Context

Apple's transformation from "Apple Computer, Inc." to the world's first trillion-dollar company was driven by a masterful expansion of its product portfolio. While the iPhone was the star, its success was bolstered and complemented by the steady performance of the Mac, the creation of the tablet market with the iPad, and the explosive growth of the Wearables category with the Apple Watch and AirPods.

This synthetic dataset was created to provide a single, unified view of this incredible journey. It allows analysts, students, and enthusiasts to explore the entire Apple hardware ecosystem side-by-side on an annual basis. Since Apple stopped reporting official unit sales in 2018, this dataset uses a combination of historical reported data and realistic, revenue-based estimations to provide a continuous timeline from 2007 to a projection for 2025.

Content

The dataset consists of a single CSV file, apple_full_product_portfolio_2007_2025.csv. The columns are structured to provide a complete overview of Apple's performance:

Identifier Columns:

Year: The calendar year.

Average_Stock_Price_USD_Annual: The approximate average AAPL stock price for the year, adjusted for splits.

Model Release Columns:

iPhone_Model_Released: The flagship iPhone model(s) launched that year.

MacBook_Model_Released: The year's most significant MacBook releases (e.g., MacBook Air, Pro, key chip updates like M1).

iPad_Model_Released: The year's most significant iPad releases (e.g., iPad, Pro, Air, Mini).

Watch_Model_Released: The year's most significant Apple Watch releases (e.g., Series number, SE, Ultra).

Product Performance Metrics (pattern repeats for each product):

[Product]_Units_Sold_Millions: Estimated units sold for the product line.

[Product]_ASP_USD: Estimated Average Selling Price for the product line.

[Product]_Revenue_Billions: Estimated revenue in billions for the product line. (Products include: iPhone, MacBook, iPad, Watch, AirPods)

Consolidated Financials:

Services_Revenue_Billions: Revenue from services like the App Store, iCloud, Apple Music, etc.

Other_Products_Revenue_Billions: Revenue from all other minor products.

Total_Revenue_Billions: The comprehensive total annual revenue for Apple Inc.

Methodology

This dataset is a carefully constructed synthetic chronicle.

Data before 2018 is based on Apple's official (but now discontinued) unit sale reports and financial statements.

Data from 2018 onwards is estimated based on Apple's public quarterly financial reports, using reported category revenues to inform unit sales and ASP calculations.

Projections for 2024-2025 are conservative forecasts based on recent market trends.

Inspiration (Potential Project Ideas) This rich, multi-product dataset opens the door for deep strategic analysis:

The Rise of an Ecosystem: Create a stacked area chart of all revenue columns to visualize how Apple's revenue mix has evolved from being iPhone-centric to a balanced portfolio with massive growth in Wearables and Services.

Impact of Generational Leaps: Did the introduction of the M1 chip for MacBooks in 2020 have a more significant impact on sales and ASP than the Touch Bar in 2016? Pinpoint key model releases and measure their financial impact.

Cannibalization or Halo Effect?: Explore the relationship between product lines. Did the explosive growth of the iPad in its early years affect MacBook sales? Does a strong iPhone year correlate with a strong Apple Watch year?

Predictive Modeling: With over 18 years of comprehensive data, can you build a model that uses the performance of individual product lines to predict Apple's total revenue or future stock price?

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

About

The following datasets were captured at a busy Belgian train station between 9pm and 10pm, it contains all 802.11 management frames that were captured. both datasets were captured with approximately 20 minutes between then.

Both datasets are represented by a pcap and CSV file. The CSV file contains the frame type, timestamps, signal strength, SSID and MAC addresses for every frame. In the pcap file, all generic 802.11 elements were removed for anonymization purposes.

Anonymization

All frames were anonymized by removing identifying information or renaming identifiers. Concretely, the following transformations were applied to both datasets:

All MAC addresses were renamed (e.g. 00:00:00:00:00:01)

All SSID's were renamed (e.g. NETWORK_1)

All generec 802.11 elements were removed from the pcap

In the pcap file, anonymization actions could lead to "corrupted" frames because length tags do not correspond with the actual data. However, the file and its frames are still readable in packet analyzing tools such as Wireshark or Scapy.

The script which was used to anonymize is available in the dataset.

