This free course is packed with useful information and practical advice that will help you transition from paper to digital tools.
The course will touch upon software-related terms, the different software solutions that are currently out there, and, lastly, the transitioning process from paper to digital.
If you are a complete beginner and you know nothing about the software-related terms, different digital solutions, or introducing the change into your lab, don’t worry! This course will begin with the very basics and gradually build up your knowledge.
Many tools and systems that speed up or automate everyday mundane tasks are already at your disposal. Moreover, the general knowledge about digital transformation needs to be improved, and all the security concerns addressed. Finally, the cultural shift needs to follow by recognizing the added value that digitalization brings about.
You will also receive your own Certificate of Achievement after you have successfully completed the course.
By the end of this free course, you will have a good understanding of the subject and know how to organize your team to evaluate, test, and implement a digital solution. 

SciNote is a trusted solution used by the researchers at the FDA. In this article we have listed critical details about 21 CFR Part 11 compliance.
Historically, companies maintained a paper trail of all their equipment, experiments and results in order to comply with cGMP (Current Good Manufacturing Practices) and cGLP (Current Good Laboratory Practices) regulations. Today, with increasing amount of automatization, digital records and computer managed equipment the volume of digital data created and transferred has rapidly surmounted what is still feasible to maintain in paper records. This situation calls for a radical switch from paper records to digital records.
In a regulated environment, all digital information is controlled by 21 CFR Part 11. This is a set of instructions and guidelines about the creation, authentication and maintenance of digital records.
Compliance is a complex and resource- consuming process, it’s not limited to just buying and installing a “21 CFR Part 11 compliant” software. The whole system needs to be verified and when all the pieces of the puzzle are linked together, the laboratory can say it is 21 CFR Part 11 compliant.
SciNote supports electronic signatures, electronic witnessing, audit trails and more.
If your lab needs to comply with 21 CFR part 11,  GLP or GMP requirements, this is the PDF for you.
GET THE 21 CFR PART 11 COMPLIANCE GUIDE

Evaluating solutions is never easy.
The webinar with Bryan Lowry from the FDA focuses on technical aspects, setting up systems, making sure that they work, looking at capabilities of the software solutions and other processes one has to take into account when selecting a new lab software.
Acquiring the software for a lab and meet the compliance requirements is challenging, but it can be done right if the team is well prepared.

Understanding the structure: Projects/Experiments/Tasks
Categorizing data is never easy – SciNote, however, offers a structure you can adopt effortlessly.
Take advantage of SciNote’s CFR21 part 11 functionalities in the patenting process:
Think about how you will look for the data in question one year from now. This usually gives some idea what the main denominator is. Is it the name of a molecule? A customer? After you decide, consider this to be your project name.
Consider different aspects that describe your data. The name of a project, experiment, or task card may include the name of a researcher working on it, when it was done, who it is for, what it is targeting, what is the end product, what was the method, and so on.
Give thought to templates. To save time in digital environments, we like to copy-paste things. Think about how you could parse your work into units that could serve you as templates – this will help you deduce what a task, an experiment, or a project should be, and how to name it.
Tracking progress of your projects and lab work
SciNote offers you several great options for tracking the progress of your projects and work.
Take advantage of user roles and grant individual permissions to data access:Monitor the progress of your work on the SciNote dashboard. The Current tasks section works as a perfect to-do list of your tasks. We made it very easy for you to track the due dates and the progress status of your tasks, so you can always be on top of your work.
Keep track of your ongoing projects in the Team projects section. On the project cards you can see, who is assigned to each project and keep track of your team members comments. Alert is displayed when any of the project’s tasks is overdue or due soon.
Set due dates to the tasks. Plan the timeline of your work in advance by setting up due dates on individual tasks. This will help you prioritize your tasks as well as giving you additional boost to get things done on time.
Read the entire content here.

Increase your efficiency & productivity by switching to an Electronic Lab Notebook.
Here are some benefits:
Save time: Experiment templates, workflow modules, automatic reports, and standard protocols from SciNote and Protocols.io repository have one thing in common – they save you a bunch of time.
Work smarter (not harder): Work smarter by keeping your data organized in a three-layered structure, assigning tasks to people, connecting the data using annotations and applying mobile devices in your lab.
Boost your productivity: There’s no better way to boost productivity than check-marking protocol steps, meeting deadlines, exchanging advice among peers via comments, and archiving completed projects.
Stay on top of things: Monitor your work progress in the dashboard, engage with your colleagues by tagging them,  use the powerful search function, and follow notifications to stay on top of things.
See the best practice examples of SciNote functionalities in action.
Using templates
Using protocol repository for repetitive protocols
Identifying reusable modules in your workflows
Connecting and cross-referencing data
Recording results and reporting
Handwriting and voice to text features on mobile devices
Saving time with SciNote
Managing a productive team
Using templates
In SciNote, you can create different templates, starting with your protocols and tasks, and even reuse a bigger piece of work, such as workflow composed out of a set of tasks. If you would like to store templates for future use and share them, one hint is to store them into a dedicated Project that serves as a template repository.
Copy entire experiment as a template
Copy individual workflow as a template
Copy individual task and protocol as a template
Using protocol repository for repetitive protocols
Protocols repository in SciNote is the home to your protocols, SOPs, or any set of instructions that you or your colleagues will likely re-use.Managing protocols in Protocols repository instead of saving them to Tasks or as pdf attachments will enable you to re-use them easier and to record their execution properly as well as increase your efficiency and traceability.You can either keep protocols only to yourself and store it under MY PROTOCOLS or have them available to your colleagues by storing them under TEAM PROTOCOLS.
Read the entire content here

Reasons that drive the global digitalization initiative:
Making the data easy to find, traceable and reproducible: Transforming the lab capabilities to store the data, move it, share it, analyze it, and know where to find it instantly, are the main drivers of labs’ success.
The initiative is driven by people: Digital data management skills are and will be crucial. You don’t have to wait for the entirety of the organization to be ready, identify a good A-team, and start. It will take off from there.
