Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,506
datasets available to search
ShareScore release 0.7.1
Dataset results
1,506 results for “objects”
ROCOv2: Radiology Objects in COntext Version 2, An Updated Multimodal Image Dataset
<p>Recent advances in deep learning techniques have enabled the development of systems for automatic analysis of medical images. These systems often require large amounts of training data with high quality labels, which is difficult and time consuming to generate.</p> <p>Here, we introduce Radiology Object in COntext Version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PubMed Open Access subset. Concepts for clinical modality, anatomy (X-ray), and directionality (X-ray) were manually curated and additionally evaluated by a radiologist. Unlike MIMIC-CXR, ROCOv2 includes seven different clinical modalities.</p> <p>It is an updated version of the ROCO dataset published in 2018, and includes 35,705 new images added to PubMed since 2018, as well as manually curated medical concepts for modality, body region (X-ray) and directionality (X-ray). The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical 2023. The participants had access to the training and validation sets after signing a user agreement.</p> <p>The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using the UMLS concepts provided with each image, e.g., to build systems to support structured medical reporting.</p> <p>Additional possible use cases for the ROCOv2 dataset include the pre-training of models for the medical domain, and the evaluation evaluation of deep learning models for multi-task learning.</p>
mouse GSE199308 scRNA data set objects
<p>scRNA data from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE199308 (Huang et al. 2023), see a detailed description of the study here: https://atlas.gs.washington.edu/mmca_v2/public/about.html</p> <p>Data were downloaded from https://atlas.gs.washington.edu/mmca_v2/public/download.html to create an AnnData (h5ad) file with meta data to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Huang et al. 2023.</p>
Figures datasets for "Wave momentum shaping for moving objects in heterogeneous and dynamic media"
<p>Source data for Figures used in the manuscript "Wave momentum shaping for moving objects in heterogeneous and dynamic media".</p>
Datasets of synthetic workflows for evaluating a multi-objective and multi-constrained scheduling approach for cyber-physical applications
<p>These datasets of synthetic workflows (task graphs) were generated to evaluate the performance and scalability of a multi-objective and multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) based on the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors with different processing core failure rates and varied sensing/actuating or other specialized capabilities. Our objectives were the minimization of the overall latency, the minimization of the overall energy consumption, and the maximization of the overall reliability of the workflow application in the specific CPS, under deadline, reliability, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random task graphs with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability, reliability threshold) were included post-generation, using appropriate values. More details are provided in README.txt.<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.<br>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p>
Supplementary micro-X-ray Fluorescence data for: "On the possible contribution of meteoritic metal to some Ni-rich Indonesian kris daggers: Comparing original daggers and newly forged analogue objects"
<p>This repository contains the micro X-Ray Fluorescence (microXRF) results described within the manuscript titled “On the possible contribution of meteoritic metal to Ni-rich Indonesian kris daggers: Comparing original daggers and newly forged analog objects” submitted to the Meteoritics and Plantetary Science (MAPS) journal by Brandstätter et al. The manuscript describes two types of microXRF results: Semi-quantitative maps and quantified line scan results. The README file contains a detailed overview of which files contain which data.</p>
Data for publication 'Detection of Artificial Seed-like Objects from UAV Imagery'
