Data Management and Life Cycle
(PA397C | INF385T | ECO395M)

Course Schedule

Quote of the Day

The schedule is tentative, we may arrange a few visits to some organizations. Details TBD.

Introduction

Understanding Data

Data Acquisition and Preprocessing

Data Management and Social Science Research

Week 0 Pre-course Back2Top

Week 1: Why this course? Back2Top

Before class

  • Readings:
    • Baker, M. (2016). 1,500 scientists lift the lid on reproducibility. Nature News, 533(7604), 452. doi:10.1038/533452a.
    • Briney, K. (2015). The Data Problem. In Data management for researchers: Organize, maintain and share your data for research success. Research Skills Series (Exeter, England). HOLLIS number:014921191. Exeter, UK: Pelagic Publishing.
    • Gentzkow, M., & Shapiro, J. M. (2014). Introduction. In Code and data for the social sciences: A practitioner’s guide.
    • Lazer, D., Pentland, A., Adamic, L., Aral, S., Barab´asi, A.-L., Brewer, D., . . . Alstyne, M. V. (2009). Computational Social Science. Science, 323(5915), 721–723. doi:10.1126/science.1167742.6

In class

  • Discussion and lecture on readings.
  • Course review: Syllabus, assignments, final project.

After class

Week 2: Data management and life cycle Back2Top

Before class

  • Readings:
    • Briney, K. (2015). Planning for Data Management. In Data management for researchers: Organize, maintain and share your data for research success. Research Skills Series (Exeter, England). HOLLIS number: 014921191. Exeter, UK: Pelagic Publishing.
    • Briney, K. (2015). The Data Lifecycle. In Data management for researchers: Organize, maintain and share your data for research success. Research Skills Series (Exeter, England). HOLLIS number: 014921191. Exeter, UK: Pelagic Publishing.
    • Ruane, J. M. (2016). Designing Ideas: What Do We Want to Know and How Can We Get There? In Introducing Social Research Methods: Essentials for Getting the Edge (pp. 67–92). Chichester, West Sussex, UK ; Hoboken, NJ: John Wiley & Sons Inc.

In class

After class

Week 3: Documenting data and version control Back2Top

Before class

  • Readings:
    • Briney, K. (2015). Documentation. In Data management for researchers: Organize, maintain and share your data for research success. Research Skills Series (Exeter, England). HOLLIS number:014921191. Exeter, UK: Pelagic Publishing.
    • Broman, K. W., & Woo, K. H. (2017). Data organization in spreadsheets (tech. rep. No. e3183v1). PeerJ Inc. doi:10.7287/peerj.preprints.3183v1.
    • Gentzkow, M., & Shapiro, J. M. (2014). Version Control. In Code and data for the social sciences: A practitioner’s guide.

In class

  • Discussion and lecture on readings.
  • Student presentation.
  • Group discussion on client projects.

After class

Week 4: Data structure, relational database, and data dictionary Back2Top

Before class

  • Readings:
    • Wickham, H. (2014). Tidy data. The Journal of Statistical Software, 59(10). http://www.jstatsoft.org/v59/i10/
    • Normalization of Database
    • Gentzkow, M., & Shapiro, J. M. (2014). Keys. In Code and data for the social sciences: A practitioner’s guide.

In class

After class

Week 5: Data types and data visualization and interaction Back2Top

Before class

  • Readings:
    • Kirk, A. (2019). Working With Data. In Data Visualisation: A Handbook for Data Driven Design (2nd edition, pp. 95–117). SAGE Publications Ltd.
    • Kirk, A. (2019). The Visualisation Design Process. In Data Visualisation: A Handbook for Data Driven Design (2nd edition, pp. 31–58). SAGE Publications Ltd.

In class

  • Discussion and lecture on readings.
  • Student presentation: Hajiyeva.
  • Group discussion on client projects.

After class

Week 6: Acquiring data: open data and open-source intelligence Back2Top

Before class:

  • Readings
    • Williams, H. J., & Blum, I. (2018). Defining Second Generation Open Source Intelligence (OSINT) for the Defense Enterprise. RAND Corporation.
  • Review Bing News Search API: what can you do with it?

In class:

After class

Week 7: Text and relation as data Back2Top

Before class

  • Readings:
    • Grimmer, J., &Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267–297. doi:10.1093/pan/mps028.
    • Provan, K. G., Veazie, M. A., Staten, L. K., & Teufel-Shone, N. I. (2005). The use of network analysis to strengthen community partnerships. Public Administration Review, 65(5), 603–613.

In class:

  • Discussion and lecture on readings.
  • Group discussion on client projects.

After class

Week 8: Data cleaning, preprocessing, and organizing + Data security Back2Top

Before class

Data cleaning, preprocessing, and organizing

  • Briney, K. (2015). Organization. In Data management for researchers: Organize, maintain and share your data for research success. Research Skills Series (Exeter, England).
  • Gentzkow, M., & Shapiro, J. M. (2014). Directories. In Code and data for the social sciences: A practitioner’s guide.
  • Miksa, T., Simms, S., Mietchen, D., & Jones, S. (2019). Ten principles for machine-actionable data management plans. PLOS Computational Biology, 15(3), e1006750. doi:10.1371/journal.pcbi.1006750.

