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 chart in Fig 4D in [1].

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

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.

The Data Visualization Corpus

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1430847%2F29f7950c3b7daf11175aab404725542c%2FGettyImages-1187621904-600x360.jpg?generation=1601115151722854&alt=media" alt="">

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!

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

Feed the Future’s Africa Lead II project partnered with The Mediae Company, a Kenya-based media education company, to develop a pilot season of Africa’s first agriculture-focused reality TV program: Don’t Lose the Plot (DLTP). Targeting youth in Kenya and Tanzania, the show aired in Kenya and Tanzania between May and July 2017. The program’s objectives were to encourage youth to consider farming as a lucrative career choice, provide information on how to start agribusinesses, and share useful agronomic information. Africa Lead commissioned Kantar Public East Africa to evaluate the impact of DLTP on knowledge, attitudes, and behavior, or intention to change behavior, related to farming and agribusiness practices. This data asset includes quantitative data collected through a cross-sectional household survey in Kenya and Tanzania. Data collection took place between August and December 2017 and targeted both viewers and non-viewers of DLTP aged 18 to 35 years. A total sample of 3,737 target individuals were interviewed in Kenya, including 406 verified viewers. In Tanzania, 3,383 target individuals were interviewed, including 527 verified viewers.

View the total value of the assets of all Federal Reserve Banks as reported in the weekly balance sheet.

This Excel file contains a quantitative analysis table for 523 manuscripts of the Epistle of Jude. The data were compiled using the open-cbgm library ./compare_witnesses function and a script that I wrote for the purpose of automating the comparison of all witnesses with every other witness and exporting the data to a single file. This script and a pdf guide can be found at https://github.com/dopeyduck/qa-table-starter. The original dataset contained 562 witnesses. This table was generated with all witnesses extant in at least 85% of variation units established during the collation process.

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).

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

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

No description is available. Visit https://dataone.org/datasets/769a7472897d0ad75524327df9673e69 for complete metadata about this dataset.

This dataset is about: (Table 4) Quantitative ICP-MS and ICP-OES data obtained from sediment core GeoB12309-5 (1 mm resolution). Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.770427 for more information.

Firstly Holtfrerich presents the Rostov Concept of the leading sector, before he sketches the development of mining in the Ruhr area by means of theoretical approaches concerning theories on production, price, and investment. In doing so, the author attempts to quantify the connections between the development of coal mining in the Ruhr district and other important sectors by means of an input-output scheme. Thereafter he examines how far the development of mining in the Ruhr area in the 19th century in its major phase of growth complies with the Rostov criteria for the leading sector. Finally Holtfrerich verifies the assumption that mining in the Ruhr district has been a leading sector of the German industrialisation.

Chart register Chart 01: Coal mining in the OBAB Dortmund, the Saar area, and the Kingdom of Prussia (1816-1913) Chart 02: Annual average price of coal in the OBAB Dortmund, nominal and real development (1816-1813) Chart 03: Number of operating coal mines in the OBAB Dortmund, and average production of each mine (1816-1892) Chart 04: Proportion of the five and ten greatest mines as to the total coal production of the mines in the OBAB Dortmund; in percent (1852-1890) Chart 05: Contributions of coal mines in the OBAB Dortmund in 1,000 marks (1850-1895) Chart 06: Tax burden for coal mining in the Lower Rhine region and in Westphalia (1880-1903) Chart 07: Burden of the coal mines in the OBAB Dortmund; coal mine contributions (“Bergwerksabgaben”) and taxes in percent of coal sales value (1816-1913) Chart 08: Annually licenced basic capital of the “Montan-Aktiengesellschaften” (coal, iron and steel corporations) founded in the Ruhr (1840-1870) Chart 10: Average number of workers per year (including mine officials) in the field of coal mining in the OBAB Dortmund (1816-1913) Chart 11: Average annual net payroll and annual net basic wages of the miners in the OBAB Dortmund (1850-1913) Chart 12: Wages in coal mining within the OBAB Dortmund (1850-1903) Chart 13: Working hours in coal mining within the OBAB Dortmund (1852-1892) Chart 14: Labour productivity in coal mining in the OBAB Dortmund (1816-1913) Chart 15: Development of capital investment: disposable steam machines (combined engine power in HP) of coal mines within the OBAB Dortmund (1851-1892) Chart 16: Development of investment: annual increase of steam machine power (in HP) (1852-1892) Chart 18: Development of capital productivity and capital intensity (1851-1892) Chart 19: Data on net value added and capital income in the field of coal mining in the OBAB Dortmund (1850-1903) Chart 20: Capital income/dividends and profits per produced ton of coal for coal mining in the Ruhr area (1850-1892) Chart 21: Proportion of the total coal produced in the Lower Rhine/Westphalian bassin, which was coked by the colliery itself, or – from 1882 on – formed into briquettes(1861-1892) Chart 22: Percentage of propulsion power in HP applied in coal mining within the OBAB Dortmund (1875-1895) Chart 23: Own consumption of coal of mines within the OBAB Dortmund (1852-1892) Chart 24: Development of the profit indicator for coal mining in the Ruhr district (1851-1892) Chart 25: Expansion of the German railway system (1835-1892) Chart 26: Figures on the development of Prussian railways (1844-1882) Chart 27: Development of average revenues for the transport of coal on various railways (1861-1877) Chart 28: Development of the proportion of means of transport with regard to the transport of coal from the Ruhr area (1851-1889) Chart 29: Division of domestic sales of the “Rheinisch-Westfälisches Kohlensyndikat” (Coal Syndicate of the Rhineland and Westphalia) per consumption groups in percent (1902-1906) Chart 30: Wroughtiron production and steel production from coal in the OBAB Dortmund and in the OBAB Bonn (part on the right bank of the Rhine) (1852-1882) Chart 31: Crude iron production in the Ruhr area, OBAB Dortmund (1837-1900) Chart 32: Price development for crude iron, bar iron and cast steel in the Ruhr district (1850-1892) Chart 33: Bar iron production in the OBAB Dortmund and in the OBAB Bonn by means of the charcoal hearth process and the “Puddelverfahren”, a method to produce steel from crude iron (1835-1870) Chart 34: The importance of the economic sectors according to their respective employment figures (1852-1875).

