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A collection of datasets and python scripts for extraction and analysis of isograms (and some palindromes and tautonyms) from corpus-based word-lists, specifically Google Ngram and the British National Corpus (BNC).Below follows a brief description, first, of the included datasets and, second, of the included scripts.1. DatasetsThe data from English Google Ngrams and the BNC is available in two formats: as a plain text CSV file and as a SQLite3 database.1.1 CSV formatThe CSV files for each dataset actually come in two parts: one labelled ".csv" and one ".totals". The ".csv" contains the actual extracted data, and the ".totals" file contains some basic summary statistics about the ".csv" dataset with the same name.The CSV files contain one row per data point, with the colums separated by a single tab stop. There are no labels at the top of the files. Each line has the following columns, in this order (the labels below are what I use in the database, which has an identical structure, see section below):

Label Data type Description

isogramy int The order of isogramy, e.g. "2" is a second order isogram

length int The length of the word in letters

word text The actual word/isogram in ASCII

source_pos text The Part of Speech tag from the original corpus

count int Token count (total number of occurences)

vol_count int Volume count (number of different sources which contain the word)

count_per_million int Token count per million words

vol_count_as_percent int Volume count as percentage of the total number of volumes

is_palindrome bool Whether the word is a palindrome (1) or not (0)

is_tautonym bool Whether the word is a tautonym (1) or not (0)

The ".totals" files have a slightly different format, with one row per data point, where the first column is the label and the second column is the associated value. The ".totals" files contain the following data:

Label

Data type

Description

!total_1grams

int

The total number of words in the corpus

!total_volumes

int

The total number of volumes (individual sources) in the corpus

!total_isograms

int

The total number of isograms found in the corpus (before compacting)

!total_palindromes

int

How many of the isograms found are palindromes

!total_tautonyms

int

How many of the isograms found are tautonyms

The CSV files are mainly useful for further automated data processing. For working with the data set directly (e.g. to do statistics or cross-check entries), I would recommend using the database format described below.1.2 SQLite database formatOn the other hand, the SQLite database combines the data from all four of the plain text files, and adds various useful combinations of the two datasets, namely:• Compacted versions of each dataset, where identical headwords are combined into a single entry.• A combined compacted dataset, combining and compacting the data from both Ngrams and the BNC.• An intersected dataset, which contains only those words which are found in both the Ngrams and the BNC dataset.The intersected dataset is by far the least noisy, but is missing some real isograms, too.The columns/layout of each of the tables in the database is identical to that described for the CSV/.totals files above.To get an idea of the various ways the database can be queried for various bits of data see the R script described below, which computes statistics based on the SQLite database.2. ScriptsThere are three scripts: one for tiding Ngram and BNC word lists and extracting isograms, one to create a neat SQLite database from the output, and one to compute some basic statistics from the data. The first script can be run using Python 3, the second script can be run using SQLite 3 from the command line, and the third script can be run in R/RStudio (R version 3).2.1 Source dataThe scripts were written to work with word lists from Google Ngram and the BNC, which can be obtained from http://storage.googleapis.com/books/ngrams/books/datasetsv2.html and [https://www.kilgarriff.co.uk/bnc-readme.html], (download all.al.gz).For Ngram the script expects the path to the directory containing the various files, for BNC the direct path to the *.gz file.2.2 Data preparationBefore processing proper, the word lists need to be tidied to exclude superfluous material and some of the most obvious noise. This will also bring them into a uniform format.Tidying and reformatting can be done by running one of the following commands:python isograms.py --ngrams --indir=INDIR --outfile=OUTFILEpython isograms.py --bnc --indir=INFILE --outfile=OUTFILEReplace INDIR/INFILE with the input directory or filename and OUTFILE with the filename for the tidied and reformatted output.2.3 Isogram ExtractionAfter preparing the data as above, isograms can be extracted from by running the following command on the reformatted and tidied files:python isograms.py --batch --infile=INFILE --outfile=OUTFILEHere INFILE should refer the the output from the previosu data cleaning process. Please note that the script will actually write two output files, one named OUTFILE with a word list of all the isograms and their associated frequency data, and one named "OUTFILE.totals" with very basic summary statistics.2.4 Creating a SQLite3 databaseThe output data from the above step can be easily collated into a SQLite3 database which allows for easy querying of the data directly for specific properties. The database can be created by following these steps:1. Make sure the files with the Ngrams and BNC data are named “ngrams-isograms.csv” and “bnc-isograms.csv” respectively. (The script assumes you have both of them, if you only want to load one, just create an empty file for the other one).2. Copy the “create-database.sql” script into the same directory as the two data files.3. On the command line, go to the directory where the files and the SQL script are. 4. Type: sqlite3 isograms.db 5. This will create a database called “isograms.db”.See the section 1 for a basic descript of the output data and how to work with the database.2.5 Statistical processingThe repository includes an R script (R version 3) named “statistics.r” that computes a number of statistics about the distribution of isograms by length, frequency, contextual diversity, etc. This can be used as a starting point for running your own stats. It uses RSQLite to access the SQLite database version of the data described above.

