Schedule#
Update
Course website and materials are currently being revised in preparation for Fall 2025.
Note: To access the slides you may need to enable Box for your NetID.
Module |
Lecture # |
Topic |
Slides |
Quizzes |
|---|---|---|---|---|
Course Overview |
Orientation Quiz |
|||
1 |
1 |
Introduction to Data Curation |
Module 1 Quiz |
|
1.1 |
What is Data Science? |
|||
1.2 |
What is Data Curation? |
|||
1.3 |
Objectives, Activities and Methods |
|||
1.4 |
Organizations, Conferences and Literature |
|||
1.5 |
Data Curation Perspectives |
|||
1.6 |
Trends in Data Curation |
|||
2 |
2 |
Data Abstractions - Relations |
Module 2 Quiz |
|
2.1 |
Data Models |
|||
2.2 |
The Problem |
|||
2.3 |
The Relational Model |
|||
2.4 |
How is the Relational Model Implemented? |
|||
2.5 |
Abstraction, Indirection & Data Independence |
|||
2.6 |
Relational Model and Curation Activities |
|||
2.7 |
Tidy Data |
|||
3 |
3 |
Data Abstractions - Trees |
Module 3 Quiz |
|
3.1 |
Text and Documents |
|||
3.2 |
The Problem |
|||
3.3 |
The Solution: (1) Descriptive Markup |
|||
3.4 |
The Solution: (2) Trees |
|||
3.5 |
Why The Solution Works |
|||
3.6 |
Implementing The Solution with XML and JSON |
|||
4 |
4 |
Data Abstractions - Ontologies |
Module 4 Quiz |
|
4.1 |
The Problem: Connecting Data to Information |
|||
4.2 |
The Solution: Ontologies |
|||
4.3 |
An ER/Ontology Example: FRBR |
|||
4.4 |
Implementing Ontologies in RDF/RDFS |
|||
4.5 |
Practical Ontologies with JSON-LD |
|||
5 |
5 |
Data Integration |
Module 5 Quiz |
|
5.1 |
Data Cleaning, Data Integration |
|||
5.2 |
Managing Heterogeneity |
|||
5.3 |
Schema Integration |
|||
5.4 |
Schema Integration: an example |
|||
5.5 |
Example: The Curated Data Lake |
|||
6 |
6 |
Data Concepts |
Module 6 Quiz |
|
6.1 |
What is data? A first attempt |
|||
6.2 |
The Identity Problem |
|||
6.3 |
Some Ontological Analysis |
|||
6.4 |
A Way Forward: Roles and Types |
|||
6.5 |
An Ontology for Data Concepts |
|||
6.6 |
What is data? |
|||
7 |
7 |
Metadata |
Module 7 Quiz |
|
7.1 |
What is Metadata? |
|||
7.2 |
Metadata Schemas |
|||
7.3 |
Common Metadata Ambiguities |
|||
7.4 |
How Does Metadata Support Data Curation? |
|||
7.5 |
Metadata in Practice |
|||
8 |
8 |
Identity |
Module 8 Quiz |
|
8.1 |
Why is Identification Important? |
|||
8.2 |
What Are We Identifying? |
|||
8.3 |
How Do We Identify? |
|||
8.4 |
Canonicalization |
|||
8.5 |
Identifiers and Identifier Systems |
|||
9 |
9 |
Preservation |
Module 9 Quiz |
|
9.1 |
Introduction to Data Preservation Challenges |
|||
9.2 |
What is Data Preservation? |
|||
9.3 |
The Preservation Integration Parallels |
|||
9.4 |
Standard Data Preservation Strategies |
|||
9.5 |
Two Data Preservation Standards |
|||
10 |
10 |
Standards |
Module 10 Quiz |
|
10.1 |
Standards and Standards Organizations |
|||
10.2 |
Some Standard Standards Maneuvers |
|||
10.3 |
Compatibility |
|||
10.4 |
Standards Organizations |
|||
11 |
11 |
Workflow, Provenance and Reproducibility |
Module 11 Quiz |
|
11.1 |
Workflow |
|||
11.2 |
Provenance |
|||
11.3 |
Workflow Systems |
|||
11.5 |
Provenance Standards |
|||
11.6 |
Computational Reproducibility |
|||
12 |
12 |
Ethics, Law, Governance, and Policy |
Module 12 Quiz |
|
12.1 |
Definitions, types, scope, issues |
|||
12.2 |
Research and Data Ethics |
|||
12.3 |
Privacy Laws |
|||
12.4 |
De-Identification Methods |
|||
12.5 |
Intellectual Property Laws |
|||
12.7 |
AI Law and Data Curation |
|||
13 |
13 |
Data Practices |
Module 13 Quiz |
|
13.1 |
Data Practices |
|||
13.2 |
What’s Going on in the Lab? |
|||
13.3 |
Data Sharing |
|||
13.4 |
Data Reuse |
|||
13.5 |
Trends in Data Curation Research |
|||
14 |
- |
Fall Break |
||
15 |
15 |
Communication |
Module 15 Quiz |
|
15.1 |
Communication and Data Curation |
|||
15.2 |
Information Overload |
|||
15.3 |
Limited Access to Research |
|||
15.4 |
Research Integrity |
|||
15.5 |
Beyond the PDF |
|||
16 |
16 |
Course Review |