cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25934/pr00008932
title: Data Challenge

description: Building the macro from the micro: towards predictive analytic models for country-wide prediction of antimicrobial resistance

To control antimicrobial resistance (AMR), we need to look to the future. Knowing it's raining by looking out the window is not enough. We need a future weather report. We need models that can show us not just the micro, but also the macro - country level forecasts of AMR trends. To bridge this gap, we propose developing a predictive analytics model to capture trends in specific AMR patterns in specific bacteria at a nationwide level.

We will use feature selection from multiple datasets, including Atlas and SIDERO-WT data, antibiotic Defined Daily Dose (DDD) rates from the European Centre for Disease Prevention and Control, Population Corrected Use (PCU) of antibiotics in animals from the European Surveillance of Veterinary Antimicrobial consumption project, vaccination and handwashing WHO rates, and AMR governance figures from a governance analysis of 114 countries published in Lancet Infectious Diseases.

We will focus on carbapenem resistance in gram negative bacteria. The more we use carbapenems, the less effective these last-line antibiotics will be. Our model will focus on predicting carbapenem resistance in European countries, given data availability. Although carbapenem use is largely associated with hospitals and not outpatient care or animals, other antibiotics can select for multidrug resistance and will be accounted for. The model will rely on data from rates 2012-2016 to predict the last available data (2017), as resistance can lag behind use and responsive antibiotic stewardship may be too little too late.

We hope prediction models can also fill gaps where diagnostic capacity is limited. We hope to build the platform for lower resource countries, focusing on antibiotics like azithromycin if used in mass prophylaxis. A predictive model could provide policymakers with accurate predictions of antimicrobial resistance trends at the country level, enabling more effective decision making.
DOI: 10.6084/m9.figshare.22557337.v1
title: Supplementary material for "Resistance exercise counteracts the impact of androgen deprivation therapy on muscle characteristics in cancer patients"

description: Table 1: Habitual dietary intake and physical activity level at baseline and following either 20 weeks of usual care (Control) or resistance exercise training (Exercise) in prostate cancer patients on androgen deprivation therapy. <br> Table 2: Type I and type II muscle fiber characteristics at baseline and following 20 weeks of androgen deprivation therapy (ADT only) or androgen deprivation therapy combined with chemotherapy (ADT + Chemotherapy) in prostate cancer patients
DOI: 10.6084/m9.figshare.20375850.v9
title: Dataset Combining experiments and integral projection model reveals when warming and food restriction increase population fitness

description: This file contains the data and scripts used in the article " Combining experiments and integral projection model reveals when warming and food restriction increase population fitness". <br> Growth, fecundity and survival data are available in df_growth.csv, df_fecundity.csv and df_survival.csv. In these files, "age" is the age of the fish, "temp" is the rearing temperature (20 °C or 30 °C), "food" is the feeding modality (ad libitum ad or restriction res), "condition" is the combination of temperature and resource modalities, "length_mm" is the fish size, fecundity is the number of eggs and "event" is the survival modality (1 = dead, 0 = alive). The scripts 01_growth.R, 02_fecundity and 03_survival.R analyze the growth, fecundity and survival curves. The 01_growth.R script uses the model written in bayesian_model.txt. <br> Integral Projection Models are written in script 04_IPM.R which uses the data cfs_bayes.Rdata (bayesian model posterior distribution), df_reproduction.csv, df_survival_probabilities.csv (Kaplan Meier estimate) and df_size_newborn.csv (data from our laboratory on the size of the newborns). IPMs results are compiled in IPM_model_outputs.csv. The script 05_IPM_sensitivity.R is used to perform sensitivity analysis. The results of these analyses are compiled in IPM_model_sensitivity.csv.
DOI: 10.5281/zenodo.7897773
title: Supplementary Materials for WEPA101

description: This archive contains data and supporting material for submission WEPA101 to IPAC23 proceedings. Subdirectory 03_warpx_training/ contains material for the first example of the article, a laser-plasma accelerator stage. Within 03_warpx_training/ are the two simulation setups, 17_1pC_2stage/ and 19_1_pC_2_beam_center_beam2_pz/. The first contains the necessary material to compute the WarpX simulation we desire to model. The second contains the necessary material to generate the training data using WarpX, as well as Jupyter notebooks for creating and training the neural networks Subdirectory iota_channel/ contains material for generating ImpactX simulations of the focusing channel based on the IOTA nonlinear lattice as well as creating and training neural networks to learn the dynamics of the focusing channel based on the IOTA lattice Subdirectory machine_learning_utilities/ contains helper functions for generating, training, and analyzing neural networks See the readme.md for more information
description: This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231 and by LLNL under Contract DE-AC52-07NA27344. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hard- ware technology, to support the nation's exascale computing imperative. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719
DOI: 10.5281/zenodo.7897772
title: Supplementary Materials for WEPA101

