Vladimir Kudinov via Unsplash

Featured Collection:
Occupant Behaviour in Buildings

Latest Research

News & Comment

  • Comment
    | Open Access

    In the past decade, there has been a surge in the number of sensitive human genomic and health datasets available to researchers via Data Access Agreements (DAAs) and managed by Data Access Committees (DACs). As this form of sharing increases, so do the challenges of achieving a reasonable level of data protection, particularly in the context of international data sharing. Here, we consider how excessive variation across DAAs can hinder these goals, and suggest a core set of clauses that could prove useful in future attempts to harmonize data governance.

    • Katie M. Saulnier
    • , David Bujold
    • , Stephanie O. M. Dyke
    • , Charles Dupras
    • , Stephan Beck
    • , Guillaume Bourque
    •  & Yann Joly
  • Comment
    | Open Access

    Climate change cannot be addressed without improving the energy efficiency of the buildings in which we live and work. The papers in this collection describe and release a series of datasets that help us understand how occupants influence and experience building energy use, both to aid future research and policy-development, and to spark wider data sharing in this important area.

    • Gesche Margarethe Huebner
    •  & Ardeshir Mahdavi
  • Comment
    | Open Access

    A special collection on multi-omics data sharing, launched today at Scientific Data, offers to the scientific community a compendium of multi-omics datasets ready for reuse, which showcase the diversity of multi-omics projects and highlights innovative approaches for preprocessing, quality control, hosting and access.

    • Ana Conesa
    •  & Stephan Beck
  • Comment
    | Open Access

    UniProt continues to support the ongoing process of making scientific data FAIR. Here we contribute to this process with a FAIRness assessment of our UniProtKB dataset followed by a critical reflection on the challenges and future directions of the adoption and validation of the FAIR principles and metrics.

    • Leyla Garcia
    • , Jerven Bolleman
    • , Sebastien Gehant
    • , Nicole Redaschi
    • , Maria Martin
    • , Alex Bateman
    • , Michele Magrane
    • , Maria Martin
    • , Sandra Orchard
    • , Shriya Raj
    • , Shadab Ahmad
    • , Emanuele Alpi
    • , Emily Bowler
    • , Ramona Britto
    • , Borisas Bursteinas
    • , Hema Bye-A-Jee
    • , Tunca Dogan
    • , Leyla Garcia
    • , Penelope Garmiri
    • , George Georghiou
    • , Leonardo Gonzales
    • , Emma Hatton-Ellis
    • , Alexandr Ignatchenko
    • , Giuseppe Insana
    • , Rizwan Ishtiaq
    • , Vishal Joshi
    • , Dushyanth Jyothi
    • , Jie Luo
    • , Yvonne Lussi
    • , Alistair MacDougall
    • , Mahdi Mahmoudy
    • , Andrew Nightingale
    • , Carla Oliveira
    • , Joseph Onwubiko
    • , Vivek Poddar
    • , Sangya Pundir
    • , Guoying Qi
    • , Ahmet Rifaioglu
    • , Daniel Rice
    • , Rabie Saidi
    • , Elena Speretta
    • , Edward Turner
    • , Nidhi Tyagi
    • , Preethi Vasudev
    • , Vladimir Volynkin
    • , Kate Warner
    • , Xavier Watkins
    • , Rossana Zaru
    • , Hermann Zellner
    • , Alan Bridge
    • , Lionel Breuza
    • , Elisabeth Coudert
    • , Damien Lieberherr
    • , Ivo Pedruzzi
    • , Sylvain Poux
    • , Manuela Pruess
    • , Nicole Redaschi
    • , Lucila Aimo
    • , Ghislaine Argoud-Puy
    • , Andrea Auchincloss
    • , Kristian Axelsen
    • , Parit Bansal
    • , Delphine Baratin
    • , Teresa Batista Neto
    • , Marie-Claude Blatter
    • , Jerven Bolleman
    • , Emmanuel Boutet
    • , Cristina Casals-Casas
    • , Beatrice Cuche
    • , Edouard De Castro
    • , Anne Estreicher
    • , Livia Famiglietti
    • , Marc Feuermann
    • , Elisabeth Gasteiger
    • , Sebastien Gehant
    • , Vivienne Gerritsen
    • , Arnaud Gos
    • , Nadine Gruaz
    • , Ursula Hinz
    • , Chantal Hulo
    • , Nevila Hyka-Nouspikel
    • , Florence Jungo
    • , Arnaud Kerhornou
    • , Philippe Lemercier
    • , Thierry Lombardot
    • , Patrick Masson
    • , Anne Morgat
    • , Sandrine Pilbout
    • , Monica Pozzato
    • , Catherine Rivoire
    • , Christian Sigrist
    • , Shyamala Sundaram
    • , Cathy Wu
    • , Cecilia Arighi
    • , Hongzhan Huang
    • , Peter McGarvey
    • , Darren Natale
    • , Leslie Arminski
    • , Chuming Chen
    • , Yongxing Chen
    • , John Garavelli
    • , Kati Laiho
    • , Karen Ross
    • , C. R. Vinayaka
    • , Qinghua Wang
    • , Yuki Wang
    • , Lai-Su Yeh
    •  & Jian Zhang
  • Comment
    | Open Access

    The Brain Imaging Data Structure (BIDS) project is a rapidly evolving effort in the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. Here we present an extension to BIDS for electroencephalography (EEG) data, EEG-BIDS, along with tools and references to a series of public EEG datasets organized using this new standard.

    • Cyril R. Pernet
    • , Stefan Appelhoff
    • , Krzysztof J. Gorgolewski
    • , Guillaume Flandin
    • , Christophe Phillips
    • , Arnaud Delorme
    •  & Robert Oostenveld
  • Comment
    | Open Access

    The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re)use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS.

    • Christopher Holdgraf
    • , Stefan Appelhoff
    • , Stephan Bickel
    • , Kristofer Bouchard
    • , Sasha D’Ambrosio
    • , Olivier David
    • , Orrin Devinsky
    • , Benjamin Dichter
    • , Adeen Flinker
    • , Brett L. Foster
    • , Krzysztof J. Gorgolewski
    • , Iris Groen
    • , David Groppe
    • , Aysegul Gunduz
    • , Liberty Hamilton
    • , Christopher J. Honey
    • , Mainak Jas
    • , Robert Knight
    • , Jean-Philippe Lachaux
    • , Jonathan C. Lau
    • , Christopher Lee-Messer
    • , Brian N. Lundstrom
    • , Kai J. Miller
    • , Jeffrey G. Ojemann
    • , Robert Oostenveld
    • , Natalia Petridou
    • , Gio Piantoni
    • , Andrea Pigorini
    • , Nader Pouratian
    • , Nick F. Ramsey
    • , Arjen Stolk
    • , Nicole C. Swann
    • , François Tadel
    • , Bradley Voytek
    • , Brian A. Wandell
    • , Jonathan Winawer
    • , Kirstie Whitaker
    • , Lyuba Zehl
    •  & Dora Hermes

Collection

Multi-Omics Data Sharing

'Multi-omics' refers to a family of complex experimental designs where researchers apply more than one molecular profiling technology – capturing, for example, the genome, proteome and metabolome – across a common set of biological samples. These experiments offer a wealth of opportunities for subsequent analyses, but the size of the resulting datasets and the diversity of the study designs makes data sharing inherently challenging. In this collection, we present a series of multi-omics studies where the authors have used innovative means to maximize the accessibility and reusability of their datasets.
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