As part of an externally funded project with members of the Cogitate Consortium, we are seeking to hire a Data Scientist or Scientific Software Engineer, ideally one with a background in research data management (RDM), FAIR data, and database administration, who will contribute to establishing the data architecture infrastructure for open and reusable data, generate experimental code, and advance the development of reproducible neuroscience tools and processing pipelines in interdisciplinary research projects. The position will involve creating tools for the efficient organization and exploration of openly shared raw and processed datasets. It is also ideal for networking in the open science community as it includes interaction with the open (neuro)science community; and it will be a unique opportunity for someone keen to contribute to the development of open science and large-scale collaborations, as well as to community efforts and dissemination. 
Your tasks --Preparing and reviewing data for open share with the community; --Developing, testing and implementing scientific software, i.e., reproducible analysis pipelines and data storage for open science building on the BIDS standard; --Reviewing code for reproducible pipelines; --Writing supporting materials and documentation for researcher end users; --Assisting staff with parallelizing scientific software and in the use of cluster and cloud computation; --Providing support and training for data management; --Liaising between the lab and the Institute’s core IT team; --Exchanging and networking within national (NFDI, MPDL, etc.) and international (RDA, EOSC, etc.) initiatives. Our offer We offer an exciting interdisciplinary field of engagement in an international scientific environment. The Institute is located in an attractive location with excellent infrastructure in Frankfurt’s Westend neighborhood. You can expect a modern, well-equipped workplace with flexible working hours (some remote working is possible) and the opportunity to participate in (international) conferences and project meetings. Further development of your personal strengths, e.g., through direct interactions with researchers forming part of the Cogitate Consortium (e.g., Christof Koch, Giulio Tononi, Stanislas Dehaene, Gabriel Kreiman, Ole Jensen, Sylvain Baillet, among many others) is possible. The position will begin earliest on May 1, 2022 and is initially limited to 18 months, with the possibility of an extension pending funding approval. Salary is paid in accordance with the collective agreement for the public sector (TVöD Bund), according to your qualifications and experience. The Max Planck Society strives for gender equality and diversity. We are also committed to increasing the number of individuals with disabilities in our workforce. Therefore, applicants of all backgrounds are welcome. Your application Your application should include: your detailed CV (including details of your educational background and skills); a cover letter that explains why this position interests you and how your skills and abilities are suitable; copies of relevant degrees and/or certificates. Please send these materials all together in a single PDF file, before April 1, 2022, by e-mail to job@ae.mpg.de using the code “TWCF Research Data” in the subject line. Please feel free to contact Tanya Brown (tanya.brown@ae.mpg.de) if you have any questions about the position.

As part of an external funded project in collaboration with Dan Marcus, Wash. U; Sean Hill, CAMH; and members of the Cogitate Consortium, we are searching for a Scientific Data Engineer to contribute to the development of generic, cross-domain metadata management framework to foster the reuse of open datasets as well as reproducibility of cognitive neuroscience datasets i.e., metadata describing the experimental context of studies employing fMRI, MEEG, and ECoG. The successful candidate will also heavily contribute to the development of data infrastructure including storage, streaming and analysis tools for reproducible science based on the BIDS standard. Efforts will be devoted to develop tools for an efficient organization and exploration of raw and processed datasets. The position is ideal for networking in the open science community as it includes interaction with the open (neuro)science community; and is a unique opportunity for someone keen to contribute to the development of open-science and large-scale collaborations and aiming to contribute to community efforts and dissemination. Your tasks in interdisciplinary research projects focus on: • Review of existing approaches and tools, Requirement specification, Conceptual design, blueprint of implementation, Proof-of-concept application to Cognitive Neuroscience • Presentation and publication of the results • Exchange and networking within national (NFDI, MPDL) and international (RDA, EOSC) initiatives • Developing, testing and implementing scientific software i.e., standardized neuroscience data acquisition, reproducible analysis pipelines and data storage for open science building on the BIDS standard • Providing training and support for students and postdocs at varied levels of competence in modern, high quality, open science/source coding practices • Providing support and training for data management • Being the lab’s interface with the Institute’s core IT team

