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Update data standards and training documentation #2

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6 changes: 3 additions & 3 deletions data/index.qmd
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Expand Up @@ -10,9 +10,9 @@ LinkedEarth develops data standards and curates compilations for paleoclimate in

LinkedEarth got started because of how impractical it was (back in 2015 or so) to work with paleoclimate data. Since then, we have created comprehensive data standards. These standards comprise three key elements:

- **Data Representation**: The [LiPD format](https://lipd.net) and its associated [LinkedEarth Ontology](http://linked.earth/ontology/#) provide structural organization for paleoclimate datasets.
- **Data Representation**: The [LiPD format](https://lipd.net) and its associated [LinkedEarth Ontology](http://linked.earth/ontology/#) provide structural organization for paleoclimate datasets. LiPD is a flexible, metadata-rich open access data format designed for paleoclimate and other paleogeoscientific datasets. We have developed packages in [python](https://joss.theoj.org/papers/10.21105/joss.08861) and [R](https://nickmckay.org/lipdR/) to make it easy to interact with LiPD data in those programming languages.

- **Standard Vocabulary**: NOAA developed the [PaST Thesaurus](https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/past-thesaurus) to establish consistent terminology across the field. ADD Lipdverse vocabs
- **Standard Vocabulary**: NOAA developed the [PaST Thesaurus](https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/past-thesaurus) to establish consistent terminology across the field. For datasets on the [LiPDverse](https://lipdverse.org), we have aligned key components of the LiPD vocabulary with the PaST Thesaurus. You can browse the [LiPDverse vocabulary here](https://lipdverse.org/vocabulary/)

- **Reporting Guidelines**: The [PaCTS standard](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019PA003632) specifies crowdsourced guidelines for reporting paleoclimate data. This collaborative effort involved over 135 international researchers and received [recognition from the American Geophysical Union](https://eos.org/research-spotlights/standardizing-the-surge-of-paleoclimate-data), at its [2019 annual meeting plenary session](https://www.youtube.com/watch?v=cjkSGnlNwX0&list=PL7Ihm2Mh3MZ5ff8PVBZ3dBLYQdwZLQlEY&index=33).

Expand All @@ -21,7 +21,7 @@ One of the major hurdles to standardize paleoclimate datasets is getting them in

### Data Compilations

[LiPDverse](https://lipdverse.org) serves as a repository housing datasets formatted according to LiPD standards. The collection features datasets in the LiPD format, which are also available through a [graph interface](https://linkedearth.graphdb.mint.isi.edu). Much of the content originated from PAGES collaborations with community-vetted metadata. The platform enables straightforward querying and visualization capabilities for paleoclimate information.
[LiPDverse](https://lipdverse.org) serves as a repository housing datasets formatted according to LiPD standards. The collection features more than 7000 datasets in the LiPD format, which are also available through a [graph interface](https://linkedearth.graphdb.mint.isi.edu). Most of these datasets have were curated as part of [international compilation efforts](https://lipdverse.org/project/), including many PAGES working groups, and have community-vetted metadata. The platform enables straightforward data access and visualization capabilities for paleoclimate data.

### How to Contribute Data

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4 changes: 3 additions & 1 deletion training/index.qmd
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Expand Up @@ -52,8 +52,10 @@ In addition to the workshops, we have online tutorials in the form of Jupyter No

##### R

* The [GeoChronR](https://nickmckay.org/GeoChronR/articles/Introduction.html) vignettes provide examples on working with the software package.
* The [geoChronR](https://nickmckay.org/GeoChronR/articles/Introduction.html) vignettes provide examples on working with the software package.
* The [Time-uncertain data analysis in R](https://linked.earth/time-uncertain-data-analysis-in-R/) book describes the theory and practice of time-uncertain data analysis in R, largely relying on the geoChronR package.
* The [actR](https://linked.earth/actR/) package has instructions and vignettes for working with this package.
* The [compositeR](https://nickmckay.org/compositeR/) package, which is still in development and is rough around the edges, also has a few examples for its use.

#### Video tutorials

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