The Interdisciplinary Data Science Conference 2027 invites researchers, practitioners, and industry experts to submit their latest findings and innovations in the field of Data Science. This conference aims to foster interdisciplinary collaboration, share cutting-edge research, and explore the diverse applications of data science across various domains.
Submissions in the following topics and beyond are welcome!
Topic 1
Focus:
Beyond
Data Science for Trust, Security & Resilience
This track bridges cybersecurity, media forensics, and societal safety with the methodological robustness and ethical frameworks needed to foster a secure digital ecosystem.
Security & Privacy for AI: Adversarial AI and countermeasures, Robustness of AI methods against disruptions and attacks, Explainable AI (XAI) for security.
Media Forensics: Deepfake detection across various data types, disinformation detection, and preventing digital societal manipulation (PeaceTech).
Advanced Statistical & ML Methods: Supervised/unsupervised learning, regression, classification, and Bayesian statistics applied to security.
Ethical & Legislative Aspects: Ethical issues in AI development, and comprehensive analysis/implications of the EU AI Act.
Privacy-preserving machine learning (e.g., Privacy and Security for Federated Learning), general data security practices across heterogeneous platforms, and long-term societal resilience against emerging digital threats.
Topic 2
Focus:
Beyond
Data Spaces, Ecosystems & Industrial Data Sovereignty
This track targets the architectures, governance models, and infrastructures that enable secure, sovereign, cross-organizational data sharing, collaborative value networks, and data-driven business innovation.
Data Governance & Sovereignty: Data Spaces architectures, standardization, and Gaia-X implementation ensuring compliance with data protection regulations.
Industrial Data Ecosystems: Digital Product Passports (DPP), tracking across supply chains, and multi-organizational data-driven circular networks.
Data-driven Business Models: Leveraging data as a central corporate resource, data-driven decision-making, and industrial innovation case studies.
Next-generation decentralized data economies, cross-border digital ecosystem policies, and novel semantic frameworks for interoperability.
Topic 3
Focus:
Beyond
Smart Industry, Robotics & Autonomous Systems
This track integrates advanced machine learning, time series analysis, and MLOps with embodied intelligence and highly automated high-tech manufacturing environments.
Smart Production & High-Tech Manufacturing: Process data analysis, predictive maintenance, quality control, and yield optimization (with a special focus on semiconductor fabrication and microelectronics).
MLOps & Data Engineering: MLOps strategies for industrial and Operational Technology (OT) contexts, building efficient data pipelines, and workflow automation.
Robotics & Embodied AI: Navigation, perception, sensor fusion, and Human-Robot Interaction (HRI).
Dynamic Systems & Control Theory: Modeling and controlling dynamic systems using ML, integrating Reinforcement Learning with co-simulation techniques.
Core Methods: Time Series Analysis (forecasting, prediction, and multivariate time series anomaly detection/generative models), Computer Vision, Deep Learning, Representation Learning, and feature engineering.
Overarching safety, trust, and governance of autonomous, robotic, and cyber-physical systems, alongside breakthroughs in alternative machine learning models beyond classical neural networks.
Topic 4
Focus:
Beyond
Data Science for Good, Sustainability & Societal Impact
This track is dedicated to leveraging foundational data science, statistical modeling, and open-source ecosystems to solve critical challenges in health, environment, education, and public governance.
Domain Applications: Health Informatics, Digital Health, Bioinformatics, Sustainable Production systems, Energy grids, and Smart Grid analytics.
Public Data Science & Open Data: Open Data Initiatives, management and analytics of public data by governmental bodies, EduTech, GovTech, and data tools for NGOs.
Foundational Analytics: Exploratory Data Analysis (EDA), effective data visualization strategies, descriptive statistics, and data interpretation for public benefit.
Topical Innovations: Semantic Technologies (Knowledge Graphs, Semantic Inference, Semantic Information Modeling), Transfer Learning, Incremental and Adaptive Learning, and Advanced Topological Data Analysis (TDA) / Topological Machine Learning.
Promoting open science and collaborative research efforts, encouraging highly interdisciplinary projects across multiple domains, and aligning AI progress with the UN Sustainable Development Goals (SDGs).
Categories:
- Research Paper
Research papers in data science and related fields, presented in a classic academic conference format. - Industry Contribution
Short contributions showcasing innovative real-world data science solutions and applications from industry and practice. - Workshops Proposal
Interactive workshop formats that complement the conference tracks, foster discussion of emerging ideas, and encourage exchange among researchers, practitioners, and industry experts.
iDSC 2027 – Submission Guidelines
7th Interdisciplinary Data Science Conference | May 19–21, 2027 | Austria
Submission Portal: meteor.springernature.com/idsc2027(opens in new tab) | | (Replaces the former Springer OCS platform)
Authors are strongly encouraged to register their working title, authors, and abstract early on METEOR. No paper upload is required at this stage. Pre-registration helps us assign appropriate reviewers early.
How to Submit
All submissions for both Research and Industry tracks are managed via the Springer Nature METEOR platform.
Create an Account / Log In: Access meteor.springernature.com/idsc2027(opens in new tab).
Abstract Pre-Registration: Open a paper slot with your title, track selection, author details, and a short abstract. No paper upload is required at this stage.
Full Submission: Upload your final paper before the respective track deadline.in new tab)
Tracks & Templates
Research Track (10–12 pages): Full papers published by Springer Nature.
Templates: Springer LaTeX Guidelines(opens in new tab) | Springer Word Template (.zip)(opens in new tab)
Industry Track (1–2 pages): Short proposals and industrial use-case summaries.
Templates: iDSC Industry Template(opens in new tab)ens in new tab)
Workshop Track (1–2 pages): Short proposals of practical application of data science
Templates: iDSC Workshop Template(opens in new tab)ens in new tab)
Important Dates
| Milestone | Date | Details |
| Pre-Registration Deadline | Feb 8, 2027 | Working title & abstract |
| Research Paper Deadline | Feb 15, 2027 | Full 10–12 page manuscript (excl. references) |
| Industry contribution Deadline | Feb 15, 2027 | 1–2 page use-case contribution |
| Workshop Proposal Deadline | Feb 15, 2027 | 1–2 page workshop proposal |
| Acceptance Notification | Mar 31, 2027 | Review results dispatched |
| Camera-Ready & Registration | Apr 30, 2027 | Final upload & conference registration |
| Conference | May 19–21, 2027 | Event dates |
All deadlines are at 23:59 Austrian local time (CET/CEST).