Experimental Research Reproducibility and DataManagement Infrastructure in SLICES-RI
Yuri Demchenko, Cees de Laat, Paola Grosso, Chrysa Papagianni, Wouter Los (University ofAmsterdam)
Different Types of Data for Different Experimental Studies
SLICES-NL Research Topics

General experimental studies and data:
Experiment data & Infra Mngt dataIndus.
verticals
demandAI-centricDIs

General experimental studies and data documentation andpublication
FAIR (Findable, Accessible, Interoperable, Reusable)
data principles are key for experimental data sharing
Metadata profiles to be defined for major types ofexperiments and supported by data and metadatamanagement tools
Infrastructure management information to berecorded as experiments environment
Research Object (RO) and FAIR Digital Object (beingdeveloped by EOSC)
Data produced for AI/ML algorithms training for smart
infrastructure optimisation and management (including energy
efficiency, performance, resilience, sustainability)
Data modelling and data lineage (stagingdocumenting)
AI/ML models serialization and portability
New Digital Infrastructure architecture elements and design
patterns
Infrastructure and design patterns
Metadata for API description, identification,
composability

Design patterns, API management,
configuration, evaluation data

Data for ML algorithm
and optimisation

Crossprop.
Cloud-toEdge
scalableDIs

Humancentric DIs

6G5.
Models and Data for monitoring andoptimisation

Breaking down in priority research topics

Simultaneous but progressive exploration of research topics

SLICES-RI Interconnection Architecture
SLICES Data Storage and Management Infrastructure

Experiment/Data
Lifecycle Stages

Data Curation &Quality Assurance(multi-purpose: experimental, technological, archival)

Researcher

Experiment
Description

Experiment
Plan &Implement

Data Analysis
Model Building

Data Collection& Preparation

Data Archiving& Publication/Sharing

Data Publishing andArchiving on EOSC
Data Sharing
Infrastructure

EOSC Data
Archiving &Sharing

Data Discovery (for experiment planning and operation)

SLICES to support experiments reproducibility to comply with Open Science

Platform
PlatformResearch
ResearchInfrastructure
Infrastructureas
asaaService
Service(PRIaaS)(PRIaaS)
Experiment Control and Monitoring

Focus on repeatability and reproducibility with the future support of replicability

Robust, reproducible experiments• Documenting all relevant parameters and environment for experiments• Automate the documentation of experiments➢ Well-structured experiment workflow may serve as documentation

Benefits for research community
1. Planning

2. Experim Setup

3. Load Test Data

• Reduce amount of work for experimenters to create reproducible experiments• Reduce amount of work for other researchers to recreate and re-run experiments• Make reproducibility an integral part of experiment design➢ Automate entire experiment (setup, execution, evaluation)

4. Experim
Workflow

SLICES to support experiments reproducibility to comply with Open ScienceExperiment
LoopFocus on repeatability and reproducibility with the future support of replicability
5. Collect Data

6. Evaluation(Store&Repeat)

Robust, reproducible experiments• Documenting all relevant parameters and environment for experiments• Automate the documentation of experiments➢ Well-structured experiment workflow may serve as documentation

Benefits for research community
9. Publication &Archiving• Reduce amount of work for experimenters to create reproducible experiments• Reduce amount of work for other researchers to recreate and re-run experiments• Make reproducibility an integral part of experiment design➢ Automate entire experiment (setup, execution, evaluation)

7.Processng(Data Anal)

8. Reporting

Experimental Data Management Infrastructure

Researcher/PI(Project Team)

PortalVirtual Research Infrastructure(Virtual Research Teams)

LayersCatalog

VRI

Data ManagementServices/Tools

DGvP

DMP

Workflow(Exp&DataAnalys)

Metadata(Registry&Tools)

Data, PID, FDO

Experiment Workflow
Management &Execution
A
D
B C
E

Visualis
ation

Experiment
MngmntExperiment
Data Ingest

Experiment Data Ingest
Data Processing
ML/Data Analytics

DataProcessing

Data Storage, Compute, Network (cloud based)

Storage &Phy Infra

ComputeStorage

Network

DGvP – Data Governance Policy DMP – Data ManagementPlan

SLICES-RI is an ESFRI Roadmap 2021 Project and Initiative• SLICES is a flexible platform designed to support large-scale,
experimental research focused on networking protocols, radio enable
experiments on novel Digital Infrastructure (DI) solutions andapplications

Data Management Infrastructure Layers
Data Management Infrastructure Layers to separate data management and governanceconcerns and actors/roles
Layer 4 - Experiment Infrastructure configuration and management
Layer 3 - Experimental data collection/recording
Data models, metadata
Layer 2 - Data processing
Data analysis, Process/ML models building, portability
Layer 1 - Data Storage, Archiving, Exchange
Datasets, metadata publication
Data Management Services and Tools (Data Management Plane)
Data Management Plan and Data Quality Assurance, FAIR compliance
Metadata registries and tools
Data Security and Data protection, GDPR
References
Future Scientific Data Infrastructure: Towards Platform Research Infrastructure as a Service (PRIaaS), by Yuri
Demchenko, Cees de Laat, Wouter Los, Proc. The International Conference on High Performance Computing andSimulation (HPCS 2020), 10-14 Dec 2020.
SLICES-RI - https://slices-ri.eu/ EOSC FAIRsFAIR - https://www.fairsfair.eu/
ESFRI Roadmap 2021 - https://www.esfri.eu/esfri-roadmap-2021
IG1157 Digital Platform Reference Architecture Concepts and Principles v5.0.1, 21 July 2020 [online]
https://www.tmforum.org/resources/reference/ig1157-digital-platform-reference-architecture-concepts-andprinciples-v5-0-0/

Contact: Yuri Demchenko <y.demchenko@uva.nl>