Find research datasets worth reusing

Progress Toward SHAPE Constrained Computational Prediction of Tertiary Interactions in RNA Structure

Supplementary repository for the "Progress Toward SHAPE Constrained Computational  Prediction of Tertiary Interactions in RNA Structure" article. Contains the simulation on the Didymium iridis lariat-capping ribozyme (DiLCrz, PDB ID: 4P8Z).

Extended data for 'Revisiting the Rist diagram for predicting operating conditions in blast furnaces with multiple injections'

Extended data for ‘Revisiting the Rist diagram for predicting operating conditions in blast furnaces with multiple injections’ (https://doi.org/10.12688/openreseurope.14275.1):                Appendix A – Terms of the energy balance in the elaboration zone                Appendix B – Heat of decomposition of coal (dry basis)                Appendix C – Energy balance in the preparation zone                Appendix D – Calculation of the flame temperature through Eq.(62)                DISIPO_OPEN DATA_1.xlsx

Multi-domain and Explainable Prediction of Changes in Web Vocabularies (code & data)

This deposit contains supplementary code & data for the paper 'Multi-domain and Explainable Prediction of Changes in Web Vocabularies' (K-CAP 2021). Web vocabularies (WV) have become a fundamental tool for structuring Web data: over 10 million sites use structured data formats and ontologies to markup content. Maintaining these vocabularies and keeping up with their changes are manual tasks with very limited automated support, impacting both publishers and users. Existing work shows that machine learning can be used to reliably predict vocabulary changes, but on specific domains (e.g. biomedicine) and with limited explanations on the impact of changes (e.g. their type, frequency, etc.). In this paper, we describe a framework that uses various supervised learning models to learn and predict changes in versioned vocabularies, independent of their domain. Using well-established results in ontology evolution we extract domain-agnostic and human-interpretable features and explain their influence on change predictability. Applying our method on 139 WV from 9 different domains, we find that ontology structural and instance data, the number of versions, and the release frequency highly correlate with predictability of change. These results can pave the way towards integrating predictive models into knowledge engineering practices and methods.

Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants.

Datasets: CommonClinvar and NewClinvar Authors: Emidio Capriotti and Piero Fariselli Article: Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants.

High Performance Predictable Quantum Efficient Detector Based on Induced-Junction Photodiodes Passivated with SiO2/SiNx

This page contains selected data from the peer-reviewed paper "High Performance Predictable Quantum Efficient Detector Based on Induced-Junction Photodiodes Passivated with SiO2/SiNx" published in Sensors by Ozhan Koybasi. Description of attached files: Figure 5. Simulated p-polarization reflectance as a function of wavelength for PQEDs mounted in trap configuration with an angle of 15° between the diodes. In this configuration the light beam undergoes 7 reflections: one at 0° degree and two reflections at 15°, 30°, and 45°. The reflectance is reported for six different thicknesses of the SiNx. Figure 6. Maximum and mean values evaluated in the wavelength interval 400–850 nm of the p-polarization reflectance as a function of SiNx thickness for PQEDs mounted in trap configuration with an angle of 15° between the diodes. Figure 7. Maximum and mean values evaluated in the wavelength interval 400–850 nm of the p-polarization reflectance as a function of SiNx thickness, when a buffer layer of 6 nm SiO2 is depos-ited before SiNx, for PQEDs mounted in trap configuration with an angle of 15° between the di-odes. Figure 8. Effective lifetime τeff vs. excess carrier density (Δn) for samples prepared with passivation processes described in Table 1. Figure 9. Photoluminescence (PL) lifetime images of samples prepared with the passivation processes described in Table 1. Figure 10. Capacitance—voltage (C—V) measurement results of MIS capacitors prepared with the passivation processes E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table 1, at a frequency of 1 kHz. Figure 11. Injection dependent effective minority carrier lifetime τeff (Δn) of test samples passivated with processes E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table 1 with simu-lation fits to extract SRV and τbulk. Figure 12. Simulated IQD as a function of reverse bias voltage for p-type inversion-layer photodi-ode that would be fabricated with passivation E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table I. The simulations were performed at a wavelength of 488 nm. Figure 13. Simulated IQD as a function of wavelength for p-type inversion-layer photodiode that would be fabricated with passivation E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table I. The simulations were performed at a reverse bias voltage of 5 V. Figure 16. Spatial uniformity of optical power responsivity of the PQEDs with SiO2/SiNx stack photodiodes P18-55-45 (a) and P18-54-44 (b).

