Find research datasets worth reusing

opencc-by-4.0Apr 2024View details →

Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures

This Dataset comprises two sub-sets of information: <ul> <li>Database and Results of the work present in the paper "Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures" published in The Journal of Physical Chemistry B (https://doi.org/10.1021/acs.jpcb.4c04432).</li> <li>Sample of the code used, in order to reproduce any of the results presented above. This can be found in the previous version of this Dataset (v1.0 https://zenodo.org/records/11216901)</li> </ul>   Regarding the sample code, an example for all ANN Models used in this work is provided. This includes the three models used: <ol> <li>One based only on Critical Properties of Ionic Liquids (CRT Model)</li> <li>One based only on Structural Properties of Ionic Liquids (STR Model)</li> <li>One combination of the previous models, taking into account both Critical and Structural Properties (COMB Model)</li> </ol> In this manner, it is possible to observe the differences between the performance of the different models, either through statiscal analysis or using graphical representation. This allows for the benchmarking to be done in a more concise way.

opencc-by-4.0Jan 2022View details →

Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties

This dataset provides data described and used in the article submitted to Journal of Advances in Modeling Earth Systems "Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties". It contains .csv files with different properties computed on outputs of the model Snow3D simulating equi-temperature metamorphism. This micro-scale model was used here with experimental micro-tomographic snow images as input and returns series of 3-D images of snow showing features of equi-temperature metamorphism at different time steps as output. In this dataset, you will find two types of files: - the microstructural properties (density, specific surface area, covariance lengths, mean curvature) computed on  the simulated images at different time steps. - the transport properties (effective conductivity, normalizes effective vapor diffusion coefficient, permeability) of the simulated images at different time steps. Finally, metadata_simulations.csv gather the information relative to the simulations.

opencc-by-4.0Sep 2022View details →

Data for the "Technical Note: assessing predicted cirrus ice properties between two deterministic ice formation parameterizations" manuscript

This repository contains the post-processed ECHAM-HAM data files for comparing KM21_GCM and ML20 of "Technical Note: assessing predicted cirrus ice properties between two deterministic ice formation parameterizations" study. The files are all netCDF4.

opencc-by-4.0Dec 2020View details →

Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Supplementary Tables

This repository contains the Supplementary Tables for  Suriyalaksh et al.  Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive  of novel ageing genes. The list of table files can be found in <a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/Supplementary%20table%20guide.pdf">Supplementary Tables guide.pdf</a> Tables S1, S2 and S3 corresponding to physical gene-gene interaction data are in a separate repository doi:10.5281/zenodo.4382337 Details about some of the Supplementary tables: TableS4_inferred_networks.csv - list of inferred GRNs for specified input combinations (set of input regulators, length of the time sequence, NI tool and prior used). <a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS5_consensus_network_member.xlsx">TableS5_consensus_network_member.xlsx</a> - list of groups of topologically similar GRNs (from Table S4) Table S6: edge lists (source,target) for each one of the three consensus networks selected according to the GS validation metrics: middle PFE/AUFE, max AUFE, max PFE. TableS6a_max_AUFE_GRN.txt - max AUFE; largest network - this is the one we used in the main analysis and discussion TableS6b_max_PFE_GRN.xt - max PFE TableS6c_middle_AUFE_PFE_GRN.txt - middle PFE/AUFE <a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS7_qRTPCR_ddCt_network_accuracy.csv">TableS7_qRTPCR_ddCt_network_accuracy.csv</a> - gene expression count differences for RNAi knockdown GRN validation experiments.  Table S8: Group membership for each one of the nodes in each one of the selected networks according to the SBM that best describes the observed network topology. Each column shows the group membership for each level in a SBM block hierarchy. Our analysis is in the second most coarse-grained level (level 1). TableS8a_max_AUFE_SBM.csv TableS8b_max_PFE_SBM.csv TableS8c_middle_AUFE_PFE_SBM.csv <a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS9_glp_gs_datasets.pdf">TableS9_glp_gs_datasets.pdf</a> - list of datasets used for defining functional clusters. TableS14a_glp_l1_vs_fem_l1_lifespan_assay.xlsx - Day13 survival of fem-3(q20)ts vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1 TableS14b_glp_l1_vs_glp_l4_lifespan_assay.xlsx - Day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L4 vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1 TableS15a_glp1_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of glp-1(e2144)ts;rrf-3(pk1426) TableS15b_fem3_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of fem-3(q20)ts TableS17_input_regulators_annotated.csv - list input regulators used as input for Network Inference Tools annotated by source type (2nd column): GenAge, known transcription factors (TF) and gene with high variability in the gene expression time series (HV). The third column lists whether that regulator has an orthologue in human (y) according to WormBase (v 278). TableS20_epistasis_lifespan_data.xlsx - Epistasis lifespan data of glp-1(e2144)ts All the image (TIF) files represent representative images in the following genetic backgrounds (below) that have been treated  with empty vector (EV) or RNAi against the gene highlighted in the title of the image. See methods section for details.  femliu1:  fem-3(q20)ts.; dhs-3p::dhs-3::gfp femsod3: fem-3(q20)ts.; sod-3p::gfp glp1lgg1: glp-1(e2144); lgg-1p:lgg-1:gfp

