Good afternoon everyone. We're gonna get started. So hi. I'm Jane Greenberg. I'm a faculty member at Drexel University College of Computing and Informatics. Great to see people. It's been just great to have an opportunity to interact with colleagues and friends. I forgot how much fun it is to attend a conference. So thank you to CNI. Getting dressed up in work clothes, that's another story, but really great to be here. So it's great to be here and it's our absolute pleasure to have an opportunity to share about the Leading Program. Leading is an IMLS National Digital Infrastructure Initiative. Leading was developed to scale up the original Leads Program. So Leads stood for LIS in Data Science and that was also a national, supported national IMLS project. And that was developed for iSchool doctoral students across the country. And iSchool doctoral students came and participated in Drexel's Data Science Education Program. They did an online curriculum and then in a boot camp. And then they had immersive experiences working with Leads Mentor Sites, major libraries, archives, data centers and so forth. And they did, worked with real data and it was an intensive summer program for PhD students. And every time I spoke about Leads and some of my colleagues, we would hear from early and mid-career people. Ooh, can I participate in something like that? I would love to participate in something like that. So the motivation for Leading is to make the program available to early and mid-career people. And so the Leading project now combines LIS, early and mid-career fellows, along with iSchool doctoral students. And you'll see on the right-hand side the overall model. And on the left-hand side, there are what are called nodes and mentor sites. And many of the mentor sites were participants in Leads and stayed on through the Leading project. On the right, you see more of a visual and there's a sort of triangle in the center where you see Drexel, you see San Diego and Montana State University. And we oversee the coordination of the fellows that participate in the program. OCLC is also a hub and they're a coeducational hub. Drexel University serves as the chief coordinating hub and oversees the data science program. So a little bit about the data for the Leading program. As I said, there's four hubs and 15 nodes. We have 31 dedicated mentors who work with the fellows. Eight Drexel LIS data science faculty. We also have a diversity, equity, and inclusivity task force. And that task force is made up of Leads fellows from the earlier program. So they are iS doctoral students. Some of them have moved on now to faculty positions and professional positions. And they've been integral to helping us with recruitment, reviewing curriculum material, and so forth. We also have an advisory board that helps oversee the Leading project as we're growing this network. And I just want to pause for a second. If you are at an institution that has had a Leads fellow or has a Leading fellow, if you are a mentor, if you're a member of our advisory board, could you please wave your hand or something so we can just sort of see. There's a few of you around here and I want to just say right over there, thank you because you're really what helps make Leading successful. I also want to just give a little kudos to Rebecca Koskala who's sitting there. Rebecca, sorry, you've got to put your hand up because part of the model for Leading, I learned a lot from Rebecca participating in the data one data net and working with fellows. Even though the fellows come to Drexel for the data science boot camp, they do the fellowship virtually. So there's our data, there are 24 fellows in the first cohort, and here's our map, there's an interactive map on the Leading website so you can see how we're expanding and we actually have, as far as Hawaii, we've got Jonathan here today, but the map can't quite do justice here in a slide because there's hubs on top of nodes on top of mentor sites. And here are our 2021 fellows. I wish we could have them all here, but we are delighted, I shouldn't say, but we are absolutely thrilled to have six of our fellows here and you'd rather hear from them than me. So I'm going to turn it over now to our first fellow, which is Jay Winkler. Thank you so much. Hello. Hi, my name is Jay Winkler and I am the metadata archivist at the ICPSR, which is a social science data archive housed at the University of Michigan. I've been working with a team from the Temple University Library on a project involving enhancing and analyzing wiki data records for black artists from the Philadelphia area. My project mentors, Sinatra Smith, Alex Wormer-Collin, and Holly Tomrin, worked with a team from the Philadelphia Museum of Art to conceptualize the project. And I did work with a second fellow at my site, Rebecca Bayek, of the Schaumburg Center at the New York Public Library. For the unfamiliar, wiki data is an attempt to apply structured data to all of the world's information. It's built on RDF triples, so each item can have statements attached showing the properties of that item. In our research, our most common triple was that a person has the occupation artist. Our project had two phases. The first was to make a large number of wiki data edits to help the Philadelphia Museum of Art improve their presence on wiki data and also improve the presence of Philadelphia's black artists more generally. To those ends, I personally made about 1,500 wiki data edits with my partner making a similar number. Most of those edits were made using Open Refined, which has really robust tools for a direct ingest into wiki data. Phase two involved designing queries that utilize Sparkle, which is a SQL-based language for querying RDF triples, and we used that to gather information from the wiki data query service. We developed a large query as a team that gathered demographic information about the artists, and we used that in order to form our research questions. What I became very interested in was how frequently race and ethnicity was tagged on wiki data. I saw that African-American was the most commonly used ethnicity in our data set, but that its use was far outpaced by null entries. Almost everyone who has their ethnicity tagged at all in wiki data is a person of color making invisible the overall whiteness of the platform. Obviously not every untagged person is white, but I believe that wiki data's structure and its editor habits make it really easy to treat whiteness as a default. My main research focus became finding a way to track whether the collection of artists with tagged ethnicities was growing or shrinking over time relative to the whole wiki data collection. The query service won't return the creation date for items, so I ran a sparkle query to get a collection of every artist on the platform that was born in the United States, and then I was able to run that list through the wiki data API to gather the creation dates for each artist's item. If my research question was how is this changing over time, ultimately my results concluded that it has stayed remarkably steady. I have two caveats up front. First, I did collapse all other tagged ethnicities into a single other category. That is not ideal, but the numbers for most are really small, and having other as a single category still provides useful insight on whether or not ethnicity tagging is happening at all. Second, I don't think this data is sufficient to conclusively say whether or not any given ethnic group is underrepresented on wiki data, but I do think the defaulting of whiteness is evident in this data. In the total