Laboratory Notebooks Research Articles

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 .

Bringing Pasteur Back to Life: Studying the Biochemistry of Yeast Fermentation Through Discussion Groups and an At-Home Lab

This hands-on, student-centered biochemistry lesson introduces beginner biochemistry students to the techniques of effectively reading and discussing primary literature, identifying fundamental biological concepts, and applying that knowledge to design their own experiments. Students begin by reading Louis Pasteur&rsquo;s article on the discovery of fermentation, a key biochemical concept in metabolism. Using guided questions while they read the primary literature, students dissect the key biochemical concepts of fermentation in student-led discussion groups. Following the group discussion, students &ldquo;act like Pasteur&rdquo; by designing their own lab experiment to collect similar data to that in the paper. For the lab activity, students utilize standard home-kitchen techniques and food-grade reagents to grow yeast in different microenvironmental conditions, such as temperature, pH, presence of oxygen, and substrate concentration in water. Here, students apply key laboratory skills such as designing experiments with proper controls, keeping a lab notebook, and communicating results. Students are given the opportunity to pursue variables they find interesting by performing experiments in their own home. Student understanding is assessed through group discussion, completion of learning issue questions, multiple choice quiz questions, midterm questions, and a lab report. This lesson features a diverse array of activities: reading and understanding the primary literature, participating in scientific discussion in small groups, and designing and performing experiments, all essential skills for any future biochemist. <em>Primary Image:</em>&nbsp;Overview of the biochemistry lesson design.

Why Do I Need a Lab Notebook? Teaching Responsible Conduct of Research with CURE Lab Notebooks

Why Do I Need a Lab Notebook? Teaching Responsible Conduct of Research with CURE Lab Notebooks

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.

Deconstructing the lab notebook and scaffolding assessments in a course-based undergraduate research experience (CURE)

Deconstructing the lab notebook and scaffolding assessments in a course-based undergraduate research experience (CURE)

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.

A focus on the use of real-world datasets for yield prediction.

The prediction of reaction yields remains a challenging task for machine learning (ML), given the vast search spaces and absence of robust training data. Wiest, Chawla et al. (https://doi.org/10.1039/D2SC06041H) show that a deep learning algorithm performs well on high-throughput experimentation data but surprisingly poorly on real-world, historical data from a pharmaceutical company. The result suggests that there is considerable room for improvement when coupling ML to electronic laboratory notebook data.

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

Connected epistemic practices in laboratory‐based engineering design projects for large‐course instruction

AbstractIn both K‐12 and university settings, instructors and curriculum developers need to create learning experiences that provide students opportunities to engage in the disciplinary practices of science and engineering. However, instructional contexts pose challenges in developing such tasks. Using a framework focusing on conceptual and material epistemic practices and tools, we used a within‐subjects comparison to explore two different types of task designs, given realistic constraints posed by a large‐enrollment university engineering laboratory course. We investigated students’ modeling and experimental activity at scale using an innovative data analysis tool, Model Maps, looking for evidence of epistemic practices in student laboratory notebooks and in their post‐project presentations. We compared inscriptions of 29 teams across three laboratory projects and found a greater number and diversity of model components in a virtual laboratory project than in two physical laboratory projects. While fitting into the same instructional “space,” the virtual laboratory task, compared to the physical tasks, better afforded iteration. The iterative development engaged students in two important features of engineering epistemic practice. First, teams had the opportunity to engage in interlocking conceptual and material practices. Second the need to reconcile experimental data with their process models elicited the authorship of free moves together with the accountability of forced moves. Implications for designing instruction to incorporate epistemic practices in engineering and science education are described.

Book Review: The Lab Book: Situated Practice in Media Studies

Book Review: The Lab Book: Situated Practice in Media Studies

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.

A simple kit to use computational notebooks for more openness, reproducibility, and productivity in research.

The ubiquitous use of computational work for data generation, processing, and modeling increased the importance of digital documentation in improving research quality and impact. Computational notebooks are files that contain descriptive text, as well as code and its outputs, in a single, dynamic, and visually appealing file that is easier to understand by nonspecialists. Traditionally used by data scientists when producing reports and informing decision-making, the use of this tool in research publication is not common, despite its potential to increase research impact and quality. For a single study, the content of such documentation partially overlaps with that of classical lab notebooks and that of the scientific manuscript reporting the study. Therefore, to minimize the amount of work required to manage all the files related to these contents and optimize their production, we present a starter kit to facilitate the implementation of computational notebooks in the research process, including publication. The kit contains the template of a computational notebook integrated into a research project that employs R, Python, or Julia. Using examples of ecological studies, we show how computational notebooks also foster the implementation of principles of Open Science, such as reproducibility and traceability. The kit is designed for beginners, but at the end we present practices that can be gradually implemented to develop a fully digital research workflow. Our hope is that such minimalist yet effective starter kit will encourage researchers to adopt this practice in their workflow, regardless of their computational background.

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.

A New Approach to Research Data Management with a Focus on Traceability: Adacta

AbstractTraceability between samples, devices and data is of great importance to the catalysis community. Adacta is a new research data management (RDM) system designed to create a traceable digital twin of a testing environment, not only storing data, but also creating a readily retrievable time‐accurate record of the critical components used to measure catalyst performance. Future developments include extending Adacta to interact with electronic laboratory notebooks and to spawn simulations directly using data and measurements stored in the database.

Exploration of organic superionic glassy conductors by process and materials informatics with lossless graph database

Data-driven material exploration is a ground-breaking research style; however, daily experimental results are difficult to record, analyze, and share. We report a data platform that losslessly describes the relationships of structures, properties, and processes as graphs in electronic laboratory notebooks. As a model project, organic superionic glassy conductors were explored by recording over 500 different experiments. Automated data analysis revealed the essential factors for a remarkable room temperature ionic conductivity of 10−4–10−3 S cm−1 and a Li+ transference number of around 0.8. In contrast to previous materials research, everyone can access all the experimental results, including graphs, raw measurement data, and data processing systems, at a public repository. Direct data sharing will improve scientific communication and accelerate integration of material knowledge.

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Continuous improvement of a bioengineering CURE: Preparing students for a changing world.

Based on recent education reform guidelines to prepare professionals who are able to handle new technological, economic, social, and environmental challenges, pedagogical modifications are deemed necessary by the educators. Specifically, in biology, the rapid changes in the content and biological products demand changes in the curriculum. We aim to address this current need by providing an example of a course that was redesigned to meet the current trends of biological engineering education. In this course-based undergraduate research experience (CURE), learning objectives and possible outcomes were developed and assessment mapping was performed to align the course objectives with the Accreditation Board for Engineering and Technology (ABET) recommendations. A description of how one can assess authentic inquiry courses while adhering to the recommendations are discussed. For example, in this particular course, students completed weekly reflection assignments, maintained lab notebooks that were graded every week, presented their research to their peers at the end of the semester, and submitted a final paper to be graded. "Holistic" engineering is crucial for the all-around development of a 21st century engineer. Altering the traditional lecturing with more hands-on learning is crucial for the development of professional and communication skills of students. Such alterations could lead to the production of well-rounded life-long learners to serve the upcoming world.

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.