This machine-readable version of John Williams' Digest of Welsh Historical Statistics is the result of a collaboration between the Statistical Directorate of the National Assembly for Wales, the History Data Service and the Centre for Data Digitisation and Analysis at Queen's University Belfast.
John Williams' Digest of Welsh Historical Statistics is intended to provide a service for those working on the history of modern Wales. It arises from a belief that the quantitative element is a necessary and important part of the historical record; from an awareness that it was an aspect that was particularly inaccessible for scholars of Welsh history; and from a conviction that some encouragement in the use of quantitative material was necessary. It is modelled on the two volumes dedicated to British historical statistics: Mitchell, B.R. and Deane, P. (1962) Abstract of British historical statistics and Mitchell, B.R. and Jones, H.G. (1971) Second abstract of British historical statistics.

Users should note there is no specific documentation for the religion statistics. The document covering population statistics has been added to provide some background information.

Locations and border maps for cities of the Holy Roman Empire as listed in the Deutsches Städtebuch (Keyser et al., eds., 1939-2003).

Historical dataset showing European Union refugee statistics by year from 1990 to 2023.

This dataset includes New York State historical shoreline positions represented as digital vector polylines from 1880 to 2015. Shorelines were compiled from topographic survey sheets from the National Oceanic and Atmospheric Administration (NOAA). Historical shoreline positions can be used to assess the movement of shorelines through time. Rates of shoreline change were calculated in ArcMap 10.5.1 using the Digital Shoreline Analysis System (DSAS) version 5.0. DSAS uses a measurement baseline method to calculate rate of change statistics. Transects are cast from the reference baseline to intersect each shoreline, establishing measurement points used to calculate shoreline change rates. For wetland shorelines these rates can be interpreted as accretion or erosion.

This dataset contains historical price data for the top global cryptocurrencies, sourced from Yahoo Finance. The data spans the following time frames for each cryptocurrency:

BTC-USD (Bitcoin): From 2014 to December 2024 ETH-USD (Ethereum): From 2017 to December 2024 XRP-USD (Ripple): From 2017 to December 2024 USDT-USD (Tether): From 2017 to December 2024 SOL-USD (Solana): From 2020 to December 2024 BNB-USD (Binance Coin): From 2017 to December 2024 DOGE-USD (Dogecoin): From 2017 to December 2024 USDC-USD (USD Coin): From 2018 to December 2024 ADA-USD (Cardano): From 2017 to December 2024 STETH-USD (Staked Ethereum): From 2020 to December 2024

Key Features:

Date: The date of the record. Open: The opening price of the cryptocurrency on that day. High: The highest price during the day. Low: The lowest price during the day. Close: The closing price of the cryptocurrency on that day. Adj Close: The adjusted closing price, factoring in stock splits or dividends (for stablecoins like USDT and USDC, this value should be the same as the closing price). Volume: The trading volume for that day.

Data Source:

The dataset is sourced from Yahoo Finance and spans daily data from 2014 to December 2024, offering a rich set of data points for cryptocurrency analysis.

Use Cases:

Market Analysis: Analyze price trends and historical market behavior of leading cryptocurrencies. Price Prediction: Use the data to build predictive models, such as time-series forecasting for future price movements. Backtesting: Test trading strategies and financial models on historical data. Volatility Analysis: Assess the volatility of top cryptocurrencies to gauge market risk. Overview of the Cryptocurrencies in the Dataset: Bitcoin (BTC): The pioneer cryptocurrency, often referred to as digital gold and used as a store of value. Ethereum (ETH): A decentralized platform for building smart contracts and decentralized applications (DApps). Ripple (XRP): A payment protocol focused on enabling fast and low-cost international transfers. Tether (USDT): A popular stablecoin pegged to the US Dollar, providing price stability for trading and transactions. Solana (SOL): A high-speed blockchain known for low transaction fees and scalability, often seen as a competitor to Ethereum. Binance Coin (BNB): The native token of Binance, the world's largest cryptocurrency exchange, used for various purposes within the Binance ecosystem. Dogecoin (DOGE): Initially a meme-inspired coin, Dogecoin has gained a strong community and mainstream popularity. USD Coin (USDC): A fully-backed stablecoin pegged to the US Dollar, commonly used in decentralized finance (DeFi) applications. Cardano (ADA): A proof-of-stake blockchain focused on scalability, sustainability, and security. Staked Ethereum (STETH): A token representing Ethereum staked in the Ethereum 2.0 network, earning staking rewards.

