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nanoindentation data associated with the publication "On the elastic microstructure of bulk metallic glasses" in Materials&Design 2023
<p>This dataset consists of indentation data measured with a conospherical tip in a Hysitron-Bruker TI980 Nanoindenter on the surface of a <100> Silicon wafer and a polished cross-sectional cut of a Zr65Cu25Al10 bulk metallic glass.</p> <p>It is associated with the following publication: <br>Birte Riechers, Catherine Ott, Saurabh Mohan Das, Christian H. Liebscher, Konrad Samwer, Peter M. Derlet and Robert Maass "On the elastic microstructure of bulk metallic glasses" Materials and Design 229, (2023) 111929. https://doi.org/10.1016/j.matdes.2023.111929</p> <p>All experimental information can be found in this paper and in the accompanying supplementary information.</p> <p>This electronic version of the data was published on the "Zenodo Data repository" found at http://zenodo.org/deposit in the community "Bundesanstalt fuer Materialforschung und -pruefung (BAM)".</p> <p>The authors have copyright to these data. You are welcome to use the data for further analysis, but are requested to cite the original publication whenever use is made of the data in publications, presentations, etc. </p> <p>Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.</p> <p>____________________________________________________________________</p> <p>The data format is defined as described below:</p> <p>In total, Fifteen text files exist that result in five different data sets.</p> <p>Two data sets represent measurements on Silicon, these are specifically the topography (mapped height profile) and indentation modulus (Si-topography.txt and Si-modulus.txt). The connected lateral information of these mapped quantities (i.e. Si-X_topography.txt and Si-Y_topography.txt; Si-X_modulus.txt and Si-Y_modulus.txt). This amounts to six .txt files connected to measurements on Silicon.</p> <p>Three data sets represent measurements on the Zr65Cu25Al10 bulk metallic glass. These are specifically the topography (MG-topography.txt), the indentation modulus (MG-modulus.txt), and the curvature-corrected indentation modulus (MG-curvcorr_modulus.txt). The connected lateral information of these mapped quantities (i.e. MG-X_topography.txt and MG-Y_topography.txt; MG-X_modulus.txt and MG-Y_modulus.txt; MG-X_curvcorr-modulus.txt and MG-Y_curvcorr-modulus.txt). This amounts to nine .txt files connected to measurements on the metallic glass.</p> <p>The files are plain text files with the data points separated by commata. Topography data is stated in units of Nanometer, modulus data is stated relative to its mean as unit-less values.</p> <p>Beside the .txt files, one figure (.pdf) with a plot of each data set is provided for reference, and the python code (Riechers_OnTheElasticMicrostructureOfBulkMetallicGlasses_zenodo.ipynb) generating these figures from the data sets is uploaded to this repository as well.</p>
Supplementary material (ubp & gwp data set): Egeler, G.-A., von Rickenbach, F., & Baur, P. (2020). Menüwahl in der Hochschulmensa: Design & Durchführung Feldexperiment (NOVANIMAL Kurzbericht). ZHAW. https://doi.org/10.21256/zhaw-1408
<p>Calculation of the greenhouse warming potential (gwp) and environmental impact points (UBP, Umweltbelastungspunkte) of 93 meals served on the fieldexperiment in the NOVANIMAL Project. Results of the fieldexperiment see here:</p> <p><a href="https://zenodo.org/deposit/4115429">Egeler, G.-A. & Baur, P. (2020). Menüwahl in der Hochschulmensa: Fleisch oder Vegi? Ergebnisse eines 12-wöchigen Feldexperiments (NOVANIMAL Working Paper No. 5). ZHAW. https://doi.org/10.21256/zhaw-1405</a></p> <p> </p>
Supplementary material (ebp data set): Egeler, G.-A., von Rickenbach, F., & Baur, P. (2020). Menüwahl in der Hochschulmensa: Design & Durchführung Feldexperiment (NOVANIMAL Kurzbericht). ZHAW. https://doi.org/10.21256/zhaw-1408
