State, County and City FIPS (Federal Information Processing Standards) codes are a set of numeric designations given to state, cities and counties by the U.S. federal government. All geographic data submitted to the FRA must have a FIPS code.

description: The US Census Bureau's online County Look-up Tool provides the unique 3-digit code for the Identification of Counties and Equivalent Entities of the United States, its Possessions, and Insular Areas.; abstract: The US Census Bureau's online County Look-up Tool provides the unique 3-digit code for the Identification of Counties and Equivalent Entities of the United States, its Possessions, and Insular Areas.

This dataset was created to facilitate the conversion of Uniform Crime Reporting (UCR) Program state and county codes to Federal Information Processing Standards (FIPS) state and county codes. The four UCR agency-level data files archived at ICPSR in Uniform Crime Reporting Program Data: United States contain UCR state and county codes as geographic identifiers. Researchers who wish to use these data with other sources, such as Census data, may want to convert these UCR codes to FIPS codes in order to link the different data sources. This file was created to facilitate this linkage. It contains state abbreviations, UCR state and county codes, FIPS state and county codes, and county names for all counties present in the UCR data files since 1990. These same FIPS codes were used to create the UCR County-Level Detailed Arrest and Offense files from 1990-1996.

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County-Level Demographic: Population, Race, Gender

Overview

This dataset provides a detailed breakdown of demographic information for counties across the United States, derived from the U.S. Census Bureau's 2023 American Community Survey (ACS). The data includes population counts by gender, race, and ethnicity, alongside unique identifiers for each county using State and County FIPS codes.

Dataset Features

The dataset includes the following columns: - County: Name of the county. - State: Name of the state the county belongs to. - State FIPS Code: Federal Information Processing Standard (FIPS) code for the state. - County FIPS Code: FIPS code for the county. - FIPS: Combined State and County FIPS codes, a unique identifier for each county. - Total Population: Total population in the county. - Male Population: Number of males in the county. - Female Population: Number of females in the county. - Total Race Responses: Total race-related responses recorded in the survey. - White Alone: Number of individuals identifying as White alone. - Black or African American Alone: Number of individuals identifying as Black or African American alone. - Hispanic or Latino: Number of individuals identifying as Hispanic or Latino.

Processing Methodology

1. Source:
   • Data was retrieved using the U.S. Census Bureau ACS API.
2. County-Level Aggregation:
   • Each county is uniquely identified using State FIPS Code and County FIPS Code.
   • These codes were concatenated to form the unified FIPS column.
3. Data Cleaning:
   • All numeric columns were converted to appropriate data types.
   • County and state names were extracted from the raw NAME field for clarity.

Why Use This Dataset?

This dataset is highly versatile and suitable for: - Demographic Analysis: - Analyze population distribution by gender, race, and ethnicity. - Geographic Studies: - Use FIPS codes to map counties geographically. - Data Visualizations: - Create visual insights into demographic trends across counties.

File Format

• The dataset is available as a CSV file with 3,000+ rows (one for each county).

Licensing

• Source: Data is sourced from the U.S. Census Bureau's 2023 American Community Survey (ACS).
• License: This dataset is in the public domain and provided under the U.S. Census Bureau’s terms of use. Attribution to the Census Bureau is appreciated.

Acknowledgments

Special thanks to the U.S. Census Bureau for making this data publicly available and to the Kaggle community for fostering a collaborative space for data analysis and exploration. """

A list of Connecticut municipalities with the 3-digit tax code and the 2010 10-digit FIPS code for county subdivisions, assigned by the U.S. Census Bureau

A listing of NYS counties with accompanying Federal Information Processing System (FIPS) and US Postal Service ZIP codes sourced from the NYS GIS Clearinghouse.

This dataset, which represents county Federal Information Processing System (FIPS) codes for each county as a raster, is utilized by reVX to compute setbacks (distances). Setbacks can be computed either locally (on a per-county basis with specified distances or multipliers) or globally under a generic setback multiplier assumption applied to either the turbine tip height or the base setback distance. A County FIPS code is a five-digit numerical identifier that uniquely identifies counties and county equivalents in the United States The initial two digits represent the FIPS state code, while the final three digits signify the county's unique code within that state. For instance, 55025 corresponds to Dane County, Wisconsin. The first two digits - 55 - represent Wisconsin, and the last three digits - 025 - denote Dane County. Further information can be accessed at the "Federal Information Processing System (FIPS) Codes for States and Counties" resource below.

