Biology subjects

Martone, M. E.

Publications and source records attributed to Martone, M. E..

14 recordsLinked to original sources

bioRxiv2026

A layered standards framework for integrating single-cell and spatial omics data into brain cell atlases

The BRAIN Initiative Cell Atlas Network (BICAN) is generating large-scale multimodal datasets to profile cell types in the human, non-human primate, and mouse brain. The diversity of single-cell and spatial transcriptomic and epigenomic assays, combined with varied experimental contexts, multiple data-generating laboratories and distributed infrastructure, poses substantial challenges for data integration and reuse in BICAN. To address this, we implemented a standards framework that enables layered integration of these data into knowledge-ready products for interoperable brain cell atlases. This framework organizes data based on three progressively structured layers. First, we introduced an assay-agnostic modeling layer that unifies the representation of single-cell and spatial omics data using a common set of biological entities and processes assessed by diverse experimental techniques. Second, we implemented harmonized metadata standards that capture key experimental features linked to biospecimen provenance across heterogeneous tissue sources, species, and preparations, supporting integration and validation while minimizing burden on data contributors. Third, we present an extensible representation for data-driven cell type taxonomies that integrates molecular data with annotations, ontology mappings, and evidence. Together, these contributions represent an end-to-end framework that transforms heterogeneous datasets into structured, interoperable resources that support broad community reuse via mapping algorithms, annotation systems, and visualization platforms. This approach links biospecimen provenance with cell-level outputs and embeds these in a standardized taxonomy format, enabling downstream applications such as cross-dataset integration, reference mapping, and knowledge-driven analysis. More broadly, our work demonstrates a generalizable strategy for enabling an efficient data-to-knowledge pipeline in a large-scale consortium setting.

genomics↗

bioRxiv2026

PRECISE TBI Model Catalog: Increasing Accessibility and Reproducibility in TBI research

Preclinical traumatic brain injury (TBI) research relies on experimental models that vary by mechanism, parameters, surgical procedures, species, strains, and ages, to name a few. While these models are crucial for understanding injury mechanisms and testing therapies, the progress in translating this knowledge to the clinic has been limited. This is in part due to fragmented resources and inconsistent reporting of critical variables. Here, we introduce the PRECISE-TBI model catalog, a centralized, queryable resource that consolidates metadata from published studies. The catalog integrates curated annotations from more than 450 papers, including details such as age, sex, strain, model type, device, and injury parameters. Where available, entries are also linked to protocols and datasets to enhance transparency and reproducibility. The Model Catalog serves as a living resource that enables cross-study comparison, identifies gaps in reporting, and connects the literature to datasets, protocols, device information, and other relevant resources. Analysis of the initial catalog entries revealed gaps in the reporting of device, age, and weight. In contrast, the reporting of sex improved over time, with over 90% of recent studies within the catalog papers reporting sex. Strain was also reported in most studies, with consistent reporting of specificity, especially for the C57 mice substrain. We expect the Model Catalog to serve as a valuable tool to enhance study design and reproducibility in preclinical TBI research while advancing FAIR data principles in the TBI field.

neuroscience↗

bioRxiv2026

Rigor and Transparency in two neurotrauma-publishing journals: editorial policies improve transparent reporting.

