Project proposal details

Please look carefully through the proposal details below. If you are interested in the project then contact the supervisor, explaining why you are interested and any background which makes you a good fit for the project.

If this is an external project, the lead supervisor may have suggested someone at Imperial College or the NHM who could act as an internal supervisor and you should also contact them. If the project is external and no internal has been proposed then you must find an internal supervisor before starting the project.

Please pay close attention to any extra notes on requirements (such as being able to drive or to speak particular languages) or the application process. There may be specific limitations on the project availability: if there are then they will be clearly shown further down the page.

Project title
Predicting funding success using big data
Contact name
David Orme
Project based at
Silwood Park (Imperial)
Imperial contact email
Project description
**(Project willbe co-supervised by David Orme, James Rosindell, and Samraat Pawar)**

The Academia is an increasingly busy and competitive world, with the rate of increase in numbers of researchers generally outstripping the rate of increase in funding available for research. Not surprisingly, funding rates and the methods for increasing funding success is of growing concern to academic institutions and researchers worldwide. The objective of this project is to use a text mining and machine learning to uncover factors that determine the successful (and if possible, failure) of grant proposals worldwide (but in particular, the UKRI, NSF, NIH, and ERC). Some of the key factors (upon which certain hypotheses may be founded) are likely to be biases in the review process driven by Gender, Subject area, and Cronyism. To the best of our knowledge, a analysis of this scope using modern quantitative methods and the increasing availability of data has never been conducted.

Relevant references:

1. Sarigöl, E., Pfitzner, R., Scholtes, I., Garas, A. & Schweitzer, F. Predicting scientific success based on coauthorship networks. EPJ Data Sci. 3, 1–16 (2014).
2. Van Dijk, D., Manor, O. & Carey, L. B. Publication metrics and success on the academic job market. Current Biology vol. 24 R516–R517 (2014).
3. Acuna, D. E., Allesina, S. & Kording, K. P. Future impact: Predicting scientific success. Nature vol. 489 201–202 (2012).
4. Mitra, T. & Gilbert, E. The language that gets people to give: Phrases that predict success on kickstarter. in Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW 49–61 (Association for Computing Machinery, 2014). doi:10.1145/2531602.2531656.
5. Allesina, S. Measuring Nepotism through Shared Last Names: The Case of Italian Academia. PLoS One 6, e21160 (2011).
6. Magua, W. et al. Are Female Applicants Disadvantaged in National Institutes of Health Peer Review? Combining Algorithmic Text Mining and Qualitative Methods to Detect Evaluative Differences in R01 Reviewers' Critiques. J. Women's Heal. 26, 560–570 (2017).
Additional requirements
Python and/or R
Available support
If permitted by covid-19 restrictions, desk space, and computer access
Selection and eligibility
None
Date uploaded
2021-01-15

Project proposal limitations

The project proposer has indicated that there are some limitations to the availability of this project. It may only be available at certain times of year or suit a specific project length. It may also need skills taught to students on a particular course or courses.

Research project proposals are usually part of an active research programme. If supervisors have stated limitations to a proposal, then they are unlikely to have any flexibility. If you are very interested in the topic but have problems with the stated limitations, the supervisor may still be happy to talk to you about other options around the proposal, but you should not expect that any alternative arrangements can be made.

Project length limitations
5 months