This dataset is metadata related to books delivered by TU Delft's institutional repository. It excludes the Maritime Archive collection. Thanks to all the staff who inputted the metadata.

This codebook was used to analyze the interview data (from 11 interviews) in the master thesis project titled "Enhancing Open Research Data Sharing and Reuse via Infrastructural and Institutional Instruments: a Case Study in Epidemiology" which is openly available on TU Delft Education Repository.

The Evidence-Based Software Portfolio Management (EBSPM) Research Repository is a collection of data of finalized software projects in four different software companies. The repository is part of the EBSPM approach as documented in the PhD-thesis EBSPM by Hennie Huijgens (2017).

This folder contains data underlying the research paper “Exploring potential contributions of open data intermediaries”. The research is about open data ecosystem and the role of open data intermediaries. The folder consists of 4 items:

1. Tentative interview questions (.pdf and .odt formats)

2. Informed consent form template (for verbal interview & written interview) (.pdf and .odt formats)

3. De-identified interview transcripts (.pdf and .odt formats)

4. Coding results (.pdf and .ods formats)

Note about the tentative interview questions:

The interviews were conducted between May and July 2023 based on the semi-structured approach. We customise the tentative interview questions accordingly for each interview and share them with the interviewees in advance (for the majority, at least three working days in advance). As semi-structured interviews, the ultimate interview questions may differ from the tentative questions based on the information provided by the interviewees and time constraints (refer to item #3).

Note about the informed consent form:

We sent the informed consent form to every interviewee in advance and requested them to return it to us before the interview. The consent form has been reviewed by TU Delft's Human Research Ethics Committee (HREC).

Note about the de-identified interview transcripts (and coding results):

The de-identified interview transcripts should be read in the context of the research on open data ecosystem and the role of open data intermediaries. We removed personally identifiable information from the transcripts. A few interviewees may risk being identifiable if their organisation is known. Hence, we removed the identification of the organisation and country in all transcripts. Partially disclosing the organisation or country for some transcripts increases the risks of identifying the non-disclosed transcripts. With verbal communication, some sentences may be less incomprehensible in writing. Thus, we did minimal edits when transcribing to improve the comprehensibility where necessary, but the main objective was to keep the transcript as close to verbatim as possible. All interviewees whose interview transcripts are recorded in this document give permission for the anonymised transcript of their interview, with personally identifiable information redacted, to be shared in 4TU.ResearchData repository so it can be used for future research and learning.

Acknowledgement:

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.

This dataset contains the outputs of Francesco Bruno’s MSc Thesis (December 2022), which is freely retrievable in the TU Delft Thesis repository. More information available in the README.txt file and in the thesis referenced below.

Supplementary data for the MSc thesis: Local roadmap adaptation for mobile manipulators in incrementally changing environments (available at TU Delft repository) by E.J. HeerkensAll information concerning the experiments can be found in the report.experiment_data.zip: all raw data and figures from the experiments described in the thesis.obstacle_ground.mp4: video presenting the real-world performance of the proposed algorithm on the robot in experiment 8 for an incremental change on the groundobstacle_air.mp4: video presenting the real-world performance of the proposed algorithm on robot in experiment 8 for an incremental change in the aircontact: evelien.heerkens@hotmail.com

Supplementary data for the MSc thesis: 'The Effects of a Social Robot’s Gestures on Learning Outcomes' (available at TU Delft repository) by F.N. Moorlag http://resolver.tudelft.nl/uuid:ae1edd23-aeeb-4d36-a7b7-8ec4524bf56dAll information concerning the experiment can be found in the report.Data.zip: all data from the experiment described in the thesisGestures_FormA.mp4: video of robot NAO explaining mathematical equivalence (Form A) with supportive gesturesNo_gestures_FormA.mp4: video of robot NAO explaining mathematical equivalence (Form A) with no gesturesRandom_FormA.mp4: video of robot NAO explaining mathematical equivalence (Form A) with random gesturescontact: fleurmoorlag@gmail.com

Function of the m-files and their dependancies is described in the full thesis at the TU Delft repository.

Subversion repository dump of raw data collected at Sand Motor (2011-2016) and corresponding tailored scripts to create standardized data products from that.

Data underlying research paper “Developing an open data intermediation business model: insights from the case of Esri”

by Ashraf Shaharudin, Bastiaan van Loenen, and Marijn Janssen from Delft University of Technology (TU Delft), the Netherlands.

This folder contains data underlying the research paper “Developing an open data intermediation business model: insights from the case of Esri”. It consists of:

1. De-identified interview transcripts

2. Informed consent form template

Note about the de-identified interview transcripts:

The de-identified interview transcripts should be read in the context of the research on open data ecosystem and the role of Esri as open data intermediaries.

The 27 interviews, involving 29 interviewees, were conducted between April 2023 and April 2024 based on the semi-structured approach. We shared the tentative interview questions with the interviewees in advance (for the majority, at least three working days prior). Since they are semi-structured interviews, the ultimate interview questions may differ from the tentative questions.

