Find research datasets worth reusing

Text-fig. 4. Charred grass from diatomite of Saint-Bauzile. a: Overview of diatomite slab with one larger specimen of charred grass (left) and several smaller, lath-shaped charcoal fragments; SM.B 22260; scale bar = 1 cm. b: Detail of vein exhibited on split grass blade, with stomata oriented parallel to vein. c: Stomata oriented in rows and bands parallel to veins exposed on split grass blade. d: Surface of grass leaf with rectangular, elongated cells with strongly undulating margins in an intercostal area. in Evidence For Wildfires During Deposition Of The Late Miocene Diatomites Of The Konservat-Lagerstätte Lake Saint-Bauzile (Ardèche, France) - Preliminary Results

Text-fig. 4. Charred grass from diatomite of Saint-Bauzile. a: Overview of diatomite slab with one larger specimen of charred grass (left) and several smaller, lath-shaped charcoal fragments; SM.B 22260; scale bar = 1 cm. b: Detail of vein exhibited on split grass blade, with stomata oriented parallel to vein. c: Stomata oriented in rows and bands parallel to veins exposed on split grass blade. d: Surface of grass leaf with rectangular, elongated cells with strongly undulating margins in an intercostal area.

Text-fig. 2. Nymphaea sp. from the Miocene Clarkia Lake flora, Locality P-33. a: Photograph of the fossil leaf. b: Sketch of leaf showing the salient features of shape, basal lobes and margin, eccentric insertion point of the abaxial petiole, and primary actinodromous venation. Dashed lines represent torn edge of lamina; dotted line is outline of right basal lobe. Line drawing by P. Martin Sander. Scale bar applies to both photo and drawing. in First Water Lily, A Leaf Of Nymphaea Sp., From The Miocene Clarkia Flora, Northern Idaho, Usa: Occurrence, Taphonomic Observations, Floristic Implications

Text-fig. 2. Nymphaea sp. from the Miocene Clarkia Lake flora, Locality P-33. a: Photograph of the fossil leaf. b: Sketch of leaf showing the salient features of shape, basal lobes and margin, eccentric insertion point of the abaxial petiole, and primary actinodromous venation. Dashed lines represent torn edge of lamina; dotted line is outline of right basal lobe. Line drawing by P. Martin Sander. Scale bar applies to both photo and drawing.

Text-fig. 3. Mastixia parva E.REID et M.CHANDLER. a–g: Holotype, V. 22972. a: Ventral view (original illustration from pl. 25, fig. 13 of Reid and Chandler 1933), reflected light. b–g: from micro-CT data. b: Dorsal view of specimen in (a) now suffering from encrustation due to pyrite decay; isosurface rendering. c: Translucent volume rendering, dorsal view showing two limbs of the locule and longitudinal groove. d–g: Digital transverse sections at various positions showing c-shaped locule, longitudinal dorsal infold, endocarp wall, and degradational cracks. h, i: V. 22983(1). h: Dorsal view showing longitudinal infold. i: Physical transverse section showing c-shaped locule and longitudinal dorsal infold. Scale bars 5 mm in (a–h), applies also to (b–g), 2 mm in (i). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision

Text-fig. 3. Mastixia parva E.REID et M.CHANDLER. a–g: Holotype, V. 22972. a: Ventral view (original illustration from pl. 25, fig. 13 of Reid and Chandler 1933), reflected light. b–g: from micro-CT data. b: Dorsal view of specimen in (a) now suffering from encrustation due to pyrite decay; isosurface rendering. c: Translucent volume rendering, dorsal view showing two limbs of the locule and longitudinal groove. d–g: Digital transverse sections at various positions showing c-shaped locule, longitudinal dorsal infold, endocarp wall, and degradational cracks. h, i: V. 22983(1). h: Dorsal view showing longitudinal infold. i: Physical transverse section showing c-shaped locule and longitudinal dorsal infold. Scale bars 5 mm in (a–h), applies also to (b–g), 2 mm in (i).

Text-fig. 4. Juglandaceae Carya (a–w). Scale bars = 1 cm. a–d: USNM PAL 772352, reflected light, palladium coated. a: Obliquelateral view of nut, apex up. b: Basal view with damage to left and clear depiction of meridional grooves. c, d: Two lateral views oriented about 130° from each other and avoiding the area of damage; the meridional grooves clear in (c). e–l: USNM PAL 772350. e: Intact nut, lateral view, apex up, reflected light. f: One half of split nut revealing in situ chalcedony locule cast, reflected light. g–k: Virtual sections from micro-CT data. g: Longitudinal section parallel to the exposed face in (f). h: Longitudinal section at 90° from (g). i: Transverse section in apical 1/3 showing locule bracketed by C-shaped lacunae (arrows). j: Equatorial transverse section showing two lobes of the locule separated by primary septum, lacuna evident below as white line. k: Transverse section near base in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.

