Find research datasets worth reusing

opencc-by-4.0Aug 2024View details →

Semi-Supervised Pre-trained Foundation Model for 3D Structural Feature Analysis of Seismic Images

Codes, trained model, and datasets for the paper "Semi-Supervised Pre-trained Foundation Model for 3D Structural Feature Analysis of Seismic Images".

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>

Bacteriophage phi29 Structural Model at Atomic Resolution

Bacteriophage phi29 structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage phi29 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com

Bacteriophage SPP1 Structural Model at Atomic Resolution

Bacteriophage SPP1 structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage SPP1 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com

Mycobacteriophage ZoeJ Structural Model at Atomic Resolution

Mycobacteriophage ZoeJ structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose mycobacteriophage ZoeJ using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com

Adenovirus D26 Structural Model at Atomic Resolution

Adenovirus D26 structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose Adenovirus D26 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com

Theoretical Models of Ultra-Cool Objects (Brown Dwarfs and Free-Floating Planets) Including Water Latent Heat Effect: Thermal Structure, Spectra, and Photometry

OVERVIEW Data presented here are from the work of Tang et al. (2021), which present a one-dimensional (vertical) atmospheric structure model for ultra-cool objects that includes moist adiabatic convection (water latent heat release effect). Model grids across a range of effective temperatures (350, 300, 250, 200 K), metallicities ([M/H] of 0.0, 0.5, 0.7, 1.5), and gravities (logg of 4.0, 4.5, 4.7, 5.0) that mimic the atmosphere condition of Y-class brown dwarf and Free-Floating Planets. ATMOSPHERIC THERMAL STRUCTURE The pressure-temperature profile are saved under the ./pT_profiles/, which have two folders: <ol> <li>pT_profile_dry_adiabat: for dry adiabatic treatment.</li> <li>pT_profile_moist_adiabat: for moist adiabatic treatment.</li> </ol> File name of each compressed files gives the effective temperature (Teff, in Kelvin), gravity (in MKS), and metallicity (in [M/H]) information as for example: t200g100nc_m0.0.cmp.gz --> Teff of 200K, gravity as 100 m/s2, and [M/H]=0.0. The table formate of the pressure-temperature profile, i.e., the .cmp files, is made to work with the [<a href="https://natashabatalha.github.io/picaso/">PICASO software</a>](https://natashabatalha.github.io/picaso/). Each .cmp file contains <ul> <li>Column 01   (x)                   : atmosphere pressure level</li> <li>Column 02   (pressure)       : atmosphere pressure in bar</li> <li>Column 03   (DEN)              : atmosphere density in cm^{-3}</li> <li>Column 04   (temperature) : atmosphere temperature in kelvin</li> <li>Column 05-14                    : molecule mixing ratios for H2, He, CH4, H2O, NH3, CO, PH3, H2S, N2, CO2</li> <li>Column 15   (MU)               : mean molecular weight in grams/mole</li> </ul> SPECTRA Model spectra are saved under the ./spectra/ with wavelength ranging from 1 to 30 micron. File name of each compressed file gives the effective temperature (Teff, in Kelvin), gravity (in MKS), metallicity (in [M/H]), adiabatic treatment, and the resolving power information as for example: sp_t200_g100_m0.0_dry_R3000.csv.gz --> Teff of 200K, gravity as 100 m/s2, [M/H]=0.0, with dry adiabatic treatment, and R=3000. Each spetrum .cv file contains two columns: <ul> <li>Column 01 wavelength [micron]</li> <li>Column 02 Flux            [W/m2/micron], the spectral fluxes are given at the top of the atmosphere, taken to be 1 Jupiter radius.</li> </ul> PHOTOMETRY Photometry data derived from the spectra at the top of the atmosphere (assuming one Jupiter radius) are given in the syn_photometry.csv. Photometry filters are from the Mauna Kea Observatory (MKO) photometry system, the Wide-field Infrared Survey Explorer (WISE), and the Spitzer Space Telescope. Filter profiles and Vega magnitude zero points are from the [<a href="http://svo2.cab.inta-csic.es/theory/fps/">SVO Filter Profile Service</a>](http://svo2.cab.inta-csic.es/theory/fps/) <ul> <li>Column 01 (gravity)  , gravity in MKS</li> <li>Column 02 (teff)       , effective temperature in Kelvin</li> <li>Column 03 ([M/H])    , metallicity</li> <li>Column 04-12           , photometry magnitude for IRAC36, IRAC45, W1, W2, GiminiM, MKO_J, MKO_H, MKO_K, MKO_Lp all in Vega system.</li> <li>Column 13 (adiabatic), adiabatic treatment; D: dry adiabatic, M: moist adiabatic</li> </ul> CREDITS Please cite Tang et al. (2021, Astrophysical Journal, in press.) [<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210507000T/abstract">ADS link</a>](https://ui.adsabs.harvard.edu/abs/2021arXiv210507000T/abstract) if you used data here in your research.

