Welcome to Cannabis Data Science. Fabulous start to the year, crunching a lot of data,
working on a lot of cool cannabis data related projects. Definitely want to get you all involved
today. That's why I'm going to share with you one of the largest data sets of lab results
out there. It's nicely curated, fresh for statistics and you to peruse. I'll give you a quick
take and then give you a bunch of ideas for projects that I'm working on. It's a meetup after
all, so just want to give you all a chance to share what's on your mind and what projects may
be at hand for you. Cannabis, I'd love to get you involved in some of these projects coming up,
especially testing. Testing is a big thing at hand. However, I'm curious, what are you interested in
at the start of the year? Any cool projects on your mind? Any questions that you'd like to answer?
Well, let's see. I do have my Massachusetts Cure-Relief COAs and so I was looking at a
LeCacha lab and I'm using Chemlytics. There are some assumptions like LeCacha labs that it's
going to be at an address that's different from Massachusetts. I'm just looking through that.
And I'm also using Spacey NLP to get the entities. I have C-PILs, various pages also.
So I'm wondering if I shouldn't just be looking for the number of pages, then grabbing the lab,
then just grabbing the address and everything on the COA, not necessarily assuming that something's
going to be a certain address. So I've just been kind of playing around with that. Thank you, Keegan.
This is phenomenal. Sneha, what Candice is talking about is, and this is actually relevant today,
samples for cannabis go through quality assurance testing. They get tested for cannabinoids.
The certificate of analysis is issued. And in states like Washington State, the public is
permitted access to these certificates. So that's why it would be phenomenal if you could go into a
retailer and know right off the bat, what are the cannabinoid percentages in this product?
Are there any contaminants? And any other cool details like who was the producer? Who tested it
in case you wanted to follow up with the lab for some follow-up questions. So all of these
are pertinent details, just it's trapped in these PDFs. So we created this tool, COA doc,
to basically parse out the data to the best of our abilities using any tool and cleverness that we
can think of. That's cool. I didn't even know that data was publicly available. Exactly. It
depends on the state. The cannabis industry, well, the cannabis regulators have made an effort
to emphasize transparency in the market because this is federally not permitted. So the states
wanted to be transparent about what they're doing, how they're regulating it, get the information to
potential consumers. Now I did put in a Freedom of Information Act for both Massachusetts, where I
reside, and Florida, where I reside a few months in the winter. And they have 10 business days to
respond via writing. And I guess her Freedom of Information Act law has expired, but also too,
my mail may need to get from Massachusetts, CCC, over to Florida, where I am now. But I'm hoping
that what I'm asking for is I'm asking for the exact same data that the state of Washington
provides, pesticides, COAs, some SOP type of knowledge that Keegan has just done amazing work
with. So I'm hoping that Massachusetts and Florida will follow, and then other states.
Well, I can help on that effort. And welcome to the group,
and Elise, just a heads up, we are recording just to save for future sake in case we
think of anything interesting. Gotcha. No worries. Thank you.
Basically, we're talking about what publicly available cannabis data is there. Well, cannabis
goes through quality control testing, ideally for the consumers and in medical states, the patients.
