The DMIS dataset is a flat file record of the matching of several data set collections. Primarily it consists of VTRs, dealer records, Observer data in conjunction with vessel permit information for the purpose of supporting North East Regional quota monitoring projects.

Data and Code to accompany the paper "Correlation Neglect in Student-to-School Matching."Abstract: We present results from three experiments containing incentivized school-choice scenarios. In these scenarios, we vary whether schools' assessments of students are based on a common priority (inducing correlation in admissions decisions) or are based on independent assessments (eliminating correlation in admissions decisions). The quality of students' application strategies declines in the presence of correlated admissions: application strategies become substantially more aggressive and fail to include attractive ``safety'' options. We provide a battery of tests suggesting that this phenomenon is at least partially driven by correlation neglect, and we discuss implications for the design and deployment of student-to-school matching mechanisms.

Data standardization is an important part of effective management. However, sometimes people have data that doesn't match. This dataset includes different ways that counties might get written by different people. It can be used as a lookup table when you need County to be your unique identifier. For example, it allows you to match St. Mary's, St Marys, and Saint Mary's so that you can use it with disparate data from other data sets.

Abstract: Scholars studying organizations often work with multiple datasets lacking shared unique identifiers or covariates. In such situations, researchers usually use approximate string (``fuzzy'') matching methods to combine datasets. String matching, although useful, faces fundamental challenges. Even when two strings appear similar to humans, fuzzy matching often does not work because it fails to adapt to the informativeness of the character combinations. In response, a number of machine-learning methods have been developed to refine string matching. Yet, the effectiveness of these methods is limited by the size and diversity of training data. This paper introduces data from a prominent employment networking site (LinkedIn) as a massive training corpus to address these limitations. We show how, by leveraging information from LinkedIn regarding organizational name-to-name links, we can improve upon existing matching benchmarks, incorporating the trillions of name pair examples from LinkedIn into various methods to improve performance by explicitly maximizing match probabilities inferred from the LinkedIn corpus. We also show how relationships between organization names can be modeled using a network representation of the LinkedIn data. In illustrative merging tasks involving lobbying firms, we document improvements when using the LinkedIn corpus in matching calibration and make all data and methods open source. Keywords: Record linkage; Interest groups; Text as data; Unstructured data

The global match data collection market is projected to grow from USD 940 million in 2023 to USD 3,530 million by 2033, at a CAGR of 16.7%. Growing adoption of data-driven decision-making in the sports industry, the increasing popularity of esports, and advancements in sensor technology are the primary factors driving the market growth. The use of match data allows teams, players, and coaches to gain insights into their performance, identify strengths and weaknesses, and make informed decisions. The market is segmented by type (sensor data, video data, and others), application (sports industry and esports), and region (North America, South America, Europe, Middle East & Africa, and Asia Pacific). North America is the largest market, followed by Europe. The Asia Pacific region is expected to witness the highest growth rate due to the increasing popularity of esports and the growing number of professional sports leagues in the region. Key players in the market include Opta, Sportradar, N3XT Sports, Sportsdata, OUTFORZ, KINEXON Sports, Stats Perform, Baidu Cloud, Bestdata, Gracenote, Genius Sports, Statscore, and Broadage.

Leveraging a massive dataset of over 421 million potential matches between single users on a leading mobile dating application, we were able to identify numerous characteristics of effective matching. Effective matching is defined as the exchange of contact information with the likely intent to meet in person. The characteristics of effective match include alignment of psychological traits (i.e., extroversion), physical traits (i.e., height), personal choices (i.e., desiring the same relationship type), and shared experiences. For nearly all characteristics, the more similar the individuals were, the higher the likelihood was of them finding each other desirable and opting to meet in person. The only exception was introversion, where introverts rarely had an effective match with other introverts. When investigating the preliminary stages of the choice process we looked at the consistency between the choice of men/women, the time it took users to make these binary choices, and the tendency of yes/no decisions. We used a biologically inspired choice model to estimate the decision process and could predict the selection and response time with nearly 60% accuracy. Given that people make their initial selection in no more than 11 s, and ultimately prefer a partner who shares numerous attributes with them, we suggest that users are less selective in their early preferences and gradually, during their conversation, converge onto clusters that share a high degree of similarity in characteristics.

Matching methods improve the validity of causal inference by reducing model dependence and offering intuitive diagnostics. While they have become a part of the standard tool kit across disciplines, matching methods are rarely used when analyzing time-series cross-sectional data. We fill this methodological gap. In the proposed approach, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated and matched control observations have similar covariate values. Assessing the quality of matches is done by examining covariate balance. Finally, we estimate both short-term and long-term average treatment effects using the difference-in-differences estimator, accounting for a time trend. We illustrate the proposed methodology through simulation and empirical studies. An open-source software package is available for implementing the proposed methods.

