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282 results for “data engineering”
Data from: Exploring the macroevolutionary impact of ecosystem engineers using an individual-based eco-evolutionary simulation
<p>Ecosystem engineers can radically reshape ecosystems by modulating the availability of resources to other organisms through modifying either physical or biological aspects of the environment. The introduction or removal of ecosystem engineers from otherwise stable ecosystems can impact the diversity of co-occurring species, such as driving local extinctions of native taxa. While these impacts are well established over ecological timescales for a wealth of taxa, the macroevolutionary implications of the onset of ecosystem engineering behaviours are less clear. Despite this uncertainty, ecosystem engineering has been implicated in several major transitions in Earth's history including the appearance of extensive bioturbation during the Cambrian substrate revolution and associated Ediacaran-Cambrian turnover and the Great Oxygenation Event. Whether ecosystem engineers are frequently associated with turnover and extinction in deep time is not known. Here we investigate this with an eco-evolutionary simulation framework in which we assign lineages the ability to impact the fitness of co-occurring taxa through phenotype-environment feedback. We explore numerous conditions, including how frequently these feedbacks occur, and whether ecosystem engineers modify or create niches. We show that there is no general expected outcome from the introduction of ecosystem engineers. In a minority of runs, ecosystem engineering lineages completely dominate, rendering all others extinct, but in others, they persist (but do not dominate), or die out. We suggest that ecosystem engineers have complex impacts but possess the capacity to profoundly shape diversity, and it is appropriate to consider them alongside other exogenous extinction drivers in deep time. </p>
Data from: Size matters: when resource accessibility by ecosystem engineering elicits wood-boring beetle demographic responses
<p>This data was used to investigate how the age and size of beaver disturbances act as predictors for primary wood-boring beetle abundance and species richness around beaver-altered habitat patches. To do so, we sampled beetles around 16 beaver-disturbed and unaltered watercourses within the Kouchibouguac National Park (Canada) and modeled beetle demographical responses to site conditions and their physical characteristics, distance from the watercourse, deadwood biomass, and the geographical location of the sites.</p>
Supporting molecular simulations data for "Pseudo-halide anion engineering for α-FAPbI3 perovskite solar cells"
<p>Supporting molecular simulations data for "Pseudo-halide anion engineering for α-FAPbI3 perovskite solar cells"</p>
Optimal Mowing Regime in Enhancing Biodiversity in Seasonal Floodplains along Engineered Channels, Survey data
<p>This is the survey dataset used in the publication " Optimal Mowing Regime in Enhancing Biodiversity in Seasonal Floodplains along Engineered Channels".</p>
X-ray computed tomography aided engineering approach for non-crimp fabric reinforced composites [Data set]
<p>The finite element models behind the publication</p> <p>Auenhammer, R.M., Jeppesen, N., Mikkelsen, L.P., Dahl, V.A., Blinzler, B.J., Asp, L.E. Robust numerical analysis of fibrous composites from X-ray computed tomography image data enabling low resolutions, <em>Composites Science and Technology, </em><strong>224</strong>, 109458, <a href="https://doi.org/10.1016/j.compscitech.2022.109458">https://doi.org/10.1016/j.compscitech.2022.109458</a>, 2022. </p> <p>The x-ray scan data which the model is based on can be found in the following publication:</p> <p>Jeppesen, N., V.A. Dahl, A.N. Christensen, A.B. Dahl, L.P. Mikkelsen, Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor. IOP Conf. Ser.: Mater. Sci. Eng. 942, 012037, <a href="https://doi.org/10.1088/1757-899X/942/1/012037">https://doi.org/10.1088/1757-899X/942/1/012037</a> 2020</p> <p>and data-set</p> <p>Jeppesen N, Dahl V A, Christensen A N, Dahl A B and Mikkelsen L P 2020 Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor [data set] Zenodo. <a href="http://dx.doi.org/10.5281/zenodo.3877522">http://dx.doi.org/10.5281/zenodo.3877522.</a></p> <p> </p>
Database of non-target invertebrates recorded in field experiments of genetically engineered Bt maize and corresponding non-Bt maize: data files
<p><span>This database represents a comprehensive collection of experimental field data from all over the world on non-target invertebrates recorded in genetically engineered/modified Bt and non-Bt maize. The three data files deposited here are described by Meissle et al. (2022), BMC Research Notes, <span><a href="https://doi.org/10.1186/s13104-022-06021-3"><span>https://doi.org/10.1186/s13104-022-06021-3</span></a></span><span> </span>.</span></p> <p><span>The database was created for a systematic review with the question if growing Bt maize changes abundance or ecological function of non-target animals compared to growing of non-GM maize, published by Meissle et al. (2022), Environmental Evidence, <a href="https://doi.org/10.1186/s13750-022-00272-0"><span>https://doi.org/10.1186/s13750-022-00272-0</span></a></span>.</p> <p>Data file 1 contains the database with 7279 records of non-target invertebrate abundance, activity density, or predation or parasitism in Bt and non-Bt maize, extracted from 120 publications. Data file 2 includes the list and definitions of variables in the database, and data file 3 represents the critical appraisal questions and answer options.</p>