description: This archive contains data and supporting material for submission WEPA101 to IPAC23 proceedings. Subdirectory 03_warpx_training/ contains material for the first example of the article, a laser-plasma accelerator stage. Within 03_warpx_training/ are the two simulation setups, 17_1pC_2stage/ and 19_1_pC_2_beam_center_beam2_pz/. The first contains the necessary material to compute the WarpX simulation we desire to model. The second contains the necessary material to generate the training data using WarpX, as well as Jupyter notebooks for creating and training the neural networks Subdirectory iota_channel/ contains material for generating ImpactX simulations of the focusing channel based on the IOTA nonlinear lattice as well as creating and training neural networks to learn the dynamics of the focusing channel based on the IOTA lattice Subdirectory machine_learning_utilities/ contains helper functions for generating, training, and analyzing neural networks See the readme.md for more information
description: This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231 and by LLNL under Contract DE-AC52-07NA27344. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hard- ware technology, to support the nation's exascale computing imperative. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719
DOI: 10.6084/m9.figshare.20375850.v10
title: Dataset Combining experiments and integral projection model reveals when warming and food restriction increase population fitness

description: This file contains the data and scripts used in the article " Combining experiments and integral projection model reveals when warming and food restriction increase population fitness". <br> Growth, fecundity and survival data are available in df_growth.csv, df_fecundity.csv and df_survival.csv. In these files, "age" is the age of the fish, "temp" is the rearing temperature (20 °C or 30 °C), "food" is the feeding modality (ad libitum ad or restriction res), "condition" is the combination of temperature and resource modalities, "length_mm" is the fish size, fecundity is the number of eggs and "event" is the survival modality (1 = dead, 0 = alive). The scripts 01_growth.R, 02_fecundity and 03_survival.R analyze the growth, fecundity and survival curves. The 01_growth.R script uses the model written in bayesian_model.txt. <br> Integral Projection Models are written in script 04_IPM.R which uses the data cfs_bayes.Rdata (bayesian model posterior distribution), df_reproduction.csv, df_survival_probabilities.csv (Kaplan Meier estimate) and df_size_newborn.csv (data from our laboratory on the size of the newborns). IPMs results are compiled in IPM_model_outputs.csv. The script 05_IPM_sensitivity.R is used to perform sensitivity analysis. The results of these analyses are compiled in IPM_model_sensitivity.csv.
DOI: 10.18712/ess10cfe03_0
title: ESS10 data from Contact forms, edition 3.0
DOI: 10.6084/m9.figshare.22786910
title: 1h of fungal keratitis

description: 1h group of fungal keratitis
DOI: 10.6084/m9.figshare.22786910.v1
title: 1h of fungal keratitis

description: 1h group of fungal keratitis
DOI: 10.18712/ess10inte03_0
title: ESS10 data from Interviewer’s questionnaire, edition 3.0
DOI: 10.18712/ess10sce02_0
title: ESS10 Self-completion - integrated file, edition 2.0
DOI: 10.6084/m9.figshare.22786952
title: Excel tables

description: This repository describes the dataset used for intraspecific (among individuals) and intraspecific (between species) comparisons, and data for female-entrance analysis.
DOI: 10.18712/ess10sccfe02_0
title: ESS10 Self-completion – data from Contact forms, edition 2.0
DOI: 10.5281/zenodo.7907676
title: A Meta-Learner Approach to Multistep-Ahead Time Series Prediction

