Project
Data Collaboratives
Website
datacollaboratives.orgThe term data collaborative refers to a new form of collaboration, beyond the public-private partnership model, in which participants from different sectors — including private companies, research institutions, and government agencies — can exchange data to help solve public problems. In the coming months and years, data collaboratives will be essential vehicles for harnessing the vast stores of privately held data toward the public good.
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The term data collaborative refers to a new form of collaboration, beyond the public-private partnership model, in which participants from different sectors — including private companies, research institutions, and government agencies — can exchange data to help solve public problems. In the coming months and years, data collaboratives will be essential vehicles for harnessing the vast stores of privately held data toward the public good.
Such first definition was laid out by S. Verhulst on Medium in 2015. Since then, the Data Collaborative framework has become a new standard to create public value from data.
The ISI Foundation has joined the Data Collaboratives team at NYU in designing a number of data collaboratives in partnership with UNICEF, Telefonica, Digital Globe, and many other private and public organizations.
Related publications
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Towards a global impact-based forecasting model for tropical cyclones
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Leave no Place Behind: Improved Geolocation in Humanitarian Documents
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GoodIT '23: Proceedings of the 2023 ACM Conference on Information Technology for Social Good 31-39 (2023)
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Emanuele Massaro, Rossano Schifanella, Matteo Piccardo, Luca Caporaso, Hannes Taubenböck, Alessandro Cescatti, Gregory Duveiller
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Strengths and limitations of relative wealth indices derived from big data in Indonesia
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Frontiers in Big Data6, 1054156 (2023)
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Handbook of Computational Social Science for PolicyChap. 2, 19-40 (2023)
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Simone Piaggesi, Serena Giurgola, Márton Karsai, Yelena Mejova, André Panisson, Michele Tizzoni
Frontiers in Big Data5, 1006352 (2022)
Data-driven micromobility network planning for demand and safety
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Environment and Planning B: Urban Analytics and City Science50(8), 1-16 (2022)
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Alice Battiston, Rossano Schifanella
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Multimodal urban mobility and multilayer transport networks
Laura Alessandretti, Luis Guillermo Natera Orozco, Meead Saberi, Michael Szell, Federico Battiston
Environment and Planning B: Urban Analytics and City Science50(8), 1-33 (2022)
Identifying urban features for vulnerable road user safety in Europe
Marina Klanjčić, Laetitia Gauvin, Michele Tizzoni, Michael Szell
EPJ Data Science11(27), 1-15 (2022)
Growing urban bicycle networks
Michael Szell, Sayat Mimar, Tyler Perlman, Gourab Ghoshal, Roberta Sinatra
Scientific Report12, 6765 (2022)
Automated Detection of Missing Links in Bicycle Networks
Anastassia Vybornova, Tiago Cunha, Astrid Gühnemann, Michael Szell
Geographical Analysis55(2), 239-267 (2022)
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WWW '21: Companion Proceedings of the Web Conference 2021 136–144 (2021)
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JMIR Public Health and Surveillance7(3), e23154 (2021)
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
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Ethics and Information Technology23, 1-6 (2021)
Data-driven strategies for optimal bicycle network growth
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Royal Society Open Science7, 201130 (2020)
Young Adult Unemployment Through the Lens of Social Media: Italy as a Case Study
Alessandra Urbinati, Kyriaki Kalimeri, Andrea Bonanomi, Alessandro Rosina, Ciro Cattuto, Daniela Paolotti
Proceeding SocInfo 2020: Lecture Notes in Computer Science book series12467, 380-396 (2020)
Extracting the multimodal fingerprint of urban transportation networks
Luis Guillermo Natera Orozco, Federico Battiston, Gerardo Iñiguez, Michael Szell
Findings13171, (2020)
Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide
Daniele Rama, Yelena Mejova, Michele Tizzoni, Kyriaki Kalimeri, Ingmar Weber
WWW '20: Proceedings of The Web Conference 2020 327-338 (2020)
Predicting City Poverty Using Satellite Imagery
Simone Piaggesi, Laetitia Gauvin, Michele Tizzoni, Ciro Cattuto, Natalia Adler, Stefaan G. Verhulst, Andrew Young, Rhiannan Price, Leonardo Adrian Ferres, André Panisson
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 90-96 (2019)
Social Media Data per lo studio della disoccupazione giovanile italiana: il progetto LikeYouth
Andrea Bonanomi, Alessandro Rosina, Ciro Cattuto, Kyriaki Kalimeri
STATISTICA & SOCIETÀ7 / 1, (2018)
Understanding Youth Unemployment in Italy via Social Media Data
Kyriaki Kalimeri, Ciro Cattuto, Andrea Bonanomi, Alessandro Rosina
International Population Conference (2017)
Bridging the digital divide: mapping Internet connectivity evolution, inequalities, and resilience in six Brazilian citiesgo
Nicolò Gozzi, Niccolò Comini, Nicola Perra
EPJ Data Science13, 69 (2024)
Gaps in gender and socioeconomic mobility disparity studies
Nature Computational Science4, 633-635 (2024)
Quantitative Information Extraction from Humanitarian Documents
Daniele Liberatore, Kyriaki Kalimeri, Derya Sever, Yelena Mejova
GoodIT '24: Proceedings of the 2024 International Conference on Information Technology for Social Good 240-248 (2024)