Find research datasets worth reusing

Fig. 2 in Theromyzon maculosum (Rathke, 1862) as a vector of potentially pathogenic fungi in aquatic ecosystems

Fig. 2. Pseudomycelium (marked as PS) and blastospores (marked as BL) of Candida albicans isolated from the river Czarna Ha´ncza in the microculture on the Nickerson agar (magn. 400x).

Fig. 5 in Theromyzon maculosum (Rathke, 1862) as a vector of potentially pathogenic fungi in aquatic ecosystems

Fig. 5. Pseudomycelium (marked as PS) and blastospores (marked as BL) of Candida tropicalis isolated from the integument of the leech Theromyzon maculosum in the microculture on the Nickerson agar (magn. 400x).

Fig. 4 in Theromyzon maculosum (Rathke, 1862) as a vector of potentially pathogenic fungi in aquatic ecosystems

Fig. 4. Pseudomycelium (marked as PS) and blastospores (marked as BL) of Candida tropicalis isolated from the river Czarna Ha´ncza in the microculture on the Nickerson agar (magn. 600x).

opencc-by-4.0Jun 2024View details →

Figure 1 in Isolation of 4-nerolidylcatechol from leaves of Piper peltatum L., and evaluation of larvicidal activity in mosquito vectors, with emphasis on Aedes aegypti (Diptera: Culicidae)

Figure 1 Molecular chemical structure of the substances 4-nerolidylcatechol (4-NC), isolated of Piper peltata, and catechol and nerolidol were obtained from Sigma-Aldrich®, used in larvicidal and cytotoxicity bioassays.

opencc-by-4.0Jun 2024View details →

Figure 3 in Isolation of 4-nerolidylcatechol from leaves of Piper peltatum L., and evaluation of larvicidal activity in mosquito vectors, with emphasis on Aedes aegypti (Diptera: Culicidae)

Figure 3 Frequency of anomalies in interphase nuclei. a – damage to Aedes aegypti neuroblasts exposed to 4-NC for two generations. b – data on Ae. aegypti oocytes exposed to 4-NC in G1.

opencc-by-4.0Jun 2024View details →

Figure 2 in Isolation of 4-nerolidylcatechol from leaves of Piper peltatum L., and evaluation of larvicidal activity in mosquito vectors, with emphasis on Aedes aegypti (Diptera: Culicidae)

Figure 2 Microphotographs of abnormalities in interphasic and metaphasic nuclei of neuroblasts and oocytes of Aedes aegypti, stained with Giemsa (pH 5.8) and lacto-acetic orcein (2%). Arrows indicate: a – normal interphasic nuclei of neuroblasts of the NC group, G 1; b and c – micronuclei in interphasic nuclei of neuroblasts of G 2 (40 and 60 µg/mL), respectively; d – budding and telophasic bridging nucleus of neuroblasts (60 µg/mL, G2); e – budding in interphasic nucleus of oocytes (40 µg/mL, G1); f – normal metaphasic chromosomes (NC, G1); g and h – chromosomal metaphases of neuroblasts showing achromatic secondary constriction (60 µg/mL, G2). Magnification: 1600×. Scale bar: 5 and 10 µm.

Fig. 4. Phylogenetic relationships generated from the 16S rRNA gene for 16 in A survey of auchenorrhynchan insects for identification of potential vectors of the 16SrIV-D phytoplasma in Florida

Fig. 4. Phylogenetic relationships generated from the 16S rRNA gene for 16SrIV phytoplasmas by using maximum likelihood (1,000 replicates) methods in MEGA. The 16S partial sequence amplified from Haplaxius crudus (indicated by the black triangle) and unidentified Cicadellidae specimen (indicated by the white triangle) from this study were included in the analysis.

Fig. 3 in A survey of auchenorrhynchan insects for identification of potential vectors of the 16SrIV-D phytoplasma in Florida

Fig. 3. (a) The number of insects tested positive for the 16SrIV-D phytoplasma by nested polymerase chain reaction assays; (b) Total number of major auchenorrhynchan insects collected by sticky traps at the Fort Lauderdale Research and Education Center from 11 Oct 2017 to 2 Nov 2018. Different scales for the number of specimens (y-axis) were used to fit the data ranges.

Fig. 2 in A survey of auchenorrhynchan insects for identification of potential vectors of the 16SrIV-D phytoplasma in Florida

Fig. 2. Species of auchenorrhynchans that were consistently collected by sticky traps at the Fort Lauderdale Research and Education Center. (a) Cedusa inflata (Derbidae); (b) Idioderma virescens (Membracidae); (c) Omolicna joi (Derbidae); (d) Haplaxius crudus (Cixiidae).

