Lowenberg Making Data Count
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This presentation was provided by Daniella Lowenberg of the California Digital Library during the NISO Virtual Conference, Advancing Altmetrics, held on Wednesday, December 13, 2017.
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Lowenberg Making Data Count
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- 2. Team 2 Stephen Abrams California Digital Library Amber Budden DataONE Trisha Cruse DataCite Martin Fenner DataCite Kristian Garza DataCite Matt Jones DataONE Dave Vieglais DataONE In partnership with: Lorraine Estelle, Project COUNTER Paul Needham, Project COUNTER Daniella Lowenberg California Digital Library John Chodacki California Digital Library
- 3. History & Project Goals 3
- 4. Making Data Count 2014 - 2015 ▪2014 - 2015 4
- 5. science metadata science data figuressoftware documents used generated derived from science data science data Data Package 5
- 6. “ 6
- 8. Journal Articles Currency of research ALMS (Citations, Views, Downloads) …but data play a larger role in the research process! Data Publication 1st class scholarly object No current infrastructure or community best practice 8
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- 10. Make Data Count 2017 - 2019 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! 10
- 11. Data Level Metrics Landscape Survey 11
- 12. Groups We Talked With ConsortiaManager CSIR DataMap Elsevier EMBL/EBI GigaScience LSAP Netherlands Institute of Ecology RAMP Project USDA University of Wisconsin 12
- 13. What We Need A better system to monitor and track usage & impact metrics 13
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- 17. Counter Code of Practice for Research Data Metrics for research data allow data repositories, libraries, funders, and other stakeholders to understand and demonstrate the value of research data 17 Citations Usage Social Media
- 18. Why Are Usage Metrics Different for Research Data? Use cases: no need to track access by institution (subscriptions), as most research data are freely available Granularity: datasets frequently include many individual files, and are aggregated, merged and split Versions: research data frequently have many versions Non-human users: Scripts and other automated tools are frequently used to fetch research data 18
- 19. Log-files Double clicks User agents Counts Data volume Processing Reporting Metric Types Access Methods Sessions
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- 22. Advisory Group ▪ Phil Bourne (UVA) ▪ Rodrigo Costas (Leiden University) ▪ Josh Greenberg (Sloan Foundation) ▪ Heather Joseph (SPARC) ▪ Jennifer Lin (CrossRef) ▪ Mark Parsons (Rensselaer Polytechnic Institute) ▪ Carly Strasser (Coko) 22
- 23. Ways to Get Involved COUNTER Code of Practice for Research Data • Please provide feedback! • Preprint Winter, 2017 Data Metrics Landscape and Early Adopters Survey • Repositories interested in implementing DLMs • Community Outreach Conferences & MDC Roadmap Github 23
- 24. 24 We need your help to make this a success https://makedatacount.org @MakeDataCount
Editor's Notes
- The issue is that data sets are very different from articles… Data are not an atomic entity Data are composed of multiple data granules. Each granule is usually described by a science metadata object. A data package a much more more complex than a PDF article, can include things i.e. figures, software, scripts Each one of these objects in the data package might be versioned in a different timescale or a different cycle than the other. Data packages can consist of objects which are present in other data packages – so there are derived datasets How do we represent a complex object? This all complicates how data are cited and counted. It is difficult to do since repositories have different practices and technical platforms- need to have an aggregation of usage data.
- A better system to monitor and track usage & impact metrics
- Two elements: processing and reporting What do you have to do to process information in a standardized way and how do you report this. Processing starts with log files from the web servers that are servers that serving research data from the repositories. Once you have cleaned up your log files the reporting aspect comes into play Metric types: Investigation: A category of COUNTER metric types that represent a user accessing information related to a dataset (i.e. a landing page or detailed descriptive metadata) or a dataset itself Request: A category of COUNTER Metric Types that represents a user accessing content. Access methods: regular users that are human and machines Session: A successful request of an online service. A single user connects to the service or database and ends by terminating activity. • Total Dataset_Investigations: Total number of times a dataset or information related to a dataset was accessed. Unique_Dataset_Investigations: Number of datasets investigated in unique user-sessions.