Raising expertise in laboratories: A digital mindset is bringing value to many laboratories who are paving the way forward today. Raising the expertise to meet the new normal is of crucial importance.
Utilizing the value of data: Data has to be managed efficiently, it has to be standardized, findable, and usable so you can draw value from it now and in the future.
Co-existence of paper and digital data (when there is no other way): A symbiotic approach towards understanding the cohesion between paper and electronic lab notebooks is explained in this blog post.
Defining your lab’s requirements
It starts by looking at your current process and clarifying what needs to be done.
Set your lab’s goals. How will the use of particular tools improve specific parts of your processes to reach your goals within the given time frames? Keeping the razor-sharp focus and determination is the most potent driver of digitalization.
Leverage the value of data by analyzing the data flow. Tracking and analyzing the data flow in your lab to leverage the value of data you are collecting is important at the start. Where is the data generated, recorded, shared, stored, sent, confirmed, reviewed, etc.? Map the flow.
Understand your need for technology. Are you trying to just use technology as a convenience to support the system you already have or are you ready to think about using better technology to change and improve the system?
Clarify your goals and current processes. Once you clarify your data flow and your main goals and KPIs, you will be able to go all the way back through the processes in your lab and see which need to be digitalized. That is when you will get the real incentive to do it.
Read the entire step-by-step quide here

Author: Liane Kober,
Molecular Biotechnology and Functional Genomics,
Fachbereich Ingenieur- und Naturwissenschaften,
Technische Hochschule Wildau
If you think about research, you generally make associations with innovation and great new things that are developed, always up to date with the equipment and laboratory organization. While attending a congress that was dealing with the smart laboratory of the future, I realized that our university and research departments were far away from the current state of the art lab tools and products. ELNs, namely electronic laboratory notebooks, have been introduced in large companies several years ago, whereas I had heard the term for the first time only a few months ago when the head of our department approached me and asked if I was interested in testing new equipment and workflow within my research project. A colleague of mine – skilled in mass spectrometry – was asked as well. Both of us agreed, although we only had about six weeks to establish the digitalized workflow. So, within two weeks we got accounts for the ELN SciNote, received Gilson’s TRACKMAN® Connected pipetting system and completed two remote trainings. We said goodbye to our paper lab notebooks and took our first steps toward a digitalized laboratory. 
SciNote and Gilson TRACKMAN Connected
Before we take a look at our new workflow, let me explain the particularities of the ELN and the pipetting system, respectively. An ELN is an online platform, on which a group of scientists can work together on projects, write and share protocols, plan experiments and store all data in one place.ELNs are structured in multiple layers to keep your data organized. Within SciNote, there are four main layers: team, projects, experiments, and tasks. In the beginning, a team is defined, consisting of different researchers. Those researchers can create projects, and within those projects, individual experiments are created that consist of multiple tasks.Each task has its own protocol, which can be self-written or imported from protocols.io – a collection of freely available protocols. For each task, you can set start and due dates, add comments, and assign them as completed. Besides that, you can add the results of a completed protocol directly to the respective task.Further integrated functions include:
Inventories that can be assigned to tasks
Activity lists to track changes made within SciNote
Generation of reports to summarize your experiment(s) and results
Manuscript writer add-on to create a draft of a manuscript
By using an ELN, we have significantly more functionalities available compared to a paper lab notebook. Those are even further extended when we take the Gilson TRACKMAN Connected into account.The Gilson system consists of a tablet with accessories that can be paired to Bluetooth®-connected pipettes. The tablet is used to create pipetting plans with defined volumes and positions in a selected format (e. g. 96 or 384 wells). A microtiter plate (MTP) can be directly attached onto the screen of the tablet. The pipettes are connected via Bluetooth, and after that the pipetting is performed almost automatically. The only task the researcher needs to complete is to set the pipette at the right position and click a button. Pipetting is visually and acoustically assisted by marking the pipetting spot through the transparent bottom of an MTP and making a sound at the end of the pipetting step. Additionally, multi-dispensing and automated mixing is possible. After the pipetting is finished, a pipetting report is created that shows all experimental details. The plans and reports can be directly uploaded to SciNote.
Read the entire article of their digitalyzation here.

In the light of the current outbreak of a novel coronavirus (COVID-19) many new challenges have stumbled in front of all of us. In SciNote we are well aware of the importance of the research community to join forces and contribute to the efforts to combat the challenges that the new virus has faced us with. 
We set up an example of the experiment in SciNote for the identification of 2019-Novel Coronavirus by real-time RT PCR that has been published by the Centers for Disease Control and Prevention (CDC) (accessible here). We show you how to easily create a well-structured experiment in SciNote in only 8 simple steps: 
Creating the project, experiment and tasks workflow in SciNote
Adding tags on the task
Importing the protocol from from protocols.io into SciNote
Loading the protocol from the protocols reporitory on the nucleic acid extraction task
Setting up the protocol steps on the rRT-PCR TAS
Assigning reagents from the inventory on the task
Adding the table (with calculations) on the protocol step
Inserting the annotated image on the protocol step
Read the full text and see the experiment setup here.

The process of going digital is all about the motivation and awareness of the entire lab team.
To fully benefit from the software, everyone needs to understand the importance of good data management, and everyone needs to be on board. Labs are different, and therefore, every team approaches this in their own way.
So, how do we do it at SciNote?
Our implementation specialists have different scientific backgrounds. They are your fellow scientists, whose knowledge and experience allows them to understand your lab’s needs.
We offer a range of top-rated services: from SciNote Academy, to email support – and the cherry on top for labs who choose more – a fully personalized onboarding.
So lets dig deeper into what the personalized onboarding actually is?
It all starts with getting your lab ready for onboarding.
1. Defining the best approach for your lab
While our development team takes care of the technical setup, an implementation specialist will organize an online meeting with your lab’s decision makers, to explain the structure and functionalities of SciNote, and make sure we choose the best and easiest way to use this software in your lab
.
2. Choose your team’s ELN specialist
We will also advise your team to choose one person from your lab who will be your internal SciNote specialist, the go-to person for the rest of the team about any SciNote related questions.
After that, the onboarding for the entire lab team starts.
