---
# System prepended metadata

title: 'Information session: writing a DMP for your PhD project (May 15, 2024)'

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Information session: writing a DMP for your PhD project (May 15, 2024)
==============================

## Welcome to the Seesion Collaborative Document

This Document is synchronized as you type, so that everyone viewing this page sees the same text. This allows you to collaborate seamlessly on documents. Here's a [Markdown cheat sheet](https://www.markdownguide.org/cheat-sheet/) in case useful. 

You can download the slides from [here](https://surfdrive.surf.nl/files/index.php/s/rg5qnQ4C1dgEeaU/download). The sldies will be made publicly available under CC-BY-4.0 once a consolidated version is created in the future.

General questions or feedback? Contact datasteward-CiTG@tudelft.nl
## 👩‍🏫👩‍💻🎓 Trainers
Xinyan Fan (CiTG data steward) / Background: Remote sensing and deep learning for natural resources

Lora Armstrong (CEG data steward) / Background: Experimental petrology of planetary interiors

## 🧠 Collaborative Notes & 🔧 Exercises
### Exercise 1: Types of data that you would like to include in the data table (5 minutes)

Nirmal: Probabilistic/Occurrence data on extreme precipitation, drought, and heat provided by KNMI and RWS. Disruption scenario data by KNMI. Network functionality parameters and KPI's provided by RWS for the Dutch main road network. Purpose: To understand the current, a-priori probabilities of extreme events on infrastructure, and enchance the probabilities. Output data: Posterior probabilities and causal pathways of extreme climate events on the road network, Prototype probability prediction algorithms, based on Bayesian Network Analysis, Simulation model, Simulated data

Juan: Concrete prisms for creep and shrinkage test, concrete prestress beams for sustained loading test, concrete samples for micromechanical test including microcsopy and CT Scan. Output data: creep and shrinkage graphs, prestress losses and deflections in time of the beams, nano and microidentation results on the concrete samples, micrograph obatined with the microscope. 

Paulina: input data: meteorological data from ECMWF (wind simulations, SEAS5), meteorological data for future climate scenarios from KNMI (wind simulations, RACMO), sea level rise scenarios from KNMI, hydrological data from KNMI (sea level simulations, WAQUA-DCSM5), observational data from Rijkswaterstaat (waterinfo, sea levels and wind). Output data: selection of extreme storm events, storm clusters / weather patterns from different machine learning algorithms, a machine learning model to predict sea levels, statistics (marginal distributions and correlation models) for extreme wind and sea levels in different future climate scenarios.    

Saul: digital elevation models for different years, hydraulic boundary conditions timeseries (discharges and water levels), dike settlement conditions, 2D hydrodynamic model (IJssel river), fragility curves for different mode of failures for different locations, flood inundation maps, training data for emulator, DL trained candidate models for surrogates, DL trained emulator for 2D hydrodynamics, flood probabilities , flood damages maps, ...


Nathalie: Meteorological data from KNMI (radar data + rain gauges + numerical weather predictions); DEM; Personal weather stations (PWS) using API from Netatmo; confidential dataset from KNMI (bought from external party); catchment boundaries; streamflow data from waterboards in NL; hydrological models, hydrodynamic model, quality control algorithms in R. Output data: processed PWS data, nowcasts, flood forecasts, improved quality control algorithm rewritten in python, data processing code. 

Qinying: electrical load data (current, voltage, power), images of samples, reports (gas composition, elemental mapping), performance indicators of the bioreactor, protocols of installation and operation

Ligaya: observational data from Rijkswaterstaat, meteorological data and climate scenario from KNMI, Digital elevation models from AHN, Land use maps; code for cleaning data; hydrodynamic model; post-processing code; output

Jiayi: Road detector data from different cities, floating car data; Simualation data from different types of models; Household data such as income and commute diatance. 
### Exercise 2: Access a folder on the Project Storage Drive (U:)


### Post-session Exercise

Please fill out your own data management plan and ask for feedback using the ‘Request Feedback’ button from DMPonline by 17:00 on **5 June**.

## Feedback
### Which parts of this session did you like or find useful?
* Very nice that you went through all the steps of the DMP with suggestions of what we should fill in normally. Also nice that you showed the DMP website and made us login to create our own DMP file. And also showed us PURE, I hadn't seen it before
* Very complete presentation and really nice that you added comparative tables to help us choose between options + links to all websites / instructive videos etc.
* The course was well structured, comprehensive, and addressed the fundaments and even more in my opinion, of the DMP. I also very much liked all the additional resources to help us out with our data management.


### What was missing or could be improved?
* Because it is such a compleet presentation there is a lot of information to absorbe in one go. I would personnaly prefer having two or three breaks of 5 min instead of just 1 break of 15 min :) Thank you for the training! 
* Due to the packed schedule of the course, more breaks would be useful though. Or it could be an idea to extend the course with one hour or so to make it more spacious for the attendants to observe the information even more effectively.