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- ItemInterlaboratory study assessing the analysis of supercapacitor electrochemistry data(New York, NY [u.a.] : Elsevier, 2023) Gittins, Jamie W.; Chen, Yuan; Arnold, Stefanie; Augustyn, Veronica; Balducci, Andrea; Brousse, Thierry; Frackowiak, Elzbieta; Gómez-Romero, Pedro; Kanwade, Archana; Köps, Lukas; Jha, Plawan Kumar; Lyu, Dongxun; Meo, Michele; Pandey, Deepak; Pang, Le; Presser, Volker; Rapisarda, Mario; Rueda-García, Daniel; Saeed, Saeed; Shirage, Parasharam M.; Ślesiński, Adam; Soavi, Francesca; Thomas, Jayan; Titirici, Maria-Magdalena; Wang, Hongxia; Xu, Zhen; Yu, Aiping; Zhang, Maiwen; Forse, Alexander C.Supercapacitors are fast-charging energy storage devices of great importance for developing robust and climate-friendly energy infrastructures for the future. Research in this field has seen rapid growth in recent years, therefore consistent reporting practices must be implemented to enable reliable comparison of device performance. Although several studies have highlighted the best practices for analysing and reporting data from such energy storage devices, there is yet to be an empirical study investigating whether researchers in the field are correctly implementing these recommendations, and which assesses the variation in reporting between different laboratories. Here we address this deficit by carrying out the first interlaboratory study of the analysis of supercapacitor electrochemistry data. We find that the use of incorrect formulae and researchers having different interpretations of key terminologies are major causes of variability in data reporting. Furthermore we highlight the more significant variation in reported results for electrochemical profiles showing non-ideal capacitive behaviour. From the insights gained through this study, we make additional recommendations to the community to help ensure consistent reporting of performance metrics moving forward.
- ItemComplex systems approaches for Earth system data analysis(Bristol : IOP Publ., 2021) Boers, Niklas; Kurths, Jürgen; Marwan, NorbertComplex systems can, to a first approximation, be characterized by the fact that their dynamics emerging at the macroscopic level cannot be easily explained from the microscopic dynamics of the individual constituents of the system. This property of complex systems can be identified in virtually all natural systems surrounding us, but also in many social, economic, and technological systems. The defining characteristics of complex systems imply that their dynamics can often only be captured from the analysis of simulated or observed data. Here, we summarize recent advances in nonlinear data analysis of both simulated and real-world complex systems, with a focus on recurrence analysis for the investigation of individual or small sets of time series, and complex networks for the analysis of possibly very large, spatiotemporal datasets. We review and explain the recent success of these two key concepts of complexity science with an emphasis on applications for the analysis of geoscientific and in particular (palaeo-) climate data. In particular, we present several prominent examples where challenging problems in Earth system and climate science have been successfully addressed using recurrence analysis and complex networks. We outline several open questions for future lines of research in the direction of data-based complex system analysis, again with a focus on applications in the Earth sciences, and suggest possible combinations with suitable machine learning approaches. Beyond Earth system analysis, these methods have proven valuable also in many other scientific disciplines, such as neuroscience, physiology, epidemics, or engineering.
