2018-01-18
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On July/August 2017, a group of researchers from University of Cambridge (life sciences), coordinated a trial of to explore the possibility of deployment or recommendation of a product for use at campus, school or departmental levels. They conclude that, in Cambridge at least, it would be inappropriate to prescribe a single product for use at Campus, College, Department or Research Group levels, because of the sheer diversity of interests, workflows and preferences within the research community. As a result, they have created a web page (http://www.gurdon.cam.ac.uk/institute-life/computing/elnguidance) with some guidance notes for prospective users and listed , plus other . We at , as the vendor of , do have a similar impression after talking to hundreds of customers. We have always been asked with questions like can you do this, or can you do that? We are trying our best to meet our customer's requirements, but still, it is not possible to develop an ELN that makes everyone happy. The solutions that Labii provides, and I am sure the other provide as well, is focused on the features that has been mostly required. What makes it so hard to meet the diversity of researchers' interests, requirements and preferences?
Different research groups usually focus on different research areas and therefore, they need different data to demonstrate their results and to support their hypothesis. Even for those groups that are working in the similar area, the type of data they generated is different because of different methods they applied. It is common for most of the biotech researchers to store the samples, the reagents, and most of these modules already available in the . However, the samples and reagents are just a small portion of data researchers are trying to track. They might need to store the plasmids and cell lines as well. Or in some other labs, researchers need to store the proteins. It is clearly not feasible to purchase a specific LIMS for each type of data.
Each type of data requires many specific attributes. For example, reagent data requires vendors, prices, volume, concentrations, et.al; protein data requires molecular weight, sequences, et.al. In LIMS, all these data stored as structured data in SQL database, with specific headers. Each type of data is created as one table in a database. It is technically impossible to efficiently put two different type of data, with all different headers in one table. Even within the same type of data, the attributions are often different. One record might have a metadata that none of the other records would have. Most of the time, the researchers themselves have no idea what other attributes might come. Diversity and the unknown of data attribution is the second reason which hinders the creation of universal .
Besides the above two issues, one other problem is the specific requirement of different records. Specifically, an equipment record needs to attach an instruction document, an experiment notebook needs signatures, a reagent requires the log of usage, et.al. The requirement of such specific functionalities is still growing.
In 2017, Labii has started working on resolving such issues, creating an that is powerful yet also flexible. We created solutions for each of above problems: We have created modules to enable users from an organization to customize what kind of data they want to store and what terms they prefer. We also make it possible to extend customizable columns with specific data type requirements. Also, we provide widgets to extend the functionalities of each record. All of these awesome updates and solutions will be available in our coming new version:
