Data Management: File Systems, Databases, and Metadata

Data Management: File Systems, Databases, and Metadata

Karen S. Baker1, Christy A. Troxell-Thomas2, William G. Pooler1 1Graduate School of Library and Information Science, University of Illinois Urbana-Champaign

2Biology Department, University of Illinois Springfield

Assembling and organizing data often occurs over time. Differing approaches to data storage, organization, and metadata may be used at different stages of project development. A comparison is provided of file systems (#1) and relational databases (#2, #3) for heterogeneous field data projects.

Overview  

1.  File system with files named and placed logically, hierarchically for data storage and organization.

Strength: Change is handled with less effort for file systems than for databases; change is a property of high value at the beginning of a project.

Weakness: File systems can not have many too many relationships, which makes some analysis difficult.

2.  Relational Database Single Key (1 to n relations) with a single key defining relations for 1-to-n queries so multiple files can be opened but specific information cannot be pulled out. This works well for data that can be assembled in a single table but not at the variable level.

Strength: More structure with some flexibility, so it can identify and access many files easily.

Weakness: There are no many to many relationships so complex analysis is difficult.

3.  Relational Database Multiple Relations (n-to-n queries)

with multiple keys that facilitate complex queries and allow subsets of data from multiple tables to be assembled into a single product.

Strength: Databases can query across many tables to support complex, efficient analysis.

Weakness: Databases are rigid designs with set rules and programmatic constraints can make changes and redesign options difficult.

Factors for making a transition

community data management readiness

personnel and resource

arrangements

stable file system

small, simple data table

nascent technical infrastructure

3.  Rela-onal  Database  Mul-ple  Rela-ons  

Emiquon Partners TFSE UIS

UIUC TNC

USF&WS Dickson Mounds

INHS (FBFS, IRBFS)

2.  Rela-onal  Database  Single  Key  

University of Illinois

By content type: þ Catalog þ Document-oriented þ Full-text þ Graphic þ Photographic þ Knowledge þ  Platform stream þ Real-time ☐ _______________ ☐ _______________

By subject: þ Spatial (Geographical) þ Temporal (Time period) þ Project þ  Theme/Phenomenon þ  Domain Botany Chemical Ecological Rivers (hydro) ☐ _______________ ☐_______________

Emiquon Science Conference March 2015

Acknowledgement Supported by National Science Foundation (NSF DEB, Rapid Grant# 1347077) and the Institute of Museum and Library Services (IMLS) Data Curation Education in Research Centers (DCERC, Award# RE-02-10-0004-10).

1.  File System: Readme file, file names & headers (e.g. Box) 2.  Relational Database Single Key: One key (e.g. FileMaker

Pro) 3.  Relational Database Multiple Relations: Multiple keys,

data dictionaries & machine readable form (e.g. Access)

Kinds  of  Metadata  

Emiquon

TFSE FBS IRBS Procedures

Thompson Lake -CH

Merwin - CH

Illinois River - CH

Bald Eagle Use Days

Waterfowl Abundance

Raptor Abundance

Emiquon Veg

Spunky Fish

Merwin Fish

GPS Coordinates

STRMP Fish

IlLTRMP Veg

Three  Approaches  to  Data  Organiza-on  

1.  File  System    

Examples  of  Kinds  of  Databases