diff --git a/Pipfile b/Pipfile
index 3fc0133d..6ad3649a 100644
--- a/Pipfile
+++ b/Pipfile
@@ -4,6 +4,7 @@ verify_ssl = true
 name = "pypi"
 
 [packages]
+feedparser = "*"
 flickrapi = "*"
 GitPython = "*"
 google-api-python-client = "*"
@@ -18,6 +19,7 @@ pillow = ">=11.3.0"  # Ensure dependency is secure
 Pyarrow = "*"
 Pygments = "*"
 python-dotenv = "*"
+PyYAML = "*"
 requests = ">=2.31.0"
 seaborn = "*"
 urllib3 = ">=2.5.0"
diff --git a/Pipfile.lock b/Pipfile.lock
index 741d5da4..c111bd7d 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "ffa89d9d058b31c7b28aca6ed62f13f1273b3f8b92f5fa4d279dd3737844d8e8"
+            "sha256": "0658ee0a643ae80db9419f8ef32887877922d18a5da188b1b87e4e9a1849a4a1"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -24,14 +24,6 @@
             "markers": "python_version >= '3.9'",
             "version": "==4.11.0"
         },
-        "appnope": {
-            "hashes": [
-                "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee",
-                "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"
-            ],
-            "markers": "python_version >= '3.6'",
-            "version": "==0.1.4"
-        },
         "argon2-cffi": {
             "hashes": [
                 "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1",
@@ -74,11 +66,11 @@
         },
         "arrow": {
             "hashes": [
-                "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80",
-                "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"
+                "sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205",
+                "sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==1.3.0"
+            "version": "==1.4.0"
         },
         "asttokens": {
             "hashes": [
@@ -133,11 +125,11 @@
         },
         "cachetools": {
             "hashes": [
-                "sha256:1c76a8960c0041fcc21097e357f882197c79da0dbff766e7317890a65d7d8ba6",
-                "sha256:38b328c0889450f05f5e120f56ab68c8abaf424e1275522b138ffc93253f7e32"
+                "sha256:09868944b6dde876dfd44e1d47e18484541eaf12f26f29b7af91b26cc892d701",
+                "sha256:3f391e4bd8f8bf0931169baf7456cc822705f4e2a31f840d218f445b9a854201"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==6.2.0"
+            "version": "==6.2.1"
         },
         "certifi": {
             "hashes": [
@@ -239,88 +231,122 @@
         },
         "charset-normalizer": {
             "hashes": [
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-                "sha256:027b776c26d38b7f15b26a5da1044f376455fb3766df8fc38563b4efbc515154",
-                "sha256:07a0eae9e2787b586e129fdcbe1af6997f8d0e5abaa0bc98c0e20e124d67e601",
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-                "sha256:13faeacfe61784e2559e690fc53fa4c5ae97c6fcedb8eb6fb8d0a15b475d2c64",
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-            "version": "==3.4.3"
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         "comm": {
             "hashes": [
@@ -483,6 +509,15 @@
             ],
             "version": "==2.21.2"
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+        "feedparser": {
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@@ -582,20 +617,20 @@
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@@ -614,11 +649,11 @@
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         "h11": {
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@@ -655,28 +690,28 @@
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-            "markers": "python_version >= '3.6'",
-            "version": "==3.10"
+            "markers": "python_version >= '3.8'",
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-            "version": "==6.30.1"
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@@ -771,11 +806,11 @@
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@@ -811,12 +846,12 @@
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@@ -828,11 +863,11 @@
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@@ -1108,11 +1143,11 @@
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@@ -1124,11 +1159,11 @@
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@@ -1172,84 +1207,84 @@
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@@ -1259,14 +1294,6 @@
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@@ -1758,6 +1777,7 @@
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+                "sha256:acbe5e8b1026c0c580d0321c8aae4b0a1e1676861d48d6e8c6586625055b606a",
+                "sha256:ad50614a02c8c2962feebe6012b52f9802deec4263946cddea37aaf28dd25a66",
+                "sha256:ada7754a10faacd4f26067e62de52d6af93b6d9542f0df73c57b9771eb3ba9c4",
+                "sha256:adc8aa88486857d2b35d75f0640b949759f79dc105f50aa2c27816b2e0dd749f",
