Data structures in calibrations of weights and mass standards

The paper addresses the transition from traditional to digital calibration certificates (DCC) for weights and mass standards, focusing on the critical aspect of machine interpretability. The main objective is to develop a methodology that converts human-readable calibration information into a format that can be processed by machines. To achieve this, the paper presents a structured approach for encoding information into the DCC framework, supplemented by practical examples that illustrate the fitting of data into the proposed structure. The study underscores the necessity of constructing semantic networks and formulating application rules to ensure consistency and harmonisation across DCCs for various weight and mass measurement devices. The results indicate that accurate digital representation of calibration information is paramount for the effectiveness of DCCs. The paper concludes by emphasising the current need for more advanced semantic networks to facilitate the seamless integration and interpretation of DCCs.

Authors

Institutions

Publication Details

Journal
Measurement Sensors
Published
2024-12-25
DOI
https://doi.org/10.1016/j.measen.2024.101468
Citations
1
Primary Topic
Scientific Measurement and Uncertainty Evaluation
Type
article
Field-Weighted Citation Impact
0.46
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Data structures in calibrations of weights and mass standards

Christian Müller‐Schöll, Julian Haller, Gisa Foyer
1 citations
Measurement Sensors
Scientific Measurement and Uncertainty Evaluation
0.46
article

Data structures in calibrations of weights and mass standards

Christian Müller‐Schöll, Julian Haller, Gisa Foyer
article en
1 citations

Abstract

The paper addresses the transition from traditional to digital calibration certificates (DCC) for weights and mass standards, focusing on the critical aspect of machine interpretability. The main objective is to develop a methodology that converts human-readable calibration information into a format that can be processed by machines. To achieve this, the paper presents a structured approach for encoding information into the DCC framework, supplemented by practical examples that illustrate the fitting of data into the proposed structure. The study underscores the necessity of constructing semantic networks and formulating application rules to ensure consistency and harmonisation across DCCs for various weight and mass measurement devices. The results indicate that accurate digital representation of calibration information is paramount for the effectiveness of DCCs. The paper concludes by emphasising the current need for more advanced semantic networks to facilitate the seamless integration and interpretation of DCCs.

Measurement SensorsVol. 38
Physikalisch-Technische Bundesanstalt (DE), Sartorius (Germany) (DE), Mettler-Toledo (Switzerland) (CH)
Openalex Percentile: Top 26%
Scientific Measurement and Uncertainty Evaluation
0.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Data structures in calibrations of weights and mass standards — Christian Müller‐Schöll, Julian Haller, et al. · Measurement Sensors (2024) | TGRS Research Map | TGRS