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Figure 5. Mutation process. This is happened by decreasing 0.2 from Med-Cold probability and adding 0.2 to Med- Hot.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>Figure 5 illustrates an example of mutation process. In Figure 5, Med-Cold:0.9 and Med-Hot:0.1<br> before mutation and become Med-Cold:0.7 and Med-Hot:0.3 after mutation. This is done by<br> decreasing 0.2 from Med-Cold probability and adding 0.2 to Med-Hot probability.</p>
Electron Backscatter Diffraction Patterns from Titanium-added Interstitial-free Steel Containing Subgrains
<h3><strong>Associated Publications</strong></h3> <ol> <li>Bennett IV, T.J. and Taleff, E.M. Dynamic Grain Growth Driven by Subgrain Boundaries in an Interstitial-Free Steel During Deformation at 850 °C. <em>Metall Mater Trans A</em> 55, 429–446 (2024). <a href="https://doi.org/10.1007/s11661-023-07256-w">https://doi.org/10.1007/s11661-023-07256-w</a>.</li> <li>Bennett IV, T.J. and Taleff, E.M. Imaging and Segmenting Grains and Subgrains using Backscattered Electron Techniques. Under review (2024).</li> </ol> <h3><strong>Data Description</strong></h3> <p>These data were collected by Thomas J. Bennett IV on July 28, 2022.</p> <p>The electron backscatter diffraction (EBSD) data and associated electron backscatter diffraction patterns (EBSPs) contained herein were acquired from a titanium-added interstitial-free (Ti-IF) steel sheet material containing numerous subgrains. The Ti-IF steel specimen that provided these data was ramped to 850 degrees Celsius over 30 minutes, held at this temperature for one hour, and then deformed at a constant true-strain rate of 10^-4 s^-1. Upon reaching a final true strain of 0.225, the specimen was air quenched while maintaining a constant stress to preserve subgrains formed during high-temperature deformation. The tensile specimen was cut from a Ti-IF steel sheet received in a hard as-rolled condition with the tensile axis parallel to the sheet rolling direction. EBSPs were acquired from a section cut from the center of the deformed gage region using a JEOL JSM-IT300HR SEM equipped with an EDAX Velocity EBSD camera at the Center for Integrated Nanotechnologies.</p> <p>The following conditions were used for EBSD data acquisition:</p> <table> <tbody> <tr> <td>Accelerating Voltage:</td> <td>20 kV</td> </tr> <tr> <td>Beam Current:</td> <td>80%</td> </tr> <tr> <td>Working Distance:</td> <td>20.0 mm</td> </tr> <tr> <td>Magnification:</td> <td>200×</td> </tr> <tr> <td>Dynamic Focus:</td> <td>44 (out of 255, arbitrary units)</td> </tr> <tr> <td>Specimen Tilt:</td> <td>70 degrees</td> </tr> <tr> <td>Scanning Grid Type:</td> <td>Square</td> </tr> <tr> <td>Step Size (x and y):</td> <td>0.5 μm</td> </tr> <tr> <td>Scan Size:</td> <td>520 (across) × 340 (down) pixels</td> </tr> <tr> <td>EBSD Camera Resolution:</td> <td>446 × 446 pixels</td> </tr> <tr> <td>EBSD Camera Binning:</td> <td>1 × 1</td> </tr> <tr> <td>EBSD Camera Exposure Time:</td> <td>10 ms</td> </tr> <tr> <td>Frame Averaging:</td> <td>None</td> </tr> <tr> <td>Specimen Tensile Direction:</td> <td>Horizontal</td> </tr> <tr> <td>Specimen Rolling Direction:</td> <td>Horizontal</td> </tr> <tr> <td>Specimen Long Transverse Direction:</td> <td>Vertical</td> </tr> <tr> <td>Specimen Short Transverse Direction:</td> <td>Normal to plane</td> </tr> <tr> <td>Pattern Center (EMSphInx Convention):</td> <td>(x_pc, y_pc, L) = (-0.2 pixels, 112.76 pixels, 21736.4 μm)</td> </tr> <tr> <td>EBSD Camera Elevation Angle:</td> <td>3 degrees</td> </tr> <tr> <td>EBSD Camera Screen Width:</td> <td>32 mm</td> </tr> <tr> <td>Pixel size on EBSD Camera Screen:</td> <td>71.749 μm/pixel ( = 32000 μm / 446 pixels)</td> </tr> </tbody> </table> <p> </p> <p><em>Note:</em> Conversions between different pattern center conventions may be found in the journal article below or at the following link: <a href="https://github.com/EMsoft-org/EMsoft/wiki/DItutorial">https://github.com/EMsoft-org/EMsoft/wiki/DItutorial</a>.