This dataset provides a comprehensive overview of key cryptocurrencies that have shaped and continue to influence the digital asset market. Whether you're conducting research, building prediction models, or analyzing trends, this dataset is an essential resource for understanding the evolution of cryptocurrencies from 2014 to December 2024.

The Framingham Township Heart Institute offers a 10-year data set on coronary heart disease

The earliest point where scientists can make reasonable estimates for the population of global regions is around 10,000 years before the Common Era (or 12,000 years ago). Estimates suggest that Asia has consistently been the most populated continent, and the least populated continent has generally been Oceania (although it was more heavily populated than areas such as North America in very early years). Population growth was very slow, but an increase can be observed between most of the given time periods. There were, however, dips in population due to pandemics, the most notable of these being the impact of plague in Eurasia in the 14th century, and the impact of European contact with the indigenous populations of the Americas after 1492, where it took almost four centuries for the population of Latin America to return to its pre-1500 level. The world's population first reached one billion people in 1803, which also coincided with a spike in population growth, due to the onset of the demographic transition. This wave of growth first spread across the most industrially developed countries in the 19th century, and the correlation between demographic development and industrial or economic maturity continued until today, with Africa being the final major region to begin its transition in the late-1900s.

The stocks of goods held by firms in El Salvador decreased by 934.59 USD Million in 2023. This dataset provides - El Salvador Changes in Inventories - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Historical dataset showing Jamaica refugee statistics by year from 1991 to 2022.

Explore the historical Whois records related to data.is.it (Domain). Get insights into ownership history and changes over time.

The Check-In Dataset is the second public-use dataset in the Dunham's Data series, a unique data collection created by Kate Elswit (Royal Central School of Speech and Drama, University of London) and Harmony Bench (The Ohio State University) to explore questions and problems that make the analysis and visualization of data meaningful for dance history through the case study of choreographer Katherine Dunham. The Check-In Dataset accounts for the comings and goings of Dunham's nearly 200 dancers, drummers, and singers and discerns who among them were working in the studio and theatre together over the years from 1937 to 1962. As with the Everyday Itinerary Dataset, the first public-use dataset from Dunham's Data, data on check-ins come from scattered sources. Due to information available, it has a greater level of ambiguity as many dates are approximated in order to achieve accurate chronological sequence. By showing who shared time and space together, the Check-In Dataset can be used to trace potential lines of transmission of embodied knowledge within and beyond the Dunham Company. Dunham's Data: Digital Methods for Dance Historical Inquiry is funded by the United Kingdom Arts and Humanities Research Council (AHRC AH/R012989/1, 2018-2022) and is part of a larger suite of ongoing digital collaborations by Bench and Elswit, Movement on the Move. The Dunham's Data team also includes digital humanities postdoctoral research assistant Antonio Jiménez-Mavillard and dance history postdoctoral research assistants Takiyah Nur Amin and Tia-Monique Uzor. For more information about Dunham's Data, please see the Dunham's Data website. Also, visit the Dunham's Data research blog to view the interactive visualizations based on the Dunham's Data.

GDP Deflator in European Union increased to 120.25 points in the first quarter of 2025 from 119.44 points in the fourth quarter of 2024. This dataset provides - European Union GDP Deflator - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The total population in Togo was estimated at 9.5 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - Togo Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Quantity and general situation of historical sites in Taichung City

Financial overview and grant giving statistics of Old Time Historical Association Inc.

Fishery statistics for the International Council for the Exploration of the Sea (ICES) area were published from 1904 to 1989 under the title "Bulletin Statistique des Pêches Maritimes". The series was renamed "ICES Fisheries Statistics' and continued until 2001.This dataset contains figures per country, year, species and area for 1903–1949, extracted from that "Bulletin Statistique des Pêches Maritimes". Overviews on the mapping of species, ICES areas and country codes are also included. Comments attached to individual values from the original files per country can be found under "country_notes".The data represent the nominal commercial catch (live weight equivalent of landings, discards excluded) of finfish, invertebrates, and seaweeds. These catch data cover the ICES Area (Northeast Atlantic, FAO Area 27). The data are expressed in the live weight equivalent of landings. The reporting units are metric tonnes. Discarded catch and other quantities not landed are not included in the data. Whilst the data have been extensively checked by the supplying countries and ICES, data presented in the dataset are not corrected for non-reported landings where such may have occurred.Catch statistics from 1950 onward for EU countries, Norway, and Iceland can be accessed through Eurostat: https://ec.europa.eu/eurostat/web/fisheries/database

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

Financial overview and grant giving statistics of Danbury New Hampshire Historical Society