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Empowering Open Science: Kick Off of the OpenAIRE-Advance H2020 project

Empowering Open Science: Kick Off of the OpenAIRE-Advance H2020 project

On 1st January 2018, OpenAIRE entered a new exciting phase with the start of the OpenAIRE-Advance project. During the Kick Off meeting, taking place from 17 to 19 January in Athens, Greece, 50 partners will gather to work on this timely continuation of OpenAIRE’s mission to support the Open Access/Open Data mandates in Europe and globally.

While sustaining the current successful infrastructure, comprised of a human network and robust technical services, OpenAIRE-Advance will work towards making Open Science the default in Europe, reshaping the scholarly communication system towards openness and transparency serving as a trusted pillar of the European Open Science Cloud (EOSC).

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Why Open Access. How to comply. What services to use.
Data Providers

Data Providers

How to make your content more visible. What to do to increase quality. How to join.
Research Admins

Research Admins

How to monitor research results. What services to use for your needs.


Why align policies and practices. How to monitor and analyze results.


RT @frankcmanista: UK National workshop for @openaire_eu focusing on OpenAIRE2020, OpenAIRE Advance, and OpenAIRE Connect is coming in Marc…

Interested in anonymizing your data?

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In the context of OpenAIRE, we are developing a data anonymization tool that will help data owners to safely share their data with experts and the scientific community. The key idea in anonymization is that identifying information is removed from the published data, so no sensitive information can be attributed to a person. The anonymization procedure is not limited to the removal of direct identifiers that might exist in a dataset, e.g. the name or the Social Security Number of a person; it also includes transforming secondary information, e.g. age, zipcode, that might indirectly lead to the true identity of an individual. This secondary information is often referred to as quasi-identifiers. Quasi-identifiers are usually presented in a more abstract or generalized form, e.g, instead of the exact age of a person (e.g., 31 years old) we can present an age category (30-40 years old). The transformation is applied so that a privacy guarantee for the anoymized dataset is provided and the data are transformed as little as possible.

Are you a Data Producer And want to work with us to anonymize your data?

Then come talk to us! Balancing the strength of the privacy guarantee and the quality of the anonymized data is a crucial point that requires working closely with data owners and field experts. To this end, we are interested in collaborating with data owners that wish to share their data, but are reluctant due to privacy concerns. We are interested in understanding the privacy threats in each case and in developing the most suitable metrics for assessing the quality of the data in each field. This knowledge will help us render our tools useful and easy to use for all data producers, while it will help you comply with EC's H2020 and other funder mandates.

Contact our team expert Dr. Manolis Terrovitis at ATHENA Research and Innovation Center: This email address is being protected from spambots. You need JavaScript enabled to view it.

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