Comparing the Marathi & Maharashtra data ecosystem against well-resourced languages and regions. These gaps represent opportunities — datasets that could be created, gathered, or digitized to unlock new AI capabilities for Marathi speakers.
No dedicated multi-turn Marathi dialogue or conversational datasets exist.
English has DailyDialog, PersonaChat, MultiWOZ; Hindi has limited options.
Critical for building Marathi chatbots, virtual assistants, and customer service AI.
No Marathi commonsense knowledge graphs or reasoning datasets.
English has ConceptNet, ATOMIC, WinoGrande, HellaSwag.
Needed for Marathi LLMs to understand cultural context and implicit knowledge.
L3Cube-MahaHate exists but is small. No large-scale, multi-platform toxicity dataset.
English has Jigsaw (1.8M comments), HateXplain with rationale annotations.
Essential for content moderation on Marathi social media platforms.
IndicSentenceSummarization has Marathi but no long-document summarization dataset.
English has CNN/DailyMail, XSum; Chinese has LCSTS.
Required for Marathi news summarization, document understanding, and report generation.
No Marathi speech emotion recognition (SER) dataset with emotion labels.
English has IEMOCAP, RAVDESS, CREMA-D; Hindi has limited options.
Needed for call center sentiment analysis, mental health monitoring, accessibility.
Existing Marathi ASR datasets are mostly read speech. No noisy, spontaneous, or code-mixed speech data.
English has CHiME, VoxCeleb, AMI Meeting Corpus.
Real-world Marathi speech recognition requires training on market, street, farm environments.
IndicTTS has 1 Marathi speaker. No multi-speaker, multi-style TTS corpus.
English has LibriTTS (2,456 speakers), VCTK (110 speakers).
Needed for natural-sounding Marathi voice assistants and audiobook generation.
BSTD and IIIT-HW exist but are small. No large-scale Marathi street sign / billboard dataset.
Chinese has CTW, RCTW with 100K+ street images.
Required for Marathi navigation apps, automated sign reading, smart city infrastructure.
No paired medical image + Marathi radiology report dataset exists.
English has MIMIC-CXR (377K images), CheXpert.
Would enable AI-assisted radiology reporting in Marathi for rural hospital networks.
No dataset for parsing Marathi document layouts (government forms, certificates, gazettes).
English has PubLayNet, DocBank. Chinese has CDLA.
Key for digitizing Maharashtra government records and automating form processing.
OpenStreetMap has partial coverage. No comprehensive, official building footprint dataset.
US has Microsoft Building Footprints (125M), Google Open Buildings covers Africa.
Needed for urban planning, disaster response, property tax assessment in MH.
No digitized, geo-referenced field boundary dataset for Maharashtra farmland.
EU has LPIS with field-level boundaries. US has CLU (Common Land Unit).
Would transform precision agriculture, crop insurance, and land record modernization.
No anonymized Marathi EHR or clinical notes dataset.
English has MIMIC-III/IV. Some de-identified Chinese EHR datasets exist.
Essential for clinical NLP, drug interaction detection, and Marathi health chatbots.
NFHS has state-level aggregates but no individual-level dietary intake data for MH.
US has NHANES with individual-level dietary recall. UK has NDNS.
Needed for personalized nutrition apps, malnutrition early warning, school meal planning.
No structured dataset of Marathi exam questions mapped to curriculum topics.
English has ARC, SciQ, MMLU. Chinese has GAOKAO benchmark.
Would power adaptive learning platforms and automated assessment in Marathi medium schools.
ASER and NAS provide samples but no continuous, longitudinal learning data.
Many OECD nations have PISA longitudinal follow-ups and national learning management data.
Critical for evidence-based education policy and personalized learning interventions.
No labeled Marathi financial sentiment or event dataset.
English has Financial PhraseBank, FiQA. Chinese has FinNL.
Needed for Marathi stock market analysis tools, financial news summarization.
Udyam has registration data but no structured dataset with financials, sectors, or outcomes.
US has SEC filings + Crunchbase. UK has Companies House full data.
Would enable MSME lending models, market analysis, and entrepreneurship research.
No labeled image dataset of diseases on Maharashtra-specific crops (sugarcane, jowar, bajra, grapes).
PlantVillage has 50K images but mainly Western crops. China has custom rice/wheat datasets.
Would enable phone-based crop disease detection apps for MH farmers.
ICRISAT VDSA has some villages. No broad, recent farm-level cost-of-cultivation microdata for MH.
US has ARMS (Agricultural Resource Management Survey) at field level.
Essential for input subsidy optimization, credit risk modeling, and procurement planning.
Indian Kanoon has some Marathi judgments but no structured, NER-tagged legal dataset.
English has CaseLaw Access Project (6.7M decisions). EU has ECHR-CASES.
Would enable legal search, case outcome prediction, and access to justice in Marathi.
RTI requests and responses exist but are not aggregated or structured as a dataset.
UK has WhatDoTheyKnow with 800K+ structured FOI requests and responses.
Could power government transparency tools, automated RTI assistance, and civic engagement.
No open, continuous traffic speed/flow dataset for Mumbai, Pune, or Nagpur.
UK has Highways England (15-min loop data). US has INRIX.
Required for traffic prediction, route optimization, and urban planning models.
No POS-tagged or semantically annotated Marathi literary text corpus (novels, poetry, drama).
English has BNC, Project Gutenberg with annotations. Japanese has BCCWJ.
Needed for digital humanities research, literary analysis, and cultural preservation.
No scraped/curated Marathi tourism review dataset for MH points of interest.
English has Yelp (6.9M reviews), TripAdvisor datasets.
Would power Marathi tourism recommendation engines and sentiment-based destination rankings.
CPCB has real-time data but no cleaned, aggregated historical time-series for all MH stations.
US EPA has AQS with decades of cleaned, station-level data. EU has EEA AirBase.
Needed for pollution forecasting, health impact studies, and environmental policy.
IndicGLUE and IndicXTREME exist but no comprehensive Marathi-specific benchmark like MMLU or C-Eval.
English has MMLU, HellaSwag, ARC. Chinese has C-Eval, CMMLU.
Critical for fairly evaluating and comparing Marathi language models.