diff --git a/scripts/automated_code_review.py b/opensource_scripts/automated_code_review.py
similarity index 100%
rename from scripts/automated_code_review.py
rename to opensource_scripts/automated_code_review.py
diff --git a/scripts/generate_documentation.py b/opensource_scripts/generate_documentation.py
similarity index 100%
rename from scripts/generate_documentation.py
rename to opensource_scripts/generate_documentation.py
diff --git a/scripts/nlp_enhancement.py b/opensource_scripts/nlp_enhancement.py
similarity index 100%
rename from scripts/nlp_enhancement.py
rename to opensource_scripts/nlp_enhancement.py
diff --git a/scripts/real_time_collaboration.py b/opensource_scripts/real_time_collaboration.py
similarity index 100%
rename from scripts/real_time_collaboration.py
rename to opensource_scripts/real_time_collaboration.py
diff --git a/scripts/real_time_collaboration_client.py b/opensource_scripts/real_time_collaboration_client.py
similarity index 100%
rename from scripts/real_time_collaboration_client.py
rename to opensource_scripts/real_time_collaboration_client.py
diff --git a/scripts/xr_integration.py b/opensource_scripts/xr_integration.py
similarity index 100%
rename from scripts/xr_integration.py
rename to opensource_scripts/xr_integration.py
diff --git a/scripts/bias_mitigation.py b/scripts/bias_mitigation.py
deleted file mode 100644
index c0f0700..0000000
--- a/scripts/bias_mitigation.py
+++ /dev/null
@@ -1,44 +0,0 @@
-python
-import pandas as pd
-from aif360.datasets import BinaryLabelDataset
-from aif360.algorithms.preprocessing import Reweighing
-from fairlearn.metrics import MetricFrame, selection_rate, demographic_parity_difference
-from sklearn.linear_model import LogisticRegression
-
-# Load and preprocess data
-def load_data():
-    data = pd.read_csv('path_to_your_dataset.csv')
-    return data
-
-# Bias detection
-def detect_bias(data, target, protected_attribute):
-    dataset = BinaryLabelDataset(df=data, label_names=[target], protected_attribute_names=[protected_attribute])
-    return dataset
-
-# Bias mitigation
-def mitigate_bias(dataset):
-    reweighing = Reweighing(unprivileged_groups=[{protected_attribute: 0}], privileged_groups=[{protected_attribute: 1}])
-    transformed_dataset = reweighing.fit_transform(dataset)
-    return transformed_dataset
-
-# Train and evaluate model
-def train_and_evaluate(data, target, protected_attribute):
-    model = LogisticRegression()
-    X = data.drop(columns=[target, protected_attribute])
-    y = data[target]
-    model.fit(X, y)
-    predictions = model.predict(X)
-    
-    metric_frame = MetricFrame(metrics={"selection_rate": selection_rate}, y_true=y, y_pred=predictions, sensitive_features=data[protected_attribute])
-    print(metric_frame.by_group)
-    print(f"Demographic parity difference: {demographic_parity_difference(y, predictions, sensitive_features=data[protected_attribute])}")
-
-if __name__ == "__main__":
-    data = load_data()
-    protected_attribute = 'gender'  # Change to your dataset's protected attribute
-    target = 'outcome'  # Change to your dataset's target variable
-    
-    dataset = detect_bias(data, target, protected_attribute)
-    mitigated_dataset = mitigate_bias(dataset)
-    
-    train_and_evaluate(mitigated_dataset.convert_to_dataframe()[0], target, protected_attribute)
diff --git a/scripts/blockchain_integration.py b/scripts/blockchain_integration.py
deleted file mode 100644
index 6725c84..0000000
--- a/scripts/blockchain_integration.py
+++ /dev/null
@@ -1,33 +0,0 @@
-python
-from web3 import Web3
-
-# Connect to a blockchain
-def connect_to_blockchain():
-    web3 = Web3(Web3.HTTPProvider('http://localhost:8545'))
-    if web3.isConnected():
-        print("Connected to blockchain")
-    return web3
-
-# Create a new transaction
-def create_transaction(web3, from_address, to_address, value):
-    tx = {
-        'from': from_address,
