Difference between revisions of "Journal:Digitalization concepts in academic bioprocess development"
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==Abstract== | ==Abstract== | ||
Digitalization with integrated devices, digital and physical assistants, [[Laboratory automation|automation]], and simulation is setting a new direction for [[laboratory]] work. Even with complex research [[workflow]]s, high staff turnover, and a limited budget, some laboratories have already shown that digitalization is indeed possible. However, academic [[bioprocess]] laboratories often struggle to follow the trend of digitalization. Due to their diverse [[research]] circumstances, high variety of team composition, goals, and limitations, the concepts are substantially different. Here, we will provide an overview on different aspects of digitalization and describe how academic laboratories successfully digitalized their working environment. The key aspect is the collaboration and communication between IT-experts and scientific staff. The developed digital infrastructure is only useful if it supports the laboratory worker and does not complicate their work. Thereby, laboratory researchers have to collaborate closely with IT experts in order for a well-developed and maintainable digitalization concept that fits their individual needs and level of complexity to emerge. This review may serve as a starting point or a collection of ideas for the transformation toward a digitalized bioprocess laboratory. | Digitalization with integrated devices, digital and physical assistants, [[Laboratory automation|automation]], and simulation is setting a new direction for [[laboratory]] work. Even with complex research [[workflow]]s, high staff turnover, and a limited budget, some laboratories have already shown that digitalization is indeed possible. However, academic [[bioprocess]] laboratories often struggle to follow the trend of digitalization. Due to their diverse [[research]] circumstances, high variety of team composition, goals, and limitations, the concepts are substantially different. Here, we will provide an overview on different aspects of digitalization and describe how academic research laboratories successfully digitalized their working environment. The key aspect is the collaboration and communication between IT-experts and scientific staff. The developed digital infrastructure is only useful if it supports the laboratory worker and does not complicate their work. Thereby, laboratory researchers have to collaborate closely with IT experts in order for a well-developed and maintainable digitalization concept that fits their individual needs and level of complexity to emerge. This review may serve as a starting point or a collection of ideas for the transformation toward a digitalized bioprocess laboratory. | ||
'''Keywords''': academic laboratories, automation, bioprocess, digitalization, FAIR data, Laboratory and Analytical Device Standard (LADS), Standardization in Lab Automation 2 (SiLA2) | '''Keywords''': academic laboratories, automation, bioprocess, digitalization, FAIR data, Laboratory and Analytical Device Standard (LADS), Standardization in Lab Automation 2 (SiLA2) | ||
==Introduction== | ==Introduction== | ||
In contrast to industry, academic [[research]] [[Laboratory|laboratories]] require more flexibility than production lines. Besides the need for flexibility, loss of knowledge due to high staff turnover in universities is another challenge that makes full laboratory digitalization hard to achieve. [1-4] "Digitalization" (what we'll be addressing) refers to an entire process, [[workflow]], or laboratory infrastructure, whereas "digitization" refers only to the procedure of converting something analog to a digital format (e.g., digitizing a standard operating procedure [SOP] from a piece of paper to a digital file). [5, 6] Working in digital laboratories has the potential for error reduction, prevention of data loss, improved [[data integrity]], faster workflow development times, possible reduction of chemical and material use, and higher [[Sample (material)|sample]] throughput, leading to modern, transparent, and reproducible research and biomanufacturing. [7-14] | |||
Developing a digitalization strategy for an academic [[bioprocess]] laboratory is an interdisciplinary task. Laboratory workers have to work closely with IT experts in order to achieve a digital infrastructure that can be maintained and is supporting their laboratory work rather than hindering it. [5, 7, 9, 15, 16] This kind of collaboration is rarely found in academic bioprocess laboratories, so even the smallest digitalization task can be a major challenge. Hardware vendors selling their devices with proprietary software and restrictive access and [[information]] about their [[Interface (computing)|interfaces]] is making digitalization even more difficult. [1, 7, 17] Therefore, academic researchers working in the field of digitalization are developing concepts that are fitting their individual needs at the time. This is why there are manifold digitalization concepts throughout academic laboratories. | |||
This review will present different digitalization concepts throughout academic bioprocess development laboratories. Before looking at different individual concepts in more detail, general aspects like [[Journal:The FAIR Guiding Principles for scientific data management and stewardship|FAIR data]] (data that is readily findable, accessible, interoperable, and reusable), standard communication protocols, [[digital twin]]s, and interaction with laboratory devices will be addressed. | |||
==Basic concepts of digitalization== | |||
===FAIR data=== | |||
With the progressing digitalization of laboratories, the generated amount of data is steadily increasing. Therefore, good data management systems will become inevitable in bioprocess development laboratories. The first step towards good data management is storing the data and metadata according to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles [18]. Both humans and machines should be able to find the data with metadata and a clear unique identifier. The data should be digitally accessible for the user with the appropriate tool. It should be noted that accessible in this context does not mean that the data are ’open’ or ’free’ but that transparency around the used data concerning its availability is given [19, 20]. Interoperable data means data are presented using vocabulary and data encoding that follows the FAIR data principles and can be read by machines. In order for data to be reusable they need to be rich in metadata and descriptive documentation on the circumstances in which the data were generated. Rich metadata should ideally describe the data in a meaningful way including the setup and context of the experiment, technical setting, and information about the provenance [8, 18, 21–23]. | |||
