Analysis of ‘Do You Know Where America Stands On Guns?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/poll-quiz-gunse on 28 January 2022.

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

About this dataset

This folder contains the data behind the quiz Do You Know Where America Stands On Guns?

guns-polls.csv contains the list of polls about guns that we used in our quiz. All polls have been taken after February 14, 2018, the date of the school shooting in Parkland, Florida.

The data is available under the Creative Commons Attribution 4.0 International License and the code is available under the MIT License. If you do find it useful, please let us know.

Source: https://github.com/fivethirtyeight/data

This dataset was created by FiveThirtyEight and contains around 100 samples along with End, Republican Support, technical information and other features such as: - Start - Support - and more.

How to use this dataset

• Analyze Question in relation to Url
• Study the influence of Population on Pollster
• More datasets

Acknowledgements

If you use this dataset in your research, please credit FiveThirtyEight

Start A New Notebook!

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

As of December 2019, 72 percent of adults in the United States do not want political campaigns to be able to micro-target them through digital ads. Respondents to a survey of U.S. adults reported that internet companies should make no information about its users available to political campaigns in order to target certain voters with online advertisements. Additionally, 7 percent of U.S. adults say that any information should be made available for a campaign's use.

Dataset Description

This dataset contains the actual and predicted federal funds target rate for the United States from 1990 to 2023. The federal funds target rate is the interest rate at which depository institutions lend their excess reserves to each other overnight. It is set by the Federal Open Market Committee (FOMC) and is a key tool used by the Federal Reserve to influence the economy.

The dataset includes the following five columns:

Release Date: The date on which the data was released by the Federal Reserve. Time: The time of day at which the data was released. Actual: The actual federal funds target rate. Predicted: The predicted federal funds target rate. Forecast: The forecast federal funds target rate.

Data Usage

This dataset can be used for a variety of purposes, including: - Analyzing trends in the federal funds target rate over time. - Forecasting the future path of the federal funds target rate. - Assessing the effectiveness of monetary policy. - Data Quality

The data for this dataset is of high quality. The Federal Reserve is a reputable source of data and the data is updated regularly.

Data Limitations

The data for this dataset is limited to the United States. Additionally, the data does not include information on the factors that influenced the Federal Open Market Committee's decision to set the federal funds target rate.

Approved Drug SMILES and Protein Sequence Dataset

This dataset provides a curated collection of approved drug Simplified Molecular Input Line Entry System (SMILES) strings and their associated protein sequences. Each small molecule has been approved by at least one regulatory body, ensuring the safety and relevance of the data for computational applications. The dataset includes 1,660 approved small molecules and their 2,093 related protein targets.

  Dataset

The data comes… See the full description on the dataset page: https://huggingface.co/datasets/alimotahharynia/approved_drug_target.

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.

Purpose

City boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.

This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers (this dataset)
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers
   • Without Coastal Buffers
4. City and County Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

Field and Abbreviation Definitions

• CDTFA_CITY: CDTFA incorporated city name
• CDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.
• CENSUS_GEOID: numeric geographic identifiers from the US Census Bureau
• CENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.
• GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• CDT_CITY_ABBR: Abbreviations of incorporated area names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 4 characters. Not present in the county-specific layers.
• CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.
• CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.
• AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or county
• CENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to

The traveltime dataset is based on the Folktables project covering US census data. The target is a binary variable encoding whether or not the individual needs to travel more than 20 minutes for work; here, having a shorter travel time is the desirable outcome. We use a subset of data from the states of California, Florida, Maine, New York, Utah, and Wyoming states in 2018. Although the folktables dataset does not have any missing values, there are some values recorded as NaN due to the Bureau's data collection methodology. We remove the "esp" column, which encodes the employment status of parents, and has 99.55% missing values. We encode the missing values in the povpip, income to poverty ratio (0.85%), to -1 in accordance to the methodology in Ding et al.. See https://arxiv.org/pdf/2108.04884 for metadata.

The cardio (a) dataset contains patient data recorded during medical examination, including 3 binary features supplied by the patient. The target class denotes the presence of cardiovascular disease. This dataset represents predictive tasks that allocate access to priority medical care for patients, and has been used for fairness evaluations in the domain.

