Data Citation at the UK Data Archive - 25th October 2012
Corti, Louise
Dr Louise Corti discusses the UK Data Archive's chosen Data Citation methodology and what they are doing to consider citing parts (subsets, versions, time slices etc) of data. Louise is an Associate Director at the UK Data Archive, having strategic and line-management responsibilities for the sections of ESDS Qualidata, Outreach & Training and Research Data Management Support Services. She is also Principal Investigator on three awards - two ESRC and one JISC - on research data management and a co-investigator on the ESRC/EPSRC TEL Ensemble project. She is currently leading the rebranding work for the UK Data Archive and is leading the Archive's work on qualitative data metadata standards as part of an international working group.
 Thank you very much for inviting me to give this presentation and I'm very pleased to be part of this and series on data citation which has already produced some wonderful presentations and some great web content as well. So very nice to be added to that portfolio. So I'm going to spend a little time at the beginning just explaining about the data archive and its collection to give you a little bit of an overview and then I'll move into talking about the data citation methodology that we've chosen and implemented at the data archive. Okay so based at the University of Essex for quite a long time now, over 40 years, and spent selecting, ingesting, dealing with a variety of social science data sources and explain a little bit about where they come from in a minute. So we've been designated by our National Archives as a place of deposit which means we can hold public records and a lot of the data that we bring in and support services that we provide support higher and higher and further education research, learning and teaching. So we kind of really can support any data user but our primary type of audience is higher and further education although that includes also many other researchers who are who want to use social science data. More recently last year we went through a long process that enabled us to gain the information security standard ISO 27001 which means we can hold disclosive data and that was a lot of auditing and a lot of work but it means we're a trusted digital repository in that sense. Excuse me for the slow action. Right so just a little picture of where we are so you get a flavour. We have got a very nice green campus with buildings that were built in the 1960s and some of the towers you can see there are actually listed buildings, they're ugly but they're listed, that our campus is based on an Italian village called San Gimignano. It doesn't quite look Italian or medieval but it's a nice place to be and we have a brand new building that was built about three years ago dedicated for the data archive and for the social research centre that conducts our panel studies in the UK and that's our nice building on the right there so just give you a flavour of what we look like. So this is the data archive website, this is our data archive website, you can go and have an explore of that later and this particular diagram says that we're part of a bigger international federation of social science data archives around the world, one in Australia sitting down there in Canberra and one in America who we work closely with at the University of Michigan so most of the western Europe do have these data archives and we're part of this bigger international network and we work very closely together on metadata standards, solutions, training materials, those kinds of things. So we have a family of services, we were running a number of different services you can see listed in grey on the left, from October the 1st we have a brand new grant, a five year grant called the UK Data Service that brings all of these data services together under one roof and we're currently being rebranded so we don't know quite what our identity looks like at the moment but it will wrap in all of these support services that support all kinds of social science data in the UK. And we have a number of research and development projects, some on metadata, some on repository systems, on many different things actually, some on providing access to geodata, others are about preservation and preservation standards but we do bring in quite a lot of grants to do kind of forward looking research. We have as an organisation more recently in the last year adopted the OAS functional model which means that our whole divisions are based around these particular sections of ingest, data management, storage, security, access, preservation so as an organisation we follow this model, we have two slightly different adaptations in that we have a sort of descriptive information here, we have an area that says, that's supposed to say pre-ingest there so we have an area of kind of metadata on its own that sits as a department and we have an access and data support unit and a lot of work goes on there to provide support and access to users but it's nice to follow a functional model and it seems to work very well. So where do our data come from, a lot of it comes from central government agencies like Office of National Statistics, like the Home Office, like the Department of Health and they give us important government series and some of them are dating back to 1960 for example the what was called the Family Expenditure Survey combined with a food survey from the 1960s, we have got a series of data going all the way back to 1960s so it's very important and time series collection. We provide access to statistical time series, some of the macro data banks from international government organisations like the World Bank and INF. We do gain grant data that comes from research projects when an academic is funded by the Economic and Social Research Council, as part of their grant they have to offer data so there's a stream of research data coming in, which can be anything from surveys to qualitative data, sets of interviews, psychology experiments, many kinds of very vastly different data sources actually. We collect data from market research agencies, polls, do have some historical sources, historical databases and records going back to the 14th century and of course we do facilitate access to data in all the other data archives around Europe so if somebody wants data from Finland we can help them acquire that. Just to give you a picture of what we do, it's about 6,000 data sets in our whole collection. We add about 230 a year, we've got about 20,000 or so active registered users and probably down there is something like 60,000 data sets a year. We have a lot of activity in our users before unit. So that's the kind of size that we're looking at. We've also on the other side done quite a lot of trying to promote use of our data so we've got a whole website here that tells you about what people are doing with data and they're kind of short one page descriptions of what data you used and what you did with them and it is about something like a 100 in there at the moment and they span between the purple ones research and the blue ones teaching. So they're a nice promotional tool for producers and for users to see kind of what's happening. So you can go and explore some of those if you want to get a flavour of what people are doing. Back to the topic of data citation. For many years as part of our end user agreement for actually accessing data through our online system you have to acknowledge the original source and there's a statement there which is part of the end user agreement number 9 that says you'll acknowledge in any publication the original data creators. Now that is a contractual agreement and people are supposed to do that but it's very hard to enforce because even though we provide a method of citation people don't necessarily do it that well particularly when the titles of the data sets are complicated. So just to show you there we've always given information citation information as a separate file which tells you that you have to cite the data like this. So we've always had a way of doing that actually even from the 70s quite early on but of course this is different from an acknowledgement it is a citation and an acknowledgement many people are using those they're not adequate as we know for the citing data and this is why we've moved to a more robust system of citation. So you all know why we should cite data I'm sure you've seen some of the wonderful presentations from before but it's about acknowledging the author's creativity and sources and identity as well, helps you find data, helps promote reproduction of research so you can get back to original results and play with them. It allows the impact of data to be tracked we now know that if you put a DOI into Google you can just immediately get some idea of what's happening to the data and it provides a good structure which I think data craters will feel that there's more incentive to deposit data now that there's more of a reward structure. So there's lots of positive things. So our wish to use persistent identifiers was going back some time and we wanted to use them and for us we've already got URLs, we've already got citation, we wanted something a bit more permanent and unique and our definition of digital objects was the thing that took us long to kind of decide exactly what we wanted to do but it should be very clear what the granularity is and also as we know there's a lot of identifiers being applied to many different things and as you probably know if you haven't you can go and get an orchid idea ready which gives you a unique idea for yourself as a researcher for some of these hard institutions. So this idea of permanent identifiers is very important across our whole community particularly for long term data providers. So our data collections are not actually digital objects, they're collections of a set of materials so one of our collections could have 100 files in it or it could have one file, it's a collection of materials that make up one particular study and that's important for us, our unit of description is a research study or a particular survey that was done. So we wanted to capture changes made to data and we do really version data but we wanted to make sure it was commonly understood we had a robust way of versioning our data and we wanted to think about what a significant change to data might be and that's very important for us and we wanted to use highly structured data so that machines could understand what we were doing. So our other requirements were our current digital preservation activities, this particular flow fitted in with that and because there's a lot of investment in planning this we wanted to get it right the first time rather than have to go back and start a different system so lots of things to think about at the time of planning. So just to kind of iterate there about 15% of our collections do have some kind of change in the first year which is a bit of a pain really but we often get issued new data sets, new variables, particularly new variables they've upgraded a variable, they've derived something different, they've replaced it, there's quite a lot of changes which we issue as new additions and it's not always clear which addition you're working with so we wanted a more robust way of kind of signalling those changes. Another example is if you're on a serious longitudinal study we're collecting data every year every time you add a