Preclinical research in traumatic brain injury (TBI) continues to significantly increase knowledge and yield a large number of peer-reviewed studies, but translation of these results to the clinical setting has been minimal. Rigor and transparency factors such as concealment of group allocation (e.g., "blinding) or ensuring that reagents are identifiable are critical in ensuring that scientific studies are replicable and translatable. Yet, nearly all efforts aimed at measuring these factors have concluded that reporting practices are problematic and incomplete. One way to improve transparency of reporting practices is to require that authors address a set of transparency related items in some way, such as a checklist or a paper section. Recently, Journal of Neurotrauma, a leading publisher of preclinical TBI research, instituted a required rigor-related section, which is explained to authors via a set of transparency, rigor, and reproducibility (TRR) instructions (one example for each manuscript type). These documents include specific transparency sections explaining blinding, power calculations, protocols, code, and data deposition. Experimental Neurology is a journal that is similar in size, impact and topic but the journal does not have explicit instructions to authors about transparency items. The purpose of this study was to assess the degree to which transparency reporting items were included in published manuscripts comparing reporting practices in the Journal of Neurotrauma and Experimental Neurology. We used a commercial software, SciScore, which is an AI tool tuned to detect rigor/transparency sentences in published manuscripts and count the number found (roughly dividing by the number expected) to obtain a score. Overall, SciScore found that in 6 of 8 items that were explicitly asked for, such as power calculations, investigator blinding, inclusion criteria, attrition, and data were significantly greater (more than 10%) compared to Experimental Neurology. However in Journal of Neurotrauma papers with the extra rigor section, 3 of 4 rigor items that were not explicitly asked for in the template rigor documents, such as subject demographics or transparent antibody reporting were not different from Experimental Neurology. One item, reporting of the sex of subjects was significantly better in Experimental Neurology. This shows that the Journal of Neurotrauma required rigor section is effective in improving reporting, but it would be far better if sex as a biological variable and transparent reporting of reagents (items present on major checklists including NIH rigor criteria) would be included.

scientific communication and education↗

bioRxiv2024

Developing a Multiscale Neural Connectivity Knowledgebase of the Autonomic Nervous System

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is a U.S. National Institutes of Health (NIH) funded effort to enhance our understanding of the neural circuitry responsible for visceral control. SPARCs mission is to identify, extract, and compile our overall existing knowledge and understanding of the autonomic nervous system (ANS) connectivity between the central nervous system and end organs. A major goal of SPARC is to use this knowledge to promote the development of the next generation of neuromodulation devices and bioelectronic medicine for nervous system diseases. As part of the SPARC program, we have been developing SCKAN, a dynamic knowledge base of ANS connectivity that contains information about the origins, terminations, and routing of ANS projections. The distillation of SPARCs connectivity knowledge into this knowledge base involves a rigorous curation process to capture connectivity information provided by experts, published literature, textbooks, and SPARC scientific data. SCKAN is used to automatically generate anatomical and functional connectivity maps on the SPARC portal. In this article, we present the design and functionality of SCKAN, including the detailed knowledge engineering process developed to populate the resource with high quality and accurate data. We discuss the process from both the perspective of SCKANs ontological representation as well as its practical applications in developing information systems. We share our techniques, strategies, tools and insights for developing a practical knowledgebase of ANS connectivity that supports continual enhancement.

bioinformatics↗

bioRxiv2023

Brain Injury Knowledge Ontology (BIKO) for traumatic brain injury: Formalizing concepts and methods used in translational traumatic brain injury research.

Traumatic brain injury (TBI) is an insult to the brain resulting from an external force and is a significant cause of morbidity and mortality in the United States. No effective clinical therapeutics currently exist for this injury. Although several therapies and procedures have been deemed successful for TBI treatment in preclinical research studies, they have yet to be translated into human patients. These discouraging results have left many scientists questioning the role of animal models in drug discovery for TBI. One major hurdle in translating the knowledge obtained in the laboratory to the clinic is the methodological variance across these studies. This variance can hinder the ability to draw conclusions from conflicting studies and aggregate data across various research studies, which ultimately impedes the ability to aggregate data across these studies. Therefore, addressing this variance is crucial for bridging the gap between the laboratory and the clinic. The increasing volume of papers and associated data being published every day makes this hurdle even more difficult to overcome. The initial steps to address these knowledge gaps are identifying these studies and creating a shared knowledge framework for mapping their terminology. We are developing the Brain Injury Knowledge Ontology (BIKO) to create a standardized model to describe methods and outcome measures used within preclinical and clinical TBI therapy studies to facilitate comparison across studies and models. The first version of BIKO focuses on modeling the major preclinical TBI models, e.g., Controlled Cortical Impact Model, Fluid Percussion Model, and Weight-Drop Model), major neurological injuries related to these models and their relationship to clinical pathophysiology. We show how BIKO provides a machine-readable way to represent the methodologies used in TBI therapeutic studies to compare models across clinically relevant features.

bioinformatics↗

bioRxiv2023

AtOM, an ontology model for standardizing use of brain atlases in tools, workflows, and data infrastructures