Data for: Pericytes' Circadian Clock Affects Endothelial Cells' Synchronization and Angiogenesis in a 3D Tissue Engineered Scaffold
<p>Raw data set and analysis files for Mastrullo et al., Frontiers in Pharmacology, 2022 <strong>DOI:</strong> 10.3389/fphar.2022.867070 </p>
Data files for the article "Engineering nanoscale hypersonic phonon transport"
<p>Data files for the article "Engineering nanoscale hypersonic phonon transport". Published in Nature Nanotechnology </p>
Data for "Physically Based Deep Learning Framework to Model Intense Precipitation Events at Engineering Scales"
<p>The dataset consists of high resolution (250 m) and low resolution (0.025 degree) climate model outputs in netCDF format. Each file contains data for one variable and one month.</p> <p>Low resolution files follow the naming scheme: montrealC_0025deg_200x200_ERA5_1m_YYYYMM_VAR.nc</p> <p>High resolution files follow the naming scheme: montrealC_250m_324x324_ERA5_TEB_100_noconv_YYYYMM_VAR.nc</p> <p>YYYYMM stands for the year (first 4 digits) and month (last 2 digits).</p> <p>_VAR indicates the variable contained in the file:</p> <ul> <li>_UU700 stands for the east-west component of wind at a pressure level of 700 hPa (hourly frequency)</li> <li>_VV700 stands for the north-south component of wind at a pressure level of 700 hPa (hourly frequency)</li> <li>When _VAR is omitted, the variable is precipitation at 1-minute temporal resolution</li> </ul>
Supporting Data for Human Factors in Developing Automated Vehicles:A Requirements Engineering Perspective
<p>This data set complements our manuscript in submission with the title:</p> <p>"Human Factors in Developing Automated Vehicles: A Requirements Engineering Perspective"</p> <p>We provide two files:</p> <p>a) the interview guide</p> <p>b) an overview that maps from themes to example quotes and codes derived from particular interview subjects</p>
Supporting data (cave length, area and volume) - Bats as ecosystem engineers in iron ore caves in the Carajás National Forest, Brazilian Amazonia
<p>Supporting data for the manuscript "Bats as ecosystem engineers in iron ore caves in the Carajás National Forest, Brazilian Amazonia", in PLOS ONE. Cave length (PH: horizontal projection, in meters), cave area (Área, in square meters) and cave volume (Volume, in cubic meters) for 1,309 caves in the Carajás National Forest area, Pará State, Brazilian Amazonia.</p>
Data for: An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean
<p><span>Soybean [<em>Glycine max (L.) Merr.</em>] </span><span>serves as one of the most economically valuable crops globally, but it is sensitive to low temperatures during the crop growing season. Currently, agriculture around the world has faced more serious abiotic stresses due to climate change, so there is an urgent need to breed cold-tolerant cultivars to resist the changing environment. The cold-tolerant trait is a complex and quantitative trait controlled by multiple genes, environmental factors, and their interaction. A total of 56 soybean samples were used, including 28 resistant varieties and 28 susceptible varieties, in the field experiments. We selected 55 SNPs (which were mapped to 39 CTgenes) from the CTgenes for distinguishing cold-tolerant lines from cold-susceptible lines. The SNP data can be applied for soybean's cold-tolerant experiment, such as soybean marker-assisted selection, soybean varieties clustering, the systems biology analysis, and further validation. </span></p>
Changes Monitoring in Hongjiannao Lake from 1987-2023 using Google Earth Engine and Analysis of Climatic and Anthropogenic Forces (Climatic Data)
<p>This dataset presents temporal (1987 to 2023) climatic data for the weather station near Hongjiannao Lake.</p>
Dataset: Problem-centred interviews results for Matching Data Life Cycle and Research Processes in Engineering Sciences
<p>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.</p>
3D Laser Scanning Data: Public Square in Murcia and Engineering Laboratory at the University of Alicante
<p>This dataset includes 3D terrestrial laser scans obtained using the Leica C10 ScanStation. The data covers two distinct scenarios:</p> <ol> <li> <p><strong>Public Square in Murcia Capital</strong>: This dataset includes two scan positions within a public square located in Murcia. Three HDTarget markers were placed, and their center or vertex coordinates are provided in the accompanying _vertices.txt file. The scans were conducted with the laser scanner leveled, but they are not registered.</p> </li> <li> <p><strong>Engineering Laboratory at the University of Alicante</strong>: This dataset consists of two scans of the Ground Engineering Laboratory at the University of Alicante. The scans were conducted with the same leveled laser scanner, and no targets were used. Between the two scans, some elements in the laboratory were slightly moved, which can be identified by comparing the point clouds.</p> </li> </ol>