description: <strong>Abstract</strong> The application of machine learning has become commonplace for problems in modern data science. The democratization of the decision process when choosing a machine learning algorithm has also received considerable attention through the use of meta features and automated machine learning for both classification and regression type problems. However, this is not the case for multistep-ahead time series problems. Time series models generally rely upon the series itself to make future predictions, as opposed to independent features used in regression and classification problems. The structure of a time series is generally described by features such as trend, seasonality, cyclicality, and irregularity. In this research, we demonstrate how time series metrics for these features, in conjunction with an ensemble based regression learner, were used to predict the standardized mean square error of candidate time series prediction models. These experiments used datasets that cover a wide feature space and enable researchers to select the single best performing model or the top N performing models. A robust evaluation was carried out to test the learner's performance on both synthetic and real time series. <strong>Proposed Dataset</strong> The dataset proposed here gives the results for 20 step ahead predictions for eight Machine Learning/Multi-step ahead prediction strategies for 5,842 time series datasets outlined here. It was used as the training data for the Meta Learners in this research. The meta features used are columns C to AE. Columns AH outlines the method/strategy used and columns AI to BB (the error) is the outcome variable for each prediction step. The description of the method/strategies is as follows: <strong>Machine Learning methods:</strong> NN: Neural Network ARIMA: Autoregressive Integrated Moving Average SVR: Support Vector Regression LSTM: Long Short Term Memory RNN: Recurrent Neural Network <strong>Multistep ahead prediction strategy:</strong> OSAP: One Step ahead strategy MRFA: Multi Resolution Forecast Aggregation
DOI: 10.5281/zenodo.7907677
title: A Meta-Learner Approach to Multistep-Ahead Time Series Prediction

description: <strong>Abstract</strong> The application of machine learning has become commonplace for problems in modern data science. The democratization of the decision process when choosing a machine learning algorithm has also received considerable attention through the use of meta features and automated machine learning for both classification and regression type problems. However, this is not the case for multistep-ahead time series problems. Time series models generally rely upon the series itself to make future predictions, as opposed to independent features used in regression and classification problems. The structure of a time series is generally described by features such as trend, seasonality, cyclicality, and irregularity. In this research, we demonstrate how time series metrics for these features, in conjunction with an ensemble based regression learner, were used to predict the standardized mean square error of candidate time series prediction models. These experiments used datasets that cover a wide feature space and enable researchers to select the single best performing model or the top N performing models. A robust evaluation was carried out to test the learner's performance on both synthetic and real time series. <strong>Proposed Dataset</strong> The dataset proposed here gives the results for 20 step ahead predictions for eight Machine Learning/Multi-step ahead prediction strategies for 5,842 time series datasets outlined here. It was used as the training data for the Meta Learners in this research. The meta features used are columns C to AE. Columns AH outlines the method/strategy used and Column AJ(the error) is the outcome variable.. The description of the method/strategies is as follows: <strong>Machine Learning methods:</strong> NN: Neural Network ARIMA: Autoregressive Integrated Moving Average SVR: Support Vector Regression LSTM: Long Short Term Memory RNN: Recurrent Neural Network <strong>Multistep ahead prediction strategy:</strong> OSAP: One Step ahead strategy MRFA: Multi Resolution Forecast Aggregation
DOI: 10.5281/zenodo.7913884
title: A Meta-Learner Approach to Multistep-Ahead Time Series Prediction

description: <strong>Abstract</strong> The application of machine learning has become commonplace for problems in modern data science. The democratization of the decision process when choosing a machine learning algorithm has also received considerable attention through the use of meta features and automated machine learning for both classification and regression type problems. However, this is not the case for multistep-ahead time series problems. Time series models generally rely upon the series itself to make future predictions, as opposed to independent features used in regression and classification problems. The structure of a time series is generally described by features such as trend, seasonality, cyclicality, and irregularity. In this research, we demonstrate how time series metrics for these features, in conjunction with an ensemble based regression learner, were used to predict the standardized mean square error of candidate time series prediction models. These experiments used datasets that cover a wide feature space and enable researchers to select the single best performing model or the top N performing models. A robust evaluation was carried out to test the learner's performance on both synthetic and real time series. <strong>Proposed Dataset</strong> The dataset proposed here gives the results for 20 step ahead predictions for eight Machine Learning/Multi-step ahead prediction strategies for 5,842 time series datasets outlined here. It was used as the training data for the Meta Learners in this research. The meta features used are columns C to AE. Columns AH outlines the method/strategy used and columns AI to BB (the error) is the outcome variable for each prediction step. The description of the method/strategies is as follows: <strong>Machine Learning methods:</strong> NN: Neural Network ARIMA: Autoregressive Integrated Moving Average SVR: Support Vector Regression LSTM: Long Short Term Memory RNN: Recurrent Neural Network <strong>Multistep ahead prediction strategy:</strong> OSAP: One Step ahead strategy MRFA: Multi Resolution Forecast Aggregation
DOI: 10.5283/epub.54091
title: Data archive of "Isotope Ratio Outlier Analysis (IROA) for HPLC–TOFMS-Based Metabolomics of Human Urine"
DOI: 10.15139/s3/79wvwc
title: State-Level Data on TANF Policy Changes During the COVID-19 Pandemic

