Carl Kesselman

Integrative structure determination combines varied experimental information with computational modeling. We describe a programmatic workflow connecting the output of these calculations directly to deposition into the Protein Data Bank (PDB), a Worldwide Protein Data Bank Core Archive that now accommodates integrative or hybrid methods structures (PDB-IHM). The python-ihm package generates deposition-ready mmCIF files describing structure models, experimental restraints, modeling protocols, and their provenance. Subsequently, programmatic interfaces support pre-deposition validation, structure deposition, and processing status tracking. We illustrate the workflow using biostructure models derived from three-dimensional electron microscopy (3DEM) and crosslinking mass spectrometry (crosslinking-MS) information. We also demonstrate the workflow scalability through submission of a collection of crosslinking-MS structure models. Making structure validation and deposition machine-accessible in turn allows incorporation into high-throughput and AI-agent-driven structure-determination workflows. The workflow provides a reproducible and scalable pathway from integrative modeling to archival deposition.

The underlying premise behind eScience is that computational methods and data-driven approaches can contribute to scientific discovery on a par with, or even superior to, traditional experimental methods; that the combination of computers, software, and extant data collections are the modern equivalent to the scientific instruments that have led to our understanding of fundamental laws in physics, chemistry, biology, and other domains. However, a robust methodology for making the results of eScience activities “scientific” is lacking, with significant consequences. In this brief paper we propose a shift in perspective as to what it means to create an eScience-based result and how the scientific validity of eScience experiments might be improved.

The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables researchers to discover datasets from across the US National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. This system is centered on a catalog that integrates detailed descriptions of biomedical datasets from individual Common Fund Programs’ Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by individual DCCs and can readily describe nearly all forms of biomedical research data. We detail its use to ingest and index data from 11 DCCs.

Scientific databases used for organizing, archiving, collaborating and sharing research data depend on a well-defined schema to accurately reflect the scientific domain and on database-driven applications for supporting key user interactions with the database. Applications that interact with a database typically depend on some form of schema mappings, such as object-relational mappings, to inform the application of how to query and manipulate the database. The presence of schema mappings, however, further exacerbates the already difficult task of evolving the database schema. Database migration utilities provide some help by coordinating schema evolution scripts with application code changes, but only automate the simplest schema mapping changes. In this paper, we present an approach to coupled database-application evolution by extending a database evolution language with model management operations. We introduce a novel set of model management operations and define their semantics and then describe how they may be integrated into schema modification operators. We then present an evaluation of the concepts from real-world usage of model mappings in scientific database deployments.

The ability to share data is critical to reproducible research, yet data sharing is often limited because issues of findability, accessibility, interoperability, and reusability—the FAIR principles—are not integrated into every step of the scientific process.

The broad sharing of research data is widely viewed as critical for the speed, quality, accessibility, and integrity of science. Despite increasing efforts to encourage data sharing, both the quality of shared data and the frequency of data reuse remain stubbornly low. We argue here that a significant reason for this unfortunate state of affairs is that the organization of research results in the Findable, Accessible, Interoperable, and Reusable (FAIR) form required for reuse is too often deferred to the end of a research project when preparing publications—by which time essential details are no longer accessible. Thus, we propose an approach to research informatics in which FAIR principles are applied continuously, from the inception of a research project and ubiquitously, to every data asset produced by experiment or computation. We suggest that this seemingly challenging task can be made feasible by the adoption of simple tools, such as lightweight identifiers (to ensure that every data asset is findable), packaging methods (to facilitate understanding of data contents), data access methods, and metadata organization and structuring tools (to support schema development and evolution). We use an example from experimental neuroscience to illustrate how these methods can work in practice.

Structures of many complex biological assemblies are increasingly determined using integrative approaches, in which data from multiple experimental methods are combined. A standalone system, called PDB-Dev, has been developed for archiving integrative structures and making them publicly available. Here, the data standards and software tools that support PDB-Dev are described along with the new and updated components of the PDB-Dev data-collection, processing and archiving infrastructure. Following the FAIR (Findable, Accessible, Interoperable and Reusable) principles, PDB-Dev ensures that the results of integrative structure determinations are freely accessible to everyone.