Tootfinder

Dirty Data in the Newsroom: Comparing Data Preparation in Journalism and Data Science
Stephen Kasica, Charles Berret, Tamara Munzner
https://arxiv.org/abs/2507.07238

Dirty Data in the Newsroom: Comparing Data Preparation in Journalism and Data Science
The work involved in gathering, wrangling, cleaning, and otherwise preparing data for analysis is often the most time consuming and tedious aspect of data work. Although many studies describe data preparation within the context of data science workflows, there has been little research on data preparation in data journalism. We address this gap with a hybrid form of thematic analysis that combines deductive codes derived from existing accounts of data science workflows and inductive codes arisin…

High Signal: Data Science | Career | AI
Great Australian Pods Podcast Directory: #GreatAusPods

cora: CORA citations (1998)
Citations among papers indexed by CORA, from 1998, an early computer science research paper search engine. If a paper i cites a paper j also in this data set, then a directed edge connects i to j. (Papers not in the data set are excluded.) Self-loops may be present. The dates of these snapshots are uncertain.
This network has 23166 nodes and 91500 edges.
Tags: Informational, Citation, Unweighted

KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
Eugenie Lai, Gerardo Vitagliano, Ziyu Zhang, Sivaprasad Sudhir, Om Chabra, Anna Zeng, Anton A. Zabreyko, Chenning Li, Ferdi Kossmann, Jialin Ding, Jun Chen, Markos Markakis, Matthew Russo, Weiyang Wang, Ziniu Wu, Michael J. Cafarella, Lei Cao, Samuel Madden, Tim Kraska

KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
Constructing real-world data-to-insight pipelines often involves data extraction from data lakes, data integration across heterogeneous data sources, and diverse operations from data cleaning to analysis. The design and implementation of data science pipelines require domain knowledge, technical expertise, and even project-specific insights. AI systems have shown remarkable reasoning, coding, and understanding capabilities. However, it remains unclear to what extent these capabilities translate…

"Applications of the Critical Incident Technique in Library and Information Science Research: A Literature Review" https://doi.org/10.1515/libri-2024-0065

Effective Training Data Synthesis for Improving MLLM Chart Understanding
Yuwei Yang, Zeyu Zhang, Yunzhong Hou, Zhuowan Li, Gaowen Liu, Ali Payani, Yuan-Sen Ting, Liang Zheng
https://arxiv.org/abs/2508.06492

Effective Training Data Synthesis for Improving MLLM Chart Understanding
Being able to effectively read scientific plots, or chart understanding, is a central part toward building effective agents for science. However, existing multimodal large language models (MLLMs), especially open-source ones, are still falling behind with a typical success rate of 30%-50% on challenging benchmarks. Previous studies on fine-tuning MLLMs with synthetic charts are often restricted by their inadequate similarity to the real charts, which could compromise model training and performa…

Science at Risk: The Urgent Need for Institutional Support of Long-Term Ecological and Evolutionary Research in an Era of Data Manipulation and Disinformation
Vincent A. Viblanc (UMR ISEM), Elise Huchard (UMR ISEM), Gilles Pinay (CEFE), Elena Orme\~no (CEFE), C\'eline Teplitsky (CEFE), Fran\c{c}ois Criscuolo (IGE), Dominique Joly (IGE), David Renault (IGE), C\'ecile Callou (IGE), Fran\c{c}oise Gourmelon (IGE), Sandrine Anquetin (IGE), B\'en\'edicte Augeard (OFB), Fabien…

Science at Risk: The Urgent Need for Institutional Support of Long-Term Ecological and Evolutionary Research in an Era of Data Manipulation and Disinformation
Planet Earth and the biodiversity it supports are in crisis. Human impact on terrestrial, marine and freshwater ecosystems and the hundreds of thousands of organisms that inhabit them is global. To what extent can we push ecosystems before they collapse? Will species adapt to these changes and at what rate? What are the consequences, for the environment and humankind? These are some of the most pressing issues to date. Clear answers can only be addressed through long-term research programs that…

MetaInfoSci: An Integrated Web Tool for Scholarly Data Analysis
Kiran Sharmaa, Parul Khurana, Ziya Uddina
https://arxiv.org/abs/2506.09056 https://

MetaInfoSci: An Integrated Web Tool for Scholarly Data Analysis
The exponential increase in academic publications has made it increasingly difficult for researchers to remain up to date and systematically synthesize knowledge scattered across vast and fragmented research domains. Literature reviews, particularly those supported by bibliometric methods, have become essential in organizing prior findings and guiding future research directions. While numerous tools exist for bibliometric analysis and network science, there is currently no single platform that …

Learning the Topic, Not the Language: How LLMs Classify Online Immigration Discourse Across Languages
Andrea Nasuto, Stefano Maria Iacus, Francisco Rowe, Devika Jain
https://arxiv.org/abs/2508.06435

Learning the Topic, Not the Language: How LLMs Classify Online Immigration Discourse Across Languages
Large language models (LLMs) are transforming social-science research by enabling scalable, precise analysis. Their adaptability raises the question of whether knowledge acquired through fine-tuning in a few languages can transfer to unseen languages that only appeared during pre-training. To examine this, we fine-tune lightweight LLaMA 3.2-3B models on monolingual, bilingual, or multilingual data sets to classify immigration-related tweets from X/Twitter across 13 languages, a domain character…

SDSS-V Milky Way Mapper (MWM): ASPCAP Stellar Parameters and Abundances in SDSS-V Data Release 19
Szabolcs M\'esz\'aros, Paula Jofr\'e, Jennifer A. Johnson, Jonathan C. Bird, Andrew R. Casey, Katia Cunha, Nathan De Lee, Peter Frinchaboy, Guillaume Guiglion, Viola Heged\H{u}s, Alex P. Ji, Juna A. Kollmeier, Melissa K. Ness, Jonah Otto, Marc H. Pinsonneault, Alexandre Roman-Lopes, Amaya Sinha, Ying-Yi Song, Guy S. Stringfellow, Keivan G. Stassun, Jamie Tayar, Andrew Tkachenko…

SDSS-V Milky Way Mapper (MWM): ASPCAP Stellar Parameters and Abundances in SDSS-V Data Release 19
The goal of this paper is to describe the science verification of Milky Way Mapper (MWM) ASPCAP data products published in Data Release 19 (DR19) of the fifth phase of the Sloan Digital Sky Survey (SDSS-V). We compare MWM ASPCAP atmospheric parameters T$_{\rm eff}$, log g, 24 abundances of 21 elements (carbon, nitrogen, and oxygen have multiple sources for deriving their abundance values) and their uncertainties determined from APOGEE spectrograph spectra with those of the literature and evalua…

A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)
https://arxiv.org/abs/2506.08153

A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper showcases the first step for creating the metrics-based architectural mode…

The most energetic transients - tidal disruptions of high-mass stars: #ExtremeNuclearTransients (ENTs) are the most energetic transients yet observed.

