Structuring unstructured data for business, economic and related research
Krieger, Ulrich
Busch, Anja
Latif, Atif
Limani, Fidan
Schumm, Irene
Saleh, Ahmed
In addition to structured data, which is often collected explicitly for research purposes, unstructured data is increasingly becoming relevant for research in economics and social sciences. These data often come from non-standard sources, such as websites, digital business reports, social media, etc., and they come in diverse formats (audio, video, text, image or multimodal) and large scale. This heterogeneity in data sets and their underlying unstructured formats give rise to new challenges for research data management such as adequate computing and storage resources, deeper knowledge of programming languages and machine learning methods for data collection, selection of appropriate metadata standards for data representation, and pre-processing and analysis. Thus it can be difficult for researchers and infrastructures to manage the complexity of re-usability of unstructured data and algorithms. To this end, the BERD@NFDI consortium will address these challenges as a contribution to the development of the National Research Data Infrastructure. The aim of BERD@NFDI is to build a powerful centralized platform for collecting, (pre-) processing, analyzing and preserving Business, Economic and Related Data. We will facilitate the integrated management of data, algorithms and other related resources and provide support in terms of services along the whole research cycle. We will dedicate special focus to unstructured (big) data, in line with the FAIR principles for research data. The presentation gives an overview of the starting position, the mission as well as the composition of the consortium and shows why the usage of unstructured data requires an extended model of empirical research. After a preliminary analysis of the community needs, we present the work program of the (entire) project.
 We have here Ulrich Krieger. Ulrich, are you there? Yeah, I'm here. Ah, wonderful Ulrich. Before I begin, before my introduction, just real quick, where are you physically at right now? And if we visit there, what's one thing you recommend that we visit? I'm here at the beautiful Baroque castle of the university, where the university library in Mannheim is located. I'm at the office and if you happen to come to Mannheim, you should check out the water tower. I guess the ensemble is really nice. Beautiful, the water. As I mentioned before, when we spoke, I did visit the water tower by chance a few months ago and had a lovely time there. But we both agreed probably when you visit Germany, Mannheim will not be on the top 10 list of things to do, but maybe the top 20 or 30. 120 I'd say. Oh boy, coming from someone living in Mannheim, very good. Good, then let's get to business. Just a few words about you. Ulrich Krieger, he's the coordinator and project manager of the Barrett at NFDI consortium based at the University of Mannheim. And just to refresh, Barrett stands for business, economic and related data. And he has a background in serving methodology and served as head of operations of the German Internet panel. His topic today will be burnt at NFDI, structuring unstructured data for business, economic and related research. So it is my pleasure to give the digital stage over to Ulrich Krieger. Please. Well, thank you. Thanks for the kind words and the introduction. And yes, I am happy to tell you about our platform, our enterprise, the Barrett at NFDI consortium. NFDI stands for the German National Data Research Infrastructure, which is a rather new funding scheme where the German state and the federal lender joined forces to enhance, to create projects that enhance the data and research infrastructure landscape in Germany. Today, I want to talk about our consortia, which is BERT. And you already mentioned what the acronym stands for, it's business, economic and related data. And I want to tell you about what it's all about. I want to start up with the use case for BERT. And with that, I'm going to take you back into time, beginning of survey research or research into human behavior. And here we have the case of unemployment research. And some of you may remember from school or somewhere there's this famous study of Marine Tal, which is this Austrian town where, as you all know, probably know the main employer closed down rather suddenly and lots of these inhabitants of Marine Tal became unemployed. Researchers from the University of Vienna, I think, jumped onto the occasion and swamped the town and observed everything that's going on to see what the consequences of unemployment are on the inhabitants of Marine Tal. They did observational studies, they interviewed respondents, or they also invaded the privacy of people's homes and checked out what they have, how their economic standing is by observing what they have in their houses or in their flats. So this is a very detailed look at one town. So this is a small scale study, very influential study, but you can't draw inference from that. So you can't say that all unemployed people are like that. And it is very driven from the researchers, so there may be some of survey or survey error in there. We moved on from that to large scale standardized studies. Like you see here in the picture, computer assisted personal interviews, where we could take a very in-depth look and long interlongage interviews with respondents about everything that's going on in their lives. And that can be generalized or potentially generalized to the general public, but these endeavors are expensive and they're getting more and