Terms of UseData Limitations and DisclaimerThe user’s use of and/or reliance on the information contained in the Document shall be at the user’s own risk and expense. MassDEP disclaims any responsibility for any loss or harm that may result to the user of this data or to any other person due to the user’s use of the Document.This is an ongoing data development project. Attempts have been made to contact all PWS systems, but not all have responded with information on their service area. MassDEP will continue to collect and verify this information. Some PWS service areas included in this datalayer have not been verified by the PWS or the municipality involved, but since many of those areas are based on information published online by the municipality, the PWS, or in a publicly available report, they are included in the estimated PWS service area datalayer.Please note: All PWS service area delineations are estimates for broad planning purposes and should only be used as a guide. The data is not appropriate for site-specific or parcel-specific analysis. Not all properties within a PWS service area are necessarily served by the system, and some properties outside the mapped service areas could be served by the PWS – please contact the relevant PWS. Not all service areas have been confirmed by the systems.Please use the following citation to reference these data:MassDEP, Water Utility Resilience Program. 2025. Community and Non-Transient Non-Community Public Water System Service Area (PubV2025_3).IMPORTANT NOTICE: This MassDEP Estimated Water Service datalayer may not be complete, may contain errors, omissions, and other inaccuracies and the data are subject to change. This version is published through MassGIS. We want to learn about the data uses. If you use this dataset, please notify staff in the Water Utility Resilience Program (WURP@mass.gov).This GIS datalayer represents approximate service areas for Public Water Systems (PWS) in Massachusetts. In 2017, as part of its “Enhancing Resilience and Emergency Preparedness of Water Utilities through Improved Mapping” (Critical Infrastructure Mapping Project ), the MassDEP Water Utility Resilience Program (WURP) began to uniformly map drinking water service areas throughout Massachusetts using information collected from various sources. Along with confirming existing public water system (PWS) service area information, the project collected and verified estimated service area delineations for PWSs not previously delineated and will continue to update the information contained in the datalayers. As of the date of publication, WURP has delineated Community (COM) and Non-Transient Non-Community (NTNC) service areas. Transient non-community (TNCs) are not part of this mapping project.Layers and Tables:The MassDEP Estimated Public Water System Service Area data comprises two polygon feature classes and a supporting table. Some data fields are populated from the MassDEP Drinking Water Program’s Water Quality Testing System (WQTS) and Annual Statistical Reports (ASR).The Community Water Service Areas feature class (PWS_WATER_SERVICE_AREA_COMM_POLY) includes polygon features that represent the approximate service areas for PWS classified as Community systems.The NTNC Water Service Areas feature class (PWS_WATER_SERVICE_AREA_NTNC_POLY) includes polygon features that represent the approximate service areas for PWS classified as Non-Transient Non-Community systems.The Unlocated Sites List table (PWS_WATER_SERVICE_AREA_USL) contains a list of known, unmapped active Community and NTNC PWS services areas at the time of publication.ProductionData UniversePublic Water Systems in Massachusetts are permitted and regulated through the MassDEP Drinking Water Program. The WURP has mapped service areas for all active and inactive municipal and non-municipal Community PWSs in MassDEP’s Water Quality Testing Database (WQTS). Community PWS refers to a public water system that serves at least 15 service connections used by year-round residents or regularly serves at least 25 year-round residents.All active and inactive NTNC PWS were also mapped using information contained in WQTS. An NTNC or Non-transient Non-community Water System refers to a public water system that is not a community water system and that has at least 15 service connections or regularly serves at least 25 of the same persons or more approximately four or more hours per day, four or more days per week, more than six months or 180 days per year, such as a workplace providing water to its employees.These data may include declassified PWSs. Staff will work to rectify the status/water services to properties previously served by declassified PWSs and remove or incorporate these service areas as needed.Maps of service areas for these systems were collected from various online and MassDEP sources to create service areas digitally in GIS. Every PWS is assigned a unique PWSID by MassDEP that incorporates the municipal ID of the municipality it serves (or the largest municipality it serves if it serves multiple municipalities). Some municipalities contain more than one PWS, but each PWS has a unique PWSID. The Estimated PWS Service Area datalayer, therefore, contains polygons with a unique PWSID for each PWS service area.A service area for a community PWS may serve all of one municipality (e.g. Watertown Water Department), multiple municipalities (e.g. Abington-Rockland Joint Water Works), all or portions of two or more municipalities (e.g. Provincetown Water Dept which serves all of Provincetown and a portion of Truro), or a portion of a municipality (e.g. Hyannis Water System, which is one of four PWSs in the town of Barnstable).Some service areas have not been mapped but their general location is represented by a small circle which serves as a placeholder. The location of these circles are estimates based on the general location of the source wells or the general estimated location of the service area - these do not represent the actual service area.Service areas were mapped initially from 2017 to 2022 and reflect varying years for which service is implemented for that service area boundary. WURP maintains the dataset quarterly with annual data updates; however, the dataset may not include all current active PWSs. A list of unmapped PWS systems is included in the USL table PWS_WATER_SERVICE_AREA_USL available for download with the dataset. Some PWSs that are not mapped may have come online after this iteration of the mapping project; these will be reconciled and mapped during the next phase of the WURP project. PWS IDs that represent regional or joint boards with (e.g. Tri Town Water Board, Randolph/Holbrook Water Board, Upper Cape Regional Water Cooperative) will not be mapped because their individual municipal service areas are included in this datalayer.PWSs that do not have corresponding sources, may be part of consecutive systems, may have been incorporated into another PWSs, reclassified as a different type of PWS, or otherwise taken offline. PWSs that have been incorporated, reclassified, or taken offline will be reconciled during the next data update.Methodologies and Data SourcesSeveral methodologies were used to create service area boundaries using various sources, including data received from the systems in response to requests for information from the MassDEP WURP project, information on file at MassDEP, and service area maps found online at municipal and PWS websites. When provided with water line data rather than generalized areas, 300-foot buffers were created around the water lines to denote service areas and then edited to incorporate generalizations. Some municipalities submitted parcel data or address information to be used in delineating service areas.Verification ProcessSmall-scale PDF file maps with roads and other infrastructure were sent to every PWS for corrections or verifications. For small systems, such as a condominium complex or residential school, the relevant parcels were often used as the basis for the delineated service area. In towns where 97% or more of their population is served by the PWS and no other service area delineation was available, the town boundary was used as the service area boundary. Some towns responded to the request for information or verification of service areas by stating that the town boundary should be used since all or nearly all of the municipality is served by the PWS.Sources of information for estimated drinking water service areasThe following information was used to develop estimated drinking water service areas:EOEEA Water Assets Project (2005) water lines (these were buffered to create service areas)Horsely Witten Report 2008Municipal Master Plans, Open Space Plans, Facilities Plans, Water Supply System Webpages, reports and online interactive mapsGIS data received from PWSDetailed infrastructure mapping completed through the MassDEP WURP Critical Infrastructure InitiativeIn the absence of other service area information, for municipalities served by a town-wide water system serving at least 97% of the population, the municipality’s boundary was used. Determinations of which municipalities are 97% or more served by the PWS were made based on the Percent Water Service Map created in 2018 by MassDEP based on various sources of information including but not limited to:The Winter population served submitted by the PWS in the ASR submittalThe number of services from WQTS as a percent of developed parcelsTaken directly from a Master Plan, Water Department Website, Open Space Plan, etc. found onlineCalculated using information from the town on the population servedMassDEP staff estimateHorsely Witten Report 2008Calculation based on Water System Areas Mapped through MassDEP WURP Critical Infrastructure Initiative, 2017-2022Information found in publicly available PWS planning documents submitted to MassDEP or as part of infrastructure planningMaintenanceThe

