Project Ladds – a summary in English


This document provides an initial overview of the different workpackages in project Ladds (LAb for the Data Drives Society). The document describes the thougths so far. Ladd’s main deliverables are:

  • Rules
    Establish ground rules that enables and balances the influence commercial and non-commercial innovation efforts and actors have in how the lab is operated and managed.
  • Professional profiles and skills
    Define competency profiles/skills needed to create data-driven innovations and link these people with those skills to the lab.
  • Tools / technology
    Deploying tools and technology, build handling skills and test technical the components/tools needed in the lab.
  • The meeting point
    Develop venue and meeting procedures so that innovators based in industry, academia, government, non-profit organizations and even individual citizens, citizens can meet to innovate together.
  • Knowledge building
    Build knowledge around data driven innovation among different actors in the region.
  • Innovation Events
    Implement events where data-driven innovations are developed.


In the establishing phase, there are limited opportunities to create a technical environment that covers everything needed in a data-driven lab. For cost reasons, the lab will be limited to what we consider to be most essential for creating data-driven innovations.

Open Source
The aim is to use Open Source where possible. The project also wants to use cloud services without fixed charges as far as possible and minimize need for having our own hardware. Our time constraints makes it necessary to use ready-made solutions to a large extent even if these lacks in transparency and openness.

At least during the relatively short establishing phase, it is therefore likely that the solutions that are not based on open source and transparency principles will be used. After the establishment phase it is reasonable to decide on the basis of rules and given economic and personnel conditions determine whether the use of open source-solutions can be extended.

Solutions developed from scratch will be too costly to produce and operate. We therefore choose to build on existing cloud services. We have chosen to use Amazon’s cloud services for two reasons. We assess that their technical environment is more mature regarding support for Machine Learning and handling sensor data. Their pricing model using only variable costs which will save us money since the intensity of the lab’s activities will vary over time. Microsoft’s cloud services his said to have a price model based on fixed costs, which, at least in this initial stage, is not ideal.

Lab environment, not a development environment
The lab consists of solutions for developing concepts and working code. The transfer if the innovation to a fully functional development environment outside of the lab should be facilitated as far as possible. Providing a fully functional development environment might be a part of a future upscaling of the lab.

Artifacts in the technical environment
The labs needs data. The data available in the lab is a combination of data from sensors which is supplied to the lab by a Lora network, data files that the project acquires from our stakeholders and existing data sources containing open data.

All data is stored in Amazon S3 storage solution. This is a cost effective solution that can smoothly handle the volumes of data that are relevant at this stage. This means that real-time analysis can not be performed. This need will probably arise and if the budget allows, also components for this will be added. This might be happening sooner than later.

The value creation from the data is primarily done by using the libraries provided by Apache Spark and other libraries that can by used by Python. To test parallelization and build knowledge about how effective the program will be if the amount of data increases significantly, a small cluster with at least two worker nodes will exist in the lab environment.

Data from the sensors is handled by Lora-WAN which is a technology for transmitting sensor data. The lab will have sensors available for lending by anyone who wants to try out some idea involving these type of sensor.

Besides sensors using LoRa Wan,  the project will also have sensors that uses bluetooth technology to count the number of mobile phones within a limited area. If possible, also this data will be transferred over the LoRa Wan. If that is impossible the will be connected to a common wireless network or a cellular network.

The interface towards the innovator is primarily “Jupyter”, an online ”notebook”. In this software you can mix text and program code so that you can describe the challenge and other interesting bits of text and then have an adjoining area of executable program code which is the concrete artefact that does what must be done in order to solve the challenge.

From the notebook, Python programs can access the different libraries included in e.g. Spark but other libraries used for the analysis and presentation of data.

Preliminary image of the labs technical environment

Sensor Data

The sensors are connected to LoRa-Wan captures the following types of data:

The temperature, amount of light, CO2 and the number of people in a room
Position, motion and temperature
Number of people (mobile phones) in a defined area
Fill ratio, and / or weight of the contents of containers
Data from these sensors is fixed test data, but the sensors can also be activated and deliver data in real time if the inventor has the need for this type of data. We also hope to be able to buy sensors and lend them to innovators who need to have them in their own facilities, buildings etc.

Data from the “bluetooth pucks” that measures the number of mobile phones in the vicinity is handled in the same way. The ambition is that all sensor data in the lab will be open, but if someone innovator wish to keep their own captured sensor data private, it will not be published as open data in the lab environment.

Open Data

Open data from existing CKAN portals are retrieved by the innovator from the respective portal, preferably via the CKAN API. Subsets of this data may be stored in the lab S3 solution or in the lab’s database server if this would make it easier for the innovator.

The Open data that will be provided is are:

Private Data
It might happen that innovators want to combine their own data with open data. It may therefore exist private data that is only accessible to those who received the explicit permission of the inventor/owner of the data. At present no such data is identified.

Data Processing
The algorithms/logic that process data is made in the web-based ”notebook” named Jupyter. Through various Python library, visualizations can also be done, but it will exist a need for more tools in the toolbox than Python and Jupyter. What additional tools that should be added is not clear at present.

The project is co-financed by