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.

MySQL Classicmodels sample database

The MySQL sample database schema consists of the following tables:

• Customers: stores customer’s data.
• Products: stores a list of scale model cars.
• ProductLines: stores a list of product line categories.
• Orders: stores sales orders placed by customers.
• OrderDetails: stores sales order line items for each sales order.
• Payments: stores payments made by customers based on their accounts.
• Employees: stores all employee information as well as the organization structure such as who reports to whom.
• Offices: stores sales office data.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8652778%2Fefc56365be54c0e2591a1aefa5041f36%2FMySQL-Sample-Database-Schema.png?generation=1670498341027618&alt=media" alt="">

This is the sample database from sqlservertutorial.net. This is a great dataset for learning SQL and practicing querying relational databases.

Database Diagram:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4146319%2Fc5838eb006bab3938ad94de02f58c6c1%2FSQL-Server-Sample-Database.png?generation=1692609884383007&alt=media" alt="">

Terms of Use

The sample database is copyrighted and cannot be used for commercial purposes. For example, it cannot be used for the following but is not limited to the purposes: - Selling - Including in paid courses

Dataset

This dataset was created by Sudhir Singh

Released under Data files © Original Authors

Contents

As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of *******; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

FooDrugs database is a development done by the Computational Biology Group at IMDEA Food Institute (Madrid, Spain), in the context of the Food Nutrition Security Cloud (FNS-Cloud) project. Food Nutrition Security Cloud (FNS-Cloud) has received funding from the European Union's Horizon 2020 Research and Innovation programme (H2020-EU.3.2.2.3. – A sustainable and competitive agri-food industry) under Grant Agreement No. 863059 – www.fns-cloud.eu (See more details about FNS-Cloud below)

FooDrugs stores information extracted from transcriptomics and text documents for foo-drug interactiosn and it is part of a demonstrator to be done in the FNS-Cloud project. The database was built using MySQL, an open source relational database management system. FooDrugs host information for a total of 161 transcriptomics GEO series with 585 conditions for food or bioactive compounds. Each condition is defined as a food/biocomponent per time point, per concentration, per cell line, primary culture or biopsy per study. FooDrugs includes information about a bipartite network with 510 nodes and their similarity scores (tau score; https://clue.io/connectopedia/connectivity_scores) related with possible drug interactions with drugs assayed in conectivity map (https://www.broadinstitute.org/connectivity-map-cmap). The information is stored in eight tables:

Table “study” : This table contains basic information about study identifiers from GEO, pubmed or platform, study type, title and abstract

Table “sample”: This table contains basic information about the different experiments in a study, like the identifier of the sample, treatment, origin type, time point or concentration.

Table “misc_study”: This table contains additional information about different attributes of the study.

Table “misc_sample”: This table contains additional information about different attributes of the sample.

Table “cmap”: This table contains information about 70895 nodes, compromising drugs, foods or bioactives, overexpressed and knockdown genes (see section 3.4). The information includes cell line, compound and perturbation type.

Table “cmap_foodrugs”: This table contains information about the tau score (see section 3.4) that relates food with drugs or genes and the node identifier in the FooDrugs network.

Table “topTable”: This table contains information about 150 over and underexpressed genes from each GEO study condition, used to calculate the tau score (see section 3.4). The information stored is the logarithmic fold change, average expression, t-statistic, p-value, adjusted p-value and if the gene is up or downregulated.

Table “nodes”: This table stores the information about the identification of the sample and the node in the bipartite network connecting the tables “sample”, “cmap_foodrugs” and “topTable”.

In addition, FooDrugs database stores a total of 6422 food/drug interactions from 2849 text documents, obtained from three different sources: 2312 documents from PubMed, 285 from DrugBank, and 252 from drugs.com. These documents describe potential interactions between 1464 food/bioactive compounds and 3009 drugs. The information is stored in two tables:

Table “texts”: This table contains all the documents with its identifiers where interactions have been identified with strategy described in section 4.

