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3,655 results for “Structural data”
Data and code for: Community structure in co-inventor networks affects time to first citation for patents
<p>This package provides the datasets and programming code needed to reproduce the results reported in the article "Community structure in co-inventor networks affects time to first citation for patents".</p> <p>v2: Added data and code pertaining to randomized-community-association test and updated README file.</p>
A structural model of the human serotonin transporter in an outward-occluded state: MD simulation data
<p>The uploads contain relevant data to supplement the study https://www.biorxiv.org/content/10.1101/637009v1, where the details of the methods are described.</p> <p>charmm_energy_minimization.inp is the input file that was used to run an energy minimization on structural models</p> <p>The two archives contain relevant MD simulation data in coordinate, parameter and trajectory files:</p> <p>hSERT_Ce.tar.gz outward-open X-ray structure PDB 5I71</p> <p>hSERT_Ceo.tar.gz outward-occluded structural model</p>
Data and simulations files for the tutorial article "Brillouin Optomechanics in Nanophotonic Structures"
<p>Data and simulations files for the tutorial article "Brillouin optomechanics in nanophotonic structures".<br> Published in APL Photonics Special issue "Optoacoustics—Advances in high-frequency optomechanics and Brillouin scattering" - DOI: 10.1063/1.5088169</p>
3D-structured Supports create complete Data Sets for Electron Crystallography
<p>Each tar file contains the raw files in HDF5 format, together with the XDS.INP file used for data integration.</p> <p>NB: The meta-data in the HDF5 files have no meaning, please refer to the respective XDS.INP file for respective information.</p>
Extended data for the paper "Reliable generation of native-like decoys limits predictive ability in fragment-based protein structure prediction"
<p>Extended data for the paper:<br> Reliable generation of native-like decoys limits predictive ability in fragment-based protein structure prediction</p> <p>Authors:<br> Shaun M Kandathil, Mario Garza-Fabre, Simon C Lovell and Julia Handl</p> <p>--------------------------------</p> <p>Contents of the zip file:</p> <p> </p> <p>Directory 'ECDFplots':<br> ----------------------<br> Data corresponding to Figure 3 for all targets, for the bilevel and ILS protocols. Data are available following stages 3 and 4 of the low-resolution protocol.</p> <p>Directory 'ScoreRMSDplots_3archivers':<br> --------------------------------------<br> Data corresponding to Figures 6 and 9 for all targets. Data corresponding to decoys obtained after low-resolution stages 3 and 4 can be found in subdirectories 'Stage3' and 'Stage4', respectively.<br> </p>
Structure of PROSS_edited human cytokine IL-24 - diffraction data
<p>Structure of PROSS_edited human cytokine IL-24 - diffraction data</p>
Structural geology data for the Pelling region, Sikkim, India
<p>This is the structural data (foliation planes, stretching lineations, crenulation lineations and fold axes) for the region around the town of Pelling, Sikkim, India. </p>
data for "Mismeasurement of the core-shell structure of black carbon-containing ambient aerosols by SP2 measurements"
<p>The data for "Mismeasurement of the core-shell structure of black carbon-containing ambient aerosols by SP2 measurements"</p>
Data and code: Gut microbiota structure differs between honey bees in winter and summer
<p>This dataset contains data and code underlying the qPCR, amplicon sequencing, and statistical analysis of the research article "Gut microbiota structure differs between honey bees in winter and summer”. Short read datasets are available under NCBI Bioproject accession PRJNA578869.</p>
UAS-SfM data from Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA
