This instructional activity introduces students to the application of statistical tools for analyzing biological data, with a focus on measures of center (mean, median, mode) and measures of spread (range, quartiles, standard deviation). Using real-world biological contexts. students learn how to summarize datasets, identify trends, and evaluate variability. The activity integrates the use of MS Excel and TI-84 Plus graphing calculators to calculate descriptive statistics and interpret results. By engaging with authentic biological data, students develop quantitative reasoning skills that enhance their ability to detect patterns, recognize variability, and draw meaningful conclusions about biological systems

This dataset contains gif images from the National Weather Service - National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) Mean Spread forecasts during the Ice in Clouds Experiment - Tropical (ICE-T) project.

Compositional data, which is data consisting of fractions or probabilities, is common in many fields including ecology, economics, physical science and political science. If these data would otherwise be normally distributed, their spread can be conveniently represented by a multivariate normal distribution truncated to the non-negative space under a unit simplex. Here this distribution is called the simplex-truncated multivariate normal distribution. For calculations on truncated distributions, it is often useful to obtain rapid estimates of their integral, mean and covariance; these quantities characterising the truncated distribution will generally possess different values to the corresponding non-truncated distribution.

In the paper Adams, Matthew (2022) Integral, mean and covariance of the simplex-truncated multivariate normal distribution. PLoS One, 17(7), Article number: e0272014. https://eprints.qut.edu.au/233964/, three different approaches that can estimate the integral, mean and covariance of any simplex-truncated multivariate normal distribution are described and compared. These three approaches are (1) naive rejection sampling, (2) a method described by Gessner et al. that unifies subset simulation and the Holmes-Diaconis-Ross algorithm with an analytical version of elliptical slice sampling, and (3) a semi-analytical method that expresses the integral, mean and covariance in terms of integrals of hyperrectangularly-truncated multivariate normal distributions, the latter of which are readily computed in modern mathematical and statistical packages. Strong agreement is demonstrated between all three approaches, but the most computationally efficient approach depends strongly both on implementation details and the dimension of the simplex-truncated multivariate normal distribution.

This dataset consists of all code and results for the associated article.

This dataset contains ERA5 surface level analysis parameter data ensemble means (see linked dataset for spreads). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.

The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.

An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that "ERA5.1 is very close to ERA5 in the lower and middle troposphere." but users of data from this period should read the technical memo 859 for further details.

Gold–Silver Pair Trading Dataset and Visualization

Author: Vineet Kumar Mittal Version: 1.0 Date: November 2025 DOI: https://doi.org/10.5281/zenodo.17537028

Overview

This repository contains the full dataset and visual analytics used in the study: "Gold–Silver Pair Trading: Mean Reversion Strategy Using Machine Learning."

It includes: - Historical gold and silver futures data (raw) - Processed dataset with spreads, ratios, and Z-scores - Key analysis charts (hedge ratio, spread, equity curve, etc.) - Reproducibility and licensing information

Contents

1. Main Analytical Dataset

File: gold_silver_live_panel.csv
Description:
Processed data containing gold/silver prices, ratio, spread, rolling statistics, and Z-scores used for the study's analysis and backtesting.

Column Definitions: See below for detailed description of each field included in gold_silver_live_panel.csv.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F30232042%2F4b0cf366a892c352a775411296a97f02%2FScreenshot%202025-11-08%20083140.jpg?generation=1762608723135200&alt=media" alt="">

2. Raw Historical Data

Files: - Gold Futures Historical Data_2015_2025.csv
- Silver Futures Historical Data_2015_2025.csv

Description:
Raw daily closing prices used to compute the ratio, spread, and other derived features.
Data sourced from Investing.com (continuous Gold and Silver Futures contracts).

Covers 2015–2025, daily frequency (excluding weekends and market holidays).

3. Visualization Outputs (Figures)

Please use below DOI to see all the figures and data.

