Literature DB >> 29756002

ERMrest: A Collaborative Data Catalog with Fine Grain Access Control.

Karl Czajkowski1, Carl Kesselman1, Robert Schuler1.   

Abstract

Creating and maintaining an accurate description of data assets and the relationships between assets is a critical aspect of making data findable, accessible, interoperable, and reusable (FAIR). Typically, such metadata are created and maintained in a data catalog by a curator as part of data publication. However, allowing metadata to be created and maintained by data producers as the data is generated rather then waiting for publication can have significant advantages in terms of productivity and repeatability. The responsibilities for metadata management need not fall on any one individual, but rather may be delegated to appropriate members of a collaboration, enabling participants to edit or maintain specific attributes, to describe relationships between data elements, or to correct errors. To support such collaborative data editing, we have created ERMrest, a relational data service for the Web that enables the creation, evolution and navigation of complex models used to describe and structure diverse file or relational data objects. A key capability of ERMrest is its ability to control operations down to the level of individual data elements, i.e. fine-grained access control, so that many different modes of data-oriented collaboration can be supported. In this paper we introduce ERMrest and describe its fine-grained access control capabilities that support collaborative editing. ERMrest is in daily use in many data driven collaborations and we describe a sample policy that is based on a common biocuration pattern.

Entities:  

Year:  2017        PMID: 29756002      PMCID: PMC5939964          DOI: 10.1109/eScience.2017.83

Source DB:  PubMed          Journal:  Proc IEEE Int Conf Escience        ISSN: 2325-372X


  1 in total

1.  The FAIR Guiding Principles for scientific data management and stewardship.

Authors:  Mark D Wilkinson; Michel Dumontier; I Jsbrand Jan Aalbersberg; Gabrielle Appleton; Myles Axton; Arie Baak; Niklas Blomberg; Jan-Willem Boiten; Luiz Bonino da Silva Santos; Philip E Bourne; Jildau Bouwman; Anthony J Brookes; Tim Clark; Mercè Crosas; Ingrid Dillo; Olivier Dumon; Scott Edmunds; Chris T Evelo; Richard Finkers; Alejandra Gonzalez-Beltran; Alasdair J G Gray; Paul Groth; Carole Goble; Jeffrey S Grethe; Jaap Heringa; Peter A C 't Hoen; Rob Hooft; Tobias Kuhn; Ruben Kok; Joost Kok; Scott J Lusher; Maryann E Martone; Albert Mons; Abel L Packer; Bengt Persson; Philippe Rocca-Serra; Marco Roos; Rene van Schaik; Susanna-Assunta Sansone; Erik Schultes; Thierry Sengstag; Ted Slater; George Strawn; Morris A Swertz; Mark Thompson; Johan van der Lei; Erik van Mulligen; Jan Velterop; Andra Waagmeester; Peter Wittenburg; Katherine Wolstencroft; Jun Zhao; Barend Mons
Journal:  Sci Data       Date:  2016-03-15       Impact factor: 6.444

  1 in total
  1 in total

1.  Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies.

Authors:  Lu Zhao; Ishaan Batta; William Matloff; Caroline O'Driscoll; Samuel Hobel; Arthur W Toga
Journal:  Neuroinformatics       Date:  2021-04
  1 in total

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