So it seems logical that, you know, they should have access to it. And in certain states,
they explicitly do. And so we're capitalizing on that. So I think there's Washington state,
and I think there's a total of six, where you're definitely allowed to get the certificate
certificates of analysis. It's just we just need to start slow and methodical and it'll lead into
the one of the insights of the day. Why are we asking you this data? Well, to learn about
cannabis, and hopefully show you how you can draw insights from similar data sets. The way I like
to say it, this data is so rich, give any good data science a chance to take a look, and they'll
probably walk away with a novel insight that no one else had thought about. So today, I'm going
to do a demonstration of that. Share with you a really simple but interesting way that you can
draw insights using just a small subset of the data. And you can now analyze the data and
hopefully draw some insights of your own. It's meetup after all. Sneha, would you want to say
a word for yourself and what you maybe want to learn or accomplish in the coming year, especially
cannabis data science related? Well, I'm currently in a master's program at CU Boulder for data
science. Data science wasn't always my field of study. I graduated in physiology from CU Boulder,
but yeah, this was honestly one of my tests for a class to attend a meetup and learn some more about
the field. And I thought this was interesting because I live in Colorado and weed is like a
big thing here. So I just wanted to learn a little bit more about cannabis's relation with data
science because I didn't even really consider it. So I think this whole thing is pretty cool and
I'm willing to learn some more. I don't have much experience, but yeah. Phenomenal. Welcome to the
group, Sneha. You're in for a treat today. We'll definitely share with you, well, hopefully some
cool insights, some cool ways to crunch data and of course the data itself. So threefold, you should
walk away with at least some bit of value. Welcome to the group, Isaac. We're just kind of doing a
quick round of introductions before getting into the data at hand. Before I let you introduce
yourself to the group, Emily, please correct me if I'm mispronouncing your name, but please let me know
if you'd like what you'd hope to get out of the group or learn here in the coming year.
Yeah, so I look to just, I guess, I don't know, I just stumbled upon the group and I am kind of new
to data analysis. I'm in my last year of my BA program for BI, data analytics with
business intelligence. So I just figured for good practice in a project, I mean, I like weed. I live
in Washington, right? So I just figured this would be a good way to dive into data science. So that's
why I'm here. Phenomenal. And in fact, that's one thing that I thought was fun about when we did
a series of Saturday morning statistics and I'll be uploading those throughout the spring. So you'll
get a treat if you want to start catching up on those because it is cannabis after all, it's fine.
And so it can be a fun way to learn about data science and statistics and the scientific method.
So it's cool to have you here and let us know if you have any particular questions
or ideas that way we can pursue those further. Now, Isaac, we had a time mix up last week,
but back on track this week, got some cool analyses. However, love to hear about some of perhaps
the work that you may have done, if you may want to share. So happy to have you here, Isaac.
Yeah, of course. And I see from the chat that you shared a data file with me. It looks like
the Washington data, but compiled into a very friendly format for analysis. Is that the file?
Exactly. And this was what we thought was the value added was simply, and remember,
it took us a while to get here. We had to diagram out the data and think about how we were going to
merge it. This isn't anything fancy and you could probably have gotten to the same results
through SQL queries, but this was basically us chomping down around 43 gigabytes of the CCRS data
to about, it's only about 20,000, to only about 20 kilobytes. So we went from about 43 gigabytes
of raw data. Of course, we're not looking at sales yet, but we boiled this down to about 20 kilobytes
of useful lab result data. So yes, you can go work with the raw data. However, if you just want a
quick glance at the lab results, then this is an effective way to make the data accessible. Also,
welcome to the group, Yasha. We're going to be doing a lot of analysis of, well, we're going to
start with analysis of lab results and we're going to use it in a peculiar way, but in a way,
I'll show you how useful lab testing is. But before I get into that, I know that Isaac and perhaps
yourself are looking at the data. So before I get into my trivial exercise, Isaac, you wouldn't want
to share? I mean, you shared with me a figure. I don't know if you're all prepared to present or
anything, but you don't have to.
I'm happy to. I mean, we were discussing about the microbiome, the EB measurements from Washington
Labs, and I just put it into a graph and I think that'd be interesting for us to all take a look
at it and see what the group thinks. I'll try to present my screen.
I love how you took this one step further. Essentially, we were just looking at detections.
So last week, we were just seeing all the different pesticides that were detected. We hadn't gotten to
the point of adding the limits. So I love that you actually did this because now this shows us not
only the microbes detected, but also roughly the percentage that falls above the failure limit.
