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Laboratory Informatics Guide 2020


There is data that has been recorded but not published so you will never know about the context and you will not be able to reproduce the experiment. In this context, metadata holds a lot of potential but this is a very complex challenge


decision, not only for personal use but for lab teams or entire university science departments. We see requirements from our customers,





not only on having a product, having a tool and then inventing a way to use it but also providing a full service that starts with planning out a strategy and then finally on-boarding everybody and training them to use the soſtware for their specific use cases.


How can your software help increase reproducibility? Reproducibility is one of the key reasons that we go to work every day. Just from personal experience and how I feel about science, so much work is being done and so little of this work can be redone and properly re-purposed for us to achieve a broader understanding. It is changing for the better, there are lots of


initiatives now and this is not an issue that just affects ELNs but I hope that ELNs can play a big role in increasing reproducibility. First of all having data stored digitally


increases reproducibility because it is going to be easy to find. Secondly storing data systematically further increases this because there is a system so even if I do not explain how I stored something you will have a general idea of the system that I used so it will be much easier for you to find, not only the relevant data but also the context or metadata. Going forward from this idea if


reproducibility is reliant on metadata because this provides a very detailed description of under what conditions I received a certain result. Te problem with this is that if I am doing some research and following a set of protocols I have no idea what metadata will be interesting for someone that will try to build upon my research later on. Tere is data that has been recorded but not


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published, so you will never know about the context and you will not be able to reproduce the experiment. In this context, metadata holds a lot of potential but this is a very complex challenge – if you measure blood cholesterol, for example. Whether it comes from a fasting person or not would be contained in the metadata. I would argue that all of that is data but the contextual links between these data points is the metadata. Tis is the area that I see ELN technologies


providing a crucial role, as they can help to create links between different pieces and that is a core functionality of ELN. Te lab notebooks are places where you


make those links but in theory, an ELN could help reproducibility by allowing future scientists to access data on protocols or instrument parameters that are not published in a scientific paper. Tis is what drives the development of our


product in the future. I would like to use the example of my car. I have a very old car that I drive because I do not need to go very far, I typically bike to work and so on. But I have this very old car, it is comfortable, I am used to it and so on but if I order an Uber and they pick me up in that type of car I would give them a very poor rating. What I am trying to say is that in science it is the same person asking the question who is also providing the answer. It is really this bias that the single person has. It is the same person that asks the question who then goes to the lab and finds the answer. Tis affects the quality of the data because


we are recording it for ourselves primarily. What we are trying to do in SciNote is to decouple the interface. Te part where you ask the questions, organising samples and so on when you are looking for the right protocols should be separated from the part where you are doing the experiments and trying to figure out the answer


and explain how something works. We are trying to create user interfaces


that are suited for the two different mindsets that scientists use be it the same person or lab manager or professor that is asking the question and the lab technician that are doing the work. We want to create an interface for them to


have efficient communication and automate as much of the linking of data as possible. We want to automate the recording of metadata as much as possible.


How do you implement this in software? Tis is the questions that we ask ourselves every day. I do not have a final answer but it drives us philosophically towards where we want to go. Standardisation is a key part, we have gone towards standardisation but we quickly come to a trap where we want to standardise data formats and this is something that is very different from what we are talking about now. Really the only standard that all scientists


agree on is that if you want to communicate a scientific discovery you need to write an introduction, material and methods, results, discussion and literature. Tat is fine because it encapsulates science very well. Understanding this structure and working our from this structure so we can standardise the way that questions are asked and defined so we can then measure quality and notify users about what is happening in the lab as results are being produced. I do not think there is a simple answer to


this question, it relies on a lot of detail and we need some time to pass for a lot of users to give feedback. We are not there yet but this is the driving force behind where we want to go.


www.scientific-computing.com/LIG20


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