Conferences
6–7 November 2017, Lisbon, Portugal
Big Data on Human and Social Sciences – History, Issues and Challenges
The Instituto de História Contemporânea / Institute of Contemporary History (IHC, FCSH-NOVA) and the History Lab (Columbia University) will be hosting an international conference to examine the challenges and impact of ‘Big Data’ in the human and social sciences, which opens up new connections and collaborations within the research community and with the civil society. The conference will be held on November 6-7, 2017, in Lisbon and it is open to all scientific areas.
Humanists and social scientists have at their disposal an unprecedented amount of data today. For sure, the wide variety of data available, including the massively growing public archival data, creates many opportunities. For instance, it allows to extend the geographical and longitudinal scope of analysis on a new scale. But nor the data existent in social and historical processes is neutral nor the ways to store, retrieve and analyse such amount of data is based upon simplistic decisions.
With their capacity to historically situate the objects of analysis, discuss meanings and vast textual corpora, as well as their strength on contextual knowledge, historians, sociologists, political scientists, economists, philosophers and anthropologists are in strong position to contribute to this revolution. Grounded on the presence of academic experts from such fields of knowledge, this Congress will cover a wide range of topics aiming to build bridges with formal, applied and natural scientists and to open windows into the public domain.
Some of the topics include but are not limited to:
- History of Big Data;
- Big Data and modes of knowledge production;
- Big Data. Pitfalls and errors;
- Development of complex datasets;
- Big Data and networks;
- Digital Humanities and historical research
- Archives, libraries and Big Data;
- Historical Big Data and statistical tools;
- Text mining and historical sources;
- Big Data management for researchers and research institutions;
- Big Data infrastructures.