This dataset is a shapefile representing the proportion of threatened endemic species (both plants and animals) in 247 countries along with associated environmental and socioeconomic drivers. The geographic coordinate system is World Geodetic System 1984 (EPSG: 4326). Information on a total of 65,125 endemic species including 27,294 globally threatened endemic species (55% threatened plant species, 45% threatened animal species) was extracted from the IUCN Red List. The categories of threatened species used in the analyses included vulnerable (VU), endangered (EN), critically endangered (CR), extinct in the wild (EW) and globally extinct (EX). We calculated the proportion of globally threatened endemic species among the total number of assessed endemic species per country (Chamberlain et al., 2020). Associated environmental socioeconomic regional correlates included: 1) Cropland: The proportion of each country covered by crops (including food, fibre and fodder crops and pasture grasses) was determined based on a FAO global map with a resolution of 5 arc-minutes (von Velthuizen et al., 2007); 2) HANPP: The proportion of net primary production appropriated by humans (HANPP) by harvesting or burning biomass and by converting natural ecosystems to managed lands with lower productivity was derived for the year 2010 from Krausmann et al. (2013); 3) Delta HANPP: We also computed the increase in HANPP over the period 1962-2010 (Krausmann et al., 2013); 4) per area GDP: The per area gross domestic product (GDP, in international $) was obtained by calculating the median value over each country of all 5 arcmin cells of a recently gridded GDP dataset (Kummu et al., 2018); 5) Human Footprint (HFP): The global terrestrial human footprint (HFP) is an index integrating the influence of built environments, population density, electric infrastructure, croplands, pasture lands, roads, railways, and navigable waterways on the environment based on remotely-sensed and bottom-up survey information (Venter et al., 2016). We extracted from a 1 km resolution HFP map the median value over each country in 2009; 6) Delta HFP: We also calculated the increase in median HFP over the period 1993-2009 (Venter et al., 2016); 7) Invasive alien plants: The richness of invasive alien vascular plant species recorded in each country was compiled by Essl et al. (2019); 8) Invasive alien animals: The richness of invasive alien animal species was derived from the Global Register of Introduced and Invasive Species database (http://griis.org/ accessed on 27-6-2018); 9) Delta temperature: Based on decadal climate maps produced by the IPCC over the last century with a 0.5° resolution, we calculated the median of the change in annual mean temperature (in °C) between 1901-1910 and 1981-1990 (Mitchell & Jones, 2005); 10) Delta rainfall: The same for annual precipitation (in mm); 11) Velocity temperature: We also calculated the median velocity of climate change based on the formula from Hamann et al. (2015) to evaluate the distance (in °) over which a species must migrate over the surface of the earth to maintain constant temperature conditions; 12) Velocity rainfall: The same for precipitation; 13) Roadless areas: The median area of a roadless fragment (in km²) was calculated from the global map of roadless areas published by Ibisch et al. (2016); 14) Wilderness areas: The proportion of wildlands (categories ‘wild woodlands' and ‘wild treeless and barren lands') was calculated from the anthropogenic biome map of Ellis et al. (2010); 15) Protected areas: The proportion of protected areas was estimated from the IUCN's shapefile of World Database on Protected Areas (https://www.iucn.org/theme/protected-areas/our-work/world-database-protected-areas); 16) Conservation spending: The mean annual conservation spending of each country (in international $) was taken from Waldron et al. (2017) to quantify investment to mitigate biodiversity loss; 17) Completeness of biodiversity information: We used data on the estimated percentage completeness of species records in GBIF, as assessed through comparison with independent estimates of native richness. Inventory effort indices available for vertebrates (Meyer et al., 2015) and vascular plants (Meyer et al., 2016) were merged into a single metric based upon an average weighted by estimated native species richness.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

