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Does the Fourth Industrial Revolution pose an existential threat to librarianship? No, it does not. Not any more than any other technological innovation (information systems, personal computers, the Internet, e-readers, Google, Google Scholar) did. However, what is very likely is that the technologies that emerge from this era will slowly (but surely) lead to profound changes in how libraries operate. Those libraries that fail to understand or embrace these technologies may, in fact, be left behind. So, we must, as always, stay abreast of trends in emerging technologies and what the literature (i.e., articles in this journal) propose as ideas for adopting (and adapting) them to better serve our patrons. With this column, my aim is to briefly discuss what the fourth industrial revolution is and its relevance within our profession.

The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient's genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature. In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature. Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content, but from a global context as well (from the Internet and from all literature in PubMed). Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach. We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases (breast cancer, prostate cancer, pancreatic cancer, lung cancer, acute myeloid leukemia, Alzheimer's disease, hemochromatosis, age-related macular degeneration (AMD), diabetes mellitus, and cystic fibrosis). We then evaluate our approach in two ways: (1) a direct comparison with the state of the art using benchmark datasets; (2) a validation study comparing the results of our approach with entries in a popular human-curated database (UniProt) for each of the previously mentioned diseases. In the benchmark comparison, our full approach achieves a 28% improvement in F1-measure (from 0.62 to 0.79) over the state-of-the-art results. For the validation study with UniProt Knowledgebase (KB), we present a thorough analysis of the results and errors. Across all diseases, our approach returned 272 triplets (disease-gene-variant) that overlapped with entries in UniProt and 5,384 triplets without overlap in UniProt. Analysis of the overlapping triplets and of a stratified sample of the non-overlapping triplets revealed accuracies of 93% and 80% for the respective categories (cumulative accuracy, 77%). We conclude that our process represents an important and broadly applicable improvement to the state of the art for curation of disease-gene-variant relationships.

PurposeThe purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods.Design/methodology/approachThe general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model.FindingsResults suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts.Research limitations/implicationsThe main limitations of this study were unavailability of labelled older texts and the limited availability of librarians.Practical implicationsThe classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases.Social implicationsThe proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable.Originality/valueThese findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.

Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.

Dr. S.R.Ranganathan's Five Laws of Library Science are applicable to all fields of library activities. Now we have entered the digital era. Information Technology is the only solution to manage all types of information which are growing rapidly. Information professionals, as well as users, are handling information technology through library automation. Despite multidimensional development in a different area of library work and service and their enrichment with the application of modern technology Five Laws of Library Science are equally applicable to library automation. The researcher has personally visited four arts and science colleges in Dindigul district, Tamilnadu, India. The College wise distribution of selected 627sample respondents is furnished. Dr. S.R.Ranganathan's \"Five Laws of Library Science\" was written seventy four years back and in spite of having so many changes in Library world; these Five Laws still fits in today's context.

IntroductionRandomised controlled trials (RCTs) are considered the gold standard for evaluating the efficacy and safety of healthcare interventions. For valid systematic reviews and evidence-based clinical guidelines, it is essential that results of all eligible RCTs are accessible. However, articles about trials published in languages other than English are often not listed in well-known and open trial databases like Medline and therefore scarcely findable. Handsearching national journals is an important approach to identify these articles and enhance their global visibility. Consequently, the results of trials conducted and published in non-English-speaking countries are not lost but rather integrated into the global body of evidence.The present study aims to evaluate the benefits of extensive handsearching in Hungary and to identify key medical fields for future efforts. We will also assess the extent of grey literature in Hungary. We will appraise the risk of bias in the identified RCTs and controlled clinical trials (CCTs; indicating quasi-randomised or possibly randomised controlled trials) and examine the reporting quality of articles in Hungarian medical journals. Additionally, we will explore whether the automation tool Paperfetcher, recommended by Cochrane for handsearching, can effectively support these efforts in a non-English language context.Methods and analysisWe will conduct a cover-to-cover handsearch of all Hungarian medical journals publishing content in the year 2023 to identify all controlled clinical trials, including RCTs, CCTs and non-RCTs, which are trials that use a clearly non-random method for allocating participants to groups. We will also search conference proceedings submitted to the Hungarian National Széchényi Library, abstract supplements from journals available via the Hungarian Medical Bibliography database, preprints available on medRxiv, Hungarian theses and dissertations, as well as Google Scholar to identify grey literature.Two independent researchers will screen the identified records, assess their eligibility, extract data and evaluate the risk of bias and reporting quality according to the CONSORT statement. To verify the availability of reports and publications derived from the identified trials in electronic databases, we will systematically search MEDLINE, the Cochrane Central Register of Controlled Trials (CENTRAL), Embase and Scopus. All identified RCTs and CCTs not yet included in CENTRAL will be added to the database. Additionally, we will compare handsearching supported by the Paperfetcher tool with unsupported handsearching to evaluate the tool’s effectiveness in a Hungarian language context.Ethics and disseminationSince the publication resulting from the handsearching activity is a retrospective review of publicly available sources of evidence, ethical approval is not required. The study findings will be submitted for publication in a peer-reviewed journal and will be presented at international conferences.