The study involved the collection of data on serious assaults that occured in three cities: Miami, Florida (1996-1997), Pittsburgh, Pennsylvania (1994-1996), and St. Louis, Missouri (1995-1996). The data were extracted from police offense reports, and included detailed information about the incidents (Part 1) as well as information about the victims, suspects, and witnesses for each incident (Parts 2-9).

Graph and download economic data for St. Louis Source Base (DISCONTINUED) (WSBASE) from 1984-01-04 to 2019-12-18 about monetary base, St. Louis, and USA.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Martin Sanchez on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This resource is a member of a series. 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. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

About this dataset

Context

The dataset tabulates the St. Louis 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 St. Louis. The dataset can be utilized to understand the population distribution of St. Louis by age. For example, using this dataset, we can identify the largest age group in St. Louis.

Key observations

The largest age group in St. Louis, MO was for the group of age 25 to 29 years years with a population of 29,055 (9.91%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in St. Louis, MO was the 80 to 84 years years with a population of 4,112 (1.40%). 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 St. Louis is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of St. Louis 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 St. Louis Population by Age. You can refer the same here

The data package includes data, model, and code that support the analyses and conclusions in the paper titled “modeling of streamflow in a 30-kilometer-long reach spanning 5 years using OpenFOAM 5.x”. The primary goal of this paper is to demonstrate that key streamflow properties such as water depth, flow velocity, and dynamic pressure in a natural river at 30-kilometer scale over 5 years can be reliably and efficiently modeled using the computational framework presented in this paper. To support the paper, various data types from remote sensing, field observations, and computational models are used. Specific details are described as follows. Firstly, the river bathymetry data was obtained from a Light Detection and Ranging (LiDAR) survey. This data is then converted to a triangulated surface format, STL, for mesh generation in OpenFOAM. The STL data can be found in Model_Setups/BaseCase_2013To2015/constant/triSurface. The OpenFOAM mesh generated using this STL file can be found in constant/polyMesh. Other model setups, boundary and initial conditions can be found in /system and /0.org under folder BaseCase_2013To2015. A similar data structure can also be found in BaseCase_2018To2019 for the simulations during 2018 and 2019. Secondly, the OpenFOAM simulations need the upstream discharge and water depth information at the upstream boundary to drive the model. These data are generated from a one-dimensional hydraulic model and the data can be found under the folder Model_Setups /1D model Mass1 data. The mass1_65.csv and mass1_191.csv files include the results of the 1D model at the model inlet and outlet, respectively. The Matlab source code Mass1ToOFBC20182019.m is used to convert these data into OpenFOAM boundary condition setups.With the above OpenFOAM model, it can generate data for water surface elevation, flow velocity, and dynamic pressure. In this paper, the water surface elevation was measured at 7 locations during different periods between 2011 and 2019. The exact survey locations (see Fig1_SurveyLocations.txt) can be found in folder Fig_1. The variation of water stage over time at the 7 locations can be found in folder /Observation_WSE. The data type include .txt, .csv, .xlsx, and .mat. The .mat data can be loaded by Matlab.We also measured the flow velocities at 12 cross-sections along the river. At each cross-section, we recorded the x, y locations, depth, three velocity components u,v,w. These data are saved to a Matlab format which can be found under folder /Observation_Velocity and /Fig_1. The relative locations of velocity survey locations to the river bathymetry can be found in Figure 1c.The water stage data at the 7 locations from OpenFOAM, 1D, and 2D hydraulic models are also provided to evaluate the long-term performance of 3D models vs 1D/2D models. The water stage data for the 7 locations from OpenFOAM have been saved to .mat format and can be found in /OpenFOAM_WSE. The water stage data from the 1D model are saved in .csv format and can be found in /Mass1_WSE. The water stage from the 2D model is saved as .mat format and can be found in / Mass2_WSEIn addition, the OpenFOAM model outputs the information of hydrostatic and hydrodynamic pressure. They are saved as .mat format under folder /Fig_11/2013_1. As the files are too large, we only uploaded the data for January 2013. The area of different ratio of dynamic pressure to static pressure for all simulation range, i.e., 2013-2015, are saved to .mat format. They can be found in /Fig_11/PA. Further, the data of wall clock time versus the solution time of the OpenFOAM modeling are also saved to .mat format under folder /Fig_13/LogsMat. In summary, the data package contains seven data types, including .txt, .csv, .xlsx, .dat, .stl, .m, and .mat. The former 4 types can be directly open using a text editor or Microsoft Office. The .mat format needs to be read by Matlab. The Matlab source code .m files need to be run with Matlab. The OpenFOAM setups can be visualized in ParaView. The .stl file can be opened in ParaView or Blender. The data in subfolders Fig_1 to Fig_10 and Fig_12 are copied from the aforementioned data folders to generate specific figures for the paper. A readME.txt file is included in each subfolder to further describe how the data in each folder are generated and used to support the paper.Please use the data package's DOI to cite the data package. Please contact yunxiang.chen@pnnl.gov if you need more data related to the paper.

About this dataset

Context

The dataset tabulates the St. Louis population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of St. Louis.

Content

The dataset constitues the following two datasets across these two themes

• St. Louis, MO Population Breakdown by Gender
• St. Louis, MO Population Breakdown by Gender and Age

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/.

