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→ Fig. 9. Antiarchan fish Bothriolepis leptocheira jeremejevi (Rohon, 1900), Sosnogorsk locality, Sosnogorsk Formation, lowermost Famennian, anterior median dorsal (A–G) and posterior median dorsal (H–M) plates of the trunk armour. A. IG KSC 155/5 in dorsal (A1) and visceral (A2) views. B. IG KSC 155/108 in dorsal (B1) and visceral (B2) views. C. IG KSC 155/97 in dorsal view. D. IG KSC 155/113 in dorsal (D1) and visceral (D2) views. E. IG KSC 155/140 in dorsal (E1) and visceral (E2) views. F. Impression of the dorsal surface of IG KSC 155/42. G. IG KSC 155/44 in dorsal view. H. Fragment of IG KSC 155/7 in dorsal view. I. IG KSC 155/1 in dorsal (I1) and visceral (I2) views. J. IG KSC 155/71 in dorsal view. K. Slightly deformed IG KSC 155/70 in dorsal (K1) and visceral (K2) views. L. IG KSC 155/158 in dorsal view. M. IG KSC 155/157 in dorsal (M1) and visceral (M2) views. Abbreviations: ADL, anterior dorso-lateral plate; alr, postlevator thickening; AMD, anterior median dorsal plate; cf.ADL, cf.AMD, and cf.MxL, area overlapping ADL, AMD or MxL respectively; cr.tp, posterior transversal internal crest; dlg1 and dlg2, anterior and posterior oblique dorsal sensory line groove; dma, tergal angle; dmr, dorsal median ridge; f.retr, levator fossa; grm, ventral median groove; l, lateral corner; mvr, median ventral ridge; MxL, mixilateral plate; npn, postnuchal notch; oa.ADL, oa.MxL and oa.PMD, area overlapped by ADL, MxL or PMD respectively; pa, posterior corner; pma, posterior marginal area; PMD, posterior median dorsal plate; pr.p, posterior process of AMD; pr.pl, external postlevator process; prv2, posterior ventral process of dorsal wall of trunk armour; pt1 and pt2, anterior and posterior ventral pit; pua, posterior unornamented area of PMD; rf, "round fossula"; sna, supranuchal area; tb, ventral tuberosity. in A new assessment of the Late Devonian antiarchan fish Bothriolepis leptocheira from South Timan (Russia) and the biotic crisis near the Frasnian-Famennian boundary
→ Fig. 9. Antiarchan fish Bothriolepis leptocheira jeremejevi (Rohon, 1900), Sosnogorsk locality, Sosnogorsk Formation, lowermost Famennian, anterior median dorsal (A–G) and posterior median dorsal (H–M) plates of the trunk armour. A. IG KSC 155/5 in dorsal (A1) and visceral (A2) views. B. IG KSC 155/108 in dorsal (B1) and visceral (B2) views. C. IG KSC 155/97 in dorsal view. D. IG KSC 155/113 in dorsal (D1) and visceral (D2) views. E. IG KSC 155/140 in dorsal (E1) and visceral (E2) views. F. Impression of the dorsal surface of IG KSC 155/42. G. IG KSC 155/44 in dorsal view. H. Fragment of IG KSC 155/7 in dorsal view. I. IG KSC 155/1 in dorsal (I1) and visceral (I2) views. J. IG KSC 155/71 in dorsal view. K. Slightly deformed IG KSC 155/70 in dorsal (K1) and visceral (K2) views. L. IG KSC 155/158 in dorsal view. M. IG KSC 155/157 in dorsal (M1) and visceral (M2) views. Abbreviations: ADL, anterior dorso-lateral plate; alr, postlevator thickening; AMD, anterior median dorsal plate; cf.ADL, cf.AMD, and cf.MxL, area overlapping ADL, AMD or MxL respectively; cr.tp, posterior transversal internal crest; dlg1 and dlg2, anterior and posterior oblique dorsal sensory line groove; dma, tergal angle; dmr, dorsal median ridge; f.retr, levator fossa; grm, ventral median groove; l, lateral corner; mvr, median ventral ridge; MxL, mixilateral plate; npn, postnuchal notch; oa.ADL, oa.MxL and oa.PMD, area overlapped by ADL, MxL or PMD respectively; pa, posterior corner; pma, posterior marginal area; PMD, posterior median dorsal plate; pr.p, posterior process of AMD; pr.pl, external postlevator process; prv2, posterior ventral process of dorsal wall of trunk armour; pt1 and pt2, anterior and posterior ventral pit; pua, posterior unornamented area of PMD; rf, "round fossula"; sna, supranuchal area; tb, ventral tuberosity.