+                "sha256:b1b553dd06e875249fd43efd727785efb57a53180e0fde321468222eabbeaafa",
+                "sha256:b1cde22f2c30ebb049a9e74c5374994157b9b70a16147d332f89c99c5960737a",
+                "sha256:b3072b16904d0b5572a15eb9d31c1954e0d3227a585fc1351aa9878729099d6c",
+                "sha256:b670c30fd87a6aec281c3c9896d3bae4b205fd75d79d06dc87c2503717e46092",
+                "sha256:b8e1e9be4fa6305a16be628959188e4fd5cd6f1b0e724d63c6d8b2a8adf74ea6",
+                "sha256:b9699fa7990368b22032baf2b2dce1f634388e4ffc03dfefaaac79f4695edc95",
+                "sha256:b9b06fe1a75e05e0713f06ea0c89ecb6452210fd60e2f1b6ddc1067b990e08d9",
+                "sha256:bbdc5640900a7dbf9dd707fe6388972f5bbd883633eb68b76591044cfe346f7e",
+                "sha256:bcf1d210dfee61a6c86551d67ee1031899c0fdbae88b2d44a569995d43797712",
+                "sha256:bd3bbba5def70b16cd1c1d7255666aad3b290fbf8d0fe7f9f91abafb73611a91",
+                "sha256:beb880a9ca0a117415f241f66d56025c02037f7c4efc6fe59b5b8454f1eaa50d",
+                "sha256:c2a34fd26588949e1e7977cfcbb17a9a42c948c100cab890c6d8d823f0586457",
+                "sha256:c9a40040aa388b037eb39416710fbcce9443498d2eaab0b9b45ae988b53f5c67",
+                "sha256:cf128350d384b777da0e68796afdcebc2e9f63f0e9f242217754e647f6d32491",
+                "sha256:cf681ac76a60b667106141e11a92a3330890257e6f559ca995fbb5265160b56e",
+                "sha256:d15431e334fba488b081d47f30f091e5d03c18527c325386091f31718952fe08",
+                "sha256:d2412be8d00a1b895f8ad827cc2116455196e20ed994bb704bf138fe91a42724",
+                "sha256:d61b355c3275acb825f8777d6c4505f42b5007e357af500939d4a35b19177259",
+                "sha256:d678e91b610c29c4b3d52a2c148b641df2b4676ffe47c59f6388d58b99cdc424",
+                "sha256:d7366b6553cdc805abcc512b849a519167db8f5e5c3472010cd1228b224265cb",
+                "sha256:dcdcb890b3ada98a03f9f2bb108489cdc7580176cb73b4f2d789e9a1dac1d472",
+                "sha256:dd8d86b5d29d1b74100982424ba53e56033dc47720a6de9ba0259cf81d7cecaa",
+                "sha256:e0a0311caedc8069d68fc2bf4c9019b58a2d5ce3cd7cb656c845f1615b577e1e",
+                "sha256:e1460ebde1bcf6d496d80b191d854adedcc619f84ff17dc1c6d550f58c9efbba",
+                "sha256:e3eb248f2feba84c692579257a043a7699e28a77d86c77b032c1d9fbb3f0219c",
+                "sha256:e5bbc701eff140ba0e872691d573b3d5d30059ea26e5785acba9132d10c8c31d",
+                "sha256:e5d9b86aa501fed9862a443c5c3116f6ead8bc9296185f369277c42542bd646b",
+                "sha256:e5deca01b271492553fdb6c7fd974659dce736a15bae5dad7ab8b93555bceb28",
+                "sha256:e80848a71c78aa328fefaba9c244d588a342c8e03bda518447b624ea64d1ff56",
+                "sha256:e819e0e37a44a78e1383bf1970076e2ccc4dc8c2bbaa2f9bd1dc987e9afff628",
+                "sha256:e9e184408a0297086f880556b6168fa927d677716f83d3472ea333b42171ee3b",
+                "sha256:edd267266a9b0448f33dc465a97cfc5d467594b600fe28e7fa2f36450e03053a",
+                "sha256:efd489fec7c311dae25e94fe7eeda4b3d06be71c68f2cf2e8ef990ffcd2cd7e8",
+                "sha256:f0b2044fdddeea5b05df832e50d2a06fe61023acb44d76978e1b060206a8a476",
+                "sha256:f274f56a926ba2dc02976ca5b11c32855cbd5925534e57cfe1fda64e04d1add2",
+                "sha256:f296ea3054e11fc58ad42e850e8b75c62d9a93a9f981ad04b2e5ae7d2186ff9c",
+                "sha256:f4794c6c3fbe8f9ac87699b131a1f26e7b4abcf6d828da46a3a52648c7930eba",
+                "sha256:f586db2e209d54fe177e58e0bc4946bea5fb0102f150b1b2f13de03e1f0976f8",
+                "sha256:f5e7101145427087e493b9c9b959da68d357c28c562792300dd21a095118ed16",
+                "sha256:f9174471d6920cbc5e82a7822de8dfd4dcea86eb828b04fc8c6519a77b0ee51e"
             ],
-            "markers": "python_version >= '3.9'",
-            "version": "==0.27.1"
+            "markers": "python_version >= '3.10'",
+            "version": "==0.28.0"
         },
         "rsa": {
             "hashes": [
@@ -2110,6 +2090,12 @@
             "markers": "python_version >= '3.9'",
             "version": "==80.9.0"
         },
+        "sgmllib3k": {
+            "hashes": [
+                "sha256:7868fb1c8bfa764c1ac563d3cf369c381d1325d36124933a726f29fcdaa812e9"
+            ],
+            "version": "==1.0.0"
+        },
         "six": {
             "hashes": [
                 "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274",
@@ -2199,14 +2185,6 @@
             "markers": "python_version >= '3.8'",
             "version": "==5.14.3"
         },
-        "types-python-dateutil": {
-            "hashes": [
-                "sha256:b9a5232c8921cf7661b29c163ccc56055c418ab2c6eabe8f917cbcc73a4c4157",
-                "sha256:c3826289c170c93ebd8360c3485311187df740166dbab9dd3b792e69f2bc1f9c"
-            ],
-            "markers": "python_version >= '3.9'",
-            "version": "==2.9.0.20251008"
-        },
         "typing-extensions": {
