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Predictive factors of large liver cell carcinoma tumor response after first conventional transarterial chemoembolization in Indonesia according to the modified response evaluation in solid tumors criteria: a preliminary study (supplemental data)

Excel data of the above-mentioned research for supplemental purposes.

FIG. 2 in Maternal Body Size and Condition Predict Measures of Reproductive Success and Future Reproductive Allocation in Territorial Eastern Red-Backed Salamanders

FIG. 2. The regression of number of surviving offspring (at 185 d posthatching) on (A) maternal body size (SVL in mm) and (B) maternal condition (residuals of the regression of maternal mass, mm, on maternal body size, SVL in mm).

FIG. 3 in Maternal Body Size and Condition Predict Measures of Reproductive Success and Future Reproductive Allocation in Territorial Eastern Red-Backed Salamanders

FIG. 3. The body size of siblings that did and did not survive to 185 d after hatching. Each point represents the mean SVL of siblings from a single female that did or did not survive. The diagonal line represents the hypothetical situation in which the body size of siblings that did and did not survive are equal.

FIG. 1 in Maternal Body Size and Condition Predict Measures of Reproductive Success and Future Reproductive Allocation in Territorial Eastern Red-Backed Salamanders

FIG. 1. The regression of (A) clutch size (number of eggs) and of (B) mean juvenile size (measured as mean snout–vent length, SVL, in mm per clutch) on maternal body size (SVL in mm).

FIG. 4 in Maternal Body Size and Condition Predict Measures of Reproductive Success and Future Reproductive Allocation in Territorial Eastern Red-Backed Salamanders

FIG. 4. The regression of number of developing oocytes (visible through the body wall) produced by females on (A) maternal body size (SVL in mm) and (B) maternal condition (residuals of the regression of maternal mass, mm, on maternal body size, SVL in mm).

opencc-by-4.0Oct 2022View details →

Source data belonged to "Establishing structure-property linkages for wicking time predictions in porous polymeric membranes using a data-driven approach"

This record contains all the necessary data to obtain the results of the study "Establishing structure-property linkages for wicking time predictions in porous polymeric membranes using a data-driven approach"

Prediction of Compressive Strength of Concrete with Silica, Ash and Fiber Using Fuzzy Logic Method

Prediction of Compressive Strength of Concrete with Silica, Ash and Fiber Using Fuzzy Logic Method References

Supplementary dataset for paper: "Approximate non-linear model predictive control with safety-augmented neural networks"

Supplementary dataset for paper Henrik Hose and Johannes Koehler and Melanie N. Zeilinger and Sebastian Trimpe "Approximate non-linear model predictive control with safety-augmented neural networks". The code to use this dataset is publicly available at <a href="https://github.com/hshose/soeampc">https://github.com/hshose/soeampc</a> The dataset contains training and testing data to train an NN controller for three standard benchmark systems, a stir tank reactor, a quadcopter, and a chain mass system. For each system, there are initial conditions as comma separated value in the `x0.txt` file, the MPC input trajectory in the `U.txt` file and the corresponding predicted state sequence in the `X.txt` file. MPC parameters are provided for each system. The dataset was computed using acados for SQP solving. The dataset also contains pretrained neural network approximations of the dataset.These are provided in the `pretrained_models.zip` file. The neural networks were trained with tensorflow.

Fast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control

If you use any of this data, please cite the following publication: @inproceedings{monforte2023fast,   title={Fast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control},   author={Monforte, Marco and Gava, Luna and Iacono, Massimiliano and Glover, Arren and Bartolozzi, Chiara},   booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},   pages={4035--4043},   year={2023} } Event-based datasets of synthetic and real trajectories of a bouncing ball. The synthetic trajectories were obtained converting frames taken using Unreal Engine to events. The ground truth is provided along with objects and camera settings. The real trajectories wer dumped from a real event camera located in front of the robot workspace. To import .log files containing events, we suggest <a href="https://github.com/event-driven-robotics/bimvee">bimvee</a> Python library. Specifically use the functions to import .log files: data = importIitYarpBinaryDataLog(filePathOrName=input_path)

FIG. 5 in Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning.

FIG. 5. Influence of parasite infection on total snail abundance. Number of original and juvenile snails observed in each parasite treatment (absent, present) on experimental day 65. Plotted values are means ± 1 SE. The y-axis is log transformed.

FIG. 2 in Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning.

FIG. 2. Example Northern Leopard Frog metamorphs observed with limb deformities. (Top left) Extra limbs. (Top right) Extra limb and boney triangle. (Bottom left) Boney triangle. (Bottom right) Absent limbs.

