Decoding IEEE 2890-2025 Part I: The New Global Baseline for Indigenous Data Governance - Relational Science

Introducing the IEEE 2890-2025 Indigenous Data Standard Hello everybody, welcome and happy 2026 cycle. We are back in relational science here. What are now when from talking to you all the way from the Andes. Actually, I'm in the Andes visiting the Amazon, working with some farmers, looking at some seed exchange and technology. And very quickly something came out to life early last year at the end and beginning of this year. So when Sierra, my Co host, we will be diving it in something really interesting related to data. Sierra, please welcome everybody. Speaker 2 Hello, hello. I am Sierra, your usual Co host coming at you from Altairola, New Zealand. Actually as of the past month or so and for the next couple of months really excited about it. But yeah, today we're going to be covering an I triple E standard that recently came out and we're going to go ahead and describe that for you given the announcement language. So through the United States Indigenous Data Sovereignty Network, there was an announcement that there's been a new milestone for Indigenous data sovereignty. It's the first global standard calling for the appropriate disclosure of an Indigenous people's relationships and links to data, and it is available for free. The effort was led by the US Indigenous State of Sovereignty Networks Co founder Doctor Stephanie Carroll, Jane Anderson from Local Context Maui Hudson and Camille Callison, with contributions from the US ID Ascends Network working Group members Doctor Randall Aki and Joe Yushreda. These recommended practices establish shared descriptors and metadata guidance for connecting people to place people and governance systems. All righty. And So what they say is that the standard is designed to support Indigenous communities and rights holders, as well as data practitioners from diverse fields and environments, including biodiversity and genomic research, machine learning, and academic, governmental, nonprofit and industry data systems. They say that for USIDSN, this marks an important step towards strengthening Indigenous LED data sovereignty and governance across the US, ensuring Indigenous nations, communities and people have tools to protect their relationships and knowledge and data futures. And as you know here, relational science, we are all about information, knowledge, data technology. And this is a huge milestone to consider, especially when in the so-called United States where's not many data rights and protections for anybody, much less Indigenous people. So what people in this field have constantly been pushing for is the creation of those standards and techniques to to assert rights over Indigenous people's data by Indigenous people. And so today we're going to go ahead and analyze this standard. We'll get into the nitty gritty of it section by section, and they'll probably be a Part 2 because we want to make sure we move through this and sort of sink our teeth into each section of it. IEEE Standard: Recommendations, Not Binding Obligations And 1st, we'll start out with just some general discussion to introduce you to it. We'll say that we should probably give you some sort of understanding of who this standard is coming out through. All righty. So I triple E, who is that? Who's coming out with the standard? I, triple E stands for the Institute of Electrical and Electronics Engineers. It's a professional association that was formed in 1963 when the American Institute of Electrical Engineers, AIEE, merged with the Institute of Radio Engineers, IRE. IEEE is known for its contributions to electrical engineering, electronics, and computer science, and it encourages scientific research and collaboration amongst its members. The I Triple E standard came out through the Society of Social Implications of Technology, which is a subset of I Triple E. And it looks like the other folks who contributed were the Indigenous Data Working Group. And then the standard lists out the members of that as well as some bodies and boards who review the creation of standards for IEEEE. So to get us started, we're going to jump in and start talking about what a standard is and sort of what the use of that and, and the the limitations of what a standard is. So I'll pass it off to Nawi to give some more context around that. Speaker 1 Yeah. And this, this is exciting definitely for Indigenous data as a whole. Now, like we can think about a standard as a, as a set of rules, if you will, and, and designed by experts, what we call a, a really a body of expert that debates different ways to have a common language as we are looking into how to catalog, classify and also describe Indigenous data. Now that that's the standardization of, of part of it. Now, the often when we come down to standards, we usually talk about, you know, the usual, is it binding? Does it have any sorts of binding of obligations, creations? And in that sense, when a standard does not create an obligation, we can refer more so closely related to a framework, a set of recommendations, a set of common set of rules that we should engage for ethical engagement with data and disclose ethically and ethically source applications of how we working. So this I Triple E standard as we will go section by section. In the first section, I Triple E does a pretty good job describing what is what this standard does and what doesn't. And one of them that keep portions of this, they mentioned that they are not IEEE. They are not responsible for the implementation and usage of this standard, which means it does not generate a sort of binding or obligations for the data actors. So this is more of a set of recommendations, as the title implies, in this standard is a set of recommendations for data