Automated Visualization Makeovers with LLMs
Siddharth Gangwar, David A. Selby, Sebastian J. Vollmer
https://arxiv.org/abs/2508.05637 https://arxiv.org/pdf/…

Automated Visualization Makeovers with LLMs
Making a good graphic that accurately and efficiently conveys the desired message to the audience is both an art and a science, typically not taught in the data science curriculum. Visualisation makeovers are exercises where the community exchange feedback to improve charts and data visualizations. Can multi-modal large language models (LLMs) emulate this task? Given a plot in the form of an image file, or the code used to generate it, an LLM, primed with a list of visualization best practices,…

KI4Demokratie: An AI-Based Platform for Monitoring and Fostering Democratic Discourse
Rudy Alexandro Garrido Veliz, Till Nikolaus Schaland, Simon Bergmoser, Florian Horwege, Somya Bansal, Ritesh Nahar, Martin Semmann, J\"org Forthmann, Seid Muhie Yimam
https://arxiv.org/abs/2506.09947…

KI4Demokratie: An AI-Based Platform for Monitoring and Fostering Democratic Discourse
Social media increasingly fuel extremism, especially right-wing extremism, and enable the rapid spread of antidemocratic narratives. Although AI and data science are often leveraged to manipulate political opinion, there is a critical need for tools that support effective monitoring without infringing on freedom of expression. We present KI4Demokratie, an AI-based platform that assists journalists, researchers, and policymakers in monitoring right-wing discourse that may undermine democratic va…

Fine-Tuning Vision-Language Models for Markdown Conversion of Financial Tables in Malaysian Audited Financial Reports
Jin Khye Tan (Faculty of Computer Science,Information Technology, Universiti Malaya), En Jun Choong, Ethan Jeremiah Chitty, Yan Pheng Choo, John Hsin Yang Wong, Chern Eu Cheah
https://arxiv.org/abs/2508.05669

Fine-Tuning Vision-Language Models for Markdown Conversion of Financial Tables in Malaysian Audited Financial Reports
Accurately extracting and representing the structure of tabular data from financial documents remains a critical challenge in document understanding, particularly for regulatory and analytical use cases. This study addresses the complexity of converting financial tables from Malaysian audited financial reports into Markdown format, a task complicated by rotated layouts, multi-level headers, and implicit structural cues. We propose a fine-tuned vision-language model (VLM), based on Qwen2.5-VL-7B…

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.

Large Language Model-based Data Science Agent: A Survey
Peiran Wang, Yaoning Yu, Ke Chen, Xianyang Zhan, Haohan Wang
https://arxiv.org/abs/2508.02744 https://

Large Language Model-based Data Science Agent: A Survey
The rapid advancement of Large Language Models (LLMs) has driven novel applications across diverse domains, with LLM-based agents emerging as a crucial area of exploration. This survey presents a comprehensive analysis of LLM-based agents designed for data science tasks, summarizing insights from recent studies. From the agent perspective, we discuss the key design principles, covering agent roles, execution, knowledge, and reflection methods. From the data science perspective, we identify key …

Overview of statistical concepts in datascience: could be usefull in case you need some preformulated text to share with stakeholders... https://towardsdatascience.com/ultimate-guide-to-statistics-for-data-science-a3d8f1fd69a7

dblp_cite: DBLP citations (2014)
Citations among papers contained in the DBLP computer science bibliography. If a paper i cites a paper j also in this data set, then a directed edge connects i to j. (Papers not in the data set are excluded.) Self-loops may be present. This snapshot from May 2014.
This network has 12590 nodes and 49759 edges.
Tags: Informational, Citation, Unweighted

This https://arxiv.org/abs/2503.17945 has been replaced.
initial toot: https://mastoxiv.page/@a…

Deep Learning Assisted Denoising of Experimental Micrographs
Microstructure imaging is crucial in materials science, but experimental images often introduce noise that obscures critical structural details. This study presents a novel deep learning approach for robust microstructure image denoising, combining phase-field simulations, Fourier transform techniques, and an attention-based neural network. The innovative framework addresses dataset limitations by synthetically generating training data by combining computational phase-field microstructures with…

This https://arxiv.org/abs/2412.04854 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

First constraint for atmospheric millicharged particles with the LUX-ZEPLIN experiment
We report on a search for millicharged particles (mCPs) produced in cosmic ray proton atmospheric interactions using data collected during the first science run of the LUX-ZEPLIN experiment. The mCPs produced by two processes -- meson decay and proton bremsstrahlung -- are considered in this study. This search utilized a novel signature unique to liquid xenon (LXe) time projection chambers (TPCs), allowing sensitivity to mCPs with masses ranging from 10 to 1000 MeV/c$^2$ and fractional charges …

Stream DaQ: Stream-First Data Quality Monitoring
Vasileios Papastergios, Anastasios Gounaris
https://arxiv.org/abs/2506.06147 https://

Stream DaQ: Stream-First Data Quality Monitoring
Data quality is fundamental to modern data science workflows, where data continuously flows as unbounded streams feeding critical downstream tasks, from elementary analytics to advanced artificial intelligence models. Existing data quality approaches either focus exclusively on static data or treat streaming as an extension of batch processing, lacking the temporal granularity and contextual awareness required for true streaming applications. In this paper, we present a novel data quality monit…

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new/
[1/1]:
Growth of Science and Women: Methodological Challenges of Using Structured Big Data

Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation
Joon Tai Kim, Tianle Chen, Ziyu Dong, Nishanth Kunchala, Alexander Guller, Daniel Ospina Acero, Roger Williams, Mrinal Kumar
https://arxiv.org/abs/2507.06321

Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation
Collecting and annotating images for the purpose of training segmentation models is often cost prohibitive. In the domain of wildland fire science, this challenge is further compounded by the scarcity of reliable public datasets with labeled ground truth. This paper presents the Centralized Copy-Paste Data Augmentation (CCPDA) method, for the purpose of assisting with the training of deep-learning multiclass segmentation models, with special focus on improving segmentation outcomes for the fire…

GloBIAS: strengthening the foundations of BioImage Analysis
A. A. Corbat (BioImage Informatics Unit, Science for Life Laboratory and Department of Information Technology, Uppsala University, Sweden), C. G. Walther (German BioImaging, Gesellschaft f\"ur Mikroskopie und Bildanalyse e.V., Konstanz, Germany, University of Vienna, Vienna, Austria), L. R. de la Ballina (Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebe…

GloBIAS: strengthening the foundations of BioImage Analysis
There is a global need for BioImage Analysis (BIA) as advances in life sciences increasingly rely on cutting-edge imaging systems that have dramatically expanded the complexity and dimensionality of biological images. Turning these data into scientific discoveries requires people with effective data management skills and knowledge of state-of-the-art image processing and data analysis, in other words, BioImage Analysts. The Global BioImage Analysts' Society (GloBIAS) aims to enhance the profile…

DELPHYNE: A Pre-Trained Model for General and Financial Time Series
Xueying Ding, Aakriti Mittal, Achintya Gopal
https://arxiv.org/abs/2506.06288 https://

DELPHYNE: A Pre-Trained Model for General and Financial Time Series
Time-series data is a vital modality within data science communities. This is particularly valuable in financial applications, where it helps in detecting patterns, understanding market behavior, and making informed decisions based on historical data. Recent advances in language modeling have led to the rise of time-series pre-trained models that are trained on vast collections of datasets and applied to diverse tasks across financial domains. However, across financial applications, existing ti…

Mapping correlations and coherence: adjacency-based approach to data visualization and regularity discovery
Guang-Xing Li
https://arxiv.org/abs/2506.05758 …

Mapping correlations and coherence: adjacency-based approach to data visualization and regularity discovery
The development of science has been transforming man's view towards nature for centuries. Observing structures and patterns in an effective approach to discover regularities from data is a key step toward theory-building. With increasingly complex data being obtained, revealing regularities systematically has become a challenge. Correlation is a most commonly-used and effective approach to describe regularities in data, yet for complex patterns, spatial inhomogeneity and complexity can often un…

