Yeah. Oh good morning. Good morning we'll go
ahead and just started. So I'm Kiri DeBose
. I am the College library for natural resources and
environment and animal sciences. Very long title. Yes
I am Natsuko Nichols. I'm the research data
consultant working for university libraries. And we're going to
talk to you today this morning about why managed data
. You guys ready to go? Yeah. So
come on guys you ready to go? All right
so we're actually gonna put it in a little bit
of perspective. Um And honestly you can ask any
of your faculty members that you work with and they
will probably have similar stories to the one you are
about to see. Yeah sure. Mhm. Mhm
. Hello My name is dr judy benign. I'm
an oncologist at N. Y. U. School
of Medicine. Hello dr judy Benign. I read
your article on the cell function I think that I
could use the day before my work on pancreatic cancer
. I am not an oncologist. I know but
I think I could use the day before my work
on pancreatic cancer. Do you have the data?
Everything you need to know is in the article No
. What is the data when you share your data
? I am not sure that will be possible.
But your work is in public central and was funded
by NIH. That is true. And it was
published in science. Which requires that you shared your
data. I did publish in science then I am
requesting your data. Can I have a copy of
your data? Mm hmm. I am not sure
where my data is. But surely you saved your
data. I did. I saved it on a
USB drive. Where is the U. S.
B. Drive? It is in the box.
Yeah, it is in a box home. I
just moved. But can I use your data?
There are many boxes. So many boxes I figure
out to label the boxes. Uh huh. Yeah
. Hello again. Thank you for sending me a
copy of your data on a USB drive. I
received the envelope yesterday. You were welcome But I
will need that bank when you were finished. That
is my only copy. I did have a question
. What is your questions? You might find the
answer in my article. No I received the data
but when I opened it up it was in hexi
decimal. Yes that is right. I cannot be
excited symbol you asked for my thing and I gave
it to you. I have done what you asked
. But is there a way to read the heck
style? You will meet the program that created the
Excel file. Yes I will. What is the
name of the program? I do not know this
program. It was a very good program. The
company that makes the program went bankrupt in 2000 and
seven. Do you have a copy of the program
? I do not use this program anymore because the
company that made it went bankrupt, maybe you could
buy a copy on your bag. Yeah. Mhm
. All right. I have good news. You
again. I talked to my colleague. She knew
a person with a copy of the software. Then
why do you need me? Everything you need to
know about the data. As in the article,
I opened the data and I could not understand it
. If you have the program you will find it
is clear. Well, I noticed that you called
your data fields Silent. Is that an abbreviation?
Yes. It is an abbreviation of my co authors
name. His name is Samuel Lee. We called
him. So I see. And what is the
content of the field called? Sambolin? Ah yes
, SAn juan is the level of CFC. Our
for expression and what is the content of the field
called? SaM to? That is logical if you
think about it. What is the content of the
field called? SaM too. I don't remember what
about 73. Is there a guide to the data
anywhere? Yes of course it is. The article
that was published in science. The article does not
tell me what the field names means. Is there
any record of what these field names means? Yes
. My co author knows what the content of San
two is and some three And send four. Can
I talk to your co author? I'm not sure
I would very much like to talk to your co
author. Well he was a graduate student. He
went back to China two years ago. Can I
have his contact information? He is in china.
His name is Sam lee. I think I cannot
use your data. You could check the article to
see if what you need is there? Please stop
talking now. So yes, talk to your faculty
. They will have similar stories. So what we're
here today is trying to figure out ways to avoid
this snafu. Mm So, so I have a
couple questions. So what kind of data do you
guys currently work with? Water quality, water quality
Social Survey? two different, completely different. Right
, anybody else? That's it. We only have
two researchers in this whole room. Video video video
, video, video, video. Oh, wow
. That's favorable interaction. That has kind of multiple
things in it. Right, My guys over here
three live tracks. Have you heard of that?
1? Come on guys in the back get measurement
And one from this table. God, you're bound
something grad students. Right. Faculty. Come on
. He's gonna be working on something special spatial data
. All right. We'll go. So you can
see we have a wide variety of going on.
