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BBQ Independent Research InitiativeIndependent research initiative · not an accredited institution · Warsaw, Poland
Operating Form
Online initiative
Domain
bbq.edu.pl
Eligibility
Not established
PROPOSED RESEARCH AGENDA

Scientific Provenance & High-Performance Systems Lab (SP-HPC Lab)

Developing exascale workflow infrastructure, immutable provenance tracking, deterministic execution runtimes, and reproducible open science ecosystems.

Laboratory Overview

The Scientific Provenance & High-Performance Systems Lab (SP-HPC Lab), headed by Prof. dr hab. Tomasz Wiśniewski, pioneers tools and system architectures that guarantee computational integrity, long-term reproducibility, and provenance tracking for data-intensive science.

From climate modeling to computational genomics, modern science relies on deeply nested software stacks and distributed compute clusters. The SP-HPC Lab creates the foundational software engineering systems that allow researchers to verify, replicate, and audit complex computational results.

Key Research Questions & Hypotheses

  1. How can multi-petabyte scientific workflow executions be cryptographically tracked with sub-millisecond logging latency?
  2. What container and kernel-level abstractions ensure strict bit-for-bit computational reproducibility across heterogeneous supercomputers?
  3. How can distributed scientific databases maintain consistency and FAIR compliance under high concurrency?

Methodologies & Technical Approaches

  1. Engineering cryptographic directed acyclic graph (DAG) capture mechanisms built directly into Linux eBPF kernel probes.
  2. Designing deterministic container layers that intercept hardware nondeterminism, timer variations, and multithreaded scheduling skews.
  3. Deploying petascale distributed object vaults with automated metadata indexing and DOI registry synchronization.

Facilities, Compute & Equipment

  • Direct 100 Gbps interconnect to the Polish Academic Research Network (PIONIER) and PL-Grid infrastructure.
  • All provenance engines deployed with fully reproducible benchmarks and automated CI verification pipelines.

Lab Seminars & Open Positions

  • Open source releases accessible via the BBQ Institute GitHub repository.
  • Collaborative infrastructure node for European Open Science Cloud (EOSC).

Key Scientific Publications & Foundation Works

Selected foundation literature and methodology references utilized by the laboratory team.

External scholarly reference · 2016Third-party work

The FAIR Guiding Principles for Scientific Data Management and Stewardship

Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E. Bourne, Jildau Bouwman, Anthony J. Brookes, Tim Clark, Mercè Crosas, Ingrid Dillo, Olivier Dumon, Scott Edmunds, Chris T. Evelo, Richard Finkers, Alejandra Gonzalez-Beltran, Alasdair J. G. Gray, Paul Groth, Carole Goble, Jeffrey S. Grethe, Jaap Heringa, Peter A. C. 't Hoen, Rob Hooft, Tobias Kuhn, Ruben Kok, Joost Kok, Scott J. Lusher, Maryann E. Martone, Albert Mons, Abel L. Packer, Bengt Persson, Philippe Rocca-Serra, Marco Roos, Rene van Schaik, Susanna-Assunta Sansone, Erik Schultes, Thierry Sengstag, Ted Slater, George Strawn, Morris A. Swertz, Mark Thompson, Johan van der Lei, Erik van Mulligen, Jan Velterop, Andra Waagmeester, Peter Wittenburg, Katherine Wolstencroft, Jun Zhao, Barend Mons

Scientific Data Vol. 3, pp. 160018(2016). DOI: 10.1038/sdata.2016.18

Defines the principles that digital research objects should be Findable, Accessible, Interoperable, and Reusable for people and machines.

External scholarly reference · 2017Third-party work

A Manifesto for Reproducible Science

Marcus R. Munafò, Brian A. Nosek, Dorothy V. M. Bishop, Katherine S. Button, Christopher D. Chambers, Nathalie Percie du Sert, Uri Simonsohn, Eric-Jan Wagenmakers, Jennifer J. Ware, John P. A. Ioannidis

Nature Human Behaviour Vol. 1, pp. 0021(2017). DOI: 10.1038/s41562-016-0021

Sets out practical measures to improve the reliability and efficiency of scientific research through stronger methods, transparency, and incentives.

External scholarly reference · 2021Third-party work

Datasheets for Datasets

Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford

Communications of the ACM Vol. 64(12), pp. 86-92(2021). DOI: 10.1145/3458723

Introduces a documentation framework for recording the motivation, composition, collection process, recommended uses, and limitations of datasets used in machine learning.