6:AM - Poster Lightning Talks
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Stirling numberFocus (optics)Multiplication signDifferent (Kate Ryan album)Reading (process)Set (mathematics)Inclusion mapProcess (computing)Goodness of fitOpen setDatabaseBasis (linear algebra)TrailNumberFrequencyContext awarenessMathematical analysisDependent and independent variablesXMLComputer animationLecture/Conference
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Stirling numberMathematical analysisTunisPhase transitionMultimediaData recoveryResultantPoint (geometry)Product (business)CausalityMotion captureTouchscreenSoftware developerDatabaseSubject indexingTable (information)Traffic reportingMathematical analysisOpen setSoftware frameworkLink (knot theory)Type theoryAreaDynamical systemProcess (computing)Demo (music)Electronic mailing listCommitment schemeMetric systemTwitterInformationContext awareness1 (number)Condition numberPrice indexStatisticsHookingNumberMultiplication signHeat transferTerm (mathematics)Computing platformQuicksortOffice suiteNetwork topologySoftware testingComputer virusAuthorizationPlanningLatent heatFrequency
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FamilyMultiplication signStirling numberMultimediaSocial softwareContent (media)Cumulative distribution functionDigital filterElectronic mailing listRevision controlSoftwareAssociative propertyInternetworkingEmailIndependence (probability theory)InformationMessage passingDigital object identifierProcess (computing)Performance appraisalSystem programmingTable (information)Peer-to-peer8 (number)LaserWindowCodeField (computer science)Thermal expansionUniform resource locatorSelf-organizationElectronic mailing listFiltrationMultimediaFunction (mathematics)Time zoneDatabaseMultiplication signSource codePerformance appraisalProduct (business)Web pageCodePerspective (visual)Digital photographyInformationCross-correlationDigital object identifierMetric systemWebsiteGoodness of fitBitDivisorSign (mathematics)Uniform resource locatorWeb 2.0Price indexCountingEndliche ModelltheorieFile archiverRange (statistics)Library (computing)WordTelecommunicationQuicksortLink (knot theory)Process (computing)Physical systemRelational databaseBlogInteractive televisionFrequencyRegulärer Ausdruck <Textverarbeitung>Message passingCASE <Informatik>Matching (graph theory)Complex analysisMeta elementMatrix (mathematics)Ocean currentAssociative propertySoftwareExecution unitMeasurementAnalytic setEmailSequelWave packetMetreStreaming mediaSigma-algebraGroup actionField (computer science)Table (information)Real numberXML
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Repository (publishing)Metric systemContent (media)Metric systemType theoryRepository (publishing)Library (computing)StatisticsMobile WebContent (media)BitCountingInformationDecision theoryWeb page
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Content (media)Repository (publishing)Latent heatLibrary (computing)Service (economics)
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Library (computing)Function (mathematics)Social softwareSystem identificationPressure volume diagramPerformance appraisalSet (mathematics)StatisticsMultimediaMathematical analysisQuantumStirling numberMaxima and minimaOpen setDivisorUser profileField (computer science)Universe (mathematics)DivisorLink (knot theory)Metric systemTerm (mathematics)Performance appraisalField (computer science)Sound effectStandard deviationSampling (statistics)Declarative programmingOpen setCASE <Informatik>RankingMultimediaBiostatisticsPhysical systemFigurate numberExterior algebraLibrary (computing)Function (mathematics)Line (geometry)Dependent and independent variablesDifferent (Kate Ryan album)Level (video gaming)Theory of relativityCategory of beingTelecommunicationPhysical lawComputer animation
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Projective planeQuicksortLibrary (computing)NumberGraph coloringDenial-of-service attackFunction (mathematics)WebsiteInstance (computer science)
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Stirling numberTwitterObject (grammar)Uniqueness quantificationError messageMessage passingCodierung <Programmierung>Source codeSocial softwareContent (media)ConsistencyMetric systemTwitterMultiplication signFehlererkennungMathematical analysisProcess (computing)InformationInteractive televisionINTEGRALImplementationLibrary (computing)Vector potentialConsistencyType theoryResultantQuicksortMetric systemCASE <Informatik>Intrusion detection systemData conversionDigital object identifierShared memoryUniverse (mathematics)Object (grammar)WebsiteIdeal (ethics)Dimensional analysisTwo-dimensional spacePoint (geometry)Position operatorLevel (video gaming)Different (Kate Ryan album)Sampling (statistics)Disk read-and-write head
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BuildingSolid geometryLibrary (computing)TelecommunicationExecution unitStrategy gameData managementUniverse (mathematics)
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Hill differential equationStirling numberMathematicsUniverse (mathematics)Data conversionPresentation of a groupComputer animation
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Presentation of a groupData conversionComputer animation
Transcript: English(auto-generated)
00:05
My name is Magnus Eriksson and I come from Minister Solutions. It's a company in Sweden that focuses mainly on research funders and our focus is in medical research. We have gotten the question from the medical funders that they would like to
00:26
have a database or something to measure the clinical impact of the research they fund. And as a response to that we created the clinical impact database. It is a specialized citation database and we have currently processed over 1.2 million citations.
