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Registering DataCite DOIs for Awards

Using DataCite DOIs for awards This page provides general guidance on registering DataCite DOIs for awards, grants, and comparable entities. DataCite DOIs are suitable for a wide range of research outputs and resources, including awards and grants. Registering DOIs for awards and grants: Enables res...

Introduction to the DataCite REST API

About the DataCite REST API The DataCite REST API enables retrieval, creation, and update of DataCite DOI metadata records and account information. The API is generally RESTful and returns results in JSON (following the JSON:API specification). REST API Reference 📘 Access the REST API Reference to ...

RegisterDOI | Simplified DOI Registration for Publishers & Researchers

Get genuine Digital Object Identifiers (DOIs) for your research articles, journals, and publications through our trusted registration service.

Apache Calcite | Proceedings of the 2018 International Conference on Management of Data

As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.

Professionalising Community Management Roles in Interdisciplinary Research Projects

In this article we discuss community management in interdisciplinary research teams, focusing on recognising and professionalising roles referred to here as the Research Community Managers (RCM). Drawing insights and examples from research and data science projects, we discuss how RCM roles address some of the research’s most pressing challenges, from promoting best practices for open research and reproducibility to engaging diverse stakeholders in community-led research and ensuring fair recognition for their contributions. We offer a Community Maturation Indicator and share examples of projects from The Alan Turing Institute, the UK's national institute for data science and Artificial Intelligence (AI), where institutionally supported RCM roles were established. With the aim to integrate RCM expertise in teams involved in data science and AI research, we provide an RCM Skills and Competencies Framework. We also propose a roadmap for professionalising RCM roles by improving recognition and rewards, potential career paths and organisational support structures. To systematically sustain and progress these roles, we recommend institutional investment in establishing RCM teams that are empowered to prioritise collaboration, transparency and community-based approaches in interdisciplinary projects, such as in data science and AI. As a team, RCMs are well placed to connect disparate teams, initiatives and resources across the organisation, building more resilient research communities that can achieve greater innovation, improved project outcomes and a strongly connected ecosystem, with impacts extending beyond their narrow contexts.

doi2bib

Easy generation of citations in BibTeX format from digital object identifiers (DOIs).

Data Transfer Initiative

Home page for the Data Transfer Initiative, a nonprofit organization dedicated to promoting data transfers

Plan S

Plan S is an initiative for open-access science publishing launched in 2018[1][2] by "cOAlition S",[3] a consortium of national research agencies and funders from twelve European countries. The plan requires scientists and researchers who benefit from state-funded research organisations and institutions to publish their work in open repositories or in journals that are available to all by 2021.[4] The "S" stands for "shock".[5]

Democratizing Data

Democratizing Data builds a community-driven data ecosystem by identifying how datasets are used and reducing barriers to accessing high-quality public data. The initiative enhances the discoverability, usability, and relevance of data for researchers, policymakers, and stakeholder communities. A suite of tools and strategic partnerships supports this work by connecting users to the data, insights, and networks needed to inform decisions and generate impact.

New fellowship program connects SDIS students with real-world opportunities in data management - School of Data and Information Sciences

SDIS alumni leaders and the UNC Research Data Management Core are creating hands-on learning opportunities for master's students through a new research data management fellowship program.

The Research Nexus vision for a more connected scholarly community

Crossref envisions “a rich and reusable open network of relationships connecting research organizations, people, things, and actions; a scholarly record that the global community can build on forever, for the benefit of society”. This Research Nexus expands on the importance of research objects being persistently and uniquely identified. The scholarly community has an established practice of connecting things such as citations to others’ work and it is increasingly critical to identify relationships beyond citations, bringing together published work, unpublished work, institutions, individuals, and identifying the actions that they take e.g., funding, publishing, creating, modifying, citing, and sharing. The Research Nexus brings together metadata and relationships to build a joined-up picture of the scholarly ecosystem and helps everyone identify these relationships and how they change through time. This vision is possible if all parts of the scholarly ecosystem (and beyond) work together, including various scholarly infrastructure organizations.

Metaudits

Audits of how completely research is described in DataCite, Crossref, ORCID, OpenAlex and repositories. In English, Spanish and German.

GainForest — Biodiversity Observations & Nature Projects

Explore field observations, biodiversity records, and nature projects from communities and organizations using GainForest.

GainForest — Biodiversity Observations & Nature Projects

Explore field observations, biodiversity records, and nature projects from communities and organizations using GainForest.

GainForest — Biodiversity Observations & Nature Projects

Explore field observations, biodiversity records, and nature projects from communities and organizations using GainForest.

Collaboratory at Columbia: An Aspen Grove of Data Science Education · Issue 3.4, Fall 2021

Many universities recognize the rapidly growing impact of data science in all fields of study and the professions and seek to embed this expertise widely across their educational offerings. There is often broad interest in developing new data science curricula, with some universities even allocating funds toward this purpose. Yet, it is often unclear what resources are needed for effective data science education, and how resources ought to be prioritized. Although university leadership might be aware of a growing number of successful data science acceleration programs and pedagogical models, many of which are either general purpose or specific to a particular discipline, there remains a lack of clarity about how these models might address their own specific needs. This article presents the Collaboratory Program at Columbia University (termed “the Collaboratory”), which is both a set of “data science in context” educational approaches, as well as a meta-model for an accelerator program that allows different institutions to respond flexibly to their own disciplinary heterogeneity in terms of data science educational needs. The novelty of the Collaboratory lies in its crowd-sourcing approach to creating new data science pedagogy and its ability to kindle transdisciplinary collaboration in doing so. By offering seed funding, it fosters proactive efforts to embed data science “in context” into more traditional domains through a cohort of compelling, transdisciplinary, crowd-sourced data science education proposals each year. Collaboratory educational offerings are required to be developed through a partnership between two faculty members, a data scientist and a domain expert from another field, or a larger team with complementary expertise. Over the past 5 years, the Collaboratory has supported the development of a wide spectrum of data science pedagogical models spread across more than 40 academic departments, centers, institutes, and professional schools at Columbia University. As a result, the Collaboratory has to date served the learning needs of more than 4,000 students. Furthermore, it has cultivated a thriving ecosystem that includes a funding mechanism and a community-support structure that all contribute to its agility and success. Here, we offer our experience and best practices in developing and managing the Collaboratory, which, we hope, will contribute to a blueprint for data science education leaders everywhere.