INFO 220-12
Research Data Services in Libraries
Spring 2022 Syllabus
Ms. Peace Ossom
E-mail
Office location: virtual
Office Hours: by appointment and during scheduled sessions (see calendar)
| Syllabus Links Textbooks CLOs Competencies Prerequisites |
Resources Canvas Login and Tutorials iSchool eBookstore |
Canvas Information: Courses will be available beginning January 26th, 6 am PT unless you are taking an intensive or a one-unit or two-unit class that starts on a different day. In that case, the class will open on the first day that the class meets.
You will be enrolled in the Canvas site automatically.
Course Description
This course offers an introduction to data services in library settings for supporting research and promoting open science practices.
Required Technology: Microsoft Office, Tableau Public - free software available at public.tableau.com
Course Requirements
Assignments
- Discussions: Students will be expected to respond substantively to readings and to each other in each module. (CLOs 1-7)
- (bonus) Skills Assessment Pre- and Post-Test: Students will reflect on their starting and ending skills in data services. (CLOs 1-7)
- Data Project: The first half of the course will consist of engaging in a project looking at secondary data and reporting findings. It consists of the following assignments:
- Data Seeking and Access Assignment: Students will find and assess a dataset for public consumption. (CLO 1)
- Research Protocol: Students will develop an introductory research plan including a data management plan and making use of open data. (CLOs 1, 3, 4, 6, 7)
- Tidy Data & Documentation: Students will submit their data in tidy data format along with documentation of their data cleaning process. (CLOs 1, 7)
- Data Report & Visualization: Students will create visualizations of their data using best practices in design and communication. (CLOs 1, 7)
- Data Services: The second half of the class focuses on lessons learned and helpful ways these experiences can be turned into services as well as reviewing what efforts are out there. These are reflected in the following assignments:
- Open Science Paper: Students will write a paper about current trends and efforts toward open science. (CLOs 2-6)
- CURATE(D) Checklist: Students will use a data curation framework by DCN to evaluating datasets for deposit. (CLOs 3, 4)
- Data Workshop Presentation: Students will use their data project from the first half of the course to develop a workshop presentation. (CLOs 1, 3, 4, 6)
Course Calendar
These items may be subject to change with fair notice. Please keep up with announcements and assignments in Canvas. Virtual sessions are optional pre-scheduled office hours.
|
Week |
Module # & Topic |
Assignment |
|
Week 1: Jan 26-28 |
1. Introduction to Research Data Services in Libraries / Starting with Data
*Virtual Session 1: 1/31/2022 @ 11 AM PT |
Pre-Class Quiz Introductions
Bonus points: Skills Assessment Pre-Test due 6/6
Discussion Board 1
Assignment 1: Data Seeking and Access due Fri, 2/04 |
|
Week 2: Jan 29- Feb 4 |
||
|
Week 3: Feb 5-11 |
2. Data Hygiene and Data Management |
Discussion Board 2
Assignment 2: Research Protocol due Fri, 2/18 |
|
Week 4: Feb 12-18 |
||
|
Week 5: Feb 19-25 |
3. Reproducible Research and Analysis
|
Discussion Board 3
Assignment 3: Tidy Data & Documentation due Fri, 3/04 Reflection 1 due Fri, 3/04 |
|
Week 6: Feb 26- Mar 4 |
||
|
Week 7: Mar 5- Mar 11 |
4. Data Visualization
*Virtual Session 2: 3/17/2021 @ 6:30 PM |
Discussion Board 4
Assignment 4: Data Visualization due Fri, 3/25 |
|
Week 8: Mar 12- 18 |
||
|
Week 9: Mar 19-25 |
||
|
Week 10: Mar 26- Apr 3 |
Spring Break |
|
|
Week 11: Apr 4-8 (short week) |
5. Open Science: Data Management |
Discussion Board 5: No discussion this module Assignment 5: Open Science Paper due Fri, 4/08 |
|
Week 12: Apr 9-15 |
6. Open Science: Data Sharing and Preservation |
Discussion Board 6
Assignment 6: CURATE(D) Checklist due Fri, 4/22 |
|
Week 13: Apr 16-22 |
||
|
Week 14: Apr 23-29 |
7. Teaching Data Literacy
*Virtual Session 3: 4/29/2021 @ 12 PM |
Discussion Board 7
|
|
Week 15: Apr 30- May 8 |
||
|
Week 16: May 9-16 |
Assignment 7: Data Workshop Presentation due Fri, 5/13 Reflection 2 due Mon, 5/16 Bonus points: Skills Assessment Post-Test due Mon, 5/16 |
|
Grading
Late assignments' grades are deducted by 5% each day late. Assignments are no longer accepted 7 days after the due date.
