Evidence map›Paper›PMID 38900708›Full record

ArticlePLoS computational biology2024

Ten simple rules to make computable knowledge shareable and reusable.

Marisa L Conte, Peter Boisvert, Philip Barrison, Farid Seifi, Zach Landis-Lewis, Allen Flynn, Charles P Friedman

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. A case for the introduction of the ICD-11 in Germany.German medical science : GMS e-journal · 2026
    Review
  3. How the National Library of Medicine should evolve in an era of artificial intelligence.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
  4. Article
  5. 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

7 authors.

Marisa L ConteDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.ORCID 0000-0001-7377-163X
Peter BoisvertDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Philip BarrisonDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.ORCID 0000-0002-8099-8506
Farid SeifiDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Zach Landis-LewisDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Allen FlynnDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Charles P FriedmanDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.

Funding

MICHIGAN MEDICAL SCIENTIST TRAINING PROGRAMT32GM007863 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI COLLINS, KATHLEEN L. · 1985 to 2024
$38.3M
NIGMS NIH HHS T32 GM007863
6 · The paper itself

Abstract

Computable biomedical knowledge (CBK) is: "the result of an analytic and/or deliberative process about human health, or affecting human health, that is explicit, and therefore can be represented and reasned upon using logic, formal standards, and mathematical approaches." Representing biomedical knowledge in a machine-interpretable, computable form increases its ability to be discovered, accessed, understood, and deployed. Computable knowledge artifacts can greatly advance the potential for implementation, reproducibility, or extension of the knowledge by users, who may include practitioners, researchers, and learners. Enriching computable knowledge artifacts may help facilitate reuse and translation into practice. Following the examples of 10 Simple Rules papers for scientific code, software, and applications, we present 10 Simple Rules intended to make shared computable knowledge artifacts more useful and reusable. These rules are mainly for researchers and their teams who have decided that sharing their computable knowledge is important, who wish to go beyond simply describing results, algorithms, or models via traditional publication pathways, and who want to both make their research findings more accessible, and to help others use their computable knowledge. These rules are roughly organized into 3 categories: planning, engineering, and documentation. Finally, while many of the following examples are of computable knowledge in biomedical domains, these rules are generalizable to computable knowledge in any research domain.

Indexed as

Computational BiologyAlgorithmsHumansInformation DisseminationKnowledgeSoftware

Identifiers

PMID38900708
PMCPMC11189186

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.