new wave it creates a new addition because you add that particular wave to the existing years of data that you have. Regrossing is sort of balancing estimates so they're fitting with current population, trends, current population statistics and sometimes we do that to data and also there can be changes to documentation, new parts added, bits that we're missing so there are these ongoing changes which means we have to reissue data so versioning is very important for us. So one of the important things, sorry to go back, one of the important things is we've wanted to distinguish between what is a major change and what's a minor change so that's been the most important thing for us because some of these have much larger impact than other changes to data and we wanted to try and make sure we're not versioning every single change that happens because that would be difficult to keep track of. And actually on the whole although there's a lot of talk about verification and replication of data in our field we've mostly seen people wanting new data for new research to ask new questions of data and so at the moment we do just make current versions available. Early versions can be accessed but not from the website you have to come and ask for them but that's something we need to think about and are is how we make previous versions available but sometimes there could be 20 different versions and additions of one data set so you can see the problem and most people really do want the most accurate up to date one. So just trying to define a little bit for you what we mean by low and high impact. Just an example of low impact there is somebody just might simply add a reference to the bibliography. They may have made spelling mistakes and that can happen quite a lot but sometimes we get variable supplied that shouldn't be in there, accidentally supplied. Sometimes there's minor changes in documentation that are not going to affect the content or the way that you use the data. Sometimes we add new terms, we do index all our data using a thesaurus, sometimes terms change they get outdated, new terms get added so we do reindex sometimes. And then sometimes we add little extra bits of documentation because they've come about and sometimes there's a change in access condition if you're changing from maybe data under public license to a more restricted license so that there can be changes in all those kinds of things to think about. And then in terms of high impact that would be something that's really going to affect the content and usability of the data set and that would be if you've added a new variable that would be important to know. We might have new value codes so somebody might have actually changed the way in which they're giving the different options for the data. Instead of a yes-no you could have had a yes-no, I really don't know that could have been added as another code. So sometimes that does happen. Sometimes the waiting variables that help you kind of balance a data set have been changed, the statisticians who own a government survey might have done a lot of work afterwards and these changes are not available till they've actually given us data. Changes in formats, yes that doesn't happen very often but it could happen. There could be file migration up to a new version of data and that could signal quite a big change for people. So they're the kinds of things that we want to distinguish between and that's very particular to our sorts of data but some of those have relevance to other kinds, scientific data, other kinds of data about what's really important and what's not so important to be used to think about. So the next step is trying to find an instance of a change collection. What is this collection and how is it changing? So we do make various changes to data and they might be internal changes that have the point of ingesting data and we don't particularly release those changes which will for us be something that changes internally but not externally. What's more likely to happen is there's low impact changes where we release the change and we have a new external instance that's got the same identifier because it's not important enough to change it. We have a high impact change where we release a new dataset and we decide that it's worth having a new identifier and I'll show you what these identifiers mean and how they change. So we started working with data sites and we've got the British Library data site who are one of the agents bought for assigning DOIs. In 2011 our director, Mackie Woolard, has been very involved in this whole area for some time. As you probably know, data sites have been founded for some time. It's actually so in format for citing data and it works quite closely with data publishers to try to promote the idea of data citations. Not only does it provide a methodology and issues DOIs, it also has quite a lot of advocacy in this area. So what's DOI? I'm sure most of you do know but it's about persistent identification of objects, of digital objects. A string of letters and numbers, nothing that complicated, a systematic string of letters and numbers. And subscribers who want to get DOIs basically join data sites and they get access to DOIs that they can get once they've got a contract and then these particular records end up in their metadata store and resolve the system. There seems to be, I'm not sure how many, last count I think in May, there was about 1.3 million objects in there, there's probably a lot more now. Of course there's other handling systems out there but we've actually chosen to go with a data site because we work very closely with our British Library on many other areas. So how they created? So as a data publisher which is what we are, we register and obtain DOIs, we mint them, we have a system for doing that with our own infrastructure, our redeeming with metadata and we use the API to get the DOIs and it's actually a fairly straightforward process and I'm not going to go too much into the technical side today. So thinking about what do you want to allocate your DOI to, it's got to be allocated to some kind of object or something, something that's got an identity. We actually use our core metadata as the theme that relates to the data collection. So you think okay core metadata, title, this is the name of a particular study, that should be quite straightforward, it's not always straightforward because sometimes the titles of the studies change which one might not expect but maybe one of the government departments decides it's changing the name completely and we need to somehow reflect that change. So our solution is that if we have metadata, is the object that our DOI identifies and that's an external instance. So it's representation of one of our data collections at a particular point in time. So when you get a DOI and you type it in somewhere, it results to a jump page which actually lists all the history of the changes to the dataset and then that then points to the catalogue record where you can go and get data. So there's a kind of front page that gives you the change, the change log and as I said before high impact changes denote a new DOI. So showing what this looks like, for example here this is a DOI on the left, I'll show you the structure of what I'll look like, hypothetically it's a study number SN, study number 2000, version 1. This denotes a study number that's got, this is a survey that's been continued over 13 years, it's got weights 1 to 13 and this is an instance specific dataset so it's fixed in one point in time and it has its own accompanying metadata. The next step would be we've actually made a change, as you can see the blue box in the middle, we've added another wave so that means we've changed our DOI so it's a slash O2 now and again this is instance specific dataset, it's actually different dataset, it's changed and it's got a different metadata record. And then finally there we've added another wave wave 15, it's changed, the DOI's now changed it's an O3, instance specific metadata, when you go to our jump page you will see that history but you will be taken to the most recent version of data because at the moment that's what our data want, that's what our users want, we don't give them instant access to some of the older waves 1 to 13 because actually our kind of data, waves 1 to 13 is already available in waves 1 to 15 so you can still get at that particular dataset but it's something that we want to think about how we do provide access to previous versions. So this is an example of our jump page, if you go to one, if I've plugged in this DOI that's 10.525 slash there, I would get a little page that gives me a kind of pot of history of what's going on so it has the most recent DOI change dataset version at the top, 5 and 3, it gives me the citation and the change log so it actually gives you a very short human description of what's happened so in February 2012 for the third edition we added in wave 2 of the study so you can instantly see the changes as you move down, you can see what the older datasets were so it's quite a simple way of defining change and turning people what the versions are doing. And then in terms of the way that we create and update DOIs, we will create, well when we create a new catalogue record so when we've got a new dataset and we're ready to publish that, we mean to brand new DOI through datasets, we update that change log and we create a new citation file because the citation has actually changed. We have something that's part of our documentation called a citation file which people can download and it gives you the way of citing data. Also if we update a catalogue record by implementing various changes we decide whether the high or low impact changes, we create or update a new DOI if there's a high impact change, we update log and create a new citation file so there's that process about bringing a new dataset and updating all the ones. So here's our format as you can see this is a kind of structured DOI that's very similar to what other people are using, you've got your own identifying organization, which for us is 10.5, 2x5, we've got our readable identifier which is UKDA, SN is what we use to study numbers, all our collections are called SNs, they're the number 1, 2, 3, 4 and the results identify our total issue, what the actual dataset is, they go up to 6,000 at the moment, we've issued about 6,000 of these things for our collections and the results version is what I was talking about something that is the current version and that will change, that will increment every time we have this high impact change. So on the left there at the bottom, high impact change means you simply just go from 1 to 2, so it's quite straightforward. So the low impact change, we don't change the version but internally we're keeping track of what's happening so we know what's the minor version. So we know that there's been a change but it's not denoted externally. And just an example here, if we have a DOI, if I plug in the DOI at the top in the data site metadata registry or search interface, it will give me a link to the dataset and if I click on that at the bottom screen, it tells me where the landing page is so I can actually go and resolve to the jump page of the data archive and it gives me the citation there. Now there's a sound of website with the dataset where you send up five fields and this is what's displayed but actually for us, this doesn't display version and it's very important