A data management workflow of biodiversity data from the field to data users

Area of Science:

• Ecology
• Biodiversity Science
• Data Management

Background:

• Biodiversity data exhibit heterogeneity across scientific communities, hindering findability, accessibility, interoperability, and reusability.
• Effective data workflows are crucial for generating reliable information across institutional and administrative boundaries.

Purpose of the Study:

• To present a case study of a data workflow for collecting, sharing, and utilizing biodiversity data from multiple sources.
• To address challenges in biodiversity data management and promote better data practices.

Main Methods:

• Initiated biodiversity data collection in 2012 focusing on Michigan prairie fens, including plant communities and the Poweshiek skipperling (Oarisma poweshiek).
• Compiled over 23,000 occurrence records adhering to Darwin Core standards, integrating media and various measurements.
• Published data via Global Biodiversity Information Facility (GBIF) and Symbiota SEINet portals.

Main Results:

• Successfully compiled and standardized a large dataset of over 23,000 occurrence records.
• Integrated diverse data types including occurrence, media, biological, chemical, and geometric measurements.
• Disseminated data through established biodiversity information platforms.

Conclusions:

• Optimized data collection using controlled vocabularies, Darwin Core terms, and data dictionaries to improve transcription efficiency.
• Ensured data validity and longevity through voucher specimens, comprehensive metadata, and published methodological details.
• Emphasized the importance of inter-partner cooperation, dynamic tools, flexible templates, and enhanced communication for data interoperability.