INFO 220-12
Data Services in Libraries
Summer 2019 Syllabus

Kate Saylor

Rachel Woodbrook

(please send email to us both and put "INFO 220-12" in the subject line. We will respond to emails and questions within 48 hours.).

Office Hours: Conducted via Zoom; please let us know if you plan to attend.
Tuesdays 8-9am PT/9-10 CT/11-12pm ET (Kate)
Fridays 12-1pm PT/1-2 CT/3-4 ET (Rachel)
Or by appointment.
You may also post questions to the "General questions" discussion board.

Syllabus Links
Canvas Login and Tutorials
iSchool eBookstore

Canvas Information: Courses will be available beginning June 3rd, 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


In the interest of reducing course costs to students, there will be no required textbook for this course. Readings will be listed and made accessible on the course Canvas site.


Assignment Policies:

  • You can generally expect to hear back about your assignments within 7 days of submission.

  • Assignments are due by midnight (Pacific Time) on the due date given.

  • Late assignments: The maximum number of points available for an assignment will be reduced by 20% (rounded to the nearest point) for each week the assignment is late. If you have extenuating circumstances, please contact the instructors as soon as possible.

Assignment Descriptions:

  • Data Management Plan: Based on real-life case studies, students will select a research scenario and draft a comprehensive data management plan for a research project using ICPSR’s “Eighteen Elements of a Data Management Plan.” (CLOs 1, 3, 7)

  • Open Data Analysis Tool Review: Students will review an openly-available data analysis tool and based on criteria relevant to researchers such as interface, ease of use, capabilities, and community uptake. (CLO 4)

  • 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 5)

  • 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 2)

  • Environmental Scan and Service Proposal: Students will select an institution, evaluate its gaps and resources, and craft a proposal for a data services program based on institution-specific needs and contexts. (CLOs 1, 6, 7)

  • Participation: Students will be expected to respond to readings, participate in discussions and complete quizzes and various activities over the course of the class. (CLOs 1, 2, 3, 4, 5, 6, 7)

Assignment Points/Due dates:



Date due*

Data Management Plan


July 2

July 9 (peer review)

July 30 (final)

Open Data Analysis Tool Review


July 16

Data Curation Assignment


July 23

Cover Letter and Professional Development Plan


August 6

Environmental Scan and Service Proposal


August 9







*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.





Introductions and Course Overview

Discussion - Intro

Quiz - Pre-class assessment


Overview of Data and Libraries

Discussion - Data terminology

Activity - Personal data write-up

Quiz - Post-module


Data education

Discussion - User needs

Activity - Data skills matrix
Quiz - Post-module


Data discovery and access

Discussion - Data seeking/Reference

Quiz - Post-module


Data Management Planning

DMP Assignment Due

Quiz - Post-module


Institutional Environment

DMP Peer Review due

Environmental Scan start

Quiz - Post-module


Data Analysis/Processing

Data Analysis Tool Review due

Quiz - Post-module


Data Curation

Data Curation Assignment due

Quiz - Post-module


Data sharing, publication, and preservation

Discussion - Ethics + Preservation

Final DMP Due

Quiz - Post-module


Data services as a profession

Cover Letter/Professional Development Plan due

Quiz - Post-module


Work Week

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.
  2. M Demonstrate oral and written communication skills necessary for professional work including collaboration and presentations.
  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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