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118 results for “spatial prediction”
Data from: Spatial scale matters for predicting plant invasions along roads
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MetaComNet: A random forest-based framework for making spatial prediction of plant-pollinator interactions
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Data from: Predicting disease risk areas through co-production of spatial models: the example of Kyasanur Forest Disease in India’s forest landscapes
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Data from: Accounting for uncertainty in marine ecosystem service predictions for spatial prioritisation
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Mimulus cardinalis plasticity analyses and R scripts for: Spatial variation in high temperature-regulated gene expression predicts evolution of plasticity with climate change in the scarlet monkeyflower
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Biogeography of the world’s worst invasive species has spatially-biased knowledge gaps but is predictable
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Data from: Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches
PLEASE NOTE, THESE DATA ARE ALSO REFERRED TO IN TWO OTHER PUBLICATIONS. PLEASE SEE http://dx.doi.org/10.1111/j.1365-2486.2008.01766.x AND http://dx.doi.org/10.1111/2041-210X.12222 FOR MORE INFORMATION. Aim: This study compares the direct, macroecological approach (MEM) for modelling species richness (SR) with the more recent approach of stacking predictions from individual species distributions (S-SDM). We implemented both approaches on the same dataset and discuss their respective theoretical assumptions, strengths and drawbacks. We also tested how both approaches performed in reproducing observed patterns of SR along an elevational gradient. Location: Two study areas in the Alps of Switzerland. Methods: We implemented MEM by relating the species counts to environmental predictors with statistical models, assuming a Poisson distribution. S-SDM was implemented by modelling each species distribution individually and then stacking the obtained prediction maps in three different ways – summing binary predictions, summing random draws of binomial trials and summing predicted probabilities – to obtain a final species count. Results: The direct MEM approach yields nearly unbiased predictions centred around the observed mean values, but with a lower correlation between predictions and observations, than that achieved by the S-SDM approaches. This method also cannot provide any information on species identity and, thus, community composition. It does, however, accurately reproduce the hump-shaped pattern of SR observed along the elevational gradient. The S-SDM approach summing binary maps can predict individual species and thus communities, but tends to overpredict SR. The two other S-SDM approaches – the summed binomial trials based on predicted probabilities and summed predicted probabilities – do not overpredict richness, but they predict many competing end points of assembly or they lose the individual species predictions, respectively. Furthermore, all S-SDM approaches fail to appropriately reproduce the observed hump-shaped patterns of SR along the elevational gradient. Main conclusions: Macroecological approach and S-SDM have complementary strengths. We suggest that both could be used in combination to obtain better SR predictions by following the suggestion of constraining S-SDM by MEM predictions.
Data from: Improving spatial predictions of taxonomic, functional and phylogenetic diversity
1. In this study, we compare two community modelling approaches to determine their ability to predict the taxonomic, functional and phylogenetic properties of plant assemblages along a broad elevation gradient and at a fine resolution. The first method is the standard stacking individual species distribution modelling (SSDM) approach, which applies a simple environmental filter to predict species assemblages. The second method couples the SSDM and macroecological modelling (MEM - SSDM-MEM) approaches to impose a limit on the number of species co-occurring at each site. Because the detection of diversity patterns can be influenced by different levels of phylogenetic or functional trees, we also examine whether performing our analyses from broad to more exact structures in the trees influences the performance of the two modelling approaches when calculating diversity indices. 2. We found that coupling the SSDM with the MEM improves the predictions for the diversity facets compared with those of the SSDM alone. The accuracy of the SSDM predictions for the diversity indices varied greatly along the elevation gradient, and when considering broad to more exact structure in the functional and phylogenetic trees, the SSDM-MEM predictions were more stable. 3. SSDM-MEM moderately but significantly improved the prediction of taxonomic diversity, which was mainly driven by the corrected number of predicted species. The performance of both modelling frameworks increased when predicting the functional and phylogenetic diversity indices. In particular, fair predictions of the taxonomic composition by SSDM-MEM led to increasingly accurate predictions of the functional and phylogenetic indices, suggesting that the compositional errors were associated with species that were functionally or phylogenetically close to the correct ones; this did not always hold for the SSDM predictions. 4. Synthesis. In this study, we tested the use of a recently published approach that couples species distribution and macroecological models to provide the first predictions of the distribution of multiple facets of plant diversity: taxonomic, functional and phylogenetic. Moderate but significant improvements were obtained; thus, our results open promising avenues for improving the predictions of different facets of biodiversityacross broad environmental gradients when functional and phylogenetic information is available.
