Trends of PEL Survey Our Recommendations in Bold, Meta-analysis support Pink

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Trends of PEL Survey Our Recommendations in Bold, Meta-analysis support Pink by Mind Map: Trends of PEL Survey  Our Recommendations in Bold, Meta-analysis support Pink

1. Cottage industry vs Enterprise

1.1. Local data storage preferred over University- or cloud-level storage, both for initial storage and back-up. Data are stored indefinitely, and are shared with departmental colleagues, if at all.

1.1.1. Local contact works with end-users to help them understand better data storage solutions and collaborative opportunities.

1.2. Cloud computing

1.2.1. cost savings

1.2.2. common behavior

1.2.3. loss of control

1.3. outside image to NSF/NIH + outside institutions

1.4. too many industries

1.4.1. spa

1.4.2. collaborate/ovpr

1.4.3. rca

1.5. fac/student recruitment more difficult/ top 3 goals harder to reach

2. RCA's role

2.1. Local department/contact is primary end-user resource

2.1.1. Collegiate IT directors work with end-users in their college to identify local contacts. Directors then work with local contacts to inform them of new services and offerings.

2.2. Past: end user seeks out info from all units

2.3. Future: RCA acts as hub of information provider to enable local points of contact

2.4. To the extent that software applications and technology tools are funded they are largely funded at the researcher level; and researchers see the future of these applications and there support increasing in importance for theie success over the next 3 years.

2.4.1. we license the right software

2.5. Spokes on the wheel idea

3. Data storage:

3.1. users expect basic storage solutions for ALL of their data

3.1.1. centralized storage must not be allocated or capped per person, but must function flex

3.2. they want storage that is accessible daily, automatically backed up, securely stored, easily sharable, saved indefinably, with unlimited capacity

3.2.1. creating an inventory of data sets will enable resuse of valuable assets to the U. OVPR and OIT inventory current data storage use and storage capacity on collegiate, University, and cloud servers.

3.3. researchers want full control over their data storage needs and in a cost-effective way

3.3.1. storage solutions must be transparent to the user with little need for support and a high level of control

4. Terminology (eResearch)

4.1. Humanities feel that this survey did not ‘speak’ to them; however, humanities has self-identified cyberinfrastructure needs. These trends were evident in the comment section of the survey. [We are going to explore further this issue.]

5. Training/Awareness

5.1. End-users are poorly informed about CI resources and data management tools and how to complying with funding requirments

5.1.1. The survey comments suggest a need for communication and/or organization of cyberinfrastructure resources. The RCA should plan for broad dissemination of the resources available to meet these needs.

5.1.2. a website, tours, a liason brought into the department to meet with staff were suggested

5.2. End-users are unaware of funds available for staff training in learning and workforce development around cyberinfrastructure. [We are going to explore further these data.]

5.2.1. one database that pulls in all the training opportunities on campus

5.3. university policy is not apparent to the user but education is expected as a centralized role

5.3.1. research and data management policy should be incorporated into orientation at a centralized level

6. Consultation

6.1. Challenge to engage end-users and University leaders in this topic, especially if the jargon is discipline-specific. [Need to research jargon for multiple disciplines.]

6.1.1. Promo handout for IT staff "Questions to ask researchers about their IT needs"

6.2. researchers ask for grant support for CI, rather than investigating local options

6.2.1. PI grant funding process should include the local IT person who consults on the grant project.

6.2.2. not to limit the project but help leverage current resources avail

6.3. Documented inventory of resources, how do you obtain access, and funding questions in all categories yield high responses in the none/don’t know categories. Can we trust these responses?

6.3.1. [Comparing to Educause responses by Friday, May 8.]

6.3.2. New node

6.4. Many departments are prased for doing great work, how to understand and model this behavior?

7. Meta-analysis

7.1. libraries: understanding scientists

7.1.1. interdisciplinary/collab issues

7.1.1.1. lack of access to shared resources

7.1.1.2. varying communication/data sharing tools

7.1.1.3. lack of knowledge on tech use

7.1.2. data storage

7.1.2.1. obstacles in sharing data across disciplines

7.1.2.1.1. access issues

7.1.2.1.2. how relevant to others?

7.1.2.2. lack of edu on database/data sharing (file naming)

7.1.2.3. security issues: competitive reasons for non-sharing

7.1.3. data management

7.1.3.1. how long to keep data

7.1.3.1.1. relevancy (science moves on)

7.1.3.1.2. indefinitely: kept for all time

7.2. Celeste

7.2.1. need for layered approach (local up to central)

7.2.2. leveraging cottage industry

7.2.2.1. merge with established behavior

7.2.2.2. PI fund local IT from grants

7.2.3. local IT staff should be more aware of research

7.2.4. because of storage restrictions, data is lost due to lack of long-term storage solutions

7.2.4.1. need storage flexibility

7.2.5. Disciplines/practices may vary but...

7.2.5.1. everyone has data storage needs

7.2.5.2. everyone needs more support

7.2.5.3. potential to build in CI into grants

7.2.5.3.1. increase awareness of existing tech

7.3. Educause

7.3.1. terminology bad

7.3.1.1. cyberinfrastructure = eResearch

7.3.2. knowledge of CI rated high, available resources are rated low

7.3.3. know HPC, but dont know about software/tools

7.3.4. collab at instititions are rated low, as are effectivness at integrating CI technologies (worse)

7.3.5. less half institutions use HPC for research (cost restrictions)

7.3.6. access to CI technologies primarily through labs/personal, second = central IT for advacned network HPC

7.3.7. for CI sucess on campus: share CI research environment with research and central IT

7.3.8. get IT involved with writting grants

7.3.9. funding for central is essential for the support of research on campus

7.3.10. better communication/outreach for central IT would improve support (get IT involved with research)

7.3.11. IT must rely on researchers' specialized IT knowledge for capacity

7.3.11.1. localize support. centralize basic needs

7.4. OVPR

7.4.1. computing/imaging needs increase

7.4.2. move toward interdisciplinary research

7.4.3. global research/collaboration

7.4.4. invest based on large needs on campus versus biggest return

7.4.5. base these priorities on "clusters"

7.4.5.1. imaging

7.4.5.2. aquatic/atmo modeling

7.4.5.3. behav/social sciences

7.4.5.4. media/arts/performing

7.4.5.5. Proteomics and spectroscopy

7.4.5.6. Research facilitation /labs/space

7.4.5.7. Spatial analysis / GIS

7.4.5.8. Digital media across arts & sciences

7.4.5.9. High performance Computing/MSI

7.4.5.10. New node

8. Software/tools

8.1. staff do not need to support all specialized software, but should understand the options avail across the universtiy

8.2. specific needs were outlined in order to be competitive

8.2.1. video conferecing

8.2.2. statistical support

8.2.2.1. SAS/SPSS/Matlab

8.2.3. collaboration tools

8.2.4. digital media/visulalization tools

8.2.4.1. image analysis, photoshop, R

8.2.5. GIS/spatial

8.2.5.1. arcGIS

8.2.6. High Performance computing/bandwidth

8.2.6.1. MSI

8.2.7. web development (basic level)

8.2.7.1. web-based software

8.2.8. grant writing support

8.2.8.1. microsoft office

8.2.9. library/citation tools

8.2.9.1. endnote

8.3. want more open source tools to reduce costs

8.4. need virtual collabroation tools, wet labs, easy process of sharing data with other institutions

9. One

9.1. new workshop

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9.3. New node

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