Open Data is most useful when it’s up-to-date. Over the past eight years, NYC Open Data has invested in dataset automation, so that data is updated without human intervention. As the demand for City data continues to increase, and new technologies continue to surface, our goal is to automate more data, at a faster rate. In the last few months we have been testing tools and processes that should bring us closer to achieving this goal. These efforts will lead to more reliable data, while allowing the Open Data Team and agency staff to work more efficiently. We plan to invest more time into testing new methods of automation, and have released a this dataset so anyone can track our progress on automations.

This dataset contains program, portfolio, and participant data from the New York State Clean Energy Dashboard (https://www.nyserda.ny.gov/Researchers-and-Policymakers/Clean-Energy-Dashboard/View-the-Dashboard). The Clean Energy Dashboard aggregates budgets and benefits progress data across dozens of programs administered by NYSERDA and utilities. The Clean Energy Dashboard features most of the programs and initiatives that contribute significantly to New York State’s aggressive clean energy goals while tracking progress against both utilities’ and New York State’s targets. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

All major capital infrastructure projects with a committed budget. Financial information and agency schedule details for each project are joined via FMS ID. Only projects in the design phase or later have project schedules displayed. This dataset is part of the Capital Projects Dashboard.

Note: Each row is uniquely identified by its Financial Management Service (FMS) ID. FMS ID is the unique ID that OMB uses for the FMS (Financial Information System). This ID can be universally joined with any OMB dataset that has the same field. The Capital Projects Dashboard is the result of joining OMB’s fiscal data with the agency’s schedule data. FMS IDs and agency projects don't always have a one-to-one relationship (i.e., one project schedule may correlate to multiple FMS IDs, and one FMS may correct to multiple projects with different schedules).

The Capital Dashboard data provides information about the projects in the MTA’s 2010 to 2014 and 2005 — 2009 Capital Programs. The data describes the planned projects and provide information about the status of Project Budgets, Scopes and Schedules. This view provides data for the current quarter only.

This dashboard is part of infrastructure.ny.gov and should not be used separately.

The dataset provides information about projects in the MTA's 5- Year Capital Programs from 2005 - 2009 to the current one. It provides quarterly updates to budgets, scopes, and schedules for planned and ongoing projects. This dataset organizes information at the agency level.

The Capital Dashboard data provides information about the projects in the MTA’s Capital Programs. The data describes the planned projects and provides information about the status of Project Budgets, Scopes and Schedules. This additional dataset provide the geo-coordinates where applicable for Capital Projects.

Analysis of ‘NY Clean Energy Dashboard Participants Progress and Plans: Beginning January 2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/481775bf-a2fe-44bf-8515-ae31ce5b4810 on 12 February 2022.

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

This dataset contains participant data from the New York State Clean Energy Dashboard (https://rev.ny.gov/cleanenergydashboard). The Clean Energy Dashboard aggregates budgets and benefits progress data across dozens of programs administered by NYSERDA and utilities. The Clean Energy Dashboard features most of the programs and initiatives that contribute significantly to New York State’s aggressive clean energy goals while tracking progress against both utilities’ and New York State’s targets.

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

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

The Temporary Program, is no longer accepting applications. *Visit Permanent Dining Out website for information: https://www.diningoutnyc.info/ The New York City Open Restaurant is an effort to implement a citywide multi-phase program to expand outdoor seating options for food establishments to promote open space, enhance social distancing, and help them rebound in these difficult economic times. For real time updates on restaurants registered in the program, please visit NYC Open Restaurants dashboard: https://bit.ly/2Z00kn8 ** Please note this Open Restaurant Applications dataset may contain multiple entries (e.g. restaurants submitting 2 or more applications). The Open Restaurants dashboard website containing real time update, noted above, will have fewer total records due to the removal of multiple applications and only list the newest entry.

This dataset is one of three Prevention Agenda Tracking Indicators posted on this site. To access the Prevention Agenda Dashboard, visit: https://www.health.ny.gov/PreventionAgendaDashboard. Each dataset consists of county level data for 68 health tracking indicators and sub-indicators for the Prevention Agenda 2013-2017: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. Each dataset includes tracking indicators for the five Priority Areas of the Prevention Agenda 2013-2017. The most recent year dataset includes the most recent county level data for all indicators. The trend dataset includes the most recent county level data and historical data, where available. Each dataset also includes the Prevention Agenda 2017 state targets for the indicators. Sub-indicators are included in these datasets to measure health disparities among socioeconomic groups. To read more about the Prevention Agenda, visit: http://www.health.ny.gov/prevention/prevention_agenda/2013-2017/.

