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College of Professional and Global Education · School of Information

Resources and Information Services in Professions and Disciplines - Data Services in Libraries
INFO 220

  • Summer 2023
  • Section 10
  • 3 Unit(s)
  • 06/05/2023 to 08/12/2023
  • Modified 05/20/2023

Canvas Information

This course will be available on Canvas beginning June 5th, 6 am PT.

You will be enrolled in the Canvas site automatically.

Contact Information

Course Co-Taught by:

Peace Ossom-Williamson
Office Hours: virtual meetings in schedule below and by request


Rafia Mirza

Office Hours: virtual meetings in schedule below and by request

Course Information

Required Technology: Tableau Public - free software available at

!!!*****Tableau is required for this course.*****!!! You can use Tableau Desktop instead of Tableau Public software which is available as an educational license by request to Tableau. Visualizations will be posted to the Tableau Public website.

Course Description and Requisites

This course offers an introduction to data services in library settings for supporting research and promoting open science practices.


INFO 210.

Classroom Protocols


Students are expected to participate fully in all class activities. It is expected that students will be open-minded and participate fully in discussions in class and debate in a mature and respectful manner. Use of derogatory, condescending, or offensive language including profanity is prohibited. Disagreement is healthy and perfectly acceptable. Expressing disagreement should always include an explanation of your reasoning and, whenever possible, evidence to support your position. In accordance with San José State University's Policies, the Student Code of Conduct, and applicable state and federal laws, discrimination based on gender, gender identity, gender expression, race, nationality, ethnicity, religion, sexual orientation, or disability is prohibited in any form.

Program Information

Course Workload

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 Goals

Core Competencies (Program Learning Outcomes) Supported

INFO 220 supports the following core competencies:

  1. 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.
  2. M Demonstrate professional leadership and communication skills.
  3. N Evaluate programs and services using measurable criteria.

Course Learning Outcomes (CLOs)

Upon successful completion of the course, students will be able to:

  1. Articulate how various data services and research data management best practices support specific phases of the research data lifecycle.
  2. Evaluate skills, experiences, and interests in relation to the current research landscape, job market, and job requirements across different types of organizational settings.
  3. Conduct a data needs assessment to identify appropriate methods and resources to manage data throughout the duration of a project.
  4. Select appropriate sources and tools to retrieve and analyze statistics and raw data in order to answer a wide range of research questions.
  5. Assess a dataset against necessary elements (organization, documentation, metadata) for sharing and make recommendations to enhance data usefulness.
  6. Demonstrate an understanding of methods to identify stakeholders and approaches for conducting an assessment of the data needs for a community or organization.
  7. Describe the broad ethical concerns that relate to research data and data service offerings in the library context.

Course Materials


No Textbooks For This Course.

Course Requirements and 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 Discussion: Students will write about current trends and efforts toward open science. (CLOs 2-6)
    • CURATE(D) Checklist: Students will use a data curation framework by DCN to evaluate 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)


Late assignments' grades are deducted by 5% each day late. Assignments are no longer accepted 7 days after the due date.













Data Seeking and Access Assignment




Research Protocol




Tidy Data & Documentation




Project Report & Visualization




Open Science Discussion Board




CURATE(D) Checklist




Data Workshop Presentation





1000 points (100%)

Grading Information

The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:

97 to 100A
94 to 96A minus
91 to 93B plus
88 to 90B
85 to 87B minus
82 to 84C plus
79 to 81C
76 to 78C minus
73 to 75D plus
70 to 72D
67 to 69D minus
Below 67F


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 (PDF), relevant university policy concerning all courses, such as student responsibilities, academic integrity, accommodations, dropping and adding, consent for recording of class, etc. and available student services (e.g. learning assistance, counseling, and other resources) are listed on the Syllabus Information web page. Make sure to visit this page to review and be aware of these university policies and resources.

Course Schedule

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. However, we highly recommend attending. If you cannot make it, the recording will be shared in Canvas afterwards.


Module # & Topic

(all due dates are 11:59 pm PT)

Week 1:

6/5 - 6/9

1. Introduction to Data Librarianship



Pre-Class Quiz


Discussion Board 1

  • Initial post due Wed, 6/07
  • Responses to 2 classmates due Fri, 6/09

Bonus points: Skills Assessment Pre-Test due Fri, 6/09

Week 2:

 6/10 - 6/16

2. Data Discovery and Access

*Virtual Session 1: June 14 @ 12 AM PT

Discussion Board 2

  • Initial post due Wed, 6/14
  • Responses to 2 classmates due Fri, 6/16

Assignment 1: Data Seeking and Access due Fri, 6/16

Week 3:

6/17 - 6/23

3. Data Hygiene and Data Management

Discussion Board 3

  • Initial post due Wed, 6/21
  • Responses to 2 classmates due Fri, 6/23


Assignment 2: Research Protocol due Fri, 6/23

Week 4:

6/24 - 6/30

4. Reproducible Research and Analysis


Discussion Board 4

  • Initial post due Wed, 6/28
  • Responses to 2 classmates due Fri, 6/30


Assignment 3: Tidy Data & Documentation due Fri, 6/30

Reflection 1 due Fri, 6/30

Week 5:

7/1 - 7/7

5. Data Visualization


*Virtual Session 2: 7/19/2023 @ 6:00 PM PT

Discussion Board 5

  • Initial post due Wed, 7/05
  • Responses to 2 classmates due Fri, 7/7


Assignment 5: Data Visualization due Fri, 7/21

Week 6:

7/8 - 7/14

Week 7:

7/15 - 7/21

Week 8:

7/22 - 7/28

6. Open Science: Data Sharing and Preservation

Discussion 6: Open Science Discussion Post due Fri, 7/28

Assignment 6: CURATE(D) Checklist due Fri, 7/28

Week 9:

7/29 - 8/4

7. Teaching Data Literacy



Discussion Board 7

  • Initial post due Wed, 8/02
  • Responses to 2 classmates due Fri, 8/04

Week 10:

8/5 - 8/11

Assignment 7: Data Workshop Presentation due Fri, 8/11

Reflection 2 due Fri, 8/11

Bonus points: Skills Assessment Post-Test due Fri, 8/11