guest ::
| Home > Publications database > odML-tables: Providing a graphical interface for odML based metadata management > print |
| Home > Publications database > odML-tables: Providing a graphical interface for odML based metadata management > print |
| 001 | 828548 | ||
| 005 | 20240313094850.0 | ||
| 037 | _ | _ | |a FZJ-2017-02502 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Sprenger, Julia |0 P:(DE-Juel1)161295 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 12th Göttingen Meeting of the German Neuroscience Society |g NWG 2017 |c Göttingen |d 2017-03-22 - 2017-03-25 |w Germany |
| 245 | _ | _ | |a odML-tables: Providing a graphical interface for odML based metadata management |
| 260 | _ | _ | |c 2017 |
| 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 |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1491198983_25427 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Experimental observations are an essential part of scientific research and are used to validate or reject a scientific hypothesis. In the scientific approach, observations are typically quantified and data are recorded for subsequent analysis. These primary data are always accompanied by information about their origin and the circumstances of their recording. Such information is typically called metadata and includes a variety of information, such as, the date of the recording, a seemingly unimportant change in measurement settings or the expectation of the experimenter (open trial vs. blind trial experiments). Metadata are crucial for performing reproducible data analysis and are essential for the interpretation of the results. They also enable queries to answer scientific questions that researchers did not previously consider (e.g. transversal studies) and are one of the main components for implementing replicable and reproducible research [1]. In addition a comprehensive metadata collection facilitates the communication between members of a project and therefore saves valuable time and effort. In neuroscience, and in particular experimental neurophysiology, the development of approaches to metadata management is 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 as hierarchically organized key-value pairs.In practice however, embedding metadata based on the odML framework into workflows for sharing data in concrete use cases of experimental and theoretical groups revealed that generating the structure of an odML document, and later filling it with metadata from the respective sources, involved extensive programming experience [2]. In addition there are always metadata, which require manual entry during or after the experiment. The lack of software support for certain processing steps and editing capabilities in these use cases 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 that is convenient for editing.odML-tables is an open-source software tool implemented in Python, which offers a graphical user interface (GUI) [4,5]. The main features of odML-tables are:1) Generation of a template (tabular) structure facilitating the initial design of an odML structure2) Conversion between odML files and tabular formats (.xls, .csv) in order to enable manual entry and modifications using spreadsheet software (e.g., Microsoft Excel, LibreOffice).3) Filtering metadata by defined search criteria to simplify access to parts of a complex odML structure4) Merging of multiple odML files5) Generation of a comparison table of similar entries within an odML fileWe show how odML-tables can be applied in a sustainable workflow for metadata management and illustrate the practical usage of odML-tables 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, 10, 26.[3] Grewe, J., Wachtler, T., & Benda, J. (2011). A Bottom-up Approach to Data Annotation in Neurophysiology. Frontiers in Neuroinformatics, 5, 16.[4] python-odmltables on PyPi: https://pypi.python.org/pypi/python-odmltables/[5] python-odmltables on GitHub: https://github.com/INM-6/python-odmltables |
| 536 | _ | _ | |a 571 - Connectivity and Activity (POF3-571) |0 G:(DE-HGF)POF3-571 |c POF3-571 |x 0 |f POF III |
| 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 HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) |0 G:(EU-Grant)720270 |c 720270 |x 2 |f H2020-Adhoc-2014-20 |
| 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 3 |
| 536 | _ | _ | |a DFG project 238707842 - Kausative Mechanismen mesoskopischer Aktivitätsmuster in der auditorischen Kategorien-Diskrimination (238707842) |0 G:(GEPRIS)238707842 |c 238707842 |x 4 |
| 700 | 1 | _ | |a Zehl, Lyuba |0 P:(DE-Juel1)145394 |b 1 |u fzj |
| 700 | 1 | _ | |a Canova, Carlos |0 P:(DE-Juel1)159172 |b 2 |u fzj |
| 700 | 1 | _ | |a Grün, Sonja |0 P:(DE-Juel1)144168 |b 3 |u fzj |
| 700 | 1 | _ | |a Denker, Michael |0 P:(DE-Juel1)144807 |b 4 |u fzj |
| 909 | C | O | |o oai:juser.fz-juelich.de:828548 |p VDB |p ec_fundedresources |p openaire |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)161295 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)145394 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)159172 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)144168 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)144807 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-571 |2 G:(DE-HGF)POF3-500 |v Connectivity and Activity |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |
| 914 | 1 | _ | |y 2017 |
| 920 | _ | _ | |l no |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 1 |
| 980 | _ | _ | |a poster |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a UNRESTRICTED |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|