cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25440/smu.12062703
title: BuzzCity mobile advertisement dataset

description: This competition involves advertisement data provided by BuzzCity Pte. Ltd. BuzzCity is a global mobile advertising network that has millions of consumers around the world on mobile phones and devices. In Q1 2012, over 45 billion ad banners were delivered across the BuzzCity network consisting of more than 10,000 publisher sites which reach an average of over 300 million unique users per month. The number of smartphones active on the network has also grown significantly. Smartphones now account for more than 32% phones that are served advertisements across the BuzzCity network. The "raw" data used in this competition has two types: publisher database and click database, both provided in CSV format. The publisher database records the publisher's (aka partner's) profile and comprises several fields: publisherid - Unique identifier of a publisher. Bankaccount - Bank account associated with a publisher (may be empty) address - Mailing address of a publisher (obfuscated; may be empty) status - Label of a publisher, which can be the following: "OK" - Publishers whom BuzzCity deems as having healthy traffic (or those who slipped their detection mechanisms) "Observation" - Publishers who may have just started their traffic or their traffic statistics deviates from system wide average. BuzzCity does not have any conclusive stand with these publishers yet "Fraud" - Publishers who are deemed as fraudulent with clear proof. Buzzcity suspends their accounts and their earnings will not be paid On the other hand, the click database records the click traffics and has several fields: id - Unique identifier of a particular click numericip - Public IP address of a clicker/visitor deviceua - Phone model used by a clicker/visitor publisherid - Unique identifier of a publisher adscampaignid - Unique identifier of a given advertisement campaign usercountry - Country from which the surfer is clicktime - Timestamp of a given click (in YYYY-MM-DD format) publisherchannel - Publisher's channel type, which can be the following: ad - Adult sites co - Community es - Entertainment and lifestyle gd - Glamour and dating in - Information mc - Mobile content pp - Premium portal se - Search, portal, services referredurl - URL where the ad banners were clicked (obfuscated; may be empty). More details about the HTTP Referer protocol can be found in this article. Related Publication: R. J. Oentaryo, E.-P. Lim, M. Finegold, D. Lo, F.-D. Zhu, C. Phua, E.-Y. Cheu, G.-E. Yap, K. Sim, M. N. Nguyen, K. Perera, B. Neupane, M. Faisal, Z.-Y. Aung, W. L. Woon, W. Chen, D. Patel, and D. Berrar. (2014).<em> </em>Detecting click fraud in online advertising: A data mining approach,<em> Journal of Machine Learning Research, 15, </em>99-140.
DOI: 10.25721/0wzz-6t54
title: En revenant de noces
DOI: 10.25721/pm20-5h29
title: La Belle Angélique
DOI: 10.25721/9g4b-6923
title: La perdriole
DOI: 10.25721/bgk0-1r55
title: Deux devinettes
DOI: 10.25721/tfmz-m718
title: Chanson de Biron
DOI: 10.25721/phra-fa70
title: C'est bien le bonjour ma petite Nônette
DOI: 10.25721/wye4-m824
title: J'étais assis dans ma chambrette
DOI: 10.25721/c31q-zp95
title: Autobiographie et commentaires par monsieur A. Seintein
DOI: 10.25721/7h7k-zj21
title: Autobiographie et commentaires par monsieur A. Seintein.  Accord et échelle du hautbois
DOI: 10.25721/9w0w-gp09
title: Autobiographie et commentaires par monsieur A. Seintein
DOI: 10.25721/w9pv-cz38
title: Autobiographie et commentaires par monsieur A. Seintein
DOI: 10.25721/5rdy-vh73
title: Ra amicgo abandounada
DOI: 10.25721/btts-ck12
title: Dus pastous à l'oumbretto
DOI: 10.25721/x4hg-a088
title: Bourrée. Air de danse
DOI: 10.25721/dpfw-sw69
title: Castagno
DOI: 10.25721/64vn-7356
title: Scottish
DOI: 10.25721/hrkp-s650
title: La traversée
DOI: 10.25721/1gvy-fp49
title: Quadrille. Première figure "Les galants et la câtine"
DOI: 10.25721/966k-r515
title: Quadrille. Quatrième figure "En avant les trois"
DOI: 10.25721/qpk0-1770
title: Quadrille. Troisième figure "Chassé-croisé"
DOI: 10.25721/zh64-tx78
title: Quadrille. Deuxième figure "en avant-deux"
DOI: 10.25721/7hz1-ev18
title: Quadrille. Cinquième figure "Le galop"
DOI: 10.25721/wb8n-sk73
title: Rythme du pas de bourrée
DOI: 10.25440/smu.12062706
title: Twitter bot profiling