Data

Specifications for the datasets


    N/o
    Dataset 1
    dataset 2




    Frames
    36306
    60984


    Beacon frames
    19693
    27983


    Request frames
    798
    1580


    Response frames
    15815
    31421


    Identified Wi-Fi Networks
    54
    70


    Identified MAC addresses
    2092
    2705


    Identified Wireless devices
    128
    186


    Capturetime
    480s
    422s

Dataset contents

The two datasets are stored in the directories 1/ and 2/. Each directory contains:

capture-X.pcap: an anonymized version of the original capture

capture-X.csv: content of each captured frame (timestamp, MAC address...) saved as a CSV file

anonymization.py is the script which was used to remove identifiers.

README.md contains the documentation about the datasets

License

Copyright 2022-2023 Benjamin Vermunicht, Beat Signer, Maxim Van de Wynckel, Vrije Universiteit Brussel

Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the “Dataset”), to deal in the Dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the Dataset is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions that make use of the Dataset.

THE DATASET IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.

Collation of data from Radio Éireann log books, at RTÉ, Donnybrook, Dublin 4.

Dataset originally created 2016 UPDATE: Packaged on 02/10/2025

I. About this Data Set

This data set is a result of close reading conducted by Patrick Egan (Pádraig Mac Aodhgáin) at Radio Teilifís Éireann log books relating to Seán Ó Riada.

Research was conducted between 2014-2018. It contains a combination of metadata from searches of the Boole Library catalogue and Seán Ó Riada Collection finding aid (or "descriptive list"), relating to music-related projects that were involving Seán Ó Riada. The PhD project was published in 2020, entitled, “Exploring ethnography and digital visualisation: a study of musical practice through the contextualisation of music related projects from the Seán Ó Riada Collection”, and a full listing of radio broadcasts is added to the dataset named "The Ó Riada Projects" at https://doi.org/10.5281/zenodo.15348617

You are invited to use and re-use this data with appropriate attribution.

The "RÉ Logs Dataset" dataset consists of 90 rows.

II. What’s included? This data set includes:

A search of log books of radio broadcasts to find all instances of shows that involved Seán Ó Riada.

III. How Was It Created? These data were created by daily visits to Radio Teilifís Éireann in Dublin, Ireland.

IV. Data Set Field Descriptions

Column headings have not been added to the dataset.

Column A - blank
Column B - type of broadcast
Column C - blank
Column D - date of broadcast
Column E - blank
Column F - blank
Column G - blank
Column H - blank
Column I - description of broadcast
Column J - blank
Column K - blank
Column J - length of broadcast

V. Rights statement The text in this data set was created by the researcher and can be used in many different ways under creative commons with attribution. All contributions to this PhD project are released into the public domain as they are created. Anyone is free to use and re-use this data set in any way they want, provided reference is given to the creator of this dataset.

VI. Creator and Contributor Information

Creator: Patrick Egan (Pádraig Mac Aodhgáin)

VII. Contact Information Please direct all questions and comments to Patrick Egan via his website at www.patrickegan.org. You can also get in touch with the Library via UCC website.

This dataset contains 63 shapefiles that represent the areas of relevance for each research project under the National Environmental Science Program Marine and Coastal Hub, northern and southern node projects for Rounds 1, 2 & 3.

Methods:
Each project map is developed using the following steps:
1. The project map was drawn based on the information provided in the research project proposals.
2. The map was refined based on feedback during the first data discussions with the project leader.
3. Where projects are finished most maps were updated based on the extents of datasets generated by the project and followup checks with the project leader.

The area mapped includes on-ground activities of the project, but also where the outputs of the project are likely to be relevant. The maps were refined by project leads, by showing them the initial map developed from the proposal, then asking them "How would you change this map to better represent the area where your project is relevant?". In general, this would result in changes such as removing areas where they were no longer intending research to be, or trimming of the extents to better represent the habitats that are relevant.

The project extent maps are intentionally low resolution (low number of polygon vertices), limiting the number of vertices 100s of points. This is to allow their easy integration into project metadata records and for presenting via interactive web maps and spatial searching. The goal of the maps was to define the project extent in a manner that was significantly more accurate than a bounding box, reducing the number of false positives generated from a spatial search. The geometry was intended to be simple enough that projects leaders could describe the locations verbally and the rough nature of the mapping made it clear that the regions of relevance are approximate.