for us so we're actually changing the way, we're adding another field to the data site, the metadata we send up because that's important that that particular version is reflected in these fields. So the five core internal fields that are used are not exactly appropriate for our kind of data so we need to add in resource type and version and we're currently doing that now. Every, I guess, collection is going to have slightly different requirements and maybe versions are not always as important. So if you put the DOI into Google, you can immediately track what's happening to data so you can get a good idea of who's actually citing that data and that's got a nice simple way for us to try and find out who's using our materials. In the past it's been very hard, we've been able to put key words like health server for England into various Thompson-Witters indices to see who's using things but it's a very manual way of finding out who's using data so we're hoping we can help track the impact to some of these datasets. And also, of course, identify the people, go to them and say would you like to give us a case study and then add that reference to our metadata which we actually keep a list of secondary analysis publications for users. So it enables us to really add value to our catalogue records and to tell users, creators and users what's happening. So we're very pleased that that seems to work very well. So just thinking about linking in different resources via metadata, at the bottom there ideally what we would like is some discovery portal that enables you to find data wherever it is. We don't currently have that in the UK but there's various registries and kind of over portals that are working towards those things. So you can see the top box on the right, especially data repository, that's what we are. In the middle there's a research council outputs repository. For us there's something called a research output system where anyone has a grant. They have a web page related to their grant and then all of their outputs have to be uploaded to this system. So you're going to find that all of the publications and outputs go in there. Also there's demand now by all our institutional repositories that if you publish you need to put your latest version of your publication into that repository. We're seeing a lot more digital objects happening now more systematically and some of these repositories are demanding that the materials go in there. So it's a way of tying these up in metadata stores so that we can begin to more easily link articles and data together. And that's still a little bit clunky at the moment but we're hoping in time that will happen. We're very impressed with the ANS research portal. That's a fantastic model to follow, really, really, very, very good. So very jealous of you guys. So we've also been doing quite a lot of awareness raising because now we had our DOIs. It was a good idea to go out there and say, look, hey guys, you need to site data properly. So we applied for some money to our research council and we had just a small grant for four months to just do some advocacy really. So we produced a brochure with them about why you should site data and this was aimed at social science researchers. That was quite widely disseminated. We also spoke to professional organisations in the social science and humanities domain. We contacted every academic publisher and editor of the journals in our domain and they've done some outreach work with post-graduates and just talked about why you need to site data and how to and why it's important. So yes, I mean even some of the communications with academic publishers have been quite encouraging. They started to include some citations from some of the smaller journals in their guidance. And already, you know, there's been quite a lot of pressure from other communities. I think we're starting more and more to see robust ways of citing data given by some of the academic publishers and journals which is very important. We've done some events with British Library data site on metadata and with a GISC who here work a lot to look at building repository infrastructures and of course metadata is absolutely critical for enabling the sharing of these. And we have things called doctoral training centres in the social sciences where universities are given a particular specialist centre to train in various areas of social science. So they provide expert advanced training and they're useful because a lot of post-graduates doing high-end research, PhDs, they're a useful new young audience to approach. So it's important to get to them as well because they'll be the next generation who are publishing. So it's been a really, really nice, short, successful project we think in combination with British Library and we produced and actually more importantly sponsored by the research council and actually the brochure produced by them, so we endorsed by them. A brochure's been used and it's very simple, it has to be very simple, it doesn't talk about technical issues but it talks about why you need to do this. And I know our sister archive in ICPSR in Michigan are doing a very similar thing, they have quite an advocacy programme at the moment. So idealised, you've probably recently seen the Data Smitation Index that's been released and I'm very pleased to have been included in that particular system, I think 20 repositories have been indexed, that's going to be starting up a part of the web of knowledge, I think it could be quite a big draw actually this one, apparently there's about two records in there, two million records in there and they're also doing advocacy work which is going to actually go quite a long way I think in promoting scholarly sort of communication and citation, so I think that's a real added bonus that's been taken seriously at that level. And I know Heather referred to this last week, this example there of Data Smitation Index and what it all looked like, pull up metadata records and show you what the collections look like, so I'm not sure how it's going to work because I don't think it's live yet, I couldn't find a live site but it looks sort of quite exciting really. So what are our challenges in the future, we seem to have sorted out our Data Sitation methodology, it seems to be quite robust, we haven't encountered any problems yet apart from needing to change the way that the data site displays some of our citations. We need to be thinking about parts of data rather than whole collections, what does that mean to sites and how permanent identifiers for single files, for example sub sets of data, if you're getting data from online system and we have an online data browsing system called Nestard that allows you to browse and visualise survey data and create tabulations online, when you sub set data or download it how do you provide a permanent reference to that particular part of data. And extracts of textual data, if you're looking at qualitative data, interview data and you want to use an extract, I'm very keen on having a permanent identification of that particular extract and that's the next challenge for us, we will be looking at in the next six months actually. Also thinking about creating better relationships between outputs, data and outputs and also making sure that we're linking funding information to grant outputs and research outputs, there's quite a lot to do, quite a lot to do in terms of linking and connecting and more powerful granular citation. And I know there's other data centres in the UK that are also looking at this kind of issue, so they're some of the challenges that we want to be working on and that really is an overview of what we're doing and I'm certainly happy to ask questions on any particular issue to provide clarification or show you more things on the website, so I hope that was useful.
 Thank you very much for inviting me to give this presentation and I'm very pleased to  be part of this and series on data citation which has already produced some wonderful  presentations and some great web content as well. So very nice to be added to that portfolio.  So I'm going to spend a little time at the beginning just explaining about the data archive  and its collection to give you a little bit of an overview and then I'll move into talking  about the data citation methodology that we've chosen and implemented at the data archive.  Okay so based at the University of Essex for quite a long time now, over 40 years, and  spent selecting, ingesting, dealing with a variety of social science data sources and  explain a little bit about where they come from in a minute. So we've been designated  by our National Archives as a place of deposit which means we can hold public records and  a lot of the data that we bring in and support services that we provide support higher and  higher and further education research, learning and teaching. So we kind of really can support  any data user but our primary type of audience is higher and further education although that  includes also many other researchers who are who want to use social science data. More  recently last year we went through a long process that enabled us to gain the information  security standard ISO 27001 which means we can hold disclosive data and that was a lot  of auditing and a lot of work but it means we're a trusted digital repository in that  sense. Excuse me for the slow action. Right so just a little picture of where we are so  you get a flavour. We have got a very nice green campus with buildings that were built  in the 1960s and some of the towers you can see there are actually listed buildings, they're  ugly but they're listed, that our campus is based on an Italian village called San  Gimignano. It doesn't quite look Italian or medieval but it's a nice place to be and  we have a brand new building that was built about three years ago dedicated for the data  archive and for the social research centre that conducts our panel studies in the UK  and that's our nice building on the right there so just give you a flavour of what we  look like. So this is the data archive website, this is our data archive website, you can  go and have an explore of that later and this particular diagram says that we're part of  a bigger international federation of social science data archives around the world, one  in Australia sitting down there in Canberra and one in America who we work closely with  at the University of Michigan so most of the western Europe do have these data archives  and we're part of this bigger international network and we work very closely together  on metadata standards, solutions, training materials, those kinds of things.  So we have a family of services, we were running a number of different services you can see  listed in grey on the left, from October the 1st we have a brand new grant, a five year  grant called the UK Data Service that brings all of these data services together under  one roof and we're currently being rebranded so we don't know quite what our identity  looks like at the moment but it will wrap in all of these support services that support  all kinds of social science data in the UK.  And we have a number of research and development projects, some on metadata, some on repository  systems, on many different things actually, some on providing access to geodata, others  are about preservation and preservation standards but we do bring in quite a lot of grants to  do kind of forward looking research.  