The Research Data Grants Program

FAVeR

The eResearch Office, in collaboration with the Library, has created a program that enables researchers to get help with the publication and publicizing of their research data.

The Research Data Grants Program (RDGP) emerged from a High-Value Collections project [1], which was part of a program funded by the Australian National Data Service (ANDS). However, unlike many other HVC projects, which focused on specific collections, our goals were: to create a platform [2] on which we could promote a range of signature collections of research data at RMIT University, and to establish an enterprise process to identify candidate datasets. The latter goal proved to be the most challenging.

A solution was found in a project workshop. Some researchers suggested that we should use a process that was already well understood by researchers: a grants program. This program would provide interested researchers with access to appropriate resources from the eResearch Office and Library. However, it was decided to the make this a competitive grants process for two reasons: there are limited resources available, and to make the program more attractive — as the successful researchers would be able to advertise this program on their academic profile.

The program operates through a periodic Call for Proposals, which specifies the method (currently a Google Form) and due date by which proposals can be submitted. All valid proposals are reviewed and ranked based on two main factors: significance, and strategic alignment. The former is defined by Significance 2.0 [3]. The latter is defined by the alignment of the dataset with RMIT University’s research strategy, as embodied by our Enabling Capability Platforms (ECPs) [4].

identify

Fig 1. The steps of the collection processing plan.

Proposals that are selected receive special assistance with the cataloguing, enrichment, storage, curation, publication, and promotion of datasets. We create a collection processing plan, in agreement with the dataset owner, that defines the steps (see Fig 1.) that are appropriate to the particular dataset and helps identify enrichment goals for each project, such as: the extraction of addition data from the collection, linking the data to external datasets, and visualisations to help describe the dataset.

wordcloud

Fig 2. Word cloud of Top 100 Hashtags
(http://dx.doi.org/10.4225/61/593f17d319bc1)

So far, four datasets and collections have been selected — of which two have been published — which include: a database of 22 million tweets that were collected around the time of the UK riots in 2011 (see Fig 2.) , a set of courier trajectories (see Fig 3.), a database of Chinese Herbs and their associated chemical compounds, and a database that records the background chemical composition of soil samples from across Victoria. Each of these datasets have presented different challenges and goals.

heatmap

Fig 3. Heat Map of Courier Trajectories
(http://dx.doi.org/10.4225/61/593f166119bc0)

We are currently undergoing a formal review of the selection process, and aim to issue a second Call for Proposals before the end of 2017.

  1. ANDS HVC Project:  https://projects.ands.org.au/id/HVC05
  2. Signature Collections Data Store: https://rd.eres.rmit.edu.au
  3. Significance 2.0: https://www.arts.gov.au/sites/g/files/net1761/f/significance-2.0.pdf
  4. ECPs: https://www.rmit.edu.au/research/research-expertise/our-focus/