Re: 2 articles every minute

I became interested in these statistics myself, sometime ago. Eventually
tracked down a fairly interesting paper on the subject. As per quality vs.
quality that may be a different discussion. Here is a source from which
such statistics may have been obtained.

*Bo-Christer Björk, Annikki Roos, Mari Lauri:*
*Global annual volume of peer reviewed scholarly articles and the share
available via different Open Access options
<http://elpub.scix.net/data/works/att/178_elpub2008.content.pdf>.*
ELPUB 2008: 178-186


On Fri, Sep 19, 2014 at 3:34 AM, Alexander Garcia Castro <
alexgarciac@gmail.com> wrote:

> Hi, I don't mean to be picky. I am just curious about statements like "2
> articles every minute". Where do they come from? Where can I get this
> stats? are this stats about journal papers? if this is true, I assume it
> is, then shouldn't we start to consider that the quality of publications is
> simply poor? Perhaps this is a challenge for us to clear the act instead of
> a challenge for the technology; and if there is a challenge for the tech
> then, IMHO, it should be how to remove rubbish from those 3000 articles per
> day "Every day, approximately 3000 new bio-medical articles are published
> on the Web".
>
> Anyway, just woke up this morning and saw this "per day, 3000 new
> bio-medical articles are published on the Web" and then "2 articles every
> minute". Just in the biomedical domain and I thought, where does it come
> from and what does it mean for us.
>
>
>
>
>
> On Fri, Sep 19, 2014 at 8:03 AM, Axel Ngonga <
> ngonga@informatik.uni-leipzig.de> wrote:
>
>> Call for Papers
>> ************
>> Supplement on Semantics-Enabled Biomedical Information Retrieval
>> Journal of Bio-Medical Semantics
>>
>> Important Data
>> *************
>> Submission Deadline: December 19th, 2014
>> Notification of acceptance/rejection: February 27th, 2015
>> Camera-Ready Paper Deadline: April 17th, 2015
>> Webpage: http://bioasq.org/project/bioasq-special-issue
>> Submission page: https://easychair.org/conferences/?conf=jbmsbioir2015
>>
>> Call
>> ***
>>
>> Every day, approximately 3000 new bio-medical articles are published on
>> the Web. This averages to more than 2 articles every minute. In addition to
>> the sheer amount of bio-medical information available on the Web, the
>> variety of this information increases everyday and ranges from structured
>> data in the form of ontologies to unstructured data in the form of
>> documents. Staying on top of this huge amount of diverse data requires
>> methods that allow detecting and integrating portions of datasets that
>> satisfy the information need of given users from sources such as documents,
>> ontologies, Linked Data sets, etc. Developing tools to achieve this bold
>> goal requires combining techniques from several disciplines including
>> Natural Language Processing (e.g., question answering, document
>> summarization, ontology verbalization), Information Retrieval (e.g.,
>> document and passage retrieval), Machine Learning (e.g., large-scale
>> hierarchical classification, clustering, etc.), Semantic Web/Linked Data
>> (e.g., reasoning, link discovery) and Databases (e.g., storage and
>> retrieval of triples, indexing, etc.).
>>
>> The aim of this supplement is to collect and present the newest results
>> from these domains in order to push the research frontier towards
>> information systems that will be able to deal with the whole diversity of
>> the Web in the bio-medical domain.
>>
>> The topics of interest include (but are not restricted to):
>>
>> * Large-scale hierarchical text classification
>> * Large-scale classification of documents onto ontology concepts
>> (semantic indexing)
>> * Classification of questions onto ontological concepts
>> * Scalable approaches to document clustering
>> * Text summarization, especially multi-document and query-focused
>> summarization
>> * Verbalization of structured information and related queries (RDF, OWL,
>> SPARQL, etc.)
>> * Question Answering over structured, semi-structured and unstructured
>> data
>> * Reasoning for information retrieval and question answering
>> * Information retrieval over fragmented sources of information
>> * Efficient indexing and storage structures for information retrieval
>> * Delivery of the retrieved information in a concise and
>> user-understandable form
>> * Relation extraction
>> * Textual entailment
>> * Natural-language generation
>> * Named entity recognition/disambiguation
>> * Fact checking
>> * Exploitation of semantic resources (terminologies, ontologies) for
>> information retrieval and question answering
>> * Normalisation of data resources with semantic resources, i.e.,
>> concept-driven data transformation
>>
>> Cheers,
>> Axel
>>
>> --
>> Axel Ngonga, Dr. rer. nat
>> Head of AKSW
>> Augustusplatz 10
>> Room P905
>> 04109 Leipzig
>> http://aksw.org/AxelNgonga
>>
>> Tel: +49 (0)341 9732341
>> Fax: +49 (0)341 9732239
>>
>>
>>
>
>
> --
> Alexander Garcia
> http://www.alexandergarcia.name/
> http://www.usefilm.com/photographer/75943.html
> http://www.linkedin.com/in/alexgarciac
>
>


-- 
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