Text-fig. 4. Juglandaceae Carya (a–w). Scale bars = 1 cm. a–d: USNM PAL 772352, reflected light, palladium coated. a: Obliquelateral view of nut, apex up. b: Basal view with damage to left and clear depiction of meridional grooves. c, d: Two lateral views oriented about 130° from each other and avoiding the area of damage; the meridional grooves clear in (c). e–l: USNM PAL 772350. e: Intact nut, lateral view, apex up, reflected light. f: One half of split nut revealing in situ chalcedony locule cast, reflected light. g–k: Virtual sections from micro-CT data. g: Longitudinal section parallel to the exposed face in (f). h: Longitudinal section at 90° from (g). i: Transverse section in apical 1/3 showing locule bracketed by C-shaped lacunae (arrows). j: Equatorial transverse section showing two lobes of the locule separated by primary septum, lacuna evident below as white line. k: Transverse section near base

opencc-by-4.0Oct 2022View details →

Figs 183–188. Pronotal shapes, dorsal views. 183 in New World genera of Galerucinae Latreille, 1802 (tribes Galerucini Latreille, 1802, Metacyclini Chapuis, 1875, and Luperini Gistel, 1848): an annotated list and identification key (Coleoptera: Chrysomelidae)

Figs 183–188. Pronotal shapes, dorsal views. 183. Socorroita carinipennis (Bowditch, 1923). 184. Socorroita elvira Bechyné, 1956. 185. Masurius sp. 186. Dircema?nigripenne (Fabricius, 1792). 187. Malacorhinus foveipennis (Jacoby, 1879). 188. Ophraea rugosa Jacoby, 1886.

opencc-by-4.0May 2022View details →

Supplementary Data for: Shifts in food webs and niche stability shaped survivorship and extinction at the end-Cretaceous

This compendium of files includes a list of statistical routines (Data S1) and cleaned fossil datasets from the Paleobiology Database (Data S2 to S3) for the Science Avances paper "Shifts in food webs and niche stability shaped survivorship and extinction at the end-Cretaceous". Data S1 and Data S2 can be opened in Microsoft Office and Data S3 can be imported in R using the load() command. Data S2 and S3 contain the same information (i.e., cleaned fossil datasets) but in different formats (xlsx vs. RData). Please, feel free to send an email to the maintainer Dr. Jorge García Girón (jogarg@unileon.es) if you face any trouble downloading, opening or using these files.

Data from: On the shape and origins of the freshwater species-area relationship

The species-area relationship (SAR) has over a 150-year-long history in ecology, but how its shape and origins vary across scales and organisms is still not fully understood. This is the first subcontinental freshwater study to examine both properties of the SAR in a spatially explicit way across major organismal groups (diatoms, insects, and fish), differing in body size and dispersal capacity. First, to describe the SAR shape, we evaluated the fit of three commonly used models, logarithmic, power, and Michaelis-Menten. Second, we proposed a hierarchical framework to explain the variability in the SAR shape, captured by the parameters of the SAR model. According to this framework, scale and species group were the top predictors of the SAR shape, climatic factors (heterogeneity and median conditions) represented the second predictor level, and metacommunity properties (intraspecific spatial aggregation, γ-diversity, and species abundance distribution), the third predictor level. We calculated the SAR as a sample-based rarefaction curve using 60 streams within landscape windows (scales) in the US, ranging from 160,000 to 6,760,000 km2. First, we found that all models provided good fits (R2 ≥ 0.93), but the frequency of the best-fitting model was strongly dependent on organism, scale, and metacommunity properties. Michaelis-Menten model was most common in fish, at the largest scales, and at the highest levels of intraspecific spatial aggregation. The power model was most frequent in diatoms and insects, at smaller scales, and in metacommunities with the lowest evenness. The logarithmic model was best fitting exclusively at the smallest scales and in species-poor metacommunities, primarily fish. Second, we tested our framework with the parameters of the most broadly used SAR model, the log-log form of the power model using a structural equation model. This model supported our framework and revealed that the SAR slope was best predicted by scale- and organism-dependent metacommunity properties, particularly spatial aggregation, while the intercept responded most strongly to species group and γ-diversity. Future research should investigate from the perspective of our framework how shifts in metacommunity properties due to climate change would alter the SAR.