Data from: Fear on the move: predator hunting mode predicts variation in prey mortality and plasticity in prey spatial response
1. Ecologists have long searched for a framework of a priori species traits to help predict predator-prey interactions in food webs. Empirical evidence has shown that predator hunting mode and predator and prey habitat domain are useful traits for explaining predator-prey interactions. Yet individual experiments have yet to replicate predator hunting mode, calling into question whether predator impacts can be attributed to hunting mode or merely species identity. 2. We tested the effects of spider predators with sit-and-wait, sit-and-pursue and active hunting modes on grasshopper habitat domain, activity and mortality in a grassland system. We replicated hunting mode by testing two spider predator species of each hunting mode on the same grasshopper prey species. We observed grasshoppers with and without each spider species in behavioral cages and measured their mortality rates, movements and habitat domains. We likewise measured the movements and habitat domains of spiders to characterize hunting modes. 3. We found that predator hunting mode explained grasshopper mortality and spider and grasshopper movement activity and habitat domain size. Sit-and-wait spider predators covered small distances over a narrow domain space and killed fewer grasshoppers than sit-and-pursue and active predators, which ranged farther distances across broader domains and killed more grasshoppers, respectively. Prey adjusted their activity levels and horizontal habitat domains in response to predator presence and hunting mode: sedentary sit-and-wait predators with narrow domains caused grasshoppers to reduce activity in the same-sized domain space; more mobile sit-and-pursue predators with broader domains caused prey to reduce their activity within a contracted horizontal (but not vertical) domain space; and highly mobile active spiders led grasshoppers to increase their activity across the same domain area. All predators impacted prey activity and sit-and-pursue predators generated strong effects on domain size. 4. This study demonstrates the validity of utilizing hunting mode and habitat domain for predicting predator-prey interactions. Results also highlight the importance of accounting for flexibility in prey movement ranges as an anti-predator response rather than treating the domain as a static attribute.
Data from: Predicting bird phenology from space: satellite-derived vegetation green-up signal uncovers spatial variation in phenological synchrony between birds and their environment
Population-level studies of how tit species (Parus spp.) track the changing phenology of their caterpillar food source have provided a model system allowing inference into how populations can adjust to changing climates, but are often limited because they implicitly assume all individuals experience similar environments. Ecologists are increasingly using satellite-derived data to quantify aspects of animals' environments, but so far studies examining phenology have generally done so at large spatial scales. Considering the scale at which individuals experience their environment is likely to be key if we are to understand the ecological and evolutionary processes acting on reproductive phenology within populations. Here, we use time series of satellite images, with a resolution of 240 m, to quantify spatial variation in vegetation green-up for a 385-ha mixed-deciduous woodland. Using data spanning 13 years, we demonstrate that annual population-level measures of the timing of peak abundance of winter moth larvae (Operophtera brumata) and the timing of egg laying in great tits (Parus major) and blue tits (Cyanistes caeruleus) is related to satellite-derived spring vegetation phenology. We go on to show that timing of local vegetation green-up significantly explained individual differences in tit reproductive phenology within the population, and that the degree of synchrony between bird and vegetation phenology showed marked spatial variation across the woodland. Areas of high oak tree (Quercus robur) and hazel (Corylus avellana) density showed the strongest match between remote-sensed vegetation phenology and reproductive phenology in both species. Marked within-population variation in the extent to which phenology of different trophic levels match suggests that more attention should be given to small-scale processes when exploring the causes and consequences of phenological matching. We discuss how use of remotely sensed data to study within-population variation could broaden the scale and scope of studies exploring phenological synchrony between organisms and their environment.