Table “TM_interactions”: This table contains information about interaction identifiers, the food and drug entities, and the start and the end positions of the context for the interaction in the document.

FNS-Cloud will overcome fragmentation problems by integrating existing FNS data, which is essential for high-end, pan-European FNS research, addressing FNS, diet, health, and consumer behaviours as well as on sustainable agriculture and the bio-economy. Current fragmented FNS resources not only result in knowledge gaps that inhibit public health and agricultural policy, and the food industry from developing effective solutions, making production sustainable and consumption healthier, but also do not enable exploitation of FNS knowledge for the benefit of European citizens. FNS-Cloud will, through three Demonstrators; Agri-Food, Nutrition & Lifestyle and NCDs & the Microbiome to facilitate: (1) Analyses of regional and country-specific differences in diet including nutrition, (epi)genetics, microbiota, consumer behaviours, culture and lifestyle and their effects on health (obesity, NCDs, ethnic and traditional foods), which are essential for public health and agri-food and health policies; (2) Improved understanding agricultural differences within Europe and what these means in terms of creating a sustainable, resilient food systems for healthy diets; and (3) Clear definitions of boundaries and how these affect the compositions of foods and consumer choices and, ultimately, personal and public health in the future. Long-term sustainability of the FNS-Cloud will be based on Services that have the capacity to link with new resources and enable cross-talk amongst them; access to FNS-Cloud data will be open access, underpinned by FAIR principles (findable, accessible, interoperable and re-useable). FNS-Cloud will work closely with the proposed Food, Nutrition and Health Research Infrastructure (FNHRI) as well as METROFOOD-RI and other existing ESFRI RIs (e.g. ELIXIR, ECRIN) in which several FNS-Cloud Beneficiaries are involved directly. (https://cordis.europa.eu/project/id/863059)

***** changes between version FooDrugs_v2 and FooDrugs_V3 (31st January 2023) are:

Increased the amount of text documents by 85.675 from PubMed and ClinicalTrials.gov, and the amount of Text Mining interactions by 168.826.

Increased the amount of transcriptomic studies by 32 GEO series.

Removed all rows in table cmap_foodrugs representing interactions with values of tau=0

Removed 43 GEO series that after manually checking didn't correspond to food compounds.

Added a new column to the table texts: citation to hold the citation of the text.

Added these columns to the table study: contributor to contain the authors of the study, publication_date to store the date of publication of the study in GEO and pubmed_id to reference the publication associated with the study if any.

Added a new column to topTable to hold the top 150 up-regulated and 150 down-regulated genes.

Introduction

This datasets have SQL injection attacks (SLQIA) as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLMAP tool has been used.

NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.

Datasets

The firts dataset was colleted to train the detection models (D1) and other collected using different attacks than those used in training to test the models and ensure their generalization (D2).

The datasets contain both benign and malicious traffic. All collected datasets are balanced.

The version of NetFlow used to build the datasets is 5.

Dataset	Aim	Samples	Benign-malicious traffic ratio
D1	Training	400,003	50%
D2	Test	57,239	50%

Infrastructure and implementation

Two sets of flow data were collected with DOROTHEA. DOROTHEA is a Docker-based framework for NetFlow data collection. It allows you to build interconnected virtual networks to generate and collect flow data using the NetFlow protocol. In DOROTHEA, network traffic packets are sent to a NetFlow generator that has a sensor ipt_netflow installed. The sensor consists of a module for the Linux kernel using Iptables, which processes the packets and converts them to NetFlow flows.

DOROTHEA is configured to use Netflow V5 and export the flow after it is inactive for 15 seconds or after the flow is active for 1800 seconds (30 minutes)

Benign traffic generation nodes simulate network traffic generated by real users, performing tasks such as searching in web browsers, sending emails, or establishing Secure Shell (SSH) connections. Such tasks run as Python scripts. Users may customize them or even incorporate their own. The network traffic is managed by a gateway that performs two main tasks. On the one hand, it routes packets to the Internet. On the other hand, it sends it to a NetFlow data generation node (this process is carried out similarly to packets received from the Internet).