<p>This resource contains the datasets supporting the model development as published in the article 'Detection of Artificial Seed-like Objects from UAV Imagery' (https://doi.org/10.3390/rs15061637).</p> <p>In the last two decades, unmanned aerial vehicle (UAV) technology has been widely utilized as an aerial survey method. Recently, a unique system of self-deployable and biodegradable microrobots akin to winged achene seeds was introduced to monitor environmental parameters in the air above the soil interface, which requires geo-localization. This research focuses on detecting these artificial seed-like objects from UAV RGB images in real-time scenarios, employing the object detection algorithm YOLO (You Only Look Once). Three environmental parameters, namely, daylight condition, background type, and flying altitude, were investigated to encompass varying data acquisition situations and their influence on detection accuracy. Artificial seeds were detected using four variants of the YOLO version 5 (YOLOv5) algorithm, which were compared in terms of accuracy and speed. The most accurate model variant was used in combination with slice-aided hyper inference (SAHI) on full resolution images to evaluate the model’s performance. It was found that the YOLOv5n variant had the highest accuracy and fastest inference speed. After model training, the best conditions for detecting artificial seed-like objects were found at a flight altitude of 4 m, on an overcast day, and against a concrete background, obtaining accuracies of 0.91, 0.90, and 0.99, respectively. YOLOv5n outperformed the other models by achieving a mAP0.5 score of 84.6% on the validation set and 83.2% on the test set. This study can be used as a baseline for detecting seed-like objects under the tested conditions in future studies.</p>
Outputs of the Jupyter Notebook - Detecting floating objects using Deep Learning and Sentinel-2 imagery
<p>The dataset contains the outputs of the notebook "Detecting floating objects using Deep Learning and Sentinel-2 imagery" published in the ocean modelling section of The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Jamila Mifdal (author), European Space Agency Φ-lab, <a href="https://github.com/jmifdal">@jmifdal</a></p> </li> <li> <p>Raquel Carmo (author), European Space Agency Φ-lab, <a href="https://github.com/raquelcarmo">@raquelcarmo</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Modelling codebase</em></p> <ul> <li> <p>Jamila Mifdal (author), European Space Agency Φ-lab, <a href="https://github.com/jmifdal">@jmifdal</a></p> </li> <li> <p>Raquel Carmo (author), European Space Agency Φ-lab, <a href="https://github.com/raquelcarmo">@raquelcarmo</a></p> </li> <li> <p>Marc Rußwurm (author), EPFL-ECEO, <a href="https://github.com/MarcCoru">@marccoru</a></p> </li> </ul>
Precipitation objects under the current and future climate: WRF 6-km hydroclimate simulation of the western US
<p>This folder includes the precipitation objects that are used in the following manuscript:</p> <p>Chen et al., Sharpening of Cold Season Storms over the Western US.</p> <p>It is generated using WRF V3.8 at PNNL. A historical simulation ("NARR") is done for 1981-2010, and five future simulations ("CanESM2", "CESM1-CAM5", "GFDL-ESM2M", "HadGEM2-ES", "MPI-ESM-MR") are done for 2041-2070 using the Pseudo Global Warming (PGW) approach. For the WRF model configuration and the simulation details, please refer to the abovementioned manuscript and Chen et al. (2018).</p> <p>This is the preliminary version of the dataset that contains the precipitation object features as analyzed in the manuscript. More data (including the WRF raw precipitation output) and the finalized scripts will be included here before the manuscript is published.</p> <p> </p> <p>Reference:</p> <p>Chen, X., L. R. Ruby, Y. Gao, Y. Liu, M. Wigmosta, and M. Richmond (2018), Predictability of Extreme Precipitation in Western U.S. Watersheds Based on Atmospheric River Occurrence, Intensity, and Duration, <em>Geophys. Res. Lett.</em> doi: <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018GL079831">10.1029/2018GL079831</a></p> <p>Chen, X., L. R. Ruby, Y. Gao, Y. Liu, and M. Wigmosta (2023), Sharpening of Cold Season Storms over the Western US, Nat. Clim. Change. doi: <a href="https://www.nature.com/articles/s41558-022-01578-0">10.1038/s41558-022-01578-0</a> </p>
Individual-donor scRNA-Seq datasets, as Seurat 4.0.5 objects
