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PICASO 3.0 Atmospheric Models of WASP-39 b for the JWST Transiting Exoplanet Community Early Release Science Program
<p><strong>OVERVIEW</strong></p> <p>The exoplanetary atmospheric models used in the recent <a href="https://www.nature.com/articles/s41586-022-05269-w">discovery of CO<sub>2 </sub>in WASP- 39 b's atmosphere</a> by the JWST transiting exoplanet community early release science program are presented here. These models are also being used to analyze multiple observations of WASP 39-b obtained using various JWST instruments and observational modes by the transiting exoplanet ERS team. The 1D Radiative-Convective-Thermochemical Equilibrium (RCTE) atmospheric models were computed using the open-source 1D climate model <a href="https://natashabatalha.github.io/picaso/">PICASO 3.0</a> (<a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>). These atmospheric models were then post-processed with condensation clouds using the open-source cloud model <a href="https://natashabatalha.github.io/virga/">VIRGA</a> (<a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a>). The atmospheric models were also post-processed with the 1D photochemical network code <a href="https://github.com/exoclime/VULCAN">VULCAN</a> (<a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a>) to explore photochemistry in WASP-39 b's atmosphere.</p> <p><strong>1D RCTE CLOUD-FREE MODELS</strong></p> <p>The base 1D RCTE grid includes atmospheric metallicity points at 0.1, 0.3, 1.0, 3.0, 10.0, 30.0, 50.0, and 100.0x solar values. The Carbon-to-Oxygen (C/O) ratio value is varied between four values - 0.23, 0.46, 0.69, and 0.92. The intrinsic temperature of the planet has been varied across 100, 200, and 300 K, whereas two values of the heat redistribution factor - 0.4 and 0.5 are included. A heat redistribution factor of 0.5 corresponds to the case of full heat redistribution. With these grid points, the grid includes a total of 8x4x3x2= 192 different models.</p> <p>These models are in the "RCTE_cloud_free.zip" folder. The naming scheme of these files is "profile_eq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_.nc" where [T_int] represents the intrinsic temperature of the planet, [MH] is the log<sub>10 </sub>of the atmospheric metallicity relative to solar, [CtoO] is the C/O ratio relative to solar, and [rfacv] is the heat-redistribution factor. So, a metallicity value of 0.3xsolar will have a [MH] value of -0.5, and a C/O 0.46 is considered 1xsolar and will correspond to [CtoO]=1. [T_int] and [rfacv] can assume values described in the previous paragraph.</p> <p><strong>1D RCTE CLOUDY MODELS</strong></p> <p>The base 1D RCTE cloud-free models were post-processed to include condensation cloud species Na<sub>2</sub>S, MnS, and MgSiO<sub>3</sub>. The cloud structure and optical property calculations were performed using the VIRGA model where the sedimentation efficiency <em>f<sub>sed </sub></em>and the vertical eddy diffusion coefficient (<em>K<sub>zz</sub></em>) are free parameters. For the cloudy models, 5 <em>f<sub>sed </sub></em> values - 0.6, 1, 3, 6, and 10 were used along with 3 different values of log<sub>10</sub><em>K<sub>zz </sub></em>- 5, 7, 9, and 11, where <em>K<sub>zz </sub></em>is in cm<sup>2</sup>/s. These models are included in the "RCTE_cloudy.zip" folder following the naming structure "profile_eq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz]_fsed_[fsed].cld.nc" where two other variables are added in the name - [log10Kzz] and [fsed]. Both of these variables can take values listed here.</p> <p><strong>PHOTOCHEMICAL CLOUD-FREE MODELS</strong></p> <p>A much smaller subset of the base 1D RCTE models were post-processed with the 1D photochemical network code VULCAN to simulate the effects of vertical mixing and photochemistry in WASP-39 b's atmosphere. log<sub>10</sub><em>K<sub>zz </sub></em>was varied again between the 5, 7, 9, and 11 for this purpose. These files are named as "profile_diseq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz].nc" and can be found in the "photochem_cloud_free.zip" folder.