This dataset includes data quality assurance information concerning the Relative Percent Difference (RPD) of laboratory duplicates. No laboratory duplicate information exists for 2010. The formula for calculating relative percent difference is: ABS(2*[(A-B)/(A+B)]). An RPD of less the 10% is considered acceptable.

Papers on duplicate records.. Visit https://dataone.org/datasets/sha256%3A8af3814a53c4db3d7260b3e66c119db96471654505927ce8a87df16ddf4592ab for complete metadata about this dataset.

Dataset Card for Stack Exchange Duplicates

This dataset contains the Stack Exchange Duplicates dataset in three formats that are easily used with Sentence Transformers to train embedding models. The data was originally extracted using the Stack Exchange API and taken from embedding-training-data. Each pair contains data from two Stack Exchange posts that were marked as duplicates. title-title-pair only has the titles, body-body-pair only the bodies, and post-post-pair has both.… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/stackexchange-duplicates.

The (beginner datasets files contains duplicate entries. Duplicate data can lead to errors in analysis and reporting, making it essential to identify and remove them.

Duplicate File: The file pretty_dd_automobile.json includes the duplicate entries found in automobile.csv.

Steps to Identify Duplicates: 1. Load the data from automobile.csv. 2. Analyze the data for duplicates with KnoDL 3. Save the identified duplicates to the file pretty_dd_automobile.json

Video Tutorial:

For a visual example of finding duplicates, you can watch the following YouTube video: Duplicate Detection in Kaggle's Automobile Dataset Using KnoDL

These steps and examples will help you correctly document the duplicate entries and provide a clear tutorial for users.

dimonds.csv 88 positions

employee.csv 2673 positions

facebook.csv 51 positions

forest.csv 4 positions

france.csv 16 positions

germany.csv 15 positions

income.csv 2762 positions

insurance.csv 1 position

iris.csv 4 positions

traffic.csv 253 positions

tweets.csv 26 positions

Displays potential software and hardware product duplicates within a manufacturer. Product duplicates have the same name, component, and manufacturer. Also displays duplicate software versions (patch level and edition must be the same) and hardware models within a product.

Dataset statistics for the original dataset compared to the de-duplicated dataset.

Dataset Card for Quora Duplicate Questions

This dataset contains the Quora Question Pairs dataset in four formats that are easily used with Sentence Transformers to train embedding models. The data was originally created by Quora for this Kaggle Competition.

  Dataset Subsets





  pair-class subset

Columns: "sentence1", "sentence2", "label" Column types: str, str, class with {"0": "different", "1": "duplicate"} Examples:{ 'sentence1': 'What is the step by step… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/quora-duplicates.

This dataset was created synthetically with the python package faker. It is intended to practice the deduplication of databases.

unique_data.csv is our main data frame without duplicates. Everything starts here. The other files (01_duplicate*, 02_duplicate*, etc...) hold only duplicate values from the unique_data.csv entries. You can mix unique_data.csv with one of the duplicate csvs or parts of the duplicate csv to get a dataset with duplicate values to practice your deduplication skills.

unique_data.csv generation process:

• Every entry has a unique identifier uuid4
• The company column is generated from a subset of 35.000 unique entries . This subset is called via random.choice(subset)
• The postcode and the city colmun is generated together from a list of tuples which contains 20% entries of the total size to inject duplicate
• The name column is generated for each entry seperatly, but may contain duplicates due to the nature and name limits of the faker (Package) generation process
• Country is US
• The street column is generated from a subset of 70.000 unique entries and 30.000 nan values. This subset is called via random.choice(subset) (high unique value count - if you like it hard please feel free to delete to make the task harder)
• The email column is generated from a subset of 40.000 unique entries and 30.000 nan values. This subset is called via random.choice(subset) (high unique value count - if you like it hard please feel free to delete to make the task harder)
• The phone column is generated from a subset of 55.000 unique entries and 30.000 nan values. This subset is called via random.choice(subset) (high unique value count - if you like it hard please feel free to delete to make the task harder)

