Find research datasets worth reusing

opencc-by-4.0Oct 2019View details →

Data-base of CH4 and ancillary data in the Belgian Coastal Zone (2017, 2018, 2019)

Data-base of CH4 and ancillary data (salinity, water temperature, chlorophyll-a concentration) in the Belgian Coastal Zone (2017, 2018, 2019)

opencc-by-4.0Nov 2019View details →

Data and R-Scripts for: Value of crowd-based water level class observations for hydrological model calibration

This dataset corresponds to the study "Value of crowd-based water level class observations for hydrological model calibration" submitted to Water Resources Research in August 2019. Please use the R-Scripts in ascending numbers and adapt the paths to where you stored the files. The helpfunctions.R will be used by some of the scripts and you might want to adapt a path in line 356 for it to be used correctly with the scripts 8a and 8b. The parameter ranges used for the HBV calibration can be found in the "Parameters and parameter ranges.pdf" If you do not wish to calibrate the model, and just perform some statistics start with script 7 and use the - CrossValidation_stats_all.txt in the LUT Tables folder which contains   all model performances. - CrossValidation_stats_WP1.txt contains also results of the upper benchmark   (only those labelled with no error and hourly). - RandomParamPerformance_Validation.txt contains the results of the random parameters   (lower benchmark). - The folders Validation Results and Calibration Results contain the files in HBV-format after the model   calibration and validatin were completed. The results of the Calibration and Validation files are also summarized   in the aforementioned txt-files within script 6 -HBV CrossValidation.R. Please be aware that for the study only the catchments Murg, Guerbe, Mentue, and Verzasca were used! If you run into trouble using the data please contact simon.etter[at]outlook.com. Co-authors are: Prof. Dr. Jan Seibert - jan.seibert[at]geo.uzh.ch Dr. Ilja (H.J.) van Meerveld - ilja.vanmeerveld[at]geo.uzh.ch Barbara Strobl - barbara.strobl[at]geo.uzh.ch

opencc-by-4.0Jul 2024View details →

UAS-SfM data from Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA

Data for: Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA Sean Reilly 1, Matthew L. Clark 2, Lika Loechler 2, Jack Spillane 2, Melina Kozanitas 3, Paris Krause 4, David Ackerly 3, Lisa Patrick Bentley 4, and Imma Oliveras Menor 1,5 1 Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK 2 Center for Interdisciplinary Geospatial Analysis, Department of Geography, Environment, and Planning, Sonoma State University, Rohnert Park, CA 94928, USA 3 Departments of Integrative Biology and Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA 4 Department of Biology, Sonoma State University, Rohnert Park, CA 94928, USA 5 AMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France Study abstract: There is a pressing need for well-informed management to reduce wildfire hazard and restore fire’s beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California. Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69 – 0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49 – 0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring. These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs.  Published in Remote Sensing of Environment Contents: This repository contains multispectral UAS-SfM data from four sites around California, USA: jcksn: Jackson Demonstration State Forest ltr: LaTour Demonstration State Forest ppwd: Pepperwood Preserve sdlmtn: Saddle Mountain Open Space Preserve Data were collected during a series of campaigns: c1: Pepperwood, 2019-09-01 to 2019-10-15 c3: Jackson, 2020-06-15 to 2020-07-02 c4: LaTour, 2020-07-07 to 2020-07-17 c6: Saddle Mountain, 2020-08-04 to 2020-08-09 c9: Jackson, 2021-07-08 to 2021-07-12 Data are included in three formats: raw: Raw outputs from Pix4D (spectral and las) reg_grnd, reg_cnpy: Las files with merged multispectral data and classified ground, registered to ALS using either ground points (grnd) or, in cases with insufficient ground points for registration, to the canopy (cnpy) hnrm: Height normalized las files, normalization performed using ALS terrain model File naming structure: site_campaign_flightzone_uas_processedstate See accompanying paper for methods on data collection and processing Data are grouped into zipped folder by product type Funding: Funding for this research was supported by CAL FIRE Forest Health and Forest Legacy (8GG18806) and California State University, Agricultural Research Institute (20-01-106) awards to L.P.B and M.L.C. S.R. was funded by the Rhodes Trust and through the University of Oxford Environmental Change Institute Small Grant Scheme. Pepperwood ground data collection was supported by funding from the Gordon and Betty Moore Foundation and National Science Foundation grants 1754475 and 1835086. Citation: <div> <div>Reilly, S., Clark, M.L., Loechler, L., Spillane, J., Kozanitas, M., Krause, P., Ackerly, D., Bentley, L.P., Menor, I.O., 2024. Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA. Remote Sensing of Environment 312, 114310. <a href="https://doi.org/10.1016/j.rse.2024.114310">https://doi.org/10.1016/j.rse.2024.114310</a></div> </div>

opencc-by-4.0Dec 2012View details →

Fig. 1 in Phylogenetic Analyses on the Tintinnid Ciliates (Protozoa, Ciliophora) Based on Multigene Sequence Data

