Reproducible Quantitative Methods

Instructor Guide, Lesson 3

Introduction to Metadata / Data and scientific authorship

yeah bar

Topics and Resources

  1. Introduction to metadata

    So what exactly is metadata? What does metadata need to include? Review the following resources:

      • Metadata Guide from Australian National Data Service (a simple working-level view of the needs, issues, processes around metadata collection and creation; not discipline specific)

      • Best Practices for Data Management from DataONE: Section 5.4 (p.5)

      • See also: Metadata Directory from Research Data Alliance - which provides a list of metadata standards used in various disciplines.

    The goals for this lesson are to help students understand the benefits of open data, how to encourage others to make their data open, and to identify what you can do with your own data to make it possible for someone to build on your work.

    This is a good time to have your students read Nine simple ways to make it easier to (re)use your data


    A helpful hint from those that came before

    Write a DATA-README Have the students work in groups to run through the process of creating a plan for data reuse, or DATA-README for the project data set.

  2. Data creation and authorship

    What defines scientific authorship, and how do data creators fit in? Conventions vary between fields, but in general, authorship may be defined by contribution to following areas:

      • Concept framework and question

      • Funding

      • Research design

      • Data collection

      • Analysis

      • Writing and manuscript preparation

      • Editing

      • Manuscript submission and revision handling

    A great overview of authorship (with a focus on ecology) in plain language: Determining authorship for a peer-reviewed scientific publication by Chris Buddle. And there's lots of outher resources- Here's a 2010 Science Magazine article on authorship conventions across disciplines, and a blog post from Hutton Institute on authorship conventions in an inderdiciplinary organization. There's also an opportunity to talk to your students about some social issues around scientific authorship, for example, see this article on gender disparities in authorship across diciplines.

    As a side note, you may want to mention ORCID to your students. It’s a great way to track scientific contributions of scientists, particularly those with common names!


  2. Write a DATA-README for project data set.

  3. Reading
  4. To prep for next week’s activities, assign the students to skim through The Quartz guide to bad data.


Authorship in Science

Select and assign a reading about authorship that is relevant to your field, for example, Chris Buddle's article from above, or Defining the Role of Authors and Contributors from the International Committee of Medical Journal Editors gives a more technical, medically-relevant guide to authorship, but conventions vary between fields.


A helpful hint from those that came before

Consult the experts Consult with your librarian to find information on authorship conventions relevant to your field.


Data Management SNAFU in 3 Short Acts (4:40)


What are the authorship conventions in your field? If class is multi-disciplinary, discuss differences in conventions among varied fields.

What makes someone an author on a paper? What kinds of contributions might a researcher make?

What if the research product is something else: code, data set, how is authorship different?

How does first use of a data set, and then use of data after previous publication differ for your field’s authorship rules? This can vary, and isn't easy to answer.

When does a data and/or code creator qualify to be an author?

What challenges are often present when working with data created by others?

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