Yaroslav O. Halchenko

International data privacy regulations impede the pooling of research data for collaborative analysis. We introduce Neurobagel, a federated network enabling cohort discovery across locally governed, access-controlled datasets. Through intuitive graphical tools and a decentralized query infrastructure, Neurobagel facilitates harmonization, control, and discovery of data according to local regulations. Today, Neurobagel is deployed by consortia and data platforms in Europe, North America, Asia, and Australia, supporting diverse and evolving regulatory frameworks.

We effortlessly extract behaviorally relevant information from dynamic visual input to understand the actions of others. Here, we compare behavioral judgments of action meaning against other models capturing semantic and visual features. We measured brain activity using fMRI while participants viewed 90 video clips depicting social and nonsocial actions in real-world contexts. Using arrangement tasks, we developed two behavioral models capturing judgments of action meaning (transitive purpose, social content), as well as three control models capturing judgments of visual content (people, objects, scene). We compared these models with semantic models based on word embeddings and visual models based on gaze and motion energy. Behavioral models of action meaning outperformed models of visual content, semantics, and low-level visual features across much of cortex. Transitivity and sociality captured large portions of unique variance throughout the action observation network, extending into regions not typically associated with action perception, such as ventral temporal cortex.

Scientific claims increasingly rely upon the interplay of code, data, and computational environments. Yet the record of how they are used together is often incomplete, scattered, or lost. This undermines rigor, reproducibility, reusability, and efficiency. Previous approaches have improved the governance of digital objects but do not specify how research objects ought to be structured and managed so they can be re-executed and extended. The community is missing a shared vocabulary for this operational layer. Building on recurrent practices and convergent patterns across disciplines, we formalize seven principles: Self-containment, Tracking, Actionability, Modularity, Portability, Ephemerality, and Distributability (STAMPED). Together they give researchers guidelines for building and assessing research objects that others can trust, rerun, and build upon. We frame each principle as a spectrum so that adoption is incremental and starts from existing practice. We support each principle with normative requirements, an interactive checklist, and a map of enabling tools. As conventions mature, tooling improves, and AI agents become increasingly involved in research workflows, the goals of rigor, reproducibility, reusability, and efficiency are becoming more attainable. STAMPED gives researchers, collaborators, reviewers, and agents a common language, concrete goals, and an aligned direction for making computational research more durable.https://github.com/stamped-principles/stamped-paper/

Neuroimaging data management and processing are tedious and error-prone, prompting reproducibility concerns. Globally, studies with heterogeneous infrastructure and governance policies lead to eclectic data processing and sharing, necessitating standardization of data workflows to ensure reusability and comparability of multi-centric datasets. The Nipoppy neuroinformatics framework facilitates such standardization by combining specification, protocol, and software to manage study-level data workflows. With its adoption, researchers can share standardized, derived datasets enabling efficient, reproducible, and inclusive research.

International data privacy regulations impede the pooling of research data for collaborative analysis. We introduce Neurobagel, a federated network enabling cohort discovery across locally governed, access-controlled datasets. Through intuitive graphical tools and a decentralized query infrastructure, Neurobagel facilitates harmonization, control, and discovery of data according to local regulations. Today, Neurobagel is deployed by consortia and data platforms in Europe, North America, Asia, and Australia, supporting diverse and evolving regulatory frameworks.

Cognitive neuroscience has advanced significantly due to the availability of openly shared datasets. Large sample sizes, large amounts of data per person, and diversity in tasks and data types are all desirable, but are difficult to achieve in a single dataset. Here, we present an open dataset with N = 101 participants and 6 hours of scanning per participant, including 6 multifaceted functional tasks, 2 hours of naturalistic movie viewing, structural T1 images and multi-shell diffusion imaging as well as autonomic physiological data. This dataset’s combination of sample size, extensive data per participant (>600 iso-hours of data), and a wide range of experimental conditions — including cognitive, affective, social, and somatic/interoceptive tasks — positions it uniquely for probing important questions in cognitive neuroscience.

The rapid growth of scientific publications and evolving experimental paradigms create significant challenges in staying up-to-date with current advances. Assertions are often unstructured and have limited provenance, which hinders reproducibility. Ontologies and knowledge graphs (KGs) offer structured solutions by capturing assertions, evidence, and provenance to support reproducibility. This paper reviews 23 ontologies - 13 focused on assertions and evidence and 10 on provenance - providing an overview of the current landscape while highlighting key challenges and opportunities for improvement.

We effortlessly extract behaviorally relevant information from dynamic visual input in order to understand the actions of others. In the current study, we develop and test different classes of models to better understand the neural representational geometries supporting action understanding. Using fMRI, we measured brain activity as participants viewed a diverse set of 90 different video clips depicting social and nonsocial actions in real-world contexts. We developed five behavioral models using arrangement tasks: two models reflecting behavioral judgments of the purpose (transitivity) and the social content (sociality) of the actions depicted in the video stimuli; and three models reflecting behavioral judgments of the visual content (people, objects, and scene) depicted in still frames of the stimuli. We evaluated how well these models predict neural representational geometry and tested them against semantic models based on verb and nonverb embeddings and visual models based on gaze and motion energy. Our results revealed that behavioral models of action meaning/purpose (transitivity, sociality) better reflect neural representational geometry than behavioral models of object, person, and scene features, as well as semantic and visual models throughout much of cortex. The sociality and transitivity models captured a large portion of unique variance throughout the action observation network, extending into regions not typically associated with action perception, like ventral temporal cortex. Overall, these findings expand our understanding of the action observation network and indicate that the social content and purpose of observed actions are predominant in cortical representation.

Cognitive neuroscience has advanced significantly due to the availability of openly shared datasets. Large sample sizes, large amounts of data per person, and diversity in tasks and data types are all desirable, but are difficult to achieve in a single dataset. Here, we present an open dataset with N = 101 participants and 6 hours of scanning per participant, with 6 multifaceted cognitive tasks including 2 hours of naturalistic movie viewing. This datasets' combination of ample sample size, extensive data per participant, more than 600 hours worth of data, and a wide range of experimental conditions - including cognitive, affective, social, and somatic/interoceptive tasks - positions it uniquely for probing important questions in cognitive neuroscience.