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Computational Investigation of Co-Aggregation and Cross-Seeding between Aβ and hIAPP Underpinning the Crosstalk in Alzheimer's Disease and Type-2 Diabetes
<p><span>The coexistence of Amyloid-β (Aβ) and human Islet Amyloid Polypeptide (hIAPP) in the brain and pancreas is associated with an increased risk of Alzheimer’s disease (AD) and type-2 diabetes (T2D) due to their co-aggregation and cross-seeding. Despite this, the molecular mechanisms underlying their interaction remain elusive. Here, we systematically investigated the cross-talk between Aβ and hIAPP using atomistic discrete molecular dynamics (DMD) simulations. Our results revealed that the amyloidogenic core regions of both Aβ (Aβ<sub>10–21</sub> and Aβ<sub>30–41</sub>) and hIAPP (hIAPP<sub>8-20</sub> and hIAPP<sub>22-29</sub>), driving their self-aggregation, also exhibited a strong tendency for cross-interaction. This propensity led to the formation of β-sheet-rich hetero-complexes, including potentially toxic β-barrel oligomers. The formation of Aβ and hIAPP hetero-aggregates did not impede the recruitment of additional peptides to grow into larger aggregates. Our cross-seeding simulations demonstrated that both Aβ and hIAPP fibrils could<a name="_Hlk163119646"></a> mutually act as seeds, assisting each other's monomers in converting into β-sheets at the exposed fibril elongation ends. The amyloidogenic core regions of Aβ and hIAPP, in both oligomeric and fibrillar states, exhibited the ability to recruit isolated peptides, thereby extending the β-sheet edges, with limited sensitivity to the amino acid sequence. These findings suggest that targeting these regions by capping them with amyloid-resistant peptide drugs may hold potential as a therapeutic approach for addressing AD, T2D, and their co-pathologies.</span></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>
Multienergy Fan Beam Computed Tomography Dataset of a Bird Chest Imaged with 3 Different X-ray Spectra
<p><strong>Summary</strong></p> <p>This dataset is a collection of X-ray projection data of a biological imaging phantom (a bird chest) imaged in an X-ray microtomography scanner, using three different X-ray spectra. The dataset also includes a metadata file for each of the scans, specifying the scan geometry and other important scan parameters, as well as photographs and example reconstructions. The dataset is designed for use in algorithm development for multienergy computed tomography.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample Information</em></p> <p>The sample is the chest of a common quail (<em>Coturnix coturnix</em>) bird obtained frozen from a local supermarket. The chest section of the frozen bird was removed using a handsaw, and left to melt and settle in a sample holder before imaging.</p> <p><em>Scanner</em></p> <p>The measurement data were acquired using an X-ray microtomography scanner in the University of Helsinki Micro-CT Laboratory. The scanner uses cone beam geometry and it is equipped with an end-window tube with a tungsten target.</p> <p><em>Scan Settings</em></p> <p>The dataset consists of three consecutive scans made using identical geometry but different X-ray spectra and detector exposure times. For each scan, 720 X-ray projections were acquired using an angle increment of 0.5 degrees. Multiple frames were averaged for each projection in order to increase signal-to-noise ratio. The scan geometry and the energy-specific settings are summarized in the following two tables.</p> <p><strong>Table 1.</strong> Imaging geometry used for collecting the data.</p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Value</strong></td> </tr> <tr> <td>Focus-center distance</td> <td>252 mm</td> </tr> <tr> <td>Focus-detector distance</td> <td>420 mm</td> </tr> <tr> <td>Geometric magnification</td> <td>5/2</td> </tr> <tr> <td>Detector pixel size</td> <td>0.200 mm</td> </tr> <tr> <td>Effective pixel size</td> <td>0.120 mm</td> </tr> <tr> <td>Projection size</td> <td>552 x 576 pixels</td> </tr> <tr> <td>Angular range</td> <td>360'</td> </tr> <tr> <td>#projections</td> <td>720</td> </tr> </tbody> </table> <p><strong>Table 2.</strong> Energy-specific settings used for collecting the data.</p> <table> <tbody> <tr> <td>Energy label</td> <td><em>U</em> (kV)</td> <td>Filtration</td> <td><em>I</em> (μA)</td> <td>Exposure time (ms)</td> <td>Frame averaging</td> </tr> <tr> <td><em>E1</em></td> <td>50</td> <td>None</td> <td>300</td> <td>125</td> <td>4</td> </tr> <tr> <td><em>E2</em></td> <td>80</td> <td>1 mm Al</td> <td>180</td> <td>125</td> <td>4</td> </tr> <tr> <td><em>E3</em></td> <td>120</td> <td>0.5 mm Cu</td> <td>120</td> <td>250</td> <td>4</td> </tr> </tbody> </table> <p><em>Data Post-Processing</em></p> <p>Before the scans were made, a dark current image and flat-field image were acquired for each scan setting. During the scans, dark current subtraction and flat-field correction were automatically applied to the X-ray projections by the measurement software.