FAIR Findable #1 - Into to FAIR and F for Findable - 30-08-17

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FAIR Findable #1 - Into to FAIR and F for Findable - 30-08-17
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#1 FINDABLE covers: -- an overview of the FAIR principles: their origins, Australian FAIR initiatives, what FAIR is (and what it is not) -- the 4 FINDABLE principles which underpin the discoverability of data -- resources to support institutional awareness and uptake of Findable principles to make your institutional data globally discoverable
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welcome everybody welcome to this the first webinar in the in the series of four webinars about the the fair data principles my name is Keith Russell I work for the Australian national data service and I am your host for today and with me I also have a Nick debugger and he will be speaking later just to give
you a bit of background usual background about Alan's what's going on the Australian national data service we work with research organisations around Australia to establish trusted partnerships provide reliable services and to enhance the capability around the research sector around research data we work together with two other increase funded projects that's RDS research data services and nectar and together we create an aligned set of joint investments to deliver transformation in the research sector so this webinar is the first in a series of four and what we will want to do in these series of four webinars is given a bit more background on the fair data principles and we've broken them up into the default principles findable accessible interoperable and reusable so for today the focus is going to be on general introduction to the fair data principles and especially to look at the first principle which is final so the
speakers for today will it's me I will give an internal introduction to the fair data principles and a little bit about findable and then I'm very grateful that Nick kleeberg has made some made time available to Nick is the director of parody sec to talk about how parody SEC has made their data findable and I think it's a great example that will sort of show what it means like in practice because the fair data principles are quite high level and general and I think the Nick can talk speak much more clearly and give a much clearer example what that actually looks like in practice and what findable can be can be how you can actually adopt findable and use it in practice so to start off what what are the fair data principles so the fair data principles were drafted by a forced 11 a force 11 is a community an international community of scholars and librarians archivists publishers and research funders that's what came together organically started in 2011 hence the force 11 and as being around ever since that date and what this group this community is looked at is to sort of facilitate change toward improved knowledge creation and sharing and as they were working on this include a dozen and 15 they came together and they said well be good to have some principles around research data and the sharing of that research data and how you can let's do that so late in 2015 they drafted these four fair data principles and in early 2016 they wrote an article in which was published in nature about it and from that moment onwards the ball started rolling and these principles started to receive attention and international recognition sort of this is actually quite useful I think a number of things to keep in mind if you look at the fair data principles and probably the reason why they are attracting so much attention is there there are a number of things there I think one of the things there to note is that they don't just look at making research start a human readable but they also look at making dirty research data machine readable and I think that offers a lot of opportunities into the future by making by thinking towards the future situation in which research start is machine readable can be harvested by machines can be pulled together can be used for big data approaches can be used for novel approaches in exploring data and knowledge creation and finding patterns and different knowledge out of that data I think is a an interesting step into the future another thing that is quite valuable I think about the fair date principles is that they are technology agnostic there's an if you read them you'll find there's no one recommendation to go with one specific technology it's formulated in a way that different types of technology can be used to solve solve the challenges another thing they did they've done quite well is to create a set of principles which are discipline independent so the principles can be your across different disciplines in different ways meeting the needs of the specific discipline also if you look at the principles it talks not only about the metadata and it not talks not only about the data but the two combined and where we're working on the metadata can enhance the visibility of the data for example all the reusability of it so you probably have noticed by now there is an acronym and it stands for findable accessible interoperable and reusable the are reusable is the one that's sometimes we results in a bit of confusion people think that it's actually reproducible but it's actually reusable it's a broader concept so just keeps just keep that in mind we'll talk about each of those principles in more detail in the coming weeks before we move into the first one findable I have a few general pointers which probably worth keeping in mind as we look at the fair data principles so
one of the questions I get sometimes is well do you want all data to be fair and I don't think that is the case I don't think that is necessary and I don't think it fits in with research practice if you look at researchers as in the process in which they create research data there are various steps in that process and in some cases huge volumes of data are being created out of experiments coming off instruments etc these huge volumes of data can't be kept or stored in that original form they often need to be manipulated analyzed process etc so these huge volumes of working data are probably not suitable to be made findable accessible interoperable and reusable it's rather the data as it moves through those steps in the final resultant analyze data is probably more suitable for that purpose researchers all stand on sometimes you scratch data to explore different experiments explore different settings see how things work not all of that data is worth keeping or worth to keep using where I till the end now there are also cases