Whenever possible, results from the MAKE-IT activities or from analysis and visualisations from CAPS will be listed in this section as downloadable Open Data or open API.
WP4 Fablabs.io User Survey
The purpose of this research study is to understand better the Fablabs.io users and how the platform could be improved for them. Fablabs.io is the online social network of the international Fab Lab community and the current official list of Fab Labs that share same principles, tools, and philosophy around the future of technology and its role in society. This survey/questionnaire asked users about them, their usage of Fablabs.io, their expectations about it and how Fablabs.io could support the global Fab Lab network and the Maker movement.
WP2 D2.2 questionnaire results June 2017 Maker Faire Barcelona
The data provide the basis for the analysis in D2.3, section 3: you can download it here. The results and analyses in this section have been derived from interviews of a random sample of 42 Maker initiatives present at the Barcelona Maker Faire, 16-18 June 2017, out of 88 registered as present. The Faire consisted of a highly diverse set of exhibitors drawn from across Barcelona and Catalonia. European experts on the MAKE-IT team conducted the interviews and validated that the Barcelona maker scene is quite typical of other large cities across Europe. The results obtained are combined, where relevant, with the instrument’s version 3 results of the ten MAKE-IT case studies from across the whole of Europe, together with relevant insights from D3.2.
WP3 BSOP Tool datasets
WP5 Input for D5.1
Deliverable 5.1 provides the initial mapping of both ICT technology and related applications developed and/or used by CAPs and technology developed and/or used by makers. Instead of a document an online tool for technology descriptions, the Technology Radar, is used. This dataset represents the input data from the online spreadsheet used for D5.1.
WP5 Input for D5.2
Continuing the work of D5.1, deliverable D5.2 describes how the interactive Technology Radar is used to describe future technology developments or trends that will impact how makers will create, communicate, organize and might even do business. This dataset represents the input data from the online spreadsheet used for D5.2.
WP5 Input for TechRadar-D5.3
Continuing the work of D5.1 and D5.2, deliverable D5.3 describes how we are using the gained insights into the Maker movement and how this project serves information back into the maker community in the shape of practical tools. This dataset represents the data available within the version of the Technology Radar developed with a new layout and features for D5.3, so makers can now access the data produced in previous deliverables – previously used for research only.
WP6 SIA Model Matrix dataset
There is no single right answer as to which model might be most appropriate for assessing the impact of Maker initiatives. Instead, we discuss the different parameters that are relevant for choosing the appropriate SIA model and provide a matrix of 69 SIA models with their respective approaches and parameters in this dataset and in deliverable D6.2.
As a younger twin of the TechRadar developed in WP5, WP7 produceda simple dashboard that shows some analytics about the makerspaces: the MakerSpacesRadar, that shows where they are distributed and the different kinds of labs. This dashboard is completely data-driven, and it is an example application of the makerlabs library (a Python module for retrieving data from several Maker CAPS); it could be interested for any stakeholder interested in having an overview of the makerspaces without the need for developing software or research. The dataset shows the distribution of Fab Labs, Hackerspaces and DIYBio Lab globally, by continent, by country and by city.
Datasets from a scientific publication for the Design for Next conference (and published in The Design Journal) and a software library that try to answer to this research question: how could the analysis of social interactions over time on such platforms improve the understanding of design-related collaborative processes? The dataset includes social network analysis of three cases of Maker initiatives developed in GitHub repositories.