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Figure 2 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175

Figure 2 Phylogenetic placement of Trebouxia photobionts from selected Parmelia spp., based on Bayesian analysis of the ITS rDNA dataset. Posterior probabilities and maximum likelihood bootstrap values are shown near the internal branches. Newly generated sequences are in bold, with collecting numbers preceding the species names. Representative Trebouxia OTUs, as described in Leavitt et al. (2015), were downloaded from Dryad database (Dryad Digital Repository, Leavitt et al. 2015). Clades with photobionts from Parmelia discordans, P. omphalodes and P. pinnatifida are highlighted.

Figure 5 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175

Figure 5 AParmelia discordans, with marginal and laminal pseudocyphellae, laminal pseudocyphellae mostly not connected with marginal ones (S F-252494) BP. omphalodes, with marginal and laminal pseudocyphellae, laminal pseudocyphellae mostly not connected with marginal ones (S F-252845) CP. pinnatifida, with marginal pseudocyphellae (UGDA L-24298) DP. pinnatifida, with marginal and laminal pseudocyphellae, laminal pseudocyphellae starting predominantly from pseudocyphellae formed at the edge of lobes (S F-239397). Scale bars: 200 μm (A, B, D), 150 μm (C).

Figure 7 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175

Figure 7 Distribution of suitable niches of P. discordans (A), P. omphalodes (B) and P. pinnatifida (C) in the Northern Hemisphere.

Figure 4 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175

Figure 4 Localities of Parmelia discordans (red), P. omphalodes (blue) and P. pinnatifida (green) used in ENM analysis.

Figure 10 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175

Figure 10 Principal components analysis (PCA) of P. discordans (red), P. omphalodes (blue) and P. pinnatifida (green), based on the bioclimatic factors from individuals.

Figure 1 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175

Figure 1 Phylogenetic relationships of Parmelia discordans, P. omphalodes and P. pinnatifida, based on Bayesian analysis of the ITS rDNA dataset. Posterior probabilities and maximum likelihood bootstrap values are shown near the internal branches. Newly generated sequences are described with herbarium numbers following the species names. GenBank Accession numbers of sequences downloaded from GenBank follow the species names. Clades with Parmelia discordans, P. omphalodes and P. pinnatifida are highlighted.

opencc-zeroDec 2019View details →

Supplementary material 4 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175

: Data type: multimedia

Figure 9 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175

Figure 9 Distribution of suitable niches of P. discordans (A), P. omphalodes (B) and P. pinnatifida (C) in Eurasia.

opencc-by-4.0Jan 2020View details →

Water isotope data for "Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction"

iCESM1.2 simulated seawater oxygen isotopes for the Early Eocene Citation: Zhu, J., Poulsen, C. J., Otto-Bliesner, B. L., Liu, Z., Brady, E. C., & Noone, D. C. (2020). Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction. Earth and Planetary Science Letters, 537, 116164. <a href="https://doi.org/10.1016/j.epsl.2020.116164">https://doi.org/10.1016/j.epsl.2020.116164</a> <ul> <li>Data set includes climatology (12 months) sea-surface temperature (TEMP) and sea-surface oxygen isotope ratio (R18O) from four Eocene simulations with 1×, 3×, 6×, and 9× preindustrial level of CO2 (284.7 ppmv), and a preindustrial simulation.</li> <li>Climatology was calculated from averaging data over the last 100 years of each simulation.</li> <li>Seawater d18O = (R18O - 1.0) * 1000.0</li> <li>TEMP and R18O are on the POP ocean grid (~1°; see here: <a href="http://www.cesm.ucar.edu/models/cesm1.2/pop2/">http://www.cesm.ucar.edu/models/cesm1.2/pop2/</a>).</li> </ul>

Data and model scripts for "Non-structural carbohydrate dynamics associated with antecedent stem water potential and air temperature in a dominant desert shrub"