in which there are there's research with interests may be commercially funded Ian's in that case there can be arguments why especially those commercial parties are not interested in having any of that research visible or public to the outside world in that they want to keep it quite to themselves that this research has taken place this also happens in case of national security and defense research so in those cases that probably does not make a lot of sense to make any of that research data findable accessible interoperable reusable one question we sometimes get is well what about data that contains data about human subjects we where there's privacy ethics considerations around the data should that data not also be kept hidden or private now there is a distinction here between open data and fair data so in open in the case of open data we're talking about making everything open in the case of fair actually talking about making it accessible through the appropriate routes and that doesn't have to be open so in in the case of human data that refers to human subjects identifiable data they might well be a very good argument why that data sure cannot be made openly available but it can be made accessible through appropriate routes in that case it would still be fair because it would still be accessible however it would just not be open we'll talk more more about that next week when we get to the accessible point well what what the fair data
principles are not about and this is something that varies only sometimes it crops up in copyright law let's talk about fair use and fair dealing that's not capitalized that's in lower case that's something completely different and it's not related to the fair data two principles one of the other things I ran into recently was turns out that a number of market research companies actually have developed their own fair data mark which talks about how these market research companies treats the data that they collect as they're doing their market research that is also lowercase and that is completely not related to the fair data principles in capitals one other thing that's worth keeping in mind is that fair is not an actual standard so some people expect say well I want to make my data fair and I want to make sure it fits all the boxes exactly you'll notice what as we started talking about the the Fair principles when digging to them in more detail it's actually not that black and white it is a set of principles it's a set of ideas about how you can approach it and how you will actually approach it in practice will probably depend on the discipline so there's not one standard therefore that will work across all disciplines another thing to keep in mind about the third out principles is that if you want to achieve if you want to make more data more fair it's not just about the research data itself but it will actually do require some work around it so it will require a layer of underlying infrastructure and that can be human infrastructural technical infrastructure which is in place so that a researcher does not have to do it all on their own but they'll actually be things in place that will make it easier for the researcher to achieve making their data fair so things that you can think about there are policies around making the data fair procedures and guidelines that might be in place be great if there are tools or platforms or software in place that actually make it easier for the researcher to make their data fair at the end of that workflow and finally it's going to be important to have the skills and the skill set available to the researchers of research men the data managers librarians research analysts in research staff all the different staff members that are involved in that process to make it easier to make the data fair down the track so I think one of the questions I get is why why is it now these specifically these fair data principles are coming up and why are these being adopted so widely or I think for one thing they've got an attractive acronym I think the other thing is that it covers quite nicely work that is already being done you look at them in more detail you'll find that some of the things that are covered there are actually things that that organizations around the country have been caring about for a while and you care about more and so some of it is probably not it's less about a completely novel approach but rather bring it together under a nice acronym in a you know well packaged form I think other reasons why it's proven to be useful to all the first of all its receiving a lot of international recognition is much as the national initiative if you look at the principles there actually there is actually quite a lot of detail hidden below them and quite useful detail the fact it is discipline independent makes it easy it is not as hard as sell as making all data open the only challenge here and this comes back to that point about the fair data principles not being a standard is that it is hard to measure it's hard to hold up to a list and say this data is fair and this data is not fair at all there is a more of a more of a scale from being less fair to more fair so if you're looking at where fair has been picked up and in various ways there's plenty of examples out there I've just picked off a few here some of them International some of the National some one disciplinary so for example in in the European Union the high-level expert group working on the European open and science cloud sort of picked up the fair data principles and embedded that in their work and their thinking around what the European open science cloud should look like if you look at the horizon 2020 funding program by the European Commission that's also drafted guidelines for data management and in those guidelines they also use the fair data principles if you look in the US NIH has just set up a data Commons pilot in which they wanted exploring and what a cloud would look like for for sharing research data and there they're also looking at the federated principles in the Netherlands initiatives being set up called go fair which is now reaching out to get more international momentum and more international partners that's also a very interesting development in that they they have they've looked at the fare principles and also how how you need different elements to support that including cultural change training and building an infrastructure to make sure that data can be made fair easy
