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Code for data and figures published in "Solar energy as an early just transition opportunity for coal-bearing states in India"
<p>The following code and data were used to generate the figures in the article "Solar energy as an early just transition opportunity for coal-bearing states in India". The article was published in Environmental Research Letters (<a href="https://iopscience.iop.org/article/10.1088/1748-9326/ac5194">https://iopscience.iop.org/article/10.1088/1748-9326/ac5194</a>)</p> <p>The code is written in R. Before running the Rmd file, create a folder called "Data" and store all the files there, except the Rmd file.</p>
Real-space imaging of phase transitions in bridged artificial kagome spin ice
<p>Open data to "Real-space imaging of phase transitions in bridged artificial kagome spin ice", published in Nature Physics (2022), https://www.nature.com/articles/s41567-022-01564-5</p>
Data on transit history and anti-fouling practices for ships arriving to the Canadian Arctic
<p>Ship biofouling is a major vector for the introduction and spread of harmful marine species globally, however, its importance in Arctic coastal ecosystems is understudied. The objective of this study was to provide insight regarding the extent of biofouling (i.e., percent cover, abundance, and species richness) on commercial ships operating in the Canadian Arctic. A questionnaire was used to collect information on transit history, anti-fouling practices, and self-reported estimates of biofouling extent from a sample of ships operating in the region during 2015 – 2016.</p>
Climate transition at the Eocene - Oligocene influenced by bathymetric changes to the Atlantic - Arctic oceanic gateways
<p>This data includes input and output fields of the Norwegian Earth System model simulations targeting the Eocene-Oligocene boundary, and the data accompanies a scientific article with the same name (DOI 10.1073/pnas.2115346119). This data is aimed to provide users enough data to reproduce the figures appearing in the article and to analyze the steady state behavior beyond what is discussed in the article. We also provide the ocean bathymetry and atmospheric topography that allows others to create a model setup similar to what is used in the article.</p> <p>For most files there are two output periods 1450-1500 is a common period for the experiments: Late Eocene and cases 1-5. This period the average over the last 50 years of the experiments that were branched of the common spinup at year 1000. The latter period 2650-2700 includes continuation of the Late Eocene and Case 2 simulations and their lower CO2 counterparts (branched of from their parent simulations at year 2000). For more information of the simulations and the setup, we refer to the parent article.</p> <p>In addition to the output files, we also share the grid files of the ocean model (grid*.nc) and the topography used by the atmospheric model (ATM_TOPO.nc). Note that we did not change the land mask between the experiments which is why there is only one ATM_TOPO, but one grid file per each experiment.</p>
Data from: Phylogenomics of elongate-bodied Springtails reveals independent transitions from aboveground to belowground habitats in deep time
<p>Soil has become a major hotspot of biodiversity studies, yet the pattern and timing of the evolution of soil organisms are poorly known because of the scarcity of palaeontological data. To overcome this limitation, we conducted a genome-based macroevolutionary study of an ancient, diversified, and widespread lineage of soil fauna, the elongate-bodied springtails (class Collembola, order Entomobryomorpha). To build the first robust backbone phylogeny of this previously refractory group, we sampled representatives of major higher taxa (6 out of 8 families, 11 out of 16 subfamilies) of the order with an emphasis on the most problematic superfamily Tomoceroidea, applied whole-genome sequencing (WGS) methods, and compared the performance of different combinations of datasets (universal single-copy orthologues/USCO versus