According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

The following data set is information obtained about counties in the United States from 2010 through 2019 through the United States Census Bureau. Information described in the data includes the age distributions, the education levels, employment statistics, ethnicity percents, houseold information, income, and other miscellneous statistics. (Values are denoted as -1, if the data is not available)

Data Dictionary

Native Hawaiian and Pacific Islander women had the highest fertility rate of any ethnicity in the United States in 2022, with about 2,237.5 births per 1,000 women. The fertility rate for all ethnicities in the U.S. was 1,656.5 births per 1,000 women. What is the total fertility rate? The total fertility rate is an estimation of the number of children who would theoretically be born per 1,000 women through their childbearing years (generally considered to be between the ages of 15 and 44) according to age-specific fertility rates. The fertility rate is different from the birth rate, in that the birth rate is the number of births in relation to the population over a specific period of time. Fertility rates around the world Fertility rates around the world differ on a country-by-country basis, and more industrialized countries tend to see lower fertility rates. For example, Niger topped the list of the countries with the highest fertility rates, and Taiwan had the lowest fertility rate.

To conduct this study, I sourced demographic data from 2010 to 2023 from the California Elections Data Archive (CEDA) for city council members and school board members. The CEDA data provide a full list of candidate names and the number of votes a given candidate received for every city council and school board election. I assigned the gender to each candidate based on the lists of popular male and female names provided by the Social Security Administration. Since the average age of city council members is 46 years old according to the Bureau of Labor Statistics, I compiled a list of popular male and female given names for babies born in the 1960s, 1970s, and 1980s. Then, I automated the gender classification as follows: for example, as “Lisa” is identified as a popular female given name by the Social Security Administration, every candidate whose first name is “Lisa” was assigned “female” in our dataset. For a gender-neutral name that appeared on the lists for both male and female given names, which included “Alex” and “Casey,” I used the following keywords “[first name] [last name] [office type (either “city council” or “school board”)] [name of the city or the school district]” to search for more information about the official’s gender online. My search returned either a picture to help clearly identify the official’s gender and/or an article that refers to the official with gendered pronouns. To identify the ethnicity of each elected official, I used the 2010 Census data and the 23AndMe Surname Discovery Tool. The 2010 Census lists surnames occurring at least 100 times, and it includes self-reported ethnicity data for individuals with a given surname. Similarly, the 23AndMe Surname Discovery Tool gives the percentage of individuals with the given surname who identify as each of four different ethnicity groups: Hispanic, White, Asian/Pacific Islander, and Black based on the 2010 US Census data. For surnames that did not appear on either the 2010 Census data or the 23AndMe Surname Discovery Tool, I used Python’s Ethnicolr library, which bases its prediction of ethnicity using either both first and last name or just the last name on the US census data (2000 and 2010), the Florida voting registration data, and the Wikipedia data.

Despite comprising of a smaller share of the U.S. population than African Americans or Hispanics, the most represented non-white U.S. CEOs were of an Asian background. They made up 55 percent of CEO positions at Fortune 500 and S&P 500 companies in 2024. By comparison, 11 percent of CEOs at the time were African American. The rise of environmental, social, and corporate governance (ESG) Investments in ESG have risen dramatically over last few years. In November 2023 there were approximately 480 billion U.S. dollars in ESG ETF assets worldwide, compared to 16 billion U.S. dollars in 2015. ESG measures were put in place to encourage companies to act responsibly, with the leading reason for ESG investing stated to be brand and reputation according to managers and asset owners. Gender diversity With the general acceptance of ESG in larger companies, there has still been a significant employment gap of women working in senior positions. For example, the share of women working as a partner or principal at EY, one of the largest accounting firms in the world, was just only 28 percent in 2023.

This dataset includes all personal names listed in the Wikipedia category “American people by ethnic or national origin” and all subcategories fitting the pattern “American People of [ ] descent”, in total more than 25,000 individuals. Each individual is represented by a row, with columns indicating binary membership (0/1) in each ethnic/national category.

Ethnicity inference is an essential tool for identifying disparities in public health and social sciences. Existing datasets linking personal names to ethnic or national origin often neglect to recognize multi-ethnic or multi-national identities. Furthermore, existing datasets use coarse classification schemes (e.g. classifying both Indian and Japanese people as “Asian”) that may not be suitable for many research questions. This dataset remedies these problems by including both very fine-grain ethnic/national categories (e.g. Afghan-Jewish) and more broad ones (e.g. European). Users can chose the categories that are relevant to their research. Since many Americans on Wikipedia are associated with multiple overlapping or distinct ethnicities/nationalities, these multi-ethnic associations are also reflected in the data.