Yes, and I understand this graph might be a little bit difficult to see what it is about, so I'll
just go try to explain in my terms. What you see here are night plots. Each figure is associated
with a number 2909, for example, 290910. Each is a one lab. And x-axis is a log of microbial
detection. So 2 is 100, 4 is 10,000. And the y-axis, because this is a histogram,
so y-axis is just the density of data. For example, on the top left, you see there are roughly
20 plus samples that have a detection around 10 to the power of 3 and all the way to the power of
4. There has been around 20 samples in each of that, each bin. And the red vertical line...
Quick question. Are we looking at total failures right now, or is this a specific analyte?
It's a specific analyte. It's an intro bacteria, and it's the type of bacteria you'll find in
people's gut. One kind of straightforward way to explain it is just a poop bacteria.
And obviously, you do want to see a lot of that in your flower. And here the results are all...
Well, first of all, I have filtered out all non-detections. Otherwise, there'll just be a
huge bar at, say, zero or whatever is the detection limit, and we won't be seeing anything
to the right, because the detections are only a fragment of clean samples. And here,
the red vertical line is the regulatory limit. That means any samples to the right of it, say,
for lab 11 on the top plot, there is one sample, two samples, three samples that failed the test.
Because their result is to the 10 to the power of four plus, so they failed.
Two thoughts come to mind. I should have suggested this last week. One of the first analyses we did
was just look at the residual solvents. We compared butane in Washington versus California.
And we were seeing that there were concentrates that were making it to the shelves in Washington
that wouldn't have passed California's quality control standards. So I wonder if something similar
may be... I wonder what... So for example, I wonder what your microbe detection limit is in Massachusetts.
So for example, I wonder if some of these samples either would or would not make it to the shelves
in Massachusetts. That's a great question. And the results are all very similar.
Limits actually differ in some cases significantly. And even entire testings. The state of Washington
only require testing intrabacteria. And while Massachusetts require four, we require AC,
the bacteria that doesn't breathe oxygen, and CC, coliform, and the most important, eSAM mold.
If a sample is moldy, it won't pass. And also EB, which is this gut bacteria.
And Washington state only requires this one type of bacteria to be tested.
So that's a very big difference to start with. And also in terms of limits, it's also different.
I believe that EB... Well, actually, I can't remember the exact number. I think EB is 10 to the power of
three versus 10 to the power of four here. And for other screens,
the residual solvents is more obvious. For example, the built-in limit for Massachusetts
is at 12 ppm, which is very, very low versus 5,000 in most states. But that's a rather a
difference in regulators' approach in making their laws.
Two more questions, and then I'll get to my second thought. The first was, what exactly are the units
here, the test value? So we've got the limit at four. So at four coliforming units, you fail.
Okay. So that was the other thing that kind of jumped out is, I guess I'm curious about the
number of tests happening in each lab. So for example, lab 11, it looks like it's a smaller lab.
So they may not be the most comparative example. But for example, it does kind of look like a lot...
For example, let me not throw them under the bus or anything, but lab 2912,
coincidentally, a large percentage are falling under the four coliforming units.
And so I think it'd be interesting to, I guess, compare the different labs to see
what's sort of the mean and variance at each lab, because this is where we were kind of
talking about it wouldn't hurt to have a standardized method, because if lab 2912 is
there maybe not incubating as long as lab 2914 may have a structural effect on their results.
Are you kind of thinking something similar? Yeah. I mean, it's definitely one approach,
but for me, what's striking on this nine different plots is the change of behavior.
Around regulatory limit. I mean, for a normal bacterial growth, you would expect it to be a
natural phenomenon and it's going to be a nice curve. Rather, what we're seeing here are
kind of almost two populations or even a cutoff around four. One of the things that we're seeing
or even a cutoff around four. Well, for example, lab 2914, you can see they have a lot of detections
just right below four, but above four, it reduced a significant amount. And you can just see
on an intuitive level that it's not a result of a typical natural phenomena. And I think that's what
it's very important evidence for us to say that there are potentially a fraud happening.