description: This dataset comprises a set of Twitter accounts in Singapore that are used for social bot profiling research conducted by the Living Analytics Research Centre (LARC) at Singapore Management University (SMU). Here a bot is defined as a Twitter account that generates contents and/or interacts with other users automatically (at least according to human judgment). In this research, Twitter bots have been categorized into three major types: Broadcast bot. This bot aims at disseminating information to general audience by providing, e.g., benign links to news, blogs or sites. Such bot is often managed by an organization or a group of people (e.g., bloggers). Consumption bot. The main purpose of this bot is to aggregate contents from various sources and/or provide update services (e.g., horoscope reading, weather update) for personal consumption or use. Spam bot. This type of bots posts malicious contents (e.g., to trick people by hijacking certain account or redirecting them to malicious sites), or promotes harmless but invalid/irrelevant contents aggressively. This categorization is general enough to cater for new, emerging types of bot (e.g., chatbots can be viewed as a special type of broadcast bots). The dataset was collected from 1 January to 30 April 2014 via the Twitter REST and streaming APIs. Starting from popular seed users (i.e., users having many followers), their follow, retweet, and user mention links were crawled. The data collection proceeds by adding those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. Using this procedure, a total of 159,724 accounts have been collected. To identify bots, the first step is to check active accounts who tweeted at least 15 times within the month of April 2014. These accounts were then manually checked and labelled, of which 589 bots were found. As many more human users are expected in the Twitter population, the remaining accounts were randomly sampled and manually checked. With this, 1,024 human accounts were identified. In total, this results in 1,613 labelled accounts. <br><br>Related Publication: R. J. Oentaryo, A. Murdopo, P. K. Prasetyo, and E.-P. Lim. (2016). On profiling bots in social media. <em>Proceedings of the International Conference on Social Informatics (SocInfo’16)</em>, 92-109. Bellevue, WA. https://doi.org/10.1007/978-3-319-47880-7_6
DOI: 10.25721/fxej-gw57
title: Le berger et la bergère
DOI: 10.25721/pyf3-fk69
title: Era proumenado d'un berge
DOI: 10.25721/zt13-cj21
title: Le berger et la bergère
DOI: 10.25721/q1gz-yc31
title: Lambskinnet
DOI: 10.5281/zenodo.4429848
title: Coffee Consumption per Capita and Covid-19 Mortality Rate

description: There is a correlation between average of "Coffee Consumption per capita" and average of "Covid-19 Mortality Rate" for countries with high coffee consumption per capita (the countries that has more than 5 kg per capita per year consumption). The Details of computations and data are provided in an attached supplementary file (Excel File Format).
DOI: 10.25440/smu.12062709
title: Twitter cascade dataset

description: This dataset comprises a set of information cascades generated by Singapore Twitter users. Here a cascade is defined as a set of tweets about the same topic. This dataset was collected via the Twitter REST and streaming APIs in the following way. Starting from popular seed users (i.e., users having many followers), we crawled their follow, retweet, and user mention links. We then added those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. With this, we have a total of 184,794 Twitter user accounts. Then tweets are crawled from these users from 1 April to 31 August 2012. In all, we got 32,479,134 tweets. To identify cascades, we extracted all the URL links and hashtags from the above tweets. And these URL links and hashtags are considered as the identities of cascades. In other words, all the tweets which contain the same URL link (or the same hashtag) represent a cascade. Mathematically, a cascade is represented as a set of user-timestamp pairs. Figure 1 provides an example, i.e. cascade C = {&lt; u1, t1 &gt;, &lt; u2, t2 &gt;, &lt; u1, t3 &gt;, &lt; u3, t4 &gt;, &lt; u4, t5 &gt;}. For evaluation, the dataset was split into two parts: four months data for training and the last one month data for testing. Table 1summarizes the basic (count) statistics of the dataset. Each line in each file represents a cascade. The first term in each line is a hashtag or URL, the second term is a list of user-timestamp pairs. Due to privacy concerns, all user identities are anonymized.
DOI: 10.25721/xnv3-yc84
title: Mouth music, (musique de bouche), air de danse d'Ecosse
DOI: 10.25721/a4y6-ss05
title: Sélection d'airs écossais
DOI: 10.25721/fx8c-q311
title: The Road to the Isles
DOI: 10.25721/ekjb-g210
title: Sean triubmais
DOI: 10.25440/smu.12062712
title: Data from: Too Materialistic to get Married and have Children?

