ATLAS Tutorial: Data Sources - Person
Summary
TLDRThis video introduces the data sources capability in the Atlas platform, demonstrating how to select and analyze various reports, such as the person report. It showcases the ability to visualize data through graphs, like the year of birth distribution and gender, race, and ethnicity composition. The script highlights the platform's functionality to identify anomalies and characterize data, providing insights into data source quality and composition.
Takeaways
- 📊 The Atlas platform offers a 'person reporting' feature that provides insights into data sources through various graphs and reports.
- 🔍 Users can select specific data sources to study and generate reports, such as the 'person report', which includes multiple graphs.
- 📈 The first graph in the person report shows the distribution of the year of birth, with the x-axis representing the year of birth and the y-axis showing the number of persons born in that year.
- 👀 Hovering over a specific year on the graph reveals the number of persons born in that year, providing detailed insights into the data distribution.
- 📊 An example from the script shows a data source with 1.3 million persons born in 1990 and an unusual spike in 1929 with 1 million patients, indicating potential anomalies.
- 👤 The person report also includes donut plots for gender distribution, revealing the proportion of male and female patients, such as 50.5% female and 49.5% male in the example provided.
- 🌐 The script mentions that the race and ethnicity data may vary depending on the data source, with some sources lacking matched concepts for these attributes.
- 🔄 Changing the data source can result in different year of birth distributions and compositions by gender, race, and ethnicity, as illustrated in the script.
- 📊 The script provides an example of a different data source where 59% of the patients are white, 9.2% are black or African-American, and 2% are Asian, with 29% of patients' race unidentified.
- 🌐 Similarly, for ethnicity, 93% of patients in the example do not have a match concept, while 6.3% are identified as Hispanic or Latino.
- 🔗 For more information about the Odyssey platform, including details on Atlas and additional data sources and reports, the script directs viewers to visit odyssey.org.
Q & A
What is the primary focus of the video?
-The primary focus of the video is to provide an introduction to the person reporting capability within the data sources capability in the Atlas platform.
How can users select a data source in the Atlas platform?
-Users can select a data source by choosing the data source they are interested in studying from the available options in the Atlas platform.
What information does the person report provide?
-The person report provides a series of graphs showing the year of birth distribution, gender, race, and ethnicity of the persons in the selected data source.
What does the first graph in the person report display?
-The first graph displays the year of birth distribution, with the x-axis showing the year of birth and the y-axis showing the number of persons born in that year.
How can anomalies in the year of birth distribution be identified?
-Anomalies in the year of birth distribution can be identified by looking for unusual spikes or drops in the graph. For example, a spike in 1929 indicates an unusual number of persons born that year.
What does the gender donut plot show?
-The gender donut plot shows the distribution of gender within the data source, classifying persons as either male or female and displaying their number and proportion.
What information is missing in the race distribution of the first data source?
-The first data source does not have any matched concepts for race, meaning that race is unknown for this population.
How does the ethnicity information appear in the first data source?
-The ethnicity information in the first data source is unknown.
What changes can be observed when switching data sources in the Atlas platform?
-When switching data sources, the person report will update to show different year of birth distributions and compositions by gender, race, and ethnicity based on the new data source.
How does the second data source differ in terms of race and ethnicity information?
-In the second data source, race information is available with 59% identified as white, 9.2% as black or African American, 2% as Asian, and 29% unknown. Ethnicity information shows 93% of patients without a matched concept, and 6.3% identified as Hispanic or Latino.
Outlines
📊 Introduction to Data Reporting in Atlas Platform
This paragraph introduces the data reporting feature within the Atlas platform. It explains how to select a data source and access various reports, specifically the 'person report'. The person report is highlighted for its ability to display graphs that represent data such as the year of birth distribution, with the ability to hover for detailed numbers, like the 1.3 million persons born in 1990. Anomalies, such as the spike in 1929 with 1 million patients, are also pointed out as valuable insights. The paragraph also mentions donut plots for attributes like gender, which in this example shows a near-even split between males and females, and notes the absence of race and ethnicity data in the current data source. Changing the data source alters the demographic distributions presented.
Mindmap
Keywords
💡Atlas platform
💡Data sources
💡Person report
💡Year of birth
💡Graphs
💡Donut plots
💡Gender
💡Race
💡Ethnicity
💡Anomalies
💡Odyssey
Highlights
Introduction to the person reporting within the data sources capability in the Atlas platform.
Ability to select data sources and specific reports for analysis.
Visualization of a year of birth graph with x-axis as year of birth and y-axis as number of persons.
Interactive feature to hover over years to see the number of persons born in that year.
Identification of 1.3 million persons born in 1990 from the data source.
Detection of unusual data spikes, such as the 1 million patients born in 1929.
Use of donut plots to represent person level attributes.
Gender classification with percentages and proportions.
50.5% of the population identified as female and 49.5% as male.
Lack of matched concepts for race in the data source.
Ethnicity data is also unknown for the population.
Change in data source leads to different year of birth distribution and demographic composition.
59% of the population identified as white in the new data source.
9.2% of the population identified as black or African-American.
2% of the population identified as Asian.
29% of patients without an identified race in the new data source.
Ethnicity identified in the new data source with 6.3% identified as Hispanic or Latino.
93% of patients do not have a match concept for ethnicity.
Invitation to explore more information about Odyssey, Atlas, and additional data sources.
Transcripts
[Music]
today we'll provide an introduction to
the person reporting within the data
sources capability in the Atlas platform
I select data sources we can select our
data source that we're interested in
studying and select any of a series of
reports here I've selected the person
report the person report shows a series
of graphs down below the first graph
shows a year of birth on this graph were
showing the x-axis being the year of
birth and the y-axis showing the number
of persons within that year of birth so
this source here if I hover over any
particular year I can see the number of
persons with that year of birth here for
example we can see that in 1990 this
data source had 1.3 million persons born
in that year this graph allows you to
see the distribution of year of birth
and also identify and characterize
potential anomalies for example here we
can see that this data source has an
unusual spike in 1929 where there are 1
million patients this would be good
information to be able to understand a
source better below we have donut plots
for a series of person level attributes
the first is gender we see that gender
is classified here by male and female
and if I hover over any segments of the
graph we can see the number and
proportion of patients belonging to that
gender here we can see that 50 point 5
percent of the population or 43 million
are female whereas forty nine point five
percent or forty two million are male
the middle graph here shows the
distribution by race here we can see
this particular source does not have any
matched concepts for race so race is
unknown for this population
and here in this graph we see ethnicity
also unknown if I change data sources we
will see the same person report appear
for a different data source in this
particular case we can see that the year
of birth distribution has changed and we
also see a different composition by
gender race and ethnicity here we can
now see in the race distribution that
this source has information where 59
percent or fifty five billion patients
are white nine point two percent of the
population are black or african-american
and two percent of the population are
Asian there are still 29 percent of the
patients who do not have an own race
identified in this data source ethnicity
is also identified in this particular
data source
most patients 93 percent do not have a
match concept however six point three
percent of the population is identified
as Hispanic or Latino for more
information about odyssey including
details on atlas and the additional data
sources reports check us out at odyssey
org
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