in the UK there's a currently a program project going on around fair in practice and taking the fair principles and exploring what that means in different disciplines the American Geophysical Union has just come up with a project in what I think was only yesterday the press release window that what they were looking at is what it will mean to make data open and fair in Earth and Space Sciences exploring that further and closer to home here in Australia one thing you might have already heard come by is the fair access to research outputs policy statement which was drafted and is now available for support by institutions around the country and the focus there is very much around research outputs in the AOC NHMRC definition as in the publications and Conference proceedings all sorts of publications materials and how those how those materials can also be made there so that was a long-winded introduction more in general about the the fair data principles what one principal I wanted to talk about today is the first of those and that's findable and if you look at the actual principles and the way it's described findable is broken down into four elements so for the research data to be findable in the principles they say that the data and the metadata should be assigned a globally unique and eternally persistent identifier well in practice that just means it needs a either a DOI or a handle or a Perl some identifier which is globally unique and eternally persistent and there's an organization that sits behind it that cares about making sure that that identifier will resolve to that data set even when that data set would move this is where that example of being technology independent comes up they don't recommend one over the other any of those solutions works as long as their identifier is in place and it gradually resolved second heading there is that they say that data should be described with rich metadata that's great however they don't specify what rich metadata means so there is this is one of those places where it's not black and white if your data fair or not what we'd say is make sure that these enough metadata assigned to alongside the data so it can be far found that it that it sort of answers the right questions for us from somebody that's searching for your data the third heading talks about the metadata and the data being registered and indexed in a searchable resource so there's different ways in several ways to tackle this and a number of number of ways to think about that is while having a search interface locally a database locally some way of making sure that your data collection can be found so a search interface but what we'd also definitely recommend is making sure that the data collections are that the descriptions of the data collections are passed on to aggregators national aggregators for example we searched art Australia but there are also other aggregators out there are more disciplinary aggregators like turn they might go out into an international disciplinary aggregator like Olek open language archives community yeah and it could also be data can also be published in national international disciplinary repositories like Pangaea for earth and environmental sciences or the in the case of astronomy for example international virtual Observatory Alliance and the systems they have in place so there's various possible routes to publish your data but make sure that it goes into a place where it's can be searched can be found and also will be indexed by search engines like Google Scholar finally last point and this really comes back to the first one is if you're going to have a globally unique and eternally persistent identifier for the data collection like a DOI or handle or perl make sure that that is actually captured in the metadata okay so that was the first that was a quick overview of findable and the way that they have described findable now I thought it would probably be of interest to show what that means in practice and it means like in practice to actually make data findable and I'm really grateful that NYX made some time available to to give us a short presentation about parody SiC and the work that's been done in parody SiC to make their data findable I think it's a great example of how they're in the course of several years and building up building up experience slope which surely made their the audio recordings more and more findable using all these different elements that I just described so I'd like to hand over now to Nick and oh and one thing I also want to add before I before I hand over to nick is paradiso also done great work on making their dateable accessible interoperable and reusable however today I've asked Nick to focus on the findable side of things so please keep that in mind in
his presentation and and there's also a lot of other world work that produce X done in those other aspects too so I would like to hand over to Nick and then it can talk about finding in parity
second great thanks very much Keith and I'd like to thank ends for the support of parity sake over the years so
Paradiso k-- has been running for some time and as you can see it's become a significant collection we have 31 most likely more than that because it's increasing almost every day but around 31 terabytes of material representing over 1,100 languages and you know 160 mm files and seven and a half thousand hours of audio so it's a significant collection and there's a huge management task involved in that and one of those tasks also is making sure that this material is findable by the people we
want to find it we have a catalogue that we've been working on for a number of years we've built our own unfortunately we didn't find one on the shelf that we could use but the catalog allows you to look at material with Geographic point of entry into a faceted search we have our ice open archives initiative and dublin core based metadata we try to be as lightweight as possible
with the metadata because our experience we're all researchers I'm a linguist my colleague Linda Berwick is a musicologist and our experience was that people just wrote into a metadata if it's too complicated so we've tried to make it as simple as possible and to make the catalog do as much of the work for you as possible so using controlled vocabularies doing predictive data entry and having you know minimal number of fields as you'll see here we have is a screenshot of catalog we have the possibility to make the metadata private so as keith was just saying fair doesn't mean that everything has to be made publicly accessible if you're constructing a collection you can keep all the metadata private and then publish it when you're ready you can also assign various kinds of access conditions including you know open subject normal conditions or closed subject to whatever conditions you want to specify because our project is really focused on language materials from small languages that is all of the 7,000 other languages that are out there in the world we include language identifiers for subject language and content language of items in the collection and this is the linchpin that lets us then feed to a number of different harvesting services that I'll show you in a minute our online catalog lets you specify geographic coordinates which then also allows you to search using that