Data from: Predicted effects of climate factors on mountain species are not uniform over different spatial scales
The selection of relevant factors and appropriate spatial scale(s) is fundamental when modelling species response to climate change. We evaluated whether the effects of climate factors on species distribution/occurrence are consistently modelled over different spatial scales in birds, and used a two-scale approach to identify species-climate correlations unlikely to represent causal effects. We used passerine birds inhabiting mountain grassland in the Apennines (Italy) as a model. We surveyed four grassland species at 400 sampling points, and built habitat selection models (territory scale) and distribution models (7 algorithms, landscape scale). We compared the effect of climatic predictors on occurrence/distribution highlighted by models over to the two spatial scales, and with the effects supposed a priori based on the climatic niche of each species. Models at the territory level included at least one climatic predictor for three species; the observed effect of climatic predictors was seldom consistent with supposed effects. At the broadest scale, distribution models for all species included climatic predictors, with varying consistence with supposed effects and findings at the finer scale. Despite the importance of climate for species distribution, occurrence could be more directly related to other factors, with important implications for understanding/predicting the impacts of climate/environmental changes. Our approach revealed key variables for grassland birds, and highlighted the scale-dependent perceived importance of climate. At the local scale, climate effects were weak or hard to interpret. We found a general lack of consistence between supposed and observed effects at the territory level, and between landscape and territory models. Our results show the importance of predicting the potential effect of climatic factors prior to the analyses, carefully selecting ecologically meaningful variables and scales, and evaluating the nature and scale of climate-species links. We call for caution when predicting under future climates, especially when mechanistic effects and consistency across scales lack.
Data from: Drivers of the spatial scale that best predict primate responses to landscape structure
Understanding the effect of landscape structure on biodiversity is critically needed to improve management strategies. To accurately evaluate such effect, landscape metrics need to be assessed at the correct scale, i.e. considering the spatial extent at which species‐landscape relationship is strongest (scale of effect, SE). Although SE is highly variable, its drivers are poorly known, but of key relevance to understand the way species use the landscape. In this study, we evaluate whether and how species traits, biological responses, landscape variables and the regional context of the study drive SE in Mexican primates. We estimated the relative abundance and immature‐to‐female ratio (a proxy of reproductive success) of howler monkeys (Alouatta palliata and A. pigra) and spider monkeys (Ateles geoffroyi) in 48 forest patches from four rainforest regions (12 patches per region) with different land‐use intensity. We then assessed the composition (forest cover, matrix functionality) and configuration (forest patch density, connectors' density, forest edge density) of local landscapes considering 13 scales (100 to 1300‐m radius) to identify the spatial extent at which each landscape variable best predict each response variable in each species and region. We found that SE did not differ significantly among the drivers evaluated. However, it tended to be lower for connectors' density than for forest patch density and forest edge density, probably because connectors' density is associated with local‐scale processes such as supplementary dynamics. Surprisingly, SE also tended to be higher in the more disturbed region than in the rest of the regions, probably because primates in the more disturbed region used larger areas of the landscape. Our findings therefore suggest that SE depends more strongly on landscape variables and regional context than on species traits and biological responses, and hence, especial caution should be taken when attempting to generalize SE to different explanatory variables and regions.
How manual object exploration is associated with 7- to 8-month-old infants' visual prediction abilities in spatial object processing.