<br> <br> <strong>PHOTOCHEMICAL CLOUDY MODELS</strong></p> <p>The photochemical models were post-processed with clouds to simulate a cloudy atmosphere with disequilibrium chemistry. The <em>f<sub>sed </sub></em> and log<sub>10</sub><em>K<sub>zz </sub></em> grid system for the RCTE cloudy models has been used again for these models as well. These files are in the "photochem_cloudy.zip" folder and are named according to the format "profile_diseq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz]_fsed_[fsed].cld.nc".</p> <p><strong>FILE FORMATTING AND USAGE</strong></p> <p>All the files are released in the <a href="https://docs.xarray.dev/en/stable/">xarray</a> format. Each model has one single xarray file containing all metadata of that model. This metadata includes the input parameters used to compute the model, for example, the metallicity, C/O ratio, and intrinsic temperature. The temperature-pressure (<em>T(P)</em>) profile and the volume mixing ratio profiles of all the different gases in each model is also included in the metadata. The computed transmission spectrum of the model planet from 0.3-6 microns is included in the same file as well. The spectrum is calculated with resampled opacities at a spectral resolution of 60,000, but they should be re-binned at a spectral resolution of 3000 or less for comparison with observed data. For cloudy models, the wavelength dependant optical depth, asymmetry parameter, and single scattering albedo for each atmospheric layer are included in these xarray files.</p> <p>We refer to this <a href="https://natashabatalha.github.io/picaso/notebooks/codehelp/data_uniformity_tutorial.html#Reading/interpreting-an-xarray-file">PICASO tutorial</a> for reading/writing these xarray files. The spectrum from these xarray files can be easily extracted using the following code.</p> <pre><code class="language-python">import xarray as xr path = "path/to/files" ds_sm = xr.open_dataset(path+"profile_eq_planet_300_grav_4.5_mh_+2.0_CO_2.0_sm_0.0486_v_0.5_.nc") # for spectrum wavelength = ds_sm['wavelength'].values transit_depth = ds_sm['transit_depth'].values # for T(P) profile temperature = ds_sm['temperature'].values pressure = ds_sm['pressure'].values</code></pre> <p><a href="https://github.com/natashabatalha/picaso/blob/master/docs/notebooks/fitdata/GridSearch.ipynb">This tutorial</a> shows how to use these models to analyze the NIRSpec Prism observations of WASP-39 b, which led to <a href="http://www.nature.com/articles/s41586-022-05269-w">CO<sub>2 </sub>detection</a>. Please note that the folders must be unzipped before using them with this notebook.</p> <p><strong>CREDITS</strong></p> <p>If you use these modeling products in your work, please cite this zenodo repository along with the following papers depending on the part of the grid being used:</p> <p>1) RCTE_cloud_free.zip</p> <p> <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a> </p> <p>2) RCTE_cloudy.zip</p> <p><a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a> </p> <p>3) photochem_cloud_free.zip</p> <p> <a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a> , <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a> </p> <p>4) photochem_cloudy.zip</p> <p> <a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a> , <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a></p> <p> </p>
"Competition between homogeneous and inhomogeneous broadening of orbital transitions in Si:Bi", N Stavrias et al, Phys Rev B 005200
<p>Raw data for the publication, including the small-signal absorption spectrum and THz pump-probe transients of Si:Bi. </p> <p>See publication for description of purpose etc:</p> <p>"Competition between homogeneous and inhomogeneous broadening of orbital transitions in Si:Bi", N Stavrias et al, Phys Rev B 005200</p> <p>See readme file for data description</p>
Data and code for "Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making" (CHI 2017)
<p>This repository contains data and analysis code for the following paper:</p> <p>Michael Fernandes, Logan Walls, Sean Munson, Jessica Hullman, and Matthew Kay. "Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making", Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI 2018. DOI: 10.1145/3173574.3173718<br> </p>
Revealing the percolation–agglomeration transition in polymer nanocomposites via MD-informed continuum RVEs with elastoplastic interphases - dataset