Developmental bias in the evolution and plasticity of beetle horn shape

The degree to which developmental systems bias the phenotypic effects of environmental and genetic variation, and how these biases affect evolution, is subject to much debate. Here, we assess whether developmental variability in horn shape aligns with the phenotypic effects of plasticity and evolutionary divergence, yielding three salient results. First, we find that most pathways previously shown to regulate horn length also affect shape. Second, we find that the phenotypic effects of manipulating divergent developmental pathways are correlated with each other as well as multivariate fluctuating asymmetry – a measure of developmental variability. Third, these effects further aligned with thermal plasticity, population differences, and macroevolutionary divergence between sister taxa and more distantly related species. Collectively, our results support the hypothesis that changes in horn shape —whether brought about by environmentally plastic responses, functional manipulations, or evolutionary divergences— converge along 'developmental lines of least resistance', i.e., are biased by the developmental system underpinning horn shape.

Genotypic sex shapes maternal care in the African Pygmy mouse, Mus minutoides

Sexually dimorphic behaviours, such as parental care, have long been thought to be mainly driven by gonadal hormones. In the past two decades, a few studies have challenged this view, highlighting the direct influence of the sex chromosome complement (XX vs XY or ZZ vs ZW). The African pygmy mouse, Mus minutoides, is a wild mouse species with naturally occurring XY sex reversal induced by a third, feminizing X* chromosome, leading to three female genotypes: XX, XX* and X*Y. Here, we show that sex reversal in X*Y females shapes a divergent maternal care strategy (maternal aggression, pup retrieval and nesting behaviours) from both XX and XX* females. Although neuroanatomical investigations were inconclusive, we show that the dopaminergic system in the anteroventral periventricular nucleus of the hypothalamus is worth investigating further as it may support differences in pup retrieval behaviour between females. Combining behaviours and neurobiology in a rodent subject to natural selection, we evaluate potential candidates for the neural basis of maternal behaviours and strengthen the underestimated role of the sex chromosomes in shaping sex differences in brain and behaviours. All things considered, we further highlight the emergence of a third sexual phenotype, challenging the binary view of phenotypic sexes.

Complex cycles of divergence and migration shape lineage structure in the common kingsnake species complex

Aim: The Nearctic is a complex patchwork of habitats and geologic features that form barriers to gene flow resulting in phylogeographic structure and speciation in many lineages. Habitats are rarely stable over geologic time, and the Nearctic has undergone major climatic changes in the past few million years. We use the common kingsnake species complex to study how climate, geography, and history influence lineage formation over a large, complex landscape. Location: Nearctic/North America Taxon: Common kingsnake, Lampropeltis getula, species complex Methods: We analyzed genome-wide sequence data from 51 snakes spanning the majority of the species complex's range. We used population clustering, generalized dissimilarity modeling, and coalescent methods to identify the number of genetic clusters within the L. getula complex, infer the environmental correlates of genetic differentiation, and estimate models of divergence and gene flow among lineages. Results: We identified three major lineages within the L. getula complex and further continuous spatial structure within lineages. The most important ecological correlates of genetic distance in the complex are related to aridity and precipitation, consistent with lineage breaks at the Great Plains/Desert ecotone and the Cochise Filter Barrier. Lineages are estimated to have undergone multiple rounds of isolation and secondary contact, with highly asymmetric migration occurring at present. Main conclusions: Changing climates combined with a large and geologically complex landscape have resulted in a mosaic of discrete and spatially continuous genetic structure. Multiple rounds of isolation and secondary contact as climate fluctuated over the past ~4.4 My have likely driven the evolution of discrete lineages that maintain high levels of gene flow. Continuous structure is strongly shaped by aridity and precipitation, suggesting roles for major precipitation gradients in helping to maintain lineage identity in the face of gene flow when lineages are in geographic contact.

Probabilistic Shaping: Experimental Optical Back-to-Back Data Set

Data set containing experimental results on B2B operation of single-carrier probabilistic shaping optical signals spanning from net bit-rates of 200G to 250G.

Flat-Field Super-Resolution Localization Microscopy with a Low-Cost Refractive Beam-Shaping Element

Raw data for the article "Flat-Field Super-Resolution Localization Microscopy with a Low-Cost Refractive Beam-Shaping Element". Each set of three files is a set of dSTORM images, taken using either top-hat illumination or a Gaussian illumination. For Sample 0, the top-hat illumination was performed first. For Sample 1, the Gaussian illumination was performed first.

