365 Data Science Database

365 Data Science is a website that provides online courses and resources for learning data science, machine learning, and data analysis.

It is common for websites that offer online courses to have **databases **to store information about their courses, students, and progress. It is also possible that they use databases for storing and organizing the data used in their courses and examples.

If you're looking for specific information about the database used by 365 Data Science, I recommend reaching out to them directly through their Website or support channels.

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The Proteome 2D-PAGE Database system for microbial research is a curated database for storing and investigating proteomics data. Software tools are available and for data submission, please contact the Database Curator. Established at the Max Plank Institution for Infection Biology, this system contains four interconnected databases: i.) 2D-PAGE Database: Two dimensional electrophoresis (2-DE) and mass spectrometry of diverse microorganisms and other organisms. This database currently contains 4971 identified spots and 1228 mass peaklists in 44 reference maps representing experiments from 24 different organisms and strains. The data were submitted by 84 Submitters from 24 Institutes and 12 nations. It also contains various software tools that are important in formatting and analyzing gels and mass peaks; software include: *TopSpot: Scanning the gel, editing the spots and saving the information *Fragmentation: Fragmentation of the gel image into sections *MS-Screener: Perl script to compare the similarity of MALDI-PMF peaklists *MS-Screener update: MS-Screener can be used to compare mass spectra (MALDI-MS(/MS) as well as ESI-MS/MS spectra) on the basis of their peak lists (.dta, .pkm, .pkt, or .txt files), to recalibrate mass spectra, to determine and eliminate exogenous contaminant peaks, and to create matrices for cluster analyses. *GelCali: Online calibration of the Mr- and pI-axis of 2-DE gels with mathematical regression methods ii.)Isotope Coded Affinity Tag (ICAT)-LC/MS database: Isotope Coded Affinity Tag (ICAT)-LC/MS data for Mycobacterium tuberculosis strain BCG versus H37Rv. iii.) FUNC_CLASS database: Functional classification of diverse microorganism. This database also integrates genomic, proteomic, and metabolic data. iv.) DIFF database: Presentation of differently regulated proteins obtained by comparative proteomic experiments using computerized gel image analysis.

1. Website usage instructions2. Website usage case instructions3. Website unit introduction

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A database and storage service resource which allows users to create, view, share, and download information from companion websites. RunMyCode allows users to create companion websites for their scientific publications. Users can share and download computer code and data from companion websites made with RunMyCode. Any software and data format is compatible with RunMyCode.

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SQLite Sakila Sample Database

Database Description

The Sakila sample database is a fictitious database designed to represent a DVD rental store. The tables of the database include film, film_category, actor, customer, rental, payment and inventory among others. The Sakila sample database is intended to provide a standard schema that can be used for examples in books, tutorials, articles, samples, and so forth. Detailed information about the database can be found on the MySQL website: https://dev.mysql.com/doc/sakila/en/

Sakila for SQLite is a part of the sakila-sample-database-ports project intended to provide ported versions of the original MySQL database for other database systems, including:

• Oracle
• SQL Server
• SQLIte
• Interbase/Firebird
• Microsoft Access

Sakila for SQLite is a port of the Sakila example database available for MySQL, which was originally developed by Mike Hillyer of the MySQL AB documentation team. This project is designed to help database administrators to decide which database to use for development of new products The user can run the same SQL against different kind of databases and compare the performance

License: BSD Copyright DB Software Laboratory http://www.etl-tools.com

Note: Part of the insert scripts were generated by Advanced ETL Processor http://www.etl-tools.com/etl-tools/advanced-etl-processor-enterprise/overview.html

Information about the project and the downloadable files can be found at: https://code.google.com/archive/p/sakila-sample-database-ports/

Other versions and developments of the project can be found at: https://github.com/ivanceras/sakila/tree/master/sqlite-sakila-db

https://github.com/jOOQ/jOOQ/tree/main/jOOQ-examples/Sakila

Direct access to the MySQL Sakila database, which does not require installation of MySQL (queries can be typed directly in the browser), is provided on the phpMyAdmin demo version website: https://demo.phpmyadmin.net/master-config/

Files Description

The files in the sqlite-sakila-db folder are the script files which can be used to generate the SQLite version of the database. For convenience, the script files have already been run in cmd to generate the sqlite-sakila.db file, as follows:

sqlite> .open sqlite-sakila.db # creates the .db file sqlite> .read sqlite-sakila-schema.sql # creates the database schema sqlite> .read sqlite-sakila-insert-data.sql # inserts the data

Therefore, the sqlite-sakila.db file can be directly loaded into SQLite3 and queries can be directly executed. You can refer to my notebook for an overview of the database and a demonstration of SQL queries. Note: Data about the film_text table is not provided in the script files, thus the film_text table is empty. Instead the film_id, title and description fields are included in the film table. Moreover, the Sakila Sample Database has many versions, so an Entity Relationship Diagram (ERD) is provided to describe this specific version. You are advised to refer to the ERD to familiarise yourself with the structure of the database.

