Okay, good afternoon. So this session today, the rest of today will be sort of in between scientific methods. It will be about data management and reproducibility. I will start with the first part, talk a little bit about issues of research data management and also send some solutions that we had at the G-note that the German neuroinformatics note have developed. And then later, Michael Denker will present and focus more about reproducibility, data analysis workflows and application of the different tools and methods. So as a background on why we are talking about this and why this course, for example, is organised by the German neuroinformatics note where there is something like a German neuroinformatics note, the G-note, a little bit of a background. So G-note is part of the INCF network. So the INCF is the International Neuroinformatics Coordinating Facility, which is an international organisation that came out of the OECD Open Science Forum that was held in the late 90s, early 2000s. And there were working groups on neuroinformatics which recommended establishing an organisation to coordinate neuroinformatics globally. And this led to the INCF being established in 2005. The INCF is a globally acting organisation, but it has a central secretariat in Stockholm. And at the moment, 17 national notes that is countries that have joined this organisation. Germany was one of the founding members of this organisation. The idea being that global efforts, globally coordinated efforts will be needed to develop neuroinformatics because it doesn't make sense that every country with its funding agencies and support is working in isolation on these methods. Science is a global effort, essentially. And also the development of tools and methods for science and standards should be done at a global scale. And neuroscience was seen as a field very much in need of such global coordination. So this basically is reflected in the mission of INCF as being a global independent coordinator of neuroinformatics developments. As mentioned, Germany was a founding member and at the same time as the INCF was established, the German government started a funding initiative, a very large funding initiative on computational neuroscience. It was called the National Network Computational Neuroscience. Now it's known as the Bernstein Network. This is the result of ten years of funding for computational neuroscience. And computational neuroscience is meant not just, as you might see me, just funding theorists who make models, to specifically fund the exchange between experimentalists and theorists because it was realized, of course, that progress in neuroscience is very much dependent on experimentalists and people who do models and who do data analysis working together and joining forces. So this was one of the core goals of this network and of this funding. And now we have a fairly large network with more than 200 research groups all over Germany. And we also have in this network several infrastructure facilities. And do you know, the German neuroinformatics node is one of these, it's hosted by the LMU in Munich and its goal is to develop solutions to help the scientists in data management and data exchange easier for the scientists. The Bernstein Network also has created the Bernstein Conference, which is an annual conference. Probably many of you have heard about it, maybe even have attended. It's meanwhile the largest European conference on computational neuroscience. So this is kind of the setting of where we are with the G-note, the link between this National Bernstein Network and the INCF Global International Community. So what do we do? What is our goal? The German node was specifically established to target the issue of cellular and systems neurophysiology because there it was seen that this field is very complex and there are so many such a large diversity of methods and data structures and science questions that it's fairly hard for scientists to collaborate. If you think of an experimentalist and a theorist, for example, who want to join in exchanging data and working on models, then it's very hard to explain to the person who has not been involved in the experiment, for example, what the data mean and how to read the data and how to analyze. So this was one of the main targets because also the number of groups working on neurophysiology is very large in the Bernstein Network. So over the years we've been working on solutions at several levels. We've been creating data conversion tools to make it easier for scientists to read data in different formats of convert data between different formats. We've created methods for data and metadata management to help the scientists in the lab organize their data and metadata. We provide data sharing services, a platform where you've seen the GIN service. This is basically a replica of what we are running in Munich where everyone can sign up and can share their data. We also provide custom solutions for data exchange and we're also involved in teaching and training activities. One of these teaching and training activities is where you are right now, the under Advanced Neural Data Analysis course. We've also been running a shorter course on Neural Data Analysis which was one week long and which provided a low barrier entry for neuroscientists, PhD students to start working on their code and start improving their data analysis in a kind of easy going way but getting in touch with the code. What you are doing here as well but you are doing it at a little bit more advanced level will, since the course is longer and will involve different further exercises and tasks of course go more in depth. These were our courses on data analysis that we provide. We've also been running for several years now the Advanced Scientific Programming in Python School which is a summer school that introduces young scientists to good scientific programming practices. This is not a course for learning Python, it's a course for learning to code better in Python because you know more about the background of the language of computers as such so you can write code that is better maintainable that can be shared so others can use your code and so forth. Currently so this year this will be held in September in Italy and the application period is currently open so if you're interested please apply now. We're also running irregular intervals, workshops and tutorials on data management. So let's go back to the issue of research data management. This is what I want to talk a little bit more in depth now. One of the focus areas that we have at the G-node. So the question is why do we care about data management? I've kind of a little bit alluded to but the question is why should you as a scientist care about data management? So one reason might be because research data is typically at the core of science. Science depends on being grounded in data and in fact we're typically focusing on publication as the output of science but there are other kind of notions that even sort of degrade the publication, the scientific publication to something which is just an advertisement for the actual science, for the scholarship. And what is the actual science? Well it's in here in this quote by quoting or paraphrasing John Clairbaugh this is from a paper of Buchhide and Donohoe. It says that it refers to computational science, to the field of computational science and there it says that the actual scholarship is the complete software developing environment and a complete set of instructions. So all the code that went into a scientific result. You can see this also or translate this easily into experimental science when you add the data. So what is the substance of your science, the scholarship? It's not the publication that comes out of it but the entire data acquisition, the experiment data acquisition, data analysis including all the code that you use because this is something that principle other scientists should be able to inspect and to see how did you get to your results, how did you get to your conclusion. So this is kind of the one argument why it would be useful or important to think about data management because you're supposed to be able to have your data available. And this also refers to something that has been a topic of many talks in the past years. The so-called replication crisis or reproducibility crisis when it has been found that many studies started with looking at psychological papers on psychology but this also applies to other fields and I'm sure also to neuroscience that many studies that are published are not really reproducible. And one problem with not being reproducible is that when you find that something, or you have a suspicion that something is not reproducible, you would like to go and check how did the original scientists, how did they get to their results, right? And this requires having the data, having the analysis code and so forth all available. Another reason why you may want to think about data management in your lab is because there are a lot of external either incentives or external pressure that's coming up. That's existing already to some degree but it's also coming up. So the funders keep requiring it. They have been for several years so when you, I don't know how many of you already wrote a grant proposal but your supervisors, when they write their grant proposal typically they have to include a few sentences about a data management plan for the project. This is so far something that is not really strictly checked or reviewed typically but since the entire scientific landscape is starting to become more open, this will certainly be more rigorously checked in the future and it's good to prepare for that. For example, at the EU level, the Horizon 2020 program, they started a couple of years ago with a so-called open data pilot where they were requiring or were encouraging the scientists who were getting EU grants to make the data open and provide a plan for how they would do that. Meanwhile, so over a couple of years, three or four years, this changed now. First it was kind of an opt-in thing that when you wrote the grant and it was not explicitly stated, this will not affect the review of your grant. This will be of your proposal, this will be completely on a scientific basis. So it was completely voluntary to say, okay, I want to make my data open after the study. But now it's the other way around, now it's an opt-out. Now it's the default, EU requires this as the default to provide a plan how to make your data open and if you don't want, then you have to opt-out. So things are turning, things are getting more into a notional understanding that science should be open and this is in fact the core of science that everything is open for discussion. Besides the funders, also there are several publishers now or several