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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.

Duplicate Listing Detection AI Market Outlook

According to our latest research, the global Duplicate Listing Detection AI market size reached USD 1.42 billion in 2024, reflecting a robust growth trajectory driven by the increasing need for data accuracy and operational efficiency across digital platforms. The market is anticipated to grow at a CAGR of 18.7% from 2025 to 2033, with the forecasted market size expected to reach USD 7.13 billion by 2033. The primary growth factor fueling this expansion is the surge in digital commerce and online platforms, where duplicate data can significantly hamper user experience and business performance.

The growth of the Duplicate Listing Detection AI market is primarily propelled by the exponential increase in digital content and user-generated data across various sectors. As e-commerce, real estate, and online marketplaces expand their digital footprints, the risk of duplicate listings has become a significant concern. Duplicate entries can lead to customer confusion, reduced trust, and inefficiencies in inventory management. AI-driven solutions are increasingly being adopted to automate the identification and removal of such duplicates, ensuring data integrity and a seamless user experience. The rising sophistication of AI models, particularly those leveraging machine learning and natural language processing, has further enhanced the accuracy and speed of duplicate detection, making these solutions indispensable for businesses operating at scale.

Another key driver is the regulatory emphasis on data quality and compliance, especially in sectors like finance, healthcare, and real estate. Governments and industry bodies are mandating stricter data governance policies, compelling organizations to invest in advanced AI tools for data cleansing and validation. The ability of Duplicate Listing Detection AI to minimize manual intervention not only reduces operational costs but also ensures compliance with industry standards. This trend is expected to intensify as data privacy regulations become more stringent, pushing organizations to adopt proactive measures for data management and integrity.

Technological advancements and the integration of AI with cloud computing are also accelerating market growth. Cloud-based deployment models offer scalability, flexibility, and cost-effectiveness, enabling even small and medium enterprises (SMEs) to leverage sophisticated AI capabilities without significant upfront investment. The proliferation of APIs and plug-and-play AI modules has democratized access to duplicate detection tools, fostering widespread adoption across diverse industry verticals. Moreover, the increasing collaboration between AI vendors and domain-specific solution providers is resulting in highly customized offerings tailored to the unique needs of different sectors, further driving market expansion.

From a regional perspective, North America currently dominates the Duplicate Listing Detection AI market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high concentration of digital-first businesses, advanced IT infrastructure, and early adoption of AI technologies in North America have positioned the region as a frontrunner. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by rapid digitalization, the expansion of e-commerce, and increasing investments in AI-driven solutions. Emerging economies in Latin America and the Middle East & Africa are also showing promising growth potential as organizations in these regions recognize the value of data quality in enhancing business outcomes.

Component Analysis

The Duplicate Listing Detection AI market by component is bifurcated into Software and Services, each playing a pivotal role in driving overall market growth. The software segment leads the market, accounting for the majority of revenue in 2024. This dominance is attributed to the continuous advancements in AI algorithms that power duplicate detection, enabling real-time identification and removal of redundant listings across platforms. Software solutions are increasingly being integrated with existing enterprise systems, offering seamless interoperability and enhanced data management capabilities. Vendors are focusing on developing intuitive user interfaces and customizable detection parameters, making these tools accessible to b

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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Genetic tools are used in applied conservation management for taxonomic identification, delineation of management units, management of wild populations, captive breeding and reintroduction, and control of invasive species, disease, and hybridisation.

To assess the extent to which genetics tools are being used for applied conservation management, we conducted a systematic literature review of over 53,767 papers focussing on wildlife research that reported results on species delineation, translocations, and population augmentation. We synthesised information on papers that used genetics tools in an expressly applied manner across all wildlife species.

We found that the application of genetics tools in conservation management was biased towards fishes, mammals, and birds and northern hemisphere locations, especially the USA and Europe.

Despite genetics tools being a highly published topic, it was difficult to find published applications of these tools in both the primary and the grey literature. Of the 115 papers on 152 species that could be considered an applied use of a genetics tool expressly for conservation management, only 49 had definable applied outcomes. The remaining 66 made recommendations, but it was often unclear if the recommendations were ever used to make conservation management decisions because of the time-lag between publication of the initial recommendation and publication of the results of the use of the tool in a conservation management situation, as well as the lack of dissemination in the primary literature.