collection, wiki data's items about African-Americans as a percentage of all artists settled in around 6% in 2014 and stayed within our percentage point of that ever since. For reference, African-Americans represent about 13% of the US population. Other accounted for about 4.5%, meaning around 89% of artists have no tagged ethnicity. When I broke this down by city, I found that Detroit had an unusually high other percentage, but most cities track pretty closely to the national data. One thing I want to point out is in the Philadelphia subset, first of all, Philadelphia has always far outpaced other cities in African-American representation. It's always hovered closer to 15%, but if you look in the little circle on the chart, you can see the effects of our project, which I found really gratifying. There's about a month where it goes from 15% to 18% in the course of a month, and that's clearly due to phase one of our project. My big takeaways from leading in general are I feel like I have a much stronger foundation in data science coding and a lot more confidence solving problems through code. I also have found that it's given me a lot of direction as an early career academic in my first role where publication is an expectation. And when the fellowship ends this month, I'm going to continue working with the Detroit Institute of Art to do a Detroit-based version of phase one of our project, and then I'm going to complete some independent research tracking these sorts of patterns in WikiData's gender and sexuality data. Thank you very much, everyone. Hi, everyone. My name is Emily Pingo-Brian, and I'm the Digital Repository Metadata Librarian at Worcester Polytechnic Institute in Worcester, Massachusetts. My project is unlocking and linking World War II Japanese-American incarceration biographical data with a focus on social network analysis. And I had a partner for this fellowship project, Lencia, whose presentation will follow mine. So for the project, we use historical records obtained by Richard Marciano from the University of Maryland who's served as our mentor. So thank you, Richard. And part of the historical dataset I used was from incident cards or also known as index cards from the Tule Lake concentration camp located in California. These cards included information regarding description of events, incidents, including crime that incarceraries were accused of by public officials, including camp police. So this information only described the event, how to category the type of event, as well as other information about the individuals. So what I was really interested in and my question for this project was how can we use this archival historical data to show how the incarcerated peoples connected with each other and reveal relationships and other events, and especially looking at communal acts of resistance and to tell us more about the life of Japanese-Americans in these concentration camps. So the objectives were promote the access and use of historical public records as data, but also understand the history of the records while using the data ethically and responsibly, and create a model overall for other narratives of zero-force displacement in America. So the approach that I used was social network analysis, which specifically addresses the field of data analytics that uses networks and graph theory to understand social structures. So for this case, social network analysis involved extracting data from the data set of index cards and applying graph algorithms found within networking tools, specifically using Python library, including Plotly, as well as NetworkX. And one of the accomplishments we're really proud of is we created Jupyter Notebook specifically. I was able to create one on social network analysis that included the Python computational work as well as the output, which are these visualizations. So in the visualization on this slide, the large blue circle, it's a simple node graph used with NetworkX, and this is pulled from a particular event on November 4th, 1943, that occurred at the Tule Lake concentration camp. It was a congregation of Japanese Americans who were dissatisfied with the situation, lack of supplies, lack of food, and this became deemed a riot by public officials. And so looking on November 4th, the category of the card was riot. This is how many card, almost 4,000 cards that were from that, recorded from that particular event, including the individuals accused of being involved in that riot. So while it can be a difficult graph to read, you can just see the scope of the event itself and how many people were involved or accused of being involved. So this riot event I was really interested in, and wanted to know more about what led up to it, what followed, tell us more about, again, the events and the lives of the incarceraries. So one particular event I found through the computations on November 1st, 1943, there was a theft, so this occurred before the riot, and there were nine cards specifically with the same exact description of nine accused incarceraries of found-picking vegetables in an area of the farm where they did not work. So I used another Python library, in this case Plotly to create a bar chart. X-axis is the different events categories from the index cards, the Y-axis is the number of cards. This was confirming, yes, there were nine cards from that specific event, but also shows other events leading up to, again, the riot event and following, including disorderly conduct, and after the riot, those that were jailed in the military stockade. So from these examination of these results, the theft that you can see in other events like disorderly conduct, they were clear displays of the rising discontent in the Tule Lake camp and also leads to questions regarding the informal of the accused incarceraries and additional or other intentional acts of, you know, what was planned, was the planned, was the theft planned, what was talked about when they were accused, what was talked about, is this why some people, some of the incarceraries were involved in the riot later. And with social network analysis, we can easily view different interactions between individuals over time and how they relate to other events, ultimately presenting additional opportunities for analysis and research. So, you know, this has been a really amazing project to work on. I feel very honored to work on this project and to work with Lencia as well as work with my mentors, Richard and also Greg Jansen from University of Maryland. Personally, I've learned a lot about the technical aspects of working with the data, how far, you know, I can push myself, how far I can work with the data itself, but also most importantly to work with historical data respectfully and sensitively. And my hope is for others to use these computer notebooks as examples and guidance when working with archival data and continue to develop new strategies and ultimately create new opportunities for research, especially with other historical events, data involving serial force displacement. Thank you very much. Hi, everyone. My name is Lencia Beltran. I'm the Special Collections Archivist at UNC Wilmington. And so, as my partner Emily said, I'm also working on the Japanese-American project. So, my focus and interest has been to visually understand the movement and spaces of how Japanese Americans lived while incarcerated during World War II. And so, with that being said, I used geographic data taken from three separate archival records. And that includes one of the data sets that Emily mentioned during her presentation, which was the incident card or index