<p>Calculation of the nutrient balance score of 93 meals served on the fieldexperiment in the NOVANIMAL Project: According two different methods: EBP and Teller Modell. Results of the fieldexperiment see here:</p> <p><a href="https://zenodo.org/deposit/4115429">Egeler, G.-A. & Baur, P. (2020). Menüwahl in der Hochschulmensa: Fleisch oder Vegi? Ergebnisse eines 12-wöchigen Feldexperiments (NOVANIMAL Working Paper No. 5). ZHAW. https://doi.org/10.21256/zhaw-1405</a></p>
Dataset on Physics-Based Indicators for Optimizing Phase Change Material Effectiveness in Building Design
<p>This research dataset includes the results as well as the EnrgyPlus models developed to investigate and validate newly proposed indicators to quantify the effectiveness of phase change materials in buildings.</p>
Supplementary codes and datasets for "Modular-topology optimization of structures and mechanisms with free material design and clustering"
<p>This repository supports Tyburec, M., Doškář, M., Zeman, J., & Kružík, M. (2022). Modular-topology optimization of structures and mechanisms with free material design and clustering. <em>Computer Methods in Applied Mechanics and Engineering</em>, <em>395</em>, 114977. <a href="https://doi.org/10.1016/j.cma.2022.114977">https://doi.org/10.1016/j.cma.2022.114977</a> (first published as preprint <a href="http://arxiv.org/abs/2111.10439">2111.10439</a> at arXiv.org).</p> <p>This repository contains:</p> <ol> <li>MATLAB source codes for <em>(modular) free material optimisation</em> and <em>hierarchical stiffness clustering</em> (folder <code>./mFMO/</code>)</li> <li>C++ source codes for <em>modular topology optimization</em> (folder <code>./MTO/</code>)</li> <li>Input/output data of the test suite (folder <code>./data/</code>)</li> </ol> <p><strong>1. Data flow</strong></p> <p>The test suite considered in the manuscript covers 4 problems:</p> <ol> <li>Messerschmitt-Bölkow-Blohm beam (labelled as <code>mbb</code>)</li> <li>Inverter compliant mechanism (labelled as <code>inv</code>)</li> <li>Gripper compliant mechanism (labelled as <code>grip</code>)</li> <li>Reusable design of both compliant mechanisms (labelled as <code>invgrip</code>)</li> </ol> <p>Each problem in the dataset is stored within a separate subfolder named according to the labels mentioned above. The final level of subdirectories <code>{X}color</code> comprises of the results for problems with <code>X</code> denoting the number of edge codes considered for each edge direction during the clustering (<code>0color</code> stands for a non-modular design and <code>1color</code> represents the design based on Periodic Unit Cell).</p> <p>Each of the folders contains outputs of the modular free material optimisation in the following form:</p> <ul> <li><code>{label}{X}.mat</code></li> <li><code>{label}{X}.til</code></li> <li><code>{label}{X}.tset</code></li> <li><code>{label}{X}guess.mat</code></li> </ul> <p>Files <code>*.til</code>, <code>*.tset</code>, and <code>*guess.mat</code> are then converted into a JSON input file for the modular topology optimization code with generator scripts which can be found in <code>./MTO/scripts</code> folder. Note that each of the problems in the test suite has its own generator script <code>generate_modular_problem_{MBB,inverter,gripper,inverterAndGripper}.mat</code>. The generator scripts make a directory named according to the key <code>MTO_{n}_kernelSensitivity</code>, where <code>n</code> denotes the resolution of each module (i.e. the number of nodes along one direction). The directory also contains the outputs of the modular topology optimisation in the form of the initial and the final state of the optimization in <code>VTK</code> files and visualisation of the final state in <code>SVG</code> files. The log file <code>log.txt</code> stores the optimized objective and progress of the value along with stopping criteria quantities during iterations.