Many online forms create barriers to filling out information, including obstacles regarding race, gender, and even relationships. Web editors with the best intentions could unintentionally create a barrier for a user, which means the user will get frustrated with a brand and abandon the form. This resource serves as a guide to ensure you are making forms that are as accessible as possible.

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This resource provides insights and methodology with reflection on the initial idea, pilot, and beta for playbook launch phases. It concludes with overall lessons that can be useful to everyone who is developing data playbooks for their organizations.

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This tool guides data practitioners through different stages to ensure that the work they conduct is as actionable as possible. It offers guidance, brainstorming exercises, and supplementary resources.

The post Mastering the Practice of Defining Problems Well: Open Data Policy Lab Releases New Problem Definition Tool appeared first on data.org.

As people have used or heard about the power of OpenAI’s GPT-4 foundation model, they have often been surprised or even astounded. Many have been enthused or excited, but others have been concerned or even frightened. What has become clear to almost everyone is something we noted four years ago—we are the first generation in the history of humanity to create machines that can make decisions that previously could only be made by people. This resource provides best practices for implementing responsible AI systems.

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This resource is designed to provide the foundations necessary for understanding and extending the current state of the art in data visualization. Gain an understanding of key visualization techniques and theory, including data models, graphical perception, and methods for visual encoding and interaction; exposure to a number of common data domains and corresponding analysis tasks, including multivariate data, geospatial data, and networks; and practical experience building and evaluating visualization applications.

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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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Data stewards seek to accelerate data collaboration in a systematic, sustainable, and responsible way. As such, they are committed to collaborating with others, with the goal of unlocking the value of data when a clear case exists that it serves the public good and that data can be used responsibly.

As such, this resource outlines a job description for data stewards. This resource is useful for organizations that are formalizing data steward roles and aspiring data stewards who are seeking more information on what training and opportunities they need to obtain such a position.

The post Wanted: Data Stewards — Drafting the Job Specs for a Re-imagined Data Stewardship Role appeared first on data.org.

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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This Data Learning Journey Playbook is a hands-on resource with independent and team activities that seek to build and sustain data culture and skills for global development organizations. This playbook is designed with people at the center— developing data skills for everyone and leveraging data as a tool for learning collaboratively.

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Data science and user experience (UX) procedures have more in common than most of us think. Both functions help improve decision-making and products by understanding users and their needs. Depending on the problem, the integration of data science and UX can support identifying a small but focused user group, surveying a larger group, mining a huge amount of data, or building products that automatically make decisions and continuously improve based on user behavior.

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An IDEA approach is essential to reflect the wide variety of lived experiences within the use and application of data. Historically, the use of data is centered around strategy for building capital, and large companies can agenda-set language and frameworks due to their extensive reach. By using IDEA as a staple in our proposed terminology, we hope to encourage organizational knowledge-building based on their unique efforts and needs.

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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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