 Okay, can you see it? We do. Thank you, Barbara. So thank you very much that I can present here a work that I have been doing together with quite a nice group at the work at the research data alliance. I present this on behalf of this group. It's, I adopt a semantic broker for variable descriptions across terminologies. And this working group started in officially already to work together in 2018. But we were an LDA working group from 2019 to 2021 and are now in a maintenance mode. So we are, we have already published some LDA endorse recommendations. And our motivation was and is still to address the intervability the eye of the fair principles to enable intervability between existing terminologies and to promote the use of fair terminologies to annotate research. So this group has four core chairs. I'm one of those. I'm one coffee from the neck vocabularies, then I'm sorry, the barrier from sees as Iro and Maria Stoica from University of Colorado. She could develop the science variable ontology. And also Alison permanent and sick machine law, where part of the core developers of this group. So, what are we addressing here what is our problem that we want to solve. We have the problem that we have many, many different vocabularies trying to describe what we observe. We call them variables. This is also known as observable properties of parameters of quantity kinds. So many of different vocabularies try to describe what we observe but it's not clear how this should be described. So all these vocabularies use different grammars different syntax for for mostly for the same things but it's not clear how to align these variable descriptions to each other. So, we are interested to represent what has been observed independently of where how and when the data position took place. And this could be an observation and measurement assimilation but also calculation. So, we try to find a semantic description of this and we are, we're heavily inspired by the complex property model from let's better inform and sign a scientific variable ontology of so it can come So let's consider an observation like mean dissolved nitrate molar concentration the unit volume and precipitation water in, for example, micromolar liter at a specific place by a specific method and let's find out what is the variable about it's the central part, it's the yellow part here it's mean dissolved nitrate molar concentration in precipitation water in a specific unit. So we are not considering the procedure and not the location so the feature where this exactly has been measured. So, what are we going to do with this description, we can find which we decompose this description in its atomic parts. And actually, we find also that it's, we should get rid of some of these parts because I'm not really part of the description itself so mean and the unit is not part of description mean because it's a statistical measure and needs many different variables or measurements to get to this information and units because a variable can be expressed in different units, and therefore it should not be part of this description, although it's a very important information for understanding the observation. But we need to decompose this in the atomic parts, and then we have to find for each atomic part, the concept, a concept from a semantic artifact to be understood to understand concretely what it, what the meaning is of it. And so we came to the, I have, I adopt recommendations. We come now to these recommendations where we can say that we find it important this is the description should be human and machine readable both are needed. The description should be explicit and sufficient to really understand what has been observed and to be reusable. We need semantic artifacts for describing these and to be, and they need to be compatible with link data. So the question should follow at the composition approach, consistent with the classes and relations in I adopt the ontology of I adopt. And if we need to reuse existing terminologies that are aligned with I adopt the I adopt framework and if this is not the case. We need to extend existing variable description and create a new variable for following the I adopt framework. So what is the out of framework about. It's a very simple ontology consists of of four classes and six relationships. And the main concept is a variable, which is a description of something observed or derived minimally consisting of no object of interest and its property. Let's take again the example dissolved nitride molar concentration per unit volume in precipitation water. So this is the whole description the whole description as a compound concept. It can be decomposed in a property, which is the type of a characteristic of of an object of interest in this case mass concentration pain when it volume other examples of abundance weight presence. And others. Object of interest is a variable that has exactly, and the variable has exactly one object of interest which is an entity whose properties observed. In this case it's nitrate. The metrics is the entity in which the object of interest is contained in this case it's precipitation. And in some variables might also include context objects more than one and give additional background information to the whole description. And in addition, a variable can also contain constraints, which limit the scope of the observation and confines the context to a particular state. And this can concern can limit various entities of the whole description. And this specific case it's it's dissolved and it constrains the object of interest, but it can also describe conditions like a specific temperature, the specific pressure. And all these components of atomic parts that are described our entities can have different roles in in different variable descriptions. So the same on entity can have different words in different descriptions. So, representing the variable. This off night at more like concentration pain and bulletin precipitation water needs to have a compound variable description. And it can be addressed as a complex concept, but can also be decomposing its different parts like property entity constraint and entity, both having different roles here. The entity nitrate is the object of interest. The metrics is here the precipitation. And these can be referred to the single concepts in a design was as here and it is ours of Oscar Thayer. And it each of these parts can really be addressed separately. So the I adult framework has the impact that it increases the fair level of variable descriptions by enabling cementing precise and fair descriptions, and by decomposing these description into atomic components and linking those to existing visualize making these description of some variables machine actionable and providing upset reusable semantic descriptions for concrete observations. It really access a semantic broker by enabling mappings between variable descriptions of across terminologies, we thought requiring to change the existing structures by, but requires to add rich human readable and machine actionable descriptions with qualified references. So we're convinced that this will not only increase the intervability but also the final ability in the real stability of data. And then I and I adopt comes with this recommendation record with the ontology that ontology a catalog of terminologies for reuse of I adopt variables. So we analyzed more than 100 terminologies to find out which parts can be described or which components can be described which terminologies. So we found out that that is a huge resource that can be reused. We also have also a unit to property look up so that we can find out from the unit which property properties can be used and described. So we have the data templates as nano publication and Cedar so we have I adopt templates express as nano publication and a Cedar template. And we have additional material for alignments to other representations about observation measurements with design patterns. And we have a catalog of existing implementations. We have also a step by step guide for meeting new variables in the vocabulary. And these are the current implementations we are working. So I am working on and test that's a vocabulary for the elder standard observations. We implemented this approach also for a bar is project for for money as variables, variables for calculating erosion and input of different nutrients and water. Then we have a nerd vocabulary server is implementing I adopt for mapping different vocalize as really as I adopt as a semantic broker for aligning these different parameters that implemented also in CF standard names in Oscar and GF bio terminology server and there's also an implementation of the OTC sense of things, which will be published soon, I think, and yeah, and we are working also on I adopt alignment to measurement ontology pattern language. So the limitations that we discovered while working on this is let you know you've now reached 15 minutes. Yeah, so I'm, that's my last slide. So the annotations. And a data annotation is still challenging, because it's not easy to find the appropriate vocabulary. So we need to further develop this approach by supporting this with an annotation service. I adopt annotations depend depend very much on how the content in tabular data is organized. So, here we think that the annotation template can help by design same entities might have different roles in different variable descriptions. And, but this is somehow confusing. We will demonstrate that this is not the case. If you apply the same variable pattern. Therefore, we have to work more on verbal design patterns. So aggregations levels for variables are needed to accommodate essential biodiversity ecosystem marine variables. So we need to extend the framework by aggregation design patterns. And we need to understand how to use I adopt in context with the different measurement observation ontologies. So we have to analyze alignments by using many measurement instances of their different complexity and demonstrate how this works. We're working really on services to have converters connectors and dynamic mapping tools at hand for for researchers to annotate the data. Thank you very much.