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12,393 results for “materiality”
The evolvement of ULVZ at LLSVP margins--supporting materials
<p>This tar.gz file is created and uploaded for the submission of the manuscript titled '<em>The evolvement of dense ULVZs at LLSVP margins and implications</em>' to the journal <em>Geophysical Research Letters</em>.</p> <p>This tar.file contains four directories /F1, /F2, /F3, /F4 with data used to plot Figures 1, 2, 3, 4, respectively, in the supporting material. The data follows the same convention as that in doi10.5281/zenodo.8394882 and 10.5281/zenodo.8394637.</p>
Supplementary Materials: ImpactX Modeling of Benchmark Tests for Space Charge Validation
<p>Supplementary materials (aka data artifact or data archive) for our HB2023 publication: " ImpactX Modeling of Benchmark Tests for Space Charge Validation" (Paper ID: THBP44).</p> <p>This work was supported by the Director, Office of Science of the U.S. Department of Energy under Contracts No. DE-AC02-05CH11231 and DE-AC02-07CH11359. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.</p>
Effect of electrode materials on resistive switching behaviour of NbOx-based memristive devices - Dataset
<p>This is the dataset of "Effect of electrode materials on resistive switching behaviour of NbOx-based memristive devices"</p>
Extra material for Biomedical Device and Sensors book
<p>Biomedical Device and Sensors book</p> <p>Chapter 6, Personal Practice 1, data: Chap6-exercice 1.csv</p> <p>Chapter 6, Personal Practice 2, data: Chap6-exercice 2.csv</p> <p>Chapter 7, example of Adruino code (under CC BY-NC-SA): MPU6050Madwick_V2.zip</p>
Supplemental Material Outcomes of SDHB pathogenic variant carriers a systematic review and meta-analysis
<p>Supplemental Material Outcomes of SDHB pathogenic variant carriers a systematic review and meta-analysis</p>
Data sets of measured cross-sectional area coordinates and material properties of an old Vindeby blade
<p>Measured raw data for determining cross-sectional area coordinates of an old Vindeby blade. Material properties are also appended.</p>
Machine Eye for Defects: Machine Learning-Based Solution to Identify and Characterize Topological Defects in Textured Images of Nematic Materials
<p><strong>Our paper has been published on Phys. Rev. Res. (doi: 10.1103/PhysRevResearch.6.013259)</strong></p> <p><strong>Our preprint paper is also avilable at arXiv(https://arxiv.org/abs/2310.06406), here is the abstract of our paper:</strong></p> <p>Topological defects play a key role in the structures and dynamics of liquid crystals (LCs) and other ordered systems. There is a recent interest in studying defects in different biological systems with distinct textures. However, a robust method to directly recognize defects and extract their structural features from various traditional and nontraditional nematic systems remains challenging to date. Here we present a machine learning solution, termed Machine Eye for Defects (MED), for automated defect analysis in images with diverse nematic textures. MED seamlessly integrates state-of-the-art object detection networks, Segment Anything Model, and vision transformer algorithms with tailored computer vision techniques. We show that MED can accurately identify the positions, winding numbers, and orientations of ±1/2 defects across distinct cellular contours, sparse vector fields of nematic directors, actin filaments, microtubules, and simulation images of Gay–Berne particles. MED performs faster than conventional defect detection method and can achieve over 90% accuracy on recognizing ±1/2 defects and their orientations from vector fields and experimental tissue images. We further demonstrate that MED can identify defect types that are not included in the training data, such as giant-core defects and defects with higher winding number. Remarkably, MED can provide correct structural information about ±1 defects. As such, MED stands poised to transform studies of diverse ordered systems by providing automated, rapid, accurate, and insightful defect analysis.</p> <p> </p> <p><strong>Repository Organization</strong></p> <p><strong>Trained Models.zip</strong></p> <p>This directory is integral for model deployment and houses all relevant pre-trained models.</p> <ul> <li><strong>plus_vit_vecUV.pt</strong>: Pre-trained model for the Plus Transformer variant.</li> <li><strong>minus_vit_theR.pt</strong>: Pre-trained model for the Minus Transformer variant.</li> <li><strong>nanodet-plus-m_416-halfenhance</strong>: A sub-directory containing all files associated with the trained Nanodet-Plus model.</li> <li><strong>configs</strong>: Configuration files for training procedures.