Make Data Count¶

Generate Fake Metrics Only¶

The quickest way to put populate tables with some data is to run the MakeDataCountApiIT integration test. For more on running integration tests see the Testing section. This will populate views and downloads.

To insert a citation you could insert a row like below, changing “72” in the example below with the dataset id for your dataset.

INSERT INTO datasetexternalcitations (id, citedbyurl, dataset_id) VALUES (1, 'https://doi.org/10.1007/s12520-017-0578-2', 72);

Full Setup¶

The recommended way to work on the Make Data Count feature is to spin up an EC2 instance that has both the Dataverse Software and Counter Processor installed. Go to the Deployment page for details on how to spin up an EC2 instance and make sure that your Ansible file is configured to install Counter Processor before running the “create” script.

(Alternatively, you can try installing Counter Processor in Vagrant. setup-counter-processor.sh might help you get it installed.)

After you have spun to your EC2 instance, set :MDCLogPath so that the Dataverse installation creates a log for Counter Processor to operate on. For more on this database setting, see the Configuration section of the Installation Guide.

Next you need to have the Dataverse installation add some entries to the log that Counter Processor will operate on. To do this, click on some published datasets and download some files.

Next you should run Counter Processor to convert the log into a SUSHI report, which is in JSON format. Before running Counter Processor, you need to put a configuration file into place. As a starting point use counter-processor-config.yaml and edit the file, paying particular attention to the following settings:

• log_name_pattern You might want something like /usr/local/payara5/glassfish/domains/domain1/logs/counter_(yyyy-mm-dd).log
• year_month You should probably set this to the current month.
• output_file This needs to be a directory that the “dataverse” Unix user can read but that the “counter” user can write to. In dev, you can probably get away with “/tmp” as the directory.
• platform Out of the box from Counter Processor this is set to Dash but this should be changed to match the name of your Dataverse installation. Examples are “Harvard Dataverse Repository” for Harvard University or “LibraData” for the University of Virginia.
• upload_to_hub This should be “False” unless you are testing sending SUSHI reports to the DataCite hub.
• simulate_date You should probably set this to tomorrow.

Once you are done with your configuration, you can run Counter Processor like this:

sudo -i

su - counter

cd /usr/local/counter-processor-0.0.1

CONFIG_FILE=counter-processor-config.yaml python36 main.py

(Please note that the Counter Processor README says you can also pass in values like START_DATE, END_DATE etc. at the command line if you find this to be more convenient.)

After main.py has finished, you should see output that a JSON file has been placed in the directory you specified in output_file. Next, pass this JSON file to curl like this, substituting the DOI of the dataset you’re testing with:

curl -X POST 'http://localhost:8080/api/admin/makeDataCount/:persistentId/addUsageMetricsFromSushiReport?reportOnDisk=/tmp/sushi_sample_logs.json&persistentId=doi:10.5072/FK2/BL2IBM

(Note that in production the persistentId of a dataset is not passed. Instead the PIDs for the datasets are pulled out of the JSON file.)

Assuming the curl command above worked, you should be able to retrieve the views and downloads via API like this, substituting the metric you’re interested in (viewsTotal, viewsUnique, downloadsTotal, downloadsUnique) and the DOI of the dataset you’re testing with:

curl -X POST 'http://localhost:8080/api/datasets/:persistentId/makeDataCount/viewsTotal/2019-01&persistentId=doi:10.5072/FK2/BL2IBM

If all this is working and you want to send data to the test instance of the Datacite hub, change upload_to_hub to “True” and contact support@datacite.org to get a JSON Web Token (JWT) to test with. Counter Processor should send the SUSHI reports for you but if you need to troubleshoot sending the reports manually, you can try the following curl command, substituting your JWT:

curl --header "Content-Type: application/json; Accept: application/json" -H "Authorization: Bearer $JSON_WEB_TOKEN" -X POST https://api.test.datacite.org/reports/ -d @sushi_report.json

For how to put citations into your dev database and how to get them out again, see Configuring Your Dataverse Installation for Make Data Count Citations section in Make Data Count of the Admin Guide.