StatOracle–02 Foundation: canon of primary data collection and example PelophylaxExamples

2 Foundation: canon of primary data collection and dataframes used in the course 2.1 How should a table with primary research data be organized? Natural sciences are based on empirical (from Greek ἐμπειρία - experience) research of nature, i.e., research based on observation and experiment. There are also purely theoretical (from Greek θεωρία - consideration, research) works in the natural sciences related to the internal development of a certain model, but such works are secondary; they are the results of the analysis of empirical, primary data. Where does this data come from? It is collected during research. Thus, the collection of empirical data is the foundation of all natural sciences, including biology. It is very important to learn to collect this data in such a way as not to limit the possibilities of working with it in the future. Most often, empirical data is collected either in numerical form or in a form that can be translated into numerical form over time. There is a point of view that where there is no mathematics, there is no natural science. Most likely, one can imagine work that does not involve mathematics, but this, of course, must be a rare case. The main problem is that the researcher must ensure that his experience reflects general patterns. You turn over a leaf and see a caterpillar on its underside. Is this a universal experience or not? You turn over the next leaf, and under it is a frog. Most likely, to establish what is hiding under such leaves, one should turn over a certain number of them and record whether there was anything under them, and if so, what exactly. A more important problem of empirical research is how universal the experience obtained in it is, how much it can be relied upon in other cases. In other words, how reliable (deserving of trust, worthy of belief) is the result obtained in this experience. You have obtained certain empirical data. What to do with it? Remember it? This is not a reliable solution. Our memory is selective; our memories are influenced by our attitude towards what we tried to remember. Memory loses one thing and substitutes another... Write it on paper? This is already a good solution. But it must meet certain requirements. Each subsequent result should be recorded immediately as soon as it is obtained. Records should be made uniformly, regardless of what they are (e.g., whether the researcher likes the result or not). Paper (or another medium) on which the results of empirical research are stored becomes an important document. It must be preserved, and it must not be edited over time! Therefore, primary records are usually made in a laboratory journal, where a specific table is built for storing data. It is very useful to store primary data in other forms as well. This can be photographs, audio recordings, printouts of instrument readings, etc. This should not replace the records in the laboratory journal, but supplement them! By the way, it is very useful, after filling a page of the laboratory journal, to photograph it and place such a digital photograph, for example, somewhere in a cloud environment... In the modern world, primary records are often made not in a paper journal, but directly in digital form – in a file (most often an electronic spreadsheet, such as Excel, Calc, Google Sheets). In this case, such a file should be treated like a paper laboratory journal – stored indefinitely, without editing existing records. This is a carrier of primary data that preserves the trace of a more important stage of research: the researcher asked nature a question, received an answer, and saved it! This file is the table for collecting empirical data, the database created during the research. Another option for organizing work is when primary data is collected in a laboratory journal (or on paper forms that the researcher works with in the field), and then transferred to a table file. In this case, both paper primary documents and the initial file should be preserved. They should be organized uniformly to reduce the probability of errors during transfer (and they should be stored so that such errors can be found). Experience working with students and researchers shows that very often major problems in work are related to the fact that the table for collecting primary data of empirical research is built incorrectly. Many reasons have influenced how to properly organize the collection and storage of primary data. Some of them have historical explanations, some are related to the specifics of statistical analysis itself. At the beginning of the course, there is no point in analyzing all these reasons in detail; it is much better to present them in the form of a certain canon. In this case, we understand the word "canon" as a clear set of rules. Of course, if necessary, one can deviate from the canonical organization of data, but this innovation is only justified if there are sufficient grounds for it. The canonical requirements listed below were previously called "commandments," but over time we abandoned such a metaphor. Violating the commandments can be considered a sin; unlike the requirements of the commandments, one can deviate from the canon when it is necessary for valid reasons. However, in most cases, one should simply follow the canon. Here is one example. The canon requires that in the primary data table, rows correspond to individual objects, observations, measurements, etc., and columns correspond to attributes. Could it have been done the other way around? Yes! In some types of analysis, it is even necessary to transpose (swap rows and columns) such a table. But data is collected this way. Among other things, because everyone (who understands) does it this way. And they do it this way because it corresponds to the logic of working with a laboratory journal. In European culture, records are made by rows, not by columns. A separate observation is a separate row, and columns reflect what needs to be considered to further work with this observation. The data file should be organized the same way as a laboratory journal page to reduce the probability of errors during data transfer. Please ensure that your understanding of the organization of primary data (and analysis files) conforms to this canon by default! Canon for building a table for collecting primary data: 1. Collection of primary data ("nature's answers" to the researcher) is a consequence of the need to obtain an answer to a specific question! 2. When building a data table, one should anticipate how, if the research is successful, an answer to the initial question will be obtained! 