<p>Data set of Kubicek, C., Jovanovic, B., & Schwarzer, G. (in press). How manual object exploration is associated with 7- to 8-month-old infants’ visual prediction abilities in spatial object processing. <em>Infancy.</em></p>
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
<p>This data set is created for a purpose of publication "<span>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.</span></p>
Deficits of hierarchical predictive coding in left spatial neglect
<p>Right brain-damaged patients with unilateral spatial neglect fail to explore the left side of space. Recent EEG and clinical evidence suggests that neglect patients might suffer deficits in predictive coding, i.e. in identifying and exploiting probabilistic associations among sensory stimuli in the environment. To gain direct insights on this issue, we focussed on the hierarchical components of predictive coding. We recorded EEG responses evoked by central, left-side or right-side tones that were presented at the end of sequences of four central tones. Left-side and right-side deviant tones produce a pre-attentive Mismatch Negativity that reflects a lower-order prediction error for the ‘Local’ deviation of the tone at the end of the sequence. Higher-order prediction errors for the frequency of these deviations in the acoustic environment, i.e. ‘Global’ deviation, are marked by the P3 response. We show that when neglect patients are immersed in an acoustic environment characterized by frequent left-side deviant tones, they display no pre-attentive Mismatch Negativity both for left-side deviant tones and infrequent omissions of the last tone, while they have Mismatch Negativity for infrequent right-side deviant tones. In the same condition, neglect patients show no P300 response to ‘Global’ prediction errors for deviant tones, including those in the non-neglected right-side, and omissions. In contrast to this, when right-side deviant tones are predominant in the acoustic environment, neglect patients have pre-attentive Mismatch Negativity both for right-side deviant tones and infrequent omissions, while they display no Mismatch Negativity for infrequent left-side deviant tones. Most importantly, in the same condition neglect patients show enhanced P300 response to infrequent left-side deviant tones, notwithstanding that these tones evoked no pre-attentive Mismatch Negativity. This latter finding indicates that ‘Global’ predictions are independent of ‘Local’ error signals provided by the Mismatch Negativity. These results qualify deficits of predictive coding in the spatial neglect syndrome and show that neglect patients base their predictive behaviour only on statistical regularities that are related to the frequent occurrence of sensory events on the right side of space.</p>
History and environment shape spatial genetic variation and predict climate maladaptation in a narrowly distributed serotinous pine, Pinus muricata
<p><span></span></p> <p>Understanding the distribution of genetic diversity and differentiation in species with disjunct and isolated populations is critical for assessing how environment shapes genetic variation and the potential response to climate change. In contrast to the large distributions and population sizes of most pine species, <em>Pinus muricata</em> (Bishop pine) occurs in a small number of isolated and disjunct populations occupying a narrow band of environmental conditions along the coast of western North America. We used genotyping by sequencing to generate population genomic data for trees sampled from nearly all existing populations of <em>P. muricata</em> (12 populations, 213 individuals, 7,828 loci) to describe the spatial arrangement of genetic differentiation and diversity. We used genetic-environment association (GEA) analyses to quantify the contribution of environmental variables to local adaptation and spatial genetic structure. Based on these results, we quantified relative levels of potential maladaptation given future climate projections at 2041 – 2060 and 2081 – 2100. Our analyses reveal pronounced spatial genetic structure across the distribution, with most populations forming genetically identifiable groups across a latitudinal gradient, and remarkable evidence for differentiation among three proximally distributed stands on Santa Cruz Island. Despite occurring in small, isolated populations, <em>P. muricata</em> do not exhibit strongly reduced diversity. GEA analyses suggested that specific soil and climate variables have contributed to local adaptation. Genomic offset analyses suggest geographic variation in potential maladaptation, with northern populations experiencing higher levels under projected climate change. Overall, our results suggest that isolation and local adaptation have shaped genetic variation among disjunct populations, and illustrate the consequences of this variation for <em>P. muricata</em> under projected climate change.</p>
Predicting changes in molluscan spatial distributions in mangrove forests in response to sea-level rise
