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opencc-by-4.0Oct 2016View details →

Neutrino beam flux prediction 2016

This data release is superseded by the<a href="https://zenodo.org/record/5734267#.YaYEsFOnxsE"> </a><a href="https://zenodo.org/record/5734307#.YaYFXlOnxsE">flux release in 2020</a>. The files in the following archive file contain the neutrino and antineutrino beam flux predictions for the T2K ND280 (near) and Super-Kamiokande (far) detectors. It supersedes the <a href="https://zenodo.org/record/5734212#.YaSq8lOnxsE">previous flux release in 2013</a>. The original description of the flux predictions is published in <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.87.012001">Phys. Rev. D.87.012001</a>. Since the publication, the flux prediction has been updated with new thin target data from the NA61/SHINE experiment, and flux predictions for antineutrino enhanced beam operation have been produced. The NA61/SHINE thin target measurements of π±, K±, KS0, Λ and p production are published in <a href="https://link.springer.com/article/10.1140/epjc/s10052-016-3898-y">Eur. Phys. J. C (2016) 76:84</a>. The updated flux prediction is described in <a href="http://journals.aps.org/prd/abstract/10.1103/PhysRevD.91.072010">Phys. Rev. D 91, 072010 (2015)</a>. The provided flux predictions include no neutrino oscillations. This tar file contains the flux predictions as well as a README.pdf file with detailed information on the included files.

opencc-by-4.0Nov 2021View details →

Neutrino beam flux prediction 2020

The files in the following archive file contain the neutrino and antineutrino beam flux predictions for the T2K ND280 (near) and Super-Kamiokande (far) detectors. It supersedes the <a href="../record/5734267#.YaStE1OnxsE">previous flux release in 2016</a>. Since the previous flux release, the flux prediction has been updated to use 2009 replica target data from the NA61/SHINE experiment to tune interactions inside of the neutrino production target. The NA61/SHINE 2009 replica target measurements of π± production are published in <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1140%2Fepjc%2Fs10052-016-4440-y&data=04%7C01%7Cteppei.katori%40kcl.ac.uk%7C392cb1750b714c1456d008d9b31a5206%7C8370cf1416f34c16b83c724071654356%7C0%7C0%7C637737749231779618%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=2aY4w0xu4PnkkAuL55OCLMd5PQUmsPtXfz9C61no2ns%3D&reserved=0">Eur. Phys. J. C (2016) 76: 617</a>. The flux prediction released here corresponds to that used in the T2K 2020 oscillation physics result paper (to be submitted to Eur. Phys. J. C). The provided flux predictions include no neutrino oscillations. A matrix which encodes the covariance between different energy bins of the fluxes at the near and far detectors for both horn operation modes is also provided in this release. The provided tar* file contains the flux predictions, covariance matrix, and a README.pdf file with detailed information on the included files. *Note, although the file name ends .gz, this is a .tar.gz file and should be unpacked as such (tar -xvf).

The angle of attack: Rapping technique predicts skill in hermit crab contests

Skill, the ability to perform a challenging behaviour well, has been shown to be an important determinant of success in a variety of contexts, including human sports, animal courtship and most recently, animal contests. Because skilful movement requires precise motor control, skill is assumed to be underpinned by traits that determine these abilities. However, while these traits determine an individual's potential to perform skilful movements (known as technique), this potential may not translate into skilful fighting due to interference from the opponent. Here, we investigate the relationship between technique and skill using the European hermit crab Pagurus bernhardus. By examining the spatial distribution of shell raps, we find that on average, technique exhibited during a "training" fight predicts the level of skill displayed in a real contest. However, our results also demonstrate substantial among-individual variation in the direction of change across the two fights, with some individuals exhibiting better technique than skill and others showing the opposite pattern. Finally, we find that winners, but not losers, progressively adapt their targeting of strikes when faced with a fully functional opponent. Our results indicate that skill is a combination of innate technique and the ability to adapt to an opponent's behaviour.

Predicted HHV values for woody biomass samples from USDA-AFRI project using the best performing models.

Predicted HHV values for samples from the USDA-AFRI project using the best-performing models from the cross-validation process.

