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717 results for “manufacturer”
Dataset related to article "Performance of dual-energy subtraction in contrast-enhanced mammography for three different manufacturers: a phantom study"
<p><span>The dataset provided here contains the raw data used in the study discussed in this article. The primary objective of the study was to conduct a comparative analysis of the performance of dual energy subtraction (DES) images acquired using contrast-enhanced mammography (CEM) systems produced by three different manufacturers. The comparison, facilitated by a CEM-specific phantom, focused on the assessment of radiation dose and image quality.</span></p> <p><span>Composed of three separate CSV files, the dataset is structured as follows:</span></p> <p><span>1) "Dose-related data": This file provides exposure parameters (including A/F combination, tube voltage and exposure) and the corresponding mean glandular dose (MGD for low-energy (LE) and high-energy (HE) images. Data are given for each CEM system and automatic exposure mode (AEC).</span></p> <p><span>2) "CNR-related data": Encapsulated in this file are data extracted from the phantom DES images. These include measurements of the mean pixel value (MPV) for each iodinated contrast detail, as well as the MPV and standard deviation (SD) for the surrounding background. These data were used to calculate the contrast-to-noise ratio (CNR) for the iodinated contrast details.</span></p> <p><span>3) “Residual CNR data”: This file includes MPV and SD data extracted from phantom DES images, which were useful for calculating the residual CNR after cancellation of the normal background tissue by the DES algorithm.</span></p>
BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of Ni-based alloy Inconel IN718
<p>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of Ni-based alloy Inconel IN718 between room temperature and 800 °C in an additively manufactured variant (laser powder bed fusion, PBF‑LB/M) and from a conventional process route (hot rolled bar). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</p>
BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of Ti-6Al-4V
<p>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of titanium alloy Ti-6Al-4V between room temperature and 400 °C in an additively manufactured variant (laser-based directed energy deposition with powder as feedstock, DED-LB/M) and from a conventional process route (hot rolled bar). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</p>
BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of austenitic stainless steel AISI 316L
<p><span>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of austenitic stainless steel AISI 316L between room temperature and 900 °C in an additively manufactured variant (laser powder bed fusion, PBF</span><span>‑</span><span>LB/M) and from a conventional process route (hot rolled sheet). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</span></p>
Dataset for paper entitled, 'Confirmation of rapid-heating β recrystallization in wire-arc additively manufactured Ti-6Al-4V'.
<p>Dataset for paper entitled, 'Confirmation of rapid-heating β recrystallization in wire-arc additively manufactured Ti-6Al-4V'. doi: https://doi.org/10.1016/j.mtla.2020.100857</p>
A Sensorized Soft Pneumatic Actuator Fabricated with Extrusion-Based Additive Manufacturing
<p>Soft pneumatic actuators with a channel network (pneu-net) based on thermoplastic elastomers are compatible with fused deposition modeling (FDM). However, conventional filament-based fused deposition modeling (FDM) printers are not well suited for thermoplastic elastomers with a shore hardness (Sh < 70A). Therefore, in this study, a pellet-based FDM printer was used to print pneumatic actuators with a shore hardness of Sh18A. Additionally, the method allowed the in situ integration of soft piezoresistive sensing elements during the fabrication. The integrated piezoresistive elements were based on conductive composites made of three different styrene-ethylene-butylene-styrene (SEBS) thermoplastic elastomers, each with a carbon black (CB) filler with a ratio of 1:1. The best sensor behavior was achieved by the SEBS material with a shore hardness of Sh50A. The dynamic and quasi-static sensor behavior were investigated on SEBS strips with integrated piezoresistive sensor composite material, and the results were compared with TPU strips from a previous study. Finally, the piezoresistive composite was used for the FDM printing of soft pneumatic actuators with a shore hardness of 18 A. It is worth mentioning that 3 h were needed for the fabrication of the soft pneumatic actuator with an integrated strain sensing element. In comparison to classical mold casting method, this is faster, since curing post-processing is not required and will help the industrialization of pneumatic actuator-based soft robotics</p>
