# Difference between revisions of "Journal:Implementation and use of cloud-based electronic lab notebook in a bioprocess engineering teaching laboratory"

Full article title Implementation and use of cloud-based electronic lab notebook in a bioprocess engineering teaching laboratory Journal of Biological Engineering Riley, Erin M.; Hattaway, Holly Z.; Felse, P. Arthur Northwestern University Email: afelse at northwestern dot edu 2017 11 40 10.1186/s13036-017-0083-2 1754-1611 Creative Commons Attribution 4.0 International https://jbioleng.biomedcentral.com/articles/10.1186/s13036-017-0083-2 https://jbioleng.biomedcentral.com/track/pdf/10.1186/s13036-017-0083-2 (PDF)

## Abstract

Background: Electronic laboratory notebooks (ELNs) are better equipped than paper laboratory notebooks (PLNs) to handle present-day life science and engineering experiments that generate large data sets and require high levels of data integrity. But limited training and a lack of workforce with ELN knowledge have restricted the use of ELN in academic and industry research laboratories, which still rely on cumbersome PLNs for record keeping. We used LabArchives, a cloud-based ELN in our bioprocess engineering lab course to train students in electronic record keeping, good documentation practices (GDPs), and data integrity.

Results: Implementation of ELN in the bioprocess engineering lab course, an analysis of user experiences, and our development actions to improve ELN training are presented here. ELN improved pedagogy and learning outcomes of the lab course through streamlined workflow, quick data recording and archiving, and enhanced data sharing and collaboration. It also enabled superior data integrity, simplified information exchange, and allowed real-time and remote monitoring of experiments. Several attributes related to positive user experiences of ELN improved between the two subsequent years in which ELN was offered. Student responses also indicate that ELN is better than PLN for compliance.

Conclusions: We demonstrated that ELN can be successfully implemented in a lab course with significant benefits to pedagogy, GDP training, and data integrity. The methods and processes presented here for ELN implementation can be adapted to many types of laboratory experiments.

Keywords: electronic lab notebook, good documentation practice, data integrity, experiment workflow, pedagogy

## Background

Data recording and reporting is of highest importance in all types of research. Data that is not recorded or recorded incorrectly is summarily invalid. Academic teaching laboratory courses have emphasized the importance of accurate record keeping and extensively trained students in good documentation practices (GDPs) based on paper lab notebooks (PLNs). Though the use of PLNs has been perfected over several decades, the large data sets generated by many contemporary life science experiments are better managed through electronic laboratory notebooks (ELNs). But the academic community has been generally slow in moving towards the use of electronic laboratory notebooks.[1][2] Lack of resources, non-standardized regulations, data security concerns, and low activation energy for changes contribute to poor adoption of ELN in academia.[3] As a result, only about five percent of academic labs use ELNs.[4] Agencies such as the National Institutes of Health (NIH) routinely emphasize the importance of data sharing and reproducibility. A report from the NIH concluded that the main reason for non-reproducibility of research data is the lack of good documentation methods rather than scientific misconduct.[5] ELNs can facilitate data sharing and simplify good documentation practices, and subsequently improve reliability of scientific data better than PLNs.[6] Also, ELNs can simplify recording and archiving of large data sets such as those generated in -omics research and in core laboratories.[7]

Academic laboratories are beginning to adopt ELNs, encouraged by the recent availability of several open-source, cloud-based ELN software options for life science research.[8] Many vendors have launched no- or low-cost versions of ELN software for academic use.[9] Machina and Wild[10] in their review article categorize the pros, cons, difficulties, and success factors in implementing ELNs in academia. Rubacha et al. classified 35 commercial ELNs in the market and developed guidelines to select the right ELN based on user requirements.[11] A more recent study identified cost and incompatibility across operating systems as the key barriers for adoption of ELNs in academia and provided a framework to build future ELNs based on user feedback results.[12] Some academic laboratories have developed surrogate ELNs by adapting software that were originally not intended for data recording. Examples include the use of Evernote, Google Docs, Microsoft OneNote, and web blogs as ELNs.[13][14][15][16] In addition, the following studies on inclusion of ELN in teaching laboratories have been reported in the literature: an ELN based on the Sakai software was used in an inquiry-based biochemistry teaching laboratory course[17], and the Pebblepad ePortfolio system was used as an ELN in a biochemistry and molecular biology lab course.[18] Weibel has also described the use of Google Docs for a paperless undergraduate physical chemistry teaching laboratory.[19]

In this paper we present the implementation and use of LabArchives, a cloud-based ELN software in our bioprocess engineering laboratory course. The multitude of pedagogical objectives accomplished through the use of LabArchives ELN is summarized in Fig. 1. For students, LabArchives facilitated legible and quick data recording, improved data sharing and collaboration, and streamlined the lab notebook submission process. For instructors, it facilitated real-time monitoring of experiment workflow, ease of grading and feedback, and simplified information sharing with students. For the lab course, it enabled superior data integrity and quality through reliable audit trails and efficient archiving of data. We also present an analysis of students’ responses on the use of the ELN, our development actions to improve student experience, and a proposal of future directions.

