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Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples (Artifact Dataset)
<p>Dataset for Peer-Reviewed Artifact of 'Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples' (NDSS 2025) </p>
Real-time emulation of leafy vegetables yield and quality response to 2℃ warming in northeast China
<p>Datasets necessary to reproduce main results in the paper.</p>
Figure 3 from: Dorri K, Modaresi F, Shakibaie MR, Moazamian E (2022) Effect of gold nanoparticles on the expression of efflux pump mexA and mexB genes of Pseudomonas aeruginosa strains by Quantitative real-time PCR. Pharmacia 69(1): 125-133. https://doi.org/10.3897/pharmacia.69.e77608
Figure 3 Susceptibility of Pseudomonas aeruginosa isolates to different antibiotics.
Figure 2 from: Dorri K, Modaresi F, Shakibaie MR, Moazamian E (2022) Effect of gold nanoparticles on the expression of efflux pump mexA and mexB genes of Pseudomonas aeruginosa strains by Quantitative real-time PCR. Pharmacia 69(1): 125-133. https://doi.org/10.3897/pharmacia.69.e77608
Figure 2 Distribution of clinical isolates of Pseudomonas aeruginosa in the studied hospitals.
Figure 19 from: Dorri K, Modaresi F, Shakibaie MR, Moazamian E (2022) Effect of gold nanoparticles on the expression of efflux pump mexA and mexB genes of Pseudomonas aeruginosa strains by Quantitative real-time PCR. Pharmacia 69(1): 125-133. https://doi.org/10.3897/pharmacia.69.e77608
Figure 19 The characteristics of the gold nanoparticles used.
Figure 13 from: Dorri K, Modaresi F, Shakibaie MR, Moazamian E (2022) Effect of gold nanoparticles on the expression of efflux pump mexA and mexB genes of Pseudomonas aeruginosa strains by Quantitative real-time PCR. Pharmacia 69(1): 125-133. https://doi.org/10.3897/pharmacia.69.e77608
Figure 13 Results of MexA gene amplification curve in Real Time PCR by cycle.
Figure 1 from: Dorri K, Modaresi F, Shakibaie MR, Moazamian E (2022) Effect of gold nanoparticles on the expression of efflux pump mexA and mexB genes of Pseudomonas aeruginosa strains by Quantitative real-time PCR. Pharmacia 69(1): 125-133. https://doi.org/10.3897/pharmacia.69.e77608
Figure 1 Frequency of clinical samples.
GRACED2021-Near-real-time global gridded daily CO2 emissions 2021
<p><strong>Description:</strong></p> <p>GRACED2021-Near-real-time global gridded daily CO<sub>2</sub> emissions 2021</p> <p><strong>The website: https://carbonmonitor-graced.com</strong></p> <ul> <li>In an era of rapid upheavals, global near-real-time and fine-grid carbon dioxide (CO<sub>2</sub>) emissions data are essential to monitoring the distribution of emissions in space and time and achieving carbon neutrality worldwide. To address this need, we presented the near-real-time Global Gridded Daily CO<sub>2</sub> Emissions Dataset (GRACED) throughout 2021.</li> <li>GRACED provides gridded CO<sub>2</sub> emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production, and from fossil fuel combustion over seven sectors (power; industry; residential consumption; ground transportation; international aviation; international shipping; domestic aviation).</li> <li>GRACED data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights. The availability of these data does not constitute publication of the data. The data providers rely on the ethics and integrity of the user to ensure that they receive fair credit for their work. If the data are obtained for potential use in a publication or presentation, we kindly ask you to inform us at the outset of the nature of this work. If the GRACED data are essential to the work, or if an important result or conclusion depends on the GRACED data, co-authorship may be appropriate. This should be discussed at an early stage in the work. Manuscripts using the GRACED data should be sent for review before they are submitted for publication so we can ensure that the quality and limitations of the data are accurately represented. </li> <li>Because GRACED2021 has a large amount of data and the Zenodo platform has a limit on the total amount of uploaded data, we recommend that data users download GRACED2021 data for free through the cloud disk link- <em><strong>https://cloud.tsinghua.edu.cn/d/24d9ed6e225d455a834e/.