Research metrics and publishing: Open data

“Open data and content can be freely used, modified, and shared by anyone for any purpose”

http://opendefinition.org/​

"Data management planning from the beginning of a research project helps to outline how data will be collected, formatted, described, stored and shared throughout, and beyond, the project lifecycle." (Australian Research Council). See links below for Data Management Plan templates and tools:

• Research Data planning at Monash
• ARDC Data Management plans guide

Monash Bridges: 

• is a collaborative research data repository for Monash University researchers and graduate research students
• ensures users retain full control over their research outputs while publishing with a DOI making them easily citable
• stores data on Monash servers providing a platform to share research outputs research datasets, media, papers, posters and presentations

Quality academic journals have their own data policies (e.g. the PLOS data policy). Multidisciplinary repositories may be designed to publish the underlying data from scholarly publications in a F.A.I.R manner.

• figshare
• Dryad

Other data respositories may have a more discipline specific focus:

• Archeology - Open Context from the Alexandria Archive Institute
• Astronomy -Astrophysics Data System from the SAO (Smithsonian Astrophysical Observatory)
• Computer Science - SourceForge, an open source directory
• DataONE
• GenBank
• Geosciences and geospatial data - Geodata Repository from the Open Source Geospatial Foundation
• IEEE DataPort (electrical and electronic engineering)
• KNB (Knowledge Network for Biocomplexity)
• PANGAEA (Publishing Network for Geoscientific & Environmental Data)
• Physics - Blue Obelisk Data Repository
• Scientific Data (Nature’s data journal)
• Social sciences - ADA (Australian Data Archive)
• TERN (terrestrial ecosystem data)

Source code:

• Github
• Git
• BitBucket
• SourceForge

To find a discipline-specific research data repository, search the Directory of Open Access Repositories (OpenDOAR) and the Registry of Research Data Repositories.

For guidance on sharing sensitive data see:

• The Australian Research Data Commons resources

Data journals publish brief articles which describe a data set(s). They are often open access and peer reviewed, and the articles can be cited. 

Examples include:

• Scientific data
  Open-access, peer-reviewed publication for descriptions of scientifically valuable datasets. Primary article-type is the Data Descriptor, designed to make data more discoverable, interpretable and reusable.
• Geoscience data journal
  Publishes short data papers cross-linked to, and citing, datasets that have been deposited in approved data centres.
• Journal of open archaeology data
  Peer reviewed data papers, describing archaeology datasets with high reuse potential.
• GigaScience
  Open-access open-data journal. Publishes 'big-data' studies from life and biomedical sciences.

A number of data journals also support 'altmetrics' that track the number of article views, number of downloads, and social media 'likes' and recommendations. These can be early indicators of the impact of data, before the long tail of formal citation metrics can be assessed. 

A number of useful guides are available for citing open data. See:

• DataCite: Cite your data
• Ball, A. and Duke, M. (2015) How to cite datasets and link to publications. DCC How-to Guides. Edinburgh: Digital Curation Centre.
• Australian Research Data Commons: Data Citation

Standards for data citation vary across the disciplines. 

​See the Monash Citing & Referencing Libguide for examples of citing data using different styles

The advantages of data citation and the impact of open data continue to evolve. Some readings below:

• The State of Open Data Report 2020
• How to track the impact of research data with metrics (DCC How-to Guides 2015)
• Data reuse and the open data citation advantage (2013)