Scholarly Context Not Found: One in Five Articles Suffers from Reference Rot

The emergence of the web has fundamentally affected most aspects of information communication, including scholarly communication. The immediacy that characterizes publishing information to the web, as well as accessing it, allows for a dramatic increase in the speed of dissemination of scholarly knowledge. But, the transition from a paper-based to a web-based scholarly communication system also poses challenges. In this paper, we focus on reference rot, the combination of link rot and content drift to which references to web resources included in Science, Technology, and Medicine (STM) articles are subject. We investigate the extent to which reference rot impacts the ability to revisit the web context that surrounds STM articles some time after their publication. We do so on the basis of a vast collection of articles from three corpora that span publication years 1997 to 2012. For over one million references to web resources extracted from over 3.5 million articles, we determine whether the HTTP URI is still responsive on the live web and whether web archives contain an archived snapshot representative of the state the referenced resource had at the time it was referenced. We observe that the fraction of articles containing references to web resources is growing steadily over time. We find one out of five STM articles suffering from reference rot, meaning it is impossible to revisit the web context that surrounds them some time after their publication. When only considering STM articles that contain references to web resources, this fraction increases to seven out of ten. We suggest that, in order to safeguard the long-term integrity of the web-based scholarly record, robust solutions to combat the reference rot problem are required. In conclusion, we provide a brief insight into the directions that are explored with this regard in the context of the Hiberlink project.     

The Role of Google Scholar in Evidence Reviews and Its Applicability to Grey Literature Searching

Google Scholar (GS), a commonly used web-based academic search engine, catalogues between 2 and 100 million records of both academic and grey literature (articles not formally published by commercial academic publishers). Google Scholar collates results from across the internet and is free to use. As a result it has received considerable attention as a method for searching for literature, particularly in searches for grey literature, as required by systematic reviews. The reliance on GS as a standalone resource has been greatly debated, however, and its efficacy in grey literature searching has not yet been investigated. Using systematic review case studies from environmental science, we investigated the utility of GS in systematic reviews and in searches for grey literature. Our findings show that GS results contain moderate amounts of grey literature, with the majority found on average at page 80. We also found that, when searched for specifically, the majority of literature identified using Web of Science was also found using GS. However, our findings showed moderate/poor overlap in results when similar search strings were used in Web of Science and GS (10–67%), and that GS missed some important literature in five of six case studies. Furthermore, a general GS search failed to find any grey literature from a case study that involved manual searching of organisations’ websites. If used in systematic reviews for grey literature, we recommend that searches of article titles focus on the first 200 to 300 results. We conclude that whilst Google Scholar can find much grey literature and specific, known studies, it should not be used alone for systematic review searches. Rather, it forms a powerful addition to other traditional search methods. In addition, we advocate the use of tools to transparently document and catalogue GS search results to maintain high levels of transparency and the ability to be updated, critical to systematic reviews.     

BioCaster: detecting public health rumors with a Web-based text mining system

SUMMARY: BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between Layman's terms and formal-coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition. Higher order event analysis is used to detect more precisely specified warning signals that can then be notified to registered users via email alerts. Evaluation of the system for topic recognition and entity identification is conducted on a gold standard corpus of annotated news articles. AVAILABILITY: The BioCaster map and ontology are freely available via a web portal at http://www.biocaster.org.     

GeneTrack--a genomic data processing and visualization framework

MOTIVATION: High-throughput 'ChIP-chip' and 'ChIP-seq' methodologies generate sufficiently large data sets that analysis poses significant informatics challenges, particularly for research groups with modest computational support. To address this challenge, we devised a software platform for storing, analyzing and visualizing high resolution genome-wide binding data. GeneTrack automates several steps of a typical data processing pipeline, including smoothing and peak detection, and facilitates dissemination of the results via the web. Our software is freely available via the Google Project Hosting environment at http://genetrack.googlecode.com     

A discussion on the availability of life-cycle assessment studies in New Zealand

Purpose

In order to understand the environmental impacts of various products, processes, or services, it should be possible to obtain life-cycle assessment (LCA) reports quickly and easily without having to delve into restricted access or hidden databases. The aim of this study is to assess the availability of environmental LCAs, water footprinting, and carbon footprinting studies conducted in New Zealand.

Methods

To review the quantitative availability of life-cycle assessment studies for New Zealand, simple online searches were performed using the Google and Google Scholar search engines. Additionally, ScienceDirect and Scopus were used to determine the availability of other peer-reviewed LCA-related reports.