01_duplicate_data_random-nan.csv generation process:

Replaces a random fraction (50%) of cells in the dataframe with np.nan. ['company', 'name', 'uuid4'] are excluded by this augmentation

02_duplicate_data_random-nan_firstname-abbreviation.csv generation process:

1. Replaces a random fraction (50%) of cells in the dataframe with np.nan. ['company', 'name', 'uuid4'] are excluded by this augmentation
2. Does first name abbreviation on random 70% of the name column values

03_duplicate_data_random-nan_firstname-abbreviation_middlename-insertion.csv generation process:

1. Replaces a random fraction (50%) of cells in the dataframe with np.nan. ['company', 'name', 'uuid4'] are excluded by this augmentation
2. Does first name abbreviation on random 70% of the name column values
3. Does a random middle name insertion at 40%of the name column values. Also does random abbreviation on the middle name in 30% of the cases

04_duplicate_data_random-nan_firstname-abbreviation_middlename-insertion_keyboarderror.csv generation process:

1. Replaces a random fraction (50%) of cells in the dataframe with np.nan. ['company', 'name', 'uuid4'] are excluded by this augmentation
2. Does first name abbreviation on random 70% of the name column values
3. Does a random middle name insertion at 40%of the name column values. Also does random abbreviation on the middle name in 30% of the cases
4. Performs keyboarderror augmentation on 60% of the values for the columns ['name', 'city', 'street', 'company', 'email', 'phone'] https://nlpaug.readthedocs.io/en/latest/augmenter/char/keyboard.html skills

Gene duplications can facilitate adaptation and may lead to inter-population divergence, causing reproductive isolation. We used whole-genome re-sequencing data from 34 butterflies to detect duplications in two Heliconius species, H. cydno and H. melpomene. Taking advantage of three distinctive signals of duplication in short-read sequencing data, we identified 744 duplicated loci in H. cydno and H. melpomene, 96% of which were validated with single molecule sequencing. We have found that duplications overlap genes significantly less than expected at random in H. melpomene, consistent with the action of background selection against duplicates in functional regions of the genome. Duplicate loci that are highly differentiated between H. melpomene and H. cydno map to four different chromosomes. Four duplications were identified with a strong signal of divergent selection, including an odorant binding protein and another in close proximity with a known wing colour pattern locus that differs between the two species.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Chromatin immunoprecipitation and sequencing (ChIP-seq) has been widely used to map DNA-binding proteins, histone proteins and their modifications. ChIP-seq data contains redundant reads termed duplicates, referring to those mapping to the same genomic location and strand. There are two main sources of duplicates: polymerase chain reaction (PCR) duplicates and natural duplicates. Unlike natural duplicates that represent true signals from sequencing of independent DNA templates, PCR duplicates are artifacts originating from sequencing of identical copies amplified from the same DNA template. In analysis, duplicates are removed from peak calling and signal quantification. Nevertheless, a significant portion of the duplicates is believed to represent true signals. Obviously, removing all duplicates will underestimate the signal level in peaks and impact the identification of signal changes across samples. Therefore, an in-depth evaluation of the impact from duplicate removal is needed. Using eight public ChIP-seq datasets from three narrow-peak and two broad-peak marks, we tried to understand the distribution of duplicates in the genome, the extent by which duplicate removal impacts peak calling and signal estimation, and the factors associated with duplicate level in peaks. The three PCR-free histone H3 lysine 4 trimethylation (H3K4me3) ChIP-seq data had about 40% duplicates and 97% of them were within peaks. For the other datasets generated with PCR amplification of ChIP DNA, as expected, the narrow-peak marks have a much higher proportion of duplicates than the broad-peak marks. We found that duplicates are enriched in peaks and largely represent true signals, more conspicuous in those with high confidence. Furthermore, duplicate level in peaks is strongly correlated with the target enrichment level estimated using nonredundant reads, which provides the basis to properly allocate duplicates between noise and signal. Our analysis supports the feasibility of retaining the portion of signal duplicates into downstream analysis, thus alleviating the limitation of complete deduplication.

