Data management¶
Data management in OpenBioMaps covers the processes used to organise, document, validate, maintain, process, and reuse biodiversity data throughout their lifecycle.
The aim is to ensure that data remain understandable, reliable, traceable, and usable beyond the original data collection activity.
OpenBioMaps provides tools for managing both the structure of the data and the processes through which data are collected, checked, transformed, queried, and used.
Database structure and metadata¶
OpenBioMaps stores data in structured database tables rather than in independent files or spreadsheets.
The database structure defines what types of information can be stored and how different types of information are related.
Metadata can be associated with tables and fields to describe their meaning, content, and intended use. Good metadata are essential for understanding and reusing data, particularly when a project is maintained over a long period or by multiple people.
Data quality and validation¶
Data quality can be improved by checking data as they enter the system and by applying validation rules during data management.
Depending on the project configuration, OpenBioMaps can check values, required fields, relationships between data, spatial information, and other project-specific constraints.
Validation can be performed during data entry or upload, as well as during subsequent data processing.
Data processing and harmonisation¶
Data collected from different sources may use different formats, terminology, taxonomies, coordinate systems, or other conventions.
OpenBioMaps can be used as part of workflows that harmonise and transform data while preserving the original information and documenting the processing steps.
Data processing may include standardising values, transforming spatial data, resolving taxonomic names, combining datasets, or preparing data for analysis and publication.
Data provenance and documentation¶
Reliable biodiversity data require more than the recorded observation itself. It is also important to retain information about when, where, how, and by whom the observation was collected and how the data have subsequently been processed.
OpenBioMaps supports the documentation of data collection and management processes through structured fields, metadata, queries, and project-specific workflows.
Keeping this information together with the data improves traceability and makes later verification and reuse possible.
Queries and derived data¶
Queries can be used to select, filter, combine, and transform data without modifying the original records.
This makes it possible to create project-specific views of the data for different purposes, such as analysis, reporting, visualisation, or publication.
Repeatable queries can also provide a documented and reproducible way of producing derived datasets.
Data export and reuse¶
OpenBioMaps data can be exported or accessed by external applications for analysis, visualisation, publication, and other purposes.
Data can be used directly from OpenBioMaps or transferred to tools such as QGIS and R, depending on the requirements of the workflow.
Data lifecycle¶
OpenBioMaps can support the different stages of a biodiversity data lifecycle, from the initial field observation to later analysis, publication, and reuse.
A typical workflow may include:
data collection;
storage in a structured database;
validation and quality control;
documentation and metadata management;
data processing and harmonisation;
analysis and visualisation;
publication or controlled sharing; and
reuse for further research or conservation activities.
These stages do not necessarily form a strictly linear process. Data may return to earlier stages as errors are identified, new information becomes available, or project requirements change.