Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Data presented in González-Flórez et al. 2023 "Insights into the size-resolved dust emission from field measurements in the Moroccan Sahara", Atmos. Chem. Phys.

Meteorological, dust and saltation data used in González-Flórez et al., 2023. Data are based on measurements taken during an intensive dust field campaign conducted in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. The campaign took place in September 2019 in a small ephemeral lake, locally named "L'Bour", located in the Lower Drâa Valley in Morocco. The description of the data is provided below: - t.nc: time series of temperature measured with four aspirated shield temperature sensors (Campbell Scientific 43502 fan-aspirated shield with 43347 RTD Temperature probe) placed at heights of 1m, 2m, 4m and 8m. - t005.nc time series of temperature measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height. - rh005.nc: time series relative humidity measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height. - wspd.nc: time series of wind speed measured with five 2-D sonic anemometers  (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m. - sdir.nc: time series of wind direction measured with five 2-D sonic anemometers  (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m. - radout.nc: time series of outgoing long wave radiation measured with a four-component net radiometer  (Campbell Scientific NR01-L radiometer) placed at 1.5m height. - p015.nc: times series barometric pressure measured with a barometer (Campbell Scientific CS106) at around 1.5m height. - u_star_law.nc: time series friction velocity calculated through the law of the wall method. - z0_law.nc: time series of roughness length calculated through the law of the wall method. - zeta_law.nc: time series of dimensionless height, zref/L, where zref is the reference height (zref=2m) and L is the Obukhov length  calculated through the law of the wall method. - psd_lower_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~1.8m height. - psd_upper_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~3.5m height and corrected for systematic bias based on an intercomparison between the two Fidas at the end of the campaign. - diff_flux_nb_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number diffusive flux calculated using the flux-gradient method. - q_15avg.nc: time series of 15-min average saltation flux calculated based on measurements with optical gate devices at heights of 0.05m, 0.15m and 0.3m as part of the Standalone AeoliaN Transport Real-time Instrument (SANTRI, Desert Research Institute). - geometric_diameters_integrated_size_bins.csv: containing the minimum, maximum and mean logarithmic optical diameter of the integrated size bins. - optical_diameters_integrated_size_bins: containing the minimum, maximum and mean logarithmic geometric diameter of the integrated size bins. SANTRI data were processed by Martina Klose (<a href="mailto:martina.klose@kit.edu">martina.klose@kit.edu</a>) and the rest by Cristina González Flórez (<a href="mailto:cristina.gonzalez@bsc.es">cristina.gonzalez@bsc.es</a>). Please, cite González-Flórez et al. (2023, ACP) if you use these data. If the data become the key main component of a paper then co-authorship may be offered. Contact Carlos Pérez García-Pando (<a href="mailto:carlos.perez@bsc.es">carlos.perez@bsc.es</a>) if more details are needed. This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 773051, FRAGMENT).