Our study highlights the relatively low publication rate of applications of genetics tools compared to the general conservation genetics field. These tools appear to have either a low percentage of translations into publication ('conservation genetics publishing gap') or a poor uptake among wildlife conservation managers ('conservation genetics gap')—the two are indistinguishable in this review.

Policy implications. Conservation genetics tools must be brought to the forefront of conservation policy and management. Users should support the use of systems and accessible databases to increase the uptake of genetic tools for conservation in applied management decisions for wildlife, reducing barriers to disseminating the results to other end users and interested parties.

This dataset provides processed and normalized/standardized indices for the management tool group focused on 'Mission and Vision Statements', including related concepts like Purpose Statements. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding Mission/Vision dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "mission statement" + "vision statement" + "mission and vision corporate". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Mission Statements + Vision Statements + Purpose Statements + Mission and Vision. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching Mission/Vision-related keywords [("mission statement" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (Mission/Vision Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Mission/Vision (1993); Mission Statements (1996); Mission and Vision Statements (1999-2017); Purpose, Mission, and Vision Statements (2022). Processing: Semantic Grouping: Data points across the different naming conventions were treated as a single conceptual series. Normalization: Combined series normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years (same names/years as Usability). Processing: Semantic Grouping: Data points treated as a single conceptual series. Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding Mission/Vision dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

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This dataset contains raw, unprocessed data files pertaining to the management tool group 'Total Quality Management' (TQM). The data originates from five distinct sources, each reflecting different facets of the tool's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization. Data Sources & File Details: Google Trends File (Prefix: GT_): Metric: Relative Search Interest (RSI) Index (0-100 scale). Keywords Used: "total quality management" + TQM + "TQM system" Time Period: January 2004 - January 2025 (Native Monthly Resolution). Scope: Global Web Search, broad categorization. Extraction Date: Data extracted January 2025. Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: Google Trends Query Google Books Ngram Viewer File (Prefix: GB_): Metric: Annual Relative Frequency (% of total n-grams in the corpus). Keywords Used: Total Quality Management + TQM + Total Quality Time Period: 1950 - 2022 (Annual Resolution). Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0. Extraction Date: Data extracted January 2025. Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: Ngram Viewer Query Crossref.org File (Prefix: CR_): Metric: Absolute count of publications per month matching keywords. Keywords Used: ("total quality management" OR "total quality" OR TQM) AND ("management" OR "system" OR "approach" OR "implementation" OR "practice" OR "framework" OR "methodology" OR "tool") Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata). Search Fields: Title, Abstract. Extraction Date: Data extracted January 2025. Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: Crossref Search Query Bain & Co. Survey - Usability File (Prefix: BU_): Metric: Original Percentage (%) of executives reporting tool usage. Tool Names/Years Included: Total Quality Management (1993, 1999, 2000, 2002, 2006, 2008, 2010, 2012, 2014, 2017, 2022); TQM (1996, 2004). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., Ronan C. et al., various years: 1994, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2023). Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1993/500; 1996/784; 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268; 2022/1068. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Total Quality Management (1993, 1999, 2000, 2002, 2006, 2008, 2010, 2012, 2014, 2017, 2022); TQM (1996, 2004). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., Ronan C. et al., various years: 1994, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2023). Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1993/500; 1996/784; 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268; 2022/1068. Reflects subjective executive perception of utility. File Naming Convention: Files generally follow the pattern: PREFIX_Tool.csv, where the PREFIX indicates the data source: GT_: Google Trends GB_: Google Books Ngram CR_: Crossref.org (Count Data for this Raw Dataset) BU_: Bain & Company Survey (Usability) BS_: Bain & Company Survey (Satisfaction) The essential identification comes from the PREFIX and the Tool Name segment. This dataset resides within the 'Management Tool Source Data (Raw Extracts)' Dataverse.