00:50
But to understand the impact we have to recognize the context of citations. It is important because less than half of the cited material is actually included and used in the recommendation.
01:05
About one quarter of the cited material is excluded, often because of faulty study design or other reasons they don't match the inclusion criteria. Another quarter of the cited material is the so-called additional
01:22
references. It's other resources, it's further reading, sometimes even patient literature. Another important aspect of the citations in clinical guidelines is that there are different updating frequencies for different guidelines.
01:41
Some guidelines are updated very seldomly, maybe once every tenth year. Other guidelines are updated very frequently, sometimes several times within a single year. By analyzing a set of guidelines, the guidelines on cancer from the Norwegian Health Directorate, we have found that around 73% of citations are recurring between the different editions of guidelines.
02:07
The guidelines in that set was updated roughly every two years between the year 2007 and 2018. The conclusion in this is that the pure number of citations in clinical guidelines can
02:21
produce an artificially high number, so you have to study the context of the citations. As I said, if less than half of the citations are used in the basis of the recommendation, one has to find those and not mix them up with all the excluded material.
02:43
Also, guidelines that are updated very frequently, you have to use them or process them in a different way to keep track of the different editions. For example, if we have one paper that's cited in a guideline that's updated once every tenth
03:00
year, and you have another article that's cited in a guideline that's updated two times a year, if you don't keep track of the different editions, you will get an unusually high number on the paper that's cited in the frequently updated one.
03:22
Oh, we keep track of that. Yeah, if I was not speaking clearly, please go to my poster and talk to me. Thank you. I am here on behalf of Patricio Cortes from Pontificia Universidad Catholica of Chile.
03:43
He also could attend this conference, so I try to do my best to represent him, so I'm going to read the notes that he sent me. His research is about the automated analysis of open access scholarly research headed by Latin American institutions from 2009 to 2018 around SDG.
04:12
Three, good health and well-being. Within the context of the open access movement led in Latin America by Cielo, a reference, and others, plus new international frameworks such as Plan S,
04:28
a greater commitment to diversify access to quality research and impact has been evident. Therefore, it is relevant to identify how the research of the period understood is disseminated, promoted, and used in open access,
04:45
and other types of access led by Latin American institutions regarding critical issues of global interest, such as the pandemic H1N1 included in the sustainable development goals, three, health and wellness.
05:06
The H1N1 pandemic is a virus never identified as a cause of human infections before 2009. After that, 74 countries have notified confirmed infections through laboratory tests.
05:30
Patricio did a study considering three working phases. The phase ones, he collected citable publications such as article reviews and conference papers,
05:45
were retrieved from journal-indexed immunoscopies database with specific subject areas linked to OECD discipline, medical and health science, which allowed to generate a framework to recover research addressing H1N1.
06:05
Phase two, selected publication where the corresponding author would be affiliated with a Latin American country. According to the definition of these indicators, leadership indicators, the amount produced of an institution as the main contributor,
06:23
that's it, the amount of documents in which the corresponding author belongs to the institution. So I will try to go to the results. He divided in three main questions.
06:42
The first question, is it an advantage to publish critical issues led by Latin American open access for disseminating on social platform and how does it impact public policies? He found a really relevant result. The open access is important for this kind of publication.
07:06
He also tried to see if there is technological transfer of this publication and he didn't find any evidence about that. And he also tried to respond if there are researchers used to sustain or generate new research and a few conclusions.
07:33
It's related to the open access, the high impact of dissertations and all that information.
