Amsterdam, Netherlands
Amsterdam, Netherlands

Elsevier is an academic publishing company that publishes medical and scientific literature. It is a part of the RELX Group. Based in Amsterdam, the company has operations in the United Kingdom, United States, Mexico, Brazil, Spain, and elsewhere. Wikipedia.


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News Article | May 10, 2017
Site: www.nature.com

Ross Mounce knows that when he shares his research papers online, he may be doing something illegal — if he uploads the final version of a paper that has appeared in a subscription-based journal. Publishers who own copyright on such papers frown on their unauthorized appearance online. Yet when Mounce has uploaded his paywalled articles to ResearchGate, a scholarly social network likened to Facebook for scientists, publishers haven’t asked him to take them down. “I’m aware that I might be breaching copyright,” says Mounce, an evolutionary biologist at the University of Cambridge, UK. “But I don’t really care.” Mounce isn’t alone in his insouciance. The unauthorized sharing of copyrighted research papers is on the rise, say analysts who track the publishing industry. Faced with this problem, science publishers seem to be changing tack in their approach to researchers who breach copyright. Instead of demanding that scientists or network operators take their papers down, some publishers are clubbing together to create systems for legal sharing of articles — called fair sharing — which could also help them to track the extent to which scientists share paywalled articles online. Free article sharing is embedded in the way science works, says Mandy Hill, managing director of academic publishing at Cambridge University Press, UK. “It is important that, as publishers, we accept this and find ways to support fair sharing of content whilst ensuring the sustainability of the research publishing business,” she says. But open-access advocates say that publishers’ plans for fair sharing will not satisfy scientists who might object to — or be unaware of — copyright restrictions, and who increasingly expect to be able to make their papers available online for free. The practice of uploading paywalled papers online seems to have ballooned in recent years — in large part because of the popularity of sites such as ResearchGate, where millions of scientists share and view articles. Publishers are watching carefully. In April, a publisher-commissioned survey of more than 5,000 scientists by the research-impact service Kudos in Wheatley, UK, suggested that 57% had uploaded their own work to scholarly communication networks; 79% of those said they checked copyright policies before they did so, but 60% thought they should be allowed to upload their articles regardless of publisher or journal policies (see ‘Copyright concerns’). No-one knows the full extent to which researchers share paywalled papers online, but a study this February gave a hint. Information scientist Hamid Jamali at Charles Sturt University in Wagga Wagga, Australia, picked 500 papers at random from ResearchGate, and found that 392 were not open-access articles (H. R Jamali Scientometrics http://dx.doi.org/10.1007/s11192-017-2291-4; 2017). Some were versions that publishers allow authors to share, such as a peer-reviewed, unedited manuscript or a preprint. But more than 50% of the uploaded versions infringed publishers’ copyright, Jamali found. A spokesperson for ResearchGate, which is based in Berlin, says that the company explicitly asks users to comply with publishers’ policies when uploading papers, and to make sure they are not breaching copyright. But it says it has no way to monitor the extent to which users might upload unauthorized papers. Some scholarly publishers have reacted to the issue with litigation threats. In late 2013, for instance, science publisher Elsevier sent 3,000 notices under the US Digital Millennium Copyright Act to the scholarly network Academia.edu and other sites, demanding that they take down papers that breached Elsevier’s copyright. The notices were also passed to individual scientists. Major infringements still prompt a legal reaction: Elsevier is currently suing Sci-Hub, a site that shares millions of paywalled research papers. Yet, when it comes to dealing with papers shared on social networks, some publishers are pulling back from litigation, says Matt McKay, a spokesperson for the International Association of Scientific, Technical and Medical Publishers (STM) in Oxford, UK. “Legal action and take-down notices are no sustainable manner to remove unauthorized content from social research networks. Rather than relying on such blunt tools, we want to talk with these sites and find long-term solutions to the problem,” he says. In a 21 March teleconference organized by the STM, science publishers discussed efforts to let scientists share full texts of papers more easily without breaching copyright. (Springer Nature, which publishes Nature, was one of the companies involved; Nature’s news team is editorially independent of its publisher.) Publishers contacted by Nature’s news team generally declined to discuss their evolving policies on article sharing in detail, but fair sharing typically means providing free links to the final versions of read-only, non-downloadable articles hosted on journal sites. Some publishers — including Springer Nature and Wiley — have adopted software that allows their authors to generate such links. An education drive in 2015 kicked off the fair-sharing discussion: the STM, following consultations with publishers and librarians, developed a website called ‘How can I share it?’ (www.howcanishareit.com) that details what different subscription journals allow in terms of archiving and sharing copyrighted articles online. (In general, many publishers permit the online sharing of peer-reviewed manuscripts, but not the final full text.) Scientists may not like publishers’ systems for fair sharing, says Stevan Harnad, a web-science and cognition expert at the University of Southampton, UK, who encourages researchers to self-archive versions of articles online. “So publishers want to track what is happening? There is no reason they should retain such control,” he says. In the long run, thinks Mounce, science will move to a system in which researchers can do what they want with their papers. “Only open access will cleanly and clearly solve the highly artificial ‘problem’ of not being allowed to share research with others,” he says. But for the publishing industry, the question of how to enable sharing of paywalled articles without breaching copyright or alienating authors will only grow in significance, says Joseph Esposito, an independent publishing consultant in New York City who works with science publishers and scholarly societies. So far, he says, journal publishers don’t seem to have lost much revenue because of scholarly networks. But publishers will have to adopt new strategies now to avoid “substantial losses” in the near future, he says.