Brain atlases are important reference resources for accurate anatomical description of neuroscience data. Open access, three-dimensional atlases serve as spatial frameworks for integrating experimental data and defining regions-of-interest in analytic workflows. However, naming conventions, parcellation criteria, area definitions, and underlying mapping methodologies differ considerably between atlases and across atlas versions. This lack of standardization impedes use of atlases in analytic tools and registration of data to different atlases. To establish a machine-readable standard for representing brain atlases, we identified four fundamental atlas elements, defined their relations, and created an ontology model. Here we present our Atlas Ontology Model (AtOM) and exemplify its use by applying it to mouse, rat, and human brain atlases. We propose minimum requirements for FAIR atlases and discuss how AtOM may facilitate atlas interoperability and data integration. AtOM provides a standardized framework for communication and use of brain atlases to create, use, and refer to specific atlas elements and versions. We argue that AtOM will accelerate analysis, sharing, and reuse of neuroscience data.

neuroscience↗

bioRxiv2022

The BRAIN Initiative Cell Census Data Ecosystem: A User's Guide

Characterizing cellular diversity at different levels of biological organization across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also required to manipulate cell types in controlled ways, and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data generating centers, data archives and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain and demonstration of prototypes for human and non-human primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed, and to accessing and using the BICCN data and its extensive resources, including the BRAIN Cell Data Center (BCDC) which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted by the BICCN toward FAIR (Wilkinson et al. 2016a) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

bioinformatics↗

bioRxiv2021

Extending and using anatomical vocabularies in the Stimulating Peripheral Activity to Relieve Conditions (SPARC) project

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is a US National Institutes of Health-funded effort to improve our understanding of the neural circuitry of the autonomic nervous system in support of bioelectronic medicine. As part of this effort, the SPARC program is generating multi-species, multimodal data, models, simulations, and anatomical maps supported by a comprehensive knowledge base of autonomic circuitry. To facilitate the organization of and integration across multi-faceted SPARC data and models, SPARC is implementing the FAIR data principles to ensure that all SPARC products are findable, accessible, interoperable, and reusable. We are therefore annotating and describing all products with a common FAIR vocabulary. The SPARC Vocabulary is built from a set of community ontologies covering major domains relevant to SPARC, including anatomy, physiology, experimental techniques, and molecules. The SPARC Vocabulary is incorporated into tools researchers use to segment and annotate their data, facilitating the application of these ontologies for annotation of research data. However, since investigators perform deep annotations on experimental data, not all terms and relationships are available in community ontologies. We therefore implemented a term management and vocabulary extension pipeline where SPARC researchers may extend the SPARC Vocabulary using InterLex, an online vocabulary management system. To ensure the quality of contributed terms, we have set up a curated term request and review pipeline specifically for anatomical terms involving expert review. Accepted terms are added to the SPARC Vocabulary and, when appropriate, contributed back to community ontologies to enhance autonomic nervous system coverage. Here, we provide an overview of the SPARC Vocabulary, the infrastructure and process for implementing the term management and review pipeline. In an analysis of > 300 anatomical contributed terms, the majority represented composite terms that necessitated combining terms within and across existing ontologies. Although these terms are not good candidates for community ontologies, they can be linked to structures contained within these ontologies. We conclude that the term request pipeline serves as a useful adjunct to community ontologies for annotating experimental data and increases the FAIRness of SPARC data.

bioinformatics↗

bioRxiv2021

The SPARC DRC: Building a resource for the autonomic nervous system community

The Data and Resource Center (DRC) of the NIH-funded SPARC program is developing databases, connectivity maps and simulation tools for the mammalian autonomic nervous system. The experimental data and mathematical models supplied to the DRC by the SPARC consortium are curated, annotated and semantically linked via a single knowledgebase. A data portal has been developed that allows discovery of data and models both via semantic search and via an interface that includes Google Map-like 2D flatmaps for displaying connectivity, and 3D anatomical organ scaffolds that provide a common coordinate framework for cross-species comparisons. We discuss examples that illustrate the data pipeline, which includes data upload, curation, segmentation (for image data), registration against the flatmaps and scaffolds, and finally display via the web portal, including the link to freely available online computational facilities that will enable neuromodulation hypotheses to be investigated by the autonomic neuroscience community and device manufacturers.