The malicious traffic collected (SQLI attacks) was performed using SQLMAP. SQLMAP is a penetration tool used to automate the process of detecting and exploiting SQL injection vulnerabilities.

The attacks were executed on 16 nodes and launch SQLMAP with the parameters of the following table.

Parameters	Description
'--banner','--current-user','--current-db','--hostname','--is-dba','--users','--passwords','--privileges','--roles','--dbs','--tables','--columns','--schema','--count','--dump','--comments', --schema'	Enumerate users, password hashes, privileges, roles, databases, tables and columns
--level=5	Increase the probability of a false positive identification
--risk=3	Increase the probability of extracting data
--random-agent	Select the User-Agent randomly
--batch	Never ask for user input, use the default behavior
--answers="follow=Y"	Predefined answers to yes

Every node executed SQLIA on 200 victim nodes. The victim nodes had deployed a web form vulnerable to Union-type injection attacks, which was connected to the MYSQL or SQLServer database engines (50% of the victim nodes deployed MySQL and the other 50% deployed SQLServer).

The web service was accessible from ports 443 and 80, which are the ports typically used to deploy web services. The IP address space was 182.168.1.1/24 for the benign and malicious traffic-generating nodes. For victim nodes, the address space was 126.52.30.0/24.
The malicious traffic in the test sets was collected under different conditions. For D1, SQLIA was performed using Union attacks on the MySQL and SQLServer databases.

However, for D2, BlindSQL SQLIAs were performed against the web form connected to a PostgreSQL database. The IP address spaces of the networks were also different from those of D1. In D2, the IP address space was 152.148.48.1/24 for benign and malicious traffic generating nodes and 140.30.20.1/24 for victim nodes.

To run the MySQL server we ran MariaDB version 10.4.12.
Microsoft SQL Server 2017 Express and PostgreSQL version 13 were used.

As of June 2024, the most popular relational database management system (RDBMS) worldwide was Oracle, with a ranking score of *******. Oracle was also the most popular DBMS overall. MySQL and Microsoft SQL server rounded out the top three.

Cleaned Meta-Kaggle Dataset

The Original Dataset - Meta-Kaggle

Explore our public data on competitions, datasets, kernels (code / notebooks) and more Meta Kaggle may not be the Rosetta Stone of data science, but we do think there's a lot to learn (and plenty of fun to be had) from this collection of rich data about Kaggle’s community and activity.

Strategizing to become a Competitions Grandmaster? Wondering who, where, and what goes into a winning team? Choosing evaluation metrics for your next data science project? The kernels published using this data can help. We also hope they'll spark some lively Kaggler conversations and be a useful resource for the larger data science community.

https://i.imgur.com/2Egeb8R.png" alt="" title="a title">

This dataset is made available as CSV files through Kaggle Kernels. It contains tables on public activity from Competitions, Datasets, Kernels, Discussions, and more. The tables are updated daily.

Please note: This data is not a complete dump of our database. Rows, columns, and tables have been filtered out and transformed.

August 2023 update

In August 2023, we released Meta Kaggle for Code, a companion to Meta Kaggle containing public, Apache 2.0 licensed notebook data. View the dataset and instructions for how to join it with Meta Kaggle here

We also updated the license on Meta Kaggle from CC-BY-NC-SA to Apache 2.0.

The Problems with the Original Dataset

• The original dataset is 32 CSV files, with 268 colums and 7GB of compressed data. Having so many tables and columns makes it hard to understand the data.
• The data is not normalized, so when you join tables you get a lot of errors.
• Some values refer to non-existing values in other tables. For example, the UserId column in the ForumMessages table has values that do not exist in the Users table.
• There are missing values.
• There are duplicate values.
• There are values that are not valid. For example, Ids that are not positive integers.
• The date and time columns are not in the right format.
• Some columns only have the same value for all rows, so they are not useful.
• The boolean columns have string values True or False.
• Incorrect values for the Total columns. For example, the DatasetCount is not the total number of datasets with the Tag according to the DatasetTags table.
• Users upvote their own messages.