3. The answer to the initial question is not only a description of the result ("what came out"), but also the determination of its statistical significance! 4. Statistical significance of the result is the probability that it arose by chance in the formation of the studied sample! 5. Empirical data contains answers to questions not yet asked and even not yet realized; they should be stored indefinitely! 6. Primary data should be duplicated in various forms (paper laboratory journal, forms, files, photos of records... )! 7. Typically: on paper and in spreadsheets – data collection, table creation; analysis – in specialized programs! 8. Transferring, recoding, reorganizing data is a source of new errors; the initial version should be preserved! 9. Are not all possible objects described? Think, choose, describe, and randomize (random selection)! 10. Table rows are individual objects or independent observations, columns are attributes; the cell contains the state of the attribute for a given object! 11. The entire data array of the study is in one table! Rows not used in the analysis can be marked in a specific column! 12. Each column contains uniform data, in the same units, in the same format, measured the same way! 13. All uniform data, in the same units, in the same format, measured the same way – in one column! 14. The group to which the object or observation belongs is specified as a separate attribute (not by the position of the record on the sheet or in the table)! 15. Each row contains all attributes (in specific columns) that define the uniqueness of the observation (so that it can be sorted)! 16. For each attribute, choose its type, method of determination or measurement, coding, format, recording accuracy, etc.! 17. Quantitative attributes: either measurable (=continuous, metric) or count (=discrete), or ordinal (=rank)! 18. Qualitative attributes: either alternative ("yes - no") or multiple ("yes, this way, or that way")! 19. If the primary data is not symbolic (photographs, etc.), a separate column is a link to them in a "cloud" storage! 20. Each cell contains one specific attribute (not, for example, two different ones, like month and year for natural observations)! 21. "0" is a specific number; absence of data is an empty cell (in programs, it can be denoted as NA, "not applicable")! 22. The order of columns should reflect the order of their determination; calculated data can be placed after empirical data! 23. In spreadsheets and statistical programs, calculations are done using formulas (they can be repeated or corrected)! 24. Attribute names and their states – preferably in Latin letters (fewer problems) + as detailed explanations as possible for yourself and others! 25. Names of files with primary data – clear, immediately understandable; their storage locations – predictable, clearly labeled! 26. The researcher's life will be simplified by a journal or file that describes the methodology, all stages of work, and anything unusual during it! 27. Data quality check – visualization of their distribution; a scatter plot will show outliers and data entry errors! 28. After obtaining the first fragment of the database, conduct a trial analysis and correct the shortcomings of the research organization! After the initial formulation of this canon, its author sought help from the collective mind. Thanks to advice from qualified Facebook friends, the requirements of this canon have been significantly improved. The authors express special gratitude to Professor Oleksandr Zhukov for his important help. A table that meets the requirements of the given canon can look, for example, as shown below. Rows are individual observations, columns are attributes. Each cell contains the state of the attribute corresponding to the column for a specific observation (row). Fig. 2.1.1. Fragment of a conditional table with primary data One of the difficulties is what constitutes an object, an individual observation (requirement #10). Suppose we are comparing the lengths of a certain number of leaves from one tree... What is an observation: an individual tree or the length of an individual leaf? Of course, the length of the leaf, a metric attribute (requirement #17). And which tree this leaf is from is already a group attribute (requirement #14). And the species of the tree, its height, or its location are further attributes (requirement #15). For all leaves from the same tree, the state of these attributes will be the same (it is not difficult to fill in such cells in spreadsheets, although one should carefully observe not to make mistakes during filling). And, for example, the height at which the leaf was located is an attribute whose state may differ for different leaves from the same tree. And if we use not all the trees from which we measured the leaves for analysis, but only some of them, we will create another column where we indicate whether this tree is considered in the current analysis or not (requirement #11). 2.2 Example file PelophylaxExamples.RData In further explanations, the specifics of program operation will be explained mainly using files that reflect the results of real research. One of these files is the PelophylaxExamples data table (Table 2.4.1). Much of the material in this textbook concerns working with this particular database. It can be obtained in one of three ways. First, it can be downloaded (PelophylaxExamples.csv) or obtained from the author of this text. The downloadable file is in .csv (Comma-Separated Values) format; this is one of the common formats for transferring data between different programs. In fact, it is a text format that presents tabular data. Each subsequent value is separated from the previous one by a delimiter (typically a comma, in the proposed file – semicolons). If the table fields are separated by commas, they cannot be used as decimal separators (we remind you that in most developed countries, commas are used as decimal separators). The file uses a fragment of data obtained by O. V. Korshunov during the preparation of his dissertation for the degree of Candidate of Biological Sciences (the authors are sincerely grateful to O. V. Korshunov for permission to use the results of his work). The original file contained descriptions of several hundred frogs based on 16 morphometric characteristics; the selected fragment includes 57 frogs and provides data on the variability of 7 morphometric characteristics. When using the electronic version of the notes, the data below can be transferred from the browser window to the Word or .pdf file into the required program. If you are using R, you can simply enter all the data and create a dataframe from them. This is not difficult. First of all, you can copy all the data used in the example file in electronic form and add them to your R script.

