Why Open Data Means Better Science – Jenny Molloy

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Why Open Data Means Better Science – Jenny Molloy, Open Knowledge Foundation. University of Minho Open Access Seminar & OpenAIRE Interoperability Workshop (7 Feb. 2013) - Session: Open Science, Open Data and Repository.

Technology News & Politics

Why Open Data
Means Better Science

Jenny Molloy

Coordinator, Open Science Working Group
Open Knowledge Foundation

Science is based on building on, reusing
and openly criticising the published body
of scientific knowledge.
For science to effectively function, and for
society to reap the full benefits from
scientific endeavours, it is crucial that
science data be made open.

Code

Code

Infrastructure
Training

Data alone is not enough
Data
ecosystem

Data ecosystem

Digital
commons

Questions?

Image credits:
Frustration Flicker CC-BY music2work2
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http://openclipart.org/detail/169987/copy--clone-icon-by-ben
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http://www.flickr.com/photos/sjcockell/4398929160/

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According to Wikipedia: Open science is the umbrella term of the movement to make scientific research, data and dissemination accessible to all levels of an inquiring society, amateur or professional. It encompasses practices such as publishing open research, campaigning for open access, encouraging scientists to practice open notebook science, and generally making it easier to publish and communicate scientific knowledge. Here (in this remixed on purpose) we will explore some of the key dimensions and opportunities behind the open science and its opportunities for digital scholars.

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Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance. Explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking. The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.

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Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance. Join Marcel Kurovski to explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking. The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds. Event: O'Reilly Artificial Intelligence Conference, New York, 18.04.2019 Speaker: Marcel Kurovski, inovex GmbH Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

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Keynote at 4th Annual KnowEscape Conference, Sofia, Bulgaria (Feb 24, 2017). http://knowescape.org/knowescape2017/ Yes, we’re open: Open science & altmetrics Abstract: Open Science is en vogue – especially after Carlos Moedas, EU-Commissioner for Research, Science and Innovation, has outlined his vision for Europe along the lines of ‘open innovation, open science, open to the world’. Open science describes the transition of ‘publishing as fast as possible’ towards ‘sharing knowledge as fast as possible’. Several reasons explain the move towards openness, it is expected, for example, that open science will increase the efficiency of science. Of course, digital media and web-based environments are keys to this development, but it also requires a systemic change to transform open science from a nice-to-have-feature into the default way of performing research. Altmetrics, i.e. social media-based metrics, are often considered drivers of open science and essential tools for changing the reward system in science. When looking closer, though, severe tensions between features as well as expectations of open science and altmetrics become apparent. The talk will argue that open science only can enfold its potential if ‘openness’ is fully embraced and supported by open metrics.

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Why Open Data Means Better Science – Jenny Molloy

1. Why Open Data Means Better Science Jenny Molloy Coordinator, Open Science Working Group Open Knowledge Foundation

5. Science is based on building on, reusing and openly criticising the published body of scientific knowledge. For science to effectively function, and for society to reap the full benefits from scientific endeavours, it is crucial that science data be made open.

8. Verify Replicate Reuse

9. Is enough?

10. Code Code Infrastructure Training Data alone is not enough Data ecosystem Data ecosystem Digital commons

11. Moving forward

12. Questions? Image credits: Frustration Flicker CC-BY music2work2 http://openclipart.org/detail/170151/loop-arrow-by-scyg http://openclipart.org/detail/88045/simple-zoom-icons-by-snifty http://openclipart.org/detail/169987/copy--clone-icon-by-ben http://pixabay.com/en/binary-system-binary-binary-code-63526/ http://www.flickr.com/photos/sjcockell/4398929160/

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