DOI: https://doi.org/10.5281/zenodo.17537028

4. Citation

If you use this dataset, please cite:

Mittal, V. K. (2025). Gold Silver Pair Trading - Mean Reversion Strategy Using Machine Learning (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17537028

5. License

Creative Commons Attribution 4.0 International (CC BY 4.0)

You are free to use, distribute, and build upon this dataset for academic and non-commercial research purposes, provided proper attribution is given.

6. Contact

For queries, collaborations, or extended analysis: Vineet Kumar Mittal

This dataset contains ERA5 initial release (ERA5t) surface level analysis parameter data from 10 member ensemble runs. ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. Ensemble means and spreads were calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data. Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble mean and ensemble spread data. The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record.

United States FRBOP: Annual Yield Spread: Moody's Baa over Aaa: Mean: Current data was reported at 0.740 % in Jun 2018. This records an increase from the previous number of 0.694 % for Mar 2018. United States FRBOP: Annual Yield Spread: Moody's Baa over Aaa: Mean: Current data is updated quarterly, averaging 0.979 % from Mar 2010 (Median) to Jun 2018, with 34 observations. The data reached an all-time high of 1.349 % in Mar 2016 and a record low of 0.663 % in Dec 2014. United States FRBOP: Annual Yield Spread: Moody's Baa over Aaa: Mean: Current data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s USA – Table US.M006: Treasury Bills Rates: Forecast: Federal Reserve Bank of Philadelphia.

United States FRBOP: Ann Yield Spread: 10Yr TBonds over 3Mos TBills: Mean: Current data was reported at 1.142 % in Jun 2018. This records a decrease from the previous number of 1.177 % for Mar 2018. United States FRBOP: Ann Yield Spread: 10Yr TBonds over 3Mos TBills: Mean: Current data is updated quarterly, averaging 1.866 % from Mar 1992 (Median) to Jun 2018, with 106 observations. The data reached an all-time high of 3.584 % in Sep 1992 and a record low of -0.208 % in Jun 2007. United States FRBOP: Ann Yield Spread: 10Yr TBonds over 3Mos TBills: Mean: Current data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s USA – Table US.M006: Treasury Bills Rates: Forecast: Federal Reserve Bank of Philadelphia.

United States FRBOP Forecast: Yield Spread (YS): 10Yr TBonds over 3Mo Tbills: Mean data was reported at 1.157 % in Jun 2018. This records a decrease from the previous number of 1.242 % for Mar 2018. United States FRBOP Forecast: Yield Spread (YS): 10Yr TBonds over 3Mo Tbills: Mean data is updated quarterly, averaging 1.848 % from Mar 1992 (Median) to Jun 2018, with 106 observations. The data reached an all-time high of 3.653 % in Dec 1992 and a record low of -0.319 % in Dec 2000. United States FRBOP Forecast: Yield Spread (YS): 10Yr TBonds over 3Mo Tbills: Mean data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s USA – Table US.M006: Treasury Bills Rates: Forecast: Federal Reserve Bank of Philadelphia.

View market daily updates and historical trends for 10-2 Year Treasury Yield Spread. from United States. Source: Department of the Treasury. Track economi…

Sample simulation data for Hurricane Harvey with mean and spread (standard deviation)

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. So far this has only been the case for the month September 2021, while it will also be the case for October, November and December 2021. For months prior to September 2021 the final release has always been equal to ERA5T, and the goal is to align the two again after December 2021. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on pressure levels from 1940 to present".

Context

Dengue fever is a mosquito-borne disease that occurs in tropical and sub-tropical parts of the world. In mild cases, symptoms are similar to the flu: fever, rash, and muscle and joint pain. In severe cases, dengue fever can cause severe bleeding, low blood pressure, and even death.

Because it is carried by mosquitoes, the transmission dynamics of dengue are related to climate variables such as temperature and precipitation. Although the relationship to climate is complex, a growing number of scientists argue that climate change is likely to produce distributional shifts that will have significant public health implications worldwide.

In recent years dengue fever has been spreading. Historically, the disease has been most prevalent in Southeast Asia and the Pacific islands. These days many of the nearly half billion cases per year are occurring in Latin America.