</p> <p><em>Data Contents</em></p> <p>This dataset contains the following files:</p> <ul> <li>The raw projection data (.tif format) for each scan and a metadata file (.txt format) describing the measurement setup, with formatting that is both human-readable and machine-readable.</li> <li>Pre-created 2D sinograms for each energy level. The sinograms have been created from the central plane of the cone beam, which reduces to fan beam geometry. The sinograms are stored in Matlab's .mat file format in data structures which also contain metadata on the measurement.</li> <li>Photographs taken during the measurement process.</li> <li>Example filtered backprojection (FBP) reconstructions of the central plane of the phantom for each energy. The reconstructions were computed using the Phoenix datos|x CT software provided with the microtomography scanner</li> </ul> <p> </p> <p><strong>Research Group</strong></p> <p>This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland (<a href="https://www.helsinki.fi/en/researchgroups/inverse-problems">https://www.helsinki.fi/en/researchgroups/inverse-problems</a>) in collaboration with the Computational Physics and Inverse Problems research group at the University of Eastern Finland, Finland (<a href="https://sites.uef.fi/inverse">https://sites.uef.fi/inverse</a>) and the X-ray Laboratory at the Department of Physics at the University of Helsinki, Finland (<a href="https://www.helsinki.fi/en/researchgroups/x-ray-laboratory">https://www.helsinki.fi/en/researchgroups/x-ray-laboratory</a>).</p> <p> </p> <p><strong>Previous Use</strong></p> <p>This dataset has been used in the following publications:</p> <p>Jussi Toivanen, Alexander Meaney, Samuli Siltanen, Ville Kolehmainen. Joint reconstruction in low dose multi-energy CT. <em>Inverse Problems and Imaging</em>, 2020, 14(4): 607-629. doi: <a href="https://doi.org/10.3934/ipi.2020028" target="_blank" rel="noopener">10.3934/ipi.2020028</a>.</p> <p>E. Cueva, A. Meaney, S. Siltanen, M. J. Ehrhardt. Synergistic multi-spectral CT reconstruction with directional total variation. <em>Philos Trans A Math Phys Eng Sci</em>. 2021 Aug 23;379(2204):20200198. doi: <a href="https://doi.org/10.1098/rsta.2020.0198">10.1098/rsta.2020.0198</a>.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected by the Inverse Problems research group, and available at <a href="https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox">https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox</a>.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We wish to thank laboratory engineer Heikki Suhonen for his guidance and assistance in conducting the measurements.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.</p>
Cone-Beam Computed Tomography Dataset of a Walnut Imaged at 4 Different Dose Levels
<p><strong>Summary</strong></p> <p>This dataset is a collection of X-ray projection images of a walnut imaged in a cone-beam computed tomography (CBCT) scanner, using four different dose levels. The dataset also includes a metadata file for each of the scans, specifying the scan geometry and other important scan parameters.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample Information</em></p> <p>The sample is a walnut in its shell. For the scanning process double-sided tape was used to attach the sample to a plastic tube placed into the rotation stage.</p> <p><em>Scanner</em></p> <p>The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22).</p> <p><em>Scan Settings</em></p> <p>The dataset consists of four different scans of the same sample. For each scan 360 X-ray projections were acquired using an angle increment of 1 degrees, with one additional frame taken at the end to estimate sample movement. The X-ray source was set at 40 kV with a 0.5 mm aluminum filter. For the different scans, the relative doses, tube currents, and exposure times were:</p> <ul> <li>100 % relative dose: tube current 1 mA, exposure time 2000 ms,</li> <li>50 % relative dose: tube current 1 mA, exposure time 1000 ms,</li> <li>25 % relative dose: tube current 0.5 mA, exposure time 1000 ms,</li> <li>10 % relative dose: tube current 0.2 mA, exposure time 1000 ms.</li> </ul> <p><em>Data Post-Processing</em></p> <p>Before the scans, two correction images were acquired for each scan setting. A dark current image was created by averaging 255 images taken with the X-ray source off. A flat-field image was created by averaging 255 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3.</p> <p><em>Data Format</em></p> <p>The X-ray projections are stored in .tif format. The metadata are contained in .txt files with formatting that is both human-readable and machine-readable.</p> <p><em>Notes</em></p> <p>Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 4 pixels, using circular boundary conditions, before performing any other operations on the projections. It was also observed that the scans are not entirely aligned, with a small angular discrepancy between each reconstruction.