Data security

  • Briney, K. (2015). Managing sensitive data. In Data management for researchers: Organize, maintain and share your data for research success. Research Skills Series (Exeter, England). HOLLIS number: 014921191. Exeter, UK: Pelagic Publishing.
  • Case: UT Data Classification Standard

In class:

  • Discussion and lecture on readings.
  • Student presentation: Barroso.
  • Group discussion on client projects.

After class

Data cleaning, preprocessing, and organizing

Data security

Week 9: Field visit: Dress for Success (3000 S I-35 Frontage Rd Suite 180, Austin, TX 78704) Back2Top

Schedule

  • 2-3pm:
  • 3-4:30pm:

Week 10: Standardization and automation Back2Top

Before class

  • Required readings:
    • Gentzkow, M., & Shapiro, J. M. (2014). Automation. In Code and data for the social sciences: A practitioner’s guide.
    • Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L., & Teal, T. K. (2017). Good enough practices in scientific computing. PLOS Computational Biology, 13(6), e1005510. doi:10.1371/journal.pcbi.1005510.
    • de Visser C, Johansson LF, Kulkarni P, Mei H, Neerincx P, Joeri van der Velde K, et al. (2023) Ten quick tips for building FAIR workflows. PLoS Comput Biol 19(9): e1011369. https://doi.org/10.1371/journal.pcbi.1011369
  • Recommended readings:
    • Wilkinson et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. doi:10.1038/sdata.2016.18.
    • Wilson, G., Aruliah, D. A., Brown, C. T., Hong, N. P. C., Davis, M., Guy, R. T., . . . Wilson, P. (2014). Best Practices for Scientific Computing. PLOS Biology, 12(1), e1001745. doi:10.1371/journal.pbio.1001745.
    • Gentzkow, M., & Shapiro, J. M. (2014). Appendix: Code Style. In Code and data for the social sciences: A practitioner’s guide.

In class

After class

Work on your customized learning modules.

Week 11: Concepts and measures in social sciences Back2Top

Before class

  • Required readings:
    • Ruane, J. M. (2016). All That Glitters Is Not Gold: Assessing the Validity and Reliability of Measures. In Introducing Social Research Methods: Essentials for Getting the Edge (pp. 117–138). Chichester, West Sussex, UK ; Hoboken, NJ: John Wiley & Sons Inc.
    • Ruane, J. M. (2016). Measure by Measure: Developing Measures—Making the Abstract Concrete. In Introducing Social Research Methods: Essentials for Getting the Edge (pp. 93–116). Chichester, West Sussex, UK ; Hoboken, NJ: John Wiley & Sons Inc.
    • Shoemaker, P. J., Tankard, J. W., & Lasorsa, D. L. (2003). Theoretical Concepts: The Building Blocks of Theory. In How to Build Social Science Theories (pp. 15–36). SAGE Publications.
  • Recommended readings:
    • Gerring, J. (1999). What Makes a Concept Good? A Criterial Framework for Understanding Concept Formation in the Social Sciences. Polity, 31(3), 357–393. doi:10.2307/3235246.

In class

Guest speaker: Lifelong Learning with Friends (2pm)

For the client (30 mins with Q&A):

  • What and how data are generated in daily operations.
  • How data are processed, shared, and collaborated in daily operations.
  • What are the relations between data and business, and who are the users of the data.

For the student team​​** (20 mins with Q&A)

  • Where is the niche for the team to fit in?
  • What are the deliverables and how they can be useful?

Weekly class activities

  • Discussion and lecture on readings.
  • Student presentation: Raza, Chavero.
  • Group discussion on client projects.

Week 12: Data reuse and data governance Back2Top

Before class

  • Readings:
    • Briney, K. (2015). Data reuse and restarting the data lifecycle. In Data management for researchers: Organize, maintain and share your data for research success. Research Skills Series (Exeter, England).
    • Ghavami, P. (2020). Data Governance and Data Security. In Big Data Management: Data Governance Principles for Big Data Analytics. De Gruyter.

In class

  • Discussion and lecture on readings.
  • Student presentation: Vanegas, Zhang.
  • Group discussion on client projects.

After class

Make sure you and your team are on track of all outstanding assignments.

Week 13: Final project workday - no class (week before Thanksgiving) Back2Top

Assignment due:

Also work on any outstanding assignments:

  • Analysis of empirical studies and presentation.
  • Client project.

Week 14: From empirical study to theory building. Final project presentation Back2Top

Before class

  • Readings:
    • Creswell, J. W. (2014). The Use of Theory. In Research design: Qualitative, quantitative, and mixed methods approaches (4th ed). Thousand Oaks: SAGE Publications.
    • Sutton, R. I., & Staw, B. M. (1995). What Theory is Not. Administrative Science Quarterly, 40(3), 371–384. doi:10.2307/2393788.
    • Shoemaker, P. J., Tankard, J. W., & Lasorsa, D. L. (2003). Theoretical and Operational Linkages. In How to Build Social Science Theories. SAGE Publications.
    • (review the article wrote by the authors in Week 1) Lazer, D. M. J., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez-Bailon, S., King, G., Margetts, H., Nelson, A., Salganik, M. J., Strohmaier, M., Vespignani, A., & Wagner, C. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060–1062. https://doi.org/10.1126/science.aaz8170

In class

  • Discussion and lecture on readings.
  • Final project presentations.

After class


© Ji Ma