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Related Concept Videos

Data Collection by Experiments01:13

Data Collection by Experiments

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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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BidCorpus: A multifaceted learning dataset for public procurement.

Weslley Lima1, Victor Silva1, Jasson Silva1

  • 1Federal University of Piauí. Campus Universitário Ministro Petrônio Portella. Teresina, Piauí, Brazil.

Data in Brief
|January 10, 2025
PubMed
Summary

We created BidCorpus, a new dataset for analyzing public procurement documents. This dataset aids in automating the detection of fraud in bidding notices, improving efficiency and transparency in public administration.

Keywords:
BERTBidding noticeNLPWeak supervision

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Area of Science:

  • Information Science
  • Public Administration
  • Computer Science

Background:

  • Digital transformation enhances public procurement efficiency, transparency, and competition.
  • Automation of data analysis and oversight in public administration is crucial.
  • Manual analysis of unstructured procurement documents is time-consuming and inefficient.

Purpose of the Study:

  • Introduce BidCorpus, a comprehensive dataset of public procurement bidding notices.
  • Facilitate automated analysis and fraud detection in public procurement documents.
  • Provide valuable resources for researchers in public procurement.

Main Methods:

  • Collected thousands of Brazilian public procurement bidding notices.
  • Utilized weak supervision, manual labeling, and BERT-based models for data annotation.
  • Trained and evaluated machine learning models on the annotated dataset.

Main Results:

  • Models trained on BidCorpus achieved over 80% accuracy in various experiments.
  • The models demonstrated robustness against intentional modifications aimed at evading fraud detection.
  • Developed and validated machine learning models for analyzing public procurement data.

Conclusions:

  • BidCorpus is a valuable resource for advancing research in public procurement.
  • Automated analysis of bidding notices can significantly improve fraud detection and efficiency.
  • Publicly available resources support further development in the field of digital public procurement.