Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort.

Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study. Investing effort in user documentation and interacting with the user base can, in turn, help developers improve quality of their software. In this work, we detail and evaluate our multi-modal teaching approach to research data management in the DataLad ecosystem, both in general and with concrete software use. Spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of research data management tips in a knowledge base, our free and open source collection of training material has made research data management and software training available to various different stakeholders over the past five years.

A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps.

Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS) - BIDS Apps - have provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging - especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad - a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n=2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.

How to establish and maintain a multimodal animal research dataset using DataLad.

Sharing of data, processing tools, and workflows require open data hosting services and management tools. Despite FAIR guidelines and the increasing demand from funding agencies and publishers, only a few animal studies share all experimental data and processing tools. We present a step-by-step protocol to perform version control and remote collaboration for large multimodal datasets. A data management plan was introduced to ensure data security in addition to a homogeneous file and folder structure. Changes to the data were automatically tracked using DataLad and all data was shared on the research data platform GIN. This simple and cost-effective workflow facilitates the adoption of FAIR data logistics and processing workflows by making the raw and processed data available and providing the technical infrastructure to independently reproduce the data processing steps. It enables the community to collect heterogeneously acquired and stored datasets not limited to a specific category of data and serves as a technical infrastructure blueprint with rich potential to improve data handling at other sites and extend to other research areas.

Large-Area Synthesis of Ferromagnetic Fe5- x GeTe2 /Graphene van der Waals Heterostructures with Curie Temperature above Room Temperature.

Van der Waals (vdW) heterostructures combining layered ferromagnets and other 2D crystals are promising building blocks for the realization of ultracompact devices with integrated magnetic, electronic, and optical functionalities. Their implementation in various technologies depends strongly on the development of a bottom-up scalable synthesis approach allowing for realizing highly uniform heterostructures with well-defined interfaces between different 2D-layered materials. It is also required that each material component of the heterostructure remains functional, which ideally includes ferromagnetic order above room temperature for 2D ferromagnets. Here, it is demonstrated that the large-area growth of Fe5- x GeTe2 /graphene heterostructures is achieved by vdW epitaxy of Fe5- x GeTe2 on epitaxial graphene. Structural characterization confirms the realization of a continuous vdW heterostructure film with a sharp interface between Fe5- x GeTe2 and graphene. Magnetic and transport studies reveal that the ferromagnetic order persists well above 300 K with a perpendicular magnetic anisotropy. In addition, epitaxial graphene on SiC(0001) continues to exhibit a high electronic quality. These results represent an important advance beyond nonscalable flake exfoliation and stacking methods, thus marking a crucial step toward the implementation of ferromagnetic 2D materials in practical applications.

Research Data Management and Data Sharing for Reproducible Research-Results of a Community Survey of the German National Research Data Infrastructure Initiative Neuroscience.

Science is changing: the volume and complexity of data are increasing, the number of studies is growing and the goal of achieving reproducible results requires new solutions for scientific data management. In the field of neuroscience, the German National Research Data Infrastructure (NFDI-Neuro) initiative aims to develop sustainable solutions for research data management (RDM). To obtain an understanding of the present RDM situation in the neuroscience community, NFDI-Neuro conducted a comprehensive survey among the neuroscience community. Here, we report and analyze the results of the survey. We focused the survey and our analysis on current needs, challenges, and opinions about RDM. The German neuroscience community perceives barriers with respect to RDM and data sharing mainly linked to (1) lack of data and metadata standards, (2) lack of community adopted provenance tracking methods, (3) lack of secure and privacy preserving research infrastructure for sensitive data, (4) lack of RDM literacy, and (5) lack of resources (time, personnel, money) for proper RDM. However, an overwhelming majority of community members (91%) indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Taking advantage of this willingness and overcoming the existing barriers requires the systematic development of standards, tools, and infrastructure, the provision of training, education, and support, as well as additional resources for RDM to the research community and a constant dialogue with relevant stakeholders including policy makers to leverage of a culture change through adapted incentivization and regulation.

Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns.

An increasing number of studies have investigated the relationships between inter-individual variability in brain regions' connectivity and behavioral phenotypes, making use of large population neuroimaging datasets. However, the replicability of brain-behavior associations identified by these approaches remains an open question. In this study, we examined the cross-dataset replicability of brain-behavior association patterns for fluid cognition and openness predictions using a previously developed region-wise approach, as well as using a standard whole-brain approach. Overall, we found moderate similarity in patterns for fluid cognition predictions across cohorts, especially in the Human Connectome Project Young Adult, Human Connectome Project Aging, and Enhanced Nathan Kline Institute Rockland Sample cohorts, but low similarity in patterns for openness predictions. In addition, we assessed the generalizability of prediction models in cross-dataset predictions, by training the model in one dataset and testing in another. Making use of the region-wise prediction approach, we showed that first, a moderate extent of generalizability could be achieved with fluid cognition prediction, and that, second, a set of common brain regions related to fluid cognition across cohorts could be identified. Nevertheless, the moderate replicability and generalizability could only be achieved in specific contexts. Thus, we argue that replicability and generalizability in connectivity-based prediction remain limited and deserve greater attention in future studies.