<p>The provided datasets correspond to the analyses of individual donor single-cell RNA Sequencing (scRNA-Seq) datasets, before their integration. The datasets have been saved as Seurat v4.0.5 objects. For clustering, we used default settings in Seurat 4.0.5 (resolution 0.8) and increased resolution, if necessary, to separate epithelium in proximal and distal. </p> <p>The *_clusters.pdf files show the suggested clusters in the individual datasets and the *_indiv_anno1.pdf files show the cell annotations according to the 84 cell states, described in the study with title "Developmental origins of cell heterogeneity in the human lung" (1st preprint version doi: https://doi.org/10.1101/2022.01.11.475631).</p> <p>The "*_cluster_annotations.csv" files provide information about the suggested annotations of the clusters.</p> <p>The "*_object_raw_and_log_counts.RData" objects contain the metadata and the UMI-counts [raw and log2(counts+1)] for each donor scRNA-Seq dataset.</p> <p> </p>
Dataset of handling noise for "Assessing the relevance of perceptually driven objective metrics in the presence of handling noise"
<p>This dataset contains the handling noise chunks used in [1] under the<br> folders "tapping" and "rustle" and the csv files used to compute the<br> results in section 5 of [1] under the "csv" folder. These chunks have<br> been extracted from seven in-house recordings and twelve of the sixteen<br> recordings from [2]. The twelve recordings from [2] have first been<br> converted from the .wma to the .wav format and resampled from 44100 Hz<br> to 48000 Hz before the extraction of the chunks. The sample rate of the<br> in-house recordings is natively 48000 Hz.</p> <p><br> [1] Angonin, C., Chourdakis E. T., and Åeng, R. A. "Assessing the<br> relevance of perceptually driven objective metrics in the presence of<br> handling noise", in 152nd Audio Engineering Society Convention,<br> Netherlands, 2022.</p> <p>[2] Kentric, P. Jackson, I. R., Fazenda, B. M, Cox, T. J., and Li, F. F.<br> "Microphone handling noise: Measurements of perceptual threshold and effects<br> on audio quality." PloS one, 10(10), 2015.</p> <p> </p>
Video Examples from: Creating Audio Object-focused Acoustic Environments for Room-Scale Virtual Reality
<p>Video recordings illustrating the issues and possible solutions mentioned in the paper.</p> <p>Please use headphones when watching the videos.</p>
Data for "Why Bananas Look Yellow: The Dominant Hue of Object Colours"
<p>These extended supplementary materials go with the article:</p> <ul> <li>Witzel & Dewis (2022) <strong>Why Bananas Look Yellow: The Dominant Hue of Object Colours</strong>. <em>Vision Research</em>.</li> </ul> <p><strong>A. SURVEYS</strong></p> <p>A pdf-printout for each of the three Qualtrics surveys illustrates details of the procedure. The layout may have been slightly different in Qualtrics (e.g., wide screen vs portrait display). Also note that the second and third surveys feature a few questions that were unrelated to the dominant-hue study (identifying a grey image).</p> <p><strong>B. STIMULI</strong></p> <p>The images used in Experiments 1-3, and the animated images used as cues to colour changes in Experiment 3 are packed in zip-files.</p> <p><strong>C. CODE</strong></p> <p>The <em>Matlab code</em> "onehue_maker.m" is a <em>function</em> that implements the dominant-hue algorithm to produce one-hue images like those in the experiments. To try out the program, the photo of the banana and the mask identifying its background are also uploaded (= first and second input to the function). The purpose of the mask is to remove the background colour from the dominant-hue computations. </p> <p><strong>D. DATA</strong></p> <p>The uploaded data is not completely raw but has been polished in the following ways:</p> <ul> <li>Pilot data has been removed (i.e., meaningless data from us and our students to try out, check and polish the survey).</li> <li>Incomplete runs have been removed (i.e., when participants quitted before completing the whole survey).</li> <li>Data irrelevant to this study have been removed (date and time; grey-identification task [see above]).