Place <- c("Krasnocuts`k", "Chernetchina", "Chernetchina", "Chernetchina", "Chernetchina", "Izbickoe", "DobritzkiyYar", "DobritzkiyYar", "KreydyanaDacha", "KreydyanaDacha", "Verbunivs`kaDacha", "Verbunivs`kaDacha", "Verbunivs`kaDacha", "Zamulivka", "Zamulivka", "ChervoniyShahtar", "Sharivka", "Sharivka", "Lipci", "Gaydary", "DobritzkiyYar", "DobritzkiyYar", "VelykaGomol`sha", "SuhaGomol`sha", "KreydyanaDacha", "Verbunivs`kaDacha", "Pechenizhsk`iyRibhoz", "Eschar", "Balakliya", "Gatishe", "Gaydary", "Gaydary", "Gaydary", "Gaydary", "Gaydary", "Gaydary", "DobritzkiyYar", "SuhaGomol`sha", "KreydyanaDacha", "KreydyanaDacha", "Verbunivs`kaDacha", "Verbunivs`kaDacha", "Balakliya", "Gorodnee", "Gubarivka", "Lipci", "Martova", "Pechenigy", "SuhaGomol`sha", "ChervonaGusarivka", "ChervonaGusarivka", "Vesele", "Petropillya", "Eschar", "Balakliya", "Zamulivka", "Liman")
East <- c(35.16, 35.13, 35.13, 35.13, 35.13, 36.73, 36.31, 36.31, 36.80, 36.80, 36.89, 36.89, 36.89, 36.46, 36.46, 37.03, 35.47, 35.47, 36.38, 36.33, 36.31, 36.31, 36.27, 36.34, 36.80, 36.89, 36.59, 36.35, 36.48, 36.52, 36.33, 36.33, 36.33, 36.33, 36.33, 36.33, 36.31, 36.34, 36.80, 36.80, 36.89, 36.89, 36.48, 35.14, 35.35, 36.38, 36.96, 36.99, 36.34, 36.86, 36.86, 37.19, 37.13, 36.35, 36.48, 36.46, 36.32) 
North <- c(50.07, 50.05, 50.05, 50.05, 50.05, 50.20, 49.56, 49.56, 49.43, 49.43, 49.42, 49.42, 49.42, 50.08, 50.08, 49.18, 50.04, 50.04, 50.21, 49.62, 49.56, 49.56, 49.57, 49.54, 49.43, 49.42, 49.52, 49.47, 49.27, 50.18, 49.62, 49.62, 49.62, 49.62, 49.62, 49.62, 49.56, 49.54, 49.43, 49.43, 49.42, 49.42, 49.27, 50.05, 50.16, 50.21, 49.93, 49.89, 49.54, 49.41, 49.41, 49.40, 49.09, 49.47, 49.27, 50.08, 49.35)
Basin <- c("Dnipro", "Dnipro", "Dnipro", "Dnipro", "Dnipro", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Dnipro", "Dnipro", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Dnipro", "Dnipro", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don", "Don")
Basin <- factor(Basin, levels = c("Dnipro", "Don"))
Sex <- c("female", "female", "female", "male", "female", "female", "male", "female", "male", "female", "male", "male", "female", "female", "female", "female", "male", "female", "male", "female", "female", "male", "male", "female", "male", "female", "male", "male", "female", "female", "male", "male", "male", "female", "male", "female", "female", "female", "male", "male", "female", "female", "male", "male", "male", "female", "female", "female", "female", "male", "female", "female", "female", "male", "male", "male", "male")
Sex <- factor(Sex, levels = c("female", "male"))
DNA <- c(14.03, 13.95, 13.99, 13.95, 14.02, 21.83, 21.43, 21.67, 21.62, 21.61, 21.61, 21.64, 21.50, 21.43, 21.60, 22.03, 14.94, 14.91, 14.86, 14.88, 14.95, 14.91, 14.80, 15.09, 14.79, 14.91, 14.72, 14.91, 14.85, 14.91, 22.97, 22.64, 22.98, 22.80, 22.81, 22.79, 22.60, 22.79, 22.74, 22.73, 22.81, 22.85, 22.85, 16.13, 16.27, 16.00, 16.18, 16.22, 15.99, 16.01, 16.07, 16.11, 16.01, 16.03, 15.92, 16.08, 16.20)
Genotype <- c("LL", "LL", "LL", "LL", "LL", "LLR", "LLR", "LLR", "LLR", "LLR", "LLR", "LLR", "LLR", "LLR", "LLR", "LLR", "LR", "LR", "LR", "LR", "LR", "LR", "LR", "LR", "LR", "LR", "LR", "LR", "LR", "LR", "LRR", "LRR", "LRR", "LRR", "LRR", "LRR", "LRR", "LRR", "LRR", "LRR", "LRR", "LRR", "LRR", "RR", "RR", "RR", "RR", "RR", "RR", "RR", "RR", "RR", "RR", "RR", "RR", "RR", "RR")
levels(Genotype) <- c("LL", "LLR", "LR", "LRR", "RR")
Lc <- c(60.3, 56.2, 59.2, 59.5, 60.2, 62.5, 58.9, 65.8, 52.8, 55.7, 52.9, 57.4, 61.6, 76.7, 80.0, 47.9, 65.9, 69.1, 66.8, 79.1, 70.7, 71.4, 55.3, 87.7, 65.0, 54.3, 66.2, 56.1, 64.1, 56.9, 58.8, 65.3, 65.5, 67.7, 69.1, 74.2, 71.5, 50.4, 56.4, 75.5, 61.8, 68.9, 72.1, 70.6, 50.8, 70.1, 82.5, 53.7, 53.5, 52.1, 54.2, 69.3, 71.0, 68.6, 79.2, 93.0, 65.6)
Ltc <- c(19.4, 18.7, 19.5, 19.9, 21.8, 22.1, 21.6, 24.1, 19.6, 20.0, 19.2, 19.9, 23.0, 24.0, 26.2, 18.9, 20.8, 22.7, 22.6, 29.9, 22.9, 24.4, 21.0, 33.8, 22.5, 19.0, 25.2, 20.9, 26.8, 20.1, 20.6, 21.5, 22.1, 29.4, 22.6, 25.5, 22.0, 20.3, 19.2, 26.8, 21.2, 24.8, 27.8, 26.6, 19.1, 27.0, 31.5, 18.9, 20.0, 18.6, 19.3, 24.7, 25.6, 26.8, 26.2, 26.5, 21.9)
Fm <- c(26.4, 26.6, 28.1, 28.5, 28.7, 30.3, 29.0, 30.6, 25.7, 25.1, 25.8, 26.3, 31.6, 34.9, 38.9, 23.8, 30.2, 35.9, 33.5, 38.1, 33.4, 35.6, 26.2, 37.6, 32.0, 26.6, 32.1, 27.3, 31.7, 26.0, 28.8, 31.5, 32.8, 33.8, 33.0, 35.2, 35.3, 23.1, 28.3, 41.1, 28.8, 31.6, 35.9, 32.6, 25.9, 36.0, 42.3, 25.9, 26.5, 24.3, 26.2, 34.1, 33.9, 33.6, 34.0, 46.2, 35.2)
Ti <- c(25.5, 24.9, 26.1, 28.6, 28.1, 29.2, 27.7, 30.4, 24.6, 25.7, 26.2, 26.7, 29.8, 34.6, 37.6, 24.6, 30.0, 34.9, 32.8, 39.4, 33.2, 34.1, 28.0, 42.3, 31.9, 27.3, 32.5, 28.5, 32.3, 28.1, 28.9, 31.9, 34.5, 36.4, 33.4, 35.6, 34.4, 24.8, 29.3, 37.2, 30.2, 34.1, 35.9, 36.2, 27.7, 37.6, 44.3, 27.3, 28.1, 26.7, 28.2, 36.2, 37.1, 36.1, 35.7, 46.1, 33.7)
Dp <- c(7.6, 6.2, 7.9, 7.5, 8.0, 8.3, 7.7, 9.6, 6.6, 6.7, 7.7, 7.8, 9.1, 9.5, 11.6, 6.5, 8.4, 9.8, 8.6, 11.6, 9.7, 9.3, 8.5, 13.9, 9.0, 7.9, 9.2, 7.7, 9.3, 9.0, 9.1, 8.2, 9.2, 10.8, 9.2, 10.7, 10.3, 6.3, 7.7, 10.6, 9.4, 10.4, 10.4, 9.7, 6.6, 10.6, 12.4, 7.2, 7.8, 6.5, 7.1, 10.4, 9.9, 10.3, 9.3, 13.8, 9.0)
Ci <- c(4.2, 4.1, 3.7, 3.8, 4.5, 3.7, 3.7, 3.4, 3.1, 3.3, 3.5, 3.8, 3.9, 4.4, 4.9, 2.7, 4.8, 4.8, 4.4, 4.7, 4.3, 5.3, 2.8, 4.7, 3.8, 3.3, 4.3, 3.7, 4.1, 3.6, 3.5, 3.8, 4.5, 4.3, 4.0, 4.0, 4.1, 3.1, 3.0, 4.2, 3.2, 3.7, 4.7, 3.7, 2.7, 3.0, 4.9, 2.8, 3.2, 2.8, 2.7, 3.2, 3.4, 3.8, 3.2, 3.1, 3.6)
Cs <- c(11.9, 15.2, 13.2, 11.4, 15.8, 14.5, 15.2, 17.0, 12.7, 14.3, 12.8, 14.6, 15.5, 16.0, 19.6, 11.8, 16.6, 17.6, 17.5, 23.3, 15.0, 18.1, 14.4, 22.7, 16.4, 12.7, 18.6, 16.1, 17.3, 15.6, 13.7, 15.1, 15.9, 18.1, 16.7, 17.4, 15.5, 11.7, 13.9, 18.9, 14.5, 17.9, 19.6, 18.7, 12.9, 17.8, 24.0, 11.9, 14.5, 12.4, 12.9, 17.4, 17.7, 19.7, 17.2, 20.2, 15.4)
PE <- data.frame(Sex, Genotype, Lc, Ltc, Fm, Ti, Dp, Ci, Cs)
row.names(PE) <- c("LL_f_603", "LL_f_562", "LL_f_592", "LL_m_595", "LL_f_602", "LLR_f_625", "LLR_m_589", "LLR_f_658", "LLR_m_528", "LLR_f_557", "LLR_m_529", "LLR_m_574", "LLR_f_616", "LLR_f_767", "LLR_f_800", "LLR_f_479", "LR_m_659", "LR_f_691", "LR_m_668", "LR_f_791", "LR_f_707", "LR_m_714", "LR_m_553", "LR_f_877", "LR_m_650", "LR_f_543", "LR_m_662", "LR_m_561", "LR_f_641", "LR_f_569", "LRR_m_588", "LRR_m_653", "LRR_m_655", "LRR_f_677", "LRR_m_691", "LRR_f_742", "LRR_f_715", "LRR_f_504", "LRR_m_564", "LRR_m_755", "LRR_f_618", "LRR_f_689", "LRR_m_721", "RR_m_706", "RR_m_508", "RR_f_701", "RR_f_825", "RR_f_537", "RR_f_535", "RR_m_521", "RR_f_542", "RR_f_693", "RR_f_710", "RR_m_686", "RR_m_792", "RR_m_930", "RR_m_656")

Now you can create a dataframe from these vectors. We will convert the individual codes into row names.