card data set. And so, before I could create the interactive maps, I had to find the locations for all five movements for 25 selected individuals. And so, I selected the individuals based on how often they appeared as being involved in the uprising at Tule Lake. So, once I found those locations, I could then find the latitude and longitude, which is what I used to create the points on the maps. And so, this first visualization here is showing individual movement for all five movements for one individual person out of the 25. And those movements are the Point of Origin, the Assembly Center first incarceration camp, the relocation to Tule Lake, and the final departure state. And so, to briefly describe what you're seeing here, the individual originated in San Diego, California. And in 1942, they were sent to the Assembly Center in Santa Anita. And from there, they were sent to Jerome, the first incarceration center in Arkansas. And on September of 1943, they were relocated to Tule Lake. And from there, it's recorded in 1946 that their last departure state was in Pennsylvania. And so, if I could have you look at the map that's on the lower bottom, this map is showing the number or size of individuals at each location for all five movements. And what I want you to take away from this map is, if you compare it with the first one that I showed you, you'll see that not all 25 individuals were sent to the same Assembly Center or the same first incarceration center or even they didn't have the same final departure state. And then the map that is on the top right, oh, real quick, I'm going to backtrack. This, the second and first map were created using Plotley, which is one of Python's libraries. And then the third map was created using Folium, which is, again, another Python library. But it's an alternative way to use interactive maps to visually see movements. Folium uses layers. So it'll allow users to either select or deselect what is visibly being shown if they want to see specific information. It also allows them to or the creator to add GeoJSON or shape files which I added, those are the blue shape files on the map. Those represent evacuation orders. And real quick, the image between the two maps are the code that I used to filter the data to find the locations. So in conclusion, my experience with leading and as a leading fellow has been a positive one. I learned a lot from my two mentors, Dr. Richard Marciano from the Advanced Information Collaboratory. I also learned a lot from my partner, Emily, and I learned that data or data science approaches and computational approaches can be used for historical research and not just numerical data. Thank you. Okay, hello. My name is Hyun Seung-goo. I'm an assessment librarian at the University of Northern Iowa. My project of mine is top-to-university library project of the University of Northern Iowa. It is a project that is focused on data and decision making for consortium acquisition. A particular goal of this project is to create visualization tool that would help identify which post-on library consortium BLC member can work together to save or share acquisition cost by showing project-based e-book usage patterns. To achieve this goal, we exported consortium e-book usage data from 2018 to 2021 from JSTOR admin porters and prepared for analysis as a pilot study. And then we created multiple natural graph modeling and overlapping usage of title in particular subject area of BLC members. In particularly, we chose a bi-part natural graph with a Python and its library X where one kind of node is a BLC member and the other kind of node is a subject. And the line between a member node and subject node define an institution's usage of a particular subject area and divide the usage of all subject areas. So this is a particular example of bi-part natural graph. If you look at the history at the center and the line from the history to individual BLC member 8, the thickness of 8 is 8 of BLC members that are similar to each other. This means that 8 BLC members, they have a similar e-book usage pattern in terms of history. However, if you look at the philosophy on the right side, you will notice that philosophy is a unique subject of BLC 1, the e-book usage of which does not have a similar e-book usage pattern as any other BLC members. So, BLC member might not need to consider course sharing in terms of philosophy. And additionally, if you look at the history and the psychology next to the history, and you will notice that the line from sociology into the individual BLC member, the thickness is similar to each other. However, as compared to history, the thickness is thinner with a lower usage of e-book usage data. So, one takeaway from this project itself, we were able to see that BLC members in the BLC area are strong or weak candidate for consortium acquisition. So, history and sociology are good example of strong candidate for consortium e-book acquisition. And philosophy is one example of weak candidates. So, in terms of takeaway from this project, I would like to step further and closer to achieving my goal of becoming a well-rounded assessment librarian that I mentioned in my application form by helping me gain immediately applicable collection-related knowledge skills that I did not have before. And helping me build extensible partnership with the BLC project, we are planning to gather more data from more vendors. We are planning to use LC classification instead of day-stored disciplines. And we are planning to build interactive network web would enable individual users to configure their threshold until they get meaningful insight. And I really wanted to thank you and support and they willing you to meet me and talk with me and get some help from me. Thank you. I'm Jennifer Proctor. I'm a Ph.D. candidate at the University of Maryland, College Park. And I worked with the OCLC on a project to detect missing diacritics and catalog records that were ingested into OCLC from works published in Russia. Okay, here we are. Okay, so the project goals were to build a machine learning model capable of identifying incorrect Cyrillic mark records on ingest. And at the scale OCLC works, this is a big data problem. Also, we wanted to develop methods for identifying and mapping corrections for transliteration errors. In order to do this, we needed to label 4.5 million rows using OpenRefine and Python. And we tested dozens of machine learning algorithms and encountered many scalability issues and got a lot of 33 terabytes of RAM cannot be allocated errors. And we had to do some research on other diacritic restoration projects of which there's about one to two dozen around the world in about six-ish languages. So this is an emerging area. And we had to develop new methods for splitting transliterated text into compound characters that would fit 3 trigrams, 4 grams, 5 grams. Because while in Cyrillic, you can just count 3 letters, 4 letters, 5 letters and when you transliterate that one can turn into 4. It can have diacritics. It can have stress marks that look like quotation marks that then the software doesn't know what to do with. And through all of that, different catalogers can parse those inconsistently. And that all complicates using computers and AI to parse these records. We labeled the data, we built machine learning models, we reassessed the data, we scaled the models and then we added some explainability methods. But throughout all of our challenges I remain super excited about this project because the challenges for libraries, archives, museums and natural language processing technology in