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FIG. 3 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape

FIG. 3. — Distribution of Choleva (Cholevopsis) punctata Brisout, 1866 in the MSS of the Sierra de Guadarrama National Park. Legends and symbols: ● subterranean sampling devices (SSDs); Δ, talus pitfall traps (TSP); ● and presence of C. (C.) punctata. The combination of the different manifestations of the aedeagus with the different morphologies of the metatrochanter is shown for each SSD following the classification of Figs 6; 7.

FIG. 9 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape

FIG. 9. — Female genitalia of Choleva (Cholevopsis) punctata Brisout, 1866: A, left lateral vision; B dorsal vision without IX ltg and IX mtg; C, dorsal vision with complete genital shield; D, ventral vision; E, detail of the female genital armor. Scale bars: A, D, 0.5 mm; E, 0.2 mm. Abbreviations: see Material and methods. The abbreviations associated with the genital shield are those used by Deuve (1993).

FIG. 8 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape

FIG. 8. — The morphological diversity of the Choleva (Cholevopsis) punctata Brisout, 1866 metatrochanter from Sierra de Guadarrama: A-F, external spiny angle (hollow arrow with continuous contour), inner spiny angle (hollow arrow with discontinuous contour), and medial spine (solid arrow). Scale bar: 1 mm.

FIG. 7 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape

FIG. 7. — Different states of evagination of the inner sac with respect to the median lobe of the aedeagus of Choleva (Cholevopsis) punctata Brisout, 1866. Categorized in columns (I-IV states) and in rows. Abbreviations: lat, lateral view; v, ventral view. Scale bars: 1 mm.

FIG. 1 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape

FIG. 1. — Locations of the 33 scree slopes and four talus that were sampled in the Sierra de Guadarrama National Park and in the surrounding area. Symbols: ●, subterranean sampling devices (SSDs); ∆, talus pitfall traps (TSPs).

FIG. 2 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape

FIG. 2. — Sampled habitats and sampling devices: A, a typical scree slope; B, placement of a subterranean sampling device (SSD); C, a talus on a scree slope; D, placement of a talus slope pitfall traps (TSP).

FIG. 5 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape

FIG. 5. — Habitus of Cholevinae Kirby, 1837 species captured in this study: A, Speonemadus angusticollis (Kraatz, 1870); B, Speonemadus clathratus (Perris, 1864); C, Speonemadus vandalitiae (Heyden, 1870); D, Attumbra josephinae josephinae (Saulcy, 1862); E, Catops fuliginosus Erichson, 1837; F, Catops fuscus fuscus (Panzer, 1794); G, Catopsimorphus (Attiscurra) marqueti Fairmaire, 1857; H, Catopsimorphus (Weiratherella) rougeti Saulcy, 1864; I, Choleva (Choleva) cisteloides (Frolich, 1799); J, Choleva (Cholevopsis) punctata Brisout, 1866; K, Sciodrepoides watsoni watsoni (Spence, 1815); L, Ptomaphagus (Ptomaphagus) tenuicornis tenuicornis (Rosenhauer, 1856). Scale bars: 1 mm.

FIG. 4 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape

FIG. 4. — Species accumulation curves for the complete inventory of the Sierra de Guadarrama National Park: A, sample-based species accumulation curve using the subterranean sampling devices (SSDs) as effort units (empty circles); 95% confidence interval as grey bands, and Chao2 curve (stripped line); B, nonparametric richness estimators.

opencc-by-4.0Mar 2022View details →

Data for "Random forest-based modeling of stream nutrients at national level in a data-scarce region"

