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Related Concept Videos

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Related Experiment Video

Updated: Sep 24, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Data Mining, Quality and Management in the Life Sciences.

Amonida Zadissa1, Rolf Apweiler2

  • 1EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK. amonida.zadissa@ebi.ac.uk.

Methods in Molecular Biology (Clifton, N.J.)
|May 4, 2022
PubMed
Summary
This summary is machine-generated.

Open science principles promote data sharing and reproducibility in life science research. EMBL-EBI resources enhance data mining, quality, and management, supporting scientific advancement and pandemic response.

Keywords:
Biology-driven portalsBiomolecular databasesData deposition databasesData managementData qualityKnowledgebases

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Area of Science:

  • Life Science Research
  • Bioinformatics
  • Data Science

Background:

  • The scientific community increasingly values open science, emphasizing data sharing and reproducibility.
  • FAIR principles (Findable, Accessible, Interoperable, Reusable) are crucial for open data in life sciences.
  • Data-intensive research necessitates effective data mining and management strategies.

Purpose of the Study:

  • To describe features of EMBL-EBI data resources that support data mining, quality, and management.
  • To highlight EMBL-EBI's response to the recent pandemic through its data resources.

Main Methods:

  • Description of features within EMBL-EBI data resources.
  • Case study of EMBL-EBI's pandemic data response.

Main Results:

  • EMBL-EBI resources offer functionalities for improved data mining and management.
  • Specific examples of how EMBL-EBI data resources supported pandemic research are presented.

Conclusions:

  • EMBL-EBI data resources are vital for advancing open science and data-driven research.
  • These resources play a key role in supporting scientific endeavors, including critical public health challenges like pandemics.