The World Ocean Isopycnal-Level Velocity (WOIL-V) climatology was derived from the United States Navy's Generalised Digital Environmental Model (GDEM) temperature and salinity profiles, using the P-Vector Method. The absolute velocity data have the same horizontal resolution and temporal variation (annual, monthly) as GDEM (T, S) fields. These data have an horizontal resolution of 0.5 degrees ×0.5 degrees, and 222 isopycnal-levels (sigma theta levels) from sigma theta = 22.200 to 27.725 (kg m-3) with the increment delta sigma theta = 0.025 (kg m-3), however in the equatorial zone (5 degrees S – 5 degrees N) they are questionable due to the geostrophic balance being the theoretical base for the P-vector inverse method. The GDEM model, which served as the base for the calculations includes data from 1920s onwards and the WOIL-V will be updated with the same frequency as the GDEM. The climatological velocity field on isopycnal surface is dynamically compatible to the GDEM (T, S) fields and provides background ocean currents for oceanographic and climatic studies, especially in ocean isopycnal modeling. The climatology was prepared by the Department of Oceanography, Naval Postgraduate School.

NMC ran the MRF every 24 h (00 UTC) with 12 h forecasts up to 240 h at a standard resolution of 200 km. The data cutoff for the model runs was 6 h and the output was ON85. There are 18 sigma levels. The output includes pressure, geopotential altitude, u and v wind components, virtual temperature, and relative humidity, among others.

`*.npz` and `*.asdf` files containing visibilities are in the TMS format (opposite that of CASA).

logo_cube.noise.npz visibilities have been rescaled such that data - model / sigma follows the expected Gaussian envelope.

HD 143006 continuum visibilities have flagged outliers removed and weights rescaled such that the data - model / sigma follows the expected Gaussian envelope, for each spectral window.

AS 209 continuum visibilities have been averaged across frequency and have their weights rescaled such that the data - model / sigma follows the expected Gaussian envelope, for each spectral window.

The "Train_AE.zip" contains the scripts for training an auto-encoder.The "Train_DL_Models.zip" contains the scripts for training deep learning-based models.The "sigma_profiles_dict.npy" contains the sigma-profiles of millions of different molecules. The SMILES of a molecule is used as key to query the corresponding sigma-profiles.The "sorted_agent_dict.npy" contains the statistical results of USPTO_TPL dataset concerning the frequency of occurrence of agents. The agents are shown in an descending manner.The "sorted_agent_combination_dict.npy" contains the statistical results of USPTO_TPL dataset concerning the frequency of occurrence of agent combinations. The combinations are shown in an descending manner.The "USPTO_TPL_own_version.xlsx" contains the reactions that used for training/validation/testing.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for SIGMA on 2025-10-07. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

The design and synthesis of a series of 2,7-diazaspiro[4.4]nonane derivatives as potent sigma receptor (SR) ligands, associated with analgesic activity, are the focus of this work. In this study, affinities at S1R and S2R were measured, and molecular modeling studies were performed to investigate the binding pose characteristics. The most promising compounds were subjected to in vitro toxicity testing and subsequently screened for in vivo analgesic properties. Compound 9d (AD258) exhibited negligible in vitro cellular toxicity and a high binding affinity to both SRs (KiS1R = 3.5 nM, KiS2R = 2.6 nM), but not for other pain-related targets, and exerted high potency in a model of capsaicin-induced allodynia, reaching the maximum antiallodynic effect at very low doses (0.6–1.25 mg/kg). Functional activity experiments showed that S1R antagonism is needed for the effects of 9d and that it did not induce motor impairment. In addition, 9d exhibited a favorable pharmacokinetic profile.

Data archived here are the external iron input data and model output data discussed in a paper entitled "Slowly sinking particles underlie dissolved iron transport across the Pacific Ocean" submitted to Global Biogeochemical Cycles. The model used in this study was developed by coupling Regional Ocean Modeling System (Shchepetkin and McWilliams, 2005) and Biogeochemical Elemental Cycling model (Moore et al., 2013). The model covers the whole North Pacific Ocean. The model horizontal resolution was set to 1/4° mesh. The external iron input data are iron fluxes due to atmospheric deposition and dissolution from seabed sediments. The model output data are dissolved iron concentrations simulated by the model and were only presented for the data in the intermediate layer (26.6-27.4 sigma-theta divided by 0.02 sigma-theta). The simulated data were regridded 1° mesh to reduce the size of the data. The model was calculated for 100 years and the simulated dissolved iron concentration are in quasi-steady state. For more details about the individual archived data, please refer to README.pdf included in the data. Reference Shchepetkin, A. F., & McWilliams, J. C. (2005). The regional oceanic modeling system (ROMS): A split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modelling, 9(4), 347-404. Moore, J. K., Lindsay, K., Doney, S. C., Long, M. C., & Misumi, K. (2013). Marine ecosystem dynamics and biogeochemical cycling in the Community Earth System Model (CESM1-BGC). Journal of Climate, 26, 9291-9312.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Sigma Success: Is Additive Manufacturing Set to Transform Industries with SASI Stock?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