more expensive. And it's harder to convince people to participate because this place is a high burden on respondents because you have to let somebody into your house, into your home. And it also tends to have misreports. It is data that's generated by the respondents themselves. So then we move on to new avenues of data research. And I will mention here this study of the IAB SMARC study, which is study where they asked respondents to download the smartphone app, which is then installed on their handheld devices, which then allows not only for standardized research, a large scale data collection, but is potentially cheaper than sending real interviews to people's homes, which has a lower burden because you can also split your questionnaires or your data collection in smaller chunks. So you can have some short 10 minute, 10, five minute survey on the go somewhere. And you can also link this to real life events. So if somebody has a contact with the employment agency, you can then after the fact ask people about their experiences, you can also use all the sensors that are in the smartphone. And our smartphones are not only telephones, but they collect all sorts of information, as you know, your location and your trajectory's speed has a microphone camera built in. So there's a lot of ways we can collect data with this with a smartphone. Fun fact, the colleagues tried to replicate one of the findings from the original Marine Tile study from the study back in the days, where they observed that unemployed residents of Marine Tile walked slower at a slower walking pace than employed residents of Marine Tile. But this couldn't be replicated in our time. So there's no difference on the walking speed that is collected with the smartphone sensors. But that just gives you an idea of the potential. That's maybe a fun story, but that should just give you an idea about the potential of the sensor data. So here we have, but then the downside that comes to this data collection is this huge amount of data, which needs to be processed for analysis, because information about locations or trajectories are not ready for analysis. So we need tools, infrastructure for post-processing, and these are lacking, which is a problem for the researcher. So we turned, so we came from this, what I'll show you here is this analytical paradigm, where we have this elaborated studies where there's a high effort prior to data collection to set up a survey or an experiment. And then you have a standard processing, which can be done by hand and is of lower effort. It's still a lot of effort, but it's compared to the low effort post data collection. Now with data where we have text, languages, images, social media data, or other digital trace data, like the one that's collected on the smartphone, it turns that up into its head where the automated recording is comparatively low effort, but the post-processing is complex, so you have more high effort post data collection. So with this unstructured data, we enrich the traditional data model where we only have the structured data that is collected by experiments, surveys, and such, where we then could apply empirical methods for cause and analysis and prediction. We now have this other part that's here where we have this unstructured data that needs to be transformed to be fed into this traditional model of empirical research. And here with this transformation, that can't be done by hand. We need artificial intelligence and machine learning algorithms to provide the data processing due to the sheer volume of the data and the complexity. So there's huge potential for exciting discoveries, the methodological costs are higher, and there's high technical burden comes with it, which then leads to data graveyards where data are just dumped somewhere and can't be used or even reused or made transparent and open as we want to have this in the scientific process these days. Just to give you an example of what the need for such an endeavor is, I have here some remarks that were given in a study where respondents said what is hindering them in their research. And you have used things where they said there should be more of a community where we can exchange on ideas of analysis or the algorithms used or we need support in publishing data and accessing data because this comes together with cost or legal problems. And this is where, and this is the case where BERT comes in. This is an important piece of a platform that we will create that is open and to link structured and unstructured data combines the best practices in machine learning to process these data. It's all reproducible and transparent and can help with the whole management of the data life cycle. So it's about collecting, processing, analyzing, preserving, giving access and then making reuse of data possible. So how we do this in BERT, we have different task areas that comes along this or that is lined up with this data life cycle. So we have these data sources that are then harvested and or shared and collected by a task area, as we call it task area two. Then there's a task area that is in charge of data quality, a huge important step. Data is not just equal, we need high quality data for the research purposes. We need to take a look at anonymization and digital documents need to be processed. Task area four is then about data analysis and semantic enrichment. And then we have two task areas that are just or that are made their main focus this on helping researchers analyze data. So there's a training aspect in here as services and we are helping with preserving data and making data accessible to the research community. This