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Research metrics and publishing: Open data

Open data

landing page image with the words Open Data        “Open data and content can be freely used, modified, and shared by anyone for any purpose”

        http://opendefinition.org/​

Have you got a Research Data Management plan?

"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:

Sharing data

Digital Object Identifiers (DOIs) provide a permanent identifier for information or data. 

DOIs are the internationally accepted standard for data citation. If you want your data to be discoverable and re-usable then you must ensure that it has a DOI. 

Publishing your data in a Monash repository such as Bridges means that a DOI will be allocated at the point of publication. Monash University manages these DOIs so that the link to your items and research files will always work, allowing them to be citable for the long-term.

Bridges FAQs

Monash Bridges: 

  • is a collaborative data repository for Monash University researchers and graduate research students
  • ensures users retain full control over their research outputs
  • allows users to describe their own research
  • can publish research outputs with a DOI making them easily citable
  • provides a platform to share research outputs including figures, datasets, media, papers, posters, presentations and filesets
  • stores data on Monash servers

For more on Digital Object Identifiers and research discoverability see the Altmetrics Guide

When you disseminate data that you own or manage, you need to think about how you want others to re-use your data and communicate any terms and conditions that you want re-users of your data to follow. There are a number of approaches that you can take to this, from very open to reasonably restricted.

For openly accessible data, a standard open licence is the most effective way of ensuring appropriate re-use. An open licence enables you to reserve some rights as the owner of the material, but to grant re-users more rights than would be available just under copyright legislation. Adopting a standard licence is often a pre-condition to depositing in a repository or archive, but licences can also be applied to resources disseminated via the web or other means.

For more detailed information about the different licences please see:

Free Book
Ball, A. (2014). ‘How to License Research Data’. DCC How-to Guides. Edinburgh: Digital Curation Centre. Available online: http://www.dcc.ac.uk/resources/how-guides

The Australian National Data Service provide guidance on sharing sensitive data.

http://www.ands.org.au/guides/sensitivedata

image of an inforgraphic on Publishing andSharing Open Data, produced by ANDS

Data citation

"Data citation refers to the practice of providing a reference to data in the same way as researchers routinely provide a bibliographic reference to outputs such as journal articles, reports and conference papers. Citing data is increasingly being recognised as one of the key practices leading to recognition of data as a primary research output."

  •     when datasets are routinely cited they will achieve greater validity and significance within the scholarly communications cycle
  •     citation of data enables recognition of scholarly effort with the potential for reward based on data outputs
  •     the use of data should be appropriately attributed in scholarly outputs as with other types of publication.

Source: http://www.ands.org.au/working-with-data/citation-and-identifiers/data-citation

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. 

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. 

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.
  • Ecological Archives - Data Papers
    Present large or expansive data sets, accompanied by metadata which describes the content, context, quality, and structure of the data.
  • GigaScience
    Open-access open-data journal. Publishes 'big-data' studies from life and biomedical sciences.

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

Standards for data citation vary across the disciplines. 

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

Discover research data, including data studies, data sets from a wide range of international data repositories, including ANDS (Australian National Data Service), and connect them with the scientific literature to track data citation.  

Other methods include:

  • demonstrating that reuse of data has contributed to the development of new knowledge
  • calculating page views, downloads, mentions in the media

Further reading:

"The 2020 State of Open Data report provides an interesting lens to view how far open research has come, and to look at opportunities for improvement in data sharing"

The State of Open Data Report 2020
https://digitalscience.figshare.com/articles/report/The_State_of_Open_Data_2020/13227875

Help

Contacts

Monash University Library research infrastructure team
+61 3 9905 9917 
researchdata@monash.edu

Monash University Copyright
+61 3 9905 5732
university.copyright@monash.edu