3. Learn on your own use case
We will go through the functionalities of the software and explain everything on your own use case. It will be easy to understand.
The team will learn all details about the data structure, team management and collaboration within SciNote; creating and managing projects, experiments, tasks, protocols and inventories; generating reports, monitoring the lab’s activities and much more.
We will conclude the onboarding process with dedicated Q&A sessions.
So, how long does it usually take to become familiar with SciNote?
In 90% of the cases, labs become confident in using the software within the 1-month onboarding period.
On average, a person needs 1 week to become a proficient SciNote user and set up their system in SciNote. This would normally spread in the course of 3 months.
If the uptake of the software will be slow, or there will be difficulties within your team, SciNote implementation specialists will be here to help.
We will make sure the adoption of the software runs smoothly. Get in touch with us and start your journey to digitalization.
“We were impressed with the security and features of the SciNote system. Everyone we spoke to at SciNote was able to clearly answer our questions and understand our concerns.”
- Stephanie Wallace, Scientific Officer, MicroMatrices Ltd, UK
“Very fast and responsive to questions. Very helpful and professional team.”
- Dominik Domanski, Ph.D., Mass Spectrometry Laboratory, Institute of Biochemistry and Biophysics, Polish Academy of Sciences

Here is a challenge for you.
Being an expert in the field of diet research, you come to the point in your career where you need to run a controlled clinical trial, include thousands of people who are willing to turn their eating habits upside down and convince the government to give you hundreds of thousands of dollars to do it.
How?
Dr Ramsden, a Clinical Investigator, NIH, was facing that challenge.
Until he realized that those studies have already been done in one of the most rigorous diet trials ever conducted – The Minnesota study.
The study which was forgotten over a quarter of a century. The study which had the crucial data of interest.
“I’ve heard the possibility that there might be some very interesting data in your father’s basement“, Dr Ramsden said in a phone call to Robert Frantz, a cardiologist at the Mayo Clinic.
Thankfully, Ivan Frantz, in charge of the study, prioritized the importance of immaculate record keeping.
The Minnesota study was a part of a massive undertaking in the early 1960s, which included a hundred thousand people in five different states. Ivan Frantz died thinking his study was a failure as the final results showed unexpected truths that denied his expectations of the outcome. Results which were indispensable for the needs of further research, decades later. The results his son found and forwarded for the benefit of further research. The Basement Tapes by Malcolm Gladwell go more into detail on the story.
The takeaway message is – the data was findable, accessible and reusable.
And that is the point of FAIR.
As Martin Schweitzer from ARDC brilliantly summarizes the above-mentioned Basement Tapes in his talk, the meaning of FAIR principles becomes clear.
To be able to maximize the impact of our research and build upon the published research, FAIR principles emphasize the fact that data and metadata related to scholarly publications need to be findable, accessible, interoperable and reusable. By both humans and machines (i.e. our computers).
“FAIR means thinking about the people who could benefit from your data”, Lambert Heller, leader of the Open Science Lab at TIB, the German National Library of Science and Technology, explains in Nature Index.
FAIR starts with FINDABLE
Findable means that it should be easy for people and computers to find the data related to your published research. It applies to the metadata as well (metadata can be understood as data about your data). What we are talking about here are the hyperlinks, DOI numbers, unique identifiers, etc. You want the link to your data to be unique. No two sources can have the same. And, making sure the link is persistent. These are long-lasting references to digital data sources. What we want to avoid are the broken pages, old links that don’t work, resources that can’t be found anymore etc.
“It also means presenting the data in a standardized way so it’s machine readable”, Heller elaborates.
That is the purpose behind data repositories that assign a globally unique and persistent identifier (PIDs, DOIs, etc.) to your deposited data. If someone is using or building upon your data, they can use the identifier to cite it.
Kate LeMay, senior research data specialist at the ARDC, adds that besides the big picture, there are both altruistic and selfish reasons for researchers to make their data FAIR. “Most people get into research because they want to make a difference,” she says. “That includes making your data as useful as possible.” FAIR can also be good for career advancement, particularly for early-career researchers. “FAIR helps you demonstrate the impact of your research when people re-use and cite your dataset,” LeMay says in the article. “It gets your name out there and can lead to new collaborations.”
Which takes us to the next point – what if the data can’t be shared and should be kept private?
ACCESSIBLE but not OPEN
Let’s talk about rhinos, for example.
Say you invested 20 years of your life into battling antipoaching activities. Your data is of great value, but you cannot disclose anything that could pinpoint the locations of threatened species.
Similar applies to national security data, defence research, or sensitive medical data that could identify or reveal info about individuals.
Data can be FAIR even if it's not open. It can be kept under mediated access controls.
“Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorization” – Go FAIR.
Sensitive data and defining the process for accessing it can still have a high level of being FAIR, even if certain data cannot be disclosed.
“As open as possible, as closed as necessary”, Heller sums up the guiding principle.
Accessible doesn't mean open, but it gives the exact conditions under which the data are accessible. Even heavily protected private data can be FAIR. This means that you’d be able to see that the data isn't open, but the exact steps that need to be taken to get access to that data are clear. And these can be as rigorous and complex as they need to be.
If the access has been granted, then the data should be accessible through the authentication and authorization procedure. More details on this are available in Martin Schweitzer’s talk on FAIR.
INTEROPERABLE to boost knowledge discovery
“Imagine the ability to link data in the Framingham Heart Study (NHLBI) with Alzheimer’s health data (NIA) to understand correlative effects in cardiovascular health with ageing and dementia. Imagine the ability to quickly obtain access to data, and related information, from published articles. Imagine the ability to link electronic health care records with personal data and with clinical and basic research data”, these are the promises and aims of the 2019 NIH Strategic Plan for Data Science.
By enabling a FAIR-data ecosystem, improving knowledge discovery becomes the main aim.
The point here is to make sure people and computers can look everything up. Interoperability goes into details. It prioritizes the precision in communication, standardizing the vocabulary and putting data into the format that is recognizable, to save us valuable time.
Martin Schweitzer describes the following use case in his talk:
You have two different files. In the first file, a table has two columns – country names and exports.