This dataset was created to help users to go between County - State Name, State-County FIPS, City, or to ZIP Code. Most importantly, this dataset was created because we shouldn't have to pay for free & public data.

Assumptions - HUD uses the most up to date Zip Code boundaries from the USPS when they post their new Quarterly data. *ZIP Codes are updated on a regular basis. Here is an example announcement from the USPS. - City data only available from 2018 onward.

Data Sources

US HUD https://www.huduser.gov/portal/datasets/usps.html

Census Bureau The table data, direct link. This data is only updated once every census, 10 years. The details of the National County text file can be found here

USPS Zip to City Lookup More information can be found here. It's a free API from the USPS. Need to create a username to pull the data.

Data Dictionary

Files 2018 -> Newer - ZIP ZIP Code - STCOUNTFP US State & County FIPS ID - CITY City for that Zip/Fips Code - STATE US State - COUNTYNAME US County Name - CLASSFP FIPS Class Code, as defined by the Census

Files 2010-2017 - ZIP ZIP Code - COUNTYNAME US County Name - STATE US State - STCOUNTFP US State & County FIPS ID - CLASSFP FIPS Class Code, as defined by the Census

FIPS Class Code Details Source Copied 7/29/17

• H1 identifies an active county or statistically equivalent entity that does not qualify under subclass C7 or H6.
• H4 identifies a legally defined inactive or nonfunctioning county or statistically equivalent entity that does not qualify under subclass H6.
• H5 identifies census areas in Alaska, a statistical county equivalent entity.
• H6 identifies a county or statistically equivalent entity that is a really coextensive or governmentally consolidated with an incorporated place, part of an incorporated place, or a consolidated city.
• C7 identifies an incorporated place that is an independent city; that is, it also serves as a county equivalent because it is not part of any county, and a minor civil division (MCD) equivalent because it is not part of any MCD.

Acknowlegement

Foto von Annie Spratt auf Unsplash

FIPS Market Outlook

The global FIPS market size is projected to witness significant growth, expanding from $10.5 billion in 2023 to an estimated $17.8 billion by 2032, achieving a compound annual growth rate (CAGR) of 6.3%. This growth trajectory is driven by the increasing demand for enhanced data security and privacy regulations across various industries, heightened by the rise in cyber threats and the growing adoption of advanced technologies that require robust encryption standards. As organizations continue to prioritize data protection to comply with regulatory requirements, the adoption of FIPS (Federal Information Processing Standards) is expected to see a significant upswing, ensuring secure and reliable information systems across diverse sectors.

One of the primary growth factors fueling the FIPS market is the escalating need for stringent data security measures amid the rising incidences of data breaches and cyber-attacks. With industries across the globe increasingly digitizing their operations, the threat landscape continues to evolve, necessitating robust security frameworks such as FIPS. Governments and regulatory bodies are mandating the implementation of FIPS-compliant solutions to safeguard sensitive information, particularly in sectors like healthcare and finance, where data sensitivity is paramount. This regulatory pressure is compelling organizations to adopt FIPS-certified systems to ensure compliance and protect their critical data assets.

Another significant growth driver is the expanding application of FIPS across various industries, beyond its traditional stronghold in government and defense sectors. The healthcare industry, for instance, is rapidly embracing FIPS standards to protect patient data under regulations like HIPAA. Similarly, the finance sector is leveraging FIPS-certified solutions to secure transaction data and prevent financial fraud. The advent of IoT and cloud computing has further widened the application scope of FIPS, as these technologies require robust encryption to counteract vulnerabilities. This diversification of applications is creating new growth opportunities for FIPS-certified products and services, further propelling market expansion.

The shift towards cloud-based deployments is also contributing to the FIPS market growth. As organizations increasingly migrate their operations to the cloud to leverage scalability and flexibility, the demand for FIPS-compliant cloud solutions is rising. Cloud service providers are integrating FIPS-certified encryption technologies into their offerings to meet the security requirements of their clients, thereby enhancing the adoption of FIPS standards. Additionally, the increasing reliance on cloud-based services in sectors like IT and telecommunications is driving the need for robust security measures, further boosting the market for FIPS-certified solutions.