07:41
Unfortunately, I don't have more time for this, but we can share his contact with you if you have interest in any question. Thank you. Yes, that's it. So again, Charlotte Spinner, AARP, Washington, D.C. I'm afraid I can't avoid the Fifty Shades reference because my entire poster is built around that theme.
08:07
You may wonder why, but we deal with the age 50 population, age 50 and older. So that's where that hook comes from. We've been finding our work, as I mentioned earlier.
08:24
Let's see, this is just a brief recap of AARP. Brief recap of AARP research, studying the needs, market conditions and trends impacting the 50 plus population. So we've been working hard to pull together not only what we've done to promote and communicate our research, as I mentioned this morning,
08:40
but also the resulting reach assembled together in our newly developed impact reports that I showed you. And I'll provide a demo of the dynamic impact report to anyone who'd like to see it during the social hour coming next. So what have we learned so far, and this is a screen capture from the poster.
09:04
So what have we learned so far? So this is where the Fifty Shades hook comes in. We kept noticing when we first started doing analysis of altmetric data and other data we gathered, is that the same studies kept getting referenced over and over and over again. And they seemed to have these very palatable, easily digestible and understandable hooks in the media.
09:27
So I coined these sexy data points, which kind of everyone at the office sort of seized on that term. So, but they're bite-sized, they're easy to understand and yet very compelling. So, for example, we have an age discrimination data point of three out of four, I'm sorry,
09:46
61 percent of U.S. adults have either seen or experienced age discrimination in the workplace. That's huge. That's a huge number. And so there's been a lot written about that. Also our Bigfoot stat, nearly three in ten people think it's more likely they're going to learn that Bigfoot is real
10:03
than they're going to be able to retire and save enough money to be able to retire. So that was a study that we did with a list of many outlandish things that people feel are more likely than being able to retire comfortably. The second surprise is that as the age of some of our data that's still being shared frequently,
10:23
we didn't expect that. But what we realized is that if the topic's relevant today, it doesn't really matter if the data are a few years old. And our web department, who doesn't really understand our research, keeps telling us, you know, some of this is two years old. We've got to take it down. We recoil in horror at that. And so this has helped us argue for keeping our data alive on our site
10:46
to be continued to be shared. And this also tells us where we need to put our resources to update these topics of interest. We've had quite a bit of interest in a 2010 loneliness study. It was quoted two times in the New York Times in 2018.
11:00
So later in 2018, we repeated the study, also to great pick up many citations. The third surprise, the diversity of the outlets using and sharing our findings on their websites and social media accounts. In policy documents, journal articles, we do get cited in professional journals, which we didn't realize.
11:20
Trade publications, retail sites, even legislator pages and legislative debate testimony, such as some testimony to the United States Senate that quoted our research. So, sorry. All of these takeaways tell us that the work does have broad reach and it does have real impact. And that's important because we're trying to become
11:42
the Pew research of the 50-plus population. So we feel like getting this data and seeing our name frequently paired with this compelling data will help us achieve that. And also will help inform our future efforts so that we can be even more impactful over time. Thank you. Okay. This is my post.
12:03
I represent the San Mateo General Teaching Hospital in Italy. By the way, I'm also an Almetric Ambassador, co-founder of the Sigmetric Group's European Association of Health Information Libraries. And I felt in love with Almetrics. As we know, the problem measuring the scientific and social impact of a research publication
12:23
has been of extreme interest to scientific researchers. We also are to answer in this new scenario. For example, how to help our researcher with data, of course, with courses, training, helping complete a CV, or something new.
12:42
How to use the data for the institution compared with the traditional meetings, which are the clinics that get the most information and the score, which research are most attractive maybe for founding grounds. So we have an idea. We collected the scientific production of the year 2011 to 2018,
13:04
about 5,000 items, of our hospital. With filmmaker 11 software, we created a database that collects citations and Almetrics of all research articles produced by our researchers. We retrieved citations of each article from Web Science and Scopus Databases.
13:25
By PubMed ED and the UI of each publication, we obtain each one score on Almetric.com. Once you have data, the system is able to connect to Web Science, to Scopus, to Almetric.com. Data can be broken by year, department, or unit.
13:44
We assess the correlation between Almetrics citation counts and traditional indices. And also, having the impact factor of each publication, we have a complete picture of the impact of our scientific output. In fact, good correlation between Almetrics and traditional metrics are observed.