</li> </ul> <p><strong>Training Data.zip</strong></p> <p>This directory contains all datasets used for the training of Nanodet-Plus, Plus Transformer, and Minus Transformer models.</p> <p><strong>Code.zip</strong></p> <p>This directory features the implementation details and example use-cases showcased in Figure 2 and Figure 3c of our associated paper. The directory also includes code corresponding to the specific versions of Nanodet-Plus and SAM models cited in our study.</p> <ul> <li><strong>nanodet</strong>: Code in this folder is adapted from <a href="https://github.com/RangiLyu/nanodet">RangiLyu/nanodet</a> (https://github.com/RangiLyu/nanodet). We have included the exact version used for compatibility.</li> <li><strong>segment_anything</strong>: Code sourced from <a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a> (https://github.com/facebookresearch/segment-anything). The specific version used is included for compatibility.</li> <li><strong>Fig2</strong>: Code for predicting topological defects in tissue cell images, citing the following reference: T. B. Saw et al., Nature 544, 212 (2017).</li> <li><strong>Fig3c</strong>: Code for predicting topological defects in microtubules images, citing the following reference: M. Golden et al., Sci. Adv. 9, eabq6120 (2023).</li> </ul> <p><strong>Initialization Steps</strong></p> <p>Before executing any code, please ensure the following:</p> <ul> <li>All files in the <strong>Trained Models</strong> directory must be available.</li> <li>Download the checkpoint <strong>sam_vit_l_0b3195.pth</strong> from <a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a>. (https://github.com/facebookresearch/segment-anything)</li> </ul> <p><strong>Acknowledgments</strong></p> <ul> <li><a href="https://github.com/RangiLyu/nanodet">RangiLyu/nanodet</a> (https://github.com/RangiLyu/nanodet)</li> <li><a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a> (https://github.com/facebookresearch/segment-anything)</li> </ul> <p>For further inquiries or issue reporting, you may contact us via email.</p> <p><strong>Contact Information</strong>: <a href="mailto:hrenae@connect.ust.hk">hrenae@connect.ust.hk</a></p>
Supplementary materials for: Striking cuticular hydrocarbon dimorphism in the mason wasp Odynerus spinipes and its possible evolutionary cause (Hymenoptera: Chrysididae, Vespidae)
<p>Cleptoparasitic wasps and bees smuggle their eggs into the nest of a host organism. Here the larvae of the cleptoparasite feed upon the food provision intended for the offspring of the host. As cleptoparasitism incurs a loss of fitness for the host organism (offspring of the host fail to develop), hosts of cleptoparasites are expected to exploit cues that alert them to potential cleptoparasite infestation. Cuticular hydrocarbons (CHCs) could serve as such cues, as insects inevitably leave traces of them behind when entering a nest. By mimicking the host's CHC profile, cleptoparasites can conceal their presence and evade detection by their host. Previous studies have provided evidence of cleptoparasites mimicking their host's CHC profile. However, the impact of this strategy on the evolution of the host's CHC profile has remained unexplored. Here, we present results from our investigation of a host-cleptoparasite system consisting of a single mason wasp species that serves syntopically as the host to three cuckoo wasp species. We found that the spiny mason wasp (<em>Odynerus spinipes</em>) is able to express two substantially different CHC profiles, each of which is seemingly mimicked by a cleptoparasitic cuckoo wasp (i.e. <em>Chrysis mediata</em> and <em>Pseudospinolia neglecta</em>). The CHC profile of the third cuckoo wasp (<em>Chrysis viridula</em>), a species not expected to benefit from mimicking its host's CHC profile because of its particular oviposition strategy, differs from the two CHC profiles of its host. Our results corroborate the idea that the similarity of the CHC profiles between cleptoparasitic cuckoo wasps and their hosts is the result of chemical mimicry. They further suggest that cleptoparasites may represent a hitherto unappreciated force that drives the evolution of their hosts' CHCs.</p>
Materials Datasets with 273 compositional and structural features extracted from Matminer
<p>Materials Datasets with 273 compositional and structural features extracted from <a href="https://github.com/hackingmaterials/matminer">Matminer</a>. Materials datasets are retrieved using the python package <a href="https://github.com/usnistgov/jarvis">jarvis-tools</a>.</p>
Scaled laboratory experiments of analogue magma intrusion in granular material: X-ray Computed Tomography imagery and displacement data
