EN

Large-Scale Analysis of the Accuracy of the Journal Classification Systems of Web of Science and Scopus

Posted on by Hans Dillaerts

Journal classification systems play an important role in bibliometric analyses. The two most important bibliographic databases, Web of Science and Scopus, each provide a journal classification system. However, no study has systematically investigated the accuracy of these classification systems. To examine and compare the accuracy of journal classification systems, we define two criteria on the basis of direct citation relations between journals and categories.

We use Criterion I to select journals that have weak connections with their assigned categories, and we use Criterion II to identify journals that are not assigned to categories with which they have strong connections. If a journal satisfies either of the two criteria, we conclude that its assignment to categories may be questionable. Accordingly, we identify all journals with questionable classifications in Web of Science and Scopus.

Furthermore, we perform a more in-depth analysis for the field of Library and Information Science to assess whether our proposed criteria are appropriate and whether they yield meaningful results. It turns out that according to our citation-based criteria Web of Science performs significantly better than Scopus in terms of the accuracy of its journal classification system.

URL : http://arxiv.org/abs/1511.00735

EN

A review of the literature on citation impact indicators

Posted on by Hans Dillaerts

Citation impact indicators nowadays play an important role in research evaluation, and consequently these indicators have received a lot of attention in the bibliometric and scientometric literature. This paper provides an in-depth review of the literature on citation impact indicators. First, an overview is given of the literature on bibliographic databases that can be used to calculate citation impact indicators (Web of Science, Scopus, and Google Scholar).

Next, selected topics in the literature on citation impact indicators are reviewed in detail. The first topic is the selection of publications and citations to be included in the calculation of citation impact indicators. The second topic is the normalization of citation impact indicators, in particular normalization for field differences.

Counting methods for dealing with co-authored publications are the third topic, and citation impact indicators for journals are the last topic. The paper concludes by offering some recommendations for future research.

URL : http://arxiv.org/abs/1507.02099

EN

Who Owns This Article? Applying Copyright’s Work-Made- for-Hire Doctrine to Librarians’ Scholarship

Posted on by Hans Dillaerts

The  Copyright  Act  of  1976  provides  that  works—including  scholarship—written within  the  scope  of  employment  belong  to  employers.  But  copyright  law  and  actual practices  widely  diverge.  The  academic  community  generally  allows  librarians  to  claim ownership of their writing, even when that ignores copyright law. Mr. Hellyer supports copyright ownership by librarians, and calls for the law and common practices to be harmonized.

URL : http://www.aallnet.org/mm/Publications/llj/LLJ-Archives/vol-108/no-1/2016-2.pdf

EN

The FAIR Guiding Principles for scientific data management and stewardship

Posted on by Hans Dillaerts

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles.

The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.

This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

URL : The FAIR Guiding Principles for scientific data management and stewardship

Alternative location : http://www.nature.com/articles/sdata201618

EN

Data Policy Recommendations for Biodiversity Data. EU BON Project Report

Posted on by Hans Dillaerts

There is a strong need for a comprehensive, coherent, and consistent data policy in Europe to increase interoperability of data and to make its reuse both easy and legal. Available single recommendations/guidelines on different topics need to be processed, structured, and unified. Within the context of the EU BON project, a team from the EU BON partners from Museum für Naturkunde Berlin, Plazi, and Pensoft has prepared this report to be used as a part of the Data Publishing Guidelines and Recommendations in the EU BON Biodiversity Portal.

The document deals with the issues: (i) Mobilizing biodiversity data, (ii) Removing legal obstacles, (iii) Changing attitudes, (iv) Data policy recommendations and is addressed to legislators, researchers, research institutions, data aggregators, funders, and publishers.

URL : Data Policy Recommendations for Biodiversity Data. EU BON Project Report

DOI : http://dx.doi.org/10.3897/rio.2.e8458

FR

Archivage pérenne en bibliothèque universitaire : bilan et perspectives

Posted on by Hans Dillaerts

Auteur/Author : Elsa Ferracci

Au vu des risques que créée l’obsolescence technologique, l’archivage pérenne des contenus numériques s’avère désormais incontournable et constitue un enjeu pour l’Enseignement supérieur et la Recherche. La préservation à très long terme nécessite cependant des compétences et des techniques spécifiques et implique des coûts, humains et financiers. Pour la bibliothèque universitaire se pose alors la question du rôle qui doit être le sien au regard de l’archivage pérenne des contenus numériques qu’elle est amenée à stocker, à diffuser ou à produire.

L’objectif de ce mémoire est de dresser un panorama de l’archivage pérenne dans l’Enseignement supérieur et la Recherche, de présenter et d’analyser un ensemble de retours d’expérience de bibliothèques universitaires ayant mené à bien, ou mettant actuellement en oeuvre, ou encore envisageant un projet d’archivage pérenne, et d’en déduire les difficultés et les obstacles qui s’opposent à une réelle avancée de l’archivage pérenne en bibliothèque universitaire.

Le mémoire envisagera la mutualisation, à divers niveaux, comme une réponse à ces difficultés.

URL : Archivage pérenne en bibliothèque universitaire : bilan et perspectives

Alternative location : http://www.enssib.fr/bibliotheque-numerique/notices/65767-archivage-perenne-en-bibliotheque-universitaire-bilan-et-perspectives

EN

Identifying and Improving Dataset References in Social Sciences Full Texts

Posted on by Hans Dillaerts

Scientific full text papers are usually stored in separate places than their underlying research datasets. Authors typically make references to datasets by mentioning them for example by using their titles and the year of publication. However, in most cases explicit links that would provide readers with direct access to referenced datasets are missing.

Manually detecting references to datasets in papers is time consuming and requires an expert in the domain of the paper. In order to make explicit all links to datasets in papers that have been published already, we suggest and evaluate a semi-automatic approach for finding references to datasets in social sciences papers.

Our approach does not need a corpus of papers (no cold start problem) and it performs well on a small test corpus (gold standard). Our approach achieved an F-measure of 0.84 for identifying references in full texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.

URL : http://arxiv.org/abs/1603.01774v1