The credit dataset contains historical financial data of borrowers, including past non-serious delinquencies. Here, a serious delinquency is considered to be 90 days past due, and this is the target variable.

The German Credit dataset (https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data) contains financial and personal information regarding loan-seeking applicants.

About this dataset

Context

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

Key observations

The largest age group in Argos, IN was for the group of age 30 to 34 years years with a population of 204 (11.20%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Argos, IN was the 85 years and over years with a population of 6 (0.33%). 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 Argos is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Argos 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 Argos Population by Age. You can refer the same here

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Techsalerator’s Business Funding Data for North America is an extensive and insightful resource designed for businesses, investors, and financial analysts who need a deep understanding of the Asian funding landscape. This dataset meticulously captures and categorizes critical information about the funding activities of companies across the continent, providing valuable insights into the financial health and investment trends within various sectors.

What the Dataset Includes: Funding Rounds: Detailed records of funding rounds for companies in North America, including the size of the round, the date it occurred, and the stages of investment (Seed, Series A, Series B, etc.).

Investment Sources: Information on the sources of investment, such as venture capital firms, private equity investors, angel investors, and corporate investors.

Financial Milestones: Key financial achievements and benchmarks reached by companies, including valuation increases, revenue milestones, and profitability metrics.

Sector-Specific Data: Insights into how different sectors are performing, with data segmented by industry verticals such as technology, healthcare, finance, and consumer goods.

Geographic Breakdown: An overview of funding trends and activities specific to each North America country, allowing users to identify regional patterns and opportunities.

EU Countries Included in the Dataset: Antigua and Barbuda Bahamas Barbados Belize Canada Costa Rica Cuba Dominica Dominican Republic El Salvador Grenada Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Trinidad and Tobago United States

Benefits of the Dataset: Informed Decision-Making: Investors and analysts can use the data to make well-informed investment decisions by understanding funding trends and financial health across different regions and sectors. Strategic Planning: Businesses can leverage the insights to identify potential investors, benchmark against industry peers, and plan their funding strategies effectively. Market Analysis: The dataset helps in analyzing market dynamics, identifying emerging sectors, and spotting investment opportunities across North America. Techsalerator’s Business Funding Data for North America is a vital tool for anyone involved in the financial and investment sectors, offering a granular view of the funding landscape and enabling more strategic and data-driven decisions.

This description provides a more detailed view of what the dataset offers and highlights the relevance and benefits for various stakeholders.

This dataset contains the cumulative estimated health benefits due to reductions in fine particulates or particulate matter less than 2.5 micrometers in diameter (PM2.5) from 2023 to 2050. The Mayor’s Office of Management and Budget (OMB) obtained the health events avoided values through collaboration with the NYC Health Department. The reductions in PM2.5 are the same reductions found in the "Forecasted Emissions and PM2.5 Reductions from City Actions" dataset. For any additional detail please refer to section 6 of the New York City Climate Budgeting Technical Appendices (https://www.nyc.gov/assets/omb/downloads/pdf/exec24-nyccbta.pdf). This dataset is going to be updated once a year during the Executive Budget.

You can find the complete collection of Climate Budget data by clicking here.

The RIPARIAS target species list is a species checklist dataset published by the Research Institute for Nature and Forest (INBO). It contains (1) the target species of the LIFE RIPARIAS project (LIFE19 NAT/BE/000953), all of them invasive alien species (IAS) of the Regulation (EU) 1143/2014 (https://ec.europa.eu/environment/nature/invasivealien/) and (2) the alert list species that currently do not occur in the LIFE RIPARIAS project area, but have proven to have negative impacts on biodiversity and need to be rapidly removed should they be encountered. The alert list was drafted within the LIFE RIPARIAS project following an evidence-based methodology involving climate matching and risk assessment (Branquart et al. 2022). By publishing this list on GBIF it can be used for general reference, early warning systems, data extractions, baseline reporting, project KPIs etc. Issues with the dataset can be reported at: https://github.com/riparias/riparias-target-list We have released this dataset to the public domain under a Creative Commons Zero waiver. We would appreciate it if you follow the INBO norms for data use (https://www.inbo.be/en/norms-data-use) when using the data. If you have any questions regarding this dataset, don't hesitate to contact us via the contact information provided in the metadata or via opendata@inbo.be. This dataset was published as open data for the LIFE RIPARIAS project (Reaching Integrated and Prompt Action in Response to Invasive Alien Species https://www.riparias.be/), with technical support provided by the Research Institute for Nature and Forest (INBO).