journals that require that you provide your data with a publication with the same kind of idea in the back that when you have a publication, people should be able to go and check in more detail how you arrived at the results that they are presenting. This at the moment typically is also not very heavily checked or reviewed. Like if you, maybe one of you has already published a paper where it was required to provide also the data. I'm pretty sure none of you has gotten any comments from the reviewers about the data. Now they are typically reviewing the paper and nobody is looking at the data. But I'm pretty sure this will also change in the coming years as the publishers also learn, they also have to recruit basically reviewers for doing it, for looking at the data. Some journals have started kind of test trials to do that and I think this is evolving and it will become more and more mandatory basically to not only provide some data, something there but also something that is accessible and readable by other scientists. And so we're going into a phase where we will see an increasing openness and this will also be reflected in the expectation of other scientists that come and say okay, here's your publication, where are your data? So we should prepare for that. And the last point or the last argument that might even be the best one is selfishness. Because I think it's in our own interest to have a good data management in our lab because it saves us time. There are studies or kind of informal studies but it's repetitively being quoted that scientists spend a lot of time just what's called munging data, meaning trying to find data, trying to read that data even in their lab. Just in their lab trying to read that data, trying to read data of their students after they've left the lab, that's a typical situation. A student is doing a PhD, maybe the data are even published and then the student leaves of course and maybe two years later there's an interest in going back to this data and it's very hard for anyone in the lab to access the data. And so it takes a lot of time. So one estimate is that scientists typically are spending about 60% of their time when they work on scientific topics, 60% of that is on data munging. Not sure if this number is really correct but it is a substantial part. I think everyone knows that from their own experience and the question is wouldn't it be really nice to only spend half of that time and have the other time free for really thinking about the science? That should be the goal and so I think this should be the largest incentive for us to try to have a good organization of data and consistent organization of data in our lab so that we make life easier for ourselves. So why is it so hard? Why do we have to spend so much time on data munging? One reason, at least in the neurosciences is that we have typically very complex experiments, we have complex research questions, the data are complex and this complexity alone makes it hard to organize the data, to find data, to document data and so forth. We are also very often relying on collaborations with others so we have to make sure that data is available and we have to exchange. The volume of the data that we are acquiring is increasing, probably all of you are experiencing this with all the new methods for recording. The volumes of data sets are increasing massively which creates also a problem in data handling. So all this together basically contributes to this situation that when we want to access our data, data that we are not immediately working with but we created in the past or generated in the past and want to go back to, that it typically takes an effort, time and effort. And having a good data organization in lab benefits us, it reduces this effort and so this, since when the effort is reduced it means we can more easily get to our data which enhances the reproducibility because we can better understand what we have been doing and it also facilitates the ability to share data with others. Or share data with ourselves. So that's again the situation when we talk about data sharing. It doesn't only mean we share with others but we also share with ourselves. That's the most important condition that we should try to arrive at and it starts in the lab. Typically in the lab we want to make sure all data is recorded, all data is available, all the information is recorded. Typically in labs there's a lot of hidden knowledge that kind of everybody knows and kind of it's transferred from HD student to PhD student. And it's also important if we want to be able to understand our data say a few years in the future that we also document this. So kind of trying to get into these practices of documenting all the information is really important. Yes. Well hidden knowledge is kind of the knowledge that's in the lab that is maybe verbally, when a PhD student comes in and he learns how to handle the setups and everything, how to do experiments. There's a lot of information that they learn by being instructed for example. But at no point is the information written down somewhere or provided with the final data set. But it may be important. So we always set the setting of this amplifier to this and this which will be important for the data analysis afterwards to know what kind of filtering appeared. Because the setup is always the same, it's not documented. So this is what I mean with hidden knowledge. The next step after sharing with yourself is sharing with a collaborator where you have an interaction where you can provide a lot of clarification and you can answer questions and so forth. And you select specific data sets. And then the yet next level is then sharing with the world which is exactly the situation when you're done with your study basically you're publishing your paper and then maybe you want or you're asked to provide the data with your publication or you decide to do a data publication. So this is also a possibility now some journals accept or have kind of an article type which is called data publication where you do not describe a new scientific finding but you describe your data set in detail, make it available so for others to use it and to reuse it. So this is all then the situation where you want to share with people you don't even know. Someone else might take your data set, might reuse it and here it is specifically challenging because there will be no interaction so you want to make sure that the scientists who access your data have a chance to understand it because otherwise they might do things with the data that are not appropriate. So you also should be in your interest to make the data be readable at all and not have some artifact there, some data there that some files that nobody can understand and nobody will ever be able to reuse. So you have basically technically shared your data. It is there but semantically nobody can make sense of it. This is a situation we want to avoid and the way to start I think is if we try to improve the situation here in the lab, in our lab. This will benefit ourselves because it's easier for us to do our research but it also will make it easier for us to provide data in a form that is understandable for everyone else. And by the way we've already had questions if there are more questions just ask me anytime. Regarding the situation of sharing with the world again I want to briefly introduce this kind of notion or these guidelines which are called the fair principles which some of you may have heard about already it's something that has been come up a few years ago and has been taken up a lot by scientists and also by providers and also by funders. The fair principles, it's an attempt to provide some guidelines of how you can make data better reusable at the end which means first of all they need to be findable. Once they are found they need to be accessible meaning you have a way of actually getting to the data they need to be interoperable that is they need to have a chance to use them to work with them and they have to be reusable that is they have to legally be reusable but also have to be understandable. So these guidelines are kind of thought as a course framework of how you should try to of goals to achieve. Primarily I would say this was thought of the situation that was considered was that the data are there and how do we provide services that will achieve that but a lot of this depends on the actual data provision the actual data that the scientists provide that are in a form that enables to achieve all these criteria for example all the documentation, all the metadata information about the experiment how the data work quite and so forth this is all important to be able to reuse the data in the end. On the kind of more general side the recommendation is for example to make the data finable is to use globally and persistent identifiers you all probably know the DOI this is an identifying a digital identifier the persistent identifier that is used to indicate or to identify resources, papers typically or data sets the idea or the goal would also be to use machine readable descriptions of a data in order to be able to of automated services to find your data and know what's in your data. Accessibility means that it must be clear how the data can be accessed it doesn't necessarily mean that it's fully open but there must be a way of finding out how you can actually access the data interoperability and this again puts some demands on the scientists what kind of formats do you use to store your data to provide your data in what kind of terms do you use for annotating your data and the reusability also has this aspect of legal reusability that is licenses when you provide data you should always have a license so that others know how they can use the data this is another topic to consider and at the Gino we provide data publishing services and there are also the users and scientists who want to make a data set available they are required to choose a license and then we often get questions what license should I choose what does it mean, what difference does it make so software developers, the open source community for example they have a long experience with licenses and there's a lot of licenses for software for research data it's not so common and we also have not so many licenses different licenses that are appropriate for research data one very common set of licenses are the creative common licenses there's a website where you can get information on those and they basically specify different levels of reuse that you allow or different conditions for reusing your data the most open one is the public domain dedication so this is strictly not a license it's basically a waving of all rights that you have as the author or the creator so you basically are waving your copyright and everyone can do