opencc-by-4.0May 2023View details →

First time-resolved measurement of infrared scintillation light in gaseous xenon

Repository with supplemental data to: First time-resolved measurement of infrared scintillation light in gaseous xenon. Piotter, M., Cichon, D., Hammann, R., Jörg, F., Hötzsch, L., Marrodán Undagoitia, T. Eur. Phys. J. C 83, 482 (2023). A pre-print of the article is available on arXiv: <a href="https://arxiv.org/abs/2303.09344">2303.09344</a> Note: When re-using the data, please make sure to cite the article (and not only the dataset)   The files contain all data related to the observed IR scintillation in gaseous xenon presented in the paper. This comprises the IR time profiles obtained via single photon counting and the measured pressure dependence of the IR light yield for the three extrapolation methods: <ul> <li>waveform_before.csv, waveform_during.csv, waveform_after.csv: These files contain the IR time profiles before, during, and after the purification of the gas (presented in figure 8 in the publication). The column dt is given in nanoseconds relative to the UV pulse and counts corresponds to counts per nanosecond per 100 UV events.</li> <li>light_yield_ir.csv: This file contains the IR light yield as a function of pressure obtained with the three extrapolation models together with the respective statistical and systematic uncertainties. The data is presented in figure 9 in the publication and all values are given in units of photons per MeV.</li> <li>waveform_495.csv, waveform_742.csv, waveform_1047.csv: These files contain the IR time profiles for xenon gas pressures of 495.0 mbar, 742.5 mbar, and 1047.0 mbar, respectively (presented in figure 10 in the publication). The column dt is given in nanoseconds relative to the UV pulse and counts corresponds to counts per nanosecond per 100 UV events.</li> </ul>   Code examples for plotting the data: The following Python code reproduces figure 9 in the publication: <pre><code class="language-python">import pandas as pd import matplotlib.pyplot as plt if __name__ == '__main__': df = pd.read_csv("light_yield_ir.csv") color_pairs = [("#365898", "#B7D0FF"), ("#AB123B", "#F0B5C5"), ("#E1992E", "#F1DAB9")] fig, ax = plt.subplots(1, figsize=(4, 3)) for fit_func_str, cs in zip(["Recombination model fit", "Exponential fit", "Linear fit"], color_pairs): # Plot systematic error ax.errorbar(df["Pressure"], df[f"IR Light Yield ({fit_func_str} fit)"], yerr=df[f"Syst. uncertainty ({fit_func_str} fit)"], ls="", elinewidth=3, capsize=0, ecolor=cs[1] ) # Plot estimator with statistical error ax.errorbar(df["Pressure"], df[f"IR Light Yield ({fit_func_str} fit)"], yerr=df[f"Stat. uncertainty ({fit_func_str} fit)"], ls="", c=cs[0], ecolor=cs[0], elinewidth=1, capsize=1, marker=".", label=fit_func_str) # Cosmetics ax.set_xlabel("Pressure [mbar]") ax.set_ylabel("IR light yield [ph / MeV]") ax.set_ylim(1200, 12_500) ax.legend(frameon=False, loc="upper left") plt.show()</code></pre>   The IR time response of figure 8 can be redrawn as follows: <pre><code class="language-python">import pandas as pd import matplotlib.pyplot as plt if __name__ == '__main__': fig, ax = plt.subplots(1, figsize=(4, 3)) for label in ["before", "during", "after"]: df = pd.read_csv(f"waveform_{label}.csv") ax.step(df["dt"], df["counts"], label=label) # Cosmetics ax.set_xlabel("$\Delta t$ between IR and UV signal [ns]") ax.set_ylabel("Counts per 1 ns per 100 UV events") ax.legend(frameon=False, loc="upper right") plt.show()</code></pre>

opencc-by-4.0Jun 2023View details →

Out-of-equilibrium charge redistribution data in a copper-oxide based superconductor by time-resolved X-ray photoelectron spectroscopy

This dataset was measured using a momentum microscope by time-resolved X-ray photoelectron spectroscopy (XPS) on the prototypical high-temperature superconductor: optimally doped BSCCO at FEL FLASH, DESY in Hamburg. With time-resolved XPS, unique access to the dynamics of individual atoms in the unit cell is granted by means of chemical shifts of the core levels. Though the induced changes are small, with a rigorous fitting procedure, it is possible to extract significant changes observed mainly at the oxygen atoms in the copper oxide planes, while other oxygen atoms as well as strontium remain largely unaffected. Although it was acquired not in the superconducting phase, the observed dynamics point to a significant coupling of energy scales involving charge-transfer processes and optical excitations. Such findings can thus provide another puzzle piece for a better understanding of high-temperature superconductivity.

opencc-by-4.0Jul 2023View details →

Tables for: The Panchromatic Hubble Andromeda Treasury XXI. The Legacy Resolved Stellar Photometry Catalog

