Talk:ESC Charrette

From Earth Science Information Partners (ESIP)

Charrette notes -- Clynnes 15:04, 11 January 2012 (MST)

N1 –

Social networking site may not be the best tool to facilitate sharing of ES type of knowledge.

Platform is not network (reference to William Gunn, Mendeley talk), need network, not web site.

Bridging IT and science falls in human factor area.

Database to connect people together, to access relevant tools and data .

ESC for next generation scientists, need certain amount of social networking capabilities that are widely used among the younger generation.

Enabling, fostering the interest in sharing are more important than determining the platform.

Key questions: who, what to share.

There are lots of existing capabilities (LinkIn, Mendeley,..), need an integration to tie all the real world technologies, including our existing data system.

Facilitate proposal collaboration, data annotation, intercomparison.

Balance between social networking vs rigor/formal science research (peer reviewed results) – 2 different layers, but connected. Sharing the process, execution, experiments and results.

Ability to take products and results generated elsewhere.

Get below the high level abstraction, constraint into a problem that can be solved.

Map capabilities into realistic milestones

How is ESC different than EC, or other existing virtual collaboratory CI capabilities? What does it provide others don’t that can benefit scientists/users?

A3/N2/G3 –

Low hanging fruit.

Catalog – google model vs amazon. Not a core role for ESC.

Browse / Search

Coherent, delightful to use, user experience.

G2 –

Community curated, recommended vs quality control/authoritative. Compliance requires different levels, and need to have standards in place.

Quality pays a role.


Establish minimum requirements (e.g. meeting community based protocol) into ESC framework.

D2 –

Brokering different services and protocols.

B2 –

Key Goal.

Casting, distributed linked data type (tools/workflow/metadata.

B3 –

PCCS is important to scientists.

Reproducibility is not the end goal. Understandability is important.


The analysis vs the collected data.

H3 –

High priority. But to what degree?

N3/K1 –

Determine which community to connect to.

Include Dataone, ESDSWG, USGS Community of Data Integration.

Interoperability, governance linkage, but loose coupled?

Prioritization, on going activities.

Be mindful of N/K categorization, N – sustainability, evolutionary, continuity; K – board participation.