Presentation is loading. Please wait.

Presentation is loading. Please wait.

SC&UA DataONE WG MemberNode subgroup Report out 20120502.

Similar presentations


Presentation on theme: "SC&UA DataONE WG MemberNode subgroup Report out 20120502."— Presentation transcript:

1 SC&UA DataONE WG MemberNode subgroup Report out

2 Ack. And Thanks

3 Where to Find our work? https://repository.dataone.org/documents/Committe es/MNcoord/Coordination_Work_Area/SC_UA_Joint_ WG_mtg_30Apr_2May/ https://repository.dataone.org/documents/Committe es/MNcoord/Coordination_Work_Area/SC_UA_Joint_ WG_mtg_30Apr_2May/ Stay tuned for more content here Will be appropriately cross-posted to docs.dataone.org

4 Persona Discussion A. Owens Presentation to sparked discussion: Discussion of Additional categories Additional qualities Suggestion: Make contact with user personas Started drafting 4 more personae

5 Personas in Draft PPSR repository (Amber Owens, Laura Moyers) GIS oriented large Government Repository (Tanner Jessel, Amber Owens, Chelsea Williamson-Barnwell Academic Institutional Repository (Suzie Allard, Holly Mercer, Miriam Davis) Replication and other Infrastructure oriented MN’s (Robert Waltz, John Cobb) Cultural Heritage data related MN (Todd Suomela and John Cobb) Group sync by May 22 and review drafts

6 Documentation Ext web presence – what would a MN want to see? How do people find the Ext documentation How to structures (more individuals less text wall) Much, Much discussion captured at and

7 MN activity assessment/metrics Discussion of what metrics and activity tracking would provide indications of success of DataONE especially with regard to MN coordination activities Comments: Make Contact with 2012 UA WG Meeting which discussed this in detail. https://docs.dataone.org/member- area/working-groups/usability-and-assessment/meetings- usability-and-assessments-working-group/joint-u-a-sc-wghttps://docs.dataone.org/member- area/working-groups/usability-and-assessment/meetings- usability-and-assessments-working-group/joint-u-a-sc-wg Current PMP has some metrics

8 Suggested Metrics (and votes-votes) 1.Number of datasets downloaded 2,1 2.Number of dataset views (some of which may have been downloaded) 1,0 3.User Activity of the MN 0,0 4.How many objects have been replicated to Tier4 MN’s store??? 0,0 5.Duration of time between MN expression of interest and operational deployment Activation time (or more generally, documenting the flow along the deployment timeline) 0,0 6.Diversity of Member Nodes along many dimensions (list the dimensions) 0,0 7.Amount of data available for retrieval via DataONE 1,0 8.Ratio on items #1 and #2 5,5 9.A measure of transdisciplinarity. This might be the number of studies that use more than one MN? 0,2 10.Survey of {potential, in development, operational} MN’s 2,0 11.Referrals (users sent to MN’s from DataONE, Users sent to DataONE from a MN, users sent to a MN from a different MN mediated by DataONE) 1,1 12.Incremental deposition above what would have occurred without DataONE 0,0 13.How many organizations want to me MN’s 3,0 14.Measure of replication multiplicity for data objects 0,0 15.Member Node engagement with DataONE 0,0 16.Curation level 0,0 17.Persistent ID citation counts 2,4 18.Number of Member Nodes (current metric),0 19.Number of datasets (current metric) 0,0 20.End user satisfaction: measures: quantity, quality, documentation 2,3 21.Number of publications resulting form use of MN’s 1,1

9 MN Metrics and Assessments comments Develop a standardize DataONE acknowledgement to include in the method part of papers. Better yet, published a paper describing DataONE that can act as a reference document

10 DataONE Scaling in MN Dimension: Q: How many MN's can DataONE Support ( Qualifiers:Current DataONE org structure, staff, collaborations, and other resources ) 10

11 Before Discussion Exercise: Write your estimate of the limit of MN's that we can support without changes in DataONE? (assume level funding etc. - even beyond yr5) 11

12 Results: MN ceiling < 50 implementation activities < 75 maintenance activity x 3 x 50 = in 2020 (I have no idea) 12

13 What limits the Scalability of DataONE MN's? Physical Cyberinfrastructure: Storage ( DataONE CI, MN's collective, replication targets, …) Network latency and bandwidth (can I finish inventory of Metadata before its time to start the next one) … Human resources and organizational structure CI staff to assist with deployment Outreach activity to get the messages Ability of DataONE to "Act" on MN requests - process/policy friction Target Market: Can we exhaust the pool of potential MN's? Can we exhaust MN pools in specific areas? discipline/subdiscipline areas? "Types" of MN's (observational, simulation, remote-sensing, …) 13

14 Presentation: Question: What things can DataONE do to alleviate these limits? - Increased funding: Easy answer - but not necessarily "smartest" - push the frontier envelope - Increase efficiency - in what areas? - Changing how we do things: For example: Focusing on deploying software reference implementations: Metacat, DSpace, Fedora, IRODs,... Adding/modifying interactions: Ex: MN-Lite Develop and promote deployment of "MN in a box" as a turnkey implementation. Develop MN-retreat - short workshop with goal of deploying MN in a week 14