A Blueprint to Design Curriculum and Pedagogy for Introductory Data Science
Elijah Meyer, Mine \c{C}etinkaya-Rundel
https://arxiv.org/abs/2508.03952 https://

A Blueprint to Design Curriculum and Pedagogy for Introductory Data Science
As the demand for jobs in data science increases, so does the demand for universities to develop and facilitate modernized data science curricula to train students for these positions. Yet, the development of these courses remains challenging, especially at the introductory level. To help instructors to meet this demand, we present a flexible blueprint that supports the development of a modernized introductory data science curriculum. This blueprint is narrated through the lens and experience i…

MLOps with Microservices: A Case Study on the Maritime Domain
Renato Cordeiro Ferreira (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Rowanne Trapmann (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Willem-Jan van den Heuvel (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)

MLOps with Microservices: A Case Study on the Maritime Domain
This case study describes challenges and lessons learned on building Ocean Guard: a Machine Learning-Enabled System (MLES) for anomaly detection in the maritime domain. First, the paper presents the system's specification, and architecture. Ocean Guard was designed with a microservices' architecture to enable multiple teams to work on the project in parallel. Then, the paper discusses how the developers adapted contract-based design to MLOps for achieving that goal. As a MLES, Ocean Guard emplo…

Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models
Mingjie Chen (Zhejiang University), Tiancheng Zhu (Huazhong University of Science,Technology), Mingxue Zhang (The State Key Laboratory of Blockchain,Data Security, Zhejiang University,Hangzhou High-Tech Zone), Yiling He (University College London), Minghao Lin (University of Southern California), Penghui Li (Columbia University), Kui Ren (The State Key Laboratory of Blockchain,Data Security, Z…

Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models
Binary code similarity detection (BCSD) serves as a fundamental technique for various software engineering tasks, e.g., vulnerability detection and classification. Attacks against such models have therefore drawn extensive attention, aiming at misleading the models to generate erroneous predictions. Prior works have explored various approaches to generating semantic-preserving variants, i.e., adversarial samples, to evaluate the robustness of the models against adversarial attacks. However, the…

Two of NASA's historic data-collecting missions
— used by scientists and earthbound agriculturalists to track carbon dioxide and crop health
— ❌ may be permanently grounded as the Trump administration looks to shrink the agency's spending.
When they launched over a decade ago,
the satellites known as the
"Orbiting Carbon Observatories" (OCOs) revolutionized the collection of carbon data and greenhouse gas science.
To put it simply, the …

NASA satellites tracking greenhouse gases may be decommissioned under Trump
The satellites targeted for removal are the only ones monitoring Earth's greenhouse gases.

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature
Brady D. Lund, Ting Wang
https://arxiv.org/abs/2506.03165

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature
This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science resea…

Explainable Hierarchical Deep Learning Neural Networks (Ex-HiDeNN)
Reza T. Batley, Chanwook Park, Wing Kam Liu, Sourav Saha
https://arxiv.org/abs/2507.05498

Explainable Hierarchical Deep Learning Neural Networks (Ex-HiDeNN)
Data-driven science and computation have advanced immensely to construct complex functional relationships using trainable parameters. However, efficiently discovering interpretable and accurate closed-form expressions from complex dataset remains a challenge. The article presents a novel approach called Explainable Hierarchical Deep Learning Neural Networks or Ex-HiDeNN that uses an accurate, frugal, fast, separable, and scalable neural architecture with symbolic regression to discover closed-f…

"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.

47 Tuc in Rubin Data Preview 1 - Exploring Early LSST Data and Science Potential: #Rubin Observatory is Just Getting Started: https://www.universetoday.com/articles/globular-clusters-the-vera-rubin-observatory-is-just-getting-started (based on ComCam images just released in bulk).

Please don't call it "politicized science". What #RFKjr and his ilk are doing is not science. Science does not discard data that disagrees with the researchers desired outcomes.
They are purposefully ignoring and hiding any information that contradicts their beliefs. That is not science, that is censorship and lies in favor of an ideology.

Please don't call it "politicized science". What #RFKjr and his ilk are doing is not science. Science does not discard data that disagrees with the researchers desired outcomes.
They are purposefully ignoring and hiding any information that contradicts their beliefs. That is not science, that is censorship and lies in favor of an ideology.

#DH2025, we're delighted to share a Turing's Humanities and Data Science event in Oxford and online on 25th Sept with a panel asking: 'How far can data science and the humanities help to answer each other’s questions?'
Express your interest here:

Network power: the humanities and data science in collaboration

cora: CORA citations (1998)
Citations among papers indexed by CORA, from 1998, an early computer science research paper search engine. If a paper i cites a paper j also in this data set, then a directed edge connects i to j. (Papers not in the data set are excluded.) Self-loops may be present. The dates of these snapshots are uncertain.
This network has 23166 nodes and 91500 edges.
Tags: Informational, Citation, Unweighted

Nonlinear Causal Discovery for Grouped Data
Konstantin G\"obler, Tobias Windisch, Mathias Drton
https://arxiv.org/abs/2506.05120 https://

Nonlinear Causal Discovery for Grouped Data
Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables. In many important domains, including neuroscience, psychology, social science, and industrial manufacturing, the causal units of interest are groups of variables rather than individual scalar measurements. Motivated by these applications, we extend nonlinear additive noise models to handle random vectors, establishing a two-step …

Inside the Vera C. Rubin Observatory, whose 3.2-gigapixel camera will produce 60PB of space image data over 10 years, to be analyzed using ML and deep learning (New York Times)
https://www.nytimes.com/2025/06/20/science…

How Vera Rubin Telescope Scientists Will Deal With 60 Million Billion Bytes of Imagery
The Vera C. Rubin Observatory will make the study of stars and galaxies more like the big data-sorting exercises of contemporary genetics and particle physics.

The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration
Yong Xiao, Li, Zin Nandar Win, He Wang, Hla Myo Tun, Win Thu Zar
https://arxiv.org/abs/2506.04584

The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration
The advent of gravitational wave astronomy (GW) has revolutionized the observation of cataclysmic cosmic events, such as black hole mergers and neutron star collisions. The Laser Interferometer Gravitational-Wave Observatory (LIGO) has been at the forefront of these discoveries. However, the immense volume and complexity of gravitational wave data present significant challenges for traditional analysis methods. This paper investigates the growing synergy between artificial intelligence (AI) and…

On Inverse Problems, Parameter Estimation, and Domain Generalization
Deborah Pereg
https://arxiv.org/abs/2506.06024 https://arxiv.org…

On Inverse Problems, Parameter Estimation, and Domain Generalization
Signal restoration and inverse problems are key elements in most real-world data science applications. In the past decades, with the emergence of machine learning methods, inversion of measurements has become a popular step in almost all physical applications, which is normally executed prior to downstream tasks that often involve parameter estimation. In this work, we analyze the general problem of parameter estimation in an inverse problem setting. First, we address the domain-shift problem b…

Next investigation should be into the councillors themselves
Warrnambool council abandons peer-reviewed flood study, citing 'supposed science' - ABC News
https://www.abc.net.au/news/2025-06-05/regiona…

Warrnambool council abandons peer-reviewed flood study, questioning 'supposed science'
An independent flood study flagging high-risk areas in Victoria's south-west is abandoned after councillors decide they do not trust the data.