PE <- data.frame(Sex, Genotype, Lc, Ltc, Fm, Ti, Dp, Ci, Cs)
row.names(PE) <- c("LL_f_603", "LL_f_562", "LL_f_592", "LL_m_595", "LL_f_602", "LLR_f_625", "LLR_m_589", "LLR_f_658", "LLR_m_528", "LLR_f_557", "LLR_m_529", "LLR_m_574", "LLR_f_616", "LLR_f_767", "LLR_f_800", "LLR_f_479", "LR_m_659", "LR_f_691", "LR_m_668", "LR_f_791", "LR_f_707", "LR_m_714", "LR_m_553", "LR_f_877", "LR_m_650", "LR_f_543", "LR_m_662", "LR_m_561", "LR_f_641", "LR_f_569", "LRR_m_588", "LRR_m_653", "LRR_m_655", "LRR_f_677", "LRR_m_691", "LRR_f_742", "LRR_f_715", "LRR_f_504", "LRR_m_564", "LRR_m_755", "LRR_f_618", "LRR_f_689", "LRR_m_721", "RR_m_706", "RR_m_508", "RR_f_701", "RR_f_825", "RR_f_537", "RR_f_535", "RR_m_521", "RR_f_542", "RR_f_693", "RR_f_710", "RR_m_686", "RR_m_792", "RR_m_930", "RR_m_656")

All individuals described in this database belong to the hybridogenic green frog complex, Pelophylax esculentus complex. These include two parental species, the pool frog Pelophylax lessonae (Camerano, 1882) and the lake frog, P. ridibundus (Pallas, 1771), as well as their diploid and triploid hybrids, known as edible frogs, P. esculentus (Linnaeus, 1758). Triploid P. esculentus hybrids are represented by two forms that differ in genome composition in their genotype. Hybrid reproduction is associated with the phenomenon of hemiclonal inheritance. The listed frog forms in various combinations can form hemiclonal population systems (HPS), where during joint reproduction both clonal and recombinant genomes are transmitted (for more details, see the review of the hybridogenic green frog complex).

	Place	East	North	Basin	Sex	DNA	Genotype	Lc	Ltc	Fm	Ti	Dp	Ci	Cs
LL_f_603	Krasnocuts`k	35.16	50.07	Dnipro	female	14.03	LL	60.3	19.4	26.4	25.5	7.6	4.2	11.9
LL_f_562	Chernetchina	35.13	50.05	Dnipro	female	13.95	LL	56.2	18.7	26.6	24.9	6.2	4.1	15.2
LL_f_592	Chernetchina	35.13	50.05	Dnipro	female	13.99	LL	59.2	19.5	28.1	26.1	7.9	3.7	13.2
LL_m_595	Chernetchina	35.13	50.05	Dnipro	male	13.95	LL	59.5	19.9	28.5	28.6	7.5	3.8	11.4
LL_f_602	Chernetchina	35.13	50.05	Dnipro	female	14.02	LL	60.2	21.8	28.7	28.1	8.0	4.5	15.8
LLR_f_625	Izbickoe	36.73	50.20	Don	female	21.83	LLR	62.5	22.1	30.3	29.2	8.3	3.7	14.5
LLR_m_589	DobritzkiyYar	36.31	49.56	Don	male	21.43	LLR	58.9	21.6	29.0	27.7	7.7	3.7	15.2
LLR_f_658	DobritzkiyYar	36.31	49.56	Don	female	21.67	LLR	65.8	24.1	30.6	30.4	9.6	3.4	17.0
LLR_m_528	KreydyanaDacha	36.80	49.43	Don	male	21.62	LLR	52.8	19.6	25.7	24.6	6.6	3.1	12.7
LLR_f_557	KreydyanaDacha	36.80	49.43	Don	female	21.61	LLR	55.7	20.0	25.1	25.7	6.7	3.3	14.3
LLR_m_529	Verbunivs`kaDacha	36.89	49.42	Don	male	21.61	LLR	52.9	19.2	25.8	26.2	7.7	3.5	12.8
LLR_m_574	Verbunivs`kaDacha	36.89	49.42	Don	male	21.64	LLR	57.4	199	26.3	26.7	7.8	3.8	14.6
LLR_f_616	Verbunivs`kaDacha	36.89	49.42	Don	female	21.50	LLR	61.6	23.0	31.6	29.8	9.1	3.9	15.5
LLR_f_767	Zamulivka	36.46	50.08	Don	female	21.43	LLR	76.7	24.0	34.9	34.6	9.5	4.4	16.0
LLR_f_800	Zamulivka	36.46	50.08	Don	female	21.60	LLR	80.0	26.2	38.9	37.6	11.6	4.9	19.6
LLR_f_479	ChervoniyShahtar	37.03	49.18	Don	female	22.03	LLR	47.9	18.9	23.8	24.6	6.5	2.7	11.8
LR_m_659	Sharivka	35.47	50.04	Dnipro	male	14.94	LR	65.9	20.8	30.2	30.0	8.4	4.8	16.6
LR_f_691	Sharivka	35.47	50.04	Dnipro	female	14.91	LR	69.1	22.7	35.9	34.9	9.8	4.8	17.6
LR_m_668	Lipci	36.38	50.21	Don	male	14.86	LR	66.8	22.6	33.5	32.8	8.6	4.4	17.5
LR_f_791	Gaydary	36.33	49.62	Don	female	14.88	LR	79.1	29.9	38.1	39.4	11.6	4.7	23.3
LR_f_707	DobritzkiyYar	36.31	49.56	Don	female	14.95	LR	70.7	22.9	33.4	33.2	9.7	4.3	15.0
LR_m_714	DobritzkiyYar	36.31	49.56	Don	male	14.91	LR	71.4	24.4	35.6	34.1	9.3	5.3	18.1
LR_m_553	VelykaGomol`sha	36.27	49.57	Don	male	14.80	LR	55.3	21.0	26.2	28.0	8.5	2.8	14.4
LR_f_877	SuhaGomol`sha	36.34	49.54	Don	female	15.09	LR	87.7	33.8	37.6	42.3	13.9	4.7	22.7
LR_m_650	KreydyanaDacha	36.80	49.43	Don	male	14.79	LR	65.0	22.5	32.0	31.9	9.0	3.8	16.4
LR_f_543	Verbunivs`kaDacha	36.89	49.42	Don	female	14.91	LR	54.3	19.0	26.6	27.3	7.9	3.3	12.7
LR_m_662	Pechenizhsk`iyRibhoz	36.59	49.52	Don	male	14.72	LR	66.2	25.2	32.1	32.5	9.2	4.3	18.6
LR_m_561	Eschar	36.35	49.47	Don	male	14.91	LR	56.1	20.9	27.3	28.5	7.7	3.7	16.1
LR_f_641	Balakliya	36.48	49.27	Don	female	14.85	LR	64.1	26.8	31.7	32.3	9.3	4.1	17.3
LR_f_569	Gatishe	36.52	50.18	Don	female	14.91	LR	56.9	20.1	26.0	28.1	9.0	3.6	15.6
LRR_m_588	Gaydary	36.33	49.62	Don	male	22.97	LRR	58.8	20.6	28.8	28.9	9.1	3.5	13.7
LRR_m_653	Gaydary	36.33	49.62	Don	male	22.64	LRR	65.3	21.5	31.5	31.9	8.2	3.8	15.1
LRR_m_655	Gaydary	36.33	49.62	Don	male	22.98	LRR	65.5	22.1	32.8	34.5	9.2	4.5	15.9
LRR_f_677	Gaydary	36.33	49.62	Don	female	22.80	LRR	67.7	29.4	33.8	36.4	10.8	4.3	18.1
LRR_m_691	Gaydary	36.33	49.62	Don	male	22.81	LRR	69.1	22.6	33.0	33.4	9.2	4.0	16.7
LRR_f_742	Gaydary	36.33	49.62	Don	female	22.79	LRR	74.2	25.5	35.2	35.6	10.7	4.0	17.4
LRR_f_715	DobritzkiyYar	36.31	49.56	Don	female	22.60	LRR	71.5	22.0	35.3	34.4	10.3	4.1	15.5
LRR_f_504	SuhaGomol`sha	36.34	49.54	Don	female	22.79	LRR	50.4	20.3	23.1	24.8	6.3	3.1	11.7
LRR_m_564	KreydyanaDacha	36.80	49.43	Don	male	22.74	LRR	56.4	19.2	28.3	29.3	7.7	3.0	13.9