whatever they want with what you provide then there's the CCPY license which requires that someone who is using your data will give you credit they cite you or cite the source where they got the data so this basically is a way of getting credit for your data, for your work you don't have to just dump it somewhere you can always ask people who are making news of it that they also acknowledge you and there's then different further constraints basically that you say okay any work that has been done with that and if it's published it must be published under the same conditions I don't want my data to be used in any commercial application or whatever so there's different further constraints as you see here for a start the CCPY license is typically this is one that most scientists choose because it's very easy and it also is very open so another thing I just wanted to mention here at this point where we're talking about more general services and general issues in data management something to look out for this is something that's currently coming up a funding program by the German government who also have recognized that improving the situation in data management is really important if we're interested in making progress in science and not only in neuroscience but in the entire scientific landscape so this is a funding program to specifically target issues of data management, data provision, standardization and so forth which is intended to create basically an expert network of training scientists together with infrastructure providers to better work together and to create better solutions for data management that kind of connects all the pillars of the science system the universities, the research institutes, IT centers and so forth the idea is of course that this is not isolated in Germany and all the groups that come up with proposals they must also make clear how they connect to international initiatives this is of course a very important requirement so there's also a lot of infrastructure at the European level the European open science cloud was started officially providing a lot of services that most scientists don't even know about and they don't know how to use them and the idea is really to work closely with the scientists bring them together with providers, service providers and also of course connect to international and provide solutions that are interoperable with international solutions of course and so we're currently preparing a proposal for a specific neuroscience research data infrastructure group that will of course interact with all the relevant other groups here in this program but also will make sure that solutions that are developed here are developed in coordination and in compliance with international standards and international solutions so let's get back to the data management in the lab I wanted to make clear that I think that the best way to start is to start really in our own labs and try to improve the situation for us and what is the situation? well the situation is for example you did an experiment a year ago and have worked on something else and now you want to go back to your experiments because maybe you read a paper that presented some result that might touch on what you did back then but in order to compare that you need to find out what was the exact stimulus parameters that you used what kind of frequencies did you use question is how do you find out say this is a parameter that was not really the control parameter used in your experiment in your study what was the kind of a side parameter did you even record it where could it be and there's lots of places where this can be typically in a typical experimental lab could be in the source code of the software that you used could be in a file that was written automatically could be part of a file name because you have some convention of how you name your files could be that it's in your notes in your spreadsheet that you use or in the notes that you write by hand or somewhere right so how do you find out do you have a way to find out and how can you make sure that in the future all the information that is important or might be important for a data set is available and stays available this refers to the overall notion of metadata besides actually the data that you are recording from your recording equipment there's a lot of metadata metadata means data about data so metadata are also data and partly very important data because for example it's very important to know what your stimuli were that you were using when you're recording something and there's a lot of metadata that can be in principle can be recorded in a way so that it's persistent that you assure you can access it even years from now besides the recorded data you need to know in order to understand your recorded data which are typically just numbers in a binary file you also need to know how to translate these numbers into the scientific quantity that you were recording was it a voltage, was it a current so you need conversion factors you need units, you need sampling rates these are very obvious otherwise you cannot do anything with the data but the question is is it always recorded then you have a lot of data that you could call heart metadata because it is data that you can record as you know beta as say key value pairs where you say temperature was so and so many degrees and sampling rate was so and so many samples per second and this you could all record in some electronic form some machinery to perform so you can have tools that make use of that and make sure that it's all collected you can automate a lot of that and automation is a way of ensuring that it's there and then there's only a small part of the metadata which is kind of very hard to really put down because it requires explaining a lot like why did you do the study what was the question you were after this is typically what you then provide in a publication but all these kind of heart parameters you could in principle save you could store and you could make sure that they are available and this is something if we try to do that we will make our lives easier and the lives of our colleague scientists so how could we do that how do we do this it's not of course something that just comes for free basically we have to work a little bit on it we have to prepare ourselves so we have to think about it how can we organize our metadata collection we should try to be clear to write down or record things unambiguously so that when we think of ourselves two years from now if I would read this the name for this parameter would I know what it is would I understand it we should try to save all the information even if it's something that we think now is not important but in the future or for other purposes for other reuses it might be important and probably the most hard thing is to be consistent to store things in the same way so the typical situation that you may have seen in this little cartoon here a few slides ago by the way these illustrations are done by Luba Sehl who's at the Foschungszentrum Uli she did this as part of her PhD thesis and I use this with permission she's fine with reusing this so for example you have some files about your dataset and the naming conventions are different for each file every day after every experiment you make up some names that's something that in the future will be very hard to understand so if we try to be consistent and that requires thinking ahead even before we start the experiment think of how we are going to do that that will go a long way already in ensuring that the data stays available for us accessible for us so how can we make this easier for us planning ahead just as much as we think about the experiment the experimental question how do we design a task for the animal for example we should also think of what are the kind of data that we will acquire what are the kind of metadata and how do we collect those and how do we store them and then once we have a good idea about that automating things again is something that's very important because that at the end then saves us time and we don't have to even think about it anymore that would be of course the ideal case it also avoids errors so for automating it's of course good if you know how to write scripts whatever scripting language you prefer but if you acquire some skills that help you automating things on your computer that would be very helpful and then of course you can use tools and you should also use conventions that other scientists use and use tools that are available to help doing this and I would like to so the situation you mentioned is really common that every lab has their conventions but the conventions are different between labs that's a typical situation and the question is there an organization that is taking care of that well in principle the INCF has some of the goals of the INCF is to promote common conventions and standards currently they have started now an activity where they identify tools and best practices in the community and endorse them meaning that they are reviewed and then the INCF says this is an endorse standard meaning it's good to use them you can use them, you can rely on them and this will also lead scientists to more and more use the same tools and conventions but it is a real problem it's a far way to go just because the space of problems to solve with tools and standards is so large but we're trying to make progress so I'd like to introduce a couple of tools and the formats we've been working on in the past years always with in mind, having in mind how to improve that situation the typical situation that we have recorded data on one hand and we have a lot of metadata a lot of information that is acquired during an experiment typically from different sources typically different machines running different computers running that might produce metadata you have some other measurement devices you do some recordings by hand so all the different information in different formats and the question is how do you bring them together and typically they are also remote from the data you have the data in some files and you have metadata in other files how do you relate them can you get to a situation where you link ideally you would want to link the metadata to the recorded data so that you are able to find data based on the recording conditions for example stimulation conditions and so forth so how could you do this well over the years we've been trying to come up with tools and solutions for these kinds of problems the challenges that we face especially in your physiology the field is so diverse we have a lot of different techniques we have a lot of different methods we have a lot of different preparations different species are used different experimental paradigms and so forth different file formats