This dataset contains raw, unprocessed data files pertaining to the management activity 'Mergers and Acquisitions' (M&A). The data originates from five distinct sources, each reflecting different facets of the activity's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization. Data Sources & File Details: Google Trends File (Prefix: GT_): Metric: Relative Search Interest (RSI) Index (0-100 scale). Keywords Used: "mergers and acquisitions" + "mergers and acquisitions corporate" Time Period: January 2004 - January 2025 (Native Monthly Resolution). Scope: Global Web Search, broad categorization. Extraction Date: Data extracted January 2025. Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: Google Trends Query Google Books Ngram Viewer File (Prefix: GB_): Metric: Annual Relative Frequency (% of total n-grams in the corpus). Keywords Used: Mergers and Acquisitions + Mergers & Acquisitions Time Period: 1950 - 2022 (Annual Resolution). Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0. Extraction Date: Data extracted January 2025. Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: Ngram Viewer Query Crossref.org File (Prefix: CR_): Metric: Absolute count of publications per month matching keywords. Keywords Used: ("mergers and acquisitions" OR "mergers & acquisitions") AND ("corporate" OR "strategy" OR "finance" OR "management" OR "deal" OR "implementation" OR "valuation") Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata). Search Fields: Title, Abstract. Extraction Date: Data extracted January 2025. Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: Crossref Search Query Bain & Co. Survey - Usability File (Prefix: BU_): Metric: Original Percentage (%) of executives reporting tool usage. Tool Names/Years Included: Mergers and Acquisitions (2006, 2008, 2010, 2012, 2014, 2017). (Note: Some sources list this as Mergers & Acquisitions). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2007, 2009, 2011, 2013, 2015, 2017). Note: Tool potentially not surveyed or reported before 2006 or after 2017 under this specific name. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Mergers and Acquisitions (2006, 2008, 2010, 2012, 2014, 2017). (Note: Some sources list this as Mergers & Acquisitions). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2007, 2009, 2011, 2013, 2015, 2017). Note: Tool potentially not surveyed or reported before 2006 or after 2017 under this specific name. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268. Reflects subjective executive perception of utility. File Naming Convention: Files generally follow the pattern: PREFIX_Tool.csv, where the PREFIX indicates the data source: GT_: Google Trends GB_: Google Books Ngram CR_: Crossref.org (Count Data for this Raw Dataset) BU_: Bain & Company Survey (Usability) BS_: Bain & Company Survey (Satisfaction) The essential identification comes from the PREFIX and the Tool Name segment. This dataset resides within the 'Management Tool Source Data (Raw Extracts)' Dataverse.

Additional file 6. List of electronic tools reported by organisations with at least two respondents to the survey. Table shows number of stakeholder survey respondents per organisation (where at least 2 respondents from the same organisation responded) and the data collection, management and analysis tools used per organisation.

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.

A structured dataset listing top tools used in HR analytics, including their primary uses and key benefits for workforce data management and analysis.

Online Project Management Software Market Outlook

According to our latest research, the global Online Project Management Software market size reached USD 6.9 billion in 2024, reflecting robust adoption across diverse industries. The market is projected to achieve a value of USD 18.2 billion by 2033, growing at a CAGR of 11.4% during the forecast period of 2025 to 2033. This growth is primarily driven by the increasing demand for real-time collaboration, the surge in remote and hybrid work models, and the growing need for workflow automation and resource optimization in organizations worldwide.

The rising complexity of business operations and the necessity for cross-functional collaboration are significant growth drivers for the Online Project Management Software market. Organizations are increasingly seeking integrated solutions that streamline project planning, tracking, resource allocation, and communication among distributed teams. The proliferation of digital transformation initiatives and the shift toward agile and DevOps methodologies further fuel the adoption of these platforms. The ability of online project management tools to provide centralized dashboards, automate repetitive tasks, and enable data-driven decision-making is transforming project execution and delivery standards across industries. As a result, businesses are prioritizing investments in advanced project management solutions to enhance productivity, minimize risks, and ensure timely project completion.

Another key growth factor is the widespread implementation of cloud-based project management software, which offers scalability, flexibility, and cost-efficiency. Cloud deployment models eliminate the need for extensive on-premises infrastructure, enabling organizations of all sizes to access sophisticated project management tools with minimal upfront investment. The surge in remote work and geographically dispersed teams has further accelerated the adoption of cloud solutions, allowing for seamless collaboration and real-time updates regardless of physical location. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) capabilities within these platforms is enabling predictive analytics, automated scheduling, and intelligent resource allocation, empowering organizations to optimize project outcomes.