This dataset displays demographics for the families and individuals residing in the Department of Homeless Services (DHS) shelter system.

The District’s 911 call center is one of the busiest in the country, historically ranking as the 4th busiest center behind those of New York City, Chicago, and Los Angeles. The goal of this tool is to help track this progress and provide insight into the daily operations of DC’s 911 call takers and dispatchers, work that happens behind the scenes but is critical to building a safer, stronger DC. This is a beta site with continuous testing and improvements. Expect updates to the dashboard as Office of Unified Communications (OUC) receives feedback on this first-of-its-kind tool.

I'm excited to share my latest project—an interactive Power BI dashboard that provides a comprehensive analysis of bike sales data from 2019 to 2024!

Key Highlights of the Dashboard:

📈 Sales Trend Analysis: Understand how bike sales have fluctuated over the years, with peaks in specific months that give us clues about seasonal demand. 🏢 Sales by Store Location: See how different cities like New York and Phoenix lead in terms of total sales revenue. 🚴‍♀️ Customer Demographics: Almost equal contributions from male and female customers—showing the broad appeal of our products. 💳 Payment Method Preferences: Breakdown of the most used payment methods, with insights that can help improve our customer experience. 📊 Revenue by Bike Model: A detailed look at which bike models drive the most revenue, helping guide product focus and inventory management. This dashboard was built to provide actionable insights into the sales performance and customer behavior of a large dataset of 100K records. It highlights the power of data visualization in turning numbers into strategic insights!

Why Power BI? Power BI's flexibility and interactive capabilities made it the ideal tool for visualizing the data, allowing users to drill down into specific details using slicers for bike models and time periods. 💡

Would love to hear your thoughts or any feedback on this project! If you’re interested in how this dashboard was built or want to discuss data visualization, feel free to reach out. Let’s transform data into stories that drive success! 🌟

Analysis of ‘NY Clean Energy Dashboard Portfolios Progress and Plans: Beginning January 2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/17ebfcf5-9dc0-490d-adf8-a1b10cd2b3d1 on 27 January 2022.

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

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

This dataset contains Portfolio-level budget data from the New York State Clean Energy Dashboard (https://rev.ny.gov/cleanenergydashboard). The Clean Energy Dashboard aggregates budgets and benefits progress data across dozens of programs administered by NYSERDA and utilities. The Clean Energy Dashboard features most of the programs and initiatives that contribute significantly to New York State’s aggressive clean energy goals while tracking progress against both utilities’ and New York State’s targets.

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

All major capital infrastructure projects with a committed budget and spend by fiscal year. Financial information and agency schedule details for each project are joined via FMS ID. Only projects in the design phase or later have project schedules displayed. This dataset is part of the Capital Projects Dashboard.

Note: Each row is uniquely identified by its Financial Management Service (FMS) ID. FMS ID is the unique ID that OMB uses for the FMS (Financial Information System). This ID can be universally joined with any OMB dataset that has the same field. The Capital Projects Dashboard is the result of joining OMB’s fiscal data with the agency’s schedule data. FMS IDs and agency projects don't always have a one-to-one relationship (ie. one project schedule may correlate to multiple FMS IDs, and one FMS may correct to multiple projects with different schedules).

In this research, the performance of the Dashboard for identifying “known unknowns” was evaluated against that of the online ChemSpider database, one of the primary resources used by mass spectrometrists, using multiple previously studied datasets reported in the peer-reviewed literature totaling 162 chemicals. These chemicals were examined using both applications via molecular formula and monoisotopic mass searches followed by rank-ordering of candidate compounds by associated references or data sources. A greater percentage of chemicals ranked in the top position when using the Dashboard, indicating an advantage of this application over ChemSpider for identifying known unknowns using data source ranking. Additional approaches are being developed for inclusion into a non-targeted analysis workflow as part of the CompTox Chemistry Dashboard. This dataset is associated with the following publication: McEachran, A., J. Sobus, and A. Williams. (Analytical and Bioanalytical Chemistry) Identifying known unknowns using the US EPAs CompTox Chemistry Dashboard. Analytical and Bioanalytical Chemistry. Springer, New York, NY, USA, 1-7, (2016).

