Empirica: a virtual lab for high-throughput macro-level experiments

AbstractVirtual labs allow researchers to design high-throughput and macro-level experiments that are not feasible in traditional in-person physical lab settings. Despite the increasing popularity of online research, researchers still face many technical and logistical barriers when designing and deploying virtual lab experiments. While several platforms exist to facilitate the development of virtual lab experiments, they typically present researchers with a stark trade-off between usability and functionality. We introduce Empirica: a modular virtual lab that offers a solution to the usability–functionality trade-off by employing a “flexible defaults” design strategy. This strategy enables us to maintain complete “build anything” flexibility while offering a development platform that is accessible to novice programmers. Empirica’s architecture is designed to allow for parameterizable experimental designs, reusable protocols, and rapid development. These features will increase the accessibility of virtual lab experiments, remove barriers to innovation in experiment design, and enable rapid progress in the understanding of human behavior.

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Pengaruh Pembelajaran Daring Berbantuan Laboratorium Virtual Terhadap Minat dan Hasil Belajar Kognitif Fisika

JARTIKA : Jurnal Riset Teknologi dan Inovasi Pendidikan ◽

10.36765/jartika.v3i2.288 ◽

2020 ◽

Vol 3 (2) ◽

pp. 351-359

Author(s):

Egidius Dewa ◽

Maria Ursula Jawa Mukin ◽

Oktavina Pandango

Keyword(s):

Online Learning ◽

Learning Outcomes ◽

Random Sampling ◽

Cognitive Learning ◽

Sampling Technique ◽

Simple Random Sampling ◽

Virtual Labs ◽

A Value ◽

Virtual Lab ◽

Learning Interest

Abstrak: Tulisan ini bertujuan untuk mengetahui pengaruh pembelajaran daring berbantuan laboratorium virtual terhadap minat belajar fisika peserta didik dan mengetahui pengaruh pembelajaran daring berbantuan laboratorium virtual terhadap hasil belajar kognitif fisika peserta didik. Jenis penelitian ini adalah quasi eksperimen dengan desain eksperimen one group pretest posttest. Sampel penelitian ini adalah peserta didik kelas X MIPA 1 yang berjumlah 33 orang yang ditentukan dengan teknik simple random sampling. Instrumen yang digunakan dalam penelitian ini adalah tes hasil belajar kognitif dan angket minat belajar peserta didik. Hasil analisis data dapat disimpulkan bahwa ada pengaruh pembelajaran daring berbantuan laboratorium virtual terhadap minat belajar peserta didik dengan nilai sig (2-tailed) lebih kecil dari 0,05 (0,0063 < 0,05) dan ada pengaruh pembelajaran daring berbantuan laboratorium virtual terhadap hasil belajar kognitif peserta didik dengan nilai sig (2-tailed) lebih kecil dari 0,05 (0,000 < 0,05) Abstract: This article aims to determine the influence of online learning with virtual labs on the learning interest of students ' physics and to know the influence of online learning with virtual labs on cognitive learning outcomes of student physics. This type of research is a quasi experiment with the experimental design of one group Pretests posttest. This research sample is a student of class X MIPA 1 which amounted 33 people determined with simple random sampling technique. The instruments used in this study are cognitive learning results test and Angket learning interest learners. Data analysis results can be concluded that there is a virtual lab-assisted online learning influence to the learning interest of learners with a value of sig (2-tailed) smaller than 0.05 (0.0063 < 0.05) and there is a virtual lab-assisted online learning influence to the outcome of the students ' cognitive learning with a sig (2-tailed) value smaller than 0.05 (0.000 < 0.05).

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High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops

International Journal of Molecular Sciences ◽

10.3390/ijms22158266 ◽

2021 ◽

Vol 22 (15) ◽

pp. 8266

Author(s):

Minsu Kim ◽

Chaewon Lee ◽

Subin Hong ◽

Song Lim Kim ◽

Jeong-Ho Baek ◽

...