opencc-zeroOct 2021View details →

Data from: Dinosaurian survivorship schedules revisited: new insights from an age-structured population model

Little is known on dinosaur population biology due to insufficient information on age-dependent fecundities and mortalities. So far, survivorship curves (hereafter SC) of only six dinosaurs (four tyrannosaurs, one ceratopsian, one hadrosaur) were erected from bone assemblages of aged specimens. They indicate high survival throughout most of their life with presumable higher mortalities after hatching and increasing mortalities towards its end. However, all studies ignored that assemblages must preserve stationary age distributions (i.e., the population's age distribution is stable and its size is constant over time as overall population fecundities match mortalities, hereafter SAD population) to infer a reliable SC for a taxon. To assess SCs of these dinosaurs, I built a simple population model with age-dependent fecundities and survival rates. Its few input parameters are maximum longevity, age at sexual maturation and maximum annual offspring number, on which information exists in these dinosaurs. As bone histological studies and scaling relationships provide estimates on its three parameters, my model is also applicable to other extinct taxa. Modelling suggests that bone assemblages did not preserve SAD populations. SCs determined for SAD populations of Albertosaurus sarcophagus, Gorgosaurus libratus, Dasplatosaurus torosus and Tyrannosaurus rex indicated that low mortalities follow high mortalities early in their life or that mortalities were rather constant throughout their life. In Psittacosaurus lujiatuensis modelling suggests low mortalities throughout most of its life that increase towards its end. The SC of Maiasaura peeblesorum was not questioned by my model as it is unable to capture sigmoidal or other composite SCs.

opencc-byOct 2022View details →

Structural models of ProPEc in inward-facing and outward-facing conformations

This deposit contains relevant data to supplement the study titled as "Structural determinants and functional significance of dimerization for osmosensing transporter ProP in Escherichia coli" to be published by Ozturk et al. (2022).  ProPEc_XylEEc_all_alignments.rtf is the list of pairwise alignments of E.coli ProP and XylE generated by different alignment tools propec-inward-facing-homology-model-based-on-4qiq.pdb is the inward-facing structural model of ProPEc. propec-outward-facing-homology-model-based-on-4gc0.pdb is the outward-facing structural model of ProPEc.

opencc-by-4.0Nov 2022View details →

Terrestrial laser scanning data Wytham Woods: individual trees and quantitative structure models (QSMs)

This dataset was used for the analysis of the following publication: Laser scanning reveals potential underestimation of biomass carbon in temperate forest. Calders, K, Verbeeck, V, Burt, A, Origo, N, Nightingale, J, Malhi, Y, Wilkes, P, Raumonen, P, Bunce, R G H and Disney, M. Ecological Solutions and Evidence (accepted) Any use of this dataset should cite the paper above (Creative Commons Attribution 4.0 International Public License). Contact: kim.calders@ugent.be   ================================================                     Dataset ================================================ General:  TLS data were collected in leaf-off conditions during late November 2015 - January 2016. Windy days were avoided to ensure data quality. We used a RIEGL VZ-400 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH). The instrument has a beam divergence of 0.35 mrad and operates in the infrared (wavelength 1550 nm) with a range up to 350 m. The pulse repetition rate for each scan was 300 kHz, the minimum range was 0.5 m and the angular sampling resolution was 0.04°. This resulted in 22,500,000 outgoing pulses for a single scan, resulting in a beam diameter of 2.45 cm and beam spacing of 3.5 cm at 50 m (for example). The azimuth angle range was 0-360° and the zenith angle range was 30-130°. Therefore an additional scan was acquired at each scan location with the scanner tilted at 90° from the vertical to complete sampling of the full hemisphere at each location. Scans were done in a larger 6 ha area using an approximate 20 m × 20 m grid, to ensure the best possible data quality within our 1.4 ha study area. Trees which had at least more than half of their stem at tree diameter 1.3 m inside the boundaries of the study area were included [ Note that this dataset contains 876 individual trees, but after applying the boundary conditions, 835 trees within the study area were used in the analysis of the paper >> see TLS_Inventory.ipynb] Full details of the methods to segment individual trees and generate the QSMs can be found in the paper Calders et al. Ecological Solutions and Evidence. Tree ID: Tree IDs can have numbers only or numbers + letters. A number only means this was a base with one stem. A number + letter means individual trees (split below 1.3m), that share a common tree base. Datasets: 1) DATA_clouds_txt & DATA_clouds_ply: Individually segmented trees in *txt and *ply format. File naming is [tree_id].*txt or [tree_ply].*tx 2) DATA_QSM_opt: optimised QSMs using TreeQSM v2.0 (https://github.com/InverseTampere/TreeQSM). File naming is [tree_id]-[dmin0]-[rcov0]-[nmin0]-[dmin]-[rcov]-[nmin]-[lcyl]-[NoGround]-[iteration].mat  3) Raw scan data can be found here: http://dx.doi.org/10.5285/ed9156e1697343e4ad82e83ed550e345   ================================================                     Paper analysis ================================================ We have provided all scripts (analysis_and_figures) that were used to: 1 ) analyse the data (TLS_Inventory.ipynb): ----- Analysis of point clouds and QSMs using TLS_Inventory.py.ipynb > tls_summary.csv (#876 trees) ----- Link with census &1.4ha > trees_summary.csv (#835 trees) 2) generate the paper figures: ----- various *.R and *.ipynb scripts in the main folder and /allometriesTLS/   ================================================                     Funding ================================================ The TLS fieldwork was funded through the Metrology for Earth Observation and Climate project (MetEOC-2), grant number ENV55 within the European Metrology Research Programme (EMRP). The EMRP is jointly funded by the EMRP participating countries within EURAMET and the European Union. Funds for purchase of the UCL RIEGL VZ-400 instrument was provided by the UK NERC National Centre for Earth Observation (NCEO) and UCL Geography. The census of the forest plot was supported by an ERC Advanced Investigator Grant to Yadvinder Malhi (GEM-TRAIT, grant number 321131).

