Tootfinder

A case study: the savings potential thanks to FAIR data in one Materials Science PhD project
Michael Seitz, Nick Garabedian, Ilia Bagov, Christian Greiner
https://arxiv.org/abs/2506.12043

A case study: the savings potential thanks to FAIR data in one Materials Science PhD project
The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles have gained significant attention as a means to enhance data sharing, collaboration, and reuse across various domains. Here, we explore the potential benefits of implementing FAIR data practices within engineering projects, with a monetary focus in the German context, but by considering aspects which are relatively universal. By examining the FAIR-data aspect of a Materials Science and Engineering PhD project, it becom…

hyperFA*IR: A hypergeometric approach to fair rankings with finite candidate pool
Mauritz N. Cartier van Dissel, Samuel Martin-Gutierrez, Lisette Esp\'in-Noboa, Ana Mar\'ia Jaramillo, Fariba Karimi
https://arxiv.org/abs/2506.14349

hyperFA*IR: A hypergeometric approach to fair rankings with finite candidate pool
Ranking algorithms play a pivotal role in decision-making processes across diverse domains, from search engines to job applications. When rankings directly impact individuals, ensuring fairness becomes essential, particularly for groups that are marginalised or misrepresented in the data. Most of the existing group fairness frameworks often rely on ensuring proportional representation of protected groups. However, these approaches face limitations in accounting for the stochastic nature of rank…

This looks very cool.
'OpenAIRE in collaboration with Area Science Park organizes a hands-on workshop titled “Where LEGO Meets FAIR Data,” designed to introduce the principles of FAIR data through a creative, interactive simulation using LEGO metaphors.'
https://www.

FPF Unveils Paper on State Data Minimization Trends
https://fpf.org/blog/fpf-unveils-paper-on-state-data-minimization-trends/
@…

The more time goes on, the more conflicted I get about #AI. (or, more specifically, generative AI. I like causal AI a fair bit)
On one hand, I hate so much about it:
the needless environmental and electronic parts waste, the impact on labor, the monopolistic nature of main organizations driving it, the endless conversations about AGI and other absurdly utopian (or dystopian) futures, the widespread theft of IP and human work, the devaluation of labor and craft… so much of it is not okay at all.
On the other hand, I kinda get it?
I like to test software for myself, so I've been dipping my toes into some popular AI tools over the past couple of years. I have a paid subscription to ChatGPT (which I don’t feel great about, I know), and I have found genuine utility in it.
More so, I feel so conflicted when I talk to people I respect and who I think are very smart and creative and they tell me how in love they are with all of these AI tools, about the complex workflows they build, about their experiments with agentic AI and integrations... These people seem so excited, so alive, so joyous about the things that this technology allows them to do. They often wouldn't have had the skills / knowledge / financial capital to do some of those things with human efforts alone. And now I see them coding their own tools, doing complex data analysis, trying creative experiments with graphics / text / video...
And I feel like a total jerk going "BUT ACTUALLY THIS IS UNETHICAL AND INEFFICIENT AND YOU SHOULD STOP IT BECAUSE AI SUCKS".
The technology behind all these gen AI models does have real utility, and it has kicked off a lot of creativity from people who wouldn't have dabbled in those kinds of projects otherwise.
So I don't know how to feel. Because I can't let go of the guilt and the real problems and the awareness of how much empty hype there is.
#technology #artificialintelligence #genAI #ChatGPT

Towards Machine-actionable FAIR Digital Objects with a Typing Model that Enables Operations
Maximilian Inckmann, Nicolas Blumenr\"ohr, Rossella Aversa
https://arxiv.org/abs/2505.16550

Towards Machine-actionable FAIR Digital Objects with a Typing Model that Enables Operations
FAIR Digital Objects support research data management aligned with the FAIR principles. To be machine-actionable, they must support operations that interact with their contents. This can be achieved by associating operations with FAIR-DO data types. However, current typing models and Data Type Registries lack support for type-associated operations. In this work, we introduce a typing model that describes type-associated and technology-agnostic FAIR Digital Object Operations in a machine-actiona…

