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Data from: Spatial scaling of environmental variables improves species-habitat models of fishes in a small, sand-bed lowland river

Habitat suitability and the distinct mobility of species depict fundamental keys for explaining and understanding the distribution of river fishes. In recent years, comprehensive data on river hydromorphology has been mapped at spatial scales down to 100 m, potentially serving high resolution species-habitat models, e.g., for fish. However, the relative importance of specific hydromorphological and in-stream habitat variables and their spatial scales of influence is poorly understood. Applying boosted regression trees, we developed species-habitat models for 13 fish species in a sand-bed lowland river based on river morphological and in-stream habitat data. First, we calculated mean values for the predictor variables in five distance classes (from the sampling site up to 4000 m up- and downstream) to identify the spatial scale that best predicts the presence of fish species. Second, we compared the suitability of measured variables and assessment scores related to natural reference conditions. Third, we identified variables which best explained the presence of fish species. The mean model quality (AUC = 0.78, area under the receiver operating characteristic curve) significantly increased when information on the habitat conditions up- and downstream of a sampling site (maximum AUC at 2500 m distance class, +0.049) and topological variables (e.g., stream order) were included (AUC = +0.014). Both measured and assessed variables were similarly well suited to predict species' presence. Stream order variables and measured cross section features (e.g., width, depth, velocity) were best-suited predictors. In addition, measured channel-bed characteristics (e.g., substrate types) and assessed longitudinal channel features (e.g., naturalness of river planform) were also good predictors. These findings demonstrate (i) the applicability of high resolution river morphological and instream-habitat data (measured and assessed variables) to predict fish presence, (ii) the importance of considering habitat at spatial scales larger than the sampling site, and (iii) that the importance of (river morphological) habitat characteristics differs depending on the spatial scale.

opencc-zeroDec 2011View details →

Data from: Spatial heterogeneity in soil nutrient supply modulates nutrient and biomass responses to multiple global change drivers in model grassland communities

Changes in the atmospheric concentration of carbon dioxide ([CO2]), nutrient availability and biotic diversity are three major drivers of the ongoing global change impacting terrestrial ecosystems worldwide. While it is well established that soil nutrient heterogeneity exerts a strong influence on the development of plant individuals and communities, it is virtually unknown how nutrient heterogeneity and global change drivers interact to affect plant performance and ecosystem functioning. We conducted a microcosm experiment to evaluate the effect of simultaneous changes in [CO2], nutrient heterogeneity (NH), nutrient availability (NA) and species evenness on the biomass and nutrient uptake patterns of assemblages formed by Lolium perenne, Plantago lanceolata and Holcus lanatus. When the nutrients were heterogeneously supplied,assemblages exhibited precise root foraging patterns, and had higher above- and belowground biomass (average increases of 32% and 29% for above- and belowground biomass, respectively). Nutrient heterogeneity also modulated the effects of NA on biomass production, complementarity in nitrogen uptake and below: aboveground ratio, as well as those of [CO2] on the nutrient use efficiency at the assemblage level. Our results show that nutrient heterogeneity has the potential to influence the response of plant assemblages to simultaneous changes in [CO2], nutrient availability and biotic diversity, and suggest that it is an important environmental factor to interpret and assess plant assemblage responses to global change.

Data from: Evaluating conservation and fisheries management strategies by linking spatial prioritisation software and ecosystem and fisheries modelling tools

1. Well-designed marine protected area (MPA) networks can deliver a range of ecological, economic and social benefits, and so a great deal of research has focused on developing spatial conservation prioritization tools to help identify important areas. 2. However, whilst these software tools are designed to identify MPA networks that both represent biodiversity and minimize impacts on stakeholders, they do not consider complex ecological processes. Thus, it is difficult to determine the impacts that proposed MPAs could have on marine ecosystem health, fisheries and fisheries sustainability. 3. Using the eastern English Channel as a case study, this paper explores an approach to address these issues by identifying a series of MPA networks using the Marxan and Marxan with Zones conservation planning software and linking them with a spatially explicit ecosystem model developed in Ecopath with Ecosim. We then use these to investigate potential trade-offs associated with adopting different MPA management strategies. 4. Limited-take MPAs, which restrict the use of some fishing gears, could have positive benefits for conservation and fisheries in the eastern English Channel, even though they generally receive far less attention in research on MPA network design. 5. Our findings, however, also clearly indicate that no-take MPAs should form an integral component of proposed MPA networks in the eastern English Channel, as they not only result in substantial increases in ecosystem biomass, fisheries catches and the biomass of commercially valuable target species, but are fundamental to maintaining the sustainability of the fisheries. 6. Synthesis and applications. Using the existing software tools Marxan with Zones and Ecopath with Ecosim in combination provides a powerful policy-screening approach. This could help inform marine spatial planning by identifying potential conflicts and by designing new regulations that better balance conservation objectives and stakeholder interests. In addition, it highlights that appropriate combinations of no-take and limited-take marine protected areas might be the most effective when making trade-offs between long-term ecological benefits and short-term political acceptability.