is a joint endeavor with many colleagues and many institutes and I just shared this slide here with everybody, everybody that is involved. So you see the starkish green, you see research institutes and universities and then in the other color, other greenish color, there's infrastructure providers, which are very important and to provide the service and together combining all these institutions, we have the knowledge in the brain power and the manpower to provide this service and put up this platform. What are the first steps? We started now with training activities, for example, there's a course that is conceived out of birds that will be offered through an existing summer school. We're reaching out to scientific community like you and we're working on two services that will go online this year. One is this service that we call Open Big Data where users can upload data, search and download data. So this will where we start off preserving and giving access to data to researchers and the other service is an open, we call data marketplace. This is an exciting endeavor where we partner with data providers such as companies and match them with interested researchers that can apply for or submit a data request and apply for data access and then make use of the data that has been provided by the data providers to benefit both parties and make scientific discovery possible. This is all that I have for you today, so I thank you for your attention and please do check out our website for developments because there's going to be more exciting developments coming up in the future follow us and if you have any questions I'm happy to take them or contact me when you watch the video next morning in New Zealand. Thank you. Thank you very much Ulrich. It has become kind of our tradition here even though we're online watching in offices and homes around the world we'd like to give our presenters a thank you applause for a great talk. Thank you very much. Yeah, already we have a couple of questions coming in. I also saw that we have a comment, actually a very nice comment. Sorry I don't have a name for that. Very nice conference, nice speakers and great organization. Thank you very much for that compliment. I can just pass it along certainly to the ZBW team for all the organization and certainly to our tech team that's keeping everything running. Okay let me begin then. I see here the first question coming up and this is from our audience member and it says are you working with test data or real data from projects and the follow-up question where did you get the data? So there's so getting the data so there's one avenue which is data harvesting that is where we where data is scraped through the web and through APIs and means and legal means possible and the other avenue is getting data that is provided by users. This should be done with real data and not test data that is that's the aim. Surprising community needs. Yeah the next one let me just read that. Hi. Yeah no problem. So were there any surprising community needs is the next question. Go ahead. It's a journalist question. No I it doesn't surprise you that I don't think it didn't surprise me that that much coming from data research. It's always the same problems or that these things need to be easy and accessible because researchers have many other tasks to do than documenting their data. This is hardly surprising but it's very important I think. Okay great thank you. Let's move on then. We're coming on the next person writes thank you Ulrich exclamation point. I'm especially interested in task area six supporting users. Can you elaborate more on this topic please? Okay so there's two things that so there's one one avenue here is classical is training. So we we partnered with with course with researchers that have existing trainings and develop new trainings that should empower researchers to just I mean just to to gain more knowledge on methodology and processes. The other I mean that you all know I mean from as I mentioned from summer school the other thing is data data the concept of data stewardship where we will be help will we help provide support for researchers to so hands on we have a dedicated person that helps a project to publish their data and documented data to take them by their hands and get data to be reusable open and fair that is maybe the year that's the other aspect that I would mention here. Okay thank you and the final question this is coming from me the last couple days we've talked a lot about open access of course and sharing data and I'm curious do you see any negative effects to that or is it all really positive? Yeah thanks I I guess it's a good thing if data are not stored on a laptop hard drive that gets then stolen or lost and I think that's possible from my background in survey research we often have this problem of the consent and how general the consent is and providing an anonymity to people that provided us with their data and said that for example if we say to that data is collected for research purposes and then we can't share it with somebody that has that it has a commercial aspect in their project or and sometimes we can't be as open as we want to be but we want to tackle this and make make secure spaces available where this data can be analyzed and then maybe counter that negative point of data sharing. Excellent and that's a good last word with that once again I want to thank Ulrich Krieger thank you Ulrich for your presentation and also taking a few minutes to answer our questions and again once again I am going to give the flowers and I will have the best and enjoy the rest of the Inconnex, take care.
