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Dairy Supply Chain Sales Dataset
<p>1.Introduction</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>2. Citation</p> <p>Please cite the following papers when using this dataset:</p> <ol> <li>I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "<strong>Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory</strong>," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted</li> </ol> <p>3. Dataset Modalities</p> <p>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.</p> <p>3.1 Data Collection</p> <p>The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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).</p> <table align="center"> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Period</strong></p> </td> <td> <p><strong>Number of Samples (days)</strong></p> </td> </tr> <tr> <td> <p><strong>product 1 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>363</p> </td> </tr> <tr> <td> <p><strong>product 1 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 1 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>365</p> </td> </tr> <tr> <td> <p><strong>product 2 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>363</p> </td> </tr> <tr> <td> <p><strong>product 2 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 2 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>365</p> </td> </tr> <tr> <td> <p><strong>product 3 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>363</p> </td> </tr> <tr> <td> <p><strong>product 3 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 3 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>365</p> </td> </tr> <tr> <td> <p><strong>product 4 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>363</p> </td> </tr> <tr> <td> <p><strong>product 4 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 4 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 5 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>363</p> </td> </tr> <tr> <td> <p><strong>product 5 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 5 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>365</p> </td> </tr> <tr> <td> <p><strong>product 6 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>362</p> </td> </tr> <tr> <td> <p><strong>product 6 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 6 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>365</p> </td> </tr> <tr> <td> <p><strong>product 7 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>362</p> </td> </tr> <tr> <td> <p><strong>product 7 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 7 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>365</p> </td> </tr> </tbody> </table> <p> </p> <p>3.2 Dataset Overview</p> <p>The following table enumerates and explains the features included across all of the included files.</p> <table> <tbody> <tr> <td> <p><strong>Feature</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p><strong>Day</strong></p> </td> <td> <p>day of the month</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Month</strong></p> </td> <td> <p>Month</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Year</strong></p> </td> <td> <p>Year</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>daily_unit_sales</strong></p> </td> <td> <p>Daily sales - the amount of products, measured in units, that during that specific day were sold</p> </td> <td> <p>units</p> </td> </tr> <tr> <td> <p><strong>previous_year_daily_unit_sales</strong></p> </td> <td> <p>Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year</p> </td> <td> <p>units</p> </td> </tr> <tr> <td> <p><strong>percentage_difference_daily_unit_sales</strong></p> </td> <td> <p>The percentage difference between the two above values</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p><strong>daily_unit_sales_kg</strong></p> </td> <td> <p>The amount of products, measured in kilograms, that during that specific day were sold</p> </td> <td> <p>kg</p> </td> </tr> <tr> <td> <p><strong>previous_year_daily_unit_sales_kg</strong></p> </td> <td> <p>Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year</p> </td> <td> <p>kg</p> </td> </tr> <tr> <td> <p><strong>percentage_difference_daily_unit_sales_kg</strong></p> </td> <td> <p>The percentage difference between the two above values</p> </td> <td> <p> kg</p> </td> </tr> <tr> <td> <p><strong>daily_unit_returns_kg</strong></p> </td> <td> <p>The percentage of the products that were shipped to selling points and were returned</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p><strong>previous_year_daily_unit_returns_kg</strong></p> </td> <td> <p>The percentage of the products that were shipped to selling points and were returned the previous year</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p><strong>points_of_distribution</strong></p> <p> </p> </td> <td> <p>The amount of sales representatives through which the product was sold to the market for this year</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>previous_year_points_of_distribution</strong></p> <p> </p> </td> <td> <p>The amount of sales representatives through which the product was sold to the market for the same day for the previous year</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p>Table 1 – Dataset Feature Description</p> <p> </p> <p>4. Structure and Format</p> <p>4.1 Dataset Structure</p> <p>The provided dataset has the following structure:</p> <p> </p> <p>Where:</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Type</strong></p> </td> <td> <p><strong>Property</strong></p> </td> </tr> <tr> <td> <p>Readme.docx</p> </td> <td> <p>Report</p> </td> <td> <p>A File that contains the documentation of the Dataset.</p> </td> </tr> <tr> <td> <p>product X</p> </td> <td> <p>Folder</p> </td> <td> <p>A folder containing the data of a product X.</p> </td> </tr> <tr> <td> <p>product X YYYY.xlsx</p> </td> <td> <p>Data file</p> </td> <td> <p>An excel file containing the sales data of product X for year YYYY.</p> </td> </tr> </tbody> </table> <p>Table 2 - Dataset File Description</p> <p>5. Acknowledgement</p> <p> </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957406 (TERMINET).</p> <p> </p> <p> </p> <p>References</p> <p> </p> <p>[1] MEVGAL is a Greek dairy production company</p>
