FRACTI framework supports large-scale collaboration and transparent investigation in finance.
problem Complex, multidisciplinary research in finance requires robust support systems.
method Defines scientific support systems, shares contributions, classifies facets, and outlines a meta-model.
result FRACTI enables provenance tracking and large-scale investigation in computational finance.
Crowdsourced investigation shows differing results for technical analysis strategies.
problem Differing results in technical analysis strategies due to lack of method.
method Collaborative scientific computational framework using Monte Carlo simulations and historical back testing.
result Results are not repeatable by other researchers, highlighting the need for transparency and robustness.
Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party …
This paper reflects on teaching Data Science to diverse students.
problem Adapting learning practices to diverse backgrounds in Data Science.
method Summarizes experiences from teaching a postgraduate Data Science module.
result Draws lessons relevant to teaching Data Science.
Data science teams collaborate extensively, using various tools and stakeholders.
problem Lack of understanding in how data science workers collaborate in practice.
method Conducted an online survey with 183 data science workers.
result Data science teams are highly collaborative and use multiple tools and stakeholders.
ML methods improve planetary science data analysis.
problem Insufficient use of ML in planetary science.
method Ten recommendations for integrating ML in planetary science.
result Expanding planetary science insights from large datasets.
Machine learning improves wildfire science and management, but requires expert knowledge.
problem Improving wildfire science and management through AI.
method Review of popular ML approaches and their application in six wildfire science domains.
result Opportunities exist for applying more advanced ML methods in wildfire science.
Causal inference from observational data is the goal of many data analyses in the health and social sciences. However, academic statistics has often frowned upon data analyses with a causal objective. The introduction of the term "data science" provides a historic opportunity to redefine data analysis in such a way tha…
Foundation models alter medical data science workflow, challenging veridical data science principles.
problem Foundation models disrupt traditional data science practices in medicine.
method Critically examined the medical foundation model lifecycle and its deviation from veridical data science principles.
result Foundation models challenge veridical data science principles of predictability, computability, and stability.
Defines data science as a natural ecosystem with challenges and missions.
problem Challenges and missions in data science due to 5D complexities and data life cycle phases.
method Systemic and data-centric view of data science as a fusion of data universe and its challenges, formalizing a general-purpose architecture.
result Essential data science as a natural ecosystem integrating specific disciplines and high-impact applications.
Data science enhances knot theory by analyzing invariant relations.
problem Understanding the complex relations between knot invariants.
method Topological data analysis applied to knot theory.
result New insights into long-standing conjectures about knots.
Article calls for data scientists to join ethics debate.
problem Impact of data science on society and ethics.
method Systematic approach from CNIL, established ethical guidelines.
result Data science requires professional ethics to build trust.
Donoho's JCGS (in press) paper is a spirited call to action for statisticians, who he points out are losing ground in the field of data science by refusing to accept that data science is its own domain. (Or, at least, a domain that is becoming distinctly defined.) He calls on writings by John Tukey, Bill Cleveland, and…
Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of da…
This paper explores data science applications in economics using a taxonomy of models and hybrid models showing higher accuracy.
problem Investigating data science applications in economics.
method Systematic literature review using Prisma method.
result Hybrid models showed higher prediction accuracy than other algorithms.
We are in the middle of a complex debate as to whether Economics is really a proper natural science. The 'Discussion & Debate' issue of this Euro. Phys. J. Special Topic volume is: 'Can economics be a Physical Science?' I discuss some aspects here.
Complexity science offers new insights into macroeconomics and finance.
problem Insufficient understanding of economic and financial phenomena.
method Adopting complexity science to better understand complex systems.
result Complex system characteristics can benefit financial analysts, regulators, and policymakers.
Simulation-based inference tackles complex inverse problems in science.
problem Challenging inverse problems in complex simulations.
method Review and identification of forces driving the field.
result Expanding the audience to appreciate the impact on science.
Proposes a methodology to improve data science ROI by addressing key business questions.
problem Companies often fail to maximize data science value, focusing on basic analysis.
method Categorizes and answers 'The Big Three' questions using data science methods.
result Shows how to apply the methodology to real business use cases.
This study analyzes data science vocabulary changes over 13 years.
problem Understanding evolution of data science terms over time.
method Exploratory Data Analysis, Latent Semantic Analysis, Latent Dirichlet Analysis, N-grams Analysis.
result Identified new vocabulary and its incorporation into scientific literature.
Economies are complex man-made systems where organisms and markets interact according to motivations and principles not entirely understood yet. The increasing dissatisfaction with the postulates of traditional economics i.e. perfectly rational agents, interacting through efficient markets in the search of equilibrium,…
Kan extensions help in data science extrapolation and learning.
problem Generalizing functions over larger sets in data science.
method Kan extensions in category theory applied to data science problems.
result Kan extensions can be used to derive classification and clustering algorithms.
SCN learns and compensates for data bias in citizen science projects.
problem Data bias in citizen science datasets.
method End-to-end learning scheme that learns and compensates for data shift.
result SCN outperforms models that ignore data bias and handles massive high-dimensional data.
New geometric methods improve optimization and data science problems.
problem Improving optimization and data science problems.
method Geometric tools for high-dimensional optimization and statistical data science.
result New algorithms and statistical guarantees for optimization and data science.
Machine learning's data-centric philosophy conflicts with natural sciences' standards.
problem Conflict between machine learning's ontology and epistemology and natural sciences' practices.
method Identifying and analyzing contexts where ML can be beneficial or harmful in natural sciences.
result ML can enhance trustworthiness in causal inference but introduces biases in emulation and labeling.
