The Excel spreadsheet contains the quantitative questions (Questions 1, 3 and 4). Each question is analysed in the form of a frequency distribution table and a pie chart.

Underlying quantitative data in support of the charts in Fig 6 in [1].

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!

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in A Portfolio Model of Quantitative Easing, PIIE Working Paper 16-7. If you use the data, please cite as: Christensen, Jens H. E., and Signe Krogstrup. (2016). A Portfolio Model of Quantitative Easing. PIIE Working Paper 16-7. Peterson Institute for International Economics.

Dataset chart Quantitative Information Social Issues Racial Mental Emotional PhD Dr.David Render Solving Categorizing Identifying Social Issues Human Impact In Part National Case Studies Chicagoland Business & Los Angeles Economic Territories

Primers used for quantitative real-time PCR, CHART-PCR and ChIP.

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 0.67 log-units (R2 = 0.86), and a specialized model focused on atmospheric compounds (MAE = 0.36 log-units, R2 = 0.97).

Deep-water benthic ostracodes from the Pliocene-Pleistocene interval of ODP Leg 107, Hole 654A (Tyrrhenian Sea) were studied. From a total of 106 samples, 40 species considered autochthonous were identified. Detailed investigations have established the biostratigraphic distribution of the most frequent ostracode taxa. The extinction levels of Agrenocythere pliocenica (a psychrospheric ostracode) in Hole 654A and in some Italian land sections lead to the conclusion that the removal of psychrospheric conditions took place in the Mediterranean Sea during or after the time interval corresponding to the Small Gephyrocapsa Zone (upper part of early Pleistocene), and not at the beginning of the Quaternary, as previously stated. Based on a reduced matrix of quantitative data of 63 samples and 20 variables of ostracodes, four varimax assemblages were extracted by a Q-mode factor analysis. Six factors and eight varimax assemblages were recognized from the Q-mode factor analysis of the quantitative data of 162 samples and 47 variables of the benthic foraminifers. The stratigraphic distributions of the varimax assemblages of the two faunistic groups were plotted against the calcareous plankton biostratigraphic scheme and compared in order to trace the relationship between the benthic foraminifers and ostracodes varimax assemblages. General results show that the two populations, belonging to quite different taxa, display almost coeval changes along the Pliocene-Pleistocene sequence of Hole 654A, essentially induced by paleoenvironmental modifications. Mainly on the base of the benthic foraminifer assemblages (which are quantitatively better represented than the ostracode assemblages), it is possible to identify such modifications as variations in sedimentation depth and in bottom oxygen content.

Underlying quantitative data in support of the charts in Figs 4A and C in [1].

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Quantity Theory of Money Redux? Will Inflation Be the Legacy of Quantitative Easing?, PIIE Policy Brief 15-7. If you use the data, please cite as: Cline, William R. (2015). Quantity Theory of Money Redux? Will Inflation Be the Legacy of Quantitative Easing?. PIIE Policy Brief 15-7. Peterson Institute for International Economics.

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.

Supplementary Materials

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.

Research in digital fabrication, specifically in 3D concrete printing (3DCP), has seen a substantial increase in publication output in the past five years, making it hard to keep up with the latest developments. The 3dcp.fyi database aims to provide the research community with a comprehensive, up-to-date, and manually curated literature dataset documenting the development of the field from its early beginnings in the late 1990s to its resurgence in the 2010s until today. The data set is compiled using a systematic approach. A thorough literature search was conducted in scientific databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) scheme. This was then enhanced iteratively with non-indexed literature through a snowball citation search. The authors of the articles were assigned unique and persistent identifiers (ORCID® IDs) through a systematic process that combined querying APIs systematically and manually curating data. The works in the data set also include references to other works, as long as those referenced works are also included within the same data set. A citation network graph is created where scientific articles are represented as vertices, and their citations to other scientific articles are the edges. The constructed network graph is subjected to detailed analysis using specific graph-theoretic algorithms, like PageRank. These algorithms evaluate the structure and connections within the graph, yielding quantitative metrics. Currently, the high-quality dataset contains more than 2600 manually curated scientific works, including journal articles, conference articles, books, and theses, with more than 40000 cross-references and 2000 authors, opening up the possibility for more detailed analysis. The data is published on https://3dcp.fyi, ready for import into several reference managers, and is continuously updated. We encourage researchers to enrich the database by submitting their publications, adding missing works, or suggesting new features.

The quantitative sensitivity is defined as the product of the groundwater residence time with the infiltration rate. When a water particle remains underground twice as long, it is twice as important for the water quality in an aquifer. The same applies to the infiltration rate. When twice as much infiltration occurs in one area as in another area (and the residence time is the same), the area is twice as important for the overall groundwater quality. The map therefore indicates which areas are important for the 'overall water quality', only taking into account conservative transport, and thus without taking degradation and retardation into account. Insight into the quantitative sensitivity is important, because the most important characteristic of groundwater systems is that the greater part remains shallow and short in the subsurface, and a very small part of an area largely determines the deeper water quality. The chart shows the quantitative sensitivity on a logarithmic scale. No data is yet available for the municipality of Vijfheerenlanden, which has been part of the province of Utrecht since 1 January 2019.

One of the major duties the Bank of England (BoE) is tasked with is keeping inflation rates low and stable. The usual tactic for keeping inflation rates down, and therefore the price of goods and services stable by the Bank of England is through lowering the Bank Rate. Such a measure was used in 2008 during the global recession when the BoE lowered the bank base rate from **** percent to *** percent. Due to the economic fears surrounding the COVID-19 virus, as of the 19th of March 2020, the bank base rate was set to its lowest ever standing. The issue with lowering interest rates is that there is an end limit as to how low they can go. Quantitative easing Quantitative easing is a measure that central banks can use to inject money into the economy to hopefully boost spending and investment. Quantitative easing is the creation of digital money in order to purchase government bonds. By purchasing large amounts of government bonds, the interest rates on those bonds lower. This in turn means that the interest rates offered on loans for the purchasing of mortgages or business loans also lowers, encouraging spending and stimulating the economy. Large enterprises jump at the opportunity After the initial stimulus of *** billion British pounds through quantitative easing in March 2020, the Bank of England announced in June that they would increase the amount by a further 100 billion British pounds. In March of 2020, the headline flow of borrowing by non-financial industries including construction, transport, real estate and the manufacturing sectors increased significantly.

The dataset and replication package of the study "A continuous open source data collection platform for architectural technical debt assessment".

Abstract

Architectural decisions are the most important source of technical debt. In recent years, researchers spent an increasing amount of effort investigating this specific category of technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic.

However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings.

In response to this concern, researchers strive to expand the scope of their study by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study.

To address this issue, this paper presents our initial attempt at tackling this problem and enabling researchers to study architectural smells at large scale, a well-known indicator of architectural technical debt. Specifically, we introduce a novel approach to data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets using Arcan, a tool for architectural smells detection (or any other tool).

Finally, we present the publicly-available dataset resulting from the first three months of execution of the pipeline, that includes over 30,000 analysed commits and releases from over 10,000 open source GitHub projects written in 5 different programming languages and amounting to over a billion of lines of code analysed.

The dataset includes 15 visual diagrams (pie and bar charts) comparing the distribution of agricultural residues, OFMSW, and used cooking oil across each state in Nigeria, province in South Africa, and county in Kenya. These summaries provide a comparative overview of regional feedstock strengths. The charts complement quantitative analyses by providing visual summaries of feedstock availability.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Can Cavendish Financial (CAV) Chart a Course for Growth?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Underlying quantitative data in support of the chart in Fig 4B in [1].