Evidence map›Paper›PMID 34506617›Full record

ArticlePloS one2021

Community development, implementation, and assessment of a NIBLSE bioinformatics sequence similarity learning resource.

Adam J Kleinschmit, Elizabeth F Ryder, Jacob L Kerby, Barbara Murdoch, Sam Donovan, Nealy F Grandgenett, Rachel E Cook, Chamindika Siriwardana, William Morgan, Mark Pauley and 3 more

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

13 authors.

Adam J KleinschmitDepartment of Natural and Applied Sciences, University of Dubuque, Dubuque, Iowa, United States of America.ORCID 0000-0003-3820-524X
Elizabeth F RyderDepartment of Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, Massachusetts, United States of America.
Jacob L KerbyDepartment of Biology, University of South Dakota, Vermillion, South Dakota, United States of America.
Barbara MurdochDepartment of Biology, Eastern Connecticut State University, Willimantic, Connecticut, United States of America.
Sam DonovanDepartment of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
Nealy F GrandgenettDepartment of Teacher Education, University of Nebraska at Omaha, Omaha, Nebraska, United States of America.
Rachel E CookDepartment of Biology, Fairmont State University, Fairmont, West Virginia, United States of America.
Chamindika SiriwardanaDepartment of Science and Mathematics, Texas A&M University - Central Texas, Killeen, Texas, United States of America.
William MorganDepartment of Biology, College of Wooster, Wooster, Ohio, United States of America.
Mark PauleyDivision of Undergraduate Education, Directorate for Education and Human Resources, National Science Foundation, Alexandria, Virginia, United States of America.
Anne RosenwaldDepartment of Biology, Georgetown University, Washington, DC, United States of America.
Eric TriplettDepartment of Microbiology and Cell Science, University of Florida, Gainesville, Florida, United States of America.
William TapprichDepartment of Biology, University of Nebraska at Omaha, Omaha, Nebraska, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As powerful computational tools and 'big data' transform the biological sciences, bioinformatics training is becoming necessary to prepare the next generation of life scientists. Furthermore, because the tools and resources employed in bioinformatics are constantly evolving, bioinformatics learning materials must be continuously improved. In addition, these learning materials need to move beyond today's typical step-by-step guides to promote deeper conceptual understanding by students. One of the goals of the Network for Integrating Bioinformatics into Life Sciences Education (NIBSLE) is to create, curate, disseminate, and assess appropriate open-access bioinformatics learning resources. Here we describe the evolution, integration, and assessment of a learning resource that explores essential concepts of biological sequence similarity. Pre/post student assessment data from diverse life science courses show significant learning gains. These results indicate that the learning resource is a beneficial educational product for the integration of bioinformatics across curricula.

Indexed as

Education, DistanceLearningBig DataBiological Science DisciplinesComputational BiologyComputer SimulationCurriculumEducational StatusHumansLinear ModelsSocial PlanningStudents

Identifiers

PMID34506617
PMCPMC8432852

What OpenQuestion holds

Textmetadata
LicenceCC0
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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.