# Top 5 ELNs for Collaborative Multi-Site Research Projects The best ELN for collaborative research is the one every site will actually use, not the one with the longest feature list. An electronic [lab notebook](/knowledge/molecular-biology/lab-notebook) (ELN) is a software system that replaces the paper notebook for recording experiments, attaching raw data, and documenting decisions, and multi-site projects fail on adoption far more often than they fail on features. The rule of thumb: pick a platform that all partners can access under their existing institutional agreements, that supports [structured data](/blog/data-analysis/what-is-structured-data) entry at the sites generating the most data, and that has a documented path for getting data out again. This guide walks through five concrete options, a side-by-side research software comparison, the criteria that decide real choices, three worked scenarios, and the mistakes that sink multi-site rollouts. ## What an ELN Must Do for a Multi-Site Project A single-lab notebook mostly needs to record what happened. A multi-site notebook has to do more, because the people reading an entry may be in a different country, a different company, or a different scientific discipline from the person who wrote it. At minimum, a shared ELN across sites needs: 1. **Common templates.** When site A records a cell passage and site B records a synthesis batch, the project needs structured fields so results can be aggregated. Free-text notebooks resist aggregation. 2. **Granular access control.** Partners need to see shared project space without seeing each lab's unrelated work. Competitive or clinical sensitivities make this non-negotiable. 3. **Timestamped, attributable records.** Every entry should show who wrote it and when, in a form that survives personnel turnover and supports publication or patent questions later. 4. **Export and interoperability.** Data written into a notebook must be exportable in standard formats. The proliferation of diverse ELN platforms is itself a documented problem, which is why interoperability tools now exist to sync data between disparate systems through their programming interfaces [1]. 5. **Institutional support.** A rollout across five universities needs training, templates, and a help path at each site. Library-led implementation support has proven effective at the institutional level, as described below [2]. ## Evaluation Criteria That Decide Real Choices ![Decision path asking about budget, discipline and existing Microsoft 365 licensing to point a consortium to one of five ELN options.](/article-images/illustrations/best-eln-for-collaborative-research-eln-choice-path.webp) Before comparing specific best ELN for collaborative research options, define the criteria your project will actually weigh. The six below come up in nearly every multi-site selection process. - **Licensing and access model.** Commercial SaaS (software delivered through a browser subscription) is fast to start but requires a procurement route at every partner. Open-source, self-hosted systems avoid per-seat fees but require server administration and someone accountable for backups. - **Structured data capture.** Look for custom fields, sample registries, and repeated entry forms rather than a blank page. - **Access control and group boundaries.** Check that you can create a project space spanning organizations with controlled roles. - **Audit trail and record integrity.** An audit trail is an immutable log of who changed what and when. Regulated work and industrial partners generally expect one. - **APIs and export.** An API (application programming interface) lets other software read or write your ELN data programmatically. This is what makes later migration and tool integration possible. - **Onboarding effort.** Adoption is the main failure mode. One medical school's institution-wide ELN rollout began with a fifteen-lab pilot before expanding, and the implementing library delivered hands-on training, one-on-one consults, and template notebooks to drive adoption [2]. The same structured evaluation habit applies to adjacent tools. Librarians evaluating reference managers for shared collaborative libraries built a structured comparison matched to user needs and workflows rather than simply picking the product with the most features [3]. Do the same exercise for your notebook. ## Side-by-Side Comparison This table is the core research software comparison for the five platforms covered here. | Platform | Model | Standout strength for multi-site work | Main watch-out | Best fit | |--|--|--|--|--| | Benchling | Commercial SaaS | Deep [molecular biology](/blog/careers/molecular-biology) workflows, registry, and API | Requires a commercial agreement for every partner | Industry-aligned biology consortia | | RSpace | Commercial with community options | Strong integrations with storage and data repositories, flexible templates | Institutional procurement takes time | Academic consortia with data-sharing deliverables | | eLabFTW | Open source, self-hosted or hosted | No per-seat licensing, full control of your server, teams and timestamps built in | You own hosting, backups, and updates | Budget-constrained multi-university projects with IT support | | Chemotion ELN | Open source | Chemistry-specific data models for molecules, reactions, and samples, plus a repository path | Chemistry-centric design is less natural for [general biology](/blog/careers/general-biology) | Chemistry and materials collaborations | | SharePoint (adapted) | Microsoft 365 subscription | Many organizations already license it, strong document collaboration | Needs deliberate adaptation to behave like a notebook | Engineering and mixed academic-industrial projects | ## The Top 5 ELNs for Collaborative Multi-Site Research Projects ### 1. Benchling Benchling is a commercial cloud platform built around molecular biology. Its notebook entries link directly to sequence files, registries of constructs and cell lines, sample inventories, and workflow trackers, so a multi-site [protein engineering](/knowledge/molecular-biology/protein-engineering) or [cell line development](/knowledge/molecular-biology/cell-line-development) project can keep sequences, notes, and results in one linked system. An API allows programmatic access, which matters when sites want to push instrument output or pull results into analysis pipelines. **Pros and cons for collaborative research.