 We have here Ulrich Krieger. Ulrich, are you there?  Yeah, I'm here.  Ah, wonderful Ulrich. Before I begin, before my introduction, just real quick,  where are you physically at right now? And if we visit there, what's one thing you recommend  that we visit?  I'm here at the beautiful Baroque castle of the university, where the university library  in Mannheim is located. I'm at the office and if you happen to come to Mannheim, you  should check out the water tower. I guess the ensemble is really nice.  Beautiful, the water. As I mentioned before, when we spoke, I did visit the water tower by chance a  few months ago and had a lovely time there. But we both agreed probably when you visit Germany,  Mannheim will not be on the top 10 list of things to do, but maybe the top 20 or 30.  120 I'd say.  Oh boy, coming from someone living in Mannheim, very good.  Good, then let's get to business. Just a few words about you. Ulrich Krieger,  he's the coordinator and project manager of the Barrett at NFDI consortium based at the  University of Mannheim. And just to refresh, Barrett stands for business, economic and related  data. And he has a background in serving methodology and served as head of operations  of the German Internet panel. His topic today will be burnt at NFDI, structuring unstructured  data for business, economic and related research. So it is my pleasure to give the digital stage  over to Ulrich Krieger. Please. Well, thank you. Thanks for the kind words and the introduction.  And yes, I am happy to tell you about our platform, our enterprise, the Barrett at NFDI consortium.  NFDI stands for the German National Data Research Infrastructure, which is a rather new  funding scheme where the German state and the federal lender joined forces to  enhance, to create projects that enhance the data and research infrastructure landscape in Germany.  Today, I want to talk about our consortia, which is BERT. And you already mentioned what the  acronym stands for, it's business, economic and related data. And I want to tell you about what  it's all about. I want to start up with the use case for BERT. And with that, I'm going to take  you back into time, beginning of survey research or research into human behavior. And here we have  the case of unemployment research. And some of you may remember from school or somewhere there's  this famous study of Marine Tal, which is this Austrian town where, as you all know, probably  know the main employer closed down rather suddenly and lots of these inhabitants of  Marine Tal became unemployed. Researchers from the University of Vienna, I think, jumped onto the  occasion and swamped the town and observed everything that's going on to see what the  consequences of unemployment are on the inhabitants of Marine Tal. They did observational studies,  they interviewed respondents, or they also invaded the privacy of people's homes and checked out what  they have, how their economic standing is by observing what they have in their houses or in  their flats. So this is a very detailed look at one town. So this is a small scale study,  very influential study, but you can't draw inference from that. So you can't say that all  unemployed people are like that. And it is very driven from the researchers, so there may be some  of survey or survey error in there. We moved on from that to large scale standardized studies.  Like you see here in the picture, computer assisted personal interviews, where we could  take a very in-depth look and long interlongage interviews with respondents about everything  that's going on in their lives. And that can be generalized or potentially generalized to the  general public, but these endeavors are expensive and they're getting more and more expensive.  And it's harder to convince people to participate because this place is a high burden on respondents  because you have to let somebody into your house, into your home. And it also tends to have misreports.  It is data that's generated by the respondents themselves. So then we move on to new avenues  of data research. And I will mention here this study of the IAB SMARC study, which is study where  they asked respondents to download the smartphone app, which is then installed on their handheld devices,  which then allows not only for standardized research, a large scale data collection,  but is potentially cheaper than sending real interviews to people's homes, which has a lower  burden because you can also split your questionnaires or your data collection in smaller chunks.  So you can have some short 10 minute, 10, five minute survey on the go somewhere. And you can  also link this to real life events. So if somebody has a contact with the employment agency, you can  then after the fact ask people about their experiences, you can also use all the sensors  that are in the smartphone. And our smartphones are not only telephones, but they collect all  sorts of information, as you know, your location and your trajectory's speed  has a microphone camera built in. So there's a lot of ways we can collect data with this  with a smartphone. Fun fact, the colleagues tried to replicate one of the findings from the  original Marine Tile study from the study back in the days, where they observed that  unemployed residents of Marine Tile walked slower at a slower walking pace than employed  residents of Marine Tile. But this couldn't be replicated in our time. So there's no difference  on the walking speed that is collected with the smartphone sensors. But that just gives you an  idea of the potential. That's maybe a fun story, but that should just give you an idea about the  potential of the sensor data. So here we have, but then the downside that comes to this data  collection is this huge amount of data, which needs to be processed for analysis, because  information about locations or trajectories are not ready for analysis. So we need tools,  infrastructure for post-processing, and these are lacking, which is a problem for the researcher.  