Cloud_ICA: A deterministic cloud-overlap algorithm for generating a complete set of independent column atmospheres
<p>In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here, we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate‐relevant scales. We build benchmarking capability around a solar heating code (Solar‐J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice‐cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors in W m−2 using a full climate re‐creation for January 2015 from a weather forecasting model. For many approximations assessed here, mean errors are ∼2 W m−2 with greater latitudinal biases and are likely to affect a model's ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.</p>
Data from: Migratory singers dynamically overlap the signal space of a breeding warbler community
<p>Migratory species inhabit many communities along their migratory routes. Across taxa, these species repeatedly move into and out of communities, interacting with each other and locally breeding species and competing for resources and niche space. However, their influence is rarely considered in analyses of ecological processes within the communities they temporarily occupy. Here, we explore the impact of migratory species on a breeding community using the framework of acoustic signal space, a limited resource in which sounds of species within communities co-exist. Migrating New World warblers (Parulidae, hereafter referred to as migrant species) often sing during refueling stops in areas and at times during which locally breeding warbler species (hereafter breeding species) are singing to establish territories and attract mates. We used eBird data to determine co-occurrence of 19 migrant and 11 breeding warbler species across spring migration in SW Michigan, generated a signal space from song recordings of these species, and examined patterns of signaling overlap experienced by breeding species as migrants moved through the community. Migrant species were present for two-thirds of the breeding season of local species, including periods when breeding species established territories and attracted mates. Signaling niche overlap experienced by individual breeding species was idiosyncratic and varied over time, yet niche overlap between migrant and breeding species occurred more commonly than between breeding species or between migrant species. Nevertheless, the proportion of niche overlap between migrant and breeding warblers was similar to overlap among breeding species. Our findings showed that singing by migrant species overlapped the signals of many breeding species, suggesting that migrants could have unexplored impacts on communication in breeding species, potentially affecting song detection and song evolution. Our study contributes to a growing body of research documenting impacts of migratory species on communities and ecosystems.</p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road segmentation models corresponding to the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography". The scripts make use of the Tensorflow with Keras framework and their additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (<a href="../records/6482346">https://zenodo.org/records/6482346</a>) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 492 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area from Palencia (Spain) and features 18 million pixels labelled with the positive "Road" class. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Fig. 3 in Quantitative Biogeographic Characterization Of Hungary Based On The Distribution Data Of Land Snails (Mollusca,Gastropoda): A Case Of Nestedness Of Species Ranges With Extensive Overlap Of Biotic Elements
Fig. 3. Distribution maps of four biotic elements found by PRABCLUS: (a) highland species, (b) general species, (c) localizes species distributed in the northern and (d) south–eastern parts of Hungary. The different shadings indicate the areas where>70%,>30%, and>0% of the species of an ele-
Fig. 1 in Quantitative Biogeographic Characterization Of Hungary Based On The Distribution Data Of Land Snails (Mollusca,Gastropoda): A Case Of Nestedness Of Species Ranges With Extensive Overlap Of Biotic Elements
Fig. 1. Biogeographic classification of Hungary based on distribution data of land snails according to the (a) hierarchical clustering of the (b) spatial units (ca. 50 km × 50 km). For clustering, the Sørensen–index and Ward–Orlóci fusion method was used. Shades of grey indicate main partitions of the cluster hierarchy, circled numbers 1–6 indicate lower level partitions mentioned in the text, numbers 1–49 identify spatial units (a) in the cluster foot and (b) in the map. Capital letters correspond to IndVal species groups listed in the text and in Appendix, lines associated to letters refer to
Fig. 2 in Quantitative Biogeographic Characterization Of Hungary Based On The Distribution Data Of Land Snails (Mollusca,Gastropoda): A Case Of Nestedness Of Species Ranges With Extensive Overlap Of Biotic Elements
Fig. 2. First two dimensions of the metric multidimensional scaling of the range data of the Hungarian land snail species. 1–4: biotic elements found by PRABCLUS; N: noise component.
Fig. 4 in Stable isotope evidence for trophic overlap of sympatric Mexican Lake Chapala silversides (Teleostei: Atherinopsidae: Chirostoma spp.)