We have as an organisation more recently in the last year adopted the OAS functional  model which means that our whole divisions are based around these particular sections  of ingest, data management, storage, security, access, preservation so as an organisation  we follow this model, we have two slightly different adaptations in that we have a sort  of descriptive information here, we have an area that says, that's supposed to say  pre-ingest there so we have an area of kind of metadata on its own that sits as a department  and we have an access and data support unit and a lot of work goes on there to provide  support and access to users but it's nice to follow a functional model and it seems  to work very well.  So where do our data come from, a lot of it comes from central government agencies like  Office of National Statistics, like the Home Office, like the Department of Health and they  give us important government series and some of them are dating back to 1960 for example  the what was called the Family Expenditure Survey combined with a food survey from the  1960s, we have got a series of data going all the way back to 1960s so it's very important  and time series collection.  We provide access to statistical time series, some of the macro data banks from international  government organisations like the World Bank and INF.  We do gain grant data that comes from research projects when an academic is funded by the  Economic and Social Research Council, as part of their grant they have to offer data so  there's a stream of research data coming in, which can be anything from surveys to qualitative  data, sets of interviews, psychology experiments, many kinds of very vastly different data sources  actually.  We collect data from market research agencies, polls, do have some historical sources, historical  databases and records going back to the 14th century and of course we do facilitate access  to data in all the other data archives around Europe so if somebody wants data from Finland  we can help them acquire that.  Just to give you a picture of what we do, it's about 6,000 data sets in our whole collection.  We add about 230 a year, we've got about 20,000 or so active registered users and probably  down there is something like 60,000 data sets a year.  We have a lot of activity in our users before unit.  So that's the kind of size that we're looking at.  We've also on the other side done quite a lot of trying to promote use of our data so  we've got a whole website here that tells you about what people are doing with data  and they're kind of short one page descriptions of what data you used and what you did with  them and it is about something like a 100 in there at the moment and they span between  the purple ones research and the blue ones teaching.  So they're a nice promotional tool for producers and for users to see kind of what's happening.  So you can go and explore some of those if you want to get a flavour of what people are  doing.  Back to the topic of data citation.  For many years as part of our end user agreement for actually accessing data through our online  system you have to acknowledge the original source and there's a statement there which  is part of the end user agreement number 9 that says you'll acknowledge in any publication  the original data creators.  Now that is a contractual agreement and people are supposed to do that but it's very hard  to enforce because even though we provide a method of citation people don't necessarily  do it that well particularly when the titles of the data sets are complicated.  So just to show you there we've always given information citation information as a separate  file which tells you that you have to cite the data like this.  So we've always had a way of doing that actually even from the 70s quite early on but of course  this is different from an acknowledgement it is a citation and an acknowledgement many  people are using those they're not adequate as we know for the citing data and this is  why we've moved to a more robust system of citation.  So you all know why we should cite data I'm sure you've seen some of the wonderful presentations  from before but it's about acknowledging the author's creativity and sources and identity  as well, helps you find data, helps promote reproduction of research so you can get back  to original results and play with them.  It allows the impact of data to be tracked we now know that if you put a DOI into Google  you can just immediately get some idea of what's happening to the data and it provides  a good structure which I think data craters will feel that there's more incentive to deposit  data now that there's more of a reward structure.  So there's lots of positive things.  So our wish to use persistent identifiers was going back some time and we wanted to use  them and for us we've already got URLs, we've already got citation, we wanted something  a bit more permanent and unique and our definition of digital objects was the thing that took  us long to kind of decide exactly what we wanted to do but it should be very clear what  the granularity is and also as we know there's a lot of identifiers being applied to many  different things and as you probably know if you haven't you can go and get an orchid  idea ready which gives you a unique idea for yourself as a researcher for some of these  hard institutions.  So this idea of permanent identifiers is very important across our whole community particularly  for long term data providers.  So our data collections are not actually digital objects, they're collections of a set of  materials so one of our collections could have 100 files in it or it could have one  file, it's a collection of materials that make up one particular study and that's important  for us, our unit of description is a research study or a particular survey that was done.  So we wanted to capture changes made to data and we do really version data but we wanted  to make sure it was commonly understood we had a robust way of versioning our data and  we wanted to think about what a significant change to data might be and that's very important  for us and we wanted to use highly structured data so that machines could understand what  we were doing.  So our other requirements were our current digital preservation activities, this particular  flow fitted in with that and because there's a lot of investment in planning this we wanted  to get it right the first time rather than have to go back and start a different system  so lots of things to think about at the time of planning.  So just to kind of iterate there about 15% of our collections do have some kind of change  in the first year which is a bit of a pain really but we often get issued new data sets,  new variables, particularly new variables they've upgraded a variable, they've derived  something different, they've replaced it, there's quite a lot of changes which we issue  as new additions and it's not always clear which addition you're working with so we  wanted a more robust way of kind of signalling those changes.  Another example is if you're on a serious longitudinal study we're collecting data every  year every time you add a new wave it creates a new addition because you add that particular  wave to the existing years of data that you have.  Regrossing is sort of balancing estimates so they're fitting with current population,  trends, current population statistics and sometimes we do that to data and also there  can be changes to documentation, new parts added, bits that we're missing so there are  these ongoing changes which means we have to reissue data so versioning is very important  for us.  So one of the important things, sorry to go back, one of the important things is we've  wanted to distinguish between what is a major change and what's a minor change so that's  been the most important thing for us because some of these have much larger impact than  other changes to data and we wanted to try and make sure we're not versioning every single  change that happens because that would be difficult to keep track of.  And actually on the whole although there's a lot of talk about verification and replication  of data in our field we've mostly seen people wanting new data for new research to ask new  questions of data and so at the moment we do just make current versions available.  Early versions can be accessed but not from the website you have to come and ask for them  but that's something we need to think about and are is how we make previous versions available  but sometimes there could be 20 different versions and additions of one data set so  you can see the problem and most people really do want the most accurate up to date one.  So just trying to define a little bit for you what we mean by low and high impact.  Just an example of low impact there is somebody just might simply add a reference to the bibliography.  They may have made spelling mistakes and that can happen quite a lot but sometimes we get  variable supplied that shouldn't be in there, accidentally supplied.  Sometimes there's minor changes in documentation that are not going to affect the content or  the way that you use the data.  Sometimes we add new terms, we do index all our data using a thesaurus, sometimes terms  change they get outdated, new terms get added so we do reindex sometimes.  And then sometimes we add little extra bits of documentation because they've come about  and sometimes there's a change in access condition if you're changing from maybe data under public  license to a more restricted license so that there can be changes in all those kinds of  things to think about.  And then in terms of high impact that would be something that's really going to affect  the content and usability of the data set and that would be if you've added a new variable  that would be important to know.  We might have new value codes so somebody might have actually changed the way in which  they're giving the different options for the data.  Instead of a yes-no you could have had a yes-no, I really don't know that could have been added  as another code.  So sometimes that does happen.  Sometimes the waiting variables that help you kind of balance a data set have been changed,  the statisticians who own a government survey might have done a lot of work afterwards and  these changes are not available till they've actually given us data.  Changes in formats, yes that doesn't happen very often but it could happen.  There could be file migration up to a new version of data and that could signal quite  a big change for people.  So they're the kinds of things that we want to distinguish between and that's very particular  to our sorts of data but some of those have relevance to other kinds, scientific data,  other kinds of data about what's really important and what's not so important to be used to  think about.  So the next step is trying to find an instance of a change collection.  What is this collection and how is it changing?  So we do make various changes to data and they might be internal changes that have the  point of ingesting data and we don't particularly release those changes which will for us be  something that changes internally but not externally.  What's more likely to happen is there's low impact changes where we release the change  and we have a new external instance that's got the same identifier because it's not important  enough to change it.  