Tootfinder

"The Seven Capital Sins of Open Science"
1. Worshiping the 'age factor'
2. Ignoring the value of data reuse and complexity
3. Disrespecting other disciplines
4. Publishing data without a supplementary paper
5. Creating and maintaining a nightmare for machines
6. Refusing to support investment in general infrastructure
7. Creating data without a FAIR and explicit data stewardship plan.

The Seven Capital Sins of Open Science
Years ago I made a set of cartoons depicting what I consider the seven major roadblocks to effectively realise an Open Science (rather FAIR science actually). These were distilled from my intensive work on the European Open Science Cloud, GO FAIR and also my presidency of CODATA.

schöne Entdeckung heute im #bibliocon25 Panel zu FAIR data: Karin Schmidgall stellt das Datenangebot Data des Literaturarchivs Marbach vor #dla h…

Ah fuck, people are using LLMs for kernel code. They really are going to fuck over everything, aren't they?
https://lwn.net/SubscriberLink/1026558/9f3079fb392c4a9a/

A GenAI System for Improved FAIR Independent Biological Database Integration
Syed N. Sakib, Kallol Naha, Sajratul Y. Rubaiat, Hasan M. Jamil
https://arxiv.org/abs/2506.17934

A GenAI System for Improved FAIR Independent Biological Database Integration
Life sciences research increasingly requires identifying, accessing, and effectively processing data from an ever-evolving array of information sources on the Linked Open Data (LOD) network. This dynamic landscape places a significant burden on researchers, as the quality of query responses depends heavily on the selection and semantic integration of data sources --processes that are often labor-intensive, error-prone, and costly. While the adoption of FAIR (Findable, Accessible, Interoperable,…

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…

Enhanced Velocity-Adaptive Scheme: Joint Fair Access and Age of Information Optimization in Vehicular Networks
Xiao Xu, Qiong Wu, Pingyi Fan, Kezhi Wang, Nan Cheng, Wen Chen, Khaled B. Letaief
https://arxiv.org/abs/2507.18328

Enhanced Velocity-Adaptive Scheme: Joint Fair Access and Age of Information Optimization in Vehicular Networks
In this paper, we consider the fair access problem and the Age of Information (AoI) under 5G New Radio (NR) Vehicle-to-Infrastructure (V2I) Mode 2 in vehicular networks. Specifically, vehicles follow Mode 2 to communicate with Roadside Units (RSUs) to obtain accurate data for driving assistance.Nevertheless, vehicles often have different velocity when they are moving in adjacent lanes, leading to difference in RSU dwelltime and communication duration. This results in unfair access to network re…

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.

#DH2025 Listening to Victoria and Thea on 'Building a FAIR data future at the Journal of Open Humanities' - I'm hoping you'll see a lot more British Library data papers over time, as along with datasheets for datasets it's a big part of making our open collections findable and usable

A Vision for Trustworthy, Fair, and Efficient Socio-Technical Control using Karma Economies
Ezzat Elokda, Andrea Censi, Emilio Frazzoli, Florian D\"orfler, Saverio Bolognani
https://arxiv.org/abs/2506.17115

A Vision for Trustworthy, Fair, and Efficient Socio-Technical Control using Karma Economies
Control systems will play a pivotal role in addressing societal-scale challenges as they drive the development of sustainable future smart cities. At the heart of these challenges is the trustworthy, fair, and efficient allocation of scarce public resources, including renewable energy, transportation, data, computation, etc.. Historical evidence suggests that monetary control -- the prototypical mechanism for managing resource scarcity -- is not always well-accepted in socio-technical resource …

Fair Data Exchange with Constant-Time Proofs
Majid Khabbazian
https://arxiv.org/abs/2506.14944 https://arxiv.org/pdf/2506.14944

Fair Data Exchange with Constant-Time Proofs
The Fair Data Exchange (FDE) protocol introduced at CCS 2024 offers atomic pay-per-file transfers with constant-size proofs, but its prover and verifier runtimes still scale linearly with the file length n. We collapse these costs to essentially constant by viewing the file as a rate-1 Reed-Solomon (RS) codeword, extending it to a lower-rate RS code with constant redundancy, encrypting this extended vector, and then proving correctness for only a small random subset of the resulting ciphertexts…

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…

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

As someone who uses #LLM s a fair bit, this sort of hallucination is good for reminding yourself that it's just bashing words together until it looks sort of like what's in its training data, especially in various RAG-type setups

Cost for research -- how cost data of research can be included in open metadata to be reused and evaluated
Julia Bartlewski, Christoph Broschinski, Gernot Deinzer, Cornelia Lang, Dirk Pieper, Bianca Schweighofer, Colin Sippl, Lisa-Marie Stein, Alexander Wagner, Silke Weisheit
https://arxiv.org/abs/2506.18517

Cost for research -- how cost data of research can be included in open metadata to be reused and evaluated
The openCost project aims to enhance transparency in research funding by making publication-related costs publicly accessible, following FAIR principles. It introduces a metadata schema for cost data, allowing aggregation and analysis across institutions. The project promotes open access and cost-efficient models, benefiting academic institutions, funders, and policymakers.

