LARA - embracing almost fully automated
experimentation from ground up by using semantic
web technologies in Life Sciences
Mark Doerr1,*,† , Stefan Born2,†
1
    University Greifswald, Felix-Hausdorff Str.4 , 17489 Greifswald, Germany
2
    Technische Universität Berlin, Institute for Biotechnology, Berlin, Germany


                                        Abstract
                                        LARA (https://gitlab.com/larasuite) is an open source lab automation and research data
                                        management system of the next generation:
                                            It utilises radical automation of most aspects of lab experimentation by applying standard-
                                        ised lab communication protocols, e.g. SiLA[1], between machines and human scientists, a
                                        new, Turing complete process- and procedure description language pythonLab [2], an open lab
                                        orchestrator [3] for running procedures and processes in the lab, open, JSON-LD based, linked
                                        data- and metadata formats, called SciDat [4], ontology based data representation and data
                                        synchronisation between different LARA instances and other repositories, like Dataverse and
                                        Zenodo (https://zenodo.org). Data / metadata can be queried through the LARA SPARQL
                                        endpoint. LARA strives for collecting and combining all data that is relevant to most common
                                        Life-Science experiments, like experiment planning, processes and procedures running the
                                        experiments (with their documented outcome), parts- and devices used in the experiments,
                                        substances, organisms, samples, etc.
                                            It is designed to reduce data inputs of scientist to the bare minimum and make data
                                        accessible and findable through deep query infrastructures.
                                            This also enables advanced Machine Learning and AI applications to access data in a
                                        machine-understandable, "semantic" form.
                                            To illustrate this interoperability between the LARA database and Machine Learning
                                        algorithms, a demonstration with a newly developed Machine Learning Framework that uses
                                        semantic technologies is planned.

                                        Keywords
                                        semantic web, life science, labautomation, robotics, machine learning,




Acknowledgments
The authors thank the German Research Foundation / Deutsche Forschungsgemeinschaft
DFG, (grant:NFDI4DCat) and the German Federal Ministry of Education and Research,

Semantic Web Applications and Tools for Health Care and Life Science conference (SWAT4HCLS 2024),
February 26–29, 2024, Leiden, The Netherlands
*
 Corresponding author.
email: mark.doerr@uni-greifswald.de (M. Doerr); Stefan.Born@posteo.de (S. Born)
url: https://gitlab.com/larasuite/ (M. Doerr); https://kiwi-biolab.de (S. Born)
orcid: 0000-0003-3270-6895 (M. Doerr); 0000-0001-7838-9157 (S. Born)
                                       © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0
                                       International (CC BY 4.0).
    CEUR
    Workshop
    Proceedings
                  http://ceur-ws.org
                  ISSN 1613-0073
                                       CEUR Workshop Proceedings (CEUR-WS.org)
BMBF (grant 01DD20002C) for their financial support.


References
[1] sila standard.org, SiLA - standardisation in lab-automation, 2023. URL: https://
    sila-standard.org.
[2] M. Doerr, S. Maak, pythonLab - labprocess- and procedure description language, 2023.
    URL: https://gitlab.com/opensourcelab/pythonlab.
[3] S. Maak, M. Doerr, Lab - orchestrator, 2023. URL: https://gitlab.com/opensourcelab/
    laborchestrator.
[4] M. Doerr, SciDat - scientfic data and metadata standard, 2023. URL: https://gitlab.
    com/opensourcelab/scientificdata/scidat.