</strong></em></li> <li>For more detailed display and introduction of data, as well as download of more GRACED data, please refer to our official website of GRACED - <strong>https://carbonmonitor-graced.com</strong>. </li> <li>Please check the README.txt file for more details.</li> </ul>
Using a real-world network to model the trade-off between stay-at-home restriction, vaccination, social distancing and working hours on COVID-19 dynamics
<p>These datasets have been obtained through thousands of well-carried simulations by using the agent-based model based on a time-dynamic graph with stochastic transmission events. </p> <p>These simulations have been conducted on MATLAB 2019a.</p>
Context shapes emotion perception and prosocial behavior to real-life laughter and crying vocalizations - Raw data
<p>This repository contains raw data for the manuscript (under review): <i>Context shapes emotion perception and prosocial behavior to real-life laughter and crying vocalizations regardless of their diverse perceptual properties. </i></p><p>Please contact Doron Atias (doron.atias@mail.huji.ac.il) if you have questions concerning the data provided in this repository.</p>
Context shapes emotion perception and prosocial behavior to real-life laughter and crying vocalizations - Code
<p>This R Markdown document includes the code for data presentation in Atias & Aviezer (2024): "Context shapes emotion perception and prosocial behavior to real-life laughter and crying vocalizations regardless of their diverse perceptual properties". Please contact Doron Atias (doron.atias@mail.huji.ac.il) if you have questions.</p>
Spike-in and real-world proteomics data sets used in publication of PRONE
<p>Spike-in and real-world data sets used in the evaluation study by Arend et al. (see reference), and some utilized in the vignettes of PRONE, an R package designed for preprocessing, normalization, and performance evaluation of normalization methods of proteomics data. </p> <h3>Overview of the Data Sets</h3> <p>Due to the unavailability of proteomics quantification data from all original publications, data were extracted from alternative sources, which are also listed in the table below. Please refer to the paper's supplementary material and GitHub repository (https://github.com/lisiarend/PRONE.Evaluation) for more comprehensive information on the data sets. A processed metadata file and the protein quantification data file are provided for all data sets. The original quantification data used to generate these two files for each data set are consistently provided in the `original_data` directory of each data set.</p> <p> </p> <div> <table> <tbody> <tr> <td> <p>Data Set</p> </td> <td> <p>Type</p> </td> <td> <p>Quantification Type</p> </td> <td> <p>Raw Data (ID)</p> </td> <td> <p>Quantification Data</p> </td> </tr> <tr> <td> <p>dS1</p> </td> <td> <p>UPS1 spike-in </p> <p>(4 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Tabb et al. <a href="https://www.zotero.org/google-docs/?gkmf6j">[1]</a></p> </td> <td> <p>Välikangas et al. <a href="https://www.zotero.org/google-docs/?Tt81W8">[10]</a> </p> </td> </tr> <tr> <td> <p>dS2</p> </td> <td> <p>UPS1 spike-in </p> <p>(6 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Ramus et al. <a href="https://www.zotero.org/google-docs/?HP8b9X">[2]</a> (PXD001819)</p> </td> <td> <p>Graw et al. <a