> "Sites must demonstrate sufficient access to water to support at least 500MW of AI infrastructure."
The actual amounts are left as an exercise.
It's fair enough that it's difficult to guess how much water would be required for an AI data centre in 2030... but in the context of development applications, a figure representing a base minimum might be expected?

AI Growth Zones: open for applications

Meta Optimality for Demographic Parity Constrained Regression via Post-Processing
Kazuto Fukuchi
https://arxiv.org/abs/2506.13947 https://

Meta Optimality for Demographic Parity Constrained Regression via Post-Processing
We address the regression problem under the constraint of demographic parity, a commonly used fairness definition. Recent studies have revealed fair minimax optimal regression algorithms, the most accurate algorithms that adhere to the fairness constraint. However, these analyses are tightly coupled with specific data generation models. In this paper, we provide meta-theorems that can be applied to various situations to validate the fair minimax optimality of the corresponding regression algori…

On Fair Epsilon Net and Geometric Hitting Set
Mohsen Dehghankar, Stavros Sintos, Abolfazl Asudeh
https://arxiv.org/abs/2507.08758 https://

On Fair Epsilon Net and Geometric Hitting Set
Fairness has emerged as a formidable challenge in data-driven decisions. Many of the data problems, such as creating compact data summaries for approximate query processing, can be effectively tackled using concepts from computational geometry, such as $\varepsilon$-nets. However, these powerful tools have yet to be examined from the perspective of fairness. To fill this research gap, we add fairness to classical geometric approximation problems of $\varepsilon$-net, $\varepsilon$-sample, and g…

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…

Large-Scale Processing and Validation of Grid Data for Assessing the Fair Spatial Distribution of PV Hosting Capacity
Ali Mohamed Ali, Yaser Raeisi, Plouton Grammatikos, Davide Pavanello, Pierre Roduit, Fabrizio Sossan
https://arxiv.org/abs/2507.08684

Large-Scale Processing and Validation of Grid Data for Assessing the Fair Spatial Distribution of PV Hosting Capacity
The integration of PV systems and increased electrification levels present significant challenges to the traditional design and operation of distribution grids. This paper presents a methodology for extracting, validating, and adapting grid data from a distribution system operator's (DSO) database to facilitate large-scale grid studies, including load flow and optimal power flow analyses. The validation process combines rule-based sanity checks and offline automated power flow analyses to ensur…

Interesting reporting and analysis by Gordon Hull about recent decisions in the US courts about AI and copyright:
https://www.newappsblog.com/2025/07/ai-and-copyright-training-data-and-transformative-fair-use.html

Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management
Luis Gasco, Hermenegildo Fabregat, Laura Garc\'ia-Sardi\~na, Paula Estrella, Daniel Deniz, Alvaro Rodrigo, Rabih Zbib
https://arxiv.org/abs/2507.13275

Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management
Advances in natural language processing and large language models are driving a major transformation in Human Capital Management, with a growing interest in building smart systems based on language technologies for talent acquisition, upskilling strategies, and workforce planning. However, the adoption and progress of these technologies critically depend on the development of reliable and fair models, properly evaluated on public data and open benchmarks, which have so far been unavailable in t…

Fair Deepfake Detectors Can Generalize
Harry Cheng, Ming-Hui Liu, Yangyang Guo, Tianyi Wang, Liqiang Nie, Mohan Kankanhalli
https://arxiv.org/abs/2507.02645

Fair Deepfake Detectors Can Generalize
Deepfake detection models face two critical challenges: generalization to unseen manipulations and demographic fairness among population groups. However, existing approaches often demonstrate that these two objectives are inherently conflicting, revealing a trade-off between them. In this paper, we, for the first time, uncover and formally define a causal relationship between fairness and generalization. Building on the back-door adjustment, we show that controlling for confounders (data distri…