LRR_m_755	KreydyanaDacha	36.80	49.43	Don	male	22.73	LRR	75.5	26.8	41.1	37.2	10.6	4.2	18.9
LRR_f_618	Verbunivs`kaDacha	36.89	49.42	Don	female	22.81	LRR	61.8	21.2	28.8	30.2	9.4	3.2	14.5
LRR_f_689	Verbunivs`kaDacha	36.89	49.42	Don	female	22.85	LRR	68.9	24.8	31.6	34.1	10.4	3.7	17.9
LRR_m_721	Balakliya	36.48	49.27	Don	male	22.85	LRR	72.1	27.8	35.9	35.9	10.4	4.7	19.6
RR_m_706	Gorodnee	35.14	50.05	Dnipro	male	16.13	RR	70.6	26.6	32.6	36.2	9.7	3.7	18.7
RR_m_508	Gubarivka	35.35	50.16	Dnipro	male	16.27	RR	50.8	19.1	25.9	27.7	6.6	2.7	12.9
RR_f_701	Lipci	36.38	50.21	Don	female	16.00	RR	70.1	27.0	36.0	37.6	10.6	3.0	17.8
RR_f_825	Martova	36.96	49.93	Don	female	16.18	RR	82.5	31.5	42.3	44.3	12.4	4.9	24.0
RR_f_537	Pechenigy	36.99	49.89	Don	female	16.22	RR	53.7	18.9	25.9	27.3	7.2	2.8	11.9
RR_f_535	SuhaGomol`sha	36.34	49.54	Don	female	15.99	RR	53.5	20.0	26.5	28.1	7.8	3.2	14.5
RR_m_521	ChervonaGusarivka	36.86	49.41	Don	male	16.01	RR	52.1	18.6	24.3	26.7	6.5	2.8	12.4
RR_f_542	ChervonaGusarivka	36.86	49.41	Don	female	16.07	RR	54.2	19.3	26.2	28.2	7.1	2.7	12.9
RR_f_693	Vesele	37.19	49.40	Don	female	16.11	RR	69.3	24.7	34.1	36.2	10.4	3.2	17.4
RR_f_710	Petropillya	37.13	49.09	Don	female	16.01	RR	71.0	25.6	33.9	37.1	9.9	3.4	17.7
RR_m_686	Eschar	36.35	49.47	Don	male	16.03	RR	68.6	26.8	33.6	36.1	10.3	3.8	19.7
RR_m_792	Balakliya	36.48	49.27	Don	male	15.92	RR	79.2	26.2	34.0	35.7	9.3	3.2	17.2
RR_m_930	Zamulivka	36.46	50.08	Don	male	16.08	RR	93.0	26.5	46.2	46.1	13.8	3.1	20.2
RR_m_656	Liman	36.32	49.35	Don	male	16.20	RR	65.6	21.9	35.2	33.7	9.0	3.6	15.4

LRR_m_588
Gaydary
36.{"translated_text": "2.3 Sample file Bufotis_viridis_database.RData\nFor consideration in this course, another database may be useful, relatively larger in size. This is part of the data that were used in the PhD thesis of one of the authors of this textbook (D.Sh.), which was defended in 2004. The thesis was devoted to population diversity of green (Bufotis viridis) and common (Bufo bufo) toads in the Left-Bank Forest-Steppe of Ukraine. Only the part of the data concerning green toads is presented here. The database characterizes toad samples collected at spawning sites using a non-selective method (relative to individuals at the spawning sites) in the late 20th century and early 21st century. The studied toad specimens were transferred for preservation to the Museum of Nature at V.N. Karazin Kharkiv National University. Sampling locations and sample sizes are shown in Fig. 2.3.1. Note: many toponyms have changed since the time of sampling; the map shows the names that existed in 2004!\n\nFig. 2.3.1. Sampling locations, numbers, conventional names, and sample sizes of Bufotis viridis from the Bufotis_viridis_database.RData database\nGeographic coordinates are provided for sampling locations (decimal format for small values). Toads were described by filling out a specific paper form. This form provided for recording the state of a significant number of morphometric characters (Table 2.3.1), as well as alternative phenetic and rank characters (Table 2.3.2). Not all characters used in the description proved useful for studying diversity. Multivariate statistical methods were used in the thesis research; primarily, proportional character values were used (result of dividing metric characters, except the first, by the first, body length). The tables present the old codes used for the listed characters in the thesis defended in 2004; the codes were changed for convenience of using the database, and the old codes are given just in case, for example, to enable comparison of results with the author's abstract of the defended thesis.\nThe described database can be downloaded from the link, as shown in Fig. 2.3.2.\n\nFig. 2.3.2. Downloading Bufotis_viridis_database.RData\nTable 2.3.1. Morphometric characters by which green toads (Bufotis viridis) were described in the Bufotis_viridis_database.RData dataframe\n\nCode\n\nCharacter\n\nCode\n\nCharacter\nOld code\n\n\n\nL01\n\nBody length\n\nC01L\n\n\n\nM02\n\nHead width\n\nP02\nRelative head width\nC02M, C02P\n\n\n\nM03\n\nHead length\n\nP03\nRelative head length\nC03M, C03P\n\n\n\nM04\n\nEye length\n\nP04\nRelative eye length\nC04M, C04P\n\n\n\nM05\n\nDistance between eyelids\n\nP05\nRelative distance between eyelids\nC05M, C05P\n\n\n\nM06\n\nEyelid width\n\nP06\nRelative eyelid width\nC06M, C06P\n\n\n\nM07\n\nDistance from anterior edge of eye to nostril\n\nP07\nRelative distance from anterior edge of eye to nostril\nC07M, C07P\n\n\n\nM08\n\nDistance between nostrils\n\nP08\nRelative distance between nostrils\nC08M, C08P\n\n\n\nM09\n\nDistance from rostrum to nostril\n\nP09\nRelative distance from rostrum to nostril\nC09M, C09P\n\n\n\nM10\n\nDistance from rostrum to corner of jaw\n\nP10\nRelative distance from rostrum to corner of jaw\nC10M, C10P\n\n\n\nM11\n\nDistance from eye to tympanum\n\nP11\nRelative distance from eye to tympanum\nC11M, C11P\n\n\n\nM12\n\nVertical diameter of tympanum\n\nP12\nRelative vertical diameter of tympanum\nC12M, C12P\n\n\n\nM13\n\nHorizontal