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Slot: Missing Data Handling Method (missing_data_handling_method)

How the source handles missing values (e.g. how Daymet handles snow-covered pixels).

Tier: recommended

Why this slot matters

How gaps were filled is often invisible downstream: an interpolated value looks identical to a measured one, so without this field an analyst overstates coverage and treats imputed exposures as if they were observed, biasing associations in unknown directions. It is the difference between apparent and real completeness.

In plain terms

Real data has holes — a cloud blocks a satellite, snow covers a sensor. This says what the dataset did about those holes: leave them empty, guess from nearby days and places, copy the last known value, and so on. Filled-in numbers can look just like real measurements, so it matters to know which is which.

URI: envar:slot/missing_data_handling_method

Applicable Classes

Name Description Modifies Slot
Uncertainty Uncertainty and quality character of a value series: per-value uncertainty ty... no

Properties

Type and Range

Property Value
Range MissingDataHandlingEnum
Domain Of Uncertainty

Cardinality and Requirements

Property Value

Examples

Value
spatiotemporal_interpolation

See Also

Identifier and Mapping Information

Annotations

property value
tier recommended
justification How gaps were filled is often invisible downstream: an interpolated value looks identical to a measured one, so without this field an analyst overstates coverage and treats imputed exposures as if they were observed, biasing associations in unknown directions. It is the difference between apparent and real completeness.
explanation Real data has holes — a cloud blocks a satellite, snow covers a sensor. This says what the dataset did about those holes: leave them empty, guess from nearby days and places, copy the last known value, and so on. Filled-in numbers can look just like real measurements, so it matters to know which is which.
covered_by None

Schema Source

Mappings

Mapping Type Mapped Value
self envar:missing_data_handling_method
native envar:missing_data_handling_method

LinkML Source

name: missing_data_handling_method
annotations:
  tier:
    tag: tier
    value: recommended
  justification:
    tag: justification
    value: 'How gaps were filled is often invisible downstream: an interpolated value
      looks identical to a measured one, so without this field an analyst overstates
      coverage and treats imputed exposures as if they were observed, biasing associations
      in unknown directions. It is the difference between apparent and real completeness.'
  explanation:
    tag: explanation
    value: 'Real data has holes — a cloud blocks a satellite, snow covers a sensor.
      This says what the dataset did about those holes: leave them empty, guess from
      nearby days and places, copy the last known value, and so on. Filled-in numbers
      can look just like real measurements, so it matters to know which is which.'
  covered_by:
    tag: covered_by
    annotations:
      omop_gaia:
        tag: omop_gaia
        annotations:
          extent:
            tag: extent
            value: absent
          status:
            tag: status
            value: asserted
          where:
            tag: where
            value: no column
          note:
            tag: note
            value: Gaia's meta_etl nodata registers the missing-value sentinel, not
              the source's missing-data handling method.
      degauss:
        tag: degauss
        annotations:
          extent:
            tag: extent
            value: absent
          status:
            tag: status
            value: asserted
          where:
            tag: where
            value: no column
          note:
            tag: note
            value: DeGAUSS records no missing-data-handling method.
      amadeus:
        tag: amadeus
        annotations:
          extent:
            tag: extent
            value: absent
          status:
            tag: status
            value: asserted
          where:
            tag: where
            value: no column
          note:
            tag: note
            value: Amadeus's THREDDS metadata declares a _FillValue sentinel but not
              the source's gap-filling method.
      cher:
        tag: cher
        annotations:
          extent:
            tag: extent
            value: absent
          status:
            tag: status
            value: asserted
          where:
            tag: where
            value: no column
          note:
            tag: note
            value: C-HER's null taxonomy classifies null cells but carries no source
              missing-data-handling method field.
      codata:
        tag: codata
        annotations:
          extent:
            tag: extent
            value: out_of_layer
          status:
            tag: status
            value: asserted
          where:
            tag: where
            value: no column
          note:
            tag: note
            value: Missing-data handling method is instance-layer; out of scope for
              CODATA.
description: How the source handles missing values (e.g. how Daymet handles snow-covered
  pixels).
title: Missing Data Handling Method
examples:
- value: spatiotemporal_interpolation
  description: Daymet fills missing cells by spatiotemporal interpolation.
from_schema: https://w3id.org/linkml/microschemas/envar
see_also:
- https://daymet.ornl.gov/
rank: 1000
domain_of:
- Uncertainty
range: MissingDataHandlingEnum