|
Item |
Points |
# |
Percentage |
|
Discussions |
25 |
6 |
15% |
|
Reflections |
15 |
2 |
3% |
|
Data Seeking and Access Assignment |
40 |
1 |
4% |
|
Research Protocol |
100 |
1 |
10% |
|
Tidy Data & Documentation |
100 |
1 |
10% |
|
Project Report & Visualization |
200 |
1 |
20% |
|
Open Science Paper |
100 |
1 |
10% |
|
CURATE(D) Checklist |
50 |
1 |
5% |
|
Data Workshop Presentation |
230 |
1 |
23% |
|
Total |
1000 points (100%) |
||
Course Workload Expectations
Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica. Other course structures will have equivalent workload expectations as described in the syllabus.
Instructional time may include but is not limited to:
Working on posted modules or lessons prepared by the instructor; discussion forum interactions with the instructor and/or other students; making presentations and getting feedback from the instructor; attending office hours or other synchronous sessions with the instructor.
Student time outside of class:
In any seven-day period, a student is expected to be academically engaged through submitting an academic assignment; taking an exam or an interactive tutorial, or computer-assisted instruction; building websites, blogs, databases, social media presentations; attending a study group;contributing to an academic online discussion; writing papers; reading articles; conducting research; engaging in small group work.
Course Prerequisites
INFO 220 has no prequisite requirements.
Course Learning Outcomes
Upon successful completion of the course, students will be able to:
- Assess a dataset against necessary elements (organization, documentation, metadata) for sharing and make recommendations to enhance data usefulness.
- Demonstrate an understanding of methods to identify stakeholders and approaches for conducting an assessment of the data needs for a community or organization.
- Describe the broad ethical concerns that relate to research data and data service offerings in the library context.
- Articulate how various data services and research data management best practices support specific phases of the research data lifecycle.
- Evaluate skills, experiences, and interests in relation to the current research landscape, job market, and job requirements across different types of organizational settings.
- Conduct a data needs assessment to identify appropriate methods and resources to manage data throughout the duration of a project.
- Select appropriate sources and tools to retrieve and analyze statistics and raw data in order to answer a wide range of research questions.
Core Competencies (Program Learning Outcomes)
INFO 220 supports the following core competencies:
- J Describe the fundamental concepts of information-seeking behaviors and how they should be considered when connecting individuals or groups with accurate, relevant and appropriate information.
- M Demonstrate professional leadership and communication skills.
- N Evaluate programs and services using measurable criteria.
Textbooks
No Textbooks For This Course.
Grading Scale
The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:
| 97 to 100 | A |
| 94 to 96 | A minus |
| 91 to 93 | B plus |
| 88 to 90 | B |
| 85 to 87 | B minus |
| 82 to 84 | C plus |
| 79 to 81 | C |
| 76 to 78 | C minus |
| 73 to 75 | D plus |
| 70 to 72 | D |
| 67 to 69 | D minus |
| Below 67 | F |
In order to provide consistent guidelines for assessment for graduate level work in the School, these terms are applied to letter grades:
- C represents Adequate work; a grade of "C" counts for credit for the course;
- B represents Good work; a grade of "B" clearly meets the standards for graduate level work or undergraduate (for BS-ISDA);
For core courses in the MLIS program (not MARA, Informatics, BS-ISDA) — INFO 200, INFO 202, INFO 204 — the iSchool requires that students earn a B in the course. If the grade is less than B (B- or lower) after the first attempt you will be placed on administrative probation. You must repeat the class if you wish to stay in the program. If - on the second attempt - you do not pass the class with a grade of B or better (not B- but B) you will be disqualified. - A represents Exceptional work; a grade of "A" will be assigned for outstanding work only.
Graduate Students are advised that it is their responsibility to maintain a 3.0 Grade Point Average (GPA). Undergraduates must maintain a 2.0 Grade Point Average (GPA).
University Policies
Per University Policy S16-9, university-wide policy information relevant to all courses, such as academic integrity, accommodations, etc. will be available on Office of Graduate and Undergraduate Programs' Syllabus Information web page at: https://www.sjsu.edu/curriculum/courses/syllabus-info.php. Make sure to visit this page, review and be familiar with these university policies and resources.
In order to request an accommodation in a class please contact the Accessible Education Center and register via the MyAEC portal.
Download Adobe Acrobat Reader to access PDF files.
More accessibility resources.