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Raw plot data for: Accelerating equilibrium isotope effect calculations. II. Stochastic implementation of direct estimators

Data for publication: K. Karandashev, J. Vanicek, Accelerating equilibrium isotope effect calculations. II. Stochastic implementation of direct estimators, J. Chem. Phys. 151, 134116 (2019) <a href="https://doi.org/10.1063/1.5124995">https://doi.org/10.1063/1.5124995</a> This data set contains the raw numerical data for reproducing Figures 1-8 in the publication.

Datasets for China's carbon sink estimates

Here are the carbon fluxes estimated by the Carbon Cycle Data Assimilation System (CCDAS) with the assimilation of in-situ CO2 and multiple satellite observations (i.e. soil moisture, FAPAR), JAMSTEC MIROC-ACTM v1, and a statistic approach from forest inventory. These data sets are used for estimating the carbon sink of China.

Raw data files for "Estimating the effect of cooling rate on the acquisition of magnetic remanence"

# Desciption of the data. The following data set is a collection of TRM acquisition curves in different cooling and field regimes for a selection of grain sizes and elongations. The data is arranged in a direcotory structure in the following way, once the 'sd_cooling_data.zip' file has been unpacked sd_cooling_data/ | +-- <grain elongation>pc     |     +-- <grain size>nm         |         +-- <field strength>uT             |             +-- <field x direction>_<field y direction>_<field z direction>                 |                 +-- 6.00e<exponent>s                     |                     +-- model.csv The model.csv file is a comma delimited file consisting of four columns:     column 1 - the time in seconds as the grain population cools,     column 2 - the temperature in degrees centigrade,     column 3 - the 'cooled magnetization' of the population i.e. each grain experiences each temperature step for a finite amount of time,     column 4 - the 'equilibrium magnetization' of the population i.e. each grain experiences each temperature step for an infinite amount of time. # Reproducing the data. Each model takes a JSON file as input to control its behaviour. Running the model is described on the 'sd-cooling' repository home page     https://www.github.org/Lesleis-Nagy/sd-cooling The specific version of the software used to generate this data set has doi: 10.5281/zenodo.5085691. The model JSON used to generate this data set is as follows with input parameters shown between angle brackets. {     "material": "magnetite",     "sizes": {         "list": [             {                 "value": "<size>",                 "fraction": 1.0             }         ],         "unit": "nm"     },     "elongations": {         "list": [             {                 "value": "<elongation>",                 "fraction": 1.0             }         ]     },     "directions": {         "list": [             {                 "value": [                     1.0,                     0.0,                     0.0                 ],                 "fraction": 1.0             }         ]     },     "applied_field": {         "strength": <field strength>,         "direction": [             <x direction>,             <y direction>,             <z direction>         ],         "unit": "uT"     },     "cooling_regime": {         "ambient_temperature": 15.0,         "initial_temperature": 579.9999,         "reference_time": <reference time>,         "temperature_at_reference_time": 15.15,         "allowable_percentage_drop": 0.005,         "stopping_temperature": 50     },     "outputs": {         "model": "model.csv"     },     "tau0": 1e-10,     "epsilon": 1e-15,     "n_polish": 0 }

A dataset of estimated net primary production of Japanese cedar plantations (ver.1)