<p>This data set contains X-ray Computed Tomography (CT) images and surface displacement data of 15 scaled laboratory experiments of analogue magma intrusion in granular material. The experimental methodology and the experimental results were described in detail by Poppe et al. (2019). Displacement data of experiment SPCTIN14 was used by Poppe et al. (2023).<br> When using the experimental imagery or their derivatives please reference at a minimum Poppe et al. (2019) and this data set (Poppe et al., 2023, Zenodo data set).<br> The included explanatory notice reproduces the experimental method and presents the structure and file types contained in this data set.</p>
Supplementary material for: "Missing nurses cause missed care: is that it? Non-Trivial Configurations of Reasons Associated with Missed Care in Austrian hospitals – A qualitative comparative analysis"
<p>Dataset with calibrated data to reproduce the Qualitative Comparative Analysis in our article. </p><p>This dataset was generated by Ana Cartaxo. It contains 81 variables with 401 observations (complete data), which were included in the MISSCARE-Austria Study and were generated using the revised MISSCARE Austria instrument (Cartaxo et al., 2022). The original data were calibrated as described in the article in preparation of performing Qualitative Comparative Analysis – variables regarding contextual factors (Nurse Characteristics, Unit Characteristics, Hospital Characteristics), reasons for missed nursing care (Demand for patient care, Relationship and communication factors, Labor resources allocation, Material resources allocation) and outcomes of missed nursing care are included in the csv. file. </p><p>The R Script (R-Code.R) was created by Ana Cartaxo, João Cartaxo und Johannes Bergmann. It contains the different functions and analysis steps to reproduce the Qualitative Comparative Analysis reported on the article above, using the dataset made available ("Calibrated_Data_Set.csv").</p>
Double Trouble (GJ 1132b JWST Transits NIRSpec G395H) — Supplementary Material
<p>Supplementary material for '<i>Double Trouble: Two Transits of the Super-Earth GJ 1132 b Observed with JWST NIRSpec G395H</i>' by May & MacDonald et al., ApJL (2023).</p><p>This repository contains four categories of data products:</p><ol><li><strong>Data reduction reproduction</strong> — Eureka! control files (.ecf) and parameter files (.epf) alongside saved Stage 3 output for both G395H visits. These allow replication of our data reduction, light curve fitting, and transmission spectra extraction using the <a href="https://github.com/kevin218/Eureka">open-source code Eureka</a>!</li><li><strong>Transmission spectra</strong> — Reduced transmission spectra from three data reduction codes (Eureka!, FIREFLy, and ExoTiC-JEDI) for both G395H visits.</li><li><strong>Stellar spectra </strong>— Flux calibrated observed stellar spectra of GJ 1132 from each G395H visit.</li><li><strong>Retrievals</strong> — Corner plots and parameter statistics summaries from atmospheric and stellar contamination retrievals applied to both G395H visits (Eureka! reduction). The retrievals used the <a href="https://github.com/MartianColonist/POSEIDON">open-source code POSEIDON</a>.</li></ol><p>For any additional data requests or questions, please contact: ryanjmac@umich.edu</p>
Supplementary material from: ZooMS confirms geometric morphometrics species identification of ancient sheep and goat
<p><span>Geometric morphometrics can effectively distinguish isolated third lower molars of present-day sheep and goat, but its applicability to archaeological specimens has yet to be established. Using a modern reference collection of 743 sheep and goats and a 2D-landmark-based geometric morphometric (GMM) protocol, this study aimed to morphometrically identify 109 archaeological specimens, used as case studies, dating from the Late Neolithic to the modern period/era. These morphometric identifications were then compared to molecular identifications via collagen peptide mass fingerprinting, known as Zooarcheology by Mass Spectrometry (ZooMS). ZooMS confirmed the morphometric identifications for 104 specimens, with the five misidentified specimens all morphometrically identified as goat. </span></p> <p><span>Modern sheep and goats have larger teeth and distinct shapes compared to their archaeological counterparts, suggesting strong differences between archaeological and modern specimens potentially linked with recent breed improvement or geographic origin of the specimens. In addition, for both species, </span><span>some of the archaeological dental morphologies do not match with any of our modern references</span><span>.</span></p> <p><span>This study validates the applicability of geometric morphometrics for identifying isolated archaeological sheep and goat teeth. It represents a stepping stone for future, non-destructive, bioarchaeological studies of the two species.</span></p>
