Laboratory Notebooks Research Articles

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.

Considerations for implementing electronic laboratory notebooks in an academic research environment.

As research becomes predominantly digitalized, scientists have the option of using electronic laboratory notebooks to record and access entries. These systems can more readily meet volume, complexity, accessibility and preservation requirements than paper notebooks. Although the technology can yield many benefits, these can be realized only by choosing a system that properly fulfills the requirements of a given context. This review explores the factors that should be considered when introducing electronic laboratory notebooks to an academically focused research group. We cite pertinent studies and discuss our own experience implementing a system within a multidisciplinary research environment. We also consider how the required financial and time investment is shared between individuals and institutions. Finally, we discuss how electronic laboratory notebooks fit into the broader context of research data management. This article is not a product review; it provides a framework for both the initial consideration of an electronic laboratory notebook and the evaluation of specific software packages.

Hands-On, Hands-Off: The Community College Genomics (ComGen) Course-Based Undergraduate Research Experience

Science is a process of discovery where failure is inherent and iteration is necessary, yet instructors often teach the scientific process as if it is a controlled, highly supervised, confirmatory practice of following directions to get a known answer. We believe this mismatch occurs because instructors often struggle to feel comfortable in facilitating open-ended inquiry and giving students the trust and autonomy to experience an authentic scientific process. In this quarter-long lab curriculum, we bring the scientific process into the classroom in the form of an authentic course-based undergraduate research experience (CURE). We present a pedagogy, which is hands-on for students and hands-off for instructors, that incorporates and celebrates the learning that occurs from failing safely and often. The research project presented in this article is a genomics-based CURE where students sequence and analyze DNA genome segments. Throughout the lesson, we present core instructional structures and techniques that are transferable to any project and help scaffold and support the learning impact of the CURE. In the following curriculum, we outline this pedagogy, applied to a model CURE focused on sequencing a bacterium, and suggest ways that both the pedagogy and the core components of our CURE (<em>i.e.,</em> journal club, posters, lab notebook, and self-assessments) transfer to other courses, and other research projects. <em>Primary Image:</em> Gita Bangera guiding Bellevue College students through the ComGen research process in a cellular biology course.

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.

“Everything Is in the Lab Book”: Multimodal Writing, Activity, and Genre Analysis of Symbolic Mediation in Medical Physics

Writing and genre scholarship has become increasingly attuned to how various nontextual features of written genres contribute to the kinds of social actions that the genres perform and to the activities that they mediate. Even though scholars have proposed different ways to account for nontextual features of genres, such attempts often remain undertheorized. By bringing together Writing, Activity, and Genre Research, and Multimodal Interaction Analysis, the authors propose a conceptual framework for multimodal activity-based analysis of genres, or Multimodal Writing, Activity, and Genre (MWAG) analysis. Furthermore, by drawing on previous studies of the laboratory notebook (lab book) genre, the article discusses the rhetorical action the genre performs and its role in mediating knowledge construction activities in science. The authors provide an illustrative example of the MWAG analysis of an emergent scientist’s lab book and discuss its contributions to his increasing participation in medical physics. The study contributes to the development of a theoretically informed analytical framework for integrative multimodal and rhetorical genre analysis, while illustrating how the proposed framework can lead to the insights into the sociorhetorical roles multimodal genres play in mediating such activities as knowledge construction and disciplinary enculturation.

The Open Reaction Database.

Chemical reaction data in journal articles, patents, and even electronic laboratory notebooks are currently stored in various formats, often unstructured, which presents a significant barrier to downstream applications, including the training of machine-learning models. We present the Open Reaction Database (ORD), an open-access schema and infrastructure for structuring and sharing organic reaction data, including a centralized data repository. The ORD schema supports conventional and emerging technologies, from benchtop reactions to automated high-throughput experiments and flow chemistry. The data, schema, supporting code, and web-based user interfaces are all publicly available on GitHub. Our vision is that a consistent data representation and infrastructure to support data sharing will enable downstream applications that will greatly improve the state of the art with respect to computer-aided synthesis planning, reaction prediction, and other predictive chemistry tasks.

