Evidence map›Paper›PMID 35271613›Full record

ArticlePloS one2022

Sim2Ls: FAIR simulation workflows and data.

Martin Hunt, Steven Clark, Daniel Mejia, Saaketh Desai, Alejandro Strachan

Abstract read
In one paragraph

Article in PloS one, 2022. 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

5 authors.

Martin HuntHalıcıoğlu Data Science Institute, University of California at San Diego, La Jolla, California, United States of America.
Steven ClarkSan Diego Supercomputer Center, University of California at San Diego, La Jolla, California, United States of America.
Daniel MejiaNetwork for Computational Nanotechnology, Purdue University, West Lafayette, Indiana, United States of America.
Saaketh DesaiCenter for Integrated Nanotechnologies, Sandia National Laboratories, Albuquerque, New Mexico, United States of America.
Alejandro StrachanNetwork for Computational Nanotechnology, Purdue University, West Lafayette, Indiana, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science and engineering workflows used in research remain poorly documented and often unavailable, involving ad hoc scripts and manual steps, hindering reproducibility and stifling progress. We introduce Sim2Ls (pronounced simtools) and the Sim2L Python library that allow developers to create and share end-to-end computational workflows with well-defined and verified inputs and outputs. The Sim2L library makes Sim2Ls, their requirements, and their services discoverable, verifies inputs and outputs, and automatically stores results in a globally-accessible simulation cache and results database. This simulation ecosystem is available in nanoHUB, an open platform that also provides publication services for Sim2Ls, a computational environment for developers and users, and the hardware to execute runs and store results at no cost. We exemplify the use of Sim2Ls using two applications and discuss best practices towards FAIR simulation workflows and associated data.

Indexed as

Data ManagementEcosystemComputer SimulationReproducibility of ResultsSoftwareWorkflow

Identifiers

PMID35271613
PMCPMC8912189

What OpenQuestion holds

Textmetadata
LicenceCC0
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.