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Entry 01

Why lab records end up everywhere: The divide between the creator, the user and the payer

Published · Anant Mirjankar (Cofounder)

In short

Lab records being scattered across physical notes (my preferred method), 100-tab spreadsheets, shared drives and instrument PCs often comes down to the software being for people who read reports, not the people at the bench who fill it in. When a tool is slower than a piece of paper or Excel, scientists work around it, and those workarounds are often what make an old result hard to find.

Most labs have a version of the same Tuesday morning (a canon event): someone needs a result from last year, and the next hour goes on finding out where it might be. This note is about why records end up in so many places despite paying for software that was supposed to fix it, and what the published evidence says. It is written for PIs, lab managers and anyone in a small biology lab or biotech who has faced this Tuesday.

Where does a lab’s record actually live?

Ask anyone in the lab where last year’s notes or results are and most of the time you’ll get a list.

  1. The notebooks the previous postdoc left behind, and you trying to piece together what they wrote.
  2. ELNs (electronic lab notebooks), for those who do actually use them.
  3. A spreadsheet buried in a Google Drive called final_v4_REAL(2).xlsx.
  4. The instrument PC with 2 TB of reruns.

The instrument PC is worth a closer look, because it often gets treated as storage (the eternal source of truth until it’s wiped) when it was never meant to be. Core facilities say so in their own rules. A UK microscopy facility’s 2016 policy page says files on its microscope computers are normally deleted 30 days (14 on some systems) after they are created or modified, and that users must copy their data elsewhere[1]. A US flow cytometry facility deletes most files left on its instrument computers every day[2]. A US light microscopy core purges its drives when space is needed, after a week’s notice to active users, and accepts no responsibility for what is left on them[3]. These are facility rules, not measurements. We found no study that counts how much is lost when an instrument PC is wiped or replaced.

Honestly, on their own none of these are wrong and are often enough to “get by”. The issue comes when an experiment is split across all of these with no chain linking them together. Finding the result then means knowing who ran it, roughly when, what they called the file, and which of the four places they trusted that Tuesday. That is the needle in the haystack, and the haystack is mostly made of workarounds.

Why do labs work around their own software?

A senior R&D consultant who advises large research organisations in India put it to us plainly: there is a huge gap between the people who create the lab software and the people who do research. Because of that gap, many tools are complicated and end up unused. Scientists choose what is convenient and familiar, even when it is slower in the long run and, on paper, less efficient.

In the labs we have spoken to, we have seen the same pattern, and a second split inside it.

  • The people who pay for lab software are usually administrators, managers or the PI. They want rich, structured data: reports, audit trails, dashboards.
  • The people who supply that data are at the bench. They want to write down what happened quickly, in a form that matches how they think about the experiment, and get back to work.

These roles overlap, especially in small labs, and the person at the bench is also the one who has to find the data again later. The tension is between the two jobs, not two groups of people.

Serving both is quite difficult. Every required field, dropdown and template that makes a report richer is another step for the person who needs to fill that in, resulting in a scientist filling up their day with admin work. Software designed without bench experience tends to optimise for the person reading the output, because that is who usually pays the software provider. The scientist who finds the tool slow does not complain to the vendor; instead, it often becomes part of the job, and they end up opening a spreadsheet.

A few papers show this happening. At a research institute in Barcelona, 28 scientists from eight labs spent three months trying out two electronic notebooks (most tried only one of them), and after discussing it, every lab preferred the general note-taking program to the purpose-built scientific one. The authors called that puzzling[4]. A group at Imperial College London that rolled out a notebook in its own lab wrote that such a system should not add a net burden for the user[5].

What do the studies show?

We looked for published evidence on how widely lab software is actually used, and why it is not. These are the studies we could check against the original paper or page. All are small, and most are local.

Study Who was asked What it found Limits
Kanza et al., 2017[6] Academic researchers in surveys run from the University of Southampton (169 in the barriers survey) 98% knew of electronic lab notebooks, 11% used one in their group, 76% were strongly interested in using one or finding out more. Among the most cited barriers were costs (74% for upfront costs, 93% for ongoing costs), difficulty capturing some kinds of information (80%) and entering data twice (74%); 22% said the tools were too difficult. Researchers who trialled two prototype notebooks all went back to Word documents. Some authors work for a notebook company, so we used only the university parts. Chemistry weighted.
Jäger et al., 2026[7] 55 researchers at a university medical centre in Halle, Germany Of 52 with documentation experience, 45 used spreadsheets and 44 paper notebooks. User-friendliness was the most often chosen criterion for an electronic notebook (47 of 52; several could be chosen). A rollout reached 237 registered users in 32 teams in 22 months. One institution. Survey answers are self reported; user numbers come from platform records. Registered users are not active users.
Foster et al., 2022[8] Users of a notebook licensed across a US medical school 60 of 233 users replied. Half of those used it for at least half their recording. 26% response rate. One school.
NFDI4Chem survey, 2020[9] German chemists Fewer than one in five used an electronic lab notebook. Sample size not given on the results page. Chemistry, Germany, six years ago.
NFDI4BIOIMAGE survey, 2021[10] 203 people working in bioimaging, about 145 of them in Germany, including research support and facility staff Knowledge and use of electronic lab notebooks was limited, although respondents saw their value. Self selected. One imaging community.
UCL data survey, 2016[11] Researchers and research students at one London university (306 completed the whole survey) A personal computer was the most common place to keep data being worked on (196 of 433 who answered, about 45%). All disciplines. One university, ten years ago.