every data acquisition system produces data in different file format there are no common standards what to converge to and that makes the situation really hard so our approach a few years ago was to start with a well-defined data model for the actual data for the recorded data to have a representation that is accessible and understandable for scientists and to have very flexible methods for the data annotation because there we have the large variety of approaches, different experiments every experiment creates different types of metadata and therefore data annotation methods must be flexible, must be adaptable to the situation in the lab or in a specific experiment and then we try to come up with solutions how to integrate the metadata with the data so that you can specifically link metadata with data so that you know what metadata belongs to which records so starting with the representation for the neo-physiology data we were involved in the development of the Neo model the Neo Python package many of you may have heard of it it's a package that defines names, object names for electrophysiology data which gives you the ability to represent whatever ephys data you have in the same form to basically work in a common way with electrophysiology data it defines objects for analog signals like voltage traces for spike trains provides ways of grouping them according to time or space or electrode so this is a very useful way and kind of minimal way but nevertheless achieving a common representation it's fairly popular it has been adopted by many labs it's easy to adopt it is Python, yes but if you are working on Python this is basically the facto standard for representing electrophysiological data another component of the Neo package is this set of I.O. modules enabling to read a lot of different electrophysiology formats and this again gives the ability to whatever your recording system data acquisition system will produce the files you will be able to read them with the Neo with the Neo modules and that makes that very versatile and very useful as a way to to represent the data the recorded data now we still haven't touched the metadata yet so we have a way here to represent the recorded data but what about the metadata the metadata as I mentioned has to be a very flexible way in a very general way so we developed a very general model for a format for recording metadata in terms of key value pairs all you need to provide is the name of the property for example sampling rate and then you provide the value together with the unit that you have a scientific representation you don't have just a number but you know what it is and you know exactly what the quantity what the quantity is and this content what you want to store here is separate from the format so AutoML provides a format where you can organize these kinds of information these kinds of key value pairs in a hierarchical way so you can group them which is a way to get an organization that reflects the structure of the information you want to store typically you would group different items different metadata items for your recording equipment under one section and then for the stimulation under another section and so forth in a way that is suitable for your experiment but nevertheless you will end up with a format or with a file that is machine readable can be used can be read in by your scripts or by tools to process the metadata and to meaningfully analyze so this idea is a fairly simple format and besides the file format the actual file format we are also providing terminologies so the idea is that you would put in everything that you know and then at the time when it gets known and you can do this with the tools for this AutoML format that you add at some place in your metadata tree it's a hierarchical tree that you add further items there so you wouldn't have a dummy entry or anything which could be misleading which I think, if I get it right is what you are referring to but you would add it afterwards so this is something that can be done dynamically and the idea is also that since the metadata is acquired in different formats you have basically a common format where you can merge all this information together in a unified way well the format per se does not make any constraints like that what you can do and what some of the tools do is what you describe that you basically have a form and you are supposed to fill that out so this is at the tool level basically then you need to make sure that something like that doesn't happen that someone fills out just something to have it done without it being necessarily being correct and by the way besides the URL a way to reference this is also something I might mention here a way to reference these kinds of tools is by this so called RRID this is a very new initiative to provide persistent identifiers for resources for digital resources that you are using in your science like for example software tools but also substances or whatever there is already a huge database for those and the idea is also that you would use these kinds of identifiers in your methods section so that there can be automated services that find literature based on the used methods for example and on the other hand the other way around enable tracking the use of methods by mentioning in the publications and this is a way that is a much less ambiguous way of referring to something for machines much less ambiguous than having a description of what you were using so I would encourage everyone who is writing a paper to go through the methods section I think that you use check whether there is an RRID for it and then just enter it ok so I said that the method is really flexible in terms of what you can store because you need to be able to adapt it to your specific experiment but then also at the same time we want to have conventions we want to use also what others are using so we are also providing terminologies that you can use so the format itself doesn't enforce any of that but if you are looking for a term for how to name some item you want to store you can make use of these terminologies and the other way around if you are using specific terms for your metadata then you can also provide them with the terminologies the idea is to have kind of a community collection of terms that are used for certain metadata in neurophysiology and as I mentioned there are several tools there are different libraries for different languages we have a visual editor where you can then once you have a file for example you want to make additional changes or additions manually so my automail is that a lot of metadata collection should happen automatically by scripts that collect the metadata from the different sources and bring them in the common format but then you also want to make some edits manually then you can use a visual editor or you can a very nice spreadsheet front and that turns the automail files into an excel sheet where you can make edits and then you can convert it back again to the automail file this was written by people at the Forschungszentrum Jülich so how would that look if you are interested in kind of case study for using automail to collect all these different sources of metadata in a lab in a monkey experiment I can refer you to this paper here by Lua Zeel she is the one who did the research and she has been using automail to collect the metadata in an experiment where recordings were done from monkey visual and motor cortex while doing a behavioral task and you see here a schematic of the metadata, data and metadata flow in this experiment and you see that there is a lot of traces for different types of the different parts of the metadata and at the end they all should be collected together in order to enable meaningful analysis and in this paper it is described some of the procedures and methods she has come up with to facilitate that what this then provides is not only data sets which are annotated in the sense that you have all the relevant metadata but also it facilitates the data analysis and then working with the data for example what they could do is they could analyze just the metadata you don't have to go to the data to doing that to understand how far along are we in the project basically what did the animal make how many trials were correct and so forth and so this is just an illustration of the statistics of performed sessions or even trials over a week here different trials with different conditions so you get a very nice overview automatically you can write basically a script that does that based on the metadata collected so this helps you keep track of what is going on and how far you are along with the data collection basically so I presented you this data model of NEO for representing electrophysiology data I presented you a format for metadata collection so how do we bring this together we've been thinking about this and came up with a data format that enables storing data in files in a way that you have a data model for the data that is inspired by the NEO data model but you also have the other metadata and you can make meaningful links between them and so this enables then having both metadata and data together and being able to go from the metadata to the data to the metadata and so for the data part here we created a data model that is similar to the NEO data in that it describes basically scientific data you have an n dimensional data array you have information about the dimensionality about the units about what kind of data it is and you have also a way to link between the data array so you can establish relations between the data this can all specify in this data format which makes it a format where the data are discoverable are understandable and where the the data structure basically in the format reflects the structure of the experimental of the underlying of the underlying data the format which is called NICS is defined by an internal data model and an API and it writes the data to a file which is in the HDF5 format which is also common standard for scientific data so any tool that can read HDF5 files can be used to read data in this NICS format and we also provide different libraries for different languages so the benefits of having data and metadata in a common format with meaningful links is that you can find data specific data traces that correspond to certain metadata so if you say I want all the spike trains from a certain unit that I recorded from under certain conditions then you can find out the data arrays in your file correspond to that because you have the links between the data and the metadata and you can also go the other way around you can say okay if you find a data trace that looks interesting what were the experimental conditions for that and this is something that only you can do when you've created this