SLICES Data Management Infrastructure for
Reproducible Experimental Research on
Digital Technologies
Yuri Demchenko, University of Amsterdam
On behalf ofYuri Demchenko, Paola Grosso, Shashank Shrestha
Acknowledgement to SLICES partners: Sebastian Gallenmuller, Serge Fdida,
Panayiotis Andreau, Damien Sauzes, Thijs RauschTrack: CompSys Research for a Responsibly Digitalised Society
ICT.OPEN2024 11 April 2024, Utrecht1

Outline• SLICES-RI: European Scientific Large-Scale Infrastructure for Computing/Communication
Experimental Studies• SLICES Data Management Infrastructure for Experimental Research• Experimental data lifecycle• Experimental Research Reproducibility as a Service• Experiment organization and workflow• Metadata definition• Tools to support data management and FAIR data principles• EOSC Data Management and Metadata Tools: MSCR, FDO, PID, others• Discussion

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SLICES Data Management Infrastructure for Experimental Research2

SLICES-RI for Research on Digital Infrastructures
Includes extensive experimentation with new technologies

Initiated in 2017, 25 partners from 15 countries:• 12 political supports from National Ministries• included in 5 national roadmaps
SLICES will enable scientific excellence andbreakthrough and will foster innovation in the ICT
domain, strengthening the impact of Europeanresearch, while contributing to European agenda toaddress societal challenges, and in particular, the twintransition to a sustainable and digital economy.

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SLICES Data Management Infrastructure for Experimental
Research

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SLICES-PP (2022-2025): Consortium members
25 Partners from 15 countries
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INRIA, FR
Sorbonne University (SU), FR
Univ of Amsterdam (uvA), NL
Univ of Thessaly (UTH), GR
CNR, IT
PSNC, PL
Mandat International (MI), CH
IoTLAB, FR
UC3M, ES
IMEC, BE
UCLan, CY
EURECOM, FRSZTAKI, HU
CINI, ITCNIT, IT
Univ Luxemburg, LU
TUM, DE
EHU, ES
KTH, SE
Univ Oulun, FI
EBOS, CYSIMULA, NO
IMT, FR
Univ Geneve, CH

SLICES Data Management Infrastructure for Experimental Research4Different Types of Data for Different Experimental
Studies
General experimental studies and data:
Experiment data & Infra Mngt dataIndus.
verticals
demandAI-centricDIs

HumancentricDIs

Data for ML algorithmand optimisation

Design patterns, API management,configuration, evaluation data

Crossprop.

Cloudto-Edgescalable
DIs

6G

Models and Data for monitoring andoptimisation

Breaking down in priority research topics

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Simultaneous but progressive exploration of research topics

SLICES Data Management Infrastructure for Experimental
Research5

Experimental Research Reproducibility as a Service (ERRaaS)• SLICES to provide the Robust Data Infrastructure for Experiment/Data Driven Research• Interoperability and integration with EOSC as Federated data infrastructure• Experiment as a Research Object (RO)• Identified with unique ID and containing smart metadata (for discovery and FAIR compliance)• Complying with the FDO/SFDO metadata schema• RO Registry and LOCrate bundles: Local and integrated with EOSC

• Containing full experiment (infrastructure) setup• Components/nodes, parametrized infrastructure description and deployment sequence• Automation of deployment wit tools: Ansible, Terraform, shell script, others• Experiment description and orchestration/workflow• Jupyter Notebook, CWL/Galaxy, Github• Interactive Experiment configuration and management (web console and CLI)

• Input/test data
• Data storage and preprocessing• Data ingest link and API• Data model and interoperable/standard data format• FAIR by design: primarily metadata management• Measurement points and monitoring

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ACM Recommendations
1. Repeatability: Same team executes
experiment using same setup
2. Reproducibility: Different teamexecutes experiment using same setup
3. Replicability: Different teamexecutes experiment using different
setup

SLICES Data Management Infrastructure for Experimental Research6

SLICES Experimental Data Lifecycle Model and Dataflow
Metadata

Experiment
DescriptionResearcherReschr Tools:
Experiment,Metadata, DataDMI Infra services: Registries,Storage, PID, FDO

Data Types
Data Models

SLICES Data Storage and Management Infrastructure

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

Experiment
Plan &Implement

Data Collection& Preparation

Data Analysis
Model Building

Data Archiving& Publication/Sharing

Data Discovery (for experiment planning and operation)