I can see that. And just to keep talking about different distributions, it also would look to me
like, you know, perhaps lab 10 has some sort of truncated distribution. It looks like they are
just starting their count at like three for some reason. And then lab, you know, 2908,
their distribution looks just entirely skewed to the left. Yes. This is different because
labs use different methods and they have different limit of detection on the lower end, which is
what I think makes a kind of comparison of mean rather difficult because in this type of
distribution, the lower end will skew the number a lot. And if they have a different cutoff,
that will change their mean. And it's so it might not be representative. I think below quantifiable
BQL wouldn't be there. It would probably be zero. If it was to be put in a number.
Yes. Yeah. I mean, there are well, when you do a analysis, when you try to find a molecule
that's of a very less amount from something, there are usually from a chemistry perspective,
two thresholds. One is limit of detection and one is limit of quantification. So if there is
enough of a response of that molecule that we're detecting, okay, that's above limit of detection.
So we know that there are some of the molecules in the sample, but there also is a gap that's
between the limit of detection and limit of quantification. If the response is, although
it's there, but it's not of enough magnitude, we won't be able to have a conclusive count of
the thing that they're trying to detect. So for example, limit of detection might be three,
limit of quantification might be 10. So anything less than 10 above three will have a result of,
okay, it's above detection limit. We know that there is some amount of it in the sample,
but we don't know how much. Yasha was talking about this gap. I love that you compiled this data and
analyzed it like this because this is where, remember there's two sides of the market, right?
There's the supply side and the consumer side. And the suppliers are often really concerned about,
so for example, a lot of the talk in the legislation is about batch size, like how big should the batch
be? But this is a good perspective from the consumer side in that, wait, before you start
working on all of these other things, maybe go back and iron out some of the other analyses.
To long story short, I don't think anybody's even talking about the labs testing microbes
differently. So if you just mention this, that hey, it doesn't look like there's a
uniform way that labs are measuring, or at least their outcomes are, we think they look different.
Could you help explain this? Or maybe the lab should focus on that. So I think this is brilliant,
brilliant analysis. If you want, I could kind of, if you're okay with it, I may change gears and just
sort of, I guess, extend your analysis by continuing looking at these lab results and try to draw just
a completely wild, different insight in a whole different realm in genetics. And this is what's
fun about this, right? So it's the same data set. Isaac and I are working on the same lab results out
of Washington state. And here Isaac's covered a structural difference between how the labs are
testing microbes, which of course has implications. And now I'll just sort of do a fun little
demonstration of another way you can look at the data. So I'm going to go ahead and take over the
screen, Isaac. So just to give you a quick background, always just trying to pin my analysis
somewhere in science, and been really interested in genetics. So just here was a, I've been always
trying to replicate cool figures. And I've been wanting to do a timeline of strains for the longest
time. Now, when did various strains come into existence? And we finally compiled enough data
that we can do just that. We're not going to walk away with as cool of a figure as this,
but it will be in the spirit of this morphology tree. Without further ado, we've got a bunch of
lab results here. We've got just shy of 53,000. We've got 52,809. Just to start showing you,
in fact, another cannabis data science member taught me this, just start looking at the data.
Let's look at this one first. Just start looking at the data, counting it. And that's a really
good first step of understanding what's happening. If we just look at all the lab tests that were
created, it looks like some of them were dated prior to 2022. And we've got them going through,
I think we can find the last lab result. So we know the last lab result occurred on December 12.
So we have data going through 2022, December 2. And we have lab results that are dated to
the beginning of 2018. So this makes me think that perhaps people were entering in old lab results
when the CCRS was enacted, which was late November, early December of 2021. This is always
a tricky part with data science, figuring out what's your actual timeline of analysis.
One second, let's see if we can't make this figure a little bit bigger. We'll have to restrict to a
timeline. I figured, okay, let's look at 2022. Well, as you can see, it's a little bit anomalous.
