Electronic Laboratory Notebook Research Articles

Finding Connections in Policies Covering Electronic Laboratory Notebook Retention and Transferal

Objective: As electronic laboratory notebook (ELN) capability continues to expand, more researchers are turning to this digital format. The University of Massachusetts Medical School developed new guidelines to outline the retention and transferal of ELNs. How do other universities approach the retention and transferal of laboratory notebooks, including ELNs? Methods: The websites of 25 universities were searched for policies or guidelines on laboratory notebook retention and transferal. A textual analysis of the policies was performed to find common themes. Results: Information on the retention and transferal of laboratory notebooks was found in record retention and research data policies/guidelines. Out of the 25 institutional websites searched, 16 policies/guidelines on research notebook retention were found and 10 institutions had policies/guidelines on transferring research notebooks when a researcher leaves the university. Only one policy had a retention recommendation for storage location specific to electronic media, including laboratory notebooks, that did not apply to its paper counterparts, the remaining policies either explicitly include multiple forms and media or do not mention multiple formats for research records at all. The minimum number of years of retention for research notebooks ranged from immediately after report completion to 7 years after completing the research with the possibility of extension depending on a wide range of external requirements. Most research notebook transferal policies and guidelines required associated researchers and students to request permission from their principal investigator (PI) before taking a copy of the notebook. Most institutions with policies also seek to retain access to research notebooks when a PI leaves an institution to protect intellectual property and respond to any cases of scientific misconduct or conflict of interest. Conclusions: Other universities have a range of approaches for the retention and transferal of laboratory notebooks, but most provide the same recommendations for both electronic and physical laboratory notebooks in their research data or record retention policies/guidelines.

Supporting Team Based Learning Using Electronic Laboratory Notebooks: Perspectives From Transnational Students

Today's engineering industries require graduates with a broad range of soft skills, which include teamwork, communication and integrity. Therefore, more accreditation bodies now recommend team-learning activities to be embedded in their engineering programmes. However, hardworking students often find group projects demotivating, especially if their contributions are not accurately reflected in their individual grades. To address these issues, we demonstrate that Electronic Laboratory Notebooks can be used to promote student collaboration and teamwork on a group project. They can also help instructors assess student contributions fairly. During our investigations, we noticed that students have used Electronic Laboratory Notebooks as social interaction tools that enable text, data, images and recorded audio to be exchanged. Consequently, we describe the experiences of 58 transnational undergraduate students in using six different software products for a team-based learning activity. According to our investigations, Electronic Laboratory Notebooks had a positive impact on supporting Team Based Learning in a new electronic engineering course. The outcomes of our investigations can help create effective teaching and learning resources for undergraduate students in Electronic Engineering. They can also help staff members make evidence-based decisions regarding the introduction of Electronic Laboratory Notebooks in undergraduate research activities.

A data-driven perspective on the colours of metal-organic frameworks.

Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is often used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale. In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards. To remedy this, we suggest and implement an alternative way of reporting colour—and chemical data in general. All data is captured in an objective, and standardised, form in an electronic lab notebook and subsequently automatically exported to a repository in open formats, from where it can be interactively explored by other researchers. We envision this to be key for a data-driven approach to chemical research.

Assessing Student Learning in a Rapidly Changing Environment: Laboratories and Exams

The purpose of this communication is to describe and document the challenges of delivering a general chemistry laboratory experience to students and of moving toward multiple small assessments and away from high-stakes examinations. By leveraging of activities that were already in place prior to the pivot to online learning, namely, an electronic laboratory notebook through bluedoorlabs and assessments that emphasized student reasoning, these challenges were addressed. Herein our approach to providing a meaningful laboratory experience and lower-stakes assessments is described. On the basis of our experiences in Spring 2020, we look forward to the fall semester, where the university is committed to reopening in a dedensified fashion with students in classrooms and laboratories.

