Data Management

What is metadata?

Metadata is “data about data,” such as the who, what, where, and when of the dataset. Examples include the date of creation, the name(s) of data collector(s), and a description of the dataset.

Why is metadata important?

Metadata provides context for the dataset. Looking through metadata can help you to understand what information the dataset is providing. It also allows you to identify if the dataset is useful for your own research. When you are searching for datasets, you are typically searching through the metadata of datasets in the collection. If you are collecting new data for an original dataset, it will be important for you to create metadata for your project so that others can find and interpret your work.

Example Dataset From Dryad

• Title of Dataset: Data from: A brain-wide analysis maps structural evolution to distinct anatomical modules
• Researchers and Institutional Affiliations: Kozol, Robert, Florida Atlantic University, et al.
• Publication Date: July 13, 2023
• Publisher: Dryad
• Research Facility: Florida Atlantic University
• Keywords: Biological sciences, concerted evolution, Astyanax mexicanus, Brain atlas, Brain evolution, Cavefish, F2 Hybrid
• License: This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.
• Abstract: Brain anatomy is highly variable and it is widely accepted that anatomical variation impacts brain function and ultimately behavior. The structural complexity of the brain, including differences in volume and shape, presents an enormous barrier to define how variability underlies differences in function. In this study, we sought to investigate the evolution of brain anatomy in relation to brain region volume and shape across the brain of a single species with variable genetic and anatomical morphs. We generated a high-resolution brain atlas for the blind Mexican cavefish and coupled the atlas with automated computational tools to directly assess variability in brain region shape and volume across all populations. We measured the volume and shape of every neuroanatomical region of the brain and assessed correlations between anatomical regions in surface fish, cavefish, and surface to cave F2 hybrids, whose phenotypes span the range of surface to cave. We find that dorsal regions of the brain are contracted in cavefish, while ventral regions have expanded. This trend is true for both volume and shape, suggesting that these two parameters share developmental mechanisms necessary for remodeling the entire brain. Given the high conservation of brain anatomy and function among vertebrate species, we expect these data to reveal generalized principles of brain evolution and show that Astyanax provides a system for functionally determining basic principles of brain evolution by utilizing the independent genetic diversity of different morphs, to test how genes influence early patterning events to drive brain-wide anatomical evolution. 

How do I cite a dataset?

Most citation styles include guidelines for citing datasets; refer to the handbook of the citation style you’re using.

Where can I find citation style guidelines?

You can find information regarding a variety of citation styles on the St. Olaf Libraries Citation Style guide. In the natural sciences, it is common for faculty to use the citation style of a specific academic journal; these journals have their own citation guidelines posted on their websites. If you need help finding information on a particular citation style, reach out to your professor or your librarian.

Kozol, Robert et al. (2023), Data from: A brain-wide analysis maps structural evolution to distinct anatomical modules, Dryad, Dataset, https://doi.org/10.5061/dryad.w9ghx3frw