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Clarifying shiny app and dataset annotations #294

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a6d8570
Added intro for dataset-level annotations, and fixed headers
dvirlar2 Jul 7, 2023
7841789
Clarified that you need to save the output of EML::shiny_attributes()…
dvirlar2 Jul 7, 2023
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6 changes: 3 additions & 3 deletions workflows/edit_eml/edit_attributelists.Rmd
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Expand Up @@ -79,11 +79,11 @@ data <- read.csv(text=rawToChar(getObject(d1c_test@mn, data_pid)))

```{r, eval = FALSE}
# From data (recommended)
EML::shiny_attributes(data = data)
atts <- EML::shiny_attributes(data = data)

# From an existing attribute table
attributeList <- get_attributes(doc$dataset$dataTable[[i]]$attributeList)
EML::shiny_attributes(data = NULL, attributes = attributeList$attributes)
atts <- EML::shiny_attributes(data = NULL, attributes = attributeList$attributes)

# From scratch
atts <- EML::shiny_attributes()
Expand Down Expand Up @@ -171,7 +171,7 @@ factors <- rbind(data.frame(attributeName = 'Location', code = names(Location),
Once you have built your attributes, factors, and custom units, you can add them to EML objects. Attributes and factors are combined to form an `attributeList` using the following command:

```{r, eval = FALSE}
attributeList <- EML::set_attributes(attributes = attributes,
attributeList <- EML::set_attributes(attributes = atts$attributes,
factors = factors)
```

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8 changes: 6 additions & 2 deletions workflows/edit_eml/edit_semantic_annotation.Rmd
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Expand Up @@ -140,11 +140,15 @@ doc$dataset$dataTable[[3]]$attributeList$attribute[[6]]$annotation$valueURI <- l

On the far right of the table of `shiny_attributes` there are 4 columns: `id`, `propertyURI`, `propertyLabel`, `valueURI`, `valueLabel` that can be filled out.

### Annotating sensitive data
### Dataset annotations

In addition to attributes being assigned annotations, the overall dataset will also be annotated. The dataset will be given two categories of annotations: one for documenting how "sensitive" the data are, and one for documenting the overall academic discipline the dataset falls under.

#### Annotating sensitive data

Sensitive datasets that might cover protected characteristics (human subjects data, endangered species locations, etc) should be annotated using the data sensitivity ontology: https://bioportal.bioontology.org/ontologies/SENSO/?p=classes&conceptid=root.

#### Dataset Annotations
#### Academic discipline annotations

As a final step in the data processing pipeline, we will categorize the dataset. We are trying to categorize datasets so we can have a general idea of what kinds of data we have at the Arctic Data Center.

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