** On the pro side, structured biology data models are mature, collaboration inside a single tenant is seamless, and the API is well documented. On the con side, every partner organization needs appropriate licensing, which complicates consortia that include small academic labs alongside companies, and your data lives in a vendor's cloud, so export planning belongs in the contract phase, not the wind-down phase. **Choose it when** the project is biology-heavy, at least one partner is commercial, and funding can cover licenses across sites. ### 2. RSpace RSpace is a commercial ELN with strong roots in academic research [data management](/blog/guides/data-management-basics-principles-processes-and-best-practices). Its templates support structured capture, and its integrations connect notebook entries to external storage and repository systems, which is valuable when a grant requires deposited, shareable data at the end of the project. Community licensing options have made it accessible to academic groups that cannot carry commercial seat costs. **Pros and cons.** The upside is a balance of ease of use with genuine data-management features, including the ability to treat notebook content as part of a broader research data lifecycle rather than a silo. The downside is the same procurement reality as any commercial tool, and the feature set is broad enough that teams should agree on templates early to avoid each site inventing its own structure. **Choose it when** the collaboration spans universities with data-sharing obligations and you want a middle path between a generic document tool and a full industrial platform. ### 3. eLabFTW eLabFTW is an open-source ELN that you host yourself, or through a hosting provider. Because the software is free and open, a consortium of universities can run one instance for the whole project without per-user licensing, and the full server means full control over where data physically resides. It supports multiple teams with permissions, timestamps entries, and exports in common formats. **Pros and cons.** Pros include zero license cost, transparency, and a documented API. The cons are operational rather than functional: someone must install, back up, secure, and update the server, and if that person leaves the project, the notebook becomes a risk. Version updates and security patches are your responsibility in a self-hosted setup. **Choose it when** partners have IT support, data sovereignty or budget rules rule out vendor cloud tools, and the project can commit a named person to administration. ### 4. Chemotion ELN Chemotion ELN is an open-source ELN developed with a chemistry focus, built around molecules, reactions, samples, and analysis data rather than generic documents. For synthetic chemistry or materials collaborations, that domain model removes a great deal of manual structuring that a general notebook would need. **Pros and cons.** Pros include the chemistry-specific data model, open-source availability, and a growing ecosystem around interoperability. A concrete example of that ecosystem is ELNdataBridge, a server-based tool that uses Python APIs to exchange data between different ELN platforms and lets researchers map individual values and entry types from one system to another [1]. Its first demonstrator connected Chemotion ELN with Herbie, a materials-science notebook, showing that chemistry-focused ELNs can now exchange structured data rather than just PDF exports [1]. The con is that its strengths are domain-specific. A multi-site immunology project would fight the data model rather than benefit from it. **Choose it when** the collaboration is in synthesis, catalysis, or materials science, and at least one site already works in the Chemotion ecosystem. ### 5. Microsoft SharePoint, Adapted as an ELN SharePoint is not an ELN out of the box, but a structured evaluation of an adapted SharePoint configuration against established ELN systems found it competitive, ranking second among six alternatives evaluated across data-management categories from collection through publication [4]. The study analyzed six application scenarios spanning small projects with minimal data to large, complex projects, and found the adapted SharePoint particularly effective for engineering research projects involving both academic and industrial partners, comfortably handling datasets on the order of 1000 samples [4]. **Pros and cons.