- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
1 Online-Ressource.
- Language
-
Englisch
- Bibliographic citation
-
A dynamic knowledge graph approach to distributed self-driving laboratories ; volume:15 ; number:1 ; day:23 ; month:1 ; year:2024 ; pages:1-14 ; date:12.2024
Nature Communications ; 15, Heft 1 (23.1.2024), 1-14, 12.2024
- Creator
-
Bai, Jiaru
Mosbach, Sebastian
Taylor, Connor J.
Karan, Dogancan
Lee, Kok Foong
Rihm, Simon D.
Akroyd, Jethro
Lapkin, Alexei A.
Kraft, Markus
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1038/s41467-023-44599-9
- URN
-
urn:nbn:de:101:1-2024032512212395493236
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:48 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Bai, Jiaru
- Mosbach, Sebastian
- Taylor, Connor J.
- Karan, Dogancan
- Lee, Kok Foong
- Rihm, Simon D.
- Akroyd, Jethro
- Lapkin, Alexei A.
- Kraft, Markus
- SpringerLink (Online service)
Other Objects (12)
Self-driving laboratories to autonomously navigate the protein fitness landscape
Managing large distributed dynamic graphs for smart city network applications
Graph Partitioning for Distributed Graph Processing
Approximating distributed graph algorithms
Scalable graph partitioning for distributed graph processing
Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems
Distributed graph partitioning for large-scale graph analytics
Laborgebäude : = Laboratories
Green laboratories
LabLinking: theory, framework, and solutions of connecting laboratories for distributed human experiments
Dynamic Graph Coloring
Semantic structural graph summaries for evolving and distributed graphs
Self-driving laboratories to autonomously navigate the protein fitness landscape
Managing large distributed dynamic graphs for smart city network applications
Graph Partitioning for Distributed Graph Processing
Approximating distributed graph algorithms
Scalable graph partitioning for distributed graph processing
Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems
Distributed graph partitioning for large-scale graph analytics
Laborgebäude : = Laboratories
Green laboratories
LabLinking: theory, framework, and solutions of connecting laboratories for distributed human experiments
Dynamic Graph Coloring
Semantic structural graph summaries for evolving and distributed graphs
Self-driving laboratories to autonomously navigate the protein fitness landscape
Managing large distributed dynamic graphs for smart city network applications
Graph Partitioning for Distributed Graph Processing
Approximating distributed graph algorithms
Scalable graph partitioning for distributed graph processing
Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems
Distributed graph partitioning for large-scale graph analytics
Laborgebäude : = Laboratories
Green laboratories
LabLinking: theory, framework, and solutions of connecting laboratories for distributed human experiments
Dynamic Graph Coloring