data set but also someone else can principle do because the links are there and everyone can use it it facilitates data sharing because it reduces the information that has to be given in addition to the actual data in order to make data reusable and understandable here's an example which shows how you can make use of this different information and you can store in the next file you can store not only the raw data you can also store the derived data in this example here the data where high density Mia recordings retinal preparation where spikes electrical signals were recorded on a Mia array and spikes were detected so this was a study where a spike sorting method was developed and so you have the raw data the voltage data and you also have the detected spikes and then what you can do for example you can pick out one channel with a few lines of code you can pick out one channel you can plot the the traces the recorded traces you can also plot the detected spikes here with the amplitude and you can also plot in the same plot the stimulus this was a stimulus light stimulus when the stimulus went on and went off periodically and you can do this with a few lines of code because all the data are in the same common format in the same file provided for the next format for example we have this data viewer which makes use of the features of NICS that you have not only numbers stored but you have really scientific data you have quantities and so this data viewer can automatically make a meaningful scientific plot with appropriate labels on the axis and you can also of course browse the metadata and then find the corresponding data to certain metadata so this is for manual exploration of data sets you can also use the NICS libraries in software for example the relaxed data acquisition software is using the NICS libraries to write the data so data and the metadata that this data acquisition system knows about are written together in a common format so this with this approach with the metadata format and the data format which are compatible because NICS uses this automated format we have a way to support and provide data and metadata collection in the lab basically from data acquisition to data sharing with access from many different languages and also make it easy to provide the data then without much effort without much further annotation effort at the point when you want to share the data so as the last point I want to talk a little bit about another service we have been developing over the past years the GIN service for data collaboration and data sharing you have already used it you have used the local installation you are also running a server in Munich where people can use this for data management for data sharing the idea is to provide something that supports the data organization working with the data version control of data and for that it uses the system uses Git and Git Annex the the web interface is a little bit inspired by GitHub and in fact this is based on a GitHub implementation an open source GitHub implementation where we have added the support for Git Annex in order to version control large files which are hard to version with Git it provides secure access and data sharing so you can share your data with colleagues for example who are not in your lab but again at the same time as you have seen you can also have a local installation in the lab and just use it for your lab the idea is to provide support for the data management along the entire data life cycle basically from acquisition to publication so if you use the system you can start version control your data sets right from the beginning typically when you make an experiment with raw data but then you start preprocessing for example spike sorting and this creates new data files and you can keep track with that system you can keep track of how your data changes you keep track of what file was added when you can work together and you keep track of who made which changes you can you will avoid data transfer because you are transferring large size only if it's necessary then if you go on to data analysis again you want to work together on it you want to keep track of who does what when you can go back to previous versions if you want to compare if something doesn't work in your data analysis it doesn't work you can go back to the state like yesterday or two days ago and find out what is the difference all these kinds of things that you can do that software developers are used when they are using github or probably many of you are also using github all these things basically you can do with data with gin and you also have if you are using the public server you have a way to access your data from outside the lab often it's difficult when you are at a conference or you are visiting in another lab then it's often hard to get to the data at home but you can with the gin server you have an access point basically through the web from wherever you are you can again give remote access to collaborators who are not in the lab and still keeping track of who makes changes and then when it comes to publication, when you have the data set there are your results your final results and you want to publish them you can easily make the data set available make it public and you can also get a UI for persistent identification and that's a citation so to summarize the points I wanted to make is that efficient data management is something that we all benefit ourselves so it should be in our own interest to have efficient data management in the lab it helps us to keep track of what is going on all the data, metadata, all the analysis it reduces the risk of losing important information so that the data set might become unreusable and for that open and machine readable formats are very useful because they can help to automate things also integration of or integrated organization of data and metadata is very useful because it provides the information of which metadata belong to which data it helps in data analysis it helps in achieving reproducibility and of course it also helps in sharing the data and with that I would like to thank the developers of the G-Note who have contributed to these tools and methods that I have been presenting and I would also like to thank a lot of people who over the years have contributed and have collaborated with us


So, just before we get started, I want to say a massive thank you to everyone who sent messages of support, commented on my Strava, messages on Facebook, messages on YouTube. I think I've had more comments and messages from people for this particular run than perhaps any other run I've done, so massive thanks for your support. So this video is going to be a complete breakdown of my Paris marathon 2023. We're going to go through the whole race from start to finish. We're going to talk about my nutrition, hydration, what kit I wore. So we're two days post-marathon now and recovery looks like one day off where I actually walked about 10 miles with the kids around Paris. We're still in Paris until about Thursday, so it's Tuesday today, I just thought I'd come out for a little light jog. So my start time for the Paris marathon was pretty early, where you were due to go off about quarter past eight in the morning. So I got up at six a.m. and I had porridge and coffee and complan, like a, like, like a hule or it's like a protein meal and a drink thing as well. So that was my breakfast, just porridge, coffee and a complan. I'm not a massive fan of the flatlay. See, you won't find me like laying out all my clothes and all my kit nice and neat and taking a photograph of it and putting it on Facebook. Everyone does that, so no, I don't bother, but I did have all my stuff ready to take in the morning and a big bag, because although the temperature was good for running, I knew that it would get very cold afterwards.  So I could put that on once I finished the race. Drop bag for the Paris marathon is at the finish line, so it doesn't go anywhere. You just put it at the finish line and it stays there until you finish. So let me tell you about the kit that I was wearing for the marathon. Nothing particularly special and I did explain it in the video I did the day before the marathon, but anyway, it will go through it anyway. So my cap here, my white cap just helping to keep the sweat off my face and often the hair out of my eyes, but quite short hair at the moment. So that's not an issue. I had my Zwift vest, so I was running with or we had a group of people from Zwift join us in Paris and lots of different times. So we didn't necessarily run together. Some people did, but so I was wearing my Zwift vest, which I feel very comfortable in anyway. It's light, it's breathable, doesn't chafe. I didn't carry a backpack. I didn't carry my phone with me. I was wearing a chest strap for heart rate. I always like to look at my heart rate afterwards. I don't look at it during the run, but after the event, I like to look at what my heart rate was. So I wore a chest strap. It's my Garmin HRM Pro. I was wearing the compression shorts that you see me wear on so many videos, along with running underwear, I think from Kalenji. In Jinji socks, those are the toe socks. If you do get in Jinji socks, make sure you get the mid-weight ones because the light ones, I find my toe pokes through them really easily. So, mid-weight in Jinji socks. And of course, on my feet, the carbon-plated shoes I've been wearing for a little while now. Not exactly new. I've done a couple of races in them. The Sorkany and Dorfin elites, which are just lovely. So fast, so cushioned, so stable. So I really, really like them. So I chose them over my vapor flies, version one. So I left the flat at around quarter seven, planning to meet my mate Marcus at about quarter past seven at the Arc de Triomphe. And then we walked down to Bag Drop. Bit of a queue at Bag Drop, a little bit worrying that we were losing time because we had to get into our pens, but we made it into Bag Drop and then walked the short distance up from the finish line and down to the start line on the Champs Elysees. Now, like all other big city marathons or marathons where there's a lot of people taking part, you are divided into waves and it's all the way down the Champs Elysees from the Arc de Triomphe at the top, all the way down to the start at the bottom. So you've got the elite runners that go up first, obviously. Then you've got VIPs and invited guests in another little section at the front. Then you've got the three hour lot. So that was myself and Marcus at the front in the three hour group. Then it extends all the way back, but even within the pens, you are set off in groups. So there are so many people in the three hour pen, the 330 pen, that they are divided up as well and you're sent off separately. And you're also sent off at different sides of the road. So on the left hand side of the road, one little group will go. And there's about a minute between each group starting. And here we are look on the Place Charles de Gaulle with the Arc de Triomphe here. And the Eiffel Tower, just the top of the Eiffel Tower sticking out up there. So this is where everyone starts to congregate and then moves down Champs Elysees over there to the start of the race. And I have to say I was quite nervous at the start line. In fact, I started to get nervous at a good couple of days before knowing what I was planning on trying to make myself too. So I was standing on that start line with confidence, but with I guess an appropriate amount of nerves that you should really have to get you going before a big race like that. There was a little bit of confusion when it came for us to start. Marcus and myself were pulled right to the front of the starting pen. And the people standing in front kind of moved away. And some runners started running. And then a group of officials go and said, no, no, no, no, no, wait, wait.   