Data Management Lab: Session 4 Review Outline

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Data Management Lab: Session 4 Review Outline (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab)

What you will learn:
1. Build awareness of research data management issues associated with digital data.
2. Introduce methods to address common data management issues and facilitate data integrity.
3. Introduce institutional resources supporting effective data management methods.
4. Build proficiency in applying these methods.
5. Build strategic skills that enable attendees to solve new data management problems.

Published in: Education, Technology, Business
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Data Management Lab: Session 4 Review Outline

  1. 1. IUPUI University Library Center for Digital Scholarship Data Management Lab: Spring 2014 Review Outline Session 1 1. Introduction to RDM a. Describe key challenges associated with managing digital research data b. Identify the potential consequences for irresponsible or inattentive data management c. Understand the life cycle approach to managing research data 2. Data Management Plans & Planning a. Summarize the basic components of US federal funding agency requirements for data management and sharing b. Define expected outcomes for data 3. Ethical & legal obligations a. Identify your legal obligations as they affect data management and protection b. Identify your ethical obligations for ensuring data confidentiality, privacy, and security c. Describe intellectual property issues for data that result in a patentable or commercial product 4. Storage & backup a. Prepare a comprehensive storage and backup plan Heather Coates, 2014
  2. 2. IUPUI University Library Center for Digital Scholarship Data Management Lab: Spring 2014 Review Outline Session 2 1. Project & data documentation a. Outline planned project and data documentation in a data management plan b. Identify metadata to describe the data set 2. Organizing data & files a. Develop a consistent and coherent file organization and naming convention scheme for all project files b. Select appropriate non-proprietary hardware and software formats for storing data c. Create protected copies of files at crucial points in your study d. Use versioning software or documentation for tracking changes to files over time Session 3 1. Quality assurance & control a. Develop data quality standards b. Develop procedures for quality assurance and quality control activities 2. Data collection a. Describe key considerations for selecting data collection tools Heather Coates, 2014
  3. 3. IUPUI University Library Center for Digital Scholarship Data Management Lab: Spring 2014 Review Outline 3. Data coding a. Use best practices for data coding 4. Data entry a. Use best practices for data entry 5. Data screening & cleaning a. Develop a screening and cleaning protocol and/or checklist 6. Automating tasks for better provenance a. Explain why automation provides better provenance than manual processes b. Identify effective tools for automating data processing and analysis Session 4 1. Ethical & legal obligations a. Identify your legal obligations for sharing and long-term preservation b. Identify how ethical and legal obligations affect data protection and sharing 2. Data protection, rights, & access a. Identify tools and platforms for storing, managing, and preserving data 3. Data sharing & re-use a. Identify the benefits to researchers of data sharing Heather Coates, 2014
  4. 4. IUPUI University Library Center for Digital Scholarship Data Management Lab: Spring 2014 Review Outline b. Evaluate resources for sharing data and openly or publicly available data 4. Data attribution & citation a. Identify two technologies enabling data citation 5. Synthesis Heather Coates, 2014

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