A collection of readily-usable volumetric models for all tissues and stages of mouse peri-implantation development as extracted from the eMouse Atlas Project (E5.0 to E9.0), as well as custom-made models of all pre-implantation stages (E0 to E4.0). These models have been converted to a commonly used 3D format (.stl), and are provided in ready-made files for digital exploration and illustration.

Specifically:

individual .stl models of embryonic subcomponents for each developmental stage (e.g. epiblast, visceral endoderm; 1 folder per EMAP embryonic stage)

ready-made .blend files where such components have been reassembled as a full embryo model, and in a scene with pre-prepared light sources and aligned camera (1 file per embryonic stage)

a .blend file with all embryonic models provided arranged in a temporal lineup; and a .blend file with a "starter-pack" of pre-made materials to use for easy illustration.

Also included are two data tables:

"EMAP_key.xlsx" indicating the source of its model, and the embryonic stage corresponding to each Theiler Stage

"EMAP_partslist.xlsx" containing the original name of each embryonic component provided (as used in EMAP), and the corresponding name used in this dataset.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Curology on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

These data were automated to provide an accurate high-resolution historical shoreline of St. Louis Bay to Petit Bois Island, MS suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808

Graph and download economic data for All Employees: Mining, Logging, and Construction in St. Louis, MO-IL (MSA) (STLNRMN) from Jan 1990 to Jan 2025 about natural resources, St. Louis, mining, IL, MO, construction, employment, and USA.

The EnviroAtlas St. Louis, Missouri Meter-Scale Urban Land Cover (MULC) dataset comprises 4188 km2 around the city of St. Louis and surrounding land in parts of eleven counties within Illinois and Missouri. These MULC data and maps were derived from several sources from multiple years: LiDAR (2008-2012); 1-m pixel, four-band (red, green, blue, and near-infrared) leaf-on aerial photography acquired from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP, 2012, 2014-2016); leaf-off 6-inch pixel four-band imagery (2015) as well as ancillary vector data (e.g., roads, building footprints.). Eight land cover classes were mapped: Water, Impervious Surfaces, Soil/Barren, Tree/Forested, Grass/Herbaceous Non Woody Vegetation, Agriculture, and Wetlands (Woody and Emergent). Wetlands were delineated using the best available existing wetlands data, which was a National Wetlands Inventory (NWI) layer. An analysis of 745 completely random and 226 stratified random photo-interpreted land cover reference points yielded a simple overall user's accuracy (MAX) of 81% and an overall fuzzy user's accuracy (RIGHT) of 90% (see confusion matrices below). This dataset was produced in three phases by the University of Missouri and the East-West Gateway Council of Governments for the Missouri Resource Assessment Partnership (MoRAP) and the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets ).

These data provide an accurate high-resolution shoreline compiled from imagery of ST LOUIS BAY, MS . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

Graph and download economic data for All Employees: Mining, Logging, and Construction in Little Rock-North Little Rock-Conway, AR (MSA) (LRSNRMN) from Jan 1990 to Dec 2024 about Little Rock, natural resources, AR, mining, construction, employment, and USA.

A two-dimensional, steady-state groundwater flow model of the St. Louis River Basin (SLRB) was developed using the analytic-element computer code, GFLOW, to provide an understanding of the regional groundwater flow system. In analytic-element models significant streams and lakes in the model domain are represented as linesink elements. Analytic-element models, such as the SLRB regional model, can be a screening model which provides a simplified version of a hydrologic system, completed ahead of a more complex modeling effort. For this study, the regional screening model was refined to focus on extensive ditching in the central SLRB, a wetland-rich area to the south of the Iron Range. Two smaller models were developed in this area—a ditch model and a pre-ditch model. The effect of ditching was evaluated by comparing two separate model runs, one with the ditch network represented as linesinks and a “pre-ditching” model with ditch linesinks removed. Additional calibration of the ditch scenario models was done to improve modeled groundwater levels in the wetlands. This USGS data release contains all of the input and output to run the model simulations described in the associated model documentation report (https://doi.org/10.3133/sir20195033).

Graph and download economic data for All Employees: Mining, Logging, and Construction in Sandusky, OH (MSA) (DISCONTINUED) (SAND739NRMN) from Jan 1990 to Dec 2014 about Sandusky, natural resources, mining, OH, construction, employment, and USA.

Graph and download economic data for All Employees: Mining, Logging, and Construction in Bay City, MI (MSA) (BAYC026NRMN) from Jan 1990 to Jan 2025 about Bay City, natural resources, mining, MI, construction, employment, and USA.

About this dataset

Context

The dataset tabulates the population of St. Louis by race. It includes the population of St. Louis across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of St. Louis across relevant racial categories.

Key observations

The percent distribution of St. Louis population by race (across all racial categories recognized by the U.S. Census Bureau): 74.73% are white, 6.59% are American Indian and Alaska Native and 18.68% are multiracial.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the St. Louis
• Population: The population of the racial category (excluding ethnicity) in the St. Louis is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of St. Louis 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 St. Louis Population by Race & Ethnicity. You can refer the same here

Graph and download economic data for All Employees: Mining, Logging, and Construction in Rochester, MN (MSA) (ROCH327NRMN) from Jan 1990 to Nov 2021 about Rochester, natural resources, MN, mining, construction, employment, and USA.

Graph and download economic data for St. Louis Fed Economic News Index: Real GDP Nowcast (STLENI) from Q2 2013 to Q1 2025 about nowcast, projection, real, GDP, rate, indexes, and USA.