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282 results for “data engineering”
The sharing of research raw data in journals indexed in the Cell & Tissue Engineering JCR category (2011-2015)
<p>The availability of research data sets is an important milestone since it can enhance the dynamics of research. This study aims to analyze the PubMed Central repository to determine the availability and type of raw data sets in Cell & Tissue Engineering journals indexed in the Journal Citation Reports. The number and types of files were registered. A search of the 21 journals from the Cell & Tissue Engineering category of the 2015 Journal Citation Reports was conducted. Information was collected from October to December 2016. A study of the supplementary material of the original articles published between 2011-2015 was performed through a search in the PubMed Central repository, which is the most used free full-text repository in biomedicine. Only articles with supplementary material were retrieved. The number and types of files were registered. In cases where a compressed file, such as a .zip or .rar file, was found, it was opened to check what kinds of files it contained.</p>
Research data supporting "Buoyancy-Driven Gradients for Biomaterial Fabrication and Tissue Engineering"
<p>Research data supporting the publication:</p> <p>Li C. et al., Advanced Materials, 2019, DOI: 10.1002/adma.201900291</p>
Campaing data obtained from the Micro Volunteering Engine
<p><span>This database supports the task allocation solution for a Spatial Crowdsourcing problem within the context of the Socio-Bee project. The goal is to resolve the task allocation challenge in a scenario where participation is altruistic, without economic incentives. Instead, recommendations are tailored to users based on their routines and past involvement, encouraging ease of participation by making tasks adaptive to their availability and location.</span></p> <p><span>The database is designed as part of the second pilot of the Socio-Bee project, a Citizen Science initiative aimed at promoting public involvement in air quality monitoring. In this project, users (citizen scientists) perform air quality measurements to create a spatially distributed data map, enhancing our understanding of environmental conditions across specific areas.</span></p> <p><span>For further details on the algorithmic design and implementation of the recommendation system, please refer to the project repository at <a href="https://github.com/mpuerta004/RecommenderSystem" target="_new">GitHub - RecommenderSystem</a>.</span></p>
Experimental data used in the article entitled "Engineering an ultra-fine grained microstructure, twins and stacking faults in PBF-LB/M Al-Si alloy via KoBo extrusion method"
<p>Dataset include</p> <p>EBSD results: KOB;O.ang and LPBF_condition.ang</p> <p>Tensile test results:</p> <p>KOBO-processed sample: KOBO.xls</p> <p>LPBF sample: SLM.xls</p>
Raw images, video, and data file for the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials"
<p>This dataset includes the raw images and data file in the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials", specifically:</p> <ul> <li>Raw images for the optimized print with the PEGDA-glycerol-water resin (Figure 2 & Figure S2)</li> <li>Raw images for the optimized print with the PEGDA-glycerol-LB resin (Figure 2)</li> <li>Raw images for the optimized print with the BSA-PEGDA-water resin (Figure 3)</li> <li>Raw images and video for the spontaneous capillary flow of LB media in a PEGDA-glycerol-LB microfluidic chip (Figure 4)</li> <li>Raw data for the UV-vis spectrum of LB media (Figure S4)</li> </ul>
Supplementary data for the discussion of the PhD thesis entitled Engineering Oleaginous Yeast for Bio-Based Future.
<p>Supplementary data corresponding to the discussion of PhD thesis named Engineering Oleaginous Yeast for Bio-Based Future.</p> <p>Authors:</p> <p>Zeynep Efsun Duman-Özdamar<sup>a,b,c</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> </p> <p> </p>
Productivity simulation framework in software engineering - Bibliometric data
<p>Bibliographic data from the Web of Science and SCOPUS relate to this search string: <em>framework AND simulation AND productivity AND (software development OR software engineering)</em>. The authors also include the Bibliometrix application report.</p>
Power engineering cost data
<div> <div>This data set contains information about various costs, budgets, materials, processes, equipment, labor and other aspects related to power engineering projects. Specifically, it is mainly used for the analysis, evaluation and prediction of project cost, so as to help project managers, engineers, cost engineers and other personnel engaged in power engineering to make reasonable decisions and plans.</div> </div>
Supplementary data for the manuscript: Image2SMILES: Transformer-based Molecular Optical Recognition Engine
<p>This is the supplementary data for the manuscript: <a href="https://chemrxiv.org/engage/chemrxiv/article-details/60c758c6469df4169bf45744">Image2SMILES: Transformer-based Molecular Optical Recognition Engine</a></p> <p>It contains pairs of image-string, generated from 1M SMILES strings. These strings were randomly chosen from PubChem database.<br> It was prepared using the code, published at <a href="https://github.com/syntelly/img2smiles_generator/">https://github.com/syntelly/img2smiles_generator/</a></p> <p>To unpack do:<br> <em>tar xvf subset_1M.tar.xz && tar xvf subset_1M_dump.tar.gz && rm subset_1M_dump.tar.gz</em></p> <p>You'll get the following data:</p> <ul> <li>subset_1M.smi - list of 1M source SMILES</li> <li>subset_1M_dump - directory with images </li> <li>subset_1M_result.csv - list of pairs FGSMILES - pathcode, first 3 chars of pathcode are corresponding subdirs in subset_1M_dump</li> <li>subset_1M_fails.csv - list of failed molecules from subset_1M.smi</li> <li>subset_1M_grpcounter.lst - list of counted groups, used in this generation</li> </ul> <p>You can generate your own data using <a href="https://github.com/syntelly/img2smiles_generator/">https://github.com/syntelly/img2smiles_generator/</a> </p>
