Notice ID 75N95021R00053
“The Government is especially interested in determining (1) the availability of qualified small business sources; (2) whether they are small businesses; HUBZone small businesses; service-disabled, veteran-owned small businesses; 8(a) small businesses; veteran-owned small businesses; woman-owned small businesses; or small disadvantaged businesses; and (3) their size classification relative to the North American Industry Classification System (NAICS) code for the proposed acquisition. Your responses to the information requested will assist the Government in determining the appropriate acquisition method, including whether a set-aside is possible…”
“Purpose and Objectives: The National Center for Advancing Translational Sciences (NCATS) requires a commercial cloud-based data aggregation and analytics Platform as a Service (PaaS) environment that can integrate, manage, secure, and analyze any kind of scientific data, and provide secure, controlled access to internal and external collaborators.
Project requirements: The vendor must be able to provide its commercial software for enterprise data integration and mangaemtn to meet the following requirements:
- A commercial software solution deployable on day one of the project and that can be configured within expedited timelines. Respondents must possess FedRAMP authorization so that an agency ATO can be granted upon award of a contract.
- An open data architecture, where data always remains under the full control of NCATS and can be easily exported in open, non-proprietary data formats via open APIs. The software should be built on an open, distributed microservices architecture with open, well-documented REST APIs that are designed to seamlessly interface with other systems, adapt to meet evolving needs, and avoid system lock-in.
- Proven data integration capabilities, including the ability to rapidly ingest unprocessed high-throughput drug screening (HTS) outputs, genomic data (including DNA sequencing, RNA-Seq, miRNA, etc.), mass spectrometry, flow cytometry, and other data types used in basic and translational biosciences research.
- The ability to maintain data and scientific provenance and reproducibility of all integrated data sources where every resource (dataset, analysis, code, plot, report) contains provenance, metadata, and can be both traced back to the exact version of all upstream dependencies, and where the dependency tree can be easily replayed given new data or updated analysis logic, while still retaining prior versions and branches.
- Dynamic data model, object-based search/discoverability and analysis workflows, allowing easy definition of objects, properties, and links that propagate from a source table, and provide natural ways to move between tabular and object-oriented interfaces and data analyses.
- Intuitive, highly configurable user interfaces that have been effectively utilized by technical bioinformaticians, cheminformaticians, and data scientists, as well as less technical biologists, chemists, and other scientists.
- Ability to perform advanced analytics and informatics in a user’s preferred coding language, as well as in non-code-based point and click tools, all within the same environment.
- Collaboration capabilities enabling teams comprising of a range of technical and less technical roles to work seamlessly and concurrently on the same data, build on insights, merge similar analytical paths, and track progress in one place.
- Ability to scale flexibly with increasing users, data, and pipeline complexity, while providing fine-grained ways to adjust resource consumption. Proven ability to scale up to thousands of users (including thousands of potential outside collaborators globally), petabytes of raw and processed data, daily updates in the terabytes, and complex bioinformatic pipelines requiring processing components developed in a variety of languages and environments.
- Proven granular, security controls with the ability for data owners to easily control all downstream uses of the originating data, and the ability to conform to NCATS security policies. 11. Proven interoperability with NCATS current IT investment landscape, and includes omnipresent APIs and plugin points that allow the system to keep up with the changing needs of NCATS, and support both third-party software and other analytic applications. NCATS also requires the ability to independently develop new configurations, plugins, integrations, and extensions to meet new and unforeseen needs and interface with external systems.
Anticipated period of performance: The anticipated period of performance is one year…”