icaoberg/sams2024-fair

SAMS 2024 pre-college project

FAIR Assessment of HuBMAP published data

Abstract

The Human BioMolecular Atlas Program (HuBMAP) aims to create a comprehensive 3D-map representation of the human body and improve data access while developing methods for tissue interrogation applicable to other studies. In its first phase, HuBMAP achieved significant milestones including the development of critical resources, standardized protocols, innovative imaging and sequencing techniques, and a reliable data integration platform. These efforts have led to the creation of high-resolution molecular and cellular maps that are essential resources for biomedical research. Researchers are expanding the map from 2D to 3D environments, incorporating niche factors such as age and ethnicity. The core value of HuBMAP is to provide freely accessible data via its online portal. Future directions include investigating changes in individual cells and neighborhoods during healthy aging and diseases, which will help develop better drugs, predict disease outcomes, and understand disease progression in clinical settings. The program adheres to the FAIR guiding principles for scientific data management and stewardship, ensuring findability, accessibility, interoperability, and reusability of data. We researched these properties of HuBMAP along with whether it has rich metadata, identifiable titles, standardized communication protocols, and open access to metadata even if the data itself is no longer available.

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Copyright © 2024 Pittsburgh Supercomputing Center. All Rights Reserved.

The Biomedical Applications Group at the Pittsburgh Supercomputing Center in the Mellon College of Science at Carnegie Mellon University.

sams2024-fair's Issues

Add logo to document

Search online for the HuBMAP logo and copy the URL. Add the URL to the section

# logo_url = ''
# st.image(logo_url)

In streamlit_app.py.

Also uncomment both lines (remove the # signs).

Identify Figures To Include

Conclusions

Insert table view at the top

Print the table below the Data Assessment section.

Add today's date

sams2024-fair/streamlit_app.py

Line 17 in 7202a7a

today = 'Today''s date'

Modify this line to reflect today's date programmatically.

You can

Method	Code	Output Format
Using datetime module	from datetime import date today = date.today()	YYYY-MM-DD
Using datetime with formatting	from datetime import datetime today = datetime.now().strftime('%Y-%m-%d')	YYYY-MM-DD
Using time module	import time today = time.strftime('%Y-%m-%d')	YYYY-MM-DD
Using pandas	import pandas as pd today = pd.Timestamp.now().strftime('%Y-%m-%d')	YYYY-MM-DD

method text

Add `has_contacts` plot

Consider the plot below that is compatible with this report

# Count how many times each boolean appears in the data
data_counts = df['has_contributors'].value_counts()

# Plot pie chart using Streamlit
fig, ax = plt.subplots()
wedges, texts, autotexts = ax.pie(data_counts,
                                  autopct='%1.1f%%',
                                  startangle=90)
ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
# Display the plot in Streamlit
st.pyplot(fig)

The code above creates a pie chart for the has_contributors column.

Reuse to create a plot for has_contacts column. Add it to the At a Glance section in the file streamlit_app.py.

Add the has_contributors plot as well.

Include Image Of How VR Looks Like for HuBMAP

Change colors of labels within pie chart

Change label colors to white

Add abstract to README

Add `group_name` plot

Print the plot below the Data Assessment section along with a sentence describing the plot.

code line 60

Place Charts Horizontal

Add and read reference

VR paper
https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2023.1162723/full

This paper is the basis for the VR component of our project
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854

Replace logo URL

Replace the logo URL with this link

https://hubmapconsortium.org/wp-content/uploads/2019/01/HuBMAP-Logo-Color.png

Seems like a smaller logo

Add Abstract section to README.md

How to Write an Abstract

An abstract is a concise summary of your research paper or project. It provides a brief overview of the key points and findings without going into too much detail. Here are the essential steps to write an effective abstract:

1. Understand the Purpose: The abstract is like a snapshot of your entire paper. Its purpose is to give readers a quick understanding of what your research is about.

2. Structure:

   • Introduction: Start with a sentence that introduces the topic of your research. What problem are you addressing or what question are you trying to answer?
   • Methods: Briefly describe the methods or approach you used in your research. Mention any experiments, surveys, or analyses conducted.
   • Results: Summarize the main findings or outcomes of your research. What did you discover or conclude?
   • Conclusion: State the significance of your findings. Why are your results important or relevant?

3. Length: Typically, an abstract is around 150-250 words long. Check if your teacher or the competition/event you're submitting to has specific length requirements.

4. Clarity and Conciseness: Use clear and simple language. Avoid jargon or technical terms that might be hard to understand without context.

5. Accuracy: Ensure that the information in your abstract accurately reflects what is in your full paper or project. Don't include information that is not discussed in your main text.

6. Write and Revise: Start by writing a draft of your abstract. Revise it to make sure it covers all the essential points and is within the word limit.

7. Proofread: Check for spelling and grammar errors. Ask someone else to read your abstract to get feedback on clarity and readability.

8. Formatting: In most cases, your abstract will be a separate paragraph at the beginning of your paper, especially for research papers. For competitions or presentations, it might be a standalone submission.