# Abbreviations in this text NPP:        Net Primary Production, average of stand ages 36-40 GCM:        Global Climate Model HT:            Historical Trend, average of five years 1996-2000 FP2050:        Future Prediction, average of five years 2046-2050 FP2100:        Future Prediction, average of five years 2096-2100 (except for a GCM HadGEM2-ES of 2095-2099) # Description The data is prepared as four files in tab-delimited text format or ten netcdf files in a zip file. The annual NPP of cedar plantations under current and future climates are calculated in 196928 meshes in Japan. The climate scenarios used are owned and distributed by the third party (National Agriculture and Food Research Organization, Japan). For the methodology on modeling, parameterization and nation-wide calculation, please check the paper below. # Reference Toriyama J, Hashimoto S, Osone Y, Yamashita N, Tsurita T, Shimizu T, Saitoh TM, Sawano S, Lehtonen A, Ishizuka S (2021) Estimating spatial variation in the effects of climate change on the net primary production of Japanese cedar plantations based on modeled carbon dynamics. PLoS ONE 16(2): e0247165. https://doi.org/10.1371/journal.pone.0247165 # List of variables in text files # site.txt (6.8 MB) mesh:        Number of third-mesh order in the Japanese grid square system lat:        Latitude (degree) of center point of third-mesh order long:        Longitude (degree) of center point of third-mesh order block:        Block number in Toriyama et al. (2021), 1, 2 and 3 for SW, CT and NW, respectively pref:        Prefecture number in the Japanese administrative system # npp_2000.txt (8.7 MB) mesh:                Number of third-mesh order in the Japanese grid square system npp_2000_cgcm:        Annual NPP (kgC m-2 year-1) in HT, MRI-CGCM3 npp_2000_csiro:        Annual NPP (kgC m-2 year-1) in HT, CSIRO-Mk3-6-0 npp_2000_gfdl:        Annual NPP (kgC m-2 year-1) in HT, GFDL-CM3 npp_2000_hadgem:    Annual NPP (kgC m-2 year-1) in HT, HadGEM2-ES npp_2000_miroc:        Annual NPP (kgC m-2 year-1) in HT, MIROC5 # npp_2050.txt (15.4 MB) mesh:                    Number of third-mesh order in the Japanese grid square system npp_rcp26_2050_cgcm:    Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, MRI-CGCM3 npp_rcp26_2050_csiro:    Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, CSIRO-Mk3-6-0 npp_rcp26_2050_gfdl:    Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, GFDL-CM3 npp_rcp26_2050_hadgem:    Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, HadGEM2-ES npp_rcp26_2050_miroc:    Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, MIROC5 npp_rcp85_2050_cgcm:    Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, MRI-CGCM3 npp_rcp85_2050_csiro:    Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, CSIRO-Mk3-6-0 npp_rcp85_2050_gfdl:    Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, GFDL-CM3 npp_rcp85_2050_hadgem:    Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, HadGEM2-ES npp_rcp85_2050_miroc:    Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, MIROC5 # npp_2100.txt (15.4 MB) mesh:                    Number of third-mesh order in the Japanese grid square system npp_rcp26_2100_cgcm:    Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, MRI-CGCM3 npp_rcp26_2100_csiro:    Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, CSIRO-Mk3-6-0 npp_rcp26_2100_gfdl:    Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, GFDL-CM3 npp_rcp26_2100_hadgem:    Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, HadGEM2-ES npp_rcp26_2100_miroc:    Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, MIROC5 npp_rcp85_2100_cgcm:    Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, MRI-CGCM3 npp_rcp85_2100_csiro:    Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, CSIRO-Mk3-6-0 npp_rcp85_2100_gfdl:    Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, GFDL-CM3 npp_rcp85_2100_hadgem:    Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, HadGEM2-ES npp_rcp85_2100_miroc:    Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, MIROC5 # npp_netcdf.zip (594 MB) The 10 files in netcdf format are compiled in a zip file for NPP data of different RCP scenarios and GCMs. Please check the content of each file by following command. ncdump -h filename # summary_map.zip (5.8 MB) The 20 files in png format are compiled in a zip file for maps of NPP and its change. The maps were created using the Generic Mapping Tools version 5 (http://gmt.soest.hawaii.edu/). The average values of five GCMs are used for mapping. # Acknowledgement This dataset was funded by the Agriculture, Forestry and Fisheries Research Council in Japan, under the project “Research on adaptation to climate change for agriculture, forestry and fisheries”.

Hourly estimates of climate variables for Greece, 2017-2018

Raster of hourly estimates of climate variables for Greece, 2017-2018. The estimates, which form the ERA5 Reanalysis data, are based on vast amounts of historical observations. From <a href="https://www.ecmwf.int/">ECMWF</a> through the Copernicus Climate Change Service. Prepared by <a href="https://www.athenarc.gr/">Athena Research Center</a> for use in the <a href="https://www.i4sea.eu/">i4SEA project</a>. The data was downloaded from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">ERA5 hourly data on single levels from 1979 to present</a> on May 16, 2019. It covers the years 2017-2018 and a rectangular region encompassing Greece. The climate variables contained are: <ul> <li>10m u-component of wind (10u)</li> <li>10m v-component of wind (10v)</li> <li>2m dewpoint temperature (2d)</li> <li>2m temperature (2t)</li> <li>Mean sea level pressure (msl)</li> <li>Mean wave direction (mwd)</li> <li>Mean wave period (mwp)</li> <li>Sea surface temperature (sst)</li> <li>Significant height of combined wind waves and swell (swh)</li> <li>Surface pressure (sp)</li> <li>Total precipitation (tp)</li> </ul> Generated using Copernicus Climate Change Service information 2019. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

