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The Dutch consortium of University Libraries and the National Library of the Netherlands (UKB), together with the Universities of The Netherlands (UNL), the Dutch national centre of expertise and repository for research data (DANS) and the Dutch Research Council (NWO), has published a practical guide on open science. Open Science Reliable science is not the sole work of superhuman geniuses, but a collaborative process. Researchers rely and build upon each other’s work. Together, we build theories, collect evidence and assess the research of colleagues. However, we can only build upon others’ work if we know exactly what our predecessors have done:What were their methods, relevant materials, data and outputs? Therefore, sound science ideally equals Open Science, in which all phases of the research cycle are as transparent and accessible as possible. About thisguide Beginning researchers are an important link in the transition to Open Science, so this guide is aimed at PhD candidates, Research Master Students, and early-career researchers from all disciplines at Dutch universities and research institutes. It is designed to accompany researchers in every step of their research, from the phase of preparing your research project and discovering relevant resources (chapter 2) to the phase of data collection and analysis (chapter 3), writing and publishing articles, data, and other research output (chapter 4), and outreach and assessment (chapter 5). Every chapter provides you with the best tools and practices to implement immediately. If the information in this guide feels overwhelming: Do not worry! Open Science is a journey, and you are not alone in this. There are always colleagues who are happy to help you out along the way. You can find fellow researchers or support staff at your localOpen Science Community (see chapter 6) and at yourUniversity Library. We hope this guide will motivate you and help you to practise Open Science, by sharing all aspects of your research with as many people as possible. The Future is Open! This guide, as an example of open science, builds upon the work of others. It is a derivative of “A Passport for Open Science. A practical guide for PhD students” written by Johann Berti, Marin Dacos, Gabriel Gallezot, Madeleine Géroudet, Sabrina Granger, Joanna Janik, Claire Josserand, Jean-François Lutz, Christine Okret-Manville, Sébastien Perrin, Noël Thiboud (Paris, 2020). Available at www.ouvrirlascience.fr and licensed under a Creative Commons CC BY-SA 4.0 Licence. The original text has been cut, edited and mixed with original content focused on the Dutch research landscape. If you have any feedback on this guide, please leave your comment(s) on PubPeer.&nbsp

The transition towards Open Science will drastically alter our approach to academic life. It will change the ways in which we reward and recognise university employees and reshape the relationship between education and research. This should be reflected in how a new generation of academics and citizens are educated. Not only through the qualifications our students receive to become productive members of society, but also by dint of the values and attitudes we teach our pupils. The aim of university education should be preparing future graduates to share their (inter)disciplinary knowledge, engage with societal stakeholders, and shape tomorrow’s society. Now is the time to explore how. This manifesto is a thought exercise that explores the (possible) relationships between Open Science and education. It attempts to point out the overlap, parallels, synergy, and possible conflicts between Open Science attitudes and practices, and contemporary views and practices in education. We aim to provoke a perspectiveon the different aspects of how Open Science relates to education and propose several concrete directions forward and possible corresponding interventions. After explaining why education from an Open Science perspective needs to be explored and strengthened, we differentiate four faces of open education: the Open Science mindset, Open Science skillset, open educational resources, and how these activities should be recognised and rewarded. We subsequently illuminate three possible paths on how to strengthen open education, ranging from content to form and system. We hope that this will spark a broader national and international conversation on the relationship between Open Science and education.

New version: typo correction. Our Accelerate Open Science Project aims to give context to various developments in the area of Open Science, and to make information about topics such as FAIR data, Research assessment and FAIR Softwareeasier accessible. As the fourthslide deck in our Info Slides Series, we present youA first Introduction to Open Science. The slide deck is licensed under a CC-BY license, which enables you to make use of it any way you want.

In our Accelerate Open Science Project (ACOS), one of our aims is to give context to various developments in the area of Open Science. One very important development in this respect is the discussion about changing the academic system of recognition and rewards to stimulate researchers to incorporate Open Science in their daily workflow. In the Netherlands, there are several developments to change existing research assessment protocols towards a more inclusive and generous approach that enables researchers to work more openly and transparent. Three perspectives are leading in this discussion, namely the perspective from a researcher, the research and the research proposals. ACOS prepared a slide deck to give an overview of these developments and highlight recommendations to facilitate change. On a personal, team, faculty and institutional level. The slide deck is licensed under a CC-BY license, which enables you to make use of it any way you want.

Our Accelerate Open Science Project aims to give context to various developments in the area of Open Science, and to make information about topics such as FAIR data easier accessible. As a second slide deck in our Info Slides Series, we present youAnswers to five questions about FAIR data. The slide deck is licensed under a CC-BY license, which enables you to make use of it any way you want. &nbsp

Alpha- and beta-band rhythms over the sensorimotor cortex are prominent and functionally relevant for movement selection. However, it remains unclear whether these rhythms modulate excitability of the same neuronal ensembles in the same direction when a movement is selected across the sensorimotor cortex. Using electrocorticography in humans (N=11), we assessed the anatomical and functional specificity of alpha- and beta-band rhythms sampled around the central sulcus during the performance of a psychophysically-controlled movement imagery task. Both rhythms displayed effector-specific task-related modulations, tracked spectral markers of action potentials in the local neural population, and showed spatially systematic phase relationships (traveling waves). Yet, alpha- and beta-band rhythms were weakly correlated, differed in their anatomical and functional properties, and travelled along opposite directions across the sensorimotor cortex. Alpha was stronger at postcentral electrodes evoking somatosensory sensations. A relative increase in alpha power in the somatosensory cortex ipsilateral to the selected arm was associated with spatially unspecific inhibition. Beta was stronger at central electrodes evoking both movements and somatosensory sensations. A focal reduction in beta power over the somatomotor cortex contralateral to the selected arm was associated with a local shift in balance from relative inhibition to excitation. These observations indicate the relevance of both inhibition and disinhibition mechanisms for precise spatiotemporal coordination of movement-related neuronal populations, and illustrate how those mechanisms are implemented through the substantially different neurophysiological properties of sensorimotor alpha and beta rhythms. Impact statement Direct cortical recordings in humans link the spectral structure of local field potentials to inhibition/disinhibition mechanisms coordinating sensorimotor neuronal populations during movement selection.