An Industrial Approach to Using Artificial Intelligence and Natural Language Processing for Accelerated Document Preparation in Drug Development

Due to the exceptionally high standards for accuracy and data integrity in scientific regulatory reporting, it is vital that any tool that aims to streamline this process is as efficient or more in gathering data as a team of scientists, without higher cost in terms of time or resources. For this reason, an artificial intelligence-based tool with parallel search, document creation, and data integrity review capabilities is being investigated as a potential solution. This paper describes a proof of concept project to develop an AI-based tool to rapidly assemble an end-of-phase 2 (EOP2) briefing document for a potential medicine. We have called the tool an Intelligent Machine for Document Preparation or IMDP. A training corpus of approximately 65,000 pdf documents derived from electronic lab notebooks and technical reports related to five molecules (including Merestinib) was ingested, and prior EOP2 documents from the remaining four molecules was used to generate training questions and answers. Then, an annotation-light natural language processing algorithm analyzed a set of structured and unstructured data regarding Merestinib. A simple user interface was created allowing scientists to query the system in natural language, and a table builder, image/plot finder, and free-text addition features were added to allow for advanced search without dependence on keywords. Three significant innovations were designed-in to improve overall performance as compared to our benchmark solution without sacrificing usability. First, the AI-based IMDP was built to improve accuracy and accelerate document creation with remarkably low amount of training. Second, image search capability was added to enrich the knowledge base, and third, the IMDP was integrated with the existing process rather than adding a step in the workflow. Finally, accuracy and total document creation time were compared with the existing tool (benchmark tool). Our experiments show that the AI-based technology reached 89% accuracy which surpassed the internal benchmark of 54% and retrieved the right information 3.6 times faster. The main contribution of this study is to show the value of artificial intelligence-based tools in accelerating all major stages of regulatory report creation while allowing a team of scientists to seamlessly collaborate.

Be FAIR to your data

Wouldn’t it be great, if experimental data were findable wherever they were? If experimental data were accessible‚ regardless of the storage place and format? If experimental data were interoperable independent of the author or its origin? If experimental data were reusable for further analysis without experimental repetition? The current state of the art of data acquisition in the laboratory is very diverse. A lot of different devices are used, analogue as well as digital ones. Usually all experimental setups and observations are summarized in a handwritten lab notebook, independently from digital or analogue sources. To change the actual and common way of laboratory data acquisition into a digital and modern one, electronic lab notebooks can be used. A challenge of science is to facilitate knowledge discovery by assisting humans and machines in their discovery of scientific data and their associated algorithms and workflows. FAIR describes a set of guiding principles to make data Findable, Accessible, Interoperable, and Reusable.

Do You Call That a Lab Notebook?

Electronic laboratory notebooks (ELNs)? What are those? At the start of every academic year, I prepare myself for a battle with my third-year engineering students about laboratory notebooks. More often than not, they either hand me a neatly written diary or loose bits of paper that are half folded and randomly glued to the back cover of a jotter. At first, my students dismiss the idea of keeping a properly documented lab notebook, but I notice a change in attitude when they are introduced to electronic notebooks.

Systematizing scientific laboratory work by a workflow and template for electronic laboratory notebooks

Effective and efficient scientific laboratory work requires systematic thinking to maximize its relevance and quality. Key aspects include: the clear, explicit articulation of context and objectives; a literature review informing the experimental plan; the use of experimental best practice. Electronic laboratory notebook (ELN) offerings continuously expand, promising improvements in workflow design. Here, the general structure of scientific projects was conceptualized, and the necessary steps reordered to logically follow project delivery. A structured workflow and an enabling template were successfully developed in a corporate ELN environment. Together, they facilitate the planning and execution of laboratory work through support of the scientific thought process, as evidenced by a commented example. In this example of practice, the resulting template is shared to enable further use and adaptation.

Raw-to-repository characterization data conversion for repeatable, replicable, and reproducible measurements