The aim of the study was to model annual total nitrogen (TN) and total phosphorus (TP) concentrations at national level using an ML approach. We used water quality data originating from the Environmental Monitoring Database KESE to train RF models for nutrient concentration prediction in 242 catchments across Estonia. A total of 82 environmental variables were used as predictors in the models. In order to yield the best results, a feature selection strategy along with hyperparameter optimization was performed when building the models. The models are applicable for predicting nutrient loads on an annual level, e.g. for the purpose of reporting national level water quality statistics in regional projects, such as HELCOM. The results showed that this relatively basic RF modeling approach can have a performance similar to process-based models. Moreover, these models are easier to reuse and apply on a larger scale, since the required inputs can be derived from freely available datasets (e.g. satellite imagery) This repository contains the input data used for building the RF models and the files describing the modeling results. The description of the files is given in the README.txt file. Virro, H., Kmoch, A., Vainu, M. and Uuemaa, E., 2022. Random forest-based modeling of stream nutrients at national level in a data-scarce region. Science of The Total Environment, 840, p.156613. <a href="https://doi.org/10.1016/j.scitotenv.2022.156613">https://doi.org/10.1016/j.scitotenv.2022.156613</a>

opencc-by-4.0Oct 2020View details →

Commodity Dataset | Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform

(Commodity data in raster format) Supplementary materials for “Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform” that had been published on Land MDPI (2020). doi:<a href="https://doi.org/10.3390/land9100377">10.3390/land9100377</a>  The data included: 1) Raster data of commodity maps (TIFF Compressed in ZIP) 2) READ ME for the dataset (DOCX) 3) Legend for raster data in ArcGIS Format (LYR)

opencc-by-4.0Nov 2021View details →

Sub-national tailoring of malaria interventions in Mainland Tanzania: simulation of the impact of strata-specific intervention combinations using modelling

Simulation dataset.

opencc-by-4.0Apr 2022View details →

Indoor localization using Wi-Fi and IMU at National Taiwan University CSIE 5F

A dataset composed of Wi-Fi fingerprints and IMU sensing data which collect by smartphone. We collected this dataset at National Taiwan University CSIE building 5F. The txt files are raw data. Fingerprint.txt is the Wi-Fi fingerprint set for reference map. Track1.txt and Track2.txt are the Wi-Fi fingerprints and IMU sensing data collect by android smartphone.   The .npy files are the preprocessed data.

opencc-by-4.0Dec 2021View details →

Fig. 3 in Ecological Analysis Of Butterflies And Day-Flying Moths Diversity Of The Gouraya National Park (Algeria)

Fig. 3. Projection of the butterfly and day-flying moth species of the three stations studied on the first two axes of the correspondence factor analysis.

opencc-by-4.0Dec 2021View details →

Fig. 2 in Ecological Analysis Of Butterflies And Day-Flying Moths Diversity Of The Gouraya National Park (Algeria)

Fig. 2. Evolution of the species richness of butterflies and day-flying moths at the three stations of Gouraya National Park, mean temperature in Bejaia (DAAE, 2012).

opencc-by-4.0May 2019View details →

Fig. 1 in Formation Of Inter-Species Links In Ungulates In The Azov-Syvash National Nature Park

Fig. 1. Dynamics of quantity of the hoof-animals in the Azov-Syvash National Nature Park in the course of 1928–2018.

Fig. 2 in Population Structure Of Ungulates In Waterberg National Park, Namibia

Fig. 2. Location of Waterberg N. P., the different vegetation types and the distribution of the seven (7) water holes in the park (Jankowitz, 1983).

Fig. 4 in Population Structure Of Ungulates In Waterberg National Park, Namibia

Fig. 4. Herd size and number of herds of White and Black Rhino based on water point census only in Waterberg Plateau Park.

Fig. 1 in Population Structure Of Ungulates In Waterberg National Park, Namibia

Fig. 1. Rainfall in the Waterberg N. P. for the years 1980 to 2017 (Sasscalweathernet.org/station_datasheet_ we.php).

Fig. 6 in Population Structure Of Ungulates In Waterberg National Park, Namibia

Fig. 6. Year to year changes in age structure (adults: black bar, Juveniles: grey bars) in ungulates in the Waterberg Plateau Park in 2008–2013, based on water point census.

Fig. 5 in Population Structure Of Ungulates In Waterberg National Park, Namibia

Fig. 5. Year to year changes in the proportion of sex (males: black bar, females: grey bars) in ungulates in the Waterberg Plateau Park in 2008–2013, based on water point census.