Organisation des données, organisation du travail en bibliothèques universitaires à l’heure du Big Data

Auteur/Author : Luc Bellier

Les bibliothèques universitaires sont confrontées à la multiplication des silos de données dont la nature et la structure sont très éloignées de celles du catalogue bibliographique.

Organisées depuis de nombreuses années autour du catalogue et de la chaîne de traitement documentaire, les bibliothèques doivent apprendre à se structurer autour de ces nouvelles données. Ce travail étudie les conséquences organisationnelles, et métier qui peuvent s’observer dans un tel contexte.

URL : Organisation des données, organisation du travail en bibliothèques universitaires à l’heure du Big Data

Alternative location : http://www.enssib.fr/bibliotheque-numerique/notices/67453-organisation-des-donnees-organisation-du-travail-en-bibliotheques-universitaires-a-l-heure-du-big-data

Au-delà des big data : Les sciences sociales et la multiplication des données numériques

Auteurs/Authors : Étienne Ollion, Julien Boelaert

Dans le débat public comme dans le monde académique, l’enthousiasme pour les big data n’a eu d’égal que les critiques que ce phénomène a suscité. « Opportunité empirique inouïe » vs « données pauvres » ; « révolution méthodologique » vs « fascination pour le nombre » ; « révolution scientifique » vs « dégradation du savoir produit » : les positions sont tranchées.

À partir d’une lecture de ces débats et des travaux en sciences sociales souvent regroupés sous ce label, l’article soutient que cette situation polarisée a de fortes chances de perdurer tant que la discussion s’organise autour du concept mal défini de big data. Il propose de distinguer différents types de données souvent regroupées sous ce terme.

Il montre ce faisant que les big data souvent évoquées ne sont qu’un aspect limité d’une transformation bien plus importante : la disponibilité croissante et massive de données numériques, qui pose des questions nouvelles à nos disciplines.

Quatre aspects sont plus particulièrement explorés : les réorganisations disciplinaires, les transformations des méthodes quantitatives, l’accès et la gestion des données, les objets des sciences sociales et leur rapport à la théorie.

URL : https://sociologie.revues.org/2613

Big data challenges for the social sciences: from society and opinion to replications

Author : Dominique Boullier

Big Data dealing with the social produce predictive correlations for the benefit of brands and web platforms. Beyond « society » and « opinion » for which the text lays out a genealogy, appear the « traces » that must be theorized as « replications » by the social sciences in order to reap the benefits of the uncertain status of entities’ widespread traceability.

High frequency replications as a collective phenomenon did exist before the digital networks emergence but now they leave traces that can be computed. The third generation of Social Sciences currently emerging must assume the specific nature of the world of data created by digital networks, without reducing them to the categories of the sciences of « society » or « opinion ».

Examples from recent works on Twitter and other digital corpora show how the search for structural effects or market-style trade-offs are prevalent even though insights about propagation, virality and memetics could help build a new theoretical framework.

URL : http://arxiv.org/abs/1607.05034

New Horizons for a Data-Driven Economy : A Roadmap for Usage and Exploitation of Big Data in Europe

Editors : José María Cavanillas, Edward Curry, Wolfgang Wahlster

In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements.

And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy.

URL : New Horizons for a Data-Driven Economy : A Roadmap for Usage and Exploitation of Big Data in Europe

Alternative location : http://link.springer.com/book/10.1007%2F978-3-319-21569-3

 

Cloud-Based Big Data Management and Analytics for Scholarly Resources: Current Trends, Challenges and Scope for Future Research

Authors : Samiya Khan, Kashish A. Shakil, Mansaf Alam

With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research.

In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems.

This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.

URL : https://arxiv.org/abs/1606.01808

Big Data Refinement

Author : Eerke A. Boiten

« Big data » has become a major area of research and associated funding, as well as a focus of utopian thinking. In the still growing research community, one of the favourite optimistic analogies for data processing is that of the oil refinery, extracting the essence out of the raw data. Pessimists look for their imagery to the other end of the petrol cycle, and talk about the « data exhausts » of our society.

Obviously, the refinement community knows how to do « refining ». This paper explores the extent to which notions of refinement and data in the formal methods community relate to the core concepts in « big data ». In particular, can the data refinement paradigm can be used to explain aspects of big data processing?

URL : http://arxiv.org/abs/1606.02017

How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China

Authors : Ying Huang, Yi Zhang, Jan Youtie, Alan L. Porter, Xuefeng Wang

How do funding agencies ramp-up their capabilities to support research in a rapidly emerging area?

This paper addresses this question through a comparison of research proposals awarded by the US National Science Foundation (NSF) and the National Natural Science Foundation of China (NSFC) in the field of Big Data.

Big data is characterized by its size and difficulties in capturing, curating, managing and processing it in reasonable periods of time. Although Big Data has its legacy in longstanding information technology research, the field grew very rapidly over a short period.

We find that the extent of interdisciplinarity is a key aspect in how these funding agencies address the rise of Big Data. Our results show that both agencies have been able to marshal funding to support Big Data research in multiple areas, but the NSF relies to a greater extent on multi-program funding from different fields.

We discuss how these interdisciplinary approaches reflect the research hot-spots and innovation pathways in these two countries.

URL : How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China

DOI : 10.1371/journal.pone.0154509