opencc-by-4.0Apr 2021View details →

Dataset from: Gravitational wave sources in our Galactic backyard - Predictions for BHBH, BHNS and NSNS binaries in LISA

The data from all simulations used in "Gravitational wave sources in our Galactic backyard: Predictions for BHBH, BHNS and NSNS binaries in LISA" Contents: <ul> <li>detections_and_totals.zip <ul> <li>Contains for .npy files that contain tables of the detections and total DCOs in Milky Way. These were calculated with <a href="https://github.com/TomWagg/detecting-DCOs-in-LISA/blob/main/simulation/postprocessing_notebooks/get_detection_rates.ipynb">this</a> and <a href="https://github.com/TomWagg/detecting-DCOs-in-LISA/blob/main/simulation/postprocessing_notebooks/get_total_DCOs_in_MW.ipynb">this</a> notebook and are included for convenience so you don't have to re-run these notebooks</li> </ul> </li> <li>simulations_4yr.zip <ul> <li>Contains 60 .h5 files that contain the main simulations for a 4-year LISA mission. Each file contains the results for a single DCO type (BHBH, BHNS or NSNS) and model variation (20 variations) are labeled as {DCO_type}_{variation}_all.h5. </li> </ul> </li> <li>simulations_10yr.zip <ul> <li>As simulations_4yr.zip but for a 10-year LISA mission</li> </ul> </li> <li>simple_mw_simulations.zip <ul> <li>As simulations_4yr.zip but using a simple model for the Milky Way (discussed in Appendix D) and only for models A and F (hence only contains 6 .h5 files)</li> </ul> </li> </ul> For a description of how to use these files to reproduce figures and results see the README.md in the associated GitHub repository: <a href="https://github.com/TomWagg/detecting-DCOs-in-LISA">https://github.com/TomWagg/detecting-DCOs-in-LISA</a> Version 0.0.1 - Changes model E to E' as discussed in paper (now we allow HeHG donors to survive common-envelopes) <ul> </ul>

Predictive simulations of core electron binding energies of halogenated species adsorbed on ice surfaces from relativistic quantum embedding calculations

This dataset collects the unprocessed (= outputs from calculations) and processed (= plots, average values for orbital and ionization energies) results discussed in the paper titled "Predictive simulations of core electron binding energies of halogenated species adsorbed on ice surfaces from relativistic quantum embedding calculations" by Richard Asamoah Opoku,  Céline Toubin, and André Severo Pereira Gomes.

Predictions of the SARS-CoV-2 B.1.1.529 Variant Spike Protein Receptor Binding Domain Structure and Neutralizing Antibody Interactions

Using AlphaFold2 and HADDOCK, we have generated a predicted structure for the SARS-CoV-2 B.1.1.529 variant's Spike receptor binding domain and then predicted the binding interaction with neutralizing antibodies. This was performed to understand the potential structural changes in the receptor binding domain of B.1.1.529 and how this may affect vaccine efficacy through antibody interaction.

opencc-by-4.0Nov 2020View details →

Polymeric Property Prediction Using Recurrent Neural Networks with Optimizations

Database of the Dielectric Polymers used for training and testing of the developed Recurrent Neural Network models (DOI:10.5281/zenodo.4280446)

Performance Prediction of Deep Learning Applications

The repository includes the source code and the datasets used to predict the performance of Deep Learning applications executed on different environments. The results are included in the AI-SPRINT project deliverable "D2.1 - First release and evaluation of the AI-SPRINT design tools".

Wave prediction at MegaRoller installation site

Wave prediction at MegaRoller installation site. This dataset specify the wave characteristics at MegaRoller installation site for power performance and load estimation.