actors to carefully designed and utilized to catalog and classify Indigenous data. Well, that being said, really doesn't really matter if you know, in many cases when we use, you know, policies, regulations is more so when the, the, the folks interacting, when Indigenous communities, Indigenous data, Indigenous information is more so to for the first time, we have a set of common language and rules that we can follow and we can attribute that is significant. You know, long time ago, maybe in 2024, we only used to say traditional ecological knowledge. And and when we were feeling pretty good, we will say Indigenous knowledge systems and then we will sometimes say like Indigenous science, but neither all of those three things, they are creating Indigenous data. And when, when people ask like what is Indigenous data, many as ourselves included, many will have some very interesting definitions of what what the extended Indigenous data was. Now the I triple E, it provides a more common set of language that we can all utilize to communicate and specifically say what Indigenous data is, how to work on it, when to use it, when not to use it, What is the extent. And that's the beauty of the standard. This is the standard that's creating this common rules. And we can utilize it pretty much in the flexibility. We're not feeling if you will, of doing something wrong because you will not generate your obligations of binding, which means you we can learn as we interact with this standard. And that is very much so comes down to each of us, each of us that we are working with Indigenous people and Indigenous data to learn and do the best way possible to implement it, operationalize it and use it, this standard as a whole. Speaker 2 Thank you. That was very thorough. I think that gives folks a really good idea. The only thing I would have to add on top of that is that standards like these, you know, they make sure with all of the disclaimer language at the beginning like now you mentioned they are not enforceable by I triple E, they're just recommendations. They don't take any responsibility for what you do with it or what the repercussions for that might be. What is coming out and is this vocalization of all of the Indigenous data governance mechanisms such as MOUSMOAS, contracts, agreements, tribal code, tribal law, There's just many, many ways that vary in levels of enforceability and formality that Indigenous peoples have and can continue to use to kind of create the appropriate level of teeth when it comes to enforceability of their rights and interests in data. And so we'll be seeing a brief coming out about that either through the Indigenous Data Alliance or the US Indigenous Data Sovereignty Network. But that's where you have the enforceability coming out right now. There's it seems to be barely any enforceability that is supported by the US federal or state governments regarding Indigenous peoples interests and rights and data. So that is still needing to come from Indigenous communities and forcing that themself. And, you know, we're probably going to see in the future certain strategic cases being taken on to try to build up a legal framework to back those rights and interests. But that is yet to come, it seems. And I doubt that that will be undertaken in the current political climate as there's too much to risk in taking those cases on, I would say, at the at the current moment. Provenance: Data's Relationality, Genealogy, and Worldviews But so we're going to move on to give you some more context around this set of standards. And for the sake of this podcast, discuss kind of our perspectives on what Indigenous provenance is. So I'll give a a quick definition, an idea for me, provenance is rights and interests and linkages. Naomi, what would you say your idea of provenance is? Speaker 1 Provenance to to to me is is you know, is the information is, is is the relationality is the genealogy that is attached to this data or the information of knowledge is giving us a very beyond just general context. When we mean like a ground context provenance it it provides world views, cosmologies. It provides the values that communities has and how they have created, generated or have contributed to the generation of data. That is not just the the location of the information, the name of the community or the language of the culture is not just that is is the values is the life of the community and it creates this very tied in to the governance of the territory and the life cultural ways of the community as a whole. And that's a very important part of the provenance in this in this standard. We are trying to reach to the point that where our information is coming when, when a lot of this cultural value and at the same time, given the appropriate attribution of exactly the location, the place specific information to how this information was created collected. How is the store and all those other things that adds the attribution of provenance and that that creates a lot of more transparency and and trust in in the data set for? Speaker 2 Sure. And so let's discuss real quick for folks why showing the connection to data is a good thing because at the end of the day, that's what the standard does, is it kind of walks Indigenous data sovereignty back to the fundamental idea that Indigenous people are connected to their data. And so for those of us who are in the Indigenous data sovereignty field are constantly working in this, this can feel fairly like rudimentary, like, you know, obvious. But this standard is coming out through a which I think is safe to call a, you know, settler colonial organization. I Triple E is not Indigenous LED or derived and the audience for it is definitely going to be data actors, which we'll get into the definition section here soon and you'll understand