Probabilistic Shaping: Experimental Loop Recirculation Data Set - 250G

Data set containing experimental results on the propagation of probabilistic shaping optical signals over pure silica core fiber (PSCF) inside a recirculating loop. The net bit-rate is fixed at 250G.

Probabilistic Shaping: Experimental Loop Recirculation Data Set - 225G

Probabilistic Shaping: Experimental Loop Recirculation Data Set - 200G

opencc-by-4.0Oct 2013View details →

BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 1. Typical feedforward artificial neural network processing unit

In Figure 1, each input X is weighted by a separate weight value w. These are then summed together to give a total input value for the neuron. This total can then be passed through a function, to transform it into the desired output value. This is then compared to the actual output value d; where the error or differences between the two sets is measured and used to correct the weight values, to bring the two sets of values closer. One way to update the weights is after each individual pattern is presented and processed. Another option is to process the error after the presentation of the whole dataset, as part of a batch update.

opencc-by-4.0Oct 2013View details →

BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 2. New neuronal model, based on matching a wave-like representation of the output.

For the new model, shown in Figure 2, the ‘differences’ between the outputs is measured and combined, to produce a kind of wave-like shape. If output point 1 has a value of 10, and output point 2 has a value of 5, for example, then this results in the shape moving down 5 points on the wave shape. This shape will, of course, change depending on the order that the dataset values are presented in. Also, if new data is presented, then that will produce a different shape. However, there is still an association between the input values and the output values and it is still this relation that is being learnt. The neural network needs to be able to learn a function that can generalise over the presented datasets, so that it can recognise the relation in previously unseen data as well. Generalising over trying to learn a wave-like shape, or trying to match the output values exactly looks quite similar, suggesting that the process is at least valid. In Figure 2, note that a transposition to move the learned shape up or down first is possible, before a weight value would try to scale it. If the initial match of the combined inputs can be as close as possible, then these adjustments will become less and will be more for fine tuning.

opencc-by-4.0Jan 2023View details →

Indirect genetic effects are shaped by demographic history and ecology in Arabidopsis thaliana

This folder contains data & code used for the study "Indirect genetic effects are shaped by demographic history and ecology in Arabidopsis thaliana" All data analyzed in the study are stored in the folder "data": <ul> <li>"pheno_file.csv": the main phenotypic file corresponding to the experiment with paired plants used to estimate Indirect Genetic Effects.</li> <li>"pheno_file_single_plants.csv": phenotypic file with measurements of plant biomasses in the absence of competition (single plants)</li> <li>"call_method_75_TAIR9.csv": genomic data (SNPs) for each accession from the RegMap panel (ref [1])</li> <li>"Data_geo_RegMap_accessions.csv": geographic localization of each accession from the RegMap panel (ref [2])</li> <li>"igeGWAS_scores.csv": Genome-Wide Association Study (GWAS) results reporting for each SNP from the RegMap panel the p-value and estimated effect sizes of their direct and indirect genetic effects</li> <li>"1001_accessions_info.csv": geographic localization and admixture group for each accession from the 1001 genomes project (ref [3])</li> <li>"snp_data_all_samples.txt": allelic value of each accession from the 1001 genomes project at the eleven top SNPs associated with IGE</li> <li>"sample_names.txt": names of  accessions listed in the file "snp_data_all_samples.txt"</li> <li>"climatic_data.csv": climatic data for each accessions from the 1001 genomes project (ref [4])</li> <li>"candidate_genes_all.csv":  list of all genes (and associated GO terms) with a non-synonymous, nonsense, or frameshift mutation in close proximity (distance < half LD decay distance) and high linkage (r2>0.5) with a SNP significantly associated with IGE</li> <li>"genes.coord.bed": list of all genes in a +- 500 kb around top IGE SNPs and their coordinates</li> <li>"AllGenes_fst.GeneID.txt": pairwise Fst computed between each pair of admixture groups, for all genes annotated in the genome of A. thaliana</li> </ul> "ABBA_BABA" subfolder contains ABBA_BABA statistics computed for each individual chromosome (Chr1-Chr5) using genomic windows of 20 kb with at least 250 SNPs per windows. ABBA-BABA statistics were computed using custom python scripts from https://github.com/simonhmartin/genomics_general "GEA" subfolder contains Genome-Environment Association results, with one file per chromosome x climatic variable. Climatique variable are indexed, following the order listed in the file "Climatic_variables.txt" within the subfolder "GEA". GEA analysis were run with the gemma program: https://github.com/genetics-statistics/GEMMA. "LD_IGE_SNPs" subfolders contains the list of SNPs located at +- 2Mb of a significant IGE SNP (one file per IGE SNP, named "SNPalias_LDSimplified.csv") and their linkage (r2) with the IGE SNP. It also contains the file "LD_windows_sizes.csv" with the half LD decay distances for all significant IGE SNP. All analysis performed to produce the tables and figures presented in the study (main manuscript & supplementary information) were done with the R script "Arabidopsis_IGE_analysis.R", which uses "manhattan_custom.R" as a source function to produce custom manhattan plots.   REFERENCES: [1] Horton MW, Hancock AM, Huang YS, Toomajian C, Atwell S, Auton A, Muliyati NW, Platt A, Sperone FG, Vilhjálmsson BJ, et al. 2012. Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel. Nature Genetics 44: 212–216. [2] Anastasio AE, Platt A, Horton M, Grotewold E, Scholl R, Borevitz JO, Nordborg M, Bergelson J. 2011. Source verification of mis-identified Arabidopsis thaliana accessions. The Plant Journal 67: 554–566. [3] 1001 Genomes Consortium. 2016. 1,135 genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell 166: 481–491. [4] Ferrero-Serrano Á, Assmann SM. 2019. Phenotypic and genome-wide association with the local environment of Arabidopsis. Nature Ecology & Evolution 3: 274–285.

