Challenges

The feedback loop is a process of collecting user feedback continuously and improving your product or service based on their opinions. In an open data context, we aim to establish effective connections between those working with the data (e.g. to reproduce the research), and those producing or entering the data at its origin (e.g. experimental setting). Connecting the data value chain may help to address some of the causes of poor data quality in the long-term.

Many feedback channels exist online: by publishing data on a platform like GitHub, users can request to update or make changes to the data to enhance the quality. Seeing who forks and contributes to the data provides better understanding of who is using data and how. Using bug tracking or forums, users can also submit requests for specific datasets. For example, see Project Open Data or COVID-19 Fallzahlen.

◎ Brainstorm

1. Suggest one or two strategies for setting up feedback loops
2. Discover and explore a new channel for engaging with data users
3. Engage in at least one existing open data publication with a question or suggestion

Making the Most of Interim Assessment Data. Jolley Bruce Christman et al 2009

The formats in which information is published – in other words, the digital base in which the information is stored - can either be “open” or “closed”. An open format is one where the specifications for the software are available to anyone, free of charge, so that anyone can use these specifications in their own software without any limitations on re-use imposed by intellectual property rights. If a file format is “closed”, this may be either because the file format is proprietary and the specification is not publicly available, or because the file format is proprietary and even though the specification has been made public, re-use is limited. [Open formats] minimise the obstacles to reusing the information they contain. -- Open Data Handbook

◎ Brainstorm

1. What are some common data formats in your field of interest?
2. Are there active and supportive user communities around them?
3. How does the software deployed by default at your institution influence data formats?
4. Discuss how data formats impact reuse culture in education or research.

opendata.swiss Update. Jonas Oesch 2018

Hacks are novel creations or solutions to problems - not always elegant, and sometimes little more than sketches or very basic prototypes - nevertheless they facilitate deep exchange among multidisciplinary teams, and often expose flaws or opportunities that are missed in other explorative methods. Hackathons are time-limited collaborative events, where people meet to tinker on data and platforms, and where "hacks" are created in response to a variety of problems or "challenges".

The events assume many different forms, but most are designed to bring researchers, engineers and other professions from various career stages and backgrounds together along topics of common interest. When the participating teams wish to use real-world data for their prototypes, a large amount of time is often invested into finding and cleaning datasets. The open data community uses hackathons - often with public sector support - to validate and improve new data sources, and create feedback loops with stakeholders.

◎ Brainstorm

1. Find out about hackathons in your chosen field, browse the challenges and results of at least one past event.
2. Make observations about how to build on top of the submitted proposals and connect to the participants.
3. How can an open platform for data events like dribdat help you to design, plan and run a hackathon?
4. What are hackathons (& characteristics thereof) that would be interesting to students, teachers, researchers, staff?
5. Is the hackathon concept transferable to other, non-digital areas? E.g. product development, prototyping, modelling, process development.

Hackathons in Large Collaborative Projects. Andrey Sadovykh et al 2019

While funder policies may suggest a scope for open science publication, content licensing can seem like a confusing jumble of opinions rather than the practical instrument it aims to be. The term "Open License" is generally used to refer to any legally binding instrument that grants permission to access, re-use, and redistribute a work with few or no restrictions. While technically not a "license," wordwide public domain dedications such as Creative Commons Zero also satisfy this definition. The Open Definition website provides a list of Conformant Licenses.

◎ Brainstorm

• Think about what kind of legal restrictions you have encountered in your work.
• Find a case study of how licensing issues have affected research or learning.
• From a fair use perspective, when may data be / may not be reused (with clear attribution)?
• Discuss how open data licensing can lead to reforms in the way collaborations are done, or institutions are governed.

Open Data in Global Environmental Research: The Belmont Forum's Open Data Survey. Birgit Schmidt et al 2016.

CC Cheat Sheet Martin-Luther-Universität Halle-Wittenberg, CC BY 4.0

Metadata is "data that provides information about other data". In other words, it is "data about data". It is not the same as the format of the data, and the term overlaps - but should not be confused with - database schema. The Open Data Handbook says that "It is essential to publish data with adequate metadata to aid both discoverability and usability of the data." There are many kinds and formats of metadata, depending on the kind of gap they are helping to address. Portals like opendata.swiss collect metadata from diverse sources and make it easy to explore.

◎ Brainstorm

1. Describe some common metadata formats that are important to your field.
2. Find out who takes responsibility for their maintenance and development, where to get publication support.
3. What about the metadata may block or benefit the open reuse of data?
4. How can users benefit from validation or discoverability features?

Research on Metadata Management System of Linkage Service of Scientific Data and Scientific Literature. Xiujuan Wang et al 2018