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Hypothesis: Does dogs exhibit different protein profile of seminal plasma and spermatozoa among breeds? What data shows: These data show the proteomic profile of seminal plasma and sperm cells of four purebred dogs (Golden Retriever n = 3, Bernese Mountain Dog n = 4, Great Dane n = 3, Maremmano-Abruzzese Sheepdog n = 3), with ages between 1 and 5 years old, kenneled at Sao Paulo State, Brazil. How it was gathered: Entire second fraction and a portion of the third semen fraction were collected into a silicone funnel attached to a graduated plastic tube by manual stimulation of the penis in the presence of a teaser bitch, when possible. Spermatozoa and seminal plasma were separated by centrifugation and prepared individually for proteomic analysis by ESI Q-Tof mass spectrometer. How the data can be interpreted: There are two dataset: "seminal plasma proteins" and "sperm cell proteins". Each dataset contains proteomic data for each dog (n = 13) in individual files named by breed.
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Replication data for our study "A kinetic model of continuous radiation damage to populations of cells: Comparison to the LQ model and application to molecular radiotherapy". Introduction The folder Code_and_Data contains the following two routines (functions) and two datasets. The code is written in Matlab (.m functions), while the datasets are saved in Matlab workspace files (.mat). If any reader wants data in any other format, please contact us and we will try to accommodate her/his request. The folder contains the following items: annealing_model_fit.m model_dif_radionuclide_stochastic.m data_unkel.mat data_iodine.mat model_dif_radionuclide_stochastic.m Function containing the implementation of our code. Detailed info about the inputs and outputs of this function can be found in it. annealing_model_fit.m Function containing the implementation of an annealing method to obtain best fits of our model to experimental data. In particular, in this case it is used to fit experimental data taken from Unkel et al of surviving fractions of different cell lines. Detailed info about the inputs and outputs of this function can be found in it. data_unkel.mat Workspace containing the experimental results taken from Unkel et al of surviving fractions of different cell lines, together to best-fits of such data to the LQ model and to our model. Experimental data (doses and log(SF)) for each of the 9 cell lines is save in variables C2, DG, FP, HP, L3, MP, P1, PT, and S2, and altogether are also reported in the structure EXPERIMENTAL_DATA. Variables named par_LQ_”cell_line” contain best-fitting LQ parameters for each cell-line, and variables SF_LQ_”cell_line” contain best-fitting LQ surviving fractions. Variables named par_opt_”cell_line” contain best-fitting parameters of our model for each cell-line, and variables SF_opt_”cell_line” contain best-fitting LQ surviving fractions. data_iodine.mat This workspace contains the sets of parameters used to create figures 2 and 3 (par_1, par_2, par_3, par_4). The variable D contains the simulated dose rate of a radioiodine treatment.
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EarVN1.0 dataset is a large-scale ear images dataset which consists of over 28,412 ear images of 164 peoples. The images cover large variation in pose, scale, illumination, occlusion, resolution, light condition. This dataset could be used on a variety of tasks, e.g., person authentication, classification. All the rights of the IEarVN1.0 database are reserved and commercial use/distribution of this database is strictly prohibited. The ear images of the volunteers are protected.
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Code for "Assemblies of Perisomatic GABAergic Neurons in the Developing Barrel Cortex" https://doi.org/10.1016/j.neuron.2019.10.007 Data will be added soon in NWB format.
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This dataset contain 5332 depth images, from a kinect v2 device, with a person appearing in all the scenes with the labels of its hands. It also contains the color information taked by the kinect mapped onto the depth image pixels. The images in the dataset has the 16 bit depth information splitted in the green and blue channels, in this order. And the red channel contains the hands masks.
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3D expression maps of the developing mouse brain of different ages derived from in situ hybridization experiments. This data was obtained from the Allen Brain Atlas and is a dependency of the VoxHunt R package.
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This data set has tomato diseased leaf images gathered across different farms located in Maharashtra. Images are acquired using different mobile camera models in order to introduce variability and classified by a team of Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth,Dapoli,India. Images are captured by keeping paper in background to avoid background effects. This entire data set can be used to design system that can be used to automatic diseased detection.
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Original microscope images and blots
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Maps of clay, silt and sand contents (g kg-1) were predicted at 0-20 cm, 20-60 cm and 60-100 cm depths intervals by random forest regression in Google Earth Engine. Gridded soil information covers a part of the Midwest Brazil, from 12° S to 20° S and from 45° W to 54° W, and is available with 250m resolution. The maps were cross-validated and had Coefficient of Determination ranging from 0.64 to 0.85 at all depth intervals.
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The uploaded data and codes are useful for conducting a bibliometric analysis to describe the research field "Seaweed Biorefinery." The bibliographic data is a collection of records describing scientific publications related to the topic “seaweed biorefinery” obtained from Web of Science (WoS) database. The codes are for processing the bibliographic data in R by applying a specialized R-package called “Bibliometrix”; the first part of the scripts is intended for descriptive analysis and the second part is for co-word analysis. The R scripts and bibliographic collection are provided in the file “R Scripts and Data of the Bibliometric Analysis.zip.” “Supplementary Information.docx” provides the details of the bibliographic data retrieval, introduces the descriptive analysis and co-word analysis, and presents the results of both analyses.
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