@misc{WeiserHubigShanmugamSubramaniam2025, author = {Weiser, Martin and Hubig, Michael and Shanmugam Subramaniam, Jayant}, title = {Reconstructing Ambient Temperature Changes in Death Time Estimation with a Bayesian Double-Exponential Approach}, journal = {Zenodo}, doi = {10.5281/zenodo.17702240}, year = {2025}, abstract = {Code and data for the reconstruction of ambient temperature drop in time of death estimation We provide Octave code and temperature measurement data for - empirircally estimating thermal sensor likelihood - estimating time and amplitude of a single sudden ambient temperature drop from temperature measurement data in two thermally different compartments.}, language = {en} } @misc{SekulicSchaibleMuelleretal.2025, author = {Sekulic, Ivan and Schaible, Jonas and M{\"u}ller, Gabriel and Plock, Matthias and Burger, Sven and Martinez-Lahuerta, Victor J. and Gaaloul, Naceur and Schneider, Philipp-Immanuel}, title = {Data publication for Physics-informed Bayesian optimization of expensive-to-evaluate black-box functions}, journal = {Zenodo}, doi = {10.5281/zenodo.16751507}, year = {2025}, language = {en} } @misc{DoerffelMikulaSchielickeetal.2025, author = {D{\"o}rffel, Tom and Mikula, Natalia and Schielicke, Lisa and Kiszler, Theresa and Faranda, Davide and Debrulle, B{\´e}reng{\`e}re and Vercauteren, Nikki}, title = {Characterizing inertial and diabatic energy transfers in tropical cyclones: Data}, doi = {10.12752/10135}, year = {2025}, abstract = {The multiscale organization of tropical cyclones (TCs) is investigated by means of three-dimensional data produced by the atmospheric model CM1. We provide a sample dataset in NetCDF format covering the TC evolution from incipient to mature under the influence of externally imposed wind shear. This dataset serves as a testbed for applying energy-tranfer analyses based on the Duchon-Robert index as well as diabatic transfer based on an asymptotic theory on TCs.}, language = {en} } @misc{BinkowskiKoulasSimosBetzetal.2025, author = {Binkowski, Felix and Koulas-Simos, Aris and Betz, Fridtjof and Plock, Matthias and Sekulic, Ivan and Manley, Phillip and Hammerschmidt, Martin and Schneider, Philipp-Immanuel and Zschiedrich, Lin and Munkhbat, Battulga and Reitzenstein, Stephan and Burger, Sven}, title = {Source code and simulation results: High Purcell enhancement in all-TMDC nanobeam resonator designs with active monolayers for nanolasers}, journal = {Zenodo}, doi = {10.5281/zenodo.16533803}, year = {2025}, language = {en} } @misc{BetzBinkowskiFischbachetal.2025, author = {Betz, Fridtjof and Binkowski, Felix and Fischbach, Jan David and Feldman, Nick and Rockstuhl, Carsten and Koenderink, A. Femius and Burger, Sven}, title = {Source code and simulation results: Hidden resonances in non-Hermitian systems with scattering thresholds}, journal = {Zenodo}, doi = {10.5281/zenodo.14651612}, pages = {doi: 10.5281/zenodo.14651612}, year = {2025}, language = {en} } @misc{BinkowskiBetzHammerschmidtetal.2024, author = {Binkowski, Felix and Betz, Fridtjof and Hammerschmidt, Martin and Zschiedrich, Lin and Burger, Sven}, title = {Source code and simulation results: Resonance modes in microstructured photonic waveguides - Efficient and accurate computation based on AAA rational approximation}, journal = {Zenodo}, doi = {10.5281/zenodo.14202408}, pages = {doi: 10.5281/zenodo.14202408}, year = {2024}, language = {en} } @misc{HajarolasvadiBaum2024, author = {Hajarolasvadi, Noushin and Baum, Daniel}, title = {Data for Training the DeepOrientation Model: Simulated cryo-ET tomogram patches}, doi = {10.12752/9686}, year = {2024}, abstract = {A major restriction to applying deep learning methods in cryo-electron tomography is the lack of annotated data. Many large learning-based models cannot be applied to these images due to the lack of adequate experimental