opencc-by-4.0Jul 2022View details →

CATH Structural domains in AlphaFold2 models for 21 model organisms

CATH structural domain assignments for AlphaFold2 models in 21 model organisms. The table cath-v4_3_0.alphafold-v2.2022-11-22.tsv contains the domain assignments with information on model quality, CATH superfamily and Class, organism, average pLDDT, percentage of residues not in secondary structure, globularity and domain origin (CATH-PDB, CATH-HMM,Pfam,newfams). Organisms included: Arabidopsis thaliana Caenorhabditis elegans Candida albicans Danio rerio Dictyostelium discoideum Drosophila melanogaster Escherichia coli Glycine max Homo sapiens Leishmania infantum Methanocaldococcus jannaschii Mus musculus Mycobacterium tuberculosis Oryza sativa Plasmodium falciparum Rattus norvegicus Saccharomyces cerevisiae Schizosaccharomyces pombe Staphylococcus aureus Trypanosoma cruzi Zea mays   The archive <pre>cath-v4_3_0.alphafold-v2.2022-11-22.by_superfamily.tgz</pre> contains all domains assigned by CATH in the dataset as PDB files, divided by superfamily. Alternatively, if you're interested in a particular organism, an individual tarball containing all CATH domains as PDB files is available e.g. cath-v4_3_0.alphafold-v2.2022-11-22.arabidopsis_thaliana.tgz All domains included in this release are named as af_[UniProt_ID]_[start]_[stop].

opencc-by-4.0Dec 2022View details →

Magnetotelluric data from Santos basin (SE Brazil) and inversion resistivity models exploring basin wedge and deep crustal structure beneath.

Magnetotelluric data Processed data from 90 magnetotelluric broadband stations acquired in are available in Electrical Data Interchange (EDI) and ModEM format. The MMT data were recorded in 2007 by WesternGeco Electromagnetics as part of the National Observatory Rio de Janeiro project funded by Petrobras. The campaign comprised a total of 92 sites from shallow water (about 50 m depth) to deep water (about 1600 m depth). The stations are placed along three NW-SE parallel profiles in the northwest part of Santos basin. The central profile  is approximately 160 km long and consists of 56 stations, while the west profile and east profile extend about 55 km each and contain 18 and 16 stations, respectively.   Models Inversion models and predicted data are present for two different starting resistivity model testes 10 and 1 Ohm.m. The inversion models were estimated using ModEM - modular system for inversion of electromagnetic geophysical data.