opencc-by-4.0Dec 2020View details →

Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.

This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive  of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms  We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references).  This repository contains 3 different files: TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions TableS2_physical_priors.xlsx - contains three tabs: ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) +  ChIP-seq datasets (GSE28350, GSE81521) from  (Hochbaum et. al, 2011, Li et. al, 2016). eY1HATAC- contains  3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016). motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1 TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources) --- Description of methodology to obtain regulatory interactions in TableS2: Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations. For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands. An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs. For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair. For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).

opencc-by-4.0Mar 2024View details →

Supplementary material for the publication: "Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties"

This dataset contains supplementary code, images and models for the publication &bdquo;Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties“.   The content will be updated and additionally linked to the corresponding git repositories.

opencc-by-4.0Feb 2024View details →

Supplementary material from: Prediction of the Cold Flow Properties of Biodiesel using the FAME Distribution and Machine Learning Techniques

The dataset is divided into three sections within the worksheet.  The first section contains the definition of the data's feedstock and its source reference. The reference includes the year, DOI (if available, as some are collected from books), publication journal, article title, and authors.  The second section describes the FAME distribution, starting from C4:0 up to C24:0, including a column of unidentified FAMEs. The third and final section describes the measured properties Cloud Point (CP), Cold Filter Plugging Point (CFPP) and Pour Point (PP).

opencc-by-4.0Nov 2024View details →

CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction

<div> <div> Application of artificial intelligence (AI) has been ubiquitous in the growth of research in the areas of basic sciences. Frequent use of machine learning (ML) and deep learning (DL) based methodologies by researchers has resulted in significant advancements in the last decade. These techniques led to notable performance enhancements in different tasks such as protein structure prediction, drug-target binding affinity prediction, and molecular property prediction. In material science literature, it is well-known that crystalline materials exhibit topological structures. Such topological structures may be represented as graphs and utilization of graph neural network (GNN) based approaches could help encoding them into an augmented representation space. Primarily, such frameworks adopt supervised learning techniques targeted towards downstream property prediction tasks on the basis of electronic properties (formation energy, bandgap, total energy, etc.) and crystalline structures. Generally, such type of frameworks rely highly on the handcrafted atom feature representations along with the structural representations. In this paper, we propose an unsupervised framework namely, CrysAtom, using untagged crystal data to generate dense vector representation of atoms, which can be utilized in existing GNN-based property predictor models to accurately predict important properties of crystals. Empirical results show that our dense representation embeds chemical properties of atoms and enhance the performance of the baseline property predictor models significantly. </div> </div>

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)

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.