Gerakan Literasi Sains melalui Pengenalan STEAM pada Anak di Komunitas “Panggon Moco” Gresik

Scientific literacy is one of the important literacies that children need to have. However, there are many findings that show the low level of scientific literacy in Indonesian children. "Panggon Moco" is one of the reading park communities (TBM) which was formed due to the community’s concern for the success of the literacy program. Until now, the literacy developed by this community is still limited to reading literacy. Therefore, it is necessary to develop other literacy such as scientific literacy. To answer this challenge, it is necessary to carry out a scientific literacy activities for children in the Panggon Moco community. A learning approach that has been proven to improve scientific literacy in children is STEAM (science, technology, engineering, art, and mathematics). The method of implementing this program includes several activities, namely (1) discussion and equalization of perceptions; (2) making Fun STEAM Lab books; (3) the STEAM experiment; and (4) STEAM literacy assistance. This activity is carried out offline and online. The results obtained from this program are increasing children's understanding of STEAM and increasing students' interest and motivation in learning.

Challenges and Advances in Information Extraction from Scientific Literature: a Review

Scientific articles have long been the primary means of disseminating scientific discoveries. Over the centuries, valuable data and potentially groundbreaking insights have been collected and buried deep in the mountain of publications. In materials engineering, such data are spread across technical handbooks specification sheets, journal articles, and laboratory notebooks in myriad formats. Extracting information from papers on a large scale has been a tedious and time-consuming job to which few researchers have wanted to devote their limited time and effort, yet is an activity that is essential for modern data-driven design practices. However, in recent years, significant progress has been made by the computer science community on techniques for automated information extraction from free text. Yet, transformative application of these techniques to scientific literature remains elusive—due not to a lack of interest or effort but to technical and logistical challenges. Using the challenges in the materials science literature as a driving motivation, we review the gaps between state-of-the-art information extraction methods and the practical application of such methods to scientific texts, and offer a comprehensive overview of work that can be undertaken to close these gaps.

The Biology of Evaluating Skin-Care Products

The goal of this inquiry activity is to evaluate a variety of skin-care products on simulated skin cells using Orbeez, a brand of superabsorbent polymers. These small beads can quickly absorb water but will dry out in the air, thus perfectly representing our own skin cells. This property makes Orbeez an acceptable model for human skin, and test material for evaluating various skin-care products. Students will observe the quantitative changes that happened to the mass of the samples to determine the percent change. They will also observe the qualitative changes that happened throughout the lab investigation and record their observations in a lab notebook. After completing the experiment, students will create a presentation of the quantitative and qualitative results.

Consolidating and Managing Data for Drug Development within a Pharmaceutical Laboratory: Comparing the Mapping and Reporting Tools from Software Applications

We present a perspective on drug development for the synthesis of an active pharmaceutical ingredient (e.g., agomelatine) within a commercial technology called Luminata and compare the results to the current method of consolidating the reaction data into Microsoft Excel. The Excel document becomes the ultimate repository of information extracted from multiple sources such as the electronic lab notebook, the laboratory information management system, the chromatography data system, in-house databases, and external data. The major needs of a pharmaceutical company are tracking the stages of multiple reactions, calculating the impurity carryover across the stages, and performing structure dereplication for an unknown impurity. As there is no standardized software available to link the different needs throughout the life cycle of process development, there is a demand for mapping tools to consolidate the route for an API synthesis and link it with analytical data while reducing transcription errors and maintaining an audit trail.

Embracing Social Engagement in Academic Medicine: Ongoing Challenges and How to Move Forward.