Fig. 1. Alignment of the ITS1-5.8S-ITS2 regions from ten reference tintinnid species: Tintinnopsis sp. 1, Tintinnopsis sp. 2, Tintinnopsis sp. 3, T. cylindrica, T. tubulosoides, T. lohmanni, Stenosemella nivalis, Codonellopsis nipponica, Favella campanula, F. taraikaensis, F. ehrenbergii, Metacylis angulata, Eutintinnus pectinis, and Amphorellopsis acuta. Agreement with other sequences is indicated by periods and disagreement by a nucleotide at a position. Gaps introduced to improve the alignment are indicated by dashes. The insertion in ITS1 of F. campanula is labeled. The ITS1 and ITS2 region sequences are shaded; the 5.8S gene sequence is unshaded.

opencc-by-4.0Dec 2012View details →

Figs 3–5 in Phylogenetic Analyses on the Tintinnid Ciliates (Protozoa, Ciliophora) Based on Multigene Sequence Data

Figs 3–5. Phylogenetic analyses and photomicrographs in this work. 3, 4 – phylogenetic analyses inferred by ML of internal transcribed spacer (ITS) and 5.8S region sequences and small subunit rDNA sequences. Topologies of trees constructed with other methods (BI, MP, or NJ) were essentially identical, lacking only a few nodes indicated by asterisks in the support values. Posterior probability values for branches of the ML tree and bootstrap values for ML, NJ, and MP trees, respectively, are given on nodes. Newly sequenced species are highlighted in bold. Scale bar in 3 corresponds to 10 substitutions per 100 nucleotide positions, scale bar in 4 corresponds to 5 substitutions per 100 nucleotide positions. 5 – photomicrographs of nine of the 10 newly sequenced tintinnid species in vivo: A – Amphorellopsis acuta; B – Favella taraikaensis; C – F. campanula; D – Tintinnopsis sp. 2; E – Stenosemella nivalis; F – Codonellopsis nipponica; G – Tintinnopsis sp. 3; H – T. lohmanni and I – T. cylindrica. Scale bars: 25 μm.

opencc-by-4.0Dec 2014View details →

Figure 6 in Genetic divergences of South and Southeast Asian frogs: a case study of several taxa based on 16S ribosomal RNA gene data with notes on the generic name Fejervarya

Figure 6. Maximum likelihood (ML) tree of bufonid frogs based on nucleotide sequences of the mitochondrial 16S rRNA gene with Leptophryne borbonica as an outgroup. The bootstrap support (>50%) is indicated at nodes in the order of ML (500) replicates. Asterisks represent Bayesian posterior probability (BPP; * ≥95%). Specimens examined in this study are indicated by boldface type.

opencc-by-4.0Dec 2014View details →

Figure 2 in Genetic divergences of South and Southeast Asian frogs: a case study of several taxa based on 16S ribosomal RNA gene data with notes on the generic name Fejervarya

Figure 2. Maximum likelihood (ML) tree based on nucleotide sequences of the mitochondrial 16S rRNA gene from 88 haplotypes of frogs (Table 1), with Xenopus laevis as an outgroup. Bootstrap support (>50%) is indicated at nodes in the order of ML (1000) replicates. Asterisks represent Bayesian posterior probability (BPP; * ≥95%).

opencc-by-4.0Dec 2008View details →

Text-fig. 6. Most parsimonious tree obtained after addition of Acaciaephyllum to the data set of Doyle (2008), with modifications discussed in the text, and with relationships of other taxa fixed with a backbone constraint tree based on results of Doyle (2008). Relative parsimony of alternative positions of Acaciaephyllum is indicated as in Text-fig. 2. Gnet = Gnetales. in Early Cretaceous Monocots: A Phylogenetic Evaluation

Text-fig. 6. Most parsimonious tree obtained after addition of Acaciaephyllum to the data set of Doyle (2008), with modifications discussed in the text, and with relationships of other taxa fixed with a backbone constraint tree based on results of Doyle (2008). Relative parsimony of alternative positions of Acaciaephyllum is indicated as in Text-fig. 2. Gnet = Gnetales.