In some cases, boundaries were drawn manually using a low number of vertices, in the process adjusting them to be more relevant to the project. In others, high resolution GIS datasets (such as the EEZ, or the Australian coastline) were used, but simplified at a resolution of 5-10km to ensure an appopriate vertices count for the final polygon extent. Reference datasets were frequently used to make adjustments to the maps, for example maps of wetlands and rivers were used to better represent the inner boundary of projects that were relevant for wetlands.

In general, the areas represented in the maps tend to show an area larger then the actual project activities, for example a project focusing on coastal restoration might include marine areas up to 50 km offshore and 50 km inshore. This buffering allows the coastline to be represented with a low number of verticies without leading to false negatives, where a project doesn't come up in a search because the area being searched is just outside the core area of a project.


Limitations of the data:
The areas represented in this data are intentionally low resolution. The polygon features from the various projects overlap significantly and thus many boundaries are hidden with default styling. This dataset is not a complete representation of the work being done by the NESP MaC projects as it was collected only 3 years into a 7 year program.

Format of the data:
The maps were drawn in QGIS using relevant reference layers and saved as shapefiles. These are then converted to GeoJSON or WKT (Well-known Text) and incorporated into the ISO19115-3 project metadata records in GeoNetwork. Updates to the map are made to the original shapefiles, and the metadata record subsequently updated.

All projects are represented as a single multi-polygon. The multiple polygons was developed by merging of separate areas into a single multi-polygon. This was done to improve compatibility with web platforms, allowing easy conversion to GeoJSON and WKT.

This dataset will be updated periodically as new NESP MaC projects are developed and as project progress and the map layers are improved. These updates will typically be annual.


Data dictionary:
NAME - Title of the layer
PROJ - Project code of the project relating to the layer
NODE - Whether the project is part of the Northern or Southern Nodes
TITLE - Title of the project
P_LEADER - Name of the Project leader and institution managing the project
PROJ_LINK - Link to the project metadata
MAP_DESC - Brief text description of the map area
MAP_TYPE - Describes whether the map extent is a 'general' area of relevance for the project work, or 'specific' where there is on ground survey or sampling activities
MOD_DATE - Last modification date to the individual map layer (prior to merging)


Updates & Processing:
These maps were created by eAtlas and IMAS Data Wranglers as part of the NESP MaC Data Management activities. As new project information is made available, the maps may be updated and republished. The update log will appear below with notes to indicate when individual project maps are updated:
20220626 - Dataset published (All shapefiles have MOD_DATE 20230626)


Location of the data:
This dataset is filed in the eAtlas enduring data repository at: data\custodian
esp-mac-3\AU_AIMS-UTAS_NESP-MaC_Project-extents-maps

In summary, this dataset provides a comprehensive record of Apple product appearances in various movies and TV shows, along with the frequency of their occurrences. Analyzing this dataset can offer valuable insights into Apple's strategic product placement endeavors and their marketing strategies within the entertainment industry. this dataset was scraped from the productplacementblog which claims is the best database in the product placement field over internet.

Note: Following the data scraping process, I incorporated additional information from the IMDb dataset available on Kaggle to enhance this dataset. This augmentation allowed me to determine the 'startYear,' 'averageRating,' and 'numVotes' columns. It is important to note that 'startYear' indicates the year of the title's initial release, not the precise release date. Furthermore, the 'imgCount' column represents the count of timestamps or scenes featuring Apple products. In cases where multiple Apple products appear within the same Movie/Show, we do not have specific scene-level granularity to discern which scenes correspond to each product. Therefore, 'imgCount' reflects the cumulative count of scenes where any Apple product was showcased

Content

• tconst: An alphanumeric unique identifier for each title.
• Title: The title of the movie or TV show.
• Movie / Show: A binary indicator (0 or 1) distinguishing between movies and TV shows.
• Season: Numeric representation of the season when the product appeared (in the case of TV shows).
• Episode: Numeric value representing the episode in which the product appeared (for TV shows).
• imgCount: The count of timestamps/scenes featuring Apple products.
• iPhone/iPad/iMac/MacBook/macOS/AirPods/Apple Watch: Eight boolean columns indicating which Apple products were featured.
• averageRating: IMDb's average rating of the title.
• numVotes: The number of IMDb votes the title received. -**startYear**: The year of the title's initial release.

Weekly statistics showing how many Mac of All Trades coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

Dataset: The files on your computer.