The National Institute for Materials Science, Japan, has been developing a materials data platform linked with a materials data repository system for rapid new material searching using materials informatics. Data conversion from raw data to a human-legible/machine-readable data file is one of the key preparation techniques prior to data analysis, where the converted data file should include metainformation. The tools developed by the authors can convert raw data to a structured data package that consists of mandatory and measurement-characterization metadata, primary and raw parameters, and formatted numerical data (FND). The FND are expressed as a matrix type with robust flexibility. This flexibility is achieved by applying the data analysis architecture of schema-on-read, not schema-on-write based on de jure standards, such as ISO documents. The primary parameters are carefully selected from raw parameters, and their vocabularies are transformed from instrument-dependent terms to general terms that everyone can readily understand. The converted data are linked with, for example, specimen information, process information, specimen handling records, and the electronic laboratory notebook. Using this raw-to-repository (R2R) conversion flow, the authors demonstrated that they can generate and store interoperable data files of x-ray photoelectron spectroscopy (XPS) spectra and depth profiles, powder x-ray diffraction patterns, (scanning) transmission electron microscope images, transmission electron diffraction patterns, electron energy-loss spectroscopy spectra, and calculated electron inelastic mean free path data. Linking measurement data to other required information ensures experimentally repeatable, replicable, and reproducible results. The mandatory and characterization metadata are used for quick electronic searching, and primary and raw parameters are convenient for setting up measurement conditions and useful for reproducibility/replicability and replicability/repeatability, respectively. The FND are human legible and machine readable using parser software, leading to a long lifetime of data utilization. The authors also developed a system to allow the semiautomatic data transfer from an instrument-controlling personal computer (PC) isolated from the communication network by adopting a Wi-Fi-capable secure digital card’s scripting capability while keeping the PC offline. They are developing further software for on-demand data manipulation after R2R data conversion. To date, it has been possible to perform XPS peak separation using an automated information compression technique without any a priori assumption. By combining R2R conversion with a high-throughput data collection system and automated data analysis routine, highly reproducible data acquisition and data analysis could be achieved, where human interaction is minimized. At this early stage, the authors demonstrate automated peak separation processing for XPS C 1s and O 1s narrow spectra of polyethylene terephthalate with very high reproducibility.

ChemStor: Using Formal Methods To Guarantee Safe Storage and Disposal of Chemicals.

While safe chemical storage and disposal are simple in principle-users should read safety specifications and place chemicals in appropriate cabinets or collection points-high-profile incidents involving improper storage and disposal of chemicals continue to occur. This paper introduces ChemStor, an open-source, automated computational system that can guarantee (mathematically verify a system is correct with respect to its specification), with regard to prescribed constraints, safe storage and disposal of chemicals used in academic, industrial, and domestic settings. ChemStor borrows concepts from formal methods-a branch of computer science capable of mathematically proving a specification or software is correct-to safely store or dispose of chemicals. If two or more chemicals can be combined in the same cabinet without forming possibly dangerous combinations of chemicals (while observing cabinet/shelf space constraints), then ChemStor determines that the storage configuration is safe. Likewise, if chemicals can be added to an existing disposal container without forming possibly dangerous combinations of chemicals (or exceeding the volume of the container), then ChemStor determines that the disposal configuration is safe. ChemStor accomplishes this by first building a chemical interaction graph, a graph that describes which chemicals may interact with each other based on their reactivity groups as determined by the United States Environmental Protection Agency. Next, ChemStor computes the chromatic number of the graph, the smallest number of colors used to color the graph such that no two vertices (chemicals) that share an edge (an interaction) share the same color. ChemStor then assigns all the chemicals of each color to a storage or disposal container after confirming that there is enough space in the container. These steps are encoded into a series of satisfiability modulo theory equations, and ChemStor uses an industry-standard tool to try to find a valid solution to these equations. The result is either a solution which dictates exactly where to store or dispose of each chemical, or an indication that no safe storage or disposal configuration could be found. To demonstrate the feasibility of ChemStor, we used the tool to analyze ten real-world chemical storage and disposal incidents that led to injuries or destruction of property. In each case, ChemStor quickly and successfully identified a proper chemical disposal or storage configuration that would have prevented the incident. In the future, ChemStor may be integrated with electronic laboratory notebooks, voice assistants, and other emerging technology to protect users of chemicals in labs, workplaces, and homes.

The Use of Mobile Applications in Research

AbstractIn science and engineering, there are a range of easy‐to‐access software applications for smart phones, tablets, and laptop and desktop computers that greatly simplify basic and advanced laboratory research activities. The available software programs include numerous mobile and web‐friendly applications for electronic laboratory notebooks, project planning, solution calculations, and advanced scientific calculations and analysis, and even simple‐to‐use apps for your favorite scientific meetings. A typical collection of mobile‐friendly applications is discussed here in the context of preparing, performing, and presenting your research. © 2019 by John Wiley & Sons, Inc.

Electronic Lab Notebooks - early research practice in teaching

Electronic Lab Notebooks - early research practice in teaching

Development and Application of a Data-Driven Reaction Classification Model: Comparison of an Electronic Lab Notebook and Medicinal Chemistry Literature.