ultraconserved elements/UCE) and modelling schemes. The fossil-calibrated timetree was used to reconstruct the evolution of body size, sensory organs, and pigmentation to establish a time frame of the ecomorphological divergences. The resultant trees based on different analyses were congruent in most nodes. Several discordant nodes were carefully evaluated by considering method fitness, morphological information, and topology test. The evaluation favoured the well-resolved topology from analyses using USCO amino acid matrices and complex site-heterogeneous models (CAT+GTR and LG+PMSF (C60)). The preferred topology supports the monophyletic superfamily Tomoceroidea as an early-diverging lineage and a sister relationship between Entomobryoidea and Isotomoidea. The family Tomoceridae was recovered as monophyletic, while Oncopoduridae was recovered as paraphyletic, with <em>Harlomillsia</em> as a sister to Tomoceridae and hence deserving a separate family status as Harlomillsiidae Yu and Zhang <strong>fam. n.</strong> Ancestral Entomobryomorpha were reconstructed as surface-living, supporting independent origins of soil-living groups across the Palaeozoic–Mesozoic, and highlighting the ancient evolutionary interaction between aboveground and belowground fauna.</p>
Spectral sensitivity transition in the compound eyes of a twilight-swarming mayfly and its visual ecological implications
<p>Aquatic insect species that leave the water after larval development, such as mayflies, have to deal with extremely different visual environments in their different life stages. Measuring the spectral sensitivity of the compound eyes of the virgin mayfly (Ephoron virgo) resulted in differences between the sensitivity of adults and larvae. Larvae were primarily green-, while adults were mostly UV-sensitive. The sensitivity of adults and larvae were the same in the UV, but in the green spectral range, adults were 3.3 times less sensitive than larvae. Transmittance spectrum measurements of larval skins covering the eye showed that the removal of exuvium during emergence cannot explain the spectral sensitivity change of the eyes. Taking numerous sky spectra from the literature, the ratio of UV and green photons in the skylight was shown to be maximal for θ ≈ − 13° solar elevation, which is in the θmax = -14.7° and θmin = -7.1° typical range of swarming that was established from webcam images of real swarmings. We suggest that spectral sensitivity of both the larval and adult eyes are adapted to the optical environment of the corresponding life stages.</p>
Supplementary information: The nuclear-spin-forbidden rovibrational transitions of water from first principles
<p><strong>Supplementary material to the manuscript <em>"The nuclear-spin-forbidden rovibrational transitions of water from first principles"</em> by Andrey Yachmenev, Guang Yang, Emil Zak, Sergei Yurchenko, and Jochen Küpper, <em>J. Chem. Phys., submitted. </em></strong><a href="https://arxiv.org/abs/2203.07945"> arXiv:2203.07945</a></p> <p>The data set contains hyperfine (spin-rovibrational) energies and dipole transition spectrum of water molecule (H<sub>2</sub><sup>16</sup>O), calculated using variational approach <a href="https://github.com/Trovemaster/TROVE">TROVE</a> and <a href="https://github.com/CFEL-CMI/richmol">RichMol</a>, and included spin-rotational and spin-spin hyperfine interactions.</p> <p>In addition, the data set includes HDF5-type richmol database file <strong><em>h2o_p48_j40_rovib.h5</em></strong> (see <a href="https://github.com/CFEL-CMI/richmol">https://github.com/CFEL-CMI/richmol</a>) containing rovibrational energies, matrix elements of nuclear spin-rotation, nuclear spin-spin, electric dipole, and electric quadrupole tensor operators of H<sub>2</sub><sup>16</sup>O, calculated using variational approach TROVE.</p> <ul> <li><strong>h2o_exomol_F.states </strong>and<strong> h2o_exomol_F.trans</strong> - hyperfine linelist of water stored in the ExoMol format (see, e.g., <a href="https://doi.org/10.1016/j.jms.2016.05.002">J. Molec. Spectrosc., 327, 73-94 (2016)</a>). The two files contain a set of hyperfine states with assignments and a set of dipole transitions (Einstein A-coefficients), respectively. The states in <strong>h2o_exomol_F.states</strong> file are arranged by quantum number of total angular momentum F = I + J (spin + rotation) in ascending order.