Data were obtained from the Wikipedia API and reviewed manually to remove stage names, pen names, mononyms, first initials (when full names are available on Wikipedia), nicknames, honorific titles, and pages that correspond to a group or event rather than an individual.

This dataset was designed for use in training classification algorithms, but may also be independently interesting inasmuch as it is a representative sample of Americans who are famous enough to have their own Wikipedia page, along with detailed information on their ethnic/national origins.

DISCLAIMER: Due to the incomplete nature of Wikipedia, data may not properly reflect all ethnic national associations for any given individual. For example, there is no guarantee that a given Cuban Jewish person will be listed in both the “American People of Cuban descent” and the “American People of Jewish descent” categories.

How racially diverse are residents in Massachusetts? This topic shows the demographic breakdown of residents by race/ethnicity and the increases in the Non-white population since 2010.

39.8% of workers from the Indian ethnic group were in 'professional' jobs in 2021 – the highest percentage out of all ethnic groups in this role.

This layer shows race and ethnicity data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Consolidated City, Census Designated Place, Incorporated Place boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P5, P9 Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

According to the 2021 Census, 81.7% of the population of England and Wales was white, 9.3% Asian, 4.0% black, 2.9% mixed and 2.1% from other ethnic groups.

This Zenodo entry details the methodology for extracting and reconciling ethnicity data from the Clinical Practice Research Datalink (CPRD), incorporating both General Practitioner (GP) and Hospital Episode Statistics (HES) sources. The approach aims to resolve discrepancies between these sources and provide a standardized single ethnicity value per patient, categorized into 6 and 12 levels according to NHS coding guidelines.

Materials and Methods:

Ethnicity data from the CPRD are recorded in multiple formats. This study harmonizes these data to achieve consistent ethnicity classification across patient records, following a hierarchal reconciliation protocol prioritizing hospital data over GP records.

Ethnicity Levels: Ethnicity data are processed to conform to two levels of granularity:

1. Six high-level categories: White, Black, Asian, Mixed, Other, Unknown
2. Twelve detailed categories: Bangladeshi, Black African, Black Caribbean, Black Other, Chinese, Indian, Mixed, Other Asian, Other, Pakistani, Unknown, White

Source Data Mapping:

• CPRD Medcodes: Directly mapped to 490 SNOMED codes
• SNOMED to NHS Codes: SNOMED codes are linked to 26 NHS ethnicity codes
• NHS to HES Codes: These NHS codes further map into 12 HES hospital ethnicities, which then consolidate into the 6 broad categories mentioned above

Algorithm (AIM-CISC):

• Hospital Data Priority: Ethnicity records from hospital sources override those from GP records unless the hospital data is classified as "Unknown", null, or empty.
• Conflict Resolution Within GP Data:
  • The frequency of recorded ethnicities determines the selection. The most frequently recorded ethnicity prevails.
  • If frequencies are tied, the most recent record is used.
  • In cases where records are equally recent, the first alphabetically listed ethnicity is selected.

Unique Patient Identifiers: Each patient is represented once in hospital data, ensuring a single source of truth for hospital-based ethnicities. This simplifies reconciliation with GP data when discrepancies arise.

Source Documentation and References:

• Reference for Code Lists: Digital ethnicity data in population-wide electronic health records in England: a description of completeness, coverage, and granularity of diversity (Pineda-Moncusí et al., 2022): https://doi.org/10.1101/2022.11.11.22282217
• GitHub Repository for Code Lists: https://github.com/BHFDSC/CCU037_01/blob/main/england/phenotypes/snomed_meaning_and_map_to_primary_code.csv
• NHS Ethnicity Codes Documentation: https://www.datadictionary.nhs.uk/attributes/ethnic_category_code_2001.html open_in_new

Notes on mapping:

Instances were noted where multiple Medcodes map back to a single SNOMED code, highlighting the importance of careful data cross-referencing. For example, two different Medcodes represent the New Zealand European ethnicity, which both map back to the identical SNOMED code.

Dataset contains ethnic group census usually resident population counts from the 2013, 2018, and 2023 Censuses, as well as the percentage change in the ethnic group population count between the 2013 and 2018 Censuses, and between the 2018 and 2023 Censuses. Data is available by regional council.

The ethnic groups are:

• European
• Māori
• Pacific peoples
• Asian
• Middle Eastern/Latin American/African
• Other ethnicity.