</p> <ul> <li>Jackson, M.A., Pascal, E., and De Graef, M. Dictionary Indexing of Electron Back-Scatter Diffraction Patterns: a Hands-On Tutorial. <em>Integr Mater Manuf Innov</em> 8, 226–246 (2019). <a href="https://doi.org/10.1007/s40192-019-00137-4">https://doi.org/10.1007/s40192-019-00137-4</a>.</li> </ul> <h3><strong>File Descriptions</strong></h3> <ul> <li>Specimen_orientation.pdf - A schematic showing specimen reference directions and the orientation used for EBSD data acquisition.</li> <li>Patterns.zip - A compressed archive containing Patterns.up2. This file contains 16-bit EBSPs and is 70,336,697,616 bytes (70.3 GB) uncompressed.</li> <li>SHT_Indexed.ang - A file containing orientation data produced by indexing Patterns.up2 using EMSphInx. Orientations are represented by Euler angles (Bunge convention) and are to be interpreted using the EDAX Setting 2 convention (see MTEX documentation at <a href="https://mtex-toolbox.github.io/EBSDReferenceFrame.html">https://mtex-toolbox.github.io/EBSDReferenceFrame.html</a>).</li> <li>SHT_Indexed.h5 - A file in HDF5 format containing orientation data and other relevant information produced by indexing Patterns.up2 using EMSphInx.</li> <li>SHT_Indexed_IPFmap.png - An image of an inverse pole figure map colored with respect to the short transverse direction showing the data from SHT_Indexed.ang.</li> </ul> <p><em>Note:</em> The basic format of "up2" files is the following. The first 4 bytes provide the version number. The second 4 bytes are the width of the patterns. The third 4 bytes are the height of the patterns. The fourth 4 bytes are the starting position of the pattern image data.</p> <h3><strong>Acknowledgments</strong></h3> <p>The authors gratefully acknowledge support from the National Science Foundation under Grant DMR-2003312 and instrumentation under Grant DMR-9974476. The authors also gratefully acknowledge support from the U.S. Department of Energy, Office of High Energy Physics under Grant DE-SC0009960. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Los Alamos National Laboratory (Contract 89233218CNA000001) and Sandia National Laboratories (Contract DE-NA-0003525). The authors thank Mr. Thomas Cayia (Arcelor Mittal) for providing the interstitial-free steel material used for this study.</p>
Molecular dynamics simulation of SpoIVFB:Pro-SigmaK complex ("pore water" added over membrane re-entrant loop)
<p>Simulation originally starting with "pore water" above the membrane re-entrant loop.</p> <p>Found here are all files needed to reproduce or visualize the results of molecular dynamics simulation of the SpoIVFB intramembrane protease bound to the transcription factor Pro-sigmaK. The protein complex was embedded in a POPE_POPG_DAG_CL bilayer using CHARMM-GUI, and the "generate pore water" feature was used to initially fill the area above the membrane re-entrant loop with water (as opposed to lipids initially being placed in this vicinity). The system was equilibrated and and simulated using OpenMM. The README file is a C-shell script that will run equilibration and 250ns of unrestrained simulation. </p> <p><br>Individual output (.out) and trajectory (.dcd) files are provided for each checkpoint of the simulation. A combined trajectory containing 250 ns of unrestrained simulation is also provided (combined_250ns_traj.dcd). Together with the step5_input.psf file, this combined dcd file can be used with common software such as VMD to visualize the molecular dynamics trajectory.</p>
Dataset for Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories
<p>Dataset utilized for our tested data in our accepted paper "Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories"</p>
Dataset: ADS-TEC Energy PLC (ADSEW) 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: ADS-TEC Energy PLC (ADSE) 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: ADS-TEC Energy PLC (ADSE) 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: ADS-TEC Energy PLC (ADSEW) 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: Integral Ad Science Holding Corp. (IAS) 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.