-        'to': to_address,
-        'value': web3.toWei(value, 'ether'),
-        'gas': 2000000,
-        'gasPrice': web3.toWei('50', 'gwei')
-    }
-    signed_tx = web3.eth.account.sign_transaction(tx, private_key='YOUR_PRIVATE_KEY')
-    tx_hash = web3.eth.sendRawTransaction(signed_tx.rawTransaction)
-    return tx_hash
-
-def main():
-    web3 = connect_to_blockchain()
-    from_address = '0xYourFromAddress'
-    to_address = '0xYourToAddress'
-    value = 0.1  # in Ether
-    tx_hash = create_transaction(web3, from_address, to_address, value)
-    print(f"Transaction hash: {tx_hash.hex()}")
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/chatbot.py b/scripts/chatbot.py
deleted file mode 100644
index f71b2bd..0000000
--- a/scripts/chatbot.py
+++ /dev/null
@@ -1,21 +0,0 @@
-python
-from flask import Flask, render_template, request
-from flask_socketio import SocketIO, send
-from transformers import pipeline
-
-app = Flask(__name__)
-app.config['SECRET_KEY'] = 'your_secret_key'
-socketio = SocketIO(app)
-chatbot = pipeline('conversational', model='microsoft/DialoGPT-medium')
-
-@app.route('/')
-def home():
-    return render_template('index.html')
-
-@socketio.on('message')
-def handle_message(msg):
-    response = chatbot(msg)
-    send(response[0]['generated_text'], broadcast=True)
-
-if __name__ == "__main__":
-    socketio.run(app, host='0.0.0.0', port=5000)
diff --git a/scripts/data_encryption.py b/scripts/data_encryption.py
deleted file mode 100644
index 9072398..0000000
--- a/scripts/data_encryption.py
+++ /dev/null
@@ -1,25 +0,0 @@
-python
-from cryptography.fernet import Fernet
-
-# Generate key
-def generate_key():
-    return Fernet.generate_key()
-
-# Encrypt data
-def encrypt_data(data, key):
-    f = Fernet(key)
-    return f.encrypt(data.encode())
-
-# Decrypt data
-def decrypt_data(encrypted_data, key):
-    f = Fernet(key)
-    return f.decrypt(encrypted_data).decode()
-
-# Example usage
-if __name__ == "__main__":
-    key = generate_key()
-    data = "Sensitive information"
-    encrypted_data = encrypt_data(data, key)
-    print(f"Encrypted Data: {encrypted_data}")
-    decrypted_data = decrypt_data(encrypted_data, key)
-    print(f"Decrypted Data: {decrypted_data}")
diff --git a/scripts/explainability.py b/scripts/explainability.py
deleted file mode 100644
index 4b86058..0000000
--- a/scripts/explainability.py
+++ /dev/null
@@ -1,34 +0,0 @@
-python
-import shap
-import lime
-import lime.lime_tabular
-import numpy as np
-import pandas as pd
-from sklearn.ensemble import RandomForestClassifier
-
-# Train example model
-def train_example_model():
-    data = pd.read_csv('path_to_your_data.csv')
-    X = data.drop('target', axis=1)
-    y = data['target']
-    model = RandomForestClassifier()
-    model.fit(X, y)
-    return model, X, y
-
-# Explain model using SHAP
-def explain_model_shap(model, X):
-    explainer = shap.TreeExplainer(model)
-    shap_values = explainer.shap_values(X)
-    shap.summary_plot(shap_values, X)
-
-# Explain model using LIME
-def explain_model_lime(model, X, instance):
-    explainer = lime.lime_tabular.LimeTabularExplainer(X.values, feature_names=X.columns, class_names=['class_0', 'class_1'], verbose=True, mode='classification')
-    exp = explainer.explain_instance(instance, model.predict_proba, num_features=5)
-    exp.show_in_notebook(show_table=True)
-
-if __name__ == "__main__":
-    model, X, y = train_example_model()
-    explain_model_shap(model, X)
-    instance = X.iloc[0]
-    explain_model_lime(model, X, instance)
diff --git a/scripts/image_video_analysis.py b/scripts/image_video_analysis.py
deleted file mode 100644
index e0a56f2..0000000
--- a/scripts/image_video_analysis.py
+++ /dev/null
@@ -1,52 +0,0 @@
-python
-import cv2
-import torch
-from torchvision import models, transforms
-from PIL import Image
-
-# Load pre-trained model
-model = models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