Vibration assisted drilling (VAD) application to the manufactured maraging steel X3NiCoMoTi18-9-5 (1.2709) and aluminium AlSi10Mg (EN AC-43000) parts
<p>Repository containing data from vibration assisted drilling (VAD) experiments on steel and Aluminum 3D powderbed manufactured parts.</p> <p>Please refer to README.MD (or .PDF), which contains a brief description of the chosen materials, parts and tools An explanation of the data aquisition and processing methods, together with the used nomenclature is given as well.</p>
Porosity distribution in sub-skin boundary area of the powderbed additively manufactured parts
<p>Repository contains measurement results of the experimental investigationn of the sub-skin porosity in additively manufactured parts. Specimens were manufactured using machine manufacturer's suggested process parameters. Four different machines (EOS M400, TRUMPF TruPrint 1000, SLM 280 and DMG MORI LASERTEC 30 2nd gen.) and four different powder materials (mararging steel 1.2709, aluminum alloy AlSi10Mg, Titanium grade 5 and stainless steel 1.4404) are covered. Influences of the relative orientation of the hatch and boundary scanning tracks was investigated. Efficiency of the mitigation strategy againts sub-skin porosity issues through distance variation between hatch and boundary tracks was evaluated.</p> <p>Please refer to README.MD (or .PDF) for more detailed information about this dataset.</p>
Modelled results for Potential Health and Economic Impacts of Shifting Manufacturing
<p>Files include modelled 100-year annual mean aerosol concentrations, zonal wind and meridional wind in the baseline and sensitivity simulations.</p>
Manufacturing of Hybrid Overmoulded FRP Components: Impact of Process and Environmental Parameters on the Mechanical Properties
<p>A manufacturing parametric study was carried out on a hybrid part, consisting of two organo sheets and injection moulded rib reinforcements, made out of a short fibre reinforced plastic. The effect of the temperature of all components, the pressure profile throughout the injection moulding process as well as the subsequent storage conditions on the mechanical properties was investigated. For this purpose a total of 96 parts was manufactured with different processing parameter combinations. Afterwards all parts were subjected to a cantilever beam test, analysing initial stiffness, deformation work and peak force. Furthermore a variety of potentially influencing factors such as temperature, humidity, order of testing, transfer durations and many more were tracked.</p> <p>Parameter definition:</p> <ul> <li><strong>part_ID</strong>: Unique part identifier.</li> <li><strong>OS-degradation</strong>: Degradation of the organo-sheet(OS) because of thermal decomposition at temperatures above 260°C. Given as percentage of degraded mass.</li> <li><strong>heating-duration</strong>: Time of heating the OS in seconds.</li> <li><strong>heating_temperature-OS</strong>: Surface temperature of the OS at the end of the heating period in °C.</li> <li><strong>heating_field-temp.</strong>: Temperature of the heating field immediately before the heating process begins in °C.</li> <li><strong>stiffness</strong>: Bending stiffness of the structure in N/mm. Determined from the force-deflection-curve between 50 and 150 N.</li> <li><strong>maximum_force</strong>: Highest force value measured during the cantilever beam test in kN.</li> <li><strong>deflection</strong>: Value corresponding to the maximum force in mm.</li> <li><strong>deformation_work</strong>: Absorbed work due to deformation in kN*mm. Determined by integrating the force-deflection-curve from 5 to 30 mm with a lower bound of 0.05 kN on the force.</li> <li><strong>corrected-x</strong>: Value of x corrected by taking into account the water intake.</li> <li><strong>rib_lengths-x</strong>: Length of the rib in mm (1à5<sup>th</sup>, 2à7<sup>th</sup> , 3à10<sup>th</sup>).</li> <li><strong>sprue_width</strong>: Diameter of the sprue in mm.</li> <li><strong>part_length</strong>: Total part length in mm.