The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

Statistical Area Level of Education of Population Aged 15 and Over_ Secondary Release Area, Statistical Area Level of Education of Population Aged 15 and Over_ Primary Release Area, Statistical Area Level of Education of Population Aged 15 and Over_ Minimum Statistical AreaThe Ministry of the Interior's Statistics Department provides the latest annual statistical data for various counties and cities on the Government Open Data Platform in XML format. When viewed in a browser, it appears as a series of characters and numbers. Typically, this format is suitable for programmers to develop applications using the data, rather than being random characters. If you wish to download the data in CSV format (which can be viewed in Excel), please refer to the Social Economic Data Service Platform on the Land Information System website (segis.moi.gov.tw) for downloading.

This data set was acquired with a DSPL HOBO HighTemp Temperature Probe and Major Fluid Sampler assembled as part of the 1991 EPR:9N_VonDamm data compilation (Chief Scientist: Dr. Karen Von Damm; Investigators: Dr. Julie Bryce, Florencia Prado, and Dr. Karen Von Damm). The data files are in Microsoft Excel format and include Fluid Chemistry and Temperature time series data and were processed after data collection. Funding was provided by NSF grant OCE03-27126. This data was cited by Oosting and Von Damm, 1996, Von Damm et al., 1997, Ravizza et al., 2001, Von Damm, 2000, Von Damm, 2004, Von Damm and Lilley, 2004, and Haymon et al., 1993.

A comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework consists of three major phases: Phase 1—Preliminary raw data sets exploration, including time formatting and combining datasets of different lengths and different time intervals; Phase 2—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme values; and Phase 3—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado) were analyzed. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.The files that are in this data package include one excel file, converted to CSV format (Billy_Barr_raw_qaqc.csv) that contains the raw meteorological data, i.e., input data used for the QA/QC analysis. The second CSV file (Billy_Barr_1hr.csv) is the QA/QC and flagged meteorological data, i.e., output data from the QA/QC analysis. The last file (QAQC_Billy_Barr_2021-03-22.R) is a script written in R that implements the QA/QC and flagging process. The purpose of the CSV data files included in this package is to provide input and output files implemented in the R script.

1.Introduction

Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.

One of the most important benefits of the sales data collection process is that it allows manufacturers to identify their most successful products and target their efforts towards those areas. For example, if a manufacturer could notice that a particular product is selling well in a certain region, this information could be utilised to develop new products, optimise the supply chain or improve existing ones to meet the changing needs of customers.

This dataset includes information about 7 of MEVGAL’s products [1]. According to the above information the data published will help researchers to understand the dynamics of the dairy market and its consumption patterns, which is creating the fertile ground for synergies between academia and industry and eventually help the industry in making informed decisions regarding product development, pricing and market strategies in the IoT playground. The use of this dataset could also aim to understand the impact of various external factors on the dairy market such as the economic, environmental, and technological factors. It could help in understanding the current state of the dairy industry and identifying potential opportunities for growth and development.

1. Citation

Please cite the following papers when using this dataset:

I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted

1. Dataset Modalities

The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.

3.1 Data Collection

The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.

The first step is to determine the specific data that is needed to support the business objectives of the industry, i.e., in this publication’s case the daily sales data.

Once the data requirements have been identified, the next step is to implement an effective sales data collection method. In MEVGAL’s case this is conducted through direct communication and reports generated each day by representatives & selling points.

It is also important for MEVGAL to ensure that the data collection process conducted is in an ethical and compliant manner, adhering to data privacy laws and regulation. The industry also has a data management plan in place to ensure that the data is securely stored and protected from unauthorised access.

The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).

File

Period

Number of Samples (days)

product 1 2020.xlsx

01/01/2020–31/12/2020

363

product 1 2021.xlsx

01/01/2021–31/12/2021

364

product 1 2022.xlsx

01/01/2022–31/12/2022

365

product 2 2020.xlsx

01/01/2020–31/12/2020

363

product 2 2021.xlsx

01/01/2021–31/12/2021

364

product 2 2022.xlsx

01/01/2022–31/12/2022

365

product 3 2020.xlsx

01/01/2020–31/12/2020

363

product 3 2021.xlsx

01/01/2021–31/12/2021

364

product 3 2022.xlsx

01/01/2022–31/12/2022

365

product 4 2020.xlsx

01/01/2020–31/12/2020

363

product 4 2021.xlsx

01/01/2021–31/12/2021

364

product 4 2022.xlsx

01/01/2022–31/12/2022

364

product 5 2020.xlsx

01/01/2020–31/12/2020

363

product 5 2021.xlsx

01/01/2021–31/12/2021

364

product 5 2022.xlsx

01/01/2022–31/12/2022

365

product 6 2020.xlsx

01/01/2020–31/12/2020

362

product 6 2021.xlsx

01/01/2021–31/12/2021

364

product 6 2022.xlsx

01/01/2022–31/12/2022

365

product 7 2020.xlsx

01/01/2020–31/12/2020

362

product 7 2021.xlsx

01/01/2021–31/12/2021

364

product 7 2022.xlsx

01/01/2022–31/12/2022

365

3.2 Dataset Overview

The following table enumerates and explains the features included across all of the included files.