opencc-by-saMar 2021View details →

A Data Resource for Prediction of Thermodynamic Properties of Small Molecules

We developed a database of 2869 experimental values of enthalpy of formation and 1403 values for entropy for substances composed of stable small molecules, derived from the literature. We developed a model for predicting enthalpy of formation and entropy from semiempirical quantum mechanical calculations of energy and atom counts, and applied the model to a comprehensive database of 16,417 small molecules. The database of small-molecule thermodynamic properties will be useful for predicting the outcome of any process that might involve the generation or destruction of volatile products, such as atmospheric chemistry, volcanism, or waste pyrolysis. Additionally, the collected experimental thermodynamic values will be of value to others developing models to predict enthalpy and entropy.

opencc-by-4.0Sep 2024View details →

Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures

This dataset comes from the following paper: Chiara Pasini, Oscar Ramponi, Stefano Pandini, Luciana Sartore, Giulia Scalet, Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures, J. of Materi Eng and Perform, 2024. <a href="https://doi.org/10.1007/s11665-024-10199-x">https://doi.org/10.1007/s11665-024-10199-x</a> It contains: <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. m of the neural network</li> <li>. inp of the Abaqus finite element simulations</li> </ul>

opencc-by-4.0Jan 2023View details →

Arthropod food webs predicted from body length ratios are improved by incorporating prey defensive properties

This dataset was used to run for Van de Walle et al. (2023). Arthropod food webs predicted from body length ratios are improved by incorporating prey defensive properties. Journal of Animal Ecology, 92(4), 913-924. <a href="https://doi.org/10.1111/1365-2656.13905">https://doi.org/10.1111/1365-2656.13905</a> "Arthropod_feeding_trials.csv" contains the results of the experimental feeding trials. Each row contains information on species taxonomy, body size and hunting strategy within a single trial. Abstract Trophic interactions are often deduced from body size differences between predators and potential prey, assuming predators prefer prey smaller than themselves because larger prey are more difficult to subdue. This hypothesis has mainly been confirmed in aquatic ecosystems, but rarely in terrestrial ecosystems, especially in arthropods. Our goal was to validate whether body size ratios can accurately predict trophic interactions in a terrestrial, plant-associated arthropod community. Additionally, we tested whether predator hunting strategy and prey taxonomy could explain possible deviations from this general rule. We collected arthropods from marram grass in coastal dunes and conducted pairwise feeding trials to explicitly test whether two individuals, of the same or different species, would predate each other. From the trial results, we constructed one of the most complete, empirically derived food webs for terrestrial arthropods associated with a single plant species. We contrasted this empirical food web with a theoretical web based on body size ratios, literature and expert knowledge. In our feeding trials, predator-prey interactions were indeed largely size-based. Moreover, the theoretical food web based on body size, activity period, microhabitat and expert knowledge converged quite well with the food web based on experimental feeding trials for both predator and prey species. However, predator hunting strategy, but mainly prey taxonomy improved predictions of predation events. Well-defended taxa, such as hard-bodied beetles, were less frequently consumed than expected based on their body size. Body size ratios predict trophic interactions among plant-associated arthropods fairly well. However, traits such as hunting strategy and anti-predator defences can explain why certain trophic interactions do not adhere to size-based rules. Feeding trials can generate insights into multiple traits underlying real-life trophic interactions among arthropods. Such insights are much needed as the dramatic global decline in arthropod species richness and abundance is knocking out many trophic interactions on which services such as pest control and nutrient cycling depend.

opencc-by-4.0Jun 2023View details →

Predicted drug-likeness and toxicity properties of Salvia fruticosa compounds

The datasets present computationally predicted drug-likeness and toxicity profiles of Salvia frutocosa compounds.

opencc-by-4.0Sep 2023View details →

Soil chemistry dataset from the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica"

