The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research

Vaibhav Bajpai; Anna Brunstrom; Anja Feldmann; Wolfgang Kellerer; Aiko Pras; Henning Schulzrinne; Georgios Smaragdakis; Matthias Wählisch; Klaus Wehrle

doi:10.48550/arXiv.1902.02165

The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research

Vaibhav Bajpai, Anna Brunstrom, Anja Feldmann, Wolfgang Kellerer, Aiko Pras, Henning Schulzrinne, Georgios Smaragdakis, Matthias Wählisch, Klaus Wehrle

Research output: Working paper › Preprint › Academic

33 Downloads (Pure)

Abstract

Reproducibility is one of the key characteristics of good science, but hard to achieve for experimental disciplines like Internet measurements and networked systems. This guide provides advice to researchers, particularly those new to the field, on designing experiments so that their work is more likely to be reproducible and to serve as a foundation for follow-on work by others.

Original language	English
Publisher	ArXiv.org
DOIs	https://doi.org/10.48550/arXiv.1902.02165
Publication status	Published - 12 Jan 2019

Keywords

cs.NI

Access to Document

10.48550/arXiv.1902.02165

1902.02165v1Final published version, 143 KB

1 Article

The dagstuhl beginners guide to reproducibility for experimental networking research
Bajpai, V., Kellerer, W., Smaragdakis, G., Brunstrom, A., Pras, A., Wählisch, M., Feldmann, A., Schulzrinne, H. & Wehrle, K., 1 Jan 2019, In: Computer communication review. 49, 1, p. 24-30 7 p.
Research output: Contribution to journal › Article › Academic › peer-review

Open Access
File
36 Citations (Scopus)

226 Downloads (Pure)

Cite this

@techreport{166d442832b24818ab9e8efa0fb4bb08,

title = "The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research",

abstract = " Reproducibility is one of the key characteristics of good science, but hard to achieve for experimental disciplines like Internet measurements and networked systems. This guide provides advice to researchers, particularly those new to the field, on designing experiments so that their work is more likely to be reproducible and to serve as a foundation for follow-on work by others. ",

keywords = "cs.NI",

author = "Vaibhav Bajpai and Anna Brunstrom and Anja Feldmann and Wolfgang Kellerer and Aiko Pras and Henning Schulzrinne and Georgios Smaragdakis and Matthias W{\"a}hlisch and Klaus Wehrle",

year = "2019",

month = jan,

day = "12",

doi = "10.48550/arXiv.1902.02165",

language = "English",

publisher = "ArXiv.org",

type = "WorkingPaper",

institution = "ArXiv.org",

}

TY - UNPB

T1 - The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research

AU - Bajpai, Vaibhav

AU - Brunstrom, Anna

AU - Feldmann, Anja

AU - Kellerer, Wolfgang

AU - Pras, Aiko

AU - Schulzrinne, Henning

AU - Smaragdakis, Georgios

AU - Wählisch, Matthias

AU - Wehrle, Klaus

PY - 2019/1/12

Y1 - 2019/1/12

N2 - Reproducibility is one of the key characteristics of good science, but hard to achieve for experimental disciplines like Internet measurements and networked systems. This guide provides advice to researchers, particularly those new to the field, on designing experiments so that their work is more likely to be reproducible and to serve as a foundation for follow-on work by others.

AB - Reproducibility is one of the key characteristics of good science, but hard to achieve for experimental disciplines like Internet measurements and networked systems. This guide provides advice to researchers, particularly those new to the field, on designing experiments so that their work is more likely to be reproducible and to serve as a foundation for follow-on work by others.

KW - cs.NI

U2 - 10.48550/arXiv.1902.02165

DO - 10.48550/arXiv.1902.02165

M3 - Preprint

BT - The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research

PB - ArXiv.org

ER -

The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research

Abstract

Keywords

Access to Document

Fingerprint

Research output

The dagstuhl beginners guide to reproducibility for experimental networking research

Cite this