New content was added to the bond order calculator page discussed at
and available at
New content was added to the bond order calculator page discussed at
and available at
In the paper, “Development of gene-based molecular markers tagging low alkaloid pauper locus in white lupin (Lupinus albus L.)”, published online on 08-13-2019 in Journal of Applied Genetics, Springer (https://link.springer.com/content/pdf/10.1007%2Fs13353-019-00508-9.pdf), the authors computed the Sokal-Michener (simple matching) and Rogers-Tanimoto coefficients with our Binary Similarity Calculator (http://www.minerazzi.com/tools/similarity/binary-similarity-calculator.php), which computes 72 different resemblance (similarity) measures.
I’m so happy to know that more and more researchers across disciplines are finding new uses for this free-to-use tool.
Building these types of tools are always fun, but are even more gratifying when these provide that extra handy help to other researchers. That’s why I decided to go the multidisciplinary way in the sciences. Their success is my success.
If you are a chemist, PhD, or student looking for tools in said discipline, you may want to check the City College Chemistry Web Resource Guide, part of the City College of New York Libraries at CUNY. This is an excellent repository of chemistry resources.
This fall, the college redesigned the site so the web address of the computational chemistry section is now https://library.ccny.cuny.edu/chemistry/computational
Happy to know that two of our chemistry tools are still listed there:
The Bond Order Calculator — http://www.minerazzi.com/tools/bond-order/calculator.php Computes bond orders of diatomic species and their ions having up to 20 electrons, including number of bonding and anti-bonding electrons, without using Molecular Orbital Theory (MOT).
The Hydrocarbons Parser http://www.minerazzi.com/tools/hydrocarbons/parser.php — Calculates boiling points and indicates sigma, pi, single, double, and triple bonds for hydrocarbons, again without using MOT.
I wish that more universities follow in the steps of CUNY and be willing to put together similar repositories, I mean computational chemistry tools, for the benefit of their students and faculties.
When you are a multidisciplinary scientist or teacher, one way of measuring your success is by looking at what students and others in different fields and countries do with the tools and resources you develop. Satisfaction goes all the way up when these help make a difference in their life.
I’m happy to know that in his 2018 PhD thesis “On Enhancing the Security of Time Constrained Mobile ContactlessTransactions” (https://pure.royalholloway.ac.uk/portal/files/33898207/Iakovos_Gurulian_PhD_Thesis.pdf), the author, Iakovos Gurulian from the Information Security Group, Department of Mathematics at the prestigious Royal Holloway, University of London, developed a Python program capable of running our Binary Similarity Calculator (http://www.minerazzi.com/tools/similarity/binary-similarity-calculator.php), which computes 72 different similarity measures. See pages 87-89, tables 4.1 and 4.2, and reference 118 of the thesis.
The Tutorial on Distance and Similarity (http://www.minerazzi.com/tutorials/distance-similarity-tutorial.pdf) was also cited as reference 60.
According to https://www.topuniversities.com/, Royal Holloway ranks 6/10 in London and 291/1000 in the world. Famous for its Founder’s Building, one of the most spectacular university buildings in the world, the College was officially opened by Queen Victoria in 1886.
This bond order tool calculates bond orders of diatomic species with up to 20 electrons, without using Molecular Orbital Theory! It is available at
We developed the tool inspired in Dr. Arijit Das set of innovative and time economic formulae for chemical education. His methodologies are suitable for computer-based learning (CBL) activities or for writing computer programs for solving chemistry problems.
Unlike with other bond order calculator tools, to use ours you don’t need to write Lewis structures, and electron configurations, or count electrons, bonds, orbitals, and atoms. Just enter a chemical formula and the tool will do the rest for you.
In my opinion, students who know how to write programs for solving chemistry problems have an edge when taking quantitative courses like analytical chemistry, instrumental analysis, chemometrics, computational chemistry, and similar courses. I think they are better prepared for multidisciplinary research work than those who cannot code.
Developing this tool was really gratifying as the work inspired us to derive an algorithm for predicting number of unpaired electrons and magnetic properties of single atoms, diatomic species, and their ions. Hopefully, this algorithm will be available early next year in the form of a new chemistry calculator.
We are also developing a tool that computes bond orders of all kind of species, including the polyatomic cases.