Reaction classification has often been considered an important task for many different applications, and has traditionally been accomplished using hand-coded rule-based approaches. However, the availability of large collections of reactions enables data-driven approaches to be developed. We present the development and validation of a 336-class machine learning-based classification model integrated within a Conformal Prediction (CP) framework to associate reaction class predictions with confidence estimations. We also propose a data-driven approach for "dynamic" reaction fingerprinting to maximize the effectiveness of reaction encoding, as well as developing a novel reaction classification system that organizes labels into four hierarchical levels (SHREC: Sheffield Hierarchical REaction Classification). We show that the performance of the CP augmented model can be improved by defining confidence thresholds to detect predictions that are less likely to be false. For example, the external validation of the model reports 95% of predictions as correct by filtering out less than 15% of the uncertain classifications. The application of the model is demonstrated by classifying two reaction data sets: one extracted from an industrial ELN and the other from the medicinal chemistry literature. We show how confidence estimations and class compositions across different levels of information can be used to gain immediate insights on the nature of reaction collections and hidden relationships between reaction classes.

Cardiac Tissue Engineering as Use Case to Connect Biomedical Research Laboratories to an Emerging Global Data Infrastructure.

Methods for cardiac tissue engineering and application in experiments are core technologies developed at the Institute of Pharmacology and Toxicology in Göttingen. As is the case in many academic research laboratories data capture and documentation may be improved to latest methods of digital research. A comprehensive information system infrastructure is the foundation of further advances toward automation of lab processes. A data management system concept is proposed and prototypically deployed that enables traceability of assets within the lab and reproducibility of published assays and results. The prototype integrates existing electronic lab notebook, experiment result database, and a newly introduced research data management system by means of a custom developed portal and integration component. The architecture concept and developed integration tools explore connection of routine experimental work in a biomedical research lab to a universal infrastructure of data.

Requirements for Documenting Electrical Cell Stimulation Experiments for Replicability and Numerical Modeling∗.

Thorough documentation of biological experiments is necessary for their replicability. This becomes even more evident when individual steps of in vitro wet-lab experiments are to be incorporated into computer simulation models. In the highly interdisciplinary field of electrical stimulation of biological cells, not only biological but also physical aspects play a crucial role. Simulations may help to identify parameters that influence cells and thereby reveal new insights into mechanisms of the cell biological system. However, missing or misleading documentation of the electrical stimulation step within wet-lab experiments may lead to discrepancies between reported and simulated electrical quantities. In addition, this threatens the replicability of electrical stimulation experiments. Thus, we argue that a minimal set of information is needed to enable a translation of electrical stimulation experiments of biological cells into computer simulation experiments and to support replicability. This set includes detailed information about the electronic devices and components, their set-up as well as the applied stimulus and shall be integrated into an existing guideline for cell biological experiments. Ideally, the documentation should also contain measured properties of the cellular and experimental environment. Furthermore, a realization of our proposed documentation requirements within electronic lab notebooks may provide a crucial step toward a more seamless integration of wet-lab data into simulations. Based on two exemplary studies, we demonstrate the relevance of our claim.

The pleiotropic effects of Innexin genes expressed in Drosophila glia encompass wing chemosensory sensilla.

The neuroanatomy of Drosophila wing chemosensilla and the analysis of their sensory organ precursor cell lineage have demonstrated that they are surprisingly related to taste perception. The microarchitecture of wing bristles limits the use of electrophysiology methods to investigate wing chemosensory mechanisms. However, by monitoring the fluorescence of the complex calcium/GCaMP, calcium flux triggered upon tastant stimulation was observed within sensilla aligned along the wing anterior nerve. This string of fluorescent puncta was impaired in wings of Innexin 2 (Inx2) mutant flies; although it is unclear whether the Innexin proteins act at the level of the wing imaginal disc, adult wing and/or at both levels. Glial cells known to shelter Innexin(s) expression have no documented role in adult chemosensory sensilla. Our data suggest that Innexin(s) are likely required for the maturation of functional wing chemosensilla in adulthood. The unexpected presence of most Innexin transcripts in adult wing RNAseq data set argues for the expression of Innexin proteins in the larval imaginal wing disc that are continued in wing chemosensilla at adulthood. OPEN PRACTICES: This article has earned an Open Data badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available as supporting materials and includes the electronic lab notebook. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki.

A quick guide for using Microsoft OneNote as an electronic laboratory notebook.