</li> <li><strong>mould_filled-x</strong>: Flag indicating whether the mould was filled (indicated by 1) at position x (1àbroad end, 2àmid, 3ànarrow end)</li> <li><strong>rib_length</strong>: Mean of the measured rib lengths in mm.</li> <li><strong>mould_filled</strong>: Mean of the flags for fill state.</li> <li><strong>day_of_testing</strong>: Day on which the part was tested (1,2,3).</li> <li><strong>no._of_test_per_day</strong>: Number of test on the respective day.</li> <li><strong>no._of_test_total</strong>: Number of tests in total.</li> <li><strong>delay_of_transfer</strong>: Duration between removal of the OS from the heating field and begin of the transfer in s.</li> <li><strong>transfer_temp_beg.-x</strong>: Surface temperature in °C of the OS at the beginning of the transfer at position x.</li> <li><strong>transfer_temp_end-x</strong>: Surface temperature in °C of the OS at the end of the transfer at position x.</li> <li><strong>tool_surface_temp.-x</strong>: Surface temperature in °C of the tool at position x.</li> <li><strong>fixing-x</strong>: Flag indicating whether the OS was fixed (indicated by 1) at position x (1àbroad end, 2àmid, 3ànarrow end).</li> <li><strong>fixing</strong>: Mean of the flags for fixing.</li> <li><strong>duration-rapid_traverse</strong>: Duration of the rapid motion phase of the press in s.</li> <li><strong>duration-deformation</strong>: Duration of the motion phase of the press deforming the OS in s.</li> <li><strong>press_profile</strong>: ID for translational velocity of the press (0àslow, 1àfast).</li> <li><strong>duration-closing</strong>: Sum of duration-rapid_traverse and duration-deformation.</li> <li><strong>duration-injection</strong>: Duration of the pure injection process in s.</li> <li><strong>duration-holding_pressure</strong>: Time for which the holding pressure was kept up in s.</li> <li><strong>temp.-cylinder-x</strong>: Mean of the temperature over one cycle at one heating band in °C.</li> <li><strong>temp-hot_runner-x</strong>: Mean of the temperature over one cycle at one heating element in the hot runner in °C.</li> <li><strong>temp.-defl._tool-x</strong>: Mean of the temperature over one cycle in the deflection tool at position x.</li> <li><strong>delay-injection</strong>: Time between the press fully closing and the beginning of injection in s.</li> <li><strong>holding_pressure-beg.-x</strong>: Holding pressure at the beginning of holding and position x in bar.</li> <li><strong>holding_pressure-end-x</strong>: Holding pressure at the ned of holding and position x in bar.</li> <li><strong>duration-form_stability-x</strong>: Time in s between maximum melt pressure and form stability, characterised by a pressure below 75 bar.</li> <li><strong>max.-pressure-melt-x</strong>: Maximum pressure during the injection process at position x in bar.</li> <li><strong>transmission-hold._press.-beg.</strong>: Ratio of pressure signal from the sensor at screw and in tool at beginning of holding.</li> <li><strong>transmission-hold._press.-end</strong>: Ratio of pressure signal from the sensor at screw and in tool at end of holding.</li> <li><strong>cooling_rate-hold._press.-x</strong>: Measured cooling rate at position x during the holding phase in °C/s.</li> <li><strong>cooling_rate-cooling-x</strong>: Measured cooling rate at position x during the cooling phase in °C/s.</li> <li><strong>tool-temp.-x</strong>: Measured tool temperature at position x in °C.</li> <li><strong>max.-melt-temp-x</strong>: Highest measured melt temperature at position x in °C.</li> <li><strong>demoulding-temp.-x</strong>: Measured tool temperature at position x at demoulding in °C.</li> <li><strong>storage-standard_atmosphere</strong>: Storage duration at standard atmosphere in h.</li> <li><strong>absoprtion_water-std.atm.</strong>: Water absorption during the storage at standard atmosphere in g.</li> <li><strong>absoprtion_water-climate_chamber.</strong>: Water absorption during the storage in the climate chamber in g.</li> <li><strong>storage-climate_chamber</strong>: Storage duration in climate chamber in h.</li> <li><strong>vert._position-climate_chamber</strong>: Vertical position in the climate chamber in cm.</li> <li><strong>air-temp.</strong>: Air temperature during manufacturing in °C.</li> <li><strong>air-rel._humidity</strong>: Relative humidity during manufacturing in %.</li> <li><strong>no.-production-day</strong>: Consecutive number indicating parts manufactured before the respective part.</li> <li><strong>factor_level</strong>: Factor level in the DOE.</li> <li><strong>prod.-date</strong>: Date of production.</li> <li><strong>prod-time</strong>: Time of production in CEST.</li> <li><strong>batch_number</strong>: ID in which batch the part was manufactured.</li> </ul>