diameter of tympanum\n\nP13\nRelative horizontal diameter of tympanum\nC13M, C13P\n\n\n\nM14\n\nDistance from rostrum to left parotoid gland\n\nP14\nRelative distance from rostrum to left parotoid gland\nC14M, C14P\n\n\n\nM15\n\nDistance from rostrum to right parotoid gland\n\nP15\nRelative distance from rostrum to right parotoid gland\nC15M, C15P\n\n\n\nM16\n\nDistance between anterior ends of parotoid glands\n\nP16\nRelative distance between anterior ends of parotoid glands\nC16M, C16P\n\n\n\nM17\n\nDistance between posterior ends of parotoid glands\n\nP17\nRelative distance between posterior ends of parotoid glands\nC17M, C17P\n\n\n\nM18\n\nLength of left parotoid gland\n\nP18\nRelative length of left parotoid gland\nC18M, C18P\n\n\n\nM19\n\nLength of right parotoid gland\n\nP19\nRelative length of right parotoid gland\nC19M, C19P\n\n\n\nM20\n\nMaximum width of left parotoid gland\n\nP20\nRelative maximum width of left parotoid gland\nC20M, C20P\n\n\n\nM21\n\nMaximum width of right parotoid gland\n\nP21\nRelative maximum width of right parotoid gland\nC21M, C21P\n\n\n\nM22\n\nMinimum distance between parotoid glands\n\nP22\nRelative minimum distance between parotoid glands\nC22M, C22P\n\n\n\nM23\n\nDistance from rostrum to constriction between parotoid glands\n\nP23\nRelative distance from rostrum to constriction between parotoid glands\nC23M, C23P\n\n\n\nM24\n\nDistance from rostrum to shoulder joint\n\nP24\nRelative distance from rostrum to shoulder joint\nC24M, C24P\n\n\n\nM25\n\nDistance from rostrum to axillary pit\n\nP25\nRelative distance from rostrum to axillary pit\nC25M, C25P\n\n\n\nM26\n\nDistance from corner of jaw to axillary pit\n\nP26\nRelative distance from corner of jaw to axillary pit\nC26M, C26P\n\n\n\nM27\n\nForearm length\n\nP27\nRelative forearm length\nC27M, C27P\n\n\n\nM28\n\nDistance from elbow joint to tip of fingers\n\nP28\nRelative distance from elbow joint to tip of fingers\nC28M, C28P\n\n\n\nM29\n\nLength of outer carpal tubercle\n\nP29\nRelative length of outer carpal tubercle\nC29M, C29P\n\n\n\nM30\n\nLength of inner carpal tubercle\n\nP30\nRelative length of inner carpal tubercle\nC30M, C30P\n\n\n\nM31\n\nLength of trunk between fore and hind legs\n\nP31\nRelative length of trunk between fore and hind legs\nC31M, C31P\n\n\n\nM32\n\nFemur length\n\nP32\nRelative femur length\nC32M, C32P\n\n\n\nM33\n\nTibia length\n\nP33\nRelative tibia length\nC33M, C33P\n\n\n\nM34\n\nLength of longest toe from inner metatarsal tubercle\n\nP34\nRelative length of longest toe from inner metatarsal tubercle\nC34M, C34P\n\n\n\nM35\n\nLength of inner toe from inner metatarsal tubercle\n\nP35\nRelative length of inner toe from inner metatarsal tubercle\nC35M, C35P\n\n\n\nM36\n\nLength of inner metatarsal tubercle\n\nP36\nRelative length of inner metatarsal tubercle\nC36M, C36P\n\nTable 2.3.2. Phenetic (alternative) and discrete (including rank) characters by which green toads (Bufotis viridis) were described in the Bufotis_viridis_database.RData dataframe\n\nCode\n\nCharacter\n\nCharacter state\n\nOld code\n\n\n\nF37\n\nDorsomedial stripe\n\n0 – absent; 1 – straight, interrupted; 2 – sinuous, continuous; 3 – straight, continuous\n\nC37F\n\n\n\nF38\n\nBrightness of dorsal spots\n\n0 – absent; 1 – dull; 2 – contrasting\n\nC38F\n\n\n\nF39\n\nRelative area of dorsal spots\n\n0 – more than background; 1 – background and spots equal; 2 – more spots\n\nC39F\n\n\n\nF40\n\nSize of dorsal spots\n\n0 – absent; 1 – smaller than eye; 2 – as eye; 3 – larger than eye\n\nC40F\n\n\n\nF41\n\nConnectivity of dorsal spots\n\n0 – spots isolated; 1 – connected in groups; 2 – single pattern\n\nC42F\n\n\n\nF42\n\nSymmetry of dorsal pattern\n\n0 – symmetric; 1 – deviation from symmetry; 2 – asymmetric\n\nC43F\n\n\n\nF43\n\nWhite border of dorsal spots\n\n0 – absent; 1 – present\n\nC44F\n\n\n\nF44\n\nBlack border of dorsal spots\n\n0 – absent; 1 – present\n\nC45F\n\n\n\nF45\n\nGland border\n\n0 – diffuse; 1 – distinct\n\nC51F\n\n\n\nF46\n\nSpines on glands\n\n0 – absent; 1 – present\n\nC52F\n\n\n\nF47\n\nGland arrangement\n\n0 – with gaps; 1 – continuous\n\nC53F\n\n\n\nF48\n\nPresence of dorsal gland rows\n\n0 – glands arranged chaotically; 1 – glands arranged in rows\n\nC54F\n\n\n\nF49\n\nGland arrangement on body side\n\n0 – absent; 1 – chaotic; 2 – lateral row in anterior part; 3 – full lateral gland row\n\nC55F\n\n\n\nF50\n\nGland arrangement under lateral row\n\n0 – absent; 1 – chaotic; 2 – arranged in rows\n\nC56F\n\n\n\nF51\n\nDots on throat\n\n0 – absent; 1 – present\n\nC57F\n\n\n\nF52\n\nSmall spots on throat\n\n0 – absent; 1 – rare; 2 – frequent\n\nC58F\n\n\n\nF53\n\nLarge spots on throat\n\n0 – absent; 1 – rare; 2 – frequent\n\nC59F\n\n\n\nF54\n\nThroat background pigmentation\n\n0 – transparent; 1 – white; 2 – pigmented\n\nC60F\n\n\n\nF55\n\nDots on chest\n\n0 – absent; 1 – present\n\nC61F\n\n\n\nF56\n\nSmall spots on chest\n\n0 – absent; 1 – rare; 2 – frequent\n\nC62F\n\n\n\nF57\n\nLarge spots on chest\n\n0 – absent; 1 – rare; 2 – frequent\n\nC63F\n\n\n\nF58\n\nChest