Forest Fire Dataset for Peninsular Malaysia (2001-2023) Extracted from Multiple-Source Remote Sensing Data using Google Earth Engine
<ul> <li>Dataset: Forest Fire data</li> <li>Time Period: 2001 to 2023</li> <li>Location: Peninsular Malaysia</li> <li>Historical Fire Source: MCD64A1 and FIRMS Hotspots</li> <li>Fire Factors Extracted: Global Remote Sensing Data from GEE</li> </ul> <p>The framework extraction process can be reffered from the following publication:</p> <ul> <li>Framework to Create Inventory Dataset for Disaster Behavior Analysis Using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis</li> <li>Journal: <em>Forests</em> <strong>2024</strong>, <em>15</em>(6), 923;</li> <li><a href="https://doi.org/10.3390/f15060923">https://doi.org/10.3390/f15060923</a></li> <li>The variables name such as AET (actual evapotranspiration) can be found from the article.</li> </ul> <p>Access the framework code from: </p> <ul> <li><a href="https://github.com/chewyeejian/GEE_FrameworkForestFireDataset">https://github.com/chewyeejian/GEE_FrameworkForestFireDataset</a></li> </ul> <p>The time sequence in the variable indicate whether it's a monthly data / yearly accumulated data / seasonal data, example:</p> <ul> <li>200101_aet (Year 2001, Month 01, value for aet (actual evapotranspiration)</li> <li>2001_aet_DJF (Average of December, January, February)</li> <li>2001_aet_MAM (Seasonal Average of March, April, May)</li> <li>2001_aet_JJA (Seasonal Average of June, July, August)</li> <li>2001_aet_SON (Seasonal Average of September, October, November)</li> <li>2001_aet_annual (Annual average of 2001)</li> </ul>
Source data - Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output
<p>Research data supporting the findings of "<em>Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output</em>" by Lennart Grabenhorst, Martina Pfeiffer, Thea Schinkel, Mirjam Kümmerlin, Gereon A. Brüggenthies, Jasmin B. Maglic, Florian Selbach, Alexander T. Murr, Philip Tinnefeld and Viktorija Glembockyte. For questions concerning this data, please reach out to Philip Tinnefeld or Viktorija Glembockyte.</p>
Input data of the multi-patch geometries used in: A. Farahat, H. M. Verhelst, J. Kiendl, M. Kapl, Isogeometric analysis for multi-patch structured Kirchhoff–Love shells, Computer Methods in Applied Mechanics and Engineering 411 (2023) 116060 DOI: 10.1016/j.cma.2023.116060
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Data related to feedstock supply, logistics and engineering work for Pilot 5 (Biosolvents) in WaysTUP! project
<p><span>The objectives of this dataset were to describe the feedstock supply, logistics and engineering work carried out for PILOT 5. A description of the design, construction and final start-up of the pilot is also included. </span><span>PILOT 5 is located in Athens (Greece). Feedstock material is source-separated biowaste provided by the Municipality of Vari-Voula-Vouliagmeni under the supervision of SUST. The necessary logistics for continuous and stable feedstock supply were arranged by SUST and verified by NTUA. </span><span>PILOT 5 is a pre-existing installation developed in the framework of the LIFE WASTE2BIO project (LIFE11 ENV/GR/000949 and</span><span> is installed in the premises of NTUA</span><span>. For the purposes of the WaysTUP!, this prototype plant was upgraded in order to meet the project’s needs. </span><span>It includes a dehydration unit, a bioconversion unit and a distillation unit for the recovery of the produced ethanol. The working volume of the bioreactors is 400L and up to</span><span> </span><span>80kg/d dried biowaste could be treated depending on the selected operational parameters. </span></p>
Data for "Diastereoselective Self-Assembly of Low-Symmetry PdnL2n Nanocages through Coordination-Sphere Engineering"
<p>In the following subdirectories are the input and outputs of cage and face analysis for:</p> <p>Published DOI: 10.1002/anie.202315451</p> <p>Previously uploaded in <span>10.5281/zenodo.8432296 and </span><a href="https://github.com/andrewtarzia/citable_data" rel="noopener noreferrer"><span>https://github.com/andrewtarzia/citable_data</span></a></p> <p>Note that all scripts are self-contained. There is some duplicate code between them.</p> scripts: <ul> <li> build_cages.py: <ul> <li>Builds the Pd2L4 cage models.</li> <li>Some manual optimisation is assumed.</li> <li>Paths for xTB and GULP are set to my machine.</li> <li>All outputs are relative to working directory.</li> </ul> </li> <li> build_dwall_triangles.py: <ul> <li>Builds the Pd3L6 cage models.</li> <li>Some manual optimisation is assumed.</li> <li>Paths for xTB and GULP are set to my machine.</li> <li>All outputs are relative to working directory.</li> </ul> </li> </ul>
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.