Find research datasets worth reusing

opencc-by-4.0Feb 2022View details →

Input features and benchmark data sets for protein complex prediction and E. coli proteome application by AF2Complex

Benchmark data sets of AF2Complex, input features for application to E. coli proteome, and predicted structural models of E. coli Ccm I as described in Predicting direct physical interactions in multimeric proteins with deep learning Mu Gao, Davi Nakajima An, Jerry M. Parks, Jeffrey Skolnick <ol> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/af2complex_bench.tar.gz">af2complex_bench.tar.gz</a>:  Benchmark data sets CP17, Dimer1193 and Oligomer562, including input features for AF2Complex/AF-Multimer, both paired and unpaired MSAs, as well as sequences, experimental structures, and results presented in the AF2Complex work (~90GB de-compressed size)</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_Ccm_I.tar.gz">ecoli_Ccm_I.tar.gz</a>: Computational models of the E. coli Ccm I system</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_set.tar.gz">ecoli_sets.tar.gz</a>: Lists of benchmark sets of positive and negative PPIs from E. coli</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_af_fea.tar.gz">ecoli_af_fea.tar.gz: </a>Pre-generated input features of E. coli proteome for protein complex prediction and modeling by AF2Complex (4,429 proteins, ~800 GB de-compressed size). This data set can be used with AF2Complex to probe the interactions of any combinations among the 4,429 proteins of E. coli.</li> </ol>

opencc-zeroFeb 2022View details →

Application of an improved calibration flight scheme in aeromagnetic interference compensation

In aeromagnetic measurement, accurate compensation for the interference magnetic field generated by the aircraft platform due to the aircraft maneuvering in the Earth's magnetic field is an important prerequisite for the accurate indentification of the magnetic anomaly signal of the detection target. The flaws of the traditional calibration flight scheme in aeromagnetic interference compensation are firstly analyzed, and then an improved scheme is proposed, featuring smaller calibration flight areas, more simplified maneuvers, shorter flight route and less influence from the magnetometer's direction. The improved scheme can enhance the robustness of the aeromagnetic interference compensation matrix, and improve the compensation efficiency, and thus both the solved magnetic compensation coefficients and compensation effects are significantly improved. The experimental results of aeromagnetic interference compensation in an area of Inner Mongolia of China show that the average accuracy of the repeated survey lines, which are compensated by the improved scheme, can be increased by 5 nT and 3 nT, respectively, compared with the uncompensated results and the results compensated by the traditional scheme. Therefore, the effectiveness and superiority of the improved calibration flight scheme is fully proved.

Supporting data for "The high-frequency tidal response of ocean worlds: Application to Europa and Ganymede"

Data files containing the static Love numbers that are used to compute the dynamic Love numbers.

Boosting the Near-Infrared Emission of Ag2S Nanoparticles by a Controllable Surface Treatment for Bioimaging Applications

Dataset of https://pubs.acs.org/doi/10.1021/acsami.1c19344

The Effect of Stromal Vascular Fraction (SVF) & Scaffolds Application on Fracture Healing with Bone Defect as Assessed Through Osteocalcin and Bone Morphogenetic Protein-2 (BMP-2) Biomarker Examination: Experimental Study on Murine Model

This data is the raw data for the manuscript with titled The Effect of Stromal Vascular Fraction (SVF) & Scaffolds Application on Fracture Healing with Bone Defect as Assessed Through Osteocalcin and Bone Morphogenetic Protein-2 (BMP-2) Biomarker Examination: Experimental Study on Murine Model.