This imaging mass cytometry (IMC) dataset serves as an example to demonstrate raw data processing and downstream analysis tools. The data was generated as part of the Integrated iMMUnoprofiling of large adaptive CANcer patient cohorts (IMMUcan) project (immucan.eu) using the Hyperion imaging system (www.fluidigm.com/products-services/instruments/hyperion). To get an overview on the technology and available analysis strategies, please visit bodenmillergroup.github.io/IMCWorkflow. The individual data files are described below:

• Patient1.zip, Patient2.zip, Patient3.zip, Patient4.zip: raw data files of 4 patient samples. Each .zip archive contains a folder in which one .mcd file (IMC raw data) and multiple .txt files (one per acquisition) can be found.
• compensation.zip: This .zip archive holds a folder which contains one .mcd file and multiple .txt files. Multiple spots of a "spillover slide" were acquired and each .txt file is named based on the spotted metal. This data is used for channel spillover correction. For more information, please refer to the original publication: Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry
• panel.csv: This file contains metadata for each antibody/channel used in the experiment. The full column indicates which channel should be analysed. The ilastik column specifies which channels were used for ilastik pixel classification and the deepcell column indicates the channels used for deepcell segmentation.
• sample_metadata.csv: This file links each patient to their cancer type (SCCHN - head and neck cancer; BCC - breast cancer; NSCLC - lung cancer; CRC - colorectal cancer).

Athough MassGIS has served trails information from the Department of Conservation and Recreation (DCR) for many years, this new Trails layer is MassGIS’ first attempt at a statewide, multi-sourced dataset. This layer was created from two primary sources, DCR Trails and the Metropolitan Area Planning Council’s (MAPC) Trailmap. Additionally, a few other trail networks were added from OpenStreetMap (OSM), municipalities, and conservation organizations, but the amount of information from these sources is relatively small.This trails dataset was created for use in the State 9-1-1 Department’s mapping application Response Assist and is intended to assist telecommunicators with lost hikers as well as potentially responding to emergencies within remote areas. Because the layer is primarily being used by 9-1-1, it was decided to focus on those trails found in wilderness areas and used as hiking trails. This resulted in the omission of many arcs from the source data. Examples of these include cart paths on golf courses, the network of paved paths on school campuses, sidewalks, and many other arcs that could functionally serve as trails but were in relatively open and developed areas.Updated with linework from OpenStreetMap in summer 2023 and published on November 14, 2023.See full metadata.Map service also available.

