[{"innerBlocks":[],"name":"core\/freeform","postId":2076,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"The National Institutes of Health is facing significant challenges when it comes to accessing, managing, analyzing, integrating and making use of the huge amounts of data generated by the biomedical research community.

To get a better handle on the data produced by researchers, NIH has developed its first strategic plan<\/a> to help the agency keep pace with the rapid changes in biomedical data science.

\u201cOur nation and the world stand at a unique moment of opportunity in biomedical research, and data science is an integral contributor,\u201d states the plan. \u201cUnderstanding basic biological mechanisms through NIH-funded research depends upon vast amounts of data and has propelled biomedicine into the sphere of \u2018Big Data\u2019 along with other sectors of the national and global economies.\u201d

In particular, NIH\u2019s data science plan seeks to <\/b>address the following challenges:
Growing costs of managing data diminish the agency\u2019s ability to enable scientists to generate data for understanding biology and improving health.
<\/li>
The current data-resource ecosystem is siloed and is not optimally integrated or interconnected.
<\/li>
Datasets exist in many different formats and are often not easily shared, findable or interoperable.
<\/li>
Currently, no general system exists to transform or harden innovative algorithms and tools created by academic scientists into enterprise-ready resources that meet industry standards of ease of use and efficiency of operation.
<\/li><\/ul>\u201cBecause of these issues, NIH has adopted a unified vision, and a corporate strategy for attaining that vision, that will best serve the biomedical research enterprise in the coming decades,\u201d states the plan. \u201cThis document, the NIH Strategic Plan for Data Science describes NIH\u2019s overarching goals, strategic objectives, and implementation tactics for modernizing the NIH-funded biomedical data-resource ecosystem.\u201d

Also See<\/b>: NIH launches \u2018Data Commons\u2019 program to aid researchers<\/a>

Specifically, the plan has five overarching goals:
Create a highly efficient and effective biomedical research data infrastructure.<\/li><\/ul>
Promote modernization of the data-resources ecosystem.<\/li><\/ul>
Support the development and dissemination of advanced data management, analytics and visualization tools.<\/li><\/ul>
Enhance workforce development for biomedical data science.<\/li><\/ul>
Enact appropriate policies to promote stewardship and sustainability.<\/li><\/ul>
To achieve these goals, NIH plans to hire a chief data strategist to guide the development and implementation of the agency\u2019s data science activities and to serve as the lead for implementing the strategic plan.","saveContent":"The National Institutes of Health is facing significant challenges when it comes to accessing, managing, analyzing, integrating and making use of the huge amounts of data generated by the biomedical research community.

To get a better handle on the data produced by researchers, NIH has developed its first strategic plan<\/a> to help the agency keep pace with the rapid changes in biomedical data science.

\u201cOur nation and the world stand at a unique moment of opportunity in biomedical research, and data science is an integral contributor,\u201d states the plan. \u201cUnderstanding basic biological mechanisms through NIH-funded research depends upon vast amounts of data and has propelled biomedicine into the sphere of \u2018Big Data\u2019 along with other sectors of the national and global economies.\u201d

In particular, NIH\u2019s data science plan seeks to <\/b>address the following challenges:
Growing costs of managing data diminish the agency\u2019s ability to enable scientists to generate data for understanding biology and improving health.
<\/li>
The current data-resource ecosystem is siloed and is not optimally integrated or interconnected.
<\/li>
Datasets exist in many different formats and are often not easily shared, findable or interoperable.
<\/li>
Currently, no general system exists to transform or harden innovative algorithms and tools created by academic scientists into enterprise-ready resources that meet industry standards of ease of use and efficiency of operation.
<\/li><\/ul>\u201cBecause of these issues, NIH has adopted a unified vision, and a corporate strategy for attaining that vision, that will best serve the biomedical research enterprise in the coming decades,\u201d states the plan. \u201cThis document, the NIH Strategic Plan for Data Science describes NIH\u2019s overarching goals, strategic objectives, and implementation tactics for modernizing the NIH-funded biomedical data-resource ecosystem.\u201d

Also See<\/b>: NIH launches \u2018Data Commons\u2019 program to aid researchers<\/a>

Specifically, the plan has five overarching goals:
Create a highly efficient and effective biomedical research data infrastructure.<\/li><\/ul>
Promote modernization of the data-resources ecosystem.<\/li><\/ul>
Support the development and dissemination of advanced data management, analytics and visualization tools.<\/li><\/ul>
Enhance workforce development for biomedical data science.<\/li><\/ul>
Enact appropriate policies to promote stewardship and sustainability.<\/li><\/ul>
To achieve these goals, NIH plans to hire a chief data strategist to guide the development and implementation of the agency\u2019s data science activities and to serve as the lead for implementing the strategic plan.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

NIH strategic plan seeks to keep pace with changes in data science

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