Dominance or Fair Play in Social Networks? A Model of Influencer Popularity Dynamic
Franco Galante, Chiara Ravazzi, Luca Vassio, Michele Garetto, Emilio Leonardi
https://arxiv.org/abs/2507.03448

Dominance or Fair Play in Social Networks? A Model of Influencer Popularity Dynamic
This paper presents a data-driven mean-field approach to model the popularity dynamics of users seeking public attention, i.e., influencers. We propose a novel analytical model that integrates individual activity patterns, expertise in producing viral content, exogenous events, and the platform's role in visibility enhancement, ultimately determining each influencer's success. We analytically derive sufficient conditions for system ergodicity, enabling predictions of popularity distributions. A…

AI, AGI, and learning efficiency
My 4-month-old kid is not DDoSing Wikipedia right now, nor will they ever do so before learning to speak, read, or write. Their entire "training corpus" will not top even 100 million "tokens" before they can speak & understand language, and do so with real intentionally.
Just to emphasize that point: 100 words-per-minute times 60 minutes-per-hour times 12 hours-per-day times 365 days-per-year times 4 years is a mere 105,120,000 words. That's a ludicrously *high* estimate of words-per-minute and hours-per-day, and 4 years old (the age of my other kid) is well after basic speech capabilities are developed in many children, etc. More likely the available "training data" is at least 1 or 2 orders of magnitude less than this.
The point here is that large language models, trained as they are on multiple *billions* of tokens, are not developing their behavioral capabilities in a way that's remotely similar to humans, even if you believe those capabilities are similar (they are by certain very biased ways of measurement; they very much aren't by others). This idea that humans must be naturally good at acquiring language is an old one (see e.g. #AI #LLM #AGI

The hunt for research data: Development of an open-source workflow for tracking institutionally-affiliated research data publications
Bryan M. Gee
https://arxiv.org/abs/2507.01228

The hunt for research data: Development of an open-source workflow for tracking institutionally-affiliated research data publications
The ability to find data is central to the FAIR principles underlying research data stewardship. As with the ability to reuse data, efforts to ensure and enhance findability have historically focused on discoverability of data by other researchers, but there is a growing recognition of the importance of stewarding data in a fashion that makes them FAIR for a wide range of potential reusers and stakeholders. Research institutions are one such stakeholder and have a range of motivations for disco…

Incorporating Fairness Constraints into Archetypal Analysis
Aleix Alcacer, Irene Epifanio
https://arxiv.org/abs/2507.12021 https://ar…

Incorporating Fairness Constraints into Archetypal Analysis
Archetypal Analysis (AA) is an unsupervised learning method that represents data as convex combinations of extreme patterns called archetypes. While AA provides interpretable and low-dimensional representations, it can inadvertently encode sensitive attributes, leading to fairness concerns. In this work, we propose Fair Archetypal Analysis (FairAA), a modified formulation that explicitly reduces the influence of sensitive group information in the learned projections. We also introduce FairKerne…

Standardized Evaluation of Fetal Phonocardiography Processing Methods
Krist\'of M\"uller, Janka Hatvani, M\'arton \'Aron Goda, Mikl\'os Koller
https://arxiv.org/abs/2507.10783

Standardized Evaluation of Fetal Phonocardiography Processing Methods
Motivation. Phonocardiography can give access to the fetal heart rate as well as direct heart sound data, and is entirely passive, using no radiation of any kind. Approach. We discuss the currently available methods for fetal heart sound detection and heart rate estimation and compare them using a common benchmarking platform and a pre-selected testing dataset. Compared to previous reviews, we evaluated the discussed methods in a standardized manner for a fair comparison. Our tests included tol…

I wouldn't have thought that quantitative analyses of retrospective national bibliographies would be that painful: data access via SRU, OAI, and REST API; another resource has a JSON dump, another one again consists of various ttl for which you have to set up your own sparql endpoint. And I've not even arrived at formats, metadata standards and cataloguing peculiarities 🤯 so everything's #FAIR

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…

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…

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 - …

BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learning
Thinh Dao, Dung Thuy Nguyen, Khoa D Doan, Kok-Seng Wong
https://arxiv.org/abs/2507.04903

BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learning
Federated Learning (FL) systems are vulnerable to backdoor attacks, where adversaries train their local models on poisoned data and submit poisoned model updates to compromise the global model. Despite numerous proposed attacks and defenses, divergent experimental settings, implementation errors, and unrealistic assumptions hinder fair comparisons and valid conclusions about their effectiveness in real-world scenarios. To address this, we introduce BackFed - a comprehensive benchmark suite desi…

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…