opencc-zeroMar 2022View details →

Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events

The data provided and the associated MATLAB code were used to build an Artificial Neural Network Model to capture the reconstruction (recovery) of various buildings subjected to tornado events in the State of Missouri. The ANN model utilizes relevant tornado, societal demographic, and structural data to determine a building's resulting damage state from an extreme wind event and the subsequent recovery time. Abstract for the publication is as follows: In a companion article, previously published in Royal Society Open Science, the authors used Graph Theory to evaluate artificial neural network models for potential social and building variables interactions contributing to building wind damage. The results promisingly highlighted the importance of social variables in modeling damage as opposed to the traditional approach of solely considering physical characteristics of a building. Within this update article, the same methods are used to evaluate two different artificial neural networks for modelling building repair and/or rebuild (recovery) time. In contrast to the damage models, the recovery models consider (A) primarily social variables and then (B) introduce structural variables. These two models are then evaluated using centrality and shortest path concepts of Graph Theory as well as validated against data from the 2011 Joplin Tornado. The results of this analysis do not show the same distinctions as were found in the analysis of the damage models from the companion article. The overarching lack of discernible and consistent differences in the recovery models suggests that social variables that drive damage are not necessarily contributions to recovery. The differences also serve to reinforce that machine learning methods are best used when the contributing variables are already well understood.

opencc-by-4.0Mar 2021View details →

Metameric evolutionary data from eCS (EVONANO) including application times

Data-set produced by and depicted in <a href="https://doi.org/10.1016/j.cmpb.2020.105886">https://doi.org/10.1016/j.cmpb.2020.105886</a>

opencc-by-4.0Jan 2022View details →

Research data supporting "Tunable microgel-templated porogel (MTP) bioink for 3D bioprinting applications"

Raw research data supporting Ouyang L. et al., 2022, Advanced Healthcare Materials <a href="https://doi.org/10.1002/adhm.202200027">https://doi.org/10.1002/adhm.202200027</a>

In-vehicle applications among Malaysians

This is an online survey dataset to study user acceptance of in-vehicle applications among Malaysians.

Breaking photoswitch activation depth limit using ionising radiation stimuli adapted to clinical application

This deposit gathers the main raw data and custom codes related to the manuscript "Breaking photoswitch activation depth limit using ionising radiation stimuli adapted to clinical application".

opencc-by-4.0Jul 2021View details →

Assessing the Linguistic Quality of REST APIs for IoT Applications

Detection of Linguistic Antipatterns in IoT APIs: The dataset contains the 19 APIs for IoT applications and a subset of their URIs and documentation. The dataset also contains the detection results of nine Linguistic Antipatterns in 19 IoT APIs.

opencc-by-4.0Jul 2021View details →

Data and script for binomial GLMs in "Lithic Analysis of Andean Sedentary Societies, a Case Study from the Chachapoyas Region, Peru, and Potential Applications." (Pratt & Guengerich)

This submission contains data and R-script that enable to reproduce the binomial generalized linear models in the paper “Lithic Analysis of Andean Sedentary Societies, a Case Study from the Chachapoyas Region, Peru, and Potential Applications” by Lauren V. Pratt & Anna Guengerich. Please cite the above paper if you use the files included in this Zenodo record in your work.

opencc-zeroApr 2022View details →

DFT investigation on the application of pure and doped X12N12 (X = B and Al) fullerene-like nano-cages towards the adsorption of temozolomide

The sensitivity of pure and doped X12 N12 (X = B and Al) fullerene-like nano-cages (FLNs) toward the anticancer drug temozolomide (TMZ) is probed herein at DFT/M06-2X-D3/6-311G(d,p) theoretical level in both gas phase and water. A noticeable affinity of the FLNs toward TMZ was observed along with the negative gas-phase adsorption energies -1.37 and -2.09 eV for the most stable configurations of pure B12N12 and Al12N12 pristines, respectively. Considerable charge transfer from TMZ to the FLNs was also revealed via NBO analysis and the Hirshfeld atomic charges, making the dipole moment vector of the molecular complexes to be oriented from the nano-cages to the TMZ moiety. Furthermore, a percentage decrease in the HOMO-LUMO energy gap (ΔEg) of 38.09 and 17.72 % was obtained for the B12N12 and Al12N12 nano-cages, respectively. The percentage change in ΔEg was found to be reduced upon doping and solvation of the FLNs. Finally, a recovery time in vacuum ultraviolet light of 1.06 s is found for the complex with pure B12N12, which in addition to the above-mentioned parameters make this boron nitride cage the best sensor for TMZ, among the FLNs considered in the present work.

opencc-by-4.0Apr 2022View details →

Application of optimized CNN to fixture layout in automotive parts. Training Dataset.

