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{"references": ["S. Hettrick, 2014:Software in research survey (2018). doi:10.5281/zenodo.1183562.", "M. Hassini, E. Redondo-Iglesias, P. Venet, Lithium-ion battery data: From production to prediction, Batteries (in press, 2023).", "P. Herring, C. B. Gopal, M. Aykol, J. H. Montoya, A. Anapolsky, P. M. Attia, W. Gent, J. S. Hummelsh\u00f8j, L. Hung, H.-K. Kwon, et al., Beep: A python library for battery evaluation and early prediction, SoftwareX 11 (2020) 100506. doi:10.1016/j.softx.2020.100506.", "V. Sulzer, S. G. Marquis, R. Timms, M. Robinson, S. J. Chapman, Python battery mathematical modelling (pybamm), Journal of Open Research Software 9 (1) (2021). doi:10.5334/JORS.309.", "M. D. Murbach, B. Gerwe, N. Dawson-Elli, L.-k. Tsui, impedance. py: A python package for electrochemical impedance analysis, Journal of Open Source Software 5 (52) (2020) 2349. doi: 10.21105/joss.02349.", "VEHLIB. URL: https://gitlab.univ-eiffel.fr/eco7/vehlib", "M. Hassini, E. Redondo-Iglesias, P. Venet, S. Gillet, Y. Zitouni, Second Life Batteries in a Mobile Charging Station: Model Based Performance Assessment, in: EVS35, 35nd International Electric Vehicle Symposium & Exhibition, Oslo, Norway, 2022. URL: https://hal.science/hal-03708744", "M. Hassini, E. Redondo-Iglesias, P. Venet, S. Gillet, Y. Zitouni, Second Life Batteries in a Mobile Charging Station: Experimental Performance Assessment, working paper or preprint (2022). URL: https://hal.science/hal-03713844v1", "M. Hassini, E. Redondo-Iglesias, P. Venet, Second-life batteries modeling for performance tracking in a mobile charging station, World Electric Vehicle Journal 14 (4) (2023) 94. doi:10.3390/ wevj14040094", "DATTES : Data Analysis Tools for Tests on Energy Storage. URL: https://gitlab.com/dattes/dattes/", "R. C. Jim\u00e9nez, M. Kuzak, M. Alhamdoosh, M. Barker, B. Batut, M. Borg, S. Capella-Gutierrez, N. C. Hong, M. Cook, M. Corpas, et al., Four simple recommendations to encourage best practices in research software, F1000Research 6 (2017). doi:10.12688/f1000research.11407.1"]}
Experiments are essential to understand the behaviour and performance of energy storage systems. In this field, a considerable amount of experimental data is generated and data processing is a tedious task. To date, research teams working in the field of energy storage tend to focus on developing their own analysis tools rather than using existing open source software. This strategy can be detrimental to the quality and reproducibility of the research. This paper presents DATTES, a free and open source software for analysing experimental battery data. The software provides a comprehensive and customizable toolkit for extracting, analysing and visualizing experimental data. It also creates gateways to other open software and tools. In this way, DATTES enables users to get the most out of their experimental data and engage in open and reproducible science.
Energy storage, [SPI.NRJ]Engineering Sciences [physics]/Electric power, Data analysis, [SPI.MAT] Engineering Sciences [physics]/Materials, Energy Storage, [SPI.MAT]Engineering Sciences [physics]/Materials, 004, QA76.75-76.765, Open Science, GNU octave, Open science, Computer software, Experiments, GNU Octave, [SPI.NRJ] Engineering Sciences [physics]/Electric power, Matlab, FAIR
Energy storage, [SPI.NRJ]Engineering Sciences [physics]/Electric power, Data analysis, [SPI.MAT] Engineering Sciences [physics]/Materials, Energy Storage, [SPI.MAT]Engineering Sciences [physics]/Materials, 004, QA76.75-76.765, Open Science, GNU octave, Open science, Computer software, Experiments, GNU Octave, [SPI.NRJ] Engineering Sciences [physics]/Electric power, Matlab, FAIR
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 10 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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| downloads | 97 |

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