<p>Data for:</p> <p>Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA<br>Sean Reilly 1, Matthew L. Clark 2, Lika Loechler 2, Jack Spillane 2, Melina Kozanitas 3, Paris Krause 4, David Ackerly 3, Lisa Patrick Bentley 4, and Imma Oliveras Menor 1,5</p> <p>1 Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK<br>2 Center for Interdisciplinary Geospatial Analysis, Department of Geography, Environment, and Planning, Sonoma State University, Rohnert Park, CA 94928, USA<br>3 Departments of Integrative Biology and Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA<br>4 Department of Biology, Sonoma State University, Rohnert Park, CA 94928, USA<br>5 AMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France</p> <p>Study abstract:</p> <p>There is a pressing need for well-informed management to reduce wildfire hazard and restore fire’s beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.</p> <p>Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69 – 0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49 – 0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.</p> <p>These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs. </p> <p>Published in Remote Sensing of Environment</p> <p><br>Contents:</p> <p>This repository contains multispectral UAS-SfM data from four sites around California, USA:<br>jcksn: Jackson Demonstration State Forest<br>ltr: LaTour Demonstration State Forest<br>ppwd: Pepperwood Preserve<br>sdlmtn: Saddle Mountain Open Space Preserve</p> <p>Data were collected during a series of campaigns:<br>c1: Pepperwood, 2019-09-01 to 2019-10-15<br>c3: Jackson, 2020-06-15 to 2020-07-02<br>c4: LaTour, 2020-07-07 to 2020-07-17<br>c6: Saddle Mountain, 2020-08-04 to 2020-08-09<br>c9: Jackson, 2021-07-08 to 2021-07-12</p> <p>Data are included in three formats:<br>raw: Raw outputs from Pix4D (spectral and las)<br>reg_grnd, reg_cnpy: Las files with merged multispectral data and classified ground, registered to ALS using either ground points (grnd) or, in cases with insufficient ground points for registration, to the canopy (cnpy)<br>hnrm: Height normalized las files, normalization performed using ALS terrain model</p> <p>File naming structure:<br>site_campaign_flightzone_uas_processedstate</p> <p>See accompanying paper for methods on data collection and processing</p> <p>Data are grouped into zipped folder by product type</p> <p>Funding:</p> <p>Funding for this research was supported by CAL FIRE Forest Health and Forest Legacy (8GG18806) and California State University, Agricultural Research Institute (20-01-106) awards to L.P.B and M.L.C. S.R. was funded by the Rhodes Trust and through the University of Oxford Environmental Change Institute Small Grant Scheme. Pepperwood ground data collection was supported by funding from the Gordon and Betty Moore Foundation and National Science Foundation grants 1754475 and 1835086.</p> <p>Citation:</p> <div> <div>Reilly, S., Clark, M.L., Loechler, L., Spillane, J., Kozanitas, M., Krause, P., Ackerly, D., Bentley, L.P., Menor, I.O., 2024. Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA. Remote Sensing of Environment 312, 114310. <a href="https://doi.org/10.1016/j.rse.2024.114310">https://doi.org/10.1016/j.rse.2024.114310</a></div> </div> <p> </p> <p> </p>
Figure 1. A in Preliminary data on the age structure of Asaccus barani (Baran's leaf-toed gecko) from southeastern Anatolia, Turkey
Figure 1. A cross-section (17 µm thick) of the femur bone of a 5-year-old Asaccus barani individual (m.c. = marrow cavity, p. = periosteal bone). The 5 LAGs are indicated by black arrows. A double LAG is highlighted by red arrow.
Data from: Mouse α-synuclein fibrils are structurally and functionally distinct from human fibrils associated with Lewy body diseases
<p>Tabular raw data corresponding to figure sets used in the study. </p> <p><span>Fig. 1 Mouse α-syn fibrils are structurally similar to human E46K-mutated and MSA-amplified α-syn fibrils</span></p> <p><span>Chemical structure and binding curves of ThT (J), Nile Red (K), and FSB (L) to mouse and human sonicated α-syn fibrils (average radii: 16.68±1.44 nm and 15.14±4.02 nm, respectively). Data points indicate means from three independent experiments and error bars are S.E.M. ****p<0.0001 and ***p<0.01 from 2-tailed t-tests. </span></p> <p><span>Fig. 2 Distinct β-fold stacking arrangements in mouse α-syn fibrils contribute to low tensile strength and resilience</span></p> <p><span>(C) Representation of proposed model of fibril fragmentation for tensile strength estimation used to simulate the MMGBSA energy of α-syn fibrils rupture and group analysis of MMGBSA energy required to disrupt a stack of six rungs. Error bars represent S.D. from 100 independent simulations. (D) Group comparison of fibril breakage under sonication conditions shown as the percent of size population of 10-100 nm (e.g., short fibrils) at 0, 2, and 30 minutes, measured by DLS. Error bars indicate S.E.M of three independent experiments with 30 acquisition measurements corresponding to each experiment. (E) Filter-trap slot-blot analysis of sonicated fibrils (i.e., PFFs) exposed to different concentrations of guanidinium chloride (GuHCl), and then remaining fibrils detected with the fibril-selective antibody MJFR14-6-4-2. Error bars indicate S.E.M from three independent experiments. (F) DLS analysis of PFFs after incubation with GuHCL. Error bars indicate S.E.M of three independent experiments with 10 acquisition measurements for each biological sample. Curves in (E) and (F) show asymmetric sigmoidal models with a goodness of fit >0.96, and **p<0.01, **p<0.001, ****p < 0.0001 from unpaired 2-tailed t-tests.