ground truth. One appealing alternative solution to the time-consuming and expensive experimental data acquisition and annotation is the generation of simulated cryo-ET images. In this context, we exploit a public cryo-ET simulator called PolNet to generate three datasets of two macromolecular structures, namely the ribosomal complex 4v4r and Thermoplasma acidophilum 20S proteasome, 3j9i. We select these two specific particles to test whether our models work for macromolecular structures with and without rotational symmetry. The three datasets contain 50, 150, and 450 tomograms with a voxel size of 10 ̊A, respectively. Here, we publish patches of size 40 × 40 × 40 extracted from the medium-sized dataset with 26,703 samples of 4v4r and 40,671 samples of 3j9i. The original tomograms from which the samples were extracted are of size 500 × 500 × 250. Finally, it should be noted that the currently published test dataset is employed for reporting the results of our paper titled "DeepOrientation: Deep Orientation Estimation of Macromolecules in Cryo-electron tomography" paper.}, language = {en} } @misc{Secker2023, author = {Secker, Christopher}, title = {Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists (Dataset)}, doi = {10.12752/9622}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-96220}, year = {2023}, abstract = {Virtual Screening Dataset for the paper "Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists" by Secker et al. (https://doi.org/10.1186/s13321-023-00746-4)}, language = {en} } @misc{KuenLoefflerTsarapkinetal.2024, author = {Kuen, Lilli and L{\"o}ffler, Lorenz and Tsarapkin, Aleksei and Zschiedrich, Lin and Feichtner, Thorsten and Burger, Sven and H{\"o}flich, Katja}, title = {Source Code and Simulation Results: Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna}, journal = {Zenodo}, doi = {10.5281/zenodo.10598255}, pages = {doi: 10.5281/zenodo.10598255}, year = {2024}, language = {en} } @misc{BetzHammerschmidtZschiedrichetal.2024, author = {Betz, Fridtjof and Hammerschmidt, Martin and Zschiedrich, Lin and Burger, Sven and Binkowski, Felix}, title = {Source code and simulation results: Efficient rational approximation of optical response functions with the AAA algorithm}, journal = {Zenodo}, doi = {10.5281/zenodo.10853692}, pages = {doi: 10.5281/zenodo.10853692}, year = {2024}, language = {en} } @misc{BinkowskiKulligBetzetal.2024, author = {Binkowski, Felix and Kullig, Julius and Betz, Fridtjof and Zschiedrich, Lin and Walther, Andrea and Wiersig, Jan and Burger, Sven}, title = {Source code and simulation results: Computing eigenfrequency sensitivities near exceptional points}, journal = {Zenodo}, doi = {10.5281/zenodo.10715639}, pages = {doi: 10.5281/zenodo.10715639}, year = {2024}, language = {en} } @misc{PlockBinkowskiZschiedrichetal.2024, author = {Plock, Matthias and Binkowski, Felix and Zschiedrich, Lin and Schneider, Phillip-Immanuel and Burger, Sven}, title = {Research data for "Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes"}, journal = {Zenodo}, doi = {10.5281/zenodo.8131611}, pages = {doi: 10.5281/zenodo.8131611}, year = {2024}, language = {en} } @misc{BetzBinkowskiBurgeretal.2023, author = {Betz, Fridtjof and Binkowski, Felix and Burger, Sven and Kuen, Lilli}, title = {RPExpand (Version 2.0)}, journal = {Zenodo}, doi = {10.5281/zenodo.10371002}, pages = {doi: 10.5281/zenodo.10371002}, year = {2023}, language = {en} } @misc{BinkowskiBetzColometal.2023, author = {Binkowski, Felix and Betz, Fridtjof and Colom, Remi and Genevet, Patrice and Burger, Sven}, title = {Source code and simulation results: Poles and zeros of electromagnetic quantities in photonic systems}, journal = {Zenodo}, doi = {10.5281/zenodo.8063931}, pages = {8063931}, year = {2023}, language = {en} } @misc{BetzBinkowskiBurger2023, author = {Betz, Fridtjof and Binkowski, Felix