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3,682 results for “ROOTS”
Model configuration files and forcing data for Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
<p>This repository includes the model configuration files, input data, and forcing data used for simulations in Bieri et al. (2025) - <em>Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration.</em></p> <ul> <li>forcing.tar.gz - Compressed folder containing HRLDAS Noah-MP model forcing NetCDF files <ul> <li>These forcing files were derived from the NASA Global Land Data Assimilation System (GLDAS; Beaudoing et al. 2020)</li> <li>The compressed file contains 3-hourly forcing files for the entire simulation period (01 Jun 2000 to 31 Dec 2019)</li> </ul> </li> <li>wrfinput_d01 - NetCDF file used as HRLDAS input file in HRLDAS Noah-MP simulations <ul> <li>Generated from WRF WPS (https://github.com/wrf-model/WPS)</li> </ul> </li> <li>Namelist files <ul> <li>namelist.hrldas.ROOT - Model namelist settings used for ROOT experiment</li> <li>namelist.hrldas.SOIL - Model namelist settings used for SOIL experiment</li> <li>namelist.hrldas.GW - Model namelist settings used for GW experiment</li> <li>namelist.hrldas.CONTROL - Model namelist settings used for FD (CONTROL) experiment</li> </ul> </li> </ul>
Characterization and Simulation Data of Cylindrical, Elliptical, Parabolic, Conical and Root-like Tips with Diameters in Sandy Loam Soil
<p>Plants use many strategies to move efficiently in soil, such as growth from the tip, tropic movements, and morphological changes. In this paper, we propose a method to translate morphological features of <em>Zea mays</em> roots into a new design of soft robots that will be able to move in soil. The method relies on image processing and curve fitting techniques to extract the profile of <em>Z</em>. <em>mays</em> primary root. We implemented an analytic translation of the root profile in a 3D model (CAD) to fabricate root-like probes by means of 3D printing technology. Then, we carried out a comparative analysis among the artificial root-like probe and probes with different tip shapes (cylindrical, conical, elliptical, and parabolic) and diameters (11, 9, 7, 5, and 3 mm). The results showed that the energy consumption and the penetration force of the bioinspired probe are better with respect to the other shapes for all the diameters of the developed probes. For 100 mm of penetration depth and 7 mm of probe diameter, the energy consumption of the bioinspired probe is 89% lesser with respect to the cylindrical probe and 26% lesser with respect to the conical probe. The penetration performance of the considered tip shapes was evaluated also by means of numerical simulations, obtaining a good agreement with the experimental results. Additional investigations on plant root morphology, movement strategies, and material properties can allow the development of innovative bioinspired solutions exploitable in challenging environments. This research can bring to breakthrough scenarios in different fields, such as exploration tasks, environmental monitoring, geotechnical studies, and medical applications</p>
Tomographic X-ray data of a lotus root filled with attenuating objects
<p>This is an open-access dataset of tomographic X-ray data of a slice of a lotus root. The dataset consists of</p> <ul> <li>the X-ray sinogram of a single 2D slice of the lotus root with two different resolutions, and</li> <li>the corresponding measurement matrices modeling the linear operation of the X-ray transform.</li> </ul> <p>Each of these sinograms was obtained from a measured 360-projection fan-beam sinogram by down-sampling and taking logarithms. The original (measured) sinogram is also provided in its original form and resolution.</p> <p>Also MATLAB code for reconstructions using filtered back-projection, Landweber iteration, and Tikhonov regularization are provided.</p> <p>Documentation of the dataset is available at <a href="https://arxiv.org/abs/1609.07299">arxiv.org/abs/1609.07299</a>. See also <a href="https://www.fips.fi/dataset.php">www.fips.fi/dataset.php</a>.</p>
