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Applications Open and Date Announcement: Summit for Academic Institutional Readiness in Data Sharing (STAIRS)

EDIT: Applications closed on May 17. Thanks to all who applied! Set your calendars – the Summit for Academic Institutional Readiness in Data Sharing (STAIRS) will be held August 5-6 2024 at the University of Minnesota, Twin Cities! The STAIRS team is also delighted to announce that applications for the summit are open now through…

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Announcing the Summit for Academic Institutional Readiness in Data Sharing (STAIRS)

The DCN is excited to announce the next phase of our project, “Sustaining Open Research: Institutional Repository Readiness.”  Building on our virtual learning series held in Summer/Fall 2023, we are excited to share our plans to host the Summit for Academic Institutional Readiness in Data Sharing (STAIRS) in August 2024! This summit is funded under…

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Beyond compliance: Curation as essential open science infrastructure

The following was adapted from a presentation by Wind Cowles, Associate Dean for Data, Research, and Teaching at Princeton University, and Mikala Narlock, Director of the Data Curation Network based at the University of Minnesota. This was presented on October 19, 2023 during a workshop titled, “Developing New Approaches to Promote Equitable and Inclusive Implementation…

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Recap: Sustaining Open Research: Virtual Learning Series

As part of our research project, “Sustaining Open Research: Institutional Repository Readiness,” the DCN hosted a virtual learning series that focused on supporting IRs that share data in demonstrating alignment with and evaluation of the Desirable Characteristics of Data Repositories for Federally Funded Research (DC-DR). Over the course of several months, we hosted four events…

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Sustaining Open Research: Data Sharing Readiness Virtual Learning Event

As part of our research project, “Sustaining Open Research: Institutional Repository Readiness,” we are happy to share the recording from our first virtual learning event on “Funding Agencies and the Desirable Characteristics.” Our speakers have also archived their slides, which are available through the DCN’s repository and linked from our project page. We also invite…

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Institutional Repository Readiness Virtual Learning Series

In April, we shared our new research project, “Sustaining Open Research: Institutional Repository Readiness,” which focuses on supporting IRs that share data in demonstrating alignment with and evaluation of the Desirable Characteristics of Data Repositories for Federally Funded Research (DC-DR). As part of this work, we are hosting a series of virtual learning days that…

Desirable Characteristics of Data Repositories: Institutional Repositories and Federally Funded Data Sharing

In May 2022, the National Science and Technology Council’s (NSTC) released the Desirable Characteristics of Data Repositories for Federally Funded Research (DC-DR). As the title suggests, these guidelines are meant to provide funding agencies guidelines to be used when advising researchers on which repositories will meet data sharing and storage requirements. While there are many…

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DCN is Ready to Support Policies Resulting from OSTP Public Access Memo

The Data Curation Network (DCN) whole-heartedly applauds the recent Office of Science and Technology Policy (OSTP) memo, Ensuring Free, Immediate, and Equitable Access to Federally Funded Research, which recommends federal funding agencies to develop policies for grant recipients that eliminates the 12-month embargo on publications and strengthens the requirements for research data management and sharing….

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New NSF-Funded Collaboration between Ithaka S+R and the Data Curation Network

We are excited to announce that the Data Curation Network will collaborate with Ithaka S+R on their recently awarded grant from the National Science Foundation to support the development of infrastructures for data sharing within data communities. The project will bring together scientists and information technology professionals for focused discussions about initiating and sustaining data…