Evidence map›Paper›PMID 32379822›Full record

ArticlePLoS computational biology2020

Ad hoc efforts for advancing data science education.

Orianna DeMasi, Alexandra Paxton, Kevin Koy

Abstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

3 authors.

Orianna DeMasiDepartment of Computer Science, University of California, Davis, California, United States of America.ORCID 0000-0002-2101-6189
Alexandra PaxtonDepartment of Psychological Sciences, University of Connecticut, Storrs, Connecticut, United States of America.ORCID 0000-0002-8603-015X
Kevin KoyIDEO, San Francisco, California, United States of America.ORCID 0000-0002-8479-4404

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With increasing demand for training in data science, extracurricular or "ad hoc" education efforts have emerged to help individuals acquire relevant skills and expertise. Although extracurricular efforts already exist for many computationally intensive disciplines, their support of data science education has significantly helped in coping with the speed of innovation in data science practice and formal curricula. While the proliferation of ad hoc efforts is an indication of their popularity, less has been documented about the needs that they are designed to meet, the limitations that they face, and practical suggestions for holding successful efforts. To holistically understand the role of different ad hoc formats for data science, we surveyed organizers of ad hoc data science education efforts to understand how organizers perceived the events to have gone-including areas of strength and areas requiring growth. We also gathered recommendations from these past events for future organizers. Our results suggest that the perceived benefits of ad hoc efforts go beyond developing technical skills and may provide continued benefit in conjunction with formal curricula, which warrants further investigation. As increasing numbers of researchers from computational fields with a history of complex data become involved with ad hoc efforts to share their skills, the lessons learned that we extract from the surveys will provide concrete suggestions for the practitioner-leaders interested in creating, improving, and sustaining future efforts.

Indexed as

CurriculumData ScienceHumansSurveys and Questionnaires

Identifiers

PMID32379822
PMCPMC7205211

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.