more of what that term means. But people handling data who are not indigenous of, and this could be their first time thinking about data and technology as being culturally contextualized or having bits of people, communities and cultures still still in them and linked to them and therefore facing the consequences or benefits of what is done with that data. As we know. And this isn't a great term to use, I think we're still looking for better terms. But Western science still very much so treats data as objective and and you know, scientific process is being objective and not linked or culturally contextualized. And the first thing that we need to do in the indigenous data sovereignty movement and scientific field is is combat. That is challenge that idea because it is the basis upon which all other aims that we have exists in protecting indigenous peoples. Data is we need to get people to understand that there are stakes there, that there is a problem space that we need to figure some things out in. And that gets negated when we look at data as this objective inconsequential entity. And so that's what the standard aims to do. That was my impression of it when I first read it is OK, we're starting, we're starting from the bare bones of and we're going to build up from there. So I'm curious now what your take on that is, why the standard just goes straight for the linkages as a starting point? Speaker 1 Well, I, I, I think as the, as you know, as you mentioned, some of the folks that are involved in, in, in developing the standard itself, there has been many of them indigenous, if not all of them indigenous people deeply connected to their own territories. And in there we, we, you touch very beautifully to the point that data is responsibility and and we are directly connected to our territories. And in that I think is trying to, if anything is trying to interpret relationality, how we relate to territory, to our cultural life ways and how that how that embarriment how the embodiment of territory and and knowledge production it, it comes data and, and in in doing so, we kind of really are trying to get to the point of the data genealogy. And that's very crucial. And, and, and I believe that perhaps is one of the reasons why we are getting very back to the fundamental way of providing 2 world views, one that looks data from a relational logic and perhaps one that looks more from a registration logic. As you mentioned, data from a weird context is, is primarily look as a zeros and ones as a binary, as a neutral. And in a sense, there's more registration logic, not necessarily attach to people in the ground or to the territories where the data came from and the two world views. It makes a significant difference. Once we are interpreting the research, the information on the data, it creates a sense of culture, sense of belonging, sense of responsibility of what are we doing when this data that now is in our hands. And, and there's a, there's a very important place to to start where once we are trying to see complementations between weird sciences and indigenous sciences, there is complementation in there. So, but we need to understand these two world views in data and the relation that we have to it. Speaker 2 And I can't even like express my excitement that this is coming out through an engineering organization because as someone who comes from an engineering background, mechanical engineering, I've taken electrical engineering classes. Like, there's not a group of people that I could more so think would view data and technology as objective and not culturally contextualized. And so speaking, speaking linkages to cultural context to them. You know, this, this the standard might seem rudimentary to many, but it's actually quite radical in who you're thinking about. It's being spoken to and it feels like a baby step, but it's actually a gigantic step forward for the movement, in my humble opinion. Dissecting Data, Actors, and Invisible Data Pillaging So now we're going to kind of start moving through the different sections of the standard itself, starting off with the definition section. You know, like any good standard, this, this document starts off with understandings of what the word shall in May. And all of these little technical terms are going to mean throughout the document. And we'll discuss those more strategically as we move throughout. But I really love the definition section of this policy. I found it super helpful. I was very relieved actually in when I first went through and I was like, yes, we get some really solid definitions because of someone who's coming from the tribal data sovereignty policy creation space. We've definitely had to go through our own process of figuring out what do we define? What are the definitions of that? And you know, trying to engage folks around that so that we don't ingrain more settler colonial notions of property and ownership in those definitions. And really think about the relationship of data and knowledge and is knowledge data? Is data knowledge. What is the relationship there? And so yeah, I I really found that helpful for policy. We're definitely in our reports going to be referencing this standard and the definitions they're in and probably putting, you know, data submergine needs to be local. At the end of the day, these standards are very helpful, but they need to be applied and transformed for local use. And so figuring out what the the local transformation of these definitions is going to look like is very exciting for application in a policy. I specifically love the definition of data. I'm going to go ahead and read it. It's a reinterpretable formal representation of information for communication, interpretation, or processing, and it goes on to have some more detail there in the definition. But