And so I'll explain to you what I think is going on. And this is why being a data scientist
involves pulling from many different disciplines. And one of those disciplines that I love to pull
from is being a historian. So really, if you're a good data scientist, you should go back and
try to find news bulletins that the Washington State Liquor and Cannabis Board issued. Because
I was following them at the time, but I'll need to dig them back up. I'm fairly certain that people
had a window for when they could start entering data into the system. So they may have said, okay,
you have until the end of March to have all your data entered into the CCRS. So as you can see,
between the big start of 2022 and April, you have a lot of data entry. So I don't know if this is
representative of the number of lab results that are happening on a day-to-day basis. This may have
just been people entering in a lot of historic lab results. So we may need to take that into
consideration. But it looks like, okay, you know, it starts to stabilize around April. And then this
may be your typical daily number of lab tests. So this is, I just love simple statistics. So
count is a statistic. And so this is just a count of lab results by day in Washington State.
And you see, okay, you know, about 100 samples are getting tested every day in the state. As you can
see, there's a little bit of a time effect, maybe a little bit of a lull during the summer. And looks
like things may be picking up in the winter. And just to kind of show you some cool things, since
it's a meetup after all, I realized what you can do is you can group these by month without too much
effort here. So I think we may even just, yes, so that way you could find out the number of lab
results that are happening per month, or I think you can even do per week, which is a fine frequency
for predictions. Weekly is my favorite for forecasting. So this way you can see how many
lab results are happening on a weekly basis. So around 500 lab results a week. Cool. Well,
as always, I like to go micro. So we started aggregate, we started just looking at how many
lab results were happening. Well, now I'd like to go micro. And it's actually kind of funny that
Isaac was just talking about microbes. And so we'll zoom in now on a particular strain, keeping it
keeping in mind that this can generalize to a bunch of different strains. And I just kind of want to
see if we can draw some particular insights here. So for example, I keep talking about runts. So
this was parent rumor has it that this was a strain that originates somewhere out of the San
Francisco Bay Area. And so I'm curious, I was curious to start doing sort of genetic lineage
tracing of strains and seeing how far back we can go. So we still have to do some of the really
ancient stuff. And so going back to some of the early hazes, so I've got some cool history to
share with you there. But I figured, okay, let's start with the present day of what we have just
picking runs for no particular reason, you can look at all the different varieties of runs. So
here, I just got a list of every different type of runs that's been grown in Washington State. So
of course, you just have just regular runs. Now you've got white runs, knockout runs, pink runs,
pink runs, runs F4, runs and cream, your red runs, ripper runs, gelato runs. So this is really cool,
right? So I would think like you could start to do lineage tracing this way. And the way I would
do it is okay. And this is Yasha where I was saying, this is a really peculiar, interesting
value added to lab results in that, how do we know when a strain came about? Well, it could be when
it sold, but what if, you know, banana runs or ripper runs never sells? Well, I was thinking,
you know, the first documentation that we have that banana runs exists, or the lab results.
And so this may be the first known occurrence of gelato runs. And so yes, we may not be able to pin
down runs itself yet until maybe we start looking at some California data. And then we can basically
find, you know, the first known test of runs in California. And then, you know, you could find the
first known test of gelato in California. Those ones are probably pretty old. But then you could
say, oh, well, you know, here's the first cross of gelato runs. Of course, you know, other people
in the country may have crossed this one. But, you know, I just think this is just an interesting
place that we can begin. Enough of that. Let's just start looking at some figures here. So here's
just the number of runs tests. So this is anything that has the runs in its name. And as you can see,
we may not want to necessarily. And this is where I was saying, the timeline selection is of critical
importance. So for example, if you were forecasting the popularity of runs, well, first off, I would
use sales. So I think we should eventually tie these to sales to see, you know, what's the total
dollar amount of runs being sold over time. So this is just number of tests. So it's a proxy for
popularity. But, you know, if we were using this whole time period for prediction, we may forecast
that, you know, runs is going to lose all of its popularity in 2023. But that may be because our
data is skewed. There's measurement error. People were entering in old data. So if we were going to
do forecasting, you know, we may actually be better off just picking, you know, the last six months
or so. Just use the last six months of data and forecast the popularity of runs moving forward.