Keyword(s):

Drought Stress ◽

High Throughput ◽

Large Scale ◽

Crop Yields ◽

Plant Traits ◽

Rapid Development ◽

Plant Responses ◽

Phenotypic Data ◽

Agricultural Technologies ◽

High Throughput Phenotyping

Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.

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Zebrafish swimming behavior as a biomarker for ototoxicity-induced hair cell damage: a high-throughput drug development platform targeting hearing loss

Translational Research ◽

10.1016/j.trsl.2015.05.002 ◽

2015 ◽

Vol 166 (5) ◽

pp. 440-450 ◽

Cited By ~ 16

Author(s):

Maki Niihori ◽

Terry Platto ◽

Suzu Igarashi ◽

Audriana Hurbon ◽

Allison M. Dunn ◽

...

Keyword(s):

Hearing Loss ◽

Drug Development ◽

Hair Cell ◽

High Throughput ◽

Cell Damage ◽

Swimming Behavior ◽

Development Platform ◽

Hair Cell Damage

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DNA-Directed Patterning for Versatile Validation and Characterization of a Lipid-Based Nanoparticle Model of SARS-CoV-2

10.26434/chemrxiv.14208455 ◽

2021 ◽

Author(s):

Molly Kozminsky ◽

Thomas Carey ◽

Lydia L. Sohn

Keyword(s):

High Throughput ◽

Glass Substrate ◽

Rapid Development ◽

Neutralizing Antibody ◽

S Protein ◽

Biological Relevance ◽

The Face ◽

Great Control ◽

Tetraspanin Cd63

Lipid-based nanoparticles have risen to the forefront of the COVID-19 pandemic—from encapsulation of vaccine components to modeling the virus, itself. Their rapid development in the face of the volatile nature of the pandemic requires high-throughput, highly flexible methods for characterization. DNA-directed patterning is a versatile method to immobilize and segregate lipid-based nanoparticles for subsequent analysis. DNA-directed patterning selectively conjugates oligonucleotides onto a glass substrate and then hybridizes them to complementary oligonucleotides tagged to the liposomes, thereby patterning them with great control and precision. The power of this method is demonstrated by characterizing a novel recapitulative lipid-based nanoparticle model of SARS-CoV-2 —S-liposomes— which present the SARS-CoV-2 spike (S) protein on their surfaces. Patterning of a mixture of S-liposomes and liposomes that display the tetraspanin CD63 into discrete regions of a substrate is used to show that ACE2 specifically binds to S-liposomes. Importantly, DNA-directed patterning of S-liposomes is used to verify the performance of a commercially available neutralizing antibody against the S protein. Ultimately, the introduction of S-liposomes to ACE2-expressing cells demonstrates the biological relevance of DNA-directed patterning. Overall, DNA-directed patterning enables a wide variety of custom assays for the characterization of any lipid-based nanoparticle.

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Labster Virtual Lab Experiments: Basic Genetics

10.1007/978-3-662-57999-2 ◽

2018 ◽

Author(s):

Sarah Stauffer ◽

Aaron Gardner ◽

Wilko Duprez ◽

Dewi Ayu Kencana Ungu ◽

Philip Wismer

Keyword(s):

Lab Experiments ◽

Virtual Lab

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The effect of colloids practicum e-module to improve students’ interest on virtual lab during the covid-19 pandemic

Jurnal Pendidikan Kimia ◽

10.24114/jpkim.v13i3.29016 ◽

2021 ◽

Vol 13 (3) ◽

pp. 172-179

Author(s):

Nelius Harefa ◽

◽

Novia Fransisca Dewi Silalahi ◽

Leony Sanga Lamsari Purba ◽

Herna Febrianty Sianipar ◽

...