Fixture layout has aroused substantial interest in the research community during the last decades. It affects the design and production of fixtures themselves, and therefore manufacturing costs. While fixturing may seem simple in conception, it requires expertise and well-trained engineers.  A general principle, usually called the 3-2-1 locating principle, ensures fixing an object, restringing its degrees of freedom. Fixtures must always comply with the principle. While most research approaches automation of the fixture layout using optimization or rule-based frameworks, this paper proposes supervised learning. The presented framework solves the 3-2-1 locating principle for sheet metal designs based on the experience of previous designs, using automotive b-pillars as a test study. There are three main contributions. 1. A novel idea to introduce sheet metal design data in a Convolutional Neural Network (CNN), projecting the geometry over a plane. The Z coordinate transforms into gray-scale pixel values, generating a topographic map. 2. The framework reuses knowledge about fixturing to layout new workpieces. The framework is an add-in integrated with the CAD environment. 3. A hyperparameter-tuned CNN for regression generates the final output. The results show high accuracy (≈ 100%) in classifying b-pillars and fast convergence in regression, proving model usability for industrial cases.   Source Code: https://colab.research.google.com/drive/18lS83mZEdwY5S-41pvcGXgUP6GbKpun_?usp=sharing

opencc-by-4.0Apr 2022View details →

On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Trained Models

Trained machine learning models and scaling values used in the paper "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model."

opencc-zeroApr 2022View details →

Optimization of cancer risk assessment model for PM2.5-bound PAHs Application in Shanxi of China SM

To optimize the models for cancer risk assessment of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs), six models of three types utilized with high frequency in recent years, including the USEPA recommendation (Model I), inhalation carcinogen unit risk (Models IIA–IID), and three exposure pathways (inhalation, dermal, and oral) (Model III), were selected to calculate potential cancer risk using the benzo[a]pyrene toxicity equivalent method. The results indicated no significant differences in the risk values between Models IIA and III, and no significant differences were found among Models IIB, IIC, and IID. However, there were significant differences between Models I and II, and between Models I and III. Furthermore, the optimization of Model I indicated that the population exposure parameters for each country should be used and age differences should not be disregarded. However, no significant differences were observed with respect to gender. In conclusion, Model I was superior to the other models; when age differences were not considered, Model IID (the USEPA's integrated risk information system) was preferred to replace Models IIB and IIC. Models IIA and III may overestimate the risk of carcinogens, and their parameters need to be reoptimized to accurately assess cancer risks.

opencc-zeroApr 2022View details →

Data from: Determining ground-state phase diagrams on quantum computers via a generalized application of adiabatic state preparation

Quantum phase transitions materialize as level crossings in the ground-state energy when the parameters of the Hamiltonian are varied. The resulting ground-state phase diagrams are straightforward to determine by exact diagonalization on classical computers, but are challenging on quantum computers because of the accuracy needed and the near degeneracy of competing states close to the level crossings. In this work, we use a local adiabatic ramp for state preparation to allow us to directly compute ground-state phase diagrams on a quantum computer via time evolution. This methodology is illustrated by examining the ground states of the XY model with a magnetic field in the z-direction in one dimension. We are able to calculate an accurate phase diagram on both two and three site systems using IBM quantum machines.

opencc-zeroMay 2022View details →

Image dataset: Applicability of hyperspectral imaging during salinity stress in rice for tracking Na+ and K+ levels in planta