for all our policy purposes, data is any sort of abstracted form of reality put through a processor, whether that's a machine or the human brain. And so I really thought that that definition was going to be really consistent with the work that we've been doing and now is something that's in a standard that is external to any of the groups that we're working with. And so to have that sort of corroborated was really exciting. So curious, now we have any definitions that call out to you, or just general ideas about the function and purpose of this section. Speaker 1 You know that the one thing that we have been using, right, like when we trying to discuss data generators and data users and and we usually call it data actors, right? That's kind of where where we are that part data actors, that definition of data actors is is finally becoming like a very common set of language to use in order to understand what data actor isn't. Who is it, who isn't, you know, kind of figure it out those things and understand that if you go to definitions, it says in here a person, device, application, system or organization that communicates, generates, uses or storage information of data. That is a very good way to, to think about is it when we say there are actors, often we interpret as a being a person. But in this context, we are looking really at anything that is 3 dimensional that can capture data and hold it. And that's the storage part, that's the, the device part. And that to me was was OK, this is this is good because in the field that I am, I am more in, in this section is in environmental markets, you know, carbon markets, biodiversity markets, payments for ecosystem services. And often when we talk about data provenance, you know, we only are referring to perhaps the individual that the, the person. But now we can actually data provenance when we add data actors as a device, a camera trap is a data actor. Therefore data provenance is the ecosystem itself. It's coming from this ecosystem itself. And that adds a very important layer on how to provide correct attribution of place specificity when we are entering in this trans and transparency of these environmental markets. And above that in the early definitions here mentioned it, it talks about the more than human relatives that is part of this definition. So it adds into this layer of understanding when do we disclose data provenance is not only in this context, is not only when it's person to person, individual to individual, it's also when it's involved a device and the territory we disclose. Also the data provenance that is coming from that is an important aspect of dealing with, you know, misappropriation or in many cases in this context dealing with biopiracy. If you're collecting data through a device, you know, often you don't see it as a data actor and you didn't need to disclose. But now when the standard we have a clarity that if the device is involved it still it needs to go through a disclosure and that way we can actually go preventing biopiracies and and miss appropriation of the data that these technologies might be acquiring. Speaker 2 Yes, and I like that we both called out those specific definitions because our next couple of sections actually expand upon those. In section 4.3 of the standard, we have an expanded definition of what Indigenous peoples data is and a bunch of examples including writings, music, performances, ceremonies, artistic work, languages, oral traditions and cultural narratives and and so forth. You know, and I think a part of this that's really exciting is in the policy process that I'm engaged with is we really tried to take media into account as part of this. There's, there's different departments at the local governance level that specifically manage that and trying to integrate them in as much as we integrate IT with the process. Because these visual formats of data representation are also really important and in many ways can be even more harmful than information represented in the written word. Because pictures tell 1000 words, as we all know. So there's even more information to possibly be compromised or benefited from in those regards. And and so when we talk about the documentation of indigenous people's heritage and all forms of media such as reports, photography, films and sound recordings, it's really nice to see that backed up explicitly in a standard like this. And in 4.4 we have data actors and we are given examples of those as well, such as researchers and research institution repositories and collections, databanks, aggregators, publishers, machines, algorithms and other sensors and data scraper. So it's really great to see human organizations, human individuals and non human tools being represented here as data actors. And yeah, we have the care data maturity model that's going to be coming out through either the Indigenous Data Alliance or US Indigenous Data esophagee Network. And that really talks about how different people managing datas and their roles and their positions can enact data sovereignty. You know, moving from principles to practice. It's more helpful to talk in terms of where people are at in their roles, in terms of what they can do, because some are really realistic for certain roles and some might be not applicable. And so it's waiting through all of that information to find what you can do in your daily life. Another thing I'll call out here in distinguishing that there's human data actors and machine data actors in a recent manuscript that I had written and submitted with my co-authors, Dr. Bruno Serafin and Lisa LaRue, is this idea of invisible and visible data pillaging per SE. The visible being by partners in agreements and breaking agreements are, are coming into the territory and, and going and places they're not supposed to go