Figure 5 in TiO nanoparticles and salinity stress in relation to artemisinin production and ADS and DBR2 expression in Artemisia absinthium L.
Figure 5. The effect of salinity stress and titanium dioxide nanoparticles on the amount of artemisinin in wormwood (the non–identical letters indicate significant difference based on Duncan test P≤ 0.05).
Figure 3 in TiO nanoparticles and salinity stress in relation to artemisinin production and ADS and DBR2 expression in Artemisia absinthium L.
Figure 3. The effect of salinity and titanium dioxide nanoparticles on ADS gene expression in wormwood (non–identical letters indicate significant difference based on Duncan test P≤ 0.05).
Figure 2 in TiO nanoparticles and salinity stress in relation to artemisinin production and ADS and DBR2 expression in Artemisia absinthium L.
Figure 2. (A) Gel electrophoresis of RNA extracted from the leaf; (B) Gel electrophoresis of Standard RT–PCR product of 16s rRNA. M: 100bp DNA size marker. Numbered wells are samples. Gel agarose 1%.
Fig. 9 in Ad-hoc devised phototraps unravel Cerambyx miles diel activity in the wild (Coleoptera: Cerambycidae)
Fig. 9 – Diel activity (%) of Cerambyx miles in the wild during two consecutive years as assessed with phototrapping: frequency of captures in 2020 with PT1 traps (upper panel), frequency of sightings in 2021 with PT2 traps (central panel), and pooled data (bottom panel). The frequency distributions on an hourly basis show the strong prevalence of the diurnal behaviour exhibited by C. miles adults (Local time, GMT+2). Field trials were conducted both years during June-July at Montánchez mountain range, Cáceres (SW Spain). See text for additional details.
Figs 3-8 in Ad-hoc devised phototraps unravel Cerambyx miles diel activity in the wild (Coleoptera: Cerambycidae)
Figs 3-8 – Some selected images (unretouched, original frames) showing phototrapped adults of Cerambyx miles; 3, a C. miles male prowling the container of a PT1 phototrap at 19.05 h. Two marked C. welensii adults are visible within the trap. Montánchez, Cáceres, 7 July 2020; 4, a C. miles male lured by a PT1 phototrap at 19.15 h before being trapped. Montánchez, Cáceres, 8 July 2020; 5, a mated pair of C. miles prowling a PT1 phototrap at 18.25 h. Two marked C. cerdo adults are visible within the trap. Note the removable cardboard panel to protect the container from direct sunlight. Montánchez, Cáceres, 8 July 2020; 6, a C. miles female feeding in the drinker of a PT2 phototrap at 14.21 h. Montánchez, Cáceres, 29 June 2021; 7, a mated pair of C. miles (while female feeds) in the platform of a PT2 phototrap at 17.56 h. Cancho Blanco, Zarza de Montánchez, Cáceres, 1 July 2021; and 8, a C. miles male feeding in the drinker of a PT2 phototrap at 14.14 h. Montánchez, Cáceres, 29 July 2021 (Local time, GMT+2).
Fig. 10 in Ad-hoc devised phototraps unravel Cerambyx miles diel activity in the wild (Coleoptera: Cerambycidae)
Fig. 10 – Pie charts depicting the diel activity (%) of Cerambyx miles depending on the data source, either field experimental data from this study (left) or literature data (right). Captures/sightings from this study (n = 139) and literature data (n = 103 records from 79 references, Table S1) were scored into three diel activity classes as either diurnal (D), crepuscular (C) or nocturnal (N). The two frequency distributions were significantly different (see text) suggesting an underlying bias in how entomologists have perceived or described the diel activity of C. miles adults.