Now. The question becomes, do others in your
field expect to have access to your data. Easy
access or ask for access. Like you have to
go to the person who request it or is it
usually posted somewhere requested requested? Right. Anybody have
anything that's really like they try to make it available
immediately. Anybody in the medical field? Yes.
Uh I have used data that is like saying,
okay, easier to use it Morris over the years
. Okay. Official. Exactly. And you can
build larger sets that way. Right. So and
then the other question becomes too. What about researches
in other fields? We saw that with the video
. Right. The Bear himself said I'm not a
psychologist yet. The panda bear was an oncologist and
wanted to use the data. Right? So you
have to think too is it's not only people in
my specific field that might want to access but what
if you're working on something like water quality? Water
quality only affects those in the environment, correct?
Nobody else nobody else uses water? You guys drink
water daily. Yes. So there's something that spreads
around a lot of different areas. So yeah usually
have your thinking cap on as to I'm in water
quality or I'm an environmentalist. Arment agricultural. And
this is my research but how my others use or
want to use my data. How about what does
data management mean to you? Uh huh scary term
that big group. Excuse that nobody wants to touch
because it's in like not nice order. It's all
messy. How do you manage it? Uh means
a lot of time. Right? And sometimes this
can relate to the life cycle of your data.
Some things have a very long span. Some things
are very short. And what's involved time? That's
number one, right. Yeah. Is anything else
or uh involved in managing data? Almost said it
organization, Right. Style stretchers. How many guys
use software when you use your data? Yeah.
So how do you know what software using? Do
you have the updated one? If you have an
old file, how do you get it into an
updated version? So all these aspects come around with
managing data. And then why are these there's mandate
for data management from granting agencies? And you hinted
at it already. You hinted at why their mandates
? Why? Why would we want to have grant
agencies telling us we need to manage our data and
make it available? Yes. They wanted to preserve
it. Why would we ever want to preserve it
? Researchers can replicate integrity reproducibility, right? Science
is based on the ability to reproduce, right?
So we got to make sure this can do it
. Any other reasons. All right. We'll get
to somewhere in a little bit. Yeah. So
then the question becomes, what's the difference between data
management and data management plan? Yeah. The data
answer plants part of data management. Yes. It's
part of it. And so data management is what
we usually think of as during the project. And
I know this is jumping. So when we talk
about the importance of data management, we start with
the idea of having a plan. And then when
we think about plan you want to think about in
advance. But then it's the data management is all
the process. It happens across the different stages of
research life cycle and a data life cycles and there
are different components different aspects. So you can't just
think about this is the one time deal. And
then the data management plan is something that happens before
everything starts. So there is a tendency that people
keep thinking about. Data management is more like a
process and the plan is more like that happens everything
before everything starts. But we started promoting the data
management process should be more like a bridge between the
tube. And as I noticed that there are different
stages when you deal with data you start with the
idea generations and you start thinking about the most of
collection data and then you start thinking about what stuff
there you use to analyze the data. And then
you start thinking about how you want to present your
research results including data. And then you start thinking
about which journal articles you want to publish your article
and research results and then what component of research results
include actual data. So all these stages as it
moves across the stages. And then as you make
progress in the research project. So it's not necessarily
just the one time deal when you plan, the
plan should stick with it and then never change.
The data management plans should be considered as a living
document and it's more of a planet head about the
post project. But then it happens all across the
stages and it's a process and we need to think
about the rich. Yeah. And then that bridge
you can see, I mean this one I like
because you have things that sometimes fall off the roads
that go under. You say I don't really need
to carry this through. And then other things you
say, Well, I might, but as Natsuko
said, it is a living document. So you
might decide after a while. This really isn't as
important as I originally thought, maybe it doesn't need
to be retained for as long as a process as
I thought for a long time. So again,
that during project that's the whole time consuming and the
plan is to help you decide what's going to happen
afterwards from the projects completed. So we are going
to talk about these for the long term and the
short term and so again, the short term during
the project and in the long term is making it
available once the project is done or once you have
a couple of publications out about it. Okay.