</li> </ul> <p>There are 3 sheets with data and three sheets with stimulus specifications for each of the three experiments. The stimulus specifications include the measures used in the analyses in "Other Factors" in the Discussion of Experiment 3. </p> <p><strong>Columns in the Data sheets are: </strong></p> <ul> <li><strong>Participant information:</strong> <em>recruit </em>(<em>soc med</em> = social media; <em>UG pool</em> = undergraduate students, <em>prolific </em>= https://www.prolific.co/); <em>coldef </em>= Colour deficiencies (<em><strong>1</strong></em> Yes, <em><strong>2</strong></em> No according to test, <em><strong>3</strong></em> No without test, <em><strong>4</strong></em> Don't know); <em>sex </em>(<em><strong>1</strong></em> male, <em><strong>2</strong></em> female, <em><strong>3</strong></em> other); <em>age </em>(in years), and <em>duration </em>(in minutes).</li> <li><strong>Main data: </strong>Column labels are composed of the following elements, separated by an underscore (_): <ul> <li>The first 3-5 letters of the object name: <strong><em>ban </em></strong>= banana, <strong><em>car </em></strong>= carrot, <strong><em>cher </em></strong>= cherry, <strong><em>dress </em></strong>= #theDress, <strong><em>fro</em></strong> = frog, <strong><em>gra </em></strong>= grapes, <strong><em>lem </em></strong>= lemon, <strong><em>let </em></strong>= lettuce, <strong><em>ora </em></strong>= orange, <strong><em>pig</em></strong>, <strong><em>ros </em></strong>= rose, <strong><em>shoe </em></strong>= #theShoe, <strong><em>stra </em></strong>= strawberry, <strong><em>zuc </em></strong>= zucchini/courgette.</li> <li>A symbol indicating the stimulus condition: <em><strong>1</strong></em> = One-Hue, <em><strong>m</strong></em> = Minus-Hue Rotation, <em><strong>p</strong></em> = Plus-Hue Rotation.</li> <li>A number identifying the measure: <em><strong>1</strong></em> = responded position; <em><strong>2</strong></em> = accuracy of the response (1 = correct); <em><strong>3</strong></em> = response time (in sec), <em><strong>4</strong></em> (Experiment 2-3) = confidence rating (between 0 and 100), <em><strong>5</strong></em> (Experiment 3) = cue confidence (cf. Figure 11.a).</li> <li>For inverted colours (Experiment 3), the column label starts with an "i" (for inverted).</li> </ul> </li> <li><strong>Practice Trials: </strong>Start with the prefix <em><strong>ex </strong></em>(for example) followed by an underscore (<em><strong>_</strong></em>) and the ID of the object; otherwise, data as in main trials.</li> <li><strong>Catch Trials (Experiment 2-3): </strong>Start with object name "d" for disk, otherwise, data as in main trials. </li> <li><strong>Eidolon Guesses (Experiment 2): </strong>Start with "guess" followed by the object ID (see main trials) followed by a number indicating the measure: <em><strong>1</strong></em> = response (yes/no), <em><strong>2</strong></em> = confidence (if positive response). In case of a positive response, the text entries are save in the variables starting with guess_txt.</li> </ul> <p><strong>Columns in the stimulus sheets are:</strong></p> <ul> <li><strong>DomHue:</strong> Angle of the dominant hue (cf. Figure 3); as principal components are relative to the average, the angle is relative to the average, not the origin.</li> <li><strong>pole1 and pole2:</strong> Poles of the dominant hue direction. "<strong>pole1_rgb" </strong>provides corresponding RGBs for illustration (cf. Figure 1).</li> <li><strong>ChromaRescaled:</strong> Rescale Factor (see Experiment 3).</li> <li><strong>MaxChr: </strong>Maximum chroma of the colour distribution in CIELUV.</li> <li><strong>M</strong>: Average chromaticities (<em>u*</em>, <em>v*</em>) of the colour distribution.</li> <li><strong>pc:</strong> Coefficients of the first principal component for <em>u*</em> and <em>v*</em>.</li> <li><strong>latent & expl</strong>: Absolute and relative explained variance, respectively; second column corresponds to orthogonal variance.</li> <li><strong>hueM & hueSD:</strong> Average and standard deviation of the hue of the colour distribution (cf. Figure 3).</li> <li><strong>rot_minus, rot_plus:</strong> The hue rotations in the rotated-hue condition (constant minus or plus 5, except for #theShoe).</li> <li><strong>oog_1hue, oog_plus, oog_minus:</strong> The proportion of out-of-gamut values.</li> <li><strong>oogdist_1hue, oogdist_minus, oogdist_plut:</strong> Average difference between clipped and original images (in CIELUV).</li> <li><strong>Mshift_1hue, Mshift_minus, Mshift_plus:</strong> Average and standard deviation of chromaticity shift due to the experimental manipulation (cf. Figure 5 and Table S1).