background pigmentation\n\n0 – transparent; 1 – white; 2 – pigmented\n\nC64F\n\n\n\nF59\n\nDots on abdomen\n\n0 – absent; 1 – present\n\nC65F\n\n\n\nF60\n\nSmall spots on abdomen\n\n0 – absent; 1 – rare; 2 – frequent\n\nC66F\n\n\n\nF61\n\nLarge spots on abdomen\n\n0 – absent; 1 – rare; 2 – frequent\n\nC67F\n\n\n\nF62\n\nAbdomen background pigmentation\n\n0 – transparent; 1 – white; 2 – pigmented\n\nC68F\n\n\n\nF63\n\nDots on groin\n\n0 – absent; 1 – present\n\nC69F\n\n\n\nF64\n\nSmall spots on groin\n\n0 – absent; 1 – rare; 2 – frequent\n\nC70F\n\n\n\nF65\n\nLarge spots on groin\n\n0 – absent; 1 – rare; 2 – frequent\n\nC71F\n\n\n\nF66\n\nGroin background pigmentation\n\n0 – transparent; 1 – white; 2 – pigmented\n\nC72F\n\n\n\nD67\n\nNumber of spot-stripes on left forearm\n\nC75D\n\n\n\nD68\n\nNumber of spot-stripes on right forearm\n\nC76D\n\n\n\nD69\n\nNumber of spot-stripes on left tibia\n\nC77D\n\n\n\nD70\n\nNumber of spot-stripes on right tibia\n\nC78D\n\n\n\nD71\n\nNumber of 2-joint tubercles on longest toe of left hind leg\n\nC79D\n\n\n\nD72\n\nNumber of 2-joint tubercles on longest toe of right hind leg\n\nC80D\n\n\n\nD73\n\nNumber of 3-joint tubercles on longest toe of left hind leg\n\nC81D\n\n\n\nD74\n\nNumber of 3-joint tubercles on longest toe of right hind leg\n\nC82D\n\n\n\nR75\n\nRank of length of finger I\n\nThe longest finger corresponds to rank 1, others in order of decreasing length. Fingers of equal length have the same rank\n\nC83D\n-\nC86D\n\n\n\nR76\n\nRank of length of finger II\n\n\n\nR77\n\nRank of length of finger III\n\n\n\nR78\n\nRank of length of finger IV"}

Code	Hybridization between a male pool frog and a female lake frog (left), and the result of this hybridization — the interspecific hybrid, edible frog (right)	Code	Hybridization between a male pool frog and a female lake frog (left), and the result of this hybridization — the interspecific hybrid, edible frog (right)	Old code
L01	Body length			S01L
M02	Width of the head	P02	Relative width of the head	S02M, S02P
M03	Length of the head	P03	Relative length of the head	S03M, S03P
M04	Length of the eye	P04	Relative length of the eye	S04M, S04P
M05	Distance between the eyelids	P05	Relative distance between the eyelids	S05M, S05P
M06	Width of the eyelid	P06	Relative width of the eyelid	S06M, S06P
M07	Distance from the anterior edge of the eye to the nostril	P07	Relative distance from the anterior edge of the eye to the nostril	S07M, S07P
M08	Distance between the nostrils	P08	Relative distance between the nostrils	S08M, S08P
M09	Distance from the rostrum to the nostril	P09	Relative distance from the rostrum to the nostril	S09M, S09P
M10	Distance from the rostrum to the angle of the jaw	P10	Relative distance from the rostrum to the angle of the jaw	S10M, S10P
M11	Distance from the eye to the tympanic membrane	P11	Relative distance from the eye to the tympanic membrane	S11M, S11P
M12	Vertical diameter of the tympanic membrane	P12	Relative vertical diameter of the tympanic membrane	S12M, S12P
M13	Horizontal diameter of the tympanic membrane	P13	Relative horizontal diameter of the tympanic membrane	S13M, S13P
M14	Distance from the rostrum to the left suprascapular gland	P14	Relative distance from the rostrum to the left suprascapular gland	S14M, S14P
M15	Distance from the rostrum to the right suprascapular gland	P15	Relative distance from the rostrum to the right suprascapular gland	S15M, S15P
M16	Distance between the anterior ends of the suprascapular glands	P16	Relative distance between the anterior ends of the suprascapular glands	S16M, S16P
M17	Distance between the posterior ends of the suprascapular glands	P17	Relative distance between the posterior ends of the suprascapular glands	S17M, S17P
M18	Length of the left suprascapular gland	P18	Relative length of the left suprascapular gland	S18M, S18P
M19	Length of the right suprascapular gland	P19	Relative length of the right suprascapular gland	S19M, S19P
M20	Greatest width of the left suprascapular gland	P20	Relative greatest width of the left suprascapular gland	S20M, S20P
M21	Greatest width of the right suprascapular gland	P21	Relative greatest width of the right suprascapular gland	S21M, S21P
M22	Smallest distance between the suprascapular glands	P22	Relative smallest distance between the suprascapular glands	S22M, S22P
M23	Distance from the rostrum to the constriction between the suprascapular glands	P23	Relative distance from the rostrum to the constriction between the suprascapular glands	S23M, S23P
M24	Distance from the rostrum to the shoulder joint	P24	Relative distance from the rostrum to the shoulder joint	S24M, S24P
M25	Distance from the rostrum to the axilla	P25	Relative distance from the rostrum to the axilla	S25M, S25P
M26	Distance from the angle of the jaw to the axilla	P26	Relative distance from the angle of the jaw to the axilla	S26M, S26P
M27	Length of the forearm	P27	Relative length of the forearm	S27M, S27P
M28	Distance from the elbow joint to the fingertips	P28	Relative distance from the elbow joint to the fingertips	S28M, S28P