</span></p> <p><br><br></p> <p><span>Fig. 3 Mouse α-syn fibrils fail to elicit robust cytokine and lysosome damage in macrophages </span></p> <p><span>(B) Internalized Alexa-647-PFFs (%area) inside cells and (C) % of LAMP1-positive vesicles positive for PFFs. Each data point represents the means of cells analyzed from at least eight images from three independent experiments. (D) ELISA analysis of the extracellular IL-6 and (E) CCL5 from MDM cultures treated with PFFs (1 μg/mL) for 3 and 24 hours. Each data point represents the mean from two technical replicates from four independent experiments. (F) % of Gal3-positive vesicles calculated per mm² of cell surface area in PFF-treated MDM cultures after 24 hours of incubation and (G) % of Gal3-positive vesicles also positive for DQ-PFFs after 48 hours of PFF incubation. Data points show the mean values from cells imaged across three independent experiments with at least eight images analyzed per group. (H) Representative images of Gal3 immunostaining after 24-hours treatment with Alexa-647- or DQ- labeled PFFs. Orthogonal views of sequential z-stacks are shown. Side left image = x,y plane, side right image = y,z plane; top image = x,z plane. Scale bar is 5 um. Error bars represent S.E.M and ***p<0.001, *p<0.05 and ns for not significant from unpaired 2-tailed t-tests (panels B, C, F, G) or from Tukey’s post-hoc test after ANOVA (panels D,E). </span></p> <p><span>Fig. 4. Mouse and human α-syn fibril uptake in neurons is similar and clathrin-dependent</span></p> <p><span>(A) Time-dependent dynamics of pHrodo-labeled mouse or human PFF (1 µg/mL) internalization over 24 hours into human-PAC-wt-SNCA<sup>+/+</sup>/Snca<sup>-/- </sup>hippocampal primary neuronal cultures at DIV7, with normalized pHrodo-channel intensity to DAPI counts at the indicated time point. Each dot represents mean values from four images each from three independent neuronal cultures. </span><span>(D) Alexa-568 intensity in dopaminergic neurons and (E) intensity exclusively in the perinuclear area 8 hours post PFF incubation. Each dot represents the mean value of one image with at least 20 images collected from three independent experiments. (F) Calculated uptake of pHrodo-labeled α-syn PFFs at 24 hours in the presence of endocytosis inhibitors with signals normalized to vehicle only controls. Each dot in panel F represents the mean value of four images evaluated per condition from three independent experiments. Error bars for each group analysis represent S.E.M., and ns is not-significant from 2-tailed t tests.</span></p> <p><span>Fig. 5. Mouse α-syn fibrils pathology propagation is more efficient than human α-syn in primary neurons. </span></p> <p><span>(A) Levels of pS129-α-syn signal assessed relative to the number of neurons in the corresponding cultures cultured from human-PAC-wt-SNCA<sup>+/+</sup>/Snca<sup>-/- </sup>hippocampal primary neuron culture treated with 1 µg/mL of α-syn PFFs for 14 days, or equivalent amounts of monomer as indicated, and stained against pS129-α-syn, Tau and NeuN. (B) To ensure the specificity of pS129-α-syn signals, control groups show the lack of signal in neurons cultured from Snca<sup>-/- </sup>mice following 14 days of incubation with α-syn PFFs or monomer as shown. (D) Abundance of distinct pS129-α-syn signals in cell bodies or neuritic morphology in neuronal cells treated with 1 µg/mL of mouse or human α-syn PFFs. (E) Proportion of pS129-α-syn occupancy in cell body and neurites in primary hippocampal cultures incubated with mouse or human α-syn PFFs for 14 days. (F) ELISA quantification of α-syn aggregate levels in cell lysates from human-PAC-wt-SNCA<sup>+/+</sup>/Snca<sup>-/- </sup> or Snca<sup>-/- </sup>neuronal cultures treated with fibril PFFs or monomeric protein for 14 days. (G) Group analysis of NeuN-positive nuclei abundance normalized to DAPI count. Each data point in a group in the graphs represents the mean of signal from an individual litter with two technical replicates per litter and at least 25 images analyzed for each replicate, with error bars indicating S.E.M. Significance was determined by 2-tailed t-tests; **p<0.001, ****p < 0.0001, ns for not significant.