 Okay, good. So I'm based on the land of the Ngunnawal Nation here in Canberra. And so I too would like to acknowledge the traditional custodians of the land and pay my respects to the elders of the Ngunnawal Nation past and present. Okay, so this presentation relates to a project which ended a few months ago and which was supported by the Atlas of Living Australia, Terrestrial Ecosystem Research Network, TURN. I'm also the Integrated Marine Observing System, ARDC and representatives from the federal government departments at various times of agriculture and environment as the portfolio moved. And the project related to improving the accessibility of data from these research infrastructures to support state of environment reporting. The particular focus was the state of environment report produced in 2021. But behind this, we were hoping also to make the data more accessible for other assessment activities including state and territory SOE reports and future iterations of the five yearly SOE process. And following discussions with the SOE authors, we identified two broad areas where they felt they needed better access to the data that were being collected by the ALA, TURN and IMOS. Specifically improved and more faceted access to integrated species distribution data. And I'm not gonna be talking so much about that today but in practical terms, this was relating to ensuring that it was easy for any of the main portions of the continent to be able to get time-based lists of how many threatened species have been recorded in that area or how many introduced species, et cetera. The second broad area was to understand just where researchers were going out into the field or into the surrounding waters and collecting environmental data, looking at the gaps and the coverage, et cetera across the Australian region. So for this second broad area, the plan was to make it easy to get summaries by state or territory or by a geographic region of the kinds of activities that have gone on in those areas, either to survey biodiversity in a reasonably standardized way, standard vegetation surveys, bird surveys, ongoing fish monitoring systems, et cetera. And also all of the environmental and climatic and landscape measurements that have been collected by TURN and IMOS at many sites and around the country. And to be able to provide these as a faceted data set that would make it easier to understand just how much activity was going on in each area. So we've developed pipelines in our, Sandia did this, for processing the data for both of these assets. I'm gonna ignore the top part, which is the species occurrence part and just highlight that for the aggregated data component here the environmental monitoring and observations effort data set which we produced as a faceted CSV file. We're taking streams of metadata from the TURN sites, the IMOS data sets in AODN and summary from the ALA of sample event based data to produce this time, space and category faceted data set. And then from that, we derived some more summary data sets that came closer to answering the kinds of questions that the researchers had. And in order to support this aggregated data set and provide the structure for the facet also in the more derived data sets, we wanted to have a reasonably understandable and not too expansive vocabulary of the earth science features that were being measured by each data set each source cited through this path. And I think I've got two slides on this so I'll skip to this one. Part of the challenge here is that obviously IMOS, TURN and ALA are using different approaches to these things. For TURN, it was primarily organized around their feature vocabulary which is a nice representation of the things that get measured at TURN sites. For IMOS, the keywords that we were able to use were basically global change master directory terms from the metadata associated with the AODN data sets. And for the ALA, we were using the structure of the national species lists. And these overlap in certain ways but also represented some challenges for integration. In particular, if you look at the land features and the ocean features on this slide, the ocean features really from GCMD is a series of categories, primarily of the measurement categories that are collected within marine data collection. Whereas the TURN way of breaking up the land side of this was much more organized around the features themselves that were being observed rather than primarily considering the measurement categories. And similarly, the organization of taxonomic groups was very different across the three. So we took a decision to use GCMD as the fundamental vocabulary but to augment it to make sure that the TURN feature terms were properly mapped. And also to substitute the biological classification from GCMD for the one that's for the Australian national species lists because they represented a more logical way to organize biodiversity data across both marine and terrestrial areas. In particular, categories such as invertebrates appear in the biological classification and don't easily map to anything that corresponds to the way that terrestrial observers might go out and carry out a reasonably consistent survey. We developed a vocabulary which is downloadable from the ECO Assets website that as seen here uses URIs from GCMD for the major categories but also has some major terms from the national species lists seen down at the bottom and additionally from the TURN feature vocabulary. But all of these are mapped via parent URIs back into the top level GCMD categories. We also produced a second file that identified the actual labels that were being used in TURN and IMOS that got mapped to each of these terms. And this was an opportunity to deal with some real world deduplication that was going on. For example, if you look at the salinity density terms in the oceans category, the metadata coming through AODN varied in how these were formatted. And so we were in any case processing the strings in order to find the alternatives there and it made sense to summarize that in a file. As I said, we produced these initially as CSVs and that's how you can download them today from the ECO Assets site. But I've been looking at using pool party to try to structure this and right now I seem to have hung pool party. So I'm quite interested in the OGC tool in case that helps me do this a little bit better. But the intention is to produce something that is easier to use in the context of vocabulary and ontology tools than the CSV file that I was presenting just now. As I said, if you go to ecoassets.org.au you can download these data. We've tried to provide quite a bit of metadata about them. The DOI for this dataset is directly referenced inside the metadata for all of the datasets that use the vocabulary. And I just want to acknowledge all of the people from the different NRIs and particularly Sandia who did most of the real work of writing the R code, et cetera to produce the pipelines that generated these data. So.