general for handling this kind of linguistic complexity is what drove me into PhD research and this is the first opportunity I've had to get my hands on data and actually work on this kind of project. So it's been fantastic. And these are the kinds of results that we've been looking at. So calibrated classifiers, the first model that we got some results on used a hash on the title, so turning strings into numbers so that they could be calculated. We hashed it with two to the eighth columns per title and that got us some results but as you can see all the arrows kind of converge together which isn't ideal. Two to the third got us a little bit better. They tended to go into their own little boxes. But it's still not great. So on top of that we still needed to use rules based and statistical modeling with Ngrams. So the other chart shows some of those Ngrams once we figured out the problems with separating out what counts as one character when one character can actually be four different characters that represent one Cyrillic letter. So we are looking at a combination of machine learning models, rules and statistical modeling together to give us this capability of helping OCLC to clean up its Cyrillic ingest at big scales. Okay, hello everyone. My name is Jonathan Young. I am a science and technology librarian at the University of Hawaii at Manoa and my project was with Montana State University and our goal was to develop a measure for an academic library to calculate a return on investment value or cost-benefit analysis based on the grant revenues coming into a university that's supported by library subscriptions. And first I want to say a disclaimer. Obviously the library supports the university in many ways beyond just grant revenues, but this is one simple way to apply data science to get a kind of bound estimate of how much support the library is giving to the university. And so my approach is shown in that figure and it basically adds two factors to the total grant amount. Using the assumption that library support of grant proposals is primarily through the cited references that are cited in the proposal. And you can see again why this is an assumption and it's a lower bound. But those two factors are one the library support percentage. So that would be the proportion of the cited references found in a grant proposal that are provided through a library subscription. And second the citation dependence or how much of a grant's probability of success is actually dependent on having cited references at all. But the main problem is that if you try to look for the data and certainly this is true for Montana State and for the University of Hawaii in Manoa, if you're given data on grant proposals, they don't include the proposal and they definitely don't include the cited references used in the proposal. So I needed to come up with an approach to try to estimate the cited references that were used in a proposal and I first tried to do natural language processing and that failed completely. But I settled on using a web of science data by looking at the cited references in a principal investigator's publications and using that as a proxy for the cited references they would use in the proposal. And so I used four, combined four data sets. One was a validation dataset from a public database ogrants.org that did have cited references in the full proposals to show that that process worked and also found that there was a difference between cited references in funded and unfunded grants so it's great to see the library actually does make a difference. And that also allowed me to calculate that citation dependence term by estimating the probability of success for a grant with zero references. And then the other three data sets were the Montana State grant data, the web of science data and the Alma Library subscription data and I combined those to get that final library support value and you can see plugging in all the numbers into that formula there. For Montana State for the last five years we come out with an ROI of $4.83 of grant revenue that's supported by the library for every $1 of library materials budget which is consistent with previous work using non-data science methods such as survey methods. So that's good to see. And I hope it'll be useful to library directors like Kenning from Montana State. But the data science approach actually has another advantage in that it can provide more data on a journal by journal level which is what I'm most interested in as not a library director but as a academic selector and liaison. And that was one of the great things about the leading project that provided the flexibility that I could tailor the project in a way that was going to help me in my role. And as an example on the bottom left at the start of the pandemic I just started my position and I was told like I'm sure many universities that I needed to re-evaluate some of the journal selections and so I created a chart like this and with as many data points as I could such as usage such as impact factor citations but it was missing what role these journals were playing in our grant revenue at the university and so I've been simultaneously trying to focus on that and I'm very happy that the leading projects have been allowing me to do that. And then going forward kind of in the far future also integrated into a visualization like a network map like I show on the right to help my role as liaison as well. And so I thank the leading program to give me the kickstart to be able to do that kind of work as a librarian. So I have the pleasure of wrapping this up here. I just would like to say thank you to all of our presenting fellows of really amazing work. This is just a selection of the incredible 24 projects that we got to highlight this year got to work with this year through our 24 fellows. I'd like to say that even just we're in our first year of the leading program of course we're extending the leads program. We've already seen some fellows secure new jobs coming out of this but one of our big goals of this program is to support career development of early career professionals. I will say we will be launching the 2022 applications for leading and so if you have doctoral students or early career professionals who would be excited to be part of this program we would really like to talk more and certainly would be glad to see applications to the program. As I wrap us up we would really like to thank IMLS for their support and for this project. A big thank you to our advisory board including our good friend Cliff Lynch our diversity and equity and inclusion board all of our fellows our mentors and faculty who are making this program a success. You can stay in touch with the cci.drexel.edu it's part of the metadata resource center it's up in the slides we'll be sharing and I think we've got about six or seven minutes for questions and so I'll turn the floor over to all of you for questions you have. Thank you. I'll give folks a moment to join us. Maybe a question to our fellows you know I think you presented really exemplary show in products we know that one of the challenges you ran into was actually scoping your work would any of you like to reflect on what that experience was like? Unsensitive question. I think I was somewhat lucky in that a lot of my scoping actually came from my mentors something I've got a forthcoming blog post about my actual the actual queries I ran and at one point in the blog post I say we cheated a little bit by creating a wiki data list prior to starting the project and had my mentors not created that list a lot of my queries