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Electronic Lab Notebooks (ELNs)


Information technology continuously develops at a fast pace. For example, the smartphones we all carry on a daily basis in our pockets are much faster than a supercomputer from the 80s which you could barely fit in an average bedroom. Unfortunately, this accelerated advancement of information technology has not yet fully reached our chemical laboratories. The paper laboratory notebook is just as popular and widespread as it was 100 years ago, with the very essence of how chemical research data is documented - literally with pen and paper - having only made little progress over the last century.

Transferring the advancement of the information technology into the lab (smartlab) does not necessarily mean tablets & touch screens in every corner of your laboratory. The term Smartlab is more about the interconnectivity of the entire workflow and handling research data according to the FAIR principles. ELNs help to assign rich metadata to experiments and make the collected data traceable as well as reusable. With the possibility to transfer data from measurement devices directly into the ELN, the ELN is the central storage with all data in one place. From the ELN, data can be easily published into repositories and transferred to suitable long-term archiving.

One of the most important things, that distinguishes an ELN from a white blank paper is the possibility to document metadata in a structured and ideally in a human as well as machine-readable way. See the following overview for more information on the differences between blank sheets, ELNs and laboratory information management systems (LIMS).

Simple system

  • Enter text
  • Add notes
  • Add files as attachments
  • Sharing
  • Searching
e.g., Evernote GoogleDrive, Dropbox, MS Sharepoint

Electronic Lab Notebook (ELN)

  • Structured metadata in human and machine-readable formats
  • Discipline-specific functions / editors
  • Rights management
  • Audit trail
  • API
e.g., Labfolder, RSpace, eLabFTW, Labguru

Laboratory Information Management System

  • Sample management
  • Instrument integration
  • Electronic signatures
  • Reporting or statistics modules
e.g., Benchling, Starlims, Limesophy

Advantages of an ELN

ELNs help to link experimental descriptions directly to the collected data so that all information can be found in one place. Furthermore, data loss is avoided by secure data storage and backups. Storing all the data in one central place helps also with knowledge management because the data is easily findable and accessible, even for new members in a research project. The biggest advantage of an ELN is that metadata is stored in a structured and standardised way. This also helps with publishing research results and transferring research data to a repository.

Avoid Data Loss

  • Linking experimental descriptions to collected data (analogue and digital)
  • Secure data storage, backups

Knowledge Management

  • Data is findable
  • Data is accessible
  • Data is available, even after change of personnel
Data LifeCycle

Standardised Documentation

  • Structured and standardised collection of metadata
  • Generation of interoperable (meta)data


  • Data provision for publication of research results
  • Simple transfer of data to repositories

FAIR Image Attribution: SangyaPundir, CC BY-SA 4.0.

The right ELN

To date, there is not one ELN, that fits the requirements for all chemical disciplines. Furthermore, only a few ELNs meet the basic requirements for chemical sciences. This is most likely due to the challenges arising from drawing and processing chemical structures, a crucial and central step in being able to correlate research data to the corresponding chemical reaction or structure. The organisation of work with ELNs that support chemistry-specific functions can offer several benefits, such as the availability of chemical structure identifiers and standardised formats as well as the integration of manifold tools and workflows that facilitate scientific work. The direct integration of tools enables a direct support of the scientist without the need to search for additional services and software, thus bringing new developments and information directly to the scientists’ awareness. Some Open Source examples for systems in chemistry that offer the necessary support for chemical structures include Indigo-ELN, LabTrove, OpenEnventory, and Chemotion ELN.

Therefore, choosing the right ELN is crucial for a successful implementation of an ELN in a laboratory. Three steps should be taken to establish an ELN: 1) Needs assessment, 2) Testing and 3) Introducing the chosen ELN.

Needs assessment
  • Analysing current situation (budget, IT resources, software environment)
  • Definition of important features
  • ELN concept (generic, discipline-specific)
  • Drawing on experiences of other research Institutions
Testing the selected products
  • Demo versions or free trial access for individual users
  • Testing no more than 2-3 ELNs
  • In-depth testing using real-life use cases from the lab
Introducing the chosen ELN
  • Run training courses, training material
  • Designate contact persons from the test team
  • Continuous mentoring

Criteria for selecting an ELN

License modelCommercial ELNs mostly use proprietary file formats, the source code is closed source and a license fee is charged. Open source ELNs are free of charge, use open standard formats, source code is openly accessible and further developed by an active developer community. Implementation and maintenance require the appropriate human resources.
Providing modelSoftware can be provided either in a cloud (SaaS - Software as a Service) or server-based for local installation at the customer's site (on-premises).
File storageData storage in the cloud of the ELN provider (or their service provider, e.g. in the EU) or the possibility of storage on own local servers/in the own cloud are offered.
ConceptConcept refers to the subject-specific orientation. Generic tools can be used in all subjects, but usually do not provide specific features, such as structure editors for chemistry.
Standard interfaceStandard interfaces enable data exchange with external programs, e.g. ChemDraw, Marvin JS, ChemDoodle, MS Office, Libre Office, ImageJ, Slack
Pragramming interfacesAn API is needed for integration into an existing IT environment, e.g. own storage solutions.
File import/exportExport/import of both human- and machine-readable formats should be possible (e.g. pdf, zip, csv, json, xml), ideally also larger units, such as the ELN.
MetadataProviding metadata is a crucial factor in ensuring findability and understanding of data. Metadata should be recorded in a structured, machine- and human-readable format.
CollaborationCollaboration simplifies the exchange of data and results. It should be possible to define roles, e.g. who has access and to what extend (role management).
Sharing/PublicationELNs allow direct exchange of data with portals for publishing/sharing research data.
SearchThe search function allows data to be retrieved and filtered.
TemplatesTemplates are used for time saving and standardization. One advantage is the option to create templates according to one’s own needs.
WorkflowsDocumenting processes significantly increases efficiency, traceability and contributes to transparency and transporting of know-how.
Security of evidenceEvidence security means the traceable documentation in compliance with legal regulations, e.g. providing the entries with a time stamp, electronic signature, versioning.
Rule complianceThe tool must enable compliance with regulatory requirements, e.g. GLP, GCP.

Table Attribution: ZB MED PUBLISSO ELN-Filter, 2921-06 english.

Finding the right ELN

There are a plethora of ELNs available and it is not always easy to figure out which is the right one for you. To help you choose an ELN, you can use the ELN Finder. Here, more than 40 filter criteria are available. Filter criteria are clearly divided into categories and the result list of the identified ELN tools are displayed in an overview alongside Brief descriptions of the individual tools.