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12,393 results for “materiality”
How Developers Locate Performance Bugs — Supplementary Material
<p><strong>Abstract:</strong></p> <p><em>Background:</em> Performance bugs can lead to severe issues regarding computation efficiency, power consumption, and user experience. Locating these bugs is a difficult task because developers have to judge for every costly operation whether runtime is consumed necessarily or unnecessarily. Objective: We wanted to investigate how developers, when locating performance bugs, navigate through the code, understand the program, and communicate the detected issues.</p> <p><em>Method:</em> We performed a qualitative user study observing twelve developers trying to fix documented performance bugs in two open source projects. The developers worked with a profiling and analysis tool that visually depicts runtime information in a list representation and embedded into the source code view.</p> <p><em>Results:</em> We identified typical navigation strategies developers used for pinpointing the bug, for instance, following method calls based on runtime consumption. The integration of visualization and code helped developers to understand the bug. Sketches visualizing data structures and algorithms turned out to be valuable for externalizing and communicating the comprehension process for complex bugs.</p> <p><em>Conclusion:</em> Fixing a performance bug is a code comprehension and navigation problem. Flexible navigation features based on executed methods and a close integration of source code and performance information support the process.</p> <p><strong>Dataset:</strong></p> <ol> <li> <p><strong>Tutorial:</strong> We provide the slides (PDF) and the video (MP4) we used in the tutorial phase of our study.</p> </li> <li> <p><strong>Locating Bugs:</strong> We also provide supplementary material for each research question. We provide the advices we prepared for each bug in case a team got stuck (PDF); the questions we asked after each bug fixing session can be found on the introduction slides (PDF).</p> <ul> <li> <p><strong>RQ1:</strong> Navigating and Understanding</p> <ul> <li> <p><strong>RQ1.1:</strong> <em>How was information from the profiling tool or other parts of the IDE used to locate the performance bug?</em> Cross-case analysis (in German) (XLSX+ODS)</p> </li> <li> <p><strong>RQ1.2:</strong> <em>Is the in-situ visualization of the profiling data beneficial compared to a traditional list representation?</em> Cross-case analysis (in German) (XLSX+ODS)</p> </li> <li> <p><strong>RQ1.3:</strong> <em>What navigation strategies do developers pursue to locate a specific performance bug?</em> Interaction logs (TXT), Navigation visualizations (SVG), Screen recordings for Bug 3 (MP4, without audio because of confidentiality)</p> </li> </ul> </li> <li> <p><strong>RQ2:</strong> Understanding and Communicating</p> <ul> <li> <p><strong>RQ2.1:</strong> <em>How do developers communicate with each other when locating a performance bug?</em> Coding (XLSX+ODS), Sketches (PDF), Screen recordings for Bug 3 (MP4, without audio because of confidentiality)</p> </li> <li> <p><strong>RQ2.2:</strong> <em>Could sketches help to understand and communicate a performance bug?</em> Coding (XLSX+ODS), Sketches (PDF), Cross-case analysis (in German) (XLSX+ODS), Sketching videos for Bug 3 (MP4, without audio because of confidentiality)</p> </li> </ul> </li> </ul> </li> <li> <p><strong>Questionnaire:</strong> The questionnaire that the participants filled out at the end of the study can be found here (PDF).</p> </li> </ol>
Training material for small RNA-seq data analysis (Galaxy Training Network tutorial)
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes small RNA-seq (sRNA-seq) data from a study published by Harrington et al. (DOI:10.1186/s12864-017-3692-8) to detect differential abundance of various classes of endogenous short interfering RNAs (esiRNAs). The goal of this study was to investigate "connections between differential retroTn and hp-derived esiRNA processing and cellular location, and to investigate the potential link between mRNA 3’ end cleavage and esiRNA biogenesis." To this end, sRNA-seq libraries were constructed from triplicate <em>Drosophila</em> tissue culture samples under conditions of either control RNAi or RNAi knockdown of a factor involved in mRNA 3’ end processing, <em>Symplekin</em>. This dataset (GEO Accession: GSE82128) consists of single-end, size-selected, non-rRNA-depleted sRNA-seq libraries. Because of the long processing time for the large original files, we have downsampled the original raw data files to include only reads that align to a subset of interesting transcript features including: (1) transposable elements, (2) <em>Drosophila</em> piRNA clusters, (3) <em>Symplekin</em>, and (4) genes encoding mass spectrometry-defined protein binding partners of <em>Symplekin</em> from Additional File 2 in the indicated paper by Harrington et al. More details on features 1 and 2 can be found here: https://github.com/bowhan/piPipes/blob/master/common/dm3/genomic_features (piRNA_Cluster, Trn). All features are from the <em>Drosophila</em> genome Apr. 2006 (BDGP R5/<em>dm3</em>) release.</p>