and Burger, Sven}, title = {RPExpand (Version 1)}, journal = {Zenodo}, doi = {10.5281/zenodo.7840116}, pages = {doi: 10.5281/zenodo.7840116}, year = {2023}, language = {en} } @misc{BetzBinkowskiHammerschmidtetal.2022, author = {Betz, Fridtjof and Binkowski, Felix and Hammerschmidt, Martin and Zschiedrich, Lin and Burger, Sven}, title = {Source code and simulation results for computing resonance expansions of quadratic quantities with regularized quasinormal modes}, journal = {Zenodo}, doi = {10.5281/zenodo.7376556}, year = {2022}, language = {en} } @misc{RickertBetzPlocketal.2022, author = {Rickert, Lucas and Betz, Fridtjof and Plock, Matthias and Burger, Sven and Heindel, Tobias}, title = {Data publication for "High-performance designs for fiber-pigtailed quantum-light sources based on quantum dots in electrically-controlled circular Bragg gratings"}, journal = {Zenodo}, doi = {10.5281/zenodo.7360516}, pages = {7360516}, year = {2022}, language = {en} } @misc{BinkowskiBetzHammerschmidtetal.2022, author = {Binkowski, Felix and Betz, Fridtjof and Hammerschmidt, Martin and Schneider, Philipp-Immanuel and Zschiedrich, Lin and Burger, Sven}, title = {Source code and simulation data for Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators}, journal = {Zenodo}, doi = {10.5281/zenodo.6614951}, year = {2022}, language = {en} } @misc{PlockAndrleBurgeretal.2022, author = {Plock, Matthias and Andrle, Kas and Burger, Sven and Schneider, Philipp-Immanuel}, title = {Research data and example scripts for the paper "Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction"}, journal = {Zenodo}, doi = {10.5281/zenodo.6359594}, year = {2022}, language = {en} } @misc{BetzColomBurgeretal.2022, author = {Betz, Fridtjof and Colom, Remi and Burger, Sven and Naydenov, Boris}, title = {Scripts for quantifying the effect of diamond nano-pillars on the fluorescence of NV centers}, journal = {Zenodo}, doi = {10.5281/zenodo.6558815}, year = {2022}, language = {en} } @misc{ColomBinkowskiBetzetal.2022, author = {Colom, Remi and Binkowski, Felix and Betz, Fridtjof and Kivshar, Yuri and Burger, Sven}, title = {Source code and simulation results for nanoantennas supporting an enhanced Purcell factor due to interfering resonances}, journal = {Zenodo}, doi = {10.5281/zenodo.6565850}, year = {2022}, language = {en} } @misc{BerioBayleAgretetal.2022, author = {Berio, Fidji and Bayle, Yann and Agret, Sylvie and Baum, Daniel and Goudemand, Nicolas and Debiais-Thibaud, M{\´e}lanie}, title = {3D models related to the publication: Hide and seek shark teeth in Random Forests: Machine learning applied to Scyliorhinus canicula}, journal = {MorphoMuseuM}, doi = {10.18563/journal.m3.164}, year = {2022}, abstract = {The present dataset contains the 3D models analyzed in Berio, F., Bayle, Y., Baum, D., Goudemand, N., and Debiais-Thibaud, M. 2022. Hide and seek shark teeth in Random Forests: Machine learning applied to Scyliorhinus canicula. It contains the head surfaces of 56 North Atlantic and Mediterranean small-spotted catsharks Scyliorhinus canicula, from which tooth surfaces were further extracted to perform geometric morphometrics and machine learning.}, language = {en} } @misc{HanikvonTycowicz2022, author = {Hanik, Martin and von Tycowicz, Christoph}, title = {Triangle meshes of shadow-recieving surfaces of ancient sundials}, doi = {10.12752/8425}, year = {2022}, abstract = {This repository contains triangle meshes of the shadow-recieving surfaces of 13 ancient sundials; three of them are from Greece and 10 from Italy. The meshes are in correspondence.}, language = {en} } @misc{GreweZachow2021, author = {Grewe, C. Martin and Zachow, Stefan}, title = {Release of the FexMM for the Open Virtual Mirror Framework}, doi = {10.12752/8532}, year = {2021}, abstract = {THIS MODEL IS FOR NON-COMMERCIAL RESEARCH PURPOSES. ONLY MEMBERS OF UNIVERSITIES OR NON-COMMERCIAL RESEARCH INSTITUTES ARE ELIGIBLE TO APPLY. 