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The IBEX knowledge-base a community resource enabling adoption and development of immunofluorescence imaging methods.
Yaniv Z
,
Anidi IU
,
Arakkal L
,
Arroyo-Mejías AJ
,
Beuschel RT
,
Börner K
,
Chu CJ
,
Clark BH
,
Clatworthy MR
,
Colautti J
,
Coscia F
,
Croteau J
,
Denha S
,
Dever R
,
Dutra WO
,
Fritzsche S
,
Fullam S
,
Gerner MY
,
Gola A
,
Gollob KJ
,
Hernandez JM
,
Hor JL
,
Ichise H
,
Jing Z
,
Jonigk D
,
Kandov E
,
Kastenmüller W
,
Koenig JFE
,
Kothurkar A
,
Kortekaas RK
,
Kreins AY
,
Lamborn IT
,
Lin Y
,
Luciano Pereira Morais K
,
Lunich A
,
Luz JCS
,
MacDonald RB
,
Makranz C
,
Maltez VI
,
McDonough JE
,
Moriarty RV
,
Ocampo-Godinez JM
,
Murakami Olyntho V
,
Oxenius A
,
Padhan K
,
Remmert K
,
Richoz N
,
Schrom EC
,
Shang W
,
Shi L
,
Shih RM
,
Speranza E
,
Stierli S
,
Teichmann SA
,
Verse TZ
,
Vierhout M
,
Wachter BT
,
Wade-Vallance AK
,
Williams M
,
Zangger N
,
Germain RN
,
Radtke AJ
.
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The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunofluorescence imaging methods. The design of the Knowledge-Base is modeled after efforts in the open-source software community and includes three facets: a development platform (GitHub), static website, and service for data archiving. The Knowledge-Base facilitates the practice of open science throughout the research life cycle by providing validation data for recommended and non-recommended reagents, such as primary and secondary antibodies. In addition to reporting negative data, the Knowledge-Base empowers method adoption and evolution by providing a venue for sharing protocols, videos, datasets, software, and publications. A dedicated discussion forum fosters a sense of community among researchers while addressing questions not covered in published manuscripts. Together, scientists from around the world are advancing scientific discovery at a faster pace, reducing wasted time and effort, and instilling greater confidence in the resulting data.
Figure 2. Computational workflow for static website generation.Data displayed on the static website is generated from human and machine-readable comma-separated value (csv), bibliography database (bib), jpg images, and markdown template (M down arrow) files. Following a new submission, custom Python scripts expand the template markdown files to include the new data. Existing data (unprocessed markdown files) remain unchanged. The website is automatically generated via Python scripts utilizing the GitHub continuous integration compute infrastructure and static website creation services (Jekyll).
Figure 3. The IBEX KB provides several ways to contribute and use data.Summary of supported data types and ways to contribute or use the KB. Crown icon indicates contributions that result in authorship on the Zenodo archival versions.
Figure 4. Guidelines for contributing data and flow chart detailing how to add a reagent contribution.(A) Details for contributing data to the IBEX KB. Files that need to be modified are highlighted in blue with data file name bolded. (B) Flow chart demonstrating how to add a reagent contribution. Files that need to be modified are highlighted in green with data file name bolded.
