
SMC Lab / Augmenting Science Journalism
Shaping tomorrows Science Journalism. For you, for us.
Explore our tools, read our blog.

AI for tomorrows
journalists!

Data analysis:
thorough & relevant!

Exploring the
sciencescape!
Inside the SMC Lab, we’re not just observing science journalism; we’re actively shaping it. Serving as an essential segment of the Science Media Center Germany, we’re pioneering advancements in how scientific discoveries and understanding reach the public.
This blog is your front-row seat to our work, offering a firsthand look at innovative projects, profound research, and our commitment to making science and news accessible for all.
But this is not all! We also focus on equipping journalists with the tools they need for tomorrow. We work at the intersection of data journalism, AI technology, and analytic tools to create a holistic approach in storytelling and information dissemination. And, best of all: you can use it for free & without attribution to us!
Let us blaze a trail through the forest of data for you – so you can use only the relevant parts in your next project!
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Build a Naïve and an Advanced RAG System – and Evaluate It
In the previous post, we laid the foundation of our RAG system. Based on the use case, we created a vector store, generated a synthetic
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Laying the Groundwork: Use Case, Vector Store, Synthetic Data, and Metrics
In the previous posts, we covered the basics: what RAG is, why it matters for journalism, and how a typical pipeline works. In the next
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How to Use RAG in Journalism?
In the previous blog post, we looked in detail at how a RAG system works step by step. In this post, we want to shift
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How Exactly Does RAG Work?
Now that we know why RAG is needed and how it helps to reduce the risk of hallucinations by compensating for the inherent limits of
Data analysis
You might know our data reports. But how do we create those? Which data stories work great, which don´t? Which projects did we start, just to scrap them later on? Why wouldn´t they work? What would it take, to make them work?
Those questions and many more will be answered here. However, our curated data sets can be found on the Public Issues Data Guide.
Paving a path

Lead: R&D / Procurator

Lead: Software Dev / DevOps

Software Developer

Software Developer

Project Manager

Software Developer

Data Scientist

Software Developer

Software Developer

Student / Data Scientist

Long term projects