-model.eval()
-
-# Preprocess image
-def preprocess_image(image_path):
-    transform = transforms.Compose([
-        transforms.ToTensor(),
-    ])
-    image = Image.open(image_path)
-    return transform(image).unsqueeze(0)
-
-# Perform object detection
-def detect_objects(image_tensor):
-    with torch.no_grad():
-        predictions = model(image_tensor)
-    return predictions
-
-# Process video
-def process_video(video_path):
-    cap = cv2.VideoCapture(video_path)
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        image_tensor = preprocess_image(frame)
-        predictions = detect_objects(image_tensor)
-        # Process predictions (e.g., draw bounding boxes on frame)
-        for prediction in predictions:
-            boxes = prediction['boxes'].cpu().numpy()
-            for box in boxes:
-                cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (255, 0, 0), 2)
-        cv2.imshow('frame', frame)
-        if cv2.waitKey(1) & 0xFF == ord('q'):
-            break
-    cap.release()
-    cv2.destroyAllWindows()
-
-if __name__ == "__main__":
-    image_path = 'path_to_your_image.jpg'
-    image_tensor = preprocess_image(image_path)
-    predictions = detect_objects(image_tensor)
-    print(f"Predictions: {predictions}")
-
-    video_path = 'path_to_your_video.mp4'
-    process_video(video_path)
diff --git a/scripts/neurosymbolic_ai.py b/scripts/neurosymbolic_ai.py
deleted file mode 100644
index a0429a2..0000000
--- a/scripts/neurosymbolic_ai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-python
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from swiplserver import PrologServer, PrologThread
-
-# Neural network definition
-class NeuralNet(nn.Module):
-    def __init__(self, input_size, hidden_size, output_size):
-        super(NeuralNet, self).__init__()
-        self.fc1 = nn.Linear(input_size, hidden_size)
-        self.fc2 = nn.Linear(hidden_size, output_size)
-
-    def forward(self, x):
-        x = F.relu(self.fc1(x))
-        x = self.fc2(x)
-        return x
-
-# Symbolic reasoning using Prolog
-def symbolic_reasoning(query):
-    with PrologServer() as server:
-        with PrologThread(server) as prolog:
-            prolog.assertz("parent(john, doe)")
-            prolog.assertz("parent(doe, jane)")
-            result = prolog.query(query)
-            return result
-
-# Hybrid model example
-def hybrid_model(inputs):
-    # Neural network inference
-    net = NeuralNet(input_size=2, hidden_size=3, output_size=1)
-    neural_output = net(torch.tensor(inputs, dtype=torch.float32))
-
-    # Symbolic reasoning
-    symbolic_output = symbolic_reasoning("parent(X, jane)")
-
-    return neural_output, symbolic_output
-
-# Example usage
-if __name__ == "__main__":
-    inputs = [0.5, 0.6]
-    neural_output, symbolic_output = hybrid_model(inputs)
-    print(f"Neural network output: {neural_output}")
-    print(f"Symbolic reasoning output: {symbolic_output}")
diff --git a/scripts/penetration_testing.py b/scripts/penetration_testing.py
deleted file mode 100644
index c6f77c9..0000000
--- a/scripts/penetration_testing.py
+++ /dev/null
@@ -1,15 +0,0 @@
-python
-import requests
-
-# Run penetration test
-def run_penetration_test(url):
-    response = requests.get(url)
-    return response.status_code, response.text
-
-# Example usage
-if __name__ == "__main__":
-    test_url = "http://localhost:8000"  # Replace with actual URL
-    status_code, response_text = run_penetration_test(test_url)
-    print(f"Status Code: {status_code}")
-    print("Response Text:")
-    print(response_text)
diff --git a/scripts/personalized_learning.py b/scripts/personalized_learning.py
deleted file mode 100644
index 95a8493..0000000
--- a/scripts/personalized_learning.py
+++ /dev/null
@@ -1,35 +0,0 @@
-python
-import gym
-from stable_baselines3 import PPO
-
-# Create personalized environment
-class PersonalizedEnv(gym.Env):