Data from: Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing
<p>This dataset contains the data for the publication "Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing".</p>
Additively Manufactured Titanium 'Alloy-Alloy Composites' and Site-Specific Property Design
<p>Paper links: Journal of Materials Characterization: https://doi.org/10.1016/j.matchar.2021.111577</p> <p>Research Gate (free preprint): https://bit.ly/3xy1Ldm</p> <p>Video written and produced by Alec Davis and Jacob Kennedy. Research was conducted at the University of Manchester and Cranfield University, UK, by Jacob Kennedy, Alec Davis, Armando Caballero, Michael White, Jon Fellowes, Ed Pickering, and Phil Prangnell. This work was supported by grants: NEWAM (EPSRC EP/R027218/1), Lightform (EPSRC EP/R001715/1), and Henry Royce Institute for Advanced Materials (EPSRC EP/R00661X/1, EP/S019367/1, EP/P025021/1, and EP/P025498/1).</p>
Fully Calculated Samples for Discrete Manufacturing Simulation Environment
<p>This dataset is a MySQL file and contains fully calculated samples for different baseline situations:</p> <p><br> </p> <table> <thead> <tr> <th>Env-Version</th> <th>ammountOfCarriers</th> <th>uncertainty</th> <th>onlyForTraining</th> <th>quantity</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>4</td> <td>1</td> <td>0</td> <td>10000</td> </tr> <tr> <td>1</td> <td>4</td> <td>1</td> <td>1</td> <td>90786</td> </tr> <tr> <td>1</td> <td>4</td> <td>3</td> <td>0</td> <td>10000</td> </tr> <tr> <td>1</td> <td>4</td> <td>3</td> <td>1</td> <td>92254</td> </tr> <tr> <td>1</td> <td>6</td> <td>1</td> <td>0</td> <td>20000</td> </tr> <tr> <td>1</td> <td>6</td> <td>1</td> <td>1</td> <td>210000</td> </tr> <tr> <td>1</td> <td>6</td> <td>3</td> <td>0</td> <td>20000</td> </tr> <tr> <td>1</td> <td>6</td> <td>3</td> <td>1</td> <td>214000</td> </tr> <tr> <td>1</td> <td>7</td> <td>3</td> <td>1</td> <td>30000</td> </tr> <tr> <td>1</td> <td>8</td> <td>3</td> <td>1</td> <td>34188</td> </tr> <tr> <td>1</td> <td>9</td> <td>3</td> <td>1</td> <td>30000</td> </tr> <tr> <td>1</td> <td>10</td> <td>3</td> <td>1</td> <td>34000</td> </tr> <tr> <td>1</td> <td>12</td> <td>3</td> <td>1</td> <td>3272</td> </tr> <tr> <td>2</td> <td>4</td> <td>1</td> <td>0</td> <td>10000</td> </tr> <tr> <td>2</td> <td>4</td> <td>1</td> <td>1</td> <td>94791</td> </tr> <tr> <td>2</td> <td>4</td> <td>3</td> <td>0</td> <td>10000</td> </tr> <tr> <td>2</td> <td>4</td> <td>3</td> <td>1</td> <td>100000</td> </tr> <tr> <td>2</td> <td>6</td> <td>1</td> <td>0</td> <td>10000</td> </tr> <tr> <td>2</td> <td>6</td> <td>1</td> <td>1</td> <td>90423</td> </tr> <tr> <td>2</td> <td>6</td> <td>3</td> <td>0</td> <td>10000</td> </tr> <tr> <td>2</td> <td>6</td> <td>3</td> <td>1</td> <td>249154</td> </tr> </tbody> </table> <p> </p> <p>How do you use the files?<br> You need a MySQL instance on your computer (e.g. via XAMPP). If you do not already have one, create a database there with the name: "rlskipping". The command for this is: </p> <blockquote> <p>CREATE DATABASE IF NOT EXISTS rlskipping;</p> </blockquote> <p>On Windows:</p> <p> 1) Open a new CMD-Shell.<br> 2) Navigate to the path where the sql file is located.<br> (e.g.: cd C:\Users\MrScience\Desktop\Work\myProject\Paper\SQL)<br> 3) Import the Files.<br> (e.g.: mysql -u root -p rlskipping < evaluierung.sql)<br> (e.g.: mysql -u root -p rlskipping < calculatedsamples.sql)</p>
Manufacture of tools for microsurgical transplantation of schistosoma sporocysts
<p>Video instruction for the making of tools for microsurgical transplantation of schistosoma sporocyst from donor snails to recipient snails.</p> <p>Produced at IHPE (http://ihpe.univ-perp.fr/)</p>
Supplementary Materials for Learning Manufacturing Computer Vision Systems Using Tiny YOLOv4