</li> <li><strong>h2o_exomol_J.states </strong>and<strong> h2o_exomol_J.trans</strong> - contain same data as <strong>h2o_exomol_F.states </strong>and<strong> h2o_exomol_F.trans</strong> files, except that the states in <strong>h2o_exomol_J.states</strong> file are arranged by rotational quantum number (J) in ascending order.</li> <li><strong>h2o_p48_j40_rovib.h5<em> - </em></strong>Richmol HDF5 database file for H<sub>2</sub><sup>16</sup>O containing rovibrational energies (in cm<sup>-1</sup>), matrix elements of nuclear spin-rotation (in kHz), spin-spin (in kHz), molecular electric dipole moment (in Debye), and molecular electric quadrupole moment (in a.u.) operators. For details on how to read this file, see <a href="https://github.com/CFEL-CMI/richmol">Richmol GitHub repository</a> and <a href="https://richmol.readthedocs.io/en/latest/">Richmol documentation</a> (<em>or contact Andrey Yachmenev at andrey.yachmenev@cfel.de</em>).</li> <li><strong>ortho_para_transitions.txt</strong> - table with strongest predicted ortho-para transitions in H<sub>2</sub><sup>16</sup>O at T = 296 K with the 10<sup>−36</sup> cm/molecule intensity cut-off.<br> <br> <strong><em>An example of hyperfine energies and hyperfine dipole spectrum calculation for water using h2o_p48_j40_rovib.h5 file from this repository may be found in the <a href="https://github.com/CFEL-CMI/richmol">Richmol GitHub repository's</a> examples folder: <a href="https://github.com/CFEL-CMI/richmol/tree/develop/examples/hyperfine">https://github.com/CFEL-CMI/richmol/tree/develop/examples/hyperfine</a></em></strong></li> </ul> <p>Structure of<strong> h2o_exomol_F.states </strong>and<strong> h2o_exomol_J.states </strong>files:</p> <table align="left"> <thead> <tr> <th scope="col">Column No.</th> <th scope="col">Kind </th> <th scope="col">Meaning </th> </tr> </thead> <tbody> <tr> <td>1</td> <td>int</td> <td>state ID number</td> </tr> <tr> <td>2</td> <td>float</td> <td>hyperfine state energy relative to the ZPE, in cm<sup>-1</sup></td> </tr> <tr> <td>3</td> <td>int</td> <td>state degeneracy</td> </tr> <tr> <td>4</td> <td>int</td> <td>value of F quantum number (total spin-rotational angular momentum)</td> </tr> <tr> <td>5</td> <td>str</td> <td>state symmetry in C<sub>2v</sub></td> </tr> <tr> <td>6</td> <td>int</td> <td>value of J quantum number (total rotational angular momentum)</td> </tr> <tr> <td>7</td> <td>str</td> <td>symmetry of state's rotational component in C<sub>2v</sub></td> </tr> <tr> <td>8</td> <td>int</td> <td>value of k<sub>a</sub> quantum number (a-axis projection of rotational angular momentum)</td> </tr> <tr> <td>9</td> <td>int</td> <td>value of k<sub>c</sub> quantum number (c-axis projection of rotational angular momentum)</td> </tr> <tr> <td>10</td> <td>int</td> <td>value of v<sub>1</sub> vibrational quantum number</td> </tr> <tr> <td>11</td> <td>int</td> <td>value of v<sub>2</sub> vibrational quantum number</td> </tr> <tr> <td>12</td> <td>int</td> <td>value of v<sub>3</sub> vibrational quantum number</td> </tr> <tr> <td>13</td> <td>int</td> <td>value of I quantum number (total nuclear spin)</td> </tr> <tr> <td>14</td> <td>float</td> <td>reference rovibrational state energy (i.e., without hyperfine effects) relative to the ZPE, in cm<sup>-1</sup></td> </tr> </tbody> </table> <p>Structure of<strong> h2o_exomol_F.trans </strong>and<strong> h2o_exomol_J.trans </strong>files:</p> <table> <thead> <tr> <th scope="col">Column No.</th> <th scope="col">Kind</th> <th scope="col">Meaning</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>int</td> <td>ID number of final transition state (col. no. 1 in <strong>h2o_exomol_F.states </strong>or<strong> h2o_exomol_J.states </strong>file)</td> </tr> <tr> <td>2</td> <td>int</td> <td>ID number of initial transition state (col. no. 1 in <strong>h2o_exomol_F.states </strong>or<strong> h2o_exomol_J.states </strong>file)</td> </tr> <tr> <td>3</td> <td>float</td> <td>Einstein A-coefficient, in s<sup>-1</sup></td> </tr> <tr> <td>4</td> <td>float</td> <td>Transition wavenumber, in cm<sup>-1</sup></td> </tr> </tbody> </table> <p> </p>
