Electronic Laboratory Notebook Research Articles

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.

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).

Teaching FAIR in computational chemistry: managing and publishing data using the twin tools of compute portals and repositories

The history of the emerging area of tools for managing research resources and the data produced from them is summarised from the perspective of two decades of use in teaching and research at one institution. These tools are a portal or electronic laboratory notebook for computational chemistry interfaced in one direction to a high-performance computing resource and in the other direction to a modern research data repository. The essential features of both these tools are described over two generations of each, with examples of student work cited as examples using persistent identifiers or PIDs, better known as DOIs. Underpinning this is the metadata describing the data being processed. The article outlines the evolution of managing such metadata-rich data and its progress towards what can now be summarised by the acronym FAIR data, itself enabling future emerging areas such as knowledge graphs.

AI4Green: An Open-Source ELN for Green and Sustainable Chemistry.

An Electronic Laboratory Notebook (ELN) combining features, including data archival, collaboration tools, and green and sustainability metrics for organic chemistry, is presented. AI4Green is a web-based application, available as open-source code and free to use. It offers the core functionality of an ELN, namely, the ability to store reactions securely and share them among different members of a research team. As users plan their reactions and record them in the ELN, green and sustainable chemistry is encouraged by automatically calculating green metrics and color-coding hazards, solvents, and reaction conditions. The interface links a database constructed from data extracted from PubChem, enabling the automatic collation of information for reactions. The application's design facilitates the development of auxiliary sustainability applications, such as our Solvent Guide. As more reaction data are captured, subsequent work will include providing "intelligent" sustainability suggestions to the user.

Leveraging socio-technical processes to support researchers

Purpose The purpose of this project was to develop research support services that address local and external research data management (RDM) support drivers within the existing organizational culture at the University of Florida. The goal was to prompt organization change to support a campus-wide electronic lab notebook. Design/methodology/approach This project used a mixed-methods research approach to cultivate an organizational change program that support technological infrastructure to benefit researchers. The mixed-methods research involved participation action research integrated with a stakeholder approach. Findings The development of the grant proposal which was unfunded led to development of continued project goals. This project confirmed the development for support for an institution-wide electronic research notebook (ERN) solution requires adherence to the summary of five key actions for developing RDM services. Failure to complete all of the key actions engenders fragmentation culture. Research limitations/implications This project includes implications for institutions to develop grant proposals with integrated budgets for research support services of funded projects; and to use the summary of key actions for developing RDM services articulated by Jones et al. (2013) in “How to Develop RDM Services – a guide for HEIs.” Both are need to support findable, accessible, interoperable and reusable data for researchers. Practical implications This project has practical implications for higher education institutions interested in leveraging socio-technical processes to advance the role of libraries as collaborator, partner and stakeholder in developing institution-wide adoption, support and training for ERN as a research support service to RDM. Social implications This paper contributes to the body of developing literature on ERN as support services to RDM lead by academic research libraries. Originality/value This project contributed to the change in organization culture resulting in the successful collaboration between the Research Office and College of Medicine to support an institution-wide ERN technological infrastructure for one year as a pilot at a large academic research institution in the southeast USA.

Electronic Lab Notebooks for Materials Synthesis

Electronic Lab Notebooks for Materials Synthesis

EnzymeML: seamless data flow and modeling of enzymatic data.

The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org .

On the use of real-world datasets for reaction yield prediction.

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as well as or better than the best previous models on two HTE datasets for the Suzuki-Miyaura and Buchwald-Hartwig reactions. However, training the AGNN on an ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions.

GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration

Sharing scientific experiments and data with the community using GitHub as an electronic laboratory notebook.