<h1>About This Dataset</h1> <p>This repository contains the supplementary materials presented in the publication “Learning Manufacturing Computer Vision Systems Using Tiny YOLO v4” by Medina, A., Bradley, R., Xu, W., Ponce, P., Anthony, B., and Molina, A. that can be found with the following DOI <a href="https://www.frontiersin.org/articles/10.3389/frobt.2024.1331249/">10.3389/frobt.2024.1331249</a></p> <p>There are three files in this repository:</p> <ol> <li>dataset.zip</li> <li>YOLOv4_object_detection.ipynb</li> <li>deploy.py</li> </ol> <h1>dataset.zip</h1> <p>This Dataset is for an example used for education purposes. It is a small dataset that is adapted from the following Kaggle repository, authored by Ruthger Righart <a href="https://www.kaggle.com/datasets/rrighart/jarlids/data">https://www.kaggle.com/datasets/rrighart/jarlids/data</a>. One of the activities proposed is to teach students how to find, download and review a free dataset, so this is the example given.</p> <p>Another activity is to teach how to label images to create a custom dataset. The images (with extension .JPG) from the original repository are used. The labels (with extension .txt) were created by the authors of the Learning Manufacturing Computer Vision Systems Using Tiny YOLOv4 paper. The authors used the free tool labelImg, from GitHub repository (<a href="https://github.com/HumanSignal/labelImg">https://github.com/HumanSignal/labelImg</a>), to label the images with object bounding boxes and corresponding labels in the YOLO format.</p> <p>The dataset contains 238 images and corresponding labels, with files named “p<num>.JPG” and “p<num>.txt”. The text labels are formatted in the YOLO format with each row in the .txt file corresponding to one object in the image. Each row contains 5 elements: The object identifier, top left corner x coordinate, top left corner y coordinate, height, and width, separated by a whitespace. The object identifier represents good cans as 0 and defective cans as 1.</p> <h1>YOLOv4_object_detection.ipynb</h1> <p>This notebook was created to give the user a step-by-step tutorial on how to train a YOLOv4 algorithm with a custom dataset using a free GPU on Google Collab, the prerequisite to use it are:</p> <ul> <li>To have ready the dataset.</li> <li>Have the training txt file with the path to all images used for training.</li> <li>Have the test txt file with the path to all images used for testing.</li> </ul> <p>There are other requirements like cloning a GitHub repository and altering certain files on that repository; however, those steps are discussed within the notebook.</p> <p>At the end of the notebook an example on how to test the trained model with images and/or videos is shown, however since Google Collab doesn’t have access to the physical computer of the user live stream video is not part of the example.</p> <h1>deploy.py</h1> <p><em>Disclaimer: This code is not optimized, and its intended purpose is to teach students how to run YOLO on a raspberry pi using the OpenCV library.</em></p> <p>To use this code with different files or datasets, be sure to change the two parameters inside the net3 variable which are the cfg file used while training the algorithm and the weights file. You should also change the class list to include your classes, keeping in mind that the classes order must correspond to the order of the labeling process and class 0 is the first one on the list.</p> <p>Also to change the Title of the created image prompt you shout go to the line calling the imshow method and change the ‘Tiny YOLOv4’ string.</p> <p>This algorithm uses the first camera it finds and opens up a display image with the detected objects surrounded by a bounding box, on top of that box the top predicted class is going to show, to change color of bounding boxes or text change the rectangle method where it says GREEN as well as in the next code line ant change the number to change the thickness of the line.</p> <p>This code has a hardcoded confidence threshold for both the YOLO objectevness score and the class score, this can be found in the NMSBoxes method and the if confidence line accordingly. The main value to change first is the if confidence value.</p> <p>To close the image, you need to press the key ‘q’ as closing the display window is not going to work as it will reopen again.</p> <p>Note: This code allows the pop-up window, which displays the detections, to be closed only when the "q" key is pressed. Simply closing the window will not work.</p>
Supplementary material from: Wolz D, Seidel-Greiff R, Behnisch T, Kruppke I, Kuznik I, Bertram P, Jäger H, Gude M, Cherif C (2024) Potentials of Polyacrylonitrile Substitution by Lignin for Continuous Manufactured Lignin/Polyacrylonitrile-Blend-Based Carbon Fibers
<p>This material contains all research data concerning the analyses conducted in the work taht was published as "<strong>Potentials of Polyacrylonitrile Substitution by Lignin for Continuous Manufactured Lignin/Polyacrylonitrile-Blend-Based Carbon Fibers</strong>" in the <strong>MDPI</strong> journal <strong>Fibers.</strong></p> <p>Process data cannot be included for reasons of confidentiality.</p>