Scenario data for article: Effects of the energy transition on environmental impacts of cobalt supply: A prospective Life Cycle Assessment study on future supply of cobalt
<p>This dataset contains the background data for the paper '<a href="https://onlinelibrary.wiley.com/doi/10.1111/jiec.13258">Effects of the energy transition on environmental impacts of the cobalt supply: A prospective Life Cycle Assessment study on the future cobalt supply</a>' as published in the Journal of Industrial Ecology.</p> <p><strong>Please note that an easier to use version of this data for LCA is available through the Premise (<a href="https://www.sciencedirect.com/science/article/pii/S136403212200226X">Sacchi et al. 2022</a>) Community Scenarios <a href="https://github.com/premise-community-scenarios/cobalt-perspective-2050">here</a>.</strong> This version is slightly adapted to fit into the Premise architecture and is compatible with ecoinvent v3.8 cutoff.</p> <p>This repository contains:</p> <ul> <li>Python code + readme to model the variables, generate presamples packages and generate LCA results based on those. (code folder)</li> <li>Input and output data for Variables 1-3 (files 1&2)</li> <li>Presamples excel sheets for each variable/scenario combination (file 3)</li> <li>Summarized LCA results (the full results can be generated through running the code provided) (file 4)</li> <li>Full LCA results used for the contribution analysis (file 5)</li> <li>Underlying data for each of the figures (file 6)</li> </ul> <p>We refer to the paper (linked above) for more information on the study.<br> </p> <p><strong>License: </strong>The metal supply scenario data is licensed under the CC-BY 4.0 license.</p> <p><strong>Access: </strong>Open access</p> <p> </p> <p>[Changelog]</p> <p>2023-03-23 - 1.3.1: Add link to Premise Community scenario page.<br> 2022-05-18 - 1.3.0: Fix minor error in data files '4 - LCA results' and '6 - Figure data' in demand amounts for total impacts.<br> 2022-04-06 - 1.2.1: Included link to article after publication<br> 2022-03-30 - 1.2.0: Included underlying figure data<br> 2022-01-24 - 1.1.1: Opened repository after paper acceptance<br> 2021-11-26 - 1.1.0: Update of code to comply with peer-review<br> 2021-07-12 - 1.0.0: Set-up of repository</p>
Dataset for Transitional Wave Climate Regions on Continental and Polar Coasts in a Warming World
<p>This dataset supports Oderiz et al. (2022) "Transitional Wave Climate Regions on Continental and Polar Coasts in a Warming World". Nature Climate Change.10.1038/s41558-022-01389-3</p> <p> </p> <p>We recomend users to also read:<br> Odériz, I., Silva, R., Mortlock, T. R., Mori, N., Shimura, T., et al. (2021). Natural variability and warming signals in global ocean wave climates. Geophysical Research Letters, 48, e2021GL093622. https://doi.org/10.1029/2021GL093622</p>
Optical constants of several multilayer transition metal dichalcogenides measured by spectroscopic ellipsometry in the 300-1700 nm range: high-index, anisotropy, and hyperbolicity
<p># Data and plotting code for "Optical constants of several multilayer transition metal dichalcogenides measured by spectroscopic ellipsometry in the 300-1700 nm range: high-index, anisotropy, and hyperbolicity" by Battulga Munkhbat, Piotr Wróbel, Tomasz J. Antosiewicz, and Timur O. Shegai, ACS Photonics (2022); https://doi.org/10.1021/acsphotonics.2c00433</p> <p><br> ## Contents</p> <p>* <TMD-material>: directories with raw and derived data for all 10 TMDs<br> * f3_dataset_*_nm_ex1_ex2_ey1_ey2_ez1_ez2.txt: obtained permittivities<br> * plot_*_v1.m: Matlab scripts for plotting data</p> <p>## Description of the data</p> <p>The raw and derived data stored in directories <TMD> contain the following files:</p> <p>* <TMD>/<date>-<TMD>.SEsnap: binary data file with collected data, CompleteEASE format<br> * <TMD>/<date>-<TMD>-E*.mat: ascii text file with permittivity data separated into individual components as exported from CompleteEASE software<br> * <TMD>/<date>-<TMD>-full.mat: ascii text file with fitted model parameters as exported from CompleteEASE software<br> * <TMD>/<TMD>-data/*.txt: selected raw data and fits for all considered samples (Mueller Matrix or Delta/Psi/depolarization).