Feature

Description

Unit

Day

day of the month

-

Month

Month

-

Year

Year

-

daily_unit_sales

Daily sales - the amount of products, measured in units, that during that specific day were sold

units

previous_year_daily_unit_sales

Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year

units

percentage_difference_daily_unit_sales

The percentage difference between the two above values

%

daily_unit_sales_kg

The amount of products, measured in kilograms, that during that specific day were sold

kg

previous_year_daily_unit_sales_kg

Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year

kg

percentage_difference_daily_unit_sales_kg

The percentage difference between the two above values

kg

daily_unit_returns_kg

The percentage of the products that were shipped to selling points and were returned

%

previous_year_daily_unit_returns_kg

The percentage of the products that were shipped to selling points and were returned the previous year

%

points_of_distribution

The amount of sales representatives through which the product was sold to the market for this year

previous_year_points_of_distribution

The amount of sales representatives through which the product was sold to the market for the same day for the previous year

Table 1 – Dataset Feature Description

1. Structure and Format

4.1 Dataset Structure

The provided dataset has the following structure:

Where:

Name

Type

Property

Readme.docx

Report

A File that contains the documentation of the Dataset.

product X

Folder

A folder containing the data of a product X.

product X YYYY.xlsx

Data file

An excel file containing the sales data of product X for year YYYY.

Table 2 - Dataset File Description

1. Acknowledgement

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957406 (TERMINET).

References

[1] MEVGAL is a Greek dairy production company

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on known details at the time of acquisition.

Mean monthly flow (ML/month) and Annual flow (ML/yr) data at key gauges in the Macalister Irrigation District (MID) as monitored by SRW. The data are provided in MS Excel format in worksheets and charts.

Data used to produce Time-series drainage volume data provided by a third party. Site information and monitoring drainage flow data provided by the Southern Rural Water are specific to the Macalister Irrigation District.

Time specific data in the range 23/07/1997 to 31/12/2013

Dataset History

This dialogue has been copied from a draft of the BA-GIP report.

A total of 197 river gauges were identified within the model area representing all of the major rivers. Daily gauge level data was sourced from the Victorian Department of Environment, Land, Water and Planning Water Measurement Information System (WMIS, 2015). A list of the river gauges is provided in the report for key river basins

Only main stems of the major rivers were included in the model. These river reaches were identified using the DEPI hydro25 spatial data set (DEPI, 2014). The river classification was used to vary river incision depth (depth below the ground surface as defined by the digital elevation model) and width attributes. In the absence of recorded stage height information, river classification was used to estimate river stage heights. A total of 22,573 river cells are included in the model. Fifty-one gauges were selected to calibrate the catchment modelling framework in unregulated catchments based on Base Flow Indexes and observed stream flows.

Drainage channels and man-made drainage features in the Macalister Irrigation District (MID) were included in the model based on available drainage network mapping. This information was sourced from Southern Rural Water (SRW) and the DEPI Corporate Spatial Data library. Drainage cells are assigned to the uppermost cells within the model to capture groundwater discharge processes. Drain cells in Modflow can only act as groundwater discharge points and as such those cells outside drainage channels will be characterised as having a bed elevation equivalent to ground surface elevation. A total of 410,504 drainage cells are incorporated in the model. Apart from 3 river gauges sourced from the WMIS, SRW also has 15 gauges monitored drainage from the MID. The measurements commenced between 1997 and 2005. Of the 15 gauges, six were selected to calibrate the catchment modelling framework based on observed discharge.

Dataset Citation

Victorian Department of Economic Development, Jobs, Transport and Resources (2015) Mean monthly flow & annual flow data - Macalister Irrigation District. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/6ba89d78-1e42-4e02-bd5c-a435ee15bef4.

A range of quarterly Excel spreadsheets and SuperTABLE datacubes. The spreadsheets contain broad level data covering all the major items of the Labour Force Survey in time series format, including …Show full descriptionA range of quarterly Excel spreadsheets and SuperTABLE datacubes. The spreadsheets contain broad level data covering all the major items of the Labour Force Survey in time series format, including seasonally adjusted and trend estimates. The datacubes contain more detailed and cross classified original data than the spreadsheets.