Processing of visual and non-visual naturalistic spatial information in the "parahippocampal place area".

The "parahippocampal place area" (PPA) in the human ventral visual stream exhibits increased hemodynamic activity correlated with the perception of landscape photos compared to faces or objects. Here, we investigate the perception of scene-related, spatial information embedded in two naturalistic stimuli. The same 14 participants were watching a Hollywood movie and listening to its audio-description as part of the open-data resource studyforrest.org. We model hemodynamic activity based on annotations of selected stimulus features, and compare results to a block-design visual localizer. On a group level, increased activation correlating with visual spatial information occurring in the movie is overlapping with a traditionally localized PPA. Activation correlating with semantic spatial information occurring in the audio-description is more restricted to the anterior PPA. On an individual level, we find significant bilateral activity in the PPA of nine individuals and unilateral activity in one individual. Results suggest that activation in the PPA generalizes to spatial information embedded in a movie and an auditory narrative, and may call for considering a functional subdivision of the PPA.

FAIRly big: A framework for computationally reproducible processing of large-scale data.

Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original computing infrastructure. We demonstrate the framework's performance using two showcases: one highlighting data sharing and transparency (using the studyforrest.org dataset) and another highlighting scalability (using the largest public brain imaging dataset available: the UK Biobank dataset).

A studyforrest extension, an annotation of spoken language in the German dubbed movie "Forrest Gump" and its audio-description.

Here we present an annotation of speech in the audio-visual movie "Forrest Gump" and its audio-description for a visually impaired audience, as an addition to a large public functional brain imaging dataset ( studyforrest.org). The annotation provides information about the exact timing of each of the more than 2500 spoken sentences, 16,000 words (including 202 non-speech vocalizations), 66,000 phonemes, and their corresponding speaker. Additionally, for every word, we provide lemmatization, a simple part-of-speech-tagging (15 grammatical categories), a detailed part-of-speech tagging (43 grammatical categories), syntactic dependencies, and a semantic analysis based on word embedding which represents each word in a 300-dimensional semantic space. To validate the dataset's quality, we build a model of hemodynamic brain activity based on information drawn from the annotation. Results suggest that the annotation's content and quality enable independent researchers to create models of brain activity correlating with a variety of linguistic aspects under conditions of near-real-life complexity.

In defense of decentralized research data management.

Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation.

REMoDNaV: robust eye-movement classification for dynamic stimulation.

Tracking of eye movements is an established measurement for many types of experimental paradigms. More complex and more prolonged visual stimuli have made algorithmic approaches to eye-movement event classification the most pragmatic option. A recent analysis revealed that many current algorithms are lackluster when it comes to data from viewing dynamic stimuli such as video sequences. Here we present an event classification algorithm-built on an existing velocity-based approach-that is suitable for both static and dynamic stimulation, and is capable of classifying saccades, post-saccadic oscillations, fixations, and smooth pursuit events. We validated classification performance and robustness on three public datasets: 1) manually annotated, trial-based gaze trajectories for viewing static images, moving dots, and short video sequences, 2) lab-quality gaze recordings for a feature-length movie, and 3) gaze recordings acquired under suboptimal lighting conditions inside the bore of a magnetic resonance imaging (MRI) scanner for the same full-length movie. We found that the proposed algorithm performs on par or better compared to state-of-the-art alternatives for static stimulation. Moreover, it yields eye-movement events with biologically plausible characteristics on prolonged dynamic recordings. Lastly, algorithm performance is robust on data acquired under suboptimal conditions that exhibit a temporally varying noise level. These results indicate that the proposed algorithm is a robust tool with improved classification accuracy across a range of use cases. The algorithm is cross-platform compatible, implemented using the Python programming language, and readily available as free and open-source software from public sources.

COVID-19 and market expectations: Evidence from option-implied densities.

We compare risk-neutral densities from equity index options across several countries during the early phase of the COVID-19 pandemic. The initial reaction in all analyzed markets was late, abrupt and simultaneous. Only a few weeks later, densities started to differ across markets.

Macroscopic information-based taste representations in insular cortex are shaped by stimulus concentration.

Taste processing is an essential ability in all animals signaling potential harm or benefit of ingestive behavior. However, current evidence for cortical taste representations remains contradictory. To address this issue, high-resolution functional MRI (fMRI) and multivariate pattern analysis were used to characterize taste-related informational content in human insular cortex, which contains primary gustatory cortex. Human participants judged pleasantness and intensity of low- and high-concentration tastes (salty, sweet, sour, and bitter) in two fMRI experiments on two different days to test for task- and concentration-invariant taste representations. We observed patterns of fMRI activity within insular cortex narrowly tuned to specific tastants consistently across tasks in all participants. Fewer patterns responded to more than one taste category. Importantly, changes in taste concentration altered the spatial layout of putative taste-specific patterns with distinct, almost nonoverlapping patterns for each taste category at different concentration levels. Together, our results point at macroscopic representations in human insular cortex as a complex function of taste category and concentration rather than representations based solely on taste identity.