Fig. 4. Canonical variate analysis (CVA) plot and deformation grids for twelve head landmarks (shown in inset photograph) of five species of Chirostoma from Lake Chapala, Mexico. Deformation grids exaggerated 3x times to better visualize shape differences. Analysis based on museum specimens from lots at Tulane University (TU), the University of Michigan Museum of Zoology (UMMZ) and Southeastern Louisiana State University (SLU): C. consocium: UMMZ 240102 (n=30); C. jordani: SLU 5031 (n=2), 5033 (n=2), 6678 (n=2); C. labarcae: SLU 6679 (n=23), TU 31966 (n=15); C. promelas: TU 31987 (n=1), 40843 (n=5), and C. sphyraena: SLU 5032 (n=1), SLU 6676 (n=6), 6841 (n=1). Analysis carried out using tps DIG V.2 (SUNY, Stonybrook University), Procrustes fit and data alignment by principle axes. CVA carried out using MorphoJ v105a.
Fig. 1 in Stable isotope evidence for trophic overlap of sympatric Mexican Lake Chapala silversides (Teleostei: Atherinopsidae: Chirostoma spp.)
Fig. 1. Lake Chapala in West-Central Mexico with eleven sampling sites identified (sites represented by circles were located> 1 km from the shoreline, sites represented by triangles were located <1 km from the shoreline). The Lerma – Santiago River Basin is shown as reference. The Lerma (inlet) and Santiago (outlet) rivers are identified. See methods section for a description of sites, including site 11, not shown in the image.
Figure 15 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 15. The variation of the Pianka mean niche overlap index deviation from random alternative within principal components 3 and 4. Spawning start: B_bjoerkna – Blicca bjoerkna, C_gibelio – Carassius gibelio, S_erythrophthalmus – Scardinius erythrophthalmus, A_brama – Abramis brama, P_fluviatilis – Perca fluviatilis, R_rutilus – Rutilus rutilus, E_lucius – Esox lucius; regression residuals of the spawning end dependence from the start: B_delta – Blicca bjoerkna, C_delta – Carassius gibelio, S_delta – Scardinius erythrophthalmus, A_delta – Abramis brama, P_delta – Perca fluviatilis, R_delta – Rutilus rutilus, E_delta – Esox lucius.
Figure 12 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 12. Spawning variance partitioning between broad-, medium-, and fine-scale temporal temperature patterns and biotope type explanatory variables. Notes: [a] – variation explained solely by broad temperature variables; [b] – variation explained solely by medium temperature variables; [c] – variation explained solely by fine temperature variables; [d] – variation explained solely by biotope type. The intersection of the ellipses corresponds to the variations explained by the respective sources together All the variance fractions shown are significant (p <0.001).
Figure 14 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 14. Relative variance of the principal components (in %). Method: ANOVA Method, Type I SS, columns denote cumulative sums of variance components.
Figure 13 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 13. The temporal variation of the principal components 1–5. the x-axis – years, the y-axis – the scores of the principal components 1–5.
Figure 10 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 10. Distribution histograms of the Pianka mean niche overlap indexes in different types of habitats: 1 – Nikolayev system of water bodies; 2 – river Protoch system and Obukhov floodplain; 3 – the channel of the river Dnipro; 4 – water bodies of the Taromske ledge.
Figure 4 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 4. Scalogram illustrating the scaling of temporally structured variation in polynomial trend residuals data. The abscissa axis – dbMEMs ordered decreasingly according to the scale of temporal patterns they represent (dbMEM 1 represents the broadest scale, dbMEM 104 the finest scale). The ordinate axis – value of R2 is the variation explained adj by individual dbMEM variables.