This deposit contains the full machine-readable tables for the accepted version of the manuscript, "The Panchromatic Hubble Andromeda Treasury XXI. The Legacy Resolved Stellar Photometry Catalog", submitted and accepted to the Astrophysical Journal Supplments.  The specific files included in this deposit are the full version of Tables 1-3 in this manuscript: <ul> <li>Table 1: Simplified table of PHAT photometry for easy use;</li> <li>Table 2: Simplified table of artificial star test results for easy use;</li> <li>Table 3: Summary of artificial star statistics as a function of brightness and stellar density.</li> </ul> The *.txt files are formatted according to the machine-readable standards adopted by the AAS Journals and CDS/Vizier. Documentation of this format can be found at these links: <ul> <li><a href="https://journals.aas.org/mrt-overview/">AAS Journals MRT overview</a></li> <li><a href="http://vizier.u-strasbg.fr/doc/catstd.htx">CDS/Vizier standards</a></li> </ul> These files can be read in python using the astropy package or with the most recent version of <a href="https://www.star.bris.ac.uk/~mbt/topcat/">TOPCAT</a> (> Version 4.8). An example script for reading these files in astropy is given here: <pre><code class="language-python"> from astropy.table import Table data = Table.read("datafile3.txt", format="ascii.cds") </code></pre> In addition, a headerless, compressed TeX (&-delimited; "full_table.tex.xz") version of Table 1, and a header-only, dataless version of Table 1 ("datafile1_headeronly.txt") are provided to give users additional tools for dealing with this large dataset.  The compression routine was applied using xz <https://tukaani.org/xz/> with the encodings <ul> <li> <code>xz -z -7 -T 0 full_table.tex</code> </li> <li> <code>xz -z -9 -T 0 datafile1.txt</code> </li> </ul> The Table 1 files on Zenodo were then split into smaller chunks to upload them to Zenodo using the GNU <a href="https://www.gnu.org/software/coreutils/manual/html_node/split-invocation.html"><code>split</code></a> routine. The full files can be recovered by recombining them before decompressing them. The list of related commands and the expected md5 hexidecimal checksums are given here: <ul> <li> <code>split --bytes=512M ../full_table.tex.xz full_table.tex.xz.</code> </li> <li> <code>split --bytes=512M ../datafile1.txt.xz datafile1.txt.xz.</code> </li> <li> <code>cat full_table.tex.xz.a* > full_table.tex.xz</code> </li> <li> <code>cat datafile1.txt.xz.a* > datafile1.txt.xz</code> </li> <li> <code>MD5 (full_table.tex.xz) = 7f9195210bec61c08d04926ada4a021a</code> </li> <li> <code>MD5 (datafile1.txt.xz) = 63205fe1051e97f4e04380dd23469893</code> </li> </ul> Finally, those interested in generating their own cuts from the DOLPHOT quality parameters can access the full photometry tables, available at MAST as a High Level Science Product via <a href="https://doi.org/10.17909/T91S30">doi.org/10.17909/T91S30</a>

opencc-by-4.0May 2023View details →

Resolving the Interpretation of Magnetic Coercivity Components from Backfield Isothermal Remanence Curves Using Unmixing of Non-linear Preisach Maps: Application to Loess-Paleosol Sequences

The data set includes: 1. Non-linear Preisach measurements for Lunca and Costinești loess-paleosol sections 2. IRM coercivity distributions from Lunca and Costinesti interpolated on a common sequence of fields 3. Lunca granulometry data 4. Median Grain size for Costineși section. 5. Magnetic susceptibility data measured at Lunca section (Constantin et al., 2015) 6. IRM acquisition curves derived from backfield IRM data through rescaling for Costinesti section 7. Costinesti rock magnetic data (Necula et al., 2015)

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Abundance and biovolume of taxonomically-resolved phytoplankton and microzooplankton imaged continuously underway with an Imaging FlowCytobot along the NES-LTER Transect in winter 2018

These data represent the abundance and biovolume of taxonomically-resolved phytoplankton and microzooplankton imaged continuously underway along the NES-LTER Transect during cruise EN608 in winter 2018. Images were obtained with an Imaging FlowCytobot (IFCB) sampling at approximately 20-min intervals from seawater supplied from 5 meters water depth. Data are provided for the subset of images during the cruise that were on the north-south transect along longitude 70 53’ W. Sizes for individuals were determined automatically by image processing, while identifications to morphological categories were done manually. Data are provided by taxon with names and machine-readable identifiers matched to the lowest taxonomic level to the World Register of Marine Species. Two data tables are provided: the level 1b for each occurrence and the level 2 that summarizes the occurrences by taxon per sample. Individual and sample identifiers are linked to images served by an external repository. This data package provides 144,281 machine-readable occurrences for incorporation into the Ocean Biogeographic Information System and the Global Ocean Observing System Essential Ocean Variables Phytoplankton biomass and diversity and the microplankton size class of Zooplankton biomass and diversity.