We have a high impact change where we release a new dataset and we decide that it's worth  having a new identifier and I'll show you what these identifiers mean and how they change.  So we started working with data sites and we've got the British Library data site who  are one of the agents bought for assigning DOIs.  In 2011 our director, Mackie Woolard, has been very involved in this whole area for  some time.  As you probably know, data sites have been founded for some time.  It's actually so in format for citing data and it works quite closely with data publishers  to try to promote the idea of data citations.  Not only does it provide a methodology and issues DOIs, it also has quite a lot of advocacy  in this area.  So what's DOI?  I'm sure most of you do know but it's about persistent identification of objects, of  digital objects.  A string of letters and numbers, nothing that complicated, a systematic string of letters  and numbers.  And subscribers who want to get DOIs basically join data sites and they get access to DOIs  that they can get once they've got a contract and then these particular records end up in  their metadata store and resolve the system.  There seems to be, I'm not sure how many, last count I think in May, there was about  1.3 million objects in there, there's probably a lot more now.  Of course there's other handling systems out there but we've actually chosen to go with  a data site because we work very closely with our British Library on many other areas.  So how they created?  So as a data publisher which is what we are, we register and obtain DOIs, we mint them,  we have a system for doing that with our own infrastructure, our redeeming with metadata  and we use the API to get the DOIs and it's actually a fairly straightforward process  and I'm not going to go too much into the technical side today.  So thinking about what do you want to allocate your DOI to, it's got to be allocated to some  kind of object or something, something that's got an identity.  We actually use our core metadata as the theme that relates to the data collection.  So you think okay core metadata, title, this is the name of a particular study, that should  be quite straightforward, it's not always straightforward because sometimes the titles  of the studies change which one might not expect but maybe one of the government departments  decides it's changing the name completely and we need to somehow reflect that change.  So our solution is that if we have metadata, is the object that our DOI identifies and  that's an external instance.  So it's representation of one of our data collections at a particular point in time.  So when you get a DOI and you type it in somewhere, it results to a jump page which  actually lists all the history of the changes to the dataset and then that then points to  the catalogue record where you can go and get data.  So there's a kind of front page that gives you the change, the change log and as I said  before high impact changes denote a new DOI.  So showing what this looks like, for example here this is a DOI on the left, I'll show  you the structure of what I'll look like, hypothetically it's a study number SN, study  number 2000, version 1.  This denotes a study number that's got, this is a survey that's been continued over 13  years, it's got weights 1 to 13 and this is an instance specific dataset so it's fixed  in one point in time and it has its own accompanying metadata.  The next step would be we've actually made a change, as you can see the blue box in the  middle, we've added another wave so that means we've changed our DOI so it's a slash  O2 now and again this is instance specific dataset, it's actually different dataset,  it's changed and it's got a different metadata record.  And then finally there we've added another wave wave 15, it's changed, the DOI's now  changed it's an O3, instance specific metadata, when you go to our jump page you will see  that history but you will be taken to the most recent version of data because at the  moment that's what our data want, that's what our users want, we don't give them instant  access to some of the older waves 1 to 13 because actually our kind of data, waves 1  to 13 is already available in waves 1 to 15 so you can still get at that particular dataset  but it's something that we want to think about how we do provide access to previous versions.  So this is an example of our jump page, if you go to one, if I've plugged in this DOI  that's 10.525 slash there, I would get a little page that gives me a kind of pot of history  of what's going on so it has the most recent DOI change dataset version at the top, 5 and  3, it gives me the citation and the change log so it actually gives you a very short  human description of what's happened so in February 2012 for the third edition we added  in wave 2 of the study so you can instantly see the changes as you move down, you can  see what the older datasets were so it's quite a simple way of defining change and turning  people what the versions are doing.  And then in terms of the way that we create and update DOIs, we will create, well when  we create a new catalogue record so when we've got a new dataset and we're ready to publish  that, we mean to brand new DOI through datasets, we update that change log and we create a new  citation file because the citation has actually changed.  We have something that's part of our documentation called a citation file which people can download  and it gives you the way of citing data.  Also if we update a catalogue record by implementing various changes we decide whether the high  or low impact changes, we create or update a new DOI if there's a high impact change,  we update log and create a new citation file so there's that process about bringing a new  dataset and updating all the ones.  So here's our format as you can see this is a kind of structured DOI that's very similar  to what other people are using, you've got your own identifying organization, which for  us is 10.5, 2x5, we've got our readable identifier which is UKDA, SN is what we use to study numbers,  all our collections are called SNs, they're the number 1, 2, 3, 4 and the results identify  our total issue, what the actual dataset is, they go up to 6,000 at the moment, we've  issued about 6,000 of these things for our collections and the results version is what  I was talking about something that is the current version and that will change, that  will increment every time we have this high impact change.  So on the left there at the bottom, high impact change means you simply just go from 1 to  2, so it's quite straightforward.  So the low impact change, we don't change the version but internally we're keeping track  of what's happening so we know what's the minor version.  So we know that there's been a change but it's not denoted externally.  And just an example here, if we have a DOI, if I plug in the DOI at the top in the data  site metadata registry or search interface, it will give me a link to the dataset and  if I click on that at the bottom screen, it tells me where the landing page is so I can  actually go and resolve to the jump page of the data archive and it gives me the citation  there.  Now there's a sound of website with the dataset where you send up five fields and this is what's  displayed but actually for us, this doesn't display version and it's very important for  us so we're actually changing the way, we're adding another field to the data site, the  metadata we send up because that's important that that particular version is reflected  in these fields.  So the five core internal fields that are used are not exactly appropriate for our kind  of data so we need to add in resource type and version and we're currently doing that  now.  Every, I guess, collection is going to have slightly different requirements and maybe  versions are not always as important.  So if you put the DOI into Google, you can immediately track what's happening to data  so you can get a good idea of who's actually citing that data and that's got a nice simple  way for us to try and find out who's using our materials.  In the past it's been very hard, we've been able to put key words like health server for  England into various Thompson-Witters indices to see who's using things but it's a very  manual way of finding out who's using data so we're hoping we can help track the impact  to some of these datasets.  And also, of course, identify the people, go to them and say would you like to give us  a case study and then add that reference to our metadata which we actually keep a list  of secondary analysis publications for users.  So it enables us to really add value to our catalogue records and to tell users, creators  and users what's happening.  So we're very pleased that that seems to work very well.  So just thinking about linking in different resources via metadata, at the bottom there  ideally what we would like is some discovery portal that enables you to find data wherever  it is.  We don't currently have that in the UK but there's various registries and kind of over  portals that are working towards those things.  So you can see the top box on the right, especially data repository, that's what we are.  In the middle there's a research council outputs repository.  For us there's something called a research output system where anyone has a grant.  They have a web page related to their grant and then all of their outputs have to be uploaded  to this system.  So you're going to find that all of the publications and outputs go in there.  Also there's demand now by all our institutional repositories that if you publish you need to  put your latest version of your publication into that repository.  We're seeing a lot more digital objects happening now more systematically and some of these  repositories are demanding that the materials go in there.  So it's a way of tying these up in metadata stores so that we can begin to more easily  link articles and data together.  And that's still a little bit clunky at the moment but we're hoping in time that will  happen.  We're very impressed with the ANS research portal.  That's a fantastic model to follow, really, really, very, very good.  So very jealous of you guys.  So we've also been doing quite a lot of awareness raising because now we had our DOIs.  It was a good idea to go out there and say, look, hey guys, you need to site data properly.  So we applied for some money to our research council and we had just a small grant for  four months to just do some advocacy really.  So we produced a brochure with them about why you should site data and this was aimed  at social science researchers.  That was quite widely disseminated.  We also spoke to professional organisations in the social science and humanities domain.  We contacted every academic publisher and editor of the journals in our domain and they've  done some outreach work with post-graduates and just talked about why you need to site  data and how to and why it's important.  So yes, I mean even some of the communications with academic publishers have been quite encouraging.  They started to include some citations from some of the smaller journals in their guidance.  And already, you know, there's been quite a lot of pressure from other communities.  I think we're starting more and more to see robust ways of citing data given by some of  the academic publishers and journals which is very important.  