</p> <p>The structure of the data file names is as follows:<br> <order-number-in-CompleteEASE>-s<sample-name>-<data-type>.txt for general ellipsometry (delta, psi, depolarization) or<br> <order-number-in-CompleteEASE>-s<sample-name>-o<in-plane-sample-rotation-number>-mm.txt for Mueller Matrix measurements.</p> <p>The following two scripts can be used to plot the raw measured data (solig lines) along with corresponding fits (black dotted lines):</p> <p>* plot_mm_v1.m: Matlab script for plotting Mueller Matrix data for WTe2 and ReS2<br> * plot_psi_delta_depol_v1.m: Matlab script for plotting psi, delta, and depolarization data for other TMDs</p> <p>The diagonal permittivity tensor data are saved in the f3_dataset_*_nm_ex1_ex2_ey1_ey2_ez1_ez2.txt files which can be plotted using the plot_permittivity_v1.m Matlab script. The format of this file is as follows:</p> <p>wavelength in nanometers; real part of epsilon_xx; imaginary part of epsilon_xx; real part of epsilon_yy; imaginary part of epsilon_yy; real part of epsilon_zz; imaginary part of epsilon_zz;</p> <p> </p> <p> </p>
Dataset for greenhouse gas modelling in diesel dependent communities transitioning to bioenergy
<p>The data presented here are from the research article entitled "Greenhouse gas mitigation potential of replacing diesel fuel with wood-based bioenergy in an arctic Indigenous community: A pilot study in Fort McPherson, Canada". Based on a pilot study realized in Northern Canada and life cycle assessment, we provide a set of key parameters and operational data gathered along the biomass supply chain to build a GHG mitigation scenario and compute the quantity and timing of GHG savings in the off-grid community of Fort McPherson, NWT. Given that GHG mitigation scenarios are often assessed against a relative fossil-fuel reference scenario, we are providing two categories of data; 1) data for the reference fossil fuel scenario and; 2) data along the upstream operations of biomass supply chains. Both categories contain data related to the operational processes as well as forest growth or decomposition of unused feedstock. Although the data presented are mostly derived from the boreal forest, they could help guide other communities beyond the boreal to develop a renewable bioenergy system and assess their GHG mitigation options.</p>
Historic data of the national electricity system transitions in Europe in 1990–2019 for retrospective evaluation of models [dataset]
<p>This data package supports empirical analysis of national electricity system transitions and retrospective evaluation of electricity system models in 1990–2019 in 31 European countries, including the EU27, Switzerland, Iceland, Norway, and the United Kingdom. The data package covers two types of content. Firstly, we provide an annotated list of 528 original data sources and references relevant for retrospective electricity system modeling with emphasis on open-access sources. Secondly, we provide a total of 1359 processed and harmonized data files in a format that is suitable as inputs to electricity system models. Four types of data files are included for each country: (i) a country file documenting national demand and economic data, (ii) technology files describing techno-economic data for each major generation technology in the country's electricity mix, (iii) resource files describing fuel prices and CO2 emissions for each fuel, and (iv) load profiles describing 24-hour national load curves for each available year. We provide these data files as comma-separated files to enable their wider reuse for retrospective evaluation of models as well as for empirical analyses of the European electricity system transitions. </p>