opencc-by-4.0Dec 2008View details →

Text-fig. 1. D&E tree of Endress and Doyle (2009), from the combined morphological and molecular analysis of Doyle and Endress (2000), with modifications based on more recent data, showing the inferred evolution of the reticulum grading character (39). Boxes under names of taxa indicate their character state; shading of branches indicates their reconstructed state based on parsimony optimization with MacClade (Maddison and Maddison 2003). Nymph = Nymphaeales, Aust = Austrobaileyales, Chlor = Chloranthaceae, Piper = Piperales, Ca = Canellales, Magnol = Magnoliales. in Early Cretaceous Monocots: A Phylogenetic Evaluation

Text-fig. 1. D&E tree of Endress and Doyle (2009), from the combined morphological and molecular analysis of Doyle and Endress (2000), with modifications based on more recent data, showing the inferred evolution of the reticulum grading character (39). Boxes under names of taxa indicate their character state; shading of branches indicates their reconstructed state based on parsimony optimization with MacClade (Maddison and Maddison 2003). Nymph = Nymphaeales, Aust = Austrobaileyales, Chlor = Chloranthaceae, Piper = Piperales, Ca = Canellales, Magnol = Magnoliales.

opencc-by-4.0Dec 2003View details →

Fig. 1 in On Trimeresurus Fasciatus (Boulenger, 1896) (Serpentes: Crotalidae), With A Discussion On Its Relationships Based On Morphological And Molecular Data

Fig. 1. Trimeresurus fasciatus. Holotype (BMNH 96.4.29.46). General view. Photograph by Jean-Christophe de Massary.

Figure 2 in Description of Nothotylenchus savadkoohensis n. sp. (Rhabditida, Anguinidae) from Iran based on morphological and molecular data

Figure 2: Light micrographs of Nothotylenchus savadkoohensis n. sp. A: Anterior body region; B & C: Non-muscular metacorpus; D: Vulva region and postvulval uterine sac; E: Female posterior body region (tail); F: Female tail tip; G: Male posterior body region and bursa; H & I: Short overlapping of pharyngeal bulb; J: Lateral field; K: Spicules (Scale bars: A-I, K=10 µm, J=5 µm).

Figure 1 in Description of Nothotylenchus savadkoohensis n. sp. (Rhabditida, Anguinidae) from Iran based on morphological and molecular data

Figure 1: Line drawings of Nothotylenchus savadkoohensis n. sp. A, B: Male and female entire body; C: Anterior body region; D: Female pharyngeal region; E: Male posterior body region; F: Female posterior body region.

Figure 4 in Description of Nothotylenchus savadkoohensis n. sp. (Rhabditida, Anguinidae) from Iran based on morphological and molecular data

Figure 4: Bayesian 50% majority rule consensus tree inferred from ITS rDNA sequence of NOtHOtylenCHUS SaVadKOOHenSiS n. sp. from Mazandaran province under the HKY + G model. Bayesian posterior probability (BPP) values>0.50 are given for appropriate clades. The newly generated sequence of the new species is in bold font.

Figure 3 in Description of Nothotylenchus savadkoohensis n. sp. (Rhabditida, Anguinidae) from Iran based on morphological and molecular data

Figure 3: Bayesian 50% majority rule consensus tree inferred from D2-D3 expansion region of LSU rDNA sequence of NOtHOtylenCHUS SaVadKOOHenSiS n. sp. from Mazandaran province under the GTR + G + I model. Bayesian posterior probability (BPP) values>0.50 are given for appropriate clades. The newly generated sequence of the new species is in bold font.

Field data used in "A field-based estimation of the variability of particle entrainment in coarse-bed rivers"

This file is a companion file to "A field-based estimation of the variability of particle entrainment in coarse-bed rivers ", co-authored by Daniel Vázquez-Tarrío, Estrella Carrero-Carralero, Raúl López, Fanny Ville, Damià Vericat and Ramon J. Batalla . This is a paper published in the Journal of Geophysical Research: Earth Surface, 129 (10) (https://doi.org/10.1029/2024JF007695). This Excel file contains all the raw data used in this research.

