|Course Archives Documentation Research and Training Centre Unit|
Time: Currently not offered
| Syllabus |
Module 1: Introduction
Unit 01: Data acquisition preservation and curation: Definition, Terminologies and Fundamental Concepts; Philosophy of Data Curation
Unit 02: Open Science and Open Data
Unit 03: Data Librarianship: Role and Functions of the Library
Module 2: Research and Government Data
Unit 04: RDM: Definition and Need, Life cycle, Issues and Challenges to RDM
Unit 05: Government data: e-Governance and Smart Cities
Module 3: Data Curation Standards and Models
Unit 06: Introduction to data curation models and data life cycle
Unit 07: Open Archival Information System(OAIS) model, Archival Information
Package(AIP), Submission Information Package(SIP), Dissemination Information, Package(DIP); ISO 16363; Digital Curation (DCC) Lifecycle Model
Unit 08: Data Curation workflow, Domain specific Data Curation
Module 4: Data Handling and Big Data
Unit 09: Concepts and Components of ETL
Unit 10: Big Data: Nature, Characteristics, Challenges and opportunities
Unit 11: Data Types and Data Models
Unit 12: Data Net, open data, open government initiatives
Unit 13: Data Cleaning and Integration; Managing, Processing, and Policy Heterogeneity; Schema Integration
Module 5: Data Curation and Related Concepts
Unit 14: Data Curation and Libraries and Repositories
Unit 15: Data Curation Planning(e.g., DMPTool)
Unit 16: Organizations for Data Preservation and curation such as : Digital Curation Centre (DCC), Digital Preservation Coalition, Library of Congress (NDIIPP)
Module 6: Data Repositories
Unit 17: Evolution and Components of Data Repositories
Unit 18: Evaluation of Data Repositories
Module 7: Linked Open Data (LOD)
Unit 19: Introduction to LOD
Unit 20: Metadata and Interoperability, Ontology
Unit 21: Case studies
Module 8: Practice
Unit 22-29: Software tools for ETL such as Spreadsheet, OpenRefine, R, Apache Spark
Unit 30-32: Data Repository Software such as CKAN, Dataverse
1. Borgman, C. L. (2015a). Big Data, Little Data, No Data: Scholarship in the Networked World. Cambridge MA: MIT Press.
2. Fearon, D. J., Gunia, B., Lake, S., Pralle, B. E., & Sallans, A. L. (2013). Research Data Management Services, SPEC Kit 334 (July 2013).
3. Gajbe, S. B., Tiwari, A., Gopalji & Singh, R. K. (2021). Evaluation and analysis of Data Management Plan tools: A parametric approach. Information Processing & Management, 58(3), 102480.
4. Inter-university Consortium for Political and Social Research. (2012). Guide to Social Science Data Preparation and Archiving: Best Practice Throughout the Data Life Cycle (No. 5th edition). Ann Arbor, MI: ICPSR. R
5. Kimpton, M., & Morris, C. M. (2013). Managing and Archiving Research Data: Local Repository and Cloud-Based Practices. In J. M. Ray (Ed.), Purdue University Press.
6. National Science Foundation. (2011). NSF Data Management Plans. Washington, DC: NSF.
7. Ray, J. M. (2014). Research data management: practical strategies for information professionals. West Lafayette, Ind.: Purdue University Press.
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