href="https://www.zotero.org/google-docs/?aiEVfL">[11]</a></p> </td> </tr> <tr> <td> <p>dS3</p> </td> <td> <p>E.coli spike-in </p> <p>(5 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Shen et al. <a href="https://www.zotero.org/google-docs/?zHIDZy">[3]</a></p> <p>(PXD003881)</p> </td> <td> <p>Sticker et al. <a href="https://www.zotero.org/google-docs/?aZ7oZu">[12]</a></p> </td> </tr> <tr> <td> <p>dS4</p> </td> <td> <p>E.coli spike-in </p> <p>(2 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Cox et al. <a href="https://www.zotero.org/google-docs/?scM19M">[4]</a></p> <p>(PXD00279)</p> </td> <td> </td> </tr> <tr> <td> <p>dS5</p> </td> <td> <p>E.coli spike-in </p> <p>(3 levels)</p> </td> <td> <p>TMT 10-plex (1)</p> </td> <td> <p>Zhu et al. <a href="https://www.zotero.org/google-docs/?qAz08E">[5]</a></p> <p>(PXD013277)</p> </td> <td> <p>Phil Wilmarth <a href="https://www.zotero.org/google-docs/?i7xmgP">[13]</a></p> </td> </tr> <tr> <td> <p>dS6</p> </td> <td> <p>yeast spike-in </p> <p>(3 levels)</p> </td> <td> <p>TMT 11-plex (1)</p> </td> <td> <p>O’Connell et al. <a href="https://www.zotero.org/google-docs/?C2XoaJ">[6]</a></p> <p>(PXD007683)</p> </td> <td> <p>Ammar et al. <a href="https://www.zotero.org/google-docs/?UWcyly">[14]</a></p> </td> </tr> <tr> <td> <p>dR1</p> </td> <td> <p>Osteogenic differentiation of hPCLSCs (4 time points)</p> </td> <td> <p>TMT 6-plex (3)</p> </td> <td> <p>Li et al. <a href="https://www.zotero.org/google-docs/?BouGSs">[8]</a> (PXD020908)</p> </td> <td> <p>MaxQuant executed in-house</p> </td> </tr> <tr> <td> <p>dR2</p> </td> <td> <p>Prospective Ovarian JHU Proteome</p> </td> <td> <p>TMT 10-plex (13)</p> </td> <td> <p>Hu et al. <a href="https://www.zotero.org/google-docs/?kVn4pf">[9]</a></p> <p>(PDC000110)</p> </td> <td> <p>MaxQuant executed in-house</p> </td> </tr> <tr> <td> <p>dR3</p> </td> <td> <p>AROM+ transgenic vs. wild-type mice</p> </td> <td> <p>LFQ</p> </td> <td> <p>Vehmas et al. <a href="https://www.zotero.org/google-docs/?qwYM2A">[7]</a></p> <p>(PXD002025)</p> </td> <td> </td> </tr> <tr> <td> <p>dR4</p> </td> <td> <p>Mycobacterium tuberculosis</p> <p>(healthy, disease vs. treated)</p> </td> <td> <p>TMT 10-plex (2)</p> </td> <td> <p>Schmidt et al. <a href="https://www.zotero.org/google-docs/?m1VHIG">[15]</a></p> <p>(PXD030883)</p> </td> <td> </td> </tr> </tbody> </table> </div> <h3>References</h3> <p>[1] D. L. Tabb et al., ‘Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography−Tandem Mass Spectrometry’, J. Proteome Res., vol. 9, no. 2, pp. 761–776, Feb. 2010, doi: 10.1021/pr9006365.<br>[2] C. Ramus et al., ‘Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods’, Data Brief, vol. 6, pp. 286–294, Mar. 2016, doi: 10.1016/j.dib.2015.11.063.<br>[3] X. Shen et al., ‘IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts’, Proc. Natl. Acad. Sci., vol. 115, no. 21, pp. E4767–E4776, May 2018, doi: 10.1073/pnas.1800541115.<br>[4] J. Cox, M. Y. Hein, C. A. Luber, I. Paron, N. Nagaraj, and M. Mann, ‘Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ *’, Mol. Cell. Proteomics, vol. 13, no. 9, pp. 2513–2526, Sep. 2014, doi: 10.1074/mcp.M113.031591.<br>[5] Y. Zhu et al., ‘DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis *’, Mol. Cell. Proteomics, vol. 19, no. 6, pp. 1047–1057, Jun. 2020, doi: 10.1074/mcp.TIR119.001646.<br>[6] J. D. O’Connell, J. A. Paulo, J. J. O’Brien, and S. P. Gygi, ‘Proteome-Wide Evaluation of Two Common Protein Quantification Methods’, J. Proteome Res., vol. 17, no. 5, pp. 1934–1942, May 2018, doi: 10.1021/acs.jproteome.8b00016.<br>[7] A. P. Vehmas et al., ‘Liver lipid metabolism is altered by increased circulating estrogen to androgen ratio in male mouse’, J. Proteomics, vol. 133, pp. 66–75, Feb. 2016, doi: 10.1016/j.jprot.2015.12.009.