Improved Bathymetric Prediction using Geological Information: SYNBATH

Manuscript in revision: Earth and Space Science, December 20, 2021 Abstract To date, approximately 20% of the ocean floor has been surveyed by ships at a spatial resolution of 400 m or better. The remaining 80% has depth predicted from satellite altimeter-derived gravity measurements at a relatively low resolution. There are many remote ocean areas in the southern hemisphere that will not be completely mapped at 400 m resolution during this decade. This study is focused on the development of synthetic bathymetry to fill the gaps. There are two types of seafloor features that are not typically well resolved by satellite gravity: abyssal hills and small seamounts (< 2.5 km tall).  We generate synthetic realizations of abyssal hills by combining the measured statistical properties of mapped abyssal hills with regional geology including fossil spreading rate/orientation, rms height from satellite gravity, and sediment thickness. With recent improvements in accuracy and resolution, It is now possible to detect all seamounts taller than about 800 m in satellite-derived gravity and their location can be determined to an accuracy of better than 1 km.  However, the width of the gravity anomaly is much greater than the actual width of the seamount so the seamount predicted from gravity will underestimate the true seamount height and overestimate its base dimension. In this study we use the amplitude of the vertical gravity gradient (VGG) to estimate the mass of the seamount and then use their characteristic shape, based on well surveyed seamounts, to replace the smooth predicted seamount with a seamount having a more realistic shape.  SYNBATH_V1.2 September 20, 2021 This version of SYNBATH has abyssal hills as described below.  Superimposed on that are 30,000 gaussian seamounts with sigma to height ratios of 2.4.  The heights were determined by fitting a uncompensated model VGG for a seamount of a particular height to the observed VGG in a 33 by 33 km area using a density of 2800 kg m^-3.  Any seamount taller than 2600 m or less than 700 m was not used. SYNBATH_V1.1 July 6, 2021 A refined version of the SYNBAPS with better blending SYNBATH_V1.0 July 1, 2021 This is the first version of SYNthetic BATHymetry (SYNBATH) that is a merge of the latest SRTM15 global bathymetry/topography grid and synthetic abyssal hill fabric based on an anisotropic power spectral model published by Goff and others [2010, 2020]. The synthetic abyssal fabric fills the voids in the real bathymetry coverage that used to be filled by predicted depth. These are global grids with 86400 columns and 43200 rows in NETCDF format. Seamount Heights used in SYNBATH_V1.2 December 15, 2021 This directory contains the locations and heights of the seamounts in the combined New and Kim Wessel (KW) catalogues.  There are three categories of seamounts. 1) good.nxybh - contains 34295 with heights successfully modeled using the VGG as described in the Sandwell 2022 publication. The file has 5 columns: name  longitude latitude base_depth height_VGG KW-00001 0.191666666667 -6.44166666667 -4060.15673828 2600 KW-00002 -0.425 -6.84166666667 -4125.54345703 2600 KW-00003 -0.075 -6.875 -4221.48730469 2600 . . . 2) uncharted.nxybh - contains 19732 seamounts that are more than 3 km from a depth sounding. The file has 5 columns: name  longitude latitude base_depth height_VGG New-00001 3.60833333333 2.74166666667 -3990.33374023 2000 New-00002 3.375 2.59166666667 -4113.46435547 1500 New-00003 3.19166666667 2.475 -4209.29638672 1200 . . . 3) well_charted.nxybh - contains 739 seamounts that are well charted by more than 50% sounding coverage over the seamount and good coverage at the summit so the summit depth is known. The file has 6 columns: name  longitude latitude base_depth height_VGG summit_depth New-00707 -8.525 71.4916666667 -2094.12524414 1000      -1044.107788 New-00786 -4.775 70.0083333333 -2978.39868164 1200      -2427.73095683 New-00808 -4.375 66.2583333333 -3378.35644531 1100      -2656.7897947 . . . In addition, there are three matching kmz-files so the locations of the seamounts can be viewed in Google Earth. good.kmz        - yellow dots uncharted.kmz        - red dots well_charted.kmz    - green dots

Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains

Dec 15, 2021   Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains   Carlos M. Carrillo and Francisco Muñoz-Arriola   Motivation The data presented here was used to investigate the uskills of precipitation in the US northern Great Plains, and it can be cited as described below. The original data for producing this data is from the Climate Forecast System (CFS) retrospective reanalysis and reforecast as well as precipitation data from the Climate Prediction Center (CPC) from the National Oceanic and Atmospheric Administration (NOAA). Also, gridded data is from the North American Regional Reanalysis (NARR) from the National Centers for Environmental Prediction (NCEP).   License Creative Commons CC-BY Disclaimer The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous. Description of the dataset Files are provided with the following features: List of cases:              files.0.00.dy.txt Low-level jet (or the GP-LLJ index) Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/ Master file: LLJ_pc_corr_1D_pdf_full.m With input data             from CFS models,                    eof1.v850.cfs.1982-2009.dy.tar         pc1.v850.cfs.1982-2009.dy.tar        from NARR model,         pc1.vwnd.narr.1982-2009.tar The geopotential height (or CGT index): Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/ Master file: Z200_mode_corr_1D_pdf_full.m With input data             xt-reco-z200.Full.123.z200.cfs.1982-2009.12-60.tar      xt-reco-z200.Full.z200.narr.1982-2009.bin.tar Precipitation at the US Great Plains: Originally located at /home/cmc542/2019/sum-pred/clim/yrcases Master file: prec_corr_cfs_1D_pdf_full.m With input data:             prec.cfs.MW.1982-2009.tar      prec.cpc.MW.1982-2009.tar Correlation patterns:      Precipitation: PREC.NGP.corr.txt      LLJ: LLJ.pcs.corr.narr.pdf.txt      Z200: Z200.pcs.corr.narr.pdf.txt   Disclaimer The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous. Description of the dataset Files are provided with the following features: List of cases:              files.0.00.dy.txt Low-level jet (or the GP-LLJ index) Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/ Master file: LLJ_pc_corr_1D_pdf_full.m With input data             from CFS models,                   eof1.v850.cfs.1982-2009.dy.tar         pc1.v850.cfs.1982-2009.dy.tar        from NARR model,         pc1.vwnd.narr.1982-2009.tar The geopotential height (or CGT index): Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/ Master file: Z200_mode_corr_1D_pdf_full.m With input data             xt-reco-z200.Full.123.z200.cfs.1982-2009.12-60.tar      xt-reco-z200.Full.z200.narr.1982-2009.bin.tar Precipitation at the US Great Plains: Originally located at /home/cmc542/2019/sum-pred/clim/yrcases Master file: prec_corr_cfs_1D_pdf_full.m With input data:             prec.cfs.MW.1982-2009.tar      prec.cpc.MW.1982-2009.tar Correlation patterns:      Precipitation: PREC.NGP.corr.txt      LLJ: LLJ.pcs.corr.narr.pdf.txt      Z200: Z200.pcs.corr.narr.pdf.txt Credit Carlos M. Carrillo and Francisco Muñoz-Arriola, 2021: “Sources of Subseasonal Predictability of Rainfall in the Northern Great Plains”, Journal of Applied Meteorology and Climatology. In review. Grant funding This research was funded by the U.S. Geological Survey (USGS), the U.S. Department of Agriculture (USDA), the Daugherty Water for Food Global Institute (DWFI) at the University of Nebraska-Lincoln (UNL), and the UNL’s Layman Award.

Explaining human mobility predictions through a pattern matching algorithm

The name of the file indicate information: {type of sequence}_{type of measure}_{sequence properites}_{additional information}.csv {type of sequence} - 'synth' for synthetic or 'london' for real mobility data from London, UK. {type of measure} - 'r2' for R-squared measure or 'corr' for Spearman's correlation {sequence properties} - for synthetic data there are three types of sequences, described in the research article (random, markovian, nonstationary). For real mobility data this part includes information about data processing parameters: (...)_london_{type of mobility sequence}_{DBSCAN epsilon value}_{DBSCAN min_pts value}. {type of mobility sequence} is 'seq' for next-place sequences and '30min' or '1H' for the next time-bin sequences and indicate the size of the time-bin. Files with 'predictability' at the end of the file contain R-squared and Spearman's correlation of measures calculated in relation to the predictability measure. R2 files include values of R-squared for all types of modelled regression functions. 'line' indicates {y = ax + b} for single variable and {y = ax + by + c} for two variables. 'expo' indicates {y = a*x^b + c} for single variable and {y = a*x^b + c*y^d + e} for two variables 'log' indicates {y = a*log(x*b) + c} for single variable and {y = a * x + c * log(y) + e + d*x * log(y)} for two variables. 'logf' indicates {y = a*log(x) + c * log(y) + e + b*log(x) * log(y)} for two variables