Radiative-transfer dataset for "Distilling machine learning's added value: Pareto fronts in atmospheric applications"
<p>This dataset goes with the journal paper "Distilling machine learning's added value: Pareto fronts in atmospheric applications" by T. Beucler, A. Grundner, S. Shamekh, P. Ukkonen, M. Chantry, and R. Lagerquist.</p> <p>Subdirectory "training" contains unnormalized (in physical units) training data. Subdirectories "validation" and "testing" contain unnormalized validation and testing data. Subdirectory "training/for_pareto_paper_2024/simple" contains training data from the simple (clear-sky) dataset discussed in the paper; subdirectory "training/for_pareto_paper_2024/complex" contains training data from the complex (multi-cloud) dataset discussed in the paper. Subdirectories "validation/for_pareto_paper_2024/simple" and "validation/for_pareto_paper_2024/complex" are analogous but for the validation data; subdirectories "testing/for_pareto_paper_2024/simple" and "testing/for_pareto_paper_2024/complex" are analogous but for the testing data.</p> <p>Subdirectories beginning with "normalized_predictors" -- "normalized_predictors/training", "normalized_predictors/validation", "normalized_predictors/testing", "normalized_predictors/training/for_pareto_paper_2024/simple", "normalized_predictors/training/for_pareto_paper_2024/complex", etc. -- are analogous to the above but containing normalized predictors (in z-scores rather than physical units).</p> <p>Every file -- after unzipping, so that the extension is ".nc" rather than ".nc.gz" -- can be read by `example_io.read_file` in the ml4rt library (https://github.com/thunderhoser/ml4rt).</p>
Figure 6. (a1), (a2), (a3), (a4), (a5), (a6), (a7) and (a8) watermarked image is degraded respectively through JPEG2000 compression, JPEG compression, median filtering, adding Salt&Pepper noise, rotating, center cropping, surrounding cropping and scaling. (b1), (b2), (b3), (b4), (b5), (b6), (b7) and (b8) The corresponding extracted watermarks.-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>This paper has described a scheme for digital watermarking of still images based on discrete<br> wavelet transform. In the proposed method, the embedded logo watermark can be extracted without<br> access to the original image. It has been confirmed that the proposed watermarking method is able<br> to extract the embedded logo watermark from the watermarked images that have degraded through<br> compression, filtering, cropping and scaling. Although this algorithm is not robust against rotation,<br> it can completely extract the watermark from watermarked images that lose about 35% of their<br> areas by cropping attack.</p>
Figure 7. Adding an Acting Module, its configuration values and its input connections-Designing a Growing Functional Modules "Artificial Brain"
<p>The fourth step consists of adding an Acting Module, its configuration values and input<br> connection as shown in figure 7. A type “CI” is assigned because it functionality will consist of<br> triggering a steering command in accordance with the perception from the Sensing Module and in<br> order to satisfy the input request from the Global Goal. Consequently, the feedback is set to “1 18”<br> where “1” is the reference to the Sensation “free” and “18” to the perception in output of the<br> Sensing Module. The identifier “18” for this perception is computed as at the total number of<br> Sensation plus one (first sensing module). Identifiers and their references are automatically updated<br> when a Sensation is added or deleted.</p>
Figure 6. Adding a Global Goal with its required type-Designing a Growing Functional Modules "Artificial Brain"
<p>The next step consists of adding a Global Goal expressing a motivation required by the<br> controller. The goal is to keep the vehicle's front free of obstacles, thus the Sensation “free” should<br> stay equal to “1”. After adding a new Global Goal, its assigned type should be “Cst” corresponding<br> to a constant output request (see figure 6). In the parameter field, its specified value is “1”.</p>
Figure 4. Adding a Sensing Module and selecting its type-Designing a Growing Functional Modules "Artificial Brain"
<p>GFM controllers learn to satisfy some predefined goals<br> while interacting with the environment and thus should be considered as artificial brains. An<br> example of the design process of a simple controller is provided herein to explain the inherent<br> methodology, to exhibit the components' interconnections and to demonstrate the control process.</p>
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.