<br>[8] J. Li et al., ‘Dynamic proteomic profiling of human periodontal ligament stem cells during osteogenic differentiation’, Stem Cell Res. Ther., vol. 12, no. 1, p. 98, Feb. 2021, doi: 10.1186/s13287-020-02123-6.<br>[9] Y. Hu et al., ‘Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma’, Cell Rep., vol. 33, no. 3, p. 108276, Oct. 2020, doi: 10.1016/j.celrep.2020.108276.<br>[10] T. Välikangas, T. Suomi, and L. L. Elo, ‘A systematic evaluation of normalization methods in quantitative label-free proteomics’, Brief. Bioinform., vol. 19, no. 1, pp. 1–11, Jan. 2018, doi: 10.1093/bib/bbw095.<br>[11] S. Graw et al., ‘proteiNorm – A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification’, ACS Omega, vol. 5, no. 40, pp. 25625–25633, Oct. 2020, doi: 10.1021/acsomega.0c02564.<br>[12] A. Sticker, L. Goeminne, L. Martens, and L. Clement, ‘Robust Summarization and Inference in Proteome-wide Label-free Quantification’, Mol. Cell. Proteomics, vol. 19, no. 7, pp. 1209–1219, Jul. 2020, doi: 10.1074/mcp.RA119.001624.<br>[13] ‘understanding_IRS’. Accessed: Mar. 07, 2024. [Online]. Available: https://pwilmart.github.io/IRS_normalization/understanding_IRS.html<br>[14] C. Ammar, M. Gruber, G. Csaba, and R. Zimmer, ‘MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins[S]’, Mol. Cell. Proteomics, vol. 18, no. 9, pp. 1880–1892, Sep. 2019, doi: 10.1074/mcp.RA119.001509.<br>[15] F. Biadglegne et al., ‘Mycobacterium tuberculosis Affects Protein and Lipid Content of Circulating Exosomes in Infected Patients Depending on Tuberculosis Disease State’, Biomedicines, vol. 10, no. 4, p. 783, Mar. 2022, doi: 10.3390/biomedicines10040783.</p>
TAU Moving Sound Events 2019 - Ambisonic, Reverberant, Real-life IR and Moving Source Dataset
<p><strong>Tampere University (TAU) Moving Sound Events 2019 - Ambisonic, Reverberant and Real-life Impulse Response and Moving Source Dataset</strong></p> <p>This dataset consists of real-life first order Ambisonic (FOA) format recordings with moving point sources each in 2D spherical space represented with azimuth and elevation angles. The dataset was generated by collecting impulse responses (IR) from a real environment using the Eigenmike spherical microphone array. The measurement was done by slowly moving a Genelec G Two loudspeaker continuously playing<br> a maximum length sequence around the array in circular trajectory in one elevation at a time. The playback volume was set to be 30 dB greater than the ambient sound level. The recording was done in a corridor inside the university with classrooms around it during work hours. The IRs were collected at elevations −40 to 40 with 10-degree increments at 1 m from the Eigenmike and at elevations −20 to 20 with 10-degree increments at 2 m. </p> <p>The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. All sound events in this dataset are moving only along azimuth with a constant angular velocity in the range [-90, 90]/s with 10-degree/s steps. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), starting spatial location in azimuth and elevation angles (in degrees), the angular velocity of motion and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the urbansound8k dataset. This dataset consists of 10 sound event classes such as air_conditioner, car_horn, children_playing, dog_bark, drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music. We do not consider air_conditioner and children_playing sound events. Further, we only include the sound event examples marked as foreground in the dataset. We used the splits 1, 8 and 9 provided in the urbansound8k as the three CV splits. These splits were chosen as they had a good number of examples for all the chosen sound event classes after selecting only the foreground examples. During the sound scene synthesis, every sound event is assigned a spatial trajectory on an arc with a constant distance from the microphone and moving with a constant angular velocity for its duration.