The market is also benefiting from the increasing focus on regulatory compliance, data security, and standardized project governance, particularly in highly regulated sectors such as BFSI and healthcare. Online project management software solutions are evolving to offer advanced security features, audit trails, and compliance management tools, addressing the concerns of organizations handling sensitive data. Furthermore, the growing trend of customizable and industry-specific solutions is expanding the marketÂ’s reach, as vendors tailor their offerings to meet the unique requirements of sectors like construction, education, and retail. As the competitive landscape intensifies, continuous innovation and the integration of emerging technologies are expected to remain central to market growth.

In the realm of project management, the introduction of Cloud-Based Project Punch-List Management has revolutionized how teams handle project tasks and deliverables. This approach allows for the seamless tracking of project punch-lists, which are essential for ensuring that all project tasks are completed before the final project handover. By utilizing cloud-based systems, project managers can update and access punch-lists in real-time, ensuring that all team members are informed of the current status and any outstanding tasks. This level of transparency and accessibility is crucial for maintaining project timelines and quality standards, particularly in industries where precision and accountability are paramount. The cloud-based model also supports remote access, allowing geographically dispersed teams to collaborate effectively and make informed decisions swiftly.

From a regional perspective, North America currently dominates the Online Project Management Software market, owing to the high adoption rate of digital tools, the presence of leading technology providers, and a mature IT infrastructure. However, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, increasing investments in e

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!

This dataset provides an in-depth look at the League of Legends Champions Korea (LCK) Spring 2024 season. It includes detailed metrics for players, champions, and matches, meticulously cleaned and organized for easy analysis and modeling.

Data Collection

The data was collected using a combination of manual efforts and automated web scraping tools. Specifically:

Source: Data was gathered from Gol.gg, a well-known platform for League of Legends statistics. Automation: Web scraping was performed using Python libraries like BeautifulSoup and Selenium to extract information on players, matches, and champions efficiently. Focus: The scripts were designed to capture relevant performance metrics for each player and champion used during the Spring 2024 split.

Data Cleaning and Processing

The raw data obtained from web scraping required significant preprocessing to ensure its usability. The following steps were taken:

Handling Raw Data:

Extracted key performance indicators like KDA, Win Rate, Games Played, and Match Durations from the source. Normalized inconsistent formats for metrics such as win rates (e.g., removing %) and durations (e.g., converting MM:SS to total seconds).

Data Cleaning:

Removed duplicate rows and ensured no missing values. Fixed inconsistencies in player and champion names to maintain uniformity. Checked for outliers in numerical metrics (e.g., unrealistically high KDA values).

Data Organization:

Created three separate tables for better data management:

Player Statistics: General player performance metrics like KDA, win rates, and average kills. Champion Statistics: Data on games played, win rates, and KDA for each champion. Match List: Details of each match, including players, champions, and results. Added sequential Player IDs to connect the three datasets, facilitating relational analysis. Date Formatting: Converted all date fields to the DD/MM/YYYY format for consistency. Removed irrelevant time data to focus solely on match dates.

Tools and Libraries Used

The following tools were used throughout the project:

Python: Libraries: Pandas, NumPy for data manipulation; BeautifulSoup, Selenium for web scraping. Visualization: Matplotlib, Seaborn, Plotly for potential analysis. Excel: Consolidated final datasets into a structured Excel file with multiple sheets. Data Validation: Used Python scripts to check for missing data, validate numerical columns, and ensure data consistency. Kaggle Integration: Cleaned datasets and a comprehensive README file were prepared for direct upload to Kaggle.

Applications

This dataset is ready for use in: Exploratory Data Analysis (EDA): Visualize player and champion performance trends across matches. Machine Learning: Develop models to predict match outcomes based on player and champion statistics. Sports Analytics: Gain insights into champion picks, win rates, and individual player strategies.

Acknowledgments

This dataset was made possible by the extensive statistics available on Gol.gg and the use of Python-based web scraping and data cleaning methodologies. It is shared under the CC BY 4.0 License to encourage reuse and collaboration.

