1. Fording the Data Stream MLA Midwest Chapter 2013
Photo by: Brian (makelessnoise)
Abigail Goben, MLS
Rebecca Raszewski, MS, AHIP

2. What We Will Be Covering
Background
Data Process
Current and Future Applications of Data
I’ll be talking about how this project started with our reference desk statistics, the data process, and current and future applications of the data

3. Background Change in record keeping: print to electronic
Loss of staff (20 hrs/week)
Save ~10-15 hrs/month compiling
Storage
Now I will summarize why we ended up moving to collecting reference desk statistics electronically. Prior to August 2011, reference desk statistics were recorded on a paper form. Cumulative data was captured only for monthly and annual reporting. A support staff member would tally up and record the data in a spreadsheet monthly. Our support staff member, who covered almost 20 hours of reference desk hours a week, moved part time into circulation. She could only work the reference desk from 3:30-7 certain nights of the week. Our department head felt this was the time to reconsider how we staffed the reference desk. We were able to get a student worker to cover those hours so we wouldn’t spend as much time as we used to on the reference desk.
Another factor in moving to collecting statistics electronically was storage. A new form was used everyday the reference desk was open. Although the paper forms were filed away, Abigail discovered that a few months of previous data had disappeared. Collecting statistics electronically would eliminate storage issues.
Photo by:Don Shall/origamidon

4. Paper Form
This is a screenshot of the paper version of our slides.

5. Google Form Front End
Abigail created the document using Google Forms. This is what it looks like. We record the type of patron, the kind of question and can also add notes. There is also an off desk option.

6. Google Form Back End
This is the Google Forms backend. It looks just like a spreadsheet. It automatically records the time of when the question was recorded. This can also be sorted like any excel file and can be saved in variety of formats. Abigail has also notes here regarding the month and year.

7. Data Process Identifying Digitizing Cleaning Metadata
Storing (short term/long term)
Sharing
Analysis
Rebecca’s Last Slide
Now that I’ve shown you the form, Abigail will talk about the majority of how the data process was integrated into this project.
Photo by: Yamamoto Tetsuya

8. Data Process: Identifying
Print Archive
Gaps
Electronic Archive
Risks
Relevancy
Over-extending librarians
Rethinking Information Practice

9. Data Process: Digitizing
Student employees
Slow process
Project documentation

10. Data Process: Cleaning
Open Refine

11. Data Process: Storing Short Term Long Term If no repository?
Short term—where are our working copies? How clustered (Excel)
Long term : Format (CSV), back ups, multiple files, access
Options if we didn’t have a repo

12. Data Process: Sharing Question that ties into data storage Licensing
When will the full data set be available?
Citation format (DataCite)

13. Data Process: Metadata
What metadata?
Standards? Ontologies?
ReadMe File (txt)
Was there metadata tied to this?
Any standards we could use
ReadMe File (txt)
Project description
Completion date
address for original authors
Photo by: Shira Golding Evergreen

14. Data Analysis Open Refine MS Excel
SPSS/STATA/R –when to use or not use
Data analysis may be depend on the kind of data you have. If you have messy, inconsistent data, you can use Open Refine. Another option is Excel. We did consider using SPSS, STATA, and R, which stands for the R Project for Statistical Computer but decided not to use those because we did not think our data was complex enough for these tools. These may be good for other projects. Google Spreadsheets is another tools.
Electronic Resources Stats
Photo by: keystricken

15. Current Applications of Data
Case 1: The Case for More Student Employees
This was to move from having one student employee to hiring two.

16. Types of Questions: 9/11-6/12
This is a duplicate of a Pivot Table Abigail created in Google Forms based on Recorded Desk Data from September 2011-June This is illustrating the 4 types of questions that we record.

17. Question Type by Hour: 9/11-6/12
This is another graph that Abigail created from Google Forms for questions by hour. Hours were mostly 8:30-5 or 6

18. Future Applications of Data
Case 2: Weekend/Nights
Case 3: Info Service Stats vs. Reference Desk Statistics(different form)
Here is what will be done in the future. UIC students had asked for a librarian for evening and weekend reference hours. In May 2013, a visiting librarian was hired to work one night a week until 8pm. In June 2013, an evening/weekend librarian was hired to work from 4-8 Tuesdays and Thursdays, Saturdays from 2-6, and Sundays from Future analysis will be done to see if these librarians are getting questions during the evenings and weekends.
We also have a separate Google Form that we use for recording chat questions, classes, consultations, and mediated searches. This form started in January The data from this will be analyzed in the future to see how much time we are spending meeting with students, teaching, etc. to use for hiring additional librarians.

19. Try it Yourself Data Set Available: 9/1/2011-6/30/2012
Sample Reference Desk Form
Both available in Google Forms, please contact us for access
Slides
6 months-a year available
Clean sample version of form

20. Contact Information
Abigail Goben, MLS Assistant Information Services Librarian & Assistant Professor Rebecca Raszewski, MS, AHIP