GENeSYS-MOD Transport Sensitivities: Data and model code for Hainsch (preprint): Identifying policy areas for the transition of the transportation sector
<p>This dataset contains all GENeSYS-MOD input data for Hainsch (preprint): Identifying policy areas for the transition of the transportation sector. doi: 10.5281/zenodo.6919452.</p> <p>With the input data files and the GAMS files, the model results presented in the preprint can be replicated.</p> <p>Furthermore, the output folder contains the result files for the base case and all sensitivities as well as the Tableau files which were used to generate the result figures.</p>
Data for the article "Tuning Spin-Orbit Torques Across the Phase Transition in VO2/NiFe Heterostructure"
<p>Data for the article "Tuning Spin-Orbit Torques Across the Phase Transition in VO2/NiFe Heterostructure" (<a href="https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202111555">https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202111555</a> and <a href="http://arxiv.org/abs/2201.12984">http://arxiv.org/abs/2201.12984</a>)</p>
The research of River Morphology transition and Sediment variation: Shule River, Northwest of China
<p>The data acquisition in this study is mainly divided into two parts: indoor statistics and field measurements to obtain. In the indoor work, satellite image data and radar digital elevation data were primarily used to measure and count the river width (Fig.1c), sinuosity, gradient, and elevation every 500m along the top of the Shule River downward (Tab.1). The river width was calculated as the distance between the outer banks, measured at a 90° angle to the river axis, including the channel bar and point bar (Mcglue et al., 2016). The classification of river morphology is mainly based on the size of sinuosity (Rust, 1978). The sinuosity greater than 1.5 is defined as a meandering river, and less than 1.5 is defined as a braided river (Fig.1c). The river gradient is counted for every two adjacent measurement points. According to the above measurement criteria, there are 237 river morphology data within the alluvial fan of the Shule River (Tab.1).</p> <p>In the field measurement process, due to the limited accuracy of satellite images in portraying river morphology. We also use UAV aerial photography to refine further the river's morphological characteristics based on satellite images, which mainly included the channel bar and point bar description. Under the guidance of sedimentological theory, we measured and sampled the gravel in the modern riverbed of Shule River (Fig.2). By measuring the grain size and orientation parameters of gravel(Fig.2a), we research the refinement characteristics of sediments from the apex to the toe (Folk, 1954). Among them, gravel grain size and orientation were measured by the quantitative characterization method of gravel orientation proposed by Huang YuanGuang et al. (Fig.2a, b), and grain size was determined by the long flat axis of gravels (Huang et al., 2018), gravel orientation was measured by the rose diagram of the relative apparent dip (Fig.2b) (Huang et al., 2018; Tao et al., 2018). A total of five gravel statistical points were included within the alluvial fan of the Shule River, and a total of 1862 gravel grain size parameters were measured (Tab.2). We also use the hand-hold X-ray fluorescence spectrometer to measure the element characteristics of each sampling point (Fig.2c, d; Tab.3), which uses intelligent one-button testing and intelligent judgment functions for elements between atomic numbers 12-92 (Mg-U) (Fig.2e; Tab.3).</p>
FIG. 16. — Masillamys cosensis n in Rongeurs de la localité éocène de Cos (Tarn-et-Garonne, Quercy, France). Comparaison avec les rongeurs de localités de la transition Éocène inférieur/Éocène moyen
FIG. 16. — Masillamys cosensis n. sp. de Cos, dents inférieures: A, UM-Cos 847, rangée dentaire droite, de p4 à m3; A1, vue occlusale; A2, vue buccale; A3, vue linguale; B, UM-Cos 81, rangée dentaire gauche, de m1 à m3; B1, vue occlusale; B2, vue buccale; B3, vue linguale; C, UM-Cos 59, m2 droite; C1, vue occlusale; C2, vue buccale; C3, vue linguale. Échelle: 1 mm.
