api

How We Add Value to Public Data With Better Curation And Documentation?

Many people ask if we can really add value to free data that can be downloaded from the Internet by anybody. We do not only work with easy-to-download data, but we know that free, public data usually requires a lot of work to become really valuable. To start with, it is not always easy to find.

How We Add Value to Public Data With Imputation and Forecasting

Public data sources are often plagued with missng values. Naively you may think that you can ignore them, but think twice: in most cases, missing data in a table is not missing information, but rather malformatted information which will destroy your beautiful visualization or stop your application from working. In this example we show how we increase the usable subset of a public dataset by 66.7%, rendering useful what would otherwise have been a deal-breaker in panel regressions or machine learning applications.

CCSI Data Observatory

The creative and cultural sectors and industries are mainly made of networks of freelancers and microenterprises, with very few medium-sized companies. Their economic performance, problems, and innovation capacities hidden. Our open collaboration to create this data observatory is committed to change this. Relying on modern data science, the re-use of open governmental data, open science data, and novel harmonized data collection we aim to fill in the gaps left in the official statistics of the European Union.

The Data Sisyphus

Sisyphus was punished by being forced to roll an immense boulder up a hill only for it to roll down every time it neared the top, repeating this action for eternity. When was a file downloaded from the internet? What happened with it sense? Are their updates? Did the bibliographical reference was made for quotations? Missing values imputed? Currency translated? Who knows about it – who created a dataset, who contributed to it? Which is the final, checked, approved by a senior manager?