The Solution

• To handle so many tables and columns I use a relational database. I use MySQL, but you can use any relational database.
• The steps to create the database are:
• Creating the database tables with the right data types and constraints. I do that by running the db_abd_create_tables.sql script.
• Downloading the CSV files from Kaggle using the Kaggle API.
• Cleaning the data using pandas. I do that by running the clean_data.py script. The script does the following steps for each table:
  • Drops the columns that are not needed.
  • Converts each column to the right data type.
  • Replaces foreign keys that do not exist with NULL.
  • Replaces some of the missing values with default values.
  • Removes rows where there are missing values in the primary key/not null columns.
  • Removes duplicate rows.
• Loading the data into the database using the LOAD DATA INFILE command.
• Checks that the number of rows in the database tables is the same as the number of rows in the CSV files.
• Adds foreign key constraints to the database tables. I do that by running the add_foreign_keys.sql script.
• Update the Total columns in the database tables. I do that by running the update_totals.sql script.
• Backup the database.

README 2025-09-10 Introduction The peatland mid infrared database (pmird) stores data from peat, vegetation, litter, and dissolved organic matter samples, in particular mid infrared spectra and other variables, from previously published and unpublished data sources. The majority of samples in the database are peat samples from northern bogs. Currently, the database contains entries from 26 studies, 11216 samples, and 3877 mid infrared spectra. The aim is to provide a harmonized data source that can be useful to re-analyse existing data, analyze peat chemistry, develop and test spectral prediction models, and provide data on various peat properties. Usage notes Download and Setup The peatland mid infrared database can be downloaded from https://doi.org/10.5281/zenodo.17092587. The publication contains the following files and folders: pmird-backup-2025-09-10.sql: A mysqldump backup of the pmird database. pmird_prepared_data: A folder that contains: Folders like c00001-2020-08-17-Hodgkins with the raw spectra for samples from each dataset in the pmird database (see below for how to import the spectra). Files like pmird_prepare_data_c00001-2020-08-17-Hodgkins.Rmd that contain the R code used to process and import the data from each dataset into the database. Corresponding html files contain the compiled scripts. pmird_prepare_data.Rmd: An Rmarkdown script that was used to run the scripts that created the database (the top level script). mysql_scripts: A folder that contains: pmird_mysql_initialization.sql: MariaDB script to initialize the database. 001-db-initialize.Rmd: Rmarkdown script that executes pmird_mysql_initialization.sql and populated dataset-independent tables. add-citations.Rmd: Rmarkdown script that adds information on references to the database. add-licenses.Rmd: Rmarkdown script that adds information on licenses to the database. add-mir-metadata-quality.Rmd Rmarkdown script that adds information on the quality of the infrared spectra to the database. Dockerfile: A Dockerfile that defines the computing environment used to create the database. renv.lock A renv.lock file that lists the R packages used to create the database. The database can be set up as follows: The downloaded database needs to be imported in a running MariaDB instance. In a linux terminal, the downloaded sql file can be imported like so: mysql -u

This dataset comprises of two .csv format files used within workstream 2 of the Wellcome Trust funded ‘Orphan drugs: High prices, access to medicines and the transformation of biopharmaceutical innovation’ project (219875/Z/19/Z). They appear in various outputs, e.g. publications and presentations.

