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Canada House Price Index: New Housing: Ontario: London data was reported at 140.500 Dec2016=100 in Mar 2025. This records a decrease from the previous number of 141.000 Dec2016=100 for Feb 2025. Canada House Price Index: New Housing: Ontario: London data is updated monthly, averaging 68.400 Dec2016=100 from Jan 1981 (Median) to Mar 2025, with 531 observations. The data reached an all-time high of 148.100 Dec2016=100 in Sep 2022 and a record low of 31.800 Dec2016=100 in Jan 1981. Canada House Price Index: New Housing: Ontario: London data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.EB003: House Price Index: Dec2016=100.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Canada House Price Index: New Housing: London data was reported at 119.700 2007=100 in Dec 2016. This records a decrease from the previous number of 119.800 2007=100 for Nov 2016. Canada House Price Index: New Housing: London data is updated monthly, averaging 74.800 2007=100 from Jan 1981 (Median) to Dec 2016, with 432 observations. The data reached an all-time high of 119.800 2007=100 in Nov 2016 and a record low of 38.100 2007=100 in Jan 1981. Canada House Price Index: New Housing: London data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.EB006: House Price Index: 2007=100.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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United Kingdom Exports of household or laundry-type washing machines to Canada was US$13.39 Thousand during 2024, according to the United Nations COMTRADE database on international trade. United Kingdom Exports of household or laundry-type washing machines to Canada - data, historical chart and statistics - was last updated on July of 2025.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
United Kingdom Imports from Canada of Household or Laundry-type Washing Machines was US$94.83 Thousand during 2024, according to the United Nations COMTRADE database on international trade. United Kingdom Imports from Canada of Household or Laundry-type Washing Machines - data, historical chart and statistics - was last updated on July of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Canada Imports from United Kingdom of Household or Laundry-type Washing Machines was US$151.41 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Canada Imports from United Kingdom of Household or Laundry-type Washing Machines - data, historical chart and statistics - was last updated on June of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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
Hiring Lab's Job Postings Tracker is being re-released as the Indeed Job Postings Index. By Chris Glynn
Indeed Hiring Lab is re-releasing our Job Postings Tracker as the Indeed Job Postings Index, a daily measure of labor market activity that is updated and will continue to be released weekly. Covering seven national markets in the US, Canada, United Kingdom, Ireland, France, Germany, and Australia, the Indeed Job Postings Index meets one of Hiring Lab’s primary goals: produce high quality and high frequency labor market metrics using Indeed’s proprietary data.
The primary difference between the Indeed Job Postings Index and the legacy Job Postings Tracker is the level. The Indeed Job Postings Index is set to 100 on February 1, 2020, and this effectively provides a uniform level shift of 100 to the existing Job Postings Tracker across all time points.The Job Postings Tracker measured the percent change in postings from February 1st, 2020. For example, if the Job Postings Tracker were 40%, the corresponding Indeed Job Postings Index on the same date would be 140. Additionally, we are now including year-over-year and month-over-month percent changes in the Indeed Job Postings Index as part of our data portal on hiringlab.org/data and on our GitHub page. Month-over-month changes are calculated as 28 day (4 week) differences to control for day of week.
As Covid-19 fades from the global labor market discussion, moving to an index better reflects current economic conditions. The Indeed Job Postings Index allows us to compare job postings more naturally across flexible date ranges as opposed to comparing to the pre-pandemic baseline. It also places Indeed’s job postings metric in a broader class of macroeconomic indexes such as the Case Shiller Index that measures house price appreciation and the Consumer Price Index that measures inflation.
Data Schema Each market covered by a Hiring Lab economist has a folder in this repo. Each folder contains the following files:
aggregate_job_postings_{country_code}.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings and new jobs postings (on Indeed for 7 days or fewer) for that market, as well as non-seasonally adjusted postings since February 1, 2020 for total job postings.
job_postings_by_sector_{country_code}.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for occupational sectors for that market. We do not share sectoral data for Ireland.
For certain markets, we also share subnational job postings trends. In the United States, we provide:
metro_job_postings_us.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in US metropolitan areas with a population of at least 500,000 people.
state_job_postings_us.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in the US states and the District of Columbia.
In Canada, we provide:
provincial_postings_ca.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in each Canadian provinces. In the United Kingdom, we provide:
regional_postings_gb.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in each region in the UK.
city_postings_gb.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in each city in the UK.
Github link: https://github.com/hiring-lab/job_postings_tracker#data-schema Hiring Lab Link: https://www.hiringlab.org/2022/12/15/introducing-the-indeed-job-postings-index/
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Canada House Price Index: New Housing: Ontario: London data was reported at 140.500 Dec2016=100 in Mar 2025. This records a decrease from the previous number of 141.000 Dec2016=100 for Feb 2025. Canada House Price Index: New Housing: Ontario: London data is updated monthly, averaging 68.400 Dec2016=100 from Jan 1981 (Median) to Mar 2025, with 531 observations. The data reached an all-time high of 148.100 Dec2016=100 in Sep 2022 and a record low of 31.800 Dec2016=100 in Jan 1981. Canada House Price Index: New Housing: Ontario: London data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.EB003: House Price Index: Dec2016=100.