So as you do short term, you have to
think yourself well, what kind of data is I'm
actually collecting and we heard from the beginning there's lots
of different data types. Right? We have videos
we had probably numeric data, water quality, three
D rails, which we have to look that up
because I have no idea what that is sort of
a survey data. So and then there comes the
question about well how do you control versions of your
data sets if you go out and run one survey
and you're going to results and then you say well
I'm gonna do another population with the same survey.
You might not want to blend those two because you
might want to compare the population groups. So how
do you control? Which version of who you gave
that survey to even? That might be the exact
same questions. So how do you put that in
there? What protocols are needed? How do you
guys know the first two that are on their I
. R. B. Rid of it. All
right. Yes I know the one doing so.
If I haven't done it and I cook what's I
because because most people don't know that one institution,
anything animal. I cook anything human. I.
R. B. And there's a few others which
Ellen will talk about later this afternoon. Okay.
Other questions become how do you organize your data files
? Because again, we know this can take a
lot of time. But if you put your organization
up in the beginning it becomes easier to find things
much more readily. And what file naming conventions are
used. Image 1234 Image 2 to 45 It's really
easy to figure out what that image is. Right
? No. So we need to think about ways
that when you just look at the file, you
can kind of tell what you're looking at or the
folder, you know? Oh, this is where
I put all my image days where I put on
my video data, this is how I've named them
. So others can understand what it is I've done
. And if you didn't handle project for maybe about
four months you come back to and go, oh
yeah, that's what I did not. What was
I thinking? So thinking of these things ahead of
time. Also thinking about where your files are actually
stored. Are they on a desktop? Are they
on a flash drive? Are they up in the
cloud? You need password protection. Are you dealing
with highly secure privacy information kinds of things? How
are you securing that? Who has access? Is
it just on your laptop that only you can get
to if it's in the cloud, Who has password
permissions to get into it? How many guys use
dropbox google drive. Right. How do you know
who has access to google drive does in the corner
. Right. So you can kind of see that
is google drive secure? No, amazon cloud secure
. No. So there's things that you have to
depend depending on your data type, you might not
be able to put it in the cloud, you
might not be able to make it up there because
it's not as secure as something that's more of a
physical format. And the other question becomes what backups
are used in case of data loss. Did the
Bear have a good backup system? No one USB
drive. Right. Which he gave to the research
. Please give that back. That's my only copy
. Right. Postal service gets the mail every single
time. Right. You've never lost a package.
Never lost the letter. Right. Yeah. So
you want to think about what kind of backups are
there? And a lot of it comes down to
documenting everything. And the more you documents easier it
becomes how many guys remember what you had for lunch
last Tuesday. Mhm. You do? What do
you have for lunch? Last Tuesday saying, what
kind of sandwich did you have chips with that?
Did you have a drink with that? What drink
? No positive. She did really well. People
go last Tuesday what they are we on right now
. So, and I didn't actually say which was
this past week Tuesday was at last last week,
Tuesday last last week. You know, last last
week about this past Tuesday, just like the one
in a couple days ago. I'm sorry. I
was thinking farther back. Okay. See I wasn't
clear Tuesday with that. So, documenting it because
sometimes you think I'll remember that Right? Sam one
. I'll remember that. And he did you remember
what that first field stood for? Right. Sam
too. It's logical if you think about it,
but he couldn't figure it out. Right? So
taking really good notes and detailed record of all processes
because sometimes you write something down and it's a normal
process, but you don't realize, oh, I
didn't calibrate that last time. And then you run
it again and you go, why am I not
getting the same number? You might not have calibrated
the system. You're using, uh software technology,
uh, drawings and use versions use. So all
of this is things that you think. Yeah,
I remember that. This is what we used to
have notebooks for right now just carries the information through
. But things you might not think about to include
, that. You should also have in there again
your data file names. How do you come up
with them? What formats are using? What directories
? So you know, Oh, I always keep
this project in this folder. I keep this one
and that one. And it becomes very easy to
find security again. Who has access? And where
is the data stored? Write these things down And
the backup system again. When was the last time
that backup was done? You don't write it down
or you're gonna remember be like, was it last
month? Two weeks ago? Last night? Remember
? So write these things down and then it's very
easy to find. Mhm. Okay, so short
term management was about the various steps that you would
face when you're dealing with data. But in general
there is a commonality to every dataset. Every type
of data you have to think about in a long
term which is more like a storage long term storage
for archival purposes. And also you have to think
about where do you actually deposit your data for long
term because many people outside of your research lab outside
your field have access and understand what your research project
was about and even about the description of data variables
. So the long term management definitely comes to the
questions about what's the strategy strategies for preservations and the
word preservation and archiving. You've probably heard or you
probably haven't heard. It's more towards like preserving like
museums and an archivist. You probably heard for museum
curation but you may have heard the word data curation
. How many people heard about data curation? Okay
so data management is something that you manage short term
and long term how you deal with the data and
how you say and how you can share your data
. But it's also about the data curation which adds
value to your data set and who can help add
value you graduate students and your assistance. And even
librarians and data curator within the libraries. Because when
you think about the long term, you have to
think about the value that will persist and sustain.