openCC (other)May 2020View details →

opencc-by-4.0Feb 2020View details →

Fig. 1 in Resolving the Ophioderma longicauda (Echinodermata: Ophiuroidea) cryptic species complex: five sisters, three of them new

Fig. 1. Phylogenetic relationships and distribution of species of Ophioderma Müller & Troschel, 1840 in the Northeast and Tropical Atlantic Ocean and the Mediterranean Sea. A. Simplified phylogenetic relationships among species of Ophioderma described in this study, modified from Weber et al. (2019). Summary of reproductive strategy: S = broadcast spawner; B = brooder. Correspondence between species names, nuclear genetic clusters and COI mitochondrial lineages is summarized from Boissin et al. (2011) and Weber et al. (2019). C6 is tentatively assigned to O. zibrowii sp. nov. because it occurs far from the type locality and brooding species are expected to have low dispersal rates, leading to low gene flow between populations. B. Distribution of species of Ophioderma in the Northeast and Tropical Atlantic Ocean. C. Distribution of species of Ophioderma in the Mediterranean Sea.

Fig. 4 in The Leiobunum rupestre species group: resolving the taxonomy of four widespread European taxa (Opiliones: Sclerosomatidae)

Fig. 4. Pedipalps of the Leiobunum rupestre group, whole pedipalps in lateral view, single femora in medial view. — A–F. ♁♁. A–B. Leiobunum rupestre Herbst, 1799, Germany, Baden-Württemberg, CJM1954. A. Pedipalpus lateral. B. Femur medial. C–D. L. gracile Thorell, 1876, Denmark, Asp, CJM3531. C. Pedipalpus lateral. D. Femur medial. — E–F. Leiobunum apenninicum (Martens, 1969), France, Alpes- Maritimes, CJM2747. E. Pedipalpus lateral. F. Femur medial. — G–M. ♀♀. G–H. Leiobunum rupestre Herbst, 1799, Germany, Mt. Arber, CJM136. G. Pedipalpus lateral. H. Femur medial. J–K. L. gracile Thorell, 1876, Denmark, Asp, CJM3531. J. Pedipalpus lateral. K. Femur medial. L–M. L. apenninicum (Martens, 1969), France, Alpes-Maritimes, CJM2747. L. Pedipalpus lateral. M. Femur medial. Arrows indicate characters mentioned in the descriptions.

Fig. 1 in The Leiobunum rupestre species group: resolving the taxonomy of four widespread European taxa (Opiliones: Sclerosomatidae)

Fig. 1. Habit of Leiobunum rupestre species group. A–B. Leiobunum rupestre Herbst, 1799, Slovenia, Pohorje Mountains, resting at rock faces. A. ♁. B. ♀. C–D. Leiobunum apenninicum (Martens, 1969), Italy, Monesi di Triora, at night. C. ♁. D. ♀. E–F. Leiobunum gracile Thorell, 1876, Denmark. E. ♁. F. ♀. Photographs: A–D by A.L.Schönhofer; E–F by S. Toft, all taken in the field.

Fig. 3 in The Leiobunum rupestre species group: resolving the taxonomy of four widespread European taxa (Opiliones: Sclerosomatidae)

Fig. 3. Body of Leiobunum C.L. Koch, 1839, dorsal view. A–B. Leiobunum rupestre Herbst, 1799, Germany, Mt. Arber, CJM136. A. ♁. B. ♀. — C–D. Leiobunum gracile Thorell, 1876, Denmark. C. ♁, 2 km N of Skaerbaek, CJM3530. D. ♀, Asp, CJM3531. — E–F. Leiobunum apenninicum (Martens, 1969), France, Alpes-Maritimes. E. ♁, CJM1508. F. ♀, CJM2747. Drawings by K. Rehbinder.

