Form 1 2019 NCBI Cloud Feedback Survey

Generic Clearance for the Collection of Qualitative Feedback on Agency Service Delivery (NIH)

2019 NCBI Cloud Feedback Survey Instrument (revised)

2019 NCBI Cloud Services Feedback Survey (NLM)

OMB: 0925-0648

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NCBI 2019 CLOUD SERVICES FEEDBACK SURVEY

OMB Control Number: 0925-0648
Expiration Date: 05/31/2021

Public reporting burden for this collection of information is estimated to average 2 minutes per response, including the time for
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aspect of this collection of information, including suggestions for reducing this burden, to NIH, Project clearance Branch, 6705
Rockledge Drive, MSC 7974, Bethesda, MD 20892-7974, ATTN: PRA (0925-0648). Do not return the completed form to this address.

1. CHECK ALL THAT APPLY IN THE FOLLOWING STATEMENTS
I (and my team) currently:
¨ Work with non-human data in the cloud on the following platforms:
¨ AWS ¨ GCP ¨ Azure other __________
¨ Work with human data in the cloud on the following platforms:
¨ AWS ¨ GCP ¨ Azure other __________
¨ Plan to start working in the cloud within the next 12 months.
2. NCBI is planning additional cloud-based data services to complement the Sequence Read Archive. Rank your
likelihood to use the following data services in the next 12 months.
(1=MOST INTERESTED, DELIVER IT NEXT, 2= SECOND PREFERENCE, …. , 9=LEAST INTERESTING.)

_________

BLAST cloud service e.g. with an opportunity to build and search private custom
databases that include your local sequence data.

_________

SRA sequence search e.g. to find accessions similar to a query string perhaps 1+
kilobases in length.

_________

SRA metadata query e.g. using BigQuery or other cloud-native database query tools to
generate worklists of SRA accessions for downstream processing.

_________

GenBank / RefSeq e.g. quickly retrieving sequence data and annotations for large
datasets.

_________

dbSNP annotations e.g. to mark-up VCF genotype files containing genomic coordinates
and alleles with dbSNP functional and allele frequency information.

_________

ClinVar e.g. to filter human called variants into subsets according to their clinical
significance and validation properties.

_________

dbGaP genotype data e.g. as VCF file objects for use in cloud-based analysis workflows.

_________

dbGaP phenotype data e.g. queried as observations via FHIR service.

_________

Other (specify): __________________________________________________


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File TitleMicrosoft Word - 2019_NCBI_STRIDES_cloud_user_interest_survey.docx
File Modified2019-10-02
File Created2019-09-23

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