Figure 5. Broad-scale components RDA 1-3 in Effects of temperature patterns on the spawining phenology and niche overlap of fish assemblages in the water bodies of the Dnipro River basin
Figure 5. Broad-scale components RDA 1-3 of the annual temperature variation. Black line – the original data, colored lines – smoothed data. The abscissa axis – the number of days from 1 July of the previous year to June 31 of the next year
◂Fig. 13 Crystallographic structure on the columellar lamellae of Dinaride Zospeum and Iberozospeum shells; (a) Zospeum spelaeum, (AJC 847), Betalov Spodmol jama, Slovenia (45.7922 14.1877), pattern of low, non-overlapping, wedges of crystallographic structure on the lamella; (b) Zospeum spelaeum, (MCBI CSR SASA 37049a), Velika Pasica, Slovenia (N45.9189 E14.4934), non-overlapping wedges of crystallographic structure on lamella in old shell; (c) Iberozospeum sp., (RMNH.MOL. 234,120), Cueva Refugio, Trucios, overview of dense, overlapping, scale-like wedges of localized, crystallographic structure on upper part of the lower lamella; (d) ibid., closeup view of c; (e) Iberozospeum sp., (RMNH.MOL. 234,104), Cueva del Comediante, Santander, upper part of the lamella of chemically treated shell showing dense, overlapping wedges of localized, crystallographic structure; (f) Iberozospeum sp., (RMNH. MOL. 234,141), Cueva a Sul, Oviedo, localized, overlapping wedges of crystallographic structure on lamella of chemically treated shell; (g) Iberozospeum vasconicum, (AJC 1849), Cueva Arrikrutz, overview of dense, localized, crystallographic structure on lower part of the lamella; h, ibid., closeup view of g. — Magnification varies for each perspective, see scale bars; Figs. a–b, g–h) imaged by M. Ruppel, (ret.) Goethe University Frankfurt am Main; Figs. c–f imaged by Dirk Vendermarel, Naturalis Biodiversity Center in Molecular investigation and description of Iberozospeum n. gen., including the description of one new species (Eupulmonata, Ellobioidea, Carychiidae)
◂Fig. 13 Crystallographic structure on the columellar lamellae of Dinaride Zospeum and Iberozospeum shells; (a) Zospeum spelaeum, (AJC 847), Betalov Spodmol jama, Slovenia (45.7922 14.1877), pattern of low, non-overlapping, wedges of crystallographic structure on the lamella; (b) Zospeum spelaeum, (MCBI CSR SASA 37049a), Velika Pasica, Slovenia (N45.9189 E14.4934), non-overlapping wedges of crystallographic structure on lamella in old shell; (c) Iberozospeum sp., (RMNH.MOL. 234,120), Cueva Refugio, Trucios, overview of dense, overlapping, scale-like wedges of localized, crystallographic structure on upper part of the lower lamella; (d) ibid., closeup view of c; (e) Iberozospeum sp., (RMNH.MOL. 234,104), Cueva del Comediante, Santander, upper part of the lamella of chemically treated shell showing dense, overlapping wedges of localized, crystallographic structure; (f) Iberozospeum sp., (RMNH. MOL. 234,141), Cueva a Sul, Oviedo, localized, overlapping wedges of crystallographic structure on lamella of chemically treated shell; (g) Iberozospeum vasconicum, (AJC 1849), Cueva Arrikrutz, overview of dense, localized, crystallographic structure on lower part of the lamella; h, ibid., closeup view of g. — Magnification varies for each perspective, see scale bars; Figs. a–b, g–h) imaged by M. Ruppel, (ret.) Goethe University Frankfurt am Main; Figs. c–f imaged by Dirk Vendermarel, Naturalis Biodiversity Center
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 2: Overlapped EEG segments
<p>In this study, the short time Fourier transformation (STFT) is used to examine the stability of the SSVEP. As the frequency resolution decreases with window size, overlapped EEG segments (Figure 2) that give sufficient frequency resolution are used. Coefficient of variation and variation speed detection features are proposed by using frequency spectrum of the segments. In Equation 1, x(t) sequence defines overlapped EEG segments in time domain, and in Equation 2 the x(f) sequence defines these segments in frequency domain and m is number of segments. </p> <p> </p>
Data archive: Niche overlap between a cold-water coral and an associated sponge for isotopically-enriched particulate food sources
<p>Data belonging to the paper: </p> <p>Dick van Oevelen, Christina E. Mueller, Tomas Lundälv, Fleur C. van Duyl, Jasper M. de Goeij, Jack J. Middelburg<span> </span>(In press) <strong>Niche overlap between a cold-water coral and an associated sponge for isotopically-enriched particulate food sources</strong>. PLOS ONE</p>
Text-fig. 3. CA climate charts for the Monte Tondo and Tossignano floras, showing climatic ranges of the Nearest Living Relatives of the fossil taxa with respect to MAT. Right-hand positoned large bold figures and shaded areas in each case indicate the Coexistence Interval, with the number of overlapping taxa being at a maximum. in Palaeoenvironmental Analysis Of The Messinian Macrofossil Floras Of Tossignano And Monte Tondo (Vena Del Gesso Basin, Romagna Apennines, Northern Italy)
Text-fig. 3. CA climate charts for the Monte Tondo and Tossignano floras, showing climatic ranges of the Nearest Living Relatives of the fossil taxa with respect to MAT. Right-hand positoned large bold figures and shaded areas in each case indicate the Coexistence Interval, with the number of overlapping taxa being at a maximum.
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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.