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

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 Figs 4A and C in [1].

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.

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.

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.

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.

We collated existing quantitative data on avian dietary composition of 58 waterbird species that make use of the Chesapeake Bay. From this database, we quantified the relative importance of forage taxa to the diets of each waterbird species. This data will enable us to develop a comprehensive suite of forage taxa indicators whose abundance and distributions can be monitored as a proxy for Chesapeake Bay ecosystem health. These data support a paired USGS authored publication.

The ability to observe and interpret images and clinical information is essential for veterinarians in clinical practice. The purpose of this study is to determine the utility of a novel teaching method in veterinary medicine, the incorporation of art interpretation using the Visual Thinking Strategies (VTS), on students’ observational and clinical interpretation skills when evaluating radiographs and patient charts. Students were asked to observe and interpret a set of radiographs and a patient chart, subsequently involved in art interpretation using VTS, and then asked to observe and interpret a different set of radiographs and a different patient chart. Qualitative and quantitative analysis was performed, including scoring of observations and interpretations by a radiologist and emergency and critical care resident. For radiographs, observation and interpretation scores increased significantly after VTS. There was no change in patient chart observation or interpretation scores after VTS. Broadly, VTS provided creative thinking and visual literacy exercises that students felt pushed students them to think more openly, notice subtleties, use evidential reasoning, identify thinking processes, and integrate details into a narrative. However, its impact on clinical reasoning, as assessed by chart observation and interpretation scores, was uncertain. Further studies are needed to determine the optimal way to incorporate art interpretation in the veterinary medical curriculum.

The dataset includes Supplementary Tables for the Thesis of Baptiste Imbert. The tables of two manuscripts are available: (1) Imbert, B et al. (2023). “Development of a Knowledge Graph Framework to Ease and Empower Translational Approaches in Plant Research: A Use-Case on Grain Legumes”. Published in: Frontiers in Artificial Intelligence 6. ISSN: 2624-8212. DOI: 10.3389/frai.2023.1191122. (2) Imbert, B et al. (in prep). "Genome-wide association study of frost tolerance in Vicia faba reveals syntenic loci in cool-season legumes and highlights relevant candidate genes"

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

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).

Key Table Information.Table Title.Island Areas: Selected Statistics by Construction Industry and Legal Form of Organization for Puerto Rico: 2022.Table ID.ISLANDAREASIND2022.IA2200IND08.Survey/Program.Economic Census of Island Areas.Year.2022.Dataset.ECNIA Economic Census of Island Areas.Source.U.S. Census Bureau, 2022 Economic Census of Island Areas, Core Statistics.Release Date.2024-12-19.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.2022 Economic Census of Island Areas tables are released on a flow basis from June through December 2024.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe. The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in Puerto Rico, have paid employees, and are classified in one of eighteen in-scope sectors defined by the 2022 NAICS..Sponsor.U.S. Department of Commerce.Methodology.Data Items and Other Identifying Records.Number of establishmentsAnnual payroll ($1,000)Number of employeesSales, value of shipments, or revenue ($1,000)Value of construction work ($1,000)Net value of construction work ($1,000)Value added ($1,000)Cost of materials, components, packaging and/or supplies used, minerals received, or purchased machinery installed ($1,000)Cost of construction work subcontracted out to others ($1,000)Total capital expenditures for buildings, structures, machinery, and equipment (new and used) ($1,000)Total rental payments and lease payments ($1,000)Gross value of depreciable assets (acquisition costs, end of year) ($1,000)Range indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesRange indicating imputed percentage of total sales, value of shipments, or revenueEach record includes a LFO code, which represents a specific legal form of organization category.The data are shown for legal form of organization.Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the Economic Census of Island Areas are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed..Geography Coverage.The data are shown for employer establishments and firms that vary by industry:At the Territory level for Puerto RicoFor information about economic census geographies, including changes for 2022, see Economic Census: Economic Geographies..Industry Coverage.The data are shown for Puerto Rico at the 2- through 5-digit 2022 NAICS code levels for the construction industry.For information about NAICS, see Economic Census Code Lists..Sampling.The Economic Census of Island Areas is a complete enumeration of establishments located in the islands (i.e., all establishments on the sampling frame are included in the sample). Therefore, the accuracy of tabulations is not affected by sampling error..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY24-0044).The primary method of disclosure avoidance protection is noise infusion. Under this method, the quantitative data values such as sales or payroll for each establishment are perturbed prior to tabulation by applying a random noise multiplier (i.e., factor). Each establishment is assigned a single noise factor, which is applied to all its quantitative data value. Using this method, most published cell totals are perturbed by at most a few percentage points.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. For more information on disclosure avoidance, see Methodology for the 2022 Economic Census- Island Areas..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, see Methodology for the 2022 Economic Census- Island Areas.For more information about survey questionnaires, Primary Business Activity/NAICS codes, and NAPCS codes, see Economic Census Technical Documentation..Weights.Because the Economic Census of Island Areas is a complete enumeration, there is no sample weighting..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector00.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate qua...