This data set contains one minute resolution flux data obtained by the Millersville University 10 m flux tower located at the PECAN Fixed PISA 3 site in Ellis, Kansas. The data are in excel format. Included in the files are daily time series plots of several parameters.

The program PanPlot 2 was developed as a visualization tool for the information system PANGAEA. It can be used as a stand-alone application to plot data versus depth or time. Data input format is tab-delimited ASCII (e.g. by export from MS-Excel or from PANGAEA). The default scales and graphic features can individualy be modified. PanPlot 2 graphs can be exported in several image formats (BMP, PNG, PDF, and SVG) which can be imported by graphic software for further processing.

!PanPlot is retired since 2017. It is free of charge, is no longer being actively developed or supported, and is provided as-is without warranty.

The U.S. Geological Survey (USGS) conducted a geophysical and sampling survey in October 2014 that focused on a series of shoreface-attached ridges offshore of western Fire Island, NY. Seismic-reflection data, surficial grab samples and bottom photographs and video were collected along the lower shoreface and inner continental shelf. The purpose of this survey was to assess the impact of Hurricane Sandy on this coastal region. These data were compared to seismic-reflection and surficial sediment data collected by the USGS in the same area in 2011 to evaluate any post-storm changes in seabed morphology and modern sediment thickness on the inner continental shelf. For more information about the WHCMSC Field Activity, see: https://cmgds.marine.usgs.gov/fan_info.php?fan=2014-009-FA.

This page provides MHO workforce data for the full period for which data is available, as follows: The data on the MHO workforce collected by the Scottish Government from 2005 to March 2012, and subsequently by the SSSC from December 2012 to date is shown in the tables below. Individual tables are available for download below in Microsoft Excel (. xlsx) and OpenDocument Spreadsheet (. ods) format.

In mountainous rivers, large relatively immobile grains partly control the local and reach-averaged flow hydraulics and sediment fluxes. When the flow depth in low relative submergence conditions plunging flow and the highly three-dimensional flow field can cause spatial distributions of bed surface elevations and grain size distributions, therefore, causing a spatially variable sediment transport rate. We conducted a set of experiments to study how the bed surface responds to this spatial variability and in particular the effect relative submergence in the formation of sediment patches around simulated large boulders. Same average sediment transport capacity, upstream sediment supply, and initial bed thickness and grain size distribution were imposed in all experiments. The detailed flow field around the boulders was obtained using a combination of laboratory measurements and a 3D flow model based on the Volume of Fluid technique. The local shear stress field displayed substantial variability and controlled the bedload transport rates and direction in which sediment moved. The divergence in shear stress caused by the hemispheres promoted size-selective bedload deposition, which formed patches of coarse sediment upstream of the hemisphere. Sediment deposition caused a decrease in local shear stress, which combined with the coarser grain size, enhanced the stability of this patch. The region downstream of the hemispheres was largely controlled by a recirculation zone and had little to no change in grain size, bed elevation, and shear stress. The formation, development and stability of sediment patches in mountain streams is controlled by the shear stress divergence and magnitude and direction of the local shear stress field.