would have just like timed out because they were way too big so I got lucky in that sense and then other than that when I started moving away from some of the stuff that was prepared for me in advance a lot of it was just what are the cities I find the most interesting is there any way I can integrate Detroit and or Ann Arbor so I could take it locally a little bit more and so yeah. A lot of my scoping work came with when do I stop labeling and start modeling how do I deal with not knowing how long a model will need to run I just have to wait and see what it's going to finish and if there are going to be results when it's going to finish or if it's going to crash while I'm sleeping and I'm going to have to start over again so some of the results are this weekend's results so I did my best to scope and we got there but some of it was a little hair raising I saw give our audience members one more moment while I shield my eyes from the blinding light I'm not seeing any movement I will thank you all for being part of our session really looking forward to continuing this project with all of our fellows and mentors and so thank you so much


Let's move to Masuda-san. We have 20 minutes to go, so keep it in less than 10 minutes. I'm sorry. Very well. Thank you very much Tanaka-san and thank you for coming all. I'll be talking about some issue of generational divide on the climate agenda. Climate change is the result of the accumulation of CO2 emissions or greenhouse gases in the atmosphere. Well over the last 200 years. Assessment report 5 of 2014, they covered long range from 1970-50 to 2011. This is the magnitude of period we have to cover and that means what we do today with all these efforts do not necessarily benefit the mature generation here except the younger ones.  That is a big issue. Already climate change is eating into the heart of the system. Today and yesterday there is an enormous typhoon hitting the heart of Japan. We call this super typhoon Hagibis. 7 million people evacuated for precursor measures centering Tokyo area. Can you imagine 7 million people evacuated and already several people have died? And it is the largest ever hitting Japan over the last 60 years. And this could be the norm in coming years also. Climate actions, mitigations in particular are of very long-term nature because of 200 years scale could be shifted to another 100 years or so. And it is not, unfortunately, it is not appealing to politicians. Politicians, I sympathize with politicians because they face with so many agenda. Employment, pension, education, healthcare, building infrastructure, all these are gender. And because they are pressed with the pressure of reelection and election, they like to shift their attention to rather short-term issues which they can sell to the voters. And quite often the long-term issues could be set aside or left behind unless there is many room to accommodate all these demand. However, however painful we have to deal with climate issues, not sitting aside. And we have to overcome the generational divide. And that kind of issue, I know there is a lot of criticism to what Greta Thunberg said. I saw many articles and videos in YouTube and elsewhere telling her what you have said is brainwashed or exaggerating the risks. But it's not the way to persuade younger generations. I saw many big speeches in the UN climate summit.  But speeches don't mean anything unless underpinned by concrete actions. And history shows very sad record. For example, if you remember there was a famous real summit in 1990. Many head of states came in real and wrote wonderful declaration of action-oriented one. They committed they will decrease CO2 emissions to 1990 level by the end of the 20th century. In a matter of 10 years they promised, they signed. Was it followed by actions? No, nothing happened. Rather, more CO2 emissions accelerated because of economic growth everywhere. And real action was taken in 1997 at COP7 of Kyoto Protocol. But Kyoto Protocol took 80 years to be effectuated because of signatory, they need ratification by many countries. Today, 15 years has passed with no actions after real summit in 1990. And we should not repeat all these mistakes made. What Greta said at UN nation shocked me. Because I was part of energy policy making together with Tanaka and others. I am partly responsible for that. And I think we have to something and we cannot leave all these younger generations left behind or keep the generation gap unnarrowed. Because we are responsible for the next generation or future generations. I am suggesting something very wild. Yes, our generation was challenged by this young little girl from Sweden on 23rd September at the United Nations. That challenge should be responded with sincerity and with concrete steps on top of what we have agreed upon in Paris in 2015. How Japanese government can respond to her accusation? Yes, Richard Cooper said no coal-fired plants. But my mother country is now constructing or planning 44 coal-fired power plants because of cost issues, because of economic growth first. It's natural for the government and industry desire for that option. But this is not the answer for the future generation. There should be a coordinated response. Not regretting in the past, but coordinated response to this young little girl representing future generation next year at the UN Climate Summit.  Then that will be the first step to narrow the gap between us and the forthcoming generation. Thank you. Thank you, Masuda-san, for kind of confessing our sin.


I am doing a trader clinic webinar for annual and lifetime members so I'll see you guys then yep guys and on the website all the information is on myinvestingclub.com the Tosh's number the webinar link is there everything guys so alright guys we'll see you next time we'll see you back in the room guys


Hi everyone, Joe for jaspyscasebreaks.com, coming at you with 2019-2020 Panini Mosaic Basketball. This is a two box break of those cellopacks right here. So the cellopacks, if you don't know where they come from, they come out of these boxes right here. That's where cellopacks come from. So we're doing two boxes of these cellopacks and this is random team break number one. UPS delivered it. Thanks UPS, it's finally here. There you go. Now there's an early bird special as you saw, the first 20 spots right here have a chance to win a bonus spot. So let's do that in a separate dice roll first. Let's roll it, let's randomize it three times for those 28 names. Name on top gets it one, two, and good luck, third and final time. Ashley Nelson, congrats to you Ash. I think you were the earliest of birds, yeah. And now you get an extra spot, the worm, the delicious, delicious worm, worms. Now let's do the randomizer for the teams, names and teams. New dice roll here, this time eight times, three and a five, eight times for names and teams. One, two, three, eight, eighth and final time. We've got Ken down to Michael after eight, eight times for the teams. Good luck everybody, four, five, eighth and final time. After eight, we've got the Spurs down to the Kings. I don't want to search for these teams, I just want to copy them. There we go. Ken with the Spurs, Ryan with the Nets, Steve with the Hawks, Andrew with the Celtics, Matt with the Knicks, Anthony with the T-Wools, Kevin with the Bulls, Andrew with the Suns, Michael with the Jazz, Christian with the Thunder, Darje is back. Hi, I was like, I know that name, Hornets, McSub with the Nuggets, Ash with the Pelicans, there you go, Brandon with the Wizards, Ash with the Raptors, Ryan with the Sixers, Zach with