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2,235 results for “engineering”
Datasets from Journal of Environmental Chemical Engineering (2020) 104657
<p>The dataset contains the raw data of the figures and tables reported in the open access publication “M. Zeppilli, B. Matturro, E. Dell’Armi, L. Cristiani, M.P. Papini, S. Rossetti, M. Majone, Reductive/oxidative sequential bioelectrochemical process for Perchloroethylene (PCE) removal: effect of the applied reductive potential and microbial community characterization, Journal of Environmental Chemical Engineering (2020) 104657.</p>
Dataset - Survey results - Applying Model-based Requirements Engineering in Three Large European Collaborative Projects
<p>This dataset and its associated report contain the results of an online survey on using a model-based requirements engineering approach in three European projects. </p>
Meteorological and ground observations in the Qinghai-Tibet Engineering Corridor
<p>Observation of meteorological factors was conducted at two permanent meteorological stations (Golmud and Wudaoliang) and one field meteorological station (Xidatan) with daily meteorological records. All three meteorological stations contain ground observations.</p>
Raw data acquired necessary to produce the plots introduced in the scientific paper: "Upper-limb kinematic reconstruction during stroke robot-aided therapy" (Medical & Biological Engineering & Computing)
<p>These files contain the raw data acquired necessary to produce the plots introduced the Figure 6 of the scientific paper: “Upper-limb kinematic reconstruction during stroke robot-aided therapy” (Medical & Biological Engineering & Computing).</p> <p>Fig. 6 shows the data recorded from two patients performing five forward/backward movements at InMotion2 robot before and after rehabilitation treatment. Mean values of the five execution have been reported in Fig. 6.</p>
Raw data corresponding to the scientific paper: "A modular telerehabilitation architecture for upper limb robotic therapy" (Advances in Mechanical Engineering 2017, Vol. 9(1) 1-13)
<p>Acquired raw data necessary to implement the adaptive control strategy grounded on multimodal information.<br> In addition, raw data for the computation of the communication parameters needed for the assessment of the implemented telerehabilitation architecture are provided.</p> <p>a) End-effector positions and velocities (x, y, vx, vy) in three conditions: healthy (Fig 9) and constraint simulated stroke behaviour (Fig 10) without robotic assistance and simulated stroke behavior with robotic assistance (Fig 11)</p> <p>b) Performance indicators and control parameters for all the recruited subjects in both conditions healthy behaviour and simulated stroke behaviour (Fig 12a and Fig 12b)</p> <p>c) Computational values for evaluating telerehabilitation performance (Table 1)</p> <p> </p> <p> </p>
Acquired data necessary to perform the control algorithm introduced in the scientific paper: "Multilevel control of an anthropomorphic prosthetic hand for grasp and slip prevention" (Advances in Mechanical Engineering, 2016, vol. 8, pp. 1-13)
<p>Acquired data necessary to perform the control algorithm introduced in this paper.</p> <p>a) Figure 6: Calibration data for the three FSRs placed on the prosthetic hand and covered with silicon caps.<br> b) Figure 9: Data for the cost during the learning of two grasping tasks of an egg: bi-digital grasp and tri-digital grasp.<br> c) Figure 10 and Figure 11: Data for the experimental results with the plastic cup and with the highlighter shown in the paper.<br> </p> <p> </p>
Dataset of the scientific paper " Multimodal robotic system for upper-limb rehabilitation in physical environment" (Advances in Mechanical Engineering)
<p>There are eight files with the following information:<br> - pos_stateXX.bin, binary file with information of the end effector position of the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - target_stateXX.bin, binary file with information of the target position for the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - emg_channelXX.bin, binary file with information of channel 1 of the EMG sensor in mV during during the whole time of the experiment<br> - color_stateXX.bin, binary file with information of color filter information during state XX of the experiment. This information is the percentage of pixels with the correct color (yellow, cyan or magenta) inside the region of interest</p> <p> </p>
Data underpinning "Engineering unsteerable quantum states with active feedback"
