MIT dropped its contract with Elsevier, the huge scientific journal company. Their library instead arranged alternate access to journals and tools for researchers to get them. They’re saving $2 million/year:
https://sparcopen.org/our-work/big-deal-knowledge-base/unbundling-profiles/mit-libraries/
#academicpublishing #OpenAccess #science #scientificpublishing
Some lovely news to share this morning: Blue Machine has been shortlisted for the #WainwrightPrize24 for Conservation :)
Hooray for the ocean! We need more people thinking about what the blue of our blue planet really is, rather than seeing it as a mysterious void, or just where the fish live. We are all citizens of an ocean planet and it's time to learn what that really means.
More here: https://wainwrightprize.com/
CSVs Are Kinda Bad. DSVs Are Kinda Good
https://matthodges.com/posts/2024-08-12-csv-bad-dsv-good/
Discussions: https://discu.eu/q/https://matthodges.com/posts/2024-08-12-csv-bad-dsv-good/
Announcing Zotero 7, the biggest update in Zotero’s 18-year history
CrowdStrike broke Debian and Rocky Linux months ago, but no one noticed
https://www.neowin.net/news/crowdstrike-broke-debian-and-rocky-linux-months-ago-but-no-one-noticed/
"Privacy-Preserving" Attribution: Mozilla Disappoints Us Yet Again
https://blog.privacyguides.org/2024/07/14/mozilla-disappoints-us-yet-again-2/
Discussions: https://discu.eu/q/https://blog.privacyguides.org/2024/07/14/mozilla-disappoints-us-yet-again-2/
I nominate Gus Atkinson for God Emperor. Any objections? #cricket
Prepping for the next Rare Earth (on cultivated meat), & remembered the Mock Turtle in Alice in Wonderland so looked up Mock Turtle Soup. And now I wish I hadn't:
"Mock turtle soup is an English soup that was created in the mid-18th century as an imitation of green turtle soup. It often uses brains and organ meats such as calf's head to duplicate the texture and flavour of the original's turtle meat after the green turtles used to make the original dish were hunted nearly to extinction. "
Ugh
For astronomers missing #SPIE by coming to #Leiden for Exoplanets 5, you can get your #astroninstrumentation fix by visiting the Boerhaave museum and amongst the science exhibits, you can see several lenses hand ground by the Huygens brothers from the 1600’s 🔭🪐 #astrodon #exo5 https://rijksmuseumboerhaave.nl/
Soon the telescope platform at ESO's Paranal Observatory in #Chile will look very different at night: all four of the 8.2 m telescopes of the VLT will be equipped with lasers! This is one of the ongoing upgrades of the GRAVITY+ instrument, which will allow us to study black holes, stars and planets like never before.
Find out more in this great article by current and former ESO communication interns Elena Reiriz Martinez and Tom Howarth: https://www.eso.org/public/blog/gravity-leap-vlti/
Astronomers who build instruments and publish in #SPIE journals - please post the preprints on #arXiv as well! #astrodon #astroinstrumentation
Before I head off on a trip to various parts of not-Barcelona, I thought I’d share a somewhat provocative paper by David Hogg and Soledad Villar. In my capacity as journal editor over the past few years I’ve noticed that there has been a phenomenal increase in astrophysics papers discussing applications of various forms of Machine Leaning (ML). This paper looks into issues around the use of ML not just in astrophysics but elsewhere in the natural sciences.
The abstract reads:
Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology – in which only the data exist – and a strong epistemology – in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in the natural sciences. Here, we identify some locations for ML in the natural sciences at which the ontology and epistemology are valuable. For example, when an expressive machine learning model is used in a causal inference to represent the effects of confounders, such as foregrounds, backgrounds, or instrument calibration parameters, the model capacity and loose philosophy of ML can make the results more trustworthy. We also show that there are contexts in which the introduction of ML introduces strong, unwanted statistical biases. For one, when ML models are used to emulate physical (or first-principles) simulations, they introduce strong confirmation biases. For another, when expressive regressions are used to label datasets, those labels cannot be used in downstream joint or ensemble analyses without taking on uncontrolled biases. The question in the title is being asked of all of the natural sciences; that is, we are calling on the scientific communities to take a step back and consider the role and value of ML in their fields; the (partial) answers we give here come from the particular perspective of physics
arXiv:2405.18095
P.S. The answer to the question posed in the title is obviously “yes”.
https://telescoper.blog/2024/05/30/is-machine-learning-good-or-bad-for-the-natural-sciences/
#AI #ArtificialIntelligence #arXiv240518095 #Astrophysics #Cosmology #DataScience #deepLearning #MachineLearning
@c_discussions The bot went a bit wrong here!
Well, this seems to be an interesting and unexpected big deal: a new paper suggests that light can evaporate water without actually needing to heat the water up first. And it could sort out a few problems in existing cloud physics:
https://news.mit.edu/2024/how-light-can-vaporize-water-without-heat-0423
Neat! Gaia spots a 33 solar mass black hole in the Milky Way!
https://www.cosmos.esa.int/web/gaia/iow_20240416
BH3 matches what is quite common in our gravitational-wave observations, but had yet to be discovered in our own galaxy
I’m a professional astrophysicist and research software engineer. I like cricket, reading and cooking.