opencc-by-4.0Dec 2022View details →

Protein structure model predictions for secreted fungal proteins

Dataset A - Alphafold2 prediction output data for 753 secreted proteins of Rhizophagus irregularis DAOM197198. Gene IDs are taken from the annotation by Yildirir et al. 2021, <a href="https://doi.org/10.1111/nph.17842">doi.org/10.1111/nph.17842</a> Dataset B - Alphafold2 prediction output data for 10 fungal effectors. These are nine effectors from Fusarium oxysporum f. sp. lycopersici and RiSLM from Rhizophagus irregularis as well as their amino acid sequences. Signal peptides and sequences preceding a predicted Kex2 processing site were removed. Dataset C - Alphafold2 prediction output data for 454 matches of a MycFOLD-HMM search across the Mycocosm genome database (<a href="https://mycocosm.jgi.doe.gov/mycocosm/home">https://mycocosm.jgi.doe.gov/mycocosm/home</a>) and 36 Glomeromycotina fungal genomes.

opencc-by-4.0Apr 2023View details →

Species detection histories used in Killion et al. (2023): Integrating Spaceborne Estimates of Structural Diversity of Habitat into Wildlife Occupancy Models

Camera trap species detection histories used for occupancy models in "Integrating Spaceborne Estimates of Structural Diversity of Habitat into Wildlife Occupancy Models".

opencc-by-4.0May 2023View details →

3D structural and probabilistic modeling of geothermal reservoir horizons in the Northern Eifel and its foreland

This repository contains the supplementary data to the submitted publication titled "3D structural and probabilistic modeling of geothermal reservoir horizons in the Northern Eifel and its foreland" which was submitted to the Journal "Geothermal Energy" (https://geothermal-energy-journal.springeropen.com/).

opencc-by-4.0Jun 2023View details →

Accompanying data for the paper "Reduced order modeling of geometrically nonlinear rotating structures using the direct parametrisation of invariant manifolds"

Links<ul><li>isSupplementTo publication-article <a href="https://doi.org/10.46298/jtcam.10430">https://doi.org/10.46298/jtcam.10430</a></li><li>isSupplementedBy software <a href="https://archive.softwareheritage.org/swh:1:dir:97292192b4790c2af01e25f4694d024561c5638c;origin=https://github.com/MORFEproject/MORFEInvariantManifold.jl;visit=swh:1:snp:cbd3f3eaf0dc99efb1d6bed706c3b4c3b67a1077;anchor=swh:1:rev:f56492ccd78890ee2b82970ae8941d6e39c0c147">https://archive.softwareheritage.org/swh:1:dir:97292192b4790c2af01e25f4694d024561c5638c;origin=https://github.com/MORFEproject/MORFEInvariantManifold.jl;visit=swh:1:snp:cbd3f3eaf0dc99efb1d6bed706c3b4c3b67a1077;anchor=swh:1:rev:f56492ccd78890ee2b82970ae8941d6e39c0c147</a></li></ul>Language<ul><li>English</li></ul>License<ul><li>Creative Commons Attribution 4.0</li></ul>Contributions<ul><li>Adrien MARTIN carried out the main part of study, defined the examples, performed the numerical simulations and drafted the manuscript;</li><li>Andrea OPRENI and Alessandra VIZZACCARO developed the methodology and built the main parts of the Julia code implementing the reduction method;</li><li>Andrea OPRENI developed the first version of the HBFEM code which has been updated for rotation in collaboration with Adrien MARTIN;</li><li>Marielle DEBEURRE performed all the simulations shown in Appendix C related to the Timoshenko beam model with continuation;</li><li>Loïc SALLES supervised the work, discussed applications to blades, and helped in designing and understanding the twisted plate model;</li><li>Attilio FRANGI supervised the work and help in the development of the methodology;</li><li>Olivier THOMAS helped in all discussions related to the comparisons with the thin beam example and wrote Appendix C;</li><li>Cyril TOUZE supervised the work, carried out most of the writing and developed the methodology;</li></ul>All authors read and approved the final manuscript.Data collection: period and details<ul><li>Datasets produced between September and December 2022</li></ul>Funding sources<ul><li>Funding from AID (Agence de l'Innovation de Défense), project REMODEL, contract number 2020 65 0057 ENSTA</li></ul>Data structure and information<ul><li>README.md: Contains the general information concerning this dataset</li></ul>Figures<ul><li>fig_1: description of the rotating beam</li><li>fig_2(a,b,c,d): Linear characteristics of the rotating cantilever beam</li><li>fig_3(a,b): FRC of the rotating cantilever beam around 1F mode</li><li>fig_4: Convergence of the non-autonomous part of DPIM for the 1F mode</li><li>fig_5(a,b,c,d,e,f): Interpolation of the coefficients of the autonomous ROM</li><li>fig_6(a,c): Hardening/softening behaviour of the rotating beam; fig 6b is a zoom on fig 6a</li><li>fig_7(a,b,c): Comparisons of FRCs obtained from interpolated ROMs with FOM solution</li><li>fig_8a: FRC of the rotating cantilever beam around 2F mode; fig 8b is a zoom of fig 8a</li><li>fig_9(a,b,c,d): fig 9 a-b-c : geometry of the blade and some modes and static displacements; fig 9d : Campbell diagram of the blade</li><li>fig_10: FRC of the twisted plate</li><li>fig_11(a,b,c): Computing time and convergence analysis with respect to mesh refinement for the fan blade</li></ul>fig_12(a,b,c,d): FRC of interpolated ROMs with increasing degrees compared to reference solutionfig_A_1: Campbell diagram of the beam : impact of Coriolis effects<ul><li>fig_C_3(a,b,c,d,e,f,g,h,i): Comparison of the results on the beam studied between DPIM and article from Thomas for 1F and 2F modes</li><li>fig_C_2(a, b): Comparison of the results on the beam studied between : DPIM, article from Thomas and results from Debeurre</li></ul>