And that comes with the data documentation, with the
standard metadata and the very secure data deposit repository,
which uh gives access to long term, which provides
a long term access. So that's something that you
may want to think about how long you gonna actually
trying to save your day to preserve your data.
That's the word. It's more like a long term
, five years, 10 years, 15 years.
And one of the leading uh from the agency NSF
National Science Foundation has come up with a different policies
and it has a different guidelines by directly. But
for example, engineering directly specifically tells that researchers should
preserve data and making uh huh the data available to
the public at least three years after the completion of
the project. So that's something that you have to
comply with the funders guidelines. But also you want
to work with activist data curator in the librarians.
How you can actually preserve your data for long term
. And when we think about the the best format
for data preservations, that's also different from the what
you actually deal with daily. So you may come
up with survey data. It maybe was this data
where SPS SS or Excel in general. And if
you want to share with colleagues tomorrow, you may
not want to convert to CSB everything in the CSP
. You lose some function. So you want to
just keep an Excel format for long, for a
little while. But then when you think about long
term, you want to also consider uh format that
is very best suited for long term preservation. So
again, thinking what needs to be saved If it
says I do want this long term, I want
this for the next 10, 15, 20 years
, what version of Windows Microsoft Excel and we have
to 2015, I think they came up with No
, I've got 2010 on my machine. But you
know, the older files, the 93 to 2007
sometimes doesn't come over as well. So again,
thinking about if I want this, maybe long long
term, then take the time to create a new
A, cmd or even a pdf or pdf A
Instead of keeping it in a proprietary software, just
because as you go forward, It's going to be
easier for that day to be accessible back into when
you get to Excel 2020 or 2025. So again
it can be imported because it's using very very limited
languages. So there so there are different formats and
we can actually give like a full list and we
have a deal a curator in the libraries who is
definitely a more expert. He's James is there and
you can talk to him after the session where he's
gonna be here all day and he also have a
session himself in the afternoon. There are like a
long list of the format that is best suited for
long term preservation and that varies by types of data
that you deal with. So that's something that you
know this is only a part of it for G
. I. S. But they're also like a
survey data. They're also like a measurement data.
And you may want to consult with the data curator
when you think about the long term preservation. So
we kind of touched on this already about this idea
of increasing access to sharing data. And again a
lot of that is driven by NIH NSF now U
. S. D. A. And a couple
of the other granting agencies because they're figuring their spelling
literally billions of dollars research grants every year. Right
? You guys you know you get your lovely award
and then they say well wait a minute why just
keep it with that researcher. Why not make it
more available because you can start building things from that
. And so right now what we have because this
is a typical representation of data. Published an article
right? You see it as a figure, you
see it as a chart. You see it as
uh table yet from here. Even on this one
, and I know it's a little small from the
screens. Can you extrapolate anything from this? Like
if you wanted to figure out? Okay, what
exactly was 26.4? What numbers do they have?