M29	Length of the outer carpal tubercle	P29	Relative length of the outer carpal tubercle	S29M, S29P
M30	Length of the inner carpal tubercle	P30	Relative length of the inner carpal tubercle	S30M, S30P
M31	Length of the trunk between the fore and hind legs	P31	Relative length of the trunk between the fore and hind legs	S31M, S31P
M32	Length of the thigh	P32	Relative length of the thigh	S32M, S32P
M33	Length of the shank	P33		S33M, S33P
M34	Length of the longest toe from the inner metatarsal tubercle	P34	Relative length of the longest toe from the inner metatarsal tubercle	S34M, S34P
M35	Length of the inner toe from the inner metatarsal tubercle	P35	Relative length of the inner toe from the inner metatarsal tubercle	S35M, S35P
M36	Length of the inner metatarsal tubercle	P36	Relative length of the inner metatarsal tubercle	S36M, S36P

Morphometric measurements were taken on fixed frogs using calipers; data were measured to an accuracy of 0.1 mm; measurement results are given in mm. The most significant of these traits is body length. All other traits can be used either as absolute values or as proportions (the ratio of the given trait to body length). Furthermore, for various purposes, indices can be calculated — complex traits that are computed as certain combinations of the original morphometric measurements. Strictly speaking, proportions (ratios of measurements to body length) are also indices, but for convenience, these concepts are narrowed as proposed in this paragraph.

Code	Hybridization between a male pool frog and a female lake frog (left), and the result of this hybridization — the interspecific hybrid, edible frog (right)	State of the trait	Old code
F37	Relative Tibia Length	0 - absent; 1 - straight, broken; 2 - wavy, continuous; 3 - straight, continuous	C37F
F38	Brightness of the spots on the back	0 - absent; 1 - faint; 2 - contrasting	C38F
F39	Relative area of the spots on the back	0 - more background; 1 - background and spots equally; 2 - more spots	C39F
F40	Size of the spots on the back	0 - absent; 1 - smaller than the eye; 2 - like the eye; 3 - larger than the eye	C40F
F41	Connectedness of the spots on the back	0 - spots isolated; 1 - joined in groups of several; 2 - a single pattern	C42F
F42	Symmetry of the pattern on the back	0 - symmetric; 1 - deviation from symmetry; 2 - asymmetric	C43F
F43	A white border of the spots on the back	0 - absent; 1 - present	C44F
F44	Black border of the spots on the back	0 - absent; 1 - present	C45F
F45	Boundary of the glands	0 - blurred; 1 - sharp	C51F
F46	Spinules on the glands	0 - absent; 1 - present	C52F
F47	Arrangement of the glands	0 - with gaps; 1 - continuous	C53F
F48	Presence of rows of dorsal glands	0 - glands arranged chaotically; 1 - glands arranged in rows	C54F
F49	Arrangement of the glands on the side of the trunk	0 - absent; 1 - chaotic; 2 - a lateral row in the anterior part; 3 - a complete lateral row of glands	C55F
F50	Arrangement of the glands below the lateral row	0 - absent; 1 - chaotic; 2 - arranged in rows	C56F
F51	Dots on the throat	0 - absent; 1 - present	C57F
F52	Small spots on the throat	0 - absent; 1 - rare; 2 - frequent	C58F
F53	Large spots on the throat	0 - absent; 1 - rare; 2 - frequent	C59F
F54	Background pigmentation of the throat	0 - transparent; 1 - white; 2 - pigmented	C60F
F55	Dots on the chest	0 - absent; 1 - present	C61F
F56	Small spots on the chest	0 - absent; 1 - rare; 2 - frequent	C62F
F57	Large spots on the chest	0 - absent; 1 - rare; 2 - frequent	C63F
F58	Background pigmentation of the chest	0 - transparent; 1 - white; 2 - pigmented	C64F
F59	Dots on the belly	0 - absent; 1 - present	C65F
F60	Small spots on the belly	0 - absent; 1 - rare; 2 - frequent	C66F
F61	Large spots on the belly	0 - absent; 1 - rare; 2 - frequent	C67F
F62	Background pigmentation of the belly	0 - transparent; 1 - white; 2 - pigmented	C68F
F63	Dots in the groin	0 - absent; 1 - present	C69F
F64	Small spots in the groin	0 - absent; 1 - rare; 2 - frequent	C70F
F65	Large spots in the groin	0 - absent; 1 - rare; 2 - frequent	C71F
F66	Background pigmentation of the groin	0 - transparent; 1 - white; 2 - pigmented	C72F
D67	Number of spot-stripes on the left forearm		C75D
D68	Number of spot-stripes on the right forearm		C76D
D69	Number of spot-stripes on the left shank		C77D
D70	Number of spot-stripes on the right shank		C78D
D71	Number of two-jointed tubercles on the longest toe of the left hind leg		C79D
D72	Number of two-jointed tubercles on the longest toe of the right hind leg		C80D
D73	Number of three-jointed tubercles on the longest toe of the left hind leg		C81D
D74	Number of three-jointed tubercles on the longest toe of the right hind leg		C82D
R75	Rank of the length of finger I of the hand	The longest toe is assigned rank 1, the others in order of decreasing length. Toes of equal length have the same rank	C83D - C86D
R76	Rank of the length of finger II of the hand
R77	Rank of the length of finger III of the hand
R78	Rank of the length of finger IV of the hand