14:03
And high percentage of paper own Almetric score. Some departments add unexpected good Almetric score compared to traditional citation. This could be a sign of particular interest of patient and patient organization. Our institutes every year promote scores on the use of social media for research
14:24
without great attendance, as you see in the photo. In conclusion, Almetrics could contribute to the creation of value and give a more complete perspective on the important question of the democratization of evaluation. It can represent an austerity and complement to citation.
14:44
In particular, we would like to explore the possibility combining the Almetrics to traditional indicators in a multidimensional model to assess the impact of scientific works over a given period of time and assess the reliability of such a complex model.
15:02
Of course, librarians might become the link between the word Almetrics, researcher, and institution. Thank you. Alright, this serves as filters. So I wanted to look at listservs to see if there was more data that we can mine for Almetrics, which I call Almetrics 1.0. And so I looked at SIGMET, AIRL, and SCALCOM.
15:23
And you can see that they all have different ranges of when they existed and when they ended. And SIGMET is a special case in that their main list moved to Google after 2018 and then actually moved to ASIST. And it's a closed community, so it's kind of a pain to get the data.
15:42
So what I did was I took the most common dates from 2004 to 2017 and I scraped all of these listserv archives. And you can see that I had around 7,000 messages for SIGMET, 34,000 for AIRL, and 7,827 for SCALCOM.
16:01
And then I wrote a bunch of code. And so I wanted to scrape DOIs, URLs, ISSNs to hopefully academic resources, but of course they were URLs to everything. But what I got, you know, I have around 8,500 DOIs from SIGMET, only 1,000 DOIs from AIRL, and 396 DOIs from SCALCOM.
16:25
And you can see the breakdown on the final data chart. So the processing has taken the most amount of time. This is why probably you and Almetrics haven't tackled this yet because I have talked about this as a data source for a long time and they keep saying, well, it's difficult, and now I know that it really is quite difficult.
16:45
So I use Python and PHP. Python handles date times with time zones really poorly, so I had to move over to PHP to handle time zones because all of these listservs have the time zone attached to the date, and it's a real pain in the butt.
17:00
And I used MySQL, and MySQL doesn't handle date times with time zones either very well. So in future work, I'm going to move to Postgres because it actually handles time zones quite well. You see the red regexes, regular expressions that I've used. CrossRef was nice enough to post a blog post about how to identify DOIs,
17:22
so I'm using their regex for this. The reason I want to do this is because, you know, the original Altmetrics Manifesto talked about altmetrics as filters, so these trusted sources of information where we could find, you know, new articles, new books, et cetera. And I think listservs are a great place for that aspect.
17:44
I don't know if we still would consider listservs as altmetrics, but I consider them social enough because there are replies to emails, there's some sort of interaction going on. Another reason that I did this is because of what we would consider academic impact. So we're all really interested in social impact these days and how to measure that,
18:04
and it's a real pain to first of all define it and then try to measure it in some meaningful way. But I think it's easier to measure academic impact, and most of these listservs have members from academia.
18:22
And so future work, I'm going to keep cleaning the data, I'm going to organize my code and post it up on GitHub once I get all this working, and publish the clean data sets, which would be in relational database tables, and then expand the listserv collection to other fields, examine citations before and after the DOIs are present in the emails, so to see if there's any sort of citation impact,
18:42
and then examine self-citations because a lot of people in listservs post their own work. So here's a new article I wrote, et cetera. So that's it. Thank you. Hi. My name is Davina. I am the librarian for scholarly communication at Royal Roads University in British Columbia, and the research question I am taking a look at looks at how academic libraries are using altmetrics
19:03
to showcase attention and engagement to research in their institutional repositories. And so at Royal Roads, we do have PLUM metrics in our repository, trying to figure out what we might do with that, what insights it can give us, and so looking at the literature to see how other libraries might be using altmetrics in this way.
19:25
So some of the key themes that came out of that review and that I speak to in the poster are around knowledge mobilization, contextualizing traditional metrics, and recruiting content. So first off, knowledge mobilization.
