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Ethnographic fieldnotes can contain richer and more thorough descriptions of social phenomena compared to other data sources. Their open-ended and flexible character makes them especially useful in explorative research. However, fieldnotes are typically highly unstructured and personalized by individual researchers, which make them harder to use as a method for data collection in collaborative and mixed methods research. More precisely, the unstructured nature of ethnographic fieldnotes presents three distinct challenges: 1) Organizability—it can be difficult to search and sort fieldnotes and thus to get an overview of them, 2) Integrability—it is difficult to meaningfully integrate fieldnotes with other more quantitative data types such as more such as surveys or geospatial data, and 3) Computational Processability—it is hard to process and analyze fieldnotes with computational methods such as topic models and network analysis. To solve these three challenges, we present a new digital tool, for the systematic collection, processing, and analysis of ethnographic fieldnotes. The tool is developed and tested as part of an interdisciplinary mixed methods pilot study on attention dynamics at a political festival in Denmark. Through case examples from this study, we show how adopting this new digital tool allowed our team to overcome the three aforementioned challenges of fieldnotes, while retaining the flexible and explorative character of ethnographic research, which is a key strength of ethnographic fieldwork. [ABSTRACT FROM AUTHOR]

Deghosting is an important technique in the marine seismic industry, as it plays a crucial role in mitigating the effects of ghost reflections from the sea surface, which can significantly impact the accuracy and resolution of subsurface imaging. In recent years, various acquisition‐based techniques have been developed to tackle the challenge of removing receiver–ghost reflections, which is the focus of our paper. These state‐of‐the‐art approaches, such as dual‐sensor or multicomponent towed streamer acquisitions, have demonstrated exceptional accuracy by combining pressure and particle motion data. However, such methods face limitations when dealing with low frequencies due to heavy noise contamination in the particle motion data. Consequently, ghost‐free data reconstruction at low frequencies typically relies on processing‐based approaches, which exclusively utilize recorded pressure data. This study presents a novel deghosting method for low‐frequency applications based on parabolic dictionary learning, which relies solely on recorded pressure data. The proposed method has the advantage of being applicable directly in a compressed domain, eliminating the need for data decompression prior to the deghosting process when compression is applied before the processing steps. This not only reduces costs related to data storage and transfer but also provides a cost‐effective alternative to conventional deghosting by operating directly on the compressed data format, which is smaller in size. The effectiveness of the proposed method was evaluated using both synthetic and field datasets. The results obtained from a synthetic data example indicate that the proposed method achieves similar results to an industry‐standard frequency–wavenumber method, while achieving a compression rate of over 7. Furthermore, the method was tested using a field dataset consisting of a full sail‐line of marine seismic acquisition. The comparison of 2D pre‐stack migrated images between the proposed method and the industry‐standard frequency–wavenumber method revealed insignificant differences, while achieving a compression ratio higher than 5 when our method was used. [ABSTRACT FROM AUTHOR]

Cyclic AMP controls neuronal ion channel activity. For example hyperpolarization-activated cyclic nucleotide–gated (HCN) and M-type K+ channels are activated by cAMP. These effects have been suggested to be involved in astrocyte control of neuronal activity, for example, by controlling the action potential firing frequency. In cortical neurons, cAMP can induce mixed-mode oscillations (MMOs) consisting of small-amplitude, subthreshold oscillations separating complete action potentials, which lowers the firing frequency greatly. We extend a model of neuronal activity by including HCN and M channels, and show that it can reproduce a series of experimental results under various conditions involving and inferring with cAMP-induced activation of HCN and M channels. In particular, we find that the model can exhibit MMOs as found experimentally, and argue that both HCN and M channels are crucial for reproducing these patterns. To understand how M and HCN channels contribute to produce MMOs, we exploit the fact that the model is a three-time scale dynamical system with one fast, two slow, and two super-slow variables. We show that the MMO mechanism does not rely on the super-slow dynamics of HCN and M channel gating variables, since the model is able to produce MMOs even when HCN and M channel activity is kept constant. In other words, the cAMP-induced increase in the average activity of HCN and M channels allows MMOs to be produced by the slow-fast subsystem alone. We show that the slow-fast subsystem MMOs are due to a folded node singularity, a geometrical structure well known to be involved in the generation of MMOs in slow-fast systems. Besides raising new mathematical questions for multiple-timescale systems, our work is a starting point for future research on how cAMP signalling, for example resulting from interactions between neurons and glial cells, affects neuronal activity via HCN and M channels. Author summary: Neurons use the frequency of electrical signals called action potentials to encode information, and various messenger systems interact with ion channels to control this so-called firing frequency. Recent experimental recordings show that the intracellular messenger cAMP can induce mixed-mode oscillations (MMOs) consisting of small-amplitude, subthreshold oscillations separating action potentials, which lowers the firing frequency greatly. We extend a recent mathematical model of neuronal electrical activity to investigate how MMOs occur from interactions between ion channels regulated by cAMP. Our simulations reproduce a range of experimental results, including cAMP-induced MMOs. We explain the model dynamics using modern geometrical methods that exploit the different timescales in the model. Our analyses show that the very slow dynamics of cAMP-regulated HCN and M ion channels is not crucial for creating MMOs, but rather that the cAMP-induced increase in their average activity is important. Our analyses suggest that both HCN and M channels are crucial for MMOs and controlling the firing frequency, which has implications for our understanding of how astrocytes control neuronal information processing. Moreover, our study raises new mathematical questions related to how super-slow dynamical variables modify MMOs. [ABSTRACT FROM AUTHOR]