diff --git a/Data/Transform/SpringerOpen_buffer_transform.zip b/Data/Transform/SpringerOpen_buffer_transform.zip
new file mode 100644
index 0000000..dbdcaa4
Binary files /dev/null and b/Data/Transform/SpringerOpen_buffer_transform.zip differ
diff --git a/DataTransformer/FileStructureTansfer(SprigerOpen).py b/DataTransformer/FileStructureTansfer(SprigerOpen).py
new file mode 100644
index 0000000..71dc14f
--- /dev/null
+++ b/DataTransformer/FileStructureTansfer(SprigerOpen).py
@@ -0,0 +1,189 @@
+import json
+import os
+import unicodedata
+
+from collections import OrderedDict
+from pprint import pprint
+
+'''
+    ========== FileStructureTransfer ==========
+    1. 本程序用于将获取的数据进行结构调整
+    2. 根据论文发表的时间年限，分别将最后的数据存储在四个 json 文件中
+        （1） newest： 发表于 2020 年之后
+        （2） oldest： 发表于 2010 年之前
+        （3） 2010-2014： 发表于 2010 年至 2014 年
+        （4） 2015-2020： 发表于 2015 年至 2020 年
+    3. 考虑到部分网站的总数据量过大，所以分成多份
+    4. 本程序运行顺序为：
+        （1） fileReader() 读取本地已爬取数据，存入待处理列表
+        （2） arDataTransform() 转换论文数据格式
+        （3） auDataTransform() 转换作者数据格式
+        （4） 存入转换后数据的存储文件夹
+'''
+
+
+# Read the data
+def fileReader(folder, dataset):
+    files = os.listdir(folder)
+    for file in files:
+        file_path = os.path.join(folder, file)
+        with open(file_path, 'r', encoding='utf-8') as json_file:
+            Data = json.load(json_file)
+            dataset.append(Data)
+
+    return dataset
+
+
+# Article data structure transfer
+def arDataTransform(au_folder, ar_dataset, num):
+    def auInfoFind(path, file_name, ar_data, num):
+        authors = ar_data.get('authors')
+        authors.extend(ar_data.get('corresponding_authors'))
+
+        file_path = os.path.join(path, file_name)
+        with open(file_path, 'r', encoding='utf-8') as file:
+            Data = json.load(file)
+
+        au_ID = []      # A new list to store author_id
+
+        # Find the author_id
+        for author in authors:
+
+            for Dict in Data:
+                Dict_name = Dict.get('first_name') + ' ' + Dict.get('last_name')
+                Dict_name = ''.join(char for char in unicodedata.normalize('NFKD', Dict_name) if
+                                    unicodedata.category(char) != 'Mn')
+
+                if Dict.get('from_article') == ar_data.get('article_id') and Dict_name == author:
+                    au_ID.append(Dict.get('author_id'))
+
+        # Change the structure
+        ar_data_transform = {
+            "article_id": ar_data['article_id'],
+            "title": ar_data['title'],
+            "authors": au_ID,
+            "authors_name": authors,
+            "submit_datetime": ar_data['submit_datetime'],
+            "publish_datetime": ar_data['publish_datetime'],
+            "keywords": ar_data['keywords'],
+            "MSC": ar_data['MSC'],
+            "URL": ar_data['URL'],
+            "DOI": ar_data['DOI'],
+            "publisher": ar_data['publisher'],
+            "journal": ar_data['journal'],
+            "volume": ar_data['volume'],
+            "issue": ar_data['issue'],
+            "page": ar_data['page']
+        }
+
+        num[0] += 1     # Update the counter
+        return ar_data_transform
+
+    # ====== Main code for function =====
+    ar_names = os.listdir(au_folder)    # Read the folder
+
+    for ar_list in ar_dataset:
+        for Dict in ar_list:
+            if num[0] % 100 == 0 and num[0] != 0:       # Alert for complete data
+                print(str(num[0]) + " copies of article data structure have been transformed.")