Accurate dengue predictions would help public health workers ... and people around the world take steps to reduce the impact of these epidemics. But predicting dengue is a hefty task that calls for the consolidation of different data sets on disease incidence, weather, and the environment, an understanding of the relationship between climate and dengue dynamics can improve research initiatives and resource allocation to help fight life-threatening pandemics.

https://www.chathampublichealth.com/wp-content/uploads/2012/10/Dengue-Fever-Outbreak-on-World-Map1.jpg" alt="image">

Content

The goal is to predict the total_cases label for each (city, year, weekofyear) in the test set. There are two cities, San Juan and Iquitos, with test data for each city spanning 5 and 3 years respectively. You will make one submission that contains predictions for both cities. The data for each city have been concatenated along with a city column indicating the source: sj for San Juan and iq for Iquitos. The test set is a pure future hold-out, meaning the test data are sequential and non-overlapping with any of the training data. Throughout, missing values have been filled as NaNs.

The data includes the following set of information on a (year, weekofyear) timescale:

(Where appropriate, units are provided as a _unit suffix on the feature name.)

City and date indicators

• city – City abbreviations: sj for San Juan and iq for Iquitos
• week_start_date – Date given in yyyy-mm-dd format

NOAA's GHCN daily climate data weather station measurements

• station_max_temp_c – Maximum temperature
• station_min_temp_c – Minimum temperature
• station_avg_temp_c – Average temperature
• station_precip_mm – Total precipitation
• station_diur_temp_rng_c – Diurnal temperature range

PERSIANN satellite precipitation measurements (0.25x0.25 degree scale)

precipitation_amt_mm – Total precipitation

NOAA's NCEP Climate Forecast System Reanalysis measurements (0.5x0.5 degree scale)

• reanalysis_sat_precip_amt_mm – Total precipitation
• reanalysis_dew_point_temp_k – Mean dew point temperature
• reanalysis_air_temp_k – Mean air temperature
• reanalysis_relative_humidity_percent – Mean relative humidity
• reanalysis_specific_humidity_g_per_kg – Mean specific humidity
• reanalysis_precip_amt_kg_per_m2 – Total precipitation
• reanalysis_max_air_temp_k – Maximum air temperature
• reanalysis_min_air_temp_k – Minimum air temperature
• reanalysis_avg_temp_k – Average air temperature
• reanalysis_tdtr_k – Diurnal temperature range

Satellite vegetation - Normalized difference vegetation index (NDVI) - NOAA's CDR Normalized Difference Vegetation Index (0.5x0.5 degree scale) measurements

• ndvi_se – Pixel southeast of city centroid
• ndvi_sw – Pixel southwest of city centroid
• ndvi_ne – Pixel northeast of city centroid
• ndvi_nw – Pixel northwest of city centroid

Acknowledgements

This data collected by various U.S. Federal Government agencies—from the Centers for Disease Control and Prevention to the National Oceanic and Atmospheric Administration in the U.S. Department of Commerce

Inspiration

• The relationship between climate and dengue dynamics
• predict the number of dengue fever cases reported each week in San Juan, Puerto Rico and Iquitos, Peru

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1940 to present".

This data deposit includes the model simulation data and intermediate data. All data is annunal mean to reduce the size.

The globalmean.zip includes the global mean radiative repsonse data.

The annualmean.zip includes the annual mean data of all model simulations done and have been regridded to 2x2.5 degree resolution.

Graph and download economic data for ICE BofA Single-B US High Yield Index Option-Adjusted Spread (BAMLH0A2HYB) from 1996-12-31 to 2025-11-30 about B Bond Rating, option-adjusted spread, yield, interest rate, interest, rate, and USA.

Locations of diffusion tubes for monitoring NO2 levels in York, along with their latest available bias corrected annual mean values. Values currently displayed correspond to calendar year 2022. For more air quality related data please visit the following datasets: • Air Quality Management Areas (AQMAs) in York • Air Quality Monitoring Stations Locations in York • York Air Quality Monitoring Stations Data • York Diffusion Tubes (NO2) Data *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.