Dataset: Art's-Way Manufacturing Co., Inc. (ARTW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Art's-Way Manufacturing Co., Inc. (ARTW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Data for the paper: The Role of Glycerol in Manufacturing Freeze-Dried Chitosan and Cellulose Foams for Mechanically Stable Scaffolds in Skin Tissue Engineering
<p>The Dataset contains all the data, described in the article "<strong>The Role of Glycerol in Manufacturing Freeze-Dried Chitosan </strong><br><strong>and Cellulose Foams for Mechanically Stable Scaffolds in Skin Tissue Engineering</strong>."</p> <p><strong><em>Abstract</em></strong><br>Various strategies have extensively explored enhancing the physical and biological properties of chitosan and cellulose scaffolds for skin tissue engineering. This study presents a straightforward method involving the addition of glycerol into highly porous structures of two polysaccharide complexes: chitosan/carboxymethyl cellulose (Chit/CMC) and chitosan/oxidized cellulose (Chit/OC); during a one-step freeze-drying process. Adding glycerol, especially to Chit/CMC, significantly increased stability, prevented degradation, and improved mechanical strength by nearly 50%. Importantly, after 21 days of incubation in enzymatic medium Chit/CMC scaffold has almost completely decomposed, while foams reinforced with glycerol exhibited only 40% mass loss. It is possible due to differences in multivalent cations and polymer chain contraction, resulting in varied hydrogen bonding <br>and, consequently, distinct physicochemical outcomes. Additionally, the scaffolds with glycerol improved the cellular activities resulting in over 40% higher proliferation of fibroblast after 21 days of incubation. It was achieved by imparting water resistance to the highly absorbent material and aiding in achieving a balance between hydrophilic and hydrophobic properties. This study clearly indicates the possible elimination of additional crosslinkers and multiple fabrication steps that can reduce the cost of scaffold production for skin tissue engineering applications while tailoring mechanical strength and degradation.</p> <p><strong>Figure 2.</strong> Morphology. SEM micrographs of the internal structure of the freeze-dried scaffolds. Results of porosity analysis. The methodology and data are described in the README file in the folder.</p> <p><strong>Figure 3.</strong> Mechanical test results. Representative stress-strain curves from the tensile test of all freeze-dried scaffolds, where (A)<br>– measurement performed in dry conditions, (B) – measurement performed in wet conditions. All are described in the README file in the folder.</p> <p><strong>Figure 4. </strong>Swelling behavior of all scaffolds. B – Two representative vials with a visual demonstration of swelling, samples marked with circles: Chit/CMC sample submerged in the PBS (blue circle) and Chit/CMC/Glyc sample floating on the surface (green circle). The arrows lead to photos of scaffolds taken from vials directly after swelling. C – Gel fraction analysis in aqueous solution after 24 <br>6 h. D – Time after which the water droplet is absorbed into the scaffold. E – Photographs of water droplet shape changes on Chit/CMC and Chit/CMC/Glyc scaffolds over time. All are described in the README file in the folder.</p> <p><strong>Figure 5</strong>. Fourier Transform Infrared Spectroscopy (ATR-FTIR) analysis results. Details are in the README file in the folder.</p> <p><strong>Figure 6.</strong> The FTIR spectra of eluates from degraded scaffolds collected on a microscopic glass slide. Details are in the README file in the folder.</p> <p><strong>Figure 7.</strong> The degradation studies of all scaffolds over 21 days of experiments in A – enzymatic medium. B – cell culture medium. Details are in the README file in the folder.</p> <p><strong>Figure 9. </strong>Cell experiments and toxicity analysis. Cytotoxicity of eluates taken from degraded scaffolds. B – Direct fibroblast seeding on scaffolds during 14 days of culture period. C – Direct fibroblast seeding on scaffolds during 14 days of culture period without control to better see the effect of glycerol. Details are in the README file in the folder.</p>
Fig. 4 in Integrated pest management of the German cockroach (Blattodea: Blattellidae) in manufactured homes in rural North Carolina
Fig. 4. Distribution of German cockroaches from trap catches at various locations within homes of individual participants.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.