Form Design Patterns

Practical patterns for combining field types in fieldwork forms.

Overview

Once you have chosen the right field types for your data (see Choosing a Field Type), the next step is combining them into effective forms. This guide presents five composite patterns — recurring field combinations that solve common recording tasks — and worked examples from archaeology, ecology, and geology.

Good form design favours recognition over recall: let users select from lists rather than remember codes. Use progressive disclosure to keep forms simple by default, revealing detailed fields only when needed. Maintain consistent interaction patterns throughout a notebook so that collectors learn the form once and can work quickly.

For a complete list of available field types, see Field Types.

Composite Patterns

These five patterns appear across disciplines whenever fieldwork forms need to go beyond single-field recording.

The Measurement Pattern

When recording a measurement, combine:

  • A Select one option or Select Field for the measurement type (e.g., length, width, depth).

  • A Controlled Number for the numeric value, with minimum and maximum bounds set to catch entry errors.

  • A unit indicator — either included in the field label (e.g., “Depth (cm)”) or as a separate Select one option field if multiple units are possible.

  • An Annotation enabled on the number field for recording uncertainty, instrument used, or measurement conditions.

The Identification Pattern

When recording an identification that may be provisional, combine:

  • A Select Field or Select Field (Hierarchical) for the identification (e.g., species, soil type, artefact class) drawn from a controlled vocabulary.

  • Use of “Uncertainty” if a simple “certain” vs. “uncertain” flag is all that is needed, OR

  • A Select one option for more nuanced confidence level (e.g., Certain, Probable, Possible, Unlikely).

  • A Checkbox labelled “Requires verification”, conditionally revealed when confidence (selected through Select one option) is not Certain.

  • A Multi-line Text Field for detailed notes, conditionally revealed alongside the verification flag.

The Complex Observation Pattern

When structuring complex observations with multiple possible outcomes, combine:

  • A Select one option for the observation type, which acts as a branching gateway.

  • Type-specific field groups revealed conditionally based on the selection.

  • Standard metadata captured automatically by the platform (recorder, timestamp, location).

  • A Take Photo field with Annotation enabled for visual documentation.

The Progressive Detail Pattern

When a form needs to support both rapid and detailed recording, combine:

  • Basic fields that are always visible for quick recording.

  • A Checkbox as a gateway question (e.g., “Record detailed measurements?”).

  • Detailed fields conditionally revealed when the checkbox is ticked.

This pattern keeps the form simple for routine records while still enabling depth when needed.

The “Other” Specification Pattern

When a controlled vocabulary needs an escape hatch for exceptions, combine:

  • A Select Field or Select one option with an “Other” option included in the list.

  • A FAIMS Text Field conditionally revealed when “Other” is selected, prompting the collector to specify.

This preserves the structure of controlled data while allowing flexibility for unanticipated values.

Discipline-Specific Examples

The patterns above can be adapted to suit different fieldwork disciplines. The examples below illustrate common field combinations and human-readable identifier (HRID) structures.

Archaeological Recording

Archaeological forms typically need to capture stratigraphic relationships, contextual inheritance, and structured identifiers:

  • Stratigraphic relationships — Use Add Related Record with defined vocabulary pairs (e.g., “cuts / cut by”, “fills / filled by”, “above / below”) to record temporal and physical relationships between contexts (temporal and physical relationships can be captured independently using multiple relationships).

  • Contextual inheritance — Finds inherit context properties, samples inherit environmental conditions, and photos inherit spatial coordinates through parent–child record relationships.

  • HRID structure — A typical archaeological identifier combines site, trench, entity type, and number: SITE-TRENCH-FEATURETYPE-NUMBER (e.g., “Perachora-T5-Hearth-023”).

Ecological Survey

Ecological survey forms often centre on transect-based observation and abundance estimation:

  • Transect observations — Use an Auto Incrementing Field for sequential observation points, combined with Take point for each location along a transect.

  • Abundance estimation — Use Controlled Number fields for percentage cover or species counts, or a Select one option field for categorical scales (e.g., DAFOR: Dominant, Abundant, Frequent, Occasional, Rare).

  • HRID structure — A typical ecological identifier combines transect, point, and date: TRANSECT-POINT-DATE (e.g., “T1-P5-20240315”).

Geological Sampling

Geological forms often involve deep sample hierarchies and orientation data:

  • Sample hierarchies — Use Add Related Record to maintain provenance through processing stages: Outcrop → Sample → Subsample → Analysis.

  • Orientation data — Use grouped Controlled Number fields for strike and dip measurements or plunge and trend for linear features.

  • Sample identifiers — Use QR / Barcode Scanner to scan a sample label in the field to avoid transcription errors (consider implementing IGSNs).

  • HRID structure — A typical geological identifier combines project, location, and sample: PROJECT-LOCATION-SAMPLE (e.g., “GEO2024-OUT3-S045”).