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.

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 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.

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 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 "/dataset">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.

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.

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

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/

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.

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

Mayor's Order 2017-115 establishes a comprehensive data policy for the District government. The data created and managed by the District government are valuable assets and are independent of the information systems in which the data reside. As such, the District government shall: maintain an inventory of its enterprise datasets; classify enterprise datasets by level of sensitivity; regularly publish the inventory, including the classifications, as an open dataset; and strategically plan and manage its investment in data.The greatest value from the District’s investment in data can only be realized when enterprise datasets are freely shared among District agencies, with federal and regional governments, and with the public to the fullest extent consistent with safety, privacy, and security. For more information, please visit https://opendata.dc.gov/pages/edi-overview. Previous years of EDI can be found on Open Data.

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.

An inventory of all FDA Datasets

The Severe Weather Data Inventory (SWDI) is an integrated database of severe weather records for the United States. SWDI enables a user to search through a variety of source data sets in the NCDC (now NCEI) archive in order to find records covering a particular time period and geographic region, and then to download the results of the search in a variety of formats. The formats currently supported are Shapefile (for GIS), KMZ (for Google Earth), CSV (comma-separated), and XML. The current data layers in SWDI are: Storm Cells from NEXRAD (Level-III Storm Structure Product); Hail Signatures from NEXRAD (Level-III Hail Product); Mesocyclone Signatures from NEXRAD (Level-III Meso Product); Digital Mesocyclone Detection Algorithm from NEXRAD (Level-III MDA Product); Tornado Signature from NEXRAD (Level-III TVS Product); Preliminary Local Storm Reports from the NOAA National Weather Service; Lightning Strikes from Vaisala NLDN.

EDI Redaction letter

An inventory of datasets and other data assets that are published by the City of Seattle on data.seattle.gov

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The DHS Data Inventory Program is working to create a single data inventory of all data within DHS, including the DHS Components, the DHS Functional Data Domains, and DHS HQ. rnrnThe Data Inventory Program is designed to exceed DHS obligations under the Foundations for Evidence-Based Policymaking Act (the Evidence Act), the OPEN Government Data Act, the DHS Data Framework Act of 2018, and DHS Delegation Number 04004 rev 00 of May 18, 2021 from Secretary Mayorkas to the Chief Data Officer. The goal is to create a data inventory that will be useful for all of DHS to help answer questions about DHS data in a timely manner and help DHS leadership plan new activities.

The Interagency Data Inventory is a product of the Data Committee of the Financial Stability Oversight Council (FSOC). The inventory catalogs the data collected by FSOC member agencies. The inventory contains information — metadata — about each data collection. It does not contain the underlying datasets. For each data collection, the inventory has basic information, such as a brief description of the collection, collecting organization, and the name and number of the form used to collect the data.The inventory may be used for identifying data gaps and for improving research and analysis to understand threats and vulnerabilities in the financial system.The inventory was first published in 2014 and has been updated each year.The new columns of information added in the 2019 update are described in the “Terms and Definitions” tab of the Excel file. Some of these columns are not relevant to all collections or are populated at the discretion of each organization that supplied the information.Some of the underlying data referenced in the inventory may not be public data due to the data’s proprietary or supervisory confidential nature.