Distributed Augmentation, Hypersweeps, and Branch Decomposition of Contour Trees for Scientific Exploration.

Contour trees describe the topology of level sets in scalar fields and are widely used in topological data analysis and visualization. A main challenge of utilizing contour trees for large-scale scientific data is their computation at scale using highperformance computing. To address this challenge, recent work has introduced distributed hierarchical contour trees for distributed computation and storage of contour trees.

Spyglass: a framework for reproducible and shareable neuroscience research.

Scientific progress depends on reliable and reproducible results. Progress can also be accelerated when data are shared and re-analyzed to address new questions. Current approaches to storing and analyzing neural data typically involve bespoke formats and software that make replication, as well as the subsequent reuse of data, difficult if not impossible.

Structured behavioral data format: An NWB extension standard for task-based behavioral neuroscience experiments.

Understanding brain function necessitates linking neural activity with corresponding behavior. Structured behavioral experiments are crucial for probing the neural computations and dynamics underlying behavior; however, adequately representing their complex data is a significant challenge. Currently, a comprehensive data standard that fully encapsulates task-based experiments, integrating neural activity with the richness of behavioral context, is lacking.

FAIR for AI: An interdisciplinary and international community building perspective.

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets.

Supercapacitor Performance of Magnetite Nanoparticles Enhanced by a Catecholate Dispersant: Experiment and Theory.

The full potential of FeO for supercapacitor applications can be achieved by addressing challenges in colloidal fabrication of high active mass electrodes. Exceptional adsorption properties of catecholate-type 3,4-dihydroxybenzoic acid (DHBA) molecules are explored for surface modification of FeO nanoparticles to enhance their colloidal dispersion as verified by sedimentation test results and Fourier-transform infrared spectroscopy measurements. Electrodes prepared in the presence of DHBA show nearly double capacitance at slow charging rates as compared to the control samples without the dispersant or with benzoic acid as a non-catecholate dispersant.

The Neurodata Without Borders ecosystem for neurophysiological data science.

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience.

Rotational dynamics and transition mechanisms of surface-adsorbed proteins.

Assembly of biomolecules at solid–water interfaces requires molecules to traverse complex orientation-dependent energy landscapes through processes that are poorly understood, largely due to the dearth of in situ single-molecule measurements and statistical analyses of the rotational dynamics that define directional selection. Emerging capabilities in high-speed atomic force microscopy and machine learning have allowed us to directly determine the orientational energy landscape and observe and quantify the rotational dynamics for protein nanorods on the surface of muscovite mica under a variety of conditions. Comparisons with kinetic Monte Carlo simulations show that the transition rates between adjacent orientation-specific energetic minima can largely be understood through traditional models of in-plane Brownian rotation across a biased energy landscape, with resulting transition rates that are exponential in the energy barriers between states.

Band alignment of monolayer CaP, CaAs, BaAs and the role of p-d orbital interactions in the formation of conduction band minima.

Recently, a number of new two-dimensional (2D) materials based on puckered phosphorene and arsenene have been predicted with moderate band gaps, good absorption properties and carrier mobilities superior to those of transition metal dichalcogenides. For heterojunction applications, it is important to know the relative band alignment of these new 2D materials. We report the band alignment of puckered CaP, CaAs and BaAs monolayers at the quasiparticle level of theory (GW), calculating band offsets for isolated monolayers according to the electron affinity rule.

Optimization and Augmentation for Data Parallel Contour Trees.

Contour trees are used for topological data analysis in scientific visualization. While originally computed with serial algorithms, recent work has introduced a vector-parallel algorithm. However, this algorithm is relatively slow for fully augmented contour trees which are needed for many practical data analysis tasks.

Giant spontaneous Hall effect in a nonmagnetic Weyl-Kondo semimetal.

Nontrivial topology in condensed-matter systems enriches quantum states of matter to go beyond either the classification into metals and insulators in terms of conventional band theory or that of symmetry-broken phases by Landau's order parameter framework. So far, focus has been on weakly interacting systems, and little is known about the limit of strong electron correlations. Heavy fermion systems are a highly versatile platform to explore this regime.

Predicting and Comparing the Performance of Array Management Libraries.

Many applications are increasingly becoming I/O-bound. To improve scalability, analytical models of parallel I/O performance are often consulted to determine possible I/O optimizations. However, I/O performance modeling has predominantly focused on applications that directly issue I/O requests to a parallel file system or a local storage device.

Electronic properties of Pb-I deficient lead halide perovskites.

The electronic structure evolution of deficient halide perovskites with a general formula (A,A')MX was investigated using the density functional theory. The focus is placed on characterization of changes in the bandgap, band alignment, effective mass, and optical properties of deficient perovskites at various concentrations of defects. We uncover unusual electronic properties of the defect corresponding to a M-X vacancy filled with an A' cation.

HDMF: Hierarchical Data Modeling Framework for Modern Science Data Standards.

A ubiquitous problem in aggregating data across different experimental and observational data sources is a lack of software infrastructure that enables flexible and extensible standardization of data and metadata. To address this challenge, we developed HDMF, a hierarchical data modeling framework for modern science data standards. With HDMF, we separate the process of data standardization into three main components: (1) data modeling and specification, (2) data I/O and storage, and (3) data interaction and data APIs.

Scalable Contour Tree Computation by Data Parallel Peak Pruning.

As data sets grow to exascale, automated data analysis and visualization are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance.

MAGI: A Method for Metabolite Annotation and Gene Integration.

Metabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, ambiguous metabolite identifications are a common challenge and biochemical interpretation is often limited by incomplete and inaccurate genome-based predictions of enzyme activities (that is, gene annotations). Metabolite Annotation and Gene Integration (MAGI) generates a metabolite-gene association score using a biochemical reaction network.

μSR study of spin freezing and persistent spin dynamics in NaCaNiF.

A new pyrochlore compound, NaCaNiF, was recently synthesized and has a single magnetic site with spin-1 Ni . We present zero field and longitudinal field muon spin rotation (μSR) measurements on this pyrochlore. Density functional theory calculations show that the most likely muon site is located between two fluorine ions, but off-centre.

Adsorption of Maleic Acid Monomer on the Surface of Hydroxyapatite and TiO: A Pathway toward Biomaterial Composites.

Poly(styrene- alt-maleic acid) adsorption on hydroxyapatite and TiO (rutile) was studied using experimental techniques and complemented by ab initio simulations of adsorption of a maleic acid segment as a subunit of the copolymer. Ab initio calculations suggest that the maleic acid segment forms a strong covalent bonding to the TiO and hydroxyapatite surfaces. If compared to vacuum, the presence of a solvent significantly reduces the adsorption strength as the polarity of the solvent increases.

Aziridinium Lead Iodide: A Stable, Low-Band-Gap Hybrid Halide Perovskite for Photovoltaics.

The low ionization energy of an A site molecule is a very important factor, which determines the thermodynamical stability of APbI hybrid halide perovskites, while the size of the molecule governs the stable phase at room temperature and, eventually, the band gap. It is challenging to achieve both a low ionization energy and the reasonable size for the PbI cage to circumvent the stability issue inherent to hybrid halide perovskites. Here we propose a new three-membered charged ring radical, which demonstrates a low ionization energy that renders a good stability for its corresponding perovskite and a reasonable cation size that translates into a suitable band gap for the photovoltaic application.

BASTet: Shareable and Reproducible Analysis and Visualization of Mass Spectrometry Imaging Data via OpenMSI.

Mass spectrometry imaging (MSI) is a transformative imaging method that supports the untargeted, quantitative measurement of the chemical composition and spatial heterogeneity of complex samples with broad applications in life sciences, bioenergy, and health. While MSI data can be routinely collected, its broad application is currently limited by the lack of easily accessible analysis methods that can process data of the size, volume, diversity, and complexity generated by MSI experiments. The development and application of cutting-edge analytical methods is a core driver in MSI research for new scientific discoveries, medical diagnostics, and commercial-innovation.

One-dimensional electron gas in strained lateral heterostructures of single layer materials.

Confinement of the electron gas along one of the spatial directions opens an avenue for studying fundamentals of quantum transport along the side of numerous practical electronic applications, with high-electron-mobility transistors being a prominent example. A heterojunction of two materials with dissimilar electronic polarisation can be used for engineering of the conducting channel. Extension of this concept to single-layer materials leads to one-dimensional electron gas (1DEG).

OpenMSI Arrayed Analysis Toolkit: Analyzing Spatially Defined Samples Using Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) has primarily been applied in localizing biomolecules within biological matrices. Although well-suited, the application of MSI for comparing thousands of spatially defined spotted samples has been limited. One reason for this is a lack of suitable and accessible data processing tools for the analysis of large arrayed MSI sample sets.

WarpIV: In Situ Visualization and Analysis of Ion Accelerator Simulations.

The generation of short pulses of ion beams through the interaction of an intense laser with a plasma sheath offers the possibility of compact and cheaper ion sources for many applications--from fast ignition and radiography of dense targets to hadron therapy and injection into conventional accelerators. To enable the efficient analysis of large-scale, high-fidelity particle accelerator simulations using the Warp simulation suite, the authors introduce the Warp In situ Visualization Toolkit (WarpIV). WarpIV integrates state-of-the-art in situ visualization and analysis using VisIt with Warp, supports management and control of complex in situ visualization and analysis workflows, and implements integrated analytics to facilitate query- and feature-based data analytics and efficient large-scale data analysis.

Thermodynamic origin of instability in hybrid halide perovskites.

Degradation of hybrid halide perovskites under the influence of environmental factors impairs future prospects of using these materials as absorbers in solar cells. First principle calculations can be used as a guideline in search of new materials, provided we can rely on their predictive capabilities. We show that the instability of perovskites can be captured using ab initio total energy calculations for reactants and products augmented with additional thermodynamic data to account for finite temperature effects.

Methods for Specifying Scientific Data Standards and Modeling Relationships with Applications to Neuroscience.

Neuroscience continues to experience a tremendous growth in data; in terms of the volume and variety of data, the velocity at which data is acquired, and in turn the veracity of data. These challenges are a serious impediment to sharing of data, analyses, and tools within and across labs. Here, we introduce BRAINformat, a novel data standardization framework for the design and management of scientific data formats.

Molecular Dynamics of Fibrinogen Adsorption onto Graphene, but Not onto Poly(ethylene glycol) Surface, Increases Exposure of Recognition Sites That Trigger Immune Response.

Changes in the conformation of blood proteins due to their binding to nonbiological surfaces is the initial step in the chain of immunological reactions to foreign bodies. Despite the large number of experimental studies that have been performed on fibrinogen adsorption to nonbiological surfaces, a clear picture describing this complex process has eluded researchers to date. Developing a better understanding of the behavior of bioactive fibrinogen motifs upon their interaction with surfaces may facilitate the design of advanced materials with improved biocompatibility.