description: SPSS Data File Used in Analyses for Studies 1 and 2Related Publication: Li, N.P., Lim, A.J.Y., Tsai M.H. (2015) Too Materialistic to get Married and have Children? PLoS ONE 10(5): e0126543. http://dx.doi.org/10.1371/journal.pone.0126543. Full text available in InK: http://ink.library.smu.edu.sg/soss_research/1676/<br>We developed new materials to induce a luxury mindset and activate materialistic values, and examined materialism’s relationship to attitudes toward marriage and having children in Singapore. Path analyses indicated that materialistic values led to more negative attitudes toward marriage, which led to more negative attitudes toward children, which in turn led to a decreased number of children desired. Results across two studies highlight, at the individual level, the tradeoff between materialistic values and attitudes toward marriage and procreation and suggest that a consideration of psychological variables such as materialistic values may allow for a better understanding of larger-scale socioeconomic issues including low fertility rates among developed countries. We discuss implications and describe how psychological factors relating to low fertility fit within evolutionary mismatch and life history theory frameworks.<br>
DOI: 10.25440/smu.12062712.v1
title: Data from: Too Materialistic to get Married and have Children?

description: SPSS Data File Used in Analyses for Studies 1 and 2Related Publication: Li, N.P., Lim, A.J.Y., Tsai M.H. (2015) Too Materialistic to get Married and have Children? PLoS ONE 10(5): e0126543. http://dx.doi.org/10.1371/journal.pone.0126543. Full text available in InK: http://ink.library.smu.edu.sg/soss_research/1676/<br>We developed new materials to induce a luxury mindset and activate materialistic values, and examined materialism’s relationship to attitudes toward marriage and having children in Singapore. Path analyses indicated that materialistic values led to more negative attitudes toward marriage, which led to more negative attitudes toward children, which in turn led to a decreased number of children desired. Results across two studies highlight, at the individual level, the tradeoff between materialistic values and attitudes toward marriage and procreation and suggest that a consideration of psychological variables such as materialistic values may allow for a better understanding of larger-scale socioeconomic issues including low fertility rates among developed countries. We discuss implications and describe how psychological factors relating to low fertility fit within evolutionary mismatch and life history theory frameworks.<br>
DOI: 10.25721/rw83-4561
title: Sélection d'airs écossais
DOI: 10.25721/aq06-4j80
title: Monymusik
DOI: 10.25721/8y2t-9e28
title: Hornpipes, air de danse écossais
DOI: 10.25721/mp0t-6951
title: A combien sont vos oignons
DOI: 10.25721/svd1-1w25
title: The Tulloch
DOI: 10.25721/b3fr-ez57
title: Si j'étais hirondelle
DOI: 10.25721/nfjr-h353
title: A recouvrance
DOI: 10.25721/w3vh-1d42
title: Je descendis dans mon jardin
DOI: 10.25721/4v7h-4a47
title: J'ai un nid de petits lièvres
DOI: 10.25440/smu.12062715
title: Topic Expertise Model

description: Community Question Answering (CQA) websites, where people share expertise on open platforms, have become large repositories of valuable knowledge. To bring the best value out of these knowledge repositories, it is critically important for CQA services to know how to find the right experts, retrieve archived similar questions and recommend best answers to new questions. To tackle this cluster of closely related problems in a principled approach, we proposed Topic Expertise Model (TEM), a novel probabilistic generative model with GMM hybrid, to jointly model topics and expertise by integrating textual content model and link structure analysis. Based on TEM results, we proposed CQARank to measure user interests and expertise score under different topics. Leveraging the question answering history based on long-term community reviews and voting, our method could find experts with both similar topical preference and high topical expertise. This package implements Gibbs sampling for Topic Expertise Model for jointly modeling topics and expertise in question answering communities. More details of our model are described in the related publication http://dl.acm.org/citation.cfm?id=2505720.Related Publication: Yang, L., Qiu, M., Gottipati, S., Zhu, F., Jiang, J., Sun, H., & Chen, Z. (2013). CQArank: jointly model topics and expertise in community question answering. Paper presented at the 22nd ACM International Conference on Information & Knowledge Management, San Francisco, California, USA. DOI: 10.1145/2505515.2505720 <br>
DOI: 10.25440/smu.12062715.v1
title: Topic Expertise Model