</p> <p> </p> <p><strong>Research Group</strong></p> <p>This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: <a href="https://www.helsinki.fi/en/researchgroups/inverse-problems">https://www.helsinki.fi/en/researchgroups/inverse-problems</a>.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at <a href="https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox">https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox</a>.</p> <p>Please note that this is a an entirely separate dataset from the Walnut datasets accessible at <a href="../record/1254206">https://zenodo.org/record/1254206</a> and <a href="https://doi.org/10.5281/zenodo.6986012">https://doi.org/10.5281/zenodo.6986012</a>, although both datasets have been created by the same research group.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.</p>
Translaminar Fracture in a Mini-Protruded Compact Tension Specimen: A Dataset of Micro-Scale Tomograms of a Thin-Ply Carbon Fibre-Epoxy Composite acquired via Synchrotron Radiation Computed Tomography During In-Situ Loading
<p>In this study, we developed a scaled-down “mini-protruded compact tension specimen” to facilitate in-situ tensile testing coupled with synchrotron radiation computed tomography (SRCT). This innovative design provides valuable insights into in-situ translaminar damage mechanisms, significantly enhancing the accuracy of data used in finite element models.</p> <p>The specimen is made of HS40 carbon fibres and ThinPreg<sup>TM </sup>736LT epoxy resin, with the layup of [90<sub>2</sub>/0/90<sub>2</sub>/0/90<sub>2</sub>/0/90<sub>2</sub>]. The translaminar fracture experiments were conducted under continuous loading and scanning using ultra-fast SRCT at the Swiss Light Source (SLS) TOMCAT beamline (Paul Scherrer Institut in Villigen, Switzerland). A polychromatic beam with an energy of 24 keV was used. The achieved voxel size was 800 <em>nm</em>, and 1000 projections per scan and 2 <em>ms</em> exposure time were acquired per scan. The GigaFRoST camera served as the detector. The scans were reconstructed into 3D volumes using the SLS’s in-house absorption-based algorithm (Gridrec) for critical loading steps during a test—both before and after a load drop (detailed in the accompanying Excel file). The tensile loading was exerted on the specimen at a rate of 0.2 <em>mm/min</em> until failure during scanning with the Deben CT500.</p>
Data from: A cerebellar granule cell–climbing fiber computation to learn to track long time intervals
<p>In classical cerebellar learning, Purkinje cells (PkCs) associate climbing fiber (CF) error signals with predictive granule cells (GrCs) active just prior (~150ms). Cerebellum also contributes to behaviors characterized by longer timescales. To investigate how GrC-CF-PkC circuits might learn seconds-long predictions, we imaged simultaneous GrC-CF activity over days of forelimb operant conditioning for delayed water reward. As mice learned reward timing, numerous GrCs developed anticipatory activity ramping at different rates until reward delivery, followed by widespread time-locked CF spiking. Relearning longer delays further lengthened GrC activations. We computed CF-dependent GrC→PkC plasticity rules, demonstrating that reward-evoked CF spikes sufficed to grade many GrC synapses by anticipatory timing. We predicted and confirmed that PkCs could thereby continuously ramp across seconds-long intervals from movement to reward. Learning thus leads to new GrC temporal bases linking predictors to remote CF reward signals—a strategy well-suited to learn to track long intervals common in cognitive domains.</p>
Figure 4 in The pros and cons of using micro-computed tomography in gross and micro-anatomical assessments of polychaetous annelids
Figure 4. Pharyngeal anatomy of Syllidae: Syllis gracilis (a-c, PTA stained) and Hediste diversicolor (d-h,). Syllis a) section through body showing the proventriculus; b) surface morphology, lines c where transverse section c image taken, line d where transverse section d image taken; c) TS showing pharyngeal tube; d) TS showing proventricle. Scale bars = 0.5 mm. Hediste e) surface morphology; f) section through pharynx, lines g and h where transverse section images taken; g) TS through anterior pharynx at level of jaws; h) TS through distal pharynx. TS through pharynx indicates that the pharynx is not symmetrical, particularly in the distal part. Scale bars e, f = 5.00 mm, g,h = 1.00 mm. Images 1a–d were produced using the SkyScan 1172 microtomograph at HCMR at 60kV / 167µA, without a filter, no camera binning, full rotation of 360°, tungsten target. Images i-h were produced using the Nikon metrology HMX ST 225 at the NHM (60 KV, 2 sec exposure, molybdenum target). Abbreviations used:; DLM–dorsal longitudinal muscles; J–jaws; M–mouth; P–pharynx; Pr–prostomium; PO–proventricle; PS–proboscidian sheath; VLM–ventral longitudinal muscles.
