Some more formatting and fixes.

index.qmd

@@ -1,38 +1,14 @@
-# Abstract
-
-Recent progress in machine learning and artificial intelligence promises to
-advance research and understanding across a wide range of fields and
-activities. In tandem, increased awareness of the importance of open data for
-reproducibility and scientific transparency is making inroads in fields that
-have not traditionally produced large publicly available datasets. Data sharing
-requirements from publishers and funders, as well as from other stakeholders,
-have also created pressure to make datasets with research and/or public
-interest value available through digital repositories. However, to make the
-best use of existing data, and facilitate the creation of useful future
-datasets, robust, interoperable and usable standards need to evolve and adapt
-over time. The open-source development model provides significant potential
-benefits to the process of standard creation and adaptation. In particular, the
-development and adaptation of standards can use long-standing socio-technical
-processes that have been key to managing the development of software, and allow
-incorporating broad community input into the formulation of these standards. By
-adhering to open-source standards to formal descriptions (e.g., by implementing
-schemata for standard specification, and/or by implementing automated standard
-validation), processes such as automated testing and continuous integration,
-which have been important in the development of open-source software, can be
-adopted in defining data and metadata standards as well. Similarly, open-source
-governance provides a range of stakeholders a voice in the development of
-standards, potentially enabling use cases and concerns that would not be taken
-into account in a top-down model of standards development. On the other hand,
-open-source models carry unique risks that need to be incorporated into the
-process.
-
-
 {{< include sections/01-introduction.qmd >}}
 {{< include sections/02-use-cases.qmd >}}
 {{< include sections/03-challenges.qmd >}}
 {{< include sections/04-cross-sector.qmd >}}
 {{< include sections/05-recommendations.qmd >}}
 {{< include sections/06-acknowledgments.qmd >}}
-{{< include sections/07-participants.qmd >}}
 
-# References
+# References
+:::{#refs}
+:::
+
+\newpage
+
+{{< include sections/07-participants.qmd >}}

sections/06-acknowledgments.qmd

@@ -3,7 +3,7 @@
 This report was produced following a
 [workshop held at NSF headquarters in Alexandria, VA on April 8th-9th, 2024](https://uwescience.github.io/2024-open-source-standards-workshop/).
 We would like to thank the speakers and participants in this workshop for the
-time and thought that they put into the workshop. A list of workshop participants is provided as an appendix.
+time and thought that they put into the workshop. A list of workshop participants is provided as an appendix (@sec-appendix).
 
 The workshop and this report were funded through [NSF grant
 #2334483](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334483&HistoricalAwards=false)

sections/07-participants.qmd

@@ -1,5 +1,4 @@
-\newpage
-# Appendix: List of participants {.appendix}
+# Appendix: List of participants {#sec-appendix .appendix}
 
 | Name                      | Affiliation                          |
 |---------------------------|--------------------------------------|
@@ -27,7 +26,7 @@
 | Ray E. Habermann          | Metadata Game Changers               |
 | Raymond (Ray) Plante      | NIST                                 |
 | Robert Hanisch            | NIST                                 |
-| Saskia de Vries           | Allen Institute for AI               |
+| Saskia de Vries           | Allen Institute for Neural Dynamics  |
 | Steven Crawford           | NASA                                 |
 | Vani Mandava              | University of Washington             |
 | Yaroslav Halchenko        | Dartmouth University                 |