15 What limits the Scalability of DataONE MN's? 1/5 Physical Cyberinfrastructure: Storage ( DataONE CI, MN's collective, replication targets, …) Network latency and bandwidth (can I finish inventory of Metadata before its time to start the next one) … Human resources and organizational structure CI staff to assist with deployment Outreach activity to get the messages Ability of DataONE to "Act" on MN requests - process/policy friction Target Market: Can we exhaust the pool of potential MN's? Can we exhaust MN pools in specific areas? discipline/subdiscipline areas? "Types" of MN's (observational, simulation, remote-sensing, …) 15

16 What limits the Scalability of DataONE MN's? 2/5 Lack of support from MN home organizations. Mission alignment Amount of peer MN support from things like DUG, etc. for deployment and operations Fail to make “why would you want to be a MN” case. Usage Outreach to MN ‘s userbase. What is the value-added for being a MN. ITK toolset size may limit the end-user base. If All we have is R than non-R users will be less interested. 16

17 What limits the Scalability of DataONE MN's? 3/5 “More Money – more problems” Complexity of organization and infrastructure will increase as DataONE grows. Situational awareness can suffer. What if one MN goes offline? Failure to broaden our base beyond bio/eco/enviro science base. Sustainability issues from the perspective of MN’s. I.E. might MN’s not join because of uncertainty DataONE Systems engineering and complexity with growth leads to a innovation “crunch” Failure to deliver of raised expectations of MN’s – failing to deliver on the promise Evolving commercial/consumer ecosystem requiring added DataONE flexibility (cloud-enabled) 17

18 What limits the Scalability of DataONE MN's? 4/5 Inability to provide governance for an expanding network. “Loss of oxygen” MN’s find a more compelling story with related similar projects. Do we collaborate or compete with other projects? Are they duplicative. Efficiency of the process of bringing on a MN. Could we end up as a repository rather tan a collection of repositories. If we “eat MN”s” we have fewer MN’s. Maybe RN’s become publicly writeable – a global shift in emphasis in response to trends. Physical infrastructure scaling costs ($, power, …) might lead to accretion or other dynamics of data storage/replication. 18

19 What limits the Scalability of DataONE MN's? 5/5 Available and widespread SW stacks that implement the DataONE API (Dspace, iRODs) Popular Data technology for certain science areas may be locked on proprietary data silos (Ex. ESRI ARCGIS) Running out of funding in one year. Funding limit Failing to provide a compelling/persuasive reason to be a MN. Our expectations and limits of our vision may limit or limits (A failure of vision) Data Discovery: We can fail to have a compelling value proposition for MN’s because of difficulty with data discovery. (Do users come to MN or DataONE for discovery 19

20 Exercise: Brainstorm actions DataONE could take to "widen the MN growth bottleneck" 20

21 Presentation: Question: What things can DataONE do to alleviate these limits? - Increased funding: Easy answer - but not necessarily "smartest" - push the frontier envelope - Increase efficiency - in what areas? - Changing how we do things: For example: Focusing on deploying software reference implementations: Metacat, DSpace, Fedora, IRODs,... Adding/modifying interactions: Ex: MN-Lite Develop and promote deployment of "MN in a box" as a turnkey implementation. Develop MN-retreat - short workshop with goal of deploying MN in a week 21

22 How to widen the Bottlenecks 1/5 Develop the DataONE User Group further. Make contact with professional societies and their conferences. This will help make a compelling case to prospective MN’s We need a few compelling MN stories as exemplars. Show return on investment case stories. Use DUG to help MN peer assistance. Consistently emphasize communications, esp. between MN’s. This would help alleviate bottlenecks of unrecognized need. It might help with “large vision” issue. 22

23 How to widen the Bottlenecks 2/5 To help with sustainability, establish involvement with MN governing organization. (engage multiple levels of MN’s organization) Capture DataONE Metadata. Help expose MN data. This will help make “compelling case” Study and verify the importance of discoveraibility. Identify next “communities” to involve/target. Complementary discipline science; complementary government decision-making (bio and climate go together for land management) (eco and publish health go together) “Peas and Carrots” 23

24 How to widen the Bottlenecks 3/5 Survey and learn from our “Coopetition”, and deploy items that we be of benefit to MN’s. Co-develop tools with collaborators or similar projects. This would distribute resource demands. Reach out to identified communities who are not currently entrained, esp.GIS community. Reach out to existing web-published data to offer value add services. Be sure your infrastructure is compatible with existing infrastructures. Diversification (and augmentation) or revenue streams: selling our expertise as a service. (might require different DataONE governance and corporate structure) 24

25 How to widen the Bottlenecks 4/5 Modify overall DataONE organization to enable scaling. DataONE offer training programs (esp for revenue) Expand Ed. efforts and learning modules. This will increase interest and increase onboarding efficiently. (Help alleviate lack od metadata standards) Strategic planning with a 3 year horizon. Helps alleviate concerns about over/under expectations. Use 3 year because 5 years is too long to predict with such mercurial technology substrate. “Our biggest threat is building a battleship that cannot maneuver in the harbor” – leviathan 25

26 How to widen the Bottlenecks 5/5 Further development through the user group Pursue more and varied funding

27 Questions 27


Download ppt "SC&UA DataONE WG MemberNode subgroup Report out 20120502."

Similar presentations


Ads by Google