Model code and data as used in the first revision submitted to Plant, Cell and Environment, Feb. 2020.  Models are coded in JAGS or OpenBUGS and run in R. Three related models are presented: 1) "mod_allometry.R" and "jags_allometry.R" run the aboveground biomass allometry model described in Methods S1, utilizing stem and leaf mass data ("data_allometry.Rdata") and the associated initial values ("inits_allometry.Rdata") 2) "mod_predawn.R" and "bugs_predawn.R" run the gap-filling model described in Methods S2, utilizing predawn water potential data ("data_predawn.Rdata") and the associated initial values ("inits_predawn.Rdata") 3) "mod_NSC.R" and "bugs_NSC.R" run the NSC model described in the main text of the manuscript, utilizing NSC and covariate data ("data_NSC.Rdata") and the associated initial values ("inits_NSC.Rdata")

opencc-by-4.0Nov 2019View details →

Models and Predictions for "The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction"

Models and Predictions This dataset contains the trained XGBoost and EA-LSTM models and the models' predictions for the paper <a href="https://github.com/gauchm/ealstm_regional_modeling">The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction</a>. For each input sequence length (10, 30, 100, 270*, 365*) and each combination of model (XGBoost, EA-LSTM), training years (3, 6, 9), number of basins (13, 26, 53, 265, 531), and seed (111-888), there are five folders. Each corresponds to a random basin sample (for 531 basins there's only one folder, since it's all basins). In each folder, there are three files: <ul> <li>\(\texttt{model.pkl}\) (XGBoost) or \(\texttt{model_epoch30.pt}\) (EA-LSTM), which stores the pickled trained model</li> <li>\(\texttt{xgboost_seedNNN.p}\) or \(\texttt{ealstm_seedNNN.p}\), which stores a pickled dictionary that maps each basin to the DataFrame of predicted and actual daily streamflow.</li> <li>\(\texttt{attributes.db}\), which stores static catchment attributes needed for inference.</li> </ul> In addition to each folder, there is a SLURM submission script called \(\texttt{<foldername>.sbatch}\) that was used to create and evaluate the model in the folder.   * sequence lengths 270 and 365 only contain data for EA-LSTM.

Data for exploring topography-based methods for downscaling subgrid precipitation for use in Earth System Models

Topography exerts major control on land surface processes. To improve representation of topographic impacts on land surface processes, a new topography-based subgrid structure has been introduced to the Energy Exascale Earth System Model representing the subgrid heterogeneity of surface elevation. Four topography-based methods of downscaling grid precipitation to the subgrids have been explored. The data utilized for the study include precipitation, surface elevation, and height rise data derived from wind speed and Brunt Vaisala parameter and outputs of downscaled precipitation and statistical metrics calculated in this study. Results show that utilizing hypsometric elevation of the subgrid landscape within the model grid cell improves downscaling of precipitation in mountainous areas. Furthermore, accounting for blocking of airflow further improves precipitation downscaling slightly in mountainous regions consistently across multiple grid sizes. The data files include: <ol> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/daily_prism_precip.zip?versionId=be97ca8d-182a-4f1e-9ae3-9da3f2b87e24">daily_prism_precip.zip</a>: high resolution precipitation data (4 km) obtained from PRISM [Daly et al. 1994, Daly et al. 2008].</li> <li> <a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/dem_4km4.nc">dem_4km4.nc</a>: 4 km surface elevation data derived from high resolution surface elevation data (90 m) obtained from HydroSHEDS [Lehner et al. 2008, Lehner and Grill 2013]</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">fr_number.zip</a>: Height rise of airflow calculated from wind speed and Brunt Vaisala parameter derived from the North American Regional Reanalysis data.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_128km.zip?versionId=5f64ec8c-4018-4d97-ae1d-eb6f15ccc564">output_from_dwnscaling_methods_at_128km.zip</a>: Output data of the downscaling methods at 128 km spatial resolution.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_96km.zip?versionId=b2c67f80-9794-41cb-9986-a4c7259ccf1c">output_from_dwnscaling_methods_at_96km.zip</a>: Output data of the downscaling methods at 96 km spatial resolution. </li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_64km.zip?versionId=b83230a0-308e-4f90-971b-6636a5add796">output_from_dwnscaling_methods_at_64km.zip</a>: Output data of the downscaling methods at 64 km spatial resolution.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_32km.zip?versionId=cc9021cc-c3b4-4c04-a558-752c151c49ba">output_from_dwnscaling_methods_at_32km.zip</a>: Output data of the downscaling methods at 32 km spatial resolution. </li> <li>ppt_spatial_downscaling_daily_data_flatten_withFr_test_filt0_v3rev_64.py: Python code used to calculate downscaled precipitation data from aggregated grid precipitation data.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/stns_precip_2015.csv">stns_precip_2015.csv</a>: Precipitation data at rain gauge stations in  the Conterminous US extracted from the Daymet station-level input datasets are used for evaluation of the downscaled results </li> </ol> Other datasets used to calculate wind speed and Brunt Vaisala parameter were extracted from the North American Regional Reanalysis (NARR) including wind speed, temperature, surface pressure, specific humidity and relative humidity [Mesinger et al. 2006].   References: Daly, C., et al. (1994). "A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain." Journal of Applied Meteorology 33(2): 140-158.  Daly, C., et al. (2008). "Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States." International Journal of Climatology 28(15): 2031-2064. Lehner, B., et al. (2008). "New Global Hydrography Derived From Spaceborne Elevation Data." Eos, Transactions American Geophysical Union 89(10): 93-94. Lehner, B. and G. Grill (2013). "Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems." Hydrological Processes 27(15): 2171-2186. Mesinger, F., et al. (2006). "NORTH AMERICAN REGIONAL REANALYSIS." Bulletin of the American Meteorological Society 87(3): 343-360.