</li> <li><strong>Mhueshift_1hue, Mhueshift_minus, Mhueshift_plus:</strong> Average and standard deviation of hue shift in CIELUV (cf. Figure S4.d-f and Table S2).</li> <li><strong>Lab_shift_1hue, Lab_shift_minus, Lab_shift_plus:</strong> Average and standard deviation of chromaticity shift in CIELAB (cf. Figure S4.a-c and Table S1).</li> <li><strong>Lab_hueshift_1hue, Lab_hueshift_minus, Lab_hueshift_plus:</strong> Average and standard deviation of hue shift in CIELAB (cf. Figure S4.g-i and Table S2).</li> <li><strong>Lab_Mhue:</strong> Hue of the average colour in CIELAB</li> <li><strong>Lab_hueM & Lab_hueSD0:</strong> Average and standard deviation of the CIELAB hue distribution.</li> <li><strong>huehist0:</strong> CIELUV hue histogram; each entry corresponds to the frequencies for 72 bins of 5-deg (cf. Figure 3); the zero indicates that the hue is relative to the origin, not to the average chromaticity.</li> </ul>
Deep Deconvolution of Object Information Modulated by a Refractive Lens Using Lucy-Richardson-Rosen Algorithm
<p>A refractive lens is one of the simplest, cost-effective and easily available imaging elements. With a spatially incoherent illumination, a refractive lens can faithfully map every object point to an image point in the sensor plane, when the object and image distances satisfy the imaging conditions. However, static imaging is limited to the depth of focus, beyond which the point-to-point mapping can be only obtained by changing either the location of the lens or the imaging sensor. In this study, the depth of focus of a refractive lens in static mode has been expanded using a recently developed computational reconstruction method, Lucy-Richardson-Rosen algorithm (LRRA). The technique consists of three steps. In this first step, the point spread functions (PSFs) were recorded along different depths and stored in the computer as PSF library. In the next step, the object intensity distribution was recorded. The LRRA was then applied to deconvolve the object information from the recorded intensity distributions in the final step. The results of LRRA were compared against two well-known reconstruction methods namely Lucy-Richardson algorithm and non-linear reconstruction. The data corresponding to experimental analysis is given in the manuscript. (Preprints Link:). The theoretical simulation data is given here.</p>
Phase Object Reconstruction for 4D-STEM using Deep Learning, (4D-STEM Training Data)
<p><strong>Overview </strong></p> <p>This repository contains 742,688 samples of simulated Convergent Beam Electron Diffraction patterns (CBEDs); the training data for the paper <a href="https://arxiv.org/abs/2202.12611">"Phase Object Reconstruction for 4D-STEM using Deep Learning"</a>. The folder contains multiple hdf5 datasets. Each dataset has a corresponding Excel-sheet containing detailed information and simulation parameters for every datapoint, as well as a summary-report containing the parameter distributions, hdf5-infos and random number generator settings. This makes every dataset reproducible, using the simulation codes provided in <a href="https://github.com/ThFriedrich/ap_data_generation">https://github.com/ThFriedrich/ap_data_generation</a>.</p> <p><strong>Technical details</strong></p> <p>Every Datapoint consists of a 3x3 set of adjacent Convergent Beam Electron Diffraction pattern (CBEDs), the coherent exit wave phase and amplitude in real and reciprocal space, and the probe functions phase and amplitude in real space. All patterns are 64x64 pixel in 16 bit unsigned integer data format.</p> <p>Every hdf5 file has the following structure:</p> <table> <tbody> <tr> <td>Attributes</td> <td>'Seed': 6108236<br> 'State': 251786606 ...