Evidence map›Paper›PMID 33169040›Full record

ArticleScientometrics2021

A comprehensive quality assessment framework for scientific events.

Sahar Vahdati, Said Fathalla, Christoph Lange, Andreas Behrend, Aysegul Say, Zeynep Say, Sören Auer

Abstract read
In one paragraph

Article in Scientometrics, 2021. 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

7 authors.

Sahar VahdatiDepartment of Computer Science, University of Oxford, Oxford, UK.
Said FathallaUniversity of Bonn, Bonn, Germany.ORCID 0000-0003-4455-2854
Christoph LangeRWTH Aachen University, Aachen, Germany.
Andreas BehrendInstitute for Telecommunications (INT), TH Köln, Germany.
Aysegul SayUniversity of Bonn, Bonn, Germany.
Zeynep SayUniversity of Bonn, Bonn, Germany.
Sören AuerL3S Research Center, Leibniz University of Hannover, Hannover, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Systematic assessment of scientific events has become increasingly important for research communities. A range of metrics (e.g., citations, h-index) have been developed by different research communities to make such assessments effectual. However, most of the metrics for assessing the quality of less formal publication venues and events have not yet deeply investigated. It is also rather challenging to develop respective metrics because each research community has its own formal and informal rules of communication and quality standards. In this article, we develop a comprehensive framework of assessment metrics for evaluating scientific events and involved stakeholders. The resulting quality metrics are determined with respect to three general categories-events, persons, and bibliometrics. Our assessment methodology is empirically applied to several series of computer science events, such as conferences and workshops, using publicly available data for determining quality metrics. We show that the metrics' values coincide with the intuitive agreement of the community on its "top conferences". Our results demonstrate that highly-ranked events share similar profiles, including the provision of outstanding reviews, visiting diverse locations, having reputed people involved, and renowned sponsors.

Indexed as

BibliometricsMetadata analysisQuality assessmentRecommendationScientific events

Identifiers

PMID33169040
PMCPMC7609385

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.