We are sincerely in debt to Dr. Arijit Das from Ramthakur College, Agartala, West Tripura, India for encouraging us to develop this tool for educators, scholars, and chemistry students.
This tool, as our Hydrocarbons Parser (http://www.minerazzi.com/tools/hydrocarbons/parser.php) is listed in the City College Chemistry Web Resources Guide at CUNY. Find them both in the guide Computational Chemistry category (https://library.ccny.cuny.edu/chemistry/computational)
We have updated and improved our Regression & Correlation Calculator to demonstrate, as shown in the above figure, that a Spearman’s Correlation Coefficient is just a Pearson’s Correlation Coefficient computed from ranks.
The tool uses an algorithm that converts values to ranks and averages any ties that might be present before calculating the correlations. This comes handy when we need to compute a Spearman’s Correlation Coefficient from ranks with a large number of ties.
We have explained in the “What is Computed?” section of the page’s tool that as the number of ties increases the classic textbook formula for computing Spearman’s correlations
increasingly overestimates the results, even if ties were averaged.
By contrast, computing a Spearman’s as a Pearson’s always work, even in the presence or absence of ties.
To illustrate the above, consider the following two sets:
X = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Y = [1, 1, 1, 1, 1, 1, 1, 1, 1, 2]
using Spearman’s classic equation rs = 0.6364 ≈ 0.64.
By contrast, rs = 0.5222 ≈ 0.52 when computed as a Pearson coefficient derived from ranks. This is a non trivial difference.
Accordingly, we can make a case as to why we should ditch for good Spearman’s classic formula.
We also demonstrate in the page’s tool why we should never arithmetically add or average Spearman’s correlation coefficients. The same goes for Pearson’s.
Early articles in the literature of correlation coefficients theory failed to recognize the non-additivity of Pearson’s and Spearman’s Correlation Coefficients.
Sadly to say, this is sometimes reflected in current research articles, textbooks, and online publications. The worst offenders are some marketers and teachers that, in order to protect their failing models, resist to consider up-to-date research on the topic.
PS. Updated on 09-14-2018 to include the numerical example and to rewrite some lines.
Some developers build form-based graphical user interfaces (GUIs) that give users the illusion of mapping the value of an input field to all other fields. Typical examples are conversion unit tools and other types of converters used in science and business oriented sites. This is frequently done by coding in the background M number of fields M number of times, with most fields hidden or dynamically coded. These M x M fields are then conditionally processed.
As M increases said strategy becomes very inefficient from both the coding and processing standpoint. Modifying these types of GUIs can be messy. For instance, to display a simple unit conversion tool with five conversion units requires the coding of 25 fields. To add an additional conversion unit requires the coding of 6 x 6 = 36 fields. Insane!
To overcome all those drawbacks, we have developed what we call a one-to-many fields mapping algorithm or O2M. The algorithm is quite simple and works as follows. Given a form with M unique text fields, randomly using one as an input field instructs the algorithm to treat the remaining ones as output fields. It does not matter which field is initially used or from where the data comes from (i.e., a user or database). Its value will be mapped to the remaining M – 1 fields. As a whole, an O2M GUI behaves as a many-to-many (M2M) solution. To grasp the concept, try one of our O2M tools at
This is a new Minerazzi tool available now at
The tool does simple linear regression and correlation analyses, computing Spearman and Pearson correlation coefficients and other relevant statistics.
The companion default example was intentionally selected to illustrate that for rank data free from ties, Spearman and Pearson correlation coefficients are the same thing.
This is a new Minerazzi tool, available now at
This tool estimates t-scores from p-values and vice versa for a given number of degrees of freedom υ. Just enter a (t,υ) or (p,υ) pair and this tool will solve for the missing term.
The tool also estimates the statistics that one would obtain if the computed estimates correspond to a set of paired variables (x, y), or to effect sizes from any two samples of same sizes (n1 and n2); i.e. samples with same degrees of freedom (υ1 and υ2).
The tool’s page lists some interesting exercises and good references for effect size conversions and meta-analysis in general. Enjoy it!
This is a new tool available now at
This tool allows you to easily generate a customized table of Student’s t-values.
That is done by iteratively calling (i.e., looping) the very same algorithm that we use for our t-values Calculator.
This tool comes handy when you don’t have statistical t-tables around or are working with p and t values, or degrees of freedom, not available from such tables. Avoid pausing a problem for annoying linear interpolation workarounds!