Information Science Roles in the Emerging Field of Data Science

There has long been discussion about the distinctions of library science,information science,and informatics,and how these areas differ and overlap with computer science.Today the term data science is emerging that generates excitement and questions about how it relates to and differs from these other areas of study.

Dynamics and interactions of blog-based science communication:A case study of blogging activities in China related to 2011 Japanese seismic and nuclear power crisis

Purpose: This paper tries to understand the dynamics of scientific communication systems during crises by investigating as a case study the blogging activities that took place during the period of the 2011 earthquake and related events in Japan. Interactions between bloggers and registered users are studied quantitatively and qualitatively at Sciencenet.cn, an influential science-related blogosphere in China.Design/methodology/approach: The editors of Sciencenet.cn compiled a special issue of science blog articles under the title Analysis of the Japanese Earthquake. We developed a spider program and downloaded from this special issue the metadata about title, content,publishing time, total read count, reply count and recommendation count, and further collected information about bloggers and recommenders. We then sent a short message to the bloggers who wrote articles on these emergencies, asking for their educational and professional background.Findings: We found that knowledge reflected in the blog articles is strongly related to the educational and professional background of bloggers. Knowledge diffusion is facilitated by interactions, such as recommendations, comments and answers. Interactions via comments and recommendations are of an assortative nature: A blog article is more likelyto be commented on and recommended by those bloggers who write on the same or similar topics than by those writing on a different one. Registered users tend to give comments on articles dealing with the topic that they recommend, and vice versa.Interaction in the intersection of two or three topics is more intense than that within one topic. The impact of blog articles is also influenced by other factors, such as the reputation of the blogger and the type of information they contain.Implications and limitations: It is confirmed that studying blogs is a valid approach within informetric studies. Yet, we only studied one triple(earthquake, tsunami, nuclear disaster) event based on data originating from one Chinese blog website. More events should be studied.Originality/value: Informetric studies based on blogs are still relatively few. Using science blogs and combining comments on a triple event with the knowledge background of bloggers in China is even less common. As such this contribution enhances our knowledge on this new form of science communication activity.

Data-driven Discovery: A New Era of Exploiting the Literature and Data

In the current data-intensive era, the traditional hands-on method of conducting scientific research by exploring related publications to generate a testable hypothesis is well on its way of becoming obsolete within just a year or two. Analyzing the literature and data to automatically generate a hypothesis might become the de facto approach to inform the core research efforts of those trying to master the exponentially rapid expansion of publications and datasets. Here, viewpoints are provided and discussed to help the understanding of challenges of data-driven discovery.

Understanding the Correlations between Social Attention and Topic Trends of Scientific Publications

Purpose： We propose and apply a simplified nowcasting model to understand the correlations between social attention and topic trends of scientific publications. Design/methodology/approach： First, topics are generated from the obesity corpus by using the latent Dirichlet allocation （LDA） algorithm and time series of keyword search trends in Google Trends are obtained. We then establish the structural time series model using data from January 2004 to December 2012, and evaluate the model using data from January 2013. We employ a state-space model to separate different non-regression components in an observational time series （i.e. the tendency and the seasonality） and apply the ＂spike and slab prior＂ and stepwise regression to analyze the correlations between the regression component and the social media attention. The two parts are combined using Markov-chain Monte Carlo sampling techniques to obtain our results. Findings： The results of our study show that （1） the number of publications on child obesity increases at a lower rate than that of diabetes publications; （2） the number of publication on a given topic may exhibit a relationship with the season or time of year; and （3） there exists a correlation between the number of publications on a given topic and its social media attention, i.e. the search frequency related to that topic as identified by Google Trends. We found that our model is also able to predict the number of publications related to a given topic.