<p class="MsoNormal"><span>Molluscs are an important component of the mangrove ecosystem, and the vertical distributions of molluscan species in this ecosystem are primarily dictated by tidal inundation. Thus, sea-level rise (SLR) may have profound effects on mangrove mollusc communities. Here, we used dynamic empirical models, based on measurements of surface elevation change, sediment accretion, and molluscan zonation patterns, to predict changes in molluscan spatial distributions in response to different sea-level rise rates in the mangrove forests of Zhenzhu Bay (Guangxi, China). The change in surface elevation was 4.76–9.61 mm yr</span><sup><span>−</span></sup><sup><span>1</span></sup><span> during the study period (2016–2020), and the magnitude of surface-elevation change decreased exponentially as original surface elevation increased. Based on our model results, we predicted that mangrove molluscs might successfully adapt to a low rate of SLR (2.00–4.57 mm yr</span><sup><span>−</span></sup><sup><span>1</span></sup><span>) by 2100, with molluscs moving seaward and those in the lower intertidal zones expanding into newly available zones. However, as SLR rate increased (4.57–8.14 mm yr</span><sup><span>−</span></sup><sup><span>1</span></sup><span>), our models predicted that surface elevations would decrease beginning in the high intertidal zones and gradually spread to the low intertidal zones. Finally, at high rates of SLR (8.14–16.00 mm yr</span><sup><span>−</span></sup><sup><span>1</span></sup><span>), surface elevations were predicted to decrease across the elevation gradient, with molluscs moving landward and species in higher intertidal zones blocked by landward barriers. Tidal inundation and the consequent increases in interspecific competition and predation pressure were predicted to threaten the survival of many molluscan groups in higher intertidal zones, especially arboreal and infaunal molluscs at the landward edge of the mangroves, resulting in a substantial reduction in the abundance of original species on the landward edge. Thus, future efforts to conserve mangrove floral and faunal diversity should prioritize species restricted to landward mangrove areas and protect potential species habitats.</span></p>
Nest shape influences colony organization in ants: spatial distribution and connectedness of colony members differs from that predicted by random movement and is affected by nest space
<p><strong>Overview</strong></p> <p>Data used for the manuscript: Nest shape influences colony organization in ants: spatial distribution and connectedness of colony members differs from that predicted by random movement and is affected by available space</p> <p><strong>Purpose of the study</strong></p> <p>Investigating how nest shape influences how <em>Temnothorax rugatulus</em> colonies spatially organize in their nests. This includes physical location of colony members and their distances from the entrance, mobile colony member distance to the brood center, worker distance to the physical center of the nest, and comparing worker distributions with those predicted by a random walk model.</p> <p><strong>Structure of the data</strong></p> <p>EMPIRICAL DATA</p> <p>WORKERS: FullDataCoordWorkers.csv, FullDataCoordWorkersRD2.csv</p> <p>Raw experimental data with worker x and y position in nests</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Day: The experimental day that the observation was collected on</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> <li>ColorID: The unique color marking assigned to an individual worker's head, thorax, abdomen1, abdomen2 (i.e., Yellow, White, Green, Green = Y,W,G,G)</li> <li>Density: The density treatment (High / Low)</li> </ul> <p>BROOD / QUEENS: FullDataCoordBrood.csv, FullDataCoordBroodRD2.csv; FullDataCoordQueen.csv, FullDataCoordQueenRD2.csv</p> <p>Raw experimental data with brood (OR) queen x and y position in nests</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Day: The experimental day that the observation was collected on</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> <li>Density: The density treatment (High / Low)</li> </ul> <p>ALATES: FullDataCoordAlate.csv</p> <p>Raw experimental data with alate (winged reproductive individuals) x and y position in nests</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Day: The experimental day that the observation was collected on</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> <li>SexID: The unique sex assignment and number given to an individual alate: Sex, SexNumber, TotalNumber (i.e., the first male alate observation that came after three queen alates making it the fourth total observation = M,1,4)</li> </ul> <p>NETLOGO SIMULATIONS: ArchitectureMoveModelFull.csv</p> <p>Raw netlogo simulation data with agent x and y positions in nests</p> <ul> <li>RunNumber: The simulation number - 1 to 4000 - there are 1000 simulations for each combination of nest shape and size</li> <li>NestSize: The size of the nest area that agents were allowed to move throughout (Small / Large)</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>TimeStep: The duration of each simulation (should be 