A discussion on some recent perspectives on pre-registration, which has become a growing trend in the past few years. This is not just limited to neuroimaging, and it applies to most scientific fields. We will start with this overview editorial by Simmons et al. (2021): https://faculty.wharton.upenn.edu/wp-content/uploads/2016/11/34-Simmons-Nelson-Simonsohn-2021a.pdf, and also talk about a more critical perspective by Pham & Oh (2021): https://www.researchgate.net/profile/Michel-Pham/publication/349545600_Preregistration_Is_Neither_Sufficient_nor_Necessary_for_Good_Science/links/60fb311e2bf3553b29096aa7/Preregistration-Is-Neither-Sufficient-nor-Necessary-for-Good-Science.pdf. I would like us to discuss the pros and cons of pre-registration, and if we have time, I may do a demonstration of how to perform a pre-registration through the Open Science Framework.

This seminar, hosted by the LIBRE hub project, will provide an in-depth introduction to the Global Open Science Hardware (GOSH) movement. Since its inception, GOSH has been instrumental in advancing open-source hardware within scientific research, fostering a diverse and active community. The seminar will cover the history of GOSH, its current initiatives, and future opportunities, with a particular focus on the contributions and activities of the Latin American branch. This session aims to inform researchers, educators, and policy-makers about the significance and impact of GOSH in promoting accessibility and collaboration in science instrumentation.

It is now widely accepted that openness and transparency are keys to improving the reproducibility of scientific research, but many challenges remain to adoption of these practices. I will discuss the growth of an ecosystem for open science within the field of neuroimaging, focusing on platforms for open data sharing and open source tools for reproducible data analysis. I will also discuss the role of the Brain Imaging Data Structure (BIDS), a community standard for data organization, in enabling this open science ecosystem, and will outline the scientific impacts of these resources.

Cognitive neuroscience has witnessed great progress since modern neuroimaging embraced an open science framework, with the adoption of shared principles (Wilkinson et al., 2016), standards (Gorgolewski et al., 2016), and ontologies (Poldrack et al., 2011), as well as practices of meta-analysis (Yarkoni et al., 2011; Dockès et al., 2020) and data sharing (Gorgolewski et al., 2015). However, while functional neuroimaging data provide correlational maps between cognitive functions and activated brain regions, its usefulness in determining causal link between specific brain regions and given behaviors or functions is disputed (Weber et al., 2010; Siddiqiet al 2022). On the contrary, neuropsychological data enable causal inference, highlighting critical neural substrates and opening a unique window into the inner workings of the brain (Price, 2018). Unfortunately, the adoption of Open Science practices in clinical settings is hampered by several ethical, technical, economic, and political barriers, and as a result, open platforms enabling access to and sharing clinical (meta)data are scarce (e.g., Larivière et al., 2021). We are working with clinicians, neuroimagers, and software developers to develop an open source platform for the storage, sharing, synthesis and meta-analysis of human clinical data to the service of the clinical and cognitive neuroscience community so that the future of neuropsychology can be transdiagnostic, open, and FAIR. We call it neurocausal (https://neurocausal.github.io).

In recent years, more and more evidence has accumulated showing that many studies in psychological research cannot be replicated, effects are often overestimated, and little is publicly known about unsuccessful studies. What are the mechanisms behind this crisis? In this talk, I will explain how we got there and why it is still difficult to break free from the current system. I will further explain which role Open Science plays within the replication crisis and how it can help to improve science. This might sound like a pessimistic, negative talk, but I will end it on a positive note, I promise!