Regionally, North America currently holds the largest share of the FIPS market, driven by the presence of key market players and stringent regulatory frameworks mandating the adoption of FIPS standards. The region's focus on cybersecurity and data protection, particularly in sectors like government and finance, is propelling the demand for FIPS-certified solutions. Meanwhile, the Asia Pacific region is expected to witness the fastest growth, with a CAGR of 7.1% during the forecast period. This growth is attributed to the rapid digital transformation in countries like China and India, coupled with increasing regulatory measures to ensure data security. Europe also shows a promising outlook, driven by stringent data protection laws such as GDPR, which are encouraging the adoption of FIPS standards to ensure compliance.

Component Analysis

The FIPS market, segmented by components into software, hardware, and services, showcases a dynamic landscape with each segment contributing significantly to the overall market growth. The software segment is a major contributor, driven by the increasing demand for encryption software and security applications that adhere to FIPS standards. As industries move towards digitalization, the need for software solutions that provide robust encryption and secure data transactions is paramount. Companies are investing heavily in developing advanced encryption software to comply with FIPS requirements, thus facilitating the growth of this segment.

Hardware components within the FIPS market are also witnessing substantial growth. This segment includes encryption modules, secure processors, and hardware security modules (HSMs) that are FIPS-certified. The demand for hardware components is particu

US County & Zipcode Historical Demographics

Easily lookup US historical demographics by county FIPS or zipcode in seconds with this file containing over 5,901 different columns including:

*Lat/Long *Boundaries *State FIPS *Population from 2010-2019 *Death Rate from 2010-2019 *Unemployment from 2001-2020 *Education from 1970-2019 *Gender and Age Population

Provided by bitrook.com to help Data Scientists clean data faster.

Data Sources

All Data Combined Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Population Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Unemployment Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Zip FIPS Crosswalk Source:

https://data.world/niccolley/us-zipcode-to-county-state

County Boundaries Source:

https://public.opendatasoft.com/explore/dataset/us-county-boundaries/table/?disjunctive.statefp&disjunctive.countyfp&disjunctive.name&disjunctive.namelsad&disjunctive.stusab&disjunctive.state_name

Age Sex Source:

https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/asrh/cc-est2019-agesex-**.csv https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/cc-est2019-agesex.pdf

Races Source:

https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/asrh/cc-est2019-alldata.csv https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/cc-est2019-alldata.pdf

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

The property’s Flood Factor as well as data on economic loss.

The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

Field Name

Type

Description

fsid

int

First Street ID (FSID) is a unique identifier assigned to each location

long

float

Longitude

lat

float

Latitude

zcta

int

ZIP code tabulation area as provided by the US Census Bureau

blkgrp_fips

int

US Census Block Group FIPS Code

tract_fips

int

US Census Tract FIPS Code

county_fips

int

County FIPS Code

cd_fips

int

Congressional District FIPS Code for the 116th Congress

state_fips

int

State FIPS Code

floodfactor

int

The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)

CS_depth_RP_YY

int

Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00

CS_chance_flood_YY

float

Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00

aal_YY_CS

int

The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low

hist1_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist1_event

string

Short name of the modeled historic event

hist1_year

int

Year the modeled historic event occurred

hist1_depth

int

Depth (in cm) of flooding to the building from this historic event

hist2_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist2_event

string

Short name of the modeled historic event

hist2_year

int

Year the modeled historic event occurred

hist2_depth

int

Depth (in cm) of flooding to the building from this historic event

adapt_id

int

A unique First Street identifier assigned to each adaptation project

adapt_name

string

Name of adaptation project

adapt_rp

int

Return period of flood event structure provides protection for when applicable

adapt_type

string

Specific flood adaptation structure type (can be one of many structures associated with a project)

fema_zone

string

Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders

footprint_flag

int

Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)

Abstract ======== This data set consists of the MESSENGER Energetic Particle and Plasma Spectrometer (EPPS) calibrated observations, also known as CDRs. The system encompasses 2 instrument subsystems - the Energetic Particle Spectrometer (EPS) and the Fast Imaging Plasma Spectrometer (FIPS). This data set contains FIPS instrument data. FIPS covers the energy/charge range of

