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Love Your Data 2017 - Rescuing unloved data

Article dated: 17 February 2017

Today's Love Your Data 2017 theme is Rescuing Unloved Data. 'Unloved data' is referred to as legacy, heritage and at-risk data which share one common theme: barrier to access. They can be data registered by hand, such as field notes, lab notebooks, handwritten transcripts or measurements, or data recorded on outdated technology or using proprietary formats, all of which are at risk.

The UK Data Service Census Support team is currently involved in rescuing data that were recorded manually some time ago: the 1961 UK Census. The team has been working on the ‘statistical archaeology’ project to retrieve ‘lost’ information from the 1961 Census Small Area Statistics (SAS) from a large set of digital images, and make it available for digital analysis, using current software tools, for the first time.

The project is being developed in partnership with the Office for National Statistics, the UK Data Service and the University of Salford. The aim is to retrieve data and metadata from aggregate statistical outputs from the 1961 Census contained in a set of approximately 141,807 digital images of tabular data taken from the microfilm and printed volumes in which it has been reposing, unavailable and unused, for over 50 years. None of the data are disclosive and statistical disclosure control will be applied before they are made available in full.

Justin Hayes, Census Support Service Manager at the UK Data Service comments: "Censuses are the largest peacetime operations carried out in the UK. The 1961 Census cost over £3 million (equivalent to around £70 million today) and employed almost 70,000 people. This hitherto little known and barely used data resource will provide a wealth of information about the population of England and Wales at the beginning of a decade of enormous social change. The 1961 Census data are definitely not unloved by us!"

If you want to know more about the project, read the '1961 Digitisation Project' blog post on the Data Impact Blog.


Read all of our Love Your Data 2017 news articles:

Monday: Defining Data Quality

Tuesday: Documenting, Describing, Defining

Wednesday: Good Data Examples

Thursday: Finding the Right Data

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