             "hashes": [
                 "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466",
@@ -2482,12 +2460,12 @@
         },
         "isort": {
             "hashes": [
-                "sha256:58d8927ecce74e5087aef019f778d4081a3b6c98f15a80ba35782ca8a2097784",
-                "sha256:9b8f96a14cfee0677e78e941ff62f03769a06d412aabb9e2a90487b3b7e8d481"
+                "sha256:1bcabac8bc3c36c7fb7b98a76c8abb18e0f841a3ba81decac7691008592499c1",
+                "sha256:5513527951aadb3ac4292a41a16cbc50dd1642432f5e8c20057d414bdafb4187"
             ],
             "index": "pypi",
-            "markers": "python_full_version >= '3.9.0'",
-            "version": "==6.1.0"
+            "markers": "python_full_version >= '3.10.0'",
+            "version": "==7.0.0"
         },
         "jedi": {
             "hashes": [
@@ -2499,11 +2477,11 @@
         },
         "matplotlib-inline": {
             "hashes": [
-                "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90",
-                "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca"
+                "sha256:d56ce5156ba6085e00a9d54fead6ed29a9c47e215cd1bba2e976ef39f5710a76",
+                "sha256:e1ee949c340d771fc39e241ea75683deb94762c8fa5f2927ec57c83c4dffa9fe"
             ],
-            "markers": "python_version >= '3.8'",
-            "version": "==0.1.7"
+            "markers": "python_version >= '3.9'",
+            "version": "==0.2.1"
         },
         "mccabe": {
             "hashes": [
@@ -2621,16 +2599,17 @@
                 "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887",
                 "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b"
             ],
+            "index": "pypi",
             "markers": "python_version >= '3.8'",
             "version": "==2.19.2"
         },
         "pytokens": {
             "hashes": [
-                "sha256:c9a4bfa0be1d26aebce03e6884ba454e842f186a59ea43a6d3b25af58223c044",
-                "sha256:db7b72284e480e69fb085d9f251f66b3d2df8b7166059261258ff35f50fb711b"
+                "sha256:532d6421364e5869ea57a9523bf385f02586d4662acbcc0342afd69511b4dd43",
+                "sha256:74d4b318c67f4295c13782ddd9abcb7e297ec5630ad060eb90abf7ebbefe59f8"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==0.1.10"
+            "version": "==0.2.0"
         },
         "pyyaml": {
             "hashes": [
@@ -2708,6 +2687,7 @@
                 "sha256:fa160448684b4e94d80416c0fa4aac48967a969efe22931448d853ada8baf926",
                 "sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0"
             ],
+            "index": "pypi",
             "markers": "python_version >= '3.8'",
             "version": "==6.0.3"
         },
@@ -2734,21 +2714,13 @@
             "markers": "python_version >= '3.8'",
             "version": "==5.14.3"
         },
-        "typing-extensions": {
-            "hashes": [
-                "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466",
-                "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548"
-            ],
-            "markers": "python_version >= '3.9'",
-            "version": "==4.15.0"
-        },
         "virtualenv": {
             "hashes": [
-                "sha256:341f5afa7eee943e4984a9207c025feedd768baff6753cd660c857ceb3e36026",
-                "sha256:44815b2c9dee7ed86e387b842a84f20b93f7f417f95886ca1996a72a4138eb1a"
+                "sha256:4f1a845d131133bdff10590489610c98c168ff99dc75d6c96853801f7f67af44",
+                "sha256:63d106565078d8c8d0b206d48080f938a8b25361e19432d2c9db40d2899c810a"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==20.34.0"
+            "version": "==20.35.3"
         },
         "wcwidth": {
             "hashes": [
diff --git a/scripts/1-fetch/arxiv_fetch.py b/scripts/1-fetch/arxiv_fetch.py
new file mode 100755
index 00000000..4daa4009
--- /dev/null
+++ b/scripts/1-fetch/arxiv_fetch.py
@@ -0,0 +1,693 @@
+#!/usr/bin/env python
+"""
+Fetch ArXiv papers with CC license information and generate count reports.
+"""
+# Standard library
+import argparse
+import csv
+import os
+import re
+import sys
+import textwrap
+import time
+import traceback
+import urllib.parse
+from collections import Counter, defaultdict
+from operator import itemgetter
+
+# Third-party
+import feedparser
+import requests
+import yaml
+from pygments import highlight
+from pygments.formatters import TerminalFormatter
+from pygments.lexers import PythonTracebackLexer
+from requests.adapters import HTTPAdapter
+from urllib3.util.retry import Retry
+
+# Add parent directory so shared can be imported
+sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
+
+# First-party/Local
+import shared  # noqa: E402
+
+# Setup
+LOGGER, PATHS = shared.setup(__file__)
+
+# Constants
+# API Configuration
+BASE_URL = "http://export.arxiv.org/api/query?"