Synthetic Data Set for Uplift Modeling (One Trial)

This dataset is designed and simulated for evaluating uplift modeling and feature selection methods. This dataset contains 10,000 samples and 36 features (one trial). The samples are equally split for control and treatment group. The generated data has three types of features: (1) uplift features influencing the treatment effect on the conversion probability; (2) classification features affecting the conversion probability but independent of the treatment effect; and (3) irrelevant features that are independent of both conversion probability and the treatment effect. To model the relationship between uplift features and the treatment effect and classification features and outcome probability, we implement six types of association patterns in the data generation process: linear, quadratic, cubic, ReLU (Rectified Linear Unit), trigonometric function sine, and cosine. In this data set, there are 36 features in total, including 10 classification features, 6 uplift features, and 20 irrelevant features. Column names: <ul> <li>Experiment group label: 'treatment_group_key'</li> <li>Feature names: ['x1_informative', 'x2_informative', 'x3_informative', 'x4_informative', 'x5_informative', 'x6_informative', 'x7_informative', 'x8_informative', 'x9_informative', 'x10_informative', 'x11_irrelevant', 'x12_irrelevant', 'x13_irrelevant', 'x14_irrelevant', 'x15_irrelevant', 'x16_irrelevant', 'x17_irrelevant', 'x18_irrelevant', 'x19_irrelevant', 'x20_irrelevant', 'x21_irrelevant', 'x22_irrelevant', 'x23_irrelevant', 'x24_irrelevant', 'x25_irrelevant', 'x26_irrelevant', 'x27_irrelevant', 'x28_irrelevant', 'x29_irrelevant', 'x30_irrelevant', 'x31_uplift_increase', 'x32_uplift_increase', 'x33_uplift_increase', 'x34_uplift_increase', 'x35_uplift_increase', 'x36_uplift_increase']</li> <li>Outcome variable:  'conversion'</li> <li>True underlying control conversion probability: 'control_conversion_prob'</li> <li>True underlying treatment conversion probability: 'treatment1_conversion_prob'</li> <li>True treatment effect:  'treatment1_true_effect'</li> <li>Note columns names with '_transformed' suffix are feature variables used in the intermediate steps during the data generation, that should be excluded for model training.</li> </ul>

opencc-byNov 2019View details →

Supplementary Data for Publication Titled "Sex differences in regulating the cardiac transcriptome within a murine model for hypertrophic cardiomyopathy"

Supplementary data for publication titled "Sex differences in regulating the cardiac transcriptome within a murine model for hypertrophic cardiomyopathy".