description: This dataset captures changes to the Temporary Assistance for Needy Families by state during the COVID-19 pandemic, from March 2020 through December 2020. It accompanies a soon-to-be submitted publication that describes the dataset and data collection processes.
DOI: 10.6073/pasta/adbd98356644c13692c6a482124c7295
title: Early-Holocene Forest at Stonewall Beach on Martha’s Vineyard 10700-9800 BP

description: Coastal erosion at Stonewall Beach on the island of Martha’s Vineyard, Massachusetts, U.S.A., has exposed a thick layer of peaty sediments rich in botanical remains, including well-preserved tree trunks. We identified the species of the tree trunks based on wood anatomy, analyzed pollen and macrofossils in the sediments, and determined the ages of the tree trunks and peat with 14C dating. The tree trunks were identified as Pinus strobus (white pine), and pollen assemblages featured high percentages of P. strobus in sediments associated with the trunks. The tree trunks and peat dated to ~10,700–9800 calibrated 14C years before present. These findings confirm that Martha’s Vineyard, like other parts of southern New England, was dominated by P. strobus forest during the early Holocene. At that time, regional climate was drier than today and Martha’s Vineyard was not yet isolated from the mainland by postglacial sea-level rise.
DOI: 10.6084/m9.figshare.22147382.v1
title: Supplementary Material for: Impact of macrocephaly, as an isolated trait, on EEG signal as measured by spectral power and multiscale entropy during the first year of life

description: Macrocephaly has been associated with neurodevelopmental disorders; however, it has been mainly studied in the context of pathological or high-risk populations and little is known about its impact, as an isolated trait, on brain development in general population. Electroencephalographic (EEG) power spectral density (PSD) and signal complexity have shown to be sensitive to neurodevelopment and its alterations. We aimed to investigate the impact of macrocephaly as isolated trait on EEG signal as measured by power spectral density (PSD) and Multiscale Entropy (MSE) during the first year of life. We recorded high density EEG resting state activity of 74 healthy full-term infants, 50 control (26 girls) and 24 macrocephalic (12 girls) aged between 3 and 11 months. We used linear regression models to assess group and age effects on EEG power spectral density (PSD) and signal complexity. Sex and brain volume measures, obtained via a 3D transfontanellar ultrasound, were also included into the models to evaluate their contribution. Our results showed lower PSD of the low alpha (8-10Hz) frequency band and lower complexity in the macrocephalic group compared to the control group. In addition, we found an increase in low alpha (8.5-10Hz) PSD and in the complexity index with age. These findings suggest that macrocephaly as an isolated trait has a significant impact on brain activity during the first year of life.
DOI: 10.5517/ccwshdj
title: CCDC 827898: Experimental Crystal Structure Determination

description: Related Article: L.Hui, S.Gao, C.G.Yan|2012|Khim.Get.Soedin.,SSSR||706|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc23gy2m
title: CSD 1950770: Experimental Crystal Structure Determination

description: Related Article: Alexandra A. Shmakova, Taisiya S. Sukhikh, Victoria V. Volchek, Vadim Yanshole, Dmitri V. Stass, Eugeniy Yu. Filatov, Eugeniy M. Glebov, Pavel A. Abramov, Maxim N. Sokolov|2020|Inorg.Chim.Acta|502|119319|doi:10.1016/j.ica.2019.119319
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc23gy3n
title: CCDC 1950771: Experimental Crystal Structure Determination

description: Related Article: Alexandra A. Shmakova, Taisiya S. Sukhikh, Victoria V. Volchek, Vadim Yanshole, Dmitri V. Stass, Eugeniy Yu. Filatov, Eugeniy M. Glebov, Pavel A. Abramov, Maxim N. Sokolov|2020|Inorg.Chim.Acta|502|119319|doi:10.1016/j.ica.2019.119319
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc23gy4p
title: CCDC 1950772: Experimental Crystal Structure Determination

description: Related Article: Alexandra A. Shmakova, Taisiya S. Sukhikh, Victoria V. Volchek, Vadim Yanshole, Dmitri V. Stass, Eugeniy Yu. Filatov, Eugeniy M. Glebov, Pavel A. Abramov, Maxim N. Sokolov|2020|Inorg.Chim.Acta|502|119319|doi:10.1016/j.ica.2019.119319
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.