Electronic Laboratory Notebook Research Articles

Application and integration of computer vision technologies for automated recognition and recording of chemical experiments

Abstract Advancements in computer vision have significantly improved motion and object recognition accuracy. These advancements should aid the automatic recognition of chemical experiments, potentially contributing to the recording of experiments. Creating an electronic laboratory notebook from experiment filming enhances convenience and allows more detailed information storage compared to traditional manual recording methods. Our previous research focused on employing object detection and action recognition to automate the recognition of chemical experiments. This paper presents a novel system that combines object detection, action recognition, multiple object tracking, and barcode recognition to automatically generate experimental flowcharts. We implemented our system as a graphical user interface-based application for laboratory use that successfully constructs flowcharts from videos of chemical experiments, including simple chemical manipulations.

Finding Relevant Retrosynthetic Disconnections for Stereocontrolled Reactions.

Machine learning-driven computer-aided synthesis planning (CASP) tools have become important tools for idea generation in the design of complex molecule synthesis but do not adequately address the stereochemical features of the target compounds. A novel approach to automated extraction of templates used in CASP that includes stereochemical information included in the US Patent and Trademark Office (USPTO) and an internal AstraZeneca database containing reactions from Reaxys, Pistachio, and AstraZeneca electronic lab notebooks is implemented in the freely available AiZynthFinder software. Three hundred sixty-seven templates covering reagent- and substrate-controlled as well as stereospecific reactions were extracted from the USPTO, while 20,724 templates were from the AstraZeneca database. The performance of these templates in multistep CASP is evaluated for 936 targets from the ChEMBL database and an in-house selection of 791 AZ designs. The potential and limitations are discussed for four case studies from ChEMBL and examples of FDA-approved drugs.

Large language models in electronic laboratory notebooks: Transforming materials science research workflows

In recent years, there has been a surge in research efforts dedicated to harnessing the capabilities of Large Language Models (LLMs) in various domains, particularly in material science. This paper delves into the transformative role of LLMs within Electronic Laboratory Notebooks (ELNs) for scientific research. ELNs represent a pivotal technological advancement, providing a digital platform for researchers to record and manage their experiments, data, and findings. This study explores the potential of LLMs to revolutionize fundamental aspects of science, including experimental methodologies, data analysis, and knowledge extraction within the ELN framework. We present a demonstrative showcase of LLM applications in ELN environments and, furthermore, we conduct a series of empirical evaluations to critically assess the practical impact of LLMs in enhancing research processes within the dynamic field of materials science. Our findings illustrate how LLMs can significantly elevate the quality and efficiency of research outcomes in ELNs, thereby advancing knowledge and innovation in materials science research and beyond.

Implementation of electronic lab notebooks (ELNs) in science laboratory classes: student response and lessons learned

As data and protocols are increasingly generated, accessed, and manipulated electronically, many labs are shifting to storing data in electronic laboratory notebooks (ELNs). ELNs provide clear advantages in readability, shareability, searchability, accessibility, and data security over their paper counterparts. Our interdisciplinary program implemented ELNs across several sections of our gateway molecular biology laboratory course and analyzed the impact on 384 students (13 sections total from Fall 2016 to Spring 2018) using surveys, focus groups, and grade data. We found that students prefer the electronic format, citing the ease of transitioning to lab reports and the longevity of their data. Course grades overall were not affected by the change. Based on our experience, we offer suggestions for smoothing the transition to an ELN system in undergraduate and graduate laboratory courses, including use during remote teaching.

Solvent flashcards: a visualisation tool for sustainable chemistry

Selecting greener solvents during experiment design is imperative for greener chemistry. While many solvent selection guides are currently used in the pharmaceutical industry, these are often paper-based guides which can make it difficult to identify and compare specific solvents. This work presents a stand-alone version of the solvent flashcards that were developed as part of the AI4Green electronic laboratory notebook. The functionality is an intuitive and interactive interface for the visualisation of data from CHEM21, a pharmaceutical solvent selection guide that categorises solvents according to “greenness”. This open-source software is written in Python, JavaScript, HTML and CSS and allows users to directly contrast and compare specific solvents by generating colour-coded flashcards. It can be installed locally using pip, or alternatively the source code is available on GitHub: https://github.com/AI4Green/solvent_flashcards. The documentation can also be found on GitHub or on the corresponding Python Package Index webpage: https://pypi.org/project/solvent-guide/.Scientific ContributionThis simple and easy-to-use digital tool provides a visualisation of solvent greenness data through a novel intuitive interface and encourages green chemistry. It offers numerous advantages over traditional solvent selection guides, allowing users to directly customise the solvent list and generate side-by-side comparisons of only the most important solvents. The release as a standalone package will maximise the benefit of this software.Graphical

Resistance Assessment as a Strategy to Increase the Adoption of Electronic Laboratory Notebooks

Electronic Lab Notebooks (ELNs) have seen a significant rise in research laboratories. However, realizing the benefits of ELNs requires the active participation of employees within an organization. This study aims to identify the factors contributing to resistance towards adopting an ELN, specifically eLabFTW. A semistructured questionnaire, validated by experts, was administered to record book requesters at the René Rachou Institute (FIOCRUZ MG). The findings revealed that individual decision-making factors include peer influence within the research group and familiarity with traditional physical logbooks. Assessing resistance can serve as a strategic tool to enhance system adoption.

Do Chemformers Dream of Organic Matter? Evaluating a Transformer Model for Multistep Retrosynthesis.

Synthesis planning of new pharmaceutical compounds is a well-known bottleneck in modern drug design. Template-free methods, such as transformers, have recently been proposed as an alternative to template-based methods for single-step retrosynthetic predictions. Here, we trained and evaluated a transformer model, called the Chemformer, for retrosynthesis predictions within drug discovery. The proprietary data set used for training comprised ∼18 M reactions from literature, patents, and electronic lab notebooks. Chemformer was evaluated for the purpose of both single-step and multistep retrosynthesis. We found that the single-step performance of Chemformer was especially good on reaction classes common in drug discovery, with most reaction classes showing a top-10 round-trip accuracy above 0.97. Moreover, Chemformer reached a higher round-trip accuracy compared to that of a template-based model. By analyzing multistep retrosynthesis experiments, we observed that Chemformer found synthetic routes, leading to commercial starting materials for 95% of the target compounds, an increase of more than 20% compared to the template-based model on a proprietary compound data set. In addition to this, we discovered that Chemformer suggested novel disconnections corresponding to reaction templates, which are not included in the template-based model. These findings were further supported by a publicly available ChEMBL compound data set. The conclusions drawn from this work allow for the design of a synthesis planning tool where template-based and template-free models work in harmony to optimize retrosynthetic recommendations.

Use of OneNote class notebook as a combined electronic laboratory notebook and content delivery tool in an introductory biochemistry laboratory course.

The COVID-19 pandemic has forced a shift in thinking regarding the safe delivery of wet laboratory courses. While we were fortunate to have the capacity to continue delivering wet laboratory experiments with physical distancing and other measures in place, modifications to the mechanisms of delivery within courses were necessary to minimize risk to students and teaching staff. One such modification was introduced in BCH370H, an introductory biochemistry laboratory course, where a OneNote Class Notebook (ONCN) was used as an electronic laboratory notebook (ELN) in place of the traditional hardbound paper laboratory notebook (PLN) used prior to the pandemic. The initial reasoning for switching to an ELN was around safety-allowing course staff and students to maintain physical distancing whenever possible and eliminating the need for teaching assistants to handle student notebooks; however, the benefits of the ONCN proved to be significantly more. OneNote acted not only as a place for students to record notes but the Class Notebook's unique features allowed easy integration of other important aspects of the course, including delivery of laboratory manuals, posting of student results, notetaking feedback, sharing of instructional materials with teaching assistants, and more. Student and teacher experiences with the ONCN as used within a fully in person biochemistry laboratory course, as well as learned best practices, are reviewed.

NMRium: Teaching nuclear magnetic resonance spectra interpretation in an online platform.

NMRium is the first web-based software that allows displaying, processing, interpretation, and teaching of 1D and 2D NMR data in a user-friendly interface. It can import the most common data formats (e.g., JCAMP-DX, Bruker, Varian, and Jeol). While the scope for the use of NMRium encompasses a variety of applications such as being a component in data repositories or electronic lab notebooks (ELN), performing structure elucidation or preparing raw spectral data for publication, it also excels in enhancing teaching of NMR interpretation. In this paper, we present some current possibilities of this new tool. Several series of exercises are already provided on https://www.nmrium.org/teaching.

Web-BO: towards increased accessibility of Bayesian optimisation (BO) for chemistry.

Historically, the chemical discovery process has predominantly been a matter of trial-and-improvement, where small modifications are made to a chemical system, guided by chemical knowledge, with the aim of optimising towards a target property or combination of properties. While a trial-and-improvement approach is frequently successful, especially when assisted by the help of serendipity, the approach is incredibly time- and resource-intensive. Complicating this further, the available chemical space that could, in theory, be explored is remarkably vast. As we are faced with near infinite possibilities and limited resources, we require improved search methods to effectively move towards desired optima, e.g. chemical systems exhibiting a target property, or several desired properties. Bayesian optimisation (BO) has recently gained significant traction in chemistry, where within the BO framework, prior knowledge is used to inform and guide the search process to optimise towards desired chemical targets, e.g. optimal reaction conditions to maximise yield, or optimal catalyst exhibiting improved catalytic activity. While powerful, implementing BO algorithms in practice is largely limited to interfacing via various APIs - requiring advanced coding experience and bespoke scripts for each optimisation task. Further, it is challenging to seamlessly link these with electronic lab notebooks via a graphical user interface (GUI). Ultimately, this limits the accessibility of BO algorithms. Here, we present Web-BO, a GUI to support BO for chemical optimisation tasks. We demonstrate its performance using an open source dataset and associated emulator, and link the platform with an existing electronic lab notebook, datalab. By providing a GUI-based BO service, we hope to improve the accessibility of data-driven optimisation tools in chemistry; https://suprashare.rcs.ic.ac.uk/web-bo/.

Using <em>OpenBIS</em> as Virtual Research Environment: An ELN-LIMS Open-Source Database Tool as a Framework within the <em>CRC 1411 Design of Particulate Products</em>

The digital transformation and consequent use of new digital technologies not only have a substantial impact on society and companies, but also on science. Analog documentation as we have known it for centuries will eventually be replaced by intelligent and FAIR (Findable, Accessible, Interoperable, and Reusable) systems. In addition to the actual research data and results, metadata now plays an important role not only for individual, independently existing projects, but for future scientific use and interdisciplinary research groups and disciplines as well. The solution presented here, consisting of an electronic laboratory notebook and laboratory information management system (ELN-LIMS) based on the openBIS (open Biology Information System) environment, offers interesting features and advantages, especially for interdisciplinary work. The Collaborative Research Centre (CRC) 1411 ‘Design of Particulate Products’ of the German Research Foundation is characterized by the cooperation of different working groups of synthesis, characterization, and simulation, and therefore serves as a model environment to present the implementation of openBIS. OpenBIS, as an open source ELN-LIMS solution following FAIR principles, provides a common set of general entries with the possibility of sharing and linking (meta-)data to improve the scientific exchange between all users.

Engaging students in a genetics course-based undergraduate research experience utilizing Caenorhabditis elegans in hybrid learning to explore human disease gene variants.

Genetic analysis in model systems using bioinformatic approaches provides a rich context for a concrete and conceptual understanding of gene structure and function. With the intent to engage students in research and explore disease biology utilizing the nematode Caenorhabditis elegans model, we developed a semester-long course-based undergraduate research experience (CURE) in a hybrid (online/in-person) learning environment-the gene-editing and evolutionary nematode exploration CURE (GENE-CURE). Using a combination of bioinformatic and molecular genetic tools, students performed structure-function analysis of disease-associated variants of uncertain significance (VUS) in human orthologs. With the aid of a series of workshop-style research sessions, students worked in teams of two to six members to identify a conserved VUS locus across species and design and test a polymerase chain reaction-based assay for targeted editing of a gene in the nematode and downstream genotyping. Research session discussions, responsible conduct of research training, electronic laboratory notebook, project reports, quizzes, and group poster presentations at a research symposium were assessed for mastery of learning objectives and research progress. Self-reflections were collected from students to assess engagement, science identity, and science efficacy. Qualitative analysis of these reflections indicated several gains suggesting that all students found many aspects of the GENE-CURE rewarding (learning process of research, self-confidence in research and science identity, and personal interest) and challenging (iterative research and failure, time management, COVID-19 pandemic, and life issues).

Results of a Three-Year Survey on the Implementation of Research Data Management and the Electronic Laboratory Notebook (ELN) Chemotion in an Advanced Inorganic Lab Course

Results of a Three-Year Survey on the Implementation of Research Data Management and the Electronic Laboratory Notebook (ELN) Chemotion in an Advanced Inorganic Lab Course

LISTER: Semiautomatic Metadata Extraction from Annotated Experiment Documentation in eLabFTW.

The availability of scientific methods, code, and data is key for reproducing an experiment. Research data should be made available following the FAIR principle (findable, accessible, interoperable, and reusable). For that, the annotation of research data with metadata is central. However, existing research data management workflows often require that metadata be created by the corresponding researchers, which takes effort and time. Here, we developed LISTER as a methodological and algorithmic solution to create and extract metadata from annotated, template-based experimental documentation using minimum effort. We focused on tailoring the integration between existing platforms by using eLabFTW as the electronic lab notebook and adopting the ISA (investigation, study, assay) model as the abstract data model framework. LISTER consists of four components: annotation language to support metadata extraction; customized eLabFTW entries using specific hierarchies, templates, and tags to structure reusable scientific documentation; a "container" concept in eLabFTW, making metadata of a particular container content extractable along with its underlying, related experiments via a single click; a Python-based app to enable easy-to-use, semiautomated metadata extraction from eLabFTW entries. LISTER outputs metadata in machine-readable .json and human-readable .xlsx formats, and Material and Methods (MM) descriptions in .docx format that could be used in a thesis or manuscript. The metadata can be used as a basis to create or extend ontologies, which, when applied to the published research data, will significantly enhance its value. DSpace is used as a data cataloging platform for hosting the extracted metadata and research data. We applied LISTER to computational biophysical chemistry, protein biochemistry, and molecular biology, and our concept should be extendable to other life science areas.

LabIMotion Electronic Lab Notebook as Research Data Management Tool in Catalysis

In the field of heterogenous catalysis, Electronic Lab Notebooks (ELNs) are only rarely employed, due to complex data structure and different needs of the community with respect to the typical features provided by wide-spread ELNs. On the contrary, LabIMotion, an extension of the open-source ELN Chemotion, adapts to the characteristic complex workflows in heterogenous catalysis; these encompass catalyst synthesis, adaptation of devices and testing rigs, activity measurements, material characterization (possibly also in situ/operando, at large scale facilities) and are complemented by mathematical modelling and simulation. Direct links to metadata catalogues like SciCat (for synchrotron/neutron characterizations) and advanced research data management tools like Adacta (for improved traceability of catalytic data, experimental setups and related resources) are envisioned. The adaptability of LabIMotion in the catalysis field is presented via the topical examples of Cu-based catalysts for methanol synthesis and noble metal-based emission control catalysts.

Ontology-Based Laboratory Data Acquisition With EnzymeML for Process Simulation of Biocatalytic Reactors

The presented work explores the use of ontologies and standardized enzymatic data to set up enzymatic reactions in process simulators, such as DWSIM. Setting up an automated workflow to start a process simulation based on enzymatic data obtained from the laboratory can help save costs and time during the development phase. Standardized conditions are crucial for accurate comparison and analysis of enzymatic data, where ontologies provide a standardized vocabulary and semantic relations between relevant concepts. To ensure standardized data, an electronic lab notebook (ELN) is used based on EnzymeML, an open standard XML-based format for enzyme kinetics data. Furthermore, two ontologies are merged and the result is extended for the use in the Python-based workflow. The resulting data is stored in a knowledge graph for research data in a machine-accessible and human-readable format. Thus, the study demonstrates a workflow that allows for the direct translation of ELN data into a process simulation via ontologies.

Exchanging Research Data with SampleDB

Exchanging research data between systems is a vital task in research data management, especially in the context of collaborations. Therefore mapping data into standardized data descriptions and offering interfaces for efficient data exchange are required. The metadata database and electronic lab notebook SampleDB supports various ways of exporting data to other services, such as Dataverse, SciCat, and JupyterHub, as well as file exports in open formats. The concept of SampleDB federations allows data exchange in loosely coupled federations of SampleDB instances.

The Impact of Digitalized Data Management on Materials Systems Workflows

AbstractThe basic modules for materials research are systems for the design, synthesis, preparation, analysis, and application of materials and materials systems. To be efficient and produce findable, accessible, interoperable, and reusable (FAIR) data, state‐of‐the‐art materials research needs to consider the integration of research data management (RDM) workflows and, in the end, the implementation of process automation concepts for all parts of the main modules. Here, the state‐of‐the‐art methods of RDM in academia are described and a perspective on the future of digitalized molecular material systems workflows is given. The different elements of an integrated research data management strategy are described, and examples of automated processes are depicted. As such, the use of electronic lab notebooks for comprehensive documentation, the use of data‐integration and data‐conversion strategies, and the establishment of two platforms that enable the automated synthesis of chemical components for materials and the analysis of materials by electron microscopy, are highlighted. Two examples of beneficial effects of successful RDM strategies are presented, showing a sophisticated tool for data prediction based on machine learning and options for creating community‐driven databases by extracting and re‐using data from different scientific projects.

Implementation and Student Perceptions of OneNote as an Electronic Laboratory Notebook in a General Biology Course

Paper Laboratory Notebooks (PLNs) have been used for centuries to document and archive the thoughts and work of inventors, scientists, students, and engineers. PLNs are the first formal means of scientific communication we teach our students and, in turn, a student’s first training on cultivating and expressing scientific thinking. In the science teaching laboratory, PLNs are at the forefront and serve various purposes for students in the science laboratory curriculum, like good documentation practices and accurate record keeping. This paper discusses the reasons for switching to OneNote ELN, the criteria for selecting OneNote ELN software, how OneNote ELN was introduced and used in the course, and students’ opinions on using this ELN. We successfully implemented the OneNote ELN in our General Biology Laboratory II sequence course for biology majors and non-major students entering the health professions. Students had a generally favorable response to using the OneNote ELN; 98% of students agreed that ELNs enabled them to keep a well-maintained laboratory notebook. Overall, student experience with ELNs was positive, with 85% of students indicating that they would recommend other future science laboratory courses they will be taking to adopt the OneNote ELN. In general, the OneNote ELN was viewed more positively in many respects than the PLNs in ease of its usage, access and organizing the notebook.

An extension of the BioAssay Ontology to include pharmacokinetic/pharmacodynamic terminology for the enrichment of scientific workflows

With the capacity to produce and record data electronically, Scientific research and the data associated with it have grown at an unprecedented rate. However, despite a decent amount of data now existing in an electronic form, it is still common for scientific research to be recorded in an unstructured text format with inconsistent context (vocabularies) which vastly reduces the potential for direct intelligent analysis. Research has demonstrated that the use of semantic technologies such as ontologies to structure and enrich scientific data can greatly improve this potential. However, whilst there are many ontologies that can be used for this purpose, there is still a vast quantity of scientific terminology that does not have adequate semantic representation. A key area for expansion identified by the authors was the pharmacokinetic/pharmacodynamic (PK/PD) domain due to its high usage across many areas of Pharma. As such we have produced a set of these terms and other bioassay related terms to be incorporated into the BioAssay Ontology (BAO), which was identified as the most relevant ontology for this work. A number of use cases developed by experts in the field were used to demonstrate how these new ontology terms can be used, and to set the scene for the continuation of this work with a look to expanding this work out into further relevant domains. The work done in this paper was part of Phase 1 of the SEED project (Semantically Enriching electronic laboratory notebook (eLN) Data).