 Figure 1: Pedagogical goals accomplished through ELN

### Rationale for using ELN in a teaching laboratory

PLNs have been used by both academia and the industry even since data collection began.[20] PLNs are still widely used, but they cannot accommodate the large sets of data generated by today’s life science experiments.[21] Legibility, data integrity, security, and archiving of PLNs are a huge logistical and financial burden. Inefficiencies inherent in using PLNs costs the drug industry about \$1 billion annually by way of lost data sharing opportunities and redundancy in data generation.[22] ELNs can effectively address several disadvantages associated with PLNs. ELNs facilitate better workflow, quick data retrieval, remote accessibility of data, and enables superior data integrity. A recent article in Science Careers identified knowledge in using ELNs as one of the key tools for successful careers in the science and technology industry.[23] We included ELN training in the bioprocess engineering lab course to better prepare students for careers in the biotechnology industry, to streamline workflow in the teaching lab, to facilitate data sharing and collaboration among student teams, and to create a culture of ELN use for the future workforce.

## Methods

### The bioprocess engineering lab course

The bioprocess engineering lab course is a graduate course offered to students in the Master of Biotechnology program at Northwestern University. The lab course includes hands-on training in bench-scale upstream and downstream bioprocessing methods, good laboratory practice, design of experiments, quality and validation, team skills, and written communication skills. Experiments are done in three- or four-member student teams, and teams perform experiments on a rotating schedule. Students are expected to share data between teams and collaborate in data analysis. All experiments need data collection at multiple time points by different team members which should be recorded using a streamlined process. Experiments in this lab course generate extensive digital data (such as preparative chromatography and bioreactor experiments) which are exported as Excel or CSV files. Students in this lab course primarily have undergraduate degrees in biology, biotechnology, or chemical engineering. Students are distributed in teams based on their educational and cultural backgrounds, gender, and personality type (based on Meyers-Briggs personality type assessment).

### Implementation of ELN in the bioprocess engineering lab course

LabArchives subscriptions were purchased at about the same cost as PLN on a per student basis. Each student created an individual account and therefore we were able to track each entry with username and time stamp. The instructor populated the master ELN with folders and files with information on all experiments. Information included a list of supplies and chemicals needed for the experiment, equipment operating manuals, calibration and preparation procedures, safety information on equipment, and safety data sheet documents. LabArchives was the single point source for information retrieval and data recording. All folder structures and information contained in the folders were then cloned into student notebooks as shown in Fig. 2. LabArchives allows for regular update of information provided by the instructor, so it was possible to communicate new information to students instantly as they became available.

 Figure 2: Screenshot of LabArchives’ document and folder system. All indented titles are contained within the folders above them.

Once students were placed in teams, each team chose a notebook leader in whose electronic notebook the data would be submitted for the entire term. The team notebook leader then shared their notebook and gave the team members the ability to view and edit the notebook as well as to turn in the assignments for grading. The assignments could be submitted by checking the assignment submission button, at which point the notebook is unable to be further edited, and timestamps ensured that the notebooks were submitted on time. The team notebook leader was also able to share their notebook with students in other teams by giving them view-only privileges to certain pages. This guaranteed data integrity while allowing the teams to share their data with others for collaborative learning.

Revisions made to LabArchives entries by students are tracked collectively for the team based on the team’s (notebook leader’s) user identification. But it was required for the student making the revisions to identify themselves and enter the reason for revision in the comments section. Thus all revisions were attributed to a particular student making the revision. Revisions made without identity were considered to be non-compliant and invalid. Attribution at the student level promotes strict compliance from all students, and makes it possible to trace the complete history of changes if necessary.

### ELN user experience evaluation

After the bioprocess engineering lab course offerings in 2015 and 2016, students were asked to complete an exit survey on the use of the ELN for course development proposes. SurveyMonkey was used to deploy the survey questions and collect feedback. The survey included queries related to ELN use, introductory lecture, and compliance, among others. Data related to student experiences in using ELN were extracted from this survey and analyzed. The lab course had enrollments of 38 and 33 students during 2015 and 2016 respectively. Student response counts for the survey were 32 (84.2% response rate) and 23 (70% response rate) during 2015 and 2016 respectively. All students who responded to the survey completed it in entirety.

Survey queries used in this study are shown in Table 1. Student experiences on various attributes important to ELN use were assessed using the Likert Scale (Query 1). Statistical significance of differences in student responses between 2015 and 2016 course offerings were evaluated using a two-sample t-test assuming unequal variances. Time taken for the students to become comfortable with the ELN was quantified as number of weeks (Query 2). Time taken for students to become comfortable with ELN use in a particular year was estimated using a weighted average composite score defined as:

${\displaystyle Compositescore=\Sigma \left(timetakentobecomecomfortable\times \%ofstudentsreportingthistime\right)}$

 1 (poor) 2 (passable) 3 (neutral/adequate) 4 (very good) 5 (excellent) Table 1. Queries used in user experience evaluation survey Query 1: Please rate your experiences in the following attributes in using ELN. Attribute Score (pick one) Introductory lecture Submission Data sharing Accessibility Query 2: How many weeks did it take for you to become comfortable with ELN software (pick one)? 1 2 3 4 5 Still not comfortable Query 3: Which system makes it easier to comply with academic rules and expectations for the lab course (pick one)? Electronic is eastier Paper is eastier Both are same Query 4: Please rate your experiences in completing the following tasks in ELN compared to PLN. Task Score (pick one) Data entry Adding figures Data sharing Editing information

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## Notes

This presentation is faithful to the original, with only a few minor changes to grammar, spelling, and presentation, including the addition of PMCID and DOI when they were missing from the original reference.