Fig. 1 in A survey of auchenorrhynchan insects for identification of potential vectors of the 16SrIV-D phytoplasma in Florida

Fig. 1. (a) Map of Florida, USA, showing Broward County in yellow; (b) South Florida showing the location of the Fort Lauderdale Research and Education Center labeled in a circle; (c) Map of the Fort Lauderdale Research and Education Center study area showing the locations of the sticky traps according to the trap identification in red. The map was generated from Google Maps, Imagery@2019, DigitalGlobe, US Geological Survey, US Department of the Interior, Reston, Virginia, USA.

opencc-by-4.0Oct 2024View details →

UNSUPERVISED MACHINE LEARNING AND VECTOR MODELS IN DESIGNING AND OPTIMIZATION OF TELECOM RETAIL CHANNELS

This paper examines the use of unsupervised machine learning and vector models in the design and optimization of retail channels for telecommunications services. Unsupervised machine learning allows you to analyze and identify hidden patterns in large volumes of untagged data, which is especially important in a dynamically changing consumer market. Vector models, in turn, provide high accuracy of demand forecasting and inventory management, contributing to an increase in the efficiency of trading channels. The synergy of these technologies allows companies to improve customer experience, optimize operational processes and increase competitiveness in the market. The main focus of the work is on data processing methods, including correlation analysis, the use of the support vector machine (SVM) method and its adaptation to solve problems related to predicting customer behavior and optimizing logistics processes.

opencc-by-4.0Nov 2024View details →

Vector biology of the soft scales Parthenolecanium corni (Bouché) and Parthenolecanium persicae (Fabricius) (Hemiptera: Coccidae) with grapevine leafroll-associated viruses and grapevine virus A

Raw tables of Elisa and RT-PCR results of LR1 and GVA transmission tests by the soft scales Parthenolecanium corni (Bouché) and Parthenolecanium persicae (Fabricius) (Hemiptera: Coccidae) to grapevine

opencc-by-4.0Jul 2021View details →

Simulation Results Data for 'Modelling new insecticide-treated bed nets for malaria-vector control: How to strategically manage resistance?'