19:41
So taking a look at what that altmetric data can tell us about whether or not that research in the repository is actually being used or maybe if there's some engagement with that research that is getting into the hands of policymakers or decision makers. So not just, you know, there were like mentions or tweets,
20:01
but can we look into that a bit further to figure out if it's being mentioned in the news, for example, or are, you know, lay people type thing, like are they speaking about that research? How is that knowledge being mobilized and how are the researchers taking that information and communicating it in a way that makes sense to non-academic audiences
20:23
and can altmetrics tell us anything about whether or not it's been communicated effectively to non-academic audiences so that it can be used in things like policy or be used by the public or have some type of impact in society in some way.
20:40
The next one is to contextualize metrics. So most of the repositories that I looked at have things like download counts and usage statistics, page views, things like that, but just taking a look at whether libraries are using altmetrics to give them any more insight or really to just paint a bigger, paint a more comprehensive picture of the type of engagement that that research is receiving
21:05
and whether or not that research is being effectively showcased in a way. So can they contextualize metrics with these types of more traditional metrics with altmetrics and then use that in the larger picture to really say that these are the institutional successes of that research.
21:27
And then the last one is content recruitment. So giving altmetrics and feeding altmetrics to scholars and to researchers who are putting their work in the repository and having that as a value-added service
21:42
to show them how their research is being engaged with. So can we actually get more researchers to deposit their work in the repository? Can we advocate, can we use this as a way to advocate for more openly available research using the repository as a service? And can altmetrics encourage scholars to want to deposit their work in the repository?
22:04
And I was just taking a look at how some libraries are doing that to show them the value of having that work in the repository specifically. Hi, I'm Nios Milan. I'm a research librarian at University of Tuberta de Catalunya. It's an online university in Barcelona.
22:21
And in GWOC, we are exploring new ways to evaluate research in a more responsible manner in line with the recommendations of the DORA declaration. Altmetrics are presented as a possible alternative metrics for analyzing the social impact of research. In this sense, GWOC decided to run a pilot last 2018
22:43
to analyze the impact of its scientific output on social media and figure out if altmetrics could be an effective way to improve research visibility beyond academia or whether altmetrics should be a complementary way to evaluate research publication or not. And how did we carry out our pilot?
23:01
First of all, we analyzed the social impact of GWOC's articles from our CRIS system published in 2016 and 2017 with DOI and using Webometric analyst tool. On the other hand, we tried to detect possible factors that could raise visibility of scientific output.
23:22
So we wanted to answer questions such as Does publishing in quarter one or two in general ranks increase the attention received on social media? Or articles without impact factor could use social media as an effective channel for scientific communication?
23:41
Or does open access publishing really improve visibility? We also analyzed articles with highest altmetric attention score related to situations received, subject category and open access. And finally, we compared GWOC's social impact in relation to other online universities.
24:01
Although we are aware that our study is biased because of the small sample, because we just retrieved altmetrics from articles with DOI, we want to share some of the conclusions of the GWOC case. We consider that it is too early to use altmetrics for research evaluation, especially because it doesn't exist as a standardization methodology, neither a general consensus.
24:25
Social media reaches beyond the academy and raises the visibility of publications that don't have impact factors such as art and humanities or law disciplines. In GWOC, 25% of our articles without altmetrics don't have impact factors.
24:42
But on the contrary, publishing in quarter one or two journals seems to guarantee greater impact on social media. In this case, at GWOC, 75% of our articles without altmetrics are in quartiles one and two. And in the case of GWOC, articles with the highest altmetric attention score are open access
25:01
and from the field of health sciences. But in general terms, we saw that the open access doesn't mean more visibility. And finally, there is no direct link between the level of attention on social media and the situations received from other scientific works. Well, we think that most of our small pilots could be of your interest because it's something different,
25:24
practical case study in an online university. Please come join me later to see our poster and we can talk in more detail about altmetrics pilots. Thank you very much. To speak for all of these lovely people here at the University Library of the University of Southern Denmark and I'd like to talk to you about our small project on how to color in the altmetric donut.
25:43
Now I know that many of you actually work for altmetric and many more of you are very well versed in how the altmetric donut works. But imagine if you will for a moment, if you're just a normal researcher and from one day to the next on your institutional website, this donut appears next to all of your newest outputs, right?
26:05
And so, as you can well imagine, our library was completely flooded with all sorts of questions. Okay, there was no flood, but we certainly did receive some. And among them were, you know, there's this number here.