Supplementary data for: "Historical glacier change on Svalbard predicts doubling of mass loss by 2100"

Supplementary datasets for: Geyman, E.C., van Pelt, W.J.J., Maloof, A.C., Faste Aas, H., and Kohler, J., 2022. "Historical glacier change on Svalbard predicts doubling of mass loss by 2100." Nature. Abstract: The melting of glaciers and ice caps accounts for about one-third of current sea-level rise, exceeding the mass loss from the more voluminous Greenland or Antarctic Ice Sheets. The Arctic archipelago of Svalbard, which hosts spatial climate gradients that are larger than the expected temporal climate shifts over the next century, is a natural laboratory to constrain the climate sensitivity of glaciers and predict their response to future warming. Here we link historical and modern glacier observations to predict that twenty-first century glacier thinning rates will more than double those from 1936 to 2010. Making use of an archive of historical aerial imagery from 1936 and 1938, we use structure-from-motion photogrammetry to reconstruct the three-dimensional geometry of 1,594 glaciers across Svalbard. We compare these reconstructions to modern ice elevation data to derive the spatial pattern of mass balance over a more than 70-year timespan, enabling us to see through the noise of annual and decadal variability to quantify how variables such as temperature and precipitation control ice loss. We find a robust temperature dependence of melt rates, whereby a 1°C rise in mean summer temperature corresponds to a decrease in area-normalized mass balance of -0.28 m yr-1 of water equivalent. Finally, we design a space-for-time substitution8 to combine our historical glacier observations with climate projections and make first-order predictions of twenty-first century glacier change across Svalbard.   Dataset description:  This dataset contains the digital elevation models (DEMs), elevation change maps, point clouds, orthophotos, and vector outlines of glacier extents based on the Norwegian Polar Institute's collection of 5,507 high-oblique aerial images captured over Svalbard in 1936/1938. The photographs were analyzed through structure-from-motion (SfM) photogrammetry to generate 3D models. We also provide an .xlsx spreadsheet containing glacier-by-glacier statistics of ice loss and climate fields. Note that all of the raster and point cloud files listed below have been georeferenced in Metashape using the ground control points (GCPs) illustrated in Main Text, Fig. 2e, but have not undergone the co-registration and bias-correction following the methods of Nuth & Kaab (2011), which was done on a glacier-by-glacier basis. However, the glacier change budgets in the .xlsx file [#5 below] do reflect the values from the glacier-by-glacier co-registered and bias-corrected DEMs. See below for descriptions of each dataset (each number below corresponds to a different zipped folder). -------------------------------------------------------------------------------------  Svalbard-wide datasets [all georeferenced Svalbard-wide datasets are in the coordinate system UTM 33N]:  1. Svalbard-wide 1936 DEM (20 m and 50 m resolution) [georeferenced .tif file]  2. Svalbard-wide 1936 orthophotomosaic (20 m resolution) [georeferenced .tif file]  3. Svalbard-wide dh (1936-2010) (20 m and 50 m resolution) [georeferenced .tif file]  4. Shapefile of 1936 glacier extents [ESRI .shp file]  5. Glacier-by-glacier statistics [.xlsx file]  -------------------------------------------------------------------------------------  Regional-datasets:  Due to file size limitations, the high-resolution (5 m) datasets are split into the 8 regions illustrated in Main Text, Fig. 2d:  Zone 1 - South Spitsbergen Zone 2 - Barentsoya-Edgeoya Zone 3 - Austfonna Zone 4 - Vestfonna Zone 5 - Northeast Spitsbergen Zone 6 - Central Spitsbergen Zone 7 - Northwest Spitsbergen Zone 8 - North Spitsbergen 6. Regional 1936 DEMs (5 m resolution) [georeferenced .tif files]  7. Regional dh (1936-2010) (5 m resolution) [georeferenced .tif files]  8. Local 1936 orthomosaics (5 m resolution) [georeferenced .tif files]  9. Unprocessed point clouds [.laz files]. These files represent the raw 3D point clouds (x,y,z) generated in Agisoft Metashape for each of the 17 local models described in Extended Data Figure 3. 10. Thumbnail-sized copies of the 5,507 historical aerial images (1936 and 1938) analyzed in this study, along with a .csv file labeling the approximate location of each photograph.