1. Download, fill, and sign the form available from: https://media.githubusercontent.com/media/mgrewe/ovmf/main/data/fexmm_license_agreement.pdf 2. Send the signed form to: fexmm@zib.de NOTE: Use an official email address of your institution for the request.}, language = {en} } @misc{TackAmbellanZachow2021, author = {Tack, Alexander and Ambellan, Felix and Zachow, Stefan}, title = {Towards novel osteoarthritis biomarkers: Multi-criteria evaluation of 46,996 segmented knee MRI data from the Osteoarthritis Initiative (Supplementary Material)}, volume = {16}, journal = {PLOS One}, number = {10}, doi = {10.12752/8328}, year = {2021}, abstract = {Convolutional neural networks (CNNs) are the state-of-the-art for automated assessment of knee osteoarthritis (KOA) from medical image data. However, these methods lack interpretability, mainly focus on image texture, and cannot completely grasp the analyzed anatomies' shapes. In this study we assess the informative value of quantitative features derived from segmentations in order to assess their potential as an alternative or extension to CNN-based approaches regarding multiple aspects of KOA A fully automated method is employed to segment six anatomical structures around the knee (femoral and tibial bones, femoral and tibial cartilages, and both menisci) in 46,996 MRI scans. Based on these segmentations, quantitative features are computed, i.e., measurements such as cartilage volume, meniscal extrusion and tibial coverage, as well as geometric features based on a statistical shape encoding of the anatomies. The feature quality is assessed by investigating their association to the Kellgren-Lawrence grade (KLG), joint space narrowing (JSN), incident KOA, and total knee replacement (TKR). Using gold standard labels from the Osteoarthritis Initiative database the balanced accuracy (BA), the area under the Receiver Operating Characteristic curve (AUC), and weighted kappa statistics are evaluated. Features based on shape encodings of femur, tibia, and menisci plus the performed measurements showed most potential as KOA biomarkers. Differentiation between healthy and severely arthritic knees yielded BAs of up to 99\%, 84\% were achieved for diagnosis of early KOA. Substantial agreement with weighted kappa values of 0.73, 0.73, and 0.79 were achieved for classification of the grade of medial JSN, lateral JSN, and KLG, respectively. The AUC was 0.60 and 0.75 for prediction of incident KOA and TKR within 5 years, respectively. Quantitative features from automated segmentations yield excellent results for KLG and JSN classification and show potential for incident KOA and TKR prediction. The validity of these features as KOA biomarkers should be further evaluated, especially as extensions of CNN-based approaches. To foster such developments we make all segmentations publicly available together with this publication.}, language = {en} } @misc{NiemannSchuetteKlus2021, author = {Niemann, Jan-Hendrik and Sch{\"u}tte, Christof and Klus, Stefan}, title = {Simulation data: Data-driven model reduction of agent-based systems using the Koopman generator}, volume = {16}, journal = {PLOS ONE}, number = {5}, doi = {http://doi.org/10.5281/zenodo.4522119}, year = {2021}, language = {en} } @misc{EhlersWesselBaum2021, author = {Ehlers, Sarah and Wessel, Andreas and Baum, Daniel}, title = {Segmentation of abdominal chordotonal organs based on semithin serial sections in the Rhododendron leafhopper Graphocephala fennahi (Cicadomorpha: Cicadellidae)}, doi = {10.12752/8326}, year = {2021}, abstract = {For mating, leafhoppers (Cicadellidae) use substrate-borne vibrational signals to communicate. We provide the first complete description of the abdominal chordotonal organs that enable the