--- name: digital-humanities-guide description: "Computational methods for humanities research including text mining and netwo..." metadata: openclaw: emoji: "📜" category: "domains" subcategory: "humanities" keywords: ["digital humanities", "philosophy", "literary studies", "art history", "linguistics", "text mining"] source: "wentor" --- # Digital Humanities Guide A skill for applying computational and quantitative methods to humanities research. Covers text mining, network analysis, spatial humanities, and digital archival methods. Designed for researchers bridging traditional humanities with data-driven approaches. ## Text Mining and Distant Reading ### Corpus Preparation ```python import re from collections import Counter def prepare_corpus(texts: list[str], stopwords: set = None) -> list[list[str]]: """ Tokenize and clean a corpus of texts for analysis. Args: texts: List of raw text strings stopwords: Set of words to remove Returns: List of tokenized, cleaned documents """ if stopwords is None: stopwords = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'is', 'was', 'are'} processed = [] for text in texts: # Lowercase and remove punctuation tokens = re.findall(r'\b[a-z]+\b', text.lower()) # Remove stopwords and short tokens tokens = [t for t in tokens if t not in stopwords and len(t) > 2] processed.append(tokens) return processed def compute_tfidf(corpus: list[list[str]]) -> dict: """Compute TF-IDF scores for term importance analysis.""" import math n_docs = len(corpus) # Document frequency df = Counter() for doc in corpus: df.update(set(doc)) # TF-IDF per document tfidf_scores = [] for doc in corpus: tf = Counter(doc) total = len(doc) scores = {} for term, count in tf.items(): tf_val = count / total idf_val = math.log(n_docs / (1 + df[term])) scores[term] = tf_val * idf_val tfidf_scores.append(scores) return tfidf_scores ``` ### Topic Modeling Apply Latent Dirichlet Allocation (LDA) to discover thematic structures in large text corpora: ```python from gensim import corpora, models def run_topic_model(corpus: list[list[str]], n_topics: int = 10, passes: int = 15) -> models.LdaModel: """ Train an LDA topic model on a preprocessed corpus. """ dictionary = corpora.Dictionary(corpus) dictionary.filter_extremes(no_below=5, no_above=0.5) bow_corpus = [dictionary.doc2bow(doc) for doc in corpus] lda_model = models.LdaModel( bow_corpus, num_topics=n_topics, id2word=dictionary, passes=passes, random_state=42, alpha='auto', eta='auto' ) return lda_model # Print top words per topic # for idx, topic in lda_model.print_topics(-1): # print(f"Topic {idx}: {topic}") ``` ## Network Analysis for Historical Research ### Correspondence and Social Networks ```python import networkx as nx def build_correspondence_network(letters: list[dict]) -> nx.Graph: """ Build a social network from historical correspondence data. Args: letters: List of dicts with 'sender', 'recipient', 'date', 'location' """ G = nx.Graph() for letter in letters: sender = letter['sender'] recipient = letter['recipient'] if G.has_edge(sender, recipient): G[sender][recipient]['weight'] += 1 else: G.add_edge(sender, recipient, weight=1) # Compute centrality measures degree_cent = nx.degree_centrality(G) betweenness = nx.betweenness_centrality(G) for node in G.nodes(): G.nodes[node]['degree_centrality'] = degree_cent[node] G.nodes[node]['betweenness'] = betweenness[node] return G # Identify the most connected and most bridging figures # sorted(degree_cent.items(), key=lambda x: x[1], reverse=True)[:10] ``` ## Spatial Humanities Map historical events, literary settings, or cultural artifacts using GIS tools: - **QGIS** for desktop spatial analysis with historical maps - **Recogito** for annotating place names in texts - **Peripleo** for linked open geodata visualization - **Palladio** for Stanford's humanities data visualization platform Georeferencing historical maps requires at least 4 ground control points with known coordinates, using polynomial or thin-plate spline transformation. ## Digital Archival Methods ### TEI Encoding The Text Encoding Initiative (TEI) is the standard for scholarly digital editions: ```xml Letters of [Historical Figure]
14 July 1789 Dear Friend,

The events of today have been most extraordinary...

 ``` ## Ethical Considerations Digital humanities research must address: copyright and fair use for digitized materials, privacy concerns for living subjects in social network analysis, algorithmic bias in NLP tools trained on modern English when applied to historical texts, and the responsibility to make digital scholarship accessible beyond the academy. --- Source attribution: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/domains/humanities/digital-humanities-guide/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Wentor AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/domains/humanities/digital-humanities-guide/SKILL.md Attribution basis: Source repository record Fields of work: Content & Media, Data & Analytics, Design, Education & Research, Marketing Roles: Content Marketer, Copywriter, Data Scientist, Researcher, UX Designer Specialties: API Tools: Gensim, Networkx Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: b6df7bd6ffb951566eadb60de30b36cce94624feb58e9479b344c86a1148b171