±«Óãtv

The Story Explorer provides a new way to enjoy and understand dramas using data about the storylines, events and characters.

Project from 2015 - present

You can try out the live prototype of the .

We have broken down the first three series of ±«Óãtv Front into 16 individual, but interlocking, storylines. Every storyline is then broken down into scenes, with each scene occurring exactly 100 years prior to the day it was broadcast. Every scene has an illustration, audio, key characters and a description of what happens.

For three featured storylines we have also highlighted the key events to help summarise them, with an option to browse the full storyline. You can browse through them, reading the descriptions, or just press play and listen to every moment. You can even subscribe to a podcast dedicated to just that storyline.

Our subsequent prototype, , evolved this design and concept is a catch-up guide for the story so far in this epic gangster drama from ±«Óãtv Two. It uses structured stories and object-based media to explore the storylines, characters and themes of the first two series of Peaky Blinders. Based on what we learnt from piloting the concepts with ±«Óãtv Front we decided to develop the concept further with TV drama  —  designing something that would be re-usable & scalable for all ±«Óãtv dramas. We chose Peaky Blinders as it uses the typical ±«Óãtv 6-part drama format, has a web of characters and storylines and looks great!

You can try out the live prototype of the  or .

Why it matters

Have you ever started a new series of a drama but struggled to remember what happened in the previous series? Or could you not quite place that character and why she's behaving suspiciously? Or did you get distracted and missed a crucial plot point? What if it's too late to watch it again in ±«Óãtv iPlayer or maybe you just don't have time?

We think there is an opportunity for the web to better support programmes and their stories. There are, of course, many fan sites and third parties that describe and discuss popular dramas and we think the ±«Óãtv could support this need too, making something informative, attractive, scalable and linked through to the rest of the ±«Óãtv and the web. Imagine the Story Explorer for your favourite drama. Doctor Who or Casualty or Luther or Poldark or Wolf Hall or The Killing.

The concept is aimed at both existing and new viewers and listeners and lets you...

• Quickly recap on what happened previously.
• Catch up on things you might have missed.
• Better understand what happened and why.
• Follow individual storylines or characters.
• More easily get into a new drama that you've previously missed.
• Find background material on real-life issues and history.
• Point to and share your favourite bits.

Goals

The aim of our experiment is to find out if it does help with the problems we've identified, specifically...

• Does it help you orientate yourself when starting a drama?
• Does it help you catch up and recap?
• Is it useful to have text summaries as well as the original media?
• Which bits of information are most important to know and is there anything missing?
• Does it make sense to break a programme into storylines, events and characters?
• Does it matter that there may be spoilers?
• Could it work as a complete listening experience?

We also aim to design a great user experience for this concept and to work out ways to efficiently create the data needed in the programme production process.

How does it work?

±«Óãtv R&D has , aiming to enhance the understanding and enjoyment of the stories that the ±«Óãtv tells. Our focus is on exploring the use of data to create new experiences for our audiences by breaking stories up into storylines, characters and moments. We have previously experimented with the stories of ,  and even .

In this work we have developed a common data model for stories based on this and designed to work across any drama or soap on TV and radio, or even to capture the stories in factual programmes and news. One of our challenges is how to efficiently create this data for programmes and we are now building tools to help production teams in the .

This project is part of the  work stream