This dataset contains a cleaned version of the Montgomery County Fleet Equipment Inventory.

✅ Data Cleaning Steps: - Removed duplicate records - Fixed spelling errors - Merged department names using Flash Fill - Removed unnecessary whitespace - Converted CSV to Excel (.XLSX) format

📂 Original Dataset Source: Montgomery County Public Dataset

Abstract

The Palestinian society's access to information and communication technology tools is one of the main inputs to achieve social development and economic change to the status of Palestinian society; on the basis of its impact on the revolution of information and communications technology that has become a feature of this era. Therefore, and within the scope of the efforts exerted by the Palestinian Central Bureau of Statistics in providing official Palestinian statistics on various areas of life for the Palestinian community, PCBS implemented the household survey for information and communications technology for the year 2019. The main objective of this report is to present the trends of accessing and using information and communication technology by households and individuals in Palestine, and enriching the information and communications technology database with indicators that meet national needs and are in line with international recommendations.

Geographic coverage

Palestine, West Bank, Gaza strip

Analysis unit

Household, Individual

Universe

All Palestinian households and individuals (10 years and above) whose usual place of residence in 2019 was in the state of Palestine.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of master sample which were enumerated in the 2017 census. Each enumeration area consists of buildings and housing units with an average of about 150 households. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

Sample size The estimated sample size is 8,040 households.

Sample Design The sample is three stages stratified cluster (pps) sample. The design comprised three stages: Stage (1): Selection a stratified sample of 536 enumeration areas with (pps) method. Stage (2): Selection a stratified random sample of 15 households from each enumeration area selected in the first stage. Stage (3): Selection one person of the (10 years and above) age group in a random method by using KISH TABLES.

Sample Strata The population was divided by: 1- Governorate (16 governorates, where Jerusalem was considered as two statistical areas) 2- Type of Locality (urban, rural, refugee camps).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaire The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on Individuals (10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Programming Consistency Check The data collection program was designed in accordance with the questionnaire's design and its skips. The program was examined more than once before the conducting of the training course by the project management where the notes and modifications were reflected on the program by the Data Processing Department after ensuring that it was free of errors before going to the field.

Using PC-tablet devices reduced data processing stages, and fieldworkers collected data and sent it directly to server, and project management withdraw the data at any time.

In order to work in parallel with Jerusalem (J1), a data entry program was developed using the same technology and using the same database used for PC-tablet devices.

Data Cleaning After the completion of data entry and audit phase, data is cleaned by conducting internal tests for the outlier answers and comprehensive audit rules through using SPSS program to extract and modify errors and discrepancies to prepare clean and accurate data ready for tabulation and publishing.

Tabulation After finalizing checking and cleaning data from any errors. Tables extracted according to prepared list of tables.

Response rate

The response rate in the West Bank reached 77.6% while in the Gaza Strip it reached 92.7%.

Sampling error estimates

Sampling Errors Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators, There is no problem to disseminate results at the national level and at the level of the West Bank and Gaza Strip.

Non-Sampling Errors Non-Sampling errors are possible at all stages of the project, during data collection or processing. These are referred to non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, as well as practical and theoretical training during the training course.

The implementation of the survey encountered non-response where the case (household was not present at home) during the fieldwork visit become the high percentage of the non response cases. The total non-response rate reached 17.5%. The refusal percentage reached 2.9% which is relatively low percentage compared to the household surveys conducted by PCBS, and the reason is the questionnaire survey is clear.