opencc-by-4.0Jan 2022View details →

# Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment) with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals dysregulated cell type gene dysregulatory mechanisms in AD.

opencc-by-4.0Jan 2022View details →

A Dataset for Utility Prediction in Computational Persuasion with Machine Learning Techniques

This dataset contains data for a new benchmark for the prediction of user's utilities with Machine Learning techniques for Computational Persuasion. This work has been accepted at AAAI-22, more information in the relative repository containing the source code: <a href="https://github.com/ivanDonadello/ML-Argument-Based-Computational-Persuasion">https://github.com/ivanDonadello/ML-Argument-Based-Computational-Persuasion</a>

opencc-by-4.0Sep 2021View details →

Data for Universal prediction of cell cycle position using transfer learning

The repo contains the data for Universal prediction of cell cycle position using transfer learning (https://www.biorxiv.org/content/10.1101/2021.04.06.438463v2). The scripts to analyze and generate all figures could be found at https://github.com/hansenlab/tricycle_paper_figs v1.1 update: add neurosphere_scvelo.qs - an R SingleCellExperiment object saved as qs file that has spliced counts, unspliced counts, and all outputs from scvelo.

opencc-by-4.0Sep 2021View details →

TransProteus, Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers from images

We present TransProteus, a dataset, for predicting the 3D structure and properties of materials, liquids, and objects inside transparent vessels from a single image without prior knowledge of the image source and camera parameters. Manipulating materials in transparent containers is essential in many fields and depends heavily on vision. This work supplies a new procedurally generated dataset consisting of 50k images of liquids and solid objects inside transparent containers. The image annotations include 3D models and material properties (color/transparency/roughness...) for the vessel and its content. The synthetic (CGI) part of the dataset was procedurally generated using 13k different objects, 500 different environments (HDRI), and 1450 material textures (PBR) combined with simulated liquids and procedurally generated vessels. In addition, we supply 104 real-world images of objects inside transparent vessels with depth maps of both the vessel and its content. Note that there are two files here: <a href="https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z">Transproteus_SimulatedLiquids2_New_No_Shift.7z</a> and <a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TranProteus2.7z?versionId=f16e7126-8750-41f7-99e6-d35ca60399cc">TranProteus2.7z </a>, contain subset of the virtual CGI data set. https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z <a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TransProteus_RealSense_RealPhotos.7z">TransProteus_RealSense_RealPhotos.7z </a>: Contain real-world photos scanned with real sense with depth map of both the vessel and its content See ReadMe file in side the downloaded files for more details The full dataset (>100gb) can be found here: <a href="https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV">https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV</a> https://<a href="http://icedrive.net/1/6cZbP5dkNG">icedrive.net/1/6cZbP5dkNG</a> See: <a href="https://arxiv.org/pdf/2109.07577.pdf"> https://arxiv.org/pdf/2109.07577.pdf</a> for more details <a href="https://zenodo.org/record/4736111#.YVOAx3tE1H4">**This dataset is complementary to LabPics dataset with 8k real images of materials in vessels in chemistry labs, medical labs, and other settings. The LabPics dataset can be downloaded from here:</a> <a href="https://zenodo.org/record/4736111#.YVOAx3tE1H4">https://zenodo.org/record/4736111#.YVOAx3tE1H4</a>   ************************************************************************************ <a href="https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z">Transproteus_SimulatedLiquids2_New_No_Shift.7z </a>and <a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TranProteus2.7z?versionId=f16e7126-8750-41f7-99e6-d35ca60399cc">TranProteus2.7z</a> The two folders contain relatively similar data styles. The data in No_Shift contain images that were generated with no camera shift in the camera paramters. If you try to predict 3d model from an image as a depth map, this is easier to use (Otherwise, you need to adapt the image using the shift). For all other purposes, both folders are the same, and you can use either or both. In addition, a  real image dataset for testing is given in the RealSense file.

PlasX model, Predicted plasmids, and Known plasmids

PlasX model and all analyses of known and predicted plasmids