This dataset contains forecasted emissions factors for electricity generation and transportation. These factors are used to convert activity data to particulate matter 2.5 (PM2.5) emissions and metric ton of carbon dioxide equivalent (mTCO2e). This dataset can be applied to "Forecast of Emissions and PM 2.5 Reductions from City Actions" and the "Forecast of Citywide Emissions" dataset to convert from activity data to emissions. For any additional detail please refer to section 6 of the New York City Climate Budgeting Technical Appendices (https://www.nyc.gov/assets/omb/downloads/pdf/exec24-nyccbta.pdf). This dataset is going to be updated once a year during the Executive Budget.

You can find the complete collection of Climate Budget data by clicking here.

This dataset includes all 7 metro counties that have made their parcel data freely available without a license or fees.

This dataset is a compilation of tax parcel polygon and point layers assembled into a common coordinate system from Twin Cities, Minnesota metropolitan area counties. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties.

NOTICE: The standard set of attributes changed to the MN Parcel Data Transfer Standard on 1/1/2019.
https://www.mngeo.state.mn.us/committee/standards/parcel_attrib/parcel_attrib.html

See section 5 of the metadata for an attribute summary.

Detailed information about the attributes can be found in the Metro Regional Parcel Attributes document.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties have polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. One primary example of this is the condominium, though some counties stacked polygons for condos. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is provided as both individual county files and as one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

This is a MetroGIS Regionally Endorsed dataset.

Additional information may be available from each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person at each individual county.

Anoka = http://www.anokacounty.us/315/GIS
Caver = http://www.co.carver.mn.us/GIS
Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx
Hennepin = https://gis-hennepin.hub.arcgis.com/pages/open-data
Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data
Scott = http://opendata.gis.co.scott.mn.us/
Washington: http://www.co.washington.mn.us/index.aspx?NID=1606

DeprecatedUpdated for PY-2023 (effective March 1, 2023, to September 30, 2024). Deprecated October 1, 2024.What does the data represent?These are named polygons that follow block group boundaries that contain 51% or greater low-to-moderate income persons as published by HUD from 2011-2015 ACS data. That data has been superseded by data developed from 2016-2020 ACS data by HUD and published at https://services.arcgis.com/VTyQ9soqVukalItT/ArcGIS/rest/services/LMISD_layers/FeatureServer/4. Target areas primarily served residential areas, and each target area ideally could self-identify as the named community.Where were they located?Target Areas of Harris County fit within the Harris County Service Area, which was the unincorporated land of Harris County, Texas plus then-cooperative cities. Any portions of otherwise qualified block groups that extended into non-service area were excluded from the target area. This prevented “double-dipping” community development resource entitlements.How accurate are they?Block group boundaries in Harris County follow visual cues such as roadways and streams. Census Bureau linework attempts to delineate these bounding features but they are seldom more accurate than within thirty feet of ground truth.Full-service city boundaries determine whether an incorporated area is within the Harris County Service Area or the non-service area. These are updated roughly quarterly in the Harris County GIS Repository layer managed by the Harris County Appraisal District. Target areas have been updated each year using this data from the late autumn to the end of each calendar year.When were they collected?When HCCSD updated the Service Area and Target Areas of Harris County in the latter part of each Program Year, it uses the current HUD LMISD dataset and HCAD full-service city boundaries to perform the update. HUD publishes an updated LMISD dataset every year, but the data HUD analyzes to create these updates only changes when an additional five-year period of American Community Survey data has accumulated. Therefore the survey data reported in the HUD LMISD were collected from 4 to 8 years prior (PY2019) to as much as 9 to 13 years prior to publishing the results (PY2023). Unless a local income survey was conducted more recently between one and four years ago, each Program Year’s target area boundaries reflect LMISD block group information collected at least four to as much as thirteen years ago.Who collected them?Harris County Community Services Department (HCCSD) collected and Harris County Housing & Community Development (HCHCD) maintains Harris County Service Area and Target Area information. As representative of one of the largest urban counties in the U.S. and the largest in Texas, the Highest Elected Official in Harris County has delegated HCHCD to implement HUD-assisted community development activities on unincorporated land and on behalf of the cooperative cities. Cooperative cities are generally those of insufficient size to become entitled to HUD funds on their own, i.e. less than 50,000 population. Through 9/30/2024 Harris County maintained agreements with 12 cooperative cities, including: Deer Park, Galena Park, Humble, Jacinto City, Katy, La Porte, Morgan's Point, Seabrook, Shoreacres, South Houston, Tomball, and Webster in PY2023. Tomball ended its agreement 9/30/2024, thereafter becoming part of the non-service area.