FIG. 4 in Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning.

FIG. 4. Influence of treatments on metamorph responses. (A) Proportion of Northern Leopard Frog tadpoles exposed to pesticide treatments (control, atrazine, Bti) and parasite treatments (absent, present) that survived to metamorphosis. (B) Larval period of Northern Leopard Frogs exposed to pesticide treatments (control, atrazine, Bti) and parasite treatments (absent, present). (C) Mass at metamorphosis of Northern Leopard Frogs exposed to pesticide treatments (control, atrazine, Bti) and parasite treatments (absent, present). Plotted values are means ± 1 SE.

FIG. 3 in Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning.

FIG. 3. Influence of pesticide treatment on the proportion of metamorphs with deformities. Proportion of parasitized Northern Leopard Frog metamorphs with deformities exposed to pesticide treatments (control, atrazine, Bti). Plotted values are means ± 1 SE. The gray points indicate proportion of metamorphs deformed within each mesocosm.

FIG. 1 in Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning.

FIG. 1. Effects of parasites on tadpole responses. Mass and Gosner developmental stage for Northern Leopard Frog tadpoles exposed to different parasite treatments (absent, present) for 7 wk. Plotted values are means ± 1 SE.

openother-openMay 2023View details →

Datasets associated with the publication: "The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models".

ECMWF HRES and ERA5 datasets for the paper "The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models". Content:  ======================================================================================== Storm Friederike: ECMWF HRES forecast, 18.01.2018 00:00 UTC - 23:00 UTC, hourly, GRIB-format. ECMWF ERA5 reanalysis, 16.01.2018 12:00 UTC - 19.01.2018 00:00 UTC, twelve-hourly data, GRIB-format. ======================================================================================== Storm Vladiana: ECMWF HRES analysis, 23.09.2016 00:00 UTC - 23.09.2016 00:00 UTC, six-hourly data, rotated North Pole (latitude: 51˚, longitude: 160˚), NetCDF-format. ======================================================================================== Storm Egon: ECMWF ERA5 reanalysis, 12.01.2017 00:00 UTC - 13.01.2017 06:00 UTC, six-hourly data, GRIB-format.

dryad32/100

Data for: Phenotypic outcomes of predator-prey coevolution are predicted by landscape variation in climate and community composition

<ol> <li>Landscape patterns of phenotypic coevolution are determined by variation in the outcome of predator-prey interactions. These outcomes may depend not only on the functional phenotypes that mediate species interactions but also on aspects of the environment that enable encounters between coevolutionary partners.</li> <li>Exploring the relationship between coevolutionary traits and the environment requires extensive sampling across the range of the interaction to determine the relationship between local ecological variation and coevolution.</li> <li>In this study, we synthesized >30 years of data on predator-prey interactions between toxic newts (Taricha granulosa) and their snake predators (Thamnophis sirtalis) to explore the environmental predictors of arms race escalation.</li> <li>We found that geographic variation in phenotypes at the interface of coevolution was best predicted by a combination of community and climatic variation. Coevolutionary phenotypes were greatest in environments with climate favorable for newt-snake overlap. We found prey toxicity was elevated in regions with more predator species, and predator resistance was higher in regions with more prey species.</li> <li>Our results suggest specific environmental conditions reinforce the process of coevolution, signifying the phenotypic outcomes of coevolutionary arms races are sensitive to local ecological contexts that vary across the landscape. </li> </ol>

opencc-zeroMay 2023View details →

opencc-by-4.0May 2023View details →

Supplementary File: followed by Predicting Religious Undergraduates' Career Development: The Salient Roles of Religious Calling, Life Satisfaction, and Quest Religiosity

Supplementary File: followed by Predicting Religious Undergraduates’ Career Development: The Salient Roles of Religious Calling, Life Satisfaction, and Quest Religiosity

openodc-odblMay 2023View details →

data for the paper "Seasonal Prediction of Regional Arctic Sea Ice Using the High-Resolution Climate Prediction System CMA-CPSv3"

CMA-CPSv3 data for the paper "Seasonal Prediction of Regional Arctic Sea Ice Using the High-Resolution Climate Prediction System CMA-CPSv3"

opencc-by-4.0May 2023View details →

A Venus in the Making? Predictions for JWST Observations of the Ultracool M-Dwarf Planet LP 890-9 c

This repository contains the pressure-temperature profiles and volume mixing ratio profiles used in a paper accepted in MNRAS Letters under the same name.