IMPACT Dashboard

The data presented here was collected about technology focused NIDILRR grants from 1983 through 2020. The data was collected from public online sources including the USPTO, Pubmed, and Google Scholar. Products were often found by navigating to a link provided by grantees. The ouputs included in the database are those that directly mention a NIDILRR grant number. Grantees were sent IMPACT reports which gave them the opportunity to add items or remove those which they did not want attributed to that grant. The data is updated on a yearly basis after newly awarded grants are reported by NIDILRR. For a more detailed look at what our database contains, click here.

If you have login access to the full NIDILRR dashboard, click here to view.

This chart shows the different types of outputs produced by NIDILRR grantees. They were only included in our dataset if they were provided directly by grantees or mentioned the NIDILRR grant number in the paper, patent or product descritpion.
These include peer reviewed papers from PubMed and Google Scholar that mention a NIDILRR grant.
These cateogories developed by the NIDILRR team were decided upon after reading the titles and abstracts of all of the grants. An advisory board then read each of the abstracts and titles and chose a category. Differences in sorting categories were disucssed amongst the board and the team and were settled.
These were the categories that NIDILRR assigned to the projects. Because we are focusing on Tech Transfer, most of the projects we considered fall into the same category of "Technology for Access and Function".
These are not normalized by funding amount. The grant categories were provided by NARIC.
The greater the number of patent citations indicates a greater patent economic impact. Patent citation counts were pulled from Google Scholar. Currently, these values are not normalized over time as patents increase in citation count rapidly when initially produced but then taper off.
This chart shows the average amount of outputs produced by each institution type and can be filtered to show one comparison to NIDILRR as a whole. This is not normalized by funding amount.
All products were available either through purchase or free download. Hardware products included primarily mechanical components. Software was entirely software based. Informational includes standards, lesson plans, building directions, safety instuctions, etc.
These include patents from the USPTO database that mention a NIDILRR grant in their funding acknowledgement.
This chart helps to highlight what categories of product are most likely to make it to market. In the future, these cateogires could be broken down further to indicate not only disability focus but what type of product is being developed. This would help to show if certain categories of product, such as software or hardware, are easier to transfer to market.
University type was found online according to the level of research activity and funding at the university. They are not normalized by funding and some groups received much more funding on average.
These are not normalized by funding amount but help highlight product transfer activities.
The greater the difference between the patent count and the count of unique patent classification codes indicates a greater lever of new technology. The smaller the different indicates a combination of existing products. Patent codes were pulled from the Google Patent Database.
This chart shows the average amount of outputs produced by each project focus group and can be filtered to show one comparison to NIDILRR as a whole. This is not normalized by funding amount.