TCTracer: Establishing Test-to-Code Traceability Links Using Dynamic and Static Techniques - Evaluation Data - Empirical Software Engineering 2021
<p>This repository provides the data artefacts for the experiments conducted using our tool TCTracer for the journal paper "TCTracer: Establishing Test-to-Code Traceability links Using Dynamic and Static Techniques" as submitted to the Empirical Software Engineering journal in 2021.</p>
NMR Relaxometry data for publication "Engineered Nonviral Protein Cages Modified for MR Imaging"
<p>NMRD profiles collected with field-cycling NMR relaxometry of a water solution of Gd-C4-IA, and of AaLS-13 and OP protein cages labeled with the gadolinium(III) complex.</p> <p>Published in: https://doi.org/10.1021/acsabm.2c00892</p> <p>NMRD data acquisition and analysis was performed with the support of the PRIN 2017A2KEPL project “Rationally designed nanogels embedding paramagnetic ions as MRI. probes”, and the European Commision through H2020 FET-Open project HIRES-MULTIDYN<br> award no. 899683 and H2020 INFRAIA iNEXT-Discovery (Structural Biology Research Infrastructures for Translational Research and Discovery) award no. 871037.</p>
Open data for publication: Advanced catalyst for CO2 photo-reduction: From controllable product selectivity by architecture engineering to improving charge transfer using stabilized Au clusters
<p>Original data for publication: Advanced catalyst for CO2 photo-reduction: From controllable product selectivity by architecture engineering to improving charge transfer using stabilized Au clusters, published in Small, 2023.</p> <p>The dataset is organized according to the Figures in the manuscript.</p>
Data and analysis of proteomic responses to hexokinase-II depletion in GAL80 and gal80Δ Saccharomyces cerevisiae with an engineered sesquiterpene-pathway
<p>Dataset 1: <a href="https://zenodo.org/api/files/ece3309f-0ca2-4773-b2e3-b1c5c839faa4/GAL80_HXK2_Vs._dhxk2p_20200324_T2_004.xlsx">GAL80_HXK2_Vs._dhxk2p_20200324_T2_004.xlsx</a></p> <p>The comparison between strain ILHA o128R+pJT9RFR (dHxk2p) and ILHA o401R+ pJT9RFR (HXK2) under the conditions with the addition of 1-Naphthaleneacetic acid and in the exponential growth phase and the ethanol growth phase. </p> <p> </p> <p>Dataset 2: <a href="https://zenodo.org/api/files/ece3309f-0ca2-4773-b2e3-b1c5c839faa4/gal80%CE%94_HXK2_Vs._dhxk2p_20200219_T1_004.xlsx">gal80Δ_HXK2_Vs._dhxk2p_20200219_T1_004.xlsx</a></p> <p>The comparison between strain ILHA NLD128-1 (dHxk2p) and ILHA NLD401 (HXK2) under the conditions with the addition of 1-Naphthaleneacetic acid and in the exponential growth phase (EXP) and the ethanol growth phase (ETH). </p> <p> </p>
Engineering cellular communication between light-activated synthetic cells and bacteria (Source data)
<p>Source data files for supplementary figures for published version of "Engineering cellular communication between light-activated synthetic cells and bacteria" https://www.biorxiv.org/content/10.1101/2022.07.22.500923v1</p>
Data from: Pollinators and plants as ecosystem engineers: post-dispersal fruits provide new habitats for other organisms
<p><span>Ecosystem engineering consists of</span><span> </span><span>a ubiquitous and fundamental class of interactions where some organisms promote state changes in biotic and abiotic materials which indirectly affect others. Since the concept was created as a counterpoint to traditional flux-based models, pollinators have never been considered to promote ecosystem engineering because they modulate the supply of resources used by seed/fruit consumers. However, dry fruits may persist in the environment for long periods after seed dispersion as empty structures that are an additional alternative for occupancy. These increase the realized niche of some organisms, being a clear example of biogenic habitats. Here, we demonstrate how pollination may boost ecosystem engineering using an illustrative case study based on a specialized interaction between a bee and an orchid with characteristic capsular fruits. We demonstrate that the orchid is fully dependent on the pollinator to set fruits. Post-dispersal fruits are voluminous and remain attached to the plant for several years. By investigating the occupation patterns of arthropods, we show that post-dispersal fruits are</span><span> </span><span>highly suitable for occupants when compared to pre-dispersal and dispersing fruits. Only 13.3% of post-dispersal fruits were never occupied, 33.3% had organisms inside and </span><span>53.3</span><span>% showed</span><span> </span><span>signs of previous occupation. We propose that pollinators are allogenic engineers and plants autogenic, since they promote state changes through their actions and their</span><span> </span><span>own physical structure, respectively. Thus, pollination is a case of cooperative ecosystem engineering whereby the effects of the interaction between two or more species expand habitat suitability. Since most plants rely on pollination to set fruits, we suggest that pollination-mediated engineering constitutes a widespread phenomenon. This offers a new perspective about the effects of pollination in nature, highlighting the importance of pollinators and opening new avenues of investigation of ecosystem dynamics and biodiversity maintenance.</span></p>