** The big pro is that most universities and companies already license Microsoft 365, so the access problem that stalls multi-site projects largely disappears. Document collaboration, permissions, and search are mature. The cons are that you must invest real design effort to impose notebook-like structure, that chemistry- or biology-specific data models are absent, and that scientific record features such as timestamps and audit trails depend on how the site is configured rather than being inherent. **Choose it when** partner organizations are already on Microsoft 365, the project is engineering or document-heavy, and nobody wants to run a new procurement. ## Emerging Tools to Watch Two developments are worth tracking even though neither is a mainstream selection yet. **IntegriLAB** is a proposed web-based ELN framework that unifies data, document, and code repositories into one system with end-to-end project tracking, demonstrated by integrating DataLad, Overleaf, and GitHub for neuroimaging research. Its distinctive feature is LabTrace, built on low-energy blockchain technology, which certifies research activities through cryptographically signed, immutable records to establish verifiable authorship and data integrity [5]. For multi-site consortia worried about provenance disputes, this direction is significant. **Interoperability layers** such as ELNdataBridge point to a future where each site keeps its preferred notebook and a synchronization layer moves defined data between them [1]. That changes the selection calculus: the question shifts from "which single platform must everyone adopt" toward "which platforms can the bridge connect." In practice, that shift is early, so for projects starting now, a single shared platform remains the safer default. ## Head-to-Head on the Criteria That Matter **Structured capture.** Benchling and Chemotion lead because their data models are domain-native. RSpace is flexible through templates. eLabFTW is adequate for structured entries. SharePoint requires deliberate configuration. **Access control across organizations.** Commercial SaaS tools handle cross-organization guest access through defined processes but require each partner to have appropriate licensing. Self-hosted eLabFTW gives you direct control but puts the burden of correct configuration on your administrator. SharePoint permissions are mature but easy to misconfigure at scale. **Audit trails and record integrity.** Commercial ELNs and eLabFTW provide attribution and timestamping as core behavior. IntegriLAB represents the strongest position in the literature, using signed, immutable records to certify who did what [5]. SharePoint's integrity depends entirely on configuration and governance choices made during setup. **Getting data out later.** Every serious candidate should offer bulk export and an API. The interoperability work around ELNdataBridge exists precisely because researchers otherwise face lock-in when different sites use different systems [1]. Write export testing into your pilot before committing. **Onboarding and support.** This is where rollouts are won or lost. The Indiana University School of Medicine experience is instructive: a small pilot with fifteen labs preceded campus-wide access, and the medical library sustained adoption through training sessions, one-on-one consults, and ready-made template notebooks, reaching 829 users by August 2021 [2]. Ongoing challenges included determining what support to offer beyond initial training and defining what successful adoption means [2]. A multi-site project should plan for the same support structure, distributed across partners. **Governance of the collaboration itself.** Tool choice is only part of the picture. Reviews of funded collaborative research programs consistently identify clear roles, shared expectations, and structured partnership processes as factors in successful delivery, lessons drawn from analyzing dozens of funded collaborative projects through application reviews, interviews, and practitioner surveys [6]. Your ELN configuration should encode those decisions, for example through role-based permissions and mandatory project metadata fields. ## Worked Scenarios ### Scenario A: Three universities plus a biotech startup, protein engineering Sites generate sequencing data, clone construction records, and assay results. Benchling fits the biology data model, and the startup likely already has licenses. The academic sites need procurement support, so start the paperwork at the slowest institution on day one. Agree on entry templates for construct records before the first experiment, because retrofitting structure across three sites is painful. Set API-based export of assay results into the shared analysis pipeline during the pilot month. ### Scenario B: Four EU and US universities, synthetic chemistry consortium, no central budget Licensing five groups on a commercial platform is not feasible, and the sites want data to remain under university control. Run one eLabFTW instance hosted at the lead university, with each site as a team and a shared project group. Alternatively, if the chemistry partners prefer richer reaction modeling, adopt Chemotion ELN and connect to any partner using a different materials notebook through the ELNdataBridge approach demonstrated for Chemotion and Herbie [1]. Either way, assign a named administrator with a backup schedule in writing, and test a full export in month one. ### Scenario C: Engineering consortium with academic and industrial partners, everyone on Microsoft 365 This is the profile where the adapted SharePoint approach performed well, including projects with datasets around 1000 samples and mixed academic-industrial participation [4]. Build a project site with a consistent page template per experiment, enforce metadata columns, and define a permissions matrix that separates shared project space from each partner's internal areas. Budget real effort for configuration and governance, because the platform's competitiveness in the published evaluation came from deliberate adaptation, not from default settings [4]. ## Common Mistakes and Limitations - **Choosing on features instead of adoption.