This dataset is about companies in New Boston. It has 18 rows. It features 3 columns: country, and tweets.

This dataset shows all rosters of the Boston Red Sox baseball team, from its founding in 1908 to 2020. The team was actually born in 1901 as the Boston Americans, until 1907, after which they were renamed to Boston Red Sox.

The Excel file includes filters for each column.

Column Description

• Season: Year of the season
• Name: Name of the player
• Age: Age of the player
• Born: Country in which the player was born
• Flag: Flag of the country
• B: Batting side
• T: Throwing hand
• Ht: Height in feet and inches
• Wt: Weight in pounds
• DoB: Date of birth
• Yrs: Years of experience, i.e. years the player was or has been in the major leagues, players in their first season are shown as “1st”
• GP: Games played
• GS: Games started
• Batting: Games appeared in the batting order (may have not batted)
• Defense: Games in lineup at a defensive position
• P: Game in lineup or announced as a pitcher
• C: Games in lineup as a catcher
• 1B: Games in lineup as a first baseman
• 2B: Games in lineup as a second baseman
• 3B: Games in lineup as a third baseman
• SS: Games in lineup as a shortstop
• LF: Games in lineup as a left fielder
• CF: Games in lineup as a center fielder
• RF: Games in lineup as a right fielder
• OF: Games in lineup as an outfielder
• DH: Games in lineup as a designated hitter
• PH: Games in lineup as a pinch hitter (may have played another position as well)
• PR: Games in lineup as a pinch runner (may have played another position as well)
• WAR: Wins Above Replacement, i.e. number of wins the player added to the team above what a replacement player would add
• All-Star: Was the player an all-star in the season?
• HOF: Is or was the player a Hall of Famer?

The Boston Marathon is one of the most iconic and prestigious road races in the world. The race has been held annually since 1897, making it the oldest marathon in the United States. This dataset contains the winners of the men's and women's Boston Marathon from 1897 to the present day.

The dataset is split into two separate files: one containing the winners of the men's Boston Marathon, and the other containing the winners of the women's Boston Marathon. Each file contains information on the year, winner's name, country, finishing time, and distance.

This dataset can be used for a variety of analyses, such as comparing the finishing times and countries of winners between the men's and women's races, exploring trends over time, and identifying outliers or interesting patterns. Additionally, this dataset can be used to inspire and motivate runners who are training for the Boston Marathon or other road races 🏃

The variables contained in the data sets are primarily concerned with perinatal outcomes and maternal health. A number of variables with respect to the social and economic status of the mothers and their families were also included (ie. Occupation, Marital status, Region). While all nine data sets are centered around these common themes and hold many variables in common, each data set has a unique combination of variables. The types of fields are wide-ranging but are primarily concerned with infant birth, maternal health, and socioeconomic status. The clinical records of the Boston Lying-in inpatient and outpatient services, and those of the New England Hospital maternity unit, are housed in the Rare Book Room, Francis A. Countway Library of Medicine, Harvard University, Boston, Massachusetts. While the information found in these records varied somewhat from one hospital to the next, each set of records was consistent throughout the period under review. Four data bases were established, one consisting exclusively of white patients for each of the three clinics and one composed of all black patients from both services of the Boston Lying-in. The four sample populations were constituted in the following ways. The clinical records of the New England Hospital’s maternity clinic exist in continuous series from 1872 to 1900. All births were recorded because there were fewer than 200 deliveries annually. The patient registers of the Boston Lying-in inpatient service span the years 1886-1900, with a gap in 1893 and 1894. A random sample of 200 cases was chosen for each year. The same procedure was followed at the outpatient clinic, whose case files extend from 1884 to 1900, excepting those years in which all were recorded because fewer births occurred, and a short period when all cases were noted even though they totaled more than 200. Because the number of black patients was small, and because the birth weight experience of blacks was distinctive in some important respects, a fourth file was created consisting of all blacks in the Lying-in inpatient and outpatient records. The preliminary data bases consisted of 3480, 2503, 3654, and 373 cases, respectively. The birth weight means in the Lying-in inpatient sample are accurate to 79 grams, and those of the outpatient clinic sample to 65 grams, at the 95 percent confidence level.

TIMSS, the Trends in International Mathematics and Science Study, is designed to help countries all over the world improve student learning in mathematics and science. It collects educational achievement data at the fourth and eighth grades to provide information about trends in performance over time together with extensive background information to address concerns about the quantity, quality, and content of instruction.

TIMSS provides important information for policy development, to foster public accountability, to allow areas of progress or decline in achievement to be identified and monitored, and to address concerns for equity.

Nearly 50 countries from all over the world participated in TIMSS 2003. A project of the IEA, based in Amsterdam, TIMSS is directed by the TIMSS & PIRLS International Study Center at Boston College in collaboration with a worldwide network of organizations and representatives from the participating countries.

TIMSS 2003 data collection results were released December 14, 2004. The first round of TIMSS, which is conducted on a four-year cycle, was in 1995 and the second in 1999. The fourth round of TIMSS was conducted in 2007. Includes data files, datasets, and supporting documentation.

Participating countries include: Argentina; Armenia; Australia; Bahrain; Basque Country, Spain; Belgium (Flemish); Botswana; Bulgaria; Chile; Chinese Taipei; Cyprus; Egypt; England; Estonia; Ghana; Hong Kong, SAR; Hungary; Indiana State, United States; Indonesia; Iran, Islamic Republic of; Israel; Italy; Japan; Jordan; Korea, Republic of; Latvia; Lebanon; Lithuania; Macedonia, Republic of; Malaysia; Moldova; Morocco; Netherlands; New Zealand; Norway; Ontario Province, Canada; Palestinian National Authority; Philippines; Quebec Province, Canada; Romania; Russian Federation; Saudi Arabia; Scotland; Serbia; Singapore; Slovak Republic; Slovenia; South Africa; Sweden; Syrian Arab Republic; Tunisia; United States; Yemen, Republic of.

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation

Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country (Portugal, Ivory Coast, and Saudi Arabia), and city (Boston) scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)–despite substantial spatial and infrastructural differences. Furthermore, our comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors–as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance. Finally, we put forth a testable hypothesis and suggest ways for future work to make more accurate and generalizable statements about human commute behaviors.

Petition subject: Court cases Original: http://nrs.harvard.edu/urn-3:FHCL:13906084 Date of creation: 1740 Petition location: Boston Selected signatures:Edward Winslow Total signatures: 1 Legal voter signatures (males not identified as non-legal): 1 Female only signatures: No Identifications of signatories: sheriff of the country of Suffolk Prayer format was printed vs. manuscript: Manuscript Additional archivist notes: Nathaniel Cunningham, George Hewes, Boston tanner, Robert Hewes, Daniel Hewes, Cato, Nero, Quagno, Scipio, Mr. Read attorney, Barker's execution, Richard Avery, Mr. Young and son, soap Location of the petition at the Massachusetts Archives of the Commonwealth: Massachusetts Archives volume 303, pages 33.1-33b Acknowledgements: Supported by the National Endowment for the Humanities (PW-5105612), Massachusetts Archives of the Commonwealth, Radcliffe Institute for Advanced Study at Harvard University, Center for American Political Studies at Harvard University, Institutional Development Initiative at Harvard University, and Harvard University Library.