Publishing and sharing sensitive data

Sensitive data has often been excluded from discussions about data publication and sharing. It was believed that sharing sensitive data is not ethical or that it is too difficult to do safely. This opinion has changed with greater understanding and use of methods to ‘de-sensitise’ (i.e., confidentialise) data; that is, modify the data to remove information so that participants or subjects are no longer identifiable, and the capacity to grant ‘conditional access’ to data. Requirements of publishers and funding bodies for researchers to publish and share their data have also seen sensitive data sharing increase. This guide outlines best practice for the publication and sharing of sensitive research data in the Australian context. The Guide follows the sequence of steps that are necessary for publishing and sharing sensitive data, as outlined in the ‘Publishing and Sharing Sensitive Data Decision Tree’. It provides the detail and context to the steps in this Decision Tree. References for further reading are provided for those that are interested. By following the sections below, and steps within, you will be able to make clear, lawful, and ethical decisions about sharing your data safely. It can be done in most cases! How the Guide interacts with your institutional policies This Guide is not intended to override institutional policies on data management or publication. Most researchers operate within the policies of their institution and/or funding arrangement and must, therefore, ensure their decisions about data publication align with these policies. This is particularly relevant for Intellectual Property, and sometimes, your classification of sensitive data (e.g., NSW Government Department of Environment & Heritage, Sensitive Data Species Policy) or selection of data repository. The Guide indicates the steps at which you should check your institutional policies

Preliminary results on Ontology-based Open Data Publishing

Despite the current interest in Open Data publishing, a formal and comprehensive methodology supporting an organization in deciding which data to publish and carrying out precise procedures for publishing high-quality data, is still missing. In this paper we argue that the Ontology-based Data Management paradigm can provide a formal basis for a principled approach to publish high quality, semantically annotated Open Data. We describe two main approaches to using an ontology for this endeavor, and then we present some technical results on one of the approaches, called bottom-up, where the specification of the data to be published is given in terms of the sources, and specific techniques allow deriving suitable annotations for interpreting the published data under the light of the ontology

Publishing and sharing multi-dimensional image data with OMERO

Imaging data are used in the life and biomedical sciences to measure the molecular and structural composition and dynamics of cells, tissues, and organisms. Datasets range in size from megabytes to terabytes and usually contain a combination of binary pixel data and metadata that describe the acquisition process and any derived results. The OMERO image data management platform allows users to securely share image datasets according to specific permissions levels: data can be held privately, shared with a set of colleagues, or made available via a public URL. Users control access by assigning data to specific Groups with defined membership and access rights. OMERO’s Permission system supports simple data sharing in a lab, collaborative data analysis, and even teaching environments. OMERO software is open source and released by the OME Consortium at www.openmicroscopy.org

A linked data approach to publishing complex scientific workflows

Past data management practices in many fields of natural science, including climate research, have focused primarily on the final research output - the research publication - with less attention paid to the chain of intermediate data results and their associated metadata, including provenance. Data were often regarded merely as an adjunct to the publication, rather than a scientific resource in their own right. In this paper, we attempt to address the issues of capturing and publishing detailed workflows associated with the climate/research datasets held by the Climatic Research Unit (CRU) at the University of East Anglia. To this end, we present a customisable approach to exposing climate research workflows for the effective re-use of the associated data, through the adoption of linked-data principles, existing widely adopted citation techniques (Digital Object Identifier) and data exchange mechanisms (Open Archives Initiative Object Reuse and Exchange)

Publishing data evidence to support educational technology claims [Editorial]

As is evident by this issue, JTATE publishes works that include rich data evidence, regardless of the method used in the research design. Detailed and careful research analyses, as well as purposeful design and construction of the write-up are critical to building a strong foundation of educational technology literature. Researchers in educational technology and technology and teacher education more specifically, who decide to follow a platinum standard for research publication, are strengthening and broadening the credibility of a relatively young field. The JTATE editors promote this line of thinking, encouraging editorial board members, reviewers, and authors to assist with this important goal

Publishing Data [Workshop]

Context: This short workshop was held on September 05 2023 in the context of the 115th Annual meeting of the German Zoological Society (DZG) in Kassel as part of the satellite workshop "Unlocking the rhythms of life with multiscale clocks", organized by the research training group "multiscale clocks" (https://www.uni-kassel.de/forschung/multiscale-clocks/welcome). Description: This short workshop specifically considers three questions that were raised in the preceding workshop part. During the working time with data and data visualizations, the participants were asked to reflect on the following: „Would you know how to obtain the underlying data?“, „Are you allowed to do with the data what you want?“, „What data/info would you require to check your hypothesis?“. The workshop consequently revolves around data publication. Designed to give a general overview, it covers access, locations and usage rights of published data from a user perspective; as a last part, the generation of reusable data is highlighted from the creator's perspective. Covered topics are open access requirements and colors; publication as supplement, in data journals, or in repositories; copyright and creative commons; research data management, life cycle, and the FAIR principles. The meaning of the FAIR principles was explored with the help of the FAIR Card Deck (unpubl.). Learning Goals: Be aware of research data management topics around data publication Understand the importance of labelling and documentation Know the FAIR principles Duration: 60 min Target Audience: Researchers (Students, PhD candidates, PostDocs) Anyone interested Prerequisites: none (participation in the preceding workshop was expected, but not absolutely necessary) Tools: For the presenter: FAIR Card Deck Mentimeter set up as "Open ended" statement collection for the take home messages from participants Upload Content: Itemized Upload Content File Description 2023-08-30_DZG-Clocks-DataPublication.key Slide deck in editable .key format 2023-08-30_DZG-Clocks-DataPublication.pptx Slide deck in editable .pptx format 2023-08-30_DZG-Clocks-DataPublication.pdf Slide deck as .pdf 2023-08-30_DataPubl_Script.xlsx Presenter's script in editable .xlsx format 2023-08-30_DataPubl_Script.pdf Presenter's script as .pdf Notes: Three FAIR Card Decks were used, with only the cards 9-5 from each deck