Availability of data, code, and plot creation for various figures throughout my PhD thesis. Rough organisation currently. Pertains to Figures 5.4, 5.8, 6.11, 6.18, 7.3, 7.12, and Table 6.1.

In order to encourage academic and related research on gender equality education and improve the academic standards of the above-mentioned topics, the Ministry of Education has formulated the "Key Points for the Ministry of Education to Award Master's and Doctoral Thesis and Journal Papers on Gender Equality Education" for awards.

New Thesis Data Sets

## Overview

New Thesis Data Sets is a dataset for object detection tasks - it contains Fruits Pineapple Mango Papaya annotations for 4,346 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset is used in Master thesis on topic "The impact of upholding environmental, social and governance principles on the market value of capital-intensive companies". The full dataset consits data on Public american companies included in S&P 500 index, traded on the New York Stock Exchange. There are data on ESG-score and its components (E, S, G), as well as components of Envitonmental pillar score. Additionaly dataset includes financial data, like market capitalization, leverage, ROCE, Capex and etc. The main sources of data are Thomson Reuters Eikon and Bloomberg terminals, along with Form 10-k by SEC. The final sample consists of 52 capital-intensive companies, time horizon: 2012-2021 [520 observations in total].

This dataset contains the results of the experiments that I ran for my master thesis. The full code (and more) can be found at https://github.com/dimitris93/msc-thesis

This record contains the data and code from the thesis: Reduced-order models to predict mesoscale mechanical behavior of polycrystalline materials. The contents of the chapter-wise zip files are described in the respective markdown files with the suffix _readme.md.

A record containing only the code from the thesis is availabe at: 10.5281/zenodo.10983507.

My PhD thesis

Computational medical image analysis - With a focus on real-time fMRI and non-parametric statistics

This data set contains the research data for the master's thesis: Integrating Explainability into Federated Learning: A Non-functional Requirement Perspective. The master's thesis was written by Nicolas Sebastian Schuler at the Computer Science Department at Karlsruhe Institute for Technology (KIT) in Germany. The data set contains: - Associate Jupyter notebooks for reproducing the figures in the master's thesis. - Generated experiment data by the federated learning simulations. - Results of the user survey conducted for the master's thesis. - Used Python Libraries. It also includes the submitted final thesis. Notice: The research data is split into multiple chunks and can be combined via the following command after downloading: $ cat thesis-results-part-* > thesis-results.tar.zst and extracted via: $ tar --zstd -xvf thesis-results.tar.zst

This dataset contains in high resolution all graphical visualizations of data analysis provided in my doctoral dissertation. The graphs are organized according to chapters and subchapters and titeled respectively. Additionally, this dataset provides all dataframes (German, English, and Armenian) in XLSX format of the manual semantic annotation based on which the graphs are generated. Among presented graphical visualizations are (Multiple) Correspondence Analysis (MCA vs. CA), Mosaic-Plots, Conditional Infererence Trees (CIT), and Context-Conditional Correlations Graphs (CCCG).

Uganda Road Fund Allocation Formula application 2014 and 2015