Data accompanying arXiv:2112.07341, "Euclid: Covariance of weak lensing pseudo-Cl estimates. Calculation, comparison to simulations, and dependence on survey geometry"

Data accompanying <a href="https://arxiv.org/abs/2112.07341">arXiv:2112.07341, "Euclid: Covariance of weak lensing pseudo-Cl estimates. Calculation, comparison to simulations, and dependence on survey geometry"</a>. Code available at <a href="https://github.com/robinupham/shear_pcl_cov">https://github.com/robinupham/shear_pcl_cov</a>.

opencc-zeroAug 2021View details →

dryad40/100

Quantitative estimates of glacial refugia for chimpanzees (Pan troglodytes) since the Last Interglacial (120,000 BP)

Paleoclimate reconstructions have enhanced our understanding of how past climates have shaped present-day biodiversity. We hypothesize that the geographic extent of Pleistocene forest refugia and suitable habitat fluctuated significantly in time during the late Quaternary for chimpanzees (Pan troglodytes). Using bioclimatic variables representing monthly temperature and precipitation estimates, past human population density data and an extensive database of georeferenced presence points, we built a model of changing habitat suitability for chimpanzees at fine spatio-temporal scales dating back to the Last Interglacial (120,000 BP). Our models cover a spatial resolution of 0.0467 degrees (approximately 5.19 km2 grid cells) and a temporal resolution of between1,000–4,000 years . Using our model, we mapped habitat stability over time using three approaches, comparing our modelled stability estimates to existing knowledge of Afrotropical refugia, as well as contemporary patterns of major keystone tropical food resources used by chimpanzees, figs (Moraceae) and palms (Arecacae). Results show habitat stability congruent with known glacial refugia across Africa, suggesting their extents may have been underestimated for chimpanzees, with potentially up to ~60,000 km2 of previously unrecognized glacial refugia. The refugia we highlight coincide with higher species richness for figs and palms. Our results provide spatio-temporally explicit insights into the role of refugia across the chimpanzee range, forming the empirical foundation for developing and testing hypotheses about behavioural, ecological and genetic diversity with additional data. This methodology can be applied to other species and geographic areas when sufficient data are available.

Area Estimates of Forest Degradation and Deforestation in the Country of Georgia by Region

Area estimates of forest degradation and deforestation in the country of Georgia by region from 1987 to 2019. Unit is square kilometers. georgia_forest_def_0512.csv: Area estimates of deforestation georgia_forest_deg_0512.csv: Area estimates of forest degradation Please cite the data as: <a href="https://www.sciencedirect.com/science/article/pii/S0034425721003680">Chen, S., Woodcock, C.E., Bullock, E.L., Arévalo, P., Torchinava, P., Peng, S. and Olofsson, P., 2021. Monitoring temperate forest degradation on Google Earth Engine using Landsat time series analysis. Remote Sensing of Environment, 265, p.112648.</a> <a href="https://authors.elsevier.com/a/1devg7qzStnwW">Click here to get 50-day free access without registration</a>

opencc-by-4.0Oct 2016View details →

Figure 10. Maximum likelihood tree estimated using Garli 2.0 with 5 in Systematics and Phylogeny of the Australian Cicada Genus Pauropsalta Goding and Froggatt, 1904 and Allied Genera (Hemiptera: Cicadidae: Cicadettini)

Figure 10. Maximum likelihood tree estimated using Garli 2.0 with 5 loci (1 mtDNA and 4 nDNA). Branch support values are bootstrap percentages from 100 non parametric bootstrap replicates. Bootstrap support values ≥ 70 are shown. Molecular voucher numbers are adjacent to species names.

New estimation of the NOx snow-source on the Antarctic Plateau - repository dataset

Notebook and data set used to present the results. For more information, please, do not hesitate to contact the corresponding author to this study.

