The dataset titled "Wellbeing Toronto - Housing" falls under the domain of Housing and is tagged with keywords such as Affordable, Affordable Housing, Housing, Housing Potential, Price, and Shelter. It is available in the format of a spreadsheet and was published on 30th April 2015. The data spans from 1st January 2008 to 31st December 2012 and covers the geographical area of Toronto. The dataset is open for access and its use is governed by the City of Toronto's Open Government Licence. The dataset is owned by the City of Toronto and any queries regarding access can be directed to opendata@toronto.ca. The dataset was published by Social Development, Finance & Administration and the author is Wellbeing Toronto. The dataset was last accessed on 30th October 2023 and is available in English. It contains a persistent identifier but does not have a globally unique identifier. The dataset does not contain data about individuals or identifiable individuals. The version of the dataset is dated 29th October 2023 and the last data refresh was on 30th April 2015. The dataset is updated annually and covers the city region. It contains 11 rows, 282 columns, and 3100 data cells. The dataset is owned by the City of Toronto Open Data organization. The dataset contains three worksheets with detailed descriptions available in the first worksheet called "IndicatorMetaData". The data is sourced from various organizations including Toronto Community Housing Corporation, City of Toronto's Shelter, Support and Housing Administration, City of Toronto Affordable Housing Office, and Statistics Canada. The dataset is licensed under the UK Open Government Licence (OGL). The metadata was created on 31st October 2023 and last modified on 8th April 2025.

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Analysis of IP holdings (active patents and trademarks) can shed light on technology and innovation at the corporate level. Insight is achieved from a variety of analyses, for example:

How does the corporate IP portfolio of a given company compare to its competitors?

Who are new entrants in the sector with similar technologies, based on their intellectual property filings?

How has a company's IP filing activity changed over time? Are patents and trademarks being filed into the similar classes as done previously, or into new or different classes, indicating a shift to new products or services, or innovation into potential new areas and technologies.

Coverage includes Intellectual Property registries from the USA, Canada and Europe.

The ORBIT (Object Recognition for Blind Image Training) -India Dataset is a collection of 105,243 images of 76 commonly used objects, collected by 12 individuals in India who are blind or have low vision. This dataset is an "Indian subset" of the original ORBIT dataset [1, 2], which was collected in the UK and Canada. In contrast to the ORBIT dataset, which was created in a Global North, Western, and English-speaking context, the ORBIT-India dataset features images taken in a low-resource, non-English-speaking, Global South context, a home to 90% of the world’s population of people with blindness. Since it is easier for blind or low-vision individuals to gather high-quality data by recording videos, this dataset, like the ORBIT dataset, contains images (each sized 224x224) derived from 587 videos. These videos were taken by our data collectors from various parts of India using the Find My Things [3] Android app. Each data collector was asked to record eight videos of at least 10 objects of their choice.

Collected between July and November 2023, this dataset represents a set of objects commonly used by people who are blind or have low vision in India, including earphones, talking watches, toothbrushes, and typical Indian household items like a belan (rolling pin), and a steel glass. These videos were taken in various settings of the data collectors' homes and workspaces using the Find My Things Android app.

The image dataset is stored in the ‘Dataset’ folder, organized by folders assigned to each data collector (P1, P2, ...P12) who collected them. Each collector's folder includes sub-folders named with the object labels as provided by our data collectors. Within each object folder, there are two subfolders: ‘clean’ for images taken on clean surfaces and ‘clutter’ for images taken in cluttered environments where the objects are typically found. The annotations are saved inside a ‘Annotations’ folder containing a JSON file per video (e.g., P1--coffee mug--clean--231220_084852_coffee mug_224.json) that contains keys corresponding to all frames/images in that video (e.g., "P1--coffee mug--clean--231220_084852_coffee mug_224--000001.jpeg": {"object_not_present_issue": false, "pii_present_issue": false}, "P1--coffee mug--clean--231220_084852_coffee mug_224--000002.jpeg": {"object_not_present_issue": false, "pii_present_issue": false}, ...). The ‘object_not_present_issue’ key is True if the object is not present in the image, and the ‘pii_present_issue’ key is True, if there is a personally identifiable information (PII) present in the image. Note, all PII present in the images has been blurred to protect the identity and privacy of our data collectors. This dataset version was created by cropping images originally sized at 1080 × 1920; therefore, an unscaled version of the dataset will follow soon.

This project was funded by the Engineering and Physical Sciences Research Council (EPSRC) Industrial ICASE Award with Microsoft Research UK Ltd. as the Industrial Project Partner. We would like to acknowledge and express our gratitude to our data collectors for their efforts and time invested in carefully collecting videos to build this dataset for their community. The dataset is designed for developing few-shot learning algorithms, aiming to support researchers and developers in advancing object-recognition systems. We are excited to share this dataset and would love to hear from you if and how you use this dataset. Please feel free to reach out if you have any questions, comments or suggestions.

REFERENCES:

Daniela Massiceti, Lida Theodorou, Luisa Zintgraf, Matthew Tobias Harris, Simone Stumpf, Cecily Morrison, Edward Cutrell, and Katja Hofmann. 2021. ORBIT: A real-world few-shot dataset for teachable object recognition collected from people who are blind or low vision. DOI: https://doi.org/10.25383/city.14294597

microsoft/ORBIT-Dataset. https://github.com/microsoft/ORBIT-Dataset

Linda Yilin Wen, Cecily Morrison, Martin Grayson, Rita Faia Marques, Daniela Massiceti, Camilla Longden, and Edward Cutrell. 2024. Find My Things: Personalized Accessibility through Teachable AI for People who are Blind or Low Vision. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems (CHI EA '24). Association for Computing Machinery, New York, NY, USA, Article 403, 1–6. https://doi.org/10.1145/3613905.3648641

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Materials prepared for ministers for an appearance.

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