to and taking pictures there. And, you know, that's, that's one battle. And another one is these algorithms and models and tools that are just scraping your information and you don't even know what's happening. And there's, you know, that's, that's a different battle of itself. That's the one we find it more difficult to figure it out. It's either easier to handle the person from the agency coming into your land and figuring out what to do with that than it is to fight these invisible forms of pillaging. So I really like that the Data's actors section acknowledged those data actors. I think he gives us a really solid foundation for policy language to acknowledge and and challenge that sort of harm caused by those entities. Why Location, Date, and Metadata Matter for Provenance So next we can move on to the Section 5, which is really more detail on when all of this comes into play. It is titled Disclosure of Indigenous Peoples Relationships and or links to data. So we're starting to get into when you would use it. And there's definitely I find that this is a 3 theme throughout the whole document is not just, you know, naming these linkages to data, but trying to get people to understand when, why, how and whom you're going to maintain those linkages. But when is a huge theme throughout, I would say. And in 6.1 we get a definition of Indigenous provenance that is actually not in the definitions section. And so I'm going to go ahead and read that for you. So 6.1 is the general is titled general and it says the provenance of Indigenous people's data should include identification information that facilitates Indigenous peoples governments, governance, decision making, participation, collaboration and engagement in and for current and future uses of Indigenous peoples data. So we talked a lot about our perception of what Indigenous data prominence is, and this is the one that is communicated in the standard and in 6.2 they tell you what should go into a provenance. And so we're going to spend a moment here talking about why these is this is important and why these different connections and linkages that are being created through these details are important to better understand the context around data. So to give you some idea for 6.2, it is the identification information. It says the provenance of indigenous peoples data should include information about the following. And then we get a a list of details, mainly discussing the location, the date, the origin point, any other relevant information, any tangible objects. So we can pause here for a moment, and I'm curious now we all toss it over to you is how does this help create the the linkages that data has? Why are these bits of information important? Why would the date be important? And why is the location important of where data was collected? Like for people who feel like this would be a burden to try and gather all of this about data, much less existing data, go ahead. Speaker 1 You know, we, we have done that previously. You know, if, if you, if we look far enough into not not too far, like anthropology, archaeology, sociology, like ethnographic studies, that's what we do, right? Like if you're in anthropology, you want to know the specific place, you want to know the date, you want to know the location, you want to know the location, you want to know the point and the meaning, which second, which area, which direction, when, which cardinal point there was entry and so forth. And that if we want to enter into machine readability, it does metadata. It's, it's giving us everything that we need to make our data actionable. What makes the data actionable? Because at the end of the day, as we, we I mentioned before, I say in, in in one world view, we can say we can look data as a register logic. You're registering values, units, numerical. You can walk away when a measurement of tree just like that, you know, this tree is measuring this height, this width and that's it. You can have those two things now that's a tree. Now imagine now that we understand the the location, the places specificity was a in the high Andes, was in the tropics, was in the temperate forest. Was it in a desert line? Does it have any usage at the local level? Is it the medicinal either for construction, either for ceremony? That adds value. When we start thinking about like the, are you saying list of species and extinction? We start looking at those things, at the cultural value. That plays specificity, why these things matter. That linkage is not only relevant for indigenous people that are getting the attribution, the credit, the ownership, the authorship of the data. Not only is doing that, but also is providing a very rich metadata that has a lot of quality, that retains quality and integrity of the data such that it cannot be in, if you will, it cannot be mistaken by an order data point that might be nearby. This metadata creates the specificity trades attributes that this data has been collected. When this and the metadata in a very good sense, it provides the integrity and the quality that we continuously go back and we talk about it. Oh, bad data, you know, well bad data just means that it's missing some data. Some data was was not collected during the metaphase phase. And this is in this section when we maintenance those things. Now that is the good part that the good implications. There is some level of ramification, if you will, when this provenance, because we're talking about location and location often we take it when GIS mapping, we give coordinate systems to the, this specific location. And we might be giving the name of the community or the name of the plant or the, the name of the territory where it might be housing a very important medicinal plant or a very important, you know, finite resource that could be of interest