NRL HYCOM 1/25 deg model output, Gulf of Mexico, 10.04 Expt 31.0, 2009-2014, At Depths The HYCOM consortium is a multi-institutional effort sponsored by the National Ocean Partnership Program (NOPP), as part of the U. S. Global Ocean Data Assimilation Experiment (GODAE), to develop and evaluate a data-assimilative hybrid isopycnal-sigma-pressure (generalized) coordinate ocean model (called HYbrid Coordinate Ocean Model or HYCOM).

After many years of research and technical preparation, the production of a new ECMWF climate reanalysis to replace ERA-Interim is in progress. ERA5 is the fifth generation of ECMWF atmospheric reanalyses of the global climate, which started with the FGGE reanalyses produced in the 1980s, followed by ERA-15, ERA-40 and most recently ERA-Interim. ERA5 will cover the period January 1950 to near real time. ERA5 is produced using high-resolution forecasts (HRES) at 31 kilometer resolution (one fourth the spatial resolution of the operational model) and a 62 kilometer resolution ten member 4D-Var ensemble of data assimilation (EDA) in CY41r2 of ECMWF's Integrated Forecast System (IFS) with 137 hybrid sigma-pressure (model) levels in the vertical, up to a top level of 0.01 hPa. Atmospheric data on these levels are interpolated to 37 pressure levels (the same levels as in ERA-Interim). Surface or single level data are also available, containing 2D parameters such as precipitation, 2 meter temperature, top of atmosphere radiation and vertical integrals over the entire atmosphere. The IFS is coupled to a soil model, the parameters of which are also designated as surface parameters, and an ocean wave model. Generally, the data is available at an hourly frequency and consists of analyses and short (12 hour) forecasts, initialized twice daily from analyses at 06 and 18 UTC. Most analyses parameters are also available from the forecasts. There are a number of forecast parameters, for example mean rates and accumulations, that are not available from the analyses. Improvements to ERA5, compared to ERA-Interim, include use of HadISST.2, reprocessed ECMWF climate data records (CDR), and implementation of RTTOV11 radiative transfer. Variational bias corrections have not only been applied to satellite radiances, but also ozone retrievals, aircraft observations, surface pressure, and radiosonde profiles. Please note: DECS is producing a CF 1.6 compliant netCDF-4/HDF5 version of ERA5...

This dataset contains 3 sets of 50 realizations of hydrostratigrahy in Egebjerg, Denmark following the GDM method (https://doi.org/10.1016/j.enggeo.2022.106833). The three sets vary in the smoothing factor (sigma) of the Low frequency - model. This dataset is used the importance of uncertainty level in geological interpretation modelling in the following paper ("Incorporating interpretation uncertainties from deterministic 3D hydrostratigraphic models in groundwater models", https://doi.org/10.5194/hess-2023-74). The three scenarios are parameterized as follows: 1) a low uncertainty scenario with sigma = 2 and the uncertainties from the GDM paper divided by three; 2) a medium uncertainty scenario with sigma = 7 and the uncertainties corresponding to the values from the GDM paper; 3) a high uncertainty scenario with sigma = 12 and the uncertainties corresponding to the values from the GDM paper multiplied by 3.

Dataset Documentation

  Overview

Dataset Name: sigma_dataset Short Description: This dataset consists of meteorological (time series) and geophysical (catchment attributes) data of 85 basins of Kazakhstan. It is intended for use in weather forecasting or modeling, as well as flood prediction based on the attributes provided. Long Description: We developed basin scale hydrometeorological forcing data for 85 basins in the conterminous Kazakhstan basin subset. Retrospective… See the full description on the dataset page: https://huggingface.co/datasets/floodpeople/sigma_dataset.