A view on big data and its relation to Informetrics

Purpose: Big data offer a huge challenge. Their very existence leads to the contradiction that the more data we have the less accessible they become,as the particular piece of information one is searching for may be buried among terabytes of other data. In this contribution we discuss the origin of big data and point to three challenges when big data arise: Data storage,data processing and generating insights.Design/methodology/approach: Computer-related challenges can be expressed by the CAP theorem which states that it is only possible to simultaneously provide any two of the three following properties in distributed applications: Consistency(C),availability(A) and partition tolerance(P). As an aside we mention Amdahl's law and its application for scientific collaboration. We further discuss data mining in large databases and knowledge representation for handling the results of data mining exercises. We further offer a short informetric study of the field of big data,and point to the ethical dimension of the big data phenomenon.Findings: There still are serious problems to overcome before the field of big data can deliver on its promises.Implications and limitations: This contribution offers a personal view,focusing on the information science aspects,but much more can be said about software aspects.Originality/value: We express the hope that the information scientists,including librarians,will be able to play their full role within the knowledge discovery,data mining and big data communities,leading to exciting developments,the reduction of scientific bottlenecks and really innovative applications.

Do Chinese and American contributions in top journals have an equal citation potential?

Purpose： We want to contribute to the evaluation of Chinese research, focusing on contributions in top journals. Design/methodology/approach： Using a Mann-Whitney test we investigate if contributions in Nature, Science or the Proceedings of the National Academy of Sciences of the United States of America （PNAS） by Chinese or American authors only, i.e. articles for which all authors have a Chinese or an American address, have a different citation potential. Findings： There is no reason to state that Chinese and American contributions in these top journals have a different citation potential. Research limitations： Because of the small numbers involved we were not able to make a distinction between publications in Nature, Science or the Proceedings of the National Academy of Sciences of the United States of America. Practical implications： These results suggest that the better Chinese research results are of a similar level as those by American colleagues. Originality/value： It is well-known that the number of citations per publication by Chinese authors is still lagging with respect to leading scientific nations and in particular compared with the USA. We have shown that this difference does not necessarily hold in contributions in Nature, Science or the Proceedings of the National Academy of Sciences of the United States of America.

一种结合有监督学习的动态主题模型

针对传统主题模型存在的不足,提出一种新的结合有监督学习的动态主题模型(Supervised Dynamic Topic Model,S-DTM)。该模型不仅能够随时间的变化对语言进行动态建模,而且结合有监督学习技术,在主题变分推理中加入标签约束,从而建立主题与标签之间的映射关系,提高主题的表达解释能力。通过在一个跨越25年"以自然语言处理领域的中文期刊论文为主导"的中文语料库上的实验,证明该模型相较于静态的有监督主题模型和无监督的动态主题模型,具有更好的语义解释概括能力,能更准确地反映文档的主题结构,更精确地捕捉主题–词汇概率分布的动态演化。

Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

The size,shape,and physical characteristics of the human skull are distinct when considering individual humans.In physical anthropology,the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective manner.For example,labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of collections.Given the multiple issues associated with the manual identification of skulls,we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features,Gabor features,fractal features,discrete wavelet transforms,and combinations of features.Each underlying facial bone exhibits unique characteristics essential to the face’s physical structure that could be exploited for identification.Therefore,we developed an automatic recognition method to classify human skulls for consistent identification compared with traditional classification approaches.Using our proposed approach,we were able to achieve an accuracy of 92.3–99.5%in the classification of human skulls with mandibles and an accuracy of 91.4–99.9%in the classification of human skills without mandibles.Our study represents a step forward in the construction of an effective automatic human skull identification system with a classification process that achieves satisfactory performance for a limited dataset of skull images.

Gauging a Firm's Innovative Performance Using an Integrated Structural Index for Patents

Purpose： In this contribution we try to find new indicators to measure characteristics of a finn＇s patents and their influence on a company＇s profits. Design/methodology/approach： We realize that patentevaluation and influence on a company＇s profits is a complicated issue requiring different perspectives. For this reason we design two types of structural h-indices, derived from the International Patent Classification （IPC）. In a case study we apply not only basic statistics but also a nested case-control methodology. Findings： The resulting indicator values based on a large dataset （19,080 patents in total） from the pharmaceutical industry show that the new structural indices are significantly correlated with a firm＇s profits. Research limitations： The new structural index and the synthetic structural index have just been applied in one case study in the pharmaceutical industry. Practical implications： Our study suggests useful implications for patentometric studies and leads to suggestions for different sized firms to include a healthy research and development （R＆D） policy management. The structural h-index can be used to gauge the profits resulting from the innovative performance of a firm＇s patent portfolio. Originality/value： Traditionally, the breadth and depth of patents of a firm and their citations are considered separately. This approach, however, does not provide an integrated insight in the major characteristics of a firm＇s patents. The Sh（Y） index, proposed in our investigation, can reflect a firm＇s innovation activities, its technological breadth, and its influence in an integrated way.

