fbpx Follow the money: how to track research funds | Science in the net

Follow the money: how to track research funds

Primary tabs

di Rita Occhipinti
Pubblicato il 28/04/2014Read time: 3 mins
Research evaluation

Sixteen digits. Nature has called it "online science passport". More than half a million researchers have already joined the initiative. The ORCID project (Open Researcher and Contributor ID) is a wide database of scientists all over the world: it collects names, short biographies, research activities, patents, citations, results, dataset. Citing from the homepage, ORCID provides “a persistent digital identifier that distinguishes you from every other researcher”. Scientists can use the code as a second signature when they submit publications (Nature, Science and many other journals, open access or not, are partners of the project), when they cite on the web or when they write on scientists’ social network, always keeping their own work and their own identity sure and recognizable. Advantages seem to be various for attendees and the number of subscriptions is growing.

Now ORCID is asking users a new kind of information: money. Researchers have to insert details about their grants: amount, frequency during the years, source public or private. The more the available data and the numbers of publications, the more metadata are necessary to understand connections and collaborations, and to track funds. The ORCID identifier provides a set of useful metadata on the web: it can follow the money and easily relate it with people and outputs. It is a sort of disclosure 2.0.

The request for information about funds tries to pander to the growing need of institutions, and of funding and regulatory agencies, to have a careful gaze on researchers’ activities. It is quite easy to gather information about funds and ranking of Universities and projects. However, it is not easy at all to associate money to single researchers and, consequently, to their scientific results. They can change affiliation over the years during their career or even during a single project, according to its different phases. For example, Horizon 2020 provides for individual grants (ERC grants, European Research Council): the PI (principal investigator) is free to move to another country, taking the money with him, if his research requires it.

Moreover, ORCID identifiers can reveal which kinds of grant work better, i.e. which ones are mainly ascribable to outstanding results. Individual (those given to the PI) grants or collaborative (those given to the project) ones? The high-risk grants to cutting-edge ideas or the ones consolidating existing projects and basic research? Funding mechanisms could find a new instrument to become more and more efficient and evidence based.

Meritocracy, transparency and efficiency: that is not all. Tracking systems and the other ORCID metadata are strictly related to openness in science. The connection between funds, scientist and achievements can be functional and fast on the web only if publications are accessible and open (in spite of the membership of Nature and Science). Tracking systems seem to be an answer to the new needs of funding agencies, and to the future direction of openness in science.


Scienza in rete è un giornale senza pubblicità e aperto a tutti per garantire l’indipendenza dell’informazione e il diritto universale alla cittadinanza scientifica. Contribuisci a dar voce alla ricerca sostenendo Scienza in rete. In questo modo, potrai entrare a far parte della nostra comunità e condividere il nostro percorso. Clicca sul pulsante e scegli liberamente quanto donare! Anche una piccola somma è importante. Se vuoi fare una donazione ricorrente, ci consenti di programmare meglio il nostro lavoro e resti comunque libero di interromperla quando credi.


  • 12093 reads

prossimo articolo

Why have neural networks won the Nobel Prizes in Physics and Chemistry?

di Chiara Sabelli
Pubblicato il 15/10/2024

This year, Artificial Intelligence played a leading role in the Nobel Prizes for Physics and Chemistry. More specifically, it would be better to say machine learning and neural networks, thanks to whose development we now have systems ranging from image recognition to generative AI like Chat-GPT. In this article, Chiara Sabelli tells the story of the research that led physicist and biologist John J. Hopfield and computer scientist and neuroscientist Geoffrey Hinton to lay the foundations of current machine learning.

Image modified from the article "Biohybrid and Bioinspired Magnetic Microswimmers" https://onlinelibrary.wiley.com/doi/epdf/10.1002/smll.201704374

Physics

The 2024 Nobel Prize in Physics was awarded to John J. Hopfield, an American physicist and biologist from Princeton University, and to Geoffrey Hinton, a British computer scientist and neuroscientist from the University of Toronto, for utilizing tools from statistical physics in the development of methods underlying today's powerful machine learning technologies.