geographic information because of the work we're doing we have lots of connections into the region in particular the Pacific and we are actively seeking collections in the Pacific collections of analog tapes that need to be digitized and you can see the various agencies there that we've collaborated with and continue to collaborate with digitizing hundreds of tapes and then putting them into a collection and making them accessible so
when we talk about findability we can talk about the sort of granularity of finding we can find collections and we can find items and we should be able to drill down into collection to find things that were particularly interested in so we can sort of characterize findability on a scale if you like from from zero to ten so if we talk about research materials
primary research materials that people have in their offices or in their homes typically the findability of those things is about zero it may be one if your colleagues know that you've done this work and you have these tapes sitting in your office but a speaker of the language trying to locate recordings that you made with their grandparents they're not going to be able to find that material so from our point of view in producing we infer that these records must exist because we know that the research has been done so we can go looking for it and then what we can do with that is we could add records to our
catalogue pointing to analog materials and we do this in some instances we also point at websites that we know exist so there are some fine websites that have language materials on them but the websites might be transient and what we then do is point at the wayback machine or the internet archive entry for that so here's an example of a text that was produced in the solomon islands put online by the project Canterbury which is a Anglican archive online archive but it's a website there's no guarantee of longevity and so by us putting it into a catalog it then makes it available and findable via the search engines that we'll see in a moment so then we increase the find ability of that to perhaps 3 out of 10 and using the language identifier so there you could see the three-letter ISO 639 3 code for languages in this case it's lkn what
we've also done is provided images of manuscripts so this is a collection of papers produced by Arthur Capel during his life he was a professor of linguistics at Sydney University when he died he left a huge number of papers which we then digitized we just set up a camera and took images of all of these papers and as you can see in the bottom right there there are a lot of handwritten original scripts which were really valuable from a research perspective but you know sitting in a box in his executives house they're completely unfindable so putting entries into the catalogue and we put this through the Heritage data management system to put a HTML framework around it and you can then find these items and you know resolve to
the level of the image know what you can't get to the transcript of the image because at the moment although we have the images there but one of the next things that we do to increase findability is to include transcripts together with recordings so here's an image from a catalogue and what we have is time aligned transcripts of recordings these allow us to play the recording and you can imagine because I won't show it to you that as the recording players it scrolls through that transcript so this is increasing findability significantly you can resolve down to the level of words and find them in the context of the recording one of the
other things that we do is we embed some metadata into the header of the wav files in our collection we create a broadcast wav format file which is the european standard for archival formats of audio files and you can see a little snippet of XML there which is extracted from a catalog and insert it into the wav file before it's all sealed up and put into our collection we use
persistent identifiers of various kinds like because the collection started as I say 15 years ago we have a internal persistent identification system which is a collection followed by an item number more recently in the last couple of years we've put DIYs through the whole collection so we have do is from the level of each file up through items and up to the collection level you can see also that we have a Zotero and Mendeley integration so that also makes things findable in that people will cite these items using this form and they can click and insert them into their Sottero armindel a toner basis we have an API we
have two seeds that we produce so people can link into collections with sis is at the collection level and that's what's harvested by research data Australia and other services a trove also harvests that material and the oai-pmh feed is primarily targeted at the open language archives community so linguists have been very good at setting up services based around these language identifiers and the Olek page allows you then to look at all the material that's produced by any one of their 60 member archives for any given language so it's a fantastic resource for finding information about the world's languages and if we update an item in our catalog then the nightly harvest from our lake will update that oh like harvest the next day so as you can see research data
Australia takes feed and and produces it in interesting ways so the benefit for us is not only that our material is more findable but some of these services present the information in our catalog in ways that we don't say you can do faceted searches and some of these services and it also links into all kinds of other services and data providers that allow you then to do interesting new searches there's the
open language archive community page they have a faceted search on the right and a whole lot of services that they provide are advertised on the left there if you're interested in languages at all the really really the the one-stop shop for finding information what's what in any archive in the world in their harvested system this is the virtual
language Observatory which is a European service funded by Eclair and in Europe they also take our feed and you can see that you can search a collection through that service as well
