Quantitative data underlying graphs published in Figs 1–5.

The "Dataset_Graph.zip" file contains the graph models of the trees in the dataset. These graph models are saved in the "pickle" format, which is a binary format used for serializing Python objects. The graph models capture the structural information and relationships of the cylinders in each tree, representing the hierarchical organization of the branches.

The Data Visualization Corpus

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Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions

The Data Visualizaion Copus

The Data Visualization corpus consists:

• 32 cheat sheets: This includes A-Z about the techniques and tricks that can be used for visualization, Python and R visualization cheat sheets, Types of charts, and their significance, Storytelling with data, etc..

• 32 Charts: The corpus also consists of a significant amount of data visualization charts information along with their python code, d3.js codes, and presentations relation to the respective charts explaining in a clear manner!

• Some recommended books for data visualization every data scientist's should read:

  1. Beautiful Visualization by Julie Steele and Noah Iliinsky
  2. Information Dashboard Design by Stephen Few
  3. Knowledge is beautiful by David McCandless (Short abstract)
  4. The Functional Art: An Introduction to Information Graphics and Visualization by Alberto Cairo
  5. The Visual Display of Quantitative Information by Edward R. Tufte
  6. storytelling with data: a data visualization guide for business professionals by cole Nussbaumer knaflic
  7. Research paper - Cheat Sheets for Data Visualization Techniques by Zezhong Wang, Lovisa Sundin, Dave Murray-Rust, Benjamin Bach

Suggestions:

In case, if you find any books, cheat sheets, or charts missing and if you would like to suggest some new documents please let me know in the discussion sections!

Resources:

• Charts: I personally recommend data viz catalogue, it's easy to understand with their explanation!
• Python codes: Plotly for python and Python graph gallery
• R codes for charts:Plotly for R
• d3 codes: Visualization codes using d3

Request to kaggle users:

• A kind request to kaggle users to create notebooks on different visualization charts as per their interest by choosing a dataset of their own as many beginners and other experts could find it useful!

• To create interactive EDA using animation with a combination of data visualization charts to give an idea about how to tackle data and extract the insights from the data

Suggestion and queries:

Feel free to use the discussion platform of this data set to ask questions or any queries related to the data visualization corpus and data visualization techniques

Kindly upvote the dataset if you find it useful or if you wish to appreciate the effort taken to gather this corpus! Thank you and have a great day!

Wikipedia is the largest and most read online free encyclopedia currently existing. As such, Wikipedia offers a large amount of data on all its own contents and interactions around them, as well as different types of open data sources. This makes Wikipedia a unique data source that can be analyzed with quantitative data science techniques. However, the enormous amount of data makes it difficult to have an overview, and sometimes many of the analytical possibilities that Wikipedia offers remain unknown. In order to reduce the complexity of identifying and collecting data on Wikipedia and expanding its analytical potential, after collecting different data from various sources and processing them, we have generated a dedicated Wikipedia Knowledge Graph aimed at facilitating the analysis, contextualization of the activity and relations of Wikipedia pages, in this case limited to its English edition. We share this Knowledge Graph dataset in an open way, aiming to be useful for a wide range of researchers, such as informetricians, sociologists or data scientists.

There are a total of 9 files, all of them in tsv format, and they have been built under a relational structure. The main one that acts as the core of the dataset is the page file, after it there are 4 files with different entities related to the Wikipedia pages (category, url, pub and page_property files) and 4 other files that act as "intermediate tables" making it possible to connect the pages both with the latter and between pages (page_category, page_url, page_pub and page_link files).

The document Dataset_summary includes a detailed description of the dataset.

Thanks to Nees Jan van Eck and the Centre for Science and Technology Studies (CWTS) for the valuable comments and suggestions.

Use of vectors in financial graphs By using mathematical vectors calculations as financial modeling then further into a new form of quantitative analysis instrument for linear financial computation graphs. A new tool in financial data analysis as an indicator.

This is the third lab in an Introductory Physical Geography/Environmental Studies course. It introduces students to different data types (qualitative vs quantitative), basic statistical analyses (correlation analysis s, t-test), and graphing techniques.

Index Time Series for Alpha Architect U.S. Quantitative Momentum ETF. The frequency of the observation is daily. Moving average series are also typically included. Under normal circumstances,the fund will invest at least 80% of its net assets (plus any borrowings for investment purposes) in U.S.- listed companies that meet the Sub-Adviser"s definition of momentum ("Momentum Companies "). The Sub-Adviser employs a multi-step, quantitative, rules-based methodology to identify a portfolio of approximately 50 to 200 equity securities with the highest relative momentum.

