Making Data Count Blog


4 Aug 2015

Today, we are pleased to announce the publication Making Data Count in Scientific Data. John Kratz and Carly Strasser led the research effort to understand the needs and values of both the researchers who create and use data and of the data managers who preserve and publish it. The Making Data Count project is a collaboration between the CDL, PLOS, and DataONE to define and implement a practical suite of metrics for evaluating the impact of datasets, which is a necessary prerequisite to widespread recognition of datasets as first class scholarly objects.

We started the project with research to understand what metrics would be meaningful to stakeholders and what metrics we can practically collect. We conducted a literature review, focus groups, and– the subject of today’s paper– a pair of online surveys for researchers and data managers.

In November and December of 2014, 247 researchers and 73 data repository managers answered our questions about data sharing, use, and metrics.Graph of interest in various metrics

Survey and anonymized data are available in the Dash repository. These responses told us, among other things, which existing Article Level Metrics (ALMs) might be profitably applied to data:

  • Social media: We should not worry excessively about capturing social media (Twitter, Facebook, etc.) activity around data yet, because there is not much to capture. Only 9% of researchers said they would “definitely” use social media to look for a dataset.
  • Page views: Page views are widely collected by repositories but neither researchers nor data managers consider them meaningful. (It stands to reason that, unlike a paper, you can't have engaged very deeply with a dataset if all you've done is read about it.)
  • Downloads: Download counts, on the other hand, are both highly valuable and practical to collect. Downloads were a resounding second-choice metric for researchers and 85% of repositories already track them.
  • Citations: Citations are the coin of the academic realm. They were by far the most interesting metric to both researchers and data managers. Unfortunately, citations are much more difficult than download counts to work with, and relatively few repositories track them. Beyond technical complexity, the biggest challenge is cultural: data citation practices are inconsistent at best, and formal data citation is rare. Despite the difficulty, the value of citations is too high to ignore, even in the short term.

We have already begun to collect data on the sample project corpus– the entire DataONE collection of 100k+ datasets. Using this pilot corpus, we see preliminary indications of researcher engagement with data across a number of online channels not previously thought to be in use by scholars. The results of this pilot will complement the survey described in today’s paper with real measurement of data-related activities “in the wild.”

For more conclusions and in-depth discussion of the initial research, see the paper, which is open access and available here: Stay tuned for analysis and results of the DataONE data-level metrics data on the Making Data Count project page:

(Original post here.)


8 Jan 2015

Last October, UC3, PLOS, and DataONE launched Making Data Count, a collaboration to develop data-level metrics (DLMs). This 12-month National Science Foundation-funded project will pilot a suite of metrics to track and measure data use that can be shared with funders, tenure & promotion committees, and other stakeholders.

To understand how DLMs might work best for researchers, we conducted an online survey and held a number of focus groups, which culminated on a very (very) rainy night last December in a discussion at the PLOS offices with researchers in town for the 2014 American Geophysical Union Fall Meeting.

Six eminent researchers participated: Ben Bond-Lamberty (Pacific Northwest National Laboratory), Jim Hansen (Columbia University), Andrew Gettelman (University Corporation for Atmospheric Research), David Schneider (UCAR), Kevin Trenberth (UCAR), and Maosheng Yao (Peking University, China)

Much of the conversation concerned how to motivate researchers to share data. Sources of external pressure that came up included publishers, funders, and peers. Publishers can require (as PLOS does) that, at a minimum, the data underlying every figure be available. Funders might refuse to ‘count’ publications based on unavailable data, and refuse to renew funding for projects that don’t release data promptly. Finally, other researchers– in some communities, at least– are already disinclined to work with colleagues who won’t share data.

However, Making Data Count is particularly concerned with the inverse– not punishing researchers who don’t share, but rewarding those who do. For a researcher, metrics demonstrating data use serve not only to prove to others that their data is valuable, but also to affirm for themselves that taking the time to share their data is worthwhile. The researchers present regarded altmetrics with suspicion and overwhelmingly affirmed that citations are the preferred currency of scholarly prestige.

Many of the technical difficulties with data citation (e.g., citing dynamic data or a particular subset) came up in the course of the conversation. One interesting point was raised by many: when citing a data subset, the needs of reproducibility and credit diverge. For reproducibility, you need to know exactly what data has been used– at a maximum level of granularity. But, credit is about resolving to a single product that the researcher gets credit for, regardless of how much of the dataset or what version of it was used– so less granular is better.

We would like to thank everyone who attended any of the focus groups. If you have ideas about how to measure data use, please let us know in the comments!
(Original post here.)


31 Oct 2014

We all know that data is important for research. So how can we quantify that? How can you get credit for the data you produce? What do you want to know about how your data is used?

If you are a researcher or data manager, we want to hear from you. Take this 5-10 minute survey and help us craft data-level metrics.

Please share widely! The survey will be open until December 1st.

Read more about the project at or check out our previous post. Thanks to John Kratz for creating the survey and jumping through IRB hoops! (Original post here.)


7 Oct 2014

We are excited to announce that UC3, in partnership with PLOS and DataONE, are launching a new project to develop data-level metrics (DLMs). This 12-month project is funded by an Early Concept Grants for Exploratory Research (EAGER) grant from the National Science Foundation, and will result in a suite of metrics that track and measure data use. The proposal is available via CDL’s eScholarship repository: More information is also available on the NSF Website .

Why DLMs? Sharing data is time consuming and researchers need incentives for undertaking the extra work. Metrics for data will provide feedback on data usage, views, and impact that will help encourage researchers to share their data. This project will explore and test the metrics needed to capture activity surrounding research data.

The DLM pilot will build from the successful open source Article-Level Metrics community project, Lagotto, originally started by PLOS in 2009. ALM provide a view into the activity surrounding an article after publication, across a broad spectrum of ways in which research is disseminated and used (e.g., viewed, shared, discussed, cited, and recommended, etc.)

About the project partners
PLOS (Public Library of Science) is a nonprofit publisher and advocacy organization founded to accelerate progress in science and medicine by leading a transformation in research communication.

Data Observation Network for Earth (DataONE) is an NSF DataNet project which is developing a distributed framework and sustainable cyberinfrastructure that meets the needs of science and society for open, persistent, robust, and secure access to well-described and easily discovered Earth observational data. DataONE development for the MDC project will be conducted by NCEAS, the National Center for Ecological Analysis and Synthesis at UC Santa Barbara. NCEAS operates one of the Coordinating Nodes for DataONE and has helped develop all of the infrastructure for the federation, and it operates the KNB Data Repository, which is a DataONE member repository.

The University of California Curation Center (UC3) at the California Digital Library is a creative partnership bringing together the expertise and resources of the University of California. Together with the UC libraries, we provide high quality and cost-effective solutions that enable campus constituencies – museums, libraries, archives, academic departments, research units and individual researchers – to have direct control over the management, curation and preservation of the information resources underpinning their scholarly activities.
(Original post here.)