CREED data

Records assessment purpose statement, relevant data summarised from above tables, relevance/reliability scores, and final assessment of data quality.

Introduction

Criteria for Reporting and Evaluating Exposure Datasets (CREED) is a framework for evaluating the relevance and reliability of a reference object for a given exposure assessment question across a series of questions. Users set criteria for it to be relevant to their question, and use a pre-defined set of reliability criteria.

The object is evaluated based on its data availability, precision, choice of methods, and a final score is calculated for both its relevance and reliability based on its useability at “Gold” and “Silver” levels of quality. This table collects the relevant data from non-CREED tables selected by the user or an automated data population process, the user-appraised scores, and any limitations the user chooses to comments on. In the data export process, the below functions are used to create two .CSV files: one for reliability and one for relevance. Scores are used to calculate a CREED score.

This table is not intended to be used as a final data product, but is included in the format because it provides traceability on how the CREED score was calculated. Because a large part of the evaluation logic is included in the eData app, this table is currently just a container with little relevant information. It is planned to migrate this logic to the eDataDRF in future.

library(eDataDRF)

initialise_CREED_data_tibble()
#> # A tibble: 0 × 6
#> # ℹ 6 variables: criterion_id <chr>, criterion_title <chr>,
#> #   required_recommended <chr>, relevant_data <chr>, score <chr>,
#> #   limitations <chr>

Variables

Criterion ID

criterion_id - string, free, mandatory

A alphanumeric serial identifier for CREED’s relevance (RV1 - RV11) and reliability (RB1 - RB19) criteria.

Criterion Title

criterion_title - string, free, mandatory

A short, descriptive title for the domain of the criterion, e.g. “Sample Medium/Matrix”.

Required or Recommended

required_recommended - string, free, mandatory

Whether the criterion is required (in which case it will be used to calculate a silver score), or recommended (gold score).

Relevant Data

relevant_data - string, free, mandatory

Any relevant data provided by the auto-population process or the user. For example:

1 compartment: Aquatic (Freshwater)

3 protocols:
Sampling Protocol - Point
Extraction Protocol - Not reported
Fractionation Protocol - Not reported

Score

score - string, free, mandatory

An assessment score based on the extent to which the data object fulfills each criterion. Criterion scores are stored as a named vector of numbers. A lower score (1) is better, a higher score (4) is worse.

CREED_choices_vocabulary()
#>      Not Met    Fully Met   Partly Met Not Reported Not Relevant 
#>            4            1            2            3            1

Limitations

limitations - string, free, mandatory

Any limitations, caveats, or other relevant comments identified by the user conducting the assessment.