So we turned, so we came from this, what I'll show you here is this analytical paradigm,  where we have this elaborated studies where there's a high effort prior to data collection  to set up a survey or an experiment. And then you have a standard processing, which can be done by  hand and is of lower effort. It's still a lot of effort, but it's compared to the low effort post  data collection. Now with data where we have text, languages, images, social media data, or other  digital trace data, like the one that's collected on the smartphone, it turns that up into its head  where the automated recording is comparatively low effort, but the post-processing is complex,  so you have more high effort post data collection. So with this unstructured data, we  enrich the traditional data model where we only have the structured data that is collected by  experiments, surveys, and such, where we then could apply empirical methods for cause and analysis  and prediction. We now have this other part that's here where we have this unstructured data that  needs to be transformed to be fed into this traditional model of empirical research. And  here with this transformation, that can't be done by hand. We need artificial intelligence and machine  learning algorithms to provide the data processing due to the sheer volume of the data and the complexity.  So there's huge potential for exciting discoveries, the methodological costs are higher,  and there's high technical burden comes with it, which then leads to data graveyards where data are  just dumped somewhere and can't be used or even reused or made transparent and open as we want  to have this in the scientific process these days. Just to give you an example of what the need for  such an endeavor is, I have here some remarks that were given in a study where respondents said  what is hindering them in their research. And you have used things where they said there should be  more of a community where we can exchange on ideas of analysis or the algorithms used or  we need support in publishing data and accessing data because this comes together with cost or  legal problems. And this is where, and this is the case where BERT comes in. This is an important  piece of a platform that we will create that is open and to link structured and unstructured data  combines the best practices in machine learning to process these data. It's all reproducible and  transparent and can help with the whole management of the data life cycle. So it's about collecting,  processing, analyzing, preserving, giving access and then making reuse of data possible.  So how we do this in BERT, we have different task areas that comes along this or that is lined  up with this data life cycle. So we have these data sources that are then harvested  and or shared and collected by a task area, as we call it task area two. Then there's a task area  that is in charge of data quality, a huge important step. Data is not just equal, we need high quality  data for the research purposes. We need to take a look at anonymization and digital documents need  to be processed. Task area four is then about data analysis and semantic enrichment. And then we  have two task areas that are just or that are made their main focus this on helping researchers  analyze data. So there's a training aspect in here as services and we are helping with preserving  data and making data accessible to the research community. This is a joint endeavor with many  colleagues and many institutes and I just shared this slide here with everybody, everybody that is  involved. So you see the starkish green, you see research institutes and universities and then in  the other color, other greenish color, there's infrastructure providers, which are very important  and to provide the service and together combining all these institutions, we have the knowledge in  the brain power and the manpower to provide this service and put up this platform. What are the  first steps? We started now with training activities, for example, there's a course  that is conceived out of birds that will be offered through an existing summer school.  We're reaching out to scientific community like you and we're working on two services that will  go online this year. One is this service that we call Open Big Data where users can upload data,  search and download data. So this will where we start off preserving and giving access to data to  researchers and the other service is an open, we call data marketplace. This is an exciting  endeavor where we partner with data providers such as companies and match them with interested  researchers that can apply for or submit a data request and apply for data access and then  make use of the data that has been provided by the data providers to benefit both parties and  make scientific discovery possible. This is all that I have for you today, so I thank you for  your attention and please do check out our website for developments because there's going to be  more exciting developments coming up in the future follow us and if you have any questions I'm happy  to take them or contact me when you watch the video next morning in New Zealand. Thank you.  Thank you very much Ulrich. It has become kind of our tradition here even though we're online  watching in offices and homes around the world we'd like to give our presenters a thank you  applause for a great talk. Thank you very much. Yeah, already we have a couple of questions coming  in. I also saw that we have a comment, actually a very nice comment. Sorry I don't have a name for  that. Very nice conference, nice speakers and great organization. Thank you very much for that  compliment. I can just pass it along certainly to the ZBW team for all the organization and  certainly to our tech team that's keeping everything running. Okay let me begin then. I see here the  first question coming up and this is from our audience member and it says are you working with  test data or real data from projects and the follow-up question where did you get the data?  So there's so getting the data so there's one avenue which is data harvesting that is where  we where data is scraped through the web and through APIs and means and legal means possible  and the other avenue is getting data that is provided by users. This should be done with  real data and not test data that is that's the aim.  Surprising community needs. Yeah the next one let me just read that. Hi.  Yeah no problem. So were there any surprising community needs is the next question. Go ahead.  