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Image and Text Introduction Data of Movies in Eleven Sites

🧠 About Dataset

Overview

The AIToolBuzz — 16,763 AI Tools Dataset is a comprehensive collection of publicly available information on artificial intelligence tools and platforms curated from AIToolBuzz.com.
It compiles detailed metadata about each tool, including name, description, category, founding year, technologies used, website, and operational status.

The dataset serves as a foundation for AI trend analysis, product discovery, market research, and NLP-based categorization projects.
It enables researchers, developers, and analysts to explore the evolution of AI tools, detect emerging sectors, and study keyword trends across industries.

Dataset Composition

• Total Entries: 16,763 AI tools
• Time Period: Data collected in October 2025
• Source: AIToolBuzz.com — a curated directory of AI products and services
• Format: CSV (comma-separated), UTF-8 encoded
• Columns: 13 descriptive fields covering both tool metadata and website status

Use Cases

• 🧩 Market & Trend Analysis — Examine growth and patterns in AI categories, technologies, and geographies.
• 🤖 NLP & ML Projects — Use keywords and descriptions for text clustering or embedding tasks.
• 🏷️ Tool Discovery & Classification — Build AI tool recommenders or taxonomies.
• 📊 Data Visualization — Create dashboards showing trends over time or by region.

Example Entries

Provenance Summary

• Source: AIToolBuzz.com — public web directory.
• Collection Method: Automated web scraping via requests + BeautifulSoup, extracting metadata from each tool’s public page.
• Date Collected: October 2025.
• License: Derived dataset — redistribution permitted with attribution (CC BY 4.0 recommended).
• Collector: Swathik Devadiga.
• Frequency: Planned quarterly updates.

Citation

If you use this dataset, please cite as: AIToolBuzz — 16,763 AI Tools (Complete Directory with Metadata). Kaggle. https://aitoolbuzz.com

License

License: CC BY 4.0 — Creative Commons Attribution 4.0 International

You are free to share and adapt the data for research or analysis with proper attribution to AIToolBuzz.com as the original source.

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The statistic shows the size of the global commercial database market, from 2013 to 2021. In 2018, the commercial database market is forecast to fall to ** billion U.S. dollars. The market's decline is linked to the growth of cloud software as a service and the rise of non-relational and open source database solutions, which offer an alternative to the SQL standard championed by the major commercial database vendors.

This folder contains the full set of code and data for the CompuCrawl database. The database contains the archived websites of publicly traded North American firms listed in the Compustat database between 1996 and 2020\u2014representing 11,277 firms with 86,303 firm/year observations and 1,617,675 webpages in the final cleaned and selected set.The files are ordered by moment of use in the work flow. For example, the first file in the list is the input file for code files 01 and 02, which create and update the two tracking files "scrapedURLs.csv" and "URLs_1_deeper.csv" and which write HTML files to its folder. "HTML.zip" is the resultant folder, converted to .zip for ease of sharing. Code file 03 then reads this .zip file and is therefore below it in the ordering.The full set of files, in order of use, is as follows:Compustat_2021.xlsx: The input file containing the URLs to be scraped and their date range.01 Collect frontpages.py: Python script scraping the front pages of the list of URLs and generating a list of URLs one page deeper in the domains.URLs_1_deeper.csv: List of URLs one page deeper on the main domains.02 Collect further pages.py: Python script scraping the list of URLs one page deeper in the domains.scrapedURLs.csv: Tracking file containing all URLs that were accessed and their scraping status.HTML.zip: Archived version of the set of individual HTML files.03 Convert HTML to plaintext.py: Python script converting the individual HTML pages to plaintext.TXT_uncleaned.zip: Archived version of the converted yet uncleaned plaintext files.input_categorization_allpages.csv: Input file for classification of pages using GPT according to their HTML title and URL.04 GPT application.py: Python script using OpenAI\u2019s API to classify selected pages according to their HTML title and URL.categorization_applied.csv: Output file containing classification of selected pages.exclusion_list.xlsx: File containing three sheets: 'gvkeys' containing the GVKEYs of duplicate observations (that need to be excluded), 'pages' containing page IDs for pages that should be removed, and 'sentences' containing (sub-)sentences to be removed.05 Clean and select.py: Python script applying data selection and cleaning (including selection based on page category), with setting and decisions described at the top of the script. This script also combined individual pages into one combined observation per GVKEY/year.metadata.csv: Metadata containing information on all processed HTML pages, including those not selected.TXT_cleaned.zip: Archived version of the selected and cleaned plaintext page files. This file serves as input for the word embeddings application.TXT_combined.zip: Archived version of the combined plaintext files at the GVKEY/year level. This file serves as input for the data description using topic modeling.06 Topic model.R: R script that loads up the combined text data from the folder stored in "TXT_combined.zip", applies further cleaning, and estimates a 125-topic model.TM_125.RData: RData file containing the results of the 125-topic model.loadings125.csv: CSV file containing the loadings for all 125 topics for all GVKEY/year observations that were included in the topic model.125_topprob.xlsx: Overview of top-loading terms for the 125 topic model.07 Word2Vec train and align.py: Python script that loads the plaintext files in the "TXT_cleaned.zip" archive to train a series of Word2Vec models and subsequently align them in order to compare word embeddings across time periods.Word2Vec_models.zip: Archived version of the saved Word2Vec models, both unaligned and aligned.08 Word2Vec work with aligned models.py: Python script which loads the trained Word2Vec models to trace the development of the embeddings for the terms \u201csustainability\u201d and \u201cprofitability\u201d over time.99 Scrape further levels down.py: Python script that can be used to generate a list of unscraped URLs from the pages that themselves were one level deeper than the front page.URLs_2_deeper.csv: CSV file containing unscraped URLs from the pages that themselves were one level deeper than the front page.For those only interested in downloading the final database of texts, the files "HTML.zip", "TXT_uncleaned.zip", "TXT_cleaned.zip", and "TXT_combined.zip" contain the full set of HTML pages, the processed but uncleaned texts, the selected and cleaned texts, and combined and cleaned texts at the GVKEY/year level, respectively.The following webpage contains answers to frequently asked questions: https://haans-mertens.github.io/faq/. More information on the database and the underlying project can be found here: https://haans-mertens.github.io/ and the following article: \u201cThe Internet Never Forgets: A Four-Step Scraping Tutorial, Codebase, and Database for Longitudinal Organizational Website Data\u201d, by Richard F.J. Haans and Marc J. Mertens in Organizational Research Methods. The full paper can be accessed here.