Supplementary Material: Evaluation of Cyanea capillata Sting Management Protocols Using Ex Vivo and In Vitro Envenomation Models
<p>Supplementary files for Doyle, T.K.; Headlam, J.L.; Wilcox, C.L.; MacLoughlin, E.; Yanagihara, A.A. Evaluation of <em>Cyanea capillata</em>Sting Management Protocols Using Ex Vivo and In Vitro Envenomation Models. <em>Toxins</em> <strong>2017</strong>, <em>9</em>, 215. Video S1: Vinegar Application to Gelatin-Adherent Cnidae</p>
Assessing the typology of person portmanteaus (supplementary material)
<p>Supplementary material for 'Assessing the typology of person portmanteaus' (doi:10.1007/s11525-017-9305-z)</p> <p> </p>
External supplementary material to: Surgery for spontaneous supratentorial intracerebral haemorrhage
<h2><strong>Supplementary figures to: Surgery for spontaneous supratentorial intracerebral haemorrhage</strong></h2> <p><strong>Wilting FNH, Sondag L, Schreuder FHBM, Dammers R, Klijn CJM, Boogaarts HD</strong><br><a href="https://doi.org/10.1002/14651858.CD015387.pub2">https://doi.org/10.1002/14651858.CD015387.pub2</a></p> <p><em>The material in this section has been supplied by the author(s) for publication under a Licence for Publication and the author(s) are solely responsible for the material. Cochrane has reviewed this material, but Cochrane has not copyedited, formatted or proofread. Cochrane accordingly gives no representations or warranties of any kind in relation to, and accepts no liability for any reliance on or use of, such material.</em></p>
Supplementary material to: Russian verbal aspect and the activation of event knowledge: Processing typical and atypical location adverbials in perfective and imperfective sentences
<p>Supplementary material for a self-paced reading experiment:</p> <ol> <li>Material: contains the verbal stimuli (experimental and filler sentences)</li> <li>raw_data.zip: E-Prime output for all 50 participants (tab-separated txt-files)</li> <li>spr_aspect_respinf.txt: Basic information on the participants; tab-separated txt-file</li> <li>spr_aspect_preprocessing_subm2_fin.R: data preprocessing and calculation of correct responses per speaker in R; R script<br>Input: <br>- Files from the "raw_data"-folder<br>- spr_aspect_respinf.txt<br>Output: <br>- spr_aspect_respinf_corr_resp.txt: same as spr_aspect_respinf.txt + number of correct responses per participant; tab-separated txt-file<br>- spr_aspect_all.txt: relevant data from all participants in one file; tab-separated txt-file<br>- spr_aspect_all_without-outlier.txt: same as spr_aspect_all.txt, but outliers set to NA; tab-separated txt-file</li> <li>spr_aspect_figures-stats_subm2_fin.R: plots figures and calculates statistics (descriptive statistics and GLMM) in R, R script<br>Input: <br>- spr_aspect_all_without-outlier.txt<br>Output:<br>- spr_aspect_avg.txt: Mean RT and SD per condition; tab-separated txt-file<br>- Figures</li> </ol>
Supplementary material for the publication: "Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties"
<p><span><span><span>This dataset contains supplementary code, images and models for the publication „Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties“.</span></span></span></p> <p> </p> <p><span><span><span>The content will be updated and additionally linked to the corresponding git repositories.</span></span></span></p>
MatSim Dataset and benchmark for one-shot visual materials and textures recognition
