Building a comprehensive data inventory as required by section 6.3 of the Directive on Open Government: “Establishing and maintaining comprehensive inventories of data and information resources of business value held by the department to determine their eligibility and priority, and to plan for their effective release.” Creating a data inventory is among the first steps in identifying federal data that is eligible for release. Departmental data inventories has been published on the Open Government portal, Open.Canada.ca, so that Canadians can see what federal data is collected and have the opportunity to indicate what data is of most interest to them, helping departments to prioritize data releases based on both external demand and internal capacity. The objective of the inventory is to provide a landscape of all federal data. While it is recognized that not all data is eligible for release due to the nature of the content, departments are responsible for identifying and including all datasets of business values as part of the inventory exercise with the exception of datasets whose title contains information that should not be released to be released to the public due to security or privacy concerns. These titles have been excluded from the inventory. Departments were provided with an open data inventory template with standardized elements to populate, and upload in the metadata catalogue, the Open Government Registry. These elements are described in the data dictionary file. Departments are responsible for maintaining up-to-date data inventories that reflect significant additions to their data holdings. For purposes of this open data inventory exercise, a dataset is defined as: “An organized collection of data used to carry out the business of a department or agency, that can be understood alone or in conjunction with other datasets”. Please note that the Open Data Inventory is no longer being maintained by Government of Canada organizations and is therefore not being updated. However, we will continue to provide access to the dataset for review and analysis.

The data inventory lists data that is created, collected and managed by the Government of Ontario, including the title, description and frequency of update for each dataset. It also shows whether a dataset is open, will become open, is under review, or is restricted from being open due to reasons outlined by the Open Data Directive. This inventory does not include dataset files or personal information. Datasets listed in the inventory are also available to view on Ontario's data catalogue. We will update this dataset regularly as data continues to be added to the inventory. Provincial agency data inventories are also available.

A JSON file of the datasets

Catalog of high value data inventories produced by Connecticut executive branch agencies, pursuant to Connecticut General Statutes Section 4-67p. Executive branch agencies update their high value data inventories annually in December. This dataset was last collected in December of 2023. High value data is defined as any data that the department head determines (A) is critical to the operation of an executive branch agency; (B) can increase executive branch agency accountability and responsiveness; (C) can improve public knowledge of the executive branch agency and its operations; (D) can further the core mission of the executive branch agency; (E) can create economic opportunity; (F) is frequently requested by the public; (G) responds to a need and demand as identified by the agency through public consultation; or (H) is used to satisfy any legislative or other reporting requirements.

This dataset provides synthetic yet realistic data for analyzing and forecasting retail store inventory demand. It contains over 73000 rows of daily data across multiple stores and products, including attributes like sales, inventory levels, pricing, weather, promotions, and holidays.

The dataset is ideal for practicing machine learning tasks such as demand forecasting, dynamic pricing, and inventory optimization. It allows data scientists to explore time series forecasting techniques, study the impact of external factors like weather and holidays on sales, and build advanced models to optimize supply chain performance.

Challenges for Data Scientists:

Challenge 1: Time Series Demand Forecasting Predict daily product demand across stores using historical sales and inventory data. Can you build an LSTM-based forecasting model that outperforms classical methods like ARIMA?

Challenge 2: Inventory Optimization Optimize inventory levels by analyzing sales trends and minimizing stockouts while reducing overstock situations.

Challenge 3: Dynamic Pricing Develop a pricing strategy based on demand, competitor pricing, and discounts to maximize revenue.

Key Data Features:

Date: Daily records from [start_date] to [end_date]. Store ID & Product ID: Unique identifiers for stores and products. Category: Product categories like Electronics, Clothing, Groceries, etc. Region: Geographic region of the store. Inventory Level: Stock available at the beginning of the day. Units Sold: Units sold during the day. Demand Forecast: Predicted demand based on past trends. Weather Condition: Daily weather impacting sales. Holiday/Promotion: Indicators for holidays or promotions.

Example Notebook Ideas

Exploratory Data Analysis (EDA): Analyze sales trends, visualize data, and identify patterns. Time Series Forecasting: Train models like ARIMA, Prophet, or LSTM to predict future demand. Pricing Analysis: Study how discounts and competitor pricing affect sales.

A. SUMMARY The dataset inventory provides a list of data maintained by departments that are candidates for open data publishing or have already been published and is collected in accordance with Chapter 22D of the Administrative Code. The inventory will be used in conjunction with department publishing plans to track progress toward meeting plan goals for each department. B. HOW THE DATASET IS CREATED This dataset is collated through 2 ways: 1. Ongoing updates are made throughout the year to reflect new datasets, this process involves DataSF staff reconciling publishing records after datasets are published 2. Annual bulk updates - departments review their inventories and identify changes and updates and submit those to DataSF for a once a year bulk update - not all departments will have changes or their changes will have been captured over the course of the prior year already as ongoing updates C. UPDATE PROCESS The dataset is synced automatically daily, but the underlying data changes manually throughout the year as needed D. HOW TO USE THIS DATASET Interpreting dates in this dataset This dataset has 2 dates: 1. Date Added - when the dataset was added to the inventory itself 2. First Published - the open data portal automatically captures the date the dataset was first created, this is that system generated date Note that in certain cases we may have published a dataset prior to it being added to the inventory. We do our best to have an accurate accounting of when something was added to this inventory and when it was published. In most cases the inventory addition will happen prior to publishing, but in certain cases it will be published and we will have missed updating the inventory as this is a manual process. First published will give an accounting of when it was actually available on the open data catalog and date added when it was added to this list. E. RELATED DATASETS Inventory of citywide enterprise systems of record Dataset Inventory: Column-Level Details

As a mandate of the New Orleans City Data Policy - Executive Order 16-01 & Policy Memorandum 135, we are taking an inventory of all City datasets. This on-going inventory process will help us to categorize and identify data that could be made publicly available. This process also assists our ability to work cross-departmentally and increases our resilience.