<div> <p>We provide the raw data used to produce plots shown in our paper "Engineering unsteerable quantum states with active feedback". The data is structured by: entangled state - number of qubits - target fidelity F*. For each parameter configuration 10 simulation runs were performed.</p> <p> </p> </div> <h2>Abstract</h2> <p>We propose active steering protocols for quantum state preparation in quantum circuits where each ancilla qubit (detector) is connected to a single system qubit, employing a simple coupling selected from a small set of steering operators. The decision is made such that the expected cost function gain in one time step is maximized. We apply these protocols to several many-qubit models. Our results are underlined by three remarkable insights. First, we show that the standard fidelity does not give a useful cost function; instead, successful steering is achieved by including local fidelity terms. Second, although the steering dynamics acts on each system qubit separately, entanglement in the generated target state is introduced, and can be tuned at will, by performing Bell measurements on ancilla qubit pairs after every time step. This implements a weak-measurement variant of entanglement swapping. Third, numerical simulations suggest that the active steering protocol can reach arbitrarily designated target states, including passively unsteerable states such as the N-qubit W state.</p>
Evolution-Guided Engineering of Trans-Acyltransferase Polyketide Synthases
<p>Data underlying the manuscript 'Evolution-Guided Engineering of <em>Trans</em>-Acyltransferase Polyketide Synthases' by Mabesoone, Leopold-Messer and Minas et al.</p> <p>The repository contains:</p> <p>Sequencing data - Genbanks files of construct designs, ab1 and fasta files for Sanger sequencing. For whole plasmid sequencing, fasta, annotated GenBank files, overviews of sequencing statistics and fastq files for selected plasmids, for which fastq files were provided by the sequencing service, are provided.</p> <p>NMR data - raw data and MestReNova files. Also includes HPLC-MS traces of isolated compounds.</p> <p>HPLC-MS data - Raw data collected on Thermo-Fisher instruments. This data can be analyzed with the Xcalibur software suite. The mzXML data can be analyzed with the Python scripts provided in the scripts folder to generate the images shown in the SI. The data collected for Bacillus and Serratia is MS1 data. The data collected for Gynuella also contains MS-MS data.</p> <p>SCA data - Python scripts, GenBank files and produced data underlying the SCA.</p> <p>Bioactivity data - Data underlying the toxicity assays in Figure S122.</p> <p> </p>
Stability Increase of Phenolic Acid Decarboxylase by a Combination of Protein and Solvent Engineering Unlocks Applications at Elevated Temperatures
<p>Enzymatic decarboxylation of biobased hydroxycinnamic acids gives access to phenolic styrenes for adhesive production. Phenolic acid decarboxylases are proficient enzymes that have been applied in aqueous systems, organic solvents, biphasic systems, and deep eutectic solvents, which makes stability a key feature. Stabilization of the enzyme would increase the total turnover number and thus reduce the energy consumption and waste accumulation associated with biocatalyst production. In this study, we used ancestral sequence reconstruction to generate thermostable decarboxylases. Investigation of a set of 16 ancestors resulted in the identification of a variant with an unfolding temperature of 78.1 °C and a half-life time of 45 h at 60 °C. Crystal structures were determined for three selected ancestors. Structural attributes were calculated to fit different regression models for predicting the thermal stability of variants that have not yet been experimentally explored. The models rely on hydrophobic clusters, salt bridges, hydrogen bonds, and surface properties and can identify more stable proteins out of a pool of candidates. Further stabilization was achieved by the application of mixtures of natural deep eutectic solvents and buffers. Our approach is a straightforward option for enhancing the industrial application of the decarboxylation process.</p>
SyDRA: An Approach to Understand Game Engine Architecture
<p>Game engines are tools to facilitate video game development. They provide graphics, sound, and physics simulation features, which would have to be otherwise implemented by developers. Even though essential for modern commercial video game development, game engines are complex and developers often struggle to understand their architecture, leading to maintainability and evolution issues that negatively affect video game productions. In this paper, we present the Subsystem-Dependency Recovery Approach (SyDRA), which helps game engine developers understand game engine architecture and therefore make informed game engine development choices. By applying this approach to 10 open-source game engines, we obtain architectural models that can be used to compare game engine architectures and identify and solve issues of excessive coupling and folder nesting. Through a controlled experiment, we show that the inspection of the architectural models derived from SyDRA enables developers to complete tasks related to architectural understanding and impact analysis in less time and with higher correctness than without these models.</p>
Unveiling Genomic Rearrangements in Engineered iPSC Lines
<p>Bionano smap files of Dual Analysis of each detected unique SV not present in parental cells. Only SVs within 12kbp of a canonical gene are presented for clarity.</p>