In Rhineland-Palatinate there are always mass movements. The Landslide Database Rhineland-Palatinate is a joint project of the State Office for Geology and Mining Rhineland-Palatinate (LGB) and the Forschungsstelle Rutschungen e.V. at Johannes Gutenberg University Mainz (FSR). Originally, the database was created at the then State Geological Office and continued at the Research Centre for Landslides. Since 2009, the two cooperation partners have been working together on a complete reprocessing. The database includes landslides, rockfalls, rockfalls, earthfalls and daybreaks in Rhineland-Palatinate. In total, there were 2,291 claims (as at: 01.06.2012), which were mainly recorded and archived by the two project partners LGB and FSR in the field. Further data come from various diploma theses and dissertations, which were supervised by the aforementioned institutions. The oldest documented case of damage occurred in 1655. Most events cover the period from 1950 to the present day. The MS Access database based on Oracle, which is specially programmed in the LGB, contains 33 different data fields, which include information on the location, geology, causes and safeguards of the mass movement. The data sets are mainly recorded by means of specially created lists of terms in order to ensure uniform documentation. The online presentation, which was created on the basis of the landslide database, shows in which areas of Rhineland-Palatinate mass movements have occurred so far. It is aimed at municipalities, engineering offices, planners, appraisers, architects and interested citizens who use this information, among other things, for the planning and preliminary exploration of construction projects. The aim is to provide clues about the range of mass movements. Possible problematic areas can thus be identified in good time, examined accordingly and an adapted approach taken. The Mapserver application represents systematically arranged tiles with an extension of 1 x 1 km, the color variation of which is due to the number of mass movements within the tile. It is expressly pointed out that for data protection reasons, a pinpoint, parcel-sharp location representation of the mass movements is dispensed with. A concrete reference to the location and the associated inference to individual plots of land are thus excluded. Furthermore, it is pointed out that the representation of a mass movement within a tile does not mean that this danger is present throughout the entire tile. Likewise, the lack of information on mass movements does not mean that they can be completely excluded there. In addition, the classification says nothing about the current activity. Planned construction projects within a tile affected by mass movements do not necessarily have to pose problems, so it is also pointed out in this context that the hazards shown here do not replace spot and object-related investigations or on-site assessments. A rockfall is a fall event in which the ground or rock material falls mostly free-falling, jumping or rolling. Demolition is often carried out along divisions. In contrast to rockfall, a rockfall includes cubatures from 10 m3 volume.

The files above are MALDI-MSI (Matrix Assisted Laser Desorption Ionization - Mass Spectrometry Imaging) experiments. The dataset contains both imzML, which is the common MSI open file type, and csv, containing x, y positions in tissue as well as m/z and intensity, which is a more user friendly format. A major difference between the two types is that the imzML contains raw data, whereas the csv data is peak-picked, explaining the discrepancy in file sizes. The data contained in this dataset should be enough to recreate the MSI parts of the manuscript.

The purpose of the experiments was to demonstrate that MALDI-MSI and ST (Spatial Transcriptomics) could be done on the same tissue. MALDI-MSI was done first, and ST second, so there is no expected difference in the tissue compared to a regular MALDI-MSI run, however, the experiments where ST was run subsequent to MALDI-MSI required non-conductive glass which is not the standard in the MSI- field. To compare, experiments done on the commonly used conductive (ITO) glass slides are also contained in this dataset

For any questions about the dataset or to request access to any files not uploaded here, please contact per.andren@farmbio.uu.se

About this dataset

Context

The dataset tabulates the Worcester population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Worcester across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Worcester was 207,621, a 0.95% increase year-by-year from 2022. Previously, in 2022, Worcester population was 205,676, a decline of 0.08% compared to a population of 205,839 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Worcester increased by 34,728. In this period, the peak population was 207,621 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Worcester is shown in this column.
• Year on Year Change: This column displays the change in Worcester population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Worcester Population by Year. You can refer the same here

This dataset was created as part of Aldrumont Ferraz Júnior's master's thesis, titled "Development of an Automated Method to Estimate the Mass of Chicken Egg Components Using Computer Vision and Artificial Intelligence."

The dataset contains videos and mass measurements of eggs collected to train artificial intelligence models capable of estimating the mass of egg components (yolk, albumen, and shell) from ovoscopic images. The eggs were filmed under four different light sources (blue, green, red, and white LEDs) in two distinct orientations (tip-up and tip-down). Each egg has a unique identification index and detailed measurements of its assessed mass.

This work contributes to the automation of egg classification and quality assessment in the poultry industry, reducing dependence on manual methods and improving estimation accuracy. If you use this dataset in your research, please credit this work accordingly.

Missing Data: Some rows in the dataset contain missing values, indicating that data collection was unsuccessful for those specific cases. The main reasons for these missing values include:

Egg damage during handling, which made it impossible to measure the mass components. Errors in mass measurement, leading to data inconsistency and exclusion. Issues during video recording, such as incorrect lighting conditions or equipment malfunctions. Data filtering criteria, where some samples were removed due to deviations greater than 2% between measured and calculated total mass. Dataset Column Descriptions data → The date when image collection and mass assessment were performed. video A → The name of the video captured for the egg under different lighting conditions, considering a specific orientation. video B → The name of the second video for the same egg, but filmed in a different orientation. index → A unique identifier for the egg, ensuring traceability between images and mass measurements. Shell → Measured mass of the eggshell (in grams). Yolk + Albumen → Measured mass of the yolk and albumen combined (in grams). Albumen → Measured mass of the albumen (in grams). Total Measured Mass → The total mass of the egg before separating its components. Yolk → Measured mass of the yolk (in grams). Total Calculated Mass → The sum of the measured masses of the egg components (shell, albumen, and yolk). Mass Difference (Calculated vs. Measured) → The difference between the total measured mass and the total calculated mass, used for data consistency analysis. 100% - (Calculated/Measured Mass) → The percentage difference between the measured and calculated mass values. Shell / Total Measured Mass → The proportion of the shell relative to the total egg mass. Yolk / Total Measured Mass → The proportion of the yolk relative to the total egg mass. Albumen / Total Measured Mass → The proportion of the albumen relative to the total egg mass. This dataset is a valuable resource for studies in computer vision, machine learning, and poultry industry applications. If you use this dataset, please cite Aldrumont Ferraz Júnior and his thesis.

THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON AUG. 26

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,

description: Roman Catholic Churches In Large Cities in Arkansas This dataset includes buildings where Roman Catholics gather for organized worship in cities with a population of 50,000 people or more. Roman Catholic Churches are Christian Churches that are subject to the papal authority in Rome. In addition to what are commonly thought of as Roman Catholic Churches, this data set also includes Newman (or Neumann) Centers and Chaldean Churches. Newman Centers are Roman Catholic Churches setup specifically to serve college or university populations. The Chaldean Church (also known as the Chaldean Church of Babylon) reunited with the Catholic Church in the 15th century. It originated in the Middle East. If a group of Roman Catholics gather for organized worship at a location that also serves another function, such as a school, these locations are included in this dataset if they otherwise meet the criteria for inclusion. Roman Catholic Shrines are included if they hold regularly scheduled mass. If a congregation celebrates mass at multiple locations, we have tried to include all such locations. This dataset excludes churches that are not subject to papal authority in Rome. Some churches may refer to themselves as "Catholic", and yet not be part of the "Roman" Catholic Church and these Churches are excluded from this dataset. Specifically Protestant Churches and their descendants which separated from the Roman Catholic Church beginning in 1517, Eastern Orthodox Churches (e.g. Russian, Greek) which separated from the Roman Catholic Church in 1054, and Episcopalian (Church of England in America) which separated from the Roman Catholic Church in 1534 are excluded. The 22 "Eastern Catholic autonomous particular churches", with the exception of the Chaldean Church, are also excluded. These are Churches which are in full communion with the Pope in Rome, but which practice their own rites which are different from the Western or Latin Roman Catholic Church. This dataset excludes rectories. Private homes, even if they are used for formal worship, are excluded from this dataset. Locations that are only used for administrative purposes are also excluded. This dataset also includes original TGS research. All data is non license restricted data. TGS has ceased making phone calls to verify information about religious locations. Therefore all entities in this dataset were €œverified€ using alternative reference sources, such as topo maps, parcel maps, various sources of imagery, and internet research. The CONTHOW attribute for these entities has been set to €œALT REF€ . Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g. the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute, the oldest record dates from 2007/09/05 and the newest record dates from 2007/09/05; abstract: Roman Catholic Churches In Large Cities in Arkansas This dataset includes buildings where Roman Catholics gather for organized worship in cities with a population of 50,000 people or more. Roman Catholic Churches are Christian Churches that are subject to the papal authority in Rome. In addition to what are commonly thought of as Roman Catholic Churches, this data set also includes Newman (or Neumann) Centers and Chaldean Churches. Newman Centers are Roman Catholic Churches setup specifically to serve college or university populations. The Chaldean Church (also known as the Chaldean Church of Babylon) reunited with the Catholic Church in the 15th century. It originated in the Middle East. If a group of Roman Catholics gather for organized worship at a location that also serves another function, such as a school, these locations are included in this dataset if they otherwise meet the criteria for inclusion. Roman Catholic Shrines are included if they hold regularly scheduled mass. If a congregation celebrates mass at multiple locations, we have tried to include all such locations. This dataset excludes churches that are not subject to papal authority in Rome. Some churches may refer to themselves as "Catholic", and yet not be part of the "Roman" Catholic Church and these Churches are excluded from this dataset. Specifically Protestant Churches and their descendants which separated from the Roman Catholic Church beginning in 1517, Eastern Orthodox Churches (e.g. Russian, Greek) which separated from the Roman Catholic Church in 1054, and Episcopalian (Church of England in America) which separated from the Roman Catholic Church in 1534 are excluded. The 22 "Eastern Catholic autonomous particular churches", with the exception of the Chaldean Church, are also excluded. These are Churches which are in full communion with the Pope in Rome, but which practice their own rites which are different from the Western or Latin Roman Catholic Church. This dataset excludes rectories. Private homes, even if they are used for formal worship, are excluded from this dataset. Locations that are only used for administrative purposes are also excluded. This dataset also includes original TGS research. All data is non license restricted data. TGS has ceased making phone calls to verify information about religious locations. Therefore all entities in this dataset were €œverified€ using alternative reference sources, such as topo maps, parcel maps, various sources of imagery, and internet research. The CONTHOW attribute for these entities has been set to €œALT REF€ . Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g. the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute, the oldest record dates from 2007/09/05 and the newest record dates from 2007/09/05

About this dataset

Context

The dataset tabulates the Boston population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Boston. The dataset can be utilized to understand the population distribution of Boston by age. For example, using this dataset, we can identify the largest age group in Boston.