Analytical prediction of scattering properties of spheroidal dust particles with machine learning

This respository includes the data used in the paper "Analytical prediction of scattering properties of spheroidal dust particles with machine learning". <ol> <li>"alldata.zip" represents the extinction and absorption coefficients, and phase matrix elements of spheroids dust particles for both training and test data. These data comes from the Oleg Dubovik's group: <a href="https://www.grasp-open.com/products/spheroid-package-release">https://www.grasp-open.com/products/spheroid-package-release</a>/.</li> <li>"<a href="/api/files/7c2cdcdd-dc47-4a19-adc8-59c944e76e54/tmat_Jacall_norm_intg.pickle?versionId=59294030-26a8-4683-9b26-8e8584c2b44b">tmat_Jacall_norm_intg.pickle</a>" contains the Jacobians simulated from linearized T-matrix model used for training in the paper and "<a href="/api/files/7c2cdcdd-dc47-4a19-adc8-59c944e76e54/tmat_fine_n_intg.pickle?versionId=3d20954d-4318-40bb-a900-a93627915c2e">tmat_fine_n_intg.pickle</a>" involves Jacobians used for testing.</li> </ol>

Predictive utility of task-related functional connectivity vs. voxel activation - Data and code archive

Functional connectivity, both in resting state and task performance, has steadily increased its share of neuroimaging research effort in the last 1.5 decades. In the current study, we investigated the predictive utility regarding behavioral performance and task information for 240 participants, aged 20-77, for both voxel activation and functional connectivity in 12 cognitive tasks, belonging to 4 cognitive reference domains (Episodic Memory, Fluid Reasoning, Perceptual Speed, and Vocabulary). We also added a model only comprising brain-structure information not specifically acquired during performance of a cognitive task. We used a simple brain-behavioral prediction technique based on Principal Component Analysis (PCA) and regression and studied the utility of both modalities in quasi out-of-sample predictions, using split-sample simulations (=5-fold Monte Carlo cross validation) with 1,000 iterations for which a regression model predicting a cognitive outcome was estimated in a training sample, with a subsequent assessment of prediction success in a non-overlapping test sample. The sample assignments were identical for functional connectivity, voxel activation, and brain structure, enabling apples-to-apples comparisons of predictive utility. All 3 models that were investigated included the demographic covariates age, gender, and years of education. A minimal reference model using simple linear regression with just these 3 covariates was included for comparison as well and was evaluated with the same resampling scheme as described above. Results of the comparison between voxel activation and functional connectivity were mixed and showed some dependency on cognitive outcome; however, mean differences in predictive utility between voxel activation and functional connectivity were rather small in terms of within-modality variability or predictive success. More notably, only in the case of Fluid Reasoning did concurrent functional neuroimaging provided compelling about cognitive performance beyond structural brain imaging or the minimal reference model.

Supplementary materials used for the research to predict phase separation in protein chains

This is a collection of all the data, codes and metadata that were used for the research titled: 'Sequence based prediction of protein phase separation into disordered condensates using machine learning'.

Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma

Original file of Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma.

Neutrophil-mediated oxidative stress and albumin structural damage predict COVID-19-associated mortality

This work reports that COVID-19-induced oxidative stress inflicts structural damages to human serum albumin (HSA) and is linked with mortality outcome in critically ill patients. Analyzing blood samples from patients and healthy individuals, the paper provides evidence that neutrophils are major sources of oxidative stress in blood and that hydrogen peroxide is highly accumulated in plasmas of non-survivors. The electron paramagnetic resonance spectra of spin-labeled fatty acids (SLFAs) bound with HSA in whole blood of control, survivor, and non-survivor subjects (n=10–11) were analyzed to probe structural damages to the protein. Non-survivors' HSA showed dramatically altered biophysical parameters that reflect remarkably fluid protein microenvironments. Following loading/unloading of 16-DSA, the results show that the transport function of HSA may be impaired in severe patients. Stratified at the means, Kaplan–Meier survival analysis indicated that lower values of S/W ratio and accumulated H2O2 in plasma significantly predicted in-hospital mortality.