Cool. Well, now here's what you came for. So this is essentially what we wanted to try to build. So
this is the model. So this is, you know, how we'll model the data. You know, the code is on GitHub.
And I found some people are interested in the code. So if you're interested in the code, go and
pause through it. There's nothing fancy and it's open source. So you're welcome to pull from it and
use it how best you please. What's more interesting to me are the visualizations. First, I'll just
explain the logic of what I've done. And then the visualization. So we've got the beginning date and
date. We know that we want to look at runs. Well, what we can do, we can get every lab test that
contains the name runs, and we can find the first lab result, the first date, created date for each
lab result for each of these varieties. I mean, I've called this the Genesis. So we can see,
okay, the soap in runs was first tested on May 25th, 2022. You know, apple fritter runs was
first tested on December 13th, 2022. There's going to be too many of them to plot aesthetically.
There's 178 varieties of runs. But I'm just going to plot a random 15 to get you an idea of what
this looks like. So here we have it. So as I said, it's not beautiful. It's not your typical
phylogenetic tree, but it's an effort. So here we have a chronological order of when
various varieties of runs were first tested in Washington state. So we see some of the more
recent varieties, I think the pink runs, the golden runs. And as time goes by, we see,
and this is where I was talking about the potential importance of this. Look at this. What was the
first variety of runs tested in 2022? It was the greasy runs. And look at this, shortly after you
have greasy runs number two. And so I was thinking this, we were talking about patenting plant
varieties. Well, one way you could say, well, I was the first one to have this plant variety tested.
So we could actually find who was the first cultivator of greasy runs. And it turns out
it was Red Ridge Farms. And then look at this, not shortly after you have a producer of
greasy runs number two, and it's a different cultivator, Sky Standard Gardens. So probably,
well, who am I to conjecture? For all we know, there could be an intense rivalry between these
cultivators. And one may be sore that the other one stole the name greasy runs first.
Because remember, these are getting entered into the Washington State traceability system.
I'm not certain if you can have unique strain names or if different people can have other
strain names. But remember, our criterion is all about first tested. So it actually wouldn't matter
if Sky Standard Gardens did test the greasy runs. It just matters who tested it first. So this was
just a fun analysis that I thought you could do. And, you know, and I was just going to demonstrate
you can have a lot of a lot of fun with this. So for example, you know, we talked about wedding
cake. So you can find all the different varieties of wedding cake. And for example, I was a big
Jack fan. So you can find all the different Jacks that people are producing to Tahoe Jack,
Jack Carrere, and Gelato. And then as I was saying, you know, we're trying to track down some of the
hazes. So you can also find, say, different varieties of hazes that people were producing,
and just start to get a timeline for these. So that was sort of my main analysis. As I was saying,
it's kind of just light and fun. We know that strain names in and of themselves don't mean
just a name at the end of the day, right? So as I was saying, right,
two competing farms, maybe if you see your neighbor produce greasy runts, you know, the next week,
you've labeled something greasy runts number two, when you go and get that tested, they may be
chemically quite different, you know, greasy runs and greasy runs number two may be quite different.
But if people if Red Ridge Farms makes a lot of clones of their greasy runts, then all of those
clones, there'll be a slight variation, right, there'll be the environmental variation. However,
all those clones will have the same genetics, and will produce relatively chemically similar plants.
I don't know, just just something to think about food for thought. But as I was saying,
this this all comes out of this data set here, where we just looked at one column. Well, here I
use two columns, more or less, I use the date that these various lab results were created,
and then I used the strain name. So you know, some strains are more popular than others. However,
look at all of this rich data here, I haven't even really touched on any of these lab results.