Key observations

The largest age group in Boston, MA was for the group of age 25 to 29 years years with a population of 83,317 (12.55%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Boston, MA was the 80 to 84 years years with a population of 8,731 (1.31%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Boston is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Boston total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Boston Population by Age. You can refer the same here

AboutThis is a dashboard summarizing all North Atlantic Aquatic Connectivity Collaborative (NAACC) non-tidal culvert assessments in the Central Massachusetts Municipal Planning Organization (CMMPO) region. The culvert data was last updated in February, 2025. The dashboard allows users to interact with NAACC culvert data in the CMMPO region, specifically the barrier conditions of culverts and which culverts have been assessed or not assessed. The culverts dataset is updated periodically from the NAACC Data Center, in which culvert assessment data is uploaded as it is collected. The culvert dataset was last updated on February 12, 2025. Culvert DataThe dataset is updated periodically from the NAACC Data Center, in which culvert assessment data is uploaded as it is collected in the field. On the dashboard, the layer displays the barrier condition of each culvert. These barriers and their symbology color include:Severe - RedSignificant - OrangeModerate - YellowMinor - BlueInsignificant - Light BlueNo Barrier - GreenNo Score/Missing Data - PinkNot Assessed - Dark GrayBy clicking the culverts (points) on the dashboard, more data is available for each structure, like when it was assessed/last updated, it's crossing code, it's aquatic organism passage (AOP) score, and more. Data Source: North Atlantic Aquatic Connectivity Collaborative (NAACC)Download Culvert Data: NAACC Data CenterArcGIS Online Data Layer Source: Culverts in MassachusettsOther Dashboard FeaturesThe dashboard also features other tools and information for the user to make use of. First, there are several filters to help read the data on the culvert map: CMMPO town CMMPO subregion, Culvert assessed or not assessedNAACC culvert barrier typeSearch by NAACC culvert crossing code (each culvert has a unique crossing code)Second, the user can interpret the data through indicators and charts, which interact with how the filters are selected as well:Culvert counter (indicator)Culverts assessed vs not assessed chartNAACC Culvert barrier types chartIcon source: https://www.flaticon.com/authors/juicy-fish

NARSTO_EPA_SS_FRESNO_BAM_PM_MASS FRACTION is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Fresno, Beta Attenuation Monitors (BAM), Particulate Mass Concentration Data product. This data set contains measurements taken from two BAMs, PM10, and PM2.5, operated at the Fresno Supersite. The MetOne BAM Monitor measured the attenuation of a beam of beta particles (electrons) generated by a 14ºC source transmitted through an aerosol sample collected on a glass fiber filter tape. Before sample collection, the beta attenuation was measured through a clean part of the tape to obtain a baseline. A sample was collected on the same location on the tape. After sample collection, the beta attenuation was measured through the exposed part of the tape. The net attenuation is proportional to the amount of mass collected on the filter. A mass flow controller controls the flow rate during sample collection at a flow rate of approximately 16.7 l/min. The mass concentration of the collected aerosol was determined from the net attenuation, the sample air flow, the sample time, and the attenuation coefficient for the instrument. The Fresno Supersite is one of several Supersites established in urban areas within the United States by the EPA to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The site is located at 3425 First Street, approximately 1 km north of the downtown commercial district. First Street was a four-lane artery with moderate traffic levels. Commercial establishments, office buildings, churches, and schools were located north and south of the monitor. Medium-density single-family homes and some apartments were located in the blocks to the east and west of First Street. The Fresno Supersite began operation in May of 1999.The EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.NARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.

Introduction

The Micro-Action-52 (MA-52) dataset is designed specifically for Micro-Action Recognition research. The Micro-Action-52 (MA-52) dataset is only to be used for non-commercial scientific purposes. You may request access to the dataset by completing the Google Form provided and the LA files. We will respond promptly upon receipt of your application. If you have difficulty filling out the form, we can also accept the application by email. Please note that the test set is… See the full description on the dataset page: https://huggingface.co/datasets/kunli-cs/MA-52.