Can you tell it from this particular table? Mhm
. Now you have no idea if it was 10
students asked, was it 100 students asked? Was
that 1000 students asked? There's no idea to figure
out what that base was to be able to pull
the numbers out? This one on the side is
a little bit nicer because they do give you percent
and they give you a number. So yeah,
I can take the time to go back and figure
out, you know, So you can tell her
multiple answer because they have a higher response rate or
number of respondents to it. So yeah, I
can kind of get that insane with this one gives
me some exact numbers besides me sitting there with a
ruler trying to figure out where exactly it was.
This person was nice and actually gave me that's three
. That's the one, that's the one that's nine
is going to be going well, that's an eight
or nine, Something like that. Sure, there's
no big difference. Right? So that's how things
used to be published. That's where you got your
data from and if you wanted the original set,
you have to contact. So the grant mandates came
out because they figured, you know, lots of
research is going on and we really need to make
it so that more things can be pulled together.
Because once you start pulling more and more data sets
together, instead of having a sampling of 100 you
can easily get to that 1000 10,000 by pulling everything
together and start looking for additional trends that might be
going on. And the big thing too is again
, as you write your management plan, they're going
to be asking you specific questions and saying, how
are you making this? So never promise something that
you can't deliver, You say, oh, I'm
gonna make it available here on this server and then
that server goes away, that was promising something that
wasn't there. So you have to be careful.
Like there's a lot of different ways to share your
data. We have our institutional repository here, that's
pretty standard, that's pretty secure, that's pretty safe
. You might find say like fig shares out there
, fix shares grown right. You guys have heard
a big share dry add so a couple of repositories
you can go out there, but you might finally
said, oh this looks good, put it there
and then they go out of business so you have
to be careful where you're gonna put things and they
are starting to check up on if you said you're
going to deposit it somewhere and then you didn't and
you're gonna ask for another grant, they'll go back
and check and then they start saying wait a minute
. You never deposited from your last project. We're
not gonna fund you because you didn't follow through on
that. You promised us you would do so we
talked a little bit about why I should make my
data more accessible. Right? Reproducibility was the one
we already came up with. Right? Because that
makes good science. If this table over here came
up with the project, came up with results published
it. That table wants to be able to duplicate
it. Right? They want to say yes.
This really did happen. I need you guys Have
you seen retractions and scientific literature? Right at the
reactions happen? Mm hmm. Not reproducible. Right
. So how can it be if you can't reproduce
it? How could it have happened in the first
place? So that helps with the validity of the
work. Right. If this table came up with
the process that work, that group tested it worked
again, we know it's valid. They came up
with the process. They couldn't do it. It's
not valid. Talked about also pulling things together.
Right? So you can do that larger analysis.
Take these small parts. Yeah. It's not gonna
fit perfectly. Because not everybody is doing the exact
same study. Like your survey is gonna be very
different than a survey done over here. About another
survey over there. But you might find commonalities and
be able to pull us together to create a good
data set. Running larger analysis. And how many
guys are planning on for the grad students? Planning
on seeing what I call academia? Land professorship,
yep. How many of my faculty are here?
All right. So what is the one thing that
faculty I'll have to go through called P and be
anti promotion and time you're right impact of your contributions
? We used to think that it's solely as cited
by articles. How many times with my articles been
sighted? How many times have my data step incited
? How many times have my data have been used
? That's another impact your contribution to your discipline is
. How often is the data set you created being
used? Mhm. So we want to really get
the most of our work we used to think about
. I have my project point A I published at
Point B and I'm done. Uh And now we're
really starting to pull out the stuff that has the
value of how you actually got your results and conclusions
. We want to make that more available because it
has a lot of value. Okay, so again
, somebody mentioned that data management takes time and it
is indeed it takes time. But if when you
are ready to take some time to uh well manage
your data, you have to have a good plan
so that your time would not be wasted. And
you do not necessarily always think about the data progress
or the research progress, particular types as you often
described as the research data site data lifecycle. But
you may want to also think about when you work
with graduate students and colleagues and faculty. And so
you need to be on the same date and what
would be expected on each state. And it's all
about documenting and it's all about documenting a process and
documenting about data. And as curiosity noted that it
is very important to focus, you know use your
time not to be wasted so that you would be
actually have and read me file for yourself in the
future and also your colleagues. And there is uh
different ways to think about the types of data.
