BITC / Biodiversity Diagnoses - Gaps 1

OK, welcome to the second half of the day. We'll have about four or five half today, but this is the second one We are going to talk about gaps. What is gap? Actually, Town has told us a lot about gaps, either in this morning talk and yesterday talk. So, I'm going to probably repeat something that we've already said and we ##### we might deviate in some other aspects. I will give you first an overview of what gaps are in general. What gap analysis is we will come to ######. And then we'll get into very specific set of gaps. The gaps that are more related to PBR (Primary Biodiversity Records) So, let's start by saying that the gap has basically two different components. And gap is basically lack of knowledge. We lack knowledge about something. Because we haven't collected data. Data are not there or we have the data but we don't know how to interpret them. So, the gap in this case is that we don't have necessarily knowledge about something. But this second specific gap Which is normally part of the first gap, part of the knowledge gap, which is the data gap. Because, we might know what the data mean But we know that those data are not enough for a good interpretation. So, a data gap means that we know where we want to go, We know what we want to have, but we don't have it. We lack data So, normally a data gaps lead to knowledge gap as well. So, what we are concern with is mostly knowledge gap. But sometimes we have to solve the data gaps first. Right? So, let's go specifically to biodiversity of knowledge gaps. So, we know that biodiversity exist. This is a reality which is ##### of life out there. And the knowledge of the biodiversity has to be gained. Biodiversity is there, it's like understanding of the universe, the universe was there But until recently, or a few centuries ago, we thought that universe were a set of closed spheres in which lights were moving around So, we need to gain knowledge about biodiversity. So, there will be always a gap between what we know and what we have gained in terms of knowledge And what we should or could eventually know. We can describe it in this plot here, when we have a zero knowledge when we know nothing And the absolute knowledge, God like knowledge which will never be achievable and some kind of knowledge which eventually can be achieved. This is not showing the entire thing. I don't know why. This is cropping the right part of, but doesn't matter much. I don't think I have many things there. Don't worry. So, what else? We might then understand there always will be absolute gaps that will remain between the absolute knowledge and the knowledge that eventually could be achieved. But there will be also relative gaps that will remain between achievable knowledge and fuctional knowledge Which is the knowledge that we need to answer something. And then there will be also practical gaps. The practical gaps go between the functional knowledge and the gained knowledge This the knowledge we do have, this is the knowledge that we need to answer some questions and this the knowledge that we could eventually get at. So, we're basically dealing with this kind of gaps here. Between what we know and we want to know. What we want to know because what we want to do is to fill this gap to fill the gap that allows us to answer something. Alright? This is what we'll do, we're trying to deal with filling this particular gap. The practical gap. So, we know that gap exists and we need to detect those gaps. We need to be able to tell where the gap is. We said before that knowledge comes from data. So, perhaps somebody went to the field and collected the data and may list and then when And then what? This knowledge, by which I mean list of species for the site, for instance may or may not be known. It may happen that we can gain knowledge from this somebody else's knowledge or the user may not know that. Both things could happen. Why should we ignore these data? Because perhaps the data were not made available as Town showed us yesterday, the data could be sitting in the cabinet which is close by key and somebody has thrown out the key We can't get a hole of that or the lecture has been thrown to the garbage can. During remodelation of the building or whatever. Somebody that could have been published but this doesn't guaranty that the data can be reached because a time #### don't reach the user. There was a time, actually I think there is still a time when taxonomical paper to be valid has to be deposited in if I remember correctly 20 institutions. Twenty libraries. If you don't happen to have access to one of those libraries you won't have access to basic taxonomical kowledge. That was until recently when the advance of electronical publication made it basically possible for anyone to get anything that has been published in recent times. But still a lot of knowledge is locked there. I don't think that Google will actually scan all those taxonomical papers of which only twenty copies exist. Perhaps one in a large library and the 19 remaining copies have been thrown away during the porch of the library. Alright. So, when do we know that there is a gap? We will know there is a gap when we are certain that data were never collected. There is a clear gap. If we don't data about some region because we know that nobody went ever there, we know there is a gap. Until Hillary went up to the mount Everest, nobody had gone