+DEFAULT_FETCH_LIMIT = 800  # Default total papers to fetch
+
+# CSV Headers
+HEADER_AUTHOR_BUCKET = ["TOOL_IDENTIFIER", "AUTHOR_BUCKET", "COUNT"]
+HEADER_CATEGORY_REPORT = [
+    "TOOL_IDENTIFIER",
+    "CATEGORY_CODE",
+    "CATEGORY_LABEL",
+    "COUNT",
+]
+HEADER_COUNT = ["TOOL_IDENTIFIER", "COUNT"]
+HEADER_YEAR = ["TOOL_IDENTIFIER", "YEAR", "COUNT"]
+
+# Search Queries
+SEARCH_QUERIES = [
+    'all:"creative commons"',
+    'all:"CC BY"',
+    'all:"CC-BY"',
+    'all:"CC BY-NC"',
+    'all:"CC-BY-NC"',
+    'all:"CC BY-SA"',
+    'all:"CC-BY-SA"',
+    'all:"CC BY-ND"',
+    'all:"CC-BY-ND"',
+    'all:"CC BY-NC-SA"',
+    'all:"CC-BY-NC-SA"',
+    'all:"CC BY-NC-ND"',
+    'all:"CC-BY-NC-ND"',
+    'all:"CC0"',
+    'all:"CC 0"',
+    'all:"CC-0"',
+]
+
+# Compiled regex patterns for CC license detection
+CC_PATTERNS = [
+    (re.compile(r"\bCC[-\s]?0\b", re.IGNORECASE), "CC0"),
+    (
+        re.compile(r"\bCC[-\s]?BY[-\s]?NC[-\s]?ND\b", re.IGNORECASE),
+        "CC BY-NC-ND",
+    ),
+    (
+        re.compile(r"\bCC[-\s]?BY[-\s]?NC[-\s]?SA\b", re.IGNORECASE),
+        "CC BY-NC-SA",
+    ),
+    (re.compile(r"\bCC[-\s]?BY[-\s]?ND\b", re.IGNORECASE), "CC BY-ND"),
+    (re.compile(r"\bCC[-\s]?BY[-\s]?SA\b", re.IGNORECASE), "CC BY-SA"),
+    (re.compile(r"\bCC[-\s]?BY[-\s]?NC\b", re.IGNORECASE), "CC BY-NC"),
+    (re.compile(r"\bCC[-\s]?BY\b", re.IGNORECASE), "CC BY"),
+    (
+        re.compile(r"\bCREATIVE\s+COMMONS\b", re.IGNORECASE),
+        "UNKNOWN CC legal tool",
+    ),
+]
+
+# ArXiv Categories - manually curated from ArXiv official taxonomy
+# Source: https://arxiv.org/category_taxonomy
+CATEGORIES = {
+    # Computer Science
+    "cs.AI": "Artificial Intelligence",
+    "cs.AR": "Hardware Architecture",
+    "cs.CC": "Computational Complexity",
+    "cs.CE": "Computational Engineering, Finance, and Science",
+    "cs.CG": "Computational Geometry",
+    "cs.CL": "Computation and Language",
+    "cs.CR": "Cryptography and Security",
+    "cs.CV": "Computer Vision and Pattern Recognition",
+    "cs.CY": "Computers and Society",
+    "cs.DB": "Databases",
+    "cs.DC": "Distributed, Parallel, and Cluster Computing",
+    "cs.DL": "Digital Libraries",
+    "cs.DM": "Discrete Mathematics",
+    "cs.DS": "Data Structures and Algorithms",
+    "cs.ET": "Emerging Technologies",
+    "cs.FL": "Formal Languages and Automata Theory",
+    "cs.GL": "General Literature",
+    "cs.GR": "Graphics",
+    "cs.GT": "Computer Science and Game Theory",
+    "cs.HC": "Human-Computer Interaction",
+    "cs.IR": "Information Retrieval",
+    "cs.IT": "Information Theory",
+    "cs.LG": "Machine Learning",
+    "cs.LO": "Logic in Computer Science",
+    "cs.MA": "Multiagent Systems",
+    "cs.MM": "Multimedia",
+    "cs.MS": "Mathematical Software",
+    "cs.NA": "Numerical Analysis",
+    "cs.NE": "Neural and Evolutionary Computing",
+    "cs.NI": "Networking and Internet Architecture",
+    "cs.OH": "Other Computer Science",
+    "cs.OS": "Operating Systems",
+    "cs.PF": "Performance",
+    "cs.PL": "Programming Languages",
+    "cs.RO": "Robotics",
+    "cs.SC": "Symbolic Computation",
+    "cs.SD": "Sound",
+    "cs.SE": "Software Engineering",
+    "cs.SI": "Social and Information Networks",
+    "cs.SY": "Systems and Control",
+    # Mathematics
+    "math.AC": "Commutative Algebra",
+    "math.AG": "Algebraic Geometry",
+    "math.AP": "Analysis of PDEs",
+    "math.AT": "Algebraic Topology",
+    "math.CA": "Classical Analysis and ODEs",
+    "math.CO": "Combinatorics",
+    "math.CT": "Category Theory",
+    "math.CV": "Complex Variables",
+    "math.DG": "Differential Geometry",
+    "math.DS": "Dynamical Systems",
+    "math.FA": "Functional Analysis",
+    "math.GM": "General Mathematics",
+    "math.GN": "General Topology",
+    "math.GR": "Group Theory",
+    "math.GT": "Geometric Topology",
+    "math.HO": "History and Overview",
+    "math.IT": "Information Theory",
+    "math.KT": "K-Theory and Homology",
+    "math.LO": "Logic",
+    "math.MG": "Metric Geometry",
+    "math.MP": "Mathematical Physics",
+    "math.NA": "Numerical Analysis",
+    "math.NT": "Number Theory",
+    "math.OA": "Operator Algebras",
+    "math.OC": "Optimization and Control",
+    "math.PR": "Probability",
+    "math.QA": "Quantum Algebra",
+    "math.RA": "Rings and Algebras",
+    "math.RT": "Representation Theory",
+    "math.SG": "Symplectic Geometry",
