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© The Author(s) 2017. The commentary raises political questions about the ways in which data has been constituted as an object vested with certain powers, influence, and rationalities. We place the emergence and transformation of professional practices such as 'data science', 'data journalism', 'data brokerage', 'data mining', 'data storage', and 'data analysis' as part of the reconfiguration of a series of fields of power and knowledge in the public and private accumulation of data. Data politics asks questions about the ways in which data has become such an object of power and explores how to critically intervene in its deployment as an object of knowledge. It is concerned with the conditions of possibility of data that involve things (infrastructures of servers, devices, and cables), language (code, programming, and algorithms), and people (scientists, entrepreneurs, engineers, information technologists, designers) that together create new worlds. We define 'data politics' as both the articulation of political questions about these worlds and the ways in which they provoke subjects to govern themselves and others by making rights claims. We contend that without understanding these conditions of possibility – of worlds, subjects and rights – it would be difficult to intervene in or shape data politics if by that it is meant the transformation of data subjects into data citizens.

The commentary raises political questions about the ways in which data has been constituted as an object vested withcertain powers, influence, and rationalities.We place the emergence and transformation of professional practices such as'data science', 'data journalism', 'data brokerage', 'data mining', 'data storage', and 'data analysis' as part of the reconfigurationof a series of fields of power and knowledge in the public and private accumulation of data. Data politics asksquestions about the ways in which data has become such an object of power and explores how to critically intervene inits deployment as an object of knowledge. It is concerned with the conditions of possibility of data that involve things(infrastructures of servers, devices, and cables), language (code, programming, and algorithms), and people (scientists,entrepreneurs, engineers, information technologists, designers) that together create new worlds. We define 'data politics'as both the articulation of political questions about these worlds and the ways in which they provoke subjects togovern themselves and others by making rights claims. We contend that without understanding these conditions ofpossibility – of worlds, subjects and rights – it would be difficult to intervene in or shape data politics if by that it is meantthe transformation of data subjects into data citizens.

The commentary raises political questions about the ways in which data has been constituted as an object vested withcertain powers, influence, and rationalities.We place the emergence and transformation of professional practices such as'data science', 'data journalism', 'data brokerage', 'data mining', 'data storage', and 'data analysis' as part of the reconfigurationof a series of fields of power and knowledge in the public and private accumulation of data. Data politics asksquestions about the ways in which data has become such an object of power and explores how to critically intervene inits deployment as an object of knowledge. It is concerned with the conditions of possibility of data that involve things(infrastructures of servers, devices, and cables), language (code, programming, and algorithms), and people (scientists,entrepreneurs, engineers, information technologists, designers) that together create new worlds. We define 'data politics'as both the articulation of political questions about these worlds and the ways in which they provoke subjects togovern themselves and others by making rights claims. We contend that without understanding these conditions ofpossibility – of worlds, subjects and rights – it would be difficult to intervene in or shape data politics if by that it is meantthe transformation of data subjects into data citizens.

This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; Profound changes in our world are exacerbating data availability challenges at the global level, in particular between scientists and other knowledge workers from regions separated by various features including historical, financial, cultural, political aspects, aside from time and space . Very few, if any, of our present problems such as biodiversity decline, climate change, and viral pandemics stop at national, disciplinary and linguistic boundaries, yet our most vital responses to the shared problems, the information generated to analyze and derive solutions, is still siloed in different languages and locations throughout the world. It is clear that in order for us to effectively respond, we need to collaborate globally and communicate information more effectively. Globalization of research requires interoperability of our observations and experimentation systems.The use of common FAIR vocabularies, that are both human and machine readable, is a key criterion in the FAIR principles (Principle I2 of Wilkinson et al 2016 specifies '(meta)data use vocabularies that follow FAIR principles'). Using common FAIR vocabularies will enable data interoperability and the necessary meta-analyses even when data have different origins and are based on multiple vocabularies. The objective of this poster is to offer an overview of the many multi-language challenges for effective Data ...

This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; Profound changes in our world are exacerbating data availability challenges at the global level, in particular between scientists and other knowledge workers from regions separated by various features including historical, financial, cultural, political aspects, aside from time and space . Very few, if any, of our present problems such as biodiversity decline, climate change, and viral pandemics stop at national, disciplinary and linguistic boundaries, yet our most vital responses to the shared problems, the information generated to analyze and derive solutions, is still siloed in different languages and locations throughout the world. It is clear that in order for us to effectively respond, we need to collaborate globally and communicate information more effectively. Globalization of research requires interoperability of our observations and experimentation systems.The use of common FAIR vocabularies, that are both human and machine readable, is a key criterion in the FAIR principles (Principle I2 of Wilkinson et al 2016 specifies '(meta)data use vocabularies that follow FAIR principles'). Using common FAIR vocabularies will enable data interoperability and the necessary meta-analyses even when data have different origins and are based on multiple vocabularies. The objective of this poster is to offer an overview of the many multi-language challenges for effective Data ...

This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; Profound changes in our world are exacerbating data availability challenges at the global level, in particular between scientists and other knowledge workers from regions separated by various features including historical, financial, cultural, political aspects, aside from time and space . Very few, if any, of our present problems such as biodiversity decline, climate change, and viral pandemics stop at national, disciplinary and linguistic boundaries, yet our most vital responses to the shared problems, the information generated to analyze and derive solutions, is still siloed in different languages and locations throughout the world. It is clear that in order for us to effectively respond, we need to collaborate globally and communicate information more effectively. Globalization of research requires interoperability of our observations and experimentation systems.The use of common FAIR vocabularies, that are both human and machine readable, is a key criterion in the FAIR principles (Principle I2 of Wilkinson et al 2016 specifies '(meta)data use vocabularies that follow FAIR principles'). Using common FAIR vocabularies will enable data interoperability and the necessary meta-analyses even when data have different origins and are based on multiple vocabularies. The objective of this poster is to offer an overview of the many multi-language challenges for effective Data ...

The Data Journalism Handbook: Towards a Critical Data Practice provides a rich and panoramic introduction to data journalism, combining both critical reflection and practical insight. It offers a diverse collection of perspectives on how data journalism is done around the world and the broader consequences of datafication in the news, serving as both a textbook and a sourcebook for this emerging field. With more than 50 chapters from leading researchers and practitioners of data journalism, it explores the work needed to render technologies and data productive for journalistic purposes. It also gives a "behind the scenes" look at the social lives of data sets, data infrastructures, and data stories in newsrooms, media organizations, start-ups, civil society organizations and beyond. The book includes sections on "doing issues with
data," "assembling data," "working with data," "experiencing data," "investigating data, platforms and algorithms," "organizing data journalism," "learning data journalism together" and "situating data journalism."

Collecting, processing and analyzing data are central activities for virtually every researcher. Hence, topics such as data sharing and the so-called FAIR data principles are becoming increasingly important. This course is meant to give a general, discipline-independent introduction to research data management with a special focus on questions related to data publication and open data. The presentation took place on 7 March 2022 and was part of the event series "Open for you! An introduction series to open science" organized by the 4EU+ University Alliance. ; Co-funded by the Erasmus+ Programme of the European Union.