26:22
On all of my publications, I have a one. So does that mean that I'm the best? Or, for instance, I have all these colors. Do I want more colors? Do I want less colors? What do all these things mean? But the most important one for us was I see this number, is this something that I can trust? And in our opinion, any metric that can be measured, it can obviously also be gamed.
26:45
So to try and address this, we sent in an editorial to Library Quarterly where we asked our other librarians to kind of crowdsource and help us to tweet and like and do all of the normal interactions
27:02
that would be followed by altmetric with this DOI. And then the idea was that we want to track and see if simple nudging will be one method to try and boost your own altmetric score. And as you can see, we've had some moderate success. But basically, I'd like to invite you all to come over to our poster,
27:21
perhaps have a drink and have a chat, and then I'd be very interested to hear how the altmetric donut is used at your institute and if you have any feelings on how I could better explain to our researchers how they can use this in their own work. Thank you. Hello, I'm Jichao Fang from the CWTS of Lido University.
27:40
In our study, we did a recheck on the status of Twitter mentions recorded by altmetric.com to see how many Twitter mentions have become unavailable or inaccessible to the public over time. We selected over 2.6 million Twitter mentions of more than 1,000, the most tweeted scientific publications recorded by altmetric.com until October 2017 as our research object.
28:08
And the tweet IDs of these types of Twitter mentions are collected from altmetric.com and are rechecked through the Twitter API in April 2019. Once the tweet ID is not accessible or is not available,
28:25
the error codes responded by API will be recorded for analysis. Finally, for 2.6 million Twitter mentions, we found about 14.3% of them were unavailable during our data collection.
28:43
And the deletion of tweets is the major reason for the unavailability. It accounts for over half of the unavailable tweets, followed by suspension and protection of Twitter user accounts. And at the publication level, all selected highly-tweeted publications in our data site
29:03
have a certain share of Twitter mentions unavailable, and the proportions are less than 20% for most of them. However, there are some publications with the most majority of Twitter mentions disappeared. For example, the top 10 publications have over 90% of Twitter mentions unavailable,
29:23
making their Twitter metrics relatively unstable if those unavailable tweets are removed. And in last month, we have finished the recheck for all of the Twitter mentions recorded by altmetric.com until 2017.
29:40
And out of 42.5 million Twitter mentions, 5.5 million have become unavailable, accounting for about 13%. So this result is quite similar with our small sample research. So in light of this result, we emphasize the importance of paying more attention
30:03
to the potential risk of unstable Twitter metrics, which might exacerbate the inconsistency among Twitter data recorded by different data aggregators. Hi, I'm Luc van Eewijk. I'm a library information specialist for a technical university in the Netherlands,
30:21
the University of Twente. You probably never heard of it. Anyway, in this position, I was responsible for the integration or implementation of Altmetric Explorer. It's not yet finished, but I've been involved all the time. And during this process, I thought maybe it's a nice idea
30:42
to create a poster about this implementation process and then preferably the ideal implementation process. So then I created this poster, and this is partly based on what we did and partly on what we did not do. And it's meant as sort of a conversation piece,
31:00
so you could look at this, and then I would very much like to discuss with you concrete cases. So I will show you how this poster works. This represents a building. So you work from the solid ground to the rooftop bar, and in the end, if you're on the rooftop bar,
31:22
well, not everything is organized, but I'll get back to that. So the solid ground, I'm not going to explain each phase. I'm just going to explain to you which pillars I chose to build this building. So I involved the management, or you could say strategy and policy,
31:41
the library and IT, S1 units, the researchers of course, marketing and communications, and funders and publishers. So these are all key players in this process. Because at first I was only focusing on the parties that are within or the players that are within the university. But then I found out that if you don't include funders and publishers
32:02
in this building, it's not really realistic, because if you want to actually make sure that altmetric is used by researchers, then the funders and publishers also play a role in this. So you will never finish the building if you don't put that pillar over there. So then I work my way up.
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I come above the ground and you have the entrance, support beams, the visitor center, command center. I invite you all to figure out what that means. And in the end, this one is important, so I will explain. You have the rooftop bar where all the key players come together and it's not finished then, so you've implemented altmetric, explorer, but you need to have an ongoing conversation
32:45
between the key players within the university, but also among universities. So that's what's represented here, and I would say that 6 a.m. is a very good example of the rooftop bar that you see here. So let's hope that we can enjoy a conversation after this presentation.
33:03
Thank you.