So for 2020 there are 542k entries. In order to download metadata data (or in my case just the DOI, date of DOI registration and the Elsevier internal ID) I’m using again the REST API of Crossref.
Actually a Crossref query is limited to provide max 1000 DOIs. Using cursors, it’s however to possible to loop further and get about 100k DOIs before the API times out. So in order to get all publications from 2020 I created a monthly batch based on the created date like:
An author can have multiple labels/refids, emails, contributor-roles. In order to get those flat in a row separated by a coma, a group step can be used, while getting it back into the stream using the “stream lookup”-step:
Merge affiliations and authors
In a third step authors and affiliations are merged by the the label:
in order to get a list with authors and affiliations
You can bundle a couple of steps as a transformation and call those steps in another transformation.
My scenario: determine publication date
I often use the Crossref Rest API to get information about publications. Depending on the publisher there are different kind of dates associated with a DOI and the dates can have different resolutions. Sometimes just a year or a year and a month.
In order to get always a specific publication date with the resolution YYYY.mm.dd I use a couple of steps and logic to determine the “relevant” publication date out from those different date fields.
To reuse those steps in different transformations without copying each time all these steps I can now save those steps as own transformation. Let’s add a “Get rows from result” and “Copy rows to result” at the beginning and and end this sub-transformation.
Then we can add a “Transformation executor” step in the main transformation. In this step we add the expected “fields” of the sub-transformation in the tab “Results row”
As output of a “transformation executor” step there are several options available:
There seems to be no option to get the results and pass through the input steps data for the same rows. Probably since the output of the sub-transformation can have more more or less rows than the input. Yet we can create a work-around by going on the the input data and add the results of the sub-transformation with a common (presorted) identifier. At the end we have the original data and the result of the sub-transformation combined.
Pentaho Data Integration (PDI) offers the input step “JSON-Input” to read out data from a JSON file or stream. Often I use this step after a REST-API-Query, so I would have the JSON-Input as a field from a previous step.
In order to test the field-extraction, it’s helpful to save some local samples of the possible responses. In the tab “File” you first can do your tests with the local file and switch later to “Source is from a previous step”.
Since recently Pentaho offers an “internal helper” to select the fields. However it unfortunately does not to work for most of my use-cases. Instead I found http://jsonpathfinder.com/ very useful.
Then add the fields with the corresponding path in PDI: