Electronic Notebook Research Articles

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.

Дигитални образователни инструменти – потенциал, приложение и перспективи

The paper examines the technologies for creating digital educational resources from the point of view of necessity, implementation, and techniques for their creation. Various digital educational tools and their capabilities according to their application are described: learning and learning management programmes, tools for creating online tests and surveys, tools for creating online quizzes and educational games, interactive worksheets, tools for video lessons, OneNote electronic notebook. A study was conducted on the opinion and attitude of teachers and students about the electronic resources used in different academic disciplines, both for teaching learning content and for control and self-control. The results of two surveys are presented – one aimed at students and the other at teachers. The opinions of 155 teachers and 180 students from four regions in Bulgaria are summarized. The two main aspects of an application – for teaching learning content and for control and self-control – have been analyzed.

Minimum Information Standards in Chemistry: A Call for Better Research Data Management Practices.

Research data management (RDM) is needed to assist experimental advances and data collection in the chemical sciences. Many funders require RDM because experiments are often paid for by taxpayers and the resulting data should be deposited sustainably for posterity. However, paper notebooks are still common in laboratories and research data is often stored in proprietary and/or dead-end file formats without experimental context. Data must mature beyond a mere supplement to a research paper. Electronic lab notebooks (ELN) and laboratory information management systems (LIMS) allow researchers to manage data better and they simplify research and publication. Thus, an agreement is needed on minimum information standards for data handling to support structured approaches to data reporting. As digitalization becomes part of curricular teaching, future generations of digital native chemists will embrace RDM and ELN as an organic part of their research.

Creating lightweight FAIR Digital Objects with RO-Crate

Creating lightweight FAIR Digital Objects with RO-Crate

Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey.

Background: Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. Methods: An online survey was conducted with a mixed question-type design. We created a questionnaire tailored to relevant topics of the bioimaging community, including specific questions on bioimaging methods and bioimage analysis, as well as more general questions on RDM principles and tools. 203 survey entries were included in the analysis covering the perspectives from various life and biomedical science disciplines and from participants at different career levels. Results: The results highlight the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. The survey participants request metadata guidelines and annotation tools and endorse the usage of image data management platforms. At present, OMERO (Open Microscopy Environment Remote Objects) is the best known and most widely used platform. Most respondents rely on image processing and analysis, which they regard as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans is limited, respondents acknowledge their potential value for data handling and publication. Conclusion: The bioimaging community acknowledges and endorses the value of RDM and data sharing. Still, there is a need for information, guidance, and standardization to foster the adoption of FAIR data handling. This survey may help inspiring targeted measures to close this gap.

Learning lab skills online: Lessons from implementing video-based instruction for a remote biotechnology lab.

The COVID-19 pandemic forced many courses to move online, presenting a particular challenge for hands-on laboratory courses. One such course in our Biotechnology track is an advanced Protein Interactions lecture/laboratory course. This 8-week course typically meets for 5 h a week in the laboratory space. For the Fall 2020 version of the course, first-person videos were produced for each of the laboratory experiments, and the corresponding experimental data produced by students in previous semesters were provided for the current students to analyze in their electronic lab notebooks and lab reports. Student perspectives and assessments were collected on course participants from Fall 2019 (in-person laboratories) and Fall 2020 (online laboratories) to compare experiences and outcomes. Analysis of the data shows that the online students appreciated the videos and gained self-confidence in the procedures, but maintained more misconceptions about the material. In addition to being unable to perform the hands-on experiments, other factors such as anxiety could also be interfering with the learning process under the pandemic conditions. The implementation process for the remote labs, student reactions, and lessons learned are discussed.

Structured Data Storage for Data-Driven Process Optimisation in Bioprinting

Bioprinting is a method to fabricate 3D models that mimic tissue. Future fields of application might be in pharmaceutical or medical context. As the number of applicants might vary between only one patient to manufacturing tissue for high-throughput drug screening, designing a process will necessitate a high degree of flexibility, robustness, as well as comprehensive monitoring. To enable quality by design process optimisation for future application, establishing systematic data storage routines suitable for automated analytical tools is highly desirable as a first step. This manuscript introduces a workflow for process design, documentation within an electronic lab notebook and monitoring to supervise the product quality over time or at different locations. Lab notes, analytical data and corresponding metadata are stored in a systematic hierarchy within the research data infrastructure Kadi4Mat, which allows for continuous, flexible data structuring and access management. To support the experimental and analytical workflow, additional features were implemented to enhance and build upon the functionality provided by Kadi4Mat, including browser-based file previews and a Python tool for the combined filtering and extraction of data. The structured research data management with Kadi4Mat enables retrospective data grouping and usage by process analytical technology tools connecting individual analysis software to machine-readable data exchange formats.

Mr Two Initiatives Furthering Open Research

This poster will present two initiatives furthering Open Research
 RSpace, is an electronic lab notebook cum research data management engine that underpins an ecosystem of connected tools enabling Open Research.
 The FAIR Data Podcast, a new podcast devoted to conversations around FAIR data and Open Research.

Exploring electronic lab notebooks (ELNs) at a R1 institution in the Southeast USA

PurposeThis study aims to build a better understanding of researcher needs regarding support for data that you create, store, and/or manage using an electronic lab notebook (ELN), also referred to as electronic research notebook (ERN). The study also articulates the need for risk assessment for ELN products used by researchers for both open data and sensitive data that require standards.Design/methodology/approachThe author used a participatory action research mixed-methods approach. A working group was formed from an ELN initial meeting. The working group team investigated several institutional ERN solutions by setting up trials, speaking with representatives from other research universities with ERN solutions and conducting internal and external research. This culminated in a broader-scale survey exploration.FindingsFindings reveal there is no single institutional ELN license solution to satisfy all scientific disciplines. There is a need to develop foundational tools needed by all, provide additional tools and uses cases with best practices that can be tailored to various labs and research processes and develop a how-to guide on how to assemble the parts to create a useful ELN solution.Research limitations/implicationsThe research implications include providing support for researchers selecting an ERN solution through a combination of online guides, short tutorials and training. There is a need to develop foundational tools, uses cases with best practices that can be tailored to various labs and research processes and how-to guide on how to assemble the parts to create a useful hybrid-ELN solution.Practical implicationsPractical implications include aligning available ERN solutions with other institution provided technologies across the research life cycle to provide researchers a suite of tools to conduct and manage their research. Further investigating educational license discounts for courses using eLabJournal, RSpace, Protocols.io, Open Science Framework, LabArchives or other ERNs currently funded by student course fees via grant funded projects are key implications.Social implicationsSocial implications include the research computing environments of researchers that use ELN solutions approved through institutional risk assessment for open data are in compliance with university regulatory frameworks for use of the software in research.Originality/valueThe originality of this study includes risk assessments of ELNs solutions to better guide researchers in the selection process. To the best of the author’s knowledge, this survey was the first exploration of ELN on campus resulting in a final report to senior stakeholders. This study also highlights a developing grant proposal to further develop support across labs and campus.

Abstract 1903: GenePattern Notebook: An integrative analytical environment for cancer research

Abstract As the availability of genomic data and analysis tools from large-scale cancer initiatives continues to increase, with single-cell studies adding new dimensions to the potential scientific insights, the need has become more urgent for a software environment that supports the rapid pace of cancer data science. The electronic analysis notebook has recently emerged as a versatile tool for this purpose, allowing scientists to combine the scientific exposition with the code that runs the analysis, creating a single “research narrative” document. The Jupyter Notebook system has become the de facto standard notebook environment in data science and genomic analysis. However, the Jupyter environment requires familiarity with programming to run analyses, and even text must be formatted using a programming-style language.To extend notebook capabilities to researchers at all levels of programming expertise, we developed the GenePattern Notebook environment, which integrates Jupyter with the hundreds of genomic tools available through the GenePattern platform. This tool allows scientists to develop, share, collaborate on, and publish their notebooks, requiring only a web browser. Investigators can design their in-silico experiments, refine workflows, launch compute-intensive analyses on cloud-based and high-performance compute resources, and publish results that others can adopt to reproduce the original analyses and modify for their own work.GenePattern Notebook provides: (1) Access to a wide range of genomic analyses within a notebook. Hundreds of analyses are available, from machine learning techniques such as clustering, classification, and dimension reduction, to omic-specific methods for gene expression analysis, proteomics, flow cytometry, sequence variation analysis, pathway analysis, and others. (2) A library of featured genomic analysis notebooks, including templates for common analysis tasks as well as cancer-specific research scenarios and compute-intensive methods. Scientists can easily copy these notebooks, use them as is, or adapt them for their research purposes. To support the growing role of single-cell analysis, we have recently released single-cell RNA-seq preprocessing, cluster harmonization, pseudotime, and RNA velocity notebooks. (3) Notebook enhancements. A rich text editor allows scientists to format text as they would in a word processor. A user interface-building tool allows notebook developers to wrap code so it is displayed as a web form, with only the necessary inputs exposed. (4) Publication and collaborative editing. Authors can publish their notebooks, where they are then made available on the community section of the notebook workspace, where other scientists may copy, run, and edit their own version.The GenePattern Notebook environment is freely available at http://genepattern-notebook.org. Citation Format: Michael M. Reich, Thorin Tabor, Edwin Juarez, Alexander Wenzel, Ted Liefeld, Barbara Hill, David Eby, Forrest Kim, Helga Thorvaldsdóttir, Pablo Tamayo, Jill P. Mesirov. GenePattern Notebook: An integrative analytical environment for cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1903.

Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey.

Background Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. Methods An online survey was conducted with a mixed question-type design. We created a questionnaire tailored to relevant topics of the bioimaging community, including specific questions on bioimaging methods and bioimage analysis, as well as more general questions on RDM principles and tools. 203 survey entries were included in the analysis covering the perspectives from various life and biomedical science disciplines and from participants at different career levels. Results The results highlight the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. The survey participants request metadata guidelines and annotation tools and endorse the usage of image data management platforms. At present, OMERO (Open Microscopy Environment Remote Objects) is the best known and most widely used platform. Most respondents rely on image processing and analysis, which they regard as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans is limited, respondents acknowledge their potential value for data handling and publication. Conclusions The bioimaging community acknowledges and endorses the value of RDM and data sharing. Still, there is a need for information, guidance, and standardization to foster the adoption of FAIR data handling. This survey may help inspiring targeted measures to close this gap.

As Data Flows Surge Data Infrastructure Groans

As Data Flows Surge Data Infrastructure Groans

Making the collective knowledge of chemistry open and machine actionable.

Large amounts of data are generated in chemistry labs-nearly all instruments record data in a digital form, yet a considerable proportion is also captured non-digitally and reported in ways non-accessible to both humans and their computational agents. Chemical research is still largely centred around paper-based lab notebooks, and the publication of data is often more an afterthought than an integral part of the process. Here we argue that a modular open-science platform for chemistry would be beneficial not only for data-mining studies but also, well beyond that, for the entire chemistry community. Much progress has been made over the past few years in developing technologies such as electronic lab notebooks that aim to address data-management concerns. This will help make chemical data reusable, however it is only one step. We highlight the importance of centring open-science initiatives around open, machine-actionable data and emphasize that most of the required technologies already exist-we only need to connect, polish and embrace them.

Ontologies4Chem: the landscape of ontologies in chemistry

Abstract For a long time, databases such as CAS, Reaxys, PubChem or ChemSpider mostly rely on unique numerical identifiers or chemical structure identifiers like InChI, SMILES or others to link data across heterogeneous data sources. The retrospective processing of information and fragmented data from text publications to maintain these databases is a cumbersome process. Ontologies are a holistic approach to semantically describe data, information and knowledge of a domain. They provide terms, relations and logic to semantically annotate and link data building knowledge graphs. The application of standard taxonomies and vocabularies from the very beginning of data generation and along research workflows in electronic lab notebooks (ELNs), software tools, and their final publication in data repositories create FAIR data straightforwardly. Thus a proper semantic description of an investigation and the why, how, where, when, and by whom data was produced in conjunction with the description and representation of research data is a natural outcome in contrast to the retrospective processing of research publications as we know it. In this work we provide an overview of ontologies in chemistry suitable to represent concepts of research and research data. These ontologies are evaluated against several criteria derived from the FAIR data principles and their possible application in the digitisation of research data management workflows.

Exploring the Determinants of Blockchain Acceptance for Research Data Management

ABSTRACT Researchers are highly interested in research data management to derive excellent research results. This study analyzes the determinants affecting the acceptance of blockchain-based electronic lab notebooks service as research data management service. A research model was established based on the technology acceptance model. The sample is collected by 585 researchers from universities and research institutes in Korea. The main research results showed that the usefulness and ease of use lowered the perceived risk and increased the intention to use. Social norms also decreased perceived risk factors and increased intention to use. Research implications are that managers should seek ways to lower the technical risks of blockchain when developing blockchain-based services. In addition, they have to allow users to easily use blockchain-based services and recommend them to other users around them.

Prediction of the Chemical Context for Buchwald‐Hartwig Coupling Reactions

We present machine learning models for predicting the chemical context for Buchwald‐Hartwig coupling reactions, i. e., what chemicals to add to the reactants to give a productive reaction. Using reaction data from in‐house electronic lab notebooks, we train two models: one based on single‐label data and one based on multi‐label data. Both models show excellent top‐3 accuracy of approximately 90 %, which suggests strong predictivity. Furthermore, there seems to be an advantage of including multi‐label data because the multi‐label model shows higher accuracy and better sensitivity for the individual contexts than the single‐label model. Although the models are performant, we also show that such models need to be re‐trained periodically as there is a strong temporal characteristic to the usage of different contexts. Therefore, a model trained on historical data will decrease in usefulness with time as newer and better contexts emerge and replace older ones. We hypothesize that such significant transitions in the context‐usage will likely affect any model predicting chemical contexts trained on historical data. Consequently, training context prediction models warrants careful planning of what data is used for training and how often the model needs to be re‐trained.

Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation

BackgroundElectronic Laboratory Notebooks (ELNs) are used to document experiments and investigations in the wet-lab. Protocols in ELNs contain a detailed description of the conducted steps including the necessary information to understand the procedure and the raised research data as well as to reproduce the research investigation. The purpose of this study is to investigate whether such ELN protocols can be used to create semantic documentation of the provenance of research data by the use of ontologies and linked data methodologies.MethodsBased on an ELN protocol of a biomedical wet-lab experiment, a retrospective provenance model of the raised research data describing the details of the experiment in a machine-interpretable way is manually engineered. Furthermore, an automated approach for knowledge acquisition from ELN protocols is derived from these results. This structure-based approach exploits the structure in the experiment’s description such as headings, tables, and links, to translate the ELN protocol into a semantic knowledge representation. To satisfy the Findable, Accessible, Interoperable, and Reuseable (FAIR) guiding principles, a ready-to-publish bundle is created that contains the research data together with their semantic documentation.ResultsWhile the manual modelling efforts serve as proof of concept by employing one protocol, the automated structure-based approach demonstrates the potential generalisation with seven ELN protocols. For each of those protocols, a ready-to-publish bundle is created and, by employing the SPARQL query language, it is illustrated that questions about the processes and the obtained research data can be answered.ConclusionsThe semantic documentation of research data obtained from the ELN protocols allows for the representation of the retrospective provenance of research data in a machine-interpretable way. Research Object Crate (RO-Crate) bundles including these models enable researchers to easily share the research data including the corresponding documentation, but also to search and relate the experiment to each other.

A System to Easily Manage Metadata in Biomedical Research Labs Based on Open-source Software.

In most biomedical labs, researchers gather metadata (i.e., all details about the experimental data) in paper notebooks, spreadsheets, or, sometimes, electronic notebooks. When data analyses occur, the related details usually go into other notebooks or spreadsheets, and more metadata are available. The whole thing rapidly becomes very complex and disjointed, and keeping track of all these things can be daunting. Organizing all the relevant data and related metadata for analysis, publication, sharing, or deposit into archives can be time-consuming, difficult, and prone to errors. By having metadata in a centralized system that contains all details from the start, the process is greatly simplified. While lab management software is available, it can be costly and inflexible. The system described here is based on a popular, freely available, and open-source wiki platform. It provides a simple but powerful way for biomedical research labs to set up a metadata management system linking the whole research process. The system enhances efficiency, transparency, reliability, and rigor, which are key factors to improving reproducibility. The flexibility afforded by the system simplifies implementation of specialized lab requirements and future needs. The protocol presented here describes how to create the system from scratch, how to use it for gathering basic metadata, and provides a fully functional version for perusal by the reader. Graphical abstract: Lab Metadata Management System.

Made for students, by students: Integrating former-student voices into a de-densified biochemistry laboratory.

The COVID-19 pandemic emphasized the importance of peer communication networks for student outcomes. Herein, we describe the use of supplemental instructional videos created by former students and integrated into electronic lab notebooks, to restore the lost community-learning component using student voices in a de-densified upper-division biochemistry laboratory.