INFO 220-10
Research Data Services in Libraries
Summer 2022 Syllabus

Peace Ossom
Office Hours: virtual appointments.

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Canvas Information: Courses will be available beginning June 1st at 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.

Virtual sessions on June 2 and July 8 are optional office-hours for additional support. Virtual sessions will be recorded and made available in the course for future viewing.

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: 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 Requirements


Assignment Policies:

  • Assignments are due by 11:59 pm (Pacific Time) on the due date given.

  • Late assignments: Because summer term moves so quickly, extensions on assignments are difficult to accommodate. Without making a prior arrangement for a late submission, your grade will be reduced by 5% for each day late. 

  • No extra credit is available for this course unless listed below. Requests for (1) additional opportunities or (2) resubmission of graded assignments will be denied.

Assignment Descriptions:

  • Discussions: Students will be expected to respond substantively to readings and to each other in each module. (CLOs 1-7)

  • Skills Assessment Pre- and Post-Test: Students will reflect on their starting and ending skills in data services. (CLOs 1-7)

  • 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 17)

  • Data Visualization: Students will create visualizations of their data using best practices in design and communication. (CLOs 17)

  • Open Science: Students will write about the progress toward open science and, from their perspectives, current opportunities, and challenges. (CLOs 2, 3, 4, 6)

  • CURATE(D) Checklist:  Students will use a data curation framework by DCN to evaluating datasets for deposit.

  • Data Workshop Presentation: Students will compile their activities into a workshop. (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.


Module Topic

(all due dates are 11:59 pm PT)

Week 1: 6/1-5

Introduction to Data Librarianship


*Virtual Session 1: 6/2/2021

Discussion Board 1

  • Initial post due *Fri*, 6/3
  • Responses to 2 classmates due Sun, 6/5


Assignment 1

Skills Assessment Pre-Test due 6/5

Week 2: 6/6-12

Data Discovery and Access

 Discussion Board 2

  • Initial post due Thu, 6/9
  • Responses to 2 classmates due Sun, 6/12


Assignment 2

Data Seeking and Access due 6/12

Week 3: 6/13-19

Data Hygiene and Data Management

 Discussion Board 3

  • Initial post due Thu, 6/16
  • Responses to 2 classmates due Sun, 6/19


Assignment 3

Research Protocol due 6/19

Week 4: 6/20-26

Reproducible Research and Analysis


Assignment 4

Tidy Data & Documentation due 6/26

Weeks 5 & 6:

Data Visualization



*Virtual Session 2: 7/08/2021

 Discussion Board 4

  • Initial post due Thu, 6/30
  • Responses to 2 classmates due Sun, 7/2


Assignment 5

Data Visualization due 7/10

Week 7: 7/11-17

Progress Toward Open Science 

 Discussion Board 5

  • Initial post due Thu,7/14
  • Responses to 2 classmates due Sun, 7/17


Assignment 6

Open Science Writeup due 7/17

Reflection 1 due 7/17

Week 8: 7/18-24

Data Sharing and Preservation

 Discussion Board 6

  • Initial post due Thu, 7/21
  • Responses to 2 classmates due Sun, 7/24


Assignment 7

CURATE(D) Checklist due 7/24

Weeks 9 & 10:

Teaching Data Literacy

Assignment 8

Data Workshop Presentation due 8/5

*Bonus* Skills Assessment Post-Test due 8/5

Reflection 2 due 8/5



Item Points # Percentage
Discussions 25 6 15%
Reflections 15 2 3%
Skills Assessment Pre-Test* 20 1 2%
Data Seeking and Access Assignment 50 1 5%
Research Protocol 100 1 10%
Tidy Data & Documentation 150 1 15%
Report and Data Visualization 200 1 20%
Open Science Write Up 50 1 5%
CURATE(D) Checklist 100 1 10%
Data Workshop Presentation 150 1 15%
Total 1000 points    

*Post-test is not required. However, you will receive 20 bonus points if completed.

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:

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

  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.


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: 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.

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