description: Community Question Answering (CQA) websites, where people share expertise on open platforms, have become large repositories of valuable knowledge. To bring the best value out of these knowledge repositories, it is critically important for CQA services to know how to find the right experts, retrieve archived similar questions and recommend best answers to new questions. To tackle this cluster of closely related problems in a principled approach, we proposed Topic Expertise Model (TEM), a novel probabilistic generative model with GMM hybrid, to jointly model topics and expertise by integrating textual content model and link structure analysis. Based on TEM results, we proposed CQARank to measure user interests and expertise score under different topics. Leveraging the question answering history based on long-term community reviews and voting, our method could find experts with both similar topical preference and high topical expertise. This package implements Gibbs sampling for Topic Expertise Model for jointly modeling topics and expertise in question answering communities. More details of our model are described in the related publication http://dl.acm.org/citation.cfm?id=2505720.Related Publication: Yang, L., Qiu, M., Gottipati, S., Zhu, F., Jiang, J., Sun, H., & Chen, Z. (2013). CQArank: jointly model topics and expertise in community question answering. Paper presented at the 22nd ACM International Conference on Information & Knowledge Management, San Francisco, California, USA. DOI: 10.1145/2505515.2505720 <br>
DOI: 10.25721/fmpd-9p17
title: Le rossignol sauvage
DOI: 10.25721/fq1g-a738
title: Bonjour ma petite Jeannette
DOI: 10.25721/ymnr-nv14
title: Pomm' de la pomm'
DOI: 10.25721/7ceq-kk20
title: Dans une ville auprès de Nantes
DOI: 10.25721/ft0t-z874
title: J'allais à la fontaine
DOI: 10.25721/etg8-t472
title: Je suis fille élégante
DOI: 10.5281/zenodo.4576401
title: MAgPIE v4.3.2 model run outputs including dynamic forestry sector

description: Archive of runs produced for the forestry paper using MAgPIE 4.3.1+ Model Code Model documentation Model tutorials
DOI: 10.25721/3qy2-aq66
title: Catherine était la fille
DOI: 10.25721/11y0-5159
title: Dessous un laurier vert
DOI: 10.25721/dvnz-3a43
title: C'est aujourd'hui que Rosette se marie
DOI: 10.25721/evwc-hq85
title: Mon père m'a mariée à l'âge de quinze ans
DOI: 10.25721/hj8t-hm30
title: Pantalon blanc, chemise de service
DOI: 10.25721/97aa-yc07
title: Le juif errant
DOI: 10.25721/24tv-x589
title: Coeur de lilas
DOI: 10.25721/te7b-1v70
title: Chanson des Johnies
DOI: 10.25721/fdv5-a224
title: Bonjour ma petite Jeannette
DOI: 10.25721/mpwb-v064
title: On m'envoyait à l'herbe cueillir du cresson
DOI: 10.25721/sq4q-p323
title: Elle s'en va de branche en branche
DOI: 10.25721/360q-jc47
title: Passant par Paris, vidant ma bouteille
DOI: 10.25721/6145-rd89
title: Venez chers camarades
DOI: 10.25440/smu.12062721
title: PMF Model for Mining User Relations

description: Advances in sentiment analysis have enabled extraction of user relations implied in online textual exchanges such as forum posts. However, recent studies in this direction only consider direct relation extraction from text. As user interactions can be sparse in online discussions, we propose to apply collaborative filtering through probabilistic matrix factorization to generalize and improve the opinion matrices extracted from forum posts. This package implements the construction of opinion matrices which are the input of PMF model. The main features include aspect identification, opinion expression identification and opinion relation extraction based on dependency path rules. More details of our methods for aspect identification, opinion identification and opinion relation extraction are described in the related paper http://aclweb.org/anthology/N13-1041.Related Publication: Qiu, M., Yang, L., & Jiang, J. (2013). Mining User Relations from Online Discussions using Sentiment Analysis and Probabilistic Matrix Factorization. <em>NAACL HLT 2013</em>, 401-410. http://aclweb.org/anthology/N13-1041 Available in InK: http://ink.library.smu.edu.sg/sis_research/1891/
DOI: 10.25440/smu.12062721.v1
title: PMF Model for Mining User Relations

