Wolfram Mathematica and MATLAB scripts for solving the Orr-Sommerfeld equation for a Carreau-Yasuda fluid over an inclined plane.
Contributors: Bruno Pelisson Chimetta, Erick Franklin
... This data presents a Wolfram Mathematica and MATLAB scripts that reproduce the solutions and figures obtained in the article "An analytical comprehensive solution for the superficial waves appearing in gravity-driven flows of liquid films".
Contributors: Susanne Moskalski, France Floc'h, Romaric Verney
... Timeseries of water level, tidal velocity, backscatter, suspended sediment concentration, and sediment flux derived from acoustic doppler current profilers. Sediment fluxes calculated according to Dyer decomposition equation and Yu et al. (2012) analytical model. Two deployments in 2013: February and September. Two locations in the estuary: Site 1 near the mouth of the river (48° 16.842’ N, 004° 16.009’ W), and Site 3 in the central estuary (48° 14.851’ N, 004° 10.140’ W). Yu, Q., Wang, Y.P., Flemming, B., Gao, S., 2012. Tide-induced suspended sediment transport: Depth-averaged concentrations and residual fluxes. Continental Shelf Research 34, 53-63.
Contributors: Guillermo Toriz, Carmen Miramontes-Corona, Alfredo Escalante, Ezequiel Delgado, Corona Gonzalez Rosa Isela, Humberto Vázquez-Torres
... Processed Data
Bridging sensory evaluation and consumer research for strategic leafy Brassica (Brassica oleracea) improvement
Contributors: Hannah Swegarden, Phillip Griffiths, Alina Stelick, Robin Dando
... Research data uploaded here provided the foundation for analysis of Qualitative Multivariate Analysis (QMA_FocusGroupObservations), descriptive analysis (QDA_Sessions123 and ConsumerClusterDemo_Kale2019), and consumer testing (ConsumerKale_FinalData). These data have been cleaned and formatted from raw datasets; they are ready for downstream analysis. Participant journal entries and raw data are available upon request.
Contributors: Nemanja Stanisic
... The data from 13,561 complete sets of annual financial statements for 4,701 companies are combined with the data from the corresponding audit reports, forming an unbalanced panel data set. The client companies included in the sample represent a supermajority of medium- and large-sized companies registered in the Republic of Serbia. The information on the auditor firm name and the type of audit opinion is hand-collected from the audit reports issued by 64 audit firms (Big 4 plus 60 other audit firms), which, again, represents a supermajority of all the auditor firms registered in this country. To the best of our knowledge, this is the largest data set used in the literature devoted to predicting the type of audit opinion. In the total sample of audit opinions (13,561), the following frequencies of the four main types of audit opinions are observed: adverse opinion (71), disclaimer of opinion (644), qualified opinion (3,706), and unqualified opinion (9,140). Feel free to use it for research purposes or to reproduce the results presented in the article. For a detailed description of the variables and their descriptive statistics, please read the article: Stanišić, N., Radojević, T., Stanić, N. (2019). Predicting the Type of Auditor Opinion: Statistics, Machine Learning, or a Combination of the Two?. The European Journal of Applied Economics, 16(2), 1-58. doi:10.5937/EJAE16-21832 that is available at: http://journal.singidunum.ac.rs/paper/predicting-the-type-of-auditor-opinion-statistics-machine-learning-or-a-combination-of-the-two.html When referring to the data set in publications please cite the data as follows: Stanisic, Nemanja (2019), “Predicting the Type of Auditor Opinion: Statistics, Machine Learning, or a Combination of the Two?”, Mendeley Data, V1, doi: 10.17632/mmcczp3g3y.1 Also, consider citing the related research paper. These data are used in a research study and may not be redistributed or used for commercial purposes. If you have any questions please feel free to contact me at email@example.com
Contributors: Chunli Dai
... Here are the results in a paper entitled "Characterization of the 2008 phreatomagmatic eruption of Okmok from ArcticDEM and InSAR: deposition, erosion, and deformation" submitted to JGR Solid Earth in 2019. It includes the 2-m resolution surface elevation change of the 2008 Okmok eruption (Fig. 2a in the paper) and the 2-m resolution post-eruptive elevation change rate map (Fig. 3), as well as the corresponding uncertainties (Fig. S3). It also includes the boundary of the proximal deposit field classified using a minimum elevation increase of 2 m, the boundary of large slope failure, and the shorelines of two lakes (Figs. 2a, S5, and S6) at different acquisition times. The GeoTIFF files can be viewed in free and open-source software QGIS, in Google Earth, or by Matlab using code https://github.com/ihowat/setsm_postprocessing/blob/master/readGeotiff.m. The shapefiles can be viewed in QGIS and Google Earth.
Contributors: Soner Buytoz
... It's the raw information of my work.
A Mathematical Model for the Berth Allocation Problem with Variable Service Time and Continuous Time Horizon
Contributors: Bruno Luís Hönigmann Cereser
... Tests, Results and Codes of the paper "A Mathematical Model for the Berth Allocation Problem with Variable Service Time and Continuous Time Horizon".
Parvimico materdei gen. et sp. nov.: A new platyrrhine from the Early Miocene of the Amazon Basin, Peru
Contributors: Richard F Kay
... SOM File S1. NEXUS-formatted file for phylogenetic analysis. (.nex file) SOM File S2. Nexus-formatted file with branch lengths for PGLS analysis. (.nex file) SOM File S3. R code for PGLS run. (R script) SOM Table S1. Extant platyrrhine frugivore mean upper first molar mesiodistal length, and sum of shearing (includes both real values and natural logs). (csv file) SOM Table S2. Molar shearing and mesiodistal lengths for platyrrhine specimens. (xlsx-formatted Excel spreadsheet) SOM Table S3. Procrustes-aligned data matrix and ancillary results from principal component analysis reported in the text. (xlsx-formatted Excel spreadsheet) SOM S1. Age determinations using U/Pb dating of detrital zircons. SOM S2. Characters and character states used in the phylogenetic analysis. SOM S3. Landmark Analyses: Additional Data and Results.
Contributors: Solam Lee
... AloNet Author: Solam Lee (firstname.lastname@example.org) AloNet is a convolutional neural network based on U-Net that can identify the hair loss and the scalp area by analying clinical photograph. This model was developed for the automated calculation of the Severity of Alopecia Tools (SALT) score in assessment of patients with alopecia areata. This repository posts the Mendeley Supplementary Materials, the program code, and the relevant data used in the paper titled "Clinically Applicable Deep Learning Framework for Measurement of the Severity of Alopecia Tool Score in Patients with Alopecia Areata". Along with the programs in the "/Program/" directory, a total of 2716 pixelwise annotations used for train the hair loss identifier (mask) and the hair loss identifier (target) could be find in the "/Data/" directory. However, please note that the clinical photograph of the patients could not be made publicly available because of strict privacy regulation. Before using AloNet program with your dataset, you should convert your dataset into numpy files. One clinical photograph (saved in .jpg with RGB format) need each annotation for the scalp area (saved in .gif with black&white color) and the hair loss (saved in .gif with black&white color), respectively. Please make sure that they have same image size each other, or the conversion will fail. We are now currently working on several postprocessing algorithms for AloNet to be available for general use. The Flask web application and its code will be made available publicly when the program is ready to use.