<p><strong>Abstract</strong>:<br>from [1]</p> <p>This contribution builds the concluding step of a multiscale approach to effectively capture the mechanical <br>behavior of polymer nanocomposites (PNCs), in this case, silica-modified polystyrene. By introducing <br>continuum-based representative volume elements (RVEs) that employ previously identified elastoplastic property <br>gradients for the interphases surrounding the fillers, the effects of particle size, particle volume fraction, <br>and agglomeration on the mechanical performance are investigated. Uniaxial tension tests are simulated with <br>the respective finite-element RVEs, and stress–strain curves are derived. The elastic and plastic material <br>properties of the RVE can then be extracted and analyzed quantitatively by fitting the stress–strain curves <br>with a Voce-type elastoplasticity formulation. <br>At small degrees of agglomeration, i.e., good particle dispersion, in combination with sufficiently large <br>particle volume fraction, percolation bands form, leading to improved elastic and plastic properties. Higher <br>degrees of agglomeration or particle clusters behave like large single particles, which has an adverse effect, i.e., <br>the nanoscale size effect is thereby neutralized. Therefore, the precise MD-informed elastoplastic interphase <br>representation of our RVEs enables the investigation of the transition from beneficial percolation to unfavorable <br>agglomeration. Ultimately, this contribution establishes a link between the effects of particle size, particle <br>volume fraction, agglomeration, and percolation, which have so far only been discussed separately in the <br>literature. <br>Our methodology offers new insights into the structure–property relations of PNCs and their resulting <br>mechanical behavior. The underlying multiscale approach with a systematic transition from molecular to <br>microscopic scales is required to complement experimental observations and exploit the full potential of PNCs. </p> <p><br><strong>Contact</strong>:</p> <p>Maximilian Ries<br>Institute of Applied Mechanics<br>Friedrich-Alexander-Universität Erlangen-Nürnberg<br>Egerlandstr. 5<br>91058 Erlangen</p> <p><strong>Software</strong>:</p> <p>All finite element simulations were performed with Simulia Abaqus/CAE2018 </p> <p><strong>License</strong>:</p> <p>Creative Commons Attribution Non Commercial 4.0 International</p> <p><strong>Context</strong>:</p> <p>Data set supplementing journal paper:</p> <p>[1] E.-M. Richter, G. Possart, P. Steinmann, S. Pfaller, & M. Ries, “Revealing the percolation–agglomeration transition in polymer nanocomposites via MD-informed continuum RVEs with elastoplastic interphases,” Composites Part B: Engineering, vol. 281, p. 111477, 2024.</p> <p><strong>Content</strong>:</p> <p>- excel sheet summarizing all RVE simulations in combination with the elastoplastic constitutive model calibration: elastoplastic_constitutive_model_calibration.xlsx<br>- input data for each RVE FE simulation in *.inp format following the naming convention:<br> agg_<degree of agglomeration>-fillercont_<filler content>Percent-fillerrad_<filler radius>nm<br> - degree of agglomeration is defined in [1]<br> - filler content is given in volume percent<br> - filler radius is given in nanometer </p> <p> </p>
Data for "Antiferromagnetic phase transition in a 3D fermionic Hubbard model"
<p>This dataset is for research article "Antiferromagnetic phase transition in a 3D fermionic Hubbard model".</p>
Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition
