Research Data supporting "A Dynamic Knowledge Graph Approach to Distributed Self-Driving Laboratories"
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Experimental data and provenance records captured in ontological format during the closed-loop optimisation of the manuscript "A Dynamic Knowledge Graph Approach to Distributed Self-Driving Laboratories".
Source Data Figure 6: Extractable form of the experiments recorded in the knowledge graph during the collaborative optimisation campaign in chronological order. For autosampler sites from which the chemicals were sourced for runs conducted in the Cambridge lab, as well as additional notes on the data points, please see Supplementary Table S7. The data points contained in this file were used to produce Fig. 6.
Source Data Supplementary Table 7: Extractable form of the Supplementary Table S7 provided in the Supplementary Information. For more details, please see the table caption and related text in the Supplementary Information.
Supplementary Data: Complete records of the collected knowledge graph triples during the collaborative optimisation campaign, including both experimental data and their provenance. The digital twins of the laboratory hardware are anonymised. This file is accessible through common plain text editors, such as Notepad, Notepad++, and Sublime Text.
Supplementary Information: Technical details on the knowledge graph implementation and experimental procedures to reproduce this work.
Supplementary Movie 1: An interactive animation of the progress of the Pareto front advancement (Fig. 6(a)) during the collaborative optimisation campaign.
Supplementary Movie 2: An interactive version of the 3D plot for the cost objective (Fig. 6(b)) during the collaborative optimisation campaign.
Supplementary Movie 3: An interactive version of the 3D plot for the yield objective (Fig. 6(c)) during the collaborative optimisation campaign.
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European Regional Development Fund (ERDF) (via Department For Communities & Local Government) (13R17P02073)
EPSRC (via Alan Turing Institute) (T2-16)
Engineering and Physical Sciences Research Council (EP/S024220/1)