---
# System prepended metadata

title: TPS coffee chat - CDT session trialing

---

# Opening up your communication
Purpose of this TPS coffee chat is to trial one of our activities for a session at the CDT conference on November 15th 2023

:::success
Aims for the [CDT conference](https://www.turing.ac.uk/events/cdt-conference):

* Establish new and enhance existing student-student connections across the CDT landscape 
* Expose attendees to topics and people to support career development 
* Improve awareness of diversity and representation challenges across AI and data science
:::

:::warning
Aim for our communication session (1,5h):
Considering how, where, when and to whom you communicate your research during the research lifecycle is an important skill for any researcher. This workshop, brought to you by the Tools, Practices and Systems Programme of The Alan Turing Institute, explores how scientific communication can be opened up to make it more accessible, collaborative and inclusive.

You will learn to consider at what technical level you should communicate your research, how to write for lay audiences, and how to make your research publications more inclusive by learning how to write alt text.  
:::


## Activity: Alt Text 

### Write your description of the image based on the figure caption 
* Regression - The line graph for regression shows CVD on the y axis and PA/SB on the x axis. There are four lines that correspond to the different numbered clusters numbered 1 to 4. All the lines are decending at different rates with number 1 is the lowest line on the left hand side, then line cluster 2, then cluster 4 and then cluster 3. 
* 




### Write your version of the alt text based on the image and 



## Feedback 
* are we doing figure caption or alt text? this is a bit confusing?
    * Change to "image description"
    * describe the difference between the two
* design your figures so they can be described
* tension between real world and toy example.
* I would include an example on how to do it for graphs, if the exercise before asked us to do it for graphs. My first reaction here was, well yeah an image is easier.



----
## Activity: Lay summaries

:::info
### Instructions
1) Please choose a text to work on below by **filling in your name**
2) ==Highlight== long phrases, with complicated grammar
3) ~~Cross-out~~ unnecessarily ~~detailed~~ words
4) ~~Cross-out~~  & Replace in **bold** jargon and passive voice

example: 
==~~In the study conducted by the team,~~ ~~it was observed~~==**We saw** that the ~~utilization~~ **use** of ~~nanoparticle technology~~ **tiny particles** in medical treatments ~~has been shown to~~ ~~potentially~~ ~~increase~~ **improves** the efficacy of ~~certain~~ drugs.==
:::

### Abstract 1: Unifying pairwise interactions in complex dynamics 
- [name=Jim]


==Scientists have developed hundreds of techniques to measure the interactions between pairs of processes in complex systems, but these computational methods—from contemporaneous correlation coefficients to causal inference methods—define and formulate interactions differently, using distinct ~~quantitative~~ theories that remain largely disconnected.== Here we introduce a ~~large assembled~~ library of 237 ~~statistics of~~ pairwise interactions, and assess their behavior on 1,053 multivariate time series ~~from a wide range of real-world and model-generated systems~~. ~~Our analysis highlights commonalities~~ **We highlight similarities** between ~~disparate mathematical formulations~~ **different types** of interactions, providing a unified picture of a rich interdisciplinary literature. Using three real-world case studies, we then show that simultaneously leveraging diverse methods can uncover those most suitable for addressing a given problem, facilitating interpretable understanding of the quantitative formulation of pairwise dependencies that drive successful performance. Our results and accompanying software enable comprehensive analysis of time-series interactions by drawing on decades of diverse methodological contributions.


### Abstract 2: Wild kangaroos become more social when caring for young and may maintain long-term affiliations with popular individuals
- [name=Alexandra]

Kangaroos are an iconic group of Australian fauna. Despite ~~considerable~~ research on kangaroo behaviour, ~~key gaps remain in our understanding of~~ their social organization in the wild. ~~In particular, ~~it remains largely unknown whether kangaroos form long-term social bonds and what factors might prompt individuals to associate or dissociate from one another. Over 6 years, we monitored the social affiliations of individually identified eastern grey kangaroos, Macropus giganteus, in a large wild population. We investigated the short-term and long-term relationships of kangaroos and the extent those relationships varied with age, sex and reproductive state. We found evidence that long-term relationships among eastern grey kangaroos are possible, especially between adult females. Those individuals that were more sociable within years were also more likely to establish affiliations across years. Contrary to previous studies, we observed females actively associating with other mothers in the years in which they had young. These data suggest that the fission–fusion dynamics of eastern grey kangaroo social behaviour allow females to modulate their social position with conspecifics according to their current reproductive state. We highlight the adaptive implications of the formation of long-term bonds and the changes in social behaviour observed in females.


### Abstract 3: Evidence of questionable research practices in clinical prediction models

- [name=Chris]

==~~Clinical prediction models~~== Models that predict health-related outcomes are widely used in medical research. ==~~The area under the receiver operating characteristic curve  (AUC) is a frequently used estimate to describe the discriminatory  ability of a clinical prediction model.~~== A widely-used technique for estimating the accuracy of these models is known as the Area Under the Receiver Operating Characteristic (AUC). ==~~The AUC is often interpreted  relative to thresholds,~~== with “good” or “excellent” models defined at  0.7, 0.8 or 0.9. These thresholds may create targets that result in  “hacking”, where researchers are motivated to re-analyse their data  until they achieve a “good” result.

We extracted AUC values from PubMed  abstracts to look for evidence of hacking. We used histograms of the  AUC values in bins of size 0.01 and compared the observed distribution  to a smooth distribution from a spline.

