The City of Rochester and its staff use data about individuals in our community to inform decisions related to policies and programs we design, fund, and carry out. City staff must understand and be accountable to best practices and standards to guide the appropriate use of this information in an ethical and accurate manner that furthers the public good. With these disaggregated data standards, the City seeks to establish useful, uniform standards that guide City staff in their collection, stewardship, analysis, and reporting of information about individuals and their demographic characteristics.This internal guide provides recommended standards and practices to City of Rochester staff for the collection, analysis, and reporting of data related to following characteristics of an individual: Race & Ethnicity; Nativity & Citizenship Status; Language Spoken at Home & English Proficiency; Age; Sex, Gender, & Sexual Orientation; Marital Status; Disability; Address / Geography; Household Income & Size; Housing Tenure; Computer & Internet Use; Employment Status; Veteran Status; and Education Level. This kind of data that describes the characteristics of individuals in our community is disaggregated data. When we summarize data about these individuals and report the data at the group level, it becomes aggregated data. These disaggregated data standards can help City staff in different roles understand how to ask individuals about various demographic traits that may describe them, the collection of which may be useful to inform the City’s programs and policies. Note that this standards document does not mandate the collection of every one of these demographic factors for all analyses or program data intake designs – instead, it prompts City staff to intentionally design surveys and other data intake tools/applications to collect the right level of data to inform the City’s decision-making while also respecting the privacy of the individuals whose information the City seeks to gather. When a City team does choose to collect any of the above-mentioned demographic information about individuals in our community, we advise that they adhere to these standards.

This report is prepared pursuant to Local Law 226 of 2019 regarding the demographics of school staff in New York City public schools. The law specifies the reporting of demographics (gender and race or ethnicity) for schools staff in three categories: teaching staff, leadership staff, and other professional and paraprofessional staff. Consistent with the law, the data is further disaggregated to show length of experience in the school and length of experience in the title. The data is shown for each school and aggregated for each community school district, by borough, and citywide. The following additional notes apply:

Analysis of ‘2019 - 2020 School Year Local Law 226 Report for the Demographics of School Staff - Ethnicity’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2329d2f8-8fa7-4758-9e48-a5807fc97215 on 27 January 2022.

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

This report is prepared pursuant to Local Law 226 of 2019 regarding the demographics of school staff in New York City public schools. The law specifies the reporting of demographics (gender and race or ethnicity) for schools staff in three categories: teaching staff, leadership staff, and other professional and paraprofessional staff. Consistent with the law, the data is further disaggregated to show length of experience in the school and length of experience in the title. The data is shown for each school and aggregated for each community school district, by borough, and citywide. The following additional notes apply:

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

Local Law 15 enacted in 2016 requires the Department of Education of the New York City School District to submit to the Council information regarding Health Education instructor data. This report inlcudes (i) the total number of health instructors employed by the department, disaggregated by full-time and part-time instructors; (ii) the total number of instructors assigned to teach at least one health education class. All information is aggregated citywide, as well as disaggregated by community school district and, when available, by school. YABC, D75 home and hospital, D79 and charter schools are excluded from this report.

Analysis of ‘2019 - 2020 School Year Local Law 226 Report for the Demographics of School Staff - Gender’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c24b5ece-25c3-470d-9a90-b11eb92fdae9 on 26 January 2022.

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

This report is prepared pursuant to Local Law 226 of 2019 regarding the demographics of school staff in New York City public schools. The law specifies the reporting of demographics (gender and race or ethnicity) for schools staff in three categories: teaching staff, leadership staff, and other professional and paraprofessional staff. Consistent with the law, the data is further disaggregated to show length of experience in the school and length of experience in the title. The data is shown for each school and aggregated for each community school district, by borough, and citywide. The following additional notes apply:

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

The Distributional Financial Accounts (DFAs) provide a quarterly measure of the distribution of U.S. household wealth since 1989, based on a comprehensive integration of disaggregated household-level wealth data with official aggregate wealth measures. The data set contains the level and share of each balance sheet item on the Financial Accounts' household wealth table (Table B.101.h), for various sub-populations in the United States. In our core data set, aggregate household wealth is allocated to each of four percentile groups of wealth: the top 1 percent, the next 9 percent (i.e., 90th to 99th percentile), the next 40 percent (50th to 90th percentile), and the bottom half (below the 50th percentile). Additionally, the data set contains the level and share of aggregate household wealth by income, age, generation, education, and race. The quarterly frequency makes the data useful for studying the business cycle dynamics of wealth concentration--which are typically difficult to observe in lower-frequency data because peaks and troughs often fall between times of measurement. These data will be updated about 10 or 11 weeks after the end of each quarter, making them a timely measure of the distribution of wealth.