And we often give like the types of data like
you know the most of data collection like you experiment
or you observe something. But there is another way
of thinking about the types of data. And I
would encourage to think about the state of data when
you think about when the progress of your data life
cycle. So when you have first have a road
data and you may not use 95 of the road
data, sensor data collects a lot of a data
and that you may only process data 55 or 1%
. So that's something that you want to think about
what component of data you would like to process and
then make it by like a finalized or published data
. And then when you publish your data it's going
to be part of your article or now that these
days journalists are requesting to share the underlying data and
so that you reader your readers would have access to
the underlying data. So there are like analyzed data
, underlying data, finalized data or curated data that
was deposited in the institutional repository with the right data
metadata standards and all that in the right format.
So that's something that you want to think about when
you're taking time for data management. Yeah. And
again, it all leads back to that whole idea
about increasing your style impact, not just article citations
or use of the other publishers that you have,
but also your data itself. So and then it's
a little bit more about the documentations. And when
you think about metadata, you probably it's easiest to
think about the metadata is data about data so that
somebody can probably pick up your data and then understand
and even reusable. So there are different ways to
think about metadata. It could be just descriptive or
more like a structured or it's more regulation. So
if you have like a database and how that each
dataset has has been structured and how it's being debated
with. So that's something that you want to think
about. When you uh what standard are you going
to use that idea? What standard you're going to
use for better documentations? Yeah. And the other
thing too is each discipline has different criteria. So
look at your societies, look at the different repositories
that you have seen that post your data because they're
going to have different schemas. So anything with us
is gonna want geographical coordinates, other groups not be
as interested in that. So looking at how they
want that data documented. So again, it's easy
to go through. It's very formulaic. So anybody
who looks at it says I know exactly what part
they looked at, how long the study ran,
what the study was about, easily all there and
again, a lot of your files can fill into
how who has the ownership of that data, who
has the rights of it, whereas the store where
access so all of that just kind of filters in
to that process, making it a lot easier.
If it's all again documented, you should go copy
paste and my metadata schema. Mm So we've done
some talking a lot of talking and we're actually gonna
have you guys work, I'm putting this into practice
. So we have two exercises were definitely get through
the first one will try to get through the second
one, but we're gonna have you guys create file
names because you know, Bringing two datasets. So
the cable here, if you want to divide into
two and you're gonna get for a minute because we're
in the library and you're dealing with a grand today
before so and it should be very clear through That's
not the right one. The way we're asking you
is you have four images of naming library. We're
going to ask you guys to create a file name
for all four of those images and write them on
the yeah, you pick any more nearby with your
group should come up with. Uh huh. Yeah
. Yeah. Yeah. Yeah. Yeah. Yeah
. Yeah. Okay. Oh yeah, we're going
to take about two more minutes. Yeah. Yes
. Right. Mhm. Something, Yeah, yeah
, yeah, yeah. Oh yes, because for
me it is important. Okay. Okay. Okay
. So we have a quick discussion. Okay.
I know it's kind of hard from depending on where
you're sitting by looking at what other tables have come
up with. Are they on the same kind of
naming file structure? What I mean, those guys
, we were talking about season and view but decided
to get rid of season just Okay. So you
did front side aerial back as Newman Dash. We
have one here that SVT lives then spring, winter
, fall summer have and and live different than VT
live now. We have an uh, oh Vermont
Virginia tech, new Mexico. So I have to
sometimes think to write such a group of my working
with. So we have new mid spring azalea.
Okay. G R N. Yeah. Okay.