FIG. 5 in Rongeurs de la localité éocène de Cos (Tarn-et-Garonne, Quercy, France). Comparaison avec les rongeurs de localités de la transition Éocène inférieur/Éocène moyen
FIG. 5. — Eogliravus Hartenberger, 1971, graphes longueur/largeur des dents supérieures (A) et inférieures (B) d'E. wildi Hartenberger, 1971 de Cos (Tarn et Garonne) (figurés bleus), E. wildi du Mas de Gimel (Hérault) (figurés rouges), E. wildi de Prémontré (Aisne) (figurés noirs) et E. hammeli Hartenberger, 1971 de Bouxwiller (Bas-Rhin) (figurés verts).
FIG. 2 in Rongeurs de la localité éocène de Cos (Tarn-et-Garonne, Quercy, France). Comparaison avec les rongeurs de localités de la transition Éocène inférieur/Éocène moyen
FIG. 2. — Eogliravus wildi Hartenberger, 1971, de Cos; A, dessins de dents supérieures en vue occlusale; A, UM-Cos 354, D4 droite; B, UM-Cos 355, P4 gauche; C, UM-Cos 356, M1 gauche; D, UM-Cos 357, M1 gauche; E, UM- Cos 359, M2 gauche; F, UM-Cos 360, M3 gauche. Échelle: 1 mm.
FIG. 11. — Masillamys cosensis n in Rongeurs de la localité éocène de Cos (Tarn-et-Garonne, Quercy, France). Comparaison avec les rongeurs de localités de la transition Éocène inférieur/Éocène moyen
FIG. 11. — Masillamys cosensis n. sp. de Cos, molaires supérieures: A, UM-Cos 85, P4-M3 gauche, vue occlusale; B, UM-Cos 83, moulage de fragment de maxillaire droit portant M3, M2, M1 et les alvéoles de P4 et P3; B1, vue buccale de M3; B2, vue occlusale; C, UM-Cos 846, P4 gauche; C1, vue occlusale; C2, vue linguale; C3, vue buccale; D, UM-Cos 140, M1-M2 gauche; E, UM-Cos 843, M1 gauche, vue occlusale; F, UM-Cos 135, M3 gauche; F1, vue occlusale; F2, vue buccale; F3, vue linguale; G, UM-Cos 152, M3 gauche; G1, vue buccale; G2, vue occlusale; G3, vue linguale. Échelle: 1 mm.
FIG. 19. — Masillamys cosensis n in Rongeurs de la localité éocène de Cos (Tarn-et-Garonne, Quercy, France). Comparaison avec les rongeurs de localités de la transition Éocène inférieur/Éocène moyen
FIG. 19. — Masillamys cosensis n. sp. de Viélase (Tarn et Garonne, MP 10/11), dents supérieures: A, VIE 129, D4 droite, vue occlusale; B, VIE 214, D4 gauche à émail altéré, bord buccal incomplet, vue occlusale; C, VIE 131, P4 droite; C1, vue occlusale; C2, vue linguale; D, VIE 122, P4 droite (Legendre et al. 1992: fig. 8b); D1, vue occlusale; D2 vue linguale; E, VIE 133, M1 gauche; E1, vue occlusale; E2, vue linguale; F, VIE 123, M1 droite; F1, vue occlusale; F2, vue linguale; G, VIE 132, M1 droite; G1, vue occlusale; G2, vue linguale; H, VIE 121, M1 gauche; H1, vue occlusale; H2, vue linguale; H3, vue buccale; I, VIE 134, M1 gauche; I1, vue occlusale; I2, vue linguale; J, VIE 135, M1-2 droite; J1, vue occlusale; J2, vue linguale; K, VIE 136, M2 droite; K1, vue occlusale; K2, vue linguale. Échelle: 1 mm.
ScienceDex guides
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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.