The data sets are part of the study titled "A web-based Delphi multi-criteria group decision-making framework for renewable energy project development processes." The study aims to outline and implement the web-based Delphi Multi-criteria Group Decision Making (Delphi-MGDM) Framework, which is intended to facilitate top-level group decision-making for renewable energy project development and long-term strategic direction setting. The datasets include: (1) the weights of criteria obtained from judgments of the experts, (2) the summary of criteria scores, (3) the comparison table dataset, and (4) the full report of the Visual PROMETHEE. “Criteria Weighing Dataset” is obtained from the judgment of experts using the AHP-Online System created by Klaus D. Goepel (available at https://bpmsg.com/ahp/ahp.php). On a pairwise comparison basis, we asked the experts to make their opinion on four (4) criteria and then the sixteen (16) sub-criteria in three rounds. The group weights after the third round are considered the final weights of criteria and sub-criteria. To rank RES using MCDA, we used the data from the literature and the Philippines’ DOE for all ten quantitative sub-criteria. However, there are six qualitative sub-criteria, so we asked the opinion of experts on how solar, wind, biomass, and hydro-power are performing in each criterion based on their knowledge and expertise. This time, we used a self-derived questionnaire and as a summary of this process, we produced the “Scoring of Options Dataset.” We got the average, minimum and maximum values of the scores to make data for the ranking in three cases (realistic, pessimistic, and optimistic). "Comparison table" dataset is composed of comparison tables for the three cases. Table A reflects the data for realistic case in which we use the averages of the qualitative inputs from experts, the averages of quantitative data obtained in ranges, and the actual value of data not given in ranges. Table B reflects the data for the optimistic case. For qualitative data, we used the minimum value of the sub-criteria to be minimized and maximum value for sub-criteria to maximized. For quantitative data in ranges, we used the minimum value of cost sub-criteria and maximum value of benefit sub-criteria. We estimated fictitious data for some quantitative data not given in ranges. Table C reflects the data for the pessimistic case. We used the same concept with Table B, but with opposite choices. For instance, we used the maximum value of cost sub-criteria and minimum value of benefit sub-criteria for quantitative data. Finally, we used Visual PROMETHEE (available at http://www.promethee-gaia.net/vpa.html) to rank renewable energy sources. The "Visual PROMETHEE Full Report" dataset is the actual report exported from the Visual PROMETHEE application – containing a partial and complete ranking of RES.

Central Bank Balance Sheet in the United States increased to 6661912 USD Million in July 9 from 6659598 USD Million in the previous week. This dataset provides - United States Central Bank Balance Sheet - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Per Imran Shah, this was the Data used in and published as supplemental material for this manuscript. Table S1. Aggregated point of departure (POD) data obtained from ToxRefDB v2.0. Table S2. Chemical structure descriptor data from DSSTox. Table S3. Chemical cluster membership. Table S5. GenRA optimal predictions for each endpoint category and cluster.

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.

Approaches quantifying relative congruence, or incongruence, of molecular divergence estimates and the fossil record have been limited. Previously proposed methods are largely node specific, assessing incongruence at particular nodes for which both fossil data and molecular divergence estimates are available. These existing metrics, and other methods that quantify incongruence across topologies including entirely extinct clades, have so far not taken into account uncertainty surrounding both the divergence estimates and the ages of fossils. They have also treated molecular divergence estimates younger than previously assessed fossil minimum estimates of clade age as if they were the same as cases in which they were older. However, these cases are not the same. Recovered divergence dates younger than compared oldest known occurrences require prior hypotheses regarding the phylogenetic position of the compared fossil record and standard assumptions about the relative timing of morphologic...