Model simulation data used in "Coupling aerosols to (cirrus) clouds in the global aerosol-climate model EMAC-MADE3" (Righi et al., Geosci. Model Dev., 2020)

This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (Geosci. Model Dev., 2020). An overview of the numerical experiments performed for this study is given in the file "experiments.dat".

opencc-by-4.0Mar 2020View details →

Data and code for "Predicting evaporation in stream temperature models – Penman, Dalton or something else?"

The uploaded files contain the data set and code used in an empirical evaluation of the application of the Penman equation for predicting evaporation from streams. <ul> <li>Fishtrap_for_stream_evap_analysis.csv - data set used in the analysis</li> <li>streamEvapAnalysis_final.r - code used to analyse the data</li> </ul>

opencc-by-4.0Mar 2020View details →

Synthetic model parameters for the comparison between an equivalent source and a minimum curvature interpolator of aeromagnetic data

Parameters of the synthetic case used in Gavazzi et al. (submitted)[1] FILES (ASCII FORMAT) param.txt General parameters of the geomagnetic field Incl. inclination of the geomagnetic field decl. declination of the geomagnetic field sources.txt Localization and magnetization of the simulated sources xs x-position of the sources (in m) ys y-position of the sources (in m) zs z-position of the sources (in m) Ms magnetization of the sources (in A/m) profiles.txt Localization of the simulated acquisition profiles xx x-position of the data yy y-position of the data zz z-position of the data eqsources.txt Localization and magnetization of the equivalent sources xdeq x-position of the sources (in m) ydeq y-position of the sources (in m) zdeq z-position of the sources (in m) JJ magnetization of the sources (in A/m) [1] Gavazzi, B., Bertrand, L., Munschy, M., Mercier de Lépinay, J., Diraison, M. & Géraud, Y. (submitted). On the use of aeromagnetism for geological interpretation part I: comparison of scalar and vector magnetometers for aeromagnetic surveys and an equivalent source interpolator for combining, gridding and transform fixed altitude and draping datasets, Journal of Geophysical Research: Solid Earth.

opencc-by-4.0Mar 2020View details →

Data from MECO(n) model simulations on "Urban greenhouse gas emissions from the Berlin area: A case study using airborne CO2 and CH4 in situ observations in summer 2018"

This tar-files contain the results of the MECO(n) model, which are published in T. Klausner, M. Mertens, H. Huntrieser, M. Galkowski, G. Kuhlmann, R. Baumann, A. Fiehn, P. Jöckel, M. Pühl, and A. Roiger: Urban greenhouse gas emissions from the Berlin area: A case study using airborne CO2 and CH4 in situ observations in summer 2018, Elementa: Science of the Anthropocene (Ref.: Ms. No. ELEMENTA-D-19-00074R1), 2019.

opencc-by-4.0Apr 2020View details →

A model of the subpacket structure of rising tone chorus emissions - wave data

This file contains the wave data obtained from simulation and used in the paper "A model of the subpacket structure of rising tone chorus emissions" submitted to JGR:Space Physics. All data are in Python Numpy binaries. Files hs.npy and ts.npy contain 1D numpy arrays of space and time coordinates of the grid covering the simulation domain. Files mus_full.npy, oms_full.npy, omws_fill.npy and phs_full.npy contain 2D numpy arrays of the refractive index, normalized wave frequency, normalized wave amplitude and wave phase, respectively. File tinds.npy gives the starting and ending points in time of each subpacket, at the spatial coordinate of the source. File input.dat contains a dictionary type data with input parameters used in the simulation.

Tidal walking on Europa's strike slip faults - insight from numerical modeling (data)

Dataset for article submitted to JGR Planets.