This section outlines the archive theoretical, political and a pedagogical frame. It explains how cases were collected and what the tags used to classify projects mean. The aim is to disclose how this archive is being built, to ask users to be critical and see if the tags are meaningful or if disagree with them and why. At the same time, revealing the theoretical part that backs up the archive seeks to promote the creation of data visualisations that support the future creation of alternative narratives.

How were cases collected?

The collected cases include projects that have to due with social objectives that seek to engage audiences through the communication of alternative visions of the world using data in digital platforms. All projects were developed by independent and mainly bottom-up organizations such as self-organized citizen groups, NGOs , independent civil society organizations, researchers. They are non-profit, not private or business sector oriented. Beyond these characteristics, the projects responds to the filter of "spaces of confrontation" criterion based on Chantal Mouffe agonistic theory (2000).The space is configured by three axes: revelation, dissenssus and confrontation. The axes serves for identifying cases that bring alternative stories that challenge dominant power.

About the typology of projects

Based on each project purpose (mainly inferred or retrieved from their "about" section) It is proposed a taxonomy of data-driven alternative narratives which are:

• Memory and archives cluster: projects the main characteristic of which is to build on the memory of specific material through data collection. Archives are power devices that create realities by storing and making pieces of evidence accessible. Archival activism is directly related to community-based archives around the world It offers access to stories, evidence, facts and arguments that can be used to advance causes and social campaigns. They can be considered as a counterculture practices itself, archiving material that has not been recognized by the dominant official structures. The cases studies tagged in fulfill one or both functions. They embrace interactive features that allow users to explore the dataset through visualizations (on maps, networks, charts). In most cases users can download the databases, and contribute to data collection (crowdsourcing data).
• Monitoring: projects in this group seek to hold accountable and increase the transparency of the work done by top-down institutions by monitoring their actions: by constantly following, tracking, and comparing the changes. Often monitoring projects are fed by crowdsourced data. The audience isn’t only an observer but also a contributor of information. The action of monitoring comes alive not only in the results of the visualization but also through the process of audience participation (Briones, 2019). Maps of power is a sub-group that monitors the influence and power relations between actors and institutions. They highlight how power is connected in different contexts through network visualizations based fundamentally on network representations. However, there are several inventive explorations in the archive.
• Reporting: this group is made up of projects that observe and share evidence about the general view of a phenomenon that has usually already occurred within a specific time range, and that can be verified by anyone. They seek above all to raise awareness and inform audiences. Although there is no completely "neutral" position, these reports tend to this position to provide facts and evidence of the phenomenon of.
• Investigative: projects in this group are usually intended to be a catalyst for further questions rather than a snapshot of the current state of affairs. Investigative projects delve deeper into a specific topic, gathering information from different angles and exploring different documentation techniques. It includes projects that come close to the porous boundary of data journalism. Investigative projects shows great predominance of territorial evidence as a relevant strategy for situating the context of the investigation. They usually combine diverse data and information sources. They also visually experiment with multiple types of media for representing different angles of the topic being investigated. It is not common to find here crowdsourced data.
• Social cohesion: the projects of this group seek to mobilize long-term movements, not just for specific events or campaigns. The projects of this cluster actively combine tactics in the public and virtual space (such as digital campaigns, physical activities and meetings such as protests, meetings, workshops, among others). They are projects that are close to the research group but that emphasize the integration of physical-digital action around the inquiry issue.

About project’s topics and sub-topics

Each project focuses on a particular topic. Topics were manually classified by defining a macro topic and related sub-thopics. The five major topicsare: Policymaking, Transparency and accountability, Human rights, Memory and archives, and Surveillance. The following diagram maps according to your topics, subtopics as well as representing the number and type of organization involved in each project. observed how the subtopics overlap and cross one topic with another. This classification seeks to give more entry points to the reading of the projects and how they represent conflicts.

About data acquisition category

The archive emphasizes observing how visualization projects for alternative narratives work with the data. There are several questions that can be asked, from do they contain a methodology section that explains how you worked with the data? Do we know who created that data and how? This classification deals with understanding and showing how the data was acquired in each project. Based on the Mirén Gutiérrez classification published in his research Data Activism and Social Change (2018), the following categorization is proposed:

• Appropriate data: it's about grabbing existing data from other platforms from which there is no access. The appropriation of data literally means the access to data that is not accessible. This is the fundamental difference with open data or data exchange. Scraping is one of the most used technique.
• Own research: project teams “conduct primary research whose findings can be datafied; or deploy data-collecting sensors, drones and other devices”. Some examples of creating data for research is through surveys, sensors, aerial imagery (community drones).
• Existing sources: produce new analysis from available, but unrelated and unexplored, datasets.
• Collaboration with other organizations: collectives, foundations, organizations among others.
• Crowdsourced: generate the means to crowdsource citizen-contributed data.
• Whistle-blowers: this is the case when organizations or individuals are recipients of data via whistleçblowers such as leaks.
• Governmental data but not public: this form of data acquisition is different from the others in that it is delivered directly by an entity such as the government to a particular organization to develop a project. It could be assumed that government data should be open to all, however this category emphasizes that they are delivered only for restricted purposes.

The treemap diagram below visualizes relationships in the forms of data acquisition, the topics addressed and the types of projects. A project can acquire its data in more than one way, combining strategies in most cases.

Many of the projects state principles such as transparency and accountability, disclosure of power relations, or the right to open information. It is striking, however, that the vast majority of cases do not put much of these principles into practice through their own data visualizations by means that they don’t release the databases used in the visualizations, among other practices. From this can be inferred:

• The weak relationship of open data culture linked to data visualizations.
• The incoherence in the discourse of institutions that use data visualization to subvert the opacity of information by institutions of power.
• The deliberate will not to share data due to other reasons not explicit in the projects.

All three are fairly generic conclusions but they provide first lines of action to foster a critical data culture. This research aims to contribute from design to a more critical and knowledgeable data culture, so it points its efforts to the first of these stated observations. How design can you contribute to a more critical data culture?

Some good practices for alternative narratives with data

Through the collection of cases, some good practices were identified that serve to promote a more critical approach to the reading and construction of visualizations for alternative narratives. Some of them are:

• Declaration of licenses of use as creative commons, copyleft or Open Data Commons Open Database License.
• Inclusion of a methodology section on data and its process of shaping research. In this section it is recommended to include how the data was acquired, who created the data and how, what were the research questions that guided the transformations in the data, among other things. Another practice recommended for this section is the disclosure of design decisions that were made to visualize the data. This should include from the description of the tools and visual models used.
• Make the data used available if possible. One of the most common practices found is the inclusion of links to data sources. In a few cases there are direct download links to the already manipulated data that are used in the data visualizations. This is recommended since it allows the public to critically understand how the visualizations have been constructed. In case the data cannot be published, it is advisable to explain why not.