Eq 1 from: Pellegrini TG, Sales LP, Aguiar P, Ferreira RL (2016) Linking spatial scale dependence of land-use descriptors and invertebrate cave community composition. Subterranean Biology 18: 17-38. https://doi.org/10.3897/subtbiol.18.8335
Eq 1 -
Occurrence dataset for Sales & Parrott (2023): "The owls are coming: positive effects of climate change in Northern ecosystems depend on grassland protection"
<p>Records from virtual databases were downloaded using the function occ() from the R package spocc. All occurrences were thoroughly assessed for their completeness and reliability. Occurrence records located exactly over centroids of municipalities and political polygons were removed from the dataset, in addition to duplicates, incomplete coordinates, and those from museums, using the suite of clean_coordinates functions from R package CoordinateCleaner. Occurrences with spatial autocorrelation structures widely divergent from the rest of the dataset coupled with coordinates outside the known extent of occurrence of the species, taken from the International Union for the Conservation of Nature, were also removed. Maintaining the contemporaneity with the climatic dataset, we also removed records dated before the year 1970.<br> </p>
Binocular - WW2 - Wehrmacht - For Sale
Questions or Feedback to: davidsikorsky.fl@gmail.com High quality, low poly 3D model of a Zeiss Diensglass 6.30 as used by the Wehrmacht during world war 2. Retextured and optimized version of a previous upload. All brand names depicted in this model are fictional, as is the logo. Intended Use: - Realtime environments, like video games - Maybe used as background asset in a film environment. - Built with VR in mind so TexelDensity is consistent. Parts are functional - Not intended for Subdivision. Features: - Textures are all PBR - All Textures are in .png 16 bit format - Single 4K Map - Modeled to real world scale - Units set to centimeters - No Plugins needed - All parts seperated and named accordingly - Triangle Count: 4820 - Vertexcount: 2700 - No LODs included. Included Files: File Formats: - Native Maya 2018 ASCII (.ma) - OBJ (Universal Format) - FBX (Universal Format) Textures Maps: - Default Substance Painter Channel Maps. - Unreal 4 Packed Maps - Unity 5 - CryEngine 3 Source: Objaverse 1.0 / Sketchfab
Study on Regulated Cannabis Sales in Pharmacies
ClinicalTrials.gov study NCT06120855. IPD Sharing: NO. Countries: 1. Publications: 0.
Responsible Marijuana Sales Practices to Reduce the Risk of Selling to Intoxicated Customers
ClinicalTrials.gov study NCT06235632. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Modified Dantien Salee Yoga Training Program in Chronic Obstructive Pulmonary Disease Rehabilitation
ClinicalTrials.gov study NCT02770677. IPD Sharing: NO. Countries: 1. Publications: 0.
Metabolic Health Improvement Program: Effects of a Workplace Sugary Beverages Sales Ban and Motivational Counseling
ClinicalTrials.gov study NCT05972109. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study to Evaluate the Impact of Direct and Surrogate Advertising and Compliance With the Bill With the Respect to Sale of Tobacco Products Around Educational Institutes
ClinicalTrials.gov study NCT01275950. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Tabular summary of shampoo sales
<p>This is the tabular summary of shampoo sales data.</p>
sale extra 30%
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1677443174.338.jpg">https://4dcity.org/imgupload/1677443174.338.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/65535/52305761215_18e31ce762_m.jpg">https://live.staticflickr.com/65535/52305761215_18e31ce762_m.jpg</a>
Love For Sale By Willy Wolff
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1677502051.7978.jpg">https://4dcity.org/imgupload/1677502051.7978.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/65535/52608851851_6c12b6f97a_m.jpg">https://live.staticflickr.com/65535/52608851851_6c12b6f97a_m.jpg</a>
Sales d'Escape Room de l'estat espanyol
<p><strong>El dataset s’ha extret a partir del nom de la sala i inclou la informació bàsica d’aquesta per tal de facilitar una visió globalitzada de la situació d’aquest tipus de sala en el marc de l’estat espanyol. A més, també inclou característiques de cada una de les sales com per exemple el públic al qual va dirigit (edat mínima, si és familiar, de terror, etc.), així com informació de l’adaptabilitat de la sala (si és apta per embarassades, per persones amb diversitat funcional o claustrofòbia, etc.). </strong></p> <p><strong>En aquelles que ho tenen disponible, s’inclou informació de la disponibilitat de cada una de les sales en el moment de l’extracció de les dades i pels próxims 6 dies per tal de poder fer una anàlisis comparativa (en funció de la temàtica, públic, etc.) de la demanda de les sales.</strong></p> <p><strong>Finalment, s’inclou informació de contacte de l’empresa encarregada de la sala d’escape room, com l’adreça, el telèfon o el correu electrònic, així com l’estat de la sala (oberta, tancada o tancada temporalment).</strong></p> <p><strong>Les dades han passat per una primera fase de tractament preliminar, però no són aptes per la seva anàlisis imminent. Moltes de les variables presenten valors en blanc, ja que les sales -suposem que menys populars- no disposen de valoracions de comentaris o, fins i tot, no disposen de valoració general. També cal destacar que la puntuació de terror únicament s’informa quan les sales són de terror, mentre que en les altres sales no s’informa. Per últim, cal tenir en compte que s’ha observat que alguns telèfons estan mal informats perquè ja venen malament de la pàgina web. </strong></p> <p><strong>Per tal de realitzar una anàlisi de les dades s’haurien de preprocesar les dades d’acord amb l’anàlisis que es vulguin realitzar.</strong></p>
Data belonging to paper The Impact of a Gradual Healthier Assortment among Vocational Schools Participating in a School Canteen Programme: Evidence from Sales and Student Survey Data
<p>Data belonging to paper <a href="https://www.mdpi.com/1660-4601/17/12/4352/htm">https://www.mdpi.com/1660-4601/17/12/4352/htm</a></p>
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