opencc-zeroDec 2015View details →

Data from: Using camera trapping and hierarchical occupancy modelling to evaluate the spatial ecology of an African mammal community

Emerging conservation paradigms have shifted from single to multi-species approaches focused on sustaining biodiversity. Multi-species hierarchical occupancy modelling provides a method for assessing biodiversity while accounting for multiple sources of uncertainty. We analysed camera trapping data with multi-species models using a Bayesian approach to estimate the distributions of a terrestrial mammal community in northern Botswana and evaluate community, group, and species-specific responses to human disturbance and environmental variables. Groupings were based on two life-history traits: body size (small, medium, large and extra-large) and diet (carnivore, omnivore and herbivore). We photographed 44 species of mammals over 6607 trap nights. Camera station-specific estimates of species richness ranged from 8 to 27 unique species, and species had a mean occurrence probability of 0·32 (95% credible interval = 0·21–0·45). At the community level, our model revealed species richness was generally greatest in floodplains and grasslands and with increasing distances into protected wildlife areas. Variation among species' responses was explained in part by our species groupings. The positive influence of protected areas was strongest for extra-large species and herbivores, while medium-sized species actually increased in the non-protected areas. The positive effect of grassland/floodplain cover, alternatively, was strongest for large species and carnivores and weakest for small species and herbivores, suggesting herbivore diversity is promoted by habitat heterogeneity. Synthesis and applications. Our results highlight the importance of protected areas and grasslands in maintaining biodiversity in southern Africa. We demonstrate the utility of hierarchical Bayesian models for assessing community, group and individual species' responses to anthropogenic and environmental variables. This framework can be used to map areas of high conservation value and predict impacts of land-use change. Our approach is particularly applicable to the growing number of camera trap studies world-wide, and we suggest broader application globally will likely result in reduced costs, improved efficiency and increased knowledge of wildlife communities.

opencc-zeroDec 2017View details →

Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models

Density estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of "marked" wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of "unmarked" wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates.

opencc-zeroDec 2016View details →

Data from: Modelling unbiased dispersal kernels over continuous space by accounting for spatial heterogeneity in marking and observation efforts

1. Although a key demographic trait determining the spatial dynamics of wild populations, dispersal is notoriously difficult to estimate in the field. Indeed, dispersal distances obtained from the monitoring of marked individuals typically lead to biased estimations of dispersal kernels as a consequence of i) restricted spatial scale of the study areas compared to species potential dispersal and ii) heterogeneity in marking and observation efforts and therfore in detection probability across space. 2. Here we propose a novel method to circumvent these issues that does not require data on observation effort per se, to correct for the variability in detection of marked individuals across space. Observed dispersal events were weighted by the distribution of departure points and an eroded spatial window approach was applied so as to deal with border effect. We conducted a set of simulations which indicated that our method was successful in correcting the effect of spatially heterogeneous detectability and produce unbiased dispersal kernels. 3. We applied this method to a real dataset on Montagu's harrier (>5000 chicks tagged), providing ca. 6000 resightings collected in entire France by a network of 1200 volunteers within a citizen-science program. The median dispersal distance observed was 32 km (range: 0.1-627 km). Once corrected for spatial heterogeneity in marking and observation efforts and border effect, the modelled dispersal kernel indicated a median dispersal distance of 78-123 km depending on the spatial scale considered (constrained within French borders or not, respectively). 4. Synthesis and applications: The current rise of citizen-science programs is likely to stretch our estimate of the ecologically-relevant spatial scale at which dispersal takes place for many taxa. Our method is particularly suited for such large scale data that typically suffer from high spatial heterogeneity in marking and observation efforts and offers the possibility to derive unbiased dispersal kernels, a key component for modelling population dynamics and species distribution in a context of environmental change. Currently, our method assumes homogeneity in both habitat and dispersal behaviour across individuals. We discuss however how to relax these hypotheses to further investigate the effect of e.g. local conspecific density or habitat quality on dispersal propensity.