Products from NCEP/NCAR Reanalysis Project (NNRP or R1) are archived in this dataset. The resolution of the global Reanalysis Model is T62 (209 km) with 28 vertical sigma levels. Results are available at 6 hour intervals. Although the initial plan is to reanalyze the data for a 40-year period (1957-1996), production has gone back to 1948 and going forward continuously. Future plans call for rerunning the entire period as next generation models are ready. There are over 80 different variables, (including geopotential height, temperature, relative humidity, u- and v- wind components, etc.) in several different coordinate systems, such as 17 pressure level stack on 2.5 by 2.5 degree grids, 28 sigma level stack on 192 by 94 Gaussian grids, and 11 isentropic level stack on 2.5 by 2.5 degree grid. They are organized as different subgroups in the archive. In addition to analyses, diagnostic terms (for example: radiative heating, convective heating) and accumulative variables (like precipitation rate) are present. The input observations are archived with quality and usage flags in WMO BUFR format. Most of the project outputs are stored in WMO GRIB format. Other files, such as restart files and zonal statistics, are saved in IEEE format. Some special periods are analyzed more than once to provide data for special research studies. For example, a special run of 1979 was made excluding most satellite inputs. This run could be used for evaluating the impact of satellite data on the analysis. During the TOGA COARE experiment period, special runs of reanalysis model without experimental data are archived under the TOGA COARE directory. For details and problems, see NCEP/NCAR Reanalysis TOGA COARE [https://rda.ucar.edu/datasets/ds090.0/inventories/TOGA-COARE/]. Monthly means are on line at ds090.2 [https://rda.ucar.edu/datasets/ds090.2/]. The R1 forecasts are in ds090.1 [https://rda.ucar.edu/datasets/ds090.1/] dataset.

Sigma (σ) receptors (SRs) have emerged as important therapeutic targets due to their roles in various biological pathways. They are classified into two subtypes: S1R, primarily distributed in the central nervous system and related to neuroprotection and neurodegenerative diseases, and S2R mainly expressed in cancer cells and associated with cell proliferation and apoptosis, as well as in neurons. Although S1R and S2R exhibit structural differences in receptor architecture and assembly, they share similar binding site features and ligand recognition mechanisms. This similarity underscores the importance of identifying selective ligands for therapeutic design, especially given the distinct physiological functions of these receptors. In this project, we developed three distinct machine learning (ML) approaches based on classification, regression, and multiclassification models to predict the activity and selectivity profiles of SR ligands. High-quality data sets were curated from public and in-house source; in turn, the data sets were systematically organized and processed for each workflow. Models were built using molecular descriptors and fingerprints, including Mordred, RDKit, ECFP4, ECFP6, and MACCS keys, and trained with various ML algorithms such as extra trees, random forest, support vector machine, k-nearest neighbors, and XGBoost. Rigorous nested and classical 5-fold cross-validation protocols were applied for model selection and validation. At the end, identification of the best workflow was performed by an external validation procedure. Among the workflows, the one-step multiclassification approach, based on extra trees combined with Mordred descriptors, showed the best predictive performance in external validation, offering a robust tool for the identification of selective S1R and S2R ligands.

Each row corresponds to a specified training set while each column corresponds to a specified test set.AUC scores for Naive Bayes classifier with 4-mer features (NB_4-mer) trained using seven versions of CV data and in each time tested on the seven versions of the independent test data.

ECMWF has in the past produced three major re-analyses: FGGE, ERA-15 and ERA-40. Now progress is being made in producing 'ERA-Interim'. ERA-Interim has progressed beyond the end of ERA-40. After completion of the first four years it was decided to revise the configuration of the system and to rerun the initial segment. Other shorter reruns for later periods have been completed as needed for technical reasons. The re-analysis is continued in near-real time in order to support climate monitoring. This experiment contains assimilated data on 60 model (sigma) levels with a time-step of six hours.

Please see this link for detailed terms of use: https://apps.ecmwf.int/datasets/licences/general/

Escherichia coli uses s factors to quickly control large gene cohorts during stress conditions. While most of its genes respond to a single s factor, approximately 5% of them have dual s factor preference. The most common are those responsive to both s⁷⁰, which controls housekeeping genes, and s³⁸, which activates genes during stationary growth and stresses. Using RNA-seq and flow-cytometry measurements, we show that 'σ⁷⁰⁺³⁸ genes' are nearly as upregulated in stationary growth as 'σ³⁸ genes'. Moreover, we find a clear quantitative relationship between their promoter sequence and their response strength to changes in σ³⁸ levels. We then propose and validate a sequence dependent model of σ⁷⁰⁺³⁸ genes, with dual sensitivity to s³⁸ and s⁷⁰, that is applicable in the exponential and stationary growth phases, as well in the transient period in between. We further propose a general model, applicable to other stresses and σ factor combinations. Given this, promoters controlling σ⁷⁰⁺³⁸ genes (and variants) could become important building blocks of synthetic circuits with predictable, sequence-dependent sensitivity to transitions between the exponential and stationary growth phases.