In the second file, there is another table that contains - country names, country codes and latitude and longitude measurements. You want to consolidate the two.
However, the country names are written slightly differently in those two tables (e.g. Congo Democratic Republic vs Congo (DRC)). The computer won’t recognize those two names as being the same. International country codes, however, are universal. So, in the document that didn’t contain country codes, these needed to be added i.e. the names replaced with country codes. That allows the two datasets to be consolidated.
The point is, if the authors would use the standard vocabulary that is findable, accessible, interoperable and reusable, which in this case means using the international country codes, the task would be done much quicker.
Currently, substantial amounts of time are being spent on consolidating data sets that use different vocabularies.
“The focus on assisting machines in their discovery and exploration of data through application of more generalized interoperability technologies and standards at the data/repository level, becomes a first-priority for good data stewardship”, Wilkinson et al. elaborate.
However, interoperability of data means that both data and metadata will have to be standardized so that datasets can be merged for further research. “The challenge is that different research fields have different cultures and requirements for data and metadata. There needs to be community ownership of these data standards,” LeMay says. “We can’t just impose them on researchers.”
REUSABLE for the benefit of authors and the progress of science
Many scientific journals and research funders now require scientists to share their data openly.
As described on the ANDS webpage, a great source of everything FAIR related, “reusable data should maintain its initial richness. For example, it should not be diminished to explain the findings in one particular publication. It needs a clear machine-readable license and provenance information on how the data was formed. It should also have discipline-specific data and metadata standards to give it rich contextual information that will allow for reuse.”
But, are researchers even motivated to shair their data? Do they want it to be reused?
The State of Open Data 2018 report shows that early career researchers are focused on the credit they receive for making data available, with regards to career progression opportunities.
However, “to provide true credit for good data practice, published, citable datasets need to be viewed as research outputs on a par with a research article in terms of career advancement and assessment. Realistically, routine inclusion of datasets, their citations and impact in grant assessments and CV evaluation is probably still years away”, Grace Baynes, VP, Research Data and New Product Development, Open Research, Springer Nature, writes in the report. “Researchers would share data more routinely, and more openly, if they genuinely believed they would get proper credit for their work that counted in advancing their academic standing and success in career development and grant applications, and for subsequent work that builds on their data”, Baynes explains.
Among the practical challenges for not sharing data, 46% of researchers stated that the most prominent challenge is “Organizing data in a presentable and useful way”.
Organizing and managing research data in a presentable way - how can electronic lab notebooks (ELNs) help?
The purpose of electronic lab notebooks extends beyond record-keeping and is evolving towards the project, data and team management platforms. Since all aspects of lab work can be unified within one platform, all data can be interconnected for better traceability. This allows researchers to organize all data related to their studies within an easy-to-use interface. Good data management is becoming a priority.
“Science funders, publishers and governmental agencies are beginning to require data management and stewardship plans for data generated in publicly funded experiments. Beyond proper collection, annotation, and archival, data stewardship includes the notion of ‘long-term care’ of valuable digital assets, with the goal that they should be discovered and re-used for downstream investigations, either alone, or in combination with newly generated data. The outcomes from good data management and stewardship, therefore, are high-quality digital publications that facilitate and simplify this ongoing process of discovery, evaluation, and reuse in downstream studies”, Wilkinson et al. elaborate.
European Commission's Guidelines on FAIR Data Management in Horizon 2020, state that “good research data management is not a goal in itself, but rather the key conduit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration and reuse”.
ELNs can contribute to better data management through an organized collection of all your research data in one place.
“The importance of using an electronic lab notebook like SciNote cannot be overstated. As a neuroscientist, I am aware that the field is currently facing a reproducibility crisis. Additionally, large funding bodies like the National Institutes of Health require indices of rigour and data management. SciNote makes it easier to address these issues because everything from the approach to the end result is all in one place”, says Jonathan Fadok, assistant professor and principle investigator, The Fadok Lab, Tulane University, USA (for more use cases and reviews, visit the Stories from Laboratories).
SciNote offers a set of functionalities that enable researchers to efficiently manage their data and prioritizes data protection:
· Keeping all research-related data in one place, annotated, traceable and searchable
· All records are timestamped and all changes within the system are recorded
· Inventory management allows information on every sample to be assigned and connected to the experiments
· Comprehensive reports are generated automatically
· It is possible to export all data in a readable format, organized by folders
· Collaboration with internal members of the team and external community or partners by inviting them and assigning them data viewing or even greater permissions when needed
· Every action by every user is automatically recorded within the system
· Activities management allows easy filtering to gain insight and overview into every detail of activities within the lab
· Entire experiments, workflows and processes can be saved as templates, cloned and re-used
· Protocol repository enables lab members to manage and share protocols
· Archiving and backups of all research data
Conclusion
Here is a challenge for you.
A quarter of a century from now, someone is looking for a crucial piece of information that was reported in your published paper.
Will they be able to find, access and reuse your data?
By Tea Pavlek

An alarming study showed that out of 238 published scientific articles, barely 46% could be reproduced. News also reported on the replication efforts which were successful for only two of five cancer papers. Furthermore,
"...more than 70% of researchers have failed to reproduce a colleague’s experiment and more than 50% have failed to reproduce their own."
Over 35% of irreproducibility has been attributed to manual errors in the performance of experiments and data reporting.
How Can Connected Lab Change This?
Facing the seriousness of today’s reproducibility crisis, we see that it is becoming absolutely crucial that observations made in the lab get associated with digital records as soon as possible, preferably in real time.
Let me show you an example of how IoT applies to scientific work:
Using the concept of Internet of Things, SciNote LLC and Gilson Inc. combined strong scientific and liquid handling background with the latest software development expertise to connect the data flow while doing experiments in the lab, in a traceable manner.
How Does it Work?
Gilson Connect devices can record and track pipette performance in real-time and transmit data to SciNote, a top-rated platform for researchers in academia or industry, who need electronic lab notebook, inventory management and project management functionalities.
