big data – Page 2 – InfoDoc MicroVeille

Learning Analytics and the Academic Library: Professional Ethics Commitments at a Crossroads

Posté le 15 août 2017 par Hans Dillaerts

Authors : Kyle M.L. Jones, Dorothea Salo

In this paper, the authors address learning analytics and the ways academic libraries are beginning to participate in wider institutional learning analytics initiatives. Since there are moral issues associated with learning analytics, the authors consider how data mining practices run counter to ethical principles in the American Library Association’s “Code of Ethics.”

Specifically, the authors address how learning analytics implicates professional commitments to promote intellectual freedom; protect patron privacy and confidentiality; and balance intellectual property interests between library users, their institution, and content creators and vendors.

The authors recommend that librarians should embed their ethical positions in technological designs, practices, and governance mechanisms.

URL : Learning Analytics and the Academic Library: Professional Ethics Commitments at a Crossroads

Alternative location : http://crl.acrl.org/index.php/crl/article/view/16603

Big data is not about size: when data transform scholarship

Posté le 8 août 2017 par Hans Dillaerts

Authors : Jean-Christophe Plantin, Carl Lagoze, Paul N. Edwards, Christian Sandvig

“Big data” discussions typically focus on scale, i.e. the problems and potentials inherent in very large collections. Here, we argue that the most important consequences of “big data” for scholarship stem not from the increasing size of datasets, but instead from a loss of control over the sources of data.

The breakdown of the “control zone” due to the uncertain provenance of data has implications for data integrity, and can be disruptive to scholarship in multiple ways. A retrospective look at the introduction of larger datasets in weather forecasting and epidemiology shows that more data can at times be counter-productive, or destabilize already existing methods.

Based on these examples, we look at two implications of “big data” for scholarship: when the presence of large datasets transforms the traditional disciplinary structure of sciences, as well as the infrastructure for scholarly communication.

URL : https://books.openedition.org/editionsmsh/9103

Data, Science and Society

Posté le 27 juillet 2017 par Hans Dillaerts

Author : Claudio Gutierrez

Reflections on the Concept of Data and its Implications for Science and Society

URL : https://arxiv.org/abs/1707.07029

Accelerating Science: A Computing Research Agenda

Posté le 3 juillet 2017 par Hans Dillaerts

Authors : Vasant G. Honavar, Mark D. Hill, Katherine Yelick

The emergence of “big data” offers unprecedented opportunities for not only accelerating scientific advances but also enabling new modes of discovery. Scientific progress in many disciplines is increasingly enabled by our ability to examine natural phenomena through the computational lens, i.e., using algorithmic or information processing abstractions of the underlying processes; and our ability to acquire, share, integrate and analyze disparate types of data.

However, there is a huge gap between our ability to acquire, store, and process data and our ability to make effective use of the data to advance discovery. Despite successful automation of routine aspects of data management and analytics, most elements of the scientific process currently require considerable human expertise and effort.

Accelerating science to keep pace with the rate of data acquisition and data processing calls for the development of algorithmic or information processing abstractions, coupled with formal methods and tools for modeling and simulation of natural processes as well as major innovations in cognitive tools for scientists, i.e., computational tools that leverage and extend the reach of human intellect, and partner with humans on a broad range of tasks in scientific discovery (e.g., identifying, prioritizing formulating questions, designing, prioritizing and executing experiments designed to answer a chosen question, drawing inferences and evaluating the results, and formulating new questions, in a closed-loop fashion).

This calls for concerted research agenda aimed at: Development, analysis, integration, sharing, and simulation of algorithmic or information processing abstractions of natural processes, coupled with formal methods and tools for their analyses and simulation; Innovations in cognitive tools that augment and extend human intellect and partner with humans in all aspects of science.

URL : https://arxiv.org/abs/1604.02006

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

Posté le 4 avril 2017 par Hans Dillaerts

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