Why is the data inventory important? • Stimulate new ideas and services. By publishing a data inventory, city departments may help to stimulate new and innovative ideas from the community. • Increase internal sharing and resilience. A data inventory can also help us access information from other departments that we need to improve service delivery and resilience planning. • Enabling better and more up-to-date processes. The process of publishing a data inventory will help us to realize the constraints of current City technology and processes, and then plan for future improvements. • Changing how we use data. A data inventory can help empower us to change how we use, share and consume our data externally and internally, ultimately transforming data into better services for citizens and fostering continuous improvement.

HHS Data Inventory: Version 1.0, published in July 2025, is a comprehensive metadata catalog designed to provide public visibility into both public and non-public data assets across all HHS Divisions. Developed in alignment with the OPEN Government Data Act (Evidence Act, Title II), the HHS Data Inventory will continue to expand and improve over time, serving as a critical resource for discovering and understanding the breadth of data assets managed by HHS.

Version 1.1 is published on Sep. 22, 2025

The State of Oregon Agency Data Inventory is an enterprise level inventory that provides a listing of all state agency data inventories, as required in HB 3361(2017), and codified in ORS276A.350-374. This statute requires that state agencies must “create and maintain an inventory of agency information resources,” and include an indication as to whether or not the information or dataset is considered “publishable.” They must then contribute this information to an enterprise level data inventory, maintained by the Chief Data Officer, for display on a centralized open data portal.
For questions about any dataset listed in this inventory, contact the agency directly. For additional information about this inventory or Oregon’s Open Standard, visit Oregon’s Open Data Program. https://data.oregon.gov/stories/s/Oregon-s-Open-Data-Standard/xr2x-d2d7/

List and description of datasets available on Open Data for Fairfax County, Virginia

The purpose of the SNF Study was to develop the techniques to make the link from biophysical measurements made on the ground to aircraft radiometric measurements and then to scale up to satellite observations. Therefore, satellite image data were acquired for the Superior National Forest study site. These data were selected from all the scenes available from Landsat 1 through 5 and SPOT platforms. Image data substantially contaminated by cloud cover or of poor radiometric quality was not acquired. Of the Landsat scenes, only one Thematic Mapper (TM) scene was acquired, the remainder were Multispectral Scanner (MSS) images. Some of the acquired image data had cloud cover in portions of the scene or other problems with the data. These problems and other comments about the images are summarized in the data set. This data set contains a listing of the scenes that passed inspection and were acquired and archived by Goddard Space Flight Center. Though these image data are no longer available from either the Goddard Space Flight Center or the ORNL DAAC, this data set has been included in the Superior National Forest data collection in order to document which satellite images were used during the project.

Graph and download economic data for Total Business Inventories (BUSINV) from Jan 1992 to Aug 2025 about inventories, headline figure, business, and USA.

An inventory of all FDA Datasets

This dataset is designed to support research and development in supply chain inventory management. It simulates real-world operations with daily, SKU-level data capturing sales, inventory levels, supplier lead times, replenishment behavior, regional distribution, and promotional effects.

It is suitable for studying demand forecasting, inventory control strategies, stockout risk analysis, cost minimization, and overall supply chain optimization. The data provides realistic complexity for exploring both traditional analytical approaches and modern data-driven solutions.

Key Features Date: Daily timestamps spanning one year of activity.

SKU-Level Detail: Unique product identifiers with varying demand patterns.

Warehouse and Region: Spatial dimensions representing distribution networks.

Units Sold: Simulated sales data with seasonal trends and random noise.

Inventory Levels: Dynamic on-hand stock that evolves over time.

Supplier Lead Times: Variable delivery delays for replenishment orders.

Reorder Points and Quantities: Inventory policy thresholds and simulated replenishments.

Promotions: Binary indicator of promotional periods influencing demand.

Stockout Events: Flags indicating when demand exceeds available inventory.

Supplier Information: Links products to specific suppliers with unique lead times.

Cost and Price: Realistic unit costs and selling prices with profit margins.

Forecasted Demand: Approximate prediction values reflecting planning estimates.

Potential Uses Demand forecasting and sales prediction.

Inventory policy simulation and evaluation.

Stockout risk modeling and mitigation planning.

Cost optimization and pricing strategy analysis.

Data exploration and feature engineering for supply chain problems.

This dataset provides a flexible and realistic foundation for testing and developing advanced solutions to complex inventory optimization challenges in supply chain networks.

EDI Redaction letter

This dataset contains an inventory of all city datasets. It is updated systematically annually and also periodically throughout the year as needed.

The DHS Data Inventory is a list of datasets used in and by the department. The dataset details include a verbose set of metadata identifying the source, quality, governance, publication, size and characteristics of the data, with pointers to the data access URL(s) and access rights for use within the department. The Data Inventory was created in response to legislative requirements of the Evidence Act (and Open Government Data Act). Metadata about datasets that are marked as 'public' are sent to Data.gov on a weekly basis.

This dataset provides values for CHANGES IN INVENTORIES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Graph and download economic data for Retailers Inventories (RETAILIMSA) from Jan 1992 to Aug 2025 about inventories, retail, and USA.

The Commercial Building Inventories provide modeled data on commercial building type, vintage, and area for each U.S. city and county. Please note this data is modeled and more precise data may be available through county assessors or other sources. Commercial building stock data is estimated using CoStar Realty Information, Inc. building stock data.

This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.