Data set of "Large superconducting diode effect in ion-beam patterned Sn-based superconductor nanowire/topological Dirac semimetal planar heterostructures"

Superconductor/topological material heterostructures are intensively studied as a platform for topological superconductivity and Majorana physics. However, the high cost of nanofabrication and the difficulty of preparing high-quality interfaces between the two dissimilar materials are common obstacles that hinder the observation of intrinsic physics and the realisation of scalable topological devices and circuits. Here, we demonstrate an innovative method to directly draw nanoscale superconducting beta-tin (β-Sn) patterns of any shape in the plane of a topological Dirac semimetal (TDS) alpha-tin (α-Sn) thin film by irradiating a focused ion beam (FIB). We utilise the property that α-Sn undergoes a phase transition to superconducting β-Sn upon heating by FIB. In β-Sn nanowires embedded in a TDS α-Sn thin film, we observe large non-reciprocal superconducting transport, where the critical current changes by 69% upon reversing the current direction. The superconducting diode rectification ratio η reaches a maximum of 35% when the magnetic field is applied parallel to the current, distinguishing itself from all the previous reports. Moreover, it oscillates between alternate signs with increasing magnetic field strength. The angular dependence of η on the magnetic field and current directions is similar to that of the chiral anomaly effect in TDS α-Sn, suggesting that the SDE may occur at the α-Sn/β-Sn interfaces where the TDS α-Sn becomes superconducting by a proximity effect. As superconducting TDSs are expected candidates for topological superconductivity and harboring Majorana bound states, the ion-beam patterned Sn-based superconductor/TDS planar structures thus show promise as a universal platform for investigating novel quantum physics and devices based on topological superconducting circuits of any shape.

opencc-by-4.0Nov 2023View details →

Precipitation hydrogen isoscape for East China from 1969 to 2017 generated based on data fusion of iGCMs simulations

The dataset includes the stable hydrogen isotope of precipitation for East China over the 1969-2017 period, at a spatial resolution of 50-60 km and a monthly temporal resolution. This dataset was built based on the Convolutional Neural Network (CNN) method, fusing observations and isotope-equipped general circulation models (iGCMs) simulations of hydrogen isotope composition. Some physical-based ancillary data are also introduced in the fusion methods, including elevation and meteorological data, to enrich the climate and terrain information in the process of data fusion.

Data from: Technological Evaluation of Fiber Effects in Wheat-Based Dough and Bread

This dataset is linked to the article by Celeste Verbeke, Els Debonne, Stien Versele, Filip Van Bockstaele and Mia Eeckhout, published in Foods (August 2024): "Technological Evaluation of Fiber Effects in Wheat-Based Dough and Bread" (DOI: https://doi.org/10.3390/foods13162582). <ul> <li>Farinogram curve data.csv & Alveogram curve data.csv & Pasting curve data.csv <ul> <li>Observations: <ul> <li>Ref = wheat flour</li> <li>PF1/5/10 = 1/5/10% pea fiber</li> <li>CF1/5/10 = 1/5/10% cocoa fiber</li> <li>AF1/5/10 = 1/5/10% apple fiber</li> <li>1/2/3/avg = Replicate 1/2/3 & average of the three replicates</li> </ul> </li> </ul> </li> <li>Dough and bread characteristics.csv <ul> <li>Observations: <ul> <li>Ref = wheat flour</li> <li>PF1/5/10 = 1/5/10% pea fiber</li> <li>CF1/5/10 = 1/5/10% cocoa fiber</li> <li>AF1/5/10 = 1/5/10% apple fiber</li> </ul> </li> <li>Abbreviations: <ul> <li>WRC = water retention capacity</li> <li>WA = water absorption</li> <li>DDT = dough development time</li> <li>STAB = stability</li> <li>SOFT = softening</li> <li>P = tenacity</li> <li>L = extensibility</li> <li>W = deformation energy</li> <li>PH = proving height</li> <li>IV = initial viscosity</li> <li>T_past = pasting temperature</li> <li>V_peak = peak viscosity</li> <li>T_peak = peak temperature</li> <li>HS = holding strength</li> <li>V_final = final viscosity</li> <li>BD = breakdown</li> <li>SB_peak = setback from peak</li> <li>SB_total = total setback</li> </ul> </li> </ul> </li> </ul>

openbsd-3-clauseAug 2024View details →

Data-driven physics-based modeling of pedestrian dynamics

Python package to create physics-based pedestrian models from crowd measurements Github: <a href="https://github.com/c-pouw/physics-based-pedestrian-modeling">https://github.com/c-pouw/physics-based-pedestrian-modeling</a>

SDUST2024MSS_AO: a mean sea surface model of the Arctic Ocean based on CryoSat-2 SAR altimeter data

This model is a mean sea surface model for ice-covered regions, using CryoSat-2 satellite SAR mode altimeter data from July 2010 to December 2023. The heights are referenced to the WGS-84 ellipsoid, and the grid size is 5 km × 5 km.