Gilson Connect includes the company’s first Internet of Things (IoT) products:
TRACKMAN® Connected, an all-in-one kit that includes a tablet with the PipettePilot™ tracker application, and
PIPETMAN® M Connected, a Bluetooth-enabled smart electronic pipette. Researchers have the ability to check their pipetting data to detect errors and help improve traceability and experiment reproducibility.
By partnering with SciNote, the Gilson Connect platform makes it possible for researchers to store data and consolidate records in a secure location that is easily accessible and sharable, effectively eliminating lost data, a significant contributor to the irreproducibility crisis.
Technology is already playing an important part in the future of science by providing scientists with useful and convenient software.
Aside from that, we also have the responsibility to raise awareness about the problems related to data reproducibility and time management in labs, while providing solutions available to the global scientific community.
K. Zupancic PhD, CEO at SciNote

Labs expressed their opinion in the recently published study “Electronic lab notebooks: can they replace paper?” that addresses the subject of electronic lab notebooks (ELNs) and whether they can replace paper in today’s digital labs. Here, we summarize key barriers to faster ELN adoption and solutions on how to tackle them, according to the findings.
Paper laboratory journals and notebooks are no longer the most efficient way to keep research records. This is mostly due to the increasing amounts of digital data generated in labs today.
Experimental data analysis often relies on digital tools and there is a growing need in many labs to implement an easy to use lab management system.
However, the adoption of digital systems in labs has been slow. In order to gain better understanding of the situation, we decided to conduct the largest study on ELN adoption so far. The study provides information on the direction in which the ELN market is moving and insight on why the ELN adoption has been so slow.
Barrier 1 – Price and budget
When talking about ELN costs and limited budgets, a large percentage of survey respondents indicated that cost was a significant barrier to ELN adoption. This includes financial outlay, staff hours, troubleshooting, and the fact that long-term use is likely to require on-going maintenance and support.
Besides that, academic institutions tend to think about the long term, so they need a solution for which they are sure that there will be no sharp changes in costs, once they establish the system within their institution.
Solution – Permanently free accounts
For academic labs with limited budgets, the study describes a couple of solutions. Even though most ELNs available on the market today are proprietary, open source and free ELNs are becoming more common.
It is important to distinguish here between free trials or demo versions of some ELNs, and ELNs that offer free accounts with full functionality and claim that free accounts will not be charged for in the future. It is advisable to go through these details with the ELN team before implementing an ELN in your lab, because, as previously mentioned, you want to avoid any unnecessary costs that might affect your institution’s budget too much in the future.
Do not confuse the term open source with access to your data. Researchers who decide to upgrade or change the source code of an open source software will do that for themselves and choose if they want to make the improvements available to the rest of the scientific community or not. But they cannot access your data. As well as you cannot access theirs.
Barrier 2 – Ease of use and complexity
The greatest concerns arise when labs start considering the implementation of the new electronic system into their everyday processes. Whether an ELN would be used by one individual or an entire team, it is important to address the complexity of the system, the learning curve of the users in the lab and the resources needed.
In the study, respondents were asked about how important the ease of use is for them (if they are to switch from paper to digital for example). 99% of respondents indicated that “ease of use would influence their ELN choice, with almost 80% rating it as very important. One comment reflected the desire for a flexible generic solution, rather than an ELN designed for a specific research area, due to anxieties that their research “doesn’t fit neatly into one category”.
Solution – Flexible and intuitive software
The process of scientific exploration and discovery is often unpredictable and not entirely linear, so an ELN would need to provide the flexibility to researchers to use it as best fits their needs. Besides flexibility, design and user experience of the software play the major role in reducing the time needed for the users to understand the software and actively use it on a daily basis.
On the other hand, it is necessary that an ELN provides more lab-oriented functionalities than the generally used note keeping applications such as Evernote, OneNote and similar. For example, the ability for the user to manage inventories, defining protocols and experiments, linking samples with results etc.
Barrier 3 – Scattered data
The studies detailed in this paper indicated worries about the ability to find, move and access data between different ELNs and lab instruments; and whether this would result in data duplication.
The study shows that among the respondents “74% expressed concerns about needing to enter data in both the lab and write-up area, due to a lack of suitable hardware or software capabilities to facilitate ELN usage inside and outside the lab. This can lead to copying and pasting printouts into paper notebooks and manually transcribing data between notebooks and computers; which can result in data loss, transcription errors and records stored haphazardly. Popular suggestions were to use mobile computers or tablets for portability in and out of the lab, and that web-based ELNs could improve accessibility.”
Solution – All in one place
It is important that an ELN supports all types of data that can be easily saved, accessed and annotated. Keeping everything in one place, from pictures to excel sheets, has major benefits when searching for data or reporting on a project. It is already becoming impossible to keep track of it all in paper notebooks.
The authors conclude that “ELNs will significantly improve reproducibility of scientific experiments, contribute to the data traceability and data annotation and enable scientists to collaborate and share results in an intuitive manner. The wider adoption of ELNs will facilitate interoperability which will ultimately change the ways scientists perform experiments and manage their data.”
Conclusion
The study focused on researchers’ current practices, their opinion towards ELNs, and their desired functionalities i.e. main priorities of different ELN features, ranging from whether respondents saw them as not important to very important. We explored and summarized the barriers to adoption within academic environments.
We can conclude that ELNs are a suitable replacement for paper notebooks. Following these findings, we defined the priorities for future ELN development and developed SciNote Electronic Lab Notebook.
SciNote is a top-rated platform for researchers in academia and industry, who need
electronic lab notebook, inventory management, and project management functionalities.
To see which SciNote plan would best fit your lab’s needs, visit: SciNote Free and Premium Academia or Industry plans.

Open source electronic lab notebooks – Beyond technicalities, a movement that shapes the future of computing and your scientific research.
The Open Source Way
To start from the beginning – “the term open source originated in the context of software development to designate a specific approach to creating computer programs. Today, however, open source designates a broader set of values—what we call the open source way. Open source projects embrace and celebrate principles of open exchange, collaborative participation, rapid prototyping, transparency and community-oriented development” – as stated by the team behind Opensource.com (supported by RedHat, the world’s open source leader).
For example, if an electronic lab notebook is an open source software, that means that the source code, the essence of the software, is available to the public. Basically, anyone is licensed to use, improve or change the software (SciNote’s source code is available on GitHub).
Open source does not mean open access to your data by other parties. We can think of it as building the house for example, I decide whether I want to share the method, i.e. the “source”, how I built my house with other people. If someone else takes the source code and modifies it for him/herself, that has no direct effect on me. I built my house, here is how I did it, if you want to build your house and change the windows or add another room to it, that’s fine.
So, if I am using an open source electronic lab notebook SciNote for example, and another institute decides that they want to add a new module to SciNote that would analyze their data, they can develop it and decide whether they want to make it available for others as well. If they do make it available, then you and me would also be able to use the specific add-ons they created.
Open source software actually gives independence to its users, more control over it. If compared to proprietary software that does not allow access to the source code, open source allows the community to step in, improve and fix the software, which increases the security and stability of the software. Open source is in direct relation with open collaboration.
This can actually decrease the costs of the software and enable its original developers to offer it to the public as a free open source solution.
“Open source is a development methodology; free software is a social movement.” Richard Stallman
Is it easier to hack an open source software?
Let us start with the most common question – is open source software hackable? Yes. Everything is. Pentagon is hackable. Google is hackable. Banks are hackable. Your own computer, phone and memory stick are hackable. Your institute’s servers are hackable. It depends on how good the hackers can be. But the chances might be lower if the software is open source. Why?
The code of an open source software is not necessarily superior to the code of a proprietary software, but in some cases, it can be considered safer because it is the only type of software whose source code can be checked for security without being dependent on the vendor and putting all the trust into their actions. As Edward Snowden pointed out at the OpenStack Summit, open source community plays an important role as a collective protector of people and even as defense against government and corporate actions. “There should not be a need to hide things from the rest of us. So, the main ethical obligation I see developers asking themselves a question – ‘How do I empower the user of this application?’ Or at least they should enter that chain of thought.” Edward Snowden, Open Source and the Power of The Collective (Full text)
While on one side open source nature of the software enables the global IT community (besides the team who developed the software in the first place) to find and fix security bugs – many eyes make all bugs shallow, on the other side the code is available to anybody (hackers included) which might make it easier for them to hack it. However, most of the data provided by experts in the field today points out that when it comes to hacking an open source or a closed source software, the difficulty is pretty much the same. It does not depend on the open or closed nature of the software, but on other things, such as: how was the software developed in the first place and whether the latest security practices have been taken into account. Basically – being closed-source really doesn’t mean that the software is more or less secure than open-source software. Open-source nature of the software doesn’t actually make a major difference for the best hackers.
It is important to note here, that the way in which laboratory data is being managed and the levels of data security that should be in place within each laboratory depend not only on one software, but on the entire organization.
“Detractors of open source software often point to its broad developer base and open source code as a potential security risk. But that’s not a fair assessment, according to Dr Ian Levy, technical director with the CESG, a department of the UK’s GCHQ intelligence agency that advises UK government on IT security. Asking whether any piece of software is secure is too broad a question. A more valuable approach is to ask what security guarantees your organization wants from a piece of software and then ask whether the software delivers that.” (Full text)
Why is SciNote an open source electronic lab notebook?
Open source nature of the software gives independence to its users. It is a movement towards transparency and collaboration on a global level, building a community of professionals to improve, fix and develop it further. Science and IT are becoming so closely linked that we cannot imagine doing science without different software solutions anymore.
SciNote electronic lab notebook scientific community of users already accounts more than 15 000 users and many of them are actively contributing either by developing their own private add-ons or by contacting the SciNote team and sharing their detailed feedback about the user experience and the improvements they would like to see in SciNote.
If you are one of them reading this, we would like to thank you.

The aim of this article is to evaluate the return on investment of implementing an electronic lab notebook in the lab.
Among numerous solutions and research tools available today, scientists are choosing electronic lab notebooks (ELNs) to complement the paper-based approach to record keeping. On the example of SciNote ELN, we demonstrate that apart from other benefits, switching to an ELN has a significant impact on researchers’ productivity. We compared the time researchers needed to perform their tasks prior and after the ELN implementation in their labs and calculated the return on investment (ROI).
On average, researchers save 9 hours per week by using an ELN, while doing the same amount of work!
For some researchers, time savings amount to 17 hours/week! They are now able to work 33% less on reporting, 16% less on scheduling and planning and 6% less on emails. After the initial learning curve and investment into software, scientists can return their investment within 3 – 4 months.
Comparison of time spent for weekly amount of work without and with SciNote
“With SciNote I save time on various tasks throughout the day and when all of it sums up I can go home earlier or invest that time in my priorities”, states David Frommholz, startup co-owner at DALEX Biotech and lab manager at Bonn-Rhein-Sieg University of Applied Sciences, Germany.
We interviewed 7 proficient SciNote users to determine the time they need to perform their tasks with and without having SciNote i.e. prior and after the ELN implementation in their labs. Interviewees have different job positions (PhD, Lab Manager, Startup co-owner, research associate etc.), and due to these differences, time gains varied across different work categories.
Image 1 and image 2 (below) show the comparison between the h/week researchers spend working on a particular subject without SciNote and with SciNote. They were asked to evaluate the time needed to perform the equal amount of work. The charts show median/work category and median/person.
On average, researchers save 9 hours per week while using an ELN. For some, time savings amount to 17 hours/week!
Users’ explanations of time savings (by category)
Meetings
For most researchers, the time they spend attending the meetings does not change so much after ELN implementation, however, the time invested in preparations for the meetings does.
“With SciNote, I can actually save 50% of time I would normally invest in preparation for and attending the meetings”, says A. Alhourani, PhD student at University of Stavanger, Norway.
How?
Structure of data within an ELN enables you to always be prepared. Your data is in order. You can talk about the progress of your work without needing to look for your files or flip through pages.
Because ELN can be used as a collaboration platform as well, some meetings can even be avoided as comments on results, feedback and delegating tasks for example, can be shared via comments and notifications.
“In academia, even more time is saved from the supervisor’s perspective, because numbers multiply as the number of students increases”, Alhourani further explains.
Emails
“Electronic lab notebook is useful when you’re asked to bring up stuff that happened a long time ago”, Alhourani says. “Without SciNote, I’d need to invest the double amount of time to prepare an email if I’m asked for a file and need to dig it out. This is where SciNote really helps”, he continues.
Besides fast search through all research data, ELN can be used to avoid certain emails overall, by posting comments directly within the software and keeping team members, students or supervisors notified instantly.
Instructions
ELN enables lab members to keep all their protocols and related research data organized in one place. In addition to the advice and mentorship from their supervisors, authorized lab members can find and access the needed information for their work. This reduces the time spent on asking for instructions.
“All information regarding each project is in SciNote, so instead of explaining all that has been done or how we did it, I can let people review the project. Everything the student needs, to be prepared for lab work, is in one place “, says Dorothea Hoepfner, PhD student at the Technical University of Munich, Germany. “Finding the protocols is much quicker due to the access to protocol repository. It is very easy to find the protocols, print them out etc”, she continues.
Class preparation and course administration
These two categories are often related and even if an ELN cannot take over such tasks in general, it can be useful as a platform on which content for lab classes can be organized.
In addition, “it is easier to review what the students did, to see whether their data is correct and in place, when compared to handwriting”, Hoepfner explains.
Scheduling and planning
“Planning experiments became more convenient with SciNote. For example, if you have to plan and compare the same experiments with different results, it is easy to have an overview, compare, plan and improve”, explains Alexandra Ehl, startup co-owner at DALEX Biotech and scientific associate at Bonn-Rhein-Sieg University of Applied Sciences, Germany.
Many labs today plan their experiments directly within an ELN as it offers more than just a way to keep records organized. Lab data can be connected within an ELN – inventory items can be assigned to experiments, results associated with protocols and more. This enables detailed overview on what has been done and how to plan the further progress of the project.
“Now that I have everything in one place in SciNote, I can replicate the experiments and build upon them which saves me up to 30% of time when planning and scheduling my future work”, elaborates Alhourani.
Workflows are one of SciNote’s unique functionalities that have proven to be useful when planning lab work. They allow users to create and define lab processes by connecting the “cards” containing notes, pictures, protocols etc. into a sequence. This allows users to better visualize the entire process that is not necessarily linear, set up due dates and follow the guidelines within each part of the process.
“I have found SciNote to be my top choice among the ELNs. I particularly like the workflows in the experiments and the integration with Microsoft Office. The workflows also help in my teaching of novice scientists. SciNote has set a new standard for the future of data management in the field of medical science”, says A. Sougiannis, NIH predoctoral fellow at University of South Carolina School of Medicine, USA.
Research
SciNote users say that up to 5 hours per week can additionally be invested in research while using an ELN.
“I save at least an hour per day that I can either invest in further research or go home earlier. SciNote allows me to stay in the lab longer, because I don’t need to spend so much time on the administration tasks afterwards”, Frommholz explains. “Before, I’d have to write everything down, collect the data and format it so that I could print it to fit into the paper notebook. Now I can just save a huge excel into SciNote. In our lab, we even started thinking about tablets to take SciNote directly from the office to the bench.”
The use of an ELN results in time savings throughout the day that sum up and give the researcher a certain amount of extra time to invest in priority activities, such as research, grant applications or networking.
“Soft benefits apply when it comes to research”, says Alhourani. “ For PhDs research is the priority, so even if they are using an ELN in their everyday work, they’d still spend the same amount or even more time on research. But an ELN helps in reducing the time needed for other tasks so that they can either invest more time in research or potentially leave home a bit earlier.”
Manuscript Writing
When starting to write a paper, many scientists face the headaches of pulling all the literature and research data together. It is difficult to start with a blank page and a typical article takes between 50 – 100 hours to write. With Manuscript Writer add-on by SciNote users can simply select relevant experiments, add references and a draft of the manuscript is generated automatically and instantly.
“SciNote makes it easy to connect the data with what you did and write the experimental part. A lot of ambiguity is saved because you’d never put the complex dataset in the paper notebook. It saves at least 25% of time needed for the experimental section of a paper. If you are working with collaborators, the benefits are even higher, because all data is in one place and it saves even more time.” Hoepfner describes.
While it doesn’t generate the final version of the manuscript, it generates a draft that can be further improved and edited.
“Without SciNote it would take so much time to compile all data. Now that we all have everything in SciNote, the lead author can take charge of everything easily”, Alhourani adds.
Reporting
ELN enables you to automatically create a report by selecting which research data you want to include i.e. which experiments, notes, protocols, results, pictures etc. Time savings here are immense as this functionality creates a detailed report in a couple of seconds.
Hoepfner points out that “reporting becomes a lot quicker with SciNote. Especially if you have a very short notice reporting request – ‘We need the data tomorrow’. You’re always prepared.”
Generating, editing and printing reports is also useful for labs who need to keep paper records as well as electronic ones. Since all project related data can be instantly presented in the form of a report, they can print it and store it regularly alongside their electronic records.
“Time spent on writing reports dropped from 3h/week to 3h/month”, Alhourani adds.
Grants
ELNs contribute to good data management practice in labs. Among the benefits of managing research data is the compliance with funding agencies’ requirements as well. ELNs cannot directly cover the tasks of preparing the grant applications (yet), but the overall time savings related to having all data organized are quite substantial. As we see on the image 3 below, time saved is often used for applying for grants, which means more funding and even greater ROI.
“I have more time to do grant writing. I gained 20% more time to do it.”, Frommholz says.
Other
While other activities greatly depend on lab’s way of work and cannot always directly be supported by an ELN, the flexibility of an ELN is an important functionality that enables labs to use it as they find suitable.
Some labs use it to store their innovative ideas – “Even random project ideas can be set up in SciNote as dummy projects that have the outline of the general idea, and saved in SciNote. You can then see it later on and develop your ideas further, even after a couple of months”, Hoepfner describes.