Data and results for manuscript: "Imaging and functional characterization of crop root systems using spectroscopic electrical impedance measurements"
<p>This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript titled:<br> <br> Imaging and functional characterization of crop root systems using spectroscopic electrical impedance measurement</p>
Data and results for manuscript "Small scale characterization of vine plant root water uptake via 3D electrical resistivity tomography and Mise-à-la-Masse method"
<p>This package contains measured raw ERT and MALM data used to generate the plots in the manuscript.</p> <p> </p>
Dataset of 100 simulated root images, with different resolutions
<p>All images were analysed with WinRhizo<sup>TM</sup> 2013, Regent method (Régent Instruments Inc 2013) and ImageJ (1.51j8, Schneider, Rasband, & Eliceiri, 2012) with the macro IJ_Rhizo (IJ_Rhizo_v0beta). We conducted batch analyses with the grey level thresholds set to automatic at all four resolutions (200, 400, 800 and 1200 DPI). We further analysed the images at 800 dpi and 1200 dpi with the threshold value manually set to 144. We considered this value suitable after visual inspection of 10 images at different thresholds using the threshold slider of WinRhizo<sup>TM</sup>. Since no debris was simulated, we used no automatic debris correction. For WinRhizo<sup>TM</sup>, the boundaries of diameter classes were set from 0.1 to 1.9 mm (with an increment of 0.1 mm).</p> <p>The dataset contains:</p> <p>- the root images in jpg format (images.zip)</p> <p>- the root architecture data in rsml format (rsml.zip)</p> <p>- the ground-truth value for each image (all_root_data_2D.csv)</p> <p>- the ground-truth value for each root in each image (ground_truth_data_2D.csv)</p> <p>- the estimation given by WinRhizo and IJ_Rhizo (all_analyses_2D.txt and all_analyses_fixed.txt)</p> <p>- the different codes used for the analysis (*.R)</p> <p>Dataset used in : Accuracy of image analysis tools for functional root traits: A comment on Delory et al., 2017 from Rose and Lobet (2019), Methods in Ecology and Evolution. </p> <p>The images in this dataset are a subset of the dataset here: https://doi.org/10.5281/zenodo.208214</p>
Seminal Root Images of Wheat TILLING Mutants
<p>These files describe a forward genetic screen for variation in seminal root traits in a subset of an exome-sequenced wheat TILLING population in the UK bread wheat cultivar - Cadenza. The submission contains:</p> <ol> <li>readme.md: Contains detailed description of the dataset and experiment.</li> <li>List_of_Lines_Phenotyped.csv: Contains a list of the subset of the Cadenza TILLING population phenotyped.</li> <li>Cadenza_TILLING_Mutants_RootNav_Measurement.csv: Contains the original root trait measurements from RootNav (Root image analysis software).</li> <li>Wheat_TILLING_Seminal_Root_Images.tar.gz: An archive containing 6,240 root images of the mutant lines and control phenotyped.<br> </li> </ol>
Identification of novel genes involved in phosphate accumulation in Lotus japonicus through Genome Wide Association mapping of root system architecture and anion content
<p>130 Lotus japonicus accessions were used. The names and accession numbers are<br> listed in S6 Table. Seeds were scarified with sandpaper and then sterilized 14 minutes in 0.05%<br> sodium hypochlorite. Subsequently, seeds were rinsed and washed 5 times in sterile distilled<br> water. For the germination, seeds were positioned in imbibed filter paper, in sterile Petri dishes,<br> and wrapped in aluminium foil. After 3 days at 21°C, young seedling were transferred to square<br> plates (12 x 12 cm) containing growth medium. Both media used in this<br> study were based on Long-Ashton solution (with two levels of phosphate concentration -20 or<br> 750 μM, LP or HP, respectively) with 0.8% MES buffer (Duchefa Biochemie,<br> Haarlem, The Netherlands), 0.8% agarose (to minimize phosphate contamination), and adjusted<br> to pH 5.7 with 1M KOH. After adding the medium, plates were dried, closed, overnight in a<br> sterile