opencc-zeroDec 2016View details →

Data from: Generalized spatial mark-resight models with an application to grizzly bears

1. The high cost associated with capture-recapture studies presents a major challenge when monitoring and managing wildlife populations. Recently-developed spatial mark-resight (SMR) models were proposed as a cost-effective alternative because they only require a single marking event. However, existing SMR models ignore the marking process and make the tenuous assumption that marked and unmarked populations have the same encounter probabilities. This assumption will be violated in most situations because the marking process results in different spatial distributions of marked and unmarked animals. 2. We developed a generalized SMR model that includes sub-models for the marking and resighting processes, thereby relaxing the assumption that marked and unmarked populations have the same spatial distributions and encounter probabilities. 3. Our simulation study demonstrated that conventional SMR models produce biased density estimates with low credible interval coverage when marked and unmarked animals had differing spatial distributions. In contrast, generalized SMR models produced unbiased density estimates with correct credible interval coverage in all scenarios. 4. We applied our SMR model to grizzly bear (Ursus arctos) data where the marking process occurred along a transportation route through Banff and Yoho National Parks, Canada. Twenty-two grizzly bears were trapped, fitted with radio-collars, and then detected along with unmarked bears on 214 remote cameras. Closed population density estimates (posterior median + 1 SD) averaged from 2012 to 2014 were much lower for conventional SMR models (7.4 + 1.0 bears per 1,000 km2) than for generalized SMR models (12.4 + 1.5). When compared to previous DNA-based estimates, conventional SMR estimates erroneously suggested a 51% decline in density. Conversely, generalized SMR estimates were similar to previous estimates, indicating that the grizzly bear population was relatively stable. 5. Synthesis and application. Conventional SMR models that ignore the marking process should only be used when marked and unmarked animals share the same spatial distribution, such as when a subset of the population has natural marks. Generalized SMR models that include the marking process are much more widely applicable. They represent a promising new approach for reducing the costs of studies aimed at understanding spatial and temporal variation in density.24-May-2017

opencc-zeroDec 2015View details →

Data from: Ignoring spatial effects results in inadequate models for variation in littoral macroinvertebrate diversity

Studies focusing on the effects of spatial processes versus environmental filtering on aquatic metacommunities have so far been focused almost entirely on relatively isolated systems, such as sets of different lakes or streams. In contrast, metacommunity patterns and underlying processes within a single aquatic system have received less attention. In this study, we aimed to examine how strongly variations in different diversity indices are affected by spatial processes (dispersal) versus local environmental conditions (species sorting) within a large lake system. Modern biodiversity research focuses on multiple diversity facets because different indices may be uncorrelated within and between facets, and they may thus describe different phenomena. We investigated the relationship of littoral macroinvertebrate diversity with environmental and spatial factors using 10 indices of species, functional and taxonomic diversity. Using spatial factors as proxies of dispersal, we decomposed variation in diversity indices into fractions attributable to environmental and spatial factors. Our results highlighted generally equal or higher importance of spatial processes in controlling the variation in diversity indices when compared to local environmental variables. Local environmental conditions accounted for higher proportion of variation only in a single index (i.e. taxonomic diversity). These findings suggest that the effects of high dispersal rates (mass effects) may override the influences of local environmental conditions (species sorting) on the diversity in highly-connected aquatic system, such as large lakes and marine coastal systems. Our results further suggest that biodiversity assessment and environmental monitoring in highly-connected systems cannot rely solely on the idea of environmental control. We hence recommend that the roles of both environmental and spatial processes should be integrated in basic and applied ecological research of aquatic systems.

opencc-zeroDec 2017View details →

Data from: A spatially explicit hierarchical model to characterize population viability