- ItemExoClock project: an open platform for monitoring the ephemerides of Ariel targets with contributions from the public(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2021) Kokori, Anastasia; Tsiaras, Angelos; Edwards, Billy; Rocchetto, Marco; Tinetti, Giovanna; Wünsche, Anaël; Paschalis, Nikolaos; Agnihotri, Vikrant Kumar; Bachschmidt, Matthieu; Bretton, Marc; Caines, Hamish; Caló, Mauro; Casali, Roland; Crow, Martin; Dawes, Simon; Deldem, Marc; Deligeorgopoulos, Dimitrios; Dymock, Roger; Evans, Phil; Falco, Carmelo; Ferratfiat, Stephane; Fowler, Martin; Futcher, Stephen; Guerra, Pere; Hurter, Francois; Jones, Adrian; Kang, Wonseok; Kim, Taewoo; Lee, Richard; Lopresti, Claudio; Marino, Antonio; Mallonn, Matthias; Mortari, Fabio; Morvan, Mario; Mugnai, Lorenzo V.; Nastasi, Alessandro; Perroud, Valère; Pereira, Cédric; Phillips, Mark; Pintr, Pavel; Raetz, Manfred; Regembal, Francois; Savage, John; Sedita, Danilo; Sioulas, Nick; Strikis, Iakovos; Thurston, Geoffrey; Tomacelli, Andrea; Tomatis, AlbertoThe Ariel mission will observe spectroscopically around 1000 exoplanets to further characterise their atmospheres. For the mission to be as efficient as possible, a good knowledge of the planets’ ephemerides is needed before its launch in 2028. While ephemerides for some planets are being refined on a per-case basis, an organised effort to collectively verify or update them when necessary does not exist. In this study, we introduce the ExoClock project, an open, integrated and interactive platform with the purpose of producing a confirmed list of ephemerides for the planets that will be observed by Ariel. The project has been developed in a manner to make the best use of all available resources: observations reported in the literature, observations from space instruments and, mainly, observations from ground-based telescopes, including both professional and amateur observatories. To facilitate inexperienced observers and at the same time achieve homogeneity in the results, we created data collection and validation protocols, educational material and easy to use interfaces, open to everyone. ExoClock was launched in September 2019 and now counts over 140 participants from more than 15 countries around the world. In this release, we report the results of observations obtained until the 15h of April 2020 for 120 Ariel candidate targets. In total, 632 observations were used to either verify or update the ephemerides of 84 planets. Additionally, we developed the Exoplanet Characterisation Catalogue (ECC), a catalogue built in a consistent way to assist the ephemeris refinement process. So far, the collaborative open framework of the ExoClock project has proven to be highly efficient in coordinating scientific efforts involving diverse audiences. Therefore, we believe that it is a paradigm that can be applied in the future for other research purposes, too.
- ItemTRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data(London : F1000 Research Ltd, 2021) Reina, Francesco; Wigg, John M.A.; Dmitrieva, Mariia; Lefebvre, Joël; Rittscher, Jens; Eggeling, ChristianSingle particle tracking (SPT) is one of the most widely used tools in optical microscopy to evaluate particle mobility in a variety of situations, including cellular and model membrane dynamics. Recent technological developments, such as Interferometric Scattering microscopy, have allowed recording of long, uninterrupted single particle trajectories at kilohertz framerates. The resulting data, where particles are continuously detected and do not displace much between observations, thereby do not require complex linking algorithms. Moreover, while these measurements offer more details into the short-term diffusion behaviour of the tracked particles, they are also subject to the influence of localisation uncertainties, which are often underestimated by conventional analysis pipelines. we thus developed a Python library, under the name of TRAIT2D (Tracking Analysis Toolbox – 2D version), in order to track particle diffusion at high sampling rates, and analyse the resulting trajectories with an innovative approach. The data analysis pipeline introduced is more localisation-uncertainty aware, and also selects the most appropriate diffusion model for the data provided on a statistical basis. A trajectory simulation platform also allows the user to handily generate trajectories and even synthetic time-lapses to test alternative tracking algorithms and data analysis approaches. A high degree of customisation for the analysis pipeline, for example with the introduction of different diffusion modes, is possible from the source code. Finally, the presence of graphical user interfaces lowers the access barrier for users with little to no programming experience.
- ItemConstraining two climate field reconstruction methodologies over the North Atlantic realm using pseudo-proxy experiments(Amsterdam [u.a.] : Elsevier, 2021) Nilsen, Tine; Talento, Stefanie; Werner, Johannes P.This study presents pseudo-proxy experiments to quantify the reconstruction skill of two climate field reconstruction methodologies for a marine proxy network subject to age uncertainties. The BARCAST methodology (Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time) is tested for sea surface temperature (SST) reconstruction for the first time over the northern North Atlantic region, and compared with a classic analogue reconstruction methodology. The reconstruction experiments are performed at annual and decadal resolution. We implement chronological uncertainties inherent to marine proxies as a novelty, using a simulated age-model ensemble covering the past millennium. Our experiments comprise different scenarios for the input data network, with the noise levels added to the target variable extending from ideal to realistic. Results show that both methodologies are able to reconstruct the Summer mean SST skillfully when the proxy network is considered absolutely dated, but the skill of the analogue method is superior to BARCAST. Only the analogue method provides skillful correlations with the true target variable in the case of a realistic noisy and age-uncertain proxy network. The spatiotemporal properties of the input target data are partly contrasting with the BARCAST model formulations, resulting in an inferior reconstruction ensemble that is similar to a white-noise stochastic process in time. The analogue method is also successful in reconstructing decadal temperatures, while BARCAST fails. The results contribute to constraining uncertainties in CFR for ocean dynamics which are highly important for climate across the globe.