LocalView is a database co-created by Soubhik Barari and Tyler Simko to advance the study of local government in the United States. It is the largest existing dataset of local government public meetings— the central policy-making process in American local government — as they are captured on video. To get started, select the file(s) that you'd like for your use case based on the year that the meeting took place. Note: we are no longer supporting file formats other than .parquet for space considerations. For potential use cases or further guidance on downloading the data in bulk, visit the companion website: localview.net. Change Log Scraping, parsing, identifying, and merging together meetings involves a large number of non-trivial decisions, many of which need to be adjusted over time particularly as the YouTube API changes. As such, when such decisions notably deviate from process or the outputs documented in the first version of this database, it will be logged here. Version 2.0 (2023-10) Data updated up until September 2023. ~10,000 new videos added, all belonging to existing channels in database. Change in data format: channelType column changed to channel_type. ST_FIPS correctly padded to be 7 characters (2 digit state code + 5 digit place FIPS code). videos with no caption available from YouTube are explicitly marked as “” in caption_text. caption_text_cleaned is actually consistently cleaned (previously stray timestamps/pause markers in some entries). acs_2018_* columns now prefixed as acs_18. additional ACS variables now available for each place: acs_18_median_gross_rent: Median gross rent in FIPS place. acs_18_median_hh_inc: Median household income in FIPS place. acs_18_median_age: Median age in FIPS place. acs_18_amind: American Indian population in FIPS place. acs_18_asian: Asian population in FIPS place. acs_18_nhapi: Native Hawaiian or Pacific Islander population in FIPS place. census_2015_* columns removed for redundancy. to avoid confusion and possible conflicts, .json and .csv file formats eliminated in favor of .parquet format. municipal voteshare (_dem2pv) variables have been removed from the public use files for a number of reasons: (1) high degree of missingness, (2) no columns estimates available, (3) potential sensitivity The matching process of videos to ST-FIPS and government types is as follows: videos are matched to channels’ previous ST-FIPS codes and/or government types if there is an umambiguous match, otherwise a (1) series of regex matches with video title/description are used to attempt to match video to the government and (2) state/place names are extracted from each video’s title/description/caption and used to match to an ST-FIPS entity if an unambiguous match; only identified videos are then uploaded to the database. to identify the date of the meeting that the video captures, we first try to extract the date from the title, otherwise we try to extract the date from the description, otherwise it is discarded. Version 1.0 (2023-02) See publication for full details on methodology choices for the Version 1.0 database.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Harvard Forest (HFR) contains population (urban) measurements in number units and were aggregated to a yearly timescale.

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Gender measures in this release include the state-level poverty ratio, which compares the proportion of females living in poverty to the proportion of males living in poverty in a given state in a given year. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

Data files

A set of 3 data files needs to be in the same directory as the executable to specify a population for FluTE. The file names consist of a prefix (e.g., "seattle") followed by a suffix.

*-tracts.dat - Tract populations and locations, from http://www.census.gov/geo/www/cenpop/cntpop2k.html. The columns in this comma-delimited file are: state FIPS code, county FIPS code, tract FIPS code, tract population, tract longitude, and tract latitude.

*-wf.dat - Tract-to-tract workerflow, extracted from stp64.us from Census 2000 Special Tabulation Product 64: Census tract of work by census tract of residence 2000. For the US-level data. Commutes over 100 miles were eliminated from the data. The columns in this space- delimited file are: home state FIPS code, home county FIPS code, home tract FIPS code, work state FIPS code, work county FIPS code, work tract FIPS code, and number of workers.

*-employment.dat - The number of employed working-age adults and the total number of working-age adults, from the Census Summary File 3 (SF3, Table PCT35). The columns in this space-delimited file are: state FIPS code, county FIPS code, tract FIPS code, number of employed 20-64 year olds, and the total number of working-age individuals (19-64 year olds).

This sample population is:

usa - The continental United States, based on the 2000 US Census. Note that the "wf" files are split into 49 separate files, one for each state and one for Washington, D.C. The wf file would be too large as a single file. When mpiflute does not find "usa-wf.dat", it looks for "usa-wf-?.dat", where "?" is the state FIPS code(s) of the population on the processor.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Harvard Forest (HFR) contains population employed in commerce (percent of total) measurements in percent units and were aggregated to a yearly timescale.

This dataset includes the count and rate per 100,000 Virginia residents for all-drug overdose deaths among Virginia residents by year and by city/county of the decedent. City/county localities are assigned using the patient's residence at time of death. Data set includes all-drug overdose death counts and rates for years 2018 through the most recent data year available. When data set is downloaded, the years will be sorted in ascending order, meaning that the earliest year will be at the top. To see data for the most recent year, please scroll down to the bottom of the data set.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Coweeta (CWT) contains population employed in service (percent of total) measurements in percent units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Harvard Forest (HFR) contains population (urban) measurements in number units and were aggregated to a yearly timescale.