</p> <p><strong>2. Running codes</strong></p> <p><strong>2.1 Modular free material optimisation</strong></p> <p>MATLAB scripts and functions for (modular) Free Material Optimization (FMO) are contained in the <code>mFMO</code> data folder. The codes have been tested with MATLAB R2019b. To run the codes the user is required to install the <a href="http://www.penopt.com">PENNON optimizer</a>. A free academic license is provided by its authors on request.</p> <p>Input files for individual problems are defined in the <code>mFMO/problems</code> folder and are launched with the <code>runproblem(problemName, numClusters)</code>, where <code>problemName</code> refers to the file in the <code>mFMO/problems</code> folder without the file extension and <code>numClusters</code> denotes the maximum number of color codes in Wang tiling formalism.</p> <p>If successful, the optimization produces output files in <code>mFMO/fmo_fig/{label}/{X}colors/{T}/</code>:</p> <ul> <li><code>{label}{X}.mat</code> (contains clustering and tiling information)</li> <li><code>{label}{X}_tmp.mat</code> (contains results of non-modular FMO)</li> <li><code>{label}{X}.til</code> (the assembly plan)</li> <li><code>{label}{X}.tset</code> (Wang tile set)</li> <li><code>{label}{X}guess.mat</code> (guess for TO)</li> </ul> <p>where <code>T</code> is the optimization time stamp.</p> <p><strong>2.2 Modular topology optimisation</strong></p> <p>All results were obtained with version <code>v1.1.2</code>, which is also provided in the folder <code>MTO</code>, and linked Intel® oneAPI Math Kernel Library and the incorporated PARDISO sparse solver. For the recent development of the code see the open git repository at <a href="https://gitlab.com/MartinDoskar/modular-topology-optimization">https://gitlab.com/MartinDoskar/modular-topology-optimization</a>. The repository also contains a detailed description of input parameters and code design.</p> <p>Modular topology optimisation code uses CMake for the cross-platform build automation. For instance, under Linux, the whole code can be compiled in the standard five steps:</p> <pre><code>cd ./MTO mkdir build cd ./build cmake -DCMAKE_BUILD_TYPE=Release .. make </code></pre> <p>All executables are automatically stored in <code>./MTO/bin/</code> folder. Individual problems can be optimized by parsing the JSON files obtained from the generator scripts as an argument to the MTO.Application binary, e.g.,</p> <pre><code>./MTO/bin/MTO.Application.exe path_to_data/mbb/2color/MTO_100_kernelSensitivity/input_modular_mbb_2colours_100.json </code></pre> <p><strong>Acknowledgement</strong></p> <p>The related research and code development was supported by the <a href="https://gacr.cz/en/">Czech Science Foundation</a>, project No. 19-26143X.</p>
Materials for Design Open Repository. High Entropy Alloys
<p>The current dataset is composed of a collection of High Entropy Alloys (HEAs). It contains the alloy composition, the number of chemical elements (No), the phase in a simple form (S_Phase), where 4 classes of phases were considered, namely amorphous (AM), intermetallic (IM), solid solution (SS), and solid solution + intermetallic (SS+IM). It contains also a second phase column (Phase), where we added the type of phase present in alloys with SS and repeated the S_Phase entry for the other cases. We have calculated 13 design parameters (see their definition below) used to design HEAs, known as the parametric approach. Finally, a set of columns containing the chemical elements and their corresponding fraction in the alloy is included. This dataset was developed in the framework of the European project ACHIEF for the discovery of novel materials to be used in industrial processes.