laminar flow hood. Two accessions, with four replicates per each accession, were placed<br> on each plate. Each plate was replicated, with mirrored position of each accession to minimize<br> any positional growth effects. Plates were placed vertically, and plants grown under long-day<br> conditions (21°C, 16 h light/8 h dark cycle) with white light bulbs emitting 50 μmol/m 2 /s and<br> roots were exposed to light. Every day at the same time, the racks were transported to the image<br> acquisition room where images of each plate were acquired with eight Epson V600 CCD flatbed<br> color image scanners (Seiko Epson) and then immediately returned to the growth chamber.</p>
Data for paper 'Segmentation of Roots in Soil with U-Net'
<p>Data for the paper 'Segmentation of Roots in Soil with U-Net'</p> <p>Paper available at: https://arxiv.org/abs/1902.11050</p> <p>Code available at: https://github.com/Abe404/segmentation_of_roots_in_soil_with_unet</p> <p>Model available at: https://doi.org/10.5281/zenodo.3484015</p>
Text-fig. 3. Borehole section in the Blansko Graben with lithology, distribution of palynomorphs, macroflora and macrofauna (modified after Čech, unpublished report). 1 – Spesovicornea pacltovae, 2 – Platanus sp., 3 – Myrtophyllum angustum (VEL.) KNOBOCH, 4 – Gleichenia sp.), 5 – percentage of land-derived palynomorphs, 6 – percentages of marine palynomorphs, 7 – glauconite, 8 – pyrite nodules, 9 – macrofauna, 10 – productive palynological samples, 11 – carbonized roots, 12 – conglomerate, 13 – sandstone, 14 – claystone, 15 – coal, 16 – granite and granodiorite of the Brno pluton. in Spesovicornea Pacltovae Gen. Nov. Et Sp. Nov., A New Elateroid Sporomorph From The Bohemian Cenomanian (Czech Republic)
Text-fig. 3. Borehole section in the Blansko Graben with lithology, distribution of palynomorphs, macroflora and macrofauna (modified after Čech, unpublished report). 1 – Spesovicornea pacltovae, 2 – Platanus sp., 3 – Myrtophyllum angustum (VEL.) KNOBOCH, 4 – Gleichenia sp.), 5 – percentage of land-derived palynomorphs, 6 – percentages of marine palynomorphs, 7 – glauconite, 8 – pyrite nodules, 9 – macrofauna, 10 – productive palynological samples, 11 – carbonized roots, 12 – conglomerate, 13 – sandstone, 14 – claystone, 15 – coal, 16 – granite and granodiorite of the Brno pluton.
Figure 6 in Identification of root-knot nematodes (Meloidogyne spp.) from greenhouses in the Middle Black Sea Region of Turkey
Figure 6. Amplification products (502 bp) with SEC 1F/1R primers on Meloidogyne populations from the Middle Black Sea Region. Sa: Samsun populations, Si: Sinop populations, Or: Ordu populations, Am: Amasya populations, C: control population, W: water, M: molecular marker with 100 bp.
Figure 2 in Identification of root-knot nematodes (Meloidogyne spp.) from greenhouses in the Middle Black Sea Region of Turkey
Figure 2. Perineal pattern variability observed in Meloidogyne ethiopica from a single egg mass population (bar: 10 µm).
Figure 1 in Identification of root-knot nematodes (Meloidogyne spp.) from greenhouses in the Middle Black Sea Region of Turkey
Figure 1. Perineal patterns of Meloidogyne arenaria, M. incognita, and M. javanica from the Middle Black Sea Region of Turkey (bar: 10 µm).
Figure 4 in Identification of root-knot nematodes (Meloidogyne spp.) from greenhouses in the Middle Black Sea Region of Turkey
Figure 4. Amplification products (420 bp) with Far/Rar primers on Meloidogyne populations from the Middle Black Sea Region. Sa: Samsun populations, Si: Sinop populations, Or: Ordu population, Co: Çorum populations, To: Tokat populations, Am: Amasya populations, C: control population, W: water, M: molecular marker with 100 bp.
Figure 5 in Identification of root-knot nematodes (Meloidogyne spp.) from greenhouses in the Middle Black Sea Region of Turkey
Figure 5. Amplification products (670 bp) with Fjav/Rjav primers on Meloidogyne populations from the Middle Black Sea Region. Sa: Samsun populations, Si: Sinop populations, Co: Çorum population, C: control population, W: water, M: molecular marker with 100 bp.