</span></p> <p><span>Fig. 6. Mouse PFF induced α-syn pathology spreads through the mouse brain to seed human α-syn pathology more efficiently than human α-syn PFFs</span></p> <p><span>(B) Group analysis of α-syn pathology propagation ratio to contralateral side in piriform cortex, (C) thalamus, and (D) dorsal striatum, quantified as proportion of pSyn neuronal inclusion spread between Ipsi and Contr. areas. (E) Analysis of pS129-α-syn pathology near the injection site within the ipsilateral dorsal striatum. Each data point (n=5 per group) in group analysis plots represents the mean of the signal from 20-25 sections from an individual animal, and error bars indicate S.E.M. Significance was determined by 2-tailed t-tests; *p<0.05, ns for not significant</span>.</p> <p><span>Fig. 7. Recruitment of human α-syn monomer into mouse PFFs leads to the generation of mouse-like fibrils.</span></p> <p><span>(A) Representative aggregation assays showing mouse and human PFF-templated aggregation with human α-syn monomer, with (B) the calculated lag phase. Data points represent normalized ThT fluorescence from three independent experiments with error bars indicating S.E.M. (C) Representative filter-trap slot-blot membranes stained with MJFR14-6-4-2 α-syn aggregate-specific antibodies for the detection of aggregated α-syn in corroboration of aggregation kinetics without amyloid dyes. </span><span> (E) Group analysis of ThT binding (fluorescence units, F.U.) from chimeric or homogenous-sequence extracted, monomer-free, sonicated α-syn fibril (PFF) products, as well as (F) Nile Red binding. Each data point in panels E and F represents a mean from an individual experiment measured in duplicate with three independent experiments.E<a name="_Hlk173655145"></a>rror bars indicate S.E.M., **p<0.01, from a 2-tailed t-test, and ***p<0.001 from Tukey’s post-hoc test after ANOVA. </span></p> <p>fig. S2. High resolution estimation of the cryo-EM map of mouse and human recombinant α-syn fibrils</p> <p>Fourier shell correlation (FSC) resolution estimation and validation for the 3D reconstruction of the cryo-EM collected images of procured mouse fibrils generated by Duke (a) and EPFL (b) research groups. (c) FSC estimation plot of human α-syn fibrils collected and generated at Duke University.</p> <p> </p> <p>fig. S7. Generation and validation of mouse and human α-syn PFFs</p> <p>(c) Group analysis of the size population proportions of sonicated mouse and human α-syn preparations, (e) representative average radii of human α-syn,(f) UV absorbance spectra and (g) coefficient extinction adjusted concentration. Each data point or S.E.M in panel c, e and g are extracted from a single acquisition from three independent experiments with ten measurements analyzed per group. Each dot in panel f is the mean of two technical replicates from three independent batches. Significance was assessed via 2-tailed t-tests with ns for not significant.</p> <p> </p> <p>fig. S9. Mouse α-syn PFFs are highly susceptible to sarkosyl denaturation.</p> <p>(a) Filter-trap slot-blot analysis of sonicated fibrils (PFFs) exposed to different concentrations of sarkosyl (left) and the remaining fibrils detected with the fibril-selective antibody MJFR14-6-4-2 in a dose-response curve (middle), with group analysis between two PFF variants at 1% Sarkosyl compared (right). Error bars indicate S.E.M from three independent experiments. (b) DLS analysis of sonicated fibrils (PFFs) exposed to sarkosyl concentrations in a dose-response curve. Error bars indicate S.E.M of three independent experiments with 10 acquisition measurements for each biological sample. *p<0.05 from unpaired 2-tailed t-tests.</p> <p> </p> <p>fig. S11. Evaluation of α-syn aggregation ELISA using control recombinant mouse and human α-syn fibril PFFs.</p> <p>Standard curves generated from mouse and human PFFs in a pan-α-syn aggregate-specific ELISA. The ELISA approach was utilized to quantify the level of aggregates present in lysates derived from the neuronal cultures. The standard curve, along with the indicated goodness of fit and corresponding r² values, provides reliable measures at physiological ranges. Each data point represents the mean from three technical replicates from two independent experiments with errors bars indicating S.E.M.