We've done some events with British Library data site on metadata and with a GISC who  here work a lot to look at building repository infrastructures and of course metadata is  absolutely critical for enabling the sharing of these.  And we have things called doctoral training centres in the social sciences where universities  are given a particular specialist centre to train in various areas of social science.  So they provide expert advanced training and they're useful because a lot of post-graduates  doing high-end research, PhDs, they're a useful new young audience to approach.  So it's important to get to them as well because they'll be the next generation who are publishing.  So it's been a really, really nice, short, successful project we think in combination  with British Library and we produced and actually more importantly sponsored by the research  council and actually the brochure produced by them, so we endorsed by them.  A brochure's been used and it's very simple, it has to be very simple, it doesn't talk  about technical issues but it talks about why you need to do this.  And I know our sister archive in ICPSR in Michigan are doing a very similar thing, they  have quite an advocacy programme at the moment.  So idealised, you've probably recently seen the Data Smitation Index that's been released  and I'm very pleased to have been included in that particular system, I think 20 repositories  have been indexed, that's going to be starting up a part of the web of knowledge, I think  it could be quite a big draw actually this one, apparently there's about two records  in there, two million records in there and they're also doing advocacy work which is  going to actually go quite a long way I think in promoting scholarly sort of communication  and citation, so I think that's a real added bonus that's been taken seriously at that  level.  And I know Heather referred to this last week, this example there of Data Smitation Index  and what it all looked like, pull up metadata records and show you what the collections  look like, so I'm not sure how it's going to work because I don't think it's live yet,  I couldn't find a live site but it looks sort of quite exciting really.  So what are our challenges in the future, we seem to have sorted out our Data Sitation  methodology, it seems to be quite robust, we haven't encountered any problems yet apart  from needing to change the way that the data site displays some of our citations.  We need to be thinking about parts of data rather than whole collections, what does that  mean to sites and how permanent identifiers for single files, for example sub sets of  data, if you're getting data from online system and we have an online data browsing  system called Nestard that allows you to browse and visualise survey data and create tabulations  online, when you sub set data or download it how do you provide a permanent reference  to that particular part of data.  And extracts of textual data, if you're looking at qualitative data, interview data and you  want to use an extract, I'm very keen on having a permanent identification of that particular  extract and that's the next challenge for us, we will be looking at in the next six months  actually.  Also thinking about creating better relationships between outputs, data and outputs and also  making sure that we're linking funding information to grant outputs and research outputs, there's  quite a lot to do, quite a lot to do in terms of linking and connecting and more powerful  granular citation.  And I know there's other data centres in the UK that are also looking at this kind of  issue, so they're some of the challenges that we want to be working on and that really  is an overview of what we're doing and I'm certainly happy to ask questions on any particular  issue to provide clarification or show you more things on the website, so I hope that  was useful.
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Thank you very much for inviting me to give this presentation and I'm very pleased to be part of this and series on data citation which has already produced some wonderful presentations and some great web content as well. So very nice to be added to that portfolio. So I'm going to spend a little time at the beginning just explaining about the data archive and its collection to give you a little bit of an overview and then I'll move into talking about the data citation methodology that we've chosen and implemented at the data archive. Okay so based at the University of Essex for quite a long time now, over 40 years, and spent selecting, ingesting, dealing with a variety of social science data sources and explain a little bit about where they come from in a minute. So we've been designated by our National Archives as a place of deposit which means we can hold public records and a lot of the data that we bring in and support services that we provide support higher and higher and further education research, learning and teaching. So we kind of really can support any data user but our primary type of audience is higher and further education although that includes also many other researchers who are who want to use social science data. More recently last year we went through a long process that enabled us to gain the information security standard ISO 27001 which means we can hold disclosive data and that was a lot of auditing and a lot of work but it means we're a trusted digital repository in that sense. Excuse me for the slow action. Right so just a little picture of where we are so you get a flavour. We have got a very nice green campus with buildings that were built in the 1960s and some of the towers you can see there are actually listed buildings, they're ugly but they're listed, that our campus is based on an Italian village called San Gimignano. It doesn't quite look Italian or medieval but it's a nice place to be and we have a brand new building that was built about three years ago dedicated for the data archive and for the social research centre that conducts our panel studies in the UK and that's our nice building on the right there so just give you a flavour of what we look like. So this is the data archive website, this is our data archive website, you can go and have an explore of that later and this particular diagram says that we're part of a bigger international federation of social science data archives around the world, one in Australia sitting down there in Canberra and one in America who we work closely with at the University of Michigan so most of the western Europe do have these data archives and we're part of this bigger international network and we work very closely together on metadata standards, solutions, training materials, those kinds of things. So we have a family of services, we were running a number of different services you can see listed in grey on the left, from October the 1st we have a brand new grant, a five year grant called the UK Data Service that brings all of these data services together under one roof and we're currently being rebranded so we don't know quite what our identity looks like at the moment but it will wrap in all of these support services that support all kinds of social science data in the UK. And we have a number of research and development projects, some on metadata, some on repository systems, on many different things actually, some on providing access to geodata, others are about preservation and preservation standards but we do bring in quite a lot of grants to do kind of forward looking research. We have as an organisation more recently in the last year adopted the OAS functional model which means that our whole divisions are based around these particular sections of ingest, data management, storage, security, access, preservation so as an organisation we follow this model, we have two slightly different adaptations in that we have a sort of descriptive information here, we have an area that says, that's supposed to say pre-ingest there so we have an area of kind of metadata on its own that sits as a department and we have an access and data support unit and a lot of work goes on there to provide support and access to users but it's nice to follow a functional model and it seems to work very well. So where do our data come from, a lot of it comes from central government agencies like Office of National Statistics, like the Home Office, like the Department of Health and they give us important government series and some of them are dating back to 1960 for example the what was called the Family Expenditure Survey combined with a food survey from the 1960s, we have got a series of data going all the way back to 1960s so it's very important and time series collection. We provide access to statistical time series, some of the macro data banks from international government organisations like the World Bank and INF. We do gain grant data that comes from research projects when an academic is funded by the Economic and Social Research Council, as part of their grant they have to offer data so there's a stream of research data coming in, which can be anything from surveys to qualitative data, sets of interviews, psychology experiments, many kinds of very vastly different data sources actually. We collect data from market research agencies, polls, do have some historical sources, historical databases and records going back to the 14th century and of course we do facilitate access to data in all the other data archives around Europe so if somebody wants data from Finland we can help them acquire that. Just to give you a picture of what we do, it's about 6,000 data sets in our whole collection. We add about 230 a year, we've got about 20,000 or so active registered users and probably down there is something like 60,000 data sets a year. We have a lot of activity in our users before unit. So that's the kind of size that we're looking at. We've also on the other side done quite a lot of trying to promote use of our data so we've got a whole website here that tells you about what people are doing with data and they're kind of short one page descriptions of what data you used and what you did with them and it is about something like a 100 in there at the moment and they span between the purple ones research and the blue ones teaching. So they're a nice promotional tool for producers and for users to see kind of what's happening. So you can go and explore some of those if you want to get a flavour of what people are doing. Back to the topic of data citation. For many years as part of our end user agreement for actually accessing data through our online system you have to acknowledge the original source and there's a statement there which is part of the end user agreement number 9 that says you'll acknowledge in any publication the original data creators. Now that is a contractual agreement and people are supposed to do that but it's very hard to enforce because even though we provide a method of citation people don't necessarily do it that well particularly when the titles of the data sets are complicated. So just to show you there we've always given information citation information as a separate file which tells you that you have to cite the data like this. So we've always had a way of doing that actually even from the 70s quite early on but of course this is different from an acknowledgement it is a citation and an acknowledgement many people are using those they're not adequate as we know for the citing data and this is why we've moved to a more robust system of citation. So you all know why we should cite data I'm sure