Methods of organizing documents by reclassification and clustering are disclosed. In one embodiment, a method of clustering electronic documents of a document corpus includes comparing, by a computer, each individual electronic document in the document corpus with each other electronic document in the document corpus, thereby forming document pairs. The electronic documents of the document pairs are compared by calculating a similarity value with respect to the electronic documents of a document pair, associating the similarity value with both electronic documents of the document pair, and applying a clustering algorithm to the document corpus using the similarity values to create a plurality of hierarchical clusters. The similarity value is based on a plurality of attributes of the electronic documents in the document corpus. The plurality of attributes includes a citation attribute, a text-based attribute and one or more of an author-attribute, a publication-attribute, an institution-attribute, a downloads-attribute, and a clustering-results-attribute.


Grant
Agency: European Commission | Branch: H2020 | Program: RIA | Phase: EINFRA-7-2014 | Award Amount: 3.46M | Year: 2015

Five years ago, a global infrastructure to uniquely attribute to researchers their scientific artefacts (articles, data, software) appeared technically and socially infeasible. Since then, DataCite has minted over 3.5m unique identifiers for data. ORCID has deployed an open solution for identification of contributors with over 850,000 registrants in less than 2 years. THOR will leverage these emerging global infrastructures to support the H2020 goal to make every researcher digital and increase creativity and efficiency of research, while bridging the R&D divide between developed and less-developed regions. We will establish interoperability between existing resources, linking digital identifiers across platforms and propagating attribution information. We will integrate PID services across the research lifecycle and data publishing workflows in four advanced research communities, and then roll-out core services and service building blocks for the wider community. These open resources will foster an open and sustainable e-infrastructure across stakeholders to avoid duplications, give economies of scale, richness of services and the ability to respond rapidly to opportunities for innovation. THOR is not just relevant to the EINFRA-7-1024 Call, but will become a pervasive element of the EINFRA family of e-Infrastructure resources over the next 3 years. It will allow data-management and curation services to exploit knowledge of data location and attribution; provide robust and persistent mechanism for linking literature and data; enable search and resolving services and generate incentives for Open Science; deliver provenance and attribution mechanisms to underpin data exchange; and provide minting and resolving services for data citation workflows. Its impact will enable third-party services, no-profit and commercial, to leverage the scholarly record.


Patent
Elsevier | Date: 2016-08-25

Provided are methods and systems for knowledge discovery utilizing knowledge profiles.


Patent
Elsevier | Date: 2016-07-21

An online document management system is disclosed. In one embodiment, the online document management system comprises: one or more editorial computers operated by one or more administrators or editors, the editorial computers send invitations and manage peer review of document submissions; one or more system computers, the system computers maintain journals, records of submitted documents and user profiles, and issue notifications; and one or more user computers; the user computers submit documents or revisions to the document management system; wherein one or more of the editorial computers coordinate with one or more of the system computers to migrate one or more documents between journals maintained by the online document management system.


Grant
Agency: NSF | Branch: Contract | Program: | Phase: SCIENCE & ENGINEERNG INDICATRS | Award Amount: 502.94K | Year: 2015

None


Patent
Elsevier | Date: 2016-02-05

Systems and methods for converting a natural language sentence into a computer-readable primitive sentence and extracting information therefrom are disclosed. A method includes identifying, by a processing device, a verbal block in the natural language sentence, splitting, by the processing device, the natural language sentence into one or more logical clauses, determining, by the processing device, a type for each logical clause, where the type indicates whether each logical clause contains an ambiguous verbal block, disambiguating, by the processing device, the ambiguous verbal block within each logical clause, where each verbal block is considered independently of a noun phrase, and constructing, by the processing device, the computer-readable primitive sentence for each ambiguous verbal block by duplicating a shared noun phrase of the ambiguous verbal block. The computer-readable primitive sentence improves functioning of a computing device by allowing the computing device to process the natural language sentence to obtain information therefrom.


Patent
Elsevier | Date: 2014-04-18

Methods for converting a natural language sentence into a set of primitive sentences. The method include identifying verbal blocks in the sentence, splitting the sentence into a set of logical clauses, disambiguating ambiguous verbal blocks within each logical clause, and constructing a primitive sentence for each verbal block by duplicating the shared noun phrases of verbal blocks.


Computer-program products and methods for automatically annotating terms, such as ambiguous terms, in an electronic text document are disclosed. In one embodiment, a method of annotating a text document includes determining, by a computing device, a term of interest within the text document. The method further includes searching a data structure including incongruous term pairs (t_(x), tt) determined from a controlled vocabulary for the term of interest appearing as a term tt, wherein the term tt is a linguistic head of a term t_(x )of the incongruous term pairs (t_(x), tt). The method further includes annotating the term of interest with a meaning provided by the controlled vocabulary only if a term t_(x )of the incongruous term pairs (t_(x), tt) associated with the term of interest in the data structure is not present within a predetermined textual distance of the term of interest in the text document.


Methods of organizing documents by reclassification and clustering are disclosed. In one embodiment, a method of clustering electronic documents of a document corpus includes comparing, by a computer, each individual electronic document in the document corpus with each other electronic document in the document corpus, thereby forming document pairs. The electronic documents of the document pairs are compared by calculating a similarity value with respect to the electronic documents of a document pair, associating the similarity value with both electronic documents of the document pair, and applying a clustering algorithm to the document corpus using the similarity values to create a plurality of hierarchical clusters. The similarity value is based on a plurality of attributes of the electronic documents in the document corpus. The plurality of attributes includes a citation attribute, a text-based attribute and one or more of an author-attribute, a publication-attribute, an institution-attribute, a downloads-attribute, and a clustering-results-attribute.

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