Curate, Connect, Inquire: A System for Findable Accessible Interoperable and Reusable (FAIR) Human-Robot Centered Datasets
Xingru Zhou, Sadanand Modak, Yao-Cheng Chan, Zhiyun Deng, Luis Sentis, Maria Esteva
https://arxiv.org/abs/2506.00220

Curate, Connect, Inquire: A System for Findable Accessible Interoperable and Reusable (FAIR) Human-Robot Centered Datasets
The rapid growth of AI in robotics has amplified the need for high-quality, reusable datasets, particularly in human-robot interaction (HRI) and AI-embedded robotics. While more robotics datasets are being created, the landscape of open data in the field is uneven. This is due to a lack of curation standards and consistent publication practices, which makes it difficult to discover, access, and reuse robotics data. To address these challenges, this paper presents a curation and access system wi…

The Cell Ontology in the age of single-cell omics
Shawn Zheng Kai Tan, Aleix Puig-Barbe, Damien Goutte-Gattat, Caroline Eastwood, Brian Aevermann, Alida Avola, James P Balhoff, Ismail Ugur Bayindir, Jasmine Belfiore, Anita Reane Caron, David S Fischer, Nancy George, Benjamin M Gyori, Melissa A Haendel, Charles Tapley Hoyt, Huseyin Kir, Tiago Lubiana, Nicolas Matentzoglu, James A Overton, Beverly Peng, Bjoern Peters, Ellen M Quardokus, Patrick L Ray, Paola Roncaglia, Andrea D Rivera, Ra…

The Cell Ontology in the age of single-cell omics
Single-cell omics technologies have transformed our understanding of cellular diversity by enabling high-resolution profiling of individual cells. However, the unprecedented scale and heterogeneity of these datasets demand robust frameworks for data integration and annotation. The Cell Ontology (CL) has emerged as a pivotal resource for achieving FAIR (Findable, Accessible, Interoperable, and Reusable) data principles by providing standardized, species-agnostic terms for canonical cell types - …

BenLOC: A Benchmark for Learning to Configure MIP Optimizers
Hongpei Li, Ziyan He, Yufei Wang, Wenting Tu, Shanwen Pu, Qi Deng, Dongdong Ge
https://arxiv.org/abs/2506.02752

BenLOC: A Benchmark for Learning to Configure MIP Optimizers
The automatic configuration of Mixed-Integer Programming (MIP) optimizers has become increasingly critical as the large number of configurations can significantly affect solver performance. Yet the lack of standardized evaluation frameworks has led to data leakage and over-optimistic claims, as prior studies often rely on homogeneous datasets and inconsistent experimental setups. To promote a fair evaluation process, we present BenLOC, a comprehensive benchmark and open-source toolkit, which no…

Probabilistic measures afford fair comparisons of AIWP and NWP model output
Tilmann Gneiting, Tobias Biegert, Kristof Kraus, Eva-Maria Walz, Alexander I. Jordan, Sebastian Lerch
https://arxiv.org/abs/2506.03744

Probabilistic measures afford fair comparisons of AIWP and NWP model output
We introduce a new measure for fair and meaningful comparisons of single-valued output from artificial intelligence based weather prediction (AIWP) and numerical weather prediction (NWP) models, called potential continuous ranked probability score (PC). In a nutshell, we subject the deterministic backbone of physics-based and data-driven models post hoc to the same statistical postprocessing technique, namely, isotonic distributional regression (IDR). Then we find PC as the mean continuous rank…

This https://arxiv.org/abs/2505.13469 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Algorithmic Tradeoffs in Fair Lending: Profitability, Compliance, and Long-Term Impact
As financial institutions increasingly rely on machine learning models to automate lending decisions, concerns about algorithmic fairness have risen. This paper explores the tradeoff between enforcing fairness constraints (such as demographic parity or equal opportunity) and maximizing lender profitability. Through simulations on synthetic data that reflects real-world lending patterns, we quantify how different fairness interventions impact profit margins and default rates. Our results demonst…