As required by federal law, state SNAP agencies verify financial and non-financial information by matching SNAP applicant and participant information to various national and state data sources to ensure they meet the program’s eligibility criteria. Data matching is an important tool for ensuring program integrity and benefit accuracy. However, information on states’ data matching practices and protocols is limited. This study was undertaken to address this knowledge gap.

China

Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label “match” or “no match”) for four product categories, computers, cameras, watches and shoes. In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test sets. For each product category, we provide training sets in four different sizes (2.000-70.000 pairs). Furthermore there are sets of ids for each training set for a possible validation split (stratified random draw) available. The test set for each product category consists of 1.100 product pairs. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web weak supervision. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites. For more information and download links for the corpus itself, please follow the links below.

Image Matching System

## Overview

Image Matching System is a dataset for classification tasks - it contains Recaptcha annotations for 8,828 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The training dataset consisting of 20 million pairs of product offers referring to the same products. The offers were extracted from 43 thousand e-shops which provide schema.org annotations including some form of product ID such as a GTIN or MPN. We also created a gold standard by manually verifying 2000 pairs of offers belonging to four different product categories.

These data were gathered in order to evaluate the implications of rational choice theory for offender rehabilitation. The hypothesis of the research was that income-enhancing prison rehabilitation programs are most effective for the economically motivated offender. The offender was characterized by demographic and socio-economic characteristics, criminal history and behavior, and work activities during incarceration. Information was also collected on type of release and post-release recidivistic and labor market measures. Recividism was measured by arrests, convictions, and reincarcerations, length of time until first arrest after release, and seriousness of offense leading to reincarceration.

The imatch program was written for Sata users to match different groups according to multiple variables. Program file: imatch.ado Help file: imatch.hlp

Data for our paper Lin, Y., Kang, M., Wu, Y., Du, Q. and Liu, T. (2019) A deep learning architecture for semantic address matching, International Journal of Geographical Information Science, DOI: 10.1080/13658816.2019.1681431

Below is an overview of each file in this dataset.

train.txt The training dataset

train_code_a.txt The index representations of the address elements (i.e., address elements represented by the corresponding indexes in the vocabulary obtained by word2vec) in Sa

train_code_b.txt The index representations of the address elements in Sb

train_lable.txt The labels of address pairs in the training dataset

dev.txt The development dataset

dev_code_a.txt The index representations of the address elements in Sa

dev_code_b.txt The index representations of the address elements in Sb

dev_lable.txt The labels of address pairs in the development dataset

test.txt The test dataset

test_code_a.txt The index representations of the address elements in Sa

test_code_b.txt The index representations of the address elements in Sb

test_lable.txt The labels of address pairs in the test dataset

This repository contains the materials needed to replicate the results presented in Mozer et al. (2019), "Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality", forthcoming in Political Analysis.

Pattern Matching Rules for Identifying Age Data.

1) Data Introduction • The FRC Match Dataset is based on the FRST Robotics Competition (FRC) competition records from 2018 to 2025, and is a robot competition match data that includes various information such as EPA (Expected Score Contribution), match win rate, team composition, and match results for each match.

2) Data Utilization (1) FRC Match Data has characteristics that: • Each row contains numerical and categorical variables such as year, event, playoff status, match stage, winning team, EPA-based probability of victory, team name and composition, and match results, which together provide team/match performance and forecasting indicators. (2) FRC Match Data can be used to: • Prediction and Assessment of Match Results: Using EPA and past match data, machine learning models can predict match wins and losses, and prediction models can be evaluated for reliability with indicators such as Brier score. • Team Strategy and Performance Analysis: By analyzing EPA, win rate, and matchup data for each team, you can use it to understand the strategic contribution, cooperation effects, seasonal trends, and strong and weak team characteristics.

Dataset

This dataset was created by neednot_toplay

Released under Apache 2.0

Contents

Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. Different types of genetic and genomic data are inter-connected by cis-regulations. On that basis, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in multiple types of molecular data, which can be used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Applied to a large lung genomic study, MODMatcher increased statistically significant genetic associations and genomic correlations by more than two-fold. In a simulation study, MODMatcher provided more robust results by using three types of omics data than two types of omics data. We further demonstrate that MODMatcher can be broadly applied to large genomic data sets containing multiple types of omics data, such as The Cancer Genome Atlas (TCGA) data sets.