{"id":23416,"date":"2020-11-16T12:09:37","date_gmt":"2020-11-16T17:09:37","guid":{"rendered":"https:\/\/www.rc.fas.harvard.edu\/?page_id=23416"},"modified":"2026-02-13T10:47:49","modified_gmt":"2026-02-13T15:47:49","slug":"data-science-research-facilitation","status":"publish","type":"page","link":"https:\/\/www.rc.fas.harvard.edu\/services\/data-science-research-facilitation\/","title":{"rendered":"Research Facilitation"},"content":{"rendered":"\n

Description<\/b>\u00a0<\/span><\/h3>\n

A research consultation and facilitation service can enable a research team to perform research using an expanded set of methodologies and skills, to accelerate research and complete deliverables in a more timely manner, and to expand the scope and amount of research pursued.\u00a0 These goals can be achieved with help from a research computing staff via consultation, facilitation, scripting, project and data management, and training.\u00a0 A research team can often benefit from incremental help from just-in-time learning to expand their knowledge and skills, to augment their collective skill set, and\/or to accomplish analysis, coding, and organizational tasks.\u00a0<\/span><\/p>\n

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Key Features and Benefits <\/b>\u00a0<\/span><\/h3>\n

There are different things that a research consulting\/facilitation team can help with a research project, with examples of those listed below.\u00a0<\/span><\/p>\n

Definitions:<\/b>\u00a0<\/span><\/h4>\n

Research Planning <\/b>is a strategy about the deployment and use of researchers, resources, tools, methodologies, and collaborations to achieve a proposed research goal.\u00a0 Research planning can be used as the basis for a grant application.\u00a0 Research planning is an opportunity to plan for the research data management lifecycle.\u00a0<\/span><\/p>\n

Data management <\/b>is the management of data as a valuable resource for a research project\/program.\u00a0 This involves consideration for aspects of the data including ownership, governance, architecture, storage, cleaning, security, discoverability, sharing, and archiving.\u00a0 All of these aspects are part of the stewardship of data throughout its research data management lifecycle.\u00a0<\/span><\/p>\n

Data processing <\/b>is the collection and manipulation of data to produce meaningful information.\u00a0 Types of data processing include validation, sorting, summarization, aggregation, analysis, reporting, and classification.\u00a0<\/span><\/p>\n

Data cleaning <\/b>is the process of detecting and correcting (or removing) corrupt or inaccurate records from a set of data.\u00a0<\/span><\/p>\n

Research workflow organization <\/b>can<\/span> involve a number of tasks, including organization of computer code within a program, structure of code within a file hierarchy, the use of a development environment to develop, compile and execute code, and the storage and versioning of code in a code management tool such as github.\u00a0<\/span><\/p>\n

Research data visualization <\/b>is the graphic representation of data that involves producing images that communicate relationships among the represented data to viewers of the images.\u00a0<\/span><\/p>\n

High performance computing <\/b>is the application of cutting-edge techniques, technologies, and systems to leverage large sets of resources to solve or approximate the solutions for complex, compute- and data-intensive problems.\u00a0 These solutions leverage optimized (often parallel) software and hardware for computation, networking, and data movement (I\/O).\u00a0<\/span><\/p>\n

Training: <\/b>Sessions to facilitate the researcher in how to leverage the most significant features of a software package, language, or system.\u00a0 The training is often focused on a particular computational or data paradigm.\u00a0\u00a0\u00a0<\/span><\/p>\n