• It is data about your data

• Describes what your data is

• Important for making your data FAIR

Here’s an example of good metadata

https://carpentries-incubator.github.io/fair-bio-practice/05-intro-to-metadata/index.html#image-metadata

• Administrative metadata
  • assigned to data before it’s creation or collection
  • E.g. funder, project collaborators, principle investigator, etc.
• Descriptive or citation metadata
  • data that helps people find your data or resource
  • E.g. keywords, persistent identifier, abstract, authors, etc.
• Structural metadata
  • How the data was collected/created and how it’s structured
  • E.g. Sampling procedure, sample size, variables, units, collection method, etc.

What kinds of data do (will) you have for your project?

• Dryad - https://datadryad.org/stash/requirements; https://datadryad.org/stash/best_practices

• Zenodo - https://about.zenodo.org/principles/

• GenBank - https://www.ncbi.nlm.nih.gov/genbank/submit_types/

• What types of metadata do you need?

• Using multiple tables

• Using multiple tabs
  • Sometimes these can be missed
  • Difficult to use in code

Not filling in zeros

* Difference between a zero and a blank cell

Using problematic null values

Using formatting to convey information organizing data

• Highlighting cells

Using formatting to convey information organizing data

• Merging cells

• Placing comments or units in cells
  • E.g. 0.123g
• Having more than one piece of information in a cell
  • “ID=g24074.t1;Parent=g24074;Alias=g24074.t1;gene=Dupd1;gene_id=Dupd1.g1;description=Similar to Dual specificity protein phosphatase [Sphaeramia orbicularis];Dbxref=InterPro:IPR000387,InterPro:IPR016130,InterPro:IPR020405,InterPro:IPR020422,InterPro:IPR029021,PANTHER:PTHR45682,PANTHER:PTHR45682:SF2,PRINTS:PR01908,PRINTS:PR01909,PFAM:PF00782,ProSitePatterns:PS00383,ProSiteProfiles:PS50054,ProSiteProfiles:PS50056,SMART:SM00195,SUPERFAMILY:SSF52799;Ontology_term=GO:0004725,GO:0016311,GO:0016791;”
• Inconsistent values
  • E.g. Using E. Coli, Escherichia coli, and Ecoli
• Using special characters, spaces, or numbers in column names

What makes data tidy?

1. Every column is a variable
2. Every row is an observation
3. Ever cell is a single value

Wrangling plate reader data

• In your group discuss what is wrong with the format of your data
• Create a repository for your metadata project
• Clone the repository to your local git
• Delegate jobs to group members:
  • write readme describing the data
  • reshape the data into a tidy form and change to csv
  • read the data into R making sure it is in the right form
• Each role will push changes to a new branch of the main branch
• As a group pull the changes merging the branches to the main branch

• What kind of metadata do you need for your project?
• How will this metadata be organized in your directory?
• Create metadata for your project data for next class