Supplementary Material DDK from childhood to adulthood
<p>Database paper: Lancheros, M., Friedrichs, D., & Laganaro, M. (2023). What Do Differences between Alternating and Sequential Diadochokinetic Tasks Tell Us about the Development of Oromotor Skills? An Insight from Childhood to Adulthood. <i>Brain Sciences</i>, <i>13</i>(4), 655.</p>
Supplementary material and dataset for article "Structure-Function Relationship of Iron Oxide Nanoflowers: Optimal Sizes for Magnetic Hyperthermia Depending on Alternating Magnetic Field Conditions"
<p>The supporting information file contains additional measurements compared to the main manuscript of the related article: TEM size histograms, SAED and XRD patterns, absorption and fluorescence spectra, DC magnetization curves, AC hysteresis loops, HR-TEM, their FTT patterns and inverse FFT images after applying a mask in the reciprocal space, and various other plots of this multi-parametric study on the structure-properties relations of magnetic iron oxide nanoflowers. Whenever needed for comparison to other experimental results or theoretical fitting, the raw data of all DC magnetization curves, AC hysteresis loops, ZFC-FC magnetization curves vs. temperature (and their derivatives on temperature) are made freely available in this dataset.</p>
Supplementary Material - Information and Software Technology
<p>Supplementary material - Article title: "Constructing the graphical structure of expert-based Bayesian networks in the context of software engineering: A systematic mapping study" - Information and Software Technology. This material contains the detailed procedure employed in the systematic mapping.</p>
Internal large field of view observations with optical microscope of fatigue damage in composite materials during bending loading
<p>Stitched microscope pictures ...</p> <p>References to this data-set should include a reference to one of the following two papers in where the data has been used.</p> <p>Mortensen U., Andersen, T.L., Mikkelsen, L.P., Observation of Edge Effect in Flexural Fatigue Test of Composites using Large Field of View Microscopy, Journal of Composite Materials. https://doi.org/10.1177/0021998320902233, 2020</p> <p>Mortensen, U.A., Mikkelsen, L.P., Andersen, T.L. Observation of the interaction between transverse cracking and fibre breaks in uni-directional non-crimp fabric composites subjected to cyclic bending fatigue damage mechanism, submitted, Feb. 2022.</p> <p>The naming of the pictures are build up by the follwing elements</p> <p>EL_05 is the plate ID,<br> J01-J09 is the sample ID where the following load level are defined:</p> <ul> <li>J01: 1 000 cycles</li> <li>J02: 2 500 cycles</li> <li>J03: 5 000 cycles</li> <li>J04: 10 000 cycles</li> <li>J05: 50 000 cycles</li> <li>J06: 100 000 cycles</li> <li>J07: 250 000 cycles</li> <li>J08: 500 000 cycles</li> <li>J09: 1 000 000 cycles</li> </ul> <p>S01-04: is the 4 surfaces inside the sample as shown in the picture saved in: xxx. The picture show the sample where the red planes indicate the polished surfaces where the microscopy pictures from the internal surfaces are taken.</p> <p>The pictures are oriented with the compression side upward and the tensile side downward similar to the orientation of the actual 4-point fatigue bending test sample.</p>
Online Supplementary Materials 2: Complete Projects and Papers included within Motivation Crowding in PES
<p>The following supplementary material is associated with the paper, Motivational Crowding in Payments for Ecosystem Service Schemes: a Global Systematic Review, published within Conservation and Society. </p> <p>The full review can be found here. </p> <p> </p> <p> </p>
Supplementary material for "Learning Conceptual Modeling: Structuring Overview, Research Themes and Paths for Future Research"
<p>This material containing a list of the bibliographical data of the final sample and the raw data set of 345 publications before excluding publications outside of our focus supplements the following literature review:</p> <p>Rosenthal, Kristina; Ternes, Benjamin; Strecker, Stefan, (2019). “Learning Conceptual Modeling: Structuring Overview, Research Themes and Paths for Future Research”. In: Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm and Uppsala, Sweden, June 8–14, 2019.</p>
Evaluation of Wound Infection Rates and Cosmetic Results of Different Suture Materials in Cesarean Skin Incision
ClinicalTrials.gov study NCT06119113. IPD Sharing: Not stated. Countries: 1. Publications: 4.
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.