Awesome! 🤩
The Dutch Universities and ICT infrastructure provider (SURF) are working on a pilot to provide Mastodon instances for the entire educational sector.
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Vincent Traag
@vtraag@social.cwts.nl
mastodon 4.7.3Senior researcher @cwts@social.cwts.nl, Leiden University. Computational social science, science studies, modelling & networks.
Searchable posts at https://www.tootfinder.ch/
1159 Followers
577 Following
27 Posts
Joined December 07, 2022
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We are proud to announce that today @cwts@social.cwts.nl launched its own Mastodon server at https://social.cwts.nl. Only people affiliated with CWTS will be able to create an account. Together with @jo_dudek@social.cwts.nl, @EleonoraDagiene@social.cwts.nl, @neesjanvaneck@social.cwts.nl and @rodrigocostas@social.cwts.nl we explain why we think this is a good idea in this blogpost: https://www.leidenmadtrics.nl/articles/take-responsibility-for-social-media
I'm happy to say that after almost 5 years, our work on comparing peer review with metrics at the institutional level is now finally available from @QSS_ISSI@scicomm.xyz at https://doi.org/10.1162/qss_a_00352.
Here's the previous thread on our preprint:
@vtraag@social.cwts.nl
⚠️ Preprint alert! ⚠️
Over the summer we preprinted our scoping review of the academic impact of open science. This is joint work from the PathOS project with @tklebel@scicomm.xyz, Ioanna Grypari, Lennart Stoy and @tonyRH@mstdn.social.
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The key insight here is that the negative observed association does not reflect the positive causal effect of open data on reproducibility. We would be completely wrong if we would draw the conclusion that open data decreases reproducibility. Especially if we would make policy recommendations, for instance around open data, it is critical that we do not make such mistakes.
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Without causal thinking, we are bound to interpret observations incorrectly. One case study in particular illustrates this well. Here, we know (because we simulate) that open data improves reproducibility in research. However, when looking at research published in journals, we observe that articles with open data are less likely to be reproducible. This in contrast to the known positive effect. What happened?
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The explanation is that journals are more likely to publish research that has open data, but also that is more rigorous. If published research has no open data, it therefore tends to be more rigorous, otherwise it would not be published at all. Research that is more rigorous tends to be more reproducible, and in our case, this effect is stronger than open data. So, published research without open data is more rigorous which in turn is more reproducible.
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We explain this and much more in greater detail in our preprint. This preprint is part of the PathOS project, and will also form an introductory chapter to our Open Science Indicator Handbook https://handbook.pathos-project.eu/
If you have any feedback or comments, we would love to hear from you!
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@jeremybowers@journa.host @claesdevreese@mastodon.social great, thanks! Having WP host their own server would be a game changer. We started our own institutional server a few weeks ago. Our move is just throwing a pebble in a pond. WP doing so would throw in a rock.
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@claesdevreese@mastodon.social I did come across this post, which seems to suggest this indeed https://journa.host/@jeremybowers/109551706041274437
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@OpenAccessElder @tklebel thank you, great to hear! Would indeed also like to see more such research.
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@claesdevreese@mastodon.social that would be great! Do you have a link or document describing their move?
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@lffontenelle@mastodon.social @textfiles@mastodon.archive.org @cwts@social.cwts.nl nice to see! We should compile a list of institutions that provide institutional social media servers.
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We find the literature is often based on case studies of OS projects, and we believe it would be better to have independent analyses of the impact of OS practices. Moreover, the focus is often on the uptake of OS practices, but we should also study the impact of OS. Finally, studying the impact of OS is complicated by questions of causality, which require more attention.
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If you have any feedback or comments, please do reach out.
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@Aschniedermann perhaps in addition, whether two independent variables are correlated does not say much about whether they should be controlled for or not.
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@eschares @tklebel @rlmcelreath yes, they should pair really well! Statistical rethinking has been a great source of inspiration. Enjoy!