Case-control comparisons are a gold standard method for diagnosing and researching neuropsychological deficits and dissociations at the single-case level. These statistical tests, developed by John Crawford and collaborators, provide quantitative criteria for the classical concepts of deficit, dissociation and double-dissociation. Much attention has been given to the control of Type I (false positive) errors for these tests, but far less to the avoidance of Type II (false negative) errors; that is, to statistical power. I will describe the origins and limits of statistical power for case-control comparisons, showing that there are hard upper limits on power, which have important implications for the design and interpretation of single-case studies. My aim is to stimulate discussion of the inferential status of single-case neuropsychological evidence, particularly with respect to contemporary ideals of open science and study preregistration.

Research tools are paramount for scientific development, they enable researchers to observe and manipulate natural phenomena, learn their principles, make predictions and develop new technologies, treatments and improve living standards. Due to their costs and the geographical distribution of manufacturing companies access to them is not widely available, hindering the pace of research, the ability of many communities to contribute to science and education and reap its benefits. One possible solution for this issue is to create research tools under the open source ethos, where all documentation about them (including their designs, building and operating instructions) are made freely available. Dubbed Open Science Hardware (OSH), this production method follows the established and successful principles of open source software and brings many advantages over traditional creation methods such as: economic savings (see Pearce 2020 for potential economic savings in developing open source research tools), distributed manufacturing, repairability, and higher customizability. This development method has been greatly facilitated by recent technological developments in fast prototyping tools, Internet infrastructure, documentation platforms and lower costs of electronic off-the-shelf components. Taken together these benefits have the potential to make research more inclusive, equitable, distributed and most importantly, more reliable and reproducible, as - 1) researchers can know their tools inner workings in minute detail - 2) they can calibrate their tools before every experiment and having them running in optimal condition everytime - 3) given their lower price point, a)students can be trained/taught with hands on classes, b) several copies of the same instrument can be built leading to a parallelization of data collection and the creation of more robust datasets. - 4) Labs across the world can share the exact same type of instruments and create collaborative projects with standardized data collection and sharing.

Every day when we fall asleep we lose consciousness, we are not there. And then, every morning, when we wake up, we regain it. What mechanisms give rise to consciousness, and how can we explain consciousness in the realm of the physical world of atoms and matter? For centuries, philosophers and scientists have aimed to crack this mystery. Much progress has been made in the past decades to understand how consciousness is instantiated in the brain, yet critical questions remain: can we develop a consciousness meter? Are computers conscious? What about other animals and babies? We have embarked in a large-scale, multicenter project to test, in the context of an open science, adversarial collaboration, two of the most prominent theories: Integrated information theory (IIT) and Global Neuronal Workspace (GNW) theory. We are collecting over 500 datasets including invasive and non-invasive recordings of the human brain, i.e.. fMRI, MEG and ECoG. We hope this project will enable theory-driven discoveries and further explorations that will help us better understand how consciousness fits inside the human brain.

Open science hardware (OSH) as a concept usually refers to artifacts, but also to a practice, a discipline and a collective of people pushing for open access to the design of science tools. Since 2016, the Global Open Science Hardware (GOSH) movement gathers actors from academia, education, the private sector and civic organisations to advocate for OSH to be ubiquitous by 2025. In Latin America, GOSH advocates have fundraised and gathered around the development of annual "residencies" for building hardware for science and education. The community is currently defining its regional strategy and identifying other regional actors working on science and technology democratization. In this presentation I will give an overview of the open hardware movement for science, with a focus on the activities and strategy of the Latin American chapter and concrete ways to engage.

The Open Science movement advocates for more transparent, equitable and reliable science. It focusses on improving existing infrastructures and spans all aspects of the scientific process, from implementing systems that reward pre-registering studies and guarantee their publication, all the way to making research data citable and freely available. In this context, open source tools (and the development ethos supporting them) are becoming more and more present in academic labs, as researchers are realizing that they can improve the quality of their work, while cutting costs. In this talk an overview of OS tools for neuroscience will be given, with a focus on software and hardware, and how their use can bring scientific independence and make research evolve faster.