The mapping of metabolite-specific data to pathways within cellular metabolism is a major data analysis step needed for biochemical interpretation. A variety of machine learning approaches, particularly deep learning approaches, have been used to predict these metabolite-to-pathway mappings, utilizing a training dataset of known metabolite-to-pathway mappings. A few such training datasets have been derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG). However, several prior published machine learning approaches utilized an erroneous KEGG-derived training dataset that used SMILES molecular representations strings (KEGG-SMILES dataset) and contained a sizable proportion (~26%) duplicate entries. The presence of so many duplicates taint the training and testing sets generated from k-fold cross-validation of the KEGG-SMILES dataset. Therefore, the k-fold cross-validation performance of the resulting machine learning models was grossly inflated by the erroneous presence of these duplicate entries. Here we describe and evaluate the KEGG-SMILES dataset so that others may avoid using it. We also identify the prior publications that utilized this erroneous KEGG-SMILES dataset so their machine learning results can be properly and critically evaluated. In addition, we demonstrate the reduction of model k-fold cross-validation (CV) performance after de-duplicating the KEGG-SMILES dataset. This is a cautionary tale about properly vetting prior published benchmark datasets before using them in machine learning approaches. We hope others will avoid similar mistakes.

The U. S. Geological Survey (USGS) makes long-term seismic hazard forecasts that are used in building codes. The hazard models usually consider only natural seismicity; non-tectonic (man-made) earthquakes are excluded because they are transitory or too small. In the past decade, however, thousands of earthquakes related to underground fluid injection have occurred in the central and eastern U.S. (CEUS), and some have caused damage. In response, the USGS is now also making short-term forecasts that account for the hazard from these induced earthquakes. A uniform earthquake catalog is assembled by combining and winnowing pre-existing source catalogs. Seismicity statistics are analyzed to develop recurrence models, accounting for catalog completeness. In the USGS hazard modeling methodology, earthquakes are counted on a map grid, recurrence models are applied to estimate the rates of future earthquakes in each grid cell, and these rates are combined with maximum-magnitude models and ground-motion models to compute the hazard. The USGS published a forecast for the years 2016 and 2017. This data set is the catalog of natural and induced earthquakes without duplicates. Duplicate events have been removed based on a hierarchy of the source catalogs. Explosions and mining related events have been deleted.

Blockchain data query: Duplicates in hemi.transactions

Dataset Card for Quora Duplicate Questions

This dataset contains the Quora Question Pairs dataset in a format that is easily used with the ParaphraseMiningEvaluator evaluator in Sentence Transformers. The data was originally created by Quora for this Kaggle Competition.

  Usage

from datasets import load_dataset from sentence_transformers.SentenceTransformer import SentenceTransformer from sentence_transformers.evaluation import ParaphraseMiningEvaluator

Load the… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/quora-duplicates-mining.

Dataset Overview:

Total Records: 749 Original Records: 700 Duplicate Records: 49 (7% of total) File Name: synthetic_claims_with_duplicates.csv Key Features:

Claim Information: Unique claim IDs (CLAIM000001 to CLAIM000700) Employee IDs (EMP0001 to EMP0700) Realistic employee names Financial Data: Amounts range: 100.00 to 20,000.00 Service codes: SVC001, SVC002, SVC003, SVC004 Departments: Finance, HR, IT, Marketing, Operations Transaction Details: Dates within the last 2 years Timestamps for submission Statuses: Submitted, Approved, Paid Random UUIDs for submitter IDs Fraud Detection: 49 exact duplicates (7%) Random distribution throughout the dataset Boolean is_duplicate flag for identification Purpose: The dataset is designed to test fraud detection systems, particularly for identifying duplicate transactions. It simulates real-world scenarios where duplicate entries might occur due to fraud or data entry errors.

Usage:

Testing duplicate transaction detection Training fraud detection models Data validation and cleaning Algorithm benchmarking The dataset is now ready for analysis in your fraud detection system.