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

Key Table Information.Table Title.Island Areas: Capital Expenditures and Rental Payments for Plant and Equipment by Manufacturing Industry for Puerto Rico and Metropolitan Areas: 2022.Table ID.ISLANDAREASIND2022.IA2200IND22.Survey/Program.Economic Census of Island Areas.Year.2022.Dataset.ECNIA Economic Census of Island Areas.Source.U.S. Census Bureau, 2022 Economic Census of Island Areas, Core Statistics.Release Date.2024-12-19.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.2022 Economic Census of Island Areas tables are released on a flow basis from June through December 2024.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe. The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in Puerto Rico, have paid employees, and are classified in one of eighteen in-scope sectors defined by the 2022 NAICS..Sponsor.U.S. Department of Commerce.Methodology.Data Items and Other Identifying Records.Number of establishmentsCapital expenditures on new buildings and other structures ($1,000)Capital expenditures on used buildings ($1,000)Gross value of depreciable assets (acquisition costs) for buildings and other structures, beginning of year ($1,000)Retirements for buildings and other structures ($1,000)Gross value of depreciable assets (acquisition costs) for buildings and other structures, end of year ($1,000)Depreciation charges on structures, additions, and related facilities ($1,000)Rental payments or lease payments for buildings and other structures ($1,000)Capital expenditures - new machinery, equipment, and vehicles ($1,000)Capital expenditures on used machinery and equipment ($1,000)Gross value of depreciable assets (acquisition costs) for machinery and equipment, beginning of year ($1,000)Retirements for machinery and equipment ($1,000)Gross value of depreciable assets (acquisition costs) for machinery and equipment, end of year ($1,000)Depreciation charges on machinery and equipment ($1,000)Rental payments or lease payments for machinery and equipment ($1,000)Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the Economic Census of Island Areas are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed..Geography Coverage.The data are shown for employer establishments and firms that vary by industry:At the Territory, Metropolitan and Micropolitan Statistical Area, and Combined Statistical Area level for Puerto RicoFor information about economic census geographies, including changes for 2022, see Economic Census: Economic Geographies..Industry Coverage.The data are shown for Puerto Rico at the 2- through 5-digit 2022 NAICS code levels for the manufacturing industry.For information about NAICS, see Economic Census Code Lists..Sampling.The Economic Census of Island Areas is a complete enumeration of establishments located in the islands (i.e., all establishments on the sampling frame are included in the sample). Therefore, the accuracy of tabulations is not affected by sampling error..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY24-0044).The primary method of disclosure avoidance protection is noise infusion. Under this method, the quantitative data values such as sales or payroll for each establishment are perturbed prior to tabulation by applying a random noise multiplier (i.e., factor). Each establishment is assigned a single noise factor, which is applied to all its quantitative data value. Using this method, most published cell totals are perturbed by at most a few percentage points.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. For more information on disclosure avoidance, see Methodology for the 2022 Economic Census- Island Areas..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, see Methodology for the 2022 Economic Census- Island Areas.For more information about survey questionnaires, Primary Business Activity/NAICS codes, and NAPCS codes, see Economic Census Technical Documentation..Weights.Because the Economic Census of Island Areas is a complete enumeration, there is no sample weighting..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector00.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data f...

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).

Key Table Information.Table Title.Island Areas: E-Commerce Statistics for American Samoa, Commonwealth of the Northern Mariana Islands, Guam, Puerto Rico, and U.S. Virgin Islands: 2022.Table ID.ISLANDAREAS2022.IA2200SUBJ04.Survey/Program.Economic Census of Island Areas.Year.2022.Dataset.ECNIA Economic Census of Island Areas.Release Date.2024-12-19.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.2022 Economic Census of Island Areas tables are released on a flow basis from June through December 2024.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in Puerto Rico, the U.S. Virgin Islands, Guam, the Commonwealth of the Northern Mariana Islands, or America Samoa, have paid employees, and are classified in one of eighteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Sales, value of shipments, or revenue ($1,000)Value of E-commerce sales, value of shipments, or revenue ($1,000)E-commerce sales, value of shipments, or revenue as a percent of total sales (%)Range indicating imputed percentage of total sales, value of shipments, or revenueDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the Economic Census of Island Areas are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed..Geography Coverage.The data are shown for employer establishments and firms that vary by industry:At the Territory level for American SamoaAt the Territory level for GuamAt the Territory level for the Commonwealth of the Northern Mariana IslandsAt the Territory level for Puerto RicoAt the Territory level for US Virgin IslandsFor information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.Sector "00” is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. The data in this table are shown for the total of all NAICS sectors except Agriculture (11). Note: Other programs may use Sector "00” to denote when multiple NAICS sectors are being displayed within the same table and/or dataset.For information about NAICS, see Economic Census Code Lists..Sampling.The Economic Census of Island Areas is a complete enumeration of establishments located in the islands (i.e., all establishments on the sampling frame are included in the sample). Therefore, the accuracy of tabulations is not affected by sampling error..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY24-0044).The primary method of disclosure avoidance protection is noise infusion. Under this method, the quantitative data values such as sales or payroll for each establishment are perturbed prior to tabulation by applying a random noise multiplier (i.e., factor). Each establishment is assigned a single noise factor, which is applied to all its quantitative data value. Using this method, most published cell totals are perturbed by at most a few percentage points.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. For more information on disclosure avoidance, see Methodology for the 2022 Economic Census- Island Areas..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, see Methodology for the 2022 Economic Census- Island Areas.For more information about survey questionnaires, Primary Business Activity/NAICS codes, and NAPCS codes, see Economic Census Technical Documentation..Weights.Because the Economic Census of Island Areas is a complete enumeration, there is no sample weighting..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector00.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity respon...

Data describing the list of images, error and uncertainty calculations for Novel Quantitative Methods to Enable Multispectral Identification of High-Purity Water Ice Exposures on Mars using High Resolution Imaging Science Experiment Images.

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.

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