</p> <ol> <li>Mean atomic radius <em>a</em> (Å) <ul> <li><span class="math-tex">\(a = \displaystyle\sum_{i=1}^{n} c_i r_i\)</span></li> </ul> </li> <li>Atomic size difference δ <ul> <li><span class="math-tex">\(\delta = \sqrt{\displaystyle\sum_{i=1}^{n} c_i \bigg(1 - \dfrac{r_i}{a} \bigg)^2}\)</span></li> </ul> </li> <li>Average melting temperature <em>T<sub>m</sub></em> (K) <ul> <li><span class="math-tex">\(T_m = \displaystyle\sum_{i=1}^{n} c_i T_{mi}\)</span></li> </ul> </li> <li>Average melting temperature standard deviation (K) <ul> <li><span class="math-tex">\(\sigma_{T_m} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i \bigg(1 - \dfrac{T_{mi}}{T_m} \bigg)^2}\)</span></li> </ul> </li> <li>Mixing enthalpy Δ<em>H<sub>mix</sub></em> (kJ/mol) <ul> <li><span class="math-tex">\(\Delta H_{mix} = 4 \displaystyle\sum_{i \neq j} c_i c_j H_{ij}\)</span></li> </ul> </li> <li>Mixing enthalpy standard deviation (kJ/mol) <ul> <li><span class="math-tex">\(\sigma_{\Delta H_{mix}} = \sqrt{\displaystyle\sum_{i \neq j} c_i c_j (H_{ij} - \Delta H_{mix})^2}\)</span></li> </ul> </li> <li>Ideal mixing entropy <em>S<sub>id</sub></em> (<em>R</em>)<strong>*</strong> <ul> <li><span class="math-tex">\(S_{id} = \Delta S_{mix} = -R \displaystyle\sum_{i=1}^{n} c_i \ln c_i\)</span></li> </ul> </li> <li>Electronegativity <em>χ</em> <ul> <li><span class="math-tex">\(\chi = \displaystyle\sum_{i=1}^{n} c_i \chi_i\)</span></li> </ul> </li> <li>Electronegativity difference in a multi-component alloy system <ul> <li><span class="math-tex">\(\Delta\chi = \displaystyle\sqrt{\sum_{i=1}^{n} c_i(\chi_i - \chi)^2}\)</span></li> </ul> </li> <li>Valence electron concentration <em>VEC</em> <ul> <li><span class="math-tex">\(VEC = \displaystyle\sum_{i=1}^{n} c_i \cdot VEC_i\)</span></li> </ul> </li> <li>Valence electron concentration standard deviation <ul> <li><span class="math-tex">\(\sigma_{VEC} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i (VEC_i - VEC)^2}\)</span></li> </ul> </li> <li>Mean bulk modulus <em>K </em>(GPa) <ul> <li><span class="math-tex">\(K = \displaystyle\sum_{i=1}^{n} c_i K_i\)</span></li> </ul> </li> <li>Bulk modulus standard deviation (GPa) <ul> <li><span class="math-tex">\(\sigma_{K} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i (K_i - K)^2}\)</span></li> </ul> </li> <li>Young's modulus <em>E</em> (GPa) <ul> <li><span class="math-tex">\(E = \displaystyle\sum_{i=1}^{n} c_i E_i\)</span></li> </ul> </li> <li>Shear modulus <em>G</em> (GPa) <ul> <li><span class="math-tex">\(G = \displaystyle\sum_{i=1}^{n} c_i G_i\)</span></li> </ul> </li> </ol> <p>where <em>n</em> is the number of components in the alloy system, <em>c<sub>i</sub></em> is the stoichiometric ratio, <em>r<sub>i</sub></em> is the atomic radius, <em>T<sub>mi</sub></em> is the melting temperature, <em>χ<sub>i</sub></em> is the Pauli electronegativity, <em>VEC<sub>i</sub></em> is the valence electron concentration, and <em>K<sub>i</sub></em> is the bulk modulus, <em>E<sub>i</sub></em> is the Young's modulus, and <em>G<sub>i</sub></em> is shear modulus for the <em>i</em>-th component of the alloy. <em>H<sub>ij</sub></em> is the binary mixing enthalpy in the liquid phase, and <em>R</em> is the gas constant.</p> <p><strong>*Note:</strong> the ideal mixing entropy <em>S<sub>id</sub></em> units in the first version of the dataset appear as kJ/mol, but they should be written in terms of the gas constant <em>R</em>, e.g., the compound Ag<sub>2</sub>Al has <em>S<sub>id</sub></em> = 0.636 <em>R</em>, where <em>R</em> = 8.314 J · K<sup>−1</sup> · mol<sup>−1</sup>. The second version the <em>S<sub>id</sub></em> units are corrected and two new features are included.</p>
Supplementary material (buffet data set): Egeler, G.-A., von Rickenbach, F., & Baur, P. (2020). Menüwahl in der Hochschulmensa: Design & Durchführung Feldexperiment (NOVANIMAL Kurzbericht). ZHAW. https://doi.org/10.21256/zhaw-1408