my Lakers, Jason with the Trailblazers, Ashley with the Bucks, Tom with the Mavs, Craig with the Cavs, Tyler Kish, I saw him earlier, the Pacers, Anthony with the Grizz, Ashley your early bird spot, got you the Warriors, nice, Derek with the Heat, Derek E, Jason with the Magic, Oliver with the Pistons, Josh with the Rockets, Colin Smith with the Clippers and Michael Lyon with the Sacramento Kings. Alright, so let's stretch this spreadsheet out, there you go, let's sort by column B, box these up right here, center them. Alright, I'm gonna pause the video, allow for a little bit of trade chatter and when we come back we'll have the breaks to go around. Alright, welcome back, no deals were done, so let's get a move on here, on Thursday the 4th, two box, Salo, Random Team 1, I think there's 12, yeah looks like 12 packs per Salo, so big thanks everybody for getting in here, thanks everybody, we got a football life in the background, I don't know who this is, somebody, there we go, nice. Now I'm sure a lot of you saw those mini breaks to get into the basketball mixer that features these Salo packs, those will be coming from a set of loose Salo packs that are distributor sent us, they just did they did not come out of these boxes, so FYI, we're doing this here, oh Charles Haley, Charles Haley story, I'm pretty sure if you find these in your local Target or Walmart ladies and gentlemen, grab as much as you can because you can't really find these anywhere, not readily that is, you can't really find these readily, not readily available. Now let's open these, let's open these packs, there'll be qualifiers for this break in here, we've got no veterans commons ship except for Luca, Yanis, and LeBron, and maybe Kobe if he's on the checklist, not sure if he is, but at least for 2019-2020 products and until further notice, that'll generally be the standing order for basketball products ladies and gentlemen, and select, any select you see everything ships in all select breaks, because of all the different tiers, that's another standing order, and then every other break I think everyone pretty much knows that it's no vet commons shipping, no vet base, people ask Joe what do you do with all that vet base, it's a very good question, we collect them all together, and then we put them into these little 500 count boxes, 100 count boxes maybe, and then we we sell them to the public, whenever our store, when our store was open, we used to do that, and all proceeds go to a few charities that we like to support, Make-A-Wish is one of them, LA's best and after-school program here in Los Angeles, we support that, so that's what we do, we keep that sort of stuff low-key, but people always wonder, what do you do with that vet base, so that's what we do, all right good luck everybody, here we go, all right so obviously these mosaic parallels will ship, that LeBron will ship, all the rookies will ship, so this regular Kevin Knox won't ship, but old-school P-Merovich, Celtics P-Merovich will ship, these will go to, there's a checklist on grouprakechecklist.com, these will go to the teams they played for the longest, the checklist has, I think we'll see, and Vince Carter, they'll have them as a raptor, just FYI, I think everything, all the other team USA guys are pretty self-explanatory on where they should go, there's Green Kendrick Nunn, they're here, nice, these parallels, now obviously these are retail, sello, hanger packs, you're not really going to expect a lot of autos out of here, so it's really these parallels that, or these LeBrons right, but parallels that we're really chasing out of here, and the special parallels in those extra packs right there, and any rookie parallels, you know, key rookie parallels, like that Kendrick Nunn, there's Jonathan Isaac Green, those are not numbered by the way, Spud Webb will definitely ship, there's Bowl Bowl, Green, that'll be for the Nuggets, the Lakers, Zach you got my Lakers, you'll get that LeBron base card, and the Kendrick Nunn for the Heat, that's for Derek E, you have Miami, Andre Drummond Green, nice John Moran, NBA debut card, going to the Milwaukee, Memphis Grizzlies, Anthony with that one, there's that Vince Carter, Amitri Silver and Taco Fall, here are the special parallels, I don't know what these are called, pink camo, pink camo is what we're looking at here, so obviously all of these will shit, so these paint parallels folks are the are the unique factor in these mosaic packs, exclusive, and we got a couple more of these breaks coming up tonight, so these aren't numbered, but again they are exclusive to these, to the oh nice John Moran pink, nice RJ Barrett for the Knicks too, John Moran, Memphis Grizzlies, Anthony, I can't imagine there'd be too many of these out there in the world, camo pink is what they call them, very nice, so far so good for the Grizz, there's another Kendrick Nunn, all right Town Horton Tucker, all right we'll grab that, where's that RJ Barrett, I'll set him aside as well, RJ Barrett hasn't really really got to where we hoped he would be yet, but he's definitely has that upside, I mean there was some conversation last summer when people were like, oh RJ Barrett better than Zion, RJ Barrett better long-term career than Zion, bigger star than Zion, so it'll be interesting, oh it says right here, there it is pink camo prism, all right good luck, next group, next box of course I think the New York Knicks definitely have to definitely have to maybe get Dolan out of there if they if they want to progress as a franchise, that guy's been handcuffing the Knicks for too long, second box, we've got we've got more of this in the store, so if you missed out on the first time around, don't miss out on the on the next time around, I'm looking at so we random team two is already full, if you look at the break schedule in the chat we'll be we'll be doing that a little bit later today, and break number three is already at nine spots left, it's only 34.99 a spot, two box, a sell-o-pack, so 24 sell-o-packs total, 12 in each box, and again the exclusive pink camo parallels, might hold any Genesis show, I don't, are there Genesis's possibilities in these retail packs, I'm not sure, I guess we'll find out if we keep ripping enough of these, I'm not sure exactly, I thought the Genesis maybe just lived in those hanger boxes, there are in these, all right so we'll look out for them, none yet, none yet, there's so many of these on May night, I always get mixed up on what parallels live where, so we'll go Donchich, we'll set that aside, ooh you pulled a Durant and Jonas, Valenchunas, Genesis out of these sell-o-packs, that's pretty nice, and the Genesis folks have that sort of blackish-blueish sort of tinge to it, they are good-looking cards, Valenchunas, RJ Barrett, no Zion, I don't think we've seen, I think we've seen any Zions here, it's been all, all gris so far for Anthony, talking about sort of like the elite tier rookies, all right, maybe some Zions in that pink camo, Kenneth says he got lucky and pulled LeBron MVP Genesis, Yannis Genesis and a jawed Genesis non-MBA debut version, that's not too shabby, out of these, out of these, sell-o-packs or just out of a mosaic in general, that's pretty nice though, either way, here we go, the pink camo, exclusive to this set here, yet another thing, another color to add to the rainbow if you're trying to go for one of those, Bobby Portis and Eric Pascal at the end, there you go ladies and gentlemen, two-box, random team one, mosaic sell-os in the books, thanks for watching, I'm Joe for jaspyscasebreaks.com, we'll see you next time for the next one, bye-bye.