you've seen some of the wonderful presentations from before but it's about acknowledging the author's creativity and sources and identity as well, helps you find data, helps promote reproduction of research so you can get back to original results and play with them. It allows the impact of data to be tracked we now know that if you put a DOI into Google you can just immediately get some idea of what's happening to the data and it provides a good structure which I think data craters will feel that there's more incentive to deposit data now that there's more of a reward structure. So there's lots of positive things. So our wish to use persistent identifiers was going back some time and we wanted to use them and for us we've already got URLs, we've already got citation, we wanted something a bit more permanent and unique and our definition of digital objects was the thing that took us long to kind of decide exactly what we wanted to do but it should be very clear what the granularity is and also as we know there's a lot of identifiers being applied to many different things and as you probably know if you haven't you can go and get an orchid idea ready which gives you a unique idea for yourself as a researcher for some of these hard institutions. So this idea of permanent identifiers is very important across our whole community particularly for long term data providers. So our data collections are not actually digital objects, they're collections of a set of materials so one of our collections could have 100 files in it or it could have one file, it's a collection of materials that make up one particular study and that's important for us, our unit of description is a research study or a particular survey that was done. So we wanted to capture changes made to data and we do really version data but we wanted to make sure it was commonly understood we had a robust way of versioning our data and we wanted to think about what a significant change to data might be and that's very important for us and we wanted to use highly structured data so that machines could understand what we were doing. So our other requirements were our current digital preservation activities, this particular flow fitted in with that and because there's a lot of investment in planning this we wanted to get it right the first time rather than have to go back and start a different system so lots of things to think about at the time of planning. So just to kind of iterate there about 15% of our collections do have some kind of change in the first year which is a bit of a pain really but we often get issued new data sets, new variables, particularly new variables they've upgraded a variable, they've derived something different, they've replaced it, there's quite a lot of changes which we issue as new additions and it's not always clear which addition you're working with so we wanted a more robust way of kind of signalling those changes. Another example is if you're on a serious longitudinal study we're collecting data every year every time you add a new wave it creates a new addition because you add that particular wave to the existing years of data that you have. Regrossing is sort of balancing estimates so they're fitting with current population, trends, current population statistics and sometimes we do that to data and also there can be changes to documentation, new parts added, bits that we're missing so there are these ongoing changes which means we have to reissue data so versioning is very important for us. So one of the important things, sorry to go back, one of the important things is we've wanted to distinguish between what is a major change and what's a minor change so that's been the most important thing for us because some of these have much larger impact than other changes to data and we wanted to try and make sure we're not versioning every single change that happens because that would be difficult to keep track of. And actually on the whole although there's a lot of talk about verification and replication of data in our field we've mostly seen people wanting new data for new research to ask new questions of data and so at the moment we do just make current versions available. Early versions can be accessed but not from the website you have to come and ask for them but that's something we need to think about and are is how we make previous versions available but sometimes there could be 20 different versions and additions of one data set so you can see the problem and most people really do want the most accurate up to date one. So just trying to define a little bit for you what we mean by low and high impact. Just an example of low impact there is somebody just might simply add a reference to the bibliography. They may have made spelling mistakes and that can happen quite a lot but sometimes we get variable supplied that shouldn't be in there, accidentally supplied. Sometimes there's minor changes in documentation that are not going to affect the content or the way that you use the data. Sometimes we add new terms, we do index all our data using a thesaurus, sometimes terms change they get outdated, new terms get added so we do reindex sometimes. And then sometimes we add little extra bits of documentation because they've come about and sometimes there's a change in access condition if you're changing from maybe data under public license to a more restricted license so that there can be changes in all those kinds of things to think about. And then in terms of high impact that would be something that's really going to affect the content and usability of the data set and that would be if you've added a new variable that would be important to know. We might have new value codes so somebody might have actually changed the way in which they're giving the different options for the data. Instead of a yes-no you could have had a yes-no, I really don't know that could have been added as another code. So sometimes that does happen. Sometimes the waiting variables that help you kind of balance a data set have been changed, the statisticians who own a government survey might have done a lot of work afterwards and these changes are not available till they've actually given us data. Changes in formats, yes that doesn't happen very often but it could happen. There could be file migration up to a new version of data and that could signal quite a big change for people. So they're the kinds of things that we want to distinguish between and that's very particular to our sorts of data but some of those have relevance to other kinds, scientific data, other kinds of data about what's really important and what's not so important to be used to think about. So the next step is trying to find an instance of a change collection. What is this collection and how is it changing? So we do make various changes to data and they might be internal changes that have the point of ingesting data and we don't particularly release those changes which will for us be something that changes internally but not externally. What's more likely to happen is there's low impact changes where we release the change and we have a new external instance that's got the same identifier because it's not important enough to change it. We have a high impact change where we release a new dataset and we decide that it's worth having a new identifier and I'll show you what these identifiers mean and how they change. So we started working with data sites and we've got the British Library data site who are one of the agents bought for assigning DOIs. In 2011 our director, Mackie Woolard, has been very involved in this whole area for some time. As you probably know, data sites have been founded for some time. It's actually so in format for citing data and it works quite closely with data publishers to try to promote the idea of data citations. Not only does it provide a methodology and issues DOIs, it also has quite a lot of advocacy in this area. So what's DOI? I'm sure most of you do know but it's about persistent identification of objects, of digital objects. A string of letters and numbers, nothing that complicated, a systematic string of letters and numbers. And subscribers who want to get DOIs basically join data sites and they get access to DOIs that they can get once they've got a contract and then these particular records end up in their metadata store and resolve the system. There seems to be, I'm not sure how many, last count I think in May, there was about 1.3 million objects in there, there's probably a lot more now. Of course there's other handling systems out there but we've actually chosen to go with a data site because we work very closely with our British Library on many other areas. So how they created? So as a data publisher which is what we are, we register and obtain DOIs, we mint them, we have a system for doing that with our own infrastructure, our redeeming with metadata and we use the API to get the DOIs and it's actually a fairly straightforward process and I'm not going to go too much into the technical side today. So thinking about what do you want to allocate your DOI to, it's got to be allocated to some kind of object or something, something that's got an identity. We actually use our core metadata as the theme that relates to the data collection. So you think okay core metadata, title, this is the name of a particular study, that should be quite straightforward, it's not always straightforward because sometimes the titles of the studies change which one might not expect but maybe one of the government departments decides it's changing the name completely and we need to somehow reflect that change. So our solution is that if we have metadata, is the object that our DOI identifies and that's an external instance. So it's representation of one of our data collections at a particular point in time. So when you get a DOI and you type it in somewhere, it results to a jump page which actually lists all the history of the changes to the dataset and then that then points to the catalogue record where you can go and get data. So there's a kind of front page that gives you the change, the change log and as I said before high impact changes denote a new DOI. So showing what this looks like, for example here this is a DOI on the left, I'll show you the structure of what I'll look like, hypothetically it's a study number SN, study number 2000, version 1. This denotes a study number that's got, this is a survey that's been continued over 13 years, it's got weights 1 to 13 and this is an instance specific dataset so it's fixed in one point in time and it has its own accompanying metadata. The next step would be we've actually made a change, as you can see the blue box in the middle, we've added another wave so that means we've changed our DOI so it's a slash O2 now and again this is instance specific dataset, it's actually different dataset, it's changed and it's got a different metadata record. And then finally there we've added another wave wave 15, it's changed, the DOI's now changed it's an O3, instance specific metadata, when you go to our jump page you will see that history but you will be taken to the most recent version of data because at the moment that's what our data want, that's what our users want, we don't give them instant access to some of the older waves 1 to 13 because actually our kind of data, waves 1 to 13 is already available in waves 1 to 15 so you can still get at that particular dataset but it's something that we