Fig. 5. Leiobunum, male genitalia. A–C. L. rupestre Herbst, 1799 in The Leiobunum rupestre species group: resolving the taxonomy of four widespread European taxa (Opiliones: Sclerosomatidae)

Fig. 5. Leiobunum, male genitalia. A–C. L. rupestre Herbst, 1799, Germany, Baden-Württemberg, CJM1954. A. Ventral view. B. Lateral view. C. Cross-section. — D–F. L. gracile Thorell, 1876, Denmark, 2 km N of Skaerbaek, CJM3530. D. Ventral view. E. Lateral view. F. Cross-section. — G–J. L. apenninicum (Martens, 1969), France, Alpes-Maritimes, CJM1508. G. Ventral view. H. Lateral view. J. Cross-section. Arrows indicate the area of the respective cross-sections.

opencc-by-4.0Oct 2019View details →

Dataset for "Atmospheric oxygen isotopic fractionation in clouds: a bin–resolved microphysics model approach"

This dataset contains the raw model outputs for the paper titled "Atmospheric oxygen isotopic fractionation in clouds: a bin–resolved microphysics model approach" by Thibault Hiron and Andrea Flossmann. The structure of the data and the explaination for the filenames are to be found in the ReadMe.txt file.

opencc-by-4.0Aug 2020View details →

Connexin-46/50 in a dynamic lipid environment resolved by CryoEM at 1.9 Å

These are the molecular dynamics (MD) data that are analyzed, in Flores et al. 2020. Each trajectory file (.dcd) has an associated structure file (.psf), which can be analyzed in VMD. Each gap junction system, Cx46 & Cx50, were simulated with either KCl or NaCl in the intracellular space: Cx46_KCl/NaCl, Cx50_KCl/NaCl. All four systems were equilibrated for 30ns and then followed up with 2 separate 100ns production runs. For MD lipid-densities the Cx50_KCl system was used as the representative dataset. Lipid densities from all other systems can be calculated with the provided TCL script calc-density.tcl.  In VMD TK-Console: > mol new <system>.psf > mol addfile <system>.dcd waitfor all > source /path/to/scripts/LipNetwork.tcl > align > source /path/to/scripts/calc-density.tcl > dmpcdensity <outname> [output] outname_ltailden.dx Convert from .dx to .mrc using UCSF-Chimera Volume_Viewer plugin. [output] outname_ltailden.mrc Using Relion: $ relion_image_handler --i outname_ltailden.mrc --o outname_ltailden_D6-Sym.mrc --sym D6 [output] outname_ltailden_D6-Sym.mrc   All trajectory files (.dcd) are 100ns longs (1,000 frames x 100ps/frame). For questions regarding MD-data analysis and full trajectory files (2ps/frame), please contact Dr. Steve Reichow (reichow@pdx.edu).

opencc-by-4.0Sep 2020View details →

DATA of "Resolving the 2D temporal evolution of subglacial water flow with dense seismic array observations."

The data set contains all data presented in the paper: Observing the subglacial hydrology network and its dynamics with a dense seismic array published in PNAS ( <a href="https://doi.org/10.1073/pnas.2023757118">https://doi.org/10.1073/pnas.2023757118</a> ) See our online presentation of this dataset: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-10710.html. The present data and code concerns the source location obtained with matched-field-processing analysis and the hydraulic potential calculation (Shreve, R. L. Movement of Water in Glaciers. J. Glaciol. 11, 205–214 (1972)). We perform source location over 1-sec long signal segment of the vertical component only. We filter the signal within the [3-7] Hz frequency range and coherently apply the MFP each 0.1 Hz within this range. To maximize our algorithm efficiency and minimize computational costs we use a gradient-based minimization algorithm (Nelder-Mead optimization) to converge to the best match between the trial and the observed phase delays rather than an exhaustive grid-search exploration. The convergence criterion is reached when the variance of values obtained over the last 5 iterations of the optimization is smaller than 1e-2 with a maximum of 3000 iterations. Our 29 different starting points used for optimization are located 250 m below the glacier surface and they uniformly cover an area of 800 x 800 m2 centered on the array. We set the initial velocity to 1800 m.sec -1. The 29 punctual locations found per signal segment (1 sec) after convergence are located all in the same place if a clear global convergence exists (i.e. high MFP output) or at up to 29 different locations if up to 29 local minima exist (i.e. low MFP output). Timeseries of physical quantities can be found here <a href="https://doi.org/10.5281/zenodo.3701520">https://doi.org/10.5281/zenodo.3701520</a> Spatial observations acquired during the same period can be found here <a href="https://doi.org/10.5281/zenodo.3971815">https://doi.org/10.5281/zenodo.3971815</a>   The RESOLVE project has been supported by a grant from LabEx OSUG@2020 (Investissement d’avenir – ANR10LABX56) and by the IDEX Université Grenoble Alpes. Most  of the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad.univ-grenoble-alpes.fr), which is supported by Grenoble research communities, and with the CiGri tool (https://github.com/oar-team/cigri) developed by Gricad, Grid5000 (https://www.grid5000.fr) and LIG (<a href="https://www.liglab.fr/">https://www.liglab.fr/</a>).   You can find more information on the method and seismic dataset used in this paper here: <a href="https://zenodo.org/deposit/5645545">https://zenodo.org/deposit/5645545</a>