perception of these signals. This supplementary data provides the aligned stack of 450 semithin serial sections of the first and second abdominal segment of an adult male Rhododendron leafhopper (Graphocephala fennahi). Further, this supplementary data comprises the segmentation files of five chordotonal organs, the exoskeleton, the segmental nerves and the spiracles of the first and the second abdominal segment. Due to time limitations, the structures of only one half of the body were segmented. The specimen was caught by hand net in September 2018 in Berlin-Tiergarten, Germany. Samples were embedded in Araldite® 502 resin and cut transversally in 1 μm thick sections using a Leica ultramicrotome and a DIATOME Histo Jumbo 6.0 mm diamond knife. Sections were placed on microscopic slides and stained with methylene blue/azur II. The images were taken by means of a 3DHISTECH PANNORAMIC SCAN II slide scanner in the Institute of Pathology Charit{\´e} in Berlin-Mitte, Germany. Images with a voxel size of 0.273809 μm x 0.273809 μm x 1 μm where obtained. The images were converted from MRXS-files to TIFF-files with the 3DHistech software Slide Converter 2.3. Using Photoshop, the images were cropped to the same canvas size and artefacts were removed. All further steps, such as alignment and segmentation, were done with the software Amira. In order to facilitate the further processing of the dataset, the voxels where resampled to a size of 0.547619 μm x 0.547619 μm x 1 μm.}, language = {en} } @misc{JaegerSutterSchneideretal.2021, author = {J{\"a}ger, Klaus and Sutter, Johannes and Schneider, Philipp-Immanuel and Hammerschmidt, Martin and Becker, Christiane}, title = {Optical simulations of nanotextured perovskite/silicon tandem solar cell}, journal = {HZB Data Service}, doi = {10.5442/ND000005}, year = {2021}, language = {en} } @misc{RaharinirinaWeberBirketal.2021, author = {Raharinirina, N. Alexia and Weber, Marcus and Birk, Ralph and Fackeldey, Konstantin and Klasse, Sarah M. and Richter, Tonio Sebastian}, title = {Different Tools and Results for Correspondence Analysis}, doi = {10.12752/8257}, year = {2021}, abstract = {This is a list of codes generated from ancient egyptian texts. The codes are used for a correspondence analysis (CA). Codes and CA software are available from the linked webpage.}, language = {en} } @misc{PfluegerKlineFernandezHerreroetal.2020, author = {Pfl{\"u}ger, Mika and Kline, R Joseph and Fern{\´a}ndez Herrero, Anal{\´i}a and Hammerschmidt, Martin and Soltwisch, Victor and Krumrey, Michael}, title = {Extracting dimensional parameters of gratings produced with self-aligned multiple patterning using grazing-incidence small-angle x-ray scattering [Source Code]}, doi = {10.24433/CO.0953516.v2}, year = {2020}, language = {en} } @misc{BaumHerterLepper2020, author = {Baum, Daniel and Herter, Felix and Lepper, Verena}, title = {Jerash Silver Scroll: Virtually Unfolded Volume}, journal = {figshare}, doi = {10.6084/m9.figshare.12145236}, year = {2020}, abstract = {A new virtual unfolding technique was applied to a silver scroll excavated in Jerash, Jordan, in 2014. As result of the unfolding, 17 lines of writing are clearly visible in the unfolded volumetric data that is published here.}, language = {en} } @misc{AmbellanTackEhlkeetal.2019, author = {Ambellan, Felix and Tack, Alexander and Ehlke, Moritz and Zachow, Stefan}, title = {Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative (Supplementary Material)}, volume = {52}, journal = {Medical Image Analysis}, number = {2}, doi = {10.12752/4.ATEZ.1.0}, pages = {109 -- 118}, year = {2019}, abstract = {We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The shape models and neural networks employed are trained using data of the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets of the SKI10 challenge. For the first time, an accuracy equivalent to the inter-observer variability of human readers has been achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We made the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation. In conclusion, combining statistical anatomical knowledge via SSMs with the localized classification via CNNs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data.