# considerations
# Machine Readability
# Definition
Machine readability refers to how easily intelligible a dataset is to a computer. GIS data is considered machine readable when it is:
- available in formats such as shapefiles, geoJSONs and CSVs
- structured in a detailed way that allows for routine or programmatic use
# More Resources
- Machine Readable, Open Data Handbook
- A Primer on Machine Readability for Online Documents and Data, Data.gov
# Human Readability
# Definition
Human readability refers to how easily accessible or intelligible a dataset is to a human being. The LMEC considers a dataset human readable if it:
- has accompanying codebooks and data dictionaries
- has clearly and thoughtfully documented methodologies and lineage
- has context with definitions or explanations of discipline-specific jargon or technical language
- easily allows a data user to make sense of or reproduce the steps used to create it
# More Resources
- Human Readable, Open Data Handbook
- Metadata and Its Importance in a Data Driven World, Villanova University
- What is Metadata and Why Do I Need It?, UNC Libraries
# Social Embeddedness
# Definition
Social embeddedness refers to the concept that datasets cannot be understood separately from the social circumstances in which they are created and used. The LMEC considers a dataset socially embedded if it:
- was created by or collaboratively in partnership with the people it represents
- has accompanying materials which outline the questions it was designed to answer
- has accompanying materials which thoughtfully reflect on the dataset's usefulness for social justice
# More resources
- Embeddedness, Encyclopedia Britannica
- The Numbers Don't Speak for Themselves, Klein & D'Ignazio
- Data as Performance – Showcasing cities through open data maps, Currie