<p>During the fieldexperiment in the NOVANIMAL Project several informations about the offer of the buffet were collected. The report of the fieldexperiment see:</p> <p><a href="https://zenodo.org/deposit/4115429">Egeler, G.-A. & Baur, P. (2020). Menüwahl in der Hochschulmensa: Fleisch oder Vegi? Ergebnisse eines 12-wöchigen Feldexperiments (NOVANIMAL Working Paper No. 5). ZHAW. https://doi.org/10.21256/zhaw-1405</a></p>
Supplementary Material: Microfluidic Fabrication Solutions for Tailor-Designed Fiber Suspensions
<p>Supplementary material for Berthet, H.; du Roure, O.; Lindner, A. Microfluidic Fabrication Solutions for Tailor-Designed Fiber Suspensions. <em>Appl. Sci.</em> <strong>2016</strong>, <em>6</em>, 385.</p> <p><strong>Video S1:</strong> Microfluidic fabrication technique of fibers by in situ photopolymerization</p> <p><strong>Video S2: </strong>In situ microfluidic measurement of the fiber’s Young’s modulus</p> <p><strong>Video S3: </strong>Microfluidic fabrication technique of fibers by super-paramagnetic particles self-assembly</p> <p><strong>Video S4: </strong>Fiber oscillating between the two lateral walls of a microfluidic channel</p> <p><strong>Video S5: </strong>Flow through a constriction of a suspension of parallel fibers fabricated by photo-polymerization</p> <p><strong>Video S6: </strong>Flow through a constriction of a suspension of rigid perpendicular fibers fabricated by photo-polymerization</p> <p><strong>Video S7: </strong>Flow through a constriction of a suspension of flexible perpendicular fibers fabricated by photo-polymerization</p> <p><strong>Video S8: </strong>Concentrated suspension of fibers flowing through a microfluidic constriction</p> <p><strong>Video S9: </strong>Fibers made by colloids self-assembly flowing through a constriction and forming non-permanent clusters.</p>
Supplementary Material for "Aiding the Design of Critical Software Systems by Iterative Exploration of Distinct Requirement Violation Scenarios"
<p>This dataset provides artifacts about an industrial case study of a Steer-by-Wire system. It collects models of the system modeled in the open-source Gamma Statechart Composition Framework. You can find more information about the framework here: <a href="https://github.com/ftsrg/gamma">https://github.com/ftsrg/gamma</a>.</p>
Analysis of policy measures on designing a renewable fuel supply chain in the transport sector- Supplementary materials
<p><strong>This repository contains supporting data for: "Analysis of policy measures on designing a renewable fuel supply chain in the transport sector"</strong></p> <ol> <li> <p><strong>Supplementary Materials</strong>: This collection encompasses the model input data, alongside a detailed formulation of the objective functions.</p> </li> <li> <p><strong>Pareto_Table</strong>: This file contains the Pareto frontier results. </p> </li> <li><strong>DoE_Results_Table</strong>: This file presents the results of various solution scenarios, each characterized by differing levels of policy measures.</li> </ol>
Dataset of the publication: Design and processing as ultrathin films of a sublimable Iron(II) spin crossover material exhibiting efficient and fast light-induced spin transition
<p>Dataset of the publication: Design and processing as ultrathin films of a sublimable Iron(II) spin crossover material exhibiting efficient and fast light-induced spin transition</p> <p>DOI: 10.1021/acs.chemmater.3c01704</p> <p>M. Gavara-Edo, F. J. Valverde-Muñoz, M. C. Muñoz, S. Elidrissi, F. Marques-Moros, J. Herrero-Martín, K. Znovjyak, M. Seredyuk, J. A. Real, E. Coronado <br><br>Chem. Mater., 35, 22, 9591-9602 (2023)</p>
Supplementary Material for Design of Frustrated Lewis Pair Catalysts for Direct Hydrogenation of CO2
<p>Supplementary Material for "Design of Frustrated Lewis Pair Catalysts for Direct Hydrogenation of CO<sub>2</sub>"</p>
Dataset for publication "Effects of Y and Ho doping on microstructure evolution during oxidation of extraordinary stable Hf-B-Si-Y/Ho-C-N films up to 1500 °C" in Materials & Design 237, (2024), 112589.