Analysis of ‘How Every NFL Team’s Fans Lean Politically?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/nfl-fandome on 28 January 2022.

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

About this dataset

Data behind the story How Every NFL Team’s Fans Lean Politically.

Google Trends Data

Google Trends data was derived from comparing 5-year search traffic for the 7 sports leagues we analyzed:

https://g.co/trends/5P8aa

Results are listed by designated market area (DMA).

The percentages are the approximate percentage of major-sports searches that were conducted for each league.

Trump's percentage is his share of the vote within the DMA in the 2016 presidential election.

SurveyMonkey Data

SurveyMonkey data was derived from a poll of American adults ages 18 and older, conducted between Sept. 1-7, 2017.

Listed numbers are the raw totals for respondents who ranked a given NFL team among their three favorites, and how many identified with a given party (further broken down by race). We also list the percentages of the entire sample that identified with each party, and were of each race.

The data is available under the Creative Commons Attribution 4.0 International License and the code is available under the MIT License. If you do find it useful, please let us know.

Source: https://github.com/fivethirtyeight/data

This dataset was created by FiveThirtyEight and contains around 0 samples along with Unnamed: 10, Unnamed: 4, technical information and other features such as: - Unnamed: 3 - Unnamed: 1 - and more.

How to use this dataset

• Analyze Unnamed: 13 in relation to Unnamed: 21
• Study the influence of Unnamed: 7 on Unnamed: 12
• More datasets

Acknowledgements

If you use this dataset in your research, please credit FiveThirtyEight

Start A New Notebook!

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

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:

• Metadata is missing or incomplete for some layers at this time and will be continuously improved.
  
• We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.

Purpose

County and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, coastal buffers are removed, leaving the land-based portions of jurisdictions. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers
   • Without Coastal Buffers (this dataset)
4. Place Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places (Coming Soon)
   
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

Field and Abbreviation Definitions

• COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system
• Place Name: CDTFA incorporated (city) or county name
• County: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information System
• Place Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area names
• CNTY Abbr: CalTrans Division of Local Assistance abbreviations of county names
• Area_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.

Accuracy

CDTFA"s source data notes the following about accuracy:

City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI =

As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.

This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 5 and the representative concentration pathway (RCP) used was RCP 8.5. More information about SSP and RCP can be referred to O'Neill et al. (2020).

Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.

More information about the six selected CMIP6 GCMs:

ACCESS-CM2 -
http://dx.doi.org/10.1071/ES19040
BCC-CSM2-MR -
https://doi.org/10.5194/gmd-14-2977-2021
CNRM-ESM2-1-
https://doi.org/10.1029/2019MS001791
MPI-ESM1-2-HR -
https://doi.org/10.5194/gmd-12-3241-2019
MRI-ESM2-0 -
https://doi.org/10.2151/jmsj.2019-051
NorESM2-MM -
https://doi.org/10.5194/gmd-13-6165-2020

Additional references:
O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.
Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0
Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.