+    "math.SP": "Spectral Theory",
+    "math.ST": "Statistics Theory",
+    # Physics
+    "physics.acc-ph": "Accelerator Physics",
+    "physics.ao-ph": "Atmospheric and Oceanic Physics",
+    "physics.app-ph": "Applied Physics",
+    "physics.atm-clus": "Atomic and Molecular Clusters",
+    "physics.atom-ph": "Atomic Physics",
+    "physics.bio-ph": "Biological Physics",
+    "physics.chem-ph": "Chemical Physics",
+    "physics.class-ph": "Classical Physics",
+    "physics.comp-ph": "Computational Physics",
+    "physics.data-an": "Data Analysis, Statistics and Probability",
+    "physics.ed-ph": "Physics Education",
+    "physics.flu-dyn": "Fluid Dynamics",
+    "physics.gen-ph": "General Physics",
+    "physics.geo-ph": "Geophysics",
+    "physics.hist-ph": "History and Philosophy of Physics",
+    "physics.ins-det": "Instrumentation and Detectors",
+    "physics.med-ph": "Medical Physics",
+    "physics.optics": "Optics",
+    "physics.plasm-ph": "Plasma Physics",
+    "physics.pop-ph": "Popular Physics",
+    "physics.soc-ph": "Physics and Society",
+    "physics.space-ph": "Space Physics",
+    # Statistics
+    "stat.AP": "Applications",
+    "stat.CO": "Computation",
+    "stat.ME": "Methodology",
+    "stat.ML": "Machine Learning",
+    "stat.OT": "Other Statistics",
+    "stat.TH": "Statistics Theory",
+    # Quantitative Biology
+    "q-bio.BM": "Biomolecules",
+    "q-bio.CB": "Cell Behavior",
+    "q-bio.GN": "Genomics",
+    "q-bio.MN": "Molecular Networks",
+    "q-bio.NC": "Neurons and Cognition",
+    "q-bio.OT": "Other Quantitative Biology",
+    "q-bio.PE": "Populations and Evolution",
+    "q-bio.QM": "Quantitative Methods",
+    "q-bio.SC": "Subcellular Processes",
+    "q-bio.TO": "Tissues and Organs",
+    # Economics
+    "econ.EM": "Econometrics",
+    "econ.GN": "General Economics",
+    "econ.TH": "Theoretical Economics",
+    # Electrical Engineering
+    "eess.AS": "Audio and Speech Processing",
+    "eess.IV": "Image and Video Processing",
+    "eess.SP": "Signal Processing",
+    "eess.SY": "Systems and Control",
+    # High Energy Physics
+    "hep-ex": "High Energy Physics - Experiment",
+    "hep-lat": "High Energy Physics - Lattice",
+    "hep-ph": "High Energy Physics - Phenomenology",
+    "hep-th": "High Energy Physics - Theory",
+    # Other Physics
+    "astro-ph": "Astrophysics",
+    "astro-ph.CO": "Cosmology and Nongalactic Astrophysics",
+    "astro-ph.EP": "Earth and Planetary Astrophysics",
+    "astro-ph.GA": "Astrophysics of Galaxies",
+    "astro-ph.HE": "High Energy Astrophysical Phenomena",
+    "astro-ph.IM": "Instrumentation and Methods for Astrophysics",
+    "astro-ph.SR": "Solar and Stellar Astrophysics",
+    "cond-mat.dis-nn": "Disordered Systems and Neural Networks",
+    "cond-mat.mes-hall": "Mesoscale and Nanoscale Physics",
+    "cond-mat.mtrl-sci": "Materials Science",
+    "cond-mat.other": "Other Condensed Matter",
+    "cond-mat.quant-gas": "Quantum Gases",
+    "cond-mat.soft": "Soft Condensed Matter",
+    "cond-mat.stat-mech": "Statistical Mechanics",
+    "cond-mat.str-el": "Strongly Correlated Electrons",
+    "cond-mat.supr-con": "Superconductivity",
+    "gr-qc": "General Relativity and Quantum Cosmology",
+    "nlin.AO": "Adaptation and Self-Organizing Systems",
+    "nlin.CD": "Chaotic Dynamics",
+    "nlin.CG": "Cellular Automata and Lattice Gases",
+    "nlin.PS": "Pattern Formation and Solitons",
+    "nlin.SI": "Exactly Solvable and Integrable Systems",
+    "nucl-ex": "Nuclear Experiment",
+    "nucl-th": "Nuclear Theory",
+    "quant-ph": "Quantum Physics",
+}
+
+# File Paths
+FILE_ARXIV_AUTHOR_BUCKET = shared.path_join(
+    PATHS["data_1-fetch"], "arxiv_4_count_by_author_bucket.csv"
+)
+FILE_ARXIV_CATEGORY_REPORT = shared.path_join(
+    PATHS["data_1-fetch"], "arxiv_2_count_by_category_report.csv"
+)
+FILE_ARXIV_COUNT = shared.path_join(PATHS["data_1-fetch"], "arxiv_1_count.csv")
+FILE_ARXIV_YEAR = shared.path_join(
+    PATHS["data_1-fetch"], "arxiv_3_count_by_year.csv"
+)
+# records metadata for each run for audit, reproducibility, and provenance
+FILE_PROVENANCE = shared.path_join(
+    PATHS["data_1-fetch"], "arxiv_provenance.yaml"
+)
+
+# Runtime variables
+QUARTER = os.path.basename(PATHS["data_quarter"])
+
+
+# parsing arguments function
+def parse_arguments():
+    """Parse command-line options, returns parsed argument namespace.