<ul> <li><strong>Name</strong>: Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition</li> <li><strong>Summary</strong>: This dataset contains answers from a panel of experts to build a) a taxonomy of determinants that explain the investment decision making on assets related to the energy transition, b) the individual contributions when sorting the taxonomy of determinantes on the different stages of the transtheoretical model for different archetypes of persons and c) the causal diagrams agreed between the different groups of experts.</li> <li><strong>License</strong>: cc-BY-SA</li> <li><strong>Acknowledge</strong>: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li><strong>Disclaimer</strong>: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li><strong>Collection Date</strong>: 22/07/2022</li> <li><strong>Publication Date</strong>: 01/06/2024</li> <li><strong>DOI</strong>: 10.5281/zenodo.11234441</li> <li><strong>Other repositories:</strong></li> <li><strong>Author</strong>: University of Deusto</li> <li><strong>Objective of collection</strong>: This data was originally collected to build a set of causal diagrams of the .</li> <li><strong>Description:</strong> <br> <ul> <li><strong>Scenarios: </strong>This dataset contains the description of 20 different scenarios used in this research activity. </li> <li><strong>File 1 - individual reasons to be coded<br></strong>This dataset compiles the reasons given by experts of different panels of the Intrinsic and Extrinsic Determinants, and the Barriers and potential Rebound effects of citizens towards a set of 20 different scenarios. The file contains the following sheets:<br> <ul> <li><strong>Methodology</strong>: Methodology followed by the coders.</li> <li><strong>Help</strong>: Short summary of the Social Cognitive Theor and Self Determination Theory used for coding. </li> <li><strong>Glossary</strong>: Glossary of terms build by the experts coding the answers. </li> <li><strong>Appliances/Flexibility/Buildings/Mobility</strong>: The contributions of each expert, the code provided by the two researchers and the consensus achived. </li> <li><strong>Summary</strong>: Assesment of the results.</li> </ul> </li> <li><strong>File 2 - individual microdata to sort determinants into causal threads from experts</strong>This dataset includes the individual sortings made by the experts of the taxonomy of determinantes into each one of the stages of the transtheoretical model. The file includes one sheet per expert where he/she has sort each determinant for each arquetype into the stage he/she thinks is more relevant to advance to the next step of the TTM. </li> <li><strong>File 3 - collective microdata to sort determinants into causal threads from EU and LATAM experts</strong> <p>This dataset compiles the results, stage by stage, of the consensus reached by each panel regarding the determining factors that make up each of the archetypes in the contexts of Europe (EU) and Latin America (LATAM). And in which stage of the change of the Transtheoretical Model (TTM) the factors should appears.</p> <ul> <li> <p><strong>Stage 1</strong>: The panels reached a consensus on the factors that describe each of the archetypes in their context. In the case of Latin America, for the panels of some countries, the existence of all eight archetypes was not evident. The number of archetypes analysed by each panel is indicated in parentheses in the following list:</p> <ul> <li> <p><strong>European panels</strong>: Group – F (8), Group–A (8). Group–FF (8), Group–M (4)</p> </li> <li> <p><strong>Latin America panels</strong>: Group-MX (5), Group-CO (8), Group-CL (7), Group-SV (7)</p> </li> </ul> </li> </ul> <ul> <li> <p><strong>Stage 2</strong>: For each of the eight archetypes, the results of the consensus for each panel are consolidated in the tabs indicated in the list below. The column on the far right shows the weights (percentage) of each factor in each stage of the TTM: Archetype-EarlyAdopter, Archetype-Uninterested, Archetype-HomoEconomicus, Archetype-Fearful, Archetype-Stubborn, Archetype-Influencer, Archetype-Careful and Archetype-Activist.</p> </li> <li> <p><strong>Stage3</strong>: In the "<em>Archetypes - Consensus Results</em>" tab, the weights of the factors for each archetype are consolidated. The far-right column calculates the average weight of each factor at each stage of the TTM (Transtheoretical Model of Change).</p> </li> <li> <p><strong>Stage 4</strong>. In the “EU vs Latam - split context” sheet, it is presented a comparative assessment between the European and Latin American results. The comparison has four tables:</p> <ul> <li> <p><em>Table (s)</em>: Difference and Agreements between both context: European & Latin American Archetypes. The table highlights the regions of determinants that mark the differences between both contexts for each archetype. If a determinant is identified by both contexts (EU, Latam), it is considered an agreement and allocated to the early TTM stage. The remaining determinants highlight the differences between the two contexts. European (-1) & Latin American (1) Archetypes FINAL Consensus (0) on TTM Stages.