Merge pull request #139 from MERLCenter/bjk127-patch-2

Update 2021-09-30-data-maturity-open-source.md

_guides/2021-09-30-data-maturity-open-source.md

@@ -72,7 +72,7 @@ Delivering value out of data requires an OSS tool that can magically offer the k
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 | Do staff demonstrate skills in data literacy and general analytics-related tasks? | Many OSS tools are accompanied with relevant documentation and even demos. These materials not only demonstrate the value of the particular tool, but can also serve to de-risk its adoption within the organization. | Manuals like those found in [R’s CRAN projects](https://cran.r-project.org/manuals.html) and [Python’s packages](https://www.python.org/doc/) give rigorous documentation for packages that support their applications. |
 | Are there specific skilled-analytics (e.g., reporting, evaluation, data engineering, data science, etc.) roles within the organization? | Along the data supply chain (e.g., data acquisition, management, analysis, reporting, etc.), OSS can be used and even integrated with one another to ensure interoperability. Each individual and team sees and engages with data in different ways, as data flows from basic program activities toward higher levels of aggregation for reporting. | Popular statistical and analysis packages in R and Python support are interoperable with many other programming languages and platforms, including SQL, D3, Stan, C++, and more. Moreover, [Jupyter](https://jupyter.org/), which is a notebook style integrated development environment (IDE) can be easily shared across teams and even reproduced. |
-| Do staff have a general understanding of data and the ways it can be used to make decisions across the organization? | Tools are only as good as the person using them, so context and domain expertise remain paramount. However, OSS tools continue to publish publicly available documentation under the [Creative Commons](https://creativecommons.org/licenses/by-nc-nd/3.0/us/) license and guides to support data skill building! | There are numerous [bookdown](https://bookdown.org/) files that can be accessed publicly with an abundance of tutorials and specialized topics like [“Quantitative Methods for Policy Analysis”](https://bookdown.org/njenk001/qmppa/), [“Hands on Programming with R”](https://rstudio-education.github.io/hopr/), and many more! |
+| Do staff have a general understanding of data and the ways it can be used to make decisions across the organization? | Tools are only as good as the person using them, so context and domain expertise remain paramount. However, OSS tools continue to publish publicly available documentation under the [Creative Commons](https://creativecommons.org/licenses/by-nc-nd/3.0/us/) license and guides to support data skill building! | There are numerous [bookdown](https://bookdown.org/) files that can be accessed publicly with an abundance of tutorials and specialized topics like [“Hands on Programming with R”](https://rstudio-education.github.io/hopr/) and many more! |
 | Is data seen as a team effort, rather than just one person's responsibility? | To become a data literate organization, teams should regularly and fully explain to all staff how data and tools connect to the mission and show how data from one area/program/team is used by others. | OSS alternatives to [JIRA](https://www.atlassian.com/software/jira), like [OpenProject](https://www.openproject.org/blog/open-source-jira-alternative/), offer sophisticated issue tracking with custom fields and workflow customization. Teams can adopt these tools to develop processes that better communicate data uses across the organization. |
 
 ### Breadth of Data Use Cases