LibGuides: Manage &amp; Share Research Data: Describe

Tips

Why is it so important to document or describe my research?

Adam Savage (Mythbusters) says it best.

An important part of the research process is documenting the plan, what actually happened, and your thoughts about what happened or didn't, and why. In some industries, lab notebooks and protocols are considered legal documents.

Tips

Always take better notes than you think you need. Many problems that happen during analysis and reporting can be prevented by taking detailed notes. Think of it as writing notes to your future self.

Think about what information would be needed to understand and analyze your data, and/or replicate your results in 20 years. Then think about how researchers in your field usually do that. Do they use lab notebooks, procedures manuals, protocols, readme.txt files, or something else? If you don't know, ask your faculty advisor or supervisor.

The answers to these two questions should tell you what about your research is important to describe and how. For more details about what you might need to document for your project and the data specifically, see the lists below.

Project-level information you should document:

Name of the project
Dataset title
Project description
Dataset abstract
Principal investigator and collaborators
Contact information
Dataset handle (DOI or URL)
Dataset citation
Data publication date
Geographic description
Time period of data collection
Subject/keywords
Project sponsor
Dataset usage rights

Data-level documentation is much more specific and may include the following information, among other things:

Data origin: experimental, observational, raw or derived, physical collections, models, images, etc.
Data type: integer, Boolean, character, floating point, etc.
Instrument(s) used
Data acquisition details: sensor deployment methods, experimental design, sensor calibration methods, etc.
File type: CSV, mat, xlsx, tiff, HDF, NetCDF, etc.
Data processing methods, software used
Data processing scripts or codes
Dataset parameter list, including
Variable names
Description of each variable
Units

BioSharing Standards
A curated, informative and educational resource on inter-related data standards, databases, and policies in the life, environmental and biomedical sciences.
Research Data Alliance: Metadata Directory
The RDA Metadata Standards Directory Working Group is supported by individuals and organizations involved in the development, implementation, and use of metadata for scientific data. The overriding goal is to develop a collaborative, open directory of metadata standards applicable to scientific data can help address infrastructure challenges.
Data Documentation Initiative (DDI)
An international standard for describing the data produced by surveys and other observational methods in the social, behavioral, economic, and health sciences. DDI is a free standard that can document and manage different stages in the research data lifecycle, such as conceptualization, collection, processing, distribution, discovery, and archiving. Documenting data with DDI facilitates understanding, interpretation, and use -- by people, software systems, and computer networks.

Examples

Data Dictionaries

The Environmental Determinants of Diabetes in the Young (TEDDY): Listed under Study Documents at https://repository.niddk.nih.gov/studies/teddy/
Multidisciplinary Approach to the Study of Pelvic Pain (MAPP I): Listed under Study Documents at https://repository.niddk.nih.gov/studies/mapp_ep/
National Aerial Photography Program (NAPP) Data Dictionary: https://www.usgs.gov/centers/eros/science/national-aerial-photography-program-napp-data-dictionary

Lab Notebooks

Columbia University Good Laboratory Notebook Practices: https://research.columbia.edu/sites/default/files/content/
RCT%20content/ReaDI%20Program/tutorial_LabNotebook_V9.pdf
MIT Guidelines for Maintaining Your Lab Notebook: https://ocw.mit.edu/courses/20-109-laboratory-fundamentals-in-biological-engineering-fall-2007/pages/labs/guide_lab_notebook/
Stanford Neuroscience How to Keep a Lab Notebook: https://neuroscience.stanford.edu/news/how-keep-lab-notebook
How to Keep a Lab Notebook: https://www.science.org/content/article/how-keep-lab-notebook

Ontologies & Controlled Vocabularies

Common Coordinate Framework Ontology: https://humanatlas.io/ccf-ontology
Human Phenotype Ontology (HPO): https://hpo.jax.org/app/about

Protocols

Isolation of Immunocomplexes from Zebrafish Brain (biochemistry): https://doi.org/10.21769/BioProtoc.4646
An Improved System to Measure Leaf Gas Exchange on Adaxial and Abaxial Surfaces: https://doi.org/10.21769/BioProtoc.4687
Subcellular RNA-seq for the Analysis of the Dendritic and Somatic Transcriptomes of Single Neurons: https://doi.org/10.21769/BioProtoc.4278
Selection of Molecules with Immunological Potential from Excretory and Secretory Products from the Nematode Haemonchus placei by Cell Proliferation and Gene Expression Assays: https://doi.org/10.21769/BioProtoc.4702
De novo transcriptome assembly workflow (computational): https://dx.doi.org/10.1038/s41598-017-09334-7

Other

Ten simple rules on how to write a standard operating procedure: https://doi.org/10.1371/journal.pcbi.1008095

Tools

IU readme template
This is a template readme file for documenting data to be archived. However, it can also be adapted as a tool for documenting data throughout a research project.

Data Reuse Checklist
This checklist is designed to help you understand what someone outside your research project (or you in 5-10 years) would need to know about your data in order to build on your work. For more information on preparing your data for reuse, check out our exercise on how to plan for data reuse.
USDA Data Dictionary Template
This blank template can be used to manually create a data dictionary.