Please cite the following if this data is used in any research or project:

Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: 10.1145/3600100.3626637

Cross-Model Version:

Representative Cities Version:

Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a

Note: Data files will be made available upon manuscript publication This dataset contains all code and data needed to reproduce the analyses in the manuscript: IDENTIFICATION OF A KEY TARGET FOR ELIMINATION OF NITROUS OXIDE, A MAJOR GREENHOUSE GAS. Blake A. Oakley (1), Trevor Mitchell (2), Quentin D. Read (3), Garrett Hibbs (1), Scott E. Gold (2), Anthony E. Glenn (2)

Department of Plant Pathology, University of Georgia, Athens, GA, USA. Toxicology and Mycotoxin Research Unit, U.S. National Poultry Research Center, United States Department of Agriculture-Agricultural Research Service, Athens, GA, USA Southeast Area, United States Department of Agriculture-Agricultural Research Service, Raleigh, NC, USA

citation will be updated upon acceptance of manuscript Brief description of study aims Denitrification is a chemical process that releases nitrous oxide (N2O), a potent greenhouse gas. The NOR1 gene is part of the denitrification pathway in Fusarium. Three experiments were conducted for this study. (1) The N2O comparative experiment compares denitrification rates, as measured by N2O production, of a variety of Fusarium spp. strains with and without the NOR1 gene. (2) The N2O substrate experiment compares denitrification rates of selected strains on different growth media (substrates). For parts 1 and 2, linear models are fit comparing N2O production between strains and/or substrates. (3) The Bioscreen growth assay tests whether there is a pleiotropic effect of the NOR1 gene. In this portion of the analysis, growth curves are fit to assess differences in growth rate and carrying capacity between selected strains with and without the NOR1 gene. Code All code is included in a .zip archive generated from a private git repository on 2022-10-13 and archived as part of this dataset. The code is contained in R scripts and RMarkdown notebooks. There are two components to the analysis: the denitrification analysis (comprising parts 1 and 2 described above) and the Bioscreen growth analysis (part 3). The scripts for each are listed and described below. Analysis of results of denitrification experiments (parts 1 and 2)

NOR1_denitrification_analysis.Rmd: The R code to analyze the experimental data comparing nitrous oxide emissions is all contained in a single RMarkdown notebook. This script analyzes the results from the comparative study and the substrate study. n2o_subgroup_figures.R: R script to create additional figures using the output from the RMarkdown notebook

Analysis of results of Bioscreen growth assay (part 3)

bioscreen_analysis.Rmd: This RMarkdown notebook contains all R code needed to analyze the results of the Bioscreen assay comparing growth of the different strains. It could be run as is. However, the model-fitting portion was run on a high-performance computing cluster with the following scripts:

bioscreen_fit_simpler.R: R script containing only the model-fitting portion of the Bioscreen analysis, fit using the Stan modeling language interfaced with R through the brms and cmdstanr packages. job_bssimple.sh: Job submission shell script used to submit the model-fitting R job to be run on USDA SciNet high-performance computing cluster.

Additional scripts developed as part of the analysis but that are not required to reproduce the analyses in the manuscript are in the deprecated/ folder. Also note the files nor1-denitrification.Rproj (RStudio project file) and gtstyle.css (stylesheet for formatting the tables in the notebooks) are included. Data Data required to run the analysis scripts are archived in this dataset, other than strain_lookup.csv, a lookup table of strain abbreviations and full names included in the code repository for convenience. They should be placed in a folder or symbolic link called project within the unzipped code repository directory.

N2O_data_2022-08-03/N2O_Comparative_Study_Trial_(n)_(date range).xlsx: These are the data from the N2O comparative study, where n is the trial number from 1-3 and date range is the begin and end date of the trial. N2O_data_2022-08-03/Nitrogen_Substrate_Study_Trial_(n)_(date range).xlsx: These are the data from the N2O substrate study, where n is the trial number from 1-3 and date range is the begin and end date of the trial. Outliers_NOR1_2022/Bioscreen_NOR1_Fungal_Growth_Assay_(substrate)_(oxygen level)_Outliers_BAO_(date).xlsx: These are the raw Bioscreen data files in MS Excel format. The format of each file name includes the substrate (minimal medium with nitrite or nitrate and lysine), oxygen level (hypoxia or normoxia), and date of the run. This repository includes code to process these files, but the processed data are also included on Ag Data Commons, so it is not necessary to run the data processing portion of the code. clean_data/bioscreen_clean_data.csv: This is an intermediate output file in CSV format generated by bioscreen_analysis.Rmd. It includes all the data from the Bioscreen assays in a clean analysis-ready format.