Data Publishing andArchiving on EOSC
Data Sharing
Infrastructure

EOSC Data
Archiving &Sharing(Zenodo)Experiment/Data
Lifecycle Stages

FAIR Data and Software: Storage, Registries, Tools• Each Data Lifecycle stage – experiment, data collection, data analysis, and finally data archiving, works with own data set, which must be linked.
• All data sets need to be stored and possibly re-used in later processes.
• Many experiments and research require already existing datasets that will be available in SLICES data repositories or can be obtained/discovered
in EOSC data repositories

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SLICES Data Management Infrastructure for Experimental Research7

Experimental Research Data Management Infrastructure
Researcher/PI(Project Team)

Layers
Virtual Research Infrastructure(Virtual Research Teams)

PortalCatalog

L5 - VRI• Virtual RI andResearcher Portal

Data Management
Services/Tools
DGvP

DMP

Workflow(Exp&DataAnalys)Experiment Workflow
Management & Execution
ABC

DVisualisation
L4 - Experiment
MngmntData, PID, FDO

Experiment Data Ingest
Data Processing
ML/Data Analytics
Data Storage, Compute, Network (cloud based)
Compute• Experiment Workflow
Management• Data Ingest

E

L3 - Experiment
Data Ingest

Metadata(Registry&Tools)

DMI layers

Storage

L2 - DataProcessing• Data Processing• Storage & PhysicalInfrastructure

L1 - Storage &Phy Infra

Network

DGvP – Data Governance Policy DMP – Data Management Plan

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SLICES Data Management Infrastructure for Experimental Research8

Generic Experiment Model for Reproducibility
Questions to be answered before
starting Experiment

Experiment Controland Monitoring
DUT/SUT
Config&Setup

Test Generator

Exp/TestParam

DUT/SUT(Device/System UnderTest)

Metadata(MRS)(names&semantics)

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DataModel/
Metadata

Measurement(DUT/SUT +Environment)

Data Collection• DUT/SUT Configuration&Setup

Data Storage• DUT/SUT data model

Environment

EnvironParam• Device/System under Test model(variables, parameters,environment)

Measurement
ConfigData Processing/Visualisation• Relational model with multipletables• Test/Stimulus Variables&Parameters• Measurement (instruments)
configuration• Metadata defined and applied forall experiment components andstage

SLICES Data Management Infrastructure for Experimental Research9Experiment Description: Metadata Requirements• SLICES Data Management Infrastructure (DMI) Requirements groups Part of SLICES DMI Blueprint(1) Architecture and services(2) General Metadata definition and management(3) Experiment description and metadata(4) Domain specific (e.g. SLICES Blueprint Architecture)(5) Metadata Management tools
What metadata should describe• Data models: storage, databases, metadata• Experiment

Existing practices• Jupyter Notebook (Python based) – Popular butlimited portability• GitHub and GitHub Actions (CI/CD tools)• Shell script• Common Workflow Language (CWL)

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generators, measurement; data storage; data
models/metadata• Dataflow: Stages, transformations,lineage/provenance, data models• Workflow: Stages, Operations/conditions,workstations

SLICES Data Management Infrastructure for Experimental Research10New/emerging technologies and tools for data and metadatamanagement• EOSC Core Metadata Tools• Research Object (RO) and ROCrate – Packaged information about and datafrom experiment – Experimental Research Profile by SLICES• DVC for Experimental data versioning and lineage in complex data processing• EOSC Catalog – Data(set) and services registration• FAIR Data Object (FDO) and PID for data publication and discovery• Machine Actionable DMP (maDMP)• Metadata Tools for researchers:• Metadata Registry Service (MRS) developed by SLICES/UCLan Cyprus• Metadata/data annotation, mapping and search• Namespace/semantics definition (SLICES namespace)• Metadata extraction for experiment description (legacy/unstructured format)

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SLICES Data Management Infrastructure for Experimental Research

11Discussion and Questions• Open to cooperation with research and industry• Pilot services planned for summer 2024
• Where to learn background information• EOSC (European Open Science Cloud) developments, services and products• RDA (Research Data Alliance) recommendations and best practices• FAIR expertise centers• Data services: datasets repositories and scientific data archives

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