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| Hauptseite > Publikationsdatenbank > odML-tables: A graphical approach to metadata management based on odML > print |
| Hauptseite > Publikationsdatenbank > odML-tables: A graphical approach to metadata management based on odML > print |
| 001 | 826006 | ||
| 005 | 20240313094923.0 | ||
| 037 | _ | _ | |a FZJ-2017-00278 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Sprenger, Julia |0 P:(DE-Juel1)161295 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 1st Symposium of the Institute for Advanced Simulations |g IAS Symposium 2016 |c Jülich |d 2016-12-05 - 2016-12-06 |w Germany |
| 245 | _ | _ | |a odML-tables: A graphical approach to metadata management based on odML |
| 260 | _ | _ | |c 2016 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
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| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1484728551_22333 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Central to quantitative sciences is the measurement of data with the aim to capture empirical, experimental observations or the outcome of model simulations. These primary data, and derived data resulting from post-processing steps, are always accompanied by information about the origin of the data and the circumstances of recording. Such information is typically called metadata. It is relevant to facilitate the communication between members of a project and is essential for the interpretation of the data. It also enables queries to answer scientific questions that researchers did not previously consider (e.g. transversal studies) and is one of the main components for implementing reproducibility [1]. In neuroscience, and in particular experimental neurophysiology, the development of approaches to metadata management are still an ongoing effort [2]. A promising metadata framework in this field is odML (open metadata Markup Language) [3]. This XML-based language is designed to represent complex metadata collections hierarchically organized as key-value pairs.In practice however, embedding odML-based metadata within multiple collaborations of INM-6 [4] revealed that setting up an odML document involves extensive programming and the manual entry of metadata into it during or after the experiment is cumbersome. The lack of software support effectively prevented our experimental partners from using odML to capture metadata into one coherent collection. To address this shortcoming, we developed odML-tables, a software solution that bridges the gap between hierarchical odML and a tabular representation of metadata and which is suitable for easy editing.odML-tables is accessible by a graphical user interface as well as from a Python interface and offers multiple features which simplify the generation and modification of odML metadata files:*Generation of a template (tabular) structure facilitating the initial design of an odML structure*Conversion of existing odML files to more easily accessible tabular formats (.xls, .csv) in order to enable manual entry and modifications using common graphical software tools (e.g., Microsoft Excel, LibreOffice).*Reverse transformation of the modified data in a standardized tabular format to the odML format*Filtering odML metadata by defined search criteria to generate overview files or simplify access parts of a complex odML structure*Merging of multiple odML files*Generation of a comparison table of similar entries within an odML fileWe show how odML-tables serves to complement a sustainable workflow for metadata management in an example use case, where we illustrate the practical usage of odML-tables ranging from structuring available metadata to daily enrichment of the metadata collection (cf. also [1,2]).References:[1] Denker, M., & Grün, S. (2016). Designing workflows for the reproducible Analysis of Electrophys-iological Data. In: Brain Inspired Computing, eds: Katrin Amunts, Lucio Grandinetti, Thomas Lippert, Nicolai Petkov. Lecture Notes in Computer Science, Springer. (in press)[2] Zehl, L., Jaillet, F., Stoewer, A., Grewe, J., Sobolev, A., Wachtler, T., Brochier, T., Riehle, A., Denker, M., & Grün, S. Handling Metadata in a Neurophysiology Laboratory. Frontiers in Neuroinformatics. (under revision)[3] Grewe, J., Wachtler, T., & Benda, J. (2011). A Bottom-up Approach to Data Annotation in Neurophysiology. Frontiers in Neuroinformatics, 5, 16.[4] Sprenger, J., Canova, C., Denker, M. & Grün, S. (2015). Data workflow management and analysis for complex electrophysiological experiments. 4th INM-Retreat, Jülich, Germany. Poster |
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| 536 | _ | _ | |a SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) |0 G:(DE-Juel1)HGF-SMHB-2013-2017 |c HGF-SMHB-2013-2017 |x 1 |f SMHB |
| 536 | _ | _ | |a DFG project 237833830 - Optogenetische Analyse der für kognitive Fähigkeiten zuständigen präfrontal-hippokampalen Netzwerke in der Entwicklung (237833830) |0 G:(GEPRIS)237833830 |c 237833830 |x 2 |
| 536 | _ | _ | |a DFG project 238707842 - Kausative Mechanismen mesoskopischer Aktivitätsmuster in der auditorischen Kategorien-Diskrimination (238707842) |0 G:(GEPRIS)238707842 |c 238707842 |x 3 |
| 536 | _ | _ | |a HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) |0 G:(EU-Grant)720270 |c 720270 |x 4 |f H2020-Adhoc-2014-20 |
| 700 | 1 | _ | |a Canova, Carlos |0 P:(DE-Juel1)159172 |b 1 |u fzj |
| 700 | 1 | _ | |a Pick, Jana |0 P:(DE-Juel1)159521 |b 2 |u fzj |
| 700 | 1 | _ | |a Zehl, Lyuba |0 P:(DE-Juel1)145394 |b 3 |u fzj |
| 700 | 1 | _ | |a Grün, Sonja |0 P:(DE-Juel1)144168 |b 4 |u fzj |
| 700 | 1 | _ | |a Denker, Michael |0 P:(DE-Juel1)144807 |b 5 |u fzj |
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