A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data
Lama Alqazlan, Zheng Fang, Michael Castelle, Rob Procter
https://arxiv.org/abs/2506.06083

A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data
The availability of big data has significantly influenced the possibilities and methodological choices for conducting large-scale behavioural and social science research. In the context of qualitative data analysis, a major challenge is that conventional methods require intensive manual labour and are often impractical to apply to large datasets. One effective way to address this issue is by integrating emerging computational methods to overcome scalability limitations. However, a critical conc…

D-Rex: Heterogeneity-Aware Reliability Framework and Adaptive Algorithms for Distributed Storage
Maxime Gonthier (University of Chicago, Argonne National Laboratory), Dante D. Sanchez-Gallegos (Universidad Carlos III de Madrid), Haochen Pan (University of Chicago), Bogdan Nicolae (Argonne National Laboratory), Sicheng Zhou (Southern University of Science and Technology), Hai Duc Nguyen (University of Chicago, Argonne National Laboratory), Valerie Hayot-Sasson (University of Chicago, Ar…

D-Rex: Heterogeneity-Aware Reliability Framework and Adaptive Algorithms for Distributed Storage
The exponential growth of data necessitates distributed storage models, such as peer-to-peer systems and data federations. While distributed storage can reduce costs and increase reliability, the heterogeneity in storage capacity, I/O performance, and failure rates of storage resources makes their efficient use a challenge. Further, node failures are common and can lead to data unavailability and even data loss. Erasure coding is a common resiliency strategy implemented in storage systems to mi…

Bilinear Quadratic Output Systems and Balanced Truncation
Heike Fa{\ss}bender (Institute for Numerical Analysis, TU Braunschweig), Serkan Gugercin (Department of Mathematics and Division of Computational Modeling and Data Analytics, Academy of Data Science, Virginia Tech), Till Peters (Institute for Numerical Analysis, TU Braunschweig)
https://

Bilinear Quadratic Output Systems and Balanced Truncation
Dynamical systems with quadratic outputs have recently attracted significant attention. In this paper, we consider bilinear dynamical systems, a special class of weakly nonlinear systems, with a quadratic output. We develop various primal-dual formulations for these systems and define the corresponding system Gramians. Conditions for the existence and uniqueness of these Gramians are established, and the generalized Lyapunov equations they satisfy are derived. Using these Gramians and their tru…

We're recruiting on our MSc Sociology and Data Analytics for next September at the University of Limerick @…

This MSc offers in depth knowledge of sociological analysis, research… | Faculty of Arts, Humanities and Social Sciences, University of Limerick
This MSc offers in depth knowledge of sociological analysis, research methods and social science analytics - including high level training in data, data science, and data mining, and a range of sequenced topics that include crime, law, and deviance, culture and media, health and well-being, political analysis and social movements, and stratification and social inequality. The combination of high level training in research methods and robust exposure to core topics in Sociology and the Social …

The SPHEREx Sky Simulator: Science Data Modeling for the First All-Sky Near-Infrared Spectral Survey
Brendan P. Crill, Yoonsoo P. Bach, Sean A. Bryan, Jean Choppin de Janvry, Ari J. Cukierman, C. Darren Dowell, Spencer W. Everett, Candice Fazar, Tatiana Goldina, Zhaoyu Huai, Howard Hui, Woong-Seob Jeong, Jae Hwan Kang, Phillip M. Korngut, Jae Joon Lee, Daniel C. Masters, Chi H. Nguyen, Jeonghyun Pyo, Teresa Symons, Yujin Yang, Michael Zemcov, Rachel Akeson, Matthew L. N. Ashby, James J…

The SPHEREx Sky Simulator: Science Data Modeling for the First All-Sky Near-Infrared Spectral Survey
We describe the SPHEREx Sky Simulator, a software tool designed to model science data for NASA's SPHEREx mission that will carry out a series of all-sky spectrophotometric surveys at $\sim$6'' spatial resolution in 102 spectral channels spanning 0.75 to 5 $μ$m. The Simulator software implements models for astrophysical emission, instrument characteristics, and survey strategy to generate realistic infrared sky scenes as they will be observed by SPHEREx. The simulated data includes a variety of…

"Violinist composes music from moth flight data to highlight insect decline"
#Music #Insects #Animals

Violinist composes music from moth flight data to highlight insect decline
Music and science collide in Moth x Human, a new composition by violinist Ellie Wilson that transforms moth flight data into a haunting tribute to these overlooked pollinators

This https://arxiv.org/abs/2502.06753 has been replaced.
link: https://scholar.google.com/scholar?q=a

SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era
Surrogate models are widely used to approximate complex systems across science and engineering to reduce computational costs. Despite their widespread adoption, the field lacks standardisation across key stages of the modelling pipeline, including data sampling, model selection, evaluation, and downstream analysis. This fragmentation limits reproducibility and cross-domain utility -- a challenge further exacerbated by the rapid proliferation of AI-driven surrogate models. We argue for the urgen…

Trustworthy Provenance for Big Data Science: a Modular Architecture Leveraging Blockchain in Federated Settings
Nicola Giuseppe Marchioro, Yannis Velegrakis, Valentine Anantharaj, Ian Foster, Sandro Luigi Fiore
https://arxiv.org/abs/2505.24675

Trustworthy Provenance for Big Data Science: a Modular Architecture Leveraging Blockchain in Federated Settings
Ensuring the trustworthiness and long-term verifiability of scientific data is a foundational challenge in the era of data-intensive, collaborative research. Provenance metadata plays a key role in this context, capturing the origin, transformation, and usage of research artifacts. However, existing solutions often fall short when applied to distributed, multi-institutional settings. This paper introduces a modular, domain-agnostic architecture for provenance tracking in federated environments,…

Microsoft has once again been named a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning (DSML) Platforms.
https://azure.microsoft.com/en-us/blog

Microsoft recognized for second consecutive year as a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms | Microsoft Azure Blog
Find out how Microsoft Azure Machine Learning can help you optimize business processes, improve customer experiences, and drive innovation.

The Impact of Carbon Targets on Firms' Carbon Performance
Xichen Sun, Xingzhi Jia, Rogelio Oliva
https://arxiv.org/abs/2508.05811 https://arxiv.org/pdf…

The Impact of Carbon Targets on Firms' Carbon Performance
Although goal-setting theory predicts that a challenging and specific goal can improve performance, the evidence regarding the effectiveness of an environmental goal in organizations is mixed. Using a panel data set consisting over 700 global firms across multiple industries, we apply a Difference-in-Differences (DiD) strategy and empirically analyze the effect of having science-based carbon emissions targets (SBTs) on firms' carbon performance. The findings in this study contribute to the deba…

#Blakes7 Series B, Episode 06 - Trial
THANIA: We reserve our opening declaration, sir.
SAMOR: Very well. Enter prosecution data. [A clerk presses some buttons.]
https://blake.torpidity.net/m/206/53

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Observable Covariance and Principal Observable Analysis for Data on Metric Spaces
Ece Karacam, Washington Mio, Osman Berat Okutan
https://arxiv.org/abs/2506.04003

