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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...

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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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FAIR data pipeline: provenance-driven data management for traceable scientific workflows.

Sonia Natalie Mitchell1,2, Andrew Lahiff3, Nathan Cummings3

  • 1Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|August 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Findable, Accessible, Interoperable, and Reusable (FAIR) data pipeline to improve transparency in scientific research. The tool enhances public trust by tracing policy decisions back to primary data and open-source code.

Keywords:
COVID-19FAIRdata managementepidemiologymodellingprovenance

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Area of Science:

  • Epidemiology
  • Data Science
  • Scientific Computing

Background:

  • Epidemiological studies rely on data, but managing rapidly changing and imprecisely identified data is challenging.
  • Lack of transparency in data provenance erodes public trust in science-based policy decisions.

Purpose of the Study:

  • To demonstrate a Findable, Accessible, Interoperable, and Reusable (FAIR) data pipeline.
  • To enhance transparency and traceability in scientific research, particularly for policy-facing applications.

Main Methods:

  • Development of a FAIR data pipeline for annotating data during analysis.
  • Implementation of a system to trace scientific outputs back to primary data through source code.

Main Results:

  • The pipeline facilitates easy annotation of data as it is used in analyses.
  • It enables tracing the provenance of scientific outputs back to the original data and analytical code.

Conclusions:

  • The FAIR data pipeline enhances transparency by clarifying the evidence supporting scientific claims.
  • This tool empowers scientists and the public to better assess scientific evidence and supports policymakers in justifying decisions.