This is the third in a series of papers devoted to studying intermediate- mass molecular outflows and their powering sources in detail and with high -angular resolution. This paper studies the intermediate-mass YSO IRAS 20050+2720 and its molecular outflow and puts the results of this and the previous studied sources in the context of intermediate-mass star formation. We carried out VLA observations of the 7mm continuum emission and OVRO observations of the 2.7mm continuum emission, CO (1-0), C18O (1-0) and HC3N (12-11) to map the core towards IRAS 20050+2720. The high-angular resolution of the observations allowed us to derive the properties of the dust emission, the molecular outflow, and the dense protostellar envelope. By adding this source to the sample of intermediate-mass protostars with outflows, we compared their properties and evolution with those of lower mass counterparts. The 2.7mm continuum emission has been resolved into three sources, labeled OVRO 1, OVRO 2, and OVRO 3. Two of them, OVRO 1 and OVRO 2, have also been detected at 7mm. OVRO 3, which is located close to the C18O emission peak, could be associated with IRAs 20050+2720. The mass of the sources, estimated from the dust continuum emission, is 6.5M_{sun} for OVRO 1, 1.8M{sun} for OVRO 2, and 1.3M{sun}_ for OVRO 3. The CO (1-0) emission traces two bipolar outflows within the OVRO field of view, a roughly east-west bipolar outflow, labeled A, driven by the intermediate-mass source OVRO 1, and a northeast-southwest bipolar outflow, labeled B, probably powered by a YSO engulfed in the circumstellar envelope surrounding OVRO 1. The multiplicity of sources observed towards IRAS 20050+2720 appears to be typical of intermediate-mass protostars, which form in dense clustered environments. In some cases, as for example IRAS 20050+2720, intermediate- mass protostars would start forming after a first generation of low-mass stars has completed their main accretion phase. The properties of intermediate-mass protostars and their outflows are not significantly different from those of low-mass stars. Although intermediate-mass outflows are intrinsically more energetic than those driven by low-mass YSOs, they do not show intrinsically more complex morphologies when observed at high angular resolution. Known intermediate-mass protostars do not form a homogeneous group. Some objects are likely in an earlier evolutionary stage as suggested by the infrared emission and the outflow properties.

This dataset contain ventilation ages calculated using the transit time distribution (TTD) method (e.g., Waugh et al., 2004) on the GLODAPv2 data synthesis product (Olsen et al., 2016). Ventilation age is defined as the time elapsed since a water parcel was last in contact with the atmosphere. Our calculated ages are estimated from measured concentrations of the transient tracers sulphur hexafluoride (SF6), and the chlorofluorocarbons (CFCs) CFC-11 and CFC-12. For these TTD calculations we have assumed full (100%) saturation of the transient tracers when subducted, which will generate a bias toward older ages in especially dense water formation regions since it is known that the saturation there is frequently lower than 100%. We assume that the solution to the Greens function is an Inverse Gaussian (IG) function. Furthermore, we have assumed a balance between advection and mixing, i.e., unity ratio between the width and the mean age of the TTDs. This assumption is typically adopted in the global ocean (e.g., Waugh et al., 2006), although there is regional variability (e.g., Stöven and Tanhua, 2014; Rajasakaren et al., 2019). Thus, some care should be taken when utilising the calculated ages in certain regions. The main reason for the published dataset is to give a user-friendly product that can be applied in ocean studies where ventilation ages are of interest, both to give an appreciation of typical ages and gradients in the ocean, and to be adopted in studies calculating biogeochemical rates. A recent example of the latter is the updated calcium carbonate dissolution study by Sulpis et al. (2021), which used these data. All included data are listed and specified in the dataset description below, and most of them are identical to the values found in GLODAPv2 (Key et al., 2015; Olsen et al., 2016). The novel addition in this dataset are the ventilation ages. The files contain both the TTD-based mean ages that are calculated as described above, and, calculated tracer ages, which assumes no mixing and are simply derived by matching the observed tracer concentration to the atmospheric history. For the atmospheric history we used (Walker et al. (2000) and Bullister (2015)), updated to 2016 by extrapolating with the same atmospheric evolution rate as the year before. The dataset consists of files covering four regions, following the GLODAPv2 data synthesis product: the Arctic Mediterranean (ARC), The Atlantic Ocean (ATL), the Indian Ocean (IND), and the Pacific Ocean (PAC). The data are provided both in comma separated (.csv) format and in Matlab® format (.mat).

