Evidence map›Paper›PMID 29218890›Full record

ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2018

Democratizing data science through data science training.

John Darrell Van Horn, Lily Fierro, Jeana Kamdar, Jonathan Gordon, Crystal Stewart, Avnish Bhattrai, Sumiko Abe, Xiaoxiao Lei, Caroline O'Driscoll, Aakanchha Sinha and 4 more

Abstract read
In one paragraph

Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

John Darrell Van HornUSC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, SHN, Los Angeles, CA 90033, USA, jvanhorn@usc.edu.
Lily Fierro
Jeana Kamdar
Jonathan Gordon
Crystal Stewart
Avnish Bhattrai
Sumiko Abe
Xiaoxiao Lei
Caroline O'Driscoll
Aakanchha Sinha
Priyambada Jain
Gully Burns
Kristina Lerman
José Luis Ambite

Funding

TR&D3: Intrinsic Surface MappingP41EB015922 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VAN HORN, JOHN DARRELL · 2012 to 2022
$14.1M
Promoting Institutional Communities for Open Data ScienceU24ES026465 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VAN HORN, JOHN DARRELL · 2015 to 2017
$7.6M
NIBIB NIH HHS P41 EB015922NIEHS NIH HHS U24 ES026465
6 · The paper itself

Abstract

The biomedical sciences have experienced an explosion of data which promises to overwhelm many current practitioners. Without easy access to data science training resources, biomedical researchers may find themselves unable to wrangle their own datasets. In 2014, to address the challenges posed such a data onslaught, the National Institutes of Health (NIH) launched the Big Data to Knowledge (BD2K) initiative. To this end, the BD2K Training Coordinating Center (TCC; bigdatau.org) was funded to facilitate both in-person and online learning, and open up the concepts of data science to the widest possible audience. Here, we describe the activities of the BD2K TCC and its focus on the construction of the Educational Resource Discovery Index (ERuDIte), which identifies, collects, describes, and organizes online data science materials from BD2K awardees, open online courses, and videos from scientific lectures and tutorials. ERuDIte now indexes over 9,500 resources. Given the richness of online training materials and the constant evolution of biomedical data science, computational methods applying information retrieval, natural language processing, and machine learning techniques are required - in effect, using data science to inform training in data science. In so doing, the TCC seeks to democratize novel insights and discoveries brought forth via large-scale data science training.

Indexed as

Computational BiologyData MiningEducation, DistanceHumansInformation Storage and RetrievalInternetMachine LearningMetadataNational Institutes of Health (U.S.)Natural Language ProcessingUnited States

Identifiers

PMID29218890
PMCPMC5731238

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.