Observable Covariance and Principal Observable Analysis for Data on Metric Spaces
Datasets consisting of objects such as shapes, networks, images, or signals overlaid on such geometric objects permeate data science. Such datasets are often equipped with metrics that quantify the similarity or divergence between any pair of elements turning them into metric spaces $(X,d)$, or a metric measure space $(X,d,μ)$ if data density is also accounted for through a probability measure $μ$. This paper develops a Lipschitz geometry approach to analysis of metric measure spaces based on…

lol, basically every single example in this post shows how the LLM is just generating context that's not in the actual image. But somehow this is sold as being better than "classical" computer vision.
I don't know folks, if I actually wanted to do "data science", with focus on the "science" bit, I'd be disturbed by that. 🤷‍♂️
https://fosstodon.org/@Posit/114597245963405210

cora: CORA citations (1998)
Citations among papers indexed by CORA, from 1998, an early computer science research paper search engine. If a paper i cites a paper j also in this data set, then a directed edge connects i to j. (Papers not in the data set are excluded.) Self-loops may be present. The dates of these snapshots are uncertain.
This network has 23166 nodes and 91500 edges.
Tags: Informational, Citation, Unweighted

Online Sparsification of Bipartite-Like Clusters in Graphs
Joyentanuj Das, Suranjan De, He Sun
https://arxiv.org/abs/2508.05437 https://arxiv.org/pdf/2508.…

Online Sparsification of Bipartite-Like Clusters in Graphs
Graph clustering is an important algorithmic technique for analysing massive graphs, and has been widely applied in many research fields of data science. While the objective of most graph clustering algorithms is to find a vertex set of low conductance, a sequence of recent studies highlights the importance of the inter-connection between vertex sets when analysing real-world datasets. Following this line of research, in this work we study bipartite-like clusters and present efficient and onlin…

47 Tuc in Rubin Data Preview 1: Exploring Early LSST Data and Science Potential
Yumi Choi (David), Knut A. G. Olsen (David), Jeffrey L. Carlin (David), Yuankun (David), Wang, Fred Moolekamp, Abi Saha, Ian Sullivan, Colin T. Slater, Douglas L. Tucker, Christina L. Adair, Peter S. Ferguson, Yijung Kang, Karla Pe\~na Ram\'irez, Markus Rabus
https:…

47 Tuc in Rubin Data Preview 1: Exploring Early LSST Data and Science Potential
We present analyses of the early data from Rubin Observatory's Data Preview 1 (DP1) for the globular cluster 47 Tuc field. The DP1 dataset for 47 Tuc includes four nights of observations from the Rubin Commissioning Camera (LSSTComCam), covering multiple bands (ugriy). We address challenges of crowding near the cluster core and toward the SMC in DP1, and demonstrate improved star-galaxy separation by fitting fifth-degree polynomials to the stellar loci in color-color diagrams and applying multi…

Wasser auf @… s Mühlen:
Dieser Aufsatz setzt sich mit Definitionen von 'reuse' auseinander und kommt zum Schluss, dass use = reuse, man also der Einfachheit halber schlicht auch von "use" sprechen kann. Also auch: Nutzungsszenarien, nutzbare Daten. Schade um das R in

Pivoting the paradigm: the role of spreadsheets in K-12 data science
Oren Tirschwell, Nicholas Jon Horton
https://arxiv.org/abs/2506.03232 https://

Pivoting the paradigm: the role of spreadsheets in K-12 data science
Spreadsheet tools are widely accessible to and commonly used by K-12 students and teachers. They have an important role in data collection and organization. Beyond data organization, spreadsheets also make data visible and easy to interact with, facilitating student engagement in data exploration and analysis. Though not suitable for all circumstances, spreadsheets can and do help foster data and computing skills for K-12 students. This paper 1) reviews prior frameworks on K-12 data tools; 2) p…

Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
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I teach undergraduate computer science labs, including for intro and more-advanced core courses. I don't publish (non-negligible) scholarly work in the area, but I've got years of craft expertise in course design, and I do follow the academic literature to some degree. In other words, In not the world's leading expert, but I have spent a lot of time thinking about course design, and consider myself competent at it, with plenty of direct experience in what knowledge & skills I can expect from students as they move through the curriculum.
I'm also strongly against most uses of what's called "AI" these days (specifically, generative deep neutral networks as supplied by our current cadre of techbro). There are a surprising number of completely orthogonal reasons to oppose the use of these systems, and a very limited number of reasonable exceptions (overcoming accessibility barriers is an example). On the grounds of environmental and digital-commons-pollution costs alone, using specifically the largest/newest models is unethical in most cases.
But as any good teacher should, I constantly question these evaluations, because I worry about the impact on my students should I eschew teaching relevant tech for bad reasons (and even for his reasons). I also want to make my reasoning clear to students, who should absolutely question me on this. That inspired me to ask a simple question: ignoring for one moment the ethical objections (which we shouldn't, of course; they're very stark), at what level in the CS major could I expect to teach a course about programming with AI assistance, and expect students to succeed at a more technically demanding final project than a course at the same level where students were banned from using AI? In other words, at what level would I expect students to actually benefit from AI coding "assistance?"
To be clear, I'm assuming that students aren't using AI in other aspects of coursework: the topic of using AI to "help you study" is a separate one (TL;DR it's gross value is not negative, but it's mostly not worth the harm to your metacognitive abilities, which AI-induced changes to the digital commons are making more important than ever).
So what's my answer to this question?
If I'm being incredibly optimistic, senior year. Slightly less optimistic, second year of a masters program. Realistic? Maybe never.
The interesting bit for you-the-reader is: why is this my answer? (Especially given that students would probably self-report significant gains at lower levels.) To start with, [this paper where experienced developers thought that AI assistance sped up their work on real tasks when in fact it slowed it down] (https://arxiv.org/abs/2507.09089) is informative. There are a lot of differences in task between experienced devs solving real bugs and students working on a class project, but it's important to understand that we shouldn't have a baseline expectation that AI coding "assistants" will speed things up in the best of circumstances, and we shouldn't trust self-reports of productivity (or the AI hype machine in general).
Now we might imagine that coding assistants will be better at helping with a student project than at helping with fixing bugs in open-source software, since it's a much easier task. For many programming assignments that have a fixed answer, we know that many AI assistants can just spit out a solution based on prompting them with the problem description (there's another elephant in the room here to do with learning outcomes regardless of project success, but we'll ignore this over too, my focus here is on project complexity reach, not learning outcomes). My question is about more open-ended projects, not assignments with an expected answer. Here's a second study (by one of my colleagues) about novices using AI assistance for programming tasks. It showcases how difficult it is to use AI tools well, and some of these stumbling blocks that novices in particular face.
But what about intermediate students? Might there be some level where the AI is helpful because the task is still relatively simple and the students are good enough to handle it? The problem with this is that as task complexity increases, so does the likelihood of the AI generating (or copying) code that uses more complex constructs which a student doesn't understand. Let's say I have second year students writing interactive websites with JavaScript. Without a lot of care that those students don't know how to deploy, the AI is likely to suggest code that depends on several different frameworks, from React to JQuery, without actually setting up or including those frameworks, and of course three students would be way out of their depth trying to do that. This is a general problem: each programming class carefully limits the specific code frameworks and constructs it expects students to know based on the material it covers. There is no feasible way to limit an AI assistant to a fixed set of constructs or frameworks, using current designs. There are alternate designs where this would be possible (like AI search through adaptation from a controlled library of snippets) but those would be entirely different tools.
So what happens on a sizeable class project where the AI has dropped in buggy code, especially if it uses code constructs the students don't understand? Best case, they understand that they don't understand and re-prompt, or ask for help from an instructor or TA quickly who helps them get rid of the stuff they don't understand and re-prompt or manually add stuff they do. Average case: they waste several hours and/or sweep the bugs partly under the rug, resulting in a project with significant defects. Students in their second and even third years of a CS major still have a lot to learn about debugging, and usually have significant gaps in their knowledge of even their most comfortable programming language. I do think regardless of AI we as teachers need to get better at teaching debugging skills, but the knowledge gaps are inevitable because there's just too much to know. In Python, for example, the LLM is going to spit out yields, async functions, try/finally, maybe even something like a while/else, or with recent training data, the walrus operator. I can't expect even a fraction of 3rd year students who have worked with Python since their first year to know about all these things, and based on how students approach projects where they have studied all the relevant constructs but have forgotten some, I'm not optimistic seeing these things will magically become learning opportunities. Student projects are better off working with a limited subset of full programming languages that the students have actually learned, and using AI coding assistants as currently designed makes this impossible. Beyond that, even when the "assistant" just introduces bugs using syntax the students understand, even through their 4th year many students struggle to understand the operation of moderately complex code they've written themselves, let alone written by someone else. Having access to an AI that will confidently offer incorrect explanations for bugs will make this worse.
To be sure a small minority of students will be able to overcome these problems, but that minority is the group that has a good grasp of the fundamentals and has broadened their knowledge through self-study, which earlier AI-reliant classes would make less likely to happen. In any case, I care about the average student, since we already have plenty of stuff about our institutions that makes life easier for a favored few while being worse for the average student (note that our construction of that favored few as the "good" students is a large part of this problem).
To summarize: because AI assistants introduce excess code complexity and difficult-to-debug bugs, they'll slow down rather than speed up project progress for the average student on moderately complex projects. On a fixed deadline, they'll result in worse projects, or necessitate less ambitious project scoping to ensure adequate completion, and I expect this remains broadly true through 4-6 years of study in most programs (don't take this as an endorsement of AI "assistants" for masters students; we've ignored a lot of other problems along the way).
There's a related problem: solving open-ended project assignments well ultimately depends on deeply understanding the problem, and AI "assistants" allow students to put a lot of code in their file without spending much time thinking about the problem or building an understanding of it. This is awful for learning outcomes, but also bad for project success. Getting students to see the value of thinking deeply about a problem is a thorny pedagogical puzzle at the best of times, and allowing the use of AI "assistants" makes the problem much much worse. This is another area I hope to see (or even drive) pedagogical improvement in, for what it's worth.
1/2

