Catches in FAO area 27 by country, species, area and year. Source: Eurostat/ICES database on catch statistics - ICES 2011, Copenhagen. Format: Archived dataset in .xls and .csv format. Version 26-06-2019 https://www.ices.dk/data/dataset-collections/Pages/Fish-catch-and-stock-assessment.aspx

These are nominal yield curves, obtained by fitting a parametric form to the prices of off-the-run nominal Treasury coupon securities. The data are available at daily frequency, from 1961 to present.

Nominal per capital consumption by consumption group, Harvested from Annual Household Survey 2015/16, Government of Nepal, National Planning Commission Secretariat, Central Bureau of Statistics.

Being relatively new to the field, electromechanical actuators in aerospace applications lack the knowledge base compared to ones accumulated for the other actuator types, especially when it comes to fault detection and characterization. Lack of health monitoring data from fielded systems and prohibitive costs of carrying out real flight tests push for the need of building system models and designing affordable but realistic experimental setups. This paper presents our approach to accomplish a comprehensive test environment equipped with fault injection and data collection capabilities. Efforts also include development of multiple models for EMA operations, both in nominal and fault conditions that can be used along with measurement data to generate effective diagnostic and prognostic estimates. A detailed description has been provided about how various failure modes are inserted in the test environment and corresponding data is collected to verify the physics based models under these failure modes that have been developed in parallel. A design of experiment study has been included to outline the details of experimental data collection. Furthermore, some ideas about how experimental results can be extended to real flight environments through actual flight tests and using real flight data have been presented. Finally, the roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators is discussed.*

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_64f98475cef1e94300362cb400a50012/view

Abstract: The data set comprises temperature data of the Earth's middle atmosphere region for the years 2002-2012 as derived from the nominal mode mid infrared spectra measurements collected by the MIPAS instrument on board of the Envisat satellite. The data version is V8. TechnicalRemarks: The data is delivered as a set of archive files with each archive containing monthly netCDF files of the respective year. Users of the IDL data language can use the programs which are delivered as an archive, too. For previous version see relations.

The Mars Express PFS data set contains raw (CODMAC Level 2) measurements from the Planetary Fourier Spectrometer collected during the first extension Mars orbit phases.

This dataset provides values for NOMINAL GDP GROWTH reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Nominal Household consumption distribution by categories, Harvested from Annual Household Survey 2015/16, Government of Nepal, National Planning Commission Secretariat, Central Bureau of Statistics.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_5d2a513a20f58239f8c449ea6c9b6ecd/view

SIA205 - Composition of Nominal Household and Equivalised Income. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Composition of Nominal Household and Equivalised Income...

TAH28 - Mean and Median equivalised nominal disposable income. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Mean and Median equivalised nominal disposable income...

