Merge pull request #1 from vanitech/day1updates

feat: Drafted a few more challenges with headings and brief descriptions

Author: arokem

_metadata.yml

@@ -6,5 +6,10 @@ authors:
     corresponding: true
     orcid: 0000-0003-0679-1985
 # Add your name and metadata here:
+  - name: Vani Mandava
+    affiliation: University of Washington
+    roles: writing
+    corresponding: true
+    orcid: 0000-0003-3592-9453
 
 bibliography: references.bib

sections/02-challenges.qmd

@@ -11,7 +11,7 @@ of a new standard [^1].
 been devoted to it.
 
 Another failure is the mismatch between developers of the standard and users.
-There is an iherent gap in both interest and ability to engage with the
+There is an inherent gap in both interest and ability to engage with the
 technical details undergirding standards and their development between the
 developers of the standard and their users. In extreme cases, these interests
 may be at odds, as developers implement sophisticated mechanisms to automate
@@ -20,6 +20,20 @@ mechanisms for evolving the standard, leaving potential users sidelined in the
 development of the standard, and limiting their ability to provide feedback
 about the practical implications of changes to the standards.
 
+## Unclear pathways for standards success
+
+Standards typically develop organically through sustained and persistent efforts from dedicated 
+groups of data practitioneers. These include scientists and the broader ecosystem of data curators and users. However there is no playbook on the structure and components of a data standard, or the pathway that moves a data implementation to a data standard. 
+As a result, data standardization lacks formal avenues for research grants. 
+
+## Cross domain funding gaps
+
+Data standardization investment is justified if the standard is generalizable beyond any specific science domain. However while the use cases are domain sciences based, data standardization is seen as a data infrastrucutre and not a science investment. Moreover due to how science research funding works, scientists lack incentives to work across domains, or work on infrastructure problems. 
+
+## Data instrumentation issues 
+
+Data for scientific observations are often generated by proprietary instrumentation due to commercialization or other profit driven incentives. There islack of regulatory oversight to adhere to available standards or evolve Significant data transformation is required to get data to a state that is amenable to standards, if available. If not available, there is lack of incentive to set aside investment or resources to invest in establishing data standards. 
+
 ## Sustainability
 
 ## The importance of automated validation