Index Time Series for Alpha Architect U.S. Quantitative Value ETF. The frequency of the observation is daily. Moving average series are also typically included. The Sub-Adviser employs a multi-step, quantitative, rules-based methodology to identify a portfolio of approximately 50 to 200 undervalued U.S. equity securities with the potential for capital appreciation. A security is considered to be undervalued when it trades at a price below the price at which the Sub-Adviser believes it would trade if the market reflected all factors relating to the company"s worth.

Inhibitory effect of antisense TcCaNA2 oligonucleotides on T. cruzi cell invasion and proliferation. (XLSX)

We propose a novel approach to predict saturation vapor pressures using group contribution-assisted graph convolutional neural networks (GC2NN), which use both, molecular descriptors like molar mass and functional group counts, as well as molecular graphs containing atom and bond features, as representations of molecular structure. Molecular graphs allow the ML model to better infer molecular connectivity and spatial relations compared to methods using other, non-structural embeddings. We achieve best results with an adaptive-depth GC2NN, where the number of evaluated graph layers depends on molecular size. We apply the model to compounds relevant for the formation of SOA, achieving strong agreement between predicted and experimentally-determined vapor pressure. In this study, we present two models: a general model with broader scope, achieving a mean absolute error (MAE) of 0.69 log-units (R2 = 0.86), and a specialized model focused on atmospheric compounds (MAE = 0.37 log-units, R2 = 0.94).

The projects map file is provided in .kml format, allowing users to view the locations of the 40 projects on Earth browsers such as Google Earth. This file serves as a guide for locating each project based on their respective project names.

Index Time Series for Invesco Quantitative Strats Glbl Eq Lw Vol Lw Crbn UCITS ETF Acc EUR. The frequency of the observation is daily. Moving average series are also typically included. NA

Dataset containing supplemental material for the publication "2D, 2.5D, or 3D? An Exploratory Study on Multilayer Network Visualizations in Virtual Reality" This dataset contains: 1) archive containing all raw quantitative results, 2) archive containing all raw qualitative data, 3) archive containing the graphs used for the experiment (.graphml file format), 4) the code to generate the graph library (C++ files using OGDF), 5) a PDF document containing detailed results (with p-values and more charts), 6) a video showing the experimentation from a participant's point of view. 7) complete graph library generated by our graph generator for the experiment

Readme file for describing the dataset

This repository contain datasets and results for the paper:

Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis

Github repository for the code:

Quantifying Language Confusion GitHub repo

DATA include the following datasets:

i) raw language graphs and

ii) the calculated language similarities from the language graphs,

iii) MTEI: the files from the experimental results of multilingual inversion attacks, and calculated language confusion entropy from the data;

iv) LCB: the files from the language confusion benchmark and calculated language confusion entropy from the data

Results include aggregated results for further analysis:

i) inversion_language_confusion: results from MTEI

ii) prompting_language_confusion: results from LCB

The dataset is comprised of quantitative plot-based data. It lists vascular plant richness values which includes total species richness and the richness of different growth forms and at different taxonomic levels. The dataset also bring about the associated environmental data per plot.

In the "Dataset_pointcloud.zip," you will find two files related to the point clouds in the dataset: "Dataset_building_other.zip" and "Dataset_tree.zip." The "Dataset_building_other.zip" file contains separate text files for each project, specifically for the "Buildings" and "Other" point clouds. On the other hand, the "Dataset_tree.zip" file includes all the point cloud files for the trees in each project. These files are in TXT format and consist of four main numbers representing each point in the point clouds. The first three numbers represent the location coordinates of the point. These coordinates typically correspond to the X, Y, and Z coordinates in a 3D space, indicating the position of the point within the project. The fourth number in each line represents the intensity value of the point.

The study examines the study quality in 8 top-tier L2 journals over 12 years. The file named "Final coded data 8-12" includes all data. the first rows in the excel file can be used to interpret the codes. There are other excel files that were used in creating the graphs for the papers. The R code for creating the graphs is in a Microsoft word file, labeled "Graphics Coding".

The "Dataset_QSM.zip" file includes three directories: "opt," "optcsv," and "trans," which correspond to each project in the dataset. The "opt" directory contains the main Quantitative Structure Model (QSM) files in ".mat" format. These files store the structural information of the tree cylinders, including their geometry and other relevant attributes. In the "optcsv" directory, you can find the extracted features from the QSM files in a more accessible format, specifically as ".csv" files. These files contain the selected features of the cylinders, making it easier to work with and analyze the QSM data. Lastly, the "trans" directory holds the transformation information files. These files provide the necessary details for converting the location coordinates of the cylinders to the project's coordinate system.

Index Time Series for Invesco Markets II PLC - Invesco Quantitative Strategies ESG Global Equity Multi-Factor UCITS ETF. The frequency of the observation is daily. Moving average series are also typically included. NA