}, language = {en} } @misc{TackMukhopadhyayZachow2018, author = {Tack, Alexander and Mukhopadhyay, Anirban and Zachow, Stefan}, title = {Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative (Supplementary Material)}, doi = {10.12752/4.TMZ.1.0}, year = {2018}, abstract = {Abstract: Objective: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof. Method: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MOAKS experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression. Results: Segmentation accuracy measured as Dice Similarity Coefficient was 83.8\% for medial menisci (MM) and 88.9\% for lateral menisci (LM) at baseline, and 83.1\% and 88.3\% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (ρ=0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16±0.93 mm vs. 0.83±0.92 mm; p<0.05). Conclusion: Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA.}, language = {en} } @misc{WeberDurmazSabrietal.2017, author = {Weber, Marcus and Durmaz, Vedat and Sabri, Peggy and Reidelbach, Marco}, title = {Supplementary simulation data for Science Manuscript ai8636}, doi = {10.12752/5.MWB.1.0}, year = {2017}, abstract = {The simulation data has been produced by Vedat Durmaz, Peggy Sabri and Marco Reidelbach inside the "Computational Molecular Design" Group headed by Marcus Weber at Zuse-Institut Berlin, Takustr. 7, D-14195 Berlin, Germany. The file contains classical simulation data for different fentanyl derivates in the MOR binding pocket at different pHs. It also includes instruction files for quantum-chemical pKa-value estimations and a description of how we derived the pKa-values from the Gaussian09 log-files.}, language = {en} } @misc{Weber2018, author = {Weber, Marcus}, title = {Supplementary: Implications of PCCA+ in Molecular Simulation}, year = {2018}, abstract = {Matlab-software and data sets to recapitulate the presented results in M. Weber: Implications of PCCA+ in Molecular Simulation. Computation, 6(1):20, 2018.}, language = {en} } @misc{KnoetelSeidelZaslanskyetal.2017, author = {Kn{\"o}tel, David and Seidel, Ronald and Zaslansky, Paul and Prohaska, Steffen and Dean, Mason N. and Baum, Daniel}, title = {Automated Segmentation of Complex Patterns in Biological Tissues: Lessons from Stingray Tessellated Cartilage (Supplementary Material)}, doi = {10.12752/4.DKN.1.0}, year = {2017}, abstract = {Supplementary data to reproduce and understand key results from the related publication, including original image data and processed data. In particular, sections from hyomandibulae harvested from specimens of round stingray Urobatis halleri, donated from another study (DOI: 10.1002/etc.2564). Specimens were from sub-adults/adults collected by beach seine from collection sites in San Diego and Seal Beach, California, USA. The hyomandibulae were mounted in clay, sealed in ethanol-humidified plastic tubes and scanned with a Skyscan 1172 desktop μCT scanner (Bruker μCT, Kontich, Belgium) in association with another study (DOI: 10.1111/joa.12508). Scans for all samples were performed with voxel sizes of 4.89 μm at 59 kV source voltage and 167 μA source current, over 360◦ sample 120 rotation. For our segmentations, the datasets were resampled to a voxel size of 9.78 μm to reduce the size of the images and speed up processing. In addition, the processed data that was generated with the visualization software Amira with techniques described in the related publication based on the mentioned specimens.}, language = {en} }