A local platform for user-friendly FAIR data management and reproducible analytics

被引:6
作者
Wieser, Florian [1 ]
Stryeck, Sarah [2 ,3 ]
Lang, Konrad [2 ,3 ]
Hahn, Christoph [4 ]
Thallinger, Gerhard G. [5 ,9 ]
Feichtinger, Julia [6 ,9 ]
Hack, Philipp [7 ]
Stepponat, Manfred [7 ]
Merchant, Nirav [8 ]
Lindstaedt, Stefanie [2 ,3 ]
Oberdorfer, Gustav [1 ,9 ]
机构
[1] Graz Univ Technol, Inst Biochem, A-8010 Graz, Austria
[2] Graz Univ Technol, Inst Interact Syst & Data Sci, A-8010 Graz, Austria
[3] Know Ctr GmbH, A-8010 Graz, Austria
[4] Karl Franzens Univ Graz, Inst Biol, A-8010 Graz, Austria
[5] Graz Univ Technol, Inst Biomed Informat, A-8010 Graz, Austria
[6] Med Univ Graz, Gottfried Schatz Res Ctr, Div Cell Biol Histol & Embryol, A-8010 Graz, Austria
[7] Graz Univ Technol, Cent Informat Technol, A-8010 Graz, Austria
[8] Univ Arizona, Data Sci Inst, BSRL 200 A, Tucson, AZ 85721 USA
[9] BioTechMed Graz, Graz, Austria
基金
奥地利科学基金会; 欧洲研究理事会;
关键词
Cyberinfrastructure; Bioinformatics; Research data management; FAIR; Teaching; CyVerse; PROTEIN-STRUCTURE PREDICTION; ROSETTA3; DESIGN;
D O I
10.1016/j.jbiotec.2021.08.004
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Collaborative research is common practice in modern life sciences. For most projects several researchers from multiple universities collaborate on a specific topic. Frequently, these research projects produce a wealth of data that requires central and secure storage, which should also allow for easy sharing among project participants. Only under best circumstances, this comes with minimal technical overhead for the researchers. Moreover, the need for data to be analyzed in a reproducible way often poses a challenge for researchers without a data science background and thus represents an overly time-consuming process. Here, we report on the integration of CyVerse Austria (CAT), a new cyberinfrastructure for a local community of life science researchers, and provide two examples how it can be used to facilitate FAIR data management and reproducible analytics for teaching and research. In particular, we describe in detail how CAT can be used (i) as a teaching platform with a defined software environment and data management/sharing possibilities, and (ii) to build a data analysis pipeline using the Docker technology tailored to the needs and interests of the researcher.
引用
收藏
页码:43 / 50
页数:8
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