CSO attributes and location information are from a variety of datasets for each state: Connecticut: Beginning with GIS data compiled by the Connecticut Department of Energy and Environmental Protection (“CT DEEP”) and displayed on their CSO Right-to-Know site (https://portal.ct.gov/DEEP/Municipal-Wastewater/Combined-Sewer-Overflows-Right-to-Know), EPA filtered the data for the purposes of this map and made corrections based upon updated information available in EPA’s files. EPA’s map only displays municipalities with CSO outfalls, whereas CT DEEP’s map includes municipalities with CSO-related bypasses at their Wastewater Treatment Facilities (but no Combined Sewer Collection System CSO outfalls). EPA’s map only displays CSO outfalls – the point at which CSOs are discharged to the receiving water - whereas CT DEEP’s map includes CSO regulators (the structure through which wastewater and stormwater exits the conveyance pipe towards the Wastewater Treatment Facility). Maine: Service containing both facility and outfall locations permitted under the Maine Pollution Elimination System (MEPDES) and administered by the Maine Department of Environmental Protection (MEDEP). The data has been collected using multiple methods over 2 decades under the direction of the Maine DEP GIS Unit. All location data was quality checked by MEDEP MEPDES Inspectors and GIS Unit staff in 2018. Massachusetts: Attribute and location information from a combination of MassDEP CSOs(https://mass-eoeea.maps.arcgis.com/apps/webappviewer/index.html?id=08c0019270254f0095a0806b155abcde) (metadata - https://mass-eoeea.maps.arcgis.com/home/item.html?id=0262b339c2c74213bdaaa15adccc0e96) and NPDES permits(https://www.epa.gov/npdes-permits/massachusetts-final-individual-npdes-permits). New Hampshire: Active CSO outfalls collected from NH NPDES permits(https://www.epa.gov/npdes-permits/new-hampshire-final-individual-npdes-permits). EPA made corrections based upon updated information available in EPA’s files. Rhode Island: RI CSO Outfall Point Features. The outfalls managed by the Narragansett Bay Commission are downloadable from a GIS file through RIGIS (Rhode Island Geographic Information System https://www.rigis.org/datasets/nbc-sewer-overflows/explore?location=41.841121%2C-71.414224%2C13.57&showTable=true). Data was intended for use in utility facility engineering structure inventory. Last updated: 2019. Downloaded: 11/19/2021. Metadata (https://www.arcgis.com/sharing/rest/content/items/2108bab269df47f988e59c18a556f37d/info/metadata/metadata.xml?format=default&output=html) Vermont: Attribute and location information from Vermont Open Geodata Poral (https://geodata.vermont.gov/datasets/VTANR::stormwater-infrastructure-point-features/explore?location=43.912839%2C-72.414150%2C9.29). Point, line, and polygon data was collected and compiled through field observations, municipal member knowledge, ortho-photo interpretation, digitization of georeferenced town plans and record drawings, and state stormwater permit plans. Accuracy of all data is for planning purposes and field verification is at the user’s discretion. VT Layer: Stormwater Infrastructure (Point Features) Metadata (https://www.arcgis.com/sharing/rest/content/items/5c9875ee609c4586bd569dbacb2d92f1/info/metadata/metadata.xml?format=default&output=html).

The purpose of this data set was to compile body mass information for all mammals on Earth so that we could investigate the patterns of body mass seen across geographic and taxonomic space and evolutionary time. We were interested in the heritability of body size across taxonomic groups (How conserved is body mass within a genus, family, and order?), in the overall pattern of body mass across continents (Do the moments and other descriptive statistics remain the same across geographic space?), and over evolutionary time (How quickly did body mass patterns iterate on the patterns seen today? Were the Pleistocene extinctions size specific on each continent, and did these events coincide with the arrival of man?). These data are also part of a larger project that seeks to integrate body mass patterns across very diverse taxa (NCEAS Working Group on Body size in ecology and paleoecology: linking pattern and process across space, time and taxonomic scales). We began with the updated version of Wilson and Reeder's (1993) taxonomic list of all known Recent mammals of the world (N = 4629 species) to which we added status, distribution, and body mass estimates compiled from the primary and secondary literature. Whenever possible, we used an average of male and female body mass, which was in turn averaged over multiple localities to arrive at our species body mass values. The sources are line referenced in the main data set, with the actual references appearing in a table within the metadata. Mammals have individual records for each continent they occur on. Please note that our data set is more than an amalgamation of smaller compilations. Although we relied heavily a data set for Chiroptera by K. E. Jones (N = 905), the CRC handbook of Mammalian Body Mass (N = 688), and a data set compiled for South America by P. Marquet (N = 505), these total less than half the records in the current database. The remainder are derived from more than 150 other sources (see reference table). Furthermore, we include a comprehensive late Pleistocene species assemblage for Africa, North and South America, and Australia (an additional 230 species). 'Late Pleistocene' is defined as approximately 11 ka for Africa, North and South America, and as 50 ka for Australia, because these times predate anthropogenic impacts on mammalian fauna. Estimates contained within this data set represent a generalized species value, averaged across gender and geographic space. Consequently, these data are not appropriate for asking population-level questions where the integration of body mass with specific environmental conditions is important. All extant orders of mammals are included, as well as several archaic groups (N = 4859 species). Because some species are found on more than one continent (particularly Chiroptera), there are 5731 entries.