This document outlines the Make Data Count (MDC) initiative to standardize and promote the tracking of research data usage metrics. MDC has developed a Code of Practice for data usage logs, built an open hub to aggregate standardized usage data, and implemented tracking and display of usage metrics at their own repositories. They encourage other repositories to follow five simple steps to Make Their Data Count: 1) Read the Code of Practice, 2) Process usage logs, 3) Send logs to the hub, 4) Pull usage metrics from the hub, and 5) Display metrics. Future work includes outreach, iteration on implementations, and expanding metrics beyond DOIs.

1. How To Make
Your Data Count
Patricia Cruse, Exec Director, DataCite
Daniella Lowenberg, Project Lead, MDC
November 26, 2018

2. Agenda
❏ What is our initiative about?
❏ Milestones in 2018
❏ Our first release
❏ Implementation at your repository
❏ Code of Practice
❏ Log Processing
❏ Sending Usage Logs
❏ Pulling & Displaying Usage & Citation Metrics
❏ Questions

3. What is MDC?

4. Imagine a world where
data are considered a
first-class research
output and are valued as
such...

5. Making Data
Count
2014 -
2015
▪2014 - 2015
5

6. “
6

8. 1. Formal recommendation for measuring data usage
2. Develop Hub for all Data Level Metrics (DLM)
3. Make usage tracking easier
4. Drive adoption by showing how it can be done
(easily)
5. Engage across all research communities
6. Iterate!
8
Make Data
Count
2017 -
2019

9. 9
DevelopNewDataLevelMetricsHub
101110
01101001
10100101
00111001
10100110
Data Citations
Repository
Usage Metrics
Future Metrics
Leverage Existing
Initiatives
Develop New
Recommendation
ServerLog
Processing
Drive Adoption - Engage Communities
Display
Data Metrics

10. Milestones thus far

11. We created a data usage metrics standard

12. We gave MDC a narrative

13. We built out an open hub for usage metrics
https://api.datacite.org/events

14. We implemented at our own repositories

15. We began to address citations

16. What does it look like?

21. How does this relate to
Scholix?

22. Scholix is not a thing - it is a change initiative
MDC & Scholix work hand in hand to advocate for best data
citation practices
● Scholix is an information framework for submitting data
citations
● MDC allows for displaying data citations back at the
repository

23. Implementation at your
repository

24. Why it is important
Community has long
grappled with the problem
of assessing and tracking
the results of scholarship
● Researchers
● Repositories
● Funders
● Publishers

25. Five simple steps to Make YOUR Data Count
1. Read the data usage metrics standard “Code of Practice for
Research Data”
2. Process your usage logs against this standard
3. Send processed and standardized usage logs to an open hub
4. Pull usage and citation metrics from an open hub
5. Display standardized usage and citation metrics on your
repository interface

26. Getting Started
We have built a “Getting Started” guide walking through
these steps as implemented in CDL’s Dash
https://github.com/CDLUC3/Make-Data-Count/blob/master/g
etting-started.md

27. 1. Code of Practice for
Research Data

29. 2. Log Processing

30. Standardized Logs
● Specialized logs that are processed against Code of Practice
● Views
● Downloads
● Users: at the country level, access during a session
● Session: de-duplicate access to page within 30 seconds

31. 3. Sending Usage Reports

33. JSON Report - HeaderJSON Report - Body

34. The Usage Metrics Hub

35. Usage Metrics Hub (hosted by DataCite)
● Aggregator of research data usage reports
● Usage reports are made available via API (in original JSON format) and soon
web interface and CSV
● Usage reports are broken down by dataset (and request method), and can
then be aggregated over time
● Information in usage reports can be combined with data citations and dataset
metadata

36. 4. Pulling Usage and
Citations

37. Pulling Usage and Citations
● Data usage metrics and citations are made available via public API, with one
“event” for each data citation or monthly usage count.
● Data citations are provided by DataCite metadata (i.e. come from data
repositories) and Crossref, with more to come
● Currently separate APIs for usage and citations, and a third API for dataset
metadata, will be combined into single API for easier retrieval of information

38. What’s next?

39. Looking Ahead
● Outreach and Adoption
○ Repository & Publishers
● Iterating on our implementation
○ Adding volume and usage by regions
○ Provide aggregation through DataCite hub
○ Beyond the DOI: metrics for other types of identifiers
○ Possible: altmetrics

40. Questions?
www.makedatacount.org
@makedatacount