diff --git a/sections/xx-cross-sector.qmd b/sections/xx-cross-sector.qmd
index 0bfedc5..11b7626 100644
--- a/sections/xx-cross-sector.qmd
+++ b/sections/xx-cross-sector.qmd
@@ -52,19 +52,50 @@ executive director, Brian Nosek, entitled "Strategy for Culture Change"
 science requires an alignment of not only incentives and values, but also
 technical infrastructure and user experience. A sociotechnical bridge between
 these pieces, which makes the adoption of standards possible, and maybe even
-easy, and the policy goals, arises from a community of practice that makes
+easy, and the policy goals, arises from a community of practice that makes the
 adoption of standards *normative*. Once all of these pieces are in place,
 making adoption of open science standards *required* through policy becomes
 more straightforward and less onerous.
 
 ## Funding
 
-While government-set policy is primarily directed towards research that is
-funded through governmental funding agencies, there are other ways in which
-funding relates to the development of open-source standards. One way is in
-funding the development of these standards. For example, the National
-Institutes of Health have provided some of the funding for the development of
-the Brain Imaging Data Structure standard in neuroscience. Where large governmental funding agencies may not have
+Government-set policy intersects with funding considerations. This is because
+it is primarily directed towards research that is funded through governmental
+funding agencies. For example, high-level policy guidance boils to practice in
+guidance for data management plans that are part of funded research. In
+response to the policy guidance, these have become increasingly more detailed
+and, for example, NSF- and NIH-funded researchers are now required to both
+formulate their plans with more clarity and increasingly also to share data
+using specified standards as a condition for funding.
+
+However, there are other ways in which funding relates to the development of
+open-source standards. For example, through the BRAIN Initiative, the National
+Institutes of Health have provided key funding for the development of the Brain
+Imaging Data Structure standard in neuroscience. Where large governmental
+funding agencies may not have the resources or agility required to fund nascent
+or unconventional ways of formulating standards, funding by non-governmental
+philanthropies and other organizations can provide alternatives. One example
+(out of many) is the Chan-Zuckerberg Initiative program for Essential Open
+Source Software, which funds foundational open-source software projects that
+have an application in biomedical sciences. Distinct from NIH funding, however,
+some of this investment focuses on the development of OSS practices. For
+example, funding to the Arrow project that focuses on developing open-source
+software maintenance skills and practices, rather than a specific biomedical
+application.
+
+
+## Industry
+
+Interactions of data and meta-data standards with commercial interests may
+provide specific sources of friction. This is because proprietary/closed
+formats of data can create difficulty at various transition points: from one
+instrument vendor to another, from data producer to downstream recipient/user,
+etc. On the other hand, in some cases cross-sector collaborations with
+commercial entities may pave the way to robust and useful standards. One
+example is the DICOM standard, which is maintained by working groups that
+encompass commercial imaging device vendors and researchers.
+
+