Predicting pasture and forest landowner intention to create early successional habitat

As human land uses expand across the landscape, the management practices of private landowners are an essential part of effective conservation. Early successional habitats (ESH) and the species that depend on them are a priority in the eastern United States, and efforts to create ESH on private lands has primarily focused on forest landowners and timber harvests. Private pasture lands in a forested landscape present an additional opportunity to create and maintain ESH, yet our understanding of landowner values and attitudes about management strategies in pastures is lacking. To address this, we surveyed private landowners in 5 Virginia counties who own ≥10.1 ha or >610 m elevation (n = 503). Our primary objective was to understand how a variety of factors such as landowner values, past experience with habitat management, and perceived barriers to carrying out habitat management are associated with private landowner intention to carry out 7 ESH management strategies (i.e., reduced mowing, reduced grazing, timber harvests within forest, timber harvests at a field-forest border, prescribed fire, use of machinery, and use of herbicides to control invasive species) for the benefit of wildlife in the next five years. We used boosted regression trees to determine which factors best predicted the intention to carry out each strategy. We were able to effectively predict (accuracy > 75%) landowner intention to engage in open pasture and timber management strategies. Landowner values were not consistent across the different management strategies; landowners likely to reduce mowing or grazing valued ecological aspects of their land (i.e., pollinator habitat water quality) whereas landowners likely to harvest timber valued hunting and revenue. Past experience with wildlife management was the strongest predictor of likelihood to reduce mowing and grazing. Our results suggest that expanding outreach efforts to include pasture management options would engage a broader set of landowners in creation of ESH, especially if such efforts highlighted the benefits to pollinator species, water quality, and enhanced opportunities for hunting and other types of recreation.

opencc-by-4.0Oct 2021View details →

Predicting wildlife susceptibility to infectious diseases atglobal scales

Dataset included as supplementary material of  the paper entitled https://doi.org/10.5281/zenodo.4914750.  It contains phylogenetic, geographical and environmental distance for birds and bats, counts of incidence of  Plasmodium relictum on birds, counts of incidence of West Nile Virus on birds and counts of incidence of coronavirus in bats, and susceptibility calculated by the random forest algorithm. Also we include the r scripts to run the models, and the outputs of the models after 1000 runs.

Datasets for: Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

The datasets and supplementary materials for the manuscript "Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy". Citations should refer directly to the manuscript (refer to the DOI <a href="https://doi.org/10.1021/acs.jcim.1c01103">10.1021/acs.jcim.1c01103</a>). The preprint version of of the manuscript is also available at: <a href="https://doi.org/10.33774/chemrxiv-2021-djd3d-v2">10.33774/chemrxiv-2021-djd3d-v2</a>   Regarding "Solvation_data-1.0.0.zip": The datasets include the curated data for: (1) Abraham solute parameters, (2) solvation free energy, (3) solvation enthalpy, (4) gas-water partition coefficient (logKw), (5) water-1-octanol partition coefficient (logPow). The fitted Abraham and Mintz solvent parameters are also included. Detailed information can be found in the "README.txt" file of the zip file.   Regarding "ML_model_files.zip": This contains the machine learning model files for SoluteML and DirectML. For the instruction on how to use it, please refer to the chemprop_solvation git repository (<a href="https://github.com/fhvermei/chemprop_solvation">https://github.com/fhvermei/chemprop_solvation</a>)

Considerations on baseline generation for Imaging AI studies illustrated on the CT-based prediction of empyema and outcome assessment.

Considerations on baseline generation for Imaging AI studies illustrated on the CT-based prediction of empyema and outcome assessment. Introduction: For AI-based classification tasks in computed tomography, a reference standard for evaluating the clinical diagnostic accuracy of individual classes is essential. To enable the implementation of an AI tool in clinical practice, this should be drawn from clinical routine data, using State-of-the-art scanners, evaluated in a blinded manner, and verified with a reference test. Methods: 2659 consecutive CTs performed between 01/2016 and 01/2021 with reported pleural effusion were retrospectively included. Pathology reports from thoracocentesis or biopsy within 7 days of CT were used as reference standard (n = 335). Two radiologists (4 and 10 PGY) blindly assessed chest CTs (n=335, 81 empyemas) for pleural CT features and ICC was determined. In addition, both pleural CT features and radiological diagnosis were extracted from written radiological reports. If needed, consensus was achieved using an experienced radiologist's opinion (29 PGY). We assessed the correlation of these findings with the following patient outcomes: mortality and median hospital stay. Results: Specificity and sensitivity for clinical detection of empyema (N=81) were 90.94 (95%-CI 86.55-94.05) and 72.84 (95%-CI: 61.63-81.85%) in all effusions, with moderate to almost perfect interrater agreement for all pleural findings associated with empyema (Cohen's kappa = 0.41-0.82). Features describing pleural enhancement or thickening achieved the highest accuracy with 87.02% and 81.49%, respectively. Empyema was associated with a longer hospital stay (median= 20 versus 14 days), and findings consistent with pleural carcinosis impacted mortality.