Keyword(s):

Data Analysis ◽

Learning Process ◽

Science Learning ◽

Learning Activities ◽

Virtual Labs ◽

Colloidal Material ◽

Virtual Lab ◽

The Creation ◽

Learning Interest

Practical learning which is generally carried out in the laboratory is one of the important lessons in the science learning process, especially chemistry. The Covid-19 pandemic has caused practical learning activities to not be accommodated in real laboratories. This situation encourages the creation of practical learning innovations, namely the use of virtual labs. In this study, students' learning interest in the use of virtual labs is described which is integrated with the use of e-modules on colloidal material. Based on the results of data analysis, 74.55% of students were interested in using the virtual lab, 10.90% very interested, 12.73% quite interested, and 1.82% lack of interested. These data indicate that the majority of students can make good use of the virtual lab and are able to optimally elaborate on the learning process. However, virtual labs are not intended to replace real laboratories but can be used as supplements and media to support learning in real laboratories.

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From Ignorance Map to Informing PKM4E Framework: Personal Knowledge Management for Empowerment

10.28945/3984 ◽

2018 ◽

Keyword(s):

Knowledge Management ◽

Design Science ◽

Knowledge Workers ◽

Rapid Development ◽

Science Research ◽

Knowledge Bases ◽

Personal Knowledge ◽

Future Research ◽

Development Platform ◽

Personal Knowledge Management

Aim/Purpose: [This Proceedings paper was revised and published in the 2018 issue of the journal Issues in Informing Science and Information Technology, Volume 15] The proposed Personal Knowledge Management (PKM) for Empowerment (PKM4E) Framework expands on the notions of the Ignorance Map and Matrix for further supporting the educational concept of a PKM system-in-progress. Background: The accelerating information abundance is depleting the very attention our cognitive capabilities are able to master, one key cause of individual and collective opportunity divides. Support is urgently needed to benefit Knowledge Workers independent of space (developed/developing countries), time (study or career phase), discipline (natural or social science), or role (student, professional, leader). Methodology: The Design Science Research (DSR) project introducing the novel PKM System (PKMS) aims to support a scenario of a ‘Decentralizing KM Revolution’ giving more power and autonomy to individuals and self-organized groups. Contribution: The portrayal of potential better solutions cannot be accommodated by one-dimensional linear text alone but necessitates the utilization of visuals, charts, and blueprints for the concept as well as the use of colors, icons, and catchy acronyms to successfully inform a diverse portfolio of audiences and potential beneficiaries. Findings: see Recommendation for Researchers Recommendations for Practitioners: The PKM4E learning cycles and workflows apply ‘cumulative synthesis’, a concept which convincingly couples the activities of researchers and entrepreneurs, and assists users to advance their capability endowments via applied learning. Recommendation for Researchers: In substituting document-centric with meme-based knowledge bases, the PKMS approach merges distinctive voluntarily shared knowledge objects/assets of diverse disciplines into a single unified digital knowledge repository and provides the means for advancing current metrics and reputation systems. Impact on Society: The PKMS features provide the means to tackle the widening opportunity divides by affording knowledge workers with continuous life-long support from trainee, student, novice, or mentee towards professional, expert, mentor, or leader. Future Research: After completing the test phase of the PKMS prototype, its transformation into a viable PKM system and cloud-based server based on a rapid development platform and a noSQL-database is estimated to take 12 months.

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HTSeq - A Python framework to work with high-throughput sequencing data

10.1101/002824 ◽

2014 ◽

Cited By ~ 242

Author(s):

Simon Anders ◽

Paul Theodor Pyl ◽

Wolfgang Huber

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Rapid Development ◽

Differential Expression Analysis ◽

Rna Seq ◽

Sequencing Data ◽

Standard Work ◽

Data Formats ◽

High Throughput Sequencing Data ◽

Python Package

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard work flows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data such as genomic coordinates, sequences, sequencing reads, alignments, gene model information, variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability: HTSeq is released as open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index, https://pypi.python.org/pypi/HTSeq