Many of the processes that govern the viability of animal populations vary spatially, yet population viability analyses (PVAs) that account explicitly for spatial variation are rare. We develop a PVA model that incorporates autocorrelation into the analysis of local demographic information to produce spatially explicit estimates of demography and viability at relatively fine spatial scales across a large spatial extent. We use a hierarchical, spatial autoregressive model for capture-recapture data from multiple locations to obtain spatially explicit estimates of adult survival (Φad), juvenile survival (Φjuv), and juvenile-to-adult transition rates (ψ), and a spatial autoregressive model for recruitment data from multiple locations to obtain spatially explicit estimates of recruitment (R). We combine local estimates of demographic rates in stage-structured population models to estimate the rate of population change (λ), then use estimates of λ (and its uncertainty) to forecast changes in local abundance and produce spatially explicit estimates of viability (probability of extirpation, Pex). We apply the model to demographic data for the Sonoran desert tortoise (Gopherus morafkai) collected across its geographic range in Arizona. There was modest spatial variation in λ (0.94–1.03), which reflected spatial variation in Φad (0.85–0.95), Φjuv (0.70–0.89), and ψ (0.07–0.13). Recruitment data were too sparse for spatially explicit estimates, therefore we used a range-wide estimate (R = 0.32 one-year old females per female per year). Spatial patterns in demographic rates were complex, but Φad, Φjuv, and λ tended to be lower and ψ higher in the northwestern portion of the range. Spatial patterns in Pex varied with local abundance. For local abundances > 500, Pex was near zero (Pex approached one in the northwestern portion of the range and remained low elsewhere. When local abundances were Pex > 0.25). This approach to PVA offers the potential to reveal spatial patterns in demography and viability that can inform conservation and management at multiple spatial scales, provide insight into scale-related investigations in population ecology, and improve basic ecological knowledge of landscape-level phenomena.

opencc-zeroDec 2018View details →

Data from: How does spatial resolution affect model performance? A case for ensemble approaches for marine benthic mesophotic communities

Aim: To investigate how changing grid size can alter model predictions of the distribution of mesophotic taxa and how it affects different modelling methods. Location: Ningaloo Marine Park, Western Australia. Taxon: Benthic mesophotic taxa: corals, macroalgae, and sponges. Methods: We determined the distributions of the major benthic taxonomic groups: corals, macroalgae, and sponges, using a number of modelling techniques and an ensemble using the 'sdm' R package. A range of grid sizes were used (10 m, 50 m, 100 m, and 250 m) to identify how model predictions were altered. Models were evaluated using the area under the curve of a receiver operator characteristic plot (AUC) and the true skill statistic (TSS) using a spatially independent dataset. Results: Grid size had a large effect on model performance across the taxonomic groups. Model outputs were compared to null surfaces and 88.8% of models performed significantly better than null. Distribution of corals was best predicted using the finest grid size (10 m) regardless of modelling method, although a model ensemble produced the best results (AUC = 0.80, TSS = 0.52). Macroalgae and sponges were better predicted at coaster grids sizes (250 m). Again, ensembles performed well for both macroalgae (AUC = 0.83, TSS = 0.63) and sponges (AUC = 0.88, TSS = 0.66). Model ensembles maintained high accuracy across grid sizes and were consistently the best, or second-best, performing method. Main Conclusions: This study has shown how grid size should be considered when producing distribution models. Identifying the most relevant grid size and being aware of the influence it may have will provide more accurate predictions of the distributions of taxa. Ensemble methods maintained good performance across scenarios and thus provide a useful tool for conservation and management especially where single modelling methods showed high levels of variability.

opencc-zeroDec 2018View details →

Data from: Use of classical bird census transects as spatial replicates for hierarchical modeling of an avian community

New monitoring programs are often designed with some form of temporal replication to deal with imperfect detection by means of occupancy models. However, classical bird census data from earlier times often lack temporal replication, precluding detection-corrected inferences about occupancy. Historical data have a key role in many ecological studies intended to document range shifts, and so need to be made comparable with present‐day data by accounting for detection probability. We analyze a classical bird census conducted in the region of Murcia (SE Spain) in 1991 and 1992 and propose a solution to estimating detection probability for such historical data when used in a community occupancy model: the spatial replication of subplots nested within larger plots allows estimation of detection probability. In our study, the basic sample units were 1‐km transects, which were considered spatial replicates in two aggregation schemes. We fit two Bayesian multispecies occupancy models, one for each aggregation scheme, and evaluated the linear and quadratic effect of forest cover and temperature, and a linear effect of precipitation on species occupancy probabilities. Using spatial rather than temporal replicates allowed us to obtain individual species occupancy probabilities and species richness accounting for imperfect detection. Species‐specific occupancy and community size decreased with increasing annual mean temperature. Both aggregation schemes yielded estimates of occupancy and detectability that were highly correlated for each species, so in the design of future surveys ecological reasons and cost‐effective sampling designs should be considered to select the most suitable aggregation scheme. In conclusion, the use of spatial replication may often allow historical survey data to be applied formally hierarchical occupancy models and be compared with modern‐day data of the species community to analyze global change process.