Quantitative results of the analysis of human bioengineered tissues corresponding to the work "Development of novel squid gladius biomaterials for cornea tissue engineering"
<p>This dataset corresponds to the quantitative data generated in the work entitled "Development of novel squid gladius biomaterials for cornea tissue engineering".</p> <p>Cornea tissue engineering is strictly dependent on the development of biomaterials fulfilling the strict biocompatibility, biomechanical and optical requirements of this organ. In this work, we have generated novel biomaterials from the squid gladius (SG) and their application in cornea tissue engineering was evaluated. Results revealed that the native SG (N-SG) was biocompatible in laboratory animals, although a local inflammatory reaction was driven by the material. Cellularized biomaterials (C-SG) demonstrated that the SG provides an adequate substrate for cell attachment and growth, and corneal epithelial cells cultured on this biomaterial were able to express crystallin alpha, a marker for this type of cells. Biomechanical analyses showed that N-SG biomaterials have higher Young modulus and lower traction deformation than control native corneas (CTR), and C-SG showed similar Young modulus than CTR. Analysis of the optical properties of these samples revealed that the diffuse transmittance of N-SG and C-SG were higher than CTR, with the diffuse reflectance showing the opposite behavior. These results confirm the putative usefulness of this abundant marine-derived biomaterial that can be obtained as a byproduct of the fishing industry.</p>
Supplementary data for Model-driven engineering of Cutaneotrichosporon oleaginosus ATCC 20509 for improved microbial oil production
<p>Supplementary data corresponding to manuscript named Model-driven engineering of <em>Cutaneotrichosporon oleaginosus</em> ATCC 20509 for improved microbial oil production. </p> <p>The Supplementary material document contains supplementary figures and tables. The content of the figures and tables are indicated below. </p> <ul> <li>Figure S1. Plasmid map of pUC57NAT containing pGpd, nourseothricin acyltransferase gene and tGpd.</li> <li>Figure S2. Plasmid maps of overexpression targets containing TEF1α promoter, ATP-citrate lyase gene, TEF1α terminator, TPI1 promoter, Acetyl-CoA carboxylase gene, TPI1, YAT1 promoter, threonine synthase gene, YAT1 terminator and ENO1 promoter, hydroxymethylglutaryl-CoA synthase gene, ENO1 terminator.</li> <li>Table S2. Nucleotide sequences of promoters, genes, and terminators from <em>C. oleaginosus.</em></li> <li>Figure S3. Calibration curve of glycerol for calculating the glycerol concentration of medium.</li> <li>Figure S4. Volcano plots displaying differentially expressed genes and fold change (log2) in expression levels in WT, Δ9 and Δ12 strains at low lipid accumulation vs high lipid accumulation conditions.</li> <li>Figure S5. Flux distribution graphs of selected reactions for overexpression in C. oleaginosus.</li> <li>Figure S6. Colony PCR products were run on 1 % agarose gel. The colony PCR was performed for WT, ACL, ACC and TS transformants.</li> <li>Table S4. qPCR outputs, CT: The threshold cycle.</li> <li>Table S5. Fatty acid profile of C. oleaginosus grown at minimal medium with or without supplement (biotin, thiamine, threonine, serine, and aspartate) at 96h.</li> <li>Table S6. Lipid content, dry cell weight, and lipid weight of WT, ACL, ACC, TS, and HMGS <em>C. oleaginosus</em> at various C/N ratio minimal medium.</li> <li>Table S7. Fatty acid profile of WT, ACL, ACC, HMGS, and TS grown at C/N30, 120, 175, 200, and 300 minimal medium at 96h.</li> <li>Figure S7. Quadratic regression analysis on lipid accumulation, biomass and lipid content of wild-type, ACL, ACC, and TS C. oleaginosus at C/N 30, 120, 175, 200, 300.</li> <li>Table S8. Regression equations, statistics of regression equations for lipid content, biomass, and lipid content of wild-type, ACL, ACC, and TS.</li> <li>Table S9. Calculated optimum C/N ratios and responses (lipid content, biomass, and total lipid) by using built regression models for wild-type, ACL, ACC, and TS.</li> </ul> <p>Authors: </p> <p>Zeynep Efsun Duman-Özdamar<sup>a,b,c</sup>, Mattijs K. Julsing<sup>c</sup>, Janine A.C. Verbokkem<sup>c</sup>, Emil Wolbert<sup>c</sup>, Vitor A.P. Martins dos Santos<sup>a,b,d</sup>, Jeroen Hugenholtz<sup>e,f</sup>, Maria Suarez-Diez<sup>b*</sup></p> <p><sup>a</sup>Bioprocess Engineering, Wageningen University & Research, 6708 PB, Wageningen, the Netherlands</p> <p><sup>b</sup>Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6708 WE, Wageningen, the Netherlands</p> <p><sup>c</sup>Wageningen Food & Biobased Research, Wageningen University & Research, 6708 WE, Wageningen, The Netherlands</p> <p><sup>d</sup>LifeGlimmer GmbH, Berlin, 12163, Germany</p> <p><sup>e</sup>Faculty of Science Swammerdam Institute for Life Sciences, University of Amsterdam, 1090 GE Amsterdam, The Netherlands</p> <p><sup>f</sup>NoPalm Ingredients BV, 6709 PA Wageningen, The Netherlands</p>
