NOAA's National Ocean Service (NOS) is responsible for the health and safety of our nation's coastal and oceanic environment. The Information and Technology Branch within the NCCOS Business Management Division requires a Scientific Data Manager to steward existing and new scientific data, metadata, and other associated documentation for the Marine Spatial Ecology Division (MSE) Biogeography Branch. The position will fulfill immediate requirements for managing data as part of a Natural Resource Damage Assessment (NRDA) project for mesophotic and deep coral restoration in the Gulf of Mexico.
This position is for a Scientific Data Manager familiar with stewarding and cataloging environmental data, including hydrographic surveys, biological and physical observations, video and photographic data, geospatial data products (i.e., GIS layers), and model output. The successful applicant will coordinate with NCCOS scientists, data managers, software developers, and other federal offices to identify and use existing software and hardware tools for organizing, storing, sharing, and tracking datasets. Specific tasks may include the following:
• Support various elements of data policies and data sharing standards.
• Make recommendations for software, hardware, and data storage requirements.
• Stay abreast of new data management products, conducting research on emerging trends and developments that could improve the storage and sharing of datasets.
• With the NCCOS Scientific Data Management Team, create, publish, and maintain dataset-, service-, and project-level data packages (including Section 508-compliant documentation) via submission to NCEI (or other data repositories) for long-term preservation and public access for general users, and data web service packages (e.g., ERDDAP, MapServer, NOAA GeoPlatform) for enhanced public access targeted to specific communities of practice.
• Disseminate stewarded datasets and services via NCCOS websites.
Data Management Planning:
• Develop procedures to maintain a data storage organization and cataloging system to share and track bathymetric surveys, seafloor imagery, groundtruthing videos, and related mapping datasets.
• Write and maintain a data management plan, including the steps and mechanisms to manage, describe, analyze, store and share data, for future hydrographic surveys and other seafloor mapping and assessment surveys in support of mesophotic and deep coral restoration
• Maintain a data repository of analysis-ready datasets, developed from multiple data sources.
• Develop, implement, and maintain change control and testing processes for modifications to data.
• Execute audits periodically to ensure that data are being properly managed in the Cloud and On-Premise.
• Devise, coordinate, and conduct mass data-cleansing initiatives for the purpose of purging and eliminating corrupt, redundant or outdated information.
Assist with the development of visualization tools for mapping and habitat assessment data.
• Assist with the development of a video/photo library that can be shared intra/interagency; organize and maintain library; explore and provide recommendations for annotation software.
• Coordinate and develop data management plans and data standards with project partners.
• Coordinate project data storage and data sharing with project partners, including NESDIS NCEI, NOS OR&R, and NMFS SEFSC.
• Two or more years of professional experience with scientific data management beyond the requirements for academic training; experience with publishing datasets
• Knowledge of database structure and theory; attention to detail with outstanding organizational and time-management skills
• Knowledge of environmental data management, including policies and procedures for data collection, ship-to-shore data management, analytical data management, data cataloguing
• Experience or training in ArcGIS, geodatabase design, scientific video, seafloor imagery (e.g., MBES, sidescan, photo mosaics), hydrographic survey data (e.g., .all, .bag, .xtf), a variety of spatial data formats (e.g., NetCDF, HDF, etc.), Python, SQL, Metadata, R, are desirable.