<br> 'Type': 'twister'<br> 'arch': 'glnxa64'<br> 'gpu': 'NVIDIA GeForce RTX 3080'<br> 'matlab_ver': '2021a'</td> </tr> <tr> <td>Dataset 'features'</td> <td> <p>Size: 64x64x9x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'labels_k'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'labels_r'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'probe_r'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'meta'</td> <td> <p>Size: 19x5000<br> Datatype: H5T_IEEE_F32LE (single)</p> </td> </tr> </tbody> </table> <p>The data was written to hdf5 in matlab. When reading from these files consider possibly different storage conventions (Row major vs. column major format). Data may need to be transposed accordingly. The integer arrays were scaled to use the full range of the uint16 datatype. The scaling values are stored under "meta". To restore the original values in floating point numbers, convert the arrays like this:</p> <p>Matlab:</p> <pre><code>hdf_file = ['db_h5_b_5_Training.h5']; n = 128; % load `n` k-space exit waves x = single(h5read(hdf_file, '/labels_k', [1,1,1,1], [64,64,2,n])); % `meta` contains parameters and scaling factors for a given datapoint in following order: [E_0(keV), cond_lens_outer_aper_ang(mrad), collection angle(rA), step_size(A), scale_cbed_1 ... scale_cbed_9, scale_phase_k, scale_amp_k, scale_phase_r, scale_amp_r, scale_probe_phase_r, scale_probe_amp_r] s = h5read(hdf_file, '/meta', [14,1], [2,n]); amplitude = zeros(64,64,n); phase = zeros(64,64,n); for ix = 1:n phase(:,:,n) = (x(:,:,1,n)*s(1,ix) / 65536) - pi; amplitude(:,:,n) = (x(:,:,2,n)*s(2,ix)) / 65536; end % The 9 CBEDs correspond to a 3x3 kernel of patterns. The order in [x,y] is: %[[3, 6, 9]; % [2, 5, 8]; % [1, 4, 7]] </code></pre>
Data set from: Rates of Compact Object Coalescences
<p><strong>Data from: Rates of Compact Object Coalescence </strong></p> <p><strong>Brief overview: </strong><br> This Zenodo entry contains the data that has been used to make the figures for the living review <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210714239M/abstract">"Rates of Compact Object Coalescence" by Ilya Mandel & Floor Broekgaarden (2021)</a>. To reproduce the figures, download all the <strong>*.csv</strong> files and run the jupyter notebook created to reproduce the results in the publicly available Github directory <a href="https://github.com/FloorBroekgaarden/Rates_of_Compact_Object_Coalescence">https://github.com/FloorBroekgaarden/Rates_of_Compact_Object_Coalescence</a> (the exact jupyter notebook can be found <a href="https://github.com/FloorBroekgaarden/Rates_of_Compact_Object_Coalescence/tree/main/plottingCode/Make_figures_Mandel_and_Broekgaarden_2021_COC_rates_review.ipynb">here</a>)</p> <p>For any suggestions, questions or inquiry, please email one, or both, of the authors: </p> <ul> <li><strong>Ilya Mandel</strong>: <em>ilya.mandel@monash.edu</em> </li> <li><strong>Floor Broekgaarden</strong>: <em>floor.broekgaarden@cfa.harvard.edu</em></li> </ul> <p>We very much welcome suggestions for additional/missing literature with rate predictions or measurements. </p> <p> </p> <p><strong>Extra figures:</strong><br> Extra figures that can be used can be found here:</p> <p><strong>Vertical figures: <a href="https://docs.google.com/presentation/d/1GqJ0k2zpnxBGwIYNeQ0BfsLSU7H2942gspL-PN_iaJY/edit?usp=sharing">https://docs.google.com/presentation/d/1GqJ0k2zpnxBGwIYNeQ0BfsLSU7H2942gspL-PN_iaJY/edit?usp=sharing</a> </strong></p> <p><br> The authors are currently working on making an interactive tool for plotting the rates that will be available soon. In the mean time, feel free to send requests for plots/figures to the authors. </p> <p><strong>Reference</strong><br> If you use this data/code for publication, please cite both the paper: <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210714239M/abstract">Mandel & Broekgaarden (2021)</a> (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210714239M/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv210714239M/abstract</a>) and the dataset on Zenodo through it's doi (see tabs on the right of this zenodo entry) <br> <br> <strong>Details datafiles: </strong></p> <p>The PDF <strong>COC_rates_supplementary_material.pdf</strong> attached (and in the Github repository) describes how each of the rates in the data files of this Zenodo entry are retrieved. The other 26 files are .csv files, where each csv file contains the rates from one specific double compact object type: NS-NS, NS-BH or BH-BH, and specific rate group (isolated binary evolution, gravitational wave observations etc.). The files in this entry are: </p> <p> </p> <ul> <li><strong>Data_Mandel_and_Broekgaarden_2021.zip </strong>all the files below conveniently in one zip file so that you only have to do 1 download. <br> </li> <li><strong>COC_rates_supplementary_material.pdf </strong> # PDF document describing how the rates are retrieved and quoted