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Devising Enabling Spaces and Affordances for Personal Knowledge Management System Design

10.28945/3744 ◽

2017 ◽

Cited By ~ 3

Author(s):

Ulrich Schmitt

Keyword(s):

Knowledge Management ◽

Personal Growth ◽

Design Science ◽

Rapid Development ◽

Science Research ◽

Personal Knowledge ◽

Prototype System ◽

Development Platform ◽

Personal Knowledge Management ◽

Nosql Database

[This Proceedings paper was revised and published in Informing Science: the International Journal of an Emerging Transdiscipline (InfoSci)] Aim/Purpose: Personal Knowledge Management (PKM) has been envisaged as a crucial tool for the growing creative class of knowledge workers, but adequate technological solutions have not been forthcoming. Background: Based on former affordance-related publications (primarily concerned with communication, community-building, collaboration, and social knowledge sharing), the common and differing narratives in relation to PKM are investigated in order to suggest further PKM capabilities and affordances in need to be conferred. Methodology: The paper follows up on a series of the author’s PKM-related publications, firmly rooted in design science research (DSR) methods and aimed at creating an innovative PKM concept and prototype system. Contribution: The affordances presented offer PKM system users the means to retain and build upon knowledge acquired in order to sustain personal growth and facilitate productive collaborations between fellow learners and/or professional acquaintances. Findings: The results call for an extension of Nonaka’s SECI model and ‘ba’ concept and provide arguments for and evidence supporting the claims that the PKM concept and system is able to facilitate better knowledge traceability and KM practices. Recommendations and Impact on Society: Together with the prior publications, the paper points to current KM shortcomings and presents a novel trans-disciplinary approach offering appealing opportunities for stakeholders engaged in the context of curation, education, research, development, business, and entrepreneurship. Its potential to tackle opportunity divides has been addressed via a PKM for Development (PKM4D) Framework. Future: DSR Activities After completing the test phase of the prototype, its transformation into a viable PKM system and cloud-based server based on a rapid development platform and a noSQL-database is estimated to take 12 months.

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Measurement and Evaluation of Calorimetric Descriptors for the Suitability for Evolutionary High-Throughput Material Development

Metals ◽

10.3390/met9020149 ◽

2019 ◽

Vol 9 (2) ◽

pp. 149 ◽

Cited By ~ 2

Author(s):

Anastasiya Toenjes ◽

Heike Sonnenberg ◽

Christina Plump ◽

Rolf Drechsler ◽

Axel von Hehl

Keyword(s):

Heating Rate ◽

High Throughput ◽

Macro Level ◽

Heating Rates ◽

Scanning Calorimetry ◽

Characterization Methods ◽

Material Development ◽

First Results ◽

Novel Method ◽

Measurement And Evaluation

A novel method for evolutionary material development by using high-throughput processing is established. For the purpose of this high-throughput approach, spherical micro samples are used, which have to be characterized, up-scaled to macro level and valued. For the evaluation of the microstructural state of the micro samples and the associated micro-properties, fast characterization methods based on physical testing methods such as calorimetry and universal microhardness measurements are developed. Those measurements result in so-called descriptors. The increase in throughput during calorimetric characterization using differential scanning calorimetry is achieved by accelerating the heating rate. Consequently, descriptors are basically measured in a non-equilibrium state. The maximum heating rate is limited by the possibility to infer the microstructural state from the calorimetric results. The substantial quality of the measured descriptors for micro samples has to be quantified and analyzed depending on the heating rate. In this work, the first results of the measurements of calorimetric descriptors with increased heating rates for 100Cr6 will be presented and discussed. The results of low and high heating rates will be compared and analyzed using additional microhardness measurements. Furthermore, the validation of the method regarding the suitability for the evolutionary material development includes up-scaling to macro level and therefore different sample masses will be investigated using micro and macro samples during calorimetry.

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