</p> <p> </p> <p>fig. S9. Evaluation of α-syn ELISA using control recombinant mouse and human α-syn fibril PFFs</p> <p>Standard curve generated from mouse and human PFFs in a pan-α-syn aggregate ELISA. The ELISA was utilized to quantify the level of aggregates present in the cell lysates. The standard curve, along with the indicated goodness of fit and corresponding r² values, provides a reliable measure for interpreting the results. Each data point represents the mean from three technical replicates from two independent experiments with errors bars indicating S.E.M.</p> <p> </p> <p>fig. S12. Elevated p-S129-α-syn levels in iPSC-derived dopaminergic neurons following treatment with mouse α-syn PFFs</p> <p>(b) Levels of pS129-α-syn in the β-III-tubulin area in iPSC-derived DA neurons after 7 days of treatment with 10 μg/mL of PFFs or control. (c) The quantity of pS129-α-syn puncta relative to the β-III-tubulin area measured in each image and compared across conditions. (d) Proportion of abundance of pS129-α-syn puncta localized in cell bodies and neurites in group analysis between mouse and human α-syn PFF treatments. Each data point in c and d represent the mean value of the images from one well (n=4) and errors bars indicate S.E.M with **p<0.01 from 2-tailed t-tests. </p> <p> </p> <p>fig. S13. Evaluation of kinetic properties in aggregation assays with cross-seeded chimeric fibrils show that the chimeric fibrils largely replicate the functional properties of their parental seeds, despite different amino acid sequences between the fibril preparations.</p> <p>(a) Representative RT-QuIC (real-time quaking induced) assays of normalized relative fluorescence values (RFUs) with mouse monomer templating on different PFF seeds in the creation of new fibrils. The lack of formation of spontaneous fibrils (gray color, no PFFs added “no template”) indicate that spontaneous aggregation is not occurring under the given aggregation conditions within the specified timeframes. Line graphs show corresponding lag in amplification (in hours, or time to initial fluorescence threshold) of the different reactions combined with different PFF concentrations added and analyzed to a linear regression model. Data points represent normalized ThT fluorescence to their maximal fluorescence in the individual reactions, accounting for differential ThT binding to different fibril structures. (b) Comparable reactions as above but with human instead of mouse monomer templating with the indicated PFF seed. </p> <p> </p>
Raw data for "A Composite Bayesian Optimisation Framework for Material and Structural Design under Uncertainty"
<p>This dataset contains the raw data for the paper "A Composite Bayesian Optimisation Framework for Material and Structural Design under Uncertainty" (submitted) by R. P. Cardoso Coelho, A. F. Carvalho Alves, T. M. Nogueira Pires and F. M. Andrade Pires (INEGI and Faculty of Engineering of the University of Porto, Portugal).</p> <p> </p> <p>The data has been generated with the development branch of piglot - an open-source optimisation toolbox (https://github.com/CM2S/piglot). The numerical simulations have been conducted with both an in-house finite element solver (Links) and with the open-source SCA implementation CRATE (https://github.com/bessagroup/CRATE).</p>
Fig. 11 in A unique late Eocene coleoid cephalopod Mississaepia from Mississippi, USA: New data on cuttlebone structure, and their phylogenetic implications
Fig. 11. Cuttlebone of sepioid cephalopod Mississaepia mississippiensis Weaver, Dockery III, and Ciampaglio, 2010; late Eocene, Mississippi, USA (A–C) and contemporary Sepia (D). A. MGS 1956. B. MGS 1956. C. MGS 1956. D. NRM−PZ Mo. 180818. Photographs (A1−D1); EDS data on chemical composition shows presence of: A2, high content of nitrogen indicating organic ingredient in silicified tissue preserved along contact between conotheca and septum; B2, nitrogen indicating organic ingredient of phosphatised sheet within the dorsal shield; C2, D2, nitrogen indicating organic ingredient of dorsal shield.
Fig. 10 in A unique late Eocene coleoid cephalopod Mississaepia from Mississippi, USA: New data on cuttlebone structure, and their phylogenetic implications
Fig. 10. Sepioid cephalopod Mississaepia mississippiensis Weaver, Dockery III, and Ciampaglio, 2010 (MGS 1963); late Eocene, Mississippi, USA. Mural part of septum lining a chamber (A), adoral surface of peripheral portion of septum (B), photographs (A1, B1); EDS data to show chemical composition (A2, B2); in both cases nitrogen indicates organic ingredient and phosphorus indicates diagenetic phosphatization of apparently originally organic material.
