From a6d8570404cec9ba125bbc62756abe394397d129 Mon Sep 17 00:00:00 2001 From: Daphne Virlar-Knight Date: Fri, 7 Jul 2023 14:25:40 -0700 Subject: [PATCH 1/2] Added intro for dataset-level annotations, and fixed headers --- workflows/edit_eml/edit_semantic_annotation.Rmd | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/workflows/edit_eml/edit_semantic_annotation.Rmd b/workflows/edit_eml/edit_semantic_annotation.Rmd index 63d14f58..1f9412ab 100644 --- a/workflows/edit_eml/edit_semantic_annotation.Rmd +++ b/workflows/edit_eml/edit_semantic_annotation.Rmd @@ -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. From 7841789a6b8dce6baa2b0127c3a4d1b3826c249c Mon Sep 17 00:00:00 2001 From: Daphne Virlar-Knight Date: Fri, 7 Jul 2023 14:29:00 -0700 Subject: [PATCH 2/2] Clarified that you need to save the output of EML::shiny_attributes() to an object --- workflows/edit_eml/edit_attributelists.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/workflows/edit_eml/edit_attributelists.Rmd b/workflows/edit_eml/edit_attributelists.Rmd index 590c79d5..7e8f9aa6 100644 --- a/workflows/edit_eml/edit_attributelists.Rmd +++ b/workflows/edit_eml/edit_attributelists.Rmd @@ -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$attributes) # From scratch atts <- EML::shiny_attributes() @@ -171,7 +171,7 @@ factors <- rbind(data.frame(attributeName = 'Location', code = names(Location), Once you have built your attributes, factors, and custom units, you can add them to EML objects. Attributes and factors are combined to form an `attributeList` using the following command: ```{r, eval = FALSE} -attributeList <- EML::set_attributes(attributes = attributes, +attributeList <- EML::set_attributes(attributes = atts$attributes, factors = factors) ```