<p><strong>The MatSim Dataset and benchmark</strong></p> <p>Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures.</p> <p>MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning).</p> <p>Based on the paper: <a href="https://arxiv.org/pdf/2212.00648.pdf">One-shot recognition of any material anywhere using contrastive learning with physics-based rendering</a></p> <p> </p> <p><strong>Benchmark_MATSIM.zip: </strong>contain the benchmark made of real-world images as described in the paper</p> <p><strong>Dataset Generation Scripts.zip: </strong>Contain the Blender (4.1) Python scripts used for generating the dataset<br><br><a href="https://zenodo.org/record/7390166/files/MatSim_object_train_split_1.zip?download=1"><strong>MatSim_object_train_split_1,2,3....zip:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper.</p> <p><strong>MatSimTrainObjectsNearField_.zip </strong>Contain train sets with near fieldlight sources</p> <p><strong><a href="https://zenodo.org/record/7390166/files/MatSim_Vessels_Train_1.zip?download=1">MatSim_Vessels_Train_1,2,3....zip </a></strong><a href="https://zenodo.org/api/files/020f90b2-7c41-44ad-86e3-69257884a569/MatSim_object_train_split_1.zip"><strong>:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.<br><br><strong>*Note: these are subsets of the dataset; the full dataset can be found at:</strong><br><a href="https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX">https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX</a></p> <p>or<br><a href="https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF">https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF</a></p> <p> </p>
Supplementary materials for "Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding"
<h1>Info</h1> <p>This dataset contains the supplementary materials for "Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding". </p> <p>For <strong>source code </strong>and <strong>detailed instructions on usage, </strong>please refer to our <a href="https://github.com/meneshail/TopoDiff/tree/main" target="_blank" rel="noopener">github</a> .</p> <h1>Supplementary data</h1> <h2>weights.tar.gz</h2> <p>The trained model weights used in the paper.</p> <h2>dataset.zip</h2> <p>CATH-60 Dataset used in the paper. In the notebook directory of our <a href="https://github.com/meneshail/TopoDiff/tree/main" target="_blank" rel="noopener">github</a> , we provide an example on encoding and visualize it with our trained encoder.</p> <h2>design.zip</h2> <p>The 21 novel mainly-beta designs selected for experiment validation. Along with the generated backbone, we also provide the prediction results from AlphaFold and ESMFold.</p> <h2>benchmark_sample.zip</h2> <p>Sampled backbones used for all benchmark experiment (All methods and variants included).</p> <h2>evaluation.tar.gz</h2> <p>Precomputed CATH reference data for coverage metric computation. Need to be downloaded for using evaluation scripts. </p>
The FAIR-Device - a non-lethal and generalist semi-automatic Malaise trap for insect biodiversity monitoring: Proof of concept - Supplementary Material
<h3>Abstract</h3> <p>Field monitoring plays a crucial role in understanding insect dynamics within ecosystems. It facilitates pest distribution assessment, control measure evaluation, and prediction of pest outbreaks. Additionally, it provides important information on bioindicators with which the state of biodiversity and ecological integrity in specific habitats and ecosystems can be accurately assessed. However, traditional monitoring systems can present various difficulties, leading to a limited temporal and spatial resolution of the obtained information. Despite recent advancements in automatic insect monitoring traps, also called e-traps, most of these systems focus exclusively on studying agricultural pests, rendering them unsuitable for monitoring diverse insect populations. To address this issue, we introduce the Field Automatic Insect Recognition (FAIR)-Device, a novel non-lethal field tool that relies on semi-automatic image capture and species identification using artificial intelligence via the iNaturalist platform. Our objective was to develop an automatic, cost-effective, and non-specific monitoring solution capable of providing high-resolution data for assessing insect diversity. During a 26-day proof-of-concept evaluation, the FAIR-Device recorded 24.8 GB of video, identifying 431 individuals from 9 orders, 50 families, and 69 genera. While improvements are possible, our device demonstrated potential as a cost-effective, non-lethal tool for monitoring insect biodiversity. Looking ahead, we envision new monitoring systems such as e-traps as valuable tools for real-time insect monitoring, offering unprecedented insights for ecological research and agricultural practices.</p> <h3>Description of the data and file structure</h3> <p>This repository complements the publication <a href="https://www.biorxiv.org/content/10.1101/2024.03.22.586299v2" target="_blank" rel="noopener">"The FAIR-Device - a non-lethal and generalist semi-automatic Malaise trap for insect biodiversity monitoring: Proof of concept".