Remember, last week, we basically just looked at okay, what pesticides are we detecting? Well,
Isaac took it one step further. And now Isaac's not only looking at microbes, but Isaac's also
looking at if the value is greater than or less than the Washington state limit. So Isaac is
so Isaac has augmented this data with the Washington state limits, which is phenomenal,
and has done a fruitful analysis. And so, as I said, I don't know how fruitful the strain
analysis was, I think Isaac's analysis was super fruitful. So hopefully, this has gotten all of
your minds thinking about some some cool ways that you can use the data. So I'm going to stop
presenting and see if any of you have any questions, thoughts or comments. And you're
welcome to chime in. That was fascinating. Oh, I have a bunch of notes that I took on it.
I still want to hog the microphone. Oh, please, please share any thoughts that come to mind.
So the first one, you showed a graph of the timeline for runs. And it seemed that there was
four strains that were tested within a couple of weeks, and then three months passed, and four
more, which is a growth cycle away. As in, it's possible that the same folks grew four, saw the
results and then decided to grow them again. But the strain name slightly different between the
first growth cycle and the second. And my curiosity is whether through the data would be able to see
whether the change was, was the yield not what they wanted? Was the potency not what they expected?
Or were there microbiological problems, which is why they wanted some sort of change in the genetics?
Or was it none of those and they just wanted to try out other stuff?
I love how you're thinking, Yasha. You're thinking really like a good microeconomist,
because really you're going to have to dive deep into this. So, for example, you may want to start
looking at specific licensees. So, for example, look at Red Ridge Farms. And exactly, we, the
data is there, it's just going to take some heavy curation. So I think you can find the yield from,
so you can basically calculate how much did Greasy Runs yield? Well, actually, that may be a certain
batch size. So depending on how many tests they've done, we may actually be able to estimate yield
that way, but we can probably, depending on the size of the cultivation, but long story short,
you could probably find yield. You could also find sales. So maybe certain strains you're selling
better than others. And then you also alluded to, this is going to be a difficult web to unweave,
because the varieties are cropping up all over the place. So that's why I wasn't, this isn't quite
a phylogenetic tree, because it's not really saying that they're related. That's just saying
when they occurred. So it's like, who knows? Who knows if these are even coming from the
same original Runs stock clones, or maybe there's some Runs seeds out there. And then as we know
about seeds, each seed will have genetic variants. And then I need to learn more about, and that's
what I'm trying to do. I'm trying to learn more about, this is Sensimilia tips, this old book on
cultivation. So I'm trying to learn more about standard cultivation techniques, because exactly,
I'm trying to find out what is the life cycle of these plants? How quickly can you cross pollinate
and create a new variety? So these are all super interesting questions. So I love how you're
thinking. That's the purpose of the analysis after all. And that's the point of the Meetup Group.
This was sort of a quick, dirty analysis, right? If you're doing something for publication, of
course, or for a business, of course, do it much more rigorously. But this was just to sort of get
your brains thinking about all the cool possibilities that are just laying to be explored. Well,
we've kind of reached the end a little soon today. I know we normally go long. I may go ahead and
wrap up a little early today, unless, like I said, there's still time for some more thoughts, comments,
and questions. But I think we've covered a lot of ground. And there's a, I don't know who to credit,
but there's a useful tip that when you're giving a presentation or a talk, that it doesn't hurt to
let the audience go five minutes early, because people kind of appreciate you for that. So that'll
be one insight for the day. And then the other insight was simply, you know, it's better to start
now than never. And that was one thing that I was thinking about. Yes, we would love to be able to
trace these strains back further, but we can at least start now. So now people in the future may
thank us for starting to track strain origins in 2022. You know, now is better than never. So now
we can, you know, start to piece out what exactly is descendant from greasy runts. And the data's
there. I think if you want, you can dig into, you know, was this greasy runts grown from clone,
or was it grown from seed? I think the data may be there if you're ambitious and you want to
dig enough. But I think you're all awesome data scientists. So I want to thank you all. Thank you
all for coming, lending your eyes, your ears, your brilliant minds. We're advancing cannabis science,
one molecule at a time. I don't know, I'm tickled with the progress that we've made. So thank you
all. And hopefully we can keep the conversation going throughout the week and then rendezvous
again next week and explore some more cannabis data.