Evidence map›Paper›PMID 38692465›Full record

ArticleJournal of biomedical informatics2024

Towards Machine-FAIR: Representing software and datasets to facilitate reuse and scientific discovery by machines.

Michael M Wagner, William R Hogan, John D Levander, Matthew Diller

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical informatics, 2024. 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

4 authors.

Michael M WagnerDepartment of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, PA 15206-3701, USA. Electronic address: mmw1@pitt.edu.
William R HoganData Science Institute, Medical College of Wisconsin, Milwaukee, WI, USA.
John D LevanderDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Matthew DillerDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.

Funding

MIDAS Informatics Services Group (ISG)U24GM110707 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI ESPINO, JESSI U, WAGNER, MICHAEL MATTHEW · 2014 to 2018
$11.6M
Apollo: Increasing Access and Use of Epidemic Models Through the Development andR01GM101151 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HOGAN, WILLIAM R, WAGNER, MICHAEL MATTHEW · 2012 to 2015
$2.2M
NIGMS NIH HHS R01 GM101151NIGMS NIH HHS U24 GM110707
6 · The paper itself

Abstract

objectiveTo use software, datasets, and data formats in the domain of Infectious Disease Epidemiology as a test collection to evaluate a novel M1 use case, which we introduce in this paper. M1 is a machine that upon receipt of a new digital object of research exhaustively finds all valid compositions of it with existing objects.

methodWe implemented a data-format-matching-only M1 using exhaustive search, which we refer to as M1

resultsPrecision of M1

conclusionAlgorithmic search of digital repositories for valid workflow compositions has potential to accelerate scientific discovery but requires a scalable solution to the problem of knowledge acquisition about semantic constraints on software inputs. Additionally, practical limitations on the logical complexity of semantic constraints must be respected, which has implications for the design of software.

Indexed as

SoftwareAlgorithmsDatabases, FactualHumansMachine LearningSemanticsAutomatic workflow compositionMachine-FAIR principlesReusing digital research objectsScientific discoveryScientific workflows

Identifiers

PMID38692465
PMCPMC11250896

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.