Now settled into low-Earth orbit, #SPHEREx (Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer) has begun delivering its sky survey data to a public archive on a weekly basis, allowing anyone to use the data to probe the secrets of the cosmos: https://science.nasa.gov/open-science/spherex-universe-map/

23andMe's Data Sold to Nonprofit Run by Its Co-Founder - 'And I Still Don't Trust It' - Slashdot
https://science.slashdot.org/story/25/07/19/0252236/23andmes-data-sold-to-nonprofit-run-by-its-co-founder---and-i-still-dont-trust-it

Retrodicting Chaotic Systems: An Algorithmic Information Theory Approach
Kamal Dingle, Boumediene Hamzi, Marcus Hutter, Houman Owhadi
https://arxiv.org/abs/2507.04780

Retrodicting Chaotic Systems: An Algorithmic Information Theory Approach
Making accurate inferences about data is a key task in science and mathematics. Here we study the problem of \emph{retrodiction}, inferring past values of a series, in the context of chaotic dynamical systems. Specifically, we are interested in inferring the starting value $x_0$ in the series $x_0,x_1,x_2,\dots,x_n$ given the value of $x_n$, and the associated function $f$ which determines the series as $f(x_i)=x_{i+1}$. Even in the deterministic case this is a challenging problem, due to mixin…

Is Your Training Pipeline Production-Ready? A Case Study in the Healthcare Domain
Daniel Lawand (University of S\~ao Paulo), Lucas Quaresma (University of S\~ao Paulo), Roberto Bolgheroni (University of S\~ao Paulo), Alfredo Goldman (University of S\~ao Paulo), Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)

Is Your Training Pipeline Production-Ready? A Case Study in the Healthcare Domain
Deploying a Machine Learning (ML) training pipeline into production requires robust software engineering practices. This differs significantly from experimental workflows. This experience report investigates this challenge in SPIRA, a project whose goal is to create an ML-Enabled System (MLES) to pre-diagnose insufficiency respiratory via speech analysis. The first version of SPIRA's training pipeline lacked critical software quality attributes. This paper presents an overview of the MLES, then…

Data Agent: A Holistic Architecture for Orchestrating Data AI Ecosystems
Zhaoyan Sun, Jiayi Wang, Xinyang Zhao, Jiachi Wang, Guoliang Li
https://arxiv.org/abs/2507.01599

Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems
Traditional Data+AI systems utilize data-driven techniques to optimize performance, but they rely heavily on human experts to orchestrate system pipelines, enabling them to adapt to changes in data, queries, tasks, and environments. For instance, while there are numerous data science tools available, developing a pipeline planning system to coordinate these tools remains challenging. This difficulty arises because existing Data+AI systems have limited capabilities in semantic understanding, rea…

A Data Science Approach to Calcutta High Court Judgments: An Efficient LLM and RAG-powered Framework for Summarization and Similar Cases Retrieval
Puspendu Banerjee, Aritra Mazumdar, Wazib Ansar, Saptarsi Goswami, Amlan Chakrabarti
https://arxiv.org/abs/2507.01058

A Data Science Approach to Calcutta High Court Judgments: An Efficient LLM and RAG-powered Framework for Summarization and Similar Cases Retrieval
The judiciary, as one of democracy's three pillars, is dealing with a rising amount of legal issues, needing careful use of judicial resources. This research presents a complex framework that leverages Data Science methodologies, notably Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) techniques, to improve the efficiency of analyzing Calcutta High Court verdicts. Our framework focuses on two key aspects: first, the creation of a robust summarization mechanism that distills…

CEMP: a platform unifying high-throughput online calculation, databases and predictive models for clean energy materials
Jifeng Wang, Jiazhe Ju, Ying Wang
https://arxiv.org/abs/2507.04423

CEMP: a platform unifying high-throughput online calculation, databases and predictive models for clean energy materials
The development of materials science is undergoing a shift from empirical approaches to data-driven and algorithm-oriented research paradigm. The state-of-the-art platforms are confined to inorganic crystals, with limited chemical space, sparse experimental data and a lack of integrated online computation for rapid validation. Here, we introduce the Clean Energy Materials Platform (CEMP), an open-access platform that integrates high-throughput computing workflows, multi-scale machine learning (…

dblp_cite: DBLP citations (2014)
Citations among papers contained in the DBLP computer science bibliography. If a paper i cites a paper j also in this data set, then a directed edge connects i to j. (Papers not in the data set are excluded.) Self-loops may be present. This snapshot from May 2014.
This network has 12590 nodes and 49759 edges.
Tags: Informational, Citation, Unweighted

This https://arxiv.org/abs/2505.24603 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches
Gaussian sketching, which consists of pre-multiplying the data with a random Gaussian matrix, is a widely used technique for multiple problems in data science and machine learning, with applications spanning computationally efficient optimization, coded computing, and federated learning. This operation also provides differential privacy guarantees due to its inherent randomness. In this work, we revisit this operation through the lens of Renyi Differential Privacy (RDP), providing a refined pri…

Shared on Bluesky from a different #DH2025 'Nwulite Obodo Open Data License — Made for sharing African datasets equitably' https://datasciencelawlab.africa/nwulite-…

Meta debuts a prototype wristband to read electrical signals from forearm muscles, letting users control devices without touch, trained on 10K peoples' EMG data (Cade Metz/New York Times)
https://www.nytimes.com/2025/07/23/science/meta-computer-wristband-r…

Meta Unveils Wristband For Controlling Computers With Hand Gestures
When you write your name in the air, you can see the letters appear on your smartphone.

