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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 04072naa a2200721 4500 | |
| 001 | oai:gup.ub.gu.se/311207 | |
| 003 | SwePub | |
| 008 | 240528s2021 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://gup.ub.gu.se/publication/3112072 URI |
| 024 | 7 | a https://doi.org/10.1038/s41467-021-26320-w2 DOI |
| 040 | a (SwePub)gu | |
| 041 | a eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Munoz-Gil, G.4 aut |
| 245 | 1 0 | a Objective comparison of methods to decode anomalous diffusion |
| 264 | c 2021-10-29 | |
| 264 | 1 | b Springer Science and Business Media LLC,c 2021 |
| 520 | a Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers. Deviations from Brownian motion leading to anomalous diffusion are ubiquitously found in transport dynamics but often difficult to characterize. Here the authors compare approaches for single trajectory analysis through an open competition, showing that machine learning methods outperform classical approaches. | |
| 650 | 7 | a NATURVETENSKAPx Matematikx Sannolikhetsteori och statistik0 (SwePub)101062 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Mathematicsx Probability Theory and Statistics0 (SwePub)101062 hsv//eng |
| 653 | a statistics | |
| 653 | a tracking | |
| 653 | a Science & Technology - Other Topics | |
| 700 | 1 | a Volpe, Giovanni,d 1979u Gothenburg University,Göteborgs universitet,Institutionen för fysik (GU),Department of Physics (GU)4 aut0 (Swepub:gu)xvolgi |
| 700 | 1 | a Garcia-March, M. A.4 aut |
| 700 | 1 | a Aghion, E.4 aut |
| 700 | 1 | a Argun, Aykutu Gothenburg University,Göteborgs universitet,Institutionen för fysik (GU),Department of Physics (GU)4 aut0 (Swepub:gu)xargua |
| 700 | 1 | a Hong, C. B.4 aut |
| 700 | 1 | a Bland, T.4 aut |
| 700 | 1 | a Bo, S. O.4 aut |
| 700 | 1 | a Conejero, J. A.4 aut |
| 700 | 1 | a Firbas, N.4 aut |
| 700 | 1 | a Orts, O.4 aut |
| 700 | 1 | a Gentili, A.4 aut |
| 700 | 1 | a Huang, Z. H.4 aut |
| 700 | 1 | a Jeon, J. H.4 aut |
| 700 | 1 | a Kabbech, H.4 aut |
| 700 | 1 | a Kim, Y.4 aut |
| 700 | 1 | a Kowalek, P.4 aut |
| 700 | 1 | a Krapf, D.4 aut |
| 700 | 1 | a Loch-Olszewska, H.4 aut |
| 700 | 1 | a Lomholt, M. A.4 aut |
| 700 | 1 | a Masson, J. B.4 aut |
| 700 | 1 | a Meyer, P. G.4 aut |
| 700 | 1 | a Park, S.4 aut |
| 700 | 1 | a Requena, B.4 aut |
| 700 | 1 | a Smal, I.4 aut |
| 700 | 1 | a Song, T.4 aut |
| 700 | 1 | a Szwabinski, J.4 aut |
| 700 | 1 | a Thapa, S.4 aut |
| 700 | 1 | a Verdier, H.4 aut |
| 700 | 1 | a Volpe, G.4 aut |
| 700 | 1 | a Widera, A.4 aut |
| 700 | 1 | a Lewenstein, M.4 aut |
| 700 | 1 | a Metzler, R.4 aut |
| 700 | 1 | a Manzo, C.4 aut |
| 710 | 2 | a Göteborgs universitetb Institutionen för fysik (GU)4 org |
| 773 | 0 | t Nature Communicationsd : Springer Science and Business Media LLCg 12:1q 12:1x 2041-1723 |
| 856 | 4 | u https://www.nature.com/articles/s41467-021-26320-w.pdf |
| 856 | 4 8 | u https://gup.ub.gu.se/publication/311207 |
| 856 | 4 8 | u https://doi.org/10.1038/s41467-021-26320-w |
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