Refuse to pay? You'll pay in blood.  In this episode, they'll pay either way. Squatters refuse to pay, law revokes their freedom. Client refuses to pay after service, their clients get a bad aftertaste. Install a wood stove without paying up, receive a smoke grenade and a company owner stops paying salary and steals, so karma made him lose all. Naturally, viewer discretion is advised. These revenge acts might be disturbing to snowflakes. Last summer I bought a house trailer and decided to rent it out. It needed some work so I posted two months free rent for putting carpet down in the living room, hooking up the hot water heater and putting in a toilet. Got a nice couple with a newborn willing to do it. Great. No lease. Month to month. No security deposit and the rent was $450 a month. The deal was that they would do the work, so I didn't ask for rent for two months. Everything was fine. Side note, there are a few little state laws that come into play. If you don't pay rent for three consecutive months you are considered a squatter, which means the landlord doesn't have to fix the property and can actually padlock it and deny you entry. I won't explain the eviction rules and violations here, let's say it's up to the judge really. Fast forward five months and still no rent money. Yes, they had a newborn, they were young and didn't have a lot of money, but the husband can spend $1,500 on a new engine for his project car. They can afford to go out to eat and spend money on electronics, but no rent. So after that third month I did nothing. The weather was turning cold. I didn't put in the furnace like I promised and if they hadn't had a newborn I would have padlocked the house. They were bragging about their tax refund, I gave them a shot to catch up and they basically said that they knew their rights and were in a rent strike, illegally I might add. I'll explain later. COVID happened, husband got laid off so I gave them a break. When he went back to work I asked for rent again and was told to frick off. I finally had enough. I told them they had 14 days to get out or I was doing a formal eviction and seeing he was on house arrest he knew what that meant. I honestly was trying to keep him from having to go back to jail and keep an eviction off of their record. I was trying to be nice and civil so they would leave and I would cut my losses, until I checked the property. Everything inside was removed. Everything they got free rent for was gone. I got pissed and filed for a formal eviction. It didn't go as they hoped. They said I didn't provide heat so they went on a rent strike. Only they didn't file that with the courts or hold the money aside. They just thought they could stop paying rent. What they put into the trailer was less than the rent. The judge agreed with me. The squatter started slamming my character in court so I replied with, well, you didn't tell me you were going on house arrest when you rented the property before then the judge looked bored, but now he perked up. He asked them if that was true, they responded that it was and he was ready to make his ruling. I received 15 months rent back at $450 a month and $2,000 in damages, because the way they removed the stuff they damaged a lot of things. As for him, the judge said he had no choice but to turn the eviction over to his parole officer. Fast forward to this month. His house arrest was violated and he was arrested. They actually had the nerve to ask me to speak on his behalf. I didn't. Ruling was his remaining six months are now being spent in jail, they have a bad eviction on their record and they owe me money. Know the law before you play the game. So, I'm a 17 year old web developer who can make basic websites and stuff to earn money for video games. The job's pretty easy for the pay. I get picked up by some local bakery so people could pre-order cakes and stuff online. Took a few hours to puke out a functional back end and a day to make it all pretty and smooth running. I had agreed for basically $10, which is insanely cheap even for India, but all I need is video game money, so I'm good with that. When I'm done with the job, they needed to purchase the domains and a server, which I explicitly told them, you pay me 10, and use another 10 to buy a domain and 5 to rent servers. Total is $25. They give me $15 so I could set their stuff up. Once the website was fully operational, I asked for my money. But apparently they didn't want to pay because, if I wasn't hosting the server anyways, why do I need compensation? You know, because apparently time has no value. The thing was I could still access the server as I had the credentials for the server. So I go home, log in and make one tiny change. The way the website works is that there's a customer page which shows your orders and there's a page for the store owners to see orders, payments, etc. Here's what I did, added a small and conspicuous may contain semen warning in all product details and the page where it shows the order confirmations to the customer, some options are changed with semen, so like semen frosting, by random. The website went down in about a week and they're back selling on Facebook. Getting someone complained or something. A few years after I started my business, I was asked to clean up and optimize a number of PCs in multiple locations, as well as set up some forms and templates for a new client who owned a local restaurant. The work, all labor apart from a little travel, was performed over the space of a month due to scheduling conflicts and school holidays, but on completing the last of it, the client confirmed verbally that he was happy with all I'd done, and to go ahead and send an invoice. I duly emailed an invoice for a sum just in excess of $400. I waited for payment, never heard anything, sent reminder emails, called and left messages, but no response. Eventually a couple of busy months had passed and I met the client by chance in the local supermarket. On asking why he'd not paid or been in touch, he said that all the PCs were as bad as they had been before I'd started, and that he had tried to contact me with no success. Because my landline and mobile phone had caller display as well as answering services, and there had been no emails, I knew the latter was BS, and as any PC user knows, a system can easily go back to pot if the user bad habits don't change. So I contacted a local debt collector, gave him the details, printouts of my call logs and post-invoicing emails, and he took them to the restaurateur. On his return his words were he's not disputing the invoice, he's saying that the work wasn't done right, so it's his word against yours. I queried if it was worth taking the guy to small claims, to which the debt