Love Your Data Week, Day 3: All’s FAIR in Love and Data Management

Welcome to day three of Love Your Data Week 2017! Today’s topic is Good Data Examples. What makes data “good” or “well managed?” The FAIR Data Principles: —Findability, Accessibility, Interoperability, and Reusability are a good place to start.  Published by Mark Wilkinson and his colleagues in 2016, these principles “put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.” ¹A brief description of the principles, excerpted from Wilkinson’s article, explains:

To be Findable:

• F1. (meta)data are assigned a globally unique and persistent identifier
• F2. data are described with rich metadata (defined by R1 below)
• F3. metadata clearly and explicitly include the identifier of the data it describes
• F4. (meta)data are registered or indexed in a searchable resource

To be Accessible:

• A1. (meta)data are retrievable by their identifier using a standardized communications protocol 
• A1.1 the protocol is open, free, and universally implementable
• A1.2 the protocol allows for an authentication and authorization procedure, where necessary 
• A2. metadata are accessible, even when the data are no longer available

To be Interoperable:

• I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
• I2. (meta)data use vocabularies that follow FAIR principles 
• I3. (meta)data include qualified references to other (meta)data 

To be Reusable:

• R1. meta(data) are richly described with a plurality of accurate and relevant attributes 
• R1.1. (meta)data are released with a clear and accessible data usage license 
• R1.2. (meta)data are associated with detailed provenance
• R1.3. (meta)data meet domain-relevant community standards”²

These guiding principles benefit all stakeholders, including, as Wilkinson states, “researchers wanting to share, get credit, and reuse each other’s data and interpretations; professional data publishers offering their services; software and tool-builders providing data analysis and processing services such as reusable workflows; funding agencies (private and public) increasingly concerned with long-term data stewardship; and a data science community mining, integrating and analyzing new and existing data to advance discovery.”³

Wilkinson identifies several examples of FAIRness, including Dataverse, FAIRDOM, and Open PHACTS, and notes that the FAIR Guiding Principles have been adopted by a wide range of data management organizations across the globe.

^1-3Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016 Mar 15;3:160018. doi: 10.1038/sdata.2016.18. PubMed PMID: 26978244; PubMed Central PMCID: PMC4792175.

Love Your Data Week, Day 3: Help Your Future Self

By Help Your Future Self, we mean Write It Down: document, document, document! Your documentation provides crucial context for your data. So whatever your preferred method of record keeping is, today is the day to make it a little bit better! Some general strategies that work for any format:

• Be clear, concise, and consistent.
• Write legibly.
• Number pages.
• Date everything, use a standard format (ex: YYYYMMDD).
• Try to organize information in a logical and consistent way.
• Define your assumptions, parameters, codes, abbreviations, etc.
• If documentation is scattered across more than one place or file (e.g., protocols & lab notebook), remind yourself of the file names and where those files are located.
• Review your notes regularly and keep them current.
• Keep all of your notes for at least 7 years after the project is completed.

Things to avoid:

• Writing illegibly.
• Using abbreviations or codes that aren’t defined.
• Using abbreviations or codes inconsistently.
• Forgetting to jot down what was unusual or what went wrong. This is usually the most important type of information when it comes to analysis and write up!

Today's Activity: If your documentation could be better, try out some of these strategies and tools:

• Readme files are a simple and low-tech way to start documenting your data better. Check out the sample readme.txt (filename = readme_template.txt) from IU.
• Cornell University RDMSG also has a guide with tips for using read me files
• Check out Kristin Briney’s post on taking better notes
• Cornell University RDMSG has some tips for writing metadata
• Data dictionaries are an easy way to document spreadsheets. Check out some examples on the Pinterest resource board.

Take a few minutes to think about how you document your data. What’s missing? Where are the gaps? Can you set up some processes to make this part of the work easier?