description: Advances in sentiment analysis have enabled extraction of user relations implied in online textual exchanges such as forum posts. However, recent studies in this direction only consider direct relation extraction from text. As user interactions can be sparse in online discussions, we propose to apply collaborative filtering through probabilistic matrix factorization to generalize and improve the opinion matrices extracted from forum posts. This package implements the construction of opinion matrices which are the input of PMF model. The main features include aspect identification, opinion expression identification and opinion relation extraction based on dependency path rules. More details of our methods for aspect identification, opinion identification and opinion relation extraction are described in the related paper http://aclweb.org/anthology/N13-1041.Related Publication: Qiu, M., Yang, L., & Jiang, J. (2013). Mining User Relations from Online Discussions using Sentiment Analysis and Probabilistic Matrix Factorization. <em>NAACL HLT 2013</em>, 401-410. http://aclweb.org/anthology/N13-1041 Available in InK: http://ink.library.smu.edu.sg/sis_research/1891/
DOI: 10.25440/smu.12062724.v1
title: Joint Behavior-Topic Model for Microblogs

description: We propose an LDA-based behavior-topic model (B-LDA) which jointly models user topic interests and behavioral patterns. We focus the study of the model on on-line social network settings such as microblogs like Twitter where the textual content is relatively short but user interactions on them are rich.Related Publication: Qiu, M., Zhu, F., & Jiang, J. (2013). It is not just what we say, but how we say them: LDA-based behavior-topic model. In <em>2013 SIAM International Conference on Data Mining (SDM’13): 2-4 May, Austin, Texas (pp. </em>794-802). Philadelphia: SIAM. http://doi.org/10.1137/1.9781611972832.88
DOI: 10.25440/smu.12062724
title: Joint Behavior-Topic Model for Microblogs

description: We propose an LDA-based behavior-topic model (B-LDA) which jointly models user topic interests and behavioral patterns. We focus the study of the model on on-line social network settings such as microblogs like Twitter where the textual content is relatively short but user interactions on them are rich.Related Publication: Qiu, M., Zhu, F., & Jiang, J. (2013). It is not just what we say, but how we say them: LDA-based behavior-topic model. In <em>2013 SIAM International Conference on Data Mining (SDM’13): 2-4 May, Austin, Texas (pp. </em>794-802). Philadelphia: SIAM. http://doi.org/10.1137/1.9781611972832.88
DOI: 10.25440/smu.12062730.v1
title: Twitter-LDA

description: Latent Dirichlet Allocation (LDA) has been widely used in textual analysis. The original LDA is used to find hidden "topics" in the documents, where a topic is a subject like "arts" or "education" that is discussed in the documents. The original setting in LDA, where each word has a topic label, may not work well with Twitter as tweets are short and a single tweet is more likely to talk about one topic. Hence, Twitter-LDA (T-LDA) has been proposed to address this issue. T-LDA also addresses the noisy nature of tweets, where it captures background words in tweets. As experiments in [7] have shown that T-LDA could capture more meaningful topics than LDA in Microblogs. The original setting in Latent Dirichlet Allocation (LDA), where each word has a topic label, may not work well with Twitter as tweets are short and a single tweet is more likely to talk about one topic. Hence, Twitter-LDA (T-LDA) has been proposed to address this issue. T-LDA also addresses the noisy nature of tweets, where it captures background words in tweets.Related Publication: Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E. P., Yan, H., & Li, X. (2011). Comparing twitter and traditional media using topic models. In <em>Advances in Information Retrieval</em> (pp. 338-349). http://doi.org/10.1007/978-3-642-20161-5_34
DOI: 10.25440/smu.12062730
title: Twitter-LDA

description: Latent Dirichlet Allocation (LDA) has been widely used in textual analysis. The original LDA is used to find hidden "topics" in the documents, where a topic is a subject like "arts" or "education" that is discussed in the documents. The original setting in LDA, where each word has a topic label, may not work well with Twitter as tweets are short and a single tweet is more likely to talk about one topic. Hence, Twitter-LDA (T-LDA) has been proposed to address this issue. T-LDA also addresses the noisy nature of tweets, where it captures background words in tweets. As experiments in [7] have shown that T-LDA could capture more meaningful topics than LDA in Microblogs. The original setting in Latent Dirichlet Allocation (LDA), where each word has a topic label, may not work well with Twitter as tweets are short and a single tweet is more likely to talk about one topic. Hence, Twitter-LDA (T-LDA) has been proposed to address this issue. T-LDA also addresses the noisy nature of tweets, where it captures background words in tweets.Related Publication: Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E. P., Yan, H., & Li, X. (2011). Comparing twitter and traditional media using topic models. In <em>Advances in Information Retrieval</em> (pp. 338-349). http://doi.org/10.1007/978-3-642-20161-5_34