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With collaborative research taking place everywhere how do we clas= sify the research data, protect it while the research is going on and then = enable everyone to see after the research is published? How do we collabora= te with the libraries on this one? How do we arrive at data management poli= cies that cut across the whole institution? What are some of the examples o= f institutions doing it successfully today?
Survey Results as of Oct 2, 2009
http://www.itana.org/wp-content/DMS_Results/Surv= eySummary.html
Survey Results as of Sept 21, 2008:
Discussion about the first set of Survey results:
*Data Management Survey*
The data management survey was mailed to the DASIG EDUCAUSE constituent = group email list. The survey asks respondents to self-rate in nine data man= agement categories. To date, there have been 21 responses. Results are in a= spreadsheet available on the wiki: https://spaces.at.internet2.edu/download/attachments/2972/data_ma= nagement_responses.xls
(AI) (Mike Fary) will send a reminder to the DASIG list seeking addition= al respondents. (AI) (Jim Phelps) will seek permission to send the survey l= ink to the EDUCAUSE CIO list. Jim will also send the survey link to the ITA= NA email list.
The highest self-ranking are in the data management and data security ma= nagement areas. The lowest marks are in the data architecture and data ware= housing areas. The data governance area has evenly distributed results.
The last question, "which areas are most critical to you and your instit= ution," probably could have been two separate questions; answers for the in= stitutional perspective and the IT perspective may differ. Two areas receiv= ed more than 50 percent of the votes: data security management, and data wa= rehousing and business intelligence management.
The survey results will be discussed at both the DASIG and ITANA session= s at EDUCAUSE. These face-to-face meetings will provide an opportunity to p= robe deeper on the items on the survey and hear feedback about which data m= anagement topics seem most important right now.
There were other areas of interest:
The data governance responses are bimodal. What are the similarities amo= ng the group at 8 and the group at 4 and under?
Data warehousing received uniformly low rankings.
Two institutions self-rated at 10 on the data security issues. (AI) (Jim P= helps) will follow-up with those institutions to determine if they would do= case studies in that area.
There may also be potential case studies for those institutions that self-= rank highly in the governance area. (AI) (Mike Fary) will follow-up with th= ose institutions.One of the next steps, once the survey is completed, is to identify the = high achievers and determine whether there are best practices to capture. (= AI) As a way to identify those achievers, Mike Fary will graph the survey r= esults by institution.
(AI) (Jim Phelps) will follow up with these high achievers, suggesting c= ase studies, online presentations/webinars, and determining whether there a= re helpful URLs with information about the respondents' areas of achievemen= t.
Example Use Cases
Broken down by Actors
Use Case |
stakeholders |
functional owner |
data custodian |
---|---|---|---|
Online archives of University functions and r= ecordings of events |
presidents office, various high level execs, = deans and dpt heads, office of news and communications, library |
the department that recorded the data |
the actual person to put it somewhere |
Multimedia archives of class support material= s (not just lectures but also study materials, etc) |
dean of students, departmental deans and dpt = heads |
the faculty member |
staff member assigned support of the research=
er, sometimes PostDoc |
Images as class materials but also products o=
f artist's work (Art history as well visual studies) |
departmental deans and heads , faculty member= s, library |
the faculty member |
staff member assigned support of the research=
er, sometimes PostDoc |
Research data - as it pertains to new NIH rul= es |
presidents office, various high level execs, = VP for research, researchers |
researcher who generated the data |
staff member assigned support of the research=
er, sometimes PostDoc |
Research data as institutional asset housed a= t the institution |
presidents office, various high level execs, = VP for research, researchers |
researcher who generated the data |
staff member assigned support of the research=
er, sometimes PostDoc |
Research data of Duke PIs at other institutio= ns or on google docs or in the "computing cloud" |
presidents office, various high level execs, = VP for research, researchers |
SEP ? |
SEP ? |
E-mails as electronic archives under e-discov= ery rules |
presidents office, various high level execs, = deans and dpt heads, University Counsel, ITSO |
none |
dpt support staff or the person who holds the=
data |
Logs as electronic archives of access to syst= ems and actions taken. |
? |
? |
? |