Computer science scans LLMs to understand and manipulate their economic forecasts.
problem Understanding and controlling the reasoning of large language models in economics.
method Brain scanning techniques applied to LLMs to identify and manipulate underlying concepts.
result LLMs can be steered to generate forecasts with specific biases, allowing for correction or simulation.
Paper relaxes optimal transport using convex functions for data science.
problem Optimal transport problem on finite spaces.
method Relaxation via strictly convex functions (Kullback-Leibler divergence, Bregman divergences). Gradient descent iterative process.
result Mathematical foundations and iterative process for the relaxed optimal transport problem.
New method calculates discrete curvature using effective resistances.
problem Calculating discrete curvature on graphs.
method Effective resistances to calculate curvature on graph nodes and links.
result Relation to established discrete curvatures and convergence to continuous curvature.
New method shows data-driven causal studies can be misleading.
problem Misattribution of causality in data-driven earth science studies.
method Subsample-based ensemble approach for robust causality analysis.
result Transfer entropy-based causal graphs can be spurious.
FinTech uses data science and AI to transform finance.
problem Transforming finance with data science and AI.
method DSAI techniques including complex system methods, quantitative methods, etc.
result DSAI enables smart FinTech for various financial sectors.
Ridge regularization simplifies model complexity in data science.
problem Overfitting in statistical models.
method Adding a penalty on the magnitude of coefficients.
result Effective in reducing model complexity and improving generalization.
This article reviews machine learning methods for behavioral sciences.
problem Applying machine learning to behavioral science data.
method Overview of classical and recent algorithms for tabular, behavioral, and textual data.
result Practical guidance for using machine learning in behavioral science.
AI+MPS workshop aims to strengthen AI's role in science.
problem AI's potential to enhance scientific discovery and education.
method Proposes activities and strategic priorities to strengthen AI-MPS link.
result AI and science are becoming increasingly intertwined.
Classic economic science is reaching the limits of its explanatory powers. Complexity science uses an increasingly larger set of different methods to analyze physical, biological, cultural, social, and economic factors, providing a broader understanding of the socio-economic dynamics involved in the development of nati…
Proposes PCS framework for veridical data science results.
problem Ensuring reliable, reproducible, and transparent data science results.
method PCS workflow with predictability, computability, and stability principles.
result PCS inference procedures demonstrate favorable performance in high-dimensional simulations.
Review of automation's role in chemical discoveries.
problem Improving autonomous discovery in chemistry.
method Classification of discovery types, assessment of autonomy, case studies.
result Rapid advancements in automation and machine learning are transforming experimentation and modeling.
ADS automates data preparation for ML/AI, reducing human effort.
problem Manual and time-consuming data preparation for ML/AI.
method Data-driven approach using statistics and ML.
result ADS automates data exploration and processing steps.
This paper uses information theory to improve risk modeling in big data.
problem Insufficient application of information theory in actuarial science.
method Explores information theory to uncover performance limits of insurance big data systems.
result Guidance for risk modeling and actuarial pricing systems.
Paper develops an attention mechanism for long-term scientific impact prediction.
problem Predicting the long-term impact of scientific papers based on citation records.
method Develops an attention mechanism to predict long-term scientific impact.
result Emphasizing the limited attention can better stand on the shoulders of giants.
New model estimates species population trends from citizen science data.
problem Interannual confounding in citizen science data.
method Double Machine Learning framework to estimate population change and propensity scores for confounding adjustment.
result Spatially detailed trend estimates from citizen science data with low error rates.
Why do nations produce scientific research? This is a fundamental problem in the field of social studies of science. The paper confronts this question here by showing vital determinants of science to explain the sources of social power and wealth creation by nations. Firstly, this study suggests a new general definitio…
Provides a compendium of data sources for various applications.
problem Lack of comprehensive data sources for data science, machine learning, and AI.
method Compilation of diverse data sources across multiple application areas.
result A comprehensive list of data sources for data scientists and machine learning experts.
The Prescriptive Canvas improves business outcomes by directly prescribing actions based on predictions.
problem Sub-optimal performance in business projects due to a two-step approach of prediction and decision-making.
method The Prescriptive Canvas methodology for framing and communicating actions directly based on predictions.
result Improves framing and communication across stakeholders for successful business impact.
Diffusion models explained via cognitive science.
problem Weak sensitivity to noise family and noise level scheduling.
method Correspondence with serial reproduction in cognitive science.
result Properties of diffusion models explained by cognitive science.
The study explores how machine learning can enhance scientific research.
problem Improving scientific models with machine learning.
method Analysis of data-driven models versus manually added variables in regression.
result Complex models may not always improve over simpler ones in scientific contexts.
Kaggle chronicles 15 years of competitions, innovation, and data science.
problem Exploring 15 years of data science competitions and innovations.
method Longitudinal trend analysis and exploratory data analysis of millions of kernels and discussion threads.
result Kaggle is a growing platform with diverse use cases and adaptable Kagglers.
New method uses mutual info and network science to explain deep learning models.
problem Interpreting deep neural networks for understanding their decision-making process.
method Coupling mutual information with network science to quantify information flow in deep learning models.
result Proposed NIF technique for codifying information flow in deep learning models.
The paper develops tensor learning methods exploiting symmetries of tensor functions.
problem Efficiently handling tensors in various scientific contexts.
method Equivariant machine learning architectures exploiting orthogonal, Lorentz, and symplectic symmetries.
result Equivariant models outperform non-equivariant baselines in time series analysis.