The deposited data were gathered using the University of Amsterdam Digital Methods Institute’s ‘Twitter Capture and Analysis Toolset’ (DMI-TCAT) before being processed and extracted from Gephi. DMI-TCAT queries Twitter’s STREAM Application Programming Interface (API) using SQL and retrieves data on a pre-set text query. It then sends the returned data for storage on a MySQL database. The tool allows for output of that data in various formats. This process aligns fully with Twitter’s service user terms and conditions. The query for the deposited dataset gathered a 1% random sample of all public tweets posted between 10-Feb-2021 and 10-Mar-2021 containing the text ‘Rare Diseases’ and/or ‘Rare Disease Day’, storing it on a local MySQL database managed by the University of Sheffield School of Sociological Studies (http://dmi-tcat.shef.ac.uk/analysis/index.php), accessible only via a valid VPN such as FortiClient and through a permitted active directory user profile. The dataset was output from the MySQL database raw as a .gexf format file, suitable for social network analysis (SNA). It was then opened using Gephi (0.9.2) data visualisation software and anonymised/pseudonymised in Gephi as per the ethical approval granted by the University of Sheffield School of Sociological Studies Research Ethics Committee on 02-Jun-201 (reference: 039187). The deposited dataset comprises of two anonymised/pseudonymised social network analysis .csv files extracted from Gephi, one containing node data (Issue-networks as excluded publics – Nodes.csv) and another containing edge data (Issue-networks as excluded publics – Edges.csv). Where participants explicitly provided consent, their original username has been provided. Where they have provided consent on the basis that they not be identifiable, their username has been replaced with an appropriate pseudonym. All other usernames have been anonymised with a randomly generated 16-digit key. The level of anonymity for each Twitter user is provided in column C of deposited file ‘Issue-networks as excluded publics – Nodes.csv’.

This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 26-Aug-2021 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman institute/School of Sociological Studies. ORDA has full permission to store this dataset and to make it open access for public re-use without restriction under a CC BY license, in line with the Wellcome Trust commitment to making all research data Open Access.

The University of Sheffield are the designated data controller for this dataset.

As of December 2022, relational database management systems (RDBMS) were the most popular type of DBMS, accounting for a ** percent popularity share. The most popular RDBMS in the world has been reported as Oracle, while MySQL and Microsoft SQL server rounded out the top three.

Approximately ** percent of the surveyed software companies in Russia mentioned PostgreSQL, making it the most popular database management system (DBMS) in the period between February and May 2022. MS SQL and MySQL followed, having been mentioned by ** percent and ** percent of respondents, respectively.

The Radiocarbon dating laboratory of IRPA/KIK was founded in the 1960s. Initially dates were reported at more or less regular intervals in the journal Radiocarbon (Schreurs 1968). Since the advent of radiocarbon dating in the 1950s it had been a common practice amongst radiocarbon laboratories to publish their dates in so-called ‘date-lists’ that were arranged per laboratory. This was first done in the Radiocarbon Supplement of the American Journal of Science and later in the specialised journal Radiocarbon. In the course of time the latter, with the added subtitle An International Journal of Cosmogenic Isotope Research, became a regular scientific journal shifting focus from date-lists to articles. Furthermore the world-wide exponential increase of radiocarbon dates made it almost impossible to publish them all in the same journal, even more so because of the broad range of applications that use radiocarbon analysis, ranging from archaeology and art history to geology and oceanography and recently also biomedical studies.The IRPA/KIK database From 1995 onwards IRPA/KIK’s Radiocarbon laboratory started to publish its dates in small publications, continuing the numbering of the preceding lists in Radiocarbon. The first booklet in this series was “Royal Institute for Cultural Heritage Radiocarbon dates XV” (Van Strydonck et al. 1995), followed by three more volumes (XVI, XVII, XVIII). The next list (XIX, 2005) was no longer printed but instead handed out as a PDF file on CD-rom. The ever increasing number of dates and the difficulties in handling all the data, however, made us look for a more permanent and easier solution. In order to improve data management and consulting, it was thus decided to gather all our dates in a web-based database. List XIX was in fact already a Microsoft Access database that was converted into a reader friendly style and could also be printed as a PDF file. However a Microsoft Access database is not the most practical solution to make information publicly available. Hence the structure of the database was recreated in Mysql and the existing content was transferred into the corresponding fields. To display the records, a web-based front-end was programmed in PHP/Apache. It features a full-text search function that allows for partial word-matching. In addition the records can be consulted in PDF format. Old records from the printed date-lists as well as new records are now added using the same Microsoft Acces back-end, which is now connected directly to the Mysql database. The main problem with introducing the old data was that not all the current criteria were available in the past (e.g. stable isotope measurements). Furthermore since all the sample information is given by the submitter, its quality largely depends on the persons willingness to contribute as well as on the accuracy and correctness of the information he provides. Sometimes problems arrive from the fact that a certain investigation (like an excavation) is carried out over a relatively long period (sometimes even more than ten years) and is directed by different people or even institutions. This can lead to differences in the labeling procedure of the samples, but also in the interpretation of structures and artifacts and in the orthography of the site’s name. Finally the submitter might change address, while the names of institutions or even regions and countries might change as well (e.g.Zaire - Congo)