</p> </li> <li> <p><em>Table (t)</em>: This table shows the difference (E, L) and agreements (X) between both context: European (E) & Latin American (L) Archetypes.</p> </li> <li> <p><em>Table (t.1)</em>: This table shows just the <strong>agreements</strong> (X) between both context: European & Latin American Archetypes.</p> </li> <li> <p><em>Table (t.2)</em>: Show the difference between both context: European (E) & Latin American Archetypes (L).</p> </li> <li> <p><em>Table (t.3)</em>: This table shows the differences (E, L) and agreements (X) between both contexts: European (E) & Latin American (L) archetypes. In this table, the main regions of factors for each archetype are coloured to highlight the set of factors that make the main differences.</p> </li> </ul> </li> </ul> </li> </ul> </li> <li><strong>5 star</strong>: ⭐⭐⭐</li> <li><strong>Preprocessing steps:</strong> Data transcription from written documents and oral discussions.</li> <li><strong>Reuse:</strong> NA</li> <li><strong>Update policy:</strong> No more updates are planned.</li> <li><strong>Ethics and legal aspects:</strong> Names of the persons involved have been removed. </li> <li><strong>Technical aspects</strong>: </li> <li><strong>Other:</strong></li> </ul>
Supporting data for "Percolation transition and bimodal density distribution in hdyrogen fluoride", J. Chem. Phys. 160, 204503 (2024)
<p>GROMACS input files for the simulation of hydrogen fluoride using the polarizable model of Pártay, Jedlovszky and Vallauri [<em>J. Chem. Phys. </em>124, 184504 (2006)] adapted for the Drude oscillator model, as published in <em>J. Chem. Phys.</em> 160, 204503 (2024) <a href="https://doi.org/10.1063/5.0207202" target="_blank" rel="noopener">https://doi.org/10.1063/5.0207202</a></p>
Can technical education in high school smooth postsecondary transitions for students with disabilities?
<p>Participation in Career Technical Education (CTE) programs has been proposed as a valuable strategy for supporting transition to independence among students with disabilities. We exploit a discontinuity created by admissions thresholds from a statewide system of CTE high schools. Our findings suggest attending CTE high schools has large positive effects on completing high school on time, employment, and earnings, including for individuals 22 years or older. Attending CTE schools also results in more time spent with non-disabled peers and higher 10th grade test scores. These results appear concentrated among male students, but the sample of female students is too small to support strong conclusions about outcomes. Notably, these estimates are for a system of CTE high schools operating at scale and serving students across a wide spectrum of disabilities, and the estimated effects appear broad based over disability type, time spent with non-disabled peers in 8th grade and previous academic performance.</p>
VITALISE - LifeSpace - Transitional Care pilot
<p>This dataset is composed of the metrics from the devices coming from the transitional care VITALISE pilot site at LifeSpace UPM and the responses from de usability questionnaire and QoL.</p> <p> </p>
Intravenous Versus Oral Transition Therapy for the Treatment of Viridans Group Streptococcal Bloodstream Infections
<p><strong>Background: </strong><em>Streptococcus</em> are amongst the most common pathogens implicated in bloodstream infections (BSI). Within the genus, S. <em>pneumoniae</em> and beta-hemolytic <em>Streptococcus</em> are the most frequently reported organisms. Viridans Group <em>Streptococcus</em> (VGS) have emerged as a more common cause and are associated with more severe complications. IV antibiotics have long been considered the standard of care, but recent studies have shown similar efficacy between IV and oral antibiotics. This is important because switching to oral antibiotics is associated with benefits like a shorter