IPinfo IP to Country ASN database

IPinfo's IP to Country ASN database is an open-access database that provides information on the country and ASN (Autonomous System Number) of a given IP address.

• It offers full accuracy and is updated daily.
• The database is licensed under CC-BY-SA 4.0, allowing for commercial usage.
• It includes both IPv4 and IPv6 addresses.
• There are two file formats available: CSV and MMDB.

Notebook

Please explore the provided notebook to learn about the dataset:

🔗 IPinfo IP to Country ASN Demo Notebook for Kaggle

Documentation

Detailed documentation for the IP to Country ASN database can be found on IPinfo's documentation page. Database samples are also available on IPinfo's GitHub repo.

🔗 Documentation: https://ipinfo.io/developers/ip-to-country-asn-database

Context and value

The IPinfo IP to Country ASN database is a subset of IPinfo's IP to Geolocation database and the ASN database.

The database provides daily updates, complete IPv4 and IPv6 coverage, and full accuracy, just like its parent databases. The database is crucial for:

• Cybersecurity and threat intelligence
• Open Source Intelligence (OSINT)
• Firewall policy configuration
• Sales intelligence
• Marketing analytics and adtech
• Personalized user experience

Whether you are running a web service or a server connected to the internet, this enterprise-ready database should be part of your tech stack.

Usage

In this dataset, we include 3 files:

• country_asn.csv → For reverse IP look-ups and running IP-based analytics
• country_asn.mmdb → For IP address information look-ups
• ips.txt → Sample IP addresses

Using the CSV dataset

As the CSV dataset has a relatively small size (~120 MB), any dataframe and database should be adequate. However, we recommend users not use the CSV file for IP address lookups. For everything else, feel free to explore the CSV file format.

Using the MMDB dataset

The MMDB dataset requires a special third-party library called the MMDB reader library. The MMDB reader library enables you to look up IP addresses at the most efficient speed possible. However, as this is a third-party library, you should install it via pip install in your notebook, which requires an internet connection to be enabled in your notebook settings.

Please see our attached demo notebook for usage examples.

IP to Country ASN provides many diverse solutions, so we encourage and share those ideas with the Kaggle community!

Sources

The geolocation data is produced by IPinfo's ProbeNet, a globe-spanning probe network infrastructure with 400+ servers. The ASN data is collected from public datasets like WHOIS, Geofeed etc. The ASN data is later parsed and structured to make it more data-friendly.

See the Data Provenance section below to learn more.

Please note that this Kaggle Dataset is not updated daily. We recommend users download our free IP to Country ASN database from IPinfo's website directly for daily updates.

Terminology

AS Organization - An AS (Autonomous System) organization is an organization that owns a block or range of IP addresses. These IP addresses are sold to them by the Regional Internet Organizations (RIRs). Even though this AS organization may own an IP address, they sometimes do not operate IP addresses directly and may rent them out to other organizations. You can check out our IP to Company data or ASN database to learn more about them.

ASN - ASN or Autonomous System Number is the unique identifying number assigned to an AS organization.

IP to ASN - Get ASN and AS organizat...

The National Conservation Easement Database (NCED) is the first national database of conservation easement information, compiling records from land trusts and public agencies throughout the United States. This public-private partnership brings together national conservation groups, local and regional land trusts, and local, state and federal agencies around a common objective. This effort helps agencies, land trusts, and other organizations plan more strategically, identify opportunities for collaboration, advance public accountability, and raise the profile of what’s happening on-the-ground in the name of conservation.For an introductory tour of the NCED and its benefits check out the story map.

🧾 All Meghalaya Web Designer Company Database – Verified & Updated Contact Directory in ExcelThe All Meghalaya Web Designer Company Database is a comprehensive, verified, and regularly updated Excel directory of active web design agencies, freelance designers, and digital studios across Meghala...

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