Travel Time Prediction and Explanation with Spatio-TemporalFeatures: A Comparative Study

Datasets are used in the paper "Travel Time Prediction and Explanation with Spatio-TemporalFeatures: A Comparative Study".

Predicting the execution time of COSMO weather forecast models

This data set is the work of E. Di Giacomo Master Thesis at the University of Bologna. Predicting the execution time of a numerical weather forecast model is a complex task. Generally, these models simulate the evolution of atmospheric weather and they are typically used for the production of weather forecasts, one or multiple times a day. Given their computational complexity, they require large computing capabilities, such as High Performance Computing systems. In these systems, job scheduling and resource allocation are carefully managed to optimize the usage of the finite and expensive hardware resources; in particular, several allocation and related pricing decisions are based on estimates of the duration of the application submitted, such as the execution time of weather forecast models. A reliable prediction of execution time allows for a better management of the overall system, an improved planning of the model execution, as well as the identification of possible anomalies during the execution, thus providing great benefits to both system administrators and users. This data set regards a particular weather forecast model, namely the COSMO model, the weather forecasting model used at the the Hydro-Meteo-Climate Structure of Arpae Emilia-Romagna. The data set contains many execution times of the COSMO meteorological model run under a variety of different scientific parameters and parallelization levels.

K-Nearest-Neighbor algorithm to predict the survival time and classification of various stages of Oral Cancer: A machine learning approach

This project predicts the survival time of a cancer patient in terms of the number of days and also classifies the dataset into various stages of cancer This is executed on the SPYDER platform using Python 3.7 on Anaconda Navigator. The dataset includes the oral cancer patient's record of 4 countries

opencc-zeroJan 2022View details →

A new model of forelimb ecomorphology for predicting the ancient habitats of fossil turtles

Various morphological proxies have been used to infer habitat preferences among fossil turtles and their early ancestors, but most are tightly linked to phylogeny, thereby minimizing their predictive power. One particularly widely used model incorporates linear measurements of the forelimb (humerus + ulna + manus) but, in addition to the issue of phylogenetic correlation, it does not estimate the likelihood of habitat assignment. Here, we introduce a new model that uses intramanual measurements (digit III metacarpal + non-ungual phalanges + ungual) to statistically estimate habitat likelihood, and that has greater predictive strength than prior estimators. Application of the model supports the hypothesis that stem-turtles were primarily terrestrial in nature, and recovers the nanhsiungchelyid Basilemys (a fossil crown-group turtle) as having lived primarily on land, despite some prior claims to the contrary.

Quasi-distributed fiber optic monitoring of thermo-hydro behavior of frozen loess for frost heave prediction

We developed a quasi-distributed fiber optic monitoring technology to investigate the thermo-hydro-mechanical (THM) behaviors of frozen loess. A combined method was proposed to measure in-situ ice content, providing critical parameters for understanding THM coupled effects. To characterize the soil freezing-thawing process, we further carried out subsurface automated multiphysics monitoring at a site located on the Loess Plateau, China, during 2020/2021 winter by employing quasi-distributed fiber optic sensing arrays. The inner mechanism of frost heave was revealed via field monitoring data and correlation analysis, which directly promoted the development of a semiempirical frost heave prediction method. For this study area, the proposed method requiring only temperature data as inputs was validated using our field monitoring results. The data presented here are the monitoring data of in-situ temperature, moisture content,  and soil displacement. The results of frost heave prediction based on the field monitoring data are shown in the files.

Clones with predictions

This contains the output of simian and the found clones with the prediction generated by the T5 model

Build Prediction in Continuous Integration Using Textual Analysis of Source Code and Traditional Software Metrics

Continuous Integration (CI) systems integrate code changes committed by software developers, tests the results of the integration, and feed developers with information about the outcome of the integration and testing. Predicting the outcome of the integration is important since it reduces the feedback time between the CI system and the developers. This data-set comprises of historical code changes extracted from the TravisTorrent data-set (found in the train-lines folder) and their corresponding feature vectors (found in the train-bag-of-words folder) for Java projects. It also includes a set of files that contains historical build records and a set of traditional software metrics.