Bases sum, H+Al (potential acidity), pH, phosphorous, remaining P (P-rem), sodium and total organic carbon distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The quantile and prediction interval data represent the spatial uncertainty of the predictions. As soon as the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica" is published, the paper will be cited here.  The .zip file contains the following folders: 1) soil_chemistry_antarctica: data containing the soil chemical attributes distribution 2) soil_chemistry_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil attributes prediction 4) soil_texture_quantile05: quantile 5% of the soil attributes prediction 5) soil_texture_quantile95: quantile 95% of the soil attributes prediction

opencc-by-4.0Nov 2024View details →

Electronic and optical properties of computationally Predicted Na-K-Sb crystals

This is the dataset for the identically-named paper "Electronic and optical properties of computationally predicted Na-K-Sb crystals". This dataset contains output files from exciting for the two considered polymorphs: hexagonal Na2KSb (hNa2KSb.zip) and cubic NaK2Sb (cNaK2Sb.zip). Inside each zip file contains: <ul> <li>INFO.OUT, INFOXS.OUT, GW_INFO.OUT, INFO_SCR.OUT</li> <li>Lattice-optimised input.xml files for both crystals</li> <li>BAND_Sxx_Ayyyy.OUT files for projected bandstructures</li> <li>PDOS_Sxx_Ayyyy.OUT files for projected density of states</li> <li>BAND.OUT, BANDLINES.OUT, BAND-QP.OUT, IDOS.OUT and TDOS/TDOS-QP.OUT for DOS</li> <li>EFERMI.OUT and EIGVAL.OUT</li> <li>EPSILON files from the dielectric tensor with/without the TDA and IQPA</li> <li>EXCITON files from the dielectric tensor with/without the TDA and IQPA</li> <li>KPATH files for considered excitons with the TDA</li> </ul>

opencc-by-4.0Jan 2022View details →

Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water

<ul> <li> Description</li> </ul> The 1520 water clusters were extracted from Ref. 1. The respective energies and atomic forces were recomputed at the revPBE-D3/def2-TZVP [2-6], B3LYP-D3/def2-TZVP [4-6, 7, 8], and  BLYP-D3/def2-TZVP [4-6, 7, 9] level. <ul> <li> Format</li> </ul> The data is stored in python compressed array format (.npz) with the atomization energy in kcal/mol and atomic forces in kcal/mol/Ang. The data set contains five np.ndarray <pre><code>import numpy as np data = np.load('revpbe.npz') data['R'] # Cartesian coordinates of nuclei in Ang. data['E'] # Total energy in kcal/mol data['F'] # Atomic forces in kcal/mol/Ang. data['N'] # Number of atoms in each structure data['Z'] # Nuclear charges</code></pre> References <ul> </ul> [1] Molpeceres G., Zaverkin V., and Kästner J., “Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – I. adsorption and desorption,” Mon. Not. R. Astron. Soc. 499, 1373 (2020). [2] P. E. Blöchl, “Projector augmented-wave method,” Phys. Rev. B 50, 17953 (1994). [3] Y. Zhang and W. Yang, “Comment on “generalized gradient approximation made simple”,” Phys. Rev. Lett. 80, 890 (1998). [4] S. Grimme, J. Antony, S. Ehrlich, and H. Krieg, “A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu,” J. Chem. Phys. 132, 154104 (2010). [5] F. Weigend and R. Ahlrichs, “Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy,” Phys. Chem. Chem. Phys. 7, 3297 (2005). [6] F. Weigend, “Accurate Coulomb-fitting basis sets for H to Rn,” Phys. Chem. Chem. Phys. 8, 1057 (2006). [7] A. D. Becke, “Density-functional thermochemistry. iii. the role of exact exchange,” J. Chem. Phys. 98, 5648 (1993). [8] P. J. Stephens, F. J. Devlin, C. F. Chabalowski, and M. J. Frisch, “Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields,” J. Phys. Chem. 98, 11623 (1994). [9] C. Lee, W. Yang, and R. G. Parr, “Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density,” Phys. Rev. B 37, 785 (1988).

opencc-by-4.0Aug 2017View details →

Individual-level trait diversity predicts phytoplankton community properties better than species richness or evenness

This archive includes the final summary tables used for the analyses.