This dataset is one of three Prevention Agenda Tracking Indicators posted on this site. To access the Prevention Agenda Dashboard, visit: https://www.health.ny.gov/PreventionAgendaDashboard. Each dataset consists of 58 state-level health tracking indicators and 31 sub-indicators for the Prevention Agenda 2013-2017: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. Priority areas include Chronic Disease; Health and Safe Environment; Healthy Women, Infants and Children; Mental Health and Substance Abuse; and HIV, STDs, Vaccine Preventable Diseases and Healthcare Associated Infections. The latest data dataset includes the most recent state level data for all indicators. The trend dataset includes the most recent state level data and historical data, where available. Each dataset also includes the Prevention Agenda 2017 state targets for the indicators. Sub-indicators are included in these datasets to measure health disparities among racial, ethnic, and socioeconomic groups and persons with disabilities. Read more about the Prevention Agenda at: http://www.health.ny.gov/prevention/prevention_agenda/2013-2017/.

Flood Depth Data (FDD) collected by a fleet of sensors deployed across 5 boroughs of New York City with a resolution of half an inch or less. The metadata for the sensors is included in the metadata.csv to identify the deployment coordinates of sensors, each with a unique deployment_id.

The depth data is collected at least every five minutes and every minute in some locations depending on the ability to harvest solar energy at that deployment location.

The final depth data field is depth_proc_mm, and the raw data is dist_mm.

The raw measurement values received from the sensor are distance measurements (dist_mm), which are simply distance measurements collected from a ranging ultrasonic-based sensor. These distance measurements are converted to depths using night_median_dist_mm which is a daily calculated median of nighttime sensor readings. Direct sunlight affects ranging measurements due to high variance in the air column between the sensor and the concrete surface that it is mounted over. Additionally, the housing internally heats up when under direct sunlight, which affects the sensor readings and appears as if the surface dips with the daily increase and decrease in temperature during the daytime.

After converting to raw depth values, a simple range filter is applied to the data removing any anomalies that lie below 10 millimeters and above unrealistic depth values (for example a person - between 5ft to 6ft), which is named depth_filt_mm.

Further, this filtered depth value is processed through data filters eliminating blips, any pulse chains, or a flat line due to garbage or a car parked underneath the sensor. The output of these filters is labeled depth_proc_mm.

This data is intended for use by communities, researchers, and New York City government agencies to better understand the frequency, severity, and impacts of flooding in New York City.

Here is the live dashboard for these sensors deployed: FloodNet Data Dashboard

More about this project at FloodNet.NYC

This is an open-source project and for more information on the sensors and build manuals see the FloodNet FloodSensor GitHub page

Analysis of ‘Coffee shop sample data (11.1.3+)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ylchang/coffee-shop-sample-data-1113 on 28 January 2022.

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

Context

This sample data module contains representative retail data from a fictional coffee chain. The source data is contained in an uploaded file named April Sales.zip. Source: IBM.

We have created sample data for a fictional coffee shop chain with three locations in New York city. The chain has purchased IBM Cognos Analytics to identify factors that contribute to their success, and ultimately to make data-informed decisions.

Amber and Sandeep are the co-founders of the coffee chain. They uploaded their data in a series of spreadsheets and created a data module. From that data, they designed an operations dashboard and a marketing dashboard.

Inventory

Amber and Sandeep have created two dashboards and one data module that is based on nine spreadsheets:

• Coffee operations: This sample dashboard demonstrates operational data from a fictional coffee chain. Location: Team content > Samples > Dashboards.
• Coffee marketing: This sample dashboard demonstrates marketing data from a fictional coffee chain. Location: Team content > Samples > Dashboards.
• Coffee sales and marketing: This sample data module contains representative retail data from a fictional coffee chain. Location: Team content > Samples > Data.
• April Sales.zip: This sample data contains representative retail data from a fictional coffee chain. This ZIP file contains nine related CSV files. Location: Team content > Samples > Data > Source files > Retail.

Content

Data

The sample data module named Coffee sales and marketing can be found in Team content > Samples > Data. There are nine tables:

• Sales Receipts
• Pastry Inventory
• Sales Targets
• Customer
• Dates
• Product
• Sales Outlet
• Staff
• Generation

Acknowledgements

https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/12/beanie-coffee-1113

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

The dashboard provides map visual timelapse about COVID-19 cases and fatalities in the United States and new and cumulatives cases, fatalities in the last 14 days in number of cases or fatalities and by 100 000 people. The dashboard displays also the vaccination in the number of doses and percent of population vaccinated. The data are visualized at county, state and country level. Case and mortality data is from The New York Times, vaccine data is from Covid Act Now, based on reports from state and local health agencies.