-    def __init__(self, user_data):
-        super(PersonalizedEnv, self).__init__()
-        self.user_data = user_data
-        self.observation_space = gym.spaces.Box(low=0, high=1, shape=(len(user_data),), dtype=float)
-        self.action_space = gym.spaces.Discrete(2)
-
-    def reset(self):
-        return self.user_data
-
-    def step(self, action):
-        reward = self.compute_reward(action)
-        done = True  # Example for simplicity
-        info = {}
-        return self.user_data, reward, done, info
-
-    def compute_reward(self, action):
-        # Define a reward function based on user data and action
-        return 1 if action == 1 else 0
-
-if __name__ == "__main__":
-    user_data = [0.5, 0.2, 0.8]  # Example user data, replace with actual data
-    env = PersonalizedEnv(user_data)
-    model = PPO("MlpPolicy", env, verbose=1)
-    model.learn(total_timesteps=10000)
-    model.save("personalized_model")
-
-    obs = env.reset()
-    action, _states = model.predict(obs)
-    print(f"Predicted action: {action}")
diff --git a/scripts/predictive_maintenance.py b/scripts/predictive_maintenance.py
deleted file mode 100644
index 9d66c0b..0000000
--- a/scripts/predictive_maintenance.py
+++ /dev/null
@@ -1,33 +0,0 @@
-python
-import pandas as pd
-from fbprophet import Prophet
-
-# Load data
-def load_data(file_path):
-    data = pd.read_csv(file_path)
-    return data
-
-# Train predictive maintenance model
-def train_model(data, date_col, metric_col):
-    df = data[[date_col, metric_col]].rename(columns={date_col: 'ds', metric_col: 'y'})
-    model = Prophet()
-    model.fit(df)
-    return model
-
-# Make future predictions
-def make_predictions(model, periods=365):
-    future = model.make_future_dataframe(periods=periods)
-    forecast = model.predict(future)
-    return forecast
-
-# Detect anomalies
-def detect_anomalies(forecast):
-    forecast['anomaly'] = forecast['yhat_upper'] < forecast['y']
-    return forecast
-
-if __name__ == "__main__":
-    data = load_data('path_to_your_data.csv')
-    model = train_model(data, 'date', 'metric')
-    forecast = make_predictions(model)
-    anomalies = detect_anomalies(forecast)
-    print(f"Anomalies detected: {anomalies[anomalies['anomaly']]}")
diff --git a/scripts/quantum_integration.py b/scripts/quantum_integration.py
deleted file mode 100644
index a40ead4..0000000
--- a/scripts/quantum_integration.py
+++ /dev/null
@@ -1,26 +0,0 @@
-python
-import pennylane as qml
-from pennylane import numpy as np
-
-# Quantum device setup
-dev = qml.device("default.qubit", wires=2)
-
-# Define a quantum circuit
-@qml.qnode(dev)
-def quantum_circuit(inputs):
-    qml.RX(inputs[0], wires=0)
-    qml.RY(inputs[1], wires=1)
-    qml.CNOT(wires=[0, 1])
-    return [qml.expval(qml.PauliZ(i)) for i in range(2)]
-
-# Hybrid quantum-classical model
-def hybrid_model(x):
-    quantum_output = quantum_circuit(x)
-    classical_output = np.sum(quantum_output)
-    return classical_output
-
-# Example usage
-if __name__ == "__main__":
-    inputs = np.array([0.5, 0.6])
-    result = hybrid_model(inputs)
-    print(f"Quantum model output: {result}")
diff --git a/scripts/recommendation_system.py b/scripts/recommendation_system.py
deleted file mode 100644
index 13e0b94..0000000
--- a/scripts/recommendation_system.py
+++ /dev/null
@@ -1,42 +0,0 @@
-python
-import pandas as pd
-from surprise import Dataset, Reader, SVD
-from surprise.model_selection import train_test_split, cross_validate
-
-# Load data
-def load_data(file_path):
-    data = pd.read_csv(file_path)
-    return data
-
-# Prepare dataset for Surprise library
-def prepare_dataset(data, user_col, item_col, rating_col):
-    reader = Reader(rating_scale=(data[rating_col].min(), data[rating_col].max()))
-    return Dataset.load_from_df(data[[user_col, item_col, rating_col]], reader)
-
-# Train recommendation model
-def train_model(data):
-    trainset, testset = train_test_split(data, test_size=0.2)