It's a journalist question. No I it doesn't surprise you that I don't think it didn't surprise me that  that much coming from data research. It's always the same problems or that these things need to be  easy and accessible because researchers have many other tasks to do than documenting their data.  This is hardly surprising but it's very important I think. Okay great thank you. Let's move on then.  We're coming on the next person writes thank you Ulrich exclamation point. I'm especially  interested in task area six supporting users. Can you elaborate more on this topic please?  Okay so there's two things that so there's one one avenue here is classical is training.  So we we partnered with with course with researchers that have existing trainings and  develop new trainings that should empower researchers to just I mean just to to gain  more knowledge on methodology and processes. The other I mean that you all know I mean from  as I mentioned from summer school the other thing is data data the concept of data stewardship  where we will be help will we help provide support for researchers to so hands on  we have a dedicated person that helps a project to publish their data and documented data to  take them by their hands and get data to be reusable open and fair that is maybe the year  that's the other aspect that I would mention here. Okay thank you and the final question  this is coming from me the last couple days we've talked a lot about open access of course  and sharing data and I'm curious do you see any negative effects to that or is it all really positive?  Yeah thanks I I guess it's a good thing if data are not stored on a laptop hard drive that gets  then stolen or lost and I think that's possible from my background in survey research we often  have this problem of the consent and how general the consent is and providing an anonymity to  people that provided us with their data and said that for example if we say to  that data is collected for research purposes and then we can't share it with somebody that has  that it has a commercial aspect in their project or and sometimes we can't be as open as we want to be  but we want to tackle this and make make secure spaces available where this data can be analyzed  and then maybe counter that negative point of data sharing. Excellent and that's a good last word  with that once again I want to thank Ulrich Krieger thank you Ulrich for your presentation  and also taking a few minutes to answer our questions and again once again  I am going to give the flowers and I will have the best and enjoy the rest of the Inconnex, take care.
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We have here Ulrich Krieger. Ulrich, are you there? Yeah, I'm here. Ah, wonderful Ulrich. Before I begin, before my introduction, just real quick, where are you physically at right now? And if we visit there, what's one thing you recommend that we visit? I'm here at the beautiful Baroque castle of the university, where the university library in Mannheim is located. I'm at the office and if you happen to come to Mannheim, you should check out the water tower. I guess the ensemble is really nice. Beautiful, the water. As I mentioned before, when we spoke, I did visit the water tower by chance a few months ago and had a lovely time there. But we both agreed probably when you visit Germany, Mannheim will not be on the top 10 list of things to do, but maybe the top 20 or 30. 120 I'd say. Oh boy, coming from someone living in Mannheim, very good. Good, then let's get to business. Just a few words about you. Ulrich Krieger, he's the coordinator and project manager of the Barrett at NFDI consortium based at the University of Mannheim. And just to refresh, Barrett stands for business, economic and related data. And he has a background in serving methodology and served as head of operations of the German Internet panel. His topic today will be burnt at NFDI, structuring unstructured data for business, economic and related research. So it is my pleasure to give the digital stage over to Ulrich Krieger. Please. Well, thank you. Thanks for the kind words and the introduction. And yes, I am happy to tell you about our platform, our enterprise, the Barrett at NFDI consortium. NFDI stands for the German National Data Research Infrastructure, which is a rather new funding scheme where the German state and the federal lender joined forces to enhance, to create projects that enhance the data and research infrastructure landscape in Germany. Today, I want to talk about our consortia, which is BERT. And you already mentioned what the acronym stands for, it's business, economic and related data. And I want to tell you about what it's all about. I want to start up with the use case for BERT. And with that, I'm going to take you back into time, beginning of survey research or research into human behavior. And here we have the case of unemployment research. And some of you may remember from school or somewhere there's this famous study of Marine Tal, which is this Austrian town where, as you all know, probably know the main employer closed down rather suddenly and lots of these inhabitants of Marine Tal became unemployed. Researchers from the University of Vienna, I think, jumped onto the occasion and swamped the town and observed everything that's going on to see what the consequences of unemployment are on the inhabitants of Marine Tal. They did observational studies, they interviewed respondents, or they also invaded the privacy of people's homes and checked out what they have, how their economic standing is by observing what they have in their houses or in their flats. So this is a very detailed look at one town. So this is a small scale study, very influential study, but you can't draw inference from that. So you can't say that all unemployed people are like that. And it is very driven from the researchers, so there may be some of survey or survey error in there. We moved on from that to large scale standardized studies. Like you see here in the picture, computer assisted personal interviews, where we could take a very in-depth look and long interlongage interviews with respondents about everything that's going on in their lives. And that can be generalized or potentially generalized to the general public, but these endeavors are expensive and they're getting more and more expensive. And it's harder to convince people to participate because this place is a high burden on respondents because you have to let somebody into your house, into your home. And it also tends to have misreports. It is data that's generated by the respondents themselves. So then we move on to new avenues of data research. And I will mention here this study of the IAB SMARC study, which is study where they asked respondents to download the smartphone app, which is then installed on their handheld devices, which then allows not only for standardized research, a large scale data collection, but is potentially cheaper than sending real interviews to people's homes, which has a lower burden because you can also split your questionnaires or your data collection in smaller chunks. So you can have some short 10 minute, 10, five minute survey on the go somewhere. And you can also link this to real life events. So if somebody has a contact with the employment agency, you can then after the fact ask people about their experiences, you can also use all the sensors that are in the smartphone. And our smartphones are not only telephones, but they collect all sorts of information, as you know, your location and your trajectory's speed has a microphone camera built in. So there's a lot of ways we can collect data with this with a smartphone. Fun fact, the colleagues tried to replicate one of the findings from the original Marine Tile study from the study back in the days, where they observed that unemployed residents of Marine Tile walked slower at a slower walking pace than employed residents of Marine Tile. But this couldn't be replicated in our time. So there's no difference on the walking speed that is collected with the smartphone sensors. But that just gives you an idea of the potential. That's maybe a fun story, but that should just give you an idea about the potential of the sensor data. So here we have, but then the downside that comes to this data collection is this huge amount of data, which needs to be processed for analysis, because information about locations or trajectories are not ready for analysis. So we need tools, infrastructure for post-processing, and these are lacking, which is a problem for the researcher. So we turned, so we came from this, what I'll show you here is this analytical paradigm, where we have this elaborated studies where there's a high effort prior to data collection to set up a survey or an experiment. And then you have a standard processing, which can be done by hand and is of lower effort. It's still a lot of effort, but it's compared to the low effort post data collection. Now with data where we have text, languages, images, social media data, or other digital trace data, like the one that's collected on the smartphone, it turns that up into its head where the automated recording is comparatively low effort, but the post-processing is complex, so you have more high effort post data collection. So with this unstructured data, we enrich the traditional data model where we only have the structured data that is collected by experiments, surveys, and such, where we then could apply empirical methods for cause and analysis and prediction. We now have this other part that's here where we have this unstructured data that needs to be transformed to be fed into this traditional model of empirical research. And here with this transformation, that can't be done by hand. We need artificial intelligence and machine learning algorithms to provide the data processing due to the sheer volume of the data and the complexity. So there's huge potential for exciting discoveries, the methodological costs are higher, and there's high technical burden comes with it, which then leads to data graveyards where data are just dumped somewhere and can't be used or even reused or made transparent and open as we want to have this in the scientific process these days. Just to give you an example of what the need for such an endeavor is, I have here some remarks that were given in a study where respondents said what is hindering them in their research. And you have used things where they said there should be more of a community where we can exchange on ideas of analysis or the algorithms used or we need support in publishing data and accessing data because this comes together with cost or legal problems. And this is where, and this is the case where BERT comes in. This is an important piece of a platform that we will create that is open and to link structured and unstructured data combines the best practices in machine learning to process these data. It's all reproducible and transparent and can help with the whole management of the data life cycle. So it's about collecting, processing, analyzing, preserving, giving access and then making reuse of data possible. So how we do this in BERT, we have different task areas that comes along this or that is lined up with this data life cycle. So we have these data sources that are then harvested and or shared and collected by a task area, as we call it task area two. Then there's a task area that is in charge of data quality, a huge important step. Data is not just equal, we need high quality data for the research purposes. We need to take a look at anonymization and digital documents need to be processed. Task area four is then about data analysis and semantic enrichment. And then we have two task areas that are just or that are made their main focus this on helping researchers analyze data. So there's a training aspect in here as services and we are helping with preserving data and making data accessible to the research community. This is a joint endeavor with many colleagues and many institutes and I just shared this slide here with everybody, everybody that is involved. So you see the starkish green, you see research institutes and universities and then in the other color, other greenish color, there's infrastructure providers, which are very important and to provide the service and together combining all these institutions, we have the knowledge in the brain power and the manpower to provide this service and put up this platform. What are the first steps? We started now with training activities, for example, there's a course that is conceived out of birds that will be offered through an existing summer school. We're reaching out to scientific community like you and we're working on two services that will go online this year. One is this service that we call Open Big Data where users can upload data, search and download data. So this will where we start off preserving and giving access to data to researchers and the other service is an open, we call data marketplace. This is an exciting endeavor where we partner with data providers such as companies and match them with interested researchers that can apply for or submit a data request and apply for data access and then make use of the data that has been provided by the data providers to benefit both parties and make scientific discovery possible. This is all that I have for you today, so I thank you for your attention and please do check out our website for developments because there's going to be more exciting developments coming up in the future follow us and if you have any questions I'm happy to take them or contact me when you watch the video next morning in New Zealand. Thank you. Thank you very much Ulrich. It has become kind of our tradition here even though we're online watching in offices and homes around the world we'd like to give our presenters a thank you applause for a great talk. Thank you very much. Yeah, already we have a couple of questions coming in. I also saw that we have a comment, actually a very nice comment. Sorry I don't have a name for that. Very nice conference, nice speakers and great organization. Thank you very much for that compliment. I can just pass it along certainly to the ZBW team for all the organization and certainly to our tech team that's keeping everything running. Okay let me begin then. I see here the first question coming up and this is from our audience member and it says are you working with test data or real data from projects and the follow-up question where did you get the data? So there's so getting the data so there's one avenue which is data harvesting that is where we where data is scraped through the web and through APIs and means and legal means possible and the other avenue is getting data that is provided by users. This should be done with real data and not test data that is that's the aim. Surprising community needs. Yeah the next one let me just read that. Hi. Yeah no problem. So were there any surprising community needs is the next question. Go ahead. It's a journalist question. No I it doesn't surprise you that I don't think it didn't surprise me that that much coming from data research. It's always the same problems or that these things need to be easy and accessible because researchers have many other tasks to do than documenting their data. This is hardly surprising but it's very important I think. Okay great thank you. Let's move on then. We're coming on the next person writes thank you Ulrich exclamation point. I'm especially interested in task area six supporting users. Can you elaborate more on this topic please? Okay so there's two things that so there's one one avenue here is classical is training. So we we partnered with with course with researchers that have existing trainings and develop new trainings that should empower researchers to just I mean just to to gain more knowledge on methodology and processes. The other I mean that you all know I mean from as I mentioned from summer school the other thing is data data the concept of data stewardship where we will be help will we help provide support for researchers to so hands on we have a dedicated person that helps a project to publish their data and documented data to take them by their hands and get data to be reusable open and fair that is maybe the year that's the other aspect that I would mention here. Okay thank you and the final question this is coming from me the last couple days we've talked a lot about open access of course and sharing data and I'm curious do you see any negative effects to that or is it all really positive? Yeah thanks I I guess it's a good thing if data are not stored on a laptop hard drive that gets then stolen or lost and I think that's possible from my background in survey research we often have this problem of the consent and how general the consent is and providing an anonymity to people that provided us with their data and said that for example if we say to that data is collected for research purposes and then we can't share it with somebody that has that it has a commercial aspect in their project or and sometimes we can't be as open as we want to be but we want to tackle this and make make secure spaces available where this data can be analyzed and then maybe counter that negative point of data sharing. Excellent and that's a good last word with that once again I want to thank Ulrich Krieger thank you Ulrich for your presentation and also taking a few minutes to answer our questions and again once again I am going to give the flowers and I will have the best and enjoy the rest of the Inconnex, take care.
project manager of the Barrett at NFDI consortium unstructured data for business, economic and related research. So it is my pleasure to give National Data Research Infrastructure, which is a moved on from that to large scale standardized So then we move on to new avenues of data collection is this huge amount of data, which needs to be processed for analysis, because text, languages, images, social media data, or data collection. So with this unstructured data, only have the structured data that is collected could apply empirical methods for cause and this traditional model of empirical research. And machine learning algorithms to provide the data and the complexity. So there's huge potential for support in publishing data and accessing data of a platform that we will create that is open transparent and can help with the whole management of the data life cycle. So it's about collecting, processing, analyzing, preserving, have these data sources that are then harvested high quality data for the research purposes. We that are made their main focus this on helping to the research community. This is a joint and giving access to data to researchers and the other service is an open, we call data we partner with data providers such as companies data that is that's the aim. Surprising community we will be help will we help provide support for say to that data is collected for research