Results and discussion

For the period under review, 20 documents were publicly available. Additionally, other searches conducted via ScienceDirect, Scopus and Google Scholar yielded a further 15 restricted documents. The results included data carbon- and water footprinting studies. The number of LCAs and carbon footprinting reports both exceeded those of water footprinting.

Conclusions

Over 35 studies were available through Internet searches. This number excludes wool which had six results (Scopus only) and many more through Google. These were not included due to possible repetition and miscounting of results.

     

Schistosomiasis and hookworm infection in humans: Disease burden,pathobiology and anthelmintic vaccines

Helminth diseases are the ancient scourges of humans and their damages are ‘silent and insidious’. Of the helminth infections, schistosomiasis and hookworm infection have a great impact. This review covers information regarding vaccine candidates against schistosomiasis and hookworms that reached at least up to the phase-1 trial and literatures regarding other vaccine candidates have been excluded. For clinical manifestations, all available literatures were included, and for epidemiology and global burden of the diseases (GBD), literatures only within 2000–2019 were included. Literatures were searched surfing various databases including PubMED, Google Scholar, and Science Direct and overall over 150 literatures were identified. Globally ~250 million people are suffering from schistosomiasis, resulting 1430 thousand DALY (disability adjusted life year) per year. On the other hand, about 1.3 billion people are infected with hookworm (HW), and according to WHO, ~878 million school-age children (SAC) are at risk. HW is estimated to cause 65,000 deaths annually, accounts for 845 thousand DALYs as well as to cause 6–35.3% loss in productivity. Despite tremendous efforts, very few anthelmintic vaccine candidates such as Na-GST-1, Na-APR-1 and Na-ASP-2 against HW, and Sm28GST/Sh28GST, Sm-p80, Sm14 and Sm-TSP-1/SmTSP-2 against schistosomiasis reached up to the clinical trials. More efforts are needed to achieve the WHO targets taken against the maladies.     

InFiRe -- a novel computational method for the identification of insertion sites in transposon mutagenized bacterial genomes

MOTIVATION: InFiRe, Insertion Finder via Restriction digest, is a novel software tool that allows for the computational identification of transposon insertion sites in known bacterial genome sequences after transposon mutagenesis experiments. The approach is based on the fact that restriction endonuclease digestions of bacterial DNA yield a unique pattern of DNA fragments with defined sizes. Transposon insertion changes the size of the hosting DNA fragment by a known number of base pairs. The exact size of this fragment can be determined by Southern blot hybridization. Subsequently, the position of insertion can be identified with computational analysis. The outlined method provides a solid basis for the establishment of a new high-throughput technology. AVAILABILITY AND IMPLEMENTATION: The software is freely available on our web server at www.infire.tu-bs.de. The algorithm was implemented in the statistical programming language R. For the most flexible use, InFiRe is provided in two different versions. A web interface offers the convenient use in a web browser. In addition, the software and source code is freely available for download as R-packages on our website. CONTACT: m.steinert@tu-bs.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

基于Google Maps API的烟草种质资源WebGIS开发研究

采用B/S架构,应用ASP.NET技术开发了烟草种质资源WebGIS。该系统采用Google Maps API作为地图服务,其数据库采用Microsoft SQL Server,服务器端程序应用C++语言开发,浏览器端基于XHTML+JavaScript。该系统对地图操作和种质资源查询进行了整合,用户可通过4种类型5种比例的地图,选择感兴趣的区域和范围。其地图标注和地址解析功能直观地展现了种质资源的地理分布状况,以及种质资源所在位置的地址名称和经纬度等。另外,用户还可通过WebGIS的查询功能获得种质资源的详细数据信息。因此,本研究开发的烟草种质资源WebGIS为种质资源的研究与利用提供了新的技术和策略。     

Skip the Trip: Air Travelers' Behavioral Responses to Pandemic Influenza

Theory suggests that human behavior has implications for disease spread. We examine the hypothesis that individuals engage in voluntary defensive behavior during an epidemic. We estimate the number of passengers missing previously purchased flights as a function of concern for swine flu or A/H1N1 influenza using 1.7 million detailed flight records, Google Trends, and the World Health Organization''s FluNet data. We estimate that concern over “swine flu,” as measured by Google Trends, accounted for 0.34% of missed flights during the epidemic. The Google Trends data correlates strongly with media attention, but poorly (at times negatively) with reported cases in FluNet. Passengers show no response to reported cases. Passengers skipping their purchased trips forwent at least $50 M in travel related benefits. Responding to actual cases would have cut this estimate in half. Thus, people appear to respond to an epidemic by voluntarily engaging in self-protection behavior, but this behavior may not be responsive to objective measures of risk. Clearer risk communication could substantially reduce epidemic costs. People undertaking costly risk reduction behavior, for example, forgoing nonrefundable flights, suggests they may also make less costly behavior adjustments to avoid infection. Accounting for defensive behaviors may be important for forecasting epidemics, but linking behavior with epidemics likely requires consideration of risk communication.     