Scientific data recording and reporting systems are of a great interest for endorsing reproducibility and transparency practices among the scientific community. Current research generates large datasets that can no longer be documented using paper lab notebooks (PLNs). In this regard, electronic laboratory notebooks (ELNs) could be a promising solution to replace PLNs and promote scientific reproducibility and transparency. We previously analyzed five ELNs and performed two survey-based studies to implement an ELN in a biomedical research institute. Among the ELNs tested, we found that Microsoft OneNote presents numerous features related to ELN best functionalities. In addition, both surveyed groups preferred OneNote over a scientifically designed ELN (PerkinElmer Elements). However, OneNote remains a general note-taking application and has not been designed for scientific purposes. We therefore provide a quick guide to adapt OneNote to an ELN workflow that can also be adjusted to other nonscientific ELNs.

An intervention to increase accuracy and understanding in undergraduate micropipette use

Abstract Micropipetting is an essential skill for the budding immunologist and is widely used across biological and physical science disciplines, yet few undergraduates are adequately introduced to this skill. The mentors of undergraduates seeking research experience point to micropipette inexperience as a common barrier to success. As part of our SEA-PHAGES authentic research laboratory, we designed a hands-on intervention to give thorough training in micropipetting to first-year undergraduates. The intervention included assignment of a pre-class video on micropipetting, an activity to orient students to the micropipette parts and functions, and a TA-led demonstration of proper micropipette use and volume adjustment. Success of the intervention was analyzed using data collected from a practical test following the lab activity. In addition, we compared the students’ end-of-semester experimental success to that of students from previous semesters lacking the micropipetting activity, using archived electronic laboratory notebooks. We found that students progressed through experiments faster and with more success after experiencing the micropipette intervention. Our analysis showed that some aspects of proper micropipette use need to be emphasized, such as proper depth of tip entry and the correct tactile discrimination of the first/second stop on the micropipette. In addition, we found that students have misconceptions and gaps in knowledge about the metric system that are an additional barrier to proper micropipette use. This project presents a successful platform for teaching micropipette technique, and demonstrates that this leads to increased likelihood of success for the students in this real-world laboratory setting.

Too many tags spoil the metadata: investigating the knowledge management of scientific research with semantic web technologies

Scientific research is increasingly characterised by the volume of documents and data that it produces, from experimental plans and raw data to reports and papers. Researchers frequently struggle to manage and curate these materials, both individually and collectively. Previous studies of Electronic Lab Notebooks (ELNs) in academia and industry have identified semantic web technologies as a means for organising scientific documents to improve current workflows and knowledge management practices. In this paper, we present a qualitative, user-centred study of researcher requirements and practices, based on a series of discipline-specific focus groups. We developed a prototype semantic ELN to serve as a discussion aid for these focus groups, and to help us explore the technical readiness of a range of semantic web technologies. While these technologies showed potential, existing tools for semantic annotation were not well-received by our focus groups, and need to be refined before they can be used to enhance current researcher practices. In addition, the seemingly simple notion of “tagging and searching” documents appears anything but; the researchers in our focus groups had extremely personal requirements for how they organise their work, so the successful incorporation of semantic web technologies into their practices must permit a significant degree of customisation and personalisation.

Procedures for systematic capture and management of analytical data in academia

Data management in universities is a challenging endeavor in particular due to the diverse infrastructure of devices and software in combination with limited budget. Nevertheless, in particular the analytical measurements and data sets need to be stored if possible digitally and in a well-organized manner. This manuscript describes how scientists can achieve a data management workflow focusing on data capture and storage by small adaptions to commonly used systems. The presented method includes data transfer options from ubiquitous devices like NMR instruments, GC (MS) or LC (MS), IR and Raman, or mass spectrometers to a central server and the visualization of the available data files in an electronic lab notebook (ELN). The given instruments were chosen according to the needs of synthetic chemists, in particular devices needed in organic, inorganic and polymer chemistry where single data files in the range of several megabytes per data set are produced. Altogether, three different data transfer systems were elaborated to allow a flexible handling of different devices running with different proprietary software: The first procedure allows data capture via the use of a mail server as data exchange point. With the second procedure, data are automatically mirrored from a local file folder to a central storage server where new files are monitored and processed. The third procedure was designed to transfer data with manual support to a central server which is supervised to register new information. All components that are necessary to install and use the herein elaborated functions are available as Open Source and the designed workflows are described step by step to facilitate the adaption of procedures in other universities accordingly if desired.