Revision as of 00:16, 20 April 2024
| Full article title | Digitalization concepts in academic bioprocess development |
|---|---|
| Journal | Engineering in Life Sciences |
| Author(s) | Habich, Tessa; Beutel, Sascha |
| Author affiliation(s) | Leibniz University Hannover |
| Primary contact | Email: beutel at iftc dot uni dash hannover dot de |
| Year published | 2024 |
| Volume and issue | 24(4) |
| Article # | 2300238 |
| DOI | 10.1002/elsc.202300238 |
| ISSN | 1618-2863 |
| Distribution license | Creative Commons Attribution-NonCommercial 4.0 International |
| Website | https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/elsc.202300238 |
| Download | https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/elsc.202300238 (PDF) |
 |
This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
Digitalization with integrated devices, digital and physical assistants, automation, and simulation is setting a new direction for laboratory work. Even with complex research workflows, high staff turnover, and a limited budget, some laboratories have already shown that digitalization is indeed possible. However, academic bioprocess laboratories often struggle to follow the trend of digitalization. Due to their diverse research circumstances, high variety of team composition, goals, and limitations, the concepts are substantially different. Here, we will provide an overview on different aspects of digitalization and describe how academic research laboratories successfully digitalized their working environment. The key aspect is the collaboration and communication between IT-experts and scientific staff. The developed digital infrastructure is only useful if it supports the laboratory worker and does not complicate their work. Thereby, laboratory researchers have to collaborate closely with IT experts in order for a well-developed and maintainable digitalization concept that fits their individual needs and level of complexity to emerge. This review may serve as a starting point or a collection of ideas for the transformation toward a digitalized bioprocess laboratory.
Keywords: academic laboratories, automation, bioprocess, digitalization, FAIR data, Laboratory and Analytical Device Standard (LADS), Standardization in Lab Automation 2 (SiLA2)
Introduction
In contrast to industry, academic research laboratories require more flexibility than production lines. Besides the need for flexibility, loss of knowledge due to high staff turnover in universities is another challenge that makes full laboratory digitalization hard to achieve. [1-4] "Digitalization" (what we'll be addressing) refers to an entire process, workflow, or laboratory infrastructure, whereas "digitization" refers only to the procedure of converting something analog to a digital format (e.g., digitizing a standard operating procedure [SOP] from a piece of paper to a digital file). [5, 6] Working in digital laboratories has the potential for error reduction, prevention of data loss, improved data integrity, faster workflow development times, possible reduction of chemical and material use, and higher sample throughput, leading to modern, transparent, and reproducible research and biomanufacturing. [7-14]
Developing a digitalization strategy for an academic bioprocess laboratory is an interdisciplinary task. Laboratory workers have to work closely with IT experts in order to achieve a digital infrastructure that can be maintained and is supporting their laboratory work rather than hindering it. [5, 7, 9, 15, 16] This kind of collaboration is rarely found in academic bioprocess laboratories, so even the smallest digitalization task can be a major challenge. Hardware vendors selling their devices with proprietary software and restrictive access and information about their interfaces is making digitalization even more difficult. [1, 7, 17] Therefore, academic researchers working in the field of digitalization are developing concepts that are fitting their individual needs at the time. This is why there are manifold digitalization concepts throughout academic laboratories.
This review will present different digitalization concepts throughout academic bioprocess development laboratories. Before looking at different individual concepts in more detail, general aspects like FAIR data (data that is readily findable, accessible, interoperable, and reusable), standard communication protocols, digital twins, and interaction with laboratory devices will be addressed.
Basic concepts of digitalization
FAIR data
With the progressing digitalization of laboratories, the generated amount of data is steadily increasing. Therefore, good data management systems will become inevitable in bioprocess development laboratories. The first step towards good data management is storing the data and metadata according to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles [18]. Both humans and machines should be able to find the data with metadata and a clear unique identifier. The data should be digitally accessible for the user with the appropriate tool. It should be noted that accessible in this context does not mean that the data are ’open’ or ’free’ but that transparency around the used data concerning its availability is given [19, 20]. Interoperable data means data are presented using vocabulary and data encoding that follows the FAIR data principles and can be read by machines. In order for data to be reusable they need to be rich in metadata and descriptive documentation on the circumstances in which the data were generated. Rich metadata should ideally describe the data in a meaningful way including the setup and context of the experiment, technical setting, and information about the provenance [8, 18, 21–23].
Abbreviations, acronyms, and initialisms
- AI: artificial intelligence
- AR: augmented reality
- CLI: command line interface
- DoE: design of experiments
- ELN: electronic laboratory notebook
- FAIR: findable, accessible, interoperable, reusable
- GUI: graphical user interface
- IoT: internet of things
- LADS: Laboratory and Analytical Device Standard
- LIMS: laboratory information management system
- ML: machine learning
- NUI: natural user interface
- OPC UA: open platform communications unified architecture
- SiLA2: Standardization in Lab Automation 2
- VR: virtual reality
- VUI: voice user interface
References
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. A fair amount of grammar and punctuation was updated from the original. In some cases important information was missing from the references, and that information was added.