opencc-by-4.0Oct 2020View details →

Time- and angle-resolved photoemission spectroscopy data and time-resolved X-ray diffraction data of TbTe3

Time- and angle-resolved photoemission spectroscopy data of bulk terbium tritelluride (TbTe3, unidirectional charge-density-wave phase, T=100K) using a laser-based femtosecond XUV source and a hemispherical analyzer for photoelectron detection at the Fritz-Haber-Institute, Berlin, Germany. The 3D (angle, energy, pump-probe-delay) datasets include the photoemission intensities for various pump-laser fluences. The time-resolved X-ray diffraction data were obtained at the Femto hard X-ray slicing source at the Swiss Light Source, and include the charge-density-wave superlattice (2 10 1+q_CDW) peak intensities as functions of pump-probe-delay for various pump-laser fluences. The data and associated metadata are stored in the NeXus data format (https://www.nexusformat.org/).

opencc-by-4.0Sep 2020View details →

Picosecond time-resolved antibunching measures nanoscale exciton motion, annihilation, and true number of chromophores

The particle-like nature of light becomes evident in the photon statistics of fluorescence of single quantum systems as photon antibunching. In multichromophoric systems, exciton diffusion and subsequent annihilation occur. These processes also yield photon antibunching but cannot be interpreted reliably. Here, we develop picosecond time-resolved antibunching (psTRAB) to identify and decode such processes. We use psTRAB to measure the true number of chromophores on well-defined multichromophoric DNA-origami structures, and precisely determine the distance-dependent rates of annihilation between excitons. Further, psTRAB allows us to measure exciton diffusion in mesoscopic H- and J-type conjugated-polymer aggregates. We distinguish between one-dimensional intra-chain and three-dimensional inter-chain exciton diffusion at different times after excitation and determine the disorder-dependent diffusion lengths. Our method provides a new lens through which excitons can be studied at the single-particle level, enabling the rational design of improved excitonic probes such as ultra-bright fluorescent nanoparticles, and materials for optoelectronic devices. Here we demonstrate the raw data of DNA Origami Microscopy on which our findings based on.

opencc-by-4.0Jan 2021View details →

Southeast US rainfall index for GRL paper "The Influence of a Resolved Gulf Stream on the Decadal Variability of Southeast US Rainfall"

The data include Southeast US rainfall index based on GPCC monthly precipitation and 30 CMIP5 model historical simulations (first realization). Each rainfall dataset, observed or model simulated, has been linearly detrended and applied with a 5-year low-pass filter. The Southeast US rainfall index is calculated as the area averaged values of 5-year low-pass filtered rainfall over land regions bounded by 25°-38°N and 266°-284°E. The unit here is mm/day.

opencc-zeroDec 2016View details →

FIGURES 81 – 86 in Different continents, same species? Resolving the taxonomy of some Holarctic Ancylis Hübner (Lepidoptera: Tortricidae)

FIGURES 81 – 86. Female genitalia (arrows denote origination of ductus seminalis). 81, A. geminana (Germany, TMG 675). 82, A. christiandiana (Austria, TOR 478 P. Huemer). 83, A. diminutana (Germany, TMG 617). 84, A. diminuatana (Ohio, TMG 412). 85, A. saliana (Florida, TMG 629). 86, A. subarcuana (Germany, TMG 620).

opencc-zeroDec 2016View details →

FIGURE 1 in Different continents, same species? Resolving the taxonomy of some Holarctic Ancylis Hübner (Lepidoptera: Tortricidae)

FIGURE 1. Neighbor-joining tree of COI DNA barcode data obtained from BOLD (K 2 P model). Clusters representing morphologically distinct species are color-coded with the corresponding species name.