One of the two miscomputations identified in the infoVal metric, namely the omission of the k constant, turns out not to be a miscomputation, since the constant was already taken into account by default in the mad() function from R (see https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/mad ).

Background: The ability to perceive, recognize and process own and others’ emotions is crucial for efficient and effective social communication. Many different tasks have been used to investigate impairments herein in patients with schizophrenia. Evidence suggests that perception, discrimination and recognition of affective facial expressions are impaired in schizophrenia patients (Green, Horan & Lee, 2015; Chan et al 2010). Importantly, not everyone may interpret the same facial expression similarly. People match their internal representation of specific facial expressions to perceived faces and variation in these internal representations may result in distortions of social reality. The impairments in face and/or emotion processing and the bias toward a more negative experience may be causally related to degradation of the internal representation itself or to disturbances in the higher order evaluation of visual input against functionally intact internal representations. Methods: We use a data-driven technique of psychophysical reverse correlation, which makes it possible to visualize internal representations on computer screens. Participants judge noisy images of faces that are created by superimposing random noise on a single constant base face. The random noise distorts the base face at the pixel level, generating facial variation across stimuli that is fully unconstrained and unaffected by researchers’ a priori expectations. The participants’ responses to a large number of faces are then used to model the facial information that was idiosyncratically diagnostic for the judgments. This analysis yields a classification image (CI) for each participant, which visualizes the facial characteristics that drive judgments of emotional expressions (i.e., their internal representation). Specifically, we used reverse correlation image classification (RCIC) to investigate and reconstruct the mental representation of trustworthiness as expressed on the face. Results: Thus far, we have reliable data from 11 patients and 9 controls. While our results are preliminary, they show that patients are capable of performing the task adequately. Both a visual inspection of the group-level visualizations of the 3 constructs probed (i.e., untrustworthy, neutral, and trustworthy) as well as the computed correlations between these visualizations suggest that the internal representation of these constructs is less expressive in patients when compared to controls. Conclusion: Being able to visualize mental representations of trustworthy and untrustworthy faces in patients with schizophrenia opens up the possibility of further use of RCIC tasks in the investigation of emotion-processing deficiencies in schizophrenia.

Background The ability to perceive, recognize and process own and others’ emotions is crucial for efficient and effective social communication. Many different tasks have been used to investigate impairments herein in patients with schizophrenia. Evidence suggests that perception, discrimination and recognition of affective facial expressions are impaired in schizophrenia patients. Importantly, not everyone may interpret the same facial expression similarly. People match their internal representation of specific facial expressions to perceived faces and variation in these internal representations may result in a distortion of social reality. The impairments in face and/or emotion processing and the bias toward a more negative experience may be causally related to degradation of the internal representation itself or to disturbances in the higher-order evaluation of visual input against functionally intact internal representations. In an attempt to develop ways of visualizing an individuals’ internal representation of an emotional face on a computer screen, we set out to visualize the representation of a male and a female face. Methods We use a data-driven technique, i.e. reverse correlation image classification (RCIC), which makes it possible to visualize internal representations of faces on computer screens. Participants judge noisy images of faces that are created by superimposing random noise on a single constant base face. The random noise distorts the base face at the pixel level, generating facial variation across stimuli that is fully unconstrained and unaffected by researchers’ a priori expectations. The participants’ responses to a large number of faces are used to model the facial information that was idiosyncratically diagnostic for the judgments. This analysis yields a classification image (CI) for each participant, which visualizes the facial characteristics that drive judgments of emotional expressions (i.e., their internal representation). We introduce an objective metric, i.e. infoVal, using gender as proof-of-principle. infoVal quantifies the probability that an observed CI was not generated by a random process and is equivalent to a modified z score. First, we test the association between infoVal and more common markers of data quality, i.e. the subjective recognizability, objective discriminability and test-retest reliability of CIs (convergent validity). Second, we use RCIC to investigate and reconstruct the mental representation of trustworthiness as expressed on the face in 32 patients with schizophrenia and 39 controls. Results Subjective ratings showed that male and female CIs were more strongly associated with masculinity and femininity, respectively, when infoVal scores where high (p

The Open Science Awards recognize researchers or research students who have used Open Science to make their research more accessible, transparent or reproducible. In the context of the first National Open Science Festival a call for Use Cases was published allowing all researchers and Phd students from Dutch universities, UMCs and research institutes to submit their use case. The call was looking for use cases that explored challenges and difficulties as well as positive experiences and successful outcomes. This collection of Use Cases is the result of this call. Use Cases in this Collection: Open science practices in Majorana research; Studies of Populations of Individuals Birds; A webtool for interactive data visualization and data sharing; Open science and open data for human factors research; cBiT: The Compendium for Biomaterial Transcriptomics; The Student Initiative for Open Science (SIOS); A practical tool for standardising future costs in economic evaluation; Making open psychological datasets more accessible and useful for research and teaching; An open-source, open-participation competition for Fast Radio Burst detection.