GENERAL INFORMATION 1. Title of Dataset: Simulation Results Data for 'Modelling new insecticide-treated bed-nets for malaria-vector control: How to strategically manage resistance?' 2. Author Information     A. Investigator Contact Information         Name: Philip G. Madgwick         Institution: Syngenta          Address: Jealott’s Hill International Research Centre, Bracknell, RG42 6EY, UK         Email: philip.madgwick@syngenta.com     B. Investigator Contact Information         Name: Ricardo Kanitz         Institution: Syngenta          Address: Syngenta Crop Protection, Rosentalstrasse 67, CH-4058 Basel, Switzerland         Email: ricardo.kanitz@syngenta.com 3. Date of data collection (single date, range, approximate date): 2021-01-13 to 2021-02-01  4. Geographic location of data collection: UK  5. Information about funding sources that supported the collection of the data:  This work was conducted during a postdoctoral research position for PGM funded by the Innovative Vector Control Consortium (IVCC). SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: NA 2. Links to publications that cite or use the data: [UPDATE] 3. Links to other publicly accessible locations of the data: NA 4. Links/relationships to ancillary data sets: NA 5. Was data derived from another source? No 6. Recommended citation for this dataset: [UPDATE] DATA & FILE OVERVIEW 1. File List:  PSData_random6.csv - 10^6 random samples of each of the 17 parameters in the model, where rows are samples and columns are parameters (with column names corresponding to the parameters identified in the rows of Table 1 of the manuscript; see also DATA-SPECIFIC INFORMATION) Data_random6_maxpsMixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_maxpsMixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_maxpsMixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Mixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_Mixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Mixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Mosaic_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_Mosaic_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Mosaic_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Rotation_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_Rotation_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Rotation_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Rotation_Fixed_revmm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_Rotation_Fixed_revmn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Rotation_Fixed_revnn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_SoloA_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_SoloA_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_SoloA_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_SoloB_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_SoloB_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_SoloB_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   2. Relationship between files, if important:  Files are named in accordance with the variables that describe each simulation setup, as described above. Each simulation dataset has 10^6 runs that correspond to the 10^6 random samples of each of the 17 parameters in the model in 'PSData_random6.csv'. 3. Additional related data collected that was not included in the current data package: NA 4. Are there multiple versions of the dataset? No METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: Data were collected using Simulator.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript.  2. Methods for processing the data: Data were processed using Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript.  3. Instrument- or software-specific information needed to interpret the data: Analysis was conducted in R version 4.0.3 (2020-10-10), using R packages identified in Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript.  4. Standards and calibration information, if appropriate: NA 5. Environmental/experimental conditions: NA 6. Describe any quality-assurance procedures performed on the data: NA 7. People involved with sample collection, processing, analysis and/or submission: NA  DATA-SPECIFIC INFORMATION FOR: PSData_random6.csv 1. Number of variables:  17 variables with column names that have the following parameter meanings (see Table 1 in the manuscript):  Population Size    = N = starting population size (and carrying capacity in logistic model); random sample range on log-scale: 10^2 - 10^9 Intrinsic Birth Rate = b = % population growth rate (in logistic model); random sample following a standard log-normal distribution with mean=0 and sd=1 Intrinsic Death Rate = d = % breeding mosquitoes that die into next generation; random sample range: 0 - 1 Female Exposure    = x_[female-symbol] = % female mosquitoes that receive a dose; random sample range: 0 - 1 Male Exposure x_[male-symbol] = % male mosquitoes that receive a dose; random sample range: 0 - 1 Initial Frequency A = f_0,A = starting frequency of allele A; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size Effectiveness 1    = m_1 = % dosed mosquitoes that die from insecticide 1; random sample range: 0 - 1 Resistance Restoration A = r_A = % return to baseline fitness with resistance allele A; random sample range: 0 - 1 Dominance of Resistance Restoration A = h^r_A = % resistance restoration in heterozygote with allele A; random sample range: 0 - 1 Resistance Cost A = c_A = % non-dosed mosquitoes that die from carrying allele A; random sample range on log-scale: 10^-3 - 10^-0.5 Dominance of Resistance Cost A = h^c_A = % resistance cost in heterozygote with allele A; random sample range: 0 - 1 Initial Frequency B = f_0,B = starting frequency of allele B; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size Effectiveness 2    = m_2 = % dosed mosquitoes that die from insecticide 2; random sample range: 0 - 1 Resistance Restoration B = r_B = % return to baseline fitness with resistance allele B; random sample range: 0 - 1 Dominance of Resistance Restoration B = h^r_B = % resistance restoration in heterozygote with allele B; random sample range: 0 - 1 Resistance Cost B = c_B = % non-dosed mosquitoes that die from carrying allele B; random sample range on log-scale: 10^-3 - 10^-0.5 Dominance of Resistance Cost B = h^c_B = % resistance cost in heterozygote with allele B; random sample range: 0 - 1 2. Number of cases/rows:  10^6, corresponding to the number of random samples  3. Variable List: NA  4. Missing data codes: NA 5. Specialized formats or other abbreviations used: NA DATA-SPECIFIC INFORMATION FOR: all other dataset files (e.g. Data_random6_maxpsMixture_Fixed_mm.csv)  1. Number of variables:  10 variables with column names that have the following meanings: A_t_50% = the recorded number of generations that it takes for resistance allele A to reach >50% frequency; 0 means that resistance allele A never reaches >50% frequency   A_f_250 = the frequency of resistance allele A at the 250th generation  A_f_bar = the mean frequency of resistance allele A over the first 250 generations   B_t_50% = the recorded number of generations that it takes for resistance allele B to reach >50% frequency; 0 means that resistance allele B never reaches >50% frequency   B_f_250 = the frequency of resistance allele B at the 250th generation  B_f_bar = the mean frequency of resistance allele B over the first 250 generations  nf_80% = the recorded number of generations that it takes for the female population size to recover to >80% of its original size in the 0th generation; 0 means that the female population size never reaches >80% recovery; 1 means that the female population size never drops below 80% of its original size in the 1st generation nf_250 = the female population size at the 250th generation nf_bar = the mean female population size over the first 250 generations  nf_ext = the recorded number of generations that it takes for the female population size to drop below 1 (i.e. population extinction); 0 means that female population size never reaches <1 2. Number of cases/rows:  10^6, corresponding to the number of random samples  3. Variable List: NA 4. Missing data codes: all missing data is recorded as 0  5. Specialized formats or other abbreviations used: NA

opencc-by-sa-4.0Aug 2021View details →

Vector image of processing techniques for self-healing soft robots

Vector images of processing techniques that can be used to manufacture self-healing soft robots. The file includes different types of additive manufacturing processes (fused filament fabrication, direct ink writing, selective laser sintering, stereolithography, inkjet printing, fused granulate fabrication), formative processes (compression moulding, solvent casting, injection moulding, casting, vacuum assisted resin transfer moulding, blow moulding), and assembly processes (folding & binding, joining & binding, stacking and binding, local thermal ablation & welding).