We removed personally identifiable information from the transcripts. Some interviewees may risk being identifiable if their organization is known. Hence, we removed the organization and country information from all transcripts.

With verbal communication, some sentences may be less incomprehensible in writing. Thus, we did minimal edits when transcribing to improve the comprehensibility where necessary, but the main objective was to keep the transcripts as close to verbatim as possible.

Note about the informed consent form template:

We sent the informed consent form to every interviewee in advance and requested that they return it to us before or during the interview.

All interviewees whose interview transcripts are recorded in this document give permission for the anonymized transcript of their interview, with personally identifiable information redacted, to be shared in 4TU.ResearchData repository so it can be used for future research and learning.

Acknowledgement:

This research is part of the 'Towards a Sustainable Open Data ECOsystem' (ODECO) project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.

This dataset contains all the data supporting the PhD thesis "Exploring the quality factor limits of room temperature nanomechanical resonators" from TU Delft, the Netherlands. The thesis aims at studying the limits of nanomechanical resonators' quality factor at room temperature. It revolves around four main facets, addressing limitations in fabrication techniques, design strategies, exploring the impact of aspect ratio on quality factor enhancement, and investigating the potential for temperature sensing. A digital version of the thesis can be found online at https://repository.tudelft.nl

General information

This dataset is part of the MSc thesis `Consensus-based single-score life cycle assessment for space missions' at Search results | TU Delft Repositories

An older version of the dataset was also used for the conference paper `A Consensus-Based Single-Score for Life Cycle Assessment of Space Missions: Preliminary Results', which can be found through the DOI 10.13009/EUCASS2023-571, or on EUCASS's website (www.eucass.eu - EUCASS Full Papers)

Abstract of the thesis

With a continuously growing number of satellites in orbit, it becomes increasingly important to assess their impacts on the Earth's environment in a standardised manner. While interest in Life Cycle Assessment (LCA) for space missions has gained in strength in the past few years – particularly in Europe – no consensus has yet been reached on a single-score LCA system.

In this thesis, a consensus-based space LCA single-score is created through an international survey of experts. The report demonstrates retroactively the single-score’s use for the ecodesign of the Delft University of Technology’s Delfi-n3Xt space mission. A discussion is also held on ways of implementing the single-score into early design phases. Overall, this thesis highlights the importance of an easy-to-understand LCA tool for space systems. It shows the necessity for a tool that is implementable during the design phase of the mission, to incentivise space actors to further consider environmental impacts.

Contents of the dataset

This dataset contains Excel sheets with:

1. the analysed answers of the survey done to reach a consensus on a space LCA single-score
2. the results of Delfi-n3Xt's LCA
3. the calculations made to obtain the recommended weighting factors.

Important note:

A specific README.txt file is part of this dataset and is recommended to be read first

This repository contains data underlying the thesis “The Causes of Regional Sea-level Change” by Carolina M. L. Camargo. A copy of the dissertation can be found at the TU Delft repository.

The data is divided in folders following the chapters of the dissertations. When using any of this data, please cite the appropriate publications. In addition, all the produced results of this dissertation has underlying data of other publications and studies, so please also acknowledge those.

Chapter 1 – Introduction

In this folder you can find:

- gmsl.xlsx – Excel table underlying Table 1.1, Table 1.2 and Figure 1.3

- masks.nc – netcdf file with ocean basins underlying Figure 1.4

- regional.xlsx – Excel table underlying bar plots in Figure 1.4

Chapter 2 – Steric Sea-level Change

In this folder you find:

- Upper_ocean folder: 0-2000m steric time series and trends. A dedicated Readme on this folder explains in more detail the files.

Note: The regional time series are only available at the 4tu link below.

- Deep_ocean folder: Deep ocean (>2000m) time series and trends, underlying Appendix of Chapter 2.

The data of this (main) chapter is also available at:

https://doi.org/10.4121/12764933

Chapter 3 – Mass-driven Sea-level Change

This folder contains files with regional barystatic (ocean mass) sea-level change trends and uncertainty, both absolute and relative fingerprints. A dedicated Readme on this folder explains in more detail the files.

The data of this chapter is also available at: https://doi.org/10.4121/16778794

Chapter 4 – Regional Sea-level Budget

This folder contains files from the regional sea-level budget analysis. A dedicated Readme on this folder explains in more detail the files.

The data of this chapter is also available at: https://doi.org/10.5281/zenodo.7007330

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In case of questions, please contact Carolina Camargo at carolina.camargo@nioz.nl or caromlcamargo@gmail.com

This repository contains MATLAB code for estimating Doppler moments using the Parametric Spectrum Estimator (PSE) and comparing these estimates with other state-of-the-art methods. The data is from the MESEWI radar, an X-band azimuthally scanning radar at TU Delft, Netherlands. Details of the code repository can be found in the README file in the software repository. One example dataset used in the software is uploaded here (with the ".mat" format of MATLAB).