Crab is a command line tool for Mac and Windows that scans file data into a SQLite database, so you can run SQL queries over it.

e.g. (Win)    C:> crab C:\some\path\MyProject
or (Mac)    $ crab /some/path/MyProject

You get a CRAB> prompt where you can enter SQL queries on the data, e.g. Count files by extension

SELECT extension, count(*) 
FROM files 
GROUP BY extension;

e.g. List the 5 biggest directories

SELECT parentpath, sum(bytes)/1e9 as GB 
FROM files 
GROUP BY parentpath 
ORDER BY sum(bytes) DESC LIMIT 5;

Crab provides a virtual table, fileslines, which exposes file contents to SQL

e.g. Count TODO and FIXME entries in any .c files, recursively

SELECT fullpath, count(*) FROM fileslines 
WHERE parentpath like '/Users/GN/HL3/%' and extension = '.c'
  and (data like '%TODO%' or data like '%FIXME%')
GROUP BY fullpath;

As well there are functions to run programs or shell commands on any subset of files, or lines within files e.g. (Mac) unzip all the .zip files, recursively

SELECT exec('unzip', '-n', fullpath, '-d', '/Users/johnsmith/Target Dir/') 
FROM files 
WHERE parentpath like '/Users/johnsmith/Source Dir/%' and extension = '.zip';

(Here -n tells unzip not to overwrite anything, and -d specifies target directory)

There is also a function to write query output to file, e.g. (Win) Sort the lines of all the .txt files in a directory and write them to a new file

SELECT writeln('C:\Users\SJohnson\dictionary2.txt', data) 
FROM fileslines 
WHERE parentpath = 'C:\Users\SJohnson\' and extension = '.txt'
ORDER BY data;

In place of the interactive prompt you can run queries in batch mode. E.g. Here is a one-liner that returns the full path all the files in the current directory

C:> crab -batch -maxdepth 1 . "SELECT fullpath FROM files"

Crab SQL can also be used in Windows batch files, or Bash scripts, e.g. for ETL processing.

Crab is free for personal use, $5/mo commercial

See more details here (mac): [http://etia.co.uk/][1] or here (win): [http://etia.co.uk/win/about/][2]

An example SQLite database (Mac data) has been uploaded for you to play with. It includes an example files table for the directory tree you get when downloading the Project Gutenberg corpus, which contains 95k directories and 123k files.

To scan your own files, and get access to the virtual tables and support functions you have to use the Crab SQLite shell, available for download from this page (Mac): [http://etia.co.uk/download/][3] or this page (Win): [http://etia.co.uk/win/download/][4]

Content

FILES TABLE

The FILES table contains details of every item scanned, file or directory. All columns are indexed except 'mode'

COLUMNS
 fileid (int) primary key -- files table row number, a unique id for each item
 name (text)        -- item name e.g. 'Hei.ttf'
 bytes (int)        -- item size in bytes e.g. 7502752
 depth (int)        -- how far scan recursed to find the item, starts at 0
 accessed (text)      -- datetime item was accessed
 modified (text)      -- datetime item was modified
 basename (text)      -- item name without path or extension, e.g. 'Hei'
 extension (text)     -- item extension including the dot, e.g. '.ttf'
 type (text)        -- item type, 'f' for file or 'd' for directory
 mode (text)        -- further type info and permissions, e.g. 'drwxr-xr-x'
 parentpath (text)     -- absolute path of directory containing the item, e.g. '/Library/Fonts/'
 fullpath (text) unique  -- parentpath of the item concatenated with its name, e.g. '/Library/Fonts/Hei.ttf'

PATHS
1) parentpath and fullpath don't support abbreviations such as ~ . or .. They're just strings.
2) Directory paths all have a '/' on the end.

FILESLINES TABLE

The FILESLINES table is for querying data content of files. It has line number and data columns, with one row for each line of data in each file scanned by Crab.

This table isn't available in the example dataset, because it's a virtual table and doesn't physically contain data.

COLUMNS
 linenumber (int) -- line number within file, restarts count from 1 at the first line of each file
 data (text)    -- data content of the files, one entry for each line

FILESLINES also duplicates the columns of the FILES table: fileid, name, bytes, depth, accessed, modified, basename, extension, type, mode, parentpath, and fullpath. This way you can restrict which files are searched without having to join tables.

Example Gutenberg data

An example SQLite database (Mac data), database.sqlite, has been uploaded for you to play with. It includes an example files table...