The ratio of Na+ and K+ is an important determinant of the magnitude of Na+ toxicity and osmotic stress in plant cells. Traditional analytical approaches involve destructive tissue sampling and chemical analysis, where real-time observation of spatio-temporal experiments across genetic or breeding populations is unrealistic. Such an approach can also be very inaccurate and prone to erroneous biological interpretation. Analysis by Hyperspectral Imaging (HSI) is an emerging non-destructive alternative for tracking plant nutrient status in a time-course with higher accuracy and reduced cost for chemical analysis. In this study, the feasibility and predictive power of HSI-based approach for spatio-temporal tracking of Na+ and K+ levels in tissue samples was explored using a panel recombinant inbred line (RIL) of rice (Oryza sativa L.; salt-sensitive IR29 x salt-tolerant Pokkali) with differential activities of the Na+ exclusion mechanism conferred by the SalTol QTL. In this panel of RILs the spectrum of salinity tolerance was represented by FL499 (super-sensitive), FL454 (sensitive), FL478 (tolerant), and FL510 (super-tolerant). Whole-plant image processing pipeline was optimized to generate HSI spectra during salinity stress at EC = 9 dS m-1. Spectral data was used to create models for Na+ and K+ prediction by partial least squares regression (PLSR). Three datasets, i.e., mean image pixel spectra, smoothened version of mean image pixel spectra, and wavelength bands, with wide differences in intensity between control and salinity facilitated the prediction models with high R2. The smoothened and filtered datasets showed significant improvements over the mean image pixel dataset. However, model prediction was not fully consistent with the empirical data. While the outcome of modeling-based prediction showed a great potential for improving the throughput capacity for salinity stress phenotyping, additional technical refinements including tissue-specific measurements is necessary to maximize the accuracy of prediction models.

Results of "Comparison of ice dynamics using full-Stokes and Blatter-Pattyn approximation: application to the Northeast Greenland Ice Stream"

This archive provides ice sheet model output produced as part of the publication: Rückamp, M., Kleiner, T., and Humbert, A.: Comparison of ice dynamics using full-Stokes and Blatter–Pattyn approximation: application to the Northeast Greenland Ice Stream, The Cryosphere, 16, 1675–1696, https://doi.org/10.5194/tc-16-1675-2022, 2022. About the data: The archive contains 2 zipped tar-files for the two 2 different regions 'icestream' and 'outlet'. Each tar-file contains ascii files for the different conducted scenarios. An exemplary filename looks like: FSvsBPlike_icestream_res12800m_E1_SlidingM1_isFS_P1P1GLS_BCstrong.txt 'FSvsBPlike' indicates the general study 'icestream' indicates the region (either icestream or outlet) 'res12800m' indicates the horizontal resolution (ranges from 12800 to 100 meter) 'E1' indicates the enhancement factor used (available values E=0.1, 1, 3 and 6) 'SlidingM1' indicates the employed sliding exponent (available values m=1 and 3) 'isFS' indicates that the full-Stokes equation was employed (either isFS or isBPlike) 'P1P1GLS' describes the employed finite element discretization (either P1P1GLS or P2P1) 'BCstrong' indicates that the strong basal boundary implementation was used (either BCstrong or BCweak) An exemplary file structure looks as follows: % Creation date:      03-May-2022 % Institution:            AWI (Bremerhaven-Germany), BADW (Munich-Germany) % Correspondence: Martin Rueckamp (martin.rueckamp@badw.de), Angelika Humbert (angelika.humbert@awi.de) % Source:                COMSOL version 5.6 (www.comsol.com) % Citation:               Rückamp et al. (2022): https://tc.copernicus.org/preprints/tc-2021-193/ % Projection:           EPSG:3413 (proj4 string: +init=epsg:3413) x(m) y(m) z_s(m) z_b(m) vx_s(m/a) vy_s(m/a) vz_s(m/a) p_s(Pa) vx_b(m/a) vy_b(m/a) vz_b(m/a) p_b(Pa)  301099.83  -1256559.78 1840.83  205.40  -0.031   0.028  -5.456  -157729.374  -2.312   2.076   0.012 14684584.911 ... The fields x, y contain x- and y coordinates of the grid nodes in EPSG:3413 projection. The fields z_s and z_b are the ice surface and ice base, respectively. The fields vx, vy, and vz are the ice velocities in horizontal (x,y) and vertical (z) direction. The field p is the pressure. The subscripts _s and _b indicate the surface and ice base, respectively.