The files contain outputs from numerical simulations using a combination of the numerical models Geospace Environment Model of Ion-Neutral Interactions (GEMINI) and Satellite-beacon Ionospheric- scintillation Global Model of the upper Atmosphere (SIGMA). The outputs provide simulated time series of Global Positioning System (GPS) scintillations through density structures generated by the Kelvin Helmholtz instability (KHI), as explained in detail in the publication. The numerical codes used to generate the outputs are described in the following publications: • Zettergren, M., Semeter, J., & Dahlgren, H. (2015). “Dynamics of density cavities generated by frictional heating: Formation, distortion, and instability”. Geophysical Research Letters, 42(23). [Available at https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066806 ] • Deshpande, K. B., Bust, G. S., Clauer, C. R., Rino, C. L., & Carrano, C. S. (2014). “Satellite-beacon Ionospheric-scintillation Global Model of the upper Atmosphere (SIGMA) I: High latitude sensitivity study of the model parameters”. Journal of Geophysical Research: Space Physics, 119, 4026-4043. doi:10.1002/2013JA019699811. [Available at https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013JA019699 ] • Deshpande, K. B., & Zettergren, M. D. (2019). “Satellite-Beacon Ionospheric Scintillation Global Model of the Upper Atmosphere (SIGMA) III: Scintillation Simulation Using A Physics-Based Plasma Model”. Geophysical Research Letters, 46(9), 4564-4572. doi:10.1029/2019GL082576. [Available at https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019GL082576 ] GEMINI is free, open-source software and can be downloaded from GitHub at https://github.com/gemini3d/. Build instructions, example simulations, and documentation are also included on this website. Uploaded by A.S.

In the Northeast and Great Lakes regions of the United States, the influence of multiple lakes on overlying air can greatly affect lake-effect snowfall over downwind communities. To assess the impact of Lake Huron on snowfall downwind of Lake Erie, we simulated a lake-effect snow event which occurred from 1-6 January 2010 using the Regional Atmospheric Modeling System (RAMS). We found that the presence of Lake Huron enhances snowfall downwind of Lake Erie by almost 20\% and leads to much heavier local snowfall totals than when Lake Huron is not present. This increase in snowfall is due to a lake-to-lake (L2L) convective band, as secondary circulations associated with lake-effect convection form over Lake Huron and persist overland between the lakes before re-intensifying over Lake Erie. As these secondary circulations move over Lake Erie, low-level convergence from the secondary circulation induces mechanical lifting which accelerates the development of convection within the L2L band. S..., This dataset was produced by simulating a lake-effect snow event which occurred between 1-3 January 2010 with the Regional Atmospheric Modeling System (RAMS), an open-source, nonhydrostatic numerical weather model. The model was initiatlized and forced at lateral boundaries with ERA5 reanalysis data. Three simulations were conducted: A CONTROL simulation, without any modifications to the reanalysis, and which used initial water temperatures from a 1-degree horizontal resolution Reynolds-averaged global dataset, a NLH simulation in which the surface of Lake Huron was changed from water to mixed forest and in which initial soil and snow data over the former area of Lake Huron were adjusted to match those of neighboring Michigan, and a VARTEMP simulation which was identical to CONTROL except that data from the Great Lakes Environmental Research Laboratory (GLERL) were used for the initial water temperatures over the Great Lakes. The model output files use a terrain-following sigma-z vertic..., , # Selected large model output files and Buffalo sounding data from: Lake Huron enhances snowfall downwind of Lake Erie: a modeling study of the 2010 near year’s Lake-effect snowfall event

https://doi.org/10.5061/dryad.2z34tmpwj

Description of the data and file structure

This data was collected for the paper entitled "Lake Huron Enhances Snowfall Downwind of Lake Erie: A Modeling Study of the 2010 New Year's Lake-Effect Snowfall Event." With the exception of the file containing radiosonde data from Buffalo Niagara International Airport, which was obtained from the Integrated Global Radiosonde Archive from the National Oceanic and Atmospheric Administration (NOAA), all other files are either raw or post-processed output from the Regional Atmospheric Modeling System (RAMS) simulations of the lake-effect snow event which occurred over the North American Great Lakes from 1-3 January 2010.

This data is not meant to replace the repository for f...