The Fundamental Data Record (FDR) for Atmospheric Composition UVN v.1.0 dataset is a cross-instrument Level-1 product [ATMOS_L1B] generated in 2023 and resulting from the ESA FDR4ATMOS project. The FDR contains selected Earth Observation Level 1b parameters (irradiance/reflectance) from the nadir-looking measurements of the ERS-2 GOME and Envisat SCIAMACHY missions for the period ranging from 1995 to 2012. The data record offers harmonised cross-calibrated spectra with focus on spectral windows in the Ultraviolet-Visible-Near Infrared regions for the retrieval of critical atmospheric constituents like ozone (O3), sulphur dioxide (SO2), nitrogen dioxide (NO2) column densities, alongside cloud parameters. The FDR4ATMOS products should be regarded as experimental due to the innovative approach and the current use of a limited-sized test dataset to investigate the impact of harmonization on the Level 2 target species, specifically SO2, O3 and NO2. Presently, this analysis is being carried out within follow-on activities. The FDR4ATMOS V1 is currently being extended to include the MetOp GOME-2 series. Product format For many aspects, the FDR product has improved compared to the existing individual mission datasets: GOME solar irradiances are harmonised using a validated SCIAMACHY solar reference spectrum, solving the problem of the fast-changing etalon present in the original GOME Level 1b data; Reflectances for both GOME and SCIAMACHY are provided in the FDR product. GOME reflectances are harmonised to degradation-corrected SCIAMACHY values, using collocated data from the CEOS PIC sites; SCIAMACHY data are scaled to the lowest integration time within the spectral band using high-frequency PMD measurements from the same wavelength range. This simplifies the use of the SCIAMACHY spectra which were split in a complex cluster structure (with own integration time) in the original Level 1b data; The harmonization process applied mitigates the viewing angle dependency observed in the UV spectral region for GOME data; Uncertainties are provided. Each FDR product provides, within the same file, irradiance/reflectance data for UV-VIS-NIR special regions across all orbits on a single day, including therein information from the individual ERS-2 GOME and Envisat SCIAMACHY measurements. FDR has been generated in two formats: Level 1A and Level 1B targeting expert users and nominal applications respectively. The Level 1A [ATMOS_L1A] data include additional parameters such as harmonisation factors, PMD, and polarisation data extracted from the original mission Level 1 products. The ATMOS_L1A dataset is not part of the nominal dissemination to users. In case of specific requirements, please contact EOHelp. Please refer to the README file for essential guidance before using the data. All the new products are conveniently formatted in NetCDF. Free standard tools, such as Panoply, can be used to read NetCDF data. Panoply is sourced and updated by external entities. For further details, please consult our Terms and Conditions page. Uncertainty characterisation One of the main aspects of the project was the characterization of Level 1 uncertainties for both instruments, based on metrological best practices. The following documents are provided: General guidance on a metrological approach to Fundamental Data Records (FDR) Uncertainty Characterisation document Effect tables NetCDF files containing example uncertainty propagation analysis and spectral error correlation matrices for SCIAMACHY (Atlantic and Mauretania scene for 2003 and 2010) and GOME (Atlantic scene for 2003) reflectance_uncertainty_example_FDR4ATMOS_GOME.nc reflectance_uncertainty_example_FDR4ATMOS_SCIA.nc Known Issues Non-monotonous wavelength axis for SCIAMACHY in FDR data version 1.0 In the SCIAMACHY OBSERVATION group of the atmospheric FDR v1.0 dataset (DOI: 10.5270/ESA-852456e), the wavelength axis (lambda variable) is not monotonically increasing. This issue affects all spectral channels (UV, VIS, NIR) in the SCIAMACHY group, while GOME OBSERVATION data remain unaffected. The root cause of the issue lies in the incorrect indexing of the lambda variable during the NetCDF writing process. Notably, the wavelength values themselves are calculated correctly within the processing chain. Temporary Workaround The wavelength axis is correct in the first record of each product. As a workaround, users can extract the wavelength axis from the first record and apply it to all subsequent measurements within the same product. The first record can be retrieved by setting the first two indices (time and scanline) to 0 (assuming counting of array indices starts at 0). Note that this process must be repeated separately for each spectral range (UV, VIS, NIR) and every daily product. Since the wavelength axis of SCIAMACHY is highly stable over time, using the first record introduces no expected impact on retrieval results. Python pseudo-code example: lambda_...

Residential property price statistics from different countries. Contains property price indicators (real series are the nominal price series deflated by the consumer price index), both in levels and in growth rates. Can be used for property market analysis. The dataset contains four different files with different metrics, including nominal index, nominal year-on-year changes, real index, and real year-on-year changes. Each file includes data in the format of date, country, and price.

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

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Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

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Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

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After we will clear our data frame, will remove missing values.

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To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

FAST5/FASTQ data used for accuracy characterization of decoding techniques applied to the HEDGEs DNA-information storage code. FASTQ data is used to evaluate the hard-decoding algorithm as explained by Press et al. in (https://doi.org/10.1073/pnas.2004821117). FAST5 data is used in evaluation for both our novel Alignment Matrix soft decoder (https://doi.org/10.5281/zenodo.11454877), and the soft decoder developed by Chandak et al. in the publication (10.1109/ICASSP40776.2020.9053441). Our code repository at https://doi.org/10.5281/zenodo.11454877 includes a GPU accelerated adaptation of Chandak et al.’s algorithm in order to scale analysis on the submitted FAST5 data, and this is the version of code used to evaluate the algorithm’s accuracy and runtime overhead.

Within the archive there are several sub-archives. Explanations for each sub-archive can be found for the corresponding archive name within the README.md file.

Wage and Payroll Statistics - Table 220-19005 : Nominal Wage Indices for employees up to supervisory level by occupational group (September 1992 = 100)

Composition of nominal household income and nominal equivalised income

This dataset is a collection of CBT results from prospective students participating in the primary school teacher professional education program.

United States US: Nominal Effective Exchange Rate Index: From INS data was reported at 119.707 2010=100 in 2017. This records a decrease from the previous number of 120.177 2010=100 for 2016. United States US: Nominal Effective Exchange Rate Index: From INS data is updated yearly, averaging 98.253 2010=100 from Dec 1979 (Median) to 2017, with 39 observations. The data reached an all-time high of 125.808 2010=100 in 2002 and a record low of 33.551 2010=100 in 1979. United States US: Nominal Effective Exchange Rate Index: From INS data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s United States – Table US.IMF.IFS: Nominal and Real Effective Exchange Rate Index: Annual.