want to think about how we do provide access to previous versions. So this is an example of our jump page, if you go to one, if I've plugged in this DOI that's 10.525 slash there, I would get a little page that gives me a kind of pot of history of what's going on so it has the most recent DOI change dataset version at the top, 5 and 3, it gives me the citation and the change log so it actually gives you a very short human description of what's happened so in February 2012 for the third edition we added in wave 2 of the study so you can instantly see the changes as you move down, you can see what the older datasets were so it's quite a simple way of defining change and turning people what the versions are doing. And then in terms of the way that we create and update DOIs, we will create, well when we create a new catalogue record so when we've got a new dataset and we're ready to publish that, we mean to brand new DOI through datasets, we update that change log and we create a new citation file because the citation has actually changed. We have something that's part of our documentation called a citation file which people can download and it gives you the way of citing data. Also if we update a catalogue record by implementing various changes we decide whether the high or low impact changes, we create or update a new DOI if there's a high impact change, we update log and create a new citation file so there's that process about bringing a new dataset and updating all the ones. So here's our format as you can see this is a kind of structured DOI that's very similar to what other people are using, you've got your own identifying organization, which for us is 10.5, 2x5, we've got our readable identifier which is UKDA, SN is what we use to study numbers, all our collections are called SNs, they're the number 1, 2, 3, 4 and the results identify our total issue, what the actual dataset is, they go up to 6,000 at the moment, we've issued about 6,000 of these things for our collections and the results version is what I was talking about something that is the current version and that will change, that will increment every time we have this high impact change. So on the left there at the bottom, high impact change means you simply just go from 1 to 2, so it's quite straightforward. So the low impact change, we don't change the version but internally we're keeping track of what's happening so we know what's the minor version. So we know that there's been a change but it's not denoted externally. And just an example here, if we have a DOI, if I plug in the DOI at the top in the data site metadata registry or search interface, it will give me a link to the dataset and if I click on that at the bottom screen, it tells me where the landing page is so I can actually go and resolve to the jump page of the data archive and it gives me the citation there. Now there's a sound of website with the dataset where you send up five fields and this is what's displayed but actually for us, this doesn't display version and it's very important for us so we're actually changing the way, we're adding another field to the data site, the metadata we send up because that's important that that particular version is reflected in these fields. So the five core internal fields that are used are not exactly appropriate for our kind of data so we need to add in resource type and version and we're currently doing that now. Every, I guess, collection is going to have slightly different requirements and maybe versions are not always as important. So if you put the DOI into Google, you can immediately track what's happening to data so you can get a good idea of who's actually citing that data and that's got a nice simple way for us to try and find out who's using our materials. In the past it's been very hard, we've been able to put key words like health server for England into various Thompson-Witters indices to see who's using things but it's a very manual way of finding out who's using data so we're hoping we can help track the impact to some of these datasets. And also, of course, identify the people, go to them and say would you like to give us a case study and then add that reference to our metadata which we actually keep a list of secondary analysis publications for users. So it enables us to really add value to our catalogue records and to tell users, creators and users what's happening. So we're very pleased that that seems to work very well. So just thinking about linking in different resources via metadata, at the bottom there ideally what we would like is some discovery portal that enables you to find data wherever it is. We don't currently have that in the UK but there's various registries and kind of over portals that are working towards those things. So you can see the top box on the right, especially data repository, that's what we are. In the middle there's a research council outputs repository. For us there's something called a research output system where anyone has a grant. They have a web page related to their grant and then all of their outputs have to be uploaded to this system. So you're going to find that all of the publications and outputs go in there. Also there's demand now by all our institutional repositories that if you publish you need to put your latest version of your publication into that repository. We're seeing a lot more digital objects happening now more systematically and some of these repositories are demanding that the materials go in there. So it's a way of tying these up in metadata stores so that we can begin to more easily link articles and data together. And that's still a little bit clunky at the moment but we're hoping in time that will happen. We're very impressed with the ANS research portal. That's a fantastic model to follow, really, really, very, very good. So very jealous of you guys. So we've also been doing quite a lot of awareness raising because now we had our DOIs. It was a good idea to go out there and say, look, hey guys, you need to site data properly. So we applied for some money to our research council and we had just a small grant for four months to just do some advocacy really. So we produced a brochure with them about why you should site data and this was aimed at social science researchers. That was quite widely disseminated. We also spoke to professional organisations in the social science and humanities domain. We contacted every academic publisher and editor of the journals in our domain and they've done some outreach work with post-graduates and just talked about why you need to site data and how to and why it's important. So yes, I mean even some of the communications with academic publishers have been quite encouraging. They started to include some citations from some of the smaller journals in their guidance. And already, you know, there's been quite a lot of pressure from other communities. I think we're starting more and more to see robust ways of citing data given by some of the academic publishers and journals which is very important. We've done some events with British Library data site on metadata and with a GISC who here work a lot to look at building repository infrastructures and of course metadata is absolutely critical for enabling the sharing of these. And we have things called doctoral training centres in the social sciences where universities are given a particular specialist centre to train in various areas of social science. So they provide expert advanced training and they're useful because a lot of post-graduates doing high-end research, PhDs, they're a useful new young audience to approach. So it's important to get to them as well because they'll be the next generation who are publishing. So it's been a really, really nice, short, successful project we think in combination with British Library and we produced and actually more importantly sponsored by the research council and actually the brochure produced by them, so we endorsed by them. A brochure's been used and it's very simple, it has to be very simple, it doesn't talk about technical issues but it talks about why you need to do this. And I know our sister archive in ICPSR in Michigan are doing a very similar thing, they have quite an advocacy programme at the moment. So idealised, you've probably recently seen the Data Smitation Index that's been released and I'm very pleased to have been included in that particular system, I think 20 repositories have been indexed, that's going to be starting up a part of the web of knowledge, I think it could be quite a big draw actually this one, apparently there's about two records in there, two million records in there and they're also doing advocacy work which is going to actually go quite a long way I think in promoting scholarly sort of communication and citation, so I think that's a real added bonus that's been taken seriously at that level. And I know Heather referred to this last week, this example there of Data Smitation Index and what it all looked like, pull up metadata records and show you what the collections look like, so I'm not sure how it's going to work because I don't think it's live yet, I couldn't find a live site but it looks sort of quite exciting really. So what are our challenges in the future, we seem to have sorted out our Data Sitation methodology, it seems to be quite robust, we haven't encountered any problems yet apart from needing to change the way that the data site displays some of our citations. We need to be thinking about parts of data rather than whole collections, what does that mean to sites and how permanent identifiers for single files, for example sub sets of data, if you're getting data from online system and we have an online data browsing system called Nestard that allows you to browse and visualise survey data and create tabulations online, when you sub set data or download it how do you provide a permanent reference to that particular part of data. And extracts of textual data, if you're looking at qualitative data, interview data and you want to use an extract, I'm very keen on having a permanent identification of that particular extract and that's the next challenge for us, we will be looking at in the next six months actually. Also thinking about creating better relationships between outputs, data and outputs and also making sure that we're linking funding information to grant outputs and research outputs, there's quite a lot to do, quite a lot to do in terms of linking and connecting and more powerful granular citation. And I know there's other data centres in the UK that are also looking at this kind of issue, so they're some of the challenges that we want to be working on and that really is an overview of what we're doing and I'm certainly happy to ask questions on any particular issue to provide clarification or show you more things on the website, so I hope that was useful.
a little time at the beginning just explaining methodology that we've chosen and implemented at data archive and for the social research centre says that we're part of a bigger international Canberra and one in America who we work closely and we work very closely together on metadata year grant called the UK Data Service that brings sections of ingest, data management, storage, Social Research Council, as part of their grant from surveys to qualitative data, sets of versions are doing. And then in terms of the way other data centres in the UK that are also that really is an overview of what we're doing