+
+    Note: The --limit parameter sets the total number of papers to fetch
+    across all search queries, not per query. ArXiv API recommends
+    maximum of 30000 results per session for optimal performance.
+    """
+    LOGGER.info("Parsing command-line options")
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--limit",
+        type=int,
+        default=DEFAULT_FETCH_LIMIT,
+        help=(
+            f"Total limit of papers to fetch across all search queries "
+            f"(default: {DEFAULT_FETCH_LIMIT}). Maximum recommended: 30000. "
+            f"Note: Individual queries limited to 500 results "
+            f"(implementation choice). "
+            f"See ArXiv API documentation: "
+            f"https://info.arxiv.org/help/api/user-manual.html"
+        ),
+    )
+    parser.add_argument(
+        "--enable-save",
+        action="store_true",
+        help="Enable saving results",
+    )
+    parser.add_argument(
+        "--enable-git",
+        action="store_true",
+        help="Enable git actions (fetch, merge, add, commit, and push)",
+    )
+    args = parser.parse_args()
+    if not args.enable_save and args.enable_git:
+        parser.error("--enable-git requires --enable-save")
+    return args
+
+
+def initialize_data_file(file_path, headers):
+    """Initialize CSV file with headers if it doesn't exist."""
+    if not os.path.isfile(file_path):
+        with open(file_path, "w", encoding="utf-8", newline="\n") as file_obj:
+            writer = csv.DictWriter(
+                file_obj, fieldnames=headers, dialect="unix"
+            )
+            writer.writeheader()
+
+
+def initialize_all_data_files(args):
+    """Initialize all data files used by this script.
+
+    Creates the data directory and initializes empty CSVs with headers.
+    """
+    if not args.enable_save:
+        return
+
+    os.makedirs(PATHS["data_1-fetch"], exist_ok=True)
+    initialize_data_file(FILE_ARXIV_COUNT, HEADER_COUNT)
+    initialize_data_file(FILE_ARXIV_CATEGORY_REPORT, HEADER_CATEGORY_REPORT)
+    initialize_data_file(FILE_ARXIV_YEAR, HEADER_YEAR)
+    initialize_data_file(FILE_ARXIV_AUTHOR_BUCKET, HEADER_AUTHOR_BUCKET)
+
+
+def get_requests_session():
+    """Create request session with retry logic"""
+    retry_strategy = Retry(
+        total=5,
+        backoff_factor=10,
+        status_forcelist=shared.STATUS_FORCELIST,
+    )
+    session = requests.Session()
+    session.headers.update({"User-Agent": shared.USER_AGENT})
+    session.mount("https://", HTTPAdapter(max_retries=retry_strategy))
+    return session
+
+
+def normalize_license_text(raw_text):
+    """
+    Convert raw license text to standardized CC license identifiers.
+
+    Uses regex patterns to identify CC licenses from paper text.
+    Returns specific license (e.g., "CC BY", "CC0") or "Unknown".
+    """
+    if not raw_text:
+        return "Unknown"
+
+    for pattern, license_type in CC_PATTERNS:
+        if pattern.search(raw_text):
+            return license_type
+
+    return "Unknown"
+
+
+def extract_license_info(entry):
+    """
+    Extract CC license information from ArXiv paper entry.
+
+    Checks rights field first, then summary field for license patterns.
+    Returns normalized license identifier or "Unknown".
+    """
+    # checking through the rights field first then summary
+    if hasattr(entry, "rights") and entry.rights:
+        license_info = normalize_license_text(entry.rights)
+        if license_info != "Unknown":
+            return license_info
+    if hasattr(entry, "summary") and entry.summary:
+        license_info = normalize_license_text(entry.summary)
+        if license_info != "Unknown":
+            return license_info
+    return "Unknown"
+
+
+def extract_category_from_entry(entry):
+    """Extract primary category from ArXiv entry."""
+    if (
+        hasattr(entry, "arxiv_primary_category")
+        and entry.arxiv_primary_category
+    ):
+        return entry.arxiv_primary_category.get("term", "Unknown")
+    if hasattr(entry, "tags") and entry.tags:
+        # Get first category from tags
+        for tag in entry.tags:
+            if hasattr(tag, "term"):
+                return tag.term
+    return "Unknown"
+
+
+def extract_year_from_entry(entry):
+    """Extract publication year from ArXiv entry."""
+    if hasattr(entry, "published") and entry.published:
+        try:
+            return entry.published[:4]  # Extract year from date string
+        except (AttributeError, IndexError) as e:
+            LOGGER.debug(
+                f"Failed to extract year from '{entry.published}': {e}"
+            )
+    return "Unknown"
+
+
+def extract_author_count_from_entry(entry):
+    """Extract number of authors from ArXiv entry."""
+    if hasattr(entry, "authors") and entry.authors:
+        try:
+            return len(entry.authors)
+        except Exception as e:
+            LOGGER.debug(f"Failed to count authors from entry.authors: {e}")
+    if hasattr(entry, "author") and entry.author:
+        return 1
+    return "Unknown"
+
+
+def bucket_author_count(n):
+    """
+    Convert author count to predefined buckets for analysis.
+
+    Buckets: "1", "2", "3", "4", "5+", "Unknown"
+    Reduces granularity for better statistical analysis.
+    """
+    if n == 1:
+        return "1"
+    if n == 2:
+        return "2"
+    if n == 3:
+        return "3"
+    if n == 4:
+        return "4"
+    if n >= 5:
+        return "5+"
+    return "Unknown"
+
+
+def save_count_data(
+    license_counts, category_counts, year_counts, author_counts
+):
+    """
+    Save all collected data to CSV files.
+
+    """
+    # license_counts: {license: count}
+    # category_counts: {license: {category_code: count}}
+    # year_counts: {license: {year: count}}
+    # author_counts: {license: {author_count(int|None): count}}
+
+    # Save license counts
+    data = []
+    for lic, c in license_counts.items():
+        data.append({"TOOL_IDENTIFIER": lic, "COUNT": c})
+    data.sort(key=itemgetter("TOOL_IDENTIFIER"))
+    with open(FILE_ARXIV_COUNT, "w", encoding="utf-8", newline="\n") as fh:
+        writer = csv.DictWriter(fh, fieldnames=HEADER_COUNT, dialect="unix")
+        writer.writeheader()
+        for row in data:
+            writer.writerow(row)
+
+    # Save category report with labels
+    data = []
+    for lic, cats in category_counts.items():
+        for code, c in cats.items():
+            label = CATEGORIES.get(code, code)
+            data.append(
+                {
+                    "TOOL_IDENTIFIER": lic,
+                    "CATEGORY_CODE": code,
+                    "CATEGORY_LABEL": label,
+                    "COUNT": c,
+                }
+            )
+    data.sort(key=itemgetter("TOOL_IDENTIFIER", "CATEGORY_CODE"))
+    with open(
+        FILE_ARXIV_CATEGORY_REPORT, "w", encoding="utf-8", newline="\n"
+    ) as fh:
+        writer = csv.DictWriter(
+            fh, fieldnames=HEADER_CATEGORY_REPORT, dialect="unix"
+        )
+        writer.writeheader()
+        for row in data:
+            writer.writerow(row)
+
+    # Save year counts
+    data = []
+    for lic, years in year_counts.items():
+        for year, c in years.items():
+            data.append({"TOOL_IDENTIFIER": lic, "YEAR": year, "COUNT": c})
+    data.sort(key=itemgetter("TOOL_IDENTIFIER", "YEAR"))
+    with open(FILE_ARXIV_YEAR, "w", encoding="utf-8", newline="\n") as fh:
+        writer = csv.DictWriter(fh, fieldnames=HEADER_YEAR, dialect="unix")
+        writer.writeheader()
+        for row in data:
+            writer.writerow(row)
+
+    # Save author buckets summary
+    data = []
+    for lic, acs in author_counts.items():
+        # build buckets across licenses
+        bucket_counts = Counter()
+        for ac, c in acs.items():
+            b = bucket_author_count(ac)
+            bucket_counts[b] += c
+        for b, c in bucket_counts.items():
+            data.append(
+                {"TOOL_IDENTIFIER": lic, "AUTHOR_BUCKET": b, "COUNT": c}
+            )
+    data.sort(key=itemgetter("TOOL_IDENTIFIER", "AUTHOR_BUCKET"))
+    with open(
+        FILE_ARXIV_AUTHOR_BUCKET, "w", encoding="utf-8", newline="\n"
+    ) as fh:
+        writer = csv.DictWriter(
+            fh, fieldnames=HEADER_AUTHOR_BUCKET, dialect="unix"
+        )
+        writer.writeheader()
+        for row in data:
+            writer.writerow(row)
+
+
+def query_arxiv(args):
+    """
+    Main function to query ArXiv API and collect CC license data.
+
+    """
+
+    LOGGER.info("Beginning to fetch results from ArXiv API")
+    session = get_requests_session()
+
+    results_per_iteration = 50
+
+    search_queries = SEARCH_QUERIES
+
+    # Data structures for counting
+    license_counts = defaultdict(int)
+    category_counts = defaultdict(lambda: defaultdict(int))
+    year_counts = defaultdict(lambda: defaultdict(int))
+    author_counts = defaultdict(lambda: defaultdict(int))
+
+    total_fetched = 0
+
+    for search_query in search_queries:
+        if total_fetched >= args.limit:
+            break
+
+        LOGGER.info(f"Searching for: {search_query}")
+        papers_found_for_query = 0
+
+        for start in range(
+            0,
+            min(args.limit - total_fetched, 500),
+            results_per_iteration,
+        ):
+            encoded_query = urllib.parse.quote_plus(search_query)
+            query = (
+                f"search_query={encoded_query}&start={start}"
+                f"&max_results={results_per_iteration}"
+            )
+
+            papers_found_in_batch = 0
+
+            try:
+                LOGGER.info(
+                    f"Fetching results {start} - "
+                    f"{start + results_per_iteration}"
+                )
+                response = session.get(BASE_URL + query, timeout=30)
+                response.raise_for_status()
+                feed = feedparser.parse(response.content)
+
+                for entry in feed.entries:
+                    if total_fetched >= args.limit:
+                        break
+
+                    license_info = extract_license_info(entry)
+
+                    if license_info != "Unknown":
+
+                        category = extract_category_from_entry(entry)
+                        year = extract_year_from_entry(entry)
+                        author_count = extract_author_count_from_entry(entry)
+
+                        # Count by license
+                        license_counts[license_info] += 1
+
+                        # Count by category and license
+                        category_counts[license_info][category] += 1
+
+                        # Count by year and license
+                        year_counts[license_info][year] += 1
+
+                        # Count by author count and license
+                        author_counts[license_info][author_count] += 1
+
+                        total_fetched += 1
+                        papers_found_in_batch += 1
+                        papers_found_for_query += 1
+
+                # arXiv recommends a 3-seconds delay between consecutive
+                # api calls for efficiency
+                time.sleep(3)
+            except requests.HTTPError as e:
+                raise shared.QuantifyingException(f"HTTP Error: {e}", 1)
+            except requests.RequestException as e:
+                raise shared.QuantifyingException(f"Request Exception: {e}", 1)
+            except KeyError as e:
+                raise shared.QuantifyingException(f"KeyError: {e}", 1)
+
+            if papers_found_in_batch == 0:
+                break
+
+        LOGGER.info(
+            f"Query '{search_query}' completed: "
+            f"{papers_found_for_query} papers found"
+        )
+
+    # Save results
+    if args.enable_save:
+        save_count_data(
+            license_counts, category_counts, year_counts, author_counts
+        )
+
+    # save provenance
+    provenance_data = {
+        "total_fetched": total_fetched,
+        "queries": search_queries,
+        "limit": args.limit,
+        "quarter": QUARTER,
+        "script": os.path.basename(__file__),
+    }
+
+    # write provenance YAML for auditing
+    try:
+        with open(FILE_PROVENANCE, "w", encoding="utf-8", newline="\n") as fh:
+            yaml.dump(provenance_data, fh, default_flow_style=False, indent=2)
+    except Exception as e:
+        LOGGER.warning("Failed to write provenance file: %s", e)
+
+    LOGGER.info(f"Total CC licensed papers fetched: {total_fetched}")
+
+
+def main():
+    """Main function."""
+    LOGGER.info("Script execution started.")
+    args = parse_arguments()
+    shared.paths_log(LOGGER, PATHS)
+    shared.git_fetch_and_merge(args, PATHS["repo"])
+    initialize_all_data_files(args)
+    query_arxiv(args)
+    args = shared.git_add_and_commit(
+        args,
+        PATHS["repo"],
+        PATHS["data_quarter"],
+        f"Add and commit new ArXiv CC license data for {QUARTER}",
+    )
+    shared.git_push_changes(args, PATHS["repo"])
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except shared.QuantifyingException as e:
+        if e.exit_code == 0:
+            LOGGER.info(e.message)
+        else:
+            LOGGER.error(e.message)
+        sys.exit(e.exit_code)
+    except SystemExit as e:
+        if e.code != 0:
+            LOGGER.error(f"System exit with code: {e.code}")
+        sys.exit(e.code)
+    except KeyboardInterrupt:
+        LOGGER.info("(130) Halted via KeyboardInterrupt.")
+        sys.exit(130)
+    except Exception:
+        traceback_formatted = textwrap.indent(
+            highlight(
+                traceback.format_exc(),
+                PythonTracebackLexer(),
+                TerminalFormatter(),
+            ),
+            "    ",
+        )
+        LOGGER.critical(f"(1) Unhandled exception:\n{traceback_formatted}")
+        sys.exit(1)
diff --git a/sources.md b/sources.md
index 2b3e1a79..3d396294 100644
--- a/sources.md
+++ b/sources.md
@@ -4,6 +4,25 @@ This project uses data from various sources that are openly licensed or in the
 public domain. Below are the sources and their respective information:
 
 
+## arXiv
+
+**Description:** arXiv is a free distribution service and an open-access archive for scholarly articles in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. All arXiv articles are available under various open licenses or are in the public domain.
+
+**API documentation link:**
+- [arXiv API User Manual](https://arxiv.org/help/api/user-manual)
+- [arXiv API Reference](https://arxiv.org/help/api)
+- [Base URL](http://export.arxiv.org/api/query)
+- [arXiv Subject Classifications](https://arxiv.org/category_taxonomy)
+- [Terms of Use for arXiv APIs](https://info.arxiv.org/help/api/tou.html)
+
+**API information:**
+- No API key required
+- Query limit: No official limit, but requests should be made responsibly
+- Data available through Atom XML format
+- Supports search by fields: title (ti), author (au), abstract (abs), comment (co), journal reference (jr), subject category (cat), report number (rn), id, all (searches all fields), and submittedDate (date filter)
+- Metadata includes licensing information for each paper
+
+
 ## CC Legal Tools
 
 **Description:** A `.txt` file provided by Timid Robot containing all legal
@@ -23,7 +42,7 @@ tool paths.
 [prioritized-tool-urls]: data/prioritized-tool-urls.txt
 
 
-## EUROPEANA DATA API
+## Europeana
 
 **Description:**
 The **Europeana Search API** provides access to digital cultural heritage metadata records aggregated from museums, libraries, and archives across Europe. This project uses the API to fetch aggregated counts of cultural heritage records by data provider, rights statement, and theme.
@@ -101,6 +120,7 @@ and access towards related query data using a programmable search engine.
   5000 requests per hour if authenticated
 - Data available through JSON format
 
+
 ## Wikipedia
 
 **Description:** The Wikipedia API allows users to query statistics of pages,