Challenging Spontaneous Quantum Collapse with XENONnT
E. Aprile, J. Aalbers, K. Abe, S. Ahmed Maouloud, L. Althueser, B. Andrieu, E. Angelino, D. Ant\'on Martin, S. R. Armbruster, F. Arneodo, L. Baudis, M. Bazyk, L. Bellagamba, R. Biondi, A. Bismark, K. Boese, A. Brown, G. Bruno, R. Budnik, C. Cai, C. Capelli, J. M. R. Cardoso, A. P. Cimental Ch\'avez, A. P. Colijn, J. Conrad, J. J. Cuenca-Garc\'ia, C. Curceanu, V. D'Andrea, L. C. Daniel Garcia, M. P. Decowski, A. Deist…

Challenging Spontaneous Quantum Collapse with XENONnT
We report on the search for X-ray radiation as predicted from dynamical quantum collapse with low-energy electronic recoil data in the energy range of 1-140 keV from the first science run of the XENONnT dark matter detector. Spontaneous radiation is an unavoidable effect of dynamical collapse models, which were introduced as a possible solution to the long-standing measurement problem in quantum mechanics. The analysis utilizes a model that for the first time accounts for cancellation effects i…

Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network
Kate Barnes, Mia Ellis-Einhorn, Carolina Ch\'avez-Ruelas, Nayera Hasan, Mohammad Fanous, Blair D. Sullivan, Sorelle Friedler, Aaron Clauset
https://arxiv.org/abs/2506.04435…

Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network
Social factors such as demographic traits and institutional prestige structure the creation and dissemination of ideas in academic publishing. One place these effects can be observed is in how central or peripheral a researcher is in the coauthorship network. Here we investigate inequities in network centrality in a hand-collected data set of 5,670 U.S.-based faculty employed in Ph.D.-granting Computer Science departments and their DBLP coauthorship connections. We introduce algorithms for comb…

The Ubiquitous Sparse Matrix-Matrix Products
Ayd{\i}n Bulu\c{c}
https://arxiv.org/abs/2508.04077 https://arxiv.org/pdf/2508.04077

The Ubiquitous Sparse Matrix-Matrix Products
Multiplication of a sparse matrix with another (dense or sparse) matrix is a fundamental operation that captures the computational patterns of many data science applications, including but not limited to graph algorithms, sparsely connected neural networks, graph neural networks, clustering, and many-to-many comparisons of biological sequencing data. In many application scenarios, the matrix multiplication takes places on an arbitrary algebraic semiring where the scalar operations are overloa…

cs_department: Aarhus Computer Science department relationships
Multiplex network consisting of 5 edge types corresponding to online and offline relationships (Facebook, leisure, work, co-authorship, lunch) between employees of the Computer Science department at Aarhus. Data hosted by Manlio De Domenico.
This network has 61 nodes and 620 edges.
Tags: Social, Relationships, Multilayer, Unweighted

Analysis of points outcome in ATP Grand Slam Tennis using big data and machine learning
Martin Illum (Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Denmark), Hans Christian Bechs{\o}fft Mikkelsen (Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Denmark), Emil Hovad (Department of Applied Mathematics and Computer Science, Technical University of Denmark, …

Analysis of points outcome in ATP Grand Slam Tennis using big data and machine learning
Tennis is one of the world's biggest and most popular sports. Multiple researchers have, with limited success, modeled the outcome of matches using probability modelling or machine learning approaches. The approach presented here predicts the outcomes of points in tennis matches. This is based on given a probability of winning a point, based on the prior history of matches, the current match, the player rankings and if the points are started with a first or second. The use of historical public …

Airalogy: AI-empowered universal data digitization for research automation
Zijie Yang, Qiji Zhou, Fang Guo, Sijie Zhang, Yexun Xi, Jinglei Nie, Yudian Zhu, Liping Huang, Chou Wu, Yonghe Xia, Xiaoyu Ma, Yingming Pu, Panzhong Lu, Junshu Pan, Mingtao Chen, Tiannan Guo, Yanmei Dou, Hongyu Chen, Anping Zeng, Jiaxing Huang, Tian Xu, Yue Zhang
https://

Airalogy: AI-empowered universal data digitization for research automation
Research data are the foundation of Artificial Intelligence (AI)-driven science, yet current AI applications remain limited to a few fields with readily available, well-structured, digitized datasets. Achieving comprehensive AI empowerment across multiple disciplines is still out of reach. Present-day research data collection is often fragmented, lacking unified standards, inefficiently managed, and difficult to share. Creating a single platform for standardized data digitization needs to overc…

Poisoning Attacks to Local Differential Privacy for Ranking Estimation
Pei Zhan (School of Cyber Science and Technology, Shandong University, State Key Laboratory of Cryptography and Digital Economy Security, Shandong University, Qingdao, China), Peng Tang (School of Cyber Science and Technology, Shandong University, State Key Laboratory of Cryptography and Digital Economy Security, Shandong University, Qingdao, China), Yangzhuo Li (School of Cyber Science and Technology, Shandong Univ…

Poisoning Attacks to Local Differential Privacy for Ranking Estimation
Local differential privacy (LDP) involves users perturbing their inputs to provide plausible deniability of their data. However, this also makes LDP vulnerable to poisoning attacks. In this paper, we first introduce novel poisoning attacks for ranking estimation. These attacks are intricate, as fake attackers do not merely adjust the frequency of target items. Instead, they leverage a limited number of fake users to precisely modify frequencies, effectively altering item rankings to maximize ga…

Data Cleaning of Data Streams
Valerie Restat, Niklas Rodenhausen, Carina Antonin, Uta St\"orl
https://arxiv.org/abs/2507.20839 https://arxiv.org/pdf/2…

Data Cleaning of Data Streams
Streaming data can arise from a variety of contexts. Important use cases are continuous sensor measurements such as temperature, light or radiation values. In the process, streaming data may also contain data errors that should be cleaned before further use. Many studies from science and practice focus on data cleaning in a static context. However, in terms of data cleaning, streaming data has particularities that distinguish it from static data. In this paper, we have therefore undertaken an i…

Millimeter-wave observations of Euclid Deep Field South using the South Pole Telescope: A data release of temperature maps and catalogs
M. Archipley, A. Hryciuk, L. E. Bleem, K. Kornoelje, M. Klein, A. J. Anderson, B. Ansarinejad, M. Aravena, L. Balkenhol, P. S. Barry, K. Benabed, A. N. Bender, B. A. Benson, F. Bianchini, S. Bocquet, F. R. Bouchet, E. Camphuis, M. G. Campitiello, J. E. Carlstrom, J. Cathey, C. L. Chang, S. C. Chapman, P. Chaubal, P. M. Chichura, A. Chokshi, T. -L. Chou…