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As part of the “From Data Quality for AI to AI for Data Quality: A Systematic Review of Tools for AI-Augmented Data Quality Management in Data Warehouses” (Tamm & Nikifovora, 2025), a systematic review of DQ tools was conducted to evaluate their automation capabilities, particularly in detecting and recommending DQ rules in data warehouse - a key component of data ecosystems.

To attain this objective, five key research questions were established.

Q1. What is the current landscape of DQ tools?

Q2. What functionalities do DQ tools offer?

Q3. Which data storage systems DQ tools support? and where does the processing of the organization’s data occur?

Q4. What methods do DQ tools use for rule detection?

Q5. What are the advantages and disadvantages of existing solutions?

Candidate DQ tools were identified through a combination of rankings from technology reviewers and academic sources. A Google search was conducted using keyword (“the best data quality tools” OR “the best data quality software” OR “top data quality tools” OR “top data quality software”) AND "2023" (search conducted in December 2023). Additionally, this list was complemented by DQ tools found in academic articles, identified with two queries in Scopus, namely "data quality tool" OR "data quality software" and ("information quality" OR "data quality") AND ("software" OR "tool" OR "application") AND "data quality rule". For selecting DQ tools for further systematic analysis, several exclusion criteria were applied. Tools from sponsored, outdated (pre-2023), non-English, or non-technical sources were excluded. Academic papers were restricted to those published within the last ten years, focusing on the computer science field.

This resulted in 151 DQ tools, which are provided in the file "DQ Tools Selection".

To structure the review process and facilitate answering the established questions (Q1-Q3), a review protocol was developed, consisting of three sections. The initial tool assessment was based on availability, functionality, and trialability (e.g., open-source, demo version, or free trial). Tools that were discontinued or lacked sufficient information were excluded. The second phase (and protocol section) focused on evaluating the functionalities of the identified tools. Initially, the core DQM functionalities were assessed, such as data profiling, custom DQ rule creation, anomaly detection, data cleansing, report generation, rule detection, data enrichment. Subsequently, additional data management functionalities such as master data management, data lineage, data cataloging, semantic discovery, and integration were considered. The final stage of the review examined the tools' compatibility with data warehouses and General Data Protection Regulation (GDPR) compliance. Tools that did not meet these criteria were excluded. As such, the 3^rd section of the protocol evaluated the tool's environment and connectivity features, such as whether it operates in the cloud, hybrid, or on-premises, its API support, input data types (.txt, .csv, .xlsx, .json), and its ability to connect to data sources including relational and non-relational databases, data warehouses, cloud data storages, data lakes. Additionally, it assessed whether the tool processes data on-premises or in the vendor’s cloud environment. Tools were excluded based on criteria such as not supporting data warehouses or processing data externally.

These protocols (filled) are available in file "DQ Tools Analysis"

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Discover the booming email validation tools market! Our comprehensive analysis reveals key trends, growth drivers, and leading companies shaping this $275M (2025 est.) industry. Learn about market segmentation, regional insights, and forecast to 2033. Boost your email marketing ROI today!

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The B2B carpet cleaning market exhibits robust growth potential, driven by increasing demand for hygiene and maintenance services across commercial sectors. While precise market size figures are unavailable, considering the presence of numerous established players like Hako, TTI, Bucher, and Zoomlion, and factoring in average CAGR growth rates within the cleaning equipment sector (let's assume a conservative 5% CAGR for illustrative purposes), a reasonable estimate for the 2025 market size could be in the range of $2.5 billion to $3 billion. This estimate accounts for both large-scale industrial cleaning equipment and smaller, more specialized machines used in office buildings and hospitality settings. Key growth drivers include the rising awareness of indoor air quality, stricter hygiene regulations in various industries (healthcare, hospitality, etc.), and increasing outsourcing of cleaning tasks to specialized providers. Trends indicate a shift towards technologically advanced, eco-friendly cleaning solutions, with a focus on efficiency and reduced environmental impact. This trend is reflected in the inclusion of companies like TASKI, known for its innovative cleaning technologies, in the list of market players. However, market restraints include the initial high investment costs of specialized equipment, the fluctuating prices of raw materials and energy, and potential economic downturns impacting overall spending on facility maintenance. The market segmentation (data not provided) would likely include various equipment types, ranging from industrial carpet extractors to smaller, handheld units, tailored to specific cleaning needs. This projected growth trajectory, even with a conservative CAGR, suggests significant opportunities for businesses operating in this market segment. Further analysis would benefit from a detailed breakdown of regional market share, specific product segment performance, and a more precise quantification of the market size. Competitive landscapes are highly influenced by the technological advancements provided by industry giants, and adapting to these shifts is key to success within this competitive landscape. The increasing awareness of sustainability, coupled with stricter environmental regulations, is driving innovation towards more eco-friendly cleaning solutions, representing a significant market opportunity for businesses able to adapt and innovate. Understanding the unique requirements of different market segments (e.g., healthcare vs. hospitality) will be critical to tailored product development and effective marketing strategies. This in-depth report provides a comprehensive analysis of the B2B carpet cleaning market, valued at approximately $2.5 billion globally in 2023. It delves into market segmentation, key players, emerging trends, and future growth projections, offering invaluable insights for businesses operating within this dynamic sector. The report leverages extensive primary and secondary research, incorporating data from industry publications, financial reports, and expert interviews to deliver a robust and accurate representation of the market landscape. This report will be an indispensable tool for industry professionals seeking to understand current market dynamics and make informed strategic decisions.