The dataset is an active collection of access data to information items in the University of Tasmania’s EPrints repository. Each night a task is scheduled to run, and this picks up in the Apache access logs from where it left off the previous night. Each download of an open access full-text item causes the generation of a database record in the MySQL database, together with a timestamp, and an approximate location of the computer system generating the download. This is achieved by looking up the IP address against the GeoIP database, with one significant difference. Downloads originating from a University of Tasmania IP address are separately identified, and removed from the ‘Australia’ category. This eliminates vanity searches from achieving high significance. Countries are coded using the ISO3166 two-letter code.

The dataset has been used to analyse the usage made of the repository and to tune it to achieve maximal visibility for the University of Tasmania. Researchers with items in the repository have used it to identify the types of use being made of their work, and to find potential collaborators. The citation of a work in a journal or conference article, for example, causes a typical step in usage, and the citing article can be searched in Google or Google Scholar to identify the authors. This enhances the dissemination experience and its value.

The software was written in the University of Tasmania by Professor Arthur Sale (in php) based on earlier work by the University of Melbourne (with permission). Mr Christian McGee wrote some critical sections of the code in perl, and set up the cron scheduling.

The dataset is generated by a computer program written by Professor Arthur Sale. The software was a test bed for ideas, and subsequently resulted in an official software set included in the EPrints distribution. This set expanded on the concepts significantly

This dataset contains the complete MySQL Employees Database, a widely used sample dataset for learning SQL, data analysis, business intelligence, and database design. It includes employee information, salaries, job titles, departments, managers, and department history, making it ideal for real-world analytical practice.

The dataset is structured into multiple tables that represent a real corporate environment with employee records spanning several decades. Users can practice SQL joins, window functions, aggregation, CTEs, subqueries, business KPIs, HR analytics, trend analysis, and more.

Malaria is the leading cause of death in the African region. Data mining can help extract valuable knowledge from available data in the healthcare sector. This makes it possible to train models to predict patient health faster than in clinical trials. Implementations of various machine learning algorithms such as K-Nearest Neighbors, Bayes Theorem, Logistic Regression, Support Vector Machines, and Multinomial Naïve Bayes (MNB), etc., has been applied to malaria datasets in public hospitals, but there are still limitations in modeling using the Naive Bayes multinomial algorithm. This study applies the MNB model to explore the relationship between 15 relevant attributes of public hospitals data. The goal is to examine how the dependency between attributes affects the performance of the classifier. MNB creates transparent and reliable graphical representation between attributes with the ability to predict new situations. The model (MNB) has 97% accuracy. It is concluded that this model outperforms the GNB classifier which has 100% accuracy and the RF which also has 100% accuracy. Methods Prior to collection of data, the researcher was be guided by all ethical training certification on data collection, right to confidentiality and privacy reserved called Institutional Review Board (IRB). Data was be collected from the manual archive of the Hospitals purposively selected using stratified sampling technique, transform the data to electronic form and store in MYSQL database called malaria. Each patient file was extracted and review for signs and symptoms of malaria then check for laboratory confirmation result from diagnosis. The data was be divided into two tables: the first table was called data1 which contain data for use in phase 1 of the classification, while the second table data2 which contains data for use in phase 2 of the classification. Data Source Collection Malaria incidence data set is obtained from Public hospitals from 2017 to 2021. These are the data used for modeling and analysis. Also, putting in mind the geographical location and socio-economic factors inclusive which are available for patients inhabiting those areas. Naive Bayes (Multinomial) is the model used to analyze the collected data for malaria disease prediction and grading accordingly. Data Preprocessing: Data preprocessing shall be done to remove noise and outlier. Transformation: The data shall be transformed from analog to electronic record. Data Partitioning The data which shall be collected will be divided into two portions; one portion of the data shall be extracted as a training set, while the other portion will be used for testing. The training portion shall be taken from a table stored in a database and will be called data which is training set1, while the training portion taking from another table store in a database is shall be called data which is training set2. The dataset was split into two parts: a sample containing 70% of the training data and 30% for the purpose of this research. Then, using MNB classification algorithms implemented in Python, the models were trained on the training sample. On the 30% remaining data, the resulting models were tested, and the results were compared with the other Machine Learning models using the standard metrics. Classification and prediction: Base on the nature of variable in the dataset, this study will use Naïve Bayes (Multinomial) classification techniques; Classification phase 1 and Classification phase 2. The operation of the framework is illustrated as follows: i. Data collection and preprocessing shall be done. ii. Preprocess data shall be stored in a training set 1 and training set 2. These datasets shall be used during classification. iii. Test data set is shall be stored in database test data set. iv. Part of the test data set must be compared for classification using classifier 1 and the remaining part must be classified with classifier 2 as follows: Classifier phase 1: It classify into positive or negative classes. If the patient is having malaria, then the patient is classified as positive (P), while a patient is classified as negative (N) if the patient does not have malaria.
Classifier phase 2: It classify only data set that has been classified as positive by classifier 1, and then further classify them into complicated and uncomplicated class label. The classifier will also capture data on environmental factors, genetics, gender and age, cultural and socio-economic variables. The system will be designed such that the core parameters as a determining factor should supply their value.

