Biodiversity analyses in an Era of global changes

In order to conserve and manage biodiversity, we need an improved understanding of essential biodiversity drivers and improved predictions of resulting biodiversity patterns in space and time. Patterns and processes behind biodiversity are of utmost importance to mitigate potential threats from global change and support conservation decisions. Read more here

Decoding the virus – what we know about Sars-CoV-2 a year on

By James Urquhart 27 January 2021

In December 2019, an unusual respiratory illness emerged in Wuhan in China. Initial cases were linked to a ‘wet market’ selling seafood and wild animals. Now, over a year on with more than 99 million confirmed cases and nearly 2 million deaths worldwide, Covid-19 is the worst global public-health crisis since the 1918 flu pandemic. As it continues to disrupt our lives, many questions still remain unanswered about Sars-CoV-2 – the virus behind the disease. We still don’t know exactly why it drastically affects some people more than others or how long immunity might keep it at bay. Read more here

The Confederation supports SwissCollNet and its digital platform of natural science collections for research

The federal government is promoting an improved access to natural science collections with a total amount of CHF 12.37 million until 2024. The digitised collections provide unique data for climate, biodiversity or agricultural research, for example. To this end, the Swiss Academy of Sciences (SCNAT) has launched the Swiss Natural History Collections Network, SwissCollNet, to collaborate with museums, universities, and botanical gardens in laying the foundations for the digitisation and long-term management and use of the collections. Read more here

SDSC Projects Information Day 2020

This year’s information session about the Swiss Data Science Center activities took place on Monday, May 18th as a Webinar. Additionally, to the call for projects to collaborate, exciting new projects that the SDSC is/was starting in 2020 and an overview of one of our poster-child collaborations from our first call were presented.

All talks were recorded and can be seen here

COVID-19 Switzerland

Laboratory-confirmed cases

The published data is based on information submitted by laboratories, doctors and hospitals. It refers to the new reports we received and reviewed. The figures might therefore deviate from those communicated by the cantons.
Epidemiological course see here

The idea behind the Sustainability Research Initiative

Watch the video to learn what the Sustainability Research Initiative is about.

How values influence decisions in science

By Vanessa Seifert 29 April 2021

Empirical evidence is not always sufficient to determine the models we use

Science involves making choices. Which hypothesis should be put to the test? Which model should be used to describe a system; and which approximations and assumptions should be enforced? Different factors figure in this decision-making process. Do we want the model to produce numerically accurate results that closely agree with our experiments, or to make new predictions? Should the use of approximations result in simple and understandable representations, or should they be justified by our background theories? Such considerations reflect the expectations scientists have for their results. Looking closely at these expectations reveals that scientists are guided by how they value and perceive the different functions of science. Read more here.

Chemical Exposure in the Human Body

Although the population is exposed to chemicals, there are no measurements that permit the situation in Switzerland to be evaluated. 

There is no doubt that people are exposed to a cocktail of chemicals on a daily basis. In Switzerland, the extent of this exposure and its impact on our health is poorly quantified or completely unknown. The reason for this is simple: no figures are available. This makes it impossible to monitor the way exposure is developing and to evaluate the efficacy of measures that are adopted.

The Chemical Products Division is working on a national biomonitoring project with the aim of collecting samples from a representative section of the population (in terms of age, language, etc.). Read more here.

Why manufacturing Covid vaccines at scale is hard

By Anthony King - 23 March 2021

The first Covid-19 vaccine candidate went into the arms of volunteers in Seattle in March 2020. It was an mRNA vaccine from Moderna. The mRNA candidate from BioNTech and Pfizer followed in April. By December 2020, these two had become the first vaccines to be approved by the US Food and Drug Administration (FDA). Hot on their heels are rivals based on adenovirus vectors from AstraZeneca and Johnson & Johnson, as well as Sputnik V from Russia. 

Early successes in developing vaccines by upstarts like Moderna and BioNTech papered over the struggles of vaccine heavyweights like Merck, GSK and Sanofi. But those companies that have surmounted the challenges of development now face the next phase: manufacturing doses on an enormous scale. And as Matt Hancock, the UK’s health secretary, told the House of Commons on 18 March ‘the process of manufacturing vaccines is complicated, and subject to unpredictability’. Read more here

Coronavirus: Next phase of reopening on 19 April

Bern, 14.04.2021 - The Federal Council is continuing its strategy of taking cautious, gradual steps towards reopening. At its meeting on 14 April, it decided on further reopening measures. From Monday, 19 April, it will again be possible to hold events with audiences and spectators subject to restrictions, for example at sports stadiums, cinemas, theatres and concert venues. Indoor sports and cultural activities will also be allowed, as well as certain matches and competitions. Restaurants will be able to reopen outside seating areas. Although the situation remains precarious, the Federal Council deems the risk associated with these reopening steps to be acceptable. Read more here


Embed your items, not just words!

Following our previous posts on recent progress in Natural Language Processing, we discuss a follow-up idea: can we extend the concept of word embeddings to any collection of items, possibly unordered? More precisely, can we learn representations from item sets, such as the product baskets in online retail or music playlists on streaming platforms? As we will see, the answer is yes, representation learning can also be applied on such datasets. Read more here.

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Calls for Papers

Calls for Papers/Contributions from scientific conferences and journals are published here as well as scientific competitions and awards.

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