Login

Recent searches

No recent searches

Search options

Only available when logged in.
social.bund.de is one of the many independent Mastodon servers you can use to participate in the fediverse.
Dies ist der Mastodon-Server der Bundesbeauftragten für den Datenschutz und die Informationsfreiheit (BfDI).

Administered by:

Server stats:

97
active users

social.bund.de: AboutProfiles directoryPrivacy policy

Mastodon: AboutGet the appKeyboard shortcutsView source codev4.3.6

#models

0 posts0 participants0 posts today
Public
bffo

From #arXiv | From #FAIR to #CURE: Guidelines for Computational #Models of Biological #Systems | #OpenScience #FAIR #CURE #Credible #Understandable #Reproducible #Extensible #Biology #Bioinformatics 🧬 🖥️ 🧪 🔓
⬇️
arxiv.org/abs/2502.15597
arxiv.org/abs/2502.15597

arXiv logo
arXiv.orgFrom FAIR to CURE: Guidelines for Computational Models of Biological SystemsGuidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of 'data', we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible.
Public
bffo

From #arXiv | From #FAIR to #CURE: Guidelines for Computational #Models of Biological #Systems | #OpenScience #FAIR #CURE #Credible #Understandable #Reproducible #Extensible #Biology #Bioinformatics 🧬 🖥️ 🧪 🔓
⬇️
arxiv.org/abs/2502.15597
arxiv.org/abs/2502.15597

arXiv logo
arXiv.orgFrom FAIR to CURE: Guidelines for Computational Models of Biological SystemsGuidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of 'data', we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible.
Replied in thread
Public
SpaceLifeForm

@martinvermeer @TatianaIlyina @DeutscherWetterdienst

I was not implying that is does not take into account radiation balance.

But, I am pretty certain that older models could not measure nighttime ocean radiation into space from the oceans.

60 years ago, forecasting was all based upon historical data. Before any weather satellites existed.

This problem probably persists.

Is there enough data from satellite coverage over the oceans to input to the models?

#WX#Climatechange#Models
Public *
MPI für Meteorologie

Here at #MPIM, we use the most advanced computers to build and run #climate #models, testing and enhancing our understanding of the Earth's climate system.

🖥️🌍 Our scientific #programmers are the backbone of the institute, essential to developing and operating these models.
These unsung heroes in climate research leverage cutting-edge technology to advance our knowledge and predictions. 💪

Curious about what it's like to be a scientific programmer at MPIM?
🎥 vimeo.com/963124805
#JoinUs

Join us at MPIM in Hamburg as a scientific programmer.
Public *
MPI für Meteorologie

#MPI_Scientist Dirk Olonscheck and Maria Rugenstein show that #climate #models underestimate the observed increase in global top-of-the atmosphere (TOA) radiation during 2001–2022. 🌍 This discrepancy is caused by both differences in surface warming patterns and in atmospheric physics that result in a weakly simulated coupling between surface warming and TOA radiation. Interestingly, models with low climate sensitivity are less discrepant than high-sensitivity models.
📄: doi.org/10.1029/2023GL106909

TOA radiation trend of different climate models.
ExploreLive feeds
About