1) Table S1. Excel file in xlsx format with stable isotope and trace elemental data of seven specimens belonging to three species of modern brachiopods. Stable isotope data have been obtained using an Isoprime dual inlet mass spectrometer plus Multiprep device, whereas trace elemental data using an Agilent 8900 QQQ-ICP-MS. Sheet 1 of the excel file ("specimens") contains information on the type of analysis performed on each specimens and the environmental data of the sampling locality for each species. Excel file composed of 8 sheets. Each Excel sheet refers to a species (L. uva 52, L. uva 58, L. neozelanica 60, L. neozelanica 56, G.vitreus 5, G. vitreus 7V, G. vitreus 7D) and contains columns with numerical values: δ13C ‰ VPDB, δ18O‰ VPDB and trace elements, where available. 2) Table S2. Excel file in xlsx format with data related to the linear and curvatur length, and the length of the growth increments on the shell external surface calculated on 31 specimens of G. vitreus and two specimens of L. uva using using a stereomicroscope Motic SMZ-171-TLed. Excel file composed of 33 sheets. 3) The README file contains the instructions, the software and the data to reproduce the main results of the study. The analysis has been performed using python. The folder "Brachiopods_as_archives_of_intra_and_interannual_environmental_variations" contains the following subfolders: - data: Contains the isotope data stored in an Excel file. - notebooks: Includes Python notebooks for reproducing the main results. - lib: Contains Python files with auxiliary methods used in the notebooks. - results: Stores the figures generated by the notebooks. - environmental.yaml: Yaml file used for building the conda environement for the python software. Contents of the data folder: - Isotopi brach x VBG.xlsx: The file is an Excel file in xlsx format containing the isotope data for different species. Each Excel sheet refers to a species (L. uva 52, L. uva 58, L. uva A+B, L. neozelanica 60, L. neozelanica 56, G.vitreus 5, G. vitreus 7 II e III, G. vitreus 7 only III, M. sanguinea K, Gryphus media) and has data for the ventral and dorsal valves. Each sheet contains the species data as K, Sinf and equilibrium field as numeric data. In addition each sheet contains 3 columns with numerical values: δ13C ‰ VPDB, δ18O‰ VPDB and St lineare. These columns respectively contains the isotope data for Carbon and Oxygen, and the measured lenght increments. Contents of the notebooks folder: - paper_figures.ipynb: Generates the main figures, including the isotope time series and power spectra plotted against the 95% confidence level curve. - supplementary_paper_figures.ipynb: Produces supplementary figures, including tests on the isotope time series and the fit of the isotope data using periodic functions based on significant periodicities. Contents of the lib folder: - periodicity_analysis.py: contains the python code used to perform the periodicity analysis of the isotope data. - utility_functions.py: contains the python code to upload and elaborate the isotope data.

All of these files are Microsoft Excel format files that contain water level data. We deployed 1-4 water level loggers and a single conductivity logger at all sites over the study period (Figure 6; Table 2). Primary water level loggers and conductivity loggers were deployed in major tidal channels connecting the marshes to the estuary. Secondary water level loggers were deployed in the upper reaches of second-order tidal channels to capture high tides and determine inundation patterns. Water level readings were collected every six minutes. We used data from the primary water level logger at each site to develop local hydrographs and inundation rates. Loggers were surveyed by RTK GPS at least once during the period of deployment. We corrected all raw water level data with local time series of barometric pressure using Solinst barometric loggers (Model 3001, Solinst Canada Ltd., Georgetown, Ontario, Canada), additional Hobo loggers (Model U-20-001-01-Ti, Onset Computer Corp., Bourne, MA, USA) or barometric pressure from local airports (distance less than 10 miles). We assessed salinity and water temperature in the tidal channels at each site with Odyssey conductivity/temperature loggers (Dataflow Systems Pty Limited, Christchurch, New Zealand), after an initial period of unsuccessful deployment of Hobo conductivity loggers (Model U-24-001, Onset Computer Corp., Bourne, MA, USA), that were recalled due to manufacture error and data inconsistencies. We converted specific conductance values obtained with the Odyssey loggers to practical salinity units (PSU) using the equation in UNESCO (1983). At Tijuana, we used salinity data from the National Estuarine Research Reserve System Centralized Data Management Office website, using the Boca Rio station (TJRBRWQ, 32.5595° N latitude, -117.1288° W longitude; cdmo.baruch.sc.edu). The water level data was used to estimate local tidal datums for all sites using procedures outlined in the NOAA Tidal Datums Handbook (NOAA 2003). Only local MHW and MHHW was calculated because the loggers were positioned in the intertidal and therefore could not be used to compute lower datums. Mean tide level (MTL) was estimated for each site by using NOAA’s VDATUM model (v.3.4) at the location of the primary water level logger or at a nearby site in the estuary if the VDATUM model domain did not include the water level logger location. At Bolinas we used NOAA published values for MTL, MHW and MHHW; the station was located about 2km from the study site.

Contains monthly data from the Assuring Transformation dataset. Data is available in Excel or CSV format. PLEASE NOTE: Some updates to the structure and numbering of the data tables and csv were applied from April 2021. This was primarily to group similar table types and content together. Additionally we have increased the amount of tables that have time series data retrospectively updated each month (green tabs). We welcome any feedback on this updated format.

Result dataset files in Excel format of the sub-set of experiments with most mature measurement set-up. Data for all initial pressure levels, but same temperature (tt=80K/300K) and same nozzle diameter (mm=05/1/2/4mm) packed in zip file, named PRE3P1A_KIT_Dmm_ttK_DATA.zip

This dataset contains leaf area index (LAI) measurements taken at peak biomass at the Sagwon MAT site on the Arctic Slope of Alaska, in 2000. This dataset also contains normalized difference vegetation index (NDVI) values for the site. The readme contains both the readme for this dataset and a general readme for this series of datasets. NOTE: This dataset contains the data in Excel format.