INFO 220-11
Data Services in Libraries
Summer 2020 Syllabus

Kate Saylor

Rachel Woodbrook

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Office Hours: Conducted via Zoom, by appointment or as needed.

Syllabus Links
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Canvas Information: Courses will be available beginning June 1st, 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 for supporting quantitative and qualitative research in library settings. In this course we will explore the following questions and topics:

  • What counts as data?

  • What is the range of data services offered in libraries? And why libraries?

  • How do data services support the research data lifecycle?

  • What are core competencies for entry into the field of data services, and what forces are shaping the job market?

  • What might a critical approach to ethics in data services look like?

Course Requirements


All readings will be linked from the course Canvas site and will be available to freely students either openly online, or through the SJSU library. For this course, we will read the entirety of The Data Librarian’s Handbook by Rice, R., & Southall, J. (2016) in addition to various articles, blog posts, etc.


Assignment Policies:

  • You can generally expect to hear back about your assignments within 1 week of the due date.

  • 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. You may have one 2-day (48-hour) no-penalty extension, no questions asked. You must let us know that you are taking the extension by the assignment deadline. Otherwise, your grade will be reduced by 20% for each week the assignment is late. No extensions will be granted for discussion assignments.

Assignment Descriptions:

  • Reading Discussions: Students will be expected to respond substantively to readings and to each other at specified points in the term, and to facilitate one of five student-led discussions. (CLOs 1, 2, 3, 4, 5, 6, 7)
  • Data Seeking Assignment: Students will practice assessing and locating appropriate resources to answer a data reference question, and reflecting on their process. (CLO 7)
  • Data Curation Assignment: Students will assess a dataset for public consumption and make recommendations using a data curation framework based on evaluating content, connections, and accompanying context. (CLO 1)
  • Data Management Plan: Students will work with a real-life case study to evaluate and propose changes to a data management plan for a research project using provided criteria. (CLOs 1, 3, 4, 6)
  • Cover Letter and Professional Development Plan: Students will write a cover letter for a position based on real job postings, detailing their current transferable skills and experience. They will also craft a plan to bridge identified knowledge gaps by locating available resources and tools for professional development. (CLO 5)
  • Environmental Scan and Service Proposal: For this final project, students will evaluate an institution’s data services gaps and resources, and craft a proposal for a service program based on institution-specific needs and contexts. (CLOs 2, 4, 5)

Assignment Points/Due dates:

Assignment Points Date due
Reading Discussion Facilitation 10 Varies
Reading Discussion Participation  15 Varies
Intro video 1 6/7/20
Data Seeking and Reference 10 6/14/20
Personal Data Organization 2 6/21/20
Data Curation  10 6/28/20
Skills matrix self-assessment 2 7/5/20
Environmental Scan Draft 5 7/12/20
Cover Letter and Professional Development Plan 10 7/19/20
Data Management Plan 10 7/26/20
Final Environmental Scan and Service Proposal  25 8/7/20
Total 100  

*Refer to class Canvas course for the most up to date information and grading rubrics; due dates subject to change at instructor discretion and with appropriate notice to students.

Course Calendar

The table below provides a summary of course topics and activities; it is subject to minor changes, which will be announced with fair notice.





Introduction to Course and Library Data Services

Discussion - Introduction


Data Discovery and Access

Discussion - Readings

Assignment - Data Seeking and Reference


Data Management Planning and Collection

Discussion - Readings

Assignment - Personal Data Organization


Data Curation

Assignment - Data Curation


Data Education

Discussion - Readings

Assignment - Skills Assessment 


Institutional Environment 

Discussion - Readings

Assignment - Environmental Scan Draft


Data services as a profession

Assignment - Cover Letter & Professional Development Plan


Data sharing, publication, and preservation

Discussion - Readings

Assignment - Data Management Plan


Data Processing and Analysis

Discussion - Readings

Assignment - Environmental Scan & Service Proposal Due


Students must meet the school’s technology requirements as listed here:

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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