Evidence map›Paper›PMID 38688980›Full record

ArticleScientific reports2024

Experiments as Code and its application to VR studies in human-building interaction.

Leonel Aguilar, Michal Gath-Morad, Jascha Grübel, Jasper Ermatinger, Hantao Zhao, Stefan Wehrli, Robert W Sumner, Ce Zhang, Dirk Helbing, Christoph Hölscher

Abstract read
In one paragraph

Article in Scientific reports, 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

10 authors.

Leonel Aguilar *Chair of Cognitive Science, ETH Zürich, Zurich, Switzerland. leonel.aguilar@gess.ethz.ch.
Michal Gath-Morad *Chair of Cognitive Science, ETH Zürich, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-7673-6290
Jascha Grübel *Chair of Cognitive Science, ETH Zürich, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-6428-4685
Jasper ErmatingerChair of Cognitive Science, ETH Zürich, Zurich, Switzerland.
Hantao ZhaoSchool of Cyber Science and Engineering, Southeast University, Nanjing, China.
Stefan WehrliDecision Science Laboratory, ETH Zürich, Zurich, Switzerland.
Robert W SumnerGeo-information Science and Remote Sensing Laboratory, Wageningen University, Wageningen, The Netherlands.
Ce ZhangData Science, Systems and Services Group, ETH Zürich, Zurich, Switzerland.
Dirk HelbingDecision Science Laboratory, ETH Zürich, Zurich, Switzerland.
Christoph HölscherChair of Cognitive Science, ETH Zürich, Zurich, Switzerland.

Funding

EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) 833168
6 · The paper itself

Abstract

Experiments as Code (ExaC) is a concept for reproducible, auditable, debuggable, reusable, & scalable experiments. Experiments are a crucial tool to understand Human-Building Interactions (HBI) and build a coherent theory around it. However, a common concern for experiments is their auditability and reproducibility. Experiments are usually designed, provisioned, managed, and analyzed by diverse teams of specialists (e.g., researchers, technicians, engineers) and may require many resources (e.g., cloud infrastructure, specialized equipment). Although researchers strive to document experiments accurately, this process is often lacking. Consequently, it is difficult to reproduce these experiments. Moreover, when it is necessary to create a similar experiment, the "wheel is very often reinvented". It appears easier to start from scratch than trying to reuse existing work. Thus valuable embedded best practices and previous experiences are lost. In behavioral studies, such as in HBI, this has contributed to the reproducibility crisis. To tackle these challenges, we propose the ExaC paradigm, which not only documents the whole experiment, but additionally provides the automation code to provision, deploy, manage, and analyze the experiment. To this end, we define the ExaC concept, provide a taxonomy for the components of a practical implementation, and provide a proof of concept with an HBI desktop VR experiment that demonstrates the benefits of its "as code" representation, that is, reproducibility, auditability, debuggability, reusability, & scalability.

Identifiers

PMID38688980
PMCPMC11061313

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.