The  distribution of 306,888 AUC values showed clear excesses above the  thresholds of 0.7, 0.8 and 0.9 and shortfalls below the thresholds.

The  AUCs for some models are over-inflated, which risks exposing patients  to sub-optimal clinical decision-making. Greater modelling transparency  is needed, including published protocols, and data and code sharing

### Abstract 1: Unifying pairwise interactions in complex dynamics 
- [name=XXX]

Scientists have developed hundreds of techniques to measure the interactions between pairs of processes in complex systems, but these computational methods—from contemporaneous correlation coefficients to causal inference methods—define and formulate interactions differently, using distinct quantitative theories that remain largely disconnected. Here we introduce a large assembled library of 237 statistics of pairwise interactions, and assess their behavior on 1,053 multivariate time series from a wide range of real-world and model-generated systems. Our analysis highlights commonalities between disparate mathematical formulations of interactions, providing a unified picture of a rich interdisciplinary literature. Using three real-world case studies, we then show that simultaneously leveraging diverse methods can uncover those most suitable for addressing a given problem, facilitating interpretable understanding of the quantitative formulation of pairwise dependencies that drive successful performance. Our results and accompanying software enable comprehensive analysis of time-series interactions by drawing on decades of diverse methodological contributions.

### Abstract 2: Wild kangaroos become more social when caring for young and may maintain long-term affiliations with popular individuals
- [name=Giulia]

Do kangaroos mums establish longer-term friendships than childless individuals?

Despite their popularity, we are yet to understand how kangaroos interact with each other and form social bonds. Here, we focused in particular on female kangaroos with young offspring. After following a group of kangaroos in the wild for 6 years, we observed that female kangaroos were more likely to interact with other  

Kangaroos are an iconic group of Australian fauna. Despite ~~considerable~~ research on kangaroo behaviour, key gaps remain in our understanding of their social organization in the wild. In particular, it remains largely unknown whether kangaroos form long-term social bonds and what factors might prompt individuals to associate or dissociate from one another. Over 6 years, we monitored the social affiliations of individually identified eastern grey kangaroos, Macropus giganteus, in a large wild population. We investigated the short-term and long-term relationships of kangaroos and the extent those relationships varied with age, sex and reproductive state. We found evidence that long-term relationships among eastern grey kangaroos are possible, especially between adult females. Those individuals that were more sociable within years were also more likely to establish affiliations across years. Contrary to previous studies, we observed females actively associating with other mothers in the years in which they had young. These data suggest that the fission–fusion dynamics of eastern grey kangaroo social behaviour allow females to modulate their social position with conspecifics according to their current reproductive state. We highlight the adaptive implications of the formation of long-term bonds and the changes in social behaviour observed in females.


### Abstract 2: Wild kangaroos become more social when caring for young and may maintain long-term affiliations with popular individuals
- [name=Emma]
kKangaroos are an iconic group of Australian **fauna**. ~~Despite considerable research on kangaroo behaviour,~~ key gaps remain in our understanding of their social organization in the wild. In particular, it remains largely unknown whether kangaroos form long-term social bonds and what factors might prompt individuals to **associate or dissociate** from one another. Over 6 years, we monitored the social affiliations of individually identified eastern grey kangaroos, Macropus giganteus, in a large wild population. We investigated the short-term and long-term relationships of kangaroos and the extent those relationships varied with age, sex and reproductive state. We found evidence that long-term relationships among eastern grey kangaroos are possible, especially between adult females. Those individuals that were more sociable within years were also more likely to establish affiliations across years. ~~Contrary to previous studies~~, we observed females actively associating with other mothers in the years in which they had young. These data suggest that the **fission–fusion dynamics** of eastern grey kangaroo social behaviour allow females to **modulate their social position with conspecifics according to their current reproductive state**. We highlight the **adaptive implications** of the formation of long-term bonds and the changes in social behaviour observed in females.


### Abstract 3: Evidence of questionable research practices in clinical prediction models
- 
- [name=Fran]

Clinical prediction models are widely used in health and medical  research. The area under the receiver operating characteristic curve  (AUC) is a frequently used estimate to describe the discriminatory  ability of a clinical prediction model. The AUC is often interpreted  relative to thresholds, with “good” or “excellent” models defined at  0.7, 0.8 or 0.9. These thresholds may create targets that result in  “hacking”, where researchers are motivated to re-analyse their data  until they achieve a “good” result.

We extracted AUC values from PubMed  abstracts to look for evidence of hacking. ==~~We used histograms of the  AUC values in bins of size 0.01 and compared the observed distribution  to a smooth distribution from a spline.~~==

The  distribution of 306,888 AUC values showed clear excesses above the  thresholds of 0.7, 0.8 and 0.9 and shortfalls below the thresholds.

The  AUCs for some models are over-inflated, which risks exposing patients  to sub-optimal clinical decision-making. Greater modelling transparency  is needed, including published protocols, and data and code sharing


## Activity: Write a good hook!

- Kangaroo mums love to be social
- Do kangaroos mums establish longer-term friendships than childless individuals?
- How credible are clinical research practices? A new study casts doubt on a vital source of health and medical knowledge.
- New study EXPOSES over-inflation of model estimates in medical research
- Why would medical researchers over-inflate their model estimation?
- Have you ever wondered what a kangaroo’s social life looks like? Well, kangaroos have stronger bonds to one another than you might think. 
- Tests that diagnose diseases are less reliable than you’d expect. Here’s why
- From stock markets to brain scans, new research harmonises hundreds of scientific methods to understand complex systems