This file contains the database and table creation scripts used in the Daily Sales & Expenses System project. It defines the structure of the MySQL database where all sales, expenses, inventory, and login records are stored before being accessed, analyzed, and visualized using Python.

The file includes: - Commands to create the database - SQL statements to create the users, sales, expenses and inventory tables - (Optionally) Sample INSERT statements to populate the tables with test data

This file is essential for anyone who wants to replicate or test the Python-MySQL integration in the project.

The Chinook Database is a sample database designed for use with multiple database platforms, such as SQL Server, Oracle, MySQL, and others. It can be easily set up by running a single SQL script, making it a convenient alternative to the popular Northwind database. Chinook is widely used in demos and testing environments, particularly for Object-Relational Mapping (ORM) tools that target both single and multiple database servers.

Supported Database Servers Chinook supports several database servers, including:

DB2 MySQL Oracle PostgreSQL SQL Server SQL Server Compact SQLite Download Instructions You can download the SQL scripts for each supported database server from the latest release assets. The appropriate SQL script file(s) for your database vendor are provided, which can be executed using your preferred database management tool.

Data Model The Chinook Database represents a digital media store, containing tables that include:

Artists Albums Media tracks Invoices Customers Sample Data The media data in Chinook is derived from a real iTunes Library, providing a realistic dataset for users. Additionally, users can generate their own SQL scripts using their personal iTunes Library by following specific instructions. Customer and employee details in the database were manually crafted with fictitious names, addresses (mappable via Google Maps), and well-structured contact information such as phone numbers, faxes, and emails. Sales data is auto-generated and spans a four-year period, using random values.

Why is it Called Chinook? The Chinook Database's name is a nod to its predecessor, the Northwind database. Chinooks are warm, dry winds found in the interior regions of North America, particularly over southern Alberta in Canada, where the Canadian Prairies meet mountain ranges. This natural phenomenon inspired the choice of name, reflecting the idea that Chinook serves as a refreshing alternative to the Northwind database.

