These tools provide users leverage to develop and interact with climate data sources. The ‘Models and Tools’ sheet collects analytics models that process/model raw data with output, such as synthesized indexes, hazard impact prediction, and assessment of risks related to climate, environment, and public health.

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Data can be a powerful tool, particularly in the social sector, with unique opportunities and challenges alike. By being more strategic in how we engage with data, we have an opportunity to advance our missions, increase impact, and promote equitable outcomes. This course was designed with and for social sector organizations seeking to build capacity in these areas.

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In today’s increasingly digital age, data is being collected at an unprecedented rate through a variety of channels. Data serves as an essential aspect of communication, a key input for decision-making, and a means to evaluate the performance of programs, projects, and policies. However, many people and organizations struggle to unlock the power of data and/or communicate it in the most effective way to achieve impact and inspire action.

This guide shares the building blocks of a data communication strategy to better understand the value and potential of an organization’s data, the two tiers to measure the impact of a strategy, and a worksheet to put a data communication strategy in place. It also provides real-world examples from mission-driven organizations.

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Multiparty data sharing in the social impact sector refers to situations where two or more organizations collaborate to collect, share, and/or analyze data to address societal challenges. Multiparty data sharing often happens among stakeholders from different sectors, including governmental agencies, private companies, and nongovernmental organizations, who come together to establish collaborative partnerships.

This document focuses on data-sharing initiatives that involve partners from across sectors, excluding business-to-business (B2B) and government-to-government (G2G) data sharing. 

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Governments play a critical role in providing data that is vital to addressing today’s most pressing problems and improving people’s lives. It is essential that governments at all levels work collaboratively to share data across the public and private sectors in a responsible manner. This resource is a repository of recent policy developments concerning open data, data reuse, and data collaboration around the world.

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Data-sharing partnerships can be win-win situations for governments and private companies. In the public sector, improved data-sharing can “improve public services, facilitate research and innovation, and inform policy-making.” Data sharing can serve societal good and help businesses identify new models to promote economic and social benefits. This resource includes insights from a landscape analysis of multi-stakeholder data-sharing partnerships in the public sector, drawing on interviews, case studies, and secondary research.

The post 6 Tips for Fostering Trust in Data-Sharing Partnerships appeared first on data.org.

The Dataverse Project is an open source web application to share, preserve, cite, explore, and analyze research data. It facilitates making data available to others, and allows you to replicate others’ work more easily. Researchers, journals, data authors, publishers, data distributors, and affiliated institutions all receive academic credit and web visibility.

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Data commons collocate data with cloud computing infrastructure and software services, applications, and tools to create powerful resources for the large-scale management, analysis, harmonization, and sharing of data.  Unlike data warehouses, data lakes, and other systems that support an organization’s business analytics, a data commons is focused on providing a resource for a community or collaboration, or in support of multiple communities. 

The term data commons derives from the more general concept of a “commons,” a term that was popularized by Elinor Ostrom, who defined it as a natural, cultural, or digital resource accessible to all members of a community, or, more broadly, of a society. An example is a pasture for animals to graze in a village. These resources are held in common, through a partnership, a not-for-profit, or another entity, but not owned privately for commercial gain [1].

An example of a data commons is the UK Biobank; a large-scale biomedical database with 500,000 participants who consent for their data to be used for the specific purpose of bonafide research on the diagnosis, prevention, and/or treatment of serious and life-threatening illnesses. Evidently, data commons can be powerful catalysts to innovation and transformation of the social impact sector especially when collaborative and synergistic action is required in the efforts to strengthen epidemic preparedness and response, financial inclusion, and enabling access to opportunity.

Data commons can be very challenging to set up and more so to maintain successfully. In this guide, we will share more examples of working data commons and three essential steps to maximize chances of success.

In this Guide

• Get inspired: explore examples of existing data commons
• Define your why and how: Understand the importance of governance in data commons
• Bootstrap: Investigate technology platforms for data commons
• Plan for the long term: Consider issues of data ingest and harmonization

Data Commons in Practice

Data Commons have been developed to address a diverse set of data sharing and research needs.  The following are some examples of data commons:

• NCI Genomic Data Commons – a data commons for cancer genomics data
• European Open Science Cloud – a European partnership supporting open science
• Australian BioCommons – a data commons supporting Australian bioscience
• datacommons.org – a data commons operated by Google

Gen3 – a general-purpose open-source platform supporting ad hoc analysis 

Terra – a platform for biomedical data

Figshare – a general data platform for storing, sharing, and discovering research data

Dataverse – open-source research data repository software

The FAIR data principles

The CARE Principles for Indigenous Data Governance

Research Data Alliance

Conclusion

Data commons support a community’s management, analysis, and sharing of data.  For this reason, data commons require a governance framework that supports the community’s values and goals.  Furthermore, these frameworks are key to ensuring resources are used for the specific purposes for which they were made available. This, in turn, enables more resources to be safely mobilized for the benefit of the community. Successful data commons tend to carefully curate and harmonize the data they contain, which reduces the time and effort required for users to analyze the data, especially when the data comes from multiple sources. This is especially helpful for emergency research and innovation. 

Please feel free to suggest any other guides you found helpful by contacting us and we may incorporate them.

[1] Elinor Ostrom. Governing the commons: The evolution of institutions for collective action. Cambridge university press, 1990.

Grateful for the contribution of Robert Grossman, Ph.D., Frederick H. Rawson Distinguished Service Professor of Medicine and Computer Science, and the Jim and Karen Frank Director of the Center for Translational Data Science (CTDS) at the University of Chicago.

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As more data is collected, data sets are getting bigger — and more challenging to work with. This resource provides 11 tips for making the most of your large data sets.

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In taking on urgent global challenges, organizations are faced with a critical hurdle: a lack of necessary data. There are many ongoing efforts to fill this data gap, including developing new technologies, sources, and collaborations. But promising data-sharing partnerships and data collaboratives (particularly between the private and public sectors) can struggle to overcome institutional and legal barriers.

This resource provides both an analytical framework and an online library of legal clauses that capture the key elements of sharing agreements for data collaboration, complete with links to several detailed guides. This resource addresses the lack of clarity around data sharing and data action issues that often result in high transaction costs and significant delays during the negotiation process.

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Once data is collected, it can be released in various ways, from closed networks within an organization to platform-dependent sharing between peer organizations, publishing with closed licenses, and publishing with fully open licenses. It’s often tempting to think that making data available to the widest possible audience is the best way to maximize that data’s impact. Still, without careful consideration, there can be a myriad of unintended consequences.

This guide introduces the benefits, limitations, and recommendations for internal and external data sharing, including open data.

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Donors have an important role in the humanitarian data ecosystem, both as drivers of increased data collection and analysis and as direct users of data. Donors regularly request data from the organizations they fund to fulfill their obligations and objectives. Some of these requests relate to sensitive information and data which needs to be protected in order to mitigate risk. This resource helps organizations navigate responsible and secure data sharing with their donors.

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