CHROMA: consensus-based colouring of multiple alignments for publication

CHROMA annotates multiple protein sequence alignments by consensus to produce formatted and coloured text suitable for incorporation into other documents for publication. The package is designed to be flexible and reliable, and has a simple-to-use graphical user interface running under Microsoft Windows. Both the executables and source code for CHROMA running under Windows and Linux (portable command-line only) are freely available at http://www.lg.ndirect.co.uk/chroma. Software enquiries should be directed to CHROMA@lg.ndirect.co.uk.     

Three options for citation tracking: Google Scholar,Scopus and Web of Science

Background

Researchers turn to citation tracking to find the most influential articles for a particular topic and to see how often their own published papers are cited. For years researchers looking for this type of information had only one resource to consult: the Web of Science from Thomson Scientific. In 2004 two competitors emerged – Scopus from Elsevier and Google Scholar from Google. The research reported here uses citation analysis in an observational study examining these three databases; comparing citation counts for articles from two disciplines (oncology and condensed matter physics) and two years (1993 and 2003) to test the hypothesis that the different scholarly publication coverage provided by the three search tools will lead to different citation counts from each.

Methods

Eleven journal titles with varying impact factors were selected from each discipline (oncology and condensed matter physics) using the Journal Citation Reports (JCR). All articles published in the selected titles were retrieved for the years 1993 and 2003, and a stratified random sample of articles was chosen, resulting in four sets of articles. During the week of November 7–12, 2005, the citation counts for each research article were extracted from the three sources. The actual citing references for a subset of the articles published in 2003 were also gathered from each of the three sources.

Results

For oncology 1993 Web of Science returned the highest average number of citations, 45.3. Scopus returned the highest average number of citations (8.9) for oncology 2003. Web of Science returned the highest number of citations for condensed matter physics 1993 and 2003 (22.5 and 3.9 respectively). The data showed a significant difference in the mean citation rates between all pairs of resources except between Google Scholar and Scopus for condensed matter physics 2003. For articles published in 2003 Google Scholar returned the largest amount of unique citing material for oncology and Web of Science returned the most for condensed matter physics.

Conclusion

This study did not identify any one of these three resources as the answer to all citation tracking needs. Scopus showed strength in providing citing literature for current (2003) oncology articles, while Web of Science produced more citing material for 2003 and 1993 condensed matter physics, and 1993 oncology articles. All three tools returned some unique material. Our data indicate that the question of which tool provides the most complete set of citing literature may depend on the subject and publication year of a given article.

     

Head Lice Surveillance on a Deregulated OTC-Sales Market: A Study Using Web Query Data

The head louse, Pediculus humanus capitis, is an obligate ectoparasite that causes infestations of humans. Studies have demonstrated a correlation between sales figures for over-the-counter (OTC) treatment products and the number of humans with head lice. The deregulation of the Swedish pharmacy market on July 1, 2009, decreased the possibility to obtain complete sale figures and thereby the possibility to obtain yearly trends of head lice infestations. In the presented study we wanted to investigate whether web queries on head lice can be used as substitute for OTC sales figures. Via Google Insights for Search and Vårdguiden medical web site, the number of queries on “huvudlöss” (head lice) and “hårlöss” (lice in hair) were obtained. The analysis showed that both the Vårdguiden series and the Google series were statistically significant (p<0.001) when added separately, but if the Google series were already included in the model, the Vårdguiden series were not statistically significant (p = 0.5689). In conclusion, web queries can detect if there is an increase or decrease of head lice infested humans in Sweden over a period of years, and be as reliable a proxy as the OTC-sales figures.     