</a> It contains the result data from the proof of concept field test of V1.0 of the FAIR-Device, conducted between July and August 2021 at the Thünen Institute of Agricultural Technology in Braunschweig. The repository comprises three compressed files (.zip):</p> <ul> <li><strong>FAIR-D_captures.zip</strong>: <ul> <li>Video captures from the field tests organized by recording day.</li> <li>Filenames indicating recording time (hh-mm-ss).</li> </ul> </li> </ul> <ul> <li><strong>FAIR-D_Tables&Code.zip</strong>: <ul> <li>Processed results from the obtained image captures, organized into: <ul> <li><strong>Monitoring2021_TotalPeriod.xlsx: </strong>Result table with taxonomic classifications, data analysis, and charts.</li> <li><strong>iNat_observations.xlsx: </strong>iNaturalist reviews analysis table.</li> <li><strong>R: </strong>code for generating article graphics.</li> </ul> </li> </ul> </li> </ul> <ul> <li><strong>FAIR-D_V1.0_3D_Models.zip</strong>: <ul> <li>Complete 3D design of the FAIR-D V1.0 in .stl format, organized into: <ul> <li><strong>3D_print_parts</strong>: 3D-printable parts with spatial coordinates for correct positioning in CAD software.</li> <li><strong>other_3Dparts</strong>: Non-printable parts, also in .stl format with spatial coordinates.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>NOTE: For visualizing the videos, we recommend <strong>VLC media player</strong> - <a href="https://www.videolan.org/vlc/">https://www.videolan.org/vlc/</a> </p>
Supplementary material for: Interindividual variability in flower pickiness by foraging bumblebees
<h3>ABSTRACT</h3> <p><span lang="EN-GB"> Pollinators navigate complex and heterogeneous “flower markets”, where floral resources vary in quality, availability, and spatial distribution. Bumblebees, as generalist foragers, visit numerous flowers during their foraging bouts, yet the factors influencing their flower choices and the individual differences in foraging behaviour remain poorly understood. Here, we tested how bees adjust their foraging in response to different reward structures. <em>Bombus terrestris</em> workers completed three foraging bouts in two artificial flower environments: one simulating a favourable environment with patches alternating high- and low-quality flowers (40% vs. 20% w/w sucrose solution), and the other a challenging environment with high-quality flowers alongside unrewarded ones (40% w/w sucrose solution vs. plain water). We hypothesised that bees would improve their foraging efficiency in both environments, but more rapidly in the more extreme one, where the greater reward difference creates stronger pressure to learn quickly. In both conditions, bees increased their sucrose intake per unit time over bouts. We also observed consistent differences in flower selectivity among individuals: in the favourable environment, bees that first visited high-quality flowers focused on them and avoided low-quality ones (became “picky”), while bees that first visited low-quality flowers kept visiting both types. Despite these differences, bees across environments and pickiness levels all reached similar sucrose intake rates by the third foraging bout, either by becoming more selective, collecting more sucrose solution, or reducing time spent foraging. These findings highlight the adaptability of bee foraging and suggest that early flower experiences may contribute to lasting individual differences in foraging behaviour.</span></p>
FOAM 02: Impedance tube measurements of two porous materials with diameter variation
<p>This dataset provides the data for Reference:</p> <p>[1] Alfonso Caiazzo, Florian Kraxberger, Christian Adams, Andreas Wurzinger, Jan Boysen, Giuseppe Petrone, Stefan Schoder, Sergio De Rosa, Manfred Kaltenbacher, and Christian Adams: FOAM 02: A dataset of impedance tube measurements with different materials and diameter variations. Acta Acustica 9 (50), 2025. <a href="https://doi.org/10.1051/aacus/2025033" target="_blank" rel="noopener noreferrer">https://doi.org/10.1051/aacus/2025033</a></p> <p> </p> <p>This dataset consists of three .csv files: </p> <ul> <li>alphas.csv: absorption coefficients vs. frequencies (comma-separated), 864 rows according <br>to 864 measurements </li> <li>targets.csv: one-hot encoded, i.e., binary, vectors of the parameter combinations), 864 rows <br>according to 864 measurements. The Read_Me.pdf gives further information on the one-hot <br>encoded vectors. </li> <li>diameter.csv: calliper diametric measurement in millimetres [mm]. This file contains <br>864 rows according to 864 measurements and 6 columns that, in order, represent: top <br>diameter at 0°, top diameter at 90°, bottom diameter at 0°, bottom diameter at 90°, <br>mean diameter, and standard deviation. </li> </ul> <p><br>The frequencies range from 150 Hz to 1600 Hz with resolution of 2 Hz. Note that these limits are <br>not strictly equal to the frequency limits of the impedance tube, see ISO 10534-2. </p> <p><br>The data and code are licensed under Apache License, Version 2.0 <br>https://opensource.org/licenses/Apache-2.0 </p> <p><br>Any reuse of the data must properly cite the dataset and its authors. </p> <p> </p> <p>Contact:<br>Univ.