Beyond Substrates: Strain Engineering of Ferroelectric Membranes
<p>Dataset for publication:</p> <p>Beyond Substrates: Strain Engineering of Ferroelectric Membranes</p> <p>D. Pesquera, E. Parsonnet, A. Qualls, R. Xu, A.J. Gubser, J. Kim, Y. Jiang, G. Velarde, Y. Huang, H.Y. Hwang, R. Ramesh, and L.W. Martin, Adv. Mater. <strong>32</strong>, 2003780 (2020).</p> <p> </p> <p>Matlab code for producing Fig.1d, Fig.2a and Fig.4b is given in .txt files</p>
Dataset supporting the paper "Molecular Approach for Engineering Interfacial Interactions in Magnetic/Topological Insulator Heterostructures. ACS Nano 14, 6285 (2020)"
<p>Dataset corresponding to theoretical calculations in the paper "Molecular Approach for Engineering Interfacial Interactions in Magnetic/Topological Insulator Heterostructures" ACS Nano 14, 6285 (2020), DOI: <a href="https://doi.org/10.1021/acsnano.0c02498">10.1021/acsnano.0c02498</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain the following files:</p> <ul> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>)</li> <li>.agr files: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).<br> </li> </ul>
Dataset: Systematic Mapping Study on the Development and Application of Sentiment Analysis Tools in Software Engineering
<p>Update: We updated the data set in March 2022 by adding newly published papers and by providing more insights on how we analyzed them. Details can be found in the file " SEnti-SMS.xlsx".</p> <p>----------</p> <p>Update: The updated version (-v2) contains the results of one more snowballing iteration and extracted information on the accuracy of the used methods.</p> <p>----------</p> <p>In 2020, we conducted a systematic literature review to explore the development and application of sentiment analysis tools in software engineering.</p> <p>Information on the execution of the SLR, its scope, the search string, etc. are presented in the paper linked below.</p> <p> </p> <p> </p>
Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials
<p>Toxicogenomics (TGx) approaches are increasingly applied to gain insight into the possible toxicity mechanisms of engineered nanomaterials (ENMs). Omics data can be valuable to elucidate the mechanism of action of chemicals and develop predictive models in toxicology. While vast amounts of transcriptomics data from ENM exposures have already been accumulated, a unified, easily accessible and reusable collection of transcriptomics data for ENMs is currently lacking. In an attempt to improve the FAIRness of already existing transcriptomics data for nanomaterials, we curated a collection of homogenized transcriptomics data from human, mouse and rat ENM exposures <em>in vitro</em> and <em>in vivo</em>.</p>
Analysis of the interacting residues between wild type SARS-CoV-2 spike protein and natural ligand hACE2, as well as three engineered alternative ligands
<p>The analysis of residue interactions between the SARS-CoV-2 spike protein and its natural (hACE2 <sup>1</sup>) and engineered binders P17 Fab <sup>2</sup>, Ty1 VHH <sup>3</sup> and LCB1 peptide <sup>4</sup> reveals that glutamine, serine and especially tyrosine residues on the ligand side are more frequent and influence spike binding efficiency, and that spike residues Glu484, Phe486, Tyr489 and Gln493 are more recurrent targets for interactions with ligands. The list of residues establishing contacts between the wild type structure of the SARS-CoV-2 spike protein and the binders defined above are described in Table 1. In Figure 1, the frequency and type of amino acids that interact with each spike residue is illustrated.</p>
Supporting data for: Low-cost anti-mycobacterial drug discovery using engineered E. coli
<p>Supporting data for: Low-cost anti-mycobacterial drug discovery using engineered E. col</p> <p>This dataset pertains to our work developing TESEC Mtb ALR, a genetically engineered strain of E. coli expressing the enzyme ALR derived from Mtb. We used the TESEC Mtb ALR strain in a high-throughput drug screen and identified benazepril as targeted inhibitor of the ALR enzyme. We then performed additional experiments to characterize the activity of benazepril against E. coli, Mtb and purified enzymes. Finally, we tested the extensibility of the platform by constructing and screening against similar strains for additional targets: Asd, CysH, DapB, and TrpD.</p> <p>These files include growth measurements, biochemical assays and other forms of biological data. They are packaged together with scripts used to analyze the data and present them in figures. Our goal in creating this archive was to present our complete analysis pipeline in the spirit of open science. It is not intended to be a stand-alone resource. Consult the associated manuscript for protocols, units of measurement and other essential technical context.</p> <p>Our scripts were written for Python 3.8. The raw data is presented as human-readable .csv files intended to be imported as Pandas DataFrames. Some data is also packaged as Python dictionaries saved with the Pickle package.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.