Section 1 of 1
Relative importance of data metrics
To inform prioritization for metrics development that will be useful across repositories. Don't worry about how hard it is to get, tell us how critical would the metric be for calculating benefits (returns) or costs (investments) or in some cases, both. These may still need some word-smithing to refine. For questions about the survey, contact cynthia.parr@ars.usda.gov.
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How important do you feel this metric is to calculation of ROI?
How important do you feel this metric is to calculation of ROI?
Question Type
Short answer
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Linear scale
Rating
Multiple choice grid
Checkbox grid
Date
Time
Not valuable
Somewhat valuable
Valuable
Very valuable
Metric is too unclear for me to answer
Not applicable to my repository
# of users accessing the data (# unique IP?) page where they could download or link
# of users finding the data
# of users identifying the applicability of the data (hits on page with detailed metadata)
# of users who can use the (visualization, e.g.) services for the data
# of users who use mediation services / staff
# of actual users of repo (unique IP on any page, minus crawlers)
Long term (number of years) Time coverage/period of data disseminated (% of datasets that have this info)
Metadata expressing Data Quality of data disseminated
Describing Standard collection methods/potential for synthesis of data
% conforming to standardized metadata (recognized specs)
Appraise dataset for uniqueness (data cannot be re-collected)
Acceptance rate for metadata
Acceptance rate for data
% with complete metadata
# citations per data repository
# of cases where datasets were important to a study
# of unique journals citing dataset from data repository
# mentions of dataset in papers
$ data storage costs (benefit to submitters, cost to repo)
Enabling transparency of research (FOI avoidance?) Fraud mitigation Money spent on avoiding fraud
# data submitters
# students or staff trained (workforce development) direct result
# of policy or management decisions impacted
# of curricula (or students reached by curricula) using dataset
alternative metrics (e.g. wikipedia pages, blogs) (towards impact on general public)
Cost of metadata creation (levels: discovery, citation, data quality, machine readable)
Time spent on applying acceptance critera
Time on Policy development
# of staff positions
Preservation/infrastructure cost
# page views
Cost of providing user support
Hardware costs
Licensing costs
Energy costs
Network costs
Travel costs
Workshop costs
Marketing costs (Engages in promotional activities)
Cost of supplies (Identifies supplies purchased?)
Ongoing software development/improvement costs
Cost in enabling efficiency of discovery and access (development of ontologies, e.g.)
Cost of enabling future discoverability and access
Cost of measuring customer satisfaction with repository
# proposals generated (impact on science/innovation)
some measure of new research inspired (stimulated) moving to data-driven
Cost of fundraising
Cost to collection development (staff time looking for great data)
Rows
1.
# of users accessing the data (# unique IP?) page where they could download or link
2.
# of users finding the data
3.
# of users identifying the applicability of the data (hits on page with detailed metadata)
4.
# of users who can use the (visualization, e.g.) services for the data
5.
# of users who use mediation services / staff
6.
# of actual users of repo (unique IP on any page, minus crawlers)
7.
Long term (number of years) Time coverage/period of data disseminated (% of datasets that have this info)
8.
Metadata expressing Data Quality of data disseminated
9.
Describing Standard collection methods/potential for synthesis of data
10.
% conforming to standardized metadata (recognized specs)
11.
Appraise dataset for uniqueness (data cannot be re-collected)
12.
Acceptance rate for metadata
13.
Acceptance rate for data
14.
% with complete metadata
15.
# citations per data repository
16.
# of cases where datasets were important to a study
17.
# of unique journals citing dataset from data repository
18.
# mentions of dataset in papers
19.
$ data storage costs (benefit to submitters, cost to repo)
20.
Enabling transparency of research (FOI avoidance?) Fraud mitigation Money spent on avoiding fraud
21.
# data submitters
22.
# students or staff trained (workforce development) direct result
23.
# of policy or management decisions impacted
24.
# of curricula (or students reached by curricula) using dataset
25.
alternative metrics (e.g. wikipedia pages, blogs) (towards impact on general public)
26.
Cost of metadata creation (levels: discovery, citation, data quality, machine readable)
27.
Time spent on applying acceptance critera
28.
Time on Policy development
29.
# of staff positions
30.
Preservation/infrastructure cost
31.
# page views
32.
Cost of providing user support
33.
Hardware costs
34.
Licensing costs
35.
Energy costs
36.
Network costs
37.
Travel costs
38.
Workshop costs
39.
Marketing costs (Engages in promotional activities)
40.
Cost of supplies (Identifies supplies purchased?)
41.
Ongoing software development/improvement costs
42.
Cost in enabling efficiency of discovery and access (development of ontologies, e.g.)
43.
Cost of enabling future discoverability and access
44.
Cost of measuring customer satisfaction with repository
45.
# proposals generated (impact on science/innovation)
46.
some measure of new research inspired (stimulated) moving to data-driven
47.
Cost of fundraising
48.
Cost to collection development (staff time looking for great data)
49.
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Not valuable
Somewhat valuable
Valuable
Very valuable
Metric is too unclear for me to answer
Not applicable to my repository
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