neuroscience↗

bioRxiv2021

Open Data Commons for Preclinical Traumatic Brain Injury Research: Empowering Data Sharing and Big Data Analytics

Traumatic brain injury (TBI) is a major unsolved public health problem worldwide with considerable preclinical research dedicated to recapitulating clinical TBI, deciphering the underlying pathophysiology, and developing therapeutics. However, the heterogeneity of clinical TBI and correspondingly in preclinical studies have made translation from bench to bedside difficult. Here, we present the potential of data sharing, data aggregation, and multivariate analytics to integrate heterogeneity and empower researchers. We introduce the Open Data Commons for Traumatic Brain Injury (ODC-TBI.org) as a user-centered web platform and cloudbased repository focused on preclinical TBI research that enables data citation with persistent identifiers, promotes data element harmonization, and follows FAIR data sharing principles. Importantly, the ODC-TBI implements data sharing at the level of individual subjects, thus enabling data reuse for granular big data analytics and data-hungry machine learning approaches. We provide use cases applying descriptive analytics and unsupervised machine learning on pooled ODC-TBI data. Descriptive statistics included subject-level data for 11 published papers (N = 1250 subjects) representing six distinct TBI models across mice and rats (implementing controlled cortical impact, closed head injury, fluid percussion injury, and CHIMERA TBI modalities). We performed principal component analysis (PCA) on cohorts of animals combined through the ODC-TBI to identify persistent inflammatory patterns across different experimental designs. Our workflow ultimately improved the sensitivity of our analyses in uncovering patterns of pro- vs anti-inflammation and oxidative stress without the multiple testing problems of univariate analyses. As the practice of open data becomes increasingly required by the scientific community, ODC-TBI provides a foundation that creates new scientific opportunities for researchers and their work, facilitates multi-dataset and multidimensional analytics, and drives collaboration across molecular and computational biologists to bridge preclinical research to the clinic.

neuroscience↗

bioRxiv2021

SPARC Data Structure: Rationale and Design of a FAIR Standard for Biomedical Research Data

The NIH Common Funds Stimulating Peripheral Activity to Relieve Conditions (SPARC) initiative is a large-scale program that seeks to accelerate the development of therapeutic devices that modulate electrical activity in nerves to improve organ function. Integral to the SPARC program are the rich anatomical and functional datasets produced by investigators across the SPARC consortium that provide key details about organ-specific circuitry, including structural and functional connectivity, mapping of cell types and molecular profiling. These datasets are provided to the research community through an open data platform, the SPARC Portal. To ensure SPARC datasets are Findable, Accessible, Interoperable and Reusable (FAIR), they are all submitted to the SPARC portal following a standard scheme established by the SPARC Curation Team, called the SPARC Data Structure (SDS). Inspired by the Brain Imaging Data Structure (BIDS), the SDS has been designed to capture the large variety of data generated by SPARC investigators who are coming from all fields of biomedical research. Here we present the rationale and design of the SDS, including a description of the SPARC curation process and the automated tools for complying with the SDS, including the SDS validator and Software to Organize Data Automatically (SODA) for SPARC. The objective is to provide detailed guidelines for anyone desiring to comply with the SDS. Since the SDS are suitable for any type of biomedical research data, it can be adopted by any group desiring to follow the FAIR data principles for managing their data, even outside of the SPARC consortium. Finally, this manuscript provides a foundational framework that can be used by any organization desiring to either adapt the SDS to suit the specific needs of their data or simply desiring to design their own FAIR data sharing scheme from scratch.

bioinformatics↗

bioRxiv2021

Alignment of biomedical data repositories with open, FAIR, citable and trustworthy principles

