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FIGURE 2 in Systematics of reptile-associated scale mites of the genus Pterygosoma (Acariformes: Pterygosomatidae) derived from external morphology
FIGURE 2. Male of Pterygosoma sp., general scheme. A, dorsal view; B, ventral view. Abbreviations: ae, aedeagus; ant, anterior setae; c.fs, coxal fields; g.c, genital cone; dm, dorso-median setae; vm, ventro-median setae; perit, peritreme; post.-lat, postero-lateral setae; sca, subcapitulum.
FIGURE 4. Fixed cheliceral digit. A in Systematics of reptile-associated scale mites of the genus Pterygosoma (Acariformes: Pterygosomatidae) derived from external morphology
FIGURE 4. Fixed cheliceral digit. A, spiniform; B, elongated; C, branched; D, rounded. Hypostomal apex. E, rounded; F, triangular; G, flattened; H, with depression; I, with teeth.
FIGURE 8 in Systematics of reptile-associated scale mites of the genus Pterygosoma (Acariformes: Pterygosomatidae) derived from external morphology
FIGURE 8. One of the 2 optimal cophylogenetic scenarios between the pterygosomatid genera and their hosts' tree from Jane 4.0 (2 cospeciation, 4 duplication, 5 duplication with host switches, 1 loss, and 0 failure to diverge events; total cost= 15). Black and grey lines represent hosts and their mites, respectively; the numbers below the nodes are support values (i.e. the percentages of solutions in which a specific event of the parasite trees is mapped to a given location on the host tree).
FIGURE 1. Pterygosoma agamae Peters, 1849, general scheme. A in Systematics of reptile-associated scale mites of the genus Pterygosoma (Acariformes: Pterygosomatidae) derived from external morphology
FIGURE 1. Pterygosoma agamae Peters, 1849, general scheme. A, dorsal view; B, ventral view. Abbreviations: perit, peritreme; ant, anterior setae; lat, lateral setae; post, posterior setae; per, peripheral setae; dm, dorso-median setae; vm, ventromedian setae; ps, pseudanal setae; g, genital setae; sca, subcapitulum; c.fs, coxal fields; Tr, trochanter; F, femur; G, genu; Ti, tibia; Ta, tarsus.
Incorporation of soil-derived covariates in progeny testing and line selection to enhance genomic prediction accuracy in soybean breeding
<p>The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a highly complex and quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture and its interaction with molecular markers through covariance structures to enhance predictive ability. A total of 797 advanced soybean breeding lines derived from 367 unique bi-parental populations were genotyped using the Illumina Infinium BARCSoySNP6K BeadChip and tested for yield for five years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including a default GBLUP model (M1), a reaction norm model (M2) accounting for the interaction between molecular markers and the environment (GE), an expansion of M2 including soil type (S), and the interaction between soil type and molecular markers (GS) (M3), and an alternative version of M3 without the GE term. Four cross-validation scenarios simulating progeny testing and line selection were implemented (CV2, CV1, CV0, and CV00). Across environments, the addition of GS in M3 decreased the amount of variability captured by both the environment (-30.4%) and residual (-39.2%) terms as compared to M1. Within environments, the GS term in M3 reduced the variability captured by the residual term by roughly 60% and 30% when compared to M1 and M2, respectively. M3 outperformed all models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). The addition of soil texture seems to structure the environment term revealing its components that could enhance or hinder the predictability of a model. The availability of soil texture before the growing season may maximize the functionality of covariance structures, particularly in scenarios with untested genotypes in untested environments. Genomic selection can optimize the efficiency of a soybean breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle.</p>
FIGURE 2 in Kalanchoe ×sogae (Crassulaceae subfam. Kalanchooideae) derived from Kalanchoe lateritia × K. sexangularis, with notes on the inheritance of hairiness and resistance against phytophagous insects in Kalanchoe hybrids
FIGURE 2. Kalanchoe ×sogae. A. At flowering maturity plants of this shrubby nothospecies reach a height 0.5–1.0 m. B. Leaves are puberulous and mid-green to most often distinctly red or strongly crimson red-infused. C. Apart from being intensely reddish-infused, virtually all the plant parts are puberulous. In bud, the flowers are bright orange-red apically. Flowers are borne erectly to slanted sideways—never pendent. Excepting the adaxial surface of the corolla lobes, the flowers, too, are conspicuously puberulous throughout. D. Especially the basal swollen part of the corolla tube is light green and distinctly 4-angled-fluted. E. Flowers, here in lateral view, of K. ×sogae (centre) are intermediate between those of K. lateritia (left, one of the parents of K. ×sogae) and K. sexangularis (right, the other parent of K. ×sogae); rounded when viewed from below; gradually narrowly cylindrical above; and with the angles distinctly and strongly orange-infused to yield a green-orange-striped appearance. F. Corolla lobes of K. ×sogae (centre) are bright yellow to strongly bright orange-infused, rather similar to those of K. lateritia (left) but differs from the bright yellow, faintly brown-tipped ones of K. sexangularis (right). All photographs taken by Gideon F. Smith.