</p> <p>Other than the license file, there are nine zip files that consist of the dataset and corresponding metadata for given split and overlap. For example, the ov3_split1.zip file consists of training and testing recordings and metadata for the case of a maximum of three temporally overlapping sound events (ov3) for the first cross-validation split (split1). Within each folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Localization, Detection and Tracking of Multiple Moving Sound Sources with Convolutional Recurrent Neural Networks'</a> work.</p> <p>Data collector (s): Fagerlund, Eemi; Koskimies, Aino; Hakala, Aapo</p>
Data and code for "Human neural dynamics of real-world and imagined navigation"
<ul> <li>Please download the zip file</li> <li>Unzip it on your computer</li> <li>Run the plot_FigureX.m scripts in Matlab</li> <li>The scripts generate the panels for the manuscript's figures</li> <li>Figure panels are saved in subfolders for your review</li> </ul>
GF6SRD: a real-world cross-sensor remote sensing super-resolution dataset
<p>We used PMS imagery and WFV imagery from Gaofen-6 satellite to construct the real-world cross-sensor remote sensing dataset, GF6SRD. This dataset is proposed in the paper "DESAT: A Distance Enhanced Strip Attention Transformer for Remote Sensing Super-Resolution"</p> <p>The code of this paper is also available at https://github.com/afomyj/DESAT</p> <p>If you find our work helpful, please cite:</p> <p>Mao Y, He G, Wang G, Yin R, Peng Y, Guan B. DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution. <em>Remote Sensing</em>. 2024; 16(22):4251. https://doi.org/10.3390/rs16224251</p> <p>The exposed portion of this resource is part of GF6SRD, containg 2000 WFV-PMS image pairs for training and 500 pairs for testing. The full GF6SRD dataset can be obtained under appropriate request to the coresponding author, He, Guojin. Email: hegj@aircas.ac.cn</p>
A real-time PCR assay to detect predation by spiny dogfish on Atlantic cod in the western North Atlantic Ocean.
Conventional observations show spiny dogfish (<i>Squalus acanthius</i> Linnaeus) rarely eat Atlantic cod (<i>Gadus morhua</i> Linnaeus; 0.02% of stomachs) in the northwestern Atlantic Ocean. Critics express concern that digestion may limit species-level prey identification, and with recovery from overfishing, dogfish populations may be suppressing cod by competition or predation. This study applied a real-time PCR TaqMan assay to identify cod in dogfish stomachs collected by cooperating fishing boats during normal trawling operations (May 2014 – May 2015; Gulf of Maine, Georges Bank). Conventional methods observed 51 different prey taxa and nearly 1600 individual prey items, but no cod were observed. Cod DNA was detected in 31 (10.5%) of the dogfish stomachs, with a higher percentage of these from the homogenate of amorphous, well-digested prey and stomach fluids (20 stomachs or 65%) than from discrete animal tissues (11 stomachs or 35%). Re-examination of photographs of these 11 tissue samples revealed one whole, partially-digested fish that could be recognized in hindsight as cod. Cod DNA was observed in dogfish stomachs year round: in January (1 of 1 trip), February (1 of 1), May (1 of 3), June (0 of 1), July (3 of 4), August (1 of 2), and October (3 of 3). Although these data suggest higher interaction rates between dogfish and cod than previously observed, addressing the population consequences of this predator-prey relationship requires a robust sampling design, estimates of digestion rates by dogfish to account for complete degradation of DNA sequences, and consideration for dogfish scavenging during fishing operations.