and WorldCat the international catalog of all libraries also takes our feed so
that's sort of on the the the big picture side of it and international search engines on the other side the people that we want to find this material out in the Pacific and we've been working very hard to get material available in forms that can be accessed by people in the Pacific on the top right there's a really interesting little project that was run in madding where they took recordings and played them at a local market and asked people in the market to comment on the recordings perhaps enrich the metadata in that way they then sent that to us in a spreadsheet which we were able to record into a catalogue and at the bottom you can see a speaker of one of the languages who happened into my office in Melbourne and went through the collection and found his grandfather speaking and he was quite amazed by by that so there's an example of how unfindable has both the material can be that he had to come into my office to find it and that's one of our big problems is how to make the material in our catalogue accessible to people who aren't perhaps always looking around on the web because they just don't expect to find material in their language on the left there's men who's working in our office in Sydney this was an ends funded project to enrich the PNG metadata in our collections and he's going through listening to material and adding metadata where he can so what are
the other ways that we're promoting the collection is by building a virtual reality project so what you're looking at there is a map of Vanuatu in each of those shards of light coming up represents a language where there's a little symbol there you can listen to a snippet of the language which comes out of the paradise thick collection and you can see some information about how much we know about the language what there's a grammar if there's a lexicon and so on and how many speakers there are of that particular language now this is generating a lot of publicity as you can see on the right there's an article from the Papua New Guinea post Korea and on the bottom right there's an article from written about this in pursuit of noble University and so on getting this publicity is important exactly so that people will then going to look in the catalog and find information or think about collections that they have that need to be digitized so this is it's an investment of time and effort to build the virtual reality but it's captured a lot of public attention and it's also you know a research output in that it it is driven by well-formed data in the in the parity sec collection we've automatically snipped 20 seconds out of audio files and used the naming convention and the metadata that's in the catalog to then feed this virtual reality display so
ultimately we do want to get this material out to the Pacific and what's amazing really is that now most people in the Pacific have mobile phones that are accessing the internet on the right you can see a poster for the internet on your phone in Port Vila and Vanuatu and on the left you can see a church but above the church there's a mobile phone tower which is now the way that people are accessing all this kind of information so we want to make our material findable for people in these remote locations even in the Highlands of Papua New Guinea or in the most remote parts of the Pacific so the catalog is is findable to them through various means including of course googling but we also need to make the data accessible interoperable and reusable for them but I'm not going to talk about that now so paradise X
created a standard metadata set that means that as the data comes in its described with as a light touch as I say we apply as much metadata to items as possible but for some of the legacy material there's just very little metadata and we have to infer what we can we also rely on people putting that metadata in online if they can or sending information to us we are always open to enriching the metadata that's in the collection the main point of the metadata is that you are able to then locate the primary records and have them play to you or see them or download them if you have the privileges so all of that makes it more accessible and findable and I hope that much theory of the metadata through API is for our discipline-specific and more general search tools that make as well we do many things to try and publicize the existence of the collection including what may seem gimmicky virtual reality or augmented reality but all of this goes to increasing public knowledge of the collection so that will it will increase find the findability but also increase our location of analog data that needs to be digitized part of all of this also requires data management training so that people know about how to build their own collections so we do a lot of training of researchers here in Australia but also in the Pacific and we also have a lot of engagement with community agencies in the Pacific and try to get funding to run digitization programs with those agencies so that's
our story about findability thanks Thank You Nick Thank You Nick
thank you for presentation it's a really interesting example of how you've taken up the findable and adopted that which is adopted that in a way which relevant to the language community and relevant to audio recordings okay so now you can
see the resources on findable hopefully there's links here to do I do iron handle minting service that ends before it's a link off to a metadata standards directory if you're looking at different metadata standards what will be a good one to use that will be relevant to your discipline as a link off to the national australian research data discovery service research data australia and if you are looking at more international perhaps disciplinary repositories where you want to make your research they are findable have a look at really three data and international initiative which lists all sorts of different repositories and parody six in there too finally if you want to have a bit of a crack at research data things we had 23 research starter things last year and there are a number of exercises there in which you can learn more about what does it may mean to make your research data more findable etc well we've picked out three of those which are especially relevant to the findable space so if you're interested have a look at those three things and see if they clean things there you might like to look at so finally I would like to
thank you all for your attention and I'd like to I'd like to thank the the National collaborative research infrastructure strategy program which funds ends and makes this all possible