Scientific Computing and Data / High Performance Computing / Policies / Acknowledgements

Policies

Acknowledge Scientific Computing and Data and CTSA

 

An acknowledgement of support from the Icahn School of Medicine at Mount Sinai and the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences is required by NIH to appear in a publication of any material, whether copyrighted or not, based on or developed with Mount Sinai-supported computing resources, including Minerva, MSDW, and REDCap. A stipulation of the National Institutes of Health (NIH) S10 award includes an accurate acknowledgement statement in publications that utilized NIH-funded resources.

All users and PIs on Minerva are required to review and submit the annual NIH acknowlegement agreement form at https://forms.hpc.mssm.edu/.

  • Campus network needed, or sign in through school VPN if off-campus

  • After signing in, you will see the list of forms pending on your submission

Please use the following acknowledgement in all your publications.

 

If you have NIH-funded projects on Minerva, you MUST include the following acknowledgement in all your publications:

 

This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this publication was also supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

 

 

If you DO NOT have NIH-funded projects on Minerva, and/or you use our other services including MSDW and REDCap, you MUST include the following acknowledgement in all your publications:

 

This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences.

 

To associate the CTSA grant UL1TR004419 to an existing publication, please follow these instructions from the NIH (see the section “Associating Funding to your Publications”).