FIGURE 1 in Kalanchoe ×sogae (Crassulaceae subfam. Kalanchooideae) derived from Kalanchoe lateritia × K. sexangularis, with notes on the inheritance of hairiness and resistance against phytophagous insects in Kalanchoe hybrids
FIGURE 1. Kalanchoe lateritia (A–B) and K. sexangularis (C–D), the parents of K. ×sogae (see Fig. 2). A. Leaves of K. lateritia are pubescent and light green to yellowish or brownish green. B. Flowers of K. lateritia have bright yellow to strongly bright orange-infused corolla lobes. C. Leaves of K. sexangularis turn a bright crimson red when exposed to high levels of solar irradiation. D. Flowers of K. sexangularis have a greenish yellow corolla tube and generally bright yellow corolla lobes that could be faintly brown-tipped. All photographs taken by Gideon F. Smith.
FIGURE 3. A in Kalanchoe ×sogae (Crassulaceae subfam. Kalanchooideae) derived from Kalanchoe lateritia × K. sexangularis, with notes on the inheritance of hairiness and resistance against phytophagous insects in Kalanchoe hybrids
FIGURE 3. A. The indigenous South African weevil Sternuchopsis sedi (Coleoptera: Curculionidae: Molytinae: Mecysolobini) on a leaf of K. sexangularis. The weevil is 7 mm long. B. The weevil deposits its eggs in the stems of kalanchoes where the hatched larvae feed on the internal tissues, usually leaving only a hollowed out and very much weakened tube that will easily snap off. Both photographs taken by Gideon F. Smith.
Dataset for In silico study of tricyclic coumarin-derived MTHFD2 inhibitors
<p>Docked structures and MD simulation trajectories of tricyclic coumarin-derived MTHFD2 inhibitors. </p> <p>All docking performed using Glide (Schrödinger) and MD simulations using Desmond (Schrödinger). </p>
Dataset for puplication: Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose Tissue-Derived Human Mesenchymal Stem cells employing 1H-1H TOCSY NMR
<p>Data set used in the publication: <strong>Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose Tissue-Derived Human Mesenchymal Stem cells employing <sup>1</sup>H-<sup>1</sup>H TOCSY NMR. </strong></p> <p>Abstract: In this work, the dynamic evolution of adipose tissue-derived human MSCs (AT-derived hMSCs) after fourteen days of cultivation, adiobocytes and osteocytes differentiation has been inspected based on 2D NMR TOCSY using machine learning techniques. Multi-class classification in addition to novelty detection of metabolites was established based on the profile of a control hMSCs sample at four days cultivation and successively detect the absence and the abundance of metabolites in differentiated MSCs following a set of <sup>1</sup>H-<sup>1</sup>H TOCSY profiles. The uploaded files are:</p> <p>File: metabolites_names.xlsx contain the names of the used metabolites.</p> <p>File: metabolites.xlsx</p> <p> column 1: metabolite abbreviation</p> <p>column 2: 2D NMR TOCSY horizontal and vertical frequencies of metabolites in the control group at 4 days cultivation (Ct d4)</p> <p>column 3: 2D NMR TOCSY horizontal and vertical frequencies of metabolites found after 14 days of cultivation (Ct d14)</p> <p>column 4: 2D NMR TOCSY horizontal and vertical frequencies of metabolites found after 14 days of differentiation into adipocytes (AT d14)</p> <p>column 5: 2D NMR TOCSY horizontal and vertical frequencies of metabolites found after14 days of differentiation into osteocytes (OS d14)</p> <p>column 6: 2D NMR TOCSY horizontal and vertical standard frequencies of all metabolites measured at broadband high resolution 600.13 MHz NMR</p>
Derived data for: 'Modelling the Time-Dependent Magnetic Fields that BepiColombo will use to Probe Down into Mercury's Mantle'
<p>Derived data for the manuscript entitled 'Modelling the Time-Dependent Magnetic Fields that BepiColombo will use to Probe Down into Mercury’s Mantle'.</p>
MUSES Leaf Area Index (LAI) Derived from AVHRR Data Monthly Global 0.05º Geographic Grid Since 1981
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global LAI product at 0.05º spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on Geographic grid and spans from 1981 to 2019 (continuously updated). It was generated from time-series Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 1981 – 2019</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Leaf Area Index (LAI) Derived from AVHRR Data 8-Day Global 0.05º Geographic Grid Since 1981
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global LAI product at 0.05º spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on Geographic grid and spans from 1981 to 2019 (continuously updated). It was generated from time-series Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 1981 – 2019</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Shorelines dataset. Deliverable 3.2 - Algorithms for satellite derived shoreline mapping and shorelines dataset, ECFAS project (GA 101004211), www.ecfas.eu.
