By SORTEE | October 4, 2021
[SORTEE member voices is a weekly Q&A with a different SORTEE member]
Name: Christine Meynard.
Date: 02 July 2021.
Position: Chargé de recherche.
Research and/or work interests: I am a macro-ecologist and biogeographer, interested in the relationships between biodiversity and environmental gradients at large scales, and their links to global change.
What do you see as the greatest challenge facing the open / reliable / transparent science movement at large or specifically in ecology and evolutionary biology?
I think most scientists agree that we need results from science to be open access, but the question of how to solve this issue in practice in a fair way for everyone is a real conundrum. This includes both publications generated through research, as well as making the data used in those publications freely and readily available. There are tensions between researchers who have more or less funding within the same countries, but also between the Global South and the Global North, interests are not the same, neither are resources.
Up until recently, even researchers without funding could publish, the question was who had access to those articles. But with the current push to publish in so-called open access journals, the policy has shifted towards a pay-to-publish one. From the publication perspective, it is not fair to ask everyone to pay to publish, but then someone has to pay the costs, and editorial houses need to be regulated in their profit margins and the services they provide. Initiatives that make the full review and publication process free are scarce but exciting; however, they are unlikely to replace the more traditional journal-based systems and would need a complete paradigm shift in the hiring and evaluation system in order to be effective. How do we go from here?
And how can we make the transition from one system to the next less painful for everyone? From the perspective of the data, which should also be made available, you can put all sorts of data online, but not everything is useful. Not only do we need to have the appropriate data types with all the relevant metadata associated with them, we also need to understand the shortcoming of each sampling strategy or dataset. How do we establish checkpoints to make sure pulling from different databases is not causing artifacts in our analyses? We need standards that are applicable through vastly different fields of study, disciplines, regions, etc.
This requires resources, training, and a lot of thought and agreement between scientists, in order for the information to be of actual use. These are all huge challenges that lay ahead of us!
Where were you born and raised?
I was born in Santiago, Chile.
What do you now know about the way science gets done that you would have found surprising before you started your training as a scientist?
When I was an undergraduate, I thought science was objective, square, and based off of facts and results. I now know that networking, connections and presentation (both oral and written) have a lot of influence on how your career plays out, publications come out, recognition you get, etc, and that all sorts of non-objective factors also play big roles in science! This is why it is so important to set up clear explicit standards to avoid discrimination, biases, etc. Of course, no system will ever be perfect. But we owe it to the next generation of scientists to make an effort and make it better.
Where to find you online?:
https://sites.google.com/site/cnmeynard/home
Twitter: @cn_meynard