-    algo = SVD()
-    algo.fit(trainset)
-    return algo, testset
-
-# Evaluate model
-def evaluate_model(algo, testset):
-    cross_validate(algo, testset, measures=['RMSE', 'MAE'], cv=5, verbose=True)
-
-# Generate recommendations
-def generate_recommendations(algo, user_id, item_ids, num_recommendations=5):
-    predictions = [(item_id, algo.predict(user_id, item_id).est) for item_id in item_ids]
-    recommendations = sorted(predictions, key=lambda x: x[1], reverse=True)[:num_recommendations]
-    return recommendations
-
-if __name__ == "__main__":
-    data = load_data('path_to_your_data.csv')
-    dataset = prepare_dataset(data, 'user_id', 'item_id', 'rating')
-    algo, testset = train_model(dataset)
-    evaluate_model(algo, testset)
-
-    user_id = 1  # Example user ID
-    item_ids = [i for i in range(100)]  # Example item IDs
-    recommendations = generate_recommendations(algo, user_id, item_ids)
-    print(f"Top recommendations for user {user_id}: {recommendations}")
diff --git a/scripts/synthetic_data_generation.py b/scripts/synthetic_data_generation.py
deleted file mode 100644
index 8afae07..0000000
--- a/scripts/synthetic_data_generation.py
+++ /dev/null
@@ -1,82 +0,0 @@
-python
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import torchvision
-import torchvision.transforms as transforms
-
-# Define GAN architecture
-class Generator(nn.Module):
-    def __init__(self):
-        super(Generator, self).__init__()
-        self.main = nn.Sequential(
-            nn.Linear(100, 256),
-            nn.ReLU(True),
-            nn.Linear(256, 512),
-            nn.ReLU(True),
-            nn.Linear(512, 1024),
-            nn.ReLU(True),
-            nn.Linear(1024, 28*28),
-            nn.Tanh()
-        )
-
-    def forward(self, input):
-        return self.main(input).view(-1, 1, 28, 28)
-
-class Discriminator(nn.Module):
-    def __init__(self):
-        super(Discriminator, self).__init__()
-        self.main = nn.Sequential(
-            nn.Linear(28*28, 1024),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Linear(1024, 512),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Linear(512, 256),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Linear(256, 1),
-            nn.Sigmoid()
-        )
-
-    def forward(self, input):
-        return self.main(input.view(-1, 28*28))
-
-# Training GAN
-def train_gan():
-    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
-    dataset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
-    dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True)
-
-    generator = Generator()
-    discriminator = Discriminator()
-    criterion = nn.BCELoss()
-    optimizer_g = optim.Adam(generator.parameters(), lr=0.0002)
-    optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0002)
-
-    for epoch in range(10):
-        for i, (images, _) in enumerate(dataloader):
-            # Train Discriminator
-            optimizer_d.zero_grad()
-            real_labels = torch.ones(images.size(0), 1)
-            fake_labels = torch.zeros(images.size(0), 1)
-            outputs = discriminator(images)
-            d_loss_real = criterion(outputs, real_labels)
-            d_loss_real.backward()
-
-            noise = torch.randn(images.size(0), 100)
-            fake_images = generator(noise)
-            outputs = discriminator(fake_images.detach())
-            d_loss_fake = criterion(outputs, fake_labels)
-            d_loss_fake.backward()
-            optimizer_d.step()
-
-            # Train Generator
-            optimizer_g.zero_grad()
-            outputs = discriminator(fake_images)
-            g_loss = criterion(outputs, real_labels)
-            g_loss.backward()
-            optimizer_g.step()
-
-        print(f'Epoch [{epoch+1}/10], d_loss: {d_loss_real.item()+d_loss_fake.item()}, g_loss: {g_loss.item()}')
-
-if __name__ == "__main__":
-    train_gan()