MLA 2014: Vendor Negotiation Strategies and Open Data

Using the meeting as an opportunity to learn something altogether new to me, I’ve started with exploring a vendor negotiation strategies session but eventually got bored and migrated to open data.

Vendor Negotiation Strategies

Speakers: N. Bernard (Buzzy) Basch, Elizabeth Lorbeer, Brad McCracken (Elsevier)

  • You gotta ask questions – is the person you’re working with entitled to negotiate?
  • Be honest — with vendors and with users
  • Where does the magic 5% come from for vendor increases, regardless of inflation?
    • Buzzy: the EBSCO report
      • the demand for writing has increased dramatically in the last 10 years
      • so much of the information purchased doesn’t get used
    • Brad: Multi-year agreement deals
    • Elizabeth: She’s argued for different percentages, even for 0% for some years

Multi-year Institutions

  • Elizabeth: Tends to work with 3-5 budget models

 

Open Data Initiatives and Trends

  • UCSF data workshop series – Megan Laurance
    • Research Data Repository Landscape & Ecosystem
    • Why Share Data?
    • Why reuse public datasets?  Use Cases!
    • Deep dive on one data repository
    • 45 min data reuse exercises
  • focus on research workflow, not data mgmt workflow
  • remember to take advantage of a captive audience – before, during, after the workshop — more content/exposure info
  • Attendance outcomes: Postdocs biggest groups, faculty the smallest.  Faculty, although smallest in number, provided greatest exposure
  • End session feedback identified learned items such as Awareness, Hands-on Practice, and Links to Resources
  • Working to partner with postdocs to have them do some trainings in the future, also partner with others in the UC system

Data, Libraries, and the 1K Challenge – Jackie Wirz et al.

  • What would you do with $1k that would improve research communication and NOT build another tool?  One-on-one  training
  • Workshops with the library, workshops with the researchers, and individual consultations
  • Data means many things to many people — great example of tomayto/tomahto
  • Working with IR and ScholComm Lib
  • Did map of library skills to research cycle
  • no one had a clue about basic data literacy, no idea of metadata, and no thought given to workflow –> aka very basic concepts is more where we are starting
  • went back to figure out how to get grad students to attend the workshop
  • motivating phrases sourced by grad students and actually attracted grad students to attend a data mgmt workshop: helps PIs and researchers, make it easier to get to graduation, easily reproducible data
  • people see the same data differently
  • detailed means different things
  • no one sees metadata
  • file mgmt is difficult — only 1 group saved the file
  • workflow varies dramatically – had no idea that workflow was unique to them

Tweets from Other Sessions of Interest

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