** The most common failure in multi-site projects is a platform that one site loves and three sites abandon. Budget for training and templates the way the Indiana University rollout did, treating the pilot phase as a real evaluation rather than a formality [2]. - **Assuming every site must use the same tool.** It is becoming technically possible to synchronize defined data between different ELN platforms through adapter layers [1]. Still, treat interoperability as a supplement, not a substitute, for a shared working space in most current projects. - **Skipping the export test.** Teams frequently discover export limitations only when a grant ends or a partner leaves. Run a full data export, including attachments and metadata, during the pilot. - **Ignoring procurement timelines.** Commercial licenses can take months to clear at public universities. Start agreements before the science starts. - **Treating the ELN as the whole data plan.** Notebooks capture records, but raw instrument files, code, and analyses often belong in connected repositories. The IntegriLAB design exists because fragmented tools and inconsistent documentation undermine reproducibility even when an ELN is in use [5]. - **Over-trusting configuration defaults.** Access control mistakes in SharePoint-style systems expose data between partners. Have someone who is not the person who configured the permissions review them. - **Expecting one ranking to fit everyone.** In the published six-way evaluation, SharePoint ranked second among the alternatives assessed for engineering project scenarios [4]. A different project profile would order those tools differently, which is why the criteria section above matters more than any single list. A final limitation: vendor capabilities change quickly, and any comparison reflects general product characteristics rather than a guarantee of current pricing or feature availability. Verify against vendor documentation and, where possible, a hands-on pilot with your own data before signing. ## Frequently Asked Questions ### What is the best ELN for collaborative research between multiple universities? There is no single best ELN for collaborative research, only a best fit for your project profile. Commercial platforms like Benchling and RSpace suit funded biology consortia, eLabFTW suits budget-limited academic groups with IT support, Chemotion ELN suits chemistry collaborations, and an adapted SharePoint suits Microsoft 365-based engineering partnerships. Decide by licensing access, data structure, and who will administer the system. ### Do all partner sites have to use the same ELN? Not necessarily, but a single shared platform is simpler today. Tools that synchronize data between different ELN platforms through their APIs now exist and have been demonstrated connecting a chemistry notebook with a materials-science notebook [1]. For most projects starting now, one shared instance remains the lower-risk choice. ### Can an open-source ELN meet institutional and compliance expectations? Yes, if it is operated responsibly. eLabFTW provides team permissions, timestamps, export, and an API, and self-hosting gives you direct control over data location and backups. The compliance burden shifts from the vendor to your administrator, so name that person and document backup and update procedures before the project starts. ### How should a multi-site ELN rollout begin? ![Five ordered steps from agreeing templates through a small pilot to campus-wide access with training and consults.](/article-images/illustrations/best-eln-for-collaborative-research-rollout-sequence.webp) Start with a small pilot before full deployment. The Indiana University School of Medicine implementation began with fifteen research labs, then expanded access campus-wide, supported by library-led training, one-on-one consults, and shared template notebooks [2]. A multi-site consortium should run the same pattern with one representative group per site. ### Can we move data from one ELN to another later? Partially, and with effort. Bulk exports and APIs make migration feasible, and new interoperability tools allow mapping individual values and entry types between different ELN systems through a configuration interface [1]. Plan the export test early, because migration is easier when your data was entered in a structured, consistent way from the start. ### Is SharePoint or a similar general tool a real ELN? An adapted SharePoint can function as one, and it ranked second among six alternatives in a published evaluation across engineering research scenarios, performing well for academic-industrial projects with datasets around 1000 samples [4]. The trade-off is that scientific record features depend on how you configure it, so it works best when partners already share the Microsoft 365 ecosystem and someone owns the governance design. ## Sources 1. [ELNdataBridge: facilitating data exchange and collaboration by linking Electronic Lab Notebooks via API.](https://pubmed.ncbi.nlm.nih.gov/40420243/) 2. [Implementing an institution-wide electronic lab notebook initiative.](https://pubmed.ncbi.nlm.nih.gov/35440896/) 3. [Reference Managers that Support Collaborative Research: Dreaming of the Perfect Fit](https://pubmed.ncbi.nlm.nih.gov/30239298/) 4. [Benchmarking and Lessons Learned from Using SharePoint as an Electronic Lab Notebook in Engineering Joint Research Projects](https://doi.org/10.3390/data10070092) 5. [IntegriLAB: a blockchain-enabled electronic lab notebook for reproducible neuroimaging research.](https://pubmed.ncbi.nlm.nih.gov/42255115/) 6. [What makes a ‘successful’ collaborative research project between public health practitioners and academics? A mixed-methods review of funding applications submitted to a local intervention evaluation scheme](https://pubmed.ncbi.nlm.nih.gov/33472643/)