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

Duplicate File Finder Market Outlook

According to our latest research, the global Duplicate File Finder market size reached USD 1.32 billion in 2024, with a robust CAGR of 13.7% expected during the forecast period. By 2033, the market is projected to attain a value of USD 4.01 billion. This impressive growth is driven by the exponential increase in digital data generation, heightened demand for storage optimization, and the pressing need for efficient data management across enterprises and individuals alike. The rapid adoption of cloud computing, coupled with the proliferation of digital transformation initiatives, is further propelling the market's expansion as organizations seek advanced solutions to minimize redundancy and enhance operational efficiency.

One of the primary growth factors fueling the Duplicate File Finder market is the explosion of unstructured data within organizations. As enterprises and individuals generate vast volumes of files, images, videos, and documents, the risk of redundant data clogging storage systems escalates. This not only inflates storage costs but also hampers data retrieval and management efficiency. Duplicate file finder solutions are thus increasingly being deployed to automate the identification and removal of redundant files, driving significant cost savings and improving overall storage utilization. The trend of digitalization across sectors such as healthcare, BFSI, and retail has further amplified the need for these solutions, as organizations strive to maintain streamlined and secure data environments.

Another significant driver is the growing emphasis on regulatory compliance and data governance. With stricter data privacy laws such as GDPR and CCPA coming into force, organizations are under pressure to ensure that sensitive information is managed, stored, and deleted appropriately. Duplicate file finders play a crucial role in this context by helping organizations locate and eliminate redundant or obsolete files, reducing the risk of data breaches and non-compliance penalties. This compliance-driven demand is particularly pronounced in regulated industries like finance, healthcare, and government, where data integrity and traceability are paramount. As a result, the adoption of duplicate file finder solutions is expected to intensify over the coming years.

The integration of artificial intelligence and machine learning technologies into duplicate file finder solutions is also catalyzing market growth. Modern solutions leverage AI algorithms to enhance accuracy in file identification, even across disparate storage systems and file formats. These intelligent tools can detect near-duplicates, analyze metadata, and provide actionable insights for data optimization, making them indispensable for enterprises with complex IT infrastructures. Furthermore, the rise of hybrid and multi-cloud environments has created new challenges for data management, further boosting demand for advanced duplicate file finder tools that can operate seamlessly across diverse platforms and storage architectures.

From a regional perspective, North America continues to dominate the Duplicate File Finder market, accounting for over 35% of the global revenue in 2024. This leadership is attributed to the regionÂ’s high rate of digital adoption, substantial investments in IT infrastructure, and the presence of major technology vendors. Meanwhile, Asia Pacific is emerging as the fastest-growing region, propelled by rapid urbanization, expanding digital economies, and increasing awareness about data management best practices. Europe also holds a significant share, driven by stringent data privacy regulations and a strong focus on enterprise IT modernization. The Middle East & Africa and Latin America are witnessing gradual adoption, supported by ongoing digital transformation initiatives and growing enterprise IT investments.

Data Deduplication Software plays a pivotal role in the modern data management landscape, particularly within the Duplicate File Finder market. As organizations continue to grapple with the challenges of managing vast amounts of digital data, the need for sophisticated software solutions that can efficiently identify and eliminate redundant data becomes increasingly critical. These software tools not only help in optimizing storage and reduc

Blockchain data query: example reservoir asks duplicates

Note: This filtered view shows only those service requests from the underlying dataset that are not marked as duplicates. -- All open rodent baiting requests and rat complaints made to 311 and all requests completed since January 1, 2011. The Department of Streets & Sanitation investigates reported rat sightings. Alley conditions are examined. If any damaged carts are identified, Sanitation Ward Offices, which distribute the carts are notified. Rodenticide is placed in rat burrows to eradicate nests. 311 sometimes receives duplicate rat complaints and requests for rodent baiting. Requests that have been labeled as Duplicates are in the same geographic area and have been entered into 311’s Customer Service Requests (CSR) system at around the same time as a previous request. Duplicate reports/requests are labeled as such in the Status field, as either "Open - Dup" or "Completed - Dup." Data is updated daily.