Social Sciences
Bitcoin(₿) is a cryptocurrency invented in 2008 by an unknown person or group of people using the pseudonym Satoshi Nakamoto. The currency began use in 2009 when its implementation was released as open-source software.
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The Dolphins network is based on observations of associations between pairs of dolphins, where edges represent associations between dolphins. The Football network is based on college football matches. Nodes represent college football teams, communities correspond to different leagues, and edges correspond to games played between them. The Polbooks network is a co-purchasing network for books. Each node in the dataset represents a book about US politics. An edge between two books indicates that they are often purchased together by customers.
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SNMDat2.0 is a comprehensive multimodal dataset, expanded from the unimodal TwiBot-20, designed for Twitter social bot detection. Specifically, we add 274587 profile images and profile background images, 86498 tweet images and 49549 tweet videos based on the original 229580 twitter users, 227979 follow relationships and 33488192 tweet text.
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<p>This data set is a modified CLARC(Customizable Artificial Community )system data based on garden city kansas socio economic data.The dataset comprises two major parts.1.Load Data: This portion includes detailed hourly electricity demand profiles across 65 distinct load points, categorized into residential, commercial, industrial, and critical infrastructure loads. Each load profile reflects realistic consumption behaviors and demand fluctuations.2.Socioeconomic Data: This segment captures essential demographic and economic indicators for each corresponding load point.
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This dataset contains anonymized Twitter data related to tourist activities in Bangkok, Thailand. It was collected to analyze travel behavior, activity preferences, and temporal patterns during events like the Songkran festival. The dataset includes timestamped activity classifications, geographic information at a generalized level, and extracted named entities relevant to tourism. The dataset is from 2019-04-05 to 2019-04-24.
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This dataset, constructed around the Jilin Baishan Incident, aims to enhance the emotion prediction capabilities of large language models. Approximately 3.5 million raw comments were collected via the Weibo API, covering key information such as user identifiers, text content, timestamps, and interaction metrics. The data underwent preprocessing steps including normalization, Chinese tokenization, stopword removal, deduplication, and anomalous sample exclusion.
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Clean energy technologies, encompassing renewable resources like solar, wind, and hydropower, are essential in the global effort to reduce greenhouse gas emissions and combat climate change. As the globe prepares to transition away from fossil fuels, understanding the factors and parameters influencing the penetration of clean energy into existing energy markets has become a critical step. Controversies surrounding the environmental impacts of renewable technologies, variability in market structures, and economic pressures on clean energy companies can complicate this transition.
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This dataset provides the full list of security controls proposed in the article "Enhancing Cybersecurity in the Judiciary: Integrating Additional Controls into the CIS Framework". The dataset includes a detailed classification of security controls derived from the CIS Controls framework and additional measures specifically tailored to address cybersecurity challenges in the Judiciary. These controls enhance operational risk management, digital asset protection, and organizational resilience.
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Virtual influencers have become a key tool in modern marketing due to their high controllability and cost-effectiveness, gradually replacing traditional influencers and celebrities. This study focuses on virtual influencers as brand endorsers, based on signaling theory and attachment theory, exploring the influence of five key characteristics - controllability, interactivity, parasocial relationship, influencer-product congruence, and source credibility on the purchase intension of Chinese Generation Z consumers. The study used a quantitative approach to collect data
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In recent years, with the rapid development of generative AI, it has become a popular topic in various fields. This study, based on the Stimulus-Organism-Response (SOR) theory, explores the impact of generative AI on customer experience, trust, purchase intention, and loyalty in cross-border e-commerce platforms, with perceived risk as a moderating variable. The empirical design uses data from a questionnaire survey to investigate how generative AI influences customer behavior.
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