This repository contains the data acquired from the MESEWI weather radar at the Delft University of Technology for Doppler processing. It contains MATLAB code for estimating the raindrop size distribution (DSD) parameters (sub-optimal estimates) and the radial mean wind and turbulence parameters. In addition to the DSD estimates, this code can generate the Plan Position Indicator (PPI) plots for terminal fall velocity and median diameter as a function of range and azimuth. This novel estimator can make use of these incoherent radar fast scans to estimate the parameters.

The MESEWI radar is an X-band azimuthally scanning radar at TU Delft, Netherlands. The data folder contains the data for several azimuthal radar scans at an elevation of 30 degrees from the ground level. The code files are in the folder Production4TURD. Details of the code repository can be found in the README.pdf file.

A photogrammetry dataset was collected using an Unmanned Aerial Vehicle (quadcopter) over a river stretch of the Black Volta at Bamboi Bridge - Ghana. Also Ground Control Points (GCPs) were collected that represent black-and-white markers in the landscape. These can be used to better geographically constrain the photogrammetric solution. GCPs have been associated with row and column pixel location in each photo in which they appear.

The raw data was processed into a 3D point cloud using the open-source software platform WebOpenDroneMap (WebODM). The point cloud was analysed for removal of vegetation using spatial filtering techniques with the intent to make a bare-earth topographical map of the dry part of the riverbed. Both filtered and unfiltered point clouds were further processed into a Digital Surface Model (unfiltered) and Digital Terrain Model (DTM). The unfiltered dataset was also processed into an RGB orthophoto. In the thesis work of Hoogendoorn (2023) further research was done on combining the results of these datasets and analyses with wet bathymetry points collected using a fishfinder equipped with Real-Time-Kinematics GNSS, and using the outcoming full bathymetry for hydraulic modelling and understanding of relationships between wetted geometry and river discharge. For more information, we refer to the MSc thesis work of Hoogendoorn (2023)

The data files consist of three (3) .zip files. Unzip these to get access to all underlying files. For a quick overview, a .qgs file can be opened in QGIS. This will display all layers in a simple GIS project. The point cloud is also visualized but may take significant time before rendered, as points first need to be cached.

References: Hoogendoorn, N. J.: 3D River Discharge Modelling using UAV photogrammetry | TU Delft Repository, Delft University of Technology, Delft, The Netherlands, 2023.

Link: https://repository.tudelft.nl/record/uuid:d4088a50-3590-4675-9600-d715800841a3

This dataset contains data collected during the development of a Graph Neural Network metamodel of the software SWMM (Storm water management model) at the Delft University of Technology, as part of Alexander Garzón's PhD project, and with the corresponding publication "Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks" <https://doi.org/10.1016/j.watres.2024.122396>.

It is being made public both to act as supplementary data for publications and the PhD project of Alexander Garzón and in order for other researchers to use this data in their own work.

This work is supported by the TU Delft AI Labs programme.

This repository was supported by the Digital Competence Centre, Delft University of Technology.

The data are aggregated opinion averages over time of polls on a 5-point scale concerning questions about climate change and the energy transition. This is the data repository for the poster I made for the ESSA conference in Glasgow 2023. My aim with this was to get a first grip on inverse modelling and share what I have learned with the community. I'm happy to share the poster for more context and content upon request via lschubotz@tudelft.nl. The code I've used this data for is available on the TU Delft GitLab. I gratefully acknowledge the support of Populytics in this research project who provided the data. Here, I provide the aggregated data as it went into the model. Column 1 is the time stamp, column 2 is the value I used, the latter being the aggregated mean value of the votes during that time stamp.

The Aidonia cemetery is an archeological site on the Peleponnese Peninsula in Greece. It was constructed in the Late Bronze Age and consists of chamber tombs dug into rock outcrops. In August 2019, a team of researchers from TU Delft and University of Stavanger acquired detailed 3D scan data of the Aidonia site using a Leica P40 laser scanner. The team scanned the site from 51 scan positions. 29 positions were focusing on the chamber tombs while the other 22 positions sampled the overall cemetery setting. The scans were combined in a common coordinate system providing a point cloud consisting of ~5.9 billion points. Analysis of the point cloud notably provided insight in the architectural setting and current status of the different tombs. The data set in this repository consist of the raw, aligned point cloud. A detailed description of the point cloud data and its application in documenting the status of the Aidoinia cemetery can be found in the linked reference.

Datasets for running Google Earth Engine (GEE) script to estimate groundwater recharge in Kumasi, Ghana. This uses a water-balance approach using satellite datasets to estimate water-balance components, ultimately groundwater recharge. The script has been created as part of the MSc thesis 'Sustainability of groundwater resources in Kumasi, Ghana', which can be found in the TU Delft education repository.
'README.txt' contains the scripts and how to run them. Files attached here are required to run the groundwater recharge model for Kumasi.