Bei dem aufbereiteten Längsschnitt-Datensatzes 2014 bis 2016 handelt es sich um „Big-Data“, weshalb der Gesamtdatensatz nur in Form einer Datenbank (MySQL) verfügbar sein wird. In dieser Datenbank liegt die Information verschiedener Variablen eines Befragten untereinander. Die vorliegende Publikation umfasst eine SQL-Datenbank mit den Meta-Daten des Sample des Gesamtdatensatzes, das einen Ausschnitt der verfügbaren Variablen des Gesamtdatensatzes darstellt und die Struktur der aufbereiteten Daten darlegen soll, und eine Datendokumentation des Samples. Für diesen Zweck beinhaltet das Sample alle Variablen der Soziodemographie, dem Freizeitverhalten, der Zusatzinformation zu einem Befragten und dessen Haushalt sowie den interviewspezifischen Variablen und Gewichte. Lediglich bei den Variablen bezüglich der Mediennutzung des Befragten, handelt es sich um eine kleine Auswahl: Für die Onlinemediennutzung wurden die Variablen aller Gesamtangebote sowie der Einzelangebote der Genre Politik und Digital aufgenommen. Die Mediennutzung von Radio, Print und TV wurde im Sample nicht berücksichtigt, da deren Struktur anhand der veröffentlichten Längsschnittdaten der Media-Analyse MA Radio, MA Pressemedien und MA Intermedia nachvollzogen werden kann.
Die Datenbank mit den tatsächlichen Befragungsdaten wäre auf Grund der Größe des Datenmaterials bereits im kritischen Bereich der Dateigröße für den normalen Up- und Download. Die tatsächlichen Befragungsergebnisse, die zur Analyse nötig sind, werden dann 2021 in Form des Gesamtdatensatzes der Media-Analyse-Daten: IntermediaPlus (2014-2016) im DBK bei GESIS veröffentlicht werden.

Die Daten sowie deren Datenaufbereitung sind ein Vorschlag eines Best-Practice Cases für Big-Data Management bzw. den Umgang mit Big-Data in den Sozialwissenschaften und mit sozialwissenschaftlichen Daten. Unter Verwendung der GESIS Software CharmStats, die im Rahmen dieses Projektes um Big-Data Features erweitert wurde, erfolgt die Dokumentation und Herstellung der Transparenz der Harmonisierungsarbeit. Durch ein Python-Skript sowie ein html-Template wurde der Arbeitsprozess um und mit CharmStats zudem stärker automatisiert.

Der aufbereitete Längsschnitt des Gesamtdatensatzes der MA IntermediaPlus für 2014 bis 2016 wird 2021 in Kooperation mit GESIS herausgegeben werden und den FAIR-Prinzipien (Wilkinson et al. 2016) entsprechend verfügbar gemacht werden. Ziel ist es durch die Harmonisierung der einzelnen Querschnitte die Datenquelle der Media-Analyse, die im Rahmen des Dissertationsprojektes „Angebots- und Publikumsfragmentierung online“ durch Inga Brentel und Céline Fabienne Kampes erfolgt, für Forschung zum sozialen und medialen Wandel in der Bundesrepublik Deutschland zugänglich zu machen.

Künftige Studiennummer des Gesamtdatensatzes der IndermediaPlus im DBK der GESIS: ZA5769 (Version 1-0-0) und der doi: https://dx.doi.org/10.4232/1.13530

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The prepared Longitudinal IntermediaPlus dataset 2014 to 2016 is a "big data", which is why the entire dataset will only be available in the form of a database (MySQL). In this database, the information of different variables of a respondent is organized in one column, one below the other. The present publication includes a SQL-Database with the meta data of a sample of the full database, which represents a section of the available variables of the total data set and is intended to show the structure of the prepared data and the data-documentation (codebook) of the sample. For this purpose, the sample contains all variables of sociodemography, free-time activities, additional information on a respondent and his household as well as the interview-specific variables and weights. Only the variables concerning the respondent's media use are a small selection: For online media use, the variables of all overall offerings as well as the individual offerings of the genres politics and digital were included. The media use of radio, print and TV was not included in the sample because its structure can be traced using the published longitudinal data of the media analysis MA Radio, MA Pressemedien and MA Intermedia.
Due to the size of the datafile, the database with the actual survey data would already be in the critical range of the file size for the common upload and download. The actual survey result...