Research Data Management: Metadata Challenge

Week 1: Introduction to Metadata

Tasks:

• Read through the Metadata LibGuide.
• Choose a dataset for which you would like to create metadata.  This can be generated data, such as a histological images from an experiment.  Or this can be aggregated data, such as an excel sheet of processed information.  Pick something that you are working with currently.

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Week 2: Data Description

Task:

• Write a description about your dataset.  Think about what you would tell someone else if you needed to hand it over for them to analyze or reuse.  It does not have to be structured or formatted (yet!) in any particular way.  Write a paragraph or make bullet points.  

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Week 3: Data Documentation

Task:

• List the important features from your description.  For example, list the details of your data collection methods and any processing steps. Did you use specific reagents? Was the data collected at a specific temperature? What instruments were used to collect the data and what were its settings?  What were all the parameters needed for a computer model?  Try looking at this section of the Metadata Libguide again.

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Example:

• Experimental Type
• Date
• Time
• Temperature
• Reagents 
• Antibody
• Antibody source
• Etc.

Week 4: Acronym and Variable Definition

Task:

• As scientists we talk in acronyms quite often, but not everyone will understand what these mean.  For example, sometimes people join a lab that have to learn the field from the beginning. Additionally, if you have to submit data to a repository, it helps to have everything defined and labelled, so that your data can reach a range of users, not just those in your field.   Having your acronyms and variables defined can save a lot of time and maker your data more reusable! (See FAIR Data). Remember to include units when applicable!

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Examples:

Acronym Definitions

• DNA: Deoxyribonucleic Acid
• PCR: Polymerase Chain Reaction
• RNA: Ribonucleic Acid
• ELISA: Enzyme-Linked Immunosorbent Assay
• GC-MS: Gas Chromatography-Mass Spectrometry

Variable Definitions

• Temp (°C): Temperature in degrees Celsius, measured during the experiment.
• OD600: Optical Density at 600 nm, used to measure bacterial growth in liquid culture.
• pH: A measure of the acidity or alkalinity of a solution, ranging from 0 to 14.
• Conc (mg/L): Concentration of the compound in milligrams per liter.
• Rxn Rate (μmol/min): Reaction rate measured as micromoles of product formed per minute.

Week 5: Create a Template

Task:

• Use your list of important features from Week 3 to create a template.  This can be a text or csv file.  You can used the Standford Libraries LibGuide example to help format your template.

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Data Standards

If you want to go the extra mile, look up the data standards that might pertain to your research field or for an experiment you conduct.

Here are some examples, with their corresponding articles:

Minimum information about a microarray experiment (MIAME) - toward standards for microarray data

The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments

Minimum Information about a Neuroscience Investigation (MINI): Electrophysiology

The minimum information about a proteomics experiment (MIAPE)

Week 6: Create A ReadMe File

Task:

• Fill out your template with your data information.
• Add your acronym and variable list.
• Put the readme file in same folder as your data.

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Congratulations!!! 🥳

You created Metadata for your research data!  Keep it up!  Create metadata for other datasets too.  Now that you have one template you can adapt for other projects and experiments.  Good luck!  And if you ever need any help feel free to contact Jacqueline Gunther, PhD at the library!

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