Figure 3 in The pros and cons of using micro-computed tomography in gross and micro-anatomical assessments of polychaetous annelids
Figure 3 Pharyngeal anatomy of Hesionidae: Hesiospina similis (a-c, PTA staining); Phyllodocidae: Phyllodoce lineata (d-f, PTA staining) and Nephtyidae Nephtys hombergi (g, Iron stain, h-i, unstained). Hesiospina a) surface morphology showing everted pharynx; b) section through everted pharynx; c) TS showing distal pharynx as indicated by line in b. Phyllodoce d) surface morphology showing everted pharynx; e) section through pharynx; f) TS showing distal pharynx as indicated by line in e. Scale bars = 0.5 mm. Nephtys images from three different individuals g) surface morphology showing everted pharynx; h) section showing pharynx but not everted; i) TS of distal pharynx indicated by line in h. Scale bar = 1.00 mm Hesiospina and Phyllodoce have long thin pharynges while Nepthys has a medium lengthed phaynx. Only Nepthys shows the cruciform muscle arrangement in the distal pharynx, in the others the muscles do not appear to form these discrete blocks. Images 3a–f were produced using the SkyScan 1172 microtomograph at HCMR at 60kV / 167µA, without a filter, no camera binning, full rotation of 360°, tungsten target. Images 3g–i were produced using the Nikon metrology HMX ST 225 at the NHM (60 KV, 2 sec exposure, molybdenum target.) Abbreviations used: OPM–outer pharyngeal muscles; P–pharynx; Pr–prostomium; PS–proboscidian sheath; T–teeth; VLM–ventral longitudinal muscle.
Figure 1 in The pros and cons of using micro-computed tomography in gross and micro-anatomical assessments of polychaetous annelids
Figure 1. Transverse sections of Hediste diversicolor after treatment with reversible stains or drying agents. a) Silver stain, the gut and main muscle blocks can be seen but also showing paper material used to stabilise the specimen surrounding the central image (molybdenum target, 131 KV, 354 millisec exposure; b) iron stain, again gut and main muscles can be seen but also ventral blood vessels linking the central ventral blood vessel to the network surrounding the gut (molybdenum target, 131 KV, 500 millisec exposure); c) Iodine shows similar anatomical features as Iron stained material (molybdenum target, 130 KV, 320 millisec exposure); d) Hexamethyldisilizane (HDMS) image shows more clearly the internal anatomy including the ventral blood vessels (molybdenum target, 110 KV, 300 millisec exposure). Scale bar = 1.00 mm. Specimens were scanned using the Nikon metrology HMX ST 225 at the NHM. Abbreviations: Ac–internal paradpodial acicula; DLM–dorsal longitudinal muscle; G–gut; Plc-V–Plexus lateral connective blood vessels; VB–ventral blood vessel; VLM–ventral longitudinal muscles
Figure 2 in The pros and cons of using micro-computed tomography in gross and micro-anatomical assessments of polychaetous annelids
Figure 2. Pharyngeal anatomy of Glyceridae: Glycera tesselata (PTA-staining) (a–c); Pilargidae: Sigambra parva (d–f) and Polynoidae: Lepidonotus clava (g-i). Glycera a) surface morphology showing everted pharynx; b) longitudinal section through everted pharynx; c) transverse section of gut as indicated by line in b); scale bars = 0.5 mm. Sigambra d) surface morphology showing everted morphology; e) longitudinal section through the pharynx; f) transverse section through distal pharynx as indicated by the line in e); scale bars = 0.5 mm. Lepidonotus g) surface morphology showing everted pharynx; h) longitudinal section through pharynx; i) transverse section through distal pharynx as indicated by line in h); scale bar = 1.00 mm. P = pharynx. All three examples show a relatively short axial pharynx approximately as wide as long. The distal part of the pharynx is characterised by distinct muscle blocks which when contracted form a cruciform cross section. Specimens were scanned using the SkyScan 1172 microtomograph at HCMR at 60kV / 167µA, without a filter, no camera binning, full rotation of 360°, tungsten target. Abbreviations used: J–jaws; P–pharynx; PG–poison glands; Pr–prostomium; PS–proboscidian sheath; RM–ring muscle.
Dataset: Themes Cloud Computing ETF (CLOD) 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: Global X Cloud Computing ETF Global X Cloud Computing ETF (CLOU) 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: Zapata Computing Holdings Inc. (ZPTA) 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: Zapata Computing Holdings Inc. (ZPTAW) 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: WisdomTree Cloud Computing Fund (WCLD) 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: Super Micro Computer, Inc. (SMCI) 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: ProShares Ultra Cloud Computing (SKYU) 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: First Trust Cloud Computing ETF (SKYY) 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: Rigetti Computing, Inc. (RGTIW) 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: Rigetti Computing, Inc. (RGTI) 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.
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.