TABLE OF CONTENTS
- 1. Use Cases
- 2. Accessing the metadata editing module
- 3. Using the metadata module
1. Use Cases
Metadata editing can be useful at multiple points during your analyses. Some examples are:
- You want to adjust errors in the metadata that you noticed during the quality controls (such as missing values etc.).
- You want to add extra metadata based on the certain analyses that you ran, such as cluster names.
- You want to curate your dataset further.
2. Accessing the metadata editing module
To start with editing your metadata, you go to the 'edit' button on the tile of your workspace, in this window you can see the 'Assign dataset' section. Next to the individual datasets, you can access the metadata editing module by clicking on the blue edit icon.
3. Using the metadata module
The metadata module has an extensive repertoire of functionalities that can be explored by the clients.
The most important ones are described here.
1. Locking columns
When clicking on the dropdown menu of a metadata variable, the user can select 'Lock' to lock a column.
This will grey out said column and will disable any further editing.
This step is irreversible, this column will not be able to be edited at any point in the future.
When the user does want to change something in this column, the remaining option is to copy the column and make the edits in the copied column.
2. Copying columns
The user can copy columns using the dropdown menu > edit column > Add column based on this column.
This can be used to add for example a clustering column based on an existing column, without losing the original input.
This is also helpful when the user would like to change input in the data from a locked column, as described above.
When adding a copy, the user can choose the naming of the copy of the column, the input will be exactly the same as the mother column.
3. Editing data
The foremost functionality of the metadata editing is of course editing the existing data.
For this, the user has to hover over the cell containing the data that needs to be changed. Here, the purple 'edit' box will appear. When clicked, this opens an editing screen where the desired changes can be made. When done, the user clicks 'apply' and the changes will be implemented.
To be able to keep track of what changes were made to the data, there is the Undo/Redo tab where an oversight is provided of everything that was done in the metadata.
In this tab, all changes are listed in chronological order.
When selecting a step (i.e. a previously made change), the metadata matrix will reboot to how it was implemented at said moment that this step was implemented.
Facets can be selected under the dropdown menu > Facet.
When selecting the text facet, the text data from that column will be displayed in groups, with the number of datapoints that every group contains.
This is also the case for the numeric facet.
The Timeline facet shows the timeline of the datapoints.
Metadata variables containing only numeric values will only be able to showcase numeric facets. The same goes for text variables.
When hovering over a certain facet (group), the user is able to edit the data. When implemented, this edits all datapoints in this facet.
When for example a certain text datapoint is named 3 times 'NA' (capital letters) and 4 times 'na' (without capital letters) and this represents the same, this should be in the same facet. By editing the 'NA' facet to 'na', the user will create a facet 'na' with 7 values in it.
The same principle is applicable to numeric facets.
6. Standard options.
The standard options include but are not limited to:
- Editing colums:
- Splitting columns
- Joining columns
- Renaming columns
- Removing columns
- Moving columns
- to beginning
- to end
- Transposing cells
- Collapsing a column
- Collapsing all other columns
- Collapsing all columns to the left
- Collapsing all columns to the right
- Expanding all columns
- Expanding all columns to the left
- Expanding all columns to the right
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