Influence of Proximity to Supporting Substrate on van der Waals Epitaxy of Atomically Thin Graphene/Hexagonal Boron Nitride Heterostructures.

Combining graphene and the insulating hexagonal boron nitride (h-BN) into two-dimensional heterostructures is promising for novel, atomically thin electronic nanodevices. A heteroepitaxial growth, in which these materials are grown on top of each other, will be crucial for their scalable device integration. However, during this so-called van der Waals epitaxy, not only the atomically thin substrate itself must be considered but also the influences from the supporting substrate below it. Here, we report not only a substantial difference between the formation of h-BN on single- (SLG) and on bi-layer epitaxial graphene (BLG) on SiC, but also vice versa, that the van der Waals epitaxy of h-BN at growth temperatures well below 1000 °C affects the varying number of graphene layers differently. Our results clearly demonstrate that the additional graphene layer in BLG enhances the distance to the corrugated, carbon-rich interface of the supporting SiC substrate and thereby diminishes its influence on the van der Waals epitaxy, leading to a homogeneous formation of a smooth, atomically thin heterostructure, which will be required for a scalable device integration of 2D heterostructures.

Nature abhors a paywall: How open science can realize the potential of naturalistic stimuli.

Naturalistic stimuli show significant potential to inform behavioral, cognitive, and clinical neuroscience. To date, this impact is still limited by the relative inaccessibility of both generated neuroimaging data as well as the supporting naturalistic stimuli. In this perspective, we highlight currently available naturalistic datasets and technical solutions such as DataLad that continue to advance our ability to share this data. We also review scientific and sociological challenges in selecting naturalistic stimuli for reproducible research. Overall, we encourage researchers to share their naturalistic datasets to the full extent possible under local copyright law.

Individual face- and house-related eye movement patterns distinctively activate FFA and PPA.

We investigated if the fusiform face area (FFA) and the parahippocampal place area (PPA) contain a representation of fixation sequences that are typically used when looking at faces or houses. Here, we instructed observers to follow a dot presented on a uniform background. The dot's movements represented gaze paths acquired separately from observers looking at face or house pictures. Even when gaze dispersion differences were controlled, face- and house-associated gaze patterns could be discriminated by fMRI multivariate pattern analysis in FFA and PPA, more so for the current observer's own gazes than for another observer's gaze. The discrimination of the observer's own gaze patterns was not observed in early visual areas (V1 - V4) or superior parietal lobule and frontal eye fields. These findings indicate a link between perception and action-the complex gaze patterns that are used to explore faces and houses-in the FFA and PPA.

Neural signatures underlying deliberation in human foraging decisions.

Humans have a remarkable capacity to mentally project themselves far ahead in time. This ability, which entails the mental simulation of events, is thought to be fundamental to deliberative decision making, as it allows us to search through and evaluate possible choices. Many decisions that humans make are foraging decisions, in which one must decide whether an available offer is worth taking, when compared to unknown future possibilities (i.e., the background). Using a translational decision-making paradigm designed to reveal decision preferences in rats, we found that humans engaged in deliberation when making foraging decisions. A key feature of this task is that preferences (and thus, value) are revealed as a function of serial choices. Like rats, humans also took longer to respond when faced with difficult decisions near their preference boundary, which was associated with prefrontal and hippocampal activation, exemplifying cross-species parallels in deliberation. Furthermore, we found that voxels within the visual cortices encoded neural representations of the available possibilities specifically following regret-inducing experiences, in which the subject had previously rejected a good offer only to encounter a low-valued offer on the subsequent trial.

Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging.

There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the "last mile" implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain.

Encoding of Auditory Temporal Gestalt in the Human Brain.

The perception of an acoustic rhythm is invariant to the absolute temporal intervals constituting a sound sequence. It is unknown where in the brain temporal Gestalt, the percept emerging from the relative temporal proximity between acoustic events, is encoded. Two different relative temporal patterns, each induced by three experimental conditions with different absolute temporal patterns as sensory basis, were presented to participants. A linear support vector machine classifier was trained to differentiate activation patterns in functional magnetic resonance imaging data to the two different percepts. Across the sensory constituents the classifier decoded which percept was perceived. A searchlight analysis localized activation patterns specific to the temporal Gestalt bilaterally to the temporoparietal junction, including the planum temporale and supramarginal gyrus, and unilaterally to the right inferior frontal gyrus (pars opercularis). We show that auditory areas not only process absolute temporal intervals, but also integrate them into percepts of Gestalt and that encoding of these percepts persists in high-level associative areas. The findings complement existing knowledge regarding the processing of absolute temporal patterns to the processing of relative temporal patterns relevant to the sequential binding of perceptual elements into Gestalt.