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p> <p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p> <p><em><strong>Reference literature:</strong></em></p> <p><em><strong>Palomar-Vázquez, J.; Pardo-Pascual, J.E.; Almonacid-Caballer, J.; Cabezas-Rabadán, C. Shoreline Analysis and Extraction Tool (SAET): A New Tool for the Automatic Extraction of Satellite-Derived Shorelines with Subpixel Accuracy. Remote Sens. 2023, 15, 3198. <a href="https://doi.org/10.3390/rs15123198">https://doi.org/10.3390/rs15123198</a></strong></em></p> <p><em><strong>J.E. Pardo-Pascual, J. Almonacid-Caballer, C. Cabezas-Rabadán, A. Fernández-Sarría, C. Armaroli, P. Ciavola, J. Montes, P.E. Souto-Ceccon, J. Palomar-Vázquez: Assessment of satellite-derived shorelines automatically extracted from Sentinel-2 imagery using SAET. Coastal Engineering, 2023, 104426, ISSN 0378-3839, <a href="https://doi.org/10.1016/j.coastaleng.2023.104426">https://doi.org/10.1016/j.coastaleng.2023.104426</a>.</strong></em></p> <p><em>Pardo-Pascual, J. E., Cabezas-Rabadán, C., Palomar-Vázquez, J., Fernández-Sarría, A., Almonacid-Caballer, J., Souto-Ceccon, P. E., Montes, J., Armaroli, C., and Ciavola, P.: Satellite-derived shorelines extracted using SAET for characterizing the effect of Storm Gloria in the Ebro Delta (W Mediterranean), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9856, <a href="https://doi.org/10.5194/egusphere-egu22-9856">https://doi.org/10.5194/egusphere-egu22-9856</a>, 2022.</em></p> <p>In relation to <strong>SAET</strong>, additional information, instructions and the <strong>open-source code</strong> are available here <a href="https://doi.org/10.5281/zenodo.5807710"><strong>https://zenodo.org/records/10256957</strong></a> and also in <strong>GitHub </strong>here<strong> <a href="https://github.com/jpalomav/SAET_master">https://github.com/jpalomav/SAET_master</a></strong></p> <p> </p> <p><strong>Description of the containing files inside the Dataset</strong></p> <p>The deliverable includes two different files: the dataset of shorelines and the accompanying report.</p> <p>The report describes the structure of the dataset of shorelines produced in Task 3.2 - Shoreline mapping validation and calibration and described in Deliverable 3.2 - Algorithms for satellite derived shoreline mapping and shorelines dataset (DOI: 10.5281/zenodo.5807711).</p> <p>The dataset is composed of three folders.</p> <ol> <li>The first folder ("SDS_vs_VHR_shoreline") contains the SDSs extracted at each test site by the different algorithms tested in ECFAS (SHOREX, Cabezas-Rabadán et al., 2021; Sánchez-García et al., 2020; CoastSat, Vos et al., 2019a,b and SAET, Palomar-Vázquez et al., 2021; Pardo-Pascual et al., 2021) to assess their performance through the comparison with shorelines photo-interpreted on coincident VHR satellite images. The SDSs obtained at all the test sites using SAET and CoastSAT are included, as well the SDSs obtained using SHOREX at the Spanish test sites. The photo-interpreted shorelines are also provided. For all the test sites it is provided the line separating the instantaneous shoreline (water/land boundary), obtained by photo-interpretation. For the sites in the Netherlands, also the wet/dry line is provided.</li> <li>The second folder ("SDS_vs_video-monitored_shorelines") presents for each of the three test sites the SDSs obtained using the different algorithms and video-derived shorelines. The SDSs at the beaches of la Victoria and Cala Millor were obtained using SAET, SHOREX and CoastSAT. In the case of Gerakas Beach, the shorelines were obtained using SAET and CoastSAT.</li> <li>The third folder ("SDS_SAET_Storm_cases") includes eight examples in different European coasts in which the SDSs before and after a storm have been obtained.</li> </ol> <p>This <strong>ECFAS dataset of shorelines</strong> is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of the ECFAS dataset of shorelines are licensed under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p> <p>This <strong>Report</strong> is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p> <p> </p> <p><em><strong>Disclaimer:</strong></em></p> <p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p> <p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p> <p> </p>
Data from: Global warming intensity of biofuel derived from switchgrass grown on marginal land in Michigan