<p>Dataset for publication "Effects of Y and Ho doping on microstructure evolution during oxidation of extraordinary stable Hf-B-Si-Y/Ho-C-N films up to 1500 °C" in Materials & Design Volume 237, January 2024, 112589, DOI 10.1016/j.matdes.2023.112589.</p> <p>XRD patterns, SAED patterns, EDS spectra, TEM images, HRTEM images.</p>
Supplementary Material: Comparing Formal Tools for System Design: a Case Study from the Railway Domain
<p>The package includes a set of models for a railway moving-block system:</p> <p>(a) a PDF document named Moving-block Model and Requirements.pdf, which includes a UML model of a moving-block system together with a set of requirements for the system;</p> <p>(b) a set of 10 folders, each one associated to a formal or semi-formal development tool. Each folder contains one or more model of the moving-block system from (a), developed by means of the tool.</p>
Text-fig. 3. Schematic geological section of the Kristina Mine near Hrádek/N. (state in 1963–1964) – height/length ratio 3:1. Explanations: vertical hatching – lignite seam, seamlet; dotted – coarse-grained sand, pea-gravel; short lines – sandy clay; white – clay; black lines – clay ironstone concretions; black dots – individual fossiliferous horizons designated as (A) plastic clay from the upper part of the main xylitic seam (about 5 m under t of the seam, (B) clay and "Blätterkohle" from the uppermost part of the first seamlet (split off the Main Coal Seam), (C) slightly sandy brown clay under the uppermost part of the Main Coal Seam, (D) base of the sandy clay with large concretions of the clay ironstone above the Main Coal Seam, (E) sandy clay (incl. clay ironstone) supplying most of leaf material with cuticles (F) 1–2 cm thin silty lenticles or thin beds of the sandy clay with xylites and Eomastixia within peagravels and coarse-grained sands, (G) coarse-grained sands with clayish silts with Fagus, Ocotea, Pterocarya, Tectocarya, (H) brown sandy clay underlying the uppermost seamlet, (I) lignite clay, base of the uppermost seamlet (J) Glyptostrobus – "Blätterkohle", base of the uppermost seamlet (according to Holý 1975, modified). in A Review Of The Early Miocene Mastixioid Flora Of The Kristina Mine At Hrádek Nad Nisou In North Bohemia (The Czech Republic)
Text-fig. 3. Schematic geological section of the Kristina Mine near Hrádek/N. (state in 1963–1964) – height/length ratio 3:1. Explanations: vertical hatching – lignite seam, seamlet; dotted – coarse-grained sand, pea-gravel; short lines – sandy clay; white – clay; black lines – clay ironstone concretions; black dots – individual fossiliferous horizons designated as (A) plastic clay from the upper part of the main xylitic seam (about 5 m under t of the seam, (B) clay and "Blätterkohle" from the uppermost part of the first seamlet (split off the Main Coal Seam), (C) slightly sandy brown clay under the uppermost part of the Main Coal Seam, (D) base of the sandy clay with large concretions of the clay ironstone above the Main Coal Seam, (E) sandy clay (incl. clay ironstone) supplying most of leaf material with cuticles (F) 1–2 cm thin silty lenticles or thin beds of the sandy clay with xylites and Eomastixia within peagravels and coarse-grained sands, (G) coarse-grained sands with clayish silts with Fagus, Ocotea, Pterocarya, Tectocarya, (H) brown sandy clay underlying the uppermost seamlet, (I) lignite clay, base of the uppermost seamlet (J) Glyptostrobus – "Blätterkohle", base of the uppermost seamlet (according to Holý 1975, modified).
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>
Figures 37–48 in Lectotype designations in Tetratomidae, Melandryidae, Boridae and Mycteridae, based on material in the Museum of Comparative Zoology, Harvard University (Coleoptera: Tenebrionoidea)
Figures 37–48. Dorsal and lateral habitus of newly designated lectotypes. 37–38. Orchesia gracilis Melsheimer, lectotype. 37) Dorsal habitus. 38) Lateral habitus. Scale lines = 2.0 mm. 39–40. Orchesia ornata Horn, lectotype. 39) Dorsal habitus. 40) Lateral habitus. Scale lines = 1.0 mm. 41–42. Amblyctis praeses LeConte, lectotype. 41) Dorsal habitus. 42) Lateral habitus. Scale lines = 5.0 mm. 43–44. Dircaea sericea Haldeman, female lectotype. 43) Dorsal habitus. 44) Lateral habitus. Scale lines = 2.0 mm. 45–46. Serropalpus obsoletus Haldeman, lectotype. 45) Dorsal habitus. 46) Lateral habitus. Scale lines = 5.0 mm. 47–48. Serropalpus substriatus Haldeman, lectotype. 47) Dorsal habitus. 48) Lateral habitus. Scale lines = 2.0 mm.