rom each study<br> </li> <li><strong>BH-BH_rates_CHE.csv</strong> # BH-BH rates for chemically homogeneous evolution </li> <li><strong>BH-BH_rates_flybys.csv </strong> # BH-BH rates for formation from wide isolated binaries with dynamical interactions from flybys</li> <li><strong>BH-BH_rates_globular-clusters.csv</strong> # BH-BH rates for dynamical formation in globular clusters </li> <li><strong>BH-BH_rates_isolated-binary-evolution.csv</strong> # BH-BH rates for isolated binary evolution </li> <li><strong>BH-BH_rates_nuclear-clusters.csv</strong> # BH-BH rates for (dynamical )formation in (active) nuclear star clusters</li> <li><strong>BH-BH_rates_observations-GWs.csv</strong> # BH-BH rates for observations from gravitational waves</li> <li><strong>BH-BH_rates_population-III.csv</strong> # BH-BH rates for population-III stars </li> <li><strong>BH-BH_rates_primordial.csv </strong> # BH-BH rates for primordial formation</li> <li><strong>BH-BH_rates_triples.csv</strong>. # BH-BH rates for formation in (hierarchical) triples </li> <li><strong>BH-BH_rates_young-stellar-clusters.csv</strong> # BH-BH rates for dynamical formation in young/open star clusters <br> </li> <li><strong>NS-BH_rates_CHE.csv</strong> # NS-BH rates for chemically homogeneous evolution </li> <li><strong>NS-BH_rates_flybys.csv </strong> # BH-BH rates for formation from wide isolated binaries with dynamical interactions from flybys</li> <li><strong>NS-BH_rates_globular-clusters.csv</strong> # NS-BH rates for dynamical formation in globular clusters </li> <li><strong>NS-BH_rates_isolated-binary-evolution.csv. </strong># NS-BH rates for isolated binary evolution </li> <li><strong>NS-BH_rates_nuclear-clusters.csv</strong> # NS-BH rates for (dynamical )formation in (active) nuclear star clusters</li> <li><strong>NS-BH_rates_observations-GWs.csv</strong> # NS-BH rates for observations from gravitational waves</li> <li><strong>NS-BH_rates_population-III.csv</strong> # NS-BH rates for population-III stars </li> <li><strong>NS-BH_rates_triples.csv</strong> # NS-BH rates for formation in (hierarchical) triples </li> <li><strong>NS-BH_rates_young-stellar-clusters.csv</strong> # BH-BH rates for dynamical formation in young/open star clusters<br> </li> <li><strong>NS-NS_rates_globular-clusters.csv </strong># NS-NS rates for dynamical formation in globular clusters </li> <li><strong>NS-NS_rates_isolated-binary-evolution.csv </strong> # NS-NS rates for isolated binary evolution </li> <li><strong>NS-NS_rates_nuclear-clusters.csv </strong># NS-NS rates for (dynamical )formation in (active) nuclear star clusters</li> <li><strong>NS-NS_rates_observations-GWs.csv</strong> # NS-NS rates for observations from gravitational waves</li> <li><strong>NS-NS_rates_observations-kilonovae.csv</strong> # NS-NS rates for observations from kilonovae</li> <li><strong>NS-NS_rates_observations-pulsars.csv</strong> # NS-NS rates for observations from Galactic pulsars</li> <li><strong>NS-NS_rates_observations-sGRBs.csv </strong># NS-NS rates for observations short gamma-ray bursts</li> <li><strong>NS-NS_rates_triples.csv </strong># NS-NS rates for formation in (hierarchical) triples </li> <li><strong>NS-NS_rates_young-stellar-clusters.csv</strong> # NS-NS rates for dynamical formation in young/open star clusters </li> </ul> <p> </p> <p><strong>Each csv file contains the following header: </strong><br> ADS year # year of the paper in the ADS entry<br> ADS month # month of the paper in the ADS entry <br> ADS abstract link # link to the ADS abstract <br> ArXiv link # link to the ArXiv version of the paper <br> First Author # name of the first author<br> label string # label of the study, that corresponds to the label in the figure<br> code (optional) # name of the code used in this study <br> type of limit (for plotting, see jupyter notebook for a dictionary) # integer, that is used to map to a certain limit visualization in the plot (e.g. scatter points vs upper limit). </p> <p>Each entry takes two columns in the csv files. One for the rates (quoted under the header 'rate [Gpc^-3 yr^-1]') and one for "notes" where we sometimes added notes about the rates (such as whether it is an upper or lower limit). </p> <p> </p>
Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform data
