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Figure 6 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section

Figure 6. Columnar section of the Hambast Formation at Baghuk Mountain with correlation of the most important index horizons.

Figure 10 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section

Figure 10. Slab of a marly shale within the lowermost part of the Baghuk Member with small ammonoids (possibly Arasella sp.). Baghuk Mountain C section, at +0.05 m. Scale bar units = 10 mm.

Figure 13. Characteristic conodonts from Baghuk Mountain section A in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section

Figure 13. Characteristic conodonts from Baghuk Mountain section A (scale bars = 100 µm), oral and oblique views; all specimens stored in the collection of the Islamic Azad University, Tehran North Branch (IAUTNB). (a) Clarkina orientalis (Barskov and Koroleva, 1970), specimen IAUTNB#115; −8.90 m. (b) Clarkina subcarinata (Sweet in Teichert et al., 1973), specimen IAUTNB#137; −7.50 m. (c) Clarkina changxingensis (Wang and Wang, 1981), specimen IAUTNB#142; −5.00 m. (d) Clarkina deflecta (Wang and Wang, 1981), IAUTNB#189; −2.50 m. (e) Clarkina bachmanni Kozur, 2004, specimen IAUTNB#162; −3.90 m. (f) Clarkina nodosa Kozur, 2004, specimen IAUTNB#203; −2.20 m. (g) Clarkina yini Mei in Mei et al., 1998, specimen IAUTNB#215; −1.95 m. (h) Clarkina abadehensis Kozur, 2004 specimen IAUTNB#231; −0.25 m. (i) Clarkina hauschkei Kozur, 2004, specimen IAUTNB#250; −0.05 m. (j) Hindeodus parvus (Kozur and Pjatakova, 1976), specimen IAUTNB#251; +2.15 m. (k) Isarcicella staeschei Dai and Zhang, 1989, specimen IAUTNB#265; +3.75 m. (l) Isarcicella isarcica (Huckriede, 1958), specimen IAUTNB#264; +3.75 m.

Figure 2 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section

Figure 2. Palaeogeographic position of the Baghuk Mountain area during the Permian–Triassic boundary time interval (after Stampfli and Borel, 2002).

Figure 15 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section

Figure 15. Selected Wuchiapingian representatives of ammonoids from Baghuk Mountain; all specimens stored in the collection of the Museum für Naturkunde, Berlin. (a) Prototoceras sp., specimen MB.C.30219 (Araxoceras beds). (b) Vedioceras sp., specimen MB.C.30220 (Vedioceras beds). (c) Eoaraxoceras sp., specimen MB.C.30221 (Araxoceras beds). (d) Urartoceras sp., specimen MB.C.30222 (Pseudotoceras beds). Scale bar units = 1 mm.

Figure 5 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section

Figure 5. Stratigraphic subdivision of the Permian–Triassic boundary sections in the Julfa sections (from Ghaderi et al., 2014) and at Baghuk Mountain (from Farshid et al., 2016) with lithostratigraphic correlation. W – Wuchiapingian.

Figure 12 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section

Figure 12. Columnar section of the basal part of the Elikah Formation at Baghuk Mountain with the position of microbial buildups, changes in bed thickness and frequency of bivalve shells.

Figure 1 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section

Figure 1. Geographic position of Permian–Triassic boundary sections, including Baghuk Mountain (BM), in Central Iran.

Figure 11 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section

Figure 11. Field photograph of in situ microbialite occurrence showing digitate upward growing branches of digitate stromatolite columns in the Baghuk Member; Baghuk Mountain K section. Scale bar units = 10 cm.

Figure 4 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section

Figure 4. Correlation of the Permian–Triassic boundary beds in Central Iranian sections. Kuh-e-Hambast rock column after Kozur (2005). Position of the conodont-based Permian–Triassic boundary after Kozur (2005; 1, 3), Farshid et al. (2016; 2) and Richoz et al. (2010; 4). Asadabad section after unpublished data.

Figure 8. Section C in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section

Figure 8. Section C with the top part of the Hambast Formation and the basal 40 m of the Elikah Formation including the Baghuk Member. View towards the west.

Figure 3 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section

Figure 3. The Permian–Triassic boundary at Baghuk Mountain section C, Central Iran. View towards the north-west, in the background, summit composed of Triassic rocks.

Figure 9 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section

Figure 9. Columnar sections of the Baghuk Member ("Boundary Clay") in some of the sections at Baghuk Mountain. Legend as in Figs. 4 and 6; EH – extinction horizon.

Figure 5. Final Ranking-Data Conflict Resolution among Same Entities in Web of Data

Finally, the last score with regard to the equation 4 has been calculated and is shown in figure 5. As it shows Geonames gains the highest score. Considering the proposed idea, Geonames' data is the most accurate. So when we are faced with data conflicts, Geonames' data must be chosen. The WordFactbook is also illustrated in the above table. We can see if WordFactbook is not removed from the synonyms list it would have more accurate data compared with DBpedia data.

Figure 3. Result of first fraction-Data Conflict Resolution among Same Entities in Web of Data

Finally we count the entities that contain any words in the synonyms list. The result of first the fraction in equation 4 is shown in figure 3.

Figure 2. Ranking datasets by Page Rank-Data Conflict Resolution among Same Entities in Web of Data

As depicted in Figure 2 DBpedia is the top ranked data set while GeoLinked Data and Eurostat are the low ranked data sets. In this stage the data sets whose rank scores are less than half of the rank scores belonging to the top ranked data set are removed from the assessment.

Figure 1. General Workflow of Algorithm-.Data Conflict Resolution among Same Entities in Web of Data

The Page Rank algorithm which is widely used in most search engines such as Google could be easily used to rank linked data. By starting from a point and random surfing, this algorithm evaluates the probability of finding any given page. The algorithm assumes a link between a page i to a page j demonstrates the importance of page j. In addition, the importance of page j is associated to the importance of page i itself and inversely proportional to the number of pages i point to. To adapt this algorithm to web of data, any page considered as a dataset and links between pages considered as links between datasets.

Figure 4. Result of Second Fraction-Data Conflict Resolution among Same Entities in Web of Data

In the last stage the size of the data set is examined. The number of instances of specialized entities and total entities are calculated. For example "London" is an instance of a specialized entity. In table 2 one of the properties of "London" is displayed in form of a triple. As is shown, the object of the triple is dbpedia-owl:Place that contains "place" as a member of the specialized entity. And 'A Trip to the Moon' is an instance of non-specialized entity because neither the subject nor the object is in the specialized entities list. After calculating the number of specialized instances and total instances the result of the second fraction of equation 4 is shown in figure 4.

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Noninvasive detection of macrophage activation with single-cell resolution through machine learning

Data related to the article "Noninvasive detection of macrophage activation with single-cell resolution through machine learning". The package contains 2 folders: - RawData:   This package contains raw data and examples of processing to extract the              variables employed to train and assess the models. - Variables: This package contains the extracted data from the various experiments              showed in the article.