So I thought, okay, let's just start easy, start sensible and stick behind the pacer for a kilometer or so. Now the pace for a three hour marathon, I don't know what you work in, but I generally tend to work in kilometers. So it's about 416 per kilometer. In miles that's around 650 per mile, 14.1, 14.2 kilometers per hour. But I wasn't going for three hours. From the outset, my plan had always been to try and beat my PV, which is just under three hours. But then a few weeks ago, having chatted to a few different people, my goal changed to try and get 255. And it was interesting to see how fast the three hour pacer went off because he was not going at three hour pace. He was going quite a bit faster than that at the start. I know that because I was actually looking at my watch for the first part of the run. I just wanted to be sure that I was going at the right pace. And yet the three hour pacer was going at around 255 pace. Obviously, one really important thing to remember about running a marathon is it might be 42.2 kilometers or 26.2 miles, technically, but when you actually run it in real life, it's gonna be a bit longer. It's always gonna measure a bit longer, almost always. So your pacing strategy needs to incorporate the fact that you might have to run two, three, four, 500 meters longer than the marathon. And that can be two or three minutes. And if you're trying for sub three hours or sub four hours, whatever it might be, and you're gonna be right on the cusp, those two or three minutes are gonna make all the difference. So I stuck with the pacer for about three kilometers. Now, either I sped up or he slowed down to a proper three hour pace. And I eventually kind of started to move ahead of him. By this point, I'd lost Marcus as well. We'd started together, but he knew that I was gonna be going a little bit quicker. So I went off ahead of the three hour pacer. So you run along the Rue de Rivoli in Paris and around through the Louvre. And then you get to 5K. And 5K is where I checked my watch and made sure I was on pace and doing okay. And then I decided to try not looking at my watch for at least the next five kilometers. So at seven kilometers, we hit Bastille and the July column, rounded the July column and headed off towards Bratavansan. Just before Bratavansan, which is a big park with a beautiful chateau, I hit 10K, looked down at my watch, 42 minutes. So pretty much bang on where I want it to be. And I decided things were going well. I didn't need to look at my watch again, just maintain pace, keep going and push through the park. Now the Paris marathon is not flat. There are definitely some undulating sections. And going into the park is one of those sections where you do climb going into the park a little bit. But then coming back out of the park around 15 kilometers, you do descend. So I made my way past 15K, did look at my watch at 15K just to glance and check. Things seem to be okay. Remember if you saw my video the other day, talked about nutrition a bit and that I wouldn't be having anything to eat or drink. Well, that was the case. Still up until 15 kilometers, I had nothing to eat, nothing to drink. Gels just made me feel really sick. And I didn't need any water, it wasn't hot. If it was hot, yes, I'd be drinking, but cold weather like it was, it was cool. Not freezing, but it was cool. I didn't need anything to drink. And that meant I could avoid the melee around the aid stations as well. Just run right around the outside and not even worry about it. Quite often there's bottles strewn all over the floor. You risk standing on one, falling over, tripping up, just hitting another runner on the way through. It can be a nightmare at those aid stations when there are loads of people. So between 15 kilometers and halfway, you've turned back on yourself and you're coming back into Paris now. And this is where things went, if anything went wrong at all during the run, this is where things went a little bit wrong. See, I'm not really that experienced at pacing without looking at my watch. I like to think I can generally work out what speed I'm doing within reason, but 15K into the marathon. And I decided not to look at my watch again until halfway. What I didn't realize was, from that point, I started to slow down each kilometer. And because I wasn't looking, I slowed down just a little bit too much. So when I got to halfway, I had a little bit of a shock because I hit halfway in one hour, 28 and a half minutes. And I really wanted to be there in one hour, 27. So I was a good minute and a half slower than I wanted to be at that point. The good thing was though, I wasn't slowing down because I was tired. I was slowing down because I just wasn't concentrating and I was being lazy. So as soon as I realized that, I sped up. I did take some water at one of the aid stations around about halfway, just after halfway, grabbed a bottle, quick sip of water, chucked it in one of the big bins. They're very keen on their recycling and the Paris Marathon. And I chucked it in one of the big green bins that they provide. So we've come back into the city now, getting on for 25 kilometers. And we dropped down to the banks of the River Seine and run along the Seine for the next few kilometers. And this is where you'll find the infamous tunnels of the Paris Marathon. So it starts with this one big, long tunnel that used to be for cars, but it's now been pedestrianized so you can walk through it. But obviously it's closed anyway for the Paris Marathon. So it's about a kilometer or so long and there's a little climb out of that tunnel. And then you've got three other shorter tunnels, all of which have a steep little climb out of them. And on tired legs, if you are knackered at the end of the Paris Marathon, those climbs are awful to do. Really horrible. So I hit 30 kilometers feeling great actually, feeling really good. And I met my brother-in-law. He was standing by the side of the road at Place de la Concorde. And he gave me my banana and my drink. Purdies, I don't know if you've heard of purdies, but it's a very nice fizzy drink. And I had some of that. I didn't eat all of the banana, about half the banana. And a few sips of the fizzy drink. But I didn't need all of it. Honestly, my confidence was so high at this point because I was on time. I knew I was going to be under three hours as long as I kept pace. And I didn't feel like I was going to drop my pace. So you know that feeling sometimes when you get to around about 20 miles in and everything just got, you can feel yourself slowing down. You can feel the energy drained from your legs. You can feel your head go. None of that was happening. And you keep waiting for it to happen, don't you? It just sometimes, you know, one out of 20 races, that doesn't happen and you just feel great. And I just felt great. And it's just kept going. So they've actually changed the route of the Paris marathon towards the end of the run. We used to spend a long time going through the Blois de Boulogne Park and it was a little bit empty, a little bit sparse. You know, with only about three or 4K to go, there weren't really very many crowds. So they've changed the route. You just spend a little bit of time in the park now. Then you come back out into the main city streets and it's much more exciting, loads more support. So that was really good. And I just felt absolutely superb as I ran through the last three or four kilometers speeding up as I went, which is awesome at the end of a marathon. Although I certainly know one person who'd say I shouldn't be speeding up at the end of the marathon. You should be so spent. You should have run so hard that you can't possibly speed up at the end of your marathon. And the last kilometer of the Paris marathon this year was pretty much all downhill. Just power down as hard as you can, round the corner and then up to the finish. And I crossed the finish line in two hours, 55 minutes and 42-ish seconds. Now I can't say it was an absolutely perfect race. There were one or two things that probably I should have done differently. And I may have got under 255 if I had been a bit more on it in the first half of the race. However, really it couldn't have gone, it couldn't have gone much better. The only other minor disappointment was that my camera died about 35K. And so I didn't turn it on again until the very end. And I just got a couple of seconds just after I crossed the finish line. But yeah, I didn't get as much film as I was hoping to. But between us, I think Victoria and myself have got enough footage to make a short film. So the Paris marathon film will be out in a few days time hopefully. Just a final quick word on nutrition.  And is that because everybody else takes gels? Is that because that's what the advertising tells you you should do? I don't know. All that sugar going into your body. No wonder so many people have stomach issues when they run because you're just putting all this awful sugar into your system and it can't digest it quick enough.  What good is that? Look, I ate a good breakfast beforehand. Yes, I'm used to running on empty. I'm used to running and burning those fat stores that I have. But it means that when I run, I can still run fast.   That's all it is. So if you do find you're throwing up everywhere when you're taking gels, then why not just try something different because you don't have to take gels to run fast. If you'd like to watch last year's Paris Marathon video, then click that link right there. That'll take you to it. Thank you so much everyone for all the support you've given all the messages of congratulations as well. Really meant a lot. It's really made me feel good. So thank you very much guys. And we'll see you on the start line next time. Bye bye.