for exploitation. Now it does have some layer of ramification that it needs to be a, a, a, a discretion or the researcher, the data actor needs to be at the discretion of the data actor in partnership with the community. What enters in this data provenance and what is left outside in confidentiality and privacy because you don't we don't want to run into these ramifications of negative impact due to a fulfilling, if you will, fulfilling data provenance which is listed in here. All the things that could be added. It does have a very important positive implications into once again the attribution, the appropriate participation, authorship of of the communities in the data. But that's put a little bit of ramification in the sense of we might be putting a risk because there is some valuable resource in there, the territories that other data actors might want to come in and intrude and do some, some sort of larger exploitation or harm of this community. So a very important balance in the data provenance in partnership with the indigenous people in those territories to find the right balance in confidentiality and the metadata. Speaker 2 And I think it would be helpful for folks to get a quick definition here of metadata. So metadata is essentially data about data. It provides additional information about a particular piece of data, helping to organize, manage, and understand the information. Metadata is found in various contexts, including digital files, libraries, websites, and databases, and plays a crucial role in data management, retrieval, and organization. That is just off the top from a quick Google. So we will move on then to the next section which is data details and we'll also go on a 6.4 which is data fields. These are just more expansion on what it means to capture provenance and what information, a very specific information that you should be collecting in order to, you know, completely capture as best as possible for the standard. The provenance of the data includes using the scientific names and any names that indigenous peoples use as applicable in the data. It includes whether permission was granted to collect it, who granted that permission, more details about when the collection occurred. And then this is really interesting. Messages, operations, actors, preferences, and contexts that led to the piece of data. So really, really capturing the origins of the data, giving that full picture. And yeah, just that that very verifiable audit trail requiring that as well. So both creating and requiring it, they're clear to mention that here. The 6.4 then focuses on data fields, says that provenance of Indigenous peoples data should be provided using the following types of data fields, a few being rights, permissions, author rights, geolocations, contributions, or contributors. So you can see that they get pretty specific here with the information that you should be capturing. I can see some sort of checklist or something emerging after the creation of the standard for folks to go in with new data that is coming into existence to to capture those contacts around them. And we'll get into a little bit here what to do about this with data that already exists when some of that information no longer can be captured for whatever reason that might be, given the time since the date that it was created. Applying the Standard: Non-Human Actors and 'Reasonable Efforts' But this next section we're going to get into here is Section 7, using this recommended practice for the provenance of Indigenous peoples data. It is mostly about using the recommended practice. The previous sections outline what that practice was, which is essentially the information you should gather to demonstrate these linkages between the data and Indigenous peoples. And so this is now what you should do with it. So they explained in 7.1 who this practice should be used by. It says all data actors who generate and engage with or otherwise have decision making power over data that discloses indigenous people's relationships and or links. And it's really great that in 7.2, like we mentioned before that they talk about non human data actors. And this is so important that I am going to read it directly and it's a few lines along, so stick with me here. But it says there are situations when a machine actor, aggregator, data scraper or other non human data actor interacts with data that data that may have a relationships and or links to indigenous peoples. In these situations, all reasonable efforts should be made by parties who design, own, control, use or benefit from non human data actors to help ensure that the non human data actor confirms, conforms with the fair principles and complies with scope and operation of the recommended practice for provenance of Indigenous people's data. So that last reference there is to the standard itself and then the one prior to that is the FAIR principles to be associated with the care principles. You can find that on the Gita or Global Indigenous Data Alliance website. It is more where the care principles are more about a interpersonal, I would say level of care around data management. FAIR is more about how you're setting up your tools and technical processes to to keep things like accessibility. In mind so one thing I want to point out here is that they talk about in these situations all reasonable efforts should be made by parties who design, own, control, use or benefit from non human data actors. I think it's important to remember that at the end of the day, there are humans attached to these tools that are doing these scrapings on websites. There's not these, you know, Marvel movie AI robots out here doing this solo. There are people who own these companies and created these tools that can be held accountable and that standards can be applicable