+
+            if int(Dict.get('volume')) <= 2009:
+                Dict = auInfoFind(au_folder, ar_names[3], Dict, num)
+                ar_dataset_new[3].append(Dict)
+
+            elif 2010 <= int(Dict.get('volume')) <= 2014:
+                Dict = auInfoFind(au_folder, ar_names[0], Dict, num)
+                ar_dataset_new[0].append(Dict)
+
+            elif 2015 <= int(Dict.get('volume')) <= 2020:
+                Dict = auInfoFind(au_folder, ar_names[1], Dict, num)
+                ar_dataset_new[1].append(Dict)
+
+            else:
+                Dict = auInfoFind(au_folder, ar_names[2], Dict, num)
+                ar_dataset_new[2].append(Dict)
+
+    # Store into the new file
+    filepaths = [
+        "./SpringerOpen_buffer_transform/Article_output/SpringerOpen_Article_output_file(2010-2014).json",
+        "./SpringerOpen_buffer_transform/Article_output/SpringerOpen_Article_output_file(2015-2020).json",
+        "./SpringerOpen_buffer_transform/Article_output/SpringerOpen_Article_output_file(newest).json",
+        "./SpringerOpen_buffer_transform/Article_output/SpringerOpen_Article_output_file(oldest).json",
+    ]
+
+    for i in range(4):
+        with open(filepaths[i], 'w', encoding='utf-8') as json_file:
+            json.dump(ar_dataset_new[i], json_file, indent=4)
+
+    print("\nComplete: All of the article data structure have been transformed.")
+
+
+# Author data structure transfer
+def auDataTransform(au_dataset, num):
+    def transform(list, num):
+        new_list = []   # New list to store transformed data
+
+        for au_data in list:
+            if num[0] % 100 == 0 and num[0] != 0:       # Alert for complete data
+                print(str(num[0]) + " copies of author data structure have been transformed.\n")
+
+            if au_data['middle_name'] is not None:
+                raw_name = au_data['first_name'] + ' ' + au_data['middle_name'] + ' ' + au_data['last_name']
+            else:
+                raw_name = au_data['first_name'] + ' ' + au_data['last_name']
+
+            au_data_transform = {
+                "author_id": au_data['author_id'],
+                "from_article": au_data['from_article'],
+                "first_name": au_data['first_name'],
+                "last_name": au_data['last_name'],
+                "middle_name": au_data['middle_name'],
+                "raw_name": raw_name,
+                "affiliation": au_data['affiliation']
+            }
+
+            new_list.append(au_data_transform)
+            num[0] += 1         # Update the counter
+
+        return new_list
+
+    for i in range(4):
+        au_list = transform(au_dataset[i], num)
+        au_dataset_new[i].extend(au_list)
+
+    # Store into the new file
+    filepaths = [
+        "./SpringerOpen_buffer_transform/Author_output/SpringerOpen_Author_output_file(2010-2014).json",
+        "./SpringerOpen_buffer_transform/Author_output/SpringerOpen_Author_output_file(2015-2020).json",
+        "./SpringerOpen_buffer_transform/Author_output/SpringerOpen_Author_output_file(newest).json",
+        "./SpringerOpen_buffer_transform/Author_output/SpringerOpen_Author_output_file(oldest).json",
+    ]
+
+    for i in range(4):
+        with open(filepaths[i], 'w', encoding='utf-8') as json_file:
+            json.dump(au_dataset_new[i], json_file, indent=4)
+
+    print("\nComplete: All of the author data structure have been transformed.")
+
+
+# ========== Main code ========== #
+# New list for storing data
+ar_dataset = []
+au_dataset = []
+
+ar_dataset_new = [[] for _ in range(4)]    # New list for transformed data
+au_dataset_new = [[] for _ in range(4)]    # New list to store transformed data
+
+num1 = [0]      # Counter for complete ar_date
+num2 = [0]      # Counter for complete au_data
+
+os.makedirs('./SpringerOpen_buffer_transform/Article_output/', exist_ok=True)
+os.makedirs('./SpringerOpen_buffer_transform/Author_output/', exist_ok=True)
+
+# Read the data
+ar_dataset = fileReader('./SpringerOpen_buffer/Article_output', ar_dataset)
+au_dataset = fileReader('./SpringerOpen_buffer/Author_output', au_dataset)
+
+# Change the structure
+# arDataTransform('./SpringerOpen_buffer/Author_output', ar_dataset, num1)
+auDataTransform(au_dataset, num2)
\ No newline at end of file