length of stay, decreased patient and hospital expenses, and a lower rate of adverse events. <strong>Objective</strong>: To determine if oral (PO) transition therapy is non-inferior to intravenous (IV)-only therapy for the treatment of uncomplicated Viridians Group <em>Streptococcus</em> (VGS) bloodstream infections (BSI). <strong>Design</strong>: Retrospective, non-inferiority cohort study. <strong>Setting</strong>: St. Luke’s University Health Network, a non-profit, academic health network. <strong>Patients</strong>: Adult inpatients with a diagnosis of VGS BSI between January 1, 2018, and December 31, 2022. <strong>Methods</strong>: Patients with VGS BSI were categorized on whether they received IV therapy for their entire treatment course or transitioned to PO therapy. The primary outcome of treatment failure was defined as a composite of all-cause mortality, hospital readmission due to infectious process, or recurrent BSI within 90 days. Secondary outcomes included the individual components of the primary outcome, adverse events, and length of stay. Non-inferiority threshold was set at 10% in the 95% CI for the difference in the proportion of patients with treatment failure between the oral transition and intravenous-only groups. <strong>Results</strong>: The primary composite outcome occurred in 20.8% of the IV group and 11.5% of the PO transition group (RR, 0.55 [95% CI 0.18 to 1.72]). Hospital length of stay was significantly longer in the IV-only group. There were no significant differences in the other outcomes. Notably, 12 of the 16 treatment-related adverse events observed in the IV-only group were associated with use of an indwelling IV catheter. <strong>Conclusions</strong>: Although the risk ratio for the composite outcome of treatment failure favors a transition to oral therapy, statistical non-inferiority could not be established.</p>
Fig. 2 in A new transitional "libelluloid" family of odonates with Mesozoic affinities in the Eocene Green River Formation of Utah, USA
Fig. 2. Anisopteran dragonfly Cordulibellula inopinata gen. et sp. nov. from Lake Uinta, Utah, USA, Eocene, holotype FHPR 11611, reconstruction of wing base (A1), detail of wing apex (A2). Abbreviations: a.l., anal loop; N, nodus; Pt, pterostigma; t, discoidal triangle.
Fig. 1 in A new transitional "libelluloid" family of odonates with Mesozoic affinities in the Eocene Green River Formation of Utah, USA
Fig. 1. Anisopteran dragonfly Cordulibellula inopinata gen. et sp. nov. from Lake Uinta, Utah, USA, Eocene, holotype FHPR 11611, part (A1), counterpart (A2), detail of wing base (A3). Abbreviations: a.l., anal loop; N, nodus; t, discoidal triangle.
Fig. 13 in Variability of conch morphology in a cephalopod species from the Cambrian to Ordovician transition strata of Siberia
Fig. 13. Restoration of the conch of Ruthenoceras elongatum Korde, 1949 from the Ust-kut Formation of Siberia, with hypothetical subspherical apex based mostly on ZPAL N. IV/4 (Fig. 5A). A. Septum in proximal view. B. Conch in lateral view with the body and proximal part of sipho exposed. C. The body and a portion of sipho in dorsal view.
Fig. 10 in Variability of conch morphology in a cephalopod species from the Cambrian to Ordovician transition strata of Siberia
Fig. 10. Contours of all the ellesmeroceratid nautiloid conchs (interpreted below as Ruthenoceras elongatum Korde, 1949) from sample Ang-4, block No. 1, probably latest Furongian Ust-kut Formation found at Pashino on the Angara River, Siberia, Russia, superimposed on the most complete specimen ZPAL N. IV/4 (Fig. 5A).
Fig. 9 in Variability of conch morphology in a cephalopod species from the Cambrian to Ordovician transition strata of Siberia
Fig. 9. Apical parts of ellesmeroceratid nautiloid conchs (interpreted below as Ruthenoceras elongatum Korde, 1949) from sample Ang-4, block No. 1, probably latest Furongian Ust-kut Formation found at Pashino on the Angara River, Siberia, Russia. A, B. ZPAL N. IV/11 and 117, respectively; conchs with low expansion rate rate in lateral (A1, B1) and dorsal (A2, B2) views. C–E. ZPAL N. IV/103, 18, and 114, respectively; conchs with high expansion rate rate in lateral (C1, D, E2), ventral (C2), and dorsal (E1) views.