opencc-by-4.0Aug 2021View details →

Instances for the two-dimensional heterogeneous vector bin packing problem

The dataset consist of 56 instances for the two-dimensional heterogeneous vector bin packing problem. Six small instances with 10, 11, 12, 13, 15 and 20 items, as well as 50 randomly generated large instances were considered. Weights and volumes of items were randomly uniformly chosen integer values from [1;15] tons and [1;25]m3, respectively. The set of large instances had 5 instances with each of the following numbers of items: 50, 70, 100, 120, 150, 200, 350, 500, 750 and 1000. File structure: - number of items - weights of items - volumes of items

2008 Nura earthquake surface rupture slip vector documentation

This online data holds information related to the surface rupture resulting from the 2008 Nura earthquake in south Kyrgyzstan. The primary dataset is a Google Earth KMZ file with GPS-locations where slip vector measurements were taken along the rupture. A downloadable ZIP file accompanies the KMZ, containing photographs linked to each data point. Both files should be stored in one folder for proper linkage. In addition, a text file is available with all measurements and associated information. Five videos obtained with the drone are available to illustrate the surface rupture zones and geological overview in the Nura settlement surroundings. The entire data was gathered in 2018.  Raster-files of high-resolution digital surface models of the surface rupture can be found on opentopography <a href="https://doi.org/10.5069/G9ZW1J4C" target="_blank" rel="noreferrer noopener">https://doi.org/10.5069/G9ZW1J4C</a> The data presented in this repository was initially disseminated in a dissertation by Magda Patyniak. This project is part of the CaTeNA-project within the Client II program of and funded by the Federal Ministry of Education and Research (BMBF; Sub-project grant 03G0878E to Manfred Strecker).

opencc-by-nc-sa-4.0May 2019View details →

DL-FRONT MERRA-2 vectorized weather fronts over North America, 1980-2018 (JSON format)

DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of hourly JSON files containing geospatial vector polylines describing the locations of four types of weather fronts—cold front, warm front, stationary front, and occluded front, over the time span 1980-2018. This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) <a href="https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/">Modern-Era Retrospective analysis for Research and Applications, Version 2</a> (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation. At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region). Each probability map was then processed to obtain polyline skeletons of the weather front boundary regions found by DL-FRONT. These vector representations of the fronts were then written to JSON files—one file for each hour. Each JSON file contains one top-level object composed of name/value pairs with the names issuanceDate, validDate, ColdFronts, WarmFronts, OccludedFronts, and StationaryFronts. The name/value pairs for createDate and validDate are always present. The other name/value pairs are only present if there is corresponding data. The values for issuanceDate and validDate are UTC timestamp strings. The ColdFronts, WarmFronts, StationaryFronts, and OccludedFronts names in the top-level object, when present, have values that are arrays. In each case, the array is composed of one or more objects. Each object represents a front of the given type. Each object is composed of five name/value pairs with the names lats, lons, cols, rows, and confidence. The value for the name confidence is a number that is the average of the values of the probability map cells intersected by the front polyline. The values associated with the names lats, lons, cols, and rows are arrays. These arrays represent the vertices of a polyline describing the location of a frontal boundary in both geospatial and grid cell coordinates.

opencc-by-4.0Jan 2023View details →

2 million histopathology stain vectors including Hematoxylin & Eosin color variations

The database includes the stain vectors including color variations of over 2 million patches. The database is adopted in "Data-driven color augmentation for H&E stained images in computational pathology", (https://www.sciencedirect.com/science/article/pii/S2153353922007830) to check is the color variation of an augmentated sample is acceptable or not. During the training, the color variation of an augmented sample is compared with the variations included in the database. If N variations from the database are found within a radius R from the components of the augmented sample, the augmented sample is considered acceptable (in terms of color variations); otherwise, it is discarded. The database is stored in a .pickle file, including vectors with six elements: the RGB components of Hematoxylin and Eosin, for every patch. Double entries are removed from the database. Code to import and to extend database with new data is available here: https://github.com/ilmaro8/Data_Driven_Color_Augmentation Stain vectors are collected from six private and public sources, to ensure that color variations can cover the variability of H&E-stained tissues: TCGA, ExaMode colon dataset, Camelyon, Puerta del Mar, Clinic, CAD.

opencc-by-4.0Jan 2023View details →

G-Code associated to the trajectories of the brush in ApPEARS deliverable D5.1 Database of vector based brush strokes and sample prints that demonstrate the range of printed materials

This is appendix for the ApPEARS Deliverable: D5.1 – Database of vector based brush strokes and sample prints that demonstrate the range of printed materials. It contains G-Code Paths associated to the trajectories of the brush.