For others, it is important to be able to access your data on the go – while changing labs or even being away on a conference. “Another benefit is that you have your data with you everywhere you go. This is especially useful if you switch labs often and move to another building”, Ehl further elaborates the benefits of having a safe, mobile friendly, web based electronic lab notebook.
If you are working in academia, but interested in building your own startup business as well, you can also gain more time to build that business. “Because of SciNote I have more time for sales and support”, Frommholz adds.
Additionally, ELNs can also be used to exchange knowledge and keep up with the literature and scientific news.
“We use SciNote for literature overview, discussion and sharing as well. We set up a project that is divided into experiments i.e. research areas. We use tasks and protocol steps to add and organize articles we can comment on. If something new was published, or you know someone would be interested in a specific article, you put it there and everyone is notified”, shares Alhourani.
Relative gain or reduction of workload
We normalized the data across all interviewed researchers to a 40-hour work week and investigated how the transition to SciNote impacted their average week.
Image 3 shows that, on average, they are now able to work 33% less on reporting, 16% less on scheduling and planning and 6% less on emails. This allows them to focus more on their research or lecturing, and gives them 17% more time to devote to grant applications.
ROI of electronic lab notebook implementation
On average, a designated person needs 1 week (56 hours) to become a highly proficient SciNote user and set up their entire system in SciNote. This would normally spread in the course of 3 months.
“It took me around 40 hours that spread over a period of 2-3 months to establish the system and transition to SciNote. I needed the most time to think how I want to organize everything. My team then took over from there and are now happily using it”, says Jonathan Fadok, assistant professor and principle investigator, The Fadok Lab, Tulane University USA.
Image 4 shows the return on investment when implementing an ELN in the lab. We assumed no gains in the first three months and included the cost of premium SciNote service.
“The main cost is related to you determining how you want to have everything organized and the transitioning itself. But after 2-3 months, you already start noticing the benefits”, Fadok concludes.
Onboarding of the lab team
The rest of the team on average needs a significantly lower amount of time to understand the software than the initial designated person (or a small team) who established the system in the first place. Based on the user’s feedback, on average 1 day (8 hours) would be enough for a new person to fully understand the system that has been set up in SciNote by their supervisor and be confident in using it.
The best approach is to have one designated person to set up the system and then introduce it to the rest of the team.
“I used to keep my notes in OneNote, which was better than paper notebook as it enabled me to search through the text. However, after a while I decided to upgrade to an ELN. I recommend to implement an ELN when starting with a completely new project and with new research data. This doesn’t cost you extra time, since you would need to write protocols also in the paper notebook, but saves you a lot of effort in the future”, advises David Dobnik, scientific associate at National Institute of Biology, Slovenia.
To summarize
By using an ELN, researchers can return their investment within 3 – 4 months and gain, on average, 9 hours of extra time per week to invest in their priority activities.
It is important to note that only 7 users were included in this study so the actual time savings might be different. However, we have seen significant time savings with 6 out of 7 people, which gives us confidence in our results.
Capabilities of today’s ELNs extend beyond the initial record keeping. They enable individual researchers and/or R&D teams to organize their data and manage their projects, inventories, protocols and more.
Some solutions go even further with their set of functionalities, to meet the requirements set by regulatory authorities, such as the FDA’s CFR 21 Part 11.
While some users do use an ELN individually, in most cases these solutions are used on the level of a lab group, an institution, a company or even the entire faculty, university or larger organization. Every lab has its own system in place. Even if the processes are paper-based, in today’s digital age scientists have to use different digital solutions for acquisition, processing and data management.
ELN’s major strongpoints
Having all data and entire IP in one place – annotated, connected and searchable
This is the main benefit ELNs can bring into the lab. Everything is traceable – from each inventory item to the latest experiment results and final project reports.
This contributes further to the overall IP protection, as data is no longer scattered across different software solutions.
Smooth transition when an employee leaves the company
Archiving, backups, organized data in one place and standardized record-keeping enable smoother and faster transition if an employee leaves the lab.
It is incomparably easier to continue where they left off or find data related to one sample that was processed years ago, for example. This results in having almost no set-backs, as the new person is ready to continue with previous work almost instantly. Costs related to such set-backs are therefore minimized.
User permissions disable an unauthorized access to data
Users have defined roles and permissions that define their access to data and actions they can perform within the software.
Timestamped activity records enable you to know exactly who did what and when
Live monitoring of results and work being done is automated and the activities of each user are recorded and time stamped.
Comments, tagging and delegating tasks within the ELN result in efficient collaboration and less meetings
Posting comments, tagging lab members, annotating data and delegating tasks by assigning users and due dates reduces the need for numerous meetings to convey the same information.
Administrative tasks such as looking for files, re-writing, flipping through pages and copy-pasting are reduced to the bare minimum
Once your system is set up within an ELN, your data becomes interactive. You can save templates and re-use them in future experiments. Your samples, reagents or instruments are directly annotated within your experiments. History of each item is one click away. If you are looking for something, just use search.
Collaboration with external partners
ELN also contributes to optimization of internal activity and collaboration with external partners. It is useful for smaller and larger organizations to standardize the data and reporting with their outsourcing partners or with customers.
Data safety and security
ELNs such as SciNote use the strongest data security protocols to protect the privacy and integrity of exchanged information. All data is encrypted and transferred over safe connections. To protect the privacy and integrity of exchanged information, SciNote is already actively working towards implementing the requirements of GDPR (General Data Protection Regulation in the EU) as well. To meet the requirements from different institutions, it can also be installed locally.

For most labs, understanding how their data is being handled is just as important as the set of features a certain software offers. And there are various reasons for it – from making sure the data is in good hands to checking various compliance checkboxes.
Despite its importance, it is not always easy to understand all the technological aspects related to data protection, which goes hand in hand with choosing the right digital lab notebook for your lab.
Are you taking enough care of your data protection?
Get a complete overview on how your data is being handled with today’s digital solutions, such as SciNote. Its team goal has always been to help scientists at every step of their research. That is why they prepared an informative whitepaper explaining various parts of SciNote’s data protection practices that enable you to manage the data according to the highest standards in scientific data management.