opencc-by-4.0May 2023View details →

Validation of an Idealized Aorta Model Analysed through Fluid-Structure Interaction Simulation with Robin-Neumann Partitioned Approach

The aorta is multiphysics system where hemodynamics and wall structural mechanic are mutually influenced. A fluid-structure interaction approach is appropriate to describe the mechanical alterations suffered by the aortic wall in response to altered hemodynamic patterns.  This work demonstrates the validation of the simulated idealized aorta model with a fluid-structure interaction (FSI) model through modified PIMPLE solver to use Robin-Neumann partitioned approach for the strongly-coupled algorithm using solids4foam v2. The validation involves the comparison of streamlines, pressure, and displacements with in vivo measurements. The geometry is reconstructed from the healthy aorta presented in 10.5281/zenodo.5801938. Our analysis shows that the streamlines and pressure pattern are comparable with the literature data acquired using rich medical imaging data. The maximum diameter deformation at the level of abdominal aorta is comparable with measured data and the diameter deformation profile along the cardiac cycle correctly follow the velocity profile. According to this results, our work shows a high-performance simulation suitable for several future works.

opencc-zeroJun 2023View details →

National forest inventory data for a size-structured forest population model

In forest communities, light competition is a key process for community assembly. Species' differences in seedling and sapling tolerance to shade cast by overstory trees is thought to determine species composition at late-successional stages. Most forests are distant from these late-successional equilibria, impeding a formal evaluation of their potential species composition. To extrapolate competitive equilibria from short-term data, we therefore introduce the JAB model, a parsimonious dynamic model with interacting size-structured populations, which focuses on sapling demography including the tolerance to overstory competition. We apply the JAB model to a two-"species" system from temperate European forests, i.e. the shade-tolerant species Fagus sylvatica L. and the group of all other competing species. Using Bayesian calibration with prior information from external Slovakian national forest inventory (NFI) data, we fit the JAB model to short timeseries from the German NFI. We use the posterior estimates of demographic rates to extrapolate that F. sylvatica will be the predominant species in 94% of the competitive equilibria, despite only predominating in 24% of the initial states. We further simulate counterfactual equilibria with parameters switched between species to assess the role of different demographic processes for competitive equilibria. These simulations confirm the hypothesis that the higher shade-tolerance of F. sylvatica saplings is key for its long-term predominance. Our results highlight the importance of demographic differences in early life stages for tree species assembly in forest communities.

opencc-zeroDec 2022View details →

Inferring the evolutionary model of community-structuring traits with convolutional kitchen sinks: Code and data

When communities are assembled through processes such as filtering or limiting similarity acting on phylogenetically conserved traits, the evolutionary signature of those traits may be reflected in patterns of community membership. We show how the model of trait evolution underlying community-structuring traits can be inferred from community membership data using both a variation of a traditional eco-phylogenetic metric--the mean pairwise distance (MPD) between taxa--and a recent machine learning tool, Convolutional Kitchen Sinks (CKS). Both methods perform well across a range of phylogenetically informative evolutionary models, but CKS outperforms MPD as tree size increases. We demonstrate CKS by inferring the evolutionary history of freeze tolerance in angiosperms. Our analysis is consistent with a late burst model of freeze tolerance, suggesting it evolved recently. We suggest that data ordered on phylogenies such as trait values, species interactions, or community presence/absence are good candidates for CKS modeling because the generative models produce structured differences between neighboring points that CKS is well-suited for. We introduce the R package kitchen to perform CKS for generic application of the technique.