-Prof. Dr. Christian Adams<br>Graz University of Technology<br>Inffeldgasse 16c<br>8010 Graz, Austria<br>christian.adams@tugraz.at</p>
Supplementary material for "Playback experiments highlight the importance of nearest-neighbor distance and social information for nest site selection in the House Martin (Delichon urbicum)"
<p><strong>Abstract</strong></p> <p>Understanding nest site selection is crucial for species conservation. Bird conservation often involves installing nesting aids to increase nest site availability and induce colonization of unoccupied sites. However, prospecting individuals must find nesting aids, which may be facilitated by social information. Here, we investigated the effectiveness of artificial nests and playback in the declining, migratory House Martin <em>Delichon urbicum</em>. We selected unoccupied sites with artificial nests along a distance gradient to occupied sites and broadcasted conspecific vocalizations during prospection times of House Martins in both the post- and the following pre-breeding periods. Visitation and colonization rates increased considerably in proximity to occupied sites. Playback during the post-breeding and pre-breeding periods enhanced visitation rates, while pre-breeding-only and post-breeding-only playback had smaller positive effects. Colonization rate increased exclusively with pre-breeding-only playback. Colonized playback and non-playback sites had similar breeding success, indicating that playback did not create ecological traps by attracting House Martins to suboptimal sites. Hence, broadcasting conspecific vocalizations informs prospecting birds of nest site availability, thereby increasing visitation, and to some degree, colonization of unoccupied House Martin sites. To boost colonization, we recommend installing artificial House Martin nests within approximately 500 meters of occupied sites and using playback of conspecific vocalizations.</p>
HYPERCOG – training material dataset
<p>The Hypercog training pack is aimed at teaching cyberphysical systems to two different types of profiles : </p><p>1) the operators at the 3 industrial sites impacted by the implementation of the HyperCOG solution (Solvay, Sidenor, Çimsa);</p><p> 2) Master students who would like to get familiar with cyberphysical systems.</p><p>The main objective for operators training was to understand the human environment where the solutions are implemented. For students it was to identify the skills needed to become familiar with cyber-physical systems. The ultimate goal was to train operators to overcome the skill gap, so they can use HyperCOG solutions in their daily work, and to produce training materials on cyber-physical systems based on the Hypercog experience, aimed at Master students .</p>
Supplementary material of the article "OpenModelica-based virtual simulator for the cardiovascular and respiratory physiology of a neonate"
<p>This document contains the adjustment data for the parameters of the model presented in the article: "<strong>OpenModelica-based virtual simulator for the cardiovascular and respiratory physiology of a neonate</strong>"</p>
Seamounts Gravity Accompanying Material
<p>Gravity effect of seamount eruptions (mass change), as spherical harmonics coefficients. Accompanying data for:</p> <p>Braitenberg, C., Pastorutti, A. Detectability of Seamount Eruptions Through a Quantum Technology Gravity Mission MOCAST+: Hunga Tonga, Fani Maoré and Other Smaller Eruptions. <em>Surv Geophys</em> (2024). doi:<a href="https://doi.org/10.1007/s10712-024-09839-7">10.1007/s10712-024-09839-7</a></p> <h4>Data format</h4> <p>The SH expansions are provided in the <a href="http://icgem.gfz-potsdam.de/ICGEM-Format-2011.pdf">ICGEM gfc format</a>.</p> <p>The "model_name" field is populated with three <em>key=value</em> pairs: name, lon, and lat of the modelled seamount.</p>
Supplemental material for / Ergänzende Materialien zu: de Capitani, A. (2023) Die Feuchtbodenfundstelle Cham-Bachgraben (Kanton Zug). Antiqua 57. Basel. ISBN 978-3-906182-33-9.