Dataset Artifact for paper "Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?"
<p>Artifacts for the paper titled <strong><em>Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?</em></strong>.</p> <p>This artifact repository contains 9 compressed folders, as follows: </p> <table> <tbody> <tr> <td><strong>ID</strong></td> <td><strong>File Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>1</td> <td>syn_circa.zip</td> <td>CIRCA10, and CIRCA50 datasets for Causal Discovery</td> </tr> <tr> <td>2</td> <td>syn_rcd.zip</td> <td>RCD10, and RCD50 datasets for Causal Discovery</td> </tr> <tr> <td>3</td> <td>syn_causil.zip</td> <td>CausIL10, and CausIL50 datasets for Causal Discovery</td> </tr> <tr> <td>4</td> <td>rca_circa.zip</td> <td>CIRCA10, and CIRCA50 datasets for RCA</td> </tr> <tr> <td>5</td> <td>rca_rcd.zip</td> <td>RCD10, and RCD50 datasets for RCA</td> </tr> <tr> <td>6</td> <td>online-boutique.zip</td> <td>Online Boutique dataset for RCA</td> </tr> <tr> <td>7</td> <td>sock-shop-1.zip</td> <td>Sock Shop 1 dataset for RCA</td> </tr> <tr> <td>8</td> <td>sock-shop-2.zip</td> <td>Sock Shop 2 dataset for RCA</td> </tr> <tr> <td>9</td> <td>train-ticket.zip</td> <td>Train Ticket dataset for RCA</td> </tr> </tbody> </table> <p>Each zip file contains the generated/collected data from the corresponding data generator or microservice benchmark systems (e.g., online-boutique.zip contains metrics data collected from the Online Boutique system). </p> <p><strong>Details about the generation of our datasets</strong></p> <p><em>1. Synthetic datasets</em></p> <p>We use three different synthetic data generators from three previous RCA studies [15, 25, 28] to create the synthetic datasets: CIRCA, RCD, and CausIL data generators. Their mechanisms are as follows:<br><br>1. CIRCA datagenerator [28] generates a random causal directed acyclic graph (DAG) based on a given number of nodes and edges. <span>From this DAG, time series data for each node is generated using a </span><span>vector auto-regression (VAR) model. A fault is injected into a node </span><span>by altering the noise term in the VAR model for two timestamps. <br></span><span><br>2. RCD data generator [25] uses the pyAgrum package [3] to generate </span><span>a random DAG based on a given number of nodes, subsequently </span><span>generating discrete time series data for each node, with values ranging from 0 to 5. A fault is introduced into a node by changing its </span><span>conditional probability distribution.<br><br>3. CausIL data generator [15] generates causal graphs and time series data that simulate </span><span>the behavior of microservice systems. It first constructs a DAG of </span><span>services and metrics based on domain knowledge, then generates </span><span>metric data for each node of the DAG using regressors trained on </span><span>real metrics data. Unlike the CIRCA and RCD data generators, the </span><span>CausIL data generator does not have the capability to inject faults.<br><br></span>To create our synthetic datasets, we first generate 10 DAGs whose nodes range from 10 to 50 for each of the synthetic data generators. Next, we generate fault-free datasets using these DAGs with different seedings, resulting in 100 cases for the CIRCA and RCD generators and 10 cases for the CausIL generator. We then create faulty datasets by introducing ten faults into each DAG and generating the corresponding faulty data, yielding 100 cases for the CIRCA and RCD data generators. The fault-free datasets (e.g. `syn_rcd`, `syn_circa`) are used to evaluate causal discovery methods, while the faulty datasets (e.g. `rca_rcd`, `rca_circa`) are used to assess RCA methods. </p> <p><em>2. Data collected from benchmark microservice systems </em></p> <p>We deploy three popular benchmark microservice systems: Sock Shop [6], Online Boutique [4], and Train Ticket [8], on a four-node Kubernetes cluster hosted by AWS. Next, we use the Istio service mesh [2] with Prometheus [5] and cAdvisor [1] to monitor and collect resource-level and service-level metrics of all services, as in previous works [ 25 , 39, 59 ]. To generate traffic, we use the load generators provided by these systems and customise them to explore all services with 100 to 200 users concurrently. We then introduce five common faults (CPU hog, memory leak, disk IO stress, network delay, and packet loss) into five different services within each system. Finally, we collect metrics data before and after the fault injection operation. An overview of our setup is presented in the Figure below.</p> <p></p> <p><strong>Code</strong></p> <p>The code to reproduce the experimental results in the paper is available at <a href="https://github.com/phamquiluan/RCAEval">https://github.com/phamquiluan/RCAEval</a>.</p> <p><strong>References</strong></p> <p>As in our paper.</p>
Data for Publication - Impact of agricultural systems on arbuscular mycorrhizal fungi community composition in Robusta coffee roots in the Democratic Republic of Congo
<p>Data used for the publication:</p> <p>"Impact of agricultural systems on arbuscular mycorrhizal fungi community composition in Robusta coffee roots in the Democratic Republic of Congo" - Ieben Broeckhoven, Arne Devriese, Olivier Honnay, Roel Merckx, and Bruno Verbist</p>
Fig. 2 in The origin and diversification of the Entorrhizales: deep evolutionary roots but recent speciation with a phylogenetic and phenotypic split between associates of the Cyperaceae and Juncaceae
Fig. 2 Collapsed chronogram for Basidiomycota and Entorrhizomycota evolution. The tree topology represents the consensus of trees inferred with BEAST from combined 18S + 28S + rpb1 domains B-C sequences from 83 Basidiomycota species, three Entorrhizomycota species and three Ascomycota species as outgroup. Alignment length = 3903. The age estimation mean is followed by the 95% highest density probability range in square brackets. Numbers on branches represent bootstrap values obtained from 1000 replicates (values ≥ 70), maximum support of 100 is encoded with bold lines. For full dataset, see Supplementary material (Fig. S1). Abbreviations: Ordo., Ordovician; Sil., Silurian; Carbon., Carboniferous; Paleo., Paleogene; N., Neogene
Fig. 4. Proscapanussansaniensis. A. Left dentary fragment with canine root and p1–p4, Petersbuch 31, NHMA P31−163A1 in Moles (Talpidae) from the late Middle Miocene of South Germany
Fig. 4. Proscapanussansaniensis. A. Left dentary fragment with canine root and p1–p4, Petersbuch 31, NHMA P31−163A1, occlusal view; ca. × 10. B. Right m1–m3, Petersbuch 31, NHMA P31−163B2, occlusal view; ca. × 10. C. Right dentary fragment with p4–m2, Petersbuch 48, NHMA P48−89A1, buccal view; ca. × 10. D. Left P4, Petersbuch 31, NHMA P31−163C1, occlusal view; ca. × 10. E. Left M1, Petersbuch 48, NHMA P48−89B2, occlusal view; ca. × 10. F. Right M2, Petersbuch 31, NHMA P31−163E3, occlusal view; ca. × 10. G. Left humerus, Petersbuch 31, NHMA P31−164A1, anterior view; ca. × 5.
Linked collectors and determiners for: Root-associated microbiota of decline-affected and asymptomatic Pinus sylvestris trees.
Natural history specimen data linked to collectors and determiners held within, "Root-associated microbiota of decline-affected and asymptomatic Pinus sylvestris trees". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1826d3c2-9e33-43e8-afaa-450b7d6e2b9b">https://bionomia.net/dataset/1826d3c2-9e33-43e8-afaa-450b7d6e2b9b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1826d3c2-9e33-43e8-afaa-450b7d6e2b9b">https://gbif.org/dataset/1826d3c2-9e33-43e8-afaa-450b7d6e2b9b</a>. Formatted as a Frictionless Data package.
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