And then you have the Southeast. Okay. Yeah
. Yeah. Yeah. We get a really large
pile name. Right? Windows is not so happy
, I have no idea about max because I don't
get along to begin with, I don't know if
it's gonna work or not but yeah, sometimes you
have character wits to watch, right? It's too
long, you can go over. So, but
you can see now if we are on a different
project and came together what kind of data, what
we have missy? Right. Yes. And so
we know we're gonna kind of cut into our break
, so feel free to walk up and run around
and we're gonna show you a lovely messy messy through
. Um So this kind of takes from the same
thing this time each table is just going to get
one file of images. You're gonna images, you're
gonna have some data about those images underneath. You
guys can just spread it around because you're gonna see
what happens when groups come together when there was no
standardization? No, normally no nothing. No direction
. You'll see how lovely. Yeah. And as
you look at how those data are listed, you
are going to see that the cap is actually in
a very clean environment compared to what company looks like
. Mm. Oh and yes, I got it
is not my cat, but that often looks like
my desk. I will admit that being organized,
librarian and it's all up here. It's all my
brain parents. Okay. Mhm. All right.
So like I said, You've been asked to provide
a spreadsheet with 38 points that has their images.
You have a sampling of over 500 images that are
involved in the project. And you can see again
by looking at what data is there. Like looking
at the I think it's the address or the location
. It's listed up very differently. Right. So
your test to do to to accept it is to
create directions. And what do you mean? But
when was that taken? Was that by year with
that Game 1? What do you do if you
have no idea what it was taken? And sometimes
you have those kinds of data, right? You
can actually answer it. So how do you have
a null field? Your table can work on creating
that read me file where you define which point?
How do you lose down? How do you describe
the location? How do you describe the prescription?
Sometimes you have date ranges. Sometimes you have things
like renovations. Do you need another field to say
date constructive date renovated like the original construction? So
thinking about things like that. Like I said,
I know we're gonna run into our brain so feel
free. And if you want to test how well
your group did that, you would pass it off
to another table and see if they understand what you
did. Again. It's a challenge in Houston.
Okay. Yeah. Otherwise we can go on break
, but I highly recommend just having fun with us
seeing what you guys have come up with because that
again translates to put in your data together. How
do you pull it in? How do you make
sure that you have to transfer the product somebody else
? But they don't go, I'm just spend the
next six months trying to sort this out. They
go, oh I see where you came from,
I see how you organize it, right? Yeah
. So for all the ones with all the data
points on there, so you're gonna want to come
up with the way that says, okay, you
know, how would I structure this out? There's
only one. Yes, wait, wait, wait
, wait, wait, wait, wait, wait
, wait, wait, wait. We have about
five minutes before our next speaker is going to come
in. So as you can see the dirty data
is not fun. I'm aware of that is from
before. But you can see how dirty you can
really get like location right? Who knew? You
could have so many ways to find a location.
Just the town street address. Geographic coordinates. You
could even just let us a right. So making
those definitions, that's why you see we want these
read me files because it comes very easy to say
if I'm going to hand the project off exactly what
you're supposed to collect for each field. So that's
kind of thought of that. And so again it
takes that tender loving care as we like to say
it. But if you invest the time in the
beginning things become a lot easier to find and use
once you have that structure built, it's just taking
the time in the beginning not to just jump in
and say I'll deal with it later, which I'm
a procrastinator myself. So that's usually my first answer
. And then I think no, every time I
do that it becomes a big jumbled mess and I
should really take time in the beginning. So I
really do my best to do that. So you
guys have any questions for us? Yes. I
never give me dirty data again, yep. All
right so the rest of this morning we are on
break until 10 a.m. Then we have Neil vines.
Who is the director of calcite? E. He's
gonna talk to you about wonderful things that you should
do with your security of your data and things that
are offered and then peter stories that will be coming
. He directs C. G. I. T
. Which is the Center for Geographic Information Technology.
Remember that? Um And now we're gonna be on
lunch with a break and we do have pizza and
salad available along with water because we figured that was
safe. And pizza who doesn't like pizza salad like
out. Um And also which we have written on
the board around the room that Bentley has a public
access folder. We'll be adding all the slides of
presentations to there by the end of today. So
if you have any questions and answers, there is
a google doc in there. So if something strikes
your fancy to ask a question about, please go
ahead and drop it in there and we will address
it at some point today. I'm good. Mm
. Yeah. Okay. Yeah. Mm.