Smoothening the Rocky Road Towards Open Science

In my last blogpost, I discussed the importance of as well as the rocky road leading towards Open Science during the corona crisis. To the disappointment of many scientists, almost no data about the corona crisis was publicly available at the time. However, a lot has changed since my last blog post; the rocks on the road seem to have smoothened and Open Science is being put into practice.

Open Science includes sharing data. One of the arguments for not sharing data was a lack of time. The Government Information Act (Wet Openbaar Bestuur (WOB) in Dutch), a law that arranges the publication of information from the Dutch government – for example for journalists, so they can critically control authorities (Brandenburg-van de Ven, 2020) – was suspended until the 1st of June by the Ministry of Health, Welfare and Sports. Now that this date has passed and the first wave is over, a lot more data is being shared (although I cannot find whether the suspension of the Government Information Act is actually over). 

The initial data made available by RIVM, the Dutch National Institute for Public Health and Environment – a knowledge and research institute aiming towards an improvement of public health and a safe living environment – concerned the cumulative covid-19 numbers per municipality (RIVM, 2020a). This dataset contains the cumulative number of confirmed cases, hospitalizations and deaths per municipality. This was a step in the right direction, but not enough; scientists were also longing for models, model codes and other data on which RIVM based its advice (Van der Velden, 2020). Fortunately, many more steps have been taken since then. The Dutch government published a corona dashboard, which shows more data regarding corona (Rijksoverheid, 2020). In particular, the dashboard shows in the number of patients admitted to the intensive care units (ICU’s); number of patients admitted to the hospital; number of confirmed cases with a distribution by age; number of active cases; and the reproduction number R, that is, the number of people that are expected to be infected by one infected person on average. There is also data about nursing homes, detailing the number of positive tested occupants per day and the number of deaths.

Rijksoverheid notes that this is the first version of the dashboard and that it is actively being developed. The website also states what data Rijskoverheid still wants to add, namely self report data of the infection radar (a tool through which Dutch participants register once a week whether they are having fever or other symptoms), data of general practitioners, data of contact tracing, data of the public health service, data of symptoms of health care workers and monitoring of sewage. The Rijksoverheid also plans to add data about behaviour, about travelling, compliance of the advice and insights from behaviour research more generally. While these are important developments to look forward to, the dashboard already now provides a good overview of the situation in The Netherlands.  

For Open Science, in addition to sharing data, sharing model codes is also an important goal, which RIVM has now met. For example, models used to compute the distribution of covid-19 have been published on a webpage (RIVM, 2020b) . By the looks of it, they have taken a lot into consideration, such as the reproduction number, the ICU’s, the average number of contacts one has per age, the delay of reporting, etc. They have released all the codes, data and articles behind their computations on their website. It includes studies on the incubation period of covid-19, the spread of the disease, the correction for delay of reporting, the computation of the reproduction number, the effect of the opening of schools, the ICU occupation and the impact of the lockdown measures on the number of contacts per person in The Netherlands. Along with the models, RIVM also shared results of the infection radar, information about transmission within families and from and to youngsters, research about herd immunity, sewage and people with covid-like symptoms at general practitioners and a first report of indirect effects of covid-19 on health and health care. 

Next to public health statistics relating to the corona crisis, RIVM is also sharing data about the psychological effects of the crisis. They launched a webpage with data about behavioural research on covid-19 in The Netherlands (RIVM, 2020c). According to this website, the World Health Organization (WHO) believes that behavioural science is an important companion in fighting pandemics. In line with this reasoning, RIVM uses scientific research and expertise about behaviour in a structured way to tackle the corona pandemic. Their research focuses on the impact of communication and policy on knowledge, attitude and behaviour during a pandemic. There is also research on the physical, social and psychological state of the population and how these are affected by covid-19 over time. With this, RIVM studies the effect of the lockdown measures on psychology, behaviour and society in the long term. 

RIVM uses different sources for their studies: the current literature on behavioural science, surveys about prevention measures, physical, social and mental well-being and how people experience the corona crisis and how they react to it. Interviews are conducted to get a better understanding of the survey results and to learn more about groups who are difficult to reach through the surveys. RIVM is also monitoring which sentiments and opinions are most pronounced on social media and study these for the improvement of policy and communication. On specific topics, fast in-depth research is being done. Knowledge of the literature, new research and expertise of partner organizations is being integrated to reach valuable insights into preventive health behaviour and health. 

The theoretical framework that is behind RIVM’s approach as well as recent literature is outlined in this document. There is also a practical step-by-step plan for the systematic and pragmatic use of the communication of government and behavioural advice for the prevention of covid-19. Accompanied by this step-by-step plan comes a summary with a diagram with three examples. 

Given all of the above, it is clear that considerable improvements have been made regarding Open Science in The Netherlands. Globally the WHO is striving for Open Science ideals and goals as well. On May 29th, 30 countries and multiple international partners and institutions signed to support the covid-19 Technology Access Pool (C-TAP) of the WHO (WHO, 2020). This is an initiative aimed at making vaccines, tests, treatments and other health technologies which will be accessible to everyone. It aims to ensure the latest and best science benefits for all of humanity and provide a one-stop shop for scientific knowledge, data and intellectual property to be shared equitably by the global community. The aim is to accelerate the discovery of vaccines, medicines and other technologies through open research, and to fast-track product development by mobilizing additional manufacturing capacity. This will help ensure faster and more equitable access to existing and new covid-19 health products. This WHO website lists what persons and organizations from different disciplines can do to take action.

Now that the situation in The Netherlands is somewhat calmer, there seems to be time to put data about the corona crisis online. Although many scientists wanted to have this information published much earlier, it is encouraging to see that they can assess what the advice is based on now. And if a second wave were to come — or another pandemic for that matter — hopefully RIVM will have learned from the criticisms and will gather enough resources to publish all data and codes sooner rather than later. This way, scientists and citizens can critically review RIVM’s advice from the start.


Brandenburg-van de Ven, T. (2020, April 21st). Kamervragen over opschorting Wob-verzoeken door ministerie van Volksgezondheid. Villamedia. Retrieved from

Rijksoverheid. (2020). Dashboard coronavirus. Retrieved from

RIVM. (2020, May 20).a Covid-19 cumulatieve aantallen per gemeente. [Data file]. Retrieved from

RIVM. (2020).b Hoe rekenmodellen bijdragen aan de bestrijding van covid-19. Retrieved from

RIVM. (2020).c Gedragswetenschappelijk onderzoek covid-19. Retrieved from

Van der Velden, L. (2020). The Rocky Road Towards Open Science. Science versus Corona. Retrieved from

WHO. (2020). Interntional community rallies to support to open research and science to fight covid-19. Retrieved from