Digital Knowledge Resources Sharing Through Online Distance Education Course

Since the electronic resources and Internet Web sites became popular, distance education courses offered via the Internet could play an important role in providing global digital resources and teaching the knowledge of international library and information standards for bibliographic databases with electronic and web resources.This paper uses the example of the author's new online course: ILS 608 Cataloging and Development of a Digital Union Catalog for Ancient Chinese Books to demonstrate the global re...

Note on Three Classes of Data Grid Operations

Traditional grid computing focuses on the movement of data to compute resources and the management of large scale simulations. Data grid computing focuses on moving the operations to the storage location and on operations on data collections. We present three types of data grid operations that facilitate data driven research: the manipulation of time series data, the reproducible execution of workflows, and the mapping of data access to software-defined networks. These data grid operations have been implemented as operations on collections within the NSF DataNet Federation Consortium project. The operations can be applied at the remote resource where data are stored, improving the ability of researchers to interact with large collections.

Heuristics based semantic annotation of biodiversity documents in Chinese

Purpose: To design an efficient high-performance algorithm for semantic annotation of biodiversity documents in Chinese.Design/methodology/approach: Data set consists of 1,000 randomly selected documents from Flora of China. Comparative evaluation of the proposed approach with the Na ve Bayes algorithm have been developed before for the same purpose.Findings: Experimental results show that the heuristics based algorithm outperformed the Na ve Bayes algorithm. The use of leading words helped improving the annotation performance while prioritizing rule application based on their weights had no significant impact on algorithm performance.Research limitations: The ICTCLAS was used to identify word boundaries off-shelf without optimatization for biodiversity domain. This may have not made the best use of the tool.Practical implications & Originality/value: The performance of heuristics based approach,enhanced by leading words analysis, reached an F value of 0.9216, which is sufficiently accurate for practical use.

Relational Topology-based Heterogeneous Network Embedding for Predicting Drug-Target Interactions

Predicting interactions between drugs and target proteins has become an essential task in the drug discovery process.Although the method of validation via wet-lab experiments has become available,experimental methods for drug-target interaction(DTI)identification remain either time consuming or heavily dependent on domain expertise.Therefore,various computational models have been proposed to predict possible interactions between drugs and target proteins.However,most prediction methods do not consider the topological structures characteristics of the relationship.In this paper,we propose a relational topologybased heterogeneous network embedding method to predict drug-target interactions,abbreviated as RTHNE_DTI.We first construct a heterogeneous information network based on the interaction between different types of nodes,to enhance the ability of association discovery by fully considering the topology of the network.Then drug and target protein nodes can be represented by the other types of nodes.According to the different topological structure of the relationship between the nodes,we divide the relationship in the heterogeneous network into two categories and model them separately.Extensive experiments on the realworld drug datasets,RTHNE_DTI produces high efficiency and outperforms other state-of-the-art methods.RTHNE_DTI can be further used to predict the interaction between unknown interaction drug-target pairs.

Features of Sina Weibo altmetrics indicator

With statistical analysis of Weibo altmetrics indicator, the paper explored features of a typical altmetrics indicator in Chinese environment. Results show that the coverage of Weibo is below 1%. However, the coverage is underestimated due to limitation of tracking time and objects. Weibo mentions and discusses articles mainly from disciplines like 'General', 'Biochemistry, genetics and molecular biology', 'Health science', 'Medicine' and 'Life science' etc. In addition to traditional distinguished interdisciplinary journals like Nature, preprint platform and open access journal like ar Xiv, PLo S ONE and SSRN also drew much attention from Weibo. Meanwhile, 'Biology Science' and 'Medical science' have the most highlighted journals. Weibo mainly tracks latest articles, reflected in that articles tracked within 180 days occupy 68.66%, it also tracks classic articles. Weibo authors prefer to disseminate, recommend and criticize articles that are adherent to daily life, funny, useful or related to health, which conveys social value and scholarly value beyond citations. Weibo altmetrics indicator is highly scattered and concentrated. 5.1% of the articles have harvested 50% of the weibos. Besides, articles tracked by Weibo gain global attention much higher than the average level.