We are looking for a new team member with the following profile: You have an open-minded and collaborative attitude towards doing groundbreaking digital humanities research; You have a firm grasp on computational methods and are willing to achieve proficiency in historical research methods; You have a Ph.D. degree in semantic web, digital humanities, language technology, or a related field by the starting date of the project; Your English is excellent and you have or are willing to obtain a working proficiency in Dutch. Why you should consider working with us: Location, location, location: Our offices are located in a historical building in downtown Amsterdam. Researchers work together in projects and a shared office space and can choose to spend part of their time at home. The team: We value a social, open and inquisitive, safe work environment where your input counts, not your job title, this means open conversations about pros and cons of a particular idea and approach based on content, not status. We also brew a mean cup of coffee and the office has a cooking club. Good to know: The Royal Netherlands Academy of Arts and Sciences is not an educational institution, this means that you can focus on building your research profile without a teaching load. We do work together with various Dutch universities so some teaching is an option. About the procedure: Apply by 10 January 2025 via https://vacatures.knaw.nl/job-invite/2438/. Please do not provide more information than what is requested (motivation letter, CV, 1-page research-statement and one illustrative publication). If the committee requires more information, they will ask. The hiring committee will review the applications and invite candidates for an online interview with an optional second interview. The first round of interviews will take place online on 22 January. Starting date & duration: the starting date is negotiable but the project team prefers to fill the vacancy sooner rather than later. The position is for three years.

Autopilot is a Python framework for performing complex behavioral neuroscience experiments by coordinating a swarm of Raspberry Pis. It was designed to not only give researchers a tool that allows them to perform the hardware-intensive experiments necessary for the next generation of naturalistic neuroscientific observation, but also to make it easier for scientists to be good stewards of the human knowledge project. Specifically, we designed Autopilot as a framework that lets its users contribute their technical expertise to a cumulative library of hardware interfaces and experimental designs, and produce data that is clean at the time of acquisition to lower barriers to open scientific practices. As autopilot matures, we have been progressively making these aspirations a reality. Currently we are preparing the release of Autopilot v0.4.0, which will include a new plugin system and wiki that makes use of semantic web technology to make a technical and contextual knowledge repository. By combining human readable text and semantic annotations in a wiki that makes contribution as easy as possible, we intend to make a communal knowledge system that gives a mechanism for sharing the contextual technical knowledge that is always excluded from methods sections, but is nonetheless necessary to perform cutting-edge experiments. By integrating it with Autopilot, we hope to make a first of its kind system that allows researchers to fluidly blend technical knowledge and open source hardware designs with the software necessary to use them. Reciprocally, we also hope that this system will support a kind of deep provenance that makes abstract "custom apparatus" statements in methods sections obsolete, allowing the scientific community to losslessly and effortlessly trace a dataset back to the code and hardware designs needed to replicate it. I will describe the basic architecture of Autopilot, recent work on its community contribution ecosystem, and the vision for the future of its development.

Many of the collaborations we encounter in our scientific careers are centered on a common idea that can be associated with certain resources, such as a dataset, an algorithm, or a model. All partners in a collaboration need to develop a common understanding of these resources, and need to be able to access them in a simple and unambiguous manner in order to avoid incorrect conclusions especially in highly cross-disciplinary contexts. While digital computers have entered to assist scientific workflows in experiment and simulation for many decades, the high degree of heterogeneity in the field had led to a scattered landscape of highly customized, lab-internal solutions to organizing and managing the resources on a project-by-project basis. Only with the availability of modern technologies such as the semantic web, platforms for collaborative coding or the development of data standards overarching different disciplines, we have tools at our disposal to make resources increasingly more accessible, understandable, and usable. However, without overarching standardization efforts and adaptation of such technologies to the workflows and needs of individual researchers, their adoption by the neuroscience community will be impeded. From the perspective of computational neuroscience, which is inherently dependent on leveraging data and methods across the field of neuroscience for inspiration and validation, I will outline my view on past and present developments towards a more rigorous use of digital resources and how they improved collaboration, and introduce emerging initiatives to support this process in the future (e.g., EBRAINS http://ebrains.eu, NFDI-Neuro http://www.nfdi-neuro.de).