Characterizing Social Media Metrics of Scholarly Papers: The Effect of Document Properties and Collaboration Patterns

A number of new metrics based on social media platforms—grouped under the term “altmetrics”—have recently been introduced as potential indicators of research impact. Despite their current popularity, there is a lack of information regarding the determinants of these metrics. Using publication and citation data from 1.3 million papers published in 2012 and covered in Thomson Reuters’ Web of Science as well as social media counts from Altmetric.com, this paper analyses the main patterns of five social media metrics as a function of document characteristics (i.e., discipline, document type, title length, number of pages and references) and collaborative practices and compares them to patterns known for citations. Results show that the presence of papers on social media is low, with 21.5% of papers receiving at least one tweet, 4.7% being shared on Facebook, 1.9% mentioned on blogs, 0.8% found on Google+ and 0.7% discussed in mainstream media. By contrast, 66.8% of papers have received at least one citation. Our findings show that both citations and social media metrics increase with the extent of collaboration and the length of the references list. On the other hand, while editorials and news items are seldom cited, it is these types of document that are the most popular on Twitter. Similarly, while longer papers typically attract more citations, an opposite trend is seen on social media platforms. Finally, contrary to what is observed for citations, it is papers in the Social Sciences and humanities that are the most often found on social media platforms. On the whole, these findings suggest that factors driving social media and citations are different. Therefore, social media metrics cannot actually be seen as alternatives to citations; at most, they may function as complements to other type of indicators.     

EntrezAJAX: direct web browser access to the Entrez Programming Utilities

Web applications for biology and medicine often need to integrate data from Entrez services provided by the National Center for Biotechnology Information. However, direct access to Entrez from a web browser is not possible due to 'same-origin' security restrictions. The use of "Asynchronous JavaScript and XML" (AJAX) to create rich, interactive web applications is now commonplace. The ability to access Entrez via AJAX would be advantageous in the creation of integrated biomedical web resources. We describe EntrezAJAX, which provides access to Entrez eUtils and is able to circumvent same-origin browser restrictions. EntrezAJAX is easily implemented by JavaScript developers and provides identical functionality as Entrez eUtils as well as enhanced functionality to ease development. We provide easy-to-understand developer examples written in JavaScript to illustrate potential uses of this service. For the purposes of speed, reliability and scalability, EntrezAJAX has been deployed on Google App Engine, a freely available cloud service. The EntrezAJAX webpage is located at     

Reconstructing the temporal ordering of biological samples using microarray data

MOTIVATION: Accurate time series for biological processes are difficult to estimate due to problems of synchronization, temporal sampling and rate heterogeneity. Methods are needed that can utilize multi-dimensional data, such as those resulting from DNA microarray experiments, in order to reconstruct time series from unordered or poorly ordered sets of observations. RESULTS: We present a set of algorithms for estimating temporal orderings from unordered sets of sample elements. The techniques we describe are based on modifications of a minimum-spanning tree calculated from a weighted, undirected graph. We demonstrate the efficacy of our approach by applying these techniques to an artificial data set as well as several gene expression data sets derived from DNA microarray experiments. In addition to estimating orderings, the techniques we describe also provide useful heuristics for assessing relevant properties of sample datasets such as noise and sampling intensity, and we show how a data structure called a PQ-tree can be used to represent uncertainty in a reconstructed ordering. AVAILABILITY: Academic implementations of the ordering algorithms are available as source code (in the programming language Python) on our web site, along with documentation on their use. The artificial 'jelly roll' data set upon which the algorithm was tested is also available from this web site. The publicly available gene expression data may be found at http://genome-www.stanford.edu/cellcycle/ and http://caulobacter.stanford.edu/CellCycle/.     

A web server for interactive and zoomable Chaos Game Representation images

Chaos Game Representation (CGR) is a generalized scale-independent Markov transition table, which is useful for the visualization and comparative study of genomic signature, or for the study of characteristic sequence motifs. However, in order to fully utilize the scale-independent properties of CGR, it should be accessible through scale-independent user interface instead of static images. Here we describe a web server and Perl library for generating zoomable CGR images utilizing Google Maps API, which is also easily searchable for specific motifs. The web server is freely accessible at , and the Perl library as well as the source code is distributed with the G-language Genome Analysis Environment under GNU General Public License.     

Chemistry and Bioactivity of the Genus Persea - A Review

This review provides the first comprehensive appraisal of bioactive compounds and their biological activities in Persea species from 1950 to 2023. Relevant articles from reputable databases, including PubMed, Web of Science, Science Direct and Google Scholar were collected, leading to the isolation of about 141 metabolite compounds, mainly flavonoids, terpenoids, fatty alcohols, lignoids, and γ-lactone derivatives. These compounds exhibit diverse biological activities, including insecticidal, antifeedant, nematicidal, antibacterial, antifungal, antiviral, cytotoxic, anti-inflammatory, and antioxidant properties. The review emphasizes the significant chemical and pharmacological potential of different Persea species, encouraging further research in various fields and medicine. Valuable insights into potential applications of Persea plants are provided.