File Metadata Scrubbing Market Outlook

According to our latest research, the global file metadata scrubbing market size reached USD 1.18 billion in 2024, demonstrating robust growth driven by increasing concerns over data privacy and regulatory compliance. The market is expected to expand at a CAGR of 17.3% from 2025 to 2033, reaching an estimated USD 5.14 billion by 2033. This growth is fueled by heightened awareness of the risks associated with metadata leakage, stricter data protection regulations, and the proliferation of digital communication channels across enterprises of all sizes.

The primary growth drivers for the file metadata scrubbing market stem from the exponential rise in digital document exchange and the corresponding surge in cyber threats targeting sensitive metadata. Organizations across sectors, especially those handling confidential information such as legal, healthcare, and financial services, are increasingly recognizing the vulnerabilities posed by unchecked metadata in documents, images, and communications. This awareness is translating into proactive investments in metadata scrubbing solutions to safeguard proprietary information, prevent inadvertent data leaks, and maintain regulatory compliance. The growing adoption of remote work and cloud-based collaboration tools further amplifies the need for robust metadata management, as documents are frequently shared beyond traditional organizational boundaries, increasing exposure risks.

Another significant factor propelling the file metadata scrubbing market is the evolving regulatory landscape. With the enforcement of stringent data protection laws such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and similar frameworks worldwide, organizations face mounting pressure to demonstrate due diligence in protecting personal and sensitive information. Metadata, often overlooked, can contain critical details such as author names, editing history, location data, and timestamps, which, if exposed, can lead to compliance violations and reputational damage. As a result, enterprises are prioritizing metadata scrubbing as an essential component of their broader data governance and risk management strategies.

Technological advancements and the integration of artificial intelligence and machine learning into metadata scrubbing tools are also contributing significantly to market expansion. Modern solutions are increasingly capable of automating the identification and removal of hidden metadata across diverse file types and communication channels, reducing manual intervention and minimizing the risk of human error. The scalability and flexibility offered by cloud-based deployment models further enhance accessibility for organizations of varying sizes, from small businesses to large enterprises. This democratization of advanced metadata protection tools is expected to accelerate market penetration, especially in emerging economies where digital transformation initiatives are gaining momentum.

From a regional perspective, North America continues to dominate the file metadata scrubbing market, accounting for over 38% of global revenue in 2024, driven by the presence of stringent privacy regulations, a high concentration of technology-driven enterprises, and early adoption of cybersecurity best practices. Europe follows closely, propelled by rigorous data protection mandates and a mature legal landscape. The Asia Pacific region, while still in a nascent stage, is witnessing rapid growth owing to increasing digitalization, rising awareness about information security, and government-led initiatives to enhance data privacy standards. Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by expanding IT infrastructure and growing regulatory frameworks.

Component Analysis

The component segment of the file metadata scrubbing market is bifurcated into software and services, each playing a pivotal role in addressing the diverse needs of end-users. Software solutions dominate the segment, accounting for nearly 68% of total market revenue in 2024. These platforms offer automated tools for detecting, analyzing, and removing sensitive metadata from documents, images, videos, and communication files. The surge in demand for integrated, user-friendly, and scalable software solutions is attributed to the gro

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