<p>Supplemental material for / Ergänzende Materialien zu: de Capitani, A. (2023) Die Seeufersiedlungen von Cham-Bachgraben (Kanton Zug). Antiqua 57. Basel. ISBN 978-3-906182-33-9.</p><p>Bleicher, Niels; Burri, Thomas; Diamantopoulou, Despoina; Eggenberger, Urs; Gut, Urs; Hofmann, Beda; Huber, Renata; Ismail-Meyer, Kristin; Pümpin, Christine; Walder, Felix; Widmer, Anna Barbara</p><p>(English Version below)</p><p>Ergänzende Datentabellen und weitere Angaben zu <strong>Band 1, Kapitel III. Mikromorphologische Untersuchung</strong> (Ismail-Meyer, K., Pümpin, Ch.), <strong>Band 1, Kapitel IV. Dendrochronologie und Dendroarchäologie</strong> (Bleicher, N., Walder, F. und Gut, U.), <strong>Band 1, Kapitel VI. Mittelalterliche Fischfache – ein Beitrag zur Fischerei am Zugersee</strong> (Huber, R.), sowie <strong>Band 2, Kapitel</strong> <strong>IV. Petrographie der Feldgesteinsartefakte unter Verwendung portabler Röntgenfluoreszenz</strong> (Burri, Th., Eggenberger, U., Hofmann, B., Diamantopoulou, D., Widmer, A. B.)</p><p> </p><p>Vorhandenen Dateien:</p><ul><li>Online-Anhang_1 (zu Band 1, Kapitel III). Ismail-Meyer/Pümpin: Cham-Bachgraben. Basisdatenbank Mikromorphologie, im Format .pdf und .xlsx.</li><li>Online-Anhang_2 (zu Band 1, Kapitel IV). Bleicher et al.: Cham-Bachgraben. Liste der Dendrogruppen, im Format .pdf und .xlsx.</li><li>Online-Anhang_3 (zu Band 1, Kapitel IV). Bleicher et al.: Cham-Bachgraben. Grafische Darstellung der Dendrogruppen, im Format .pdf.</li><li>Online-Anhang_4 (zu Band 1, Kapitel VI). Huber: Cham-Bachgraben. Holzliste aller Staken, im Format .pdf und .xlsx.</li><li>Online-Anhang_5 (zu Band 1, Kapitel VI). Huber: Hünenberg Dersbachstrasse 61. Holzliste aller Staken, im Format .pdf und .xlsx.</li><li>Online-Anhang_6 (zu Band 2, Kapitel IV). Burri et al.: Cham-Bachgraben. Chemismus der Mineralien, im Format .pdf und .xlsx.</li><li>Online-Anhang_7 (zu Band 2, Kapitel IV). Burri et al.: Cham-Bachgraben. Chemismus der Gesteine, im Format .pdf und .xlsx.</li><li>Online-Anhang_8 (zu Band 2, Kapitel IV). Burri et al.: pXRF Methodik Zusatzinformationen, im Format .pdf und .docx.</li></ul><p> </p><p>Supplementary data and further details on <strong>volume 1, chapter III. Mikromorphological Investigation</strong> (Ismail-Meyer, K., Pümpin, Ch.), <strong>volume 1, chapter IV. Dendrochronology and Dendroarchaeology</strong> (Bleicher, N., Walder, F. und Gut, U.), <strong>volume 1, chapter VI. Medieval Fish Traps - A Contribution to Fishing on Lake Zug </strong>(Huber, R.), as well as <strong>volume 2, chapter</strong> <strong>IV. Petrography of Field Rock Artifacts Using Portable X-Ray Fluorescence </strong>(Burri, Th., Eggenberger, U., Hofmann, B., Diamantopoulou, D., Widmer, A. B.)</p><p> </p><p>Contents:</p><p> </p><ul><li>Online-Anhang_1 (supplement to volume 1, chapter III). Ismail-Meyer/Pümpin: Cham-Bachgraben. Mikromorphology Database, in .pdf and .xlsx format.</li></ul><p> </p><ul><li>Online-Anhang_2 (supplement to volume 1, chapter IV). Bleicher et al.: Cham-Bachgraben. List of Dendro Groups, in .pdf and .xlsx format.