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Related Concept Videos

Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Cell Lines

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Biostatistics: Overview01:20

Biostatistics: Overview

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Synthetic Biology02:55

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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Data issues in the life sciences.

Anne E Thessen1, David J Patterson

  • 1Center for Library and Informatics, Marine Biological Laboratory, 7 MBL Street, Woods Hole, MA 02543 USA.

Zookeys
|December 31, 2011
PubMed
Summary
This summary is machine-generated.

The "Big New Biology" faces challenges in data sharing, with technical hurdles surmountable. Sociological issues, like diverse data cultures and lack of incentives, impede progress in life sciences data integration.

Keywords:
data issuesescienceincentivesinformaticslife sciencestandards

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Area of Science:

  • Life Sciences
  • Bioinformatics
  • Data Science

Background:

  • The Life Sciences are transitioning into data-intensive fields, termed the "Big New Biology".
  • This transformation presents significant technical and sociological challenges.
  • Key issues include the need for comprehensive standards, data sharing incentives, and robust infrastructure.

Purpose of the Study:

  • To review the technical and sociological challenges in the data-centric transformation of Life Sciences.
  • To assess the feasibility of overcoming these challenges and identify key impediments.

Main Methods:

  • Literature review and analysis of current trends in Life Sciences data management.
  • Examination of technological advancements and sociological factors influencing data sharing.

Main Results:

  • Technical challenges, including standards, bandwidth, and distributed computing, are expected to be overcome.
  • Sociological issues, such as understanding heterogeneous data cultures and lack of incentives for data sharing and infrastructure development, are the primary determinants of progress.
  • Funding agencies are pushing for open data, but cultural and incentive-based barriers persist.

Conclusions:

  • While technical aspects of the "Big New Biology" are progressing, sociological factors are critical for successful data integration.
  • Addressing the diverse data cultures and creating incentives for scientists and institutions are essential for advancing open data in Life Sciences.