to. You might just have to dig a little further to find them because their face isn't going to be shown in what they're doing. Just adds an extra layer. But that at the end of the day, there are humans attached to these technologies that do need to be held accountable. But this, you know, in these situations, all reasonable efforts. So in my experience with contracts and standards and agreements and things, reasonable efforts is very interpretational and there's reasons for that, both on the people creating standards and the people who are going to be potentially in court, You know, should this language be transferred to other binding documents that are going to create the meaning of that? But you can tell that it's important because they go ahead in the standard here and they define it. They say that reasonable efforts entail fair, proper or appropriate actions under the circumstances and are subject to assessment by Indigenous peoples, colleagues, peers and the extended community of practice. Whenever I see reasonable somewhere, I know that it has been left loose, so that there's some flexibility there. You don't want to box your own self in with these kind of standards. What it does though is it leaves it up for interpretation by potentially judicial bodies or others who are determining in that situation going to build out in the future what the definition of reasonable is going to mean. It just depends on court cases. One again, not around the standard because it's not. It doesn't have implications like that. But should this language be transferred to actual legal agreements? But I thought it was really important here that while this definition is very strategically loose, that what they do make sure to explicitly mention is that this is whatever reasonable is, is under the assessment by indigenous peoples. So reclaiming back that power. And to me that says, you know, even in the future, if reasonable in the courts is determined to be one thing that these, you know, data scrapers and things like that are they've they've done they've they've created harm, but they did everything, you know, within reasonable ability to be able to stop that, that the standard is still calling that indigenous peoples at the end of the day are the ones who are going to determine what reasonable means. And I think that's really powerful to say. Any thoughts on that? Naway for that section there about, you know, using the practice and the distinction between human and non human data actors? Speaker 1 Yeah, I think it's a it's, it's more so thinking, thinking carefully how how indigenous data is defined and and that should be a way to guide how we are disclosing this data that's created by non indigenal, non indigenal non human actors in in this context. But if if anything is is really looking, looking at a source where the data is generated and how it was generated. So it it adds an extra layer of thinking and carefully defining or looking how the data should be described and and disclosed. But there still is within within the definition. I think is a as important definition here more so when the movements that we are seeing when rights of nature. So when we are adding these other layer that's coming parallel to the standard, this might be a very good instrument to start thinking about how rights of nature can utilise this standard to start disclosing or providing the provenance of the data that's generating in the modern human space. Wrapping Up Part 1: What's Next in the Standard Analysis Yes, OK. And so for our Part 1, we're going to conclude here for now. We will have a Part 2 where we continue to move through Sections 78910 and 1112 and 13 and 14. So I'm scrolling down here. There's smaller sections once you get towards the end, but we will pick back up with section 7.3, application of this recommended practice for the governance of Indigenous Peoples data. And we start to look into not just which data actors there are, who this would be applicable to, but in different steps of their management, whether that's generating, transferring and receiving, collecting or storing. You're using, you're reusing or transforming. You know, there's a specific guidance that's given here. So we'll pick back up with that. And then in that episode, if we have some time after concluding, we can do some synthesis of our thoughts on, you know, at the end of the day, what all of this is going to mean. So we're really excited to see you in Part 2. Sierra signing off. Speaker 1 Yeah. We we will be back when all of this definitely Part 2. We will we need to add a little bit more how that looks from early career all the way to sectors and industry. And that will be the missing sections that we live in for Part 2. Stay tuned. And if you having look at the IEEE standard when this episode in mind, that might be a good time to download it open source, read it, leave your comments. If you have a specific questions for the sections that we cover and you want us to go back and kind of touch points and provide some examples on how we might thinking about these things. Now is the time in the other sections. If you are like, oh, I would love to hear about more in the application. What does that mean when I'm working in this particular sector? Now is the time for you to happening in relational Science podcast. Leave your comments we will be putting in our social media, leave us comments in there. If you have questions, concerns, recommendations to delve deeper into some section, provide some more details than that, please do so. We're looking forward for Part 2 be hopefully in February we will come up with Part 2. If not, if we will make it happen in Part 2 in February. So that way we have those two things out before things get even crazier for 2026. Thank you. This is worry all the way from the Andes.