</li></ul><p> </p><ul><li>Online-Anhang_3 (supplement to volume 1, chapter IV). Bleicher et al.: Cham-Bachgraben. Graphical representation of the Dendro Groups, in .pdf format.</li></ul><p> </p><ul><li>Online-Anhang_4 (supplement to volume 1, chapter VI). Huber: Cham-Bachgraben. List of all wooden stakes, in .pdf and .xlsx format.</li></ul><p> </p><ul><li>Online-Anhang_5 (supplement to volume 1, chapter VI). Huber: Hünenberg Dersbachstrasse 61. List of all wooden stakes, in .pdf and .xlsx format.</li></ul><p> </p><ul><li>Online-Anhang_6 (supplement to volume 2, chapter IV). Burri et al.: Cham-Bachgraben. Chemism of Minerals, in .pdf and .xlsx format.</li></ul><p> </p><ul><li>Online-Anhang_7 (supplement to volume 2, chapter IV). Burri et al.: Cham-Bachgraben. Chemism of the Rocks, in .pdf and .xlsx format.</li></ul><p> </p><ul><li>Online-Anhang_8 (supplement to volume 2, chapter IV). Burri et al.: pXRF Methodology Additional Information, in .pdf and .docx format.</li></ul>
Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling - Supplementary Material
<p>Supplementary material for the manuscript "Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling".</p> <p>This deposit contains all data and visualization scripts needed to replicate results in the manuscript.This includes user created figures, model input files, model output files, configuration files for running the workflow, and all scripts needed to process results.</p> <p>In addition to the European Commission, we acknowledge that Trevor Barnes' contribution to this paper was funded via a Mitacs Globalink Research Award, grant number IT2569</p>
Fig. 19 in Quaternary Murid Rodents Of Timor Part I: New Material Of Coryphomys Buehleri Schaub, 1937, And Description Of A Second Species Of The Genus
Fig. 19. Isolated right petrosal bone (ANWCP31) referred to the genus Coryphomys, and tentatively assigned to C. buehleri. This specimen is burnt and has lost much of the pars cochlearis; other parts are well preserved and appear undistorted. The three views are of A, the tympanic surface; B, the posterior endocranial fossa surface; and C, lateral surface. Crosshatched areas are fractured or abraded. Abbreviations as in figure 17. Scale bar 5 5 mm.
Fig. 16 in Quaternary Murid Rodents Of Timor Part I: New Material Of Coryphomys Buehleri Schaub, 1937, And Description Of A Second Species Of The Genus
Fig. 16. Four partial dentaries referred to Coryphomys musseri ANWCP32 (holotype): a left dentary fragment with m1–3 in medial (A) and lateral (B) views; ANWCP27: a left dentary fragment with lower incisor and m1 in medial (C) and lateral (D) views; ANWCP10: a right dentary fragment with m1–3 in medial (E) and lateral (F) views; AMF 68753: a right dentary fragment with m1–3 in medial (G) and lateral (H) views. Scale bar represents 5 mm and applies to all images. Abbreviations: dp, digastric process; imc, inferior masseteric crest; par, postalveolar ridge; smf, superior mandibular foramen; tmf, fossa for deep part of transverse mandibular muscle. Scale bar represents 5 mm and applies to all images.
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.