Fig. 6 in Variability of conch morphology in a cephalopod species from the Cambrian to Ordovician transition strata of Siberia
Fig. 6. Suture lines of ellesmeroceratid nautiloids from sample Ang-4, block No. 1, probably latest Furongian Ust-kut Formation found at Pashino on the Angara River, Siberia, Russia. A–H. ZPAL N. IV/109, 111, 10, 27, 56, 51, 48, and 9, respectively. Scale bars 2 mm.
Fig. 1 in Variability of conch morphology in a cephalopod species from the Cambrian to Ordovician transition strata of Siberia
Fig. 1. Loose block No. 1 of a stromatolitic limestone with abundant nautiloid conchs of the Ust-kut Formation (latest Furongian or earliest Tremadocian) found on the left bank of the Angara River at the former village Pashino. A. The block partially exploited for fossils. B. Stromatolite columns with empty cavities and a laminar cover above. C. A piece of the rock with exposed nautiloids. D. Polished rock surface (note similarity of the specimen in the middle to the holotype of Ruthenoceras elongatum Korde, 1949). E. Naturally abraded upper surface of the stromatolite columns.
Fig. 12 in Variability of conch morphology in a cephalopod species from the Cambrian to Ordovician transition strata of Siberia
Fig. 12. Relationships between the basic conch geometry aspects of ellesmeroceratid nautiloids from sample Ang-4, block No. 1, probably latest Furongian Ust-kut Formation found at Pashino on the Angara River, Siberia, Russia. A. With an increase of septum depth its obliquity increases even stronger but the correlation is rather loose. B. If non-linear correspondence to ontogeny of these phragmocone aspects is ignored, the pattern of variability appears roughly unimodal. C–E. Also the distribution of indices of the living chamber elongation, septum inclination and depth does not reveal any multimodality. The regression lines in A and B are intuitive (drawn by hand and not computed) because only one dimension (aperture height) is measurable in smallest conchs.
Fig. 11 in Variability of conch morphology in a cephalopod species from the Cambrian to Ordovician transition strata of Siberia
Fig. 11. Ontogenetic change of conch geometry aspects of ellesmeroceratid nautiloids from sample Ang-4, block No. 1, probably latest Furongian Ust-kut Formation found at Pashino on the Angara River, Siberia, Russia. Aperture height is used as a measure of an individual age. A–C. Characters with linear growth pattern. D–G. Characters with non-linear growth pattern. The regression lines are intuitive (drawn by hand and not computed) because only one dimension (aperture height) is measurable in smallest conchs.
Fig. 7 in Variability of conch morphology in a cephalopod species from the Cambrian to Ordovician transition strata of Siberia
Fig. 7. Medial sections of ellesmeroceratid nautiloids interpreted below as Ruthenoceras elongatum Korde, 1949, from sample Ang-4, block No. 1, probably latest Furongian Ust-kut Formation found at Pashino on the Angara River, Siberia, Russia. A, B. Mature phragmocones with moderately oblique septa, ZPAL N. IV/14 and 15, respectively. C. Almost complete juvenile phragmocone ZPAL N. IV/92 showing extend of diaphragms in the siphuncle. D–G. Apical parts of phragmocones (not strictly medial sections), ZPAL N. IV/115, 118, 121, and 125, respectively. H. Mature phragmocone ZPAL N. IV/16 with extremely oblique septa. I. Straight part of the phragmocone ZPAL N. IV/17 with oblique diaphragms. Wet ground surfaces (A1, B, C1, D–G, H1, I) and acetate peels (A2, C2, H2).
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.