Dataset Card for 100 Richest People In World

  Dataset Summary

This dataset contains the list of Top 100 Richest People in the World Column Information:-

Name - Person Name NetWorth - His/Her Networth Age - Person Age Country - The country person belongs to Source - Information Source Industry - Expertise Domain

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[More Information Needed]… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/100-richest-people-in-world.

Analysis of ‘World's Billionaires’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/seriadiallo1/world-billionaires on 14 February 2022.

--- Dataset description provided by original source is as follows ---

The richest people in the world, yearly rank from 2002 to 2021

This dataset contains 200 rows and 7 columns.

The World's Billionaires is an annual ranking by documented net worth of the world's wealthiest billionaires compiled and published in March annually by the American business magazine Forbes. The list was first published in March 1987. The total net worth of each individual on the list is estimated and is cited in United States dollars, based on their documented assets and accounting for debt. Royalty and dictators whose wealth comes from their positions are excluded from these lists. This ranking is an index of the wealthiest documented individuals, excluding and ranking against those with wealth that is not able to be completely ascertained. (wikipedia)

--- Original source retains full ownership of the source dataset ---

A dataset listing the richest zip codes in United States Virgin Islands per the most current US Census data, including information on rank and average income.

In 2023, the real median household income in the state of Alabama was 60,660 U.S. dollars. The state with the highest median household income was Massachusetts, which was 106,500 U.S. dollars in 2023. The average median household income in the United States was at 80,610 U.S. dollars.

United States US: Account: Income: Richest 60%: % Aged 15+ data was reported at 97.904 % in 2014. This records an increase from the previous number of 92.810 % for 2011. United States US: Account: Income: Richest 60%: % Aged 15+ data is updated yearly, averaging 95.357 % from Dec 2011 (Median) to 2014, with 2 observations. The data reached an all-time high of 97.904 % in 2014 and a record low of 92.810 % in 2011. United States US: Account: Income: Richest 60%: % Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Banking Indicators. Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (income, richest 60%, % age 15+). [ts: data are available for multiple waves].; ; Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank.; Weighted average;

This upload is constituted of four datasets of specimens from American herbaria covering different levels of information precision and different floras - from temperate to equatorial.

Three of these datasets consist of selected specimens from herbaria located in different geographic and environmental regions. Each specimen of these three datasets was annotated with the following fields: family, genus, species name, fertile / non-fertile, presence / absence of flower(s), presence / absence of fruit(s). The resulting dataset was composed of 163,233 herbarium specimens belonging to 7,782 species, 1,906 genera, and 236 families. Specimens were annotated as “fertile” if any reproductive structures were present, such as sporangia (ferns), cones (gymnosperms), flowers, or fruits (angiosperms). Non-fertile specimens were those that lacked any reproductive structures.

The fourth dataset consists of 20,371 herbarium specimens from 11 genera in the sunflower family (Asteraceae). The main difference in this dataset is that it is annotated with fine-grained phenophase scores rather than presence/absence attributes (see description below).

Each of these datasets is described below:

NEVP: this dataset of New England vascular plant (NEVP) specimens was produced by members of the Consortium of Northeastern Herbaria. The dataset comprises 42,658 digitized specimens that belong to 1,375 species and come from several North American institutions. Most of the specimens in this dataset are from the north-temperate region of the northeastern United States.

FSU: this dataset was produced by the Florida State University's Robert K. Godfrey Herbarium (FSU), a collection that focuses on northern Florida and the U.S. Southeast Coastal Plain, one of North America's biodiversity hotspots. This dataset contains 54,263 digitized herbarium specimen records that belong to 3,870 species, making it the taxonomically richest dataset in this study. Most species in this dataset grow under subtropical or warm temperate conditions in the southeastern region of the United States.

CAY: this dataset comes from the IRD’s Herbarium of French Guiana (CAY). CAY is dedicated to the Guayana Shield flora, with a strong focus on tropical tree species. This dataset is composed of 66,312 herbarium specimens that belong to 3,024 species. All digitized specimens of this herbarium are accessible online. Most specimens were collected in the tropical rainforests of French Guiana, with the remaining specimens coming mostly from Suriname and Guyana.

PHENO: this dataset includes 20,371 herbarium specimens of 139 species in the Asteraceae produced in a study of phenological trends in the U.S. Southeast Coastal Plain. The dataset is composed of specimen records from 57 herbaria. Each recorded specimen was annotated for quartile percentages (0, 25, 50, 75, or 100%) of (i) closed buds, (ii) buds transformed into flowers, and (iii) fruits. According to the distribution of these three categories for each specimen, a phenophase code was computed.

Datasets format

These datasets are grouped in 3 tasks:

fertility detection

flowers and/or fruit detection

phenophase classification

The first 2 tasks are carried on the first 3 previous datasets and thus are based on the same set of images, unlike the third task which has its own disjoint set of images. This is why the dataset is presented into two separated files, one for each set of images.

Fertility detection & flower/fruit detection

These tasks are contained into the herbarium_fertility_annotations.zip archive. It consists of 3 files:

metadata.csv: general information about all the herbarium specimens for these tasks

id: specimen identifier

collection: which of NEVP, FSU or CAY does the specimen come from

herbarium: institution of origin of the specimen, especially for NEVP collection

clade, family, genus, species: classification of the specimen

URL: URL of the scan

fertility_task.csv: specific information regarding the fertility detection task

id: specimen identifier

is_fertile: True if the specimen has an expression of fertility, False otherwise

train_test_set: which subset does the specimen belong to; possible values are: train, random_test, species_test and herbarium_test

flower_fruit_task.csv: specific information regarding the flower/fruit detection task

id: specimen identifier, note that in this case not all the specimen described in metadata.csv are included in this task

has_flower: True if the specimen has at least one flower, False otherwise

has_fruit: True if the specimen has at least one fruit, False otherwise

train_test_set: which subset does the specimen belong to; possible values are: train, random_test, species_test and herbarium_test

Phenophase classification

These tasks are contained into the herbarium_asteraceae_phenophase_annotations.zip archive. It consists of a single file:

annotations.csv:

id: specimen identifier

URL: URL of the scan

genus: genus of the specimen

phenophase: integer from 1 to 9 describing the phenophase of the specimen

train_test_set: which subset does the specimen belong to; possible values are: train and test

Additional ressources

More information can be found in the related paper: Lorieul, T., K. D. Pearson, E. R. Ellwood, H. Goëau, J.-F. Molino, P. W. Sweeney, J. M. Yost, J. Sachs, E. Mata-Montero, G. Nelson, P. S. Soltis, P. Bonnet, and A. Joly. 2019. Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras. Applications in Plant Sciences 7(3): e1233.

For an example of usage of these datasets as well as a baseline, see: http://doi.org/10.5281/zenodo.2549996

This dataset provides values for GOLD RESERVES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

A dataset listing California counties by population for 2024.

A dataset listing the 20 richest counties in Virginia for 2024, including information on rank, county, population, average income, and median income.

A dataset listing the 20 richest counties in Maryland for 2024, including information on rank, county, population, average income, and median income.

A dataset listing Florida counties by population for 2024.

A dataset listing the 20 richest counties in Michigan for 2024, including information on rank, county, population, average income, and median income.

A dataset listing the 20 richest cities in Montana for 2024, including information on rank, city, county, population, average income, and median income.

A dataset listing Georgia counties by population for 2024.

A dataset listing New York counties by population for 2024.