Data from: Modeling spatial expansion of invasive alien species: relative contributions of environmental and anthropogenic factors to the spreading of the harlequin ladybird in France

Species distribution models (SDM) have often been used to predict the potential ranges of introduced species and prioritize management strategies. However, this approach assumes equilibrium between occurrences and environmental gradients, an assumption which is violated during the invasion process, where many suitable sites are empty because the species has not yet reached them. Here we considered the invasive ladybird Harmonia axyridis as a case study to show the benefits of using a dynamic colonization–extinction model that does not assume equilibrium. We used a multi-year occupancy model incorporating environmental, anthropogenic and neighborhood effects, to identify factors that explained spreading variation of this species in France from 2004, when only a few occupied sites were detected, to 2011. We found that anthropogenic factors (urbanization, agriculture, vineyards, and presence/absence of highways) explained more variation in the diffusion process than environmental factors (winter and summer temperatures, wind-speed, and rainfall). The surface of urbanization was the major anthropogenic factor increasing the probability of colonization. The average summer temperature was the main environmental factor affecting colonization, with a negative effect when high or low. The neighborhood effect revealed that colonization was mostly influenced by contributions coming from a radius of 24 km around the focal cell. The contribution of neighborhood decreases over time, suggesting that H. axyridis is reaching its equilibrium in France. This is confirmed by the small discrepancy observed between the performance of our approach and a SDM approach when predicting a single year occupancy pattern at the end of the study period. Our approach has the advantage of explicitly modelling the state of the biological system during the spatial expansion and identifying colonization constraints. This allows managers to explore the effect of different actions on the system at key moments of the invasion process, hence providing a powerful approach to prioritize management strategies.

Data from: Examining temporal sample scale and model choice with spatial capture-recapture models in the common leopard Panthera pardus

Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the "true" explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km2 (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.

Data from: Conservation versus livelihoods: spatial management of non-timber forest product harvests in a two-dimensional model

Areas of high biodiversity often coincide with communities living in extreme poverty. As a livelihood support, these communities often harvest wild products from the environment. But harvest activities can have negative impacts on fragile and globally important ecosystems. This paper examines trade-offs in ecological protection and community welfare from the harvest of wild products. With a novel model and empirical evidence, I show that management of harvest activity does not always resolve these trade-offs. In a model of continuous harvests in a two-dimensional landscape, managed harvest activity improves welfare, but is uniformly bad for other ecosystem services that are sensitive to the presence (as opposed to the intensity) of human activity. Empirical results from a unique dataset of mushroom harvesters in Yunnan, China suggest more experienced, poorer, and more vulnerable individuals tend to rely on more distant harvests. Thus, policies that limit the extent of forest travel, such as protected areas, may protect fragile ecosystems but can have a disproportionately negative effect on those most vulnerable.

opencc-by-4.0Oct 2023View details →

03/04 - Spatial and Amplitude Dynamics of Neurostimulation: Insights from the Acute Intrahippocampal Kainate Seizure Mouse Model

Dataset #3 of 4

opencc-by-4.0Oct 2023View details →

01/04 - Spatial and Amplitude Dynamics of Neurostimulation: Insights from the Acute Intrahippocampal Kainate Seizure Mouse Model

Dataset #1 of 4

opencc-zeroJan 2024View details →

spatiAlign: An Unsupervised Contrastive Learning Model for Data Integration of Spatially Resolved Transcriptomics

Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. Here, we propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.

opencc-by-4.0Mar 2024View details →

Dataset for Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes

This data set is created for a purpose of publication "Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes". It contains of dataset of kicks and lstm models for predictions a force of kicks upon IMU data. Detailed description of file names are in readme file. Folders are divided into specific kicks - turning or side kick in sport or traditional versions.

opencc-by-4.0Apr 2024View details →

Imbalanced regressive neural network model for whistler-mode hiss waves: spatial and temporal evolution

This dataset contains the whistler-mode hiss waves obtained from the Van Allen Probes. It is accompanied by the manuscript "Imbalanced regressive neural network model for whistler-mode hiss waves: spatial and temporal evolution".