there except perhaps for #### because we will never know the #### came up to the Everest. And I doubt there is so any bug there or they had time to look for any bug there, but still there are places where we haven't ever gone. We know that data were collected but it's now lost. We can know that the data have been lost sometimes. We know the data were collected but haven't located this data yet. We know that somebody did in some place an expedition, so there must be a ##### somewhere but it seems we can't find it That's the data gap. Or we know that data were collected but we know that this data were not enough or not good for the particular purpose we want to achieve. That's the most common or those two the most common gaps. There are data out there but they aren't enough. So, we need to sample more or get more data out of it. On top of it, we may refer to a very interesting concept very recently explained which is Digital Accessible Knowledge. Or DAK for short. OK, now it's copying both sides. a digital accessible knowledge regarding the biodiversity's define based on the primary data that are both digital and accessible in standard format. Right? This concept appeared on this paper that you have actually other there, I think is this one. And it's a really important concept. Because it basically sets the #### for getting the data or for which data could we get. If data are digital, they are far more easily reachable than the data that are in analog forms. So, this digital accessible knowledge or DAK. Do you pronounce it DAK or ...? [Town]: D A K, I don't know [Arturo]: Yeah, you have to set a rule for that [Town]: #### accessible knowledge. [Arturo]: OK, this DAK is the opposite to what #### called "Dark data". Dark data which are data that have been basically lost and cannot be recovered. But there are lots of data which are somewhere between Somewhere between DAK and Dark, we can call them "Gray Data". And they might actually form the majority of the data that are available. So, data can be lost if data won't become DAK. A way to ensure that gaps can be filled is to ensure that gray data become DAK, become known digital data We can't possibly do anything about dark data, they have been lost. But gray data which may be in isolated media or obsolete media All discs for instance, may eventually with some efforts get to a digital form that now can be accessed. And if data in analog form but can be digitized they will become digital or this thing takes time in effort, actually. And quite a lot of it. Infrastructures or standards may have to move the data from gray into digital. But it will be basically mother of having the right documentation If documentation about gray data doesn't exist is quite difficult to digitize them. Documentation metadata about data, means if I give a #### (lecture) that has three columns there with some data in them, either I know what the data mean because it's documented or I can't use the data. OK, I may suppose I've seen a movie, which movie? What's the name of that movie? "Close Encounters of the Third King", do you remember? that the French translator was the one that said "Okay, those series of numbers were coordinated because none of them was 108 in of the blocks and none of the started by being more than 19 and the rest two block were less than 68 and so for. OK. So, we can possibly detect what a column means or what a bit of an analog data mean Byt the only way to be sure is that the data properly has been documented. Then there are services that will take care of this digitization of the data or there is effort put by people Personally effort digitizing #### may have moving things from analog to digital. The basically what we want to be is here. We would like to have everything into this block here. Digital and accessible. It may be digital, may be #### than it's only accessible to some. [Town]: Or it's just behind the password. [Arturo]: Or behind your password. We need a data to fill gaps. We may need to get new data or we may try to locate data that perhaps filling the gaps. And some of these gaps filling may be easy, some of these gap filling may be difficult. Those are examples of places where accessible data may be existing in analog form Forms in which data may be existing in digital form. But some of them are easy to get or to combine Such as databases tend to be quite easy to mind for the data we need And if the files are not well-structured or need some kinds of processing then maybe slightly harder to get a data But eventually we may. However, if the data are digital ##### are locked, then we may be out of ###(luck). We may need to pay a lot or to convince somebody to release the password as Town pointed out So that maybe much harder. Analog and things can be also harder to combine specially lost data What about the future? In the future, data will probably be always digital but not necessarily always. And the future opens an opportunity in avenues to get data for gap filling by new techniques or new procedures such as the development of automated surveys and monitoring. When Town was explaining in the morning that he may spend one day trying to learn how particular #### sings And I was thinking OK, yes. What Town will probably develop in future is a kind of automatic voice recording system in his laptop or even better in his iPhone that will work very much like #### or like a #### that you point there and it will say OK, this is