By following these steps, you'll be able to write a clear and effective abstract that highlights the key aspects of your research or project.

aesthetic title

add data access plot

test run

Add Methods section to streamlit_app.py

The Methods section of your paper explains how you conducted your research or project. It provides enough detail so that someone else could replicate your work. Here’s how to write it effectively:

1. Purpose: Begin by stating the purpose of your Methods section. Explain what your research question or objective was and how your methods helped you answer it.

2. Description of Methods:

   • Participants or Materials: Describe who or what was involved in your study. Include details like age, gender (if relevant), and any characteristics that are important to your study.
   • Procedure: Outline the steps you took to conduct your research. Include the sequence of events, any measurements taken, and how data was collected.

3. Tools and Instruments: Mention any tools, instruments, software, or equipment you used during your research. Provide enough detail so that others could understand how you collected and analyzed data.

4. Data Analysis: Briefly explain how you analyzed the data you collected. Mention any statistical methods or software used to interpret your findings.

5. Ethical Considerations: If your research involved human participants or animals, mention any ethical considerations you followed. This could include obtaining consent, protecting privacy, or following guidelines for animal welfare.

6. Clarity and Detail: Be clear and specific in your descriptions. Avoid unnecessary details, but include enough information so that someone else could replicate your study.

7. Review and Revise: After writing your Methods section, review it to ensure all necessary details are included and that it flows logically. Revise for clarity and accuracy.

8. Formatting: Typically, the Methods section comes after the Introduction and before the Results sections in your paper.

By following these steps, you can write a clear and informative Methods section that demonstrates how you conducted your research or project.

formatting_check

Updating Graphs

Add `has_dataset_metadata` plot

Consider the plot below that is compatible with this report

# Count how many times each boolean appears in the data
data_counts = df['has_contributors'].value_counts()

# Plot pie chart using Streamlit
fig, ax = plt.subplots()
wedges, texts, autotexts = ax.pie(data_counts,
                                  autopct='%1.1f%%',
                                  startangle=90)
ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
# Display the plot in Streamlit
st.pyplot(fig)

The code above creates a pie chart for the has_contributors column.

Reuse to create a plot for has_dataset_metadata column. Add it to the At a Glance section in the file streamlit_app.py.

Add `has_donor_metadata` plot

Consider the plot below that is compatible with this report

# Count how many times each boolean appears in the data
data_counts = df['has_contributors'].value_counts()

# Plot pie chart using Streamlit
fig, ax = plt.subplots()
wedges, texts, autotexts = ax.pie(data_counts,
                                  autopct='%1.1f%%',
                                  startangle=90)
ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
# Display the plot in Streamlit
st.pyplot(fig)

The code above creates a pie chart for the has_contributors column.

Reuse to create a plot for has_donor_metadata column. Add it to the At a Glance section in the file streamlit_app.py.

Add title to application

Edit the file streamlit_app.py and edit the lines

title = '# Title'
st.write(title)

to add a title to the document.

Reformat data_access_level chart

Hide columns in table

Relabel Charts

Add `has_data` plot

Consider the plot below that is compatible with this report

# Count how many times each boolean appears in the data
data_counts = df['has_contributors'].value_counts()

# Plot pie chart using Streamlit
fig, ax = plt.subplots()
wedges, texts, autotexts = ax.pie(data_counts,
                                  autopct='%1.1f%%',
                                  startangle=90)
ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
# Display the plot in Streamlit
st.pyplot(fig)

The code above creates a pie chart for the has_contributors column.

Reuse to create a plot for has_data column. Add it to the At a Glance section in the file streamlit_app.py.

Create tools section

Re-organize sections

Add Today's Date

Add data access plot

Update References for VR Introduction

Add shadow and change figure size

For has plots.

add introduction title

Add introduction to `streamlit_app.py`

Add an intro to the document.

Edit the variable intro in streamlit_app.py

intro = '''
This is some text
'''
st.write(intro)

And include the text you built in the Google Docs. You can decide two summarize in one or two paragraphs or leave as bullet points.

The number of datasets is `None`

There are datasets. This is not correct.

added method text

references

Fix Overlapping Charts

Charts are overlapping on screen, and need to be moved spatially to be readable.

Compute number of datasets

Modify this block of code in streamlit_app.py

number_of_datasets = None
text = f'There are {number_of_datasets} published datasets'
st.write(text)

and modify the variable number_of_datasets to compute the number of datasets.

PS Remember the number of datasets is the number of rows in the data frame

Acknowledgements

We need to acknowledge the people who helped us

Add bibliography section

Add a bibliography section to the end of the document.

The bibliography should contain the list of articles listed in the Google Doc.

The bibliography lives above the copyright.

create map

Add shadow and change figure size - new

Add your name as an author

Edit the file streamlit_app.py and add your name.

add in introduction headline

VR Introduction

Add `data_access_level` plot

Consider the plot below that is compatible with this report

# Count how many times each boolean appears in the data
data_counts = df['has_contributors'].value_counts()

# Plot pie chart using Streamlit
fig, ax = plt.subplots()
wedges, texts, autotexts = ax.pie(data_counts,
                                  autopct='%1.1f%%',
                                  startangle=90)
ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
# Display the plot in Streamlit
st.pyplot(fig)

The code above creates a pie chart for the has_contributors column.

Reuse to create a plot for data_access_level column. Add it to the At a Glance section in the file streamlit_app.py.