Replication package for Real Effects of Financial Distress: The Role of Heterogeneity
<p>The package contains synthetic data and codes to replicate the figures and tables in the paper and appendix. Please refer to the readme file for further details.</p>
Replication Package: "Simulation-based Test Case Generation for Unmanned Aerial Vehicles in the Neighborhood of Real Flights"
<p><strong>Structure:</strong></p> <p>For each of the experiments, conducted for our Research questions, we include the following:</p> <p>1. An spreadsheet file containing the aggregated experiment results</p> <ul> <li>first tab: aggregated data for all the 10 repetitions, and metrics reported in the paper</li> <li>raw logs for each of 10 repetitions in separate tabs</li> </ul> <p>2. Experiment summary for each of the 10 repetitions</p> <ul> <li>plots of the evaluated solutions at each iteration, and their fitness values</li> <li>the plot of the overall progress of the fitness values over iterations</li> <li>the raw log containing the details of computations for each iteration</li> </ul> <p>3. Experiment simulation logs (raw simulation outputs)</p> <ul> <li>'.ulg' flight logs of the solutions evaluated at each iteration ( 'n' parallel simulations at each iteration)</li> <li>only the repetition with the best final result is included. We exclude the other repetitions since the total space needed is too large (more than 200 GB).</li> </ul>
GRAND-SLAM analysis of simulated and real SLAM-seq datasets for grandRescue
<p>These are several processed datasets for the purpose of investigating 4sU-induced toxicity, mapping impairment and the potential of grandRescue to alleviate these effects.</p> <p>Published dataset: Narain dataset (Narain, et al.): <a href="https://doi.org/10.1016/j.molcel.2021.06.016">https://doi.org/10.1016/j.molcel.2021.06.016</a></p> <p> </p> <p>The zip files contain the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The *.tsv.gz files are the GRAND-SLAM output tables.</p> <p><br> To generate the GRAND-SLAM output yourself, first prepare the mouse & human genomes. Then run the following command with the respective cit-files, prefixes (*.cit) and genomes (e.g. h.ens90 or m.ens102):</p> <p>gedi -e Slam -trim5p 15 -reads *.cit -genomic m.ens102 -prefix grandslam_t15/* -plot -D -modelall</p> <p>To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.</p> <p>You can also start from scratch (i.e., the json file):</p> <p> Prepare the human/mouse genome and their rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j *.json rnaseq_mapping.sh report.sh grandslam.sh</p> <p>Software versions:</p> <p> gedi toolkit 1.0.5<br> GRAND-SLAM 2.0.7<br> STAR version 2.7.10b</p>
Accurate real space iterative reconstruction (RESIRE) algorithm for tomography
<p>This is the code and dataset for the paper: Accurate real space iterative reconstruction (RESIRE) algorithm for tomography. Three dataset are provided in this deposit:</p> <p>1. the simulation of vesicle model</p> <p>2. the biomineral data</p> <p>3. Amorphous Ta thinfilm data</p> <p>The algorithm are written in matlab and cuda code. Users can either use the cpu or gpu versions.</p> <p>1. The cpu code can only do tomography reconstructions with single tilt axis (say y-axis)</p> <p>2. The gpu code can do with multiple tilt axes, say ZYX<br> To use the gpu cude, you need to download and install CUDA toolkit from the NVIDA website https://developer.nvidia.com/cuda-downloads. Then, open Matlab and compile cuda functions, for example:<br> mexcuda -R2018a RT3_1GPU.cu<br> The mexw64 cuda files attached in this folder are precompiled for windows, cuda version = 11.x</p> <p>If you use the code or the data, please cite our paper:<br> Pham, M., Yuan, Y., Rana, A. et al. Accurate real space iterative reconstruction (RESIRE) algorithm for tomography. Sci Rep 13, 5624 (2023). https://doi.org/10.1038/s41598-023-31124-7</p> <p>If you have question, feel free to email us at minhrose@ucla.edu</p>
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.