Millimeter-wave observations of Euclid Deep Field South using the South Pole Telescope: A data release of temperature maps and catalogs
Context. The South Pole Telescope third-generation camera (SPT-3G) has observed over 10,000 square degrees of sky at 95, 150, and 220 GHz (3.3, 2.0, 1.4 mm, respectively) overlapping the ongoing 14,000 square-degree Euclid Wide Survey. The Euclid collaboration recently released Euclid Deep Field observations in the first quick data release (Q1). Aims. With the goal of releasing complementary millimeter-wave data and encouraging legacy science, we performed dedicated observations of a 57-square-…

cs_department: Aarhus Computer Science department relationships
Multiplex network consisting of 5 edge types corresponding to online and offline relationships (Facebook, leisure, work, co-authorship, lunch) between employees of the Computer Science department at Aarhus. Data hosted by Manlio De Domenico.
This network has 61 nodes and 620 edges.
Tags: Social, Relationships, Multilayer, Unweighted

#DH2025 thanks @flochiff.bsky.social for sharing this link as I wanted to follow up on Pandore! 'Pandore: automating text-processing workflows for humanities researchers' from Sorbonne Université and ObTIC - Observatoire des textes, des idées et des corpus

faculty_hiring: Faculty hiring networks (Comp. Sci., Business, History)
Three networks of faculty hiring in Computer Science Departments, Business Schools, and History Departments. Each node is a PhD-granting institution in the respective field, and a directed edge (i,j) indicates that a person received their PhD from node i and was tenure-track faculty at node j during time of collection (2011-2013). All data collected from faculty public rosters at the sampled institutions.
Thi…

Carbonate formation and fluctuating habitability on Mars: #Mars Science Laboratory Curiosity rover data may explain why planet was likely harsh desert for most of recent past.

Buckaroo: A Direct Manipulation Visual Data Wrangler
Annabelle Warner, Andrew McNutt, Paul Rosen, El Kindi Rezig
https://arxiv.org/abs/2507.16073 https://

Buckaroo: A Direct Manipulation Visual Data Wrangler
Preparing datasets -- a critical phase known as data wrangling -- constitutes the dominant phase of data science development, consuming upwards of 80% of the total project time. This phase encompasses a myriad of tasks: parsing data, restructuring it for analysis, repairing inaccuracies, merging sources, eliminating duplicates, and ensuring overall data integrity. Traditional approaches, typically through manual coding in languages such as Python or using spreadsheets, are not only laborious bu…

cora: CORA citations (1998)
Citations among papers indexed by CORA, from 1998, an early computer science research paper search engine. If a paper i cites a paper j also in this data set, then a directed edge connects i to j. (Papers not in the data set are excluded.) Self-loops may be present. The dates of these snapshots are uncertain.
This network has 23166 nodes and 91500 edges.
Tags: Informational, Citation, Unweighted

Learning Lineage Constraints for Data Science Operations
Jinjin Zhao
https://arxiv.org/abs/2506.18252 https://arxiv.org/pdf/2506.1825…

Learning Lineage Constraints for Data Science Operations
Data science workflows often integrate functionalities from a diverse set of libraries and frameworks. Tasks such as debugging require data lineage that crosses library boundaries. The problem is that the way that "lineage" is represented is often intimately tied to particular data models and data manipulation paradigms. Inspired by the use of intermediate representations (IRs) in cross-library performance optimizations, this vision paper proposes a similar architecture for lineage - how do we …

faculty_hiring: Faculty hiring networks (Comp. Sci., Business, History)
Three networks of faculty hiring in Computer Science Departments, Business Schools, and History Departments. Each node is a PhD-granting institution in the respective field, and a directed edge (i,j) indicates that a person received their PhD from node i and was tenure-track faculty at node j during time of collection (2011-2013). All data collected from faculty public rosters at the sampled institutions.
Thi…

Towards Next Generation Data Engineering Pipelines
Kevin M. Kramer, Valerie Restat, Sebastian Strasser, Uta St\"orl, Meike Klettke
https://arxiv.org/abs/2507.13892

Towards Next Generation Data Engineering Pipelines
Data engineering pipelines are a widespread way to provide high-quality data for all kinds of data science applications. However, numerous challenges still remain in the composition and operation of such pipelines. Data engineering pipelines do not always deliver high-quality data. By default, they are also not reactive to changes. When new data is coming in which deviates from prior data, the pipeline could crash or output undesired results. We therefore envision three levels of next generatio…

faculty_hiring: Faculty hiring networks (Comp. Sci., Business, History)
Three networks of faculty hiring in Computer Science Departments, Business Schools, and History Departments. Each node is a PhD-granting institution in the respective field, and a directed edge (i,j) indicates that a person received their PhD from node i and was tenure-track faculty at node j during time of collection (2011-2013). All data collected from faculty public rosters at the sampled institutions.
Thi…

faculty_hiring: Faculty hiring networks (Comp. Sci., Business, History)
Three networks of faculty hiring in Computer Science Departments, Business Schools, and History Departments. Each node is a PhD-granting institution in the respective field, and a directed edge (i,j) indicates that a person received their PhD from node i and was tenure-track faculty at node j during time of collection (2011-2013). All data collected from faculty public rosters at the sampled institutions.
Thi…

sp_infectious: Art exhibit dynamic contacts (2011)
This dataset contains the daily dynamic contact networks collected during the Infectious SocioPatterns event that took place at the Science Gallery in Dublin, Ireland, during the artscience exhibition INFECTIOUS: STAY AWAY. Each file in the downloadable package contains a tab-separated list representing the active contacts during 20-second intervals of one day of data collection. Each line has the form “t i j“, where i and j are the a…

sp_infectious: Art exhibit dynamic contacts (2011)
This dataset contains the daily dynamic contact networks collected during the Infectious SocioPatterns event that took place at the Science Gallery in Dublin, Ireland, during the artscience exhibition INFECTIOUS: STAY AWAY. Each file in the downloadable package contains a tab-separated list representing the active contacts during 20-second intervals of one day of data collection. Each line has the form “t i j“, where i and j are the a…

cs_department: Aarhus Computer Science department relationships
Multiplex network consisting of 5 edge types corresponding to online and offline relationships (Facebook, leisure, work, co-authorship, lunch) between employees of the Computer Science department at Aarhus. Data hosted by Manlio De Domenico.
This network has 61 nodes and 620 edges.
Tags: Social, Relationships, Multilayer, Unweighted

faculty_hiring: Faculty hiring networks (Comp. Sci., Business, History)
Three networks of faculty hiring in Computer Science Departments, Business Schools, and History Departments. Each node is a PhD-granting institution in the respective field, and a directed edge (i,j) indicates that a person received their PhD from node i and was tenure-track faculty at node j during time of collection (2011-2013). All data collected from faculty public rosters at the sampled institutions.
Thi…

sp_infectious: Art exhibit dynamic contacts (2011)
This dataset contains the daily dynamic contact networks collected during the Infectious SocioPatterns event that took place at the Science Gallery in Dublin, Ireland, during the artscience exhibition INFECTIOUS: STAY AWAY. Each file in the downloadable package contains a tab-separated list representing the active contacts during 20-second intervals of one day of data collection. Each line has the form “t i j“, where i and j are the a…

sp_infectious: Art exhibit dynamic contacts (2011)
This dataset contains the daily dynamic contact networks collected during the Infectious SocioPatterns event that took place at the Science Gallery in Dublin, Ireland, during the artscience exhibition INFECTIOUS: STAY AWAY. Each file in the downloadable package contains a tab-separated list representing the active contacts during 20-second intervals of one day of data collection. Each line has the form “t i j“, where i and j are the a…