This document discusses using natural language processing (NLP) to automatically match project contributors and creators based on their descriptions. The approach uses Word2Vec to convert descriptions to vectors and BERT for named entity recognition and categorization. The matching algorithm calculates expertise, mandatory skills, and good-to-have skills similarity scores. Future plans include integrating video, skills assessments, and a contribution request flow. The objective is to get a perfect match based on descriptions.

1. Problem and Objective Approach and Solution Future Plans to be implemented
#OPENSCI
NLP
Automated Matching through
Natural Language Processing

2. Approach and Solution Future Plans to be implemented
Problem and Objective
Open
Inclusive
Open
Transparent
Open
Accessible
Open
Reproducible
welcome participation by and
collaboration with diverse
people and organizations.
Scientific processes and
results should be visible,
accessible, and
understandable.
Data, tools, software,
documentation, and
publications should be
accessible to all (FAIR).
Scientific processes and
results should be open such
that they are reproducible
by members of the
community.
Open Science Principles from
NASA

3. Problem and Objective Future Plans to be implemented
Approach and Solution
Solution
Website
Frontend(Bootstrap)
Back end(node.js)
Automated Matching Algorithm
depending on NLP (AI)
Search and Filter
Personalized Dashboard
Chat System

4. Problem and Objective Future Plans to be implemented
Approach and Solution
Experiment matching to one of NASA Open Science Projects to get 3
matches [Perfect Match] – [Very Good Match] – [Good Match]
Cloudspotting on
MARS
Mandatory Skills
Data Analysis,
Research,
Image Processing
Good to Have Skills
Machine Learning
NASA Project from the Data
This matching works vice versa with the Creator. In casing of signing in as a creator, all suitable contributor
matches will be listed based on our algorithm
CS: Combined Similarity
ES: Expertise Similarity
MS: Mandatory Skills similarity
GS: Good to have skills Similarity

5. Problem and Objective Future Plans to be implemented
Approach and Solution
#3 Types of matching
+
+
Expertise Level
Similarity
1 2 3
Mandatory Skills
Similarity
Good to Have Skills
Similarity
= Combined Similarity
1 1 1 = 3 / Perfect Match
+ +
1 <1 1 = 2-3 / Very Good Match
+ +
0.7 <=1 0.7 = 1.4 – 2.4 / Good Match
+ +

6. Problem and Objective Future Plans to be implemented
Approach and Solution
Expertise Level Similarity
1 2 3
Mandatory Skills
Similarity
Good to Have Skills
Similarity
Required: Expert
Similarity = 1 – | 0.8 – 0.8 | = 1
Incoming: Expert
Required: Expert
Similarity = 1 – | 0.8 – 0.5 | = 0.7
Incoming: Intermediate
Required: Expert
Similarity = 1 – | 0.5 – 0.2 | = 0.3
Incoming: Beginner
Bioinformatics
Research
Teamwork
Cosine Similarity
Similar Unrelated Opposite
Level score
Beginner 0.2
Intermediate 0.5
Expert 0.8
Similarity =
1 – |score1– score2|

7. Problem and Objective Approach and Solution Future Plans to be implemented
1. Data extraction: Utilize Word2Vec to convert descriptions
into vectors.
2. Model choice: Employ BERT, a transformer-based model,
for Named Entity Recognition (NER).
3. Categorization: Classify extracted data into various
categories using BERT.
Project Contributor:
Enter a description for the project
and type of contributors needed
Creator:
Enter a self-description
stating previous
experiences using your
skills.
#Objective: Getting a Perfect match based on descriptions
Matching Algorithm
1. Video Integration
2. Assessments for new contributors to
ensure skills validity.
3. Case Handle: Sign up as a contributor
and a creator at the same time
4. Implement Contribution Requests Flow
Features to be added
If you want to discuss how it will be done
kindly connect with us on:
ahmagdy.am1@gmail.com
anashelal851@gmail.com
amirawad004@gmail.com