MPIData: Easy Axis Orientation Estimation

This data repository contains magnetic particle imaging data. The files are stored in the <a href="https://arxiv.org/pdf/1602.06072.pdf">MDF file format</a> and the folder structure is organized as an <a href="https://github.com/MagneticParticleImaging/MPIFiles.jl">MDF store</a>. The data is used in <a href="https://github.com/IBIResearch/EasyAxisOrientationEstimation">this</a> code example.

opencc-by-4.0Aug 2008View details →

Fig. 1 in Estimating Population Size And Distribution Of Hume'S Pheasant In Northern Thailand

Fig. 1. Study area is covered by two LANDSAT 7/ETM+ images, path 131/row 46 and path 131/row 47, which were acquired in Feb.2002.

opencc-by-4.0Sep 2017View details →

Fig. 1 in Preliminary estimation of home range size for Meristogenys orphnocnemis, a common Bornean Ranid, in an altered forest ecosystem using radiotelemetry

Fig. 1. Section of stream in SAFE Project experimental site, known as logged forest edge (LFE) stream, where all radiotracking occurred.

opencc-by-4.0Sep 2018View details →

Fig. 1 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore

Fig. 1. Map showing the location of the Central Catchment Nature Reserve on mainland Singapore. All 27 camera points are indicated with a red circle. Black squares indicate the three camera points added to the 1 km2 grid. The six cage traps are marked with a blue cross. Dotted circles indicate areas the last remaining patches of primary forest in Singapore.

opencc-by-4.0Sep 2018View details →

Fig. 3 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore

Fig. 3. Map of the Central Catchment Nature Reserve showing the day and night fixes of the collared pig. The home ranges are calculated from the monthly 95% Kernel Density Estimate (KDE), while the aggregate home range was calculated from the 99% KDE from all six months. The Seletar Expressway (SLE) is pointed out on the map and the satellite overlay was adapted from Google Earth.

opencc-by-4.0Sep 2018View details →

Fig. 2 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore

Fig. 2. The density map showing the number of activity centres per kilometer square, the locations of the camera points (circles), 143 out of 856 GPS locations from the collared pig (black dots) and the boundary of the Central Catchment Nature Reserve. Only a fraction of the GPS locations was plotted to prevent the colored pixels from being obscured. Each pixel is 1 km2. X and Y coordinates are in kilometers.

opencc-by-4.0Nov 2002View details →

Fig. 54. Estimated m2 in Morphology And Relationships Of Apternodus And Other Extinct, Zalambdodont, Placental Mammals

Fig. 54. Estimated m2 area (m2 length × m2 width) in square millimeters across selected taxa. See figure 20 for taxonomic abbreviations and figure 52 for measurements taken. Dots indicate individual specimens measured. Crosses denote extinct taxa. Diamonds indicate 95% confidence intervals of the mean; nonoverlapping diamonds indicate a significant difference at alpha = 0.05, assuming normality.

opencc-by-4.0Jun 2008View details →

Fig. 1 in Estimating body mass in New World ''monkeys'' (Platyrrhini, Primates), with a consideration of the Miocene platyrrhine, Chilecebus carrascoensis

Fig. 1. Cladogram from molecular phylogenies (Canavez et al., 1999; von Dornum and Ruvolo, 1999) of platyrrhine primates used in the phylogenetically corrected regressions of body mass on morphometric variables. This cladogram is a synthetic topology of these two phylogenetic analyses. The topologies for the two analyses were congruent for overlapping taxa, with two exceptions. First the Callicebus/Cacjao/ Chiropotes/Pitehca clade was basal to all other Platyrrhini in von Dornum and Ruvolo (1999), but was allied with the Ateles/Bachyteles/Lagothrix/Allouata clade in Canavez et al. (1999). Second, Aotus was allied with Saimiri and Cebus in Canvez et al. (1999), but left in an unresolved polytomy in von Dornum and Ruvolo (1999). Therefore both nodes are conservatively left in polytomies here.

opencc-by-4.0Jun 2009View details →

Fig. 3 in Estimating size at first maturity (L ) from Gonadossomatic Index (GSI) data

Fig. 3. Individual distribution of female gonadossomatic index (GSI) according to the total length in four fish species. Animals captured using seine and gill nets over 17 monthly samples (November/2002 to May/2004) from two sites (lagoa do Casamento and lagoa dos Gateados, Rio Grande do Sul State, Brazil). Vertical lines or areas represent published estimates of size at first maturity (L50).