collector said, even if you could prove he confirmed he was satisfied with the work, they might insist you get his computers back to their pre-invoice state again, do you really want to spend more time doing that? Of course the answer was no, so I stood it over in my mind and came up with a plan. At this point it was late November so, creating two throwaway email accounts in female names, I got in touch with the restaurant to book a large party for Valentine's night the following February. I put it down as, my husband's surprise 40th birthday party, confirmed that my husband's sister, and cc'd her in the message with the other throwaway. Said I couldn't make the journey north, but would happily pay the $10 ahead deposit as her share towards the night. Of course, as time went on the ideas grew arms and legs. The numbers attending increased, until the owner suggested he'd reserve the whole restaurant for the evening, and they'd happily arranged the seating to suit us. But could I ensure the deposit was sorted ASAP please? Of course I confirmed that the sister was a scatterbrain and that I'd ensure the check was with him very soon. He emailed the sister using the cc address and she confirmed it had been posted. To keep him on side, I asked for a proposed menu in advance so that I could send it to all the attendees for pre-ordering. Naturally they were delighted that they'd know this as it makes their life much easier. Consequently the numbers for all three courses were emailed in, with a few fussy eater variables thrown in for good measure. Needless to say by the beginning of February he was getting quite antsy about there being no sign of the deposit, but I reassured him that the sister's check must have been lost in the post, so she'd send another by special delivery, if they could ensure someone was there to sign for it. I knew the owner lived about 25 miles away, and the restaurant didn't open till 5 p.m., so he'd have to come in very early and hang around waiting for it. A week before D-Day and he'd obviously had enough. He emailed in a spat saying they'd turned away numerous inquiries, had no deposit, and could no longer hold back on taking other bookings. This time I didn't bother replying. My part was done. My wife and I were booked in at another restaurant close by for our own Valentine's meal, after which we took a walk past the restaurateur's business premises to see just two cars in their parking lot, one of which was his. I'm not sure how much he must have lost out on that night, but knowing his prices I'd bet it was significantly more than the $400 plus I'd invoiced him. Of course lessons were learned by me too, get written or emailed confirmation of job satisfaction for one, and not letting new clients go unbilled for too long was another. Naturally I had no hesitation letting all and sundry know how he'd behaved either, so he was blacklisted, or forced to pay up front for any work, by IT and other professionals I knew locally. A friend of mine used to install wood stoves and sell firewood. He still sells firewood but stopped bothering with wood stoves. This one guy bought a wood stove and wanted it installed. My friend did this but told him he would need a higher chimney for better air drawing. The guy refused it, I'm assuming maybe he didn't want a higher chimney for the look, and my friend kept insisting. The owner said no and when the wood stove was done, he complained it was hard to get a fire going due to the chimney being too short, leading to poor drawing of air. My friend came with a chimney extension and it worked fine, but he decided he didn't want to pay still, even though the job was done and done well. So a few weeks later my friend was passing through the area and saw no body was home, but there was a fire going to keep the house warm, owner must have been at work. So my friend climbed up onto the roof and got his chimney extension back. It was A plus, but not enough to cover everything that was installed and the time put in. Where things get funny is that once he took the extension off the chimney, due to poor drawing of air, the house started to fill up with smoke as the fire burned.  Try getting that smell out of stuff. Nice. Probably a layer of soot on everything. To my recollection the owner did not have any pets, so no lives were harmed, but when everything you own is black and reeks like smoke and your home is freezing in the middle of winter, I bet you would have rather have paid someone for their products and services.  When they know from the point they hire you, no matter how good of a job you do, they will try to find some reason not to pay you. I'm a trade painter that worked for around five years essentially running a company for an owner who took a very hands off approach. He was essentially a name and working capital, not much else. After getting an offer to work for a bigger commercial company and my old boss's realization he would have to run his own company, as there really wasn't anyone else in house that was qualified, he resorted to acting like a child to try and make me stay. Made me run around the world to receive my final pay which I never even got and refused to return my tools spread throughout various jobs. Revenge time. As luck would have it another even better offer from an even bigger company ended up in my lap that wanted to subcontract me a very substantial amount of their work, but I'm going to need more employees for that. Hmm. So I go through and call each and every member of the original company and offer them a three dollar an hour raise to work for me which all of them accepted, after all they know me, they've worked for me for years, and it's more money, no brainer. They barely know the owner and what they do know, they don't like. I then asked them to grab only my tools, which was the vast majority of tools on all the jobs from their respective jobs when they leave for the weekend on Friday. Monday rolls around and my old boss is getting calls left and right from supervisors asking why nobody's at work and what's going on. He was so hands off he didn't even have employees phone numbers to call and ask them. He's essentially without employees and little to no tools to complete any jobs at this point. Fast forward six months and his company has now closed, he's lost all his work. His new addition to his house has come to a grinding halt halfway though construction, he's hurting big time. I never got my last check, but I did get a great group of workers and a company of my own, so I guess I'll just call it even. Thank you for watching Royal AI. Be sure to subscribe and hit the notification bell to receive future episodes. Share your experience in the comments or tell us what you think of these stories.