Ideal World? Tuesday afternoon What gaps, challenges, obstacles prevent us from attaining the vision now? What new research investments should be made?

Ideal World Supports multiple approaches to Systems Research –Single discipline, cross discipline, multi-discipline –Strive to improve data access Enable more interactions between and within disciplines –Develop software for making it easier to examine data Enable faster interactions –Be able to take quick snapshot over the web. –Flexible usable data transfer methods. Accelerate the learning process –Allow merging of data from a variety of sources. Test ideas more quickly –Make data available in a variety of spatial and temporal scales. More of an open system that allows more people to access and understand more information. Need some evaluation and feedback on model/data/process applicability. Facilitates assessments from data and models. Facilitates understanding and communication between disciplines and communities. CI is one example. Moving towards a more common language. Serves multiple communities: public, fellow scientists, outside scientists, assessment communities. Incorporates disparate pieces of data, models and output into a complete understanding of the Arctic.

Four factors for promoting synthesis – Getting to the Ideal World Improved data description, quality and feedback Improved data sharing and access Improved mechanisms for promoting activities across disciplines. Arctic data and system model framework

Improved data description, quality and feedback Present mechanisms exist to address this Community based quality assessment –E.g. Bugzilla, Amazon reviews, shared knowledge base Understanding the heritage for the data –Data citation and DOI links Authority of the data –Identifying who is responsible for improving the data, answering the questions Improved metadata as a joint effort –Supplied by whoever created the data –Make this a living, evolving effort

Improved data sharing Mechanisms exist to address this Move toward web accessible, –URL accessible data access –Data easily repurposed Distributed data Communication between groups Improve of understanding of available data Examine data quickly Data extraction team Searchable data resources

Improved mechanisms for promoting activities across disciplines. Encourage multi-disciplinary meetings Encourage multi-disciplinary work –No one’s job description –Short, fast proposal turn-around Make key datasets available in simplified, easily viewable work Make data available for specific locations and regions. Promote common language

Arctic data and system model framework Community based forum to integrate modules into a system involving data and models. Discussion about system components – physical, biological, ecological, social. Develop model intercomparisons, and bring datasets together with models. Merging data integration tools with model output for comparisons and assessment. Applications addressing multiple scales. Discuss with product users what we want to come out of these efforts. Encourage multiple communities to be involved in the model development.

Arctic System Science Data Centers to support synthesis work CI Tools Disciplinary work Data needs Model needs: Products Cross disciplinary Data needs Model needs Products Multi-disciplinary Data needs Model needs Products The ARROWS are the Interactions that need greater support

Followed by more details on discussion of the four topics Data description Data Sharing Activities across disciplines Multiscale, Community Arctic data and system model framework

Gaps have been identified: Assessing the quality of the data. Where are the data? What is the quality of the data? Whom can we talk to about the data/model output? Possible solutions: –Amazon.com reviews Reviews of reviews –MySpace.com approach “Let me tell you about what I thought of these data…” –Apple Itunes approach People who bought this song, also bought … All of these approaches are highly applicable to data centers.

Web of Science for Datasets Mature heritage can show what other data are available. Mechanisms for assessing the usability and quality of the data. Tracking of data use. –Hard to get people to acknowledge data use, let alone cite it formally. –Doi issue?

Assessing the quality of the data. Identifying a data authority (PI, team or data center). Discussions attached to the data. (“Bug reports” automatically sent to whoever downloads the data.) Problems should be addressed once. – captured in knowledge base

Community based modeling forum Driven by the community Global models Regional models System models Data management and modeling have to merge Two strands: –Data related issues (quality, usability) –Models with community

Community Workshop We need to get people to talk to people: –Mediators, facilitators, people skills We need system modeling Data management and system modeling have to merge. We need to push forward component modeling and data access in agreement. –Data management is further along

Community Workshop Work on putting the pieces of a system model together We need to take a step toward working on the components of a system model.

Promoting collaborative science Common proposals between investigators in multiple fields to address a common problem. Move beyond the synthesis mode. Don’t need to be pan-Arctic. Innovative methods to encourage cross- disciplinary work. e.g. on-line fora Need to try multi-disciplinary (not just two disciplines).