50000)</li> <li>xcor: a list of every agent x coordinate position at the end of the simulation</li> <li>ycor: a list of every agent y coordinate position at the end of the simulation</li> </ul> <p>REFERENCE DATA </p> <p>NEST BINS: Empirical</p> <p>BinsNullFull.csv</p> <p>Null data sheet with eight bins for tube and circle nests in every colony</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Bin: Nest section identifier (1-8)</li> </ul> <p>BinCoordFull.csv</p> <p>Reference binning coordinates to group empirical coordinates into nest sections</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>CoordID: The unique coordinate identifier within each colony and nest combination</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> </ul> <p>NEST BINS: Netlogo Simulations</p> <p>BinsNullNetlogo.csv</p> <p>Null data sheet with eight bins for tube and circle nests in each simulation treatment</p> <ul> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>NestSize: The size treatment for simulations (Small / Large)</li> <li>Bin: Nest section identifier (1-8)</li> </ul> <p>BinCoordNetlogo.csv</p> <p>Reference binning coordinates to group Netlogo simulation coordinates into nest sections</p> <ul> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>NestSize: The size treatment for simulations (Small / Large)</li> <li>ScaledX: X-axis coordinate</li> <li>ScaledY: Y-axis coordinate</li> <li>CoordID: The unique coordinate identifier within each colony and nest combination</li> </ul> <p>CORNERS: Empirical</p> <p>CornerFull.csv</p> <p>Whether a nest section has a corner or not</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Bin: Nest section identifier (1-8)</li> <li>Corner: Presence of a corner (Y / N)</li> </ul> <p>CORNERS: Empirical</p> <p>CornerFullSim.csv</p> <ul> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Bin: Nest section identifier (1-8)</li> <li>Corner: Presence of a corner (Y / N)</li> </ul> <p>REFERENCE DATA </p> <p>DISTANCES IN THE NEST: Empirical</p> <p>DistBinsFull.csv</p> <p>Reference coordinates for the entrance of nest sections (Bin) front-to-back and shortest distance to the entrance from each nest section entrance</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Distance: Reference shortest distance from a nest section to the entrance</li> <li>Bin: Nest section identifier (1-8)</li> <li>BinX: X-axis reference coodinate for a nest section entrance</li> <li>BinY: Y-axis reference coodinate for a nest section entrance</li> <li>Xmax: Max X-axis coordinate possible within the nest</li> <li>Ymax: Max Y-axis coordinate possible within the nest</li> <li>MaxDist: Max possible shortest distance from the nest entrance</li> <li>TubeRatio: Ratio of shortest distance to the nest entrance in circle nest / tube nest</li> </ul> <p>DISTANCES IN THE NEST: Netlogo Simulations</p> <p>DistBinsFullNetlogo.csv</p> <p>Reference coordinates for the entrance of nest sections (Bin) front-to-back and shortest distance to the entrance from each nest section entrance</p> <ul> <li>NestSize: The size treatment for simulations (Small / Large)</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Distance: Reference shortest distance from a nest section to the entrance</li> <li>Bin: Nest section identifier (1-8)</li> <li>BinX: X-axis reference coodinate for a nest section entrance</li> <li>BinY: Y-axis reference coodinate for a nest section entrance</li> <li>Xmax: Max X-axis coordinate possible within the nest</li> <li>Ymax: Max Y-axis coordinate possible within the nest</li> <li>MaxDist: Max possible shortest distance from the nest entrance</li> <li>TubeRatio: Ratio of shortest distance to the nest entrance in circle nest / tube nest</li> </ul> <p>REFERENCE DATA </p> <p>WORKER SITE FIDELITY (SPATIAL FIDELITY & OCCURRENCE ZONE SIZES), ALSO RELATING SIZES TO DISTANCES IN THE NEST</p> <p>ColorRefFull.csv</p> <p>Reference of all possible unique color identifiers paint marked workers</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Head: Head color mark</li> <li>Thorax: Thorax color mark</li> <li>Abd1: Left side abdomen mark</li> <li>Abd2: Right side abdomen mark</li> </ul> <p>NestAreaFull.csv</p> <p>Reference for colony size (number of workers in the colony) and nest area</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Number.ants: Number of workers in the colony after painting</li> <li>Diameter: The diameter of the circle nest</li> <li>Area: The area of the nest</li> </ul> <p>ScalingCircleSFZ.csv</p> <p>Reference to scale the radius of circle nests to make coordinates representing fidelity zone bins</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Scaling: The scaling factor that is applied to the radius of each circle nest</li> </ul>
Knowledge Graph Neural Network with Spatial-Aware Capsule for Drug-Drug Interaction Prediction
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Multimodal contrastive learning for spatial gene expression prediction using histology images
<p>we employed two human breast cancer datasets and one human cutaneous squamous cell carcinoma (cSCC) dataset.</p>
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.