Published evidence on how far lab software is adopted, and why. Each row was checked against the original paper or page on 13 October 2026.

Read together, these say something consistent. The tools exist, and many scientists know about them and even say they are interested. Many still do not use them, and the reasons they give range from cost and ease to information the tool cannot capture, rather than a dislike of software in general. When the official tool does not fit, records go somewhere that does. What is convenient at the time becomes a hunt for the future.

The buyer and user split is the part we would most like to see measured, and no study we found does that directly. The closest is Foster and colleagues[8]: a school licensed a notebook for everyone, and half of the users who answered used it for at least half their recording. That is not a failure, but it shows a licence alone does not make a tool part of how people work.

Is this only a lab problem?

While Reddit’s r/labrats shows how this issue is common within labs and academia, healthcare has been studied more closely and the pattern looks familiar, though laboratories are not hospitals and we offer this only as an analogue. In an observational study of 57 US physicians, every hour with patients came with nearly two further hours of record keeping and desk work during the clinic day[12]. At five hospitals, researchers studying barcode medication systems recorded 15 types of workaround and concluded that design and workflow shortcomings encouraged them[13].

Who is at fault?

Most of the people we spoke to have quite high turnover in their labs, with students filling in for the semester or rotations having fast handover rates. And what was a convenient workaround at that time becomes a chore for the next person. However, we don’t think it’s right to blame the person who did the workaround. In most cases they did it because the software simply wasn’t built for what they needed to record, or was too complicated to use in the middle of an experiment. In the Southampton study, 74% named having to enter the same data twice as a barrier[6], and in the Halle study, user-friendliness was the criterion chosen most often for an electronic notebook, picked by 90%[7]. Science and experiments already take up enough time and effort. When time is of the essence, the discovery takes priority over admin work.

What good lab software should do instead

Too often, a lab has to change how it works to suit a new piece of software: new templates, new habits, new places to type things. We think it should be the other way round. Good software should adapt to the lab, fitting the notebooks, spreadsheets and folders people already use, so that scientists do not have to choose between doing the work and recording it. It is the problem we spend our days on at Vantaria Labs.

Sources

  1. UCL Laboratory for Molecular Cell Biology. Light Microscope Data Policy. University College London, . ucldata.atlassian.net/wiki/spaces/LMCBLMic/pages/38011058/LMCB+Light+Microscope+Data+PolicyChecked
  2. Cornell Institute of Biotechnology. Flow Cytometry Facility data retention policy. Cornell University, date unknown. biotech.cornell.edu/core-facilities-brc/facilities/flow-cytometry-facility/data-retention-policy-flow-cytometryChecked
  3. University of Kentucky Light Microscopy Core. Data retention. University of Kentucky, . research.uky.edu/light-microscopy-core/resources/data-retentionChecked
  4. Guerrero S, Dujardin G, Cabrera-Andrade A, et al. Analysis and implementation of an electronic laboratory notebook in a biomedical research institute. PLoS One 11(8):e0160428, . doi.org/10.1371/journal.pone.0160428Checked
  5. Higgins SG, Nogiwa-Valdez AA, Stevens MM. Considerations for implementing electronic laboratory notebooks in an academic research environment. Nature Protocols 17:179-189, . doi.org/10.1038/s41596-021-00645-8Checked
  6. Kanza S, Willoughby C, Gibbins N, et al. Electronic lab notebooks: can they replace paper?. Journal of Cheminformatics 9:31, . doi.org/10.1186/s13321-017-0221-3Checked
  7. Jäger C, Zagrodnick M, Schwede B, Christoph J. A case study of rolling out an electronic lab notebook, with a survey-informed onboarding framework (short description, not the full title). PLoS One 21(8):e0355558, . doi.org/10.1371/journal.pone.0355558Checked
  8. Foster ED, Whipple EC, Rios GR. Implementing an institution-wide electronic lab notebook initiative. Journal of the Medical Library Association 110(2):222-227, . doi.org/10.5195/jmla.2022.1407Checked
  9. Results of the first NFDI4Chem community survey published. NFDI4Chem, . www.nfdi4chem.de/results-of-the-first-nfdi4chem-community-survey-published/Checked
  10. Schmidt C, Hanne J, Moore J, et al. Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey. F1000Research 11:638 (version 2), . doi.org/10.12688/f1000research.121714.2Checked
  11. Fellous-Sigrist M. UCL researchers and their research data: practices, challenges and recommendations (report on the 2016 RDM survey). UCL Library Services, date unknown. discovery.ucl.ac.uk/id/eprint/1540140/Checked
  12. Sinsky C, Colligan L, Li L, et al. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Annals of Internal Medicine 165(11):753-760, . doi.org/10.7326/M16-0961Checked
  13. Koppel R, Wetterneck T, Telles JL, Karsh BT. Workarounds to barcode medication administration systems: their occurrences, causes, and threats to patient safety. Journal of the American Medical Informatics Association 15(4):408-423, . doi.org/10.1197/jamia.M2616Checked

Change log

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