Increasing attention is being paid to the operation of biomedical data repositories in light of efforts to improve how scientific data is handled and made available for the long term. Multiple groups have produced recommendations for functions that biomedical repositories should support, with many using requirements of the FAIR data principles as guidelines. However, FAIR is but one set of principles that has arisen out of the open science community. They are joined by principles governing open science, data citation and trustworthiness, all of which are important aspects for biomedical data repositories to support. Together, these define a framework for data repositories that we call OFCT: Open, FAIR, Citable and Trustworthy. Here we developed an instrument using the open source PolicyModels toolkit that attempts to operationalize key aspects of OFCT principles and piloted the instrument by evaluating eight biomedical community repositories listed by the NIDDK Information Network (dkNET.org). Repositories included both specialist repositories that focused on a particular data type or domain, in this case diabetes and metabolomics, and generalist repositories that accept all data types and domains. The goal of this work was both to obtain a sense of how much the design of current biomedical data repositories align with these principles and to augment the dkNET listing with additional information that may be important to investigators trying to choose a repository, e.g., does the repository fully support data citation? The evaluation was performed from March to November 2020 through inspection of documentation and interaction with the sites by the authors. Overall, although there was little explicit acknowledgement of any of the OFCT principles in our sample, the majority of repositories provided at least some support for their tenets.

scientific communication and education↗

bioRxiv2020

The Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types

The challenge of defining and cataloging the building blocks of the brain requires a standardized approach to naming neurons and organizing knowledge about their properties. The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, and others are generating vast amounts of data and characterizing large numbers of neurons throughout the nervous system. The neuroscientific literature contains many neuron names (e.g. parvalbumin-positive interneuron or layer 5 pyramidal cell) that are commonly used and generally accepted. However, it is often unclear how such common usage types relate to the many proposed evidence-based types that are based on the results of new techniques. Further, comparing different models across labs remains a significant challenge. Here, we propose an interoperable knowledge representation, the Neuron Phenotype Ontology (NPO) that provides a standardized and machine computable approach for naming and normalizing phenotypes in cell types by using community ontology identifiers as a common language. The NPO provides a framework for systematically organizing knowledge about cellular properties and enables interoperability with existing neuron naming schemes. We evaluate the NPO by populating a knowledge base with three independent cortical neuron classifications derived from published data sets that describe neurons according to molecular, morphological, electrophysiological and synaptic properties. Competency queries to this knowledge base demonstrate that this knowledge model enables interoperability between the three test cases and common usage neuron names from the literature.

neuroscience↗

bioRxiv2020

Rigor and Transparency Index, a new metric of quality for assessing biological and medical science methods.

The reproducibility crisis in science is a multifaceted problem involving practices and incentives, both in the laboratory and in publication. Fortunately, some of the root causes are known and can be addressed by scientists and authors alike. After careful consideration of the available literature, the National Institutes of Health identified several key problems with the way that scientists conduct and report their research and introduced guidelines to improve the rigor and reproducibility of pre-clinical studies. Many journals have implemented policies addressing these same criteria. We currently have, however, no comprehensive data on how these guidelines are impacting the reporting of research. Using SciScore, an automated tool developed to review the methods sections of manuscripts for the presence of criteria associated with the NIH and other reporting guidelines, e.g., ARRIVE, RRIDs, we have analyzed ~1.6 million PubMed Central papers to determine the degree to which articles were addressing these criteria. The tool scores each paper on a ten point scale identifying sentences that are associated with compliance with criteria associated with increased rigor (5 pts) and those associated with key resource identification and authentication (5 pts). From these data, we have built the Rigor and Transparency Index, which is the average score for analyzed papers in a particular journal. Our analyses show that the average score over all journals has increased since 1997, but remains below five, indicating that less than half of the rigor and reproducibility criteria are routinely addressed by authors. To analyze the data further, we examined the prevalence of individual criteria across the literature, e.g., the reporting of a subject’s sex (21-37% of studies between 1997 and 2019), the inclusion of sample size calculations (2-10%), whether the study addressed blinding (3-9%), or the identifiability of key biological resources such as antibodies (11-43%), transgenic organisms (14-22%), and cell lines (33-39%). The greatest increase in prevalence for rigor criteria was seen in the use of randomization of subjects (10-30%), while software tool identifiability improved the most among key resource types (42-87%). We further analyzed individual journals over time that had implemented specific author guidelines covering rigor criteria, and found that in some journals, they had a big impact, whereas in others they did not. We speculate that unless they are enforced, author guidelines alone do little to improve the number of criteria addressed by authors. Our Rigor and Transparency Index did not correlate with the impact factors of journals.View Full Text

scientific communication and education↗