<p>Energy crops for biofuel production, especially switchgrass (<em>Panicum virgatum</em>), are of interest from a climate change perspective. Here, we use outputs from a crop growth model and life cycle assessment (LCA) to examine the global warming intensity (GWI; g CO<sub>2</sub> MJ<sup>-1</sup>) and greenhouse gas (GHG) mitigation potential (Mg CO<sub>2</sub> year<sup>-1</sup>) of biofuel systems based on a spatially explicit analysis of switchgrass grown on marginal land (abandoned former cropland) in Michigan, USA. We find that marginal lands in Michigan can annually produce over 0.57 hm<sup>3</sup> of liquid biofuel derived from nitrogen-fertilized switchgrass, mitigating 1.2-1.5 Tg of CO<sub>2</sub> per year. About 96% of these biofuels can meet the Renewable Fuel Standard (60% reduction in lifecycle GHG emissions compared with conventional gasoline; GWI ≤ 37.2 g CO<sub>2</sub> MJ<sup>-1</sup>). Furthermore, 73-75% of these biofuels are carbon-negative (GWI less than zero) due to enhanced soil organic carbon (SOC) sequestration. However, simulations indicate that SOC levels would fail to increase and even decrease on the 11% of lands where SOC stocks >> 200 Mg C ha<sup>-1</sup>, leading to carbon intensities greater than gasoline. Results highlight the strong climate mitigation potential of switchgrass grown on marginal lands as well as the needs to avoid carbon rich soils such as histosols and wetlands and to ensure that productivity will be sufficient to provide net mitigation.</p>
A cryopreservation method to recover laboratory- and field-derived bacterial communities from mosquito larval habitats
<p>Mosquitoes develop in a wide range of aquatic habitats containing highly diverse and variable bacterial communities that shape both larval and adult traits, including the capacity of adult females of some mosquito species to vector disease-causing organisms to humans. However, while most mosquito studies control for host genotype and environmental conditions, the impact of microbiota variation on phenotypic outcomes of mosquitoes is often unaccounted for. The inability to conduct reproducible intra- and inter-laboratory studies of mosquito-microbiota interactions has also greatly limited our ability to identify microbial targets for mosquito-borne disease control. Here, we developed an approach to isolate and cryopreserve bacterial communities derived from lab- and field-based larval-rearing environments of the yellow fever mosquito <em>Aedes</em> <em>aegypti</em>–a primary vector of dengue, Zika, and chikungunya viruses. We then validated the use of our approach to generate experimental microcosms colonized by standardized lab- and field-derived bacterial communities. <span>Our results overall reveal minimal effects of cryopreservation on the recovery of both lab- and field-derived bacteria when directly compared with isolation from non-cryopreserved fresh material. Our results also reveal improved reproducibility of bacterial communities in replicate microcosms generated using cryopreserved stocks over fresh material. Communities in replicate microcosms further captured the majority of total bacterial diversity present in both lab- and field-based larval environments, although the relative richness of recovered taxa as compared to non-recovered taxa was substantially lower in microcosms containing field-derived bacteria. Altogether, these results provide a critical next step toward the standardization of mosquito studies to include larval-rearing environments colonized by defined microbial communities. They also lay the foundation for long-term studies of mosquito-microbe interactions and the identification and manipulation of taxa with potential to reduce mosquito vectorial capacity.</span></p>
Figure 9 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 9. Responses to the jumping spider, high detail text prompt by the Canva engine (#5).
Figure 10 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 10. Responses to the jumping spider, high detail text prompt by the Canva engine (#5).
Figure 20 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 20. Responses to the jumping spider, high detail text prompt by the Simplified engine (#9).
supplementary data for Synthesis, Characterization, and Preliminary Antimicrobial Study of Some New Phthalimide Phenyl Hydrazide Derivatives
<p>supplementary data for Synthesis, Characterization, and Preliminary Antimicrobial Study of Some New Phthalimide Phenyl Hydrazide Derivatives</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.