opencc-by-4.0May 2024View details →

Synthetic Shape Dataset for Numerosity: Exploring Six Configurations of Complexity

The Synthetic Shape Dataset for Numerosity is a meticulously crafted collection designed to advance the understanding and training of machine learning models in the realm of numerosity perception, mirroring human neurocognitive abilities. Comprising six distinct configurations ranging from fundamental to intricate, this dataset offers a comprehensive exploration of shape complexity. Each configuration presents a unique array of synthetic images, where shapes are dynamically generated and randomly distributed against contrasting backgrounds. Configuration 1 serves as the foundation, featuring only white circles against a black background, with each circle sharing a uniform size. As complexity escalates through the configurations, additional elements are introduced, including variations in shape type, size, orientation, and pixel intensity. One notable feature of this dataset is that no shapes overlap or touch each other, ensuring clarity and precision in each image. The total dataset comprises 73,686 images, with each configuration meticulously crafted to offer distinct challenges for numerosity perception tasks. The shape generation process is divided into two types of shape sizes: <ul> <li> Bounded: In this category, there is no correlation between the pixel count for shapes contained in an image and the target numerosity count. Each of the six configurations contains 9,212 images, totaling 55,272 images. </li> <li> Unbounded: Here, there is a correlation between the pixel count for shapes in an image and the target numerosity count. Each configuration consists of 3,069 images, totaling 18,414 images. </li> </ul> The configurations are as follows: <ol> <li> Configuration 1: Features only white circles against a black background, with uniform circle size. </li> <li> Configuration 2: Similar to Configuration 1, but circles vary in size. </li> <li> Configuration 3: Presents a black background with full white circles, triangles, squares, and pentagons. Shapes have a uniform orientation but do not share a uniform size. </li> <li> Configuration 4: Similar to Configuration 3, but shapes do not have a uniform orientation. </li> <li> Configuration 5: Similar to Configuration 4, but shapes also vary in pixel intensity, exhibiting different shades of grey. </li> <li> Configuration 6: Features a white background with full black circles, triangles, squares, and pentagons. Shapes do not have a uniform orientation. </li> </ol> The dataset is split into training and test sets: <ul> <li> Training Set: Contains 61,440 images, with each image containing 1-8 shapes, evenly distributed across target numerosity counts. </li> <li> Test Set: Comprises 12,246 images, including a variety of numerosity counts ranging from 0 to 12 shapes per image. </li> </ul> Additionally, each image is accompanied by structured label information provided in a CSV file: <ul> <li>id: A unique number assigned to each image.</li> <li>config: Indicates the configuration of the image.</li> <li>target: Denotes the label for the image, representing the number of shapes contained within it.</li> <li>shape: Specifies whether the image contains bounded or unbounded shapes. Options include 'bounded' or 'unbounded'.</li> <li>numerosity_id: Identifies the image in the creation process (not crucial for end-users).</li> <li>shape_id: Identifies the image in the creation process (not crucial for end-users).</li> <li>split: Labels each image as belonging to either the training or test dataset. Options are 'train' or 'test'.</li> <li>path: Provides the path to the image file.</li> </ul> This dataset serves as a valuable resource for researchers seeking to explore and enhance machine learning models' ability to comprehend numerosity in various contexts.