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Excemplify: a flexible template based solution, parsing and managing data in spreadsheets for experimentalists
Citation
Shi, L and Jong, L and Wittig, U and Lucarelli, P and Stepath, M and Mueller, S and D'Alessandro, LA and Klingmuller, U and Muller, W, Excemplify: a flexible template based solution, parsing and managing data in spreadsheets for experimentalists, Journal of Integrative Bioinformatics, 10, (2) Article 220. ISSN 1613-4516 (2013) [Refereed Article]
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Copyright Statement
Copyright 2013 The Authors Licenced under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) http://creativecommons.org/licenses/by-nc-nd/3.0/
DOI: doi:10.2390/biecoll-jib-2013-220
Abstract
In systems biology, quantitative experimental data is the basis of building mathematical
models. In most of the cases, they are stored in Excel files and hosted locally. To have a
public database for collecting, retrieving and citing experimental raw data as well as experimental
conditions is important for both experimentalists and modelers. However, the
great effort needed in the data handling procedure and in the data submission procedure
becomes the crucial limitation for experimentalists to contribute to a database, thereby impeding
the database to deliver its benefit. Moreover, manual copy and paste operations
which are commonly used in those procedures increase the chance of making mistakes.
Excemplify, a web-based application, proposes a flexible and adaptable template-based
solution to solve these problems. Comparing to the normal template based uploading approach,
which is supported by some public databases, rather than predefining a format that
is potentiall impractical, Excemplify allows users to create their own experiment-specific
content templates in different experiment stages and to build corresponding knowledge
bases for parsing. Utilizing the embedded knowledge of used templates, Excemplify is
able to parse experimental data from the initial setup stage and generate following stages
spreadsheets automatically. The proposed solution standardizes the flows of data traveling
according to the standard procedures of applying the experiment, cuts down the amount
of manual effort and reduces the chance of mistakes caused by manual data handling. In
addition, it maintains the context of meta-data from the initial preparation manuscript and
improves the data consistency. It interoperates and complements RightField and SEEK as
well.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | knowledge management, systems biology |
| Research Division: | Information and Computing Sciences |
| Research Group: | Software engineering |
| Research Field: | Software engineering not elsewhere classified |
| Objective Division: | Expanding Knowledge |
| Objective Group: | Expanding knowledge |
| Objective Field: | Expanding knowledge in the biological sciences |
| UTAS Author: | Jong, L (Dr Lenneke Jong) |
| ID Code: | 102658 |
| Year Published: | 2013 |
| Deposited By: | IMAS Research and Education Centre |
| Deposited On: | 2015-09-02 |
| Last Modified: | 2018-02-16 |
| Downloads: | 177 View Download Statistics |
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