Figure 26–36 in Lectotype designations in Tetratomidae, Melandryidae, Boridae and Mycteridae, based on material in the Museum of Comparative Zoology, Harvard University (Coleoptera: Tenebrionoidea)
Figure 26–36. Dorsal and lateral habitus of newly designated lectotypes. 26. Melandrya striata var. bicolor Melsheimer, lectotype, lateral habitus. Scale line = 2.0 mm. 27–28. Melandrya striata var. thoracica Melsheimer, lectotype. 27) Dorsal habitus. 28) Lateral habitus. Scale lines = 5.0 mm. 29–30. Hypulus fulminans LeConte, lectotype. 29) Dorsal habitus. 30) Lateral habitus. Scale lines = 1.0 mm. 31–32. Microscapha arctica Horn, lectotype. 31) Dorsal habitus. 32) Lateral habitus. Scale lines = 0.5 mm. 33–34. Microscapha clavicornis LeConte, lectotype. 33) Dorsal habitus. 34) Lateral habitus. Scale lines = 1.0 mm. 35–36. Orchesia castanea Melsheimer, lectotype. 35) Dorsal habitus. 36) Lateral habitus. Scale lines = 2.0 mm.
Figure 14–25 in Lectotype designations in Tetratomidae, Melandryidae, Boridae and Mycteridae, based on material in the Museum of Comparative Zoology, Harvard University (Coleoptera: Tenebrionoidea)
Figure 14–25. Dorsal and lateral habitus of newly designated lectotypes. 14) Dircaea riversi LeConte, lectotype, lateral habitus. Scale line = 5.0 mm. 15–16. Hypulus trifasciatus Melsheimer, lectotype. 15) Dorsal habitus. 16) Lateral habitus. Scale lines = 2.0 mm. 17–18. Microtonus sericans LeConte, lectotype. 17) Dorsal habitus. 18) Lateral habitus. Scale lines = 1.0 mm. 19–20. Scraptia flavicollis Haldeman, lectotype. 19) Dorsal habitus. Scale line = 2.0 mm. 20) Lateral habitus. Scale line = 1.0 mm. 21–22. Scraptia rugosa Haldeman, lectotype. 21) Dorsal habitus. 22) Lateral habitus. Scale lines = 1.0 mm. 23–24. Melandrya maculata LeConte, male lectotype. 23) Dorsal habitus. 24) Lateral habitus. Scale lines = 2.0 mm. 25) Melandrya striata var. bicolor Melsheimer, lectotype, dorsal habitus.
Figures 49–58 in Lectotype designations in Tetratomidae, Melandryidae, Boridae and Mycteridae, based on material in the Museum of Comparative Zoology, Harvard University (Coleoptera: Tenebrionoidea)
Figures 49–58. Dorsal and lateral habitus of newly designated lectotypes. 49–50. Carebara longula LeConte, lectotype. 49) Dorsal habitus. 50) Lateral habitus. Scale lines = 2.0 mm. 51–52. Hallomenus quadripustulata Melsheimer, lectotype. 51) Dorsal habitus. 52) Lateral habitus. Scale lines = 1.0 mm. 53–54. Mycterus canescens Horn, male lectotype. 53) Dorsal habitus. 54) Lateral habitus. Scale lines = 2.0 mm. 55–56. Mycterus quadricollis Horn, male lectotype. 55) Dorsal habitus. 56) Lateral habitus. Scale lines = 2.0 mm. 57–58. Crymodes discicollis LeConte, lectotype. 57) Dorsal habitus. 58) Lateral habitus. Scale lines = 2.0 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.