DSpace provides a batch metadata editing tool. The batch editing tool is able to produce a comma delimited file in the CSV format. The batch editing tool facilitates the user to perform the following:
For information about configuration options for the Batch Metadata Editing tool, see Batch Metadata Editing Configuration
The following table summarizes the basics.
Arguments short and (long) forms):
Required. The filename of the resulting CSV.
The Item, Collection, or Community handle or Database ID to export. If not specified, all items will be exported.
Include all the metadata fields that are not normally changed (e.g. provenance) or those fields you configured in the
Display the help page.
To run the batch editing exporter, at the command line:
[dspace]/bin/dspace metadata-export -f name_of_file.csv -i 1023/24
[dspace]/bin/dspace metadata-export -f /batch_export/col_14.csv -i /1989.1/24
In the above example we have requested that a collection, assigned handle '1989.1/24' export the entire collection to the file 'col_14.csv' found in the '/batch_export' directory.
The following table summarizes the basics.
Arguments short and (long) forms:
Required. The filename of the CSV file to load.
Silent mode. The import function does not prompt you to make sure you wish to make the changes.
The email address of the user. This is only required when adding new items.
When adding new items, the program will queue the items up to use the Collection Workflow processes.
when adding new items using a workflow, send notification emails.
When adding new items, use the Collection template, if it exists.
Display the brief help page.
Silent Mode should be used carefully. It is possible (and probable) that you can overlay the wrong data and cause irreparable damage to the database.
To run the batch importer, at the command line:
[dspace]/bin/dspace metadata-import -f name_of_file.csv
[dspace]/bin/dspace metadata-import -f /dImport/col_14.csv
If you are wishing to upload new metadata without bitstreams, at the command line:
[dspace]/bin/dspace metadata-import -f /dImport/new_file.csv -e firstname.lastname@example.org -w -n -t
In the above example we threw in all the arguments. This would add the metadata and engage the workflow, notification, and templates to all be applied to the items that are being added.
It is not recommended to import CSV files of more than 1,000 lines. When importing files larger than this, it is hard to accurately verify the changes that the import tool states it will make, and large files may cause 'Out Of Memory' errors part way through the process.
The csv files that this tool can import and export abide by the RFC4180 CSV format. This means that new lines, and embedded commas can be included by wrapping elements in double quotes. Double quotes can be included by using two double quotes. The code does all this for you, and any good csv editor such as Excel or OpenOffice will comply with this convention.
File Structure. The first row of the csv must define the metadata values that the rest of the csv represents. The first column must always be "id" which refers to the item's id. All other columns are optional. The other columns contain the dublin core metadata fields that the data is to reside.
A typical heading row looks like:
Subsequent rows in the csv file relate to items. A typical row might look like:
350,2292,Item title,"Smith, John",2008
If you want to store multiple values for a given metadata element, they can be separated with the double-pipe '||' (or another character that you defined in your
modules/bulkedit.cfg file. For example:
Elements are stored in the database in the order that they appear in the csv file. You can use this to order elements where order may matter, such as authors, or controlled vocabulary such as Library of Congress Subject Headings.
When importing a csv file, the importer will overlay the data onto what is already in the repository to determine the differences. It only acts on the contents of the csv file, rather than on the complete item metadata. This means that the CSV file that is exported can be manipulated quite substantially before being re-imported. Rows (items) or Columns (metadata elements) can be removed and will be ignored. For example, if you only want to edit item abstracts, you can remove all of the other columns and just leave the abstract column. (You do need to leave the ID column intact. This is mandatory).
Items can be moved between collections by editing the collection handles in the 'collection' column. Multiple collections can be included. The first collection is the 'owning collection'. The owning collection is the primary collection that the item appears in. Subsequent collections (separated by the field separator) are treated as mapped collections. These are the same as using the map item functionality in the DSpace user interface. To move items between collections, or to edit which other collections they are mapped to, change the data in the collection column.
New metadata-only items can be added to DSpace using the batch metadata importer. To do this, enter a plus sign '+' in the first 'id' column. The importer will then treat this as a new item. If you are using the command line importer, you will need to use the -e flag to specify the user email address or id of the user that is registered as submitting the items.
It is possible to perform metadata deletes across the board of certain metadata fields from an exported file. For example, let's say you have used keywords (dc.subject) that need to be removed en masse. You would leave the column (dc.subject) intact, but remove the data in the corresponding rows.
It is possible to perform certain 'actions' on items. This is achieved by adding an 'action' column to the CSV file (after the id, and collection columns). There are three possible actions:
If an action makes no change (for example, asking to withdraw an item that is already withdrawn) then, just like metadata that has not changed, this will be ignored.
It is possible that you have data in one Dublin Core (DC) element and you wish to really have it in another. An example would be that your staff have input Library of Congress Subject Headings in the Subject field (dc.subject) instead of the LCSH field (dc.subject.lcsh). Follow these steps and your data is migrated upon import:
Metadata values in CSV export seem to have duplicate columns