BRIAN MACWHINNEY: In a way that's sort of a segue right here. I guess I had this little fantasy in my mind that we were going to spend two days talking about sharing and standards and as Tim has just pointed out haven't really gotten to it. But, you know, the real world is the real world. Right? I mean, you know, I know Tim's data rather intimately and I know how there are these places where, you know, the way he codes, he's not happy the way I reformat it and this kind of thing that we, you know. And there really are, you know, details that have to be worked out in terms of linking up the gesture line. I think, you know, there's the problem of should the gesture be in a footnote or should it be in a column or should it be in a row and how should it be displayed. Right? I mean just to begin, you know, one of millions of issues. So, you know, that would have been a great discussion, but we didn't have the time I guess to do all that, you know. So I want to talk a little bit about these issues but, you know, the depth I think will not all be there. This is on sharing standards and this work has been funded by NSF and NIH. Actually, McArthur, I forgot them, long ago also funded this. NSF has funded the Top Bank Project, the Diver Project to my colleague here to the right, the SCOTUS Project which is, SCOTUS by the way, can anyone guess what SCOTUS means? AUDIENCE MEMBER: Dennis Scodis? BRIAN MACWHINNEY: Very good. No. Supreme Court of the United States. Okay? How about that? Isn't that cute? And it's Dennis Scodis which is also -- Dennis Scodis was a poet though, not a lawyer. Right? Logician. Right. Thank you. Okay. That's the Oxford there. Okay. And it's Chile's funding from NIH and also now, we're getting funding for a project called PHONE which is to look at the phonological level and building a project called Aphasia Bank which is also known as Can't Talk Bank, but don't tell anyone I said that. Okay. It wasn't said. Well, I'll tell you we've only done -- why video. I told you my answer about why video earlier that I thought time scales was a crucial reason and that video actually captures the interaction of the time scales in the moment. But we've sort of said that. I think another important thing, I don't see any IES people here, Institute of Education, you know, everything has to be evidence based analysis. I think that videos can fit in very well with evidence based analysis as long as you have a research design sort of on the side there. You know, it could be micro and these are some of the eight standard methods that everyone would approve in research design. Video just, you know, fits in well with all of these. In fact, some of these could only be done well with video. It's just that sometimes you will need, say, a design like I've forgotten who it was, same consultant, different communities. Who was that? Whose paper was that? Okay. Rogers. Thank you. You had some more. Actually, on that very slide you had you had a few more and you just didn't have time for the question I wanted to know about them. But there's no reason that -- another one is diffusion analysis. This is great stuff. In a museum study, too, right, was a fusion analysis in a sense, and you can use video for all of these standard research methods. I just really, really think that's important. Not to think that video is just this blah thing you do and you can't prove anything blah, you know. So standards. Now, we have developed standards and actually, Tim, occasionally, for example, and other people have had input to them. And my approach to standards is that XML is actually standards, neutral assistant and notation of syntax and that within our XML standard our goal is to try to be sort of a Hindu like, that is to take in the many different religions and make them all merged under one. So we spend time after time trying to take different people's standards and actually crunching their data. And it's a lot of work. It's programs that can reformat one standard into the other, and eventually they all become inter-translatable. But you have to then also do a lot of work to verify that you went from one standard to the other and came back and you had identical data. It's called round tripping. Okay. And, of course, we have analytic tools, transcription tools, linkage to media. You know, all of this technology I think is now getting fairly mature, and I don't think the technology really is in any way the barrier. And actually, I don't really, really think the standards are a barrier either. I think we need to have a lot of communication and talk about them, and there are some rough edges. We've recently tried to pull in Elan gestural data, and we found that our chat coding system has to be hacked in certain ways. And so there are certain places where, you know things have to be done, but they can all be done. Conduct standardization as Roy and others have said, that's a problem that's going to go away. And another thing we've done is streaming media server which is locally deployable. So we have streaming media from the child servers at CMU, but we also, I'll have another slide on this later, can also deploy that so that if you don't really, really want your data to be public you can run all the software locally and still get the same effects within your local group. Then, of course, the meta data issue. Not all groups subscribe to OLAC, put all the data which is Online Language Archive Community. There's the Online Archive Initiative and so on. And if we also put ISBN numbers on everybody's corpus and so on and so forth. So there's a lot of meta data. And that's an open ended thing. Meta data can go on I think till the end of the field, and that's good. But we have frameworks for distributing it and there are depositories and all that stuff. So the technology's really there. The standards of available programs are available, stable, and tested. Streaming is solved, and now, by the way, Bennett Brentendahl over here is not talking, but he should be in a sense a major player in this. Bennett? Yes. And he's, you know, developing a project with NSF support called Super Lab. Is that the right name for it or you have another name? ROY PEA: Social Informatics. BRIAN MACWHINNEY: Okay. And Super Lab is the program inside? Yeah, Social Informatics Data Grid. So it's not just SID. Right, okay. Yeah, right, okay. And taking a lot of this and making it on the grid is the emphasis there. And then, of course, collaborative commentary as Roy talked about and I guess Ken talked about. In fact, everybody talked about it. Ricki talked about it, too. Right. So really, Orion and Web Diver and Project PAD and Talk (Inaudible) Viewer and Clan Web Data and all these things are allowing us to do that. So technology's really there. Data sharing is a bit more questionable. And I sort of -- my view of data sharing in this community has slightly changed over the last two days. What I see here is data sharing within labs. I think this -- and then maybe that will change in the future, and that would be great. But the reality is that I see people who when they talk about data sharing, they mean sharing with my students. Which, you know, is still maybe a useful thing. Within Chile's we take a very different approach which is data sharing within the community and that the shared database becomes a definition of the community. So now, I wrote this before I sort of started to have a change of feeling on this regarding this field. But in any case, it's certainly true that data sharing's not important for an established researcher. They don't have to share their data. It isn't going to change their career in any way. It's more a question of what's important for the field. And I think for the field it's pretty important. You might say, for example, would Google have happened if there hadn't been open access to data? You know, just think of that. The other way is that raw data is really infinitely rich. And you're not really going to ever get scooped. Okay. I've never seen a case. You might not get cited, and that we have to do something about. That's happened about three times in the 25 years of Chile's where people didn't get cited for the work they did. But really they never, ever got scooped. Okay. I believe a tenured faculty have a responsibility to share data. That's what tenure is about.