Fig. 4 in A unique late Eocene coleoid cephalopod Mississaepia from Mississippi, USA: New data on cuttlebone structure, and their phylogenetic implications
Fig. 4. Sepioid cephalopod Mississaepia mississippiensis Weaver, Dockery III, and Ciampaglio, 2010 (MGS 1945); late Eocene, Mississippi, USA. Inner surface of the phragmocone exposing a small fragment of brownish transparent septum preserved. Abbreviatons: lwph, lateral wall of the phragmocone; mlad, median line indicating apertural direction; mlpd, median line indicating posterior direction; mpls, mural part of last septum; rr, ribby relief; sr, septal ridge; trs, transparent fragmentary septum; vsdsh, ventral side of dorsal shield.
Fig. 3 in A unique late Eocene coleoid cephalopod Mississaepia from Mississippi, USA: New data on cuttlebone structure, and their phylogenetic implications
Fig. 3. Sepioid cephalopod Mississaepia mississippiensis Weaver, Dockery III, and Ciampaglio, 2010 (MGS 1948); late Eocene, Mississippi, USA. Median cuttlebone section to show loosely mineralized dorsal shield (bottom), small cup−like protoconch covered by thin layer of the dorsal shield (on the left) and curved hollow phragmocone exhibiting two long chambers and next short chambers; to the right from the last preserved (eighth?) septum inner surface of phragmocone is transversely ribbed. Abbreviations: dsh dorsal shield; p, protoconch; rr, ribbed relief of the inner surface of the phragmocone; 1s, 2s, 3s, 8s, first, second, third, eighth septa.
Fig. 2 in A unique late Eocene coleoid cephalopod Mississaepia from Mississippi, USA: New data on cuttlebone structure, and their phylogenetic implications
Fig. 2. Sketch map showing the location of the Yazoo Clay, the Miss Lite Clay Pit in the northwest corner of the town of Jackson and the Moodys Branch Formation, Town Creek locality south of Jackson in Hinds County, Mississippi, USA.
Fig. 1 in A unique late Eocene coleoid cephalopod Mississaepia from Mississippi, USA: New data on cuttlebone structure, and their phylogenetic implications
Fig. 1. Cuttlebone of Late Eocene sepioid cephalopod Mississaepia mississippiensis Weaver, Dockery III, and Ciampaglio, 2010 from Mississippi, USA with a missing anterior−most part (MGS 1945), in left lateral (A) and ventral (B) views. Abbreviations: dsh, dorsal shield; lwph, lateral wall of the phragmocone; phr, phragmocone; vg, ventral groove of spine; vp, ventral plate.
Рис. 3–6. Coprophilus, строение эΔеагуса. 3, 5 – C. (Zonyptilus) pseudopiceus Gildenkov, 2015; 4, 6 – C. (Zonyptilus) schubertii (Motschulsky, 1860); 3–4 – вентраΛьно; 5–6 – ΛатераΛьно. Масштабная Λинейка 0.25 мм. Figs 3–6. Coprophilus, structure of aedeagus. 3, 5 – C. (Zonyptilus) pseudopiceus Gildenkov, 2015; 4, 6 – C. (Zonyptilus) schubertii (Motschulsky, 1860); 3–4 – ventral view; 5–6 – lateral view. Scale bars: 0.25 mm. in New data on distribution of Coprophilus Latreille, 1829 (Coleoptera: Staphylinidae: Oxytelinae) in the south of European part of Russia, in the Caucasus and Turkey
Рис. 3–6. Coprophilus, строение эΔеагуса. 3, 5 – C. (Zonyptilus) pseudopiceus Gildenkov, 2015; 4, 6 – C. (Zonyptilus) schubertii (Motschulsky, 1860); 3–4 – вентраΛьно; 5–6 – ΛатераΛьно. Масштабная Λинейка 0.25 мм. Figs 3–6. Coprophilus, structure of aedeagus. 3, 5 – C. (Zonyptilus) pseudopiceus Gildenkov, 2015; 4, 6 – C. (Zonyptilus) schubertii (Motschulsky, 1860); 3–4 – ventral view; 5–6 – lateral view. Scale bars: 0.25 mm.
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.