Map shows percentage change in the census usually resident population count for ethnic groups between the 2018 and 2023 Censuses.

Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

​

Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

​

Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

​

Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

​

About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

​

Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

​

Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

​

Ethnicity concept quality rating

Ethnicity is rated as high quality.

Ethnicity – 2023 Census: Information by concept has more information, for example, definitions and data quality.

​

Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

​

Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

​

Symbol

-998 Not applicable

​

Percentages

To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies.

In the 2022 to 2023 school year, pupils from the Chinese ethnic group had the highest Attainment 8 score out of all ethnic groups (65.5 out of 90.0).

This layer shows health insurance coverage sex and race by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Sums may add to more than the total, as people can be in multiple race groups (for example, Hispanic and Black)This layer is symbolized to show the percent of population with no health insurance coverage. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B27010, C27001B, C27001C, C27001D, C27001E, C27001F, C27001G, C27001H, C27001I (Not all lines of these tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..For more information on understanding race and Hispanic origin data, please see the Census 2010 Brief entitled, Overview of Race and Hispanic Origin: 2010, issued March 2011. (pdf format).The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Datasource: Statistics Canada. 2003. Profile for Canada, Provinces, Territories, Census Divisions and Census Subdivisions, 2001 Census (table). Cumulative Electronic Profiles. Statistics Canada Catalogue no. 95F0495XCB01001. Ottawa. October 22, 2003. http://www12.statcan.ca/english/census01/products/standard/profiles/List... (accessed November 7, 2008). Statistics Canada. 2009. 2001 Census Semi-Custom Profile of Selected CSD Aggregates in Yukon, 2001 Census (tables). J5543 and CRO0105914. Ottawa. November 7, 2008. Footnotes: A value of 0 in any given cell represents one of the following: 1) value is actually zero; 2) value may be random rounded to zero; or 3) value is more than zero but is suppressed for confidentiality reasons. This table is based on 20% data. Values have been subjected to a confidentiality procedure known as random rounding. For Statistics Canada's definition of terms, http://www12.statcan.ca/english/census01/Products/Reference/dict/atoz.htm.

To improve access to capital, the City of Chicago seeded a $2MM revolving loan fund and partnered with Accion to create the Chicago Microlending Institute (CMI). There is a full list of loans in the https://data.cityofchicago.org/id/dpkg-upyz dataset. Certain data elements could not be included for privacy reasons but are summarized in this dataset. Loans without a date are not included in the summary.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Scientific conferences incorporate diversity-focused events into their programming to increase their diversity and inclusivity and to improve the conference experience for scientists from underrepresented groups (URGs). While simply adding diversity-focused events to conferences is positive, maximizing their impact requires that conferences organizeand schedule these events to minimize well-acknowledged, problematic patterns such as the minority tax. To our knowledge, the programming of diversity-focused events at conferences has not been systematically reviewed to identify the extent of these shortcomings and how they can be addressed. This dataset describes temporal trends in the types of diversity-focused events held at biology conferences, the targeted audiences of those events, and scheduling conflicts that occur with each event. Methods Time-series: We gathered publicly available conference programs for the selected biology conferences (Table 1) for the years 2010 through 2019. Not all conferences had programs available for all years, particularly as time from the present increased, thus sample sizes varied across the time series from 17 to 28. Programs were searched for diversity-focused events by both reading through the entire program and conducting keyword searches. The following keywords were used: diversity, gender, female, woman, women, black, race, ethnic*, minorit*, inclusiv*, LGBT*, where asterisks indicate wild-card search terms. For each program, we first scored (yes/no) on whether there were any diversity-focused events. We then scored whether each event was “women-focused” - where the event was specific to women; “ethnic/racial minority groups-focused” – where the event was specific to any URG based on ethnicity and/or race; and/or “LGBTQ+-focused” - where the event was specific to any part of the LGBTQ+ community. Using these scores, we calculated for each calendar year the percent of conferences with (1) any kind of diversity-focused event, (2) women-focused events, (3) ethnic/racial minorities-focused events, and (4) LGBTQ+-focused events. Table 1. Biology conferences were acquired from a list of societies affiliated with the American Association for the Advancement of Science (https://www.aaas.org/group/60/list-aaas-affiliates). We included a conference if its primary focus was on the biological sciences, regardless of whether the conference was hosted by an academic, professional, or not-for-profit organization. Recent publicly available conference programs were used to examine how conferences incorporated diversity-focused events into their schedules.