Nudged Presents, Shadow Sea, The Cogheart Adventures, Book 4, by Peter Bunzel, narrated by Penelope Rawlins. Prologue. First, there was darkness. Then patches of watery green light. Then fish. Whole schools of them. With luminous fins bright as knives, glowing scales that shimmered like armor. They swam past the shadow of a submarine base that clung to a cliff edge beside a fathomless trench which stretched like a scar across the ocean floor. The base was the shape of a giant rusted wheel with spokes that ran from exterior to its center. Parts Sample complete. Ready to continue?


Not a lot of distractions here. It's just pristinely quiet, with possibly the exception of one bird, but That was a great opportunity. Oh, it was, yeah. Oh, and today he was so frantic, David. You know, there's this perch right outside my window. We've all watched the pounding and pecking, but but isn't that like the way it goes in meditation that you You know, you you seem to have to watch the pounding of the thoughts until there's just a sense of total embrace, you might say, or going beyond the idea that that even these thoughts are disturbing peace of mind. So that's the workbook training of A Course in Miracles.  And so first of all, you are allowing those thoughts into awareness and as we came out here to do a Christmas retreat and a New Year retreat, the bird showed up on cue and the pecking and the pounding, it stands out in this monastery setting, because it's just so still that it's the only thing going on in the whole the whole area. 49 acres here that we have and beyond, that's the only thing happening is this pecking and pounding. And then, so you start off with I can be hurt by nothing but my thoughts starting to realize that it's not anything in the world that is disturbing my mind or upsetting me, but it's just these thoughts. And then, and then finally you move into Lesson 23, I can escape from the world I see by giving up attack thoughts. That's the culmination of the first 22 and then the 23 is the culmination of a beautiful cause-effect relationship, that it's only attack thoughts that are upsetting. And that's a grievance. And then, I could see peace instead of this I am Spirit. I am the Holy Son of God Himself. You know, that takes you right into beyond the desperation into the stillness that is really what the desperation is about. And there was this, I will be still an instant and go home, that workbook lesson where it's really an invitation where Jesus says even your childhood home is just a faded memory that really never happened and a distorted faded memory and that there is a child in you, you know, just give him an instant to be himself. He's talking about the Christ child. Just give him an instant. Give allegiance in your mind to the Christ child to be still an instant and go home. He needs your protection. He says in reality the Christ doesn't need protection, but in the workbook training it's like we need to give that Christ idea ascendance in our mind lest we give the attack thoughts ascendance in our mind and therefore seem to be something that we are really not. We feel like an alien lost and desperate to go home. As desperate as that bird is to get inside and really it's such a strong witness to us that really that's really why we're here. That's really what we're doing. We're going inward.


Today we are opening a $25 Walmart Mystery Cube versus a $50 Walmart Mystery Box. What's up guys, we're back with another epic video. Today we're going to be opening the Walmart Mystery Cube, which I have not opened one of these newer ones yet. It looks like it has a legacy pack, two booster packs, five rare cards, 55 common cards. I'm sure they're going to be really good cards, and then we have the box. We've opened several of these at this point, so we kind of know what to expect from that one. This one on the other hand, we don't really, but first we have a giveaway. I'll be giving away these three cards.  Let me know what you think will win, the Mystery Cube or the Mystery Box. All right, because we kind of know what's in this box, because I mean, they say mystery, but we all know there's going to be fists of the gadgets in here. But there's some other cool stuff. We're going to start with this one because the cube is a little bit more mysterious to me. So I want to finish with that one. Let's go into your, imagine if there's not, you know, fists of the gadgets in here. That would be huge. That'd be a big play. I think last time we actually pulled the most expensive card and I was kind of like, is this a good card? It's $7, by the way, the most expensive card. Just letting you guys know. Yeah, it's really expensive, really, really nice. Let's start off with this is no, that doesn't feel like fists of the gadgets. Let's see if I can find fists of the gadgets. Yep, there it is. Starting off with our favorite pack, the fists of the gadgets. Can we pull the junk? I already forgot again. What's he called? Magiston Maiden chronograph sorcerer and a boot up Admiral Dynamo. Man, I've never even heard of these boot up cards besides this one set. Have they ever been used before? The boot up Adderals. Adderals, what I give up. All right, let's go to the next thing. Yeah, that's maybe that's why they quit using them because you can't you can't say I'm or at least I can't. I don't know. Maybe I was like, Konami, we're going to have to stop. No, not the pay of ever listening to anything I've said. Magiston Maiden. We got the brotherhood of Firefist spirit. Subterra final battle. Cool. And the ultimate fire formation sent to we pulled this one literally every time we have a mystery box. So not only is it not mysterious about what packs you're going to get. It's not mysterious about what polls you're going to get. We get the same polls. Oh, wait, wait, wait, wait. Oh, wow. Yeah, third pack, baby. Look at that. I pulled them all out from the beginning, so we're not we're not finishing with the gadgets. We're starting with the gadgets.  Yeah, he is an angry teenager right there. We got the rest of Liger and the tin key. All right, fire formation, tin key. We'll take that's probably OK, you know, for this set, I guess. So far, it is literally the only poll they have out of the mystery box. So let's see what we can do. Let's go to the deck next. Let's see what the deck will be. Be something cool. It's the same deck. All right. So it looks like there is a little bit of variance in the deck because we used to get those mocking it extra. Remember, was that this? Was it the same product? OK, what is there? Three more packs. We'll start with this one. Flames of destruction, I think, has literally been in every single one so far. Cool, cool. We have yet to pull anything too crazy. I think we got a Cerberus, which was pretty nice. So I'm not complaining about that, but let's see if we can pull something else like Imperm, Ghostbell. You guys know, World Legacy's Nightmare. Also, call buys in here is a common. We have yet to pull that, I think. Pico Felina, didn't this just get a reprint in like, yeah, in Grand Creators, it's got a Super Rare. OK, we got the Crystal Keeper. That's pretty cool. Prompthorn, three trolling trolls. It's the three fists of the gadgets packs. Oh my gosh, guys, they have faces. Fists of the gadgets number one, fists of the gadgets number two, and fists of the gadgets number three. That's them, the three trolling trolls in every mystery box. Trickstar Nightstage, that's pretty funny. And oh, a Nightmare Phoenix, yes, OK. We're pulling some nice Nightmare cards. I will take that and Revenger, Revengered Executor. This is a nice card in Super Rare. I really like the foiling of the Super Rare. I think it looks really nice on this. I definitely prefer it to Maximum Gold, to be honest. OK, let's go into the next one and then we have a Mystery