<p>Data files used in the publication: "Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform", submitted to Physics of Plasma August 2022. To be used in conjunction with analysis software KVIST.</p> <p>KVIST can be found at:</p> <ul> <li>https://doi.org/10.5281/zenodo.7017043</li> <li>https://github.com/Planetary-Surfaces-and-Spacecraft-Lab/KVIST</li> </ul> <p>Data files generated with:</p> <p>Truitt, A. (2020). Simulation of Forced Korteweg De Vries Equation as Applied to Small Orbital Debris. Digital Repository at the University of Maryland. https://doi.org/10.13016/FOR0-XJYD</p>
Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures
<p>This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe).</p> <p>The dataset contains:</p> <p>- The EnergyPlus baseline models employed as a case study for each climate.</p> <p>- The Pareto fronts obtained after the multi-objective optimization in each climate.</p> <p>- The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.</p>
Objects_09/29/22
Documentation material from the Mastic pilot of the Mingei project
HOWS-CL-25: Household Objects Within Simulation Dataset for Continual Learning
<p>HOWS-CL-25 (Household Objects Within Simulation dataset for Continual Learning) is a synthetic dataset especially designed for object classification on mobile robots operating in a changing environment (like a household), where it is important to learn new, never seen objects on the fly.<br> This dataset can also be used for other learning use-cases, like instance segmentation or depth estimation.<br> Or where household objects or continual learning are of interest.</p> <p>Our dataset contains 150,795 unique synthetic images using 25 different household categories with 925 3D models in total. For each of those categories, we generated about 6000 RGB images. In addition, we also provide a corresponding depth, segmentation, and normal image.</p> <p>The dataset was created with BlenderProc [Denninger et al. (2019)], a procedural pipeline to generate images for deep learning.<br> This tool created a virtual room with randomly textured floors, walls, and a light source with randomly chosen light intensity and color. After that, a 3D model is placed in the resulting room. This object gets customized by randomly assigning materials, including different textures, to achieve a diverse dataset. Moreover, each object might be deformed with a random<br> displacement texture.<br> We use 774 3D models from the ShapeNet dataset [A. X. Chang et al. (2015)] and the other models from various internet sites. Please note that we had to manually fix and filter most of the models with Blender before using them in the pipeline!</p> <p>For continual learning (CL), we provide two different loading schemes:<br> - Five sequences with five categories each<br> - Twelve sequences with three categories in the first and two in the other sequences.</p> <p>In addition to the RGB, depth, segmentation, and normal images, we also provide the calculated features of the RGB images (by ResNet50) as used in our RECALL paper.<br> In those two loading schemes, ten percent of the images are used for validation, where we ensure that an object instance is either in the training or the validation set, not in both. This avoids learning to recognize certain instances by heart.</p> <p>We recommend using those loading schemes to compare your approach with others.</p> <p>Here we provide three files for download:<br> - HOWS_CL_25.zip [124GB]: This is the original dataset with the RGB, depth, segmentation, and normal images, as well as the loading schemes. It is divided into three archive parts. To open the dataset, please ensure to download all three parts.<br> - HOWS_CL_25_hdf5_features.zip [2.5GB]: This only contains the calculated features from the RGB input by a ResNet50 in a .hdf5 file. Download this if you want to use the dataset for learning and/or want to compare your approach to our RECALL approach (where we used the same features).<br> - README.md: Some additional explanation.</p> <p>For further information and code examples, please have a look at our website: https://github.com/DLR-RM/RECALL.</p>
frog scRNA data set objects
<p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qiu et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p> <p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Briggs et al. 2018 and Qiu et al. 2022.</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.