Society/Conference

Year analyzed

Society/Conference

Year analyzed

American Dairy Science Association

2018

Ecological Society of America

2019

American Ornithological Society

2018

Entomological Society of America

2018

American Physiological Society

2018

International Biometrics Society - Eastern North America

2018

American Phytopathological Society

2018

Microscopy Society of America

2018

American Society for Horticultural Science

2018

Mycological Society of America

2017

American Society for Microbiology

2019

Phycological Society of America

2019

American Society of Agronomy

2018

Poultry Science Association

2018

American Society of Mammalogists

2018

Society for Integrative and Comparative Biology

2018

American Society of Plant Biologists

2019

Society for Neuroscience

2018

Animal Behavior Society

2019

Society for the Study of Evolution

2018

Association for the Sciences of Limnology and Oceanography - Ocean Sciences Meeting

2018

Society of American Foresters

2019

Association of Southeastern Biologists

2018

Society of Toxicology

2018

Behavior Genetics Association

2018

The Wildlife Society

2018

Biophysical Society

2018

Weed Science Society of America

2018

Botanical Society of America

2018

Survey of event-scheduling and targeted audiences: Using one recent program from each conference (years 2017 through 2019), we searched for diversity-focused events by both reading through the entire program and conducting keyword searches. The keywords used are listed above in the Time Series section. From these searches, we found 87 diversity-focused events from 21 out of the 29 conferences. Target audience: For each conference, we used the title and any other description of the event to classify the targeted audience as either an underrepresented group (URG) or the broader conference community. For example, events with titles such as “Inclusive Teaching Workshop” were classified as broadly targeted, whereas events with titles such as “Minority Social” were classified as URG-targeted. However, if any event contained the explicit statement that “all are welcome” (or similar), the event was classified as targeted at the broader conference community. Event format: We also used the titles and other event descriptions to classify the formats of events. Events were classified as socials, workshops, symposia, plenary lectures, forums and town halls, orientations, or poster sessions. The most common events were socials, workshops, and symposia (e.g., “LGBTQ+ Networking Event and Social”, “Workshop for Creating an Inclusive Research Environment”, and “Symposium Honoring the Roles of Women in Microbiology”, respectively). Breaks or scientific sessions: We used the conference schedule to identify whether each diversity-focused event occurred during a scheduled break versus the main scientific sessions. We defined a break as a period that was either explicitly labeled as a break (e.g., lunch, dinner) or occurred outside the daily start or end of conference-wide scientific events, which included workshops, plenary lectures, poster sessions, and contributed oral presentations. Number of conflicting events: We used the conference schedule to count the number of events that overlapped with each diversity-focused event for more than 15 minutes. Events were only counted as separate events if they occurred in separate rooms. “Business” events and other closed, invitation-only events were not included in this calculation. Overlap for an average conference event: Because the baseline number of overlapping events can vary with the size of a conference, we conducted a randomized survey to calculate how many events overlapped with an “average” event at a conference. For each day of a conference, we used a random number generator to identify a single hour with conference activity and counted the number of overlapping events within the first 15 minutes of that hour. The number of events conflicting with an average event was calculated as the total number of overlapping events minus 1. This number was averaged across the different days for each conference. To validate our randomized survey, we also contacted the organizers of each conference to request attendance numbers for the surveyed years - 15 conferences provided this information. Conflict with an average event was strongly correlated with the size of the conference, thus, we concluded that our method of random surveys was a reliable method for quantifying how busy a conference was.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

Datasource: Statistics Canada. 2003. Profile for Canada, Provinces, Territories, Census Divisions and Census Subdivisions, 2001 Census (table). Cumulative Electronic Profiles. Statistics Canada Catalogue no. 95F0495XCB01001. Ottawa. October 22, 2003. http://www12.statcan.ca/english/census01/products/standard/profiles/List... (accessed November 7, 2008). Statistics Canada. 2009. 2001 Census Semi-Custom Profile of Selected CSD Aggregates in Yukon, 2001 Census (tables). J5543 and CRO0105914. Ottawa. November 7, 2008. Footnotes: A value of 0 in any given cell represents one of the following: 1) value is actually zero; 2) value may be random rounded to zero; or 3) value is more than zero but is suppressed for confidentiality reasons. This table is based on 20% data. Values have been subjected to a confidentiality procedure known as random rounding. For Statistics Canada's definition of terms, http://www12.statcan.ca/english/census01/Products/Reference/dict/atoz.htm.