Academic medical centers have historically been defined by scientific discovery for health advancement. However, the mounting challenges of modern medicine are fueled by the social, economic, and political determinants of health that predict vulnerability and accelerate poor outcomes. To surmount looming threats to health, the academic medical mindset must equally prioritize social engagement-work that directly addresses the systemic social causes of health and illness-alongside the traditional pedagogy of laboratory-based, translational, and clinical research. Considerable barriers still exist, rooted in historical priorities and significant funding structured to reward scientific achievements. Academic medicine has the agency to support elements of restructuring to help prioritize research, education, and training to more prominently include social engagement. Crucial steps to ensure the success of this process include prioritizing financial commitments to community-engaged scholarship and programmatic work and rigorous recognition of faculty who work on socially engaged scholarship within promotion schemes. The COVID pandemic presents an unprecedented opportunity for academic medicine to reflect on the breadth of the work we promote and encourage, work that reflects all the complex elements of health-those that can be documented in a lab notebook and those rooted in social systems and structures that we have neglected for too long.

An Approach to Transitioning Undergraduate Biochemistry Laboratory Courses Online

An Approach to Transitioning Undergraduate Biochemistry Laboratory Courses Online

ELabFTW as an Open Science tool to improve the quality and translation of preclinical research.

Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our life-science lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small life-science labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research and our goals towards data quality management.

Standardized Data, Scalable Documentation, Sustainable Storage – EnzymeML As A Basis For FAIR Data Management In Biocatalysis

AbstractThe often reported reproducibility crisis in the biomedical sciences also applies to enzymology and biocatalysis, and mainly results from incomplete reporting of reaction conditions. In this Concept article, an infrastructure based on EnzymeML is sketched, which enables reporting, exchange, and storage of enzymatic data according to the FAIR data principles. EnzymeML is a novel data exchange format for enzymology and biocatalysis, which facilitates the application of the STRENDA Guidelines and thus makes data on enzyme‐catalyzed reactions findable, accessible, interoperable, and reusable. EnzymeML enables the comprehensive documentation of metadata, thus fostering reproducibility and replicability in enzymology and biocatalysis. An EnzymeML Application Programming Interface integrates electronic lab notebooks with modelling platforms and databases on enzymatic reactions, and thus enables the seamless flow of enzymatic data from measurement to modelling to publication, without the need for manual intervention such as reformatting or editing. EnzymeML serves as a valuable tool for the design of biocatalytic experiments and contributes to the vision of a unified research data infrastructure for catalysis research.

Linking Labs, Writing, and Information Literacy to Improve Student Success

Our research and technical writing class develops discipline specific communication and critical thinking skills as a flexible hybrid corequisite to first upper division science laboratory courses. It scaffolds ethics, writing mechanics, information literacy, self-promotion and synthesis assignments focused on the science writing genre (lab notebooks, research papers, posters, grants, online, CVs). Students generate and interpret data from graphs, construct explanations, and practice linking empirical claims to evidence. Instructor, peer and writing tutor feedback is provided on drafts to fulfill California State University’s graduate writing assessment requirement. Significant improvement on final research papers following the course interventions was measured.

ELabFTW as an Open Science tool to improve the quality and translation of preclinical research

Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our life-science lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small life-sceience labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research and our goals towards data quality management.

ELabFTW as an Open Science tool to improve the quality and translation of preclinical research

Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our life-science lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small life-science labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research and our goals towards data quality management.

A marathon, not a sprint – neuroimaging, Open Science and ethics

Open Science is calling for a radical re-thinking of existing scientific practices. Within the neuroimaging community, Open Science practices are taking the form of open data repositories and open lab notebooks. The broad sharing of data that accompanies Open Science, however, raises some difficult ethical and legal issues. With neuroethics as a focusing lens, we explore eight central concerns posed by open data with regard to human brain imaging studies: respect for individuals and communities, concern for marginalized communities, consent, privacy protections, participatory research designs, contextual integrity, fusions of clinical and research goals, and incidental findings. Each consideration assists in bringing nuance to the potential benefits for open data sharing against associated challenges. We combine current understandings with forward-looking solutions to key issues. We conclude by underscoring the need for new policy tools to enhance the potential for responsible open data.