This dataset contains raw, unprocessed data files pertaining to the management tool 'Knowledge Management' (KM), including related concepts like Intellectual Capital Management and Knowledge Transfer. The data originates from five distinct sources, each reflecting different facets of the tool's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization. Data Sources & File Details: Google Trends File (Prefix: GT_): Metric: Relative Search Interest (RSI) Index (0-100 scale). Keywords Used: "knowledge management" + "knowledge management organizational" Time Period: January 2004 - January 2025 (Native Monthly Resolution). Scope: Global Web Search, broad categorization. Extraction Date: Data extracted January 2025. Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: Google Trends Query Google Books Ngram Viewer File (Prefix: GB_): Metric: Annual Relative Frequency (% of total n-grams in the corpus). Keywords Used: Knowledge Management + Intellectual Capital Management + Knowledge Transfer Time Period: 1950 - 2022 (Annual Resolution). Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0. Extraction Date: Data extracted January 2025. Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: Ngram Viewer Query Crossref.org File (Prefix: CR_): Metric: Absolute count of publications per month matching keywords. Keywords Used: ("knowledge management" OR "intellectual capital management" OR "knowledge transfer") AND ("organizational" OR "management" OR "learning" OR "innovation" OR "sharing" OR "system") Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata). Search Fields: Title, Abstract. Extraction Date: Data extracted January 2025. Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: Crossref Search Query Bain & Co. Survey - Usability File (Prefix: BU_): Metric: Original Percentage (%) of executives reporting tool usage. Tool Names/Years Included: Knowledge Management (1999, 2000, 2002, 2004, 2006, 2008, 2010). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2001, 2003, 2005, 2007, 2009, 2011). Note: Tool potentially not surveyed or reported after 2010 under this specific name. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Knowledge Management (1999, 2000, 2002, 2004, 2006, 2008, 2010). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2001, 2003, 2005, 2007, 2009, 2011). Note: Tool potentially not surveyed or reported after 2010 under this specific name. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230. Reflects subjective executive perception of utility. File Naming Convention: Files generally follow the pattern: PREFIX_Tool.csv, where the PREFIX indicates the data source: GT_: Google Trends GB_: Google Books Ngram CR_: Crossref.org (Count Data for this Raw Dataset) BU_: Bain & Company Survey (Usability) BS_: Bain & Company Survey (Satisfaction) The essential identification comes from the PREFIX and the Tool Name segment. This dataset resides within the 'Management Tool Source Data (Raw Extracts)' Dataverse.

This dataset features only products from Ulta.com that include detailed ingredient lists, ideal for product transparency tools, clean label research, and beauty data modeling.

Designed for professionals and researchers working in beauty tech, compliance, formulation, and product analysis, it focuses on ingredient-rich listings for advanced use cases.

What’s Inside:

• Product Name

• Brand

• Full Ingredient List

• Category (e.g., Hair, Skin, Makeup)

• Product URL

• Price (if available)

• Description

• Images

• Date Extracted

Best For:

• Clean beauty app builders

• Ingredient risk assessment and allergen tracking

• Comparative cosmetic formulation

• Beauty AI and ML dataset training

• Ingredient transparency dashboards for e-commerce

Updates:

Available weekly or monthly or on request

This dataset contains raw, unprocessed data files pertaining to the management tool group focused on 'Activity-Based Costing' (ABC) and 'Activity-Based Management' (ABM). The data originates from five distinct sources, each reflecting different facets of the tool's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization. Data Sources & File Details: Google Trends File (Prefix: GT_): Metric: Relative Search Interest (RSI) Index (0-100 scale). Keywords Used: "activity based costing" + "activity based management" + "activity based costing management" Time Period: January 2004 - January 2025 (Native Monthly Resolution). Scope: Global Web Search, broad categorization. Extraction Date: Data extracted January 2025. Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: Google Trends Query Google Books Ngram Viewer File (Prefix: GB_): Metric: Annual Relative Frequency (% of total n-grams in the corpus). Keywords Used: Activity Based Management + Activity Based Costing Time Period: 1950 - 2022 (Annual Resolution). Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0. Extraction Date: Data extracted January 2025. Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: Ngram Viewer Query Crossref.org File (Prefix: CR_): Metric: Absolute count of publications per month matching keywords. Keywords Used: ("activity based costing" OR "activity based management") AND ("management" OR "accounting" OR "cost control" OR "financial" OR "analysis" OR "system") Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata). Search Fields: Title, Abstract. Extraction Date: Data extracted January 2025. Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: Crossref Search Query Bain & Co. Survey - Usability File (Prefix: BU_): Metric: Original Percentage (%) of executives reporting tool usage. Tool Names/Years Included: Activity-Based Costing (1993); Activity-Based Management (1999, 2000, 2002, 2004). (Note: Some sources use Activity Based Management). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 1994, 2001, 2003, 2005). Note: Tool potentially not surveyed or reported after 2004 under these specific names. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1993/500; 1999/475; 2000/214; 2002/708; 2004/960. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Activity-Based Costing (1993); Activity-Based Management (1999, 2000, 2002, 2004). (Note: Some sources use Activity Based Management). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 1994, 2001, 2003, 2005). Note: Tool potentially not surveyed or reported after 2004 under these specific names. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1993/500; 1999/475; 2000/214; 2002/708; 2004/960. Reflects subjective executive perception of utility. File Naming Convention: Files generally follow the pattern: PREFIX_Tool.csv, where the PREFIX indicates the data source: GT_: Google Trends GB_: Google Books Ngram CR_: Crossref.org (Count Data for this Raw Dataset) BU_: Bain & Company Survey (Usability) BS_: Bain & Company Survey (Satisfaction) The essential identification comes from the PREFIX and the Tool Name segment. This dataset resides within the 'Management Tool Source Data (Raw Extracts)' Dataverse.

Maps of the number, size, and species of trees in forests across the western United States are desirable for many applications such as estimating terrestrial carbon resources, predicting tree mortality following wildfires, and for forest inventory. However, detailed mapping of trees for large areas is not feasible with current technologies, but statistical methods for matching the forest plot data with biophysical characteristics of the landscape offer a practical means to populate landscapes with a limited set of forest plot inventory data. We used a modified random forests approach with Landscape Fire and Resource Management Planning Tools (LANDFIRE) vegetation and biophysical predictors as the target data, to which we imputed plot data collected by the USDA Forest Service’s Forest Inventory Analysis (FIA) to the landscape at 30-meter (m) grid resolution (Riley et al. 2016). This method imputes the plot with the best statistical match, according to a “forest” of decision trees, to each pixel of gridded landscape data. In this work, we used the LANDFIRE data set as the gridded target data because it is publicly available, offers seamless coverage of variables needed for fire models, and is consistent with other data sets, including burn probabilities and flame length probabilities generated for the continental United States. The main output of this project (the GeoTIFF available in this data publication) is a map of imputed plot identifiers at 30×30 m spatial resolution for the western United States for landscape conditions circa 2009. The map of plot identifiers can be linked to the FIA databases available through the FIA DataMart or to the ACCDB/CSV files included in this data publication to produce tree-level maps or to map other plot attributes. These ACCDB/CSV files also contain attributes regarding the FIA PLOT CN (a unique identifier for each time a plot is measured), the inventory year, the state code and abbreviation, the unit code, the county code, the plot number, the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a unique identifier for each tree and tree visit. Application of the dataset to research questions other than those related to aboveground biomass and carbon should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. Such detailed forest structure data are not available for large areas of public and private lands in the United States, which rely on forest inventory at fixed plot locations at sparse densities. While direct sampling technologies such as light detection and ranging (LiDAR) may eventually make broad coverage of detailed forest inventory feasible, no such data sets at the scale of the western United States are currently available.When linking the tree list raster (“CN_text” field) to the FIA data via the plot CN field (“CN” in the “PLOT” table and “PLT_CN” in other tables), note that this field is unique to a single visit to a plot. The raster contains a “Value” field, which also appears in the ACCDB/CSV files in the “tl_id” field in order to facilitate this linkage. All plot CNs utilized in this analysis were single condition, 100% forested, physically located in the Rocky Mountain Research Station (RMRS) and Pacific Northwest Research Station (PNW) obtained from FIA in December of 2012.

Original metadata date was 01/03/2018. Minor metadata updates made on 04/30/2019.