Reproduction package for "Revisiting the reproducibility of empirical software engineering studies based on data retrieved from development repositories"
<p>Reproduction package for "Revisiting the reproducibility of empirical software engineering studies based on data retrieved from development repositories", published in Information and Software Technology, Volume 164, December 2023. DOI: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.infsof.2023.107318" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.infsof.2023.107318</span></span></a></p>
Open Data for publication: Engineering ligand chemistry on Au25 nanocluster: From unique ligand addition to precisely controllable ligand exchange
<p>Open Data for publication: <strong>Engineering ligand chemistry on Au<sub>25</sub> nanocluster: From unique ligand addition to precisely controllable ligand exchange</strong><br> Published in Chemical Science, 2023</p> <p>Authors: Jiangtao Zhao, Abolfazl Ziarati, Arnulf Rosspeintner, Yanan Wang and Thomas Bürgi</p>
Data for: Magnetic-field-assisted molecular beam epitaxy: Engineering of Fe3O4 ultrathin films on MgO(111)
<p>Molecular beam epitaxy is widely used for engineering low-dimensional materials. Here, we present a novel extension of the capabilities of this method by assisting epitaxial growth with the presence of an external magnetic field (MF). MF-assisted epitaxial growth was implemented under ultra-high vacuum conditions thanks to specialized sample holders for generating in-plane or out-of-plane MF and dedicated manipulator stations with heating and cooling options. The significant impact of MF on the magnetic properties was shown for ultra-thin epitaxial magnetite films grown on MgO(111). Using in situ and ex situ characterization methods, scanning tunneling microscopy, conversion electron Mössbauer spectroscopy, and the magneto-optic Kerr effect, we showed that the in-plane MF applied during the reactive deposition of 10 nm Fe3O4(111)/MgO(111) heterostructures influenced the growth morphology of the magnetite films, which affects both in-plane and out-of-plane characteristics of the magnetization process. The observed changes are explained in terms of modification of the effective magnetic anisotropy.</p>
Data from: Human activities modulate reciprocal effects of a subterranean ecological engineer rodent, Tachyoryctes macrocephalus, on Afroalpine vegetation cover
<p class="MsoNormalCxSpFirst"><span>Human activities, directly and indirectly, impact ecological engineering activities of subterranean rodents. As engineering activities of burrowing rodents are affected by, and reciprocally affect vegetation cover via feeding, burrowing and mound building, human influence such as settlements and livestock grazing, could have cascading effects on biodiversity and ecosystem processes such as bioturbation. However, there is limited understanding of the relationship between human activities and burrowing rodents. The aim of this study was therefore to understand how human activities influence the ecological engineering activity of the giant root-rat (<em>Tachyoryctes macrocephalus</em>), a subterranean rodent species endemic to the Afroalpine ecosystem of the Bale Mountains of Ethiopia. We collected data on human impact, burrowing activity and vegetation during February and March of 2021. Using path analysis, we tested (1) direct effects of human settlement on the patterns of livestock grazing intensity, (2) direct and indirect impacts of humans and livestock grazing intensity on the root-rat burrow density, and (3) whether human settlement and livestock grazing influence the effects of giant root-rat burrow density on vegetation and <em>vice versa</em>. We found lower levels of livestock grazing intensity further from human settlement than in its proximity. We also found a significantly increased giant root-rat burrow density with increasing livestock grazing intensity. Seasonal settlement and livestock grazing intensity had an indirect negative and positive effect on giant root-rat burrow density, respectively, both via vegetation cover. Analysing the reciprocal effects of giant root-rat on vegetation, we found a significantly decreased vegetation cover with increasing density of giant root-rat burrows, and indirectly with increasing livestock grazing intensity via giant root-rat burrow density. Our results demonstrate that giant root-rats play a synanthropic engineering role that affects vegetation structure and ecosystem processes. </span></p>
NGS data from: Deploying synthetic coevolution and machine learning to engineer protein-protein interactions
<p>Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a synthetic platform for protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes<span class="Apple-converted-space"> </span>drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pre-trained protein language models to expand, <em>in silico</em>, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of generating protein complexes with diverse molecular recognition properties as tools for biotechnology and synthetic biology.</p>
ScienceDex guides
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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.