Pack. Oh, Dimension of Chaos, every time. All right, Unlimited Dimension of Chaos. I think I need an Unlimited Scarlight Ghost, so I'm just saying, this could be the time. This could be the moment. Let's see if we can make it happen. Raid Raptor Skull Eagle, DD Savant Galilay. Dark Contract with a Witch, DD Savant Kepler. Speaking of DDDs, pack, one, or not one, you got top A, but that's basically winning to me. Magspector, Super Soldier Soul. Gay Way to Chaos, that's pretty cool. We got a Super Rare, Samurai and Fluffle Wings. Yeah, we might wanna check some of these DDD prices just in case, because I heard a lot of the stuff got bought out. It's probably the high rarity though, not these rares, but very cool, Gay Way to Chaos. And the final pack from the Mystery Box. We have a Mystery Pack. So today we have a Mystery Box, a Mystery Pack, and a Mystery Cube, that's a lot of mystery. Too bad there's not much of a mystery. I guess this is technically a mystery. I'm guessing these aren't all the same, right? OK, this is different than last time. We got a Pharaoh Servant, definitely better than, what was it, it was the, ah, well, what's that, was it? All right, I thought for a few seconds and already forgot. So, OK, I don't know what said it was. It was the one that's like purple and green, if you know what I'm talking about. Pharaoh Servant, if you've watched the last video, you know, even though we didn't get that this time, we did get Pharaoh Servant, which is much better. OK, let's do this, can we get Jynzo? Cold Wave, very good card. OK, Science Soldier, he's a Soldier of Science. Where's the Math Soldier? That's what I want, we got the Regulation of Tribe. The Dark Fire Soldier, number two. The Solomon Wishes, I wish to gain 500 life points every time I draw a card. Jynzo Javelin, I don't really understand why that's the wish, but Type Zero Magic Crusher. Oni Tank, rocks in 34 and appropriate. Wait, did we do that Patrick wrong? We might have, but we didn't get anything. We did get Oni Tank, rocks in 34. That was an all right showing from the box. Let's see if the cube can beat it. So, we'll open it up sort of and then we'll check out the outsides of it so we can see it a little better. So, basically we're gonna get a Pot of Extravagance and Ash Blossom BLS, is that first edition from IOC, yeah, we got a Slyther GBI, Secret Rare by the way. Oh, I haven't even heard about this card in a while. Phantasmate, do people still play that? I haven't heard that in a while and we're also gonna get these packs, which is what Secret of Eternity is kind of funny. Vision of Chaos and Galactic Overlord. I'm excited for this. We're gonna get one Legacy Pack, two Booster Packs, five Rare Cards, 55 Common Cards, very nice. And Blue Eyes White Dragon, yeah, we're gonna get L.O.B. Pack as well. I can't wait, definitely not gonna be disappointed with this, this is gonna be great. All right Yugi, what can you provide for us? Oh, our packs. Oh, these are not bad. Wait, Breakers of Shadow, Duelist Alliance and L.O.B. These are actually not too bad. Okay, we didn't completely get scammed, except that they probably bent our cards with the way they go into this cube. They might wanna make these cubes a little bigger. Let's see what random cards we got before we open these packs. All right, so what did say five Rare, 55 Common? So there's literally a Rare, I figure those, oh, here are our Rares. There's four of them, wait no, that's three. Okay, so then we have some Commons. Is there anything good in here? I'm gonna be looking for something decent. Dino Miscus, that's not a bad card, I guess. Chinator, Sagita at the Sanctuary, Goldfang, Will of the Salamand, great, Trickstar Nightshade. Not looking too good so far, Thunder Dragon Matrix. If you wanna Thunder Dragon them up, Waking the Demons fun, okay, that's fun. Gokie Heel Ogre, Crusadia, Sky Striker Mecha, Eagle Booster, I just bought one of these, dang it. Okay, I bought the Super though and then I pulled one and then we got another one, okay. All right, maybe I shouldn't have done that. Every time I buy something, I pull it. There's a Seal Strategist for hire, cool, Gokie Pull. Okay, so we have two more Rares apparently, let's see. We have, oh that's not it, okay. Oh yeah, look at that, we got five Rares, how exciting. All right, where's my Extrav, where's my Slyther, my BLS, come on, my Ash Blossom. Where they at, come on. All right, so so far we have a couple of Commons that I like that are like 10 cents. So we're gonna start off with Breakers of Shadow. If you pick the cube, it's looking pretty rough.  Hopefully we can do something here that will turn around. Bosch has some cool cards in it, let's see if we can pull them. Ooh, we got a Lapras on the first card, very nice. We got a Bite Bite Turtle. The Dragon Buster Destruction Swordsman. All of these literally look the same. The Destruction Swordsman, like the Buster ones, they all look the same, Dynamist Rush, cool. Dark Doriado, okay, Doriado's got another version. Shurenewi Smith, he loved to see it, Shurenewi's cool.  Wait, there's the Hollow? Oh, there's only two cards left. I was getting really into those cards. Pendulum Storm, is that okay? I'm not really sure. Fiendish Rhino Warrior, cool looking at least. Two more packs, we gotta finish with LOP. If we pull a Blue Eyes, White Dragon, we can win right now, Dualist Alliance. There's some good cards in here. There's Ghost Rares, there's Ultimate Rares, Seeker Rares, you're not guaranteed a foil in this set, because this is from 2014. So it is pretty cool to get something this old. This is actually older than the LOP, because the LOP was printed in 2017, it's a reprint. Battle Guard Howling, UA Stadium, those Ultra Athletes, baby. Resonance Insect, isn't this card? It got a reprint as a super in an OTS pack because of, I guess, Beedrooper. Pretty good card. Yangzing Prina, Aria, the Melodious Diva. We can also get the Shitol stuff in here, which is pretty nice. A Battle Guard Rage. Magical Spring, Secret Rare. Oh, wow. Whip Snake and Hymn of Light. We got a Secret Rare, Magical Spring. I've never heard of this card, which probably means it's not very good. But we will still take a nice Secret Rare. That is pretty, pretty cool. Onto the final booster pack, Legend of Crushed Eyes. You see that? There's the Crushed Eyes. All right, let's keep going. Let's see if we can pull something good. Okay, let's not mess up this pack trick. So I think it would be one, two, three, four, and then this would be, no, wait. The fourth card would be the foil.  Is that right? I don't think that's, I don't know if that's right, but we're gonna do that. Metabat, very cool. Green Phantom King, the King of Phantom. The Larvas, very cool. Sparks, oh, best card ever made. 200 damage right to the face. Flame Manipulator, he can manipulate those sparks. You guys see that? It's literally the same thing. Very cool. Dark World Thorns, I said it was very cool. Ray of Temperature, how many times did I say that in this video? Let me know in the comments. Stop Defense, okay, so it should be after this if there is a foil. We want Blue Eyes, we want Exodia, Red Eyes, Dark Magician, all that stuff. Here we go. Main Eater Bug, I was like a feck monster. What is this? Main Eater Bug, super rare, we will take that. That is pretty good, yeah, Main Eater Bug. Okay, that turned out to be a pretty close battle. Let me know in the comments who you think won. If you guys enjoyed this video, make sure to subscribe to the channel. Also, check out the live channel and the shorts channel in the description below. Shout out to TCG, Trust Your Cards, To Info Show, Tomato Juice, Stanley Minance, Mimic Echo, Specky Butthole, and G-Batter, Daxter, Ian Musa, and Junior Barting. Thank you guys for supporting the channel, and I'll see you guys next time. Peace.