[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82045-en":3,"doc-seo-82045-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82045,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","DaDaDa: A Dataset for Data Pricing in Data Marketplaces","High-quality data is essential for machine learning, yet data marketplaces need reliable pricing for data products whose economic properties differ from traditional goods. Traditional cost and income approaches break down because replication is near-zero cost and revenue is difficult to predict. Sales comparison is promising but limited by missing standardized benchmarks across platforms. DaDaDa provides metadata for 16,147 data products from nine major marketplaces to train pricing models and establish price benchmarks, and supports pricing, classification, and retrieval.","[Experiment, Analysis, and Benchmark] DaDaDa: ADataset for Data Pricing in Data Marketplaces  \nQiheng Sun 1,2 , Hongwei Zhang2 , Junxu Liu 1 , Xiaokai Mao2 , Jinfei Liu2 , Kui Ren2 , Haibo Hu 1  \n1The Hong Kong Polytechnic University, Hong Kong, China  \n2Zhejiang University, Hangzhou, China  \n{qiheng.sun, junxu.liu, [haibo.hu}@polyu.edu.hk](haibo.hu}@polyu.edu.hk)  \n{hongweizhang, xiaokaimao, jinfeiliu, [kuiren}@zju.edu.cn](kuiren}@zju.edu.cn)  \narXiv :2607 .08785v1 [ cs .LG] 13 Jun 2026  \nAbstract—High-quality data drives machine learning advances across industries. Recognizing the value of data, data transactions are increasingly common, giving rise to many data marketplaces, e.g., AWS Marketplace, Databricks, and Datarade. However, determining the appropriate prices for data products remains a significant challenge due to the unique properties of data products. Traditional pricing methods in economics can be categorized into the cost approach, the income approach, and the sales comparison approach. The cost approach fails in data pricing due to near-zero marginal cost from data replication, and the income approach fails due to inherently unpredictable data revenue. The sales comparison approach remains viable, yet its application is hindered by the absence of standardized pricing benchmarks for data products across marketplaces. To address this challenge, we introduce DaDaDa, the first dataset for data product pricing, containing metadata for 16,147 data products from 9 major data marketplaces worldwide. DaDaDa enables the training of pricing models, thereby establishing price benchmarks for new data products. In addition, DaDaDa can be utilized for other important tasks in data markets, such as data product classification and retrieval. Experiments and a retrieval prototype demonstrate the effectiveness of DaDaDa for pricing, classification, and retrieval of data products. The dataset and code are available at [https://github.com/ZJU-DIVER/DaDaDa](https://github.com/ZJU-DIVER/DaDaDa).  \nIndex Terms—Data Pricing; Data Marketplaces  \nI. INTRODUCTION  \nThe immense value created by data-driven machine learning models stems from accessible, diverse, and well-structured data [32] . However, publicly available data are running out, as noted in recent discussions of AI data scarcity [47] . Moreover, such data often lack domain specificity and structural consistency. In contrast, private data sources not only retain vast amounts of unused data but also provide curated, taskspecific datasets. This gap between the need for high-quality data and the limitations of public sources drives the growth of private data transactions [43], giving rise to dedicated marketplaces such as AWS Marketplace [2], Databricks [8], and Datarade [10] . These marketplaces provide a convenient avenue for showcasing data products and facilitate connections between data sellers and buyers.  \nA critical function of marketplaces is enabling pricing for products, which determines transaction viability and market liquidity [56] . There are three commonly used pricing methods:  \n(1) the cost approach, (2) the income approach, and (3) the sales comparison approach [30], [38], [41] . The cost approach sets the price of a product by assessing the cost of replacing or  \nreproducing it. Evaluating the cost of data products is difficult due to the fact that they can be replicated and distributed at almost zero cost. The income approach determines the price of a product based on the income that it is expected to bring. Predicting the income of data products is challenging because they can generate different revenues across use cases and be sold multiple times to distinct customers. Due to inherent issues with these two methods in the context of data products, the sales comparison approach is more applicable and widely adopted in data markets. The sales comparison approach utilizes existing pricing information of similar products to estimate the price of the commodity to be sold","cbCaisid2fAvNe5j","https://ap.wps.com/l/cbCaisid2fAvNe5j","pdf",1254091,1,14,"English","en",105,"# Introduction\n## Data marketplaces and the need for pricing\n## Limitations of traditional pricing approaches\n## Challenges for sales comparison across marketplaces\n## DaDaDa dataset design and coverage","[{\"question\":\"Why do cost-approach pricing and income-approach pricing perform poorly for data products?\",\"answer\":\"Cost-approach pricing struggles because data products can be replicated and distributed at almost zero marginal cost. Income-approach pricing struggles because expected revenue varies across use cases and multiple sales to different customers are common.\"},{\"question\":\"What makes the sales comparison approach promising for data marketplaces?\",\"answer\":\"Sales comparison leverages pricing signals from similar existing products, reduces information asymmetry between buyers and sellers, and supports transaction completion.\"},{\"question\":\"How does DaDaDa address the lack of standardized pricing benchmarks across marketplaces?\",\"answer\":\"DaDaDa is a dataset containing metadata for 16,147 data products from nine major marketplaces worldwide. It provides structured price references that enable training of pricing models and supports related tasks like classification and retrieval.\"}]",1784177789,35,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"dadada-a-dataset-for-data-pricing-in-data-marketplaces","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/dadada-a-dataset-for-data-pricing-in-data-marketplaces/82045/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do cost-approach pricing and income-approach pricing perform poorly for data products?","Question",{"text":75,"@type":76},"Cost-approach pricing struggles because data products can be replicated and distributed at almost zero marginal cost. Income-approach pricing struggles because expected revenue varies across use cases and multiple sales to different customers are common.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What makes the sales comparison approach promising for data marketplaces?",{"text":80,"@type":76},"Sales comparison leverages pricing signals from similar existing products, reduces information asymmetry between buyers and sellers, and supports transaction completion.",{"name":82,"@type":73,"acceptedAnswer":83},"How does DaDaDa address the lack of standardized pricing benchmarks across marketplaces?",{"text":84,"@type":76},"DaDaDa is a dataset containing metadata for 16,147 data products from nine major marketplaces worldwide. 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