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Electronic Lab Notebooks (ELNs)


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Introduction

An Electronic Lab Notebook (ELN) is a software meant for documenting experiments, resulting research data, and processes. In its most basic form, an ELN replicates an interface similar to a page in a physical lab notebook, that allows input via computer or mobile device. More advanced forms often offer other features such as protocol templates, collaboration tools, support for electronic signatures, and the ability to manage the lab inventory. Ultimately, ELNs will replace physical lab notebooks as part of the digital transformation, (Kwok, 2018; Lindstädt et al., 2019; LMA RDMWG, n.d.; Vandendorpe et al., 2024; Vandendorpe et al., 2023; Vieten et al., 2023) as it makes sense to document and handle digital data with a digital tool.

Uses of ELNs

ELNs offer features and functions that can pave the way for significant time savings and knowledge transfer in daily laboratory work. For example, ELNs support the annotation of raw data (e.g. with tags or metadata (Rathmann et al., 2021; Vandendorpe et al., 2024)) without having to switch between different media formats. Annotating data makes it searchable, discoverable, traceable and reusable (Vandendorpe et al., 2024). ELNs also bring data and their description closer together through embedded multimedia files (e.g. videos of experimental setups), links to shared resources (e.g. chemical databases or analysis software), links to other experiments, as well as direct links to (raw) data sets and analysis workflows (Rehwald et al., 2022). ELNs also allow for the versioning of experiment descriptions (Rehwald et al., 2022) and the structuring and visualisation of workflows and processes (Rathmann et al., 2021). ELNs also have the ability to manage inventories of samples, reagents and other supplies, and track equipment and equipment maintenance schedules (LMA RDMWG, n.d.). ELNs also provide for collaboration (LMA RDMWG, n.d.) through a common medium (Rehwald et al., 2022). Last but not least, ELNs provide for auditing (LMA RDMWG, n.d.), security and safety. Indeed, ELNs are fireproof, waterproof and cannot be lost, misplaced or stolen. ELNs can also be automatically backed up. They allow timestamping (RFC 3161 using DFN-PKI) and finalisation to prevent further changes (Rehwald et al., 2022). They also support electronic signatures (Cozat et al., 2021) and require access management (Rehwald et al., 2022).

Benefits and drawbacks

Benefits

  • Boosting efficiency of everyday tasks
  • Connecting to a digital research environment and integration into your Research Data Management (RDM) strategy
  • Complying to the FAIR data principles
  • Contributing to Good Research Practice (GRP)

Drawbacks

ELNs still have some disadvantages compared to physical lab notebooks, as network interruptions can temporarily limit access to data (Kwok, 2018). In addition, they may lack chronological continuity, formatting flexibility, haptic perception and resistance to liquids (for end devices). Another major drawback appears to be data security risks, especially when used to collect and document sensitive data. The best solution to this is the use of private servers on site or private, institutionally based cloud services (Guerrero et al., 2016).

Ergonomics

Several tools and techniques can be used to overcome the difficulties of using a computer when conducting experiments: tablets in the lab, plug-ins (such as voice input and Optical Character Recognition (OCR) plug-ins), linking experiments to raw data files and results, automatic date and time stamping to prove provenance, and integrating ELNs with other research software to capture data and information (LMA RDMWG, n.d.).

The role of ELNs in data security and protection

Some ELNs can help you keep your data secure by meeting the trustworthiness criteria defined in FDA 21 CFR Part 11 (USA) and EU Annex 11 (EU), including the provision of audit capabilities. ELNs can also provide two-factor authentication and unique credentials. Another way to address data security risks is to use of private servers on site or private, institutionally based cloud services (Guerrero et al., 2016). If you are setting up an ELN at your institution, you should also develop a data security plan (LMA RDMWG, n.d.). If you intend to collect sensitive data (e.g. sensitive personal data, sensitive environmental data) with the ELN, you should involve your institution’s data protection officer (i.e. the person who oversees the application of and compliance with regulations designed to protect important information from corruption, compromise or loss within an organisation (Data Management Wiki, 2023; Crocetti et al., 2021)) in the implementation process to ensure compliance with your institution’s data protection regulations and the General Data Protection Regulation (GDPR) (i.e. the regulation “on the protection of natural persons with regard to the processing of personal data and on the free movement of such data” (European Union, 2016)). These regulations may restrict the physical location and transfer of data, excluding the use of cloud-hosted ELNs (Kwok, 2018; Higgins et al., 2022). You can also contact your RDM support team or relevant initiatives (e.g. the National Research Data Infrastructure for Personal Health Data (NFDI4Health)) to find out more about anonymisation and pseudonymisation services, as well as your organisation’s existing policies and procedures for handling sensitive data and how an ELN might fit into this picture. Ideally, this should be done at the stage of writing your Data Management Plan (DMP). It is important to note that privacy issues are not addressed by the ELN itself, but rather by how you set it up and store your data (Vandendorpe et al., 2024).

Selecting an ELN

To select an ELN, we recommend that you define selection criteria that reflect the needs of your institution and labs. You can then use these criteria in a matrix to compare them with the features offered by available ELNs (Vandendorpe et al., 2024), or enter these criteria into the ELN Finder. The ELN Finder is a tool developed together by the University and State Library Darmstadt and the ZB MED - Information Centre for Life Sciences. It is an interactive tool for filtering ELNs based on 40 filter criteria (Vandendorpe et al., 2024).

Important criteria

  • Discipline
  • Proprietary vs. open-source
  • Software as a Service (SaaS) vs. on-premises
  • Performance and stability
  • ELN vs. LIMS: ELNs are sometimes confused with Laboratory Information Management Systems (LIMS). They both streamline laboratory workflow and data management and are complementary, but they have different functionalities and features. A LIMS is a comprehensive software for managing and tracking laboratory operations and data. A LIMS covers sample management and tracking, workflow management and automation and quality control throughout the laboratory. On the other hand, an ELN focuses on experimental data acquisition, experiment documentation and (real-time) collaboration (Integrated Software Solutions, 2023; Halton, 2024).

Features for selecting an ELN based on the complexity of the system

Basic systems

Basic systems are tools used as ELNs that were not originally designed for this purpose (Dirnagl & Przesdzing, 2016). They allow traditional text entry, which can be searched and made available on multiple devices via the cloud. They also allow files (e.g. images, spreadsheets) to be attached to text and the attachments to be viewed, annotated and searched. Such systems include Word, Evernote and Dropbox. Basic systems have the advantage of being inexpensive, easily accessible and already familiar to many researchers. However, considerable effort is required to achieve the functionality of a traditional ELN with such a system (Vandendorpe & Lindstädt, 2020; Bobrov et al., 2021). They typically do not include any kind of audit trail (i.e. saving all previous versions of a note and logging changes), certification (Dirnagl & Przesdzing, 2016), or the ability to digitally sign or timestamp entries, with workarounds such as signing exported files required (Higgins et al., 2022).

Specialised systems

Specialised systems are ELNs that allow unstructured data entry and offer a wide range of functionalities (Dirnagl & Przesdzing, 2016). These specialised systems have all the features of basic systems and more; including the ability to integrate original data (Dirnagl & Przesdzing, 2016) and capture freehand and chemical drawings (LMA RDMWG, n.d.). Specialized systems also provide subject-specific features/editors and templates, task allocation between colleagues, complex rights management (i.e. with roles and individual rights) within institutes and workgroups (Dirnagl & Przesdzing, 2016), basic inventory management (i.e. the ability to manage the quantity and location of samples and reagents) and extensions/APIs for customisation (Vandendorpe & Lindstädt, 2020; Bobrov et al., 2021).

Finally, they comply with the FDA 21 CFR Part 11. This part requires version control (Dirnagl & Przesdzing, 2016), full audit trails, electronic signatures on completed records, witnessing and freezing (i.e., making a record immutable after the author and a witness have signed it), and prevents the author from deleting records. Examples of specialised systems: include Arxspan, Biovia, eCat, eLabJournal, eLabFTW, iLabber, Labfolder, and RSpace.

Specialised systems have the advantage of offering easy entry through free online versions and the ability to install and store data locally. They can also be integrated into your own IT environment via Application Programming Interfaces (APIs) (Vandendorpe & Lindstädt, 2020; Bobrov et al., 2021).

High-end systems

High-end systems are ELNs that are offered as a module of a comprehensive laboratory management system (Dirnagl & Przesdzing, 2016). They have all the features of specialised systems and more. High-end ELNs integrate a LIMS (e.g. IBDS E-WORKBOOK, iLAB Laboratory Execution System) that allows complete tracking of samples and reagents through all experiments. They are also directly linked to laboratory equipment such as microscopes, spectrometers and sequencers. High-end systems provide workflows for specific samples, experiments and tasks. They can automatically provide raw data and metadata (e.g. date of last calibration) from laboratory instruments. Finally, high-end systems allow data mining (i.e. the aggregation and clustering of structured data) and analysis of raw data within the system. Such systems include Hivebench and Limsophy. High-end systems have the advantage of completeness of functionality and all their components fit together seamlessly. However, they can be complex to use and few allow users to use a language other than English (by 2020) (Dirnagl & Przesdzing, 2016). In addition, they are often cloud-hosted solutions, which means that data control and security remain in the hands of the ELN provider (Vandendorpe & Lindstädt, 2020; Bobrov et al., 2021), and that they are more expensive (Higgins et al., 2022). They also use proprietary formats, which can increase the risk of vendor lock-in (i.e. making users dependent on the ELN and unable to use their data with another ELN without significant switching costs (Vendor Lock-In, n.d.)) (Vandendorpe & Lindstädt, 2020; Bobrov et al., 2021).

Implementing the ELN

Steps

To successfully select and maintain an ELN, you must first analyse the current situation and assess your institution’s needs. You will then need to define selection criteria against which you will test pre-selected ELNs to choose the one that best meets your needs. Once you have selected an ELN, you need to licence it and then introduce it to your institution’s research groups. The introduction phase consists of ensuring that all technical requirements (such as a stable wireless connection) are met, creating and implementing a distribution plan, training users and setting up support services. Finally, you will need to monitor the application (Adam & Lindstädt, 2021; Vandendorpe et al., 2024).

Timeframe

Testing pre-selected ELNs takes three to six months, depending on when sufficient knowledge of the products’ suitability has been acquired (Vandendorpe et al., 2024). Setting up an ELN (especially the inventory functions) initially takes time. However, having an ELN saves time in the long term, e.g. by linking experiments to samples and by managing and ordering supplies (LMA RDMWG, n.d.).

eLabFTW

eLabFTW is a free and open-source (Rathmann et al., 2021) ELN developed by Deltablot (France) (Cozat et al., 2021). eLabFTW is a web-based application that runs on all major operating systems. It can be used in research and teaching (e.g. for laboratory exercises) (Cozat et al., 2021). In addition to a search function (Rathmann et al., 2021) and the ability to take notes, eLabFTW allows you to create experiments and collections of experiments (Cozat et al., 2021), log work steps, document data and results (Rathmann et al., 2021) with metadata, and create a database for a variety of objects (e.g. lab materials, lab equipment). It is also a good collaborative tool with which you can manage the lab (Cozat et al., 2021). eLabFTW will also help you comply with GRP as it prevents data deletion and provides immutability through timestamps (Rathmann et al., 2021). Being open source, it is freely modifiable and highly customisable. It also benefits from community development, by scientists for scientists (Cozat et al., 2021). Its interface has been translated into 17 languages as of June 2024 (CARPi, 2021). To see what it looks like, watch this video tutorial with eLabFTW and Labfolder from ZB MED on YouTube.

Resources

More Info

It is also advisable to talk to other members of your research community to find out about their experiences with ELNs (e.g. advantages and disadvantages of different tools, selection process).

If you have any further questions about the management and analysis of your microbial research data, please contact us: helpdesk@nfdi4microbiota.de (by emailing us you agree to the privacy policy on our website: Contact)

References

  1. Kwok, R. (2018). How to pick an electronic laboratory notebook. Nature, 560(7717), 269–270. https://doi.org/10.1038/d41586-018-05895-3
  2. Lindstädt, B., Vandendorpe, J., & von der Ropp, S. (2019). Research Data Management.
  3. LMA RDMWG. Retrieved September 1, 2023, from https://datamanagement.hms.harvard.edu/collect-analyze/electronic-lab-notebooks
  4. Vandendorpe, J., Adam, B., Wilbrandt, J., Lindstädt, B., & Förstner, K. U. (2024). Ten simple rules for implementing electronic lab notebooks (ELNs). PLOS Computational Biology, 20(6), 1–9. https://doi.org/10.1371/journal.pcbi.1012170
  5. Vandendorpe, J., Wilbrandt, J., Grünwald, K., & Magel, M. (2023). NFDI4Microbiota World Café on Electronic Lab Notebooks (ELNs).
  6. Vieten, A., Lindstädt, B., & Vandendorpe, J. (2023). Introduction to Research Data Management.
  7. Rathmann, T., Kastrup, D., Weiß, B., Lindstädt, B., Shutsko, A., & Vandendorpe, J. (2021). Workshop on Research Data Management.
  8. Rehwald, S., Leimer, S., Lindstädt, B., Shutsko, A., & Vandendorpe, J. (2022). Workshop on Research Data Management in Medical and Biomedical Sciences.
  9. Cozat, R., Grenzebach, G., Lindstädt, B., Shutsko, A., & Vandendorpe, J. (2021). Workshop on Research Data Management.
  10. Guerrero, S., Dujardin, G., Cabrera-Andrade, A., Paz-y-Miño, C., Indacochea, A., Inglés-Ferrándiz, M., Nadimpalli, H. P., Collu, N., Dublanche, Y., De Mingo, I., & Camargo, D. (2016). Analysis and Implementation of an Electronic Laboratory Notebook in a Biomedical Research Institute. PLOS ONE, 11(8), e0160428. https://doi.org/10.1371/journal.pone.0160428
  11. Data Management Wiki. (2023). Data protection officer. DAMA-NL. https://datamanagement.wiki/role/data_protection_officer
  12. Crocetti, P., Peterson, S., & Hefner, K. (2021). What is data protection and why is it important? TechTarget. https://www.techtarget.com/searchdatabackup/definition/data-protection
  13. European Union. (2016). EUR-Lex - 02016R0679-20160504 - EN - EUR-Lex. Europa.eu. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A02016R0679-20160504
  14. Higgins, S. G., Nogiwa-Valdez, A. A., & Stevens, M. M. (2022). Considerations for implementing electronic laboratory notebooks in an academic research environment. Nature Protocols, 17(2). https://doi.org/10.1038/s41596-021-00645-8
  15. Integrated Software Solutions. (2023). What Is The Difference Between ELN And LIMS? LinkedIn. https://www.linkedin.com/pulse/what-difference-between-eln-lims-integrated-software-solutions-ltd/
  16. Halton, R. (2024). LIMS vs ELN: Understanding The Difference & How To Choose The Right One. Sapio Sciences. https://www.sapiosciences.com/blog/lims-vs-eln
  17. Dirnagl, U., & Przesdzing, I. (2016). A pocket guide to electronic laboratory notebooks in the academic life sciences. F1000Res., 5, 2.
  18. Vandendorpe, J., & Lindstädt, B. (2020). Research Data Management.
  19. Bobrov, E., Adam, L.-S., Söring, S., Jäckel, D., Herwig, A., Lindstädt, B., Vandendorpe, J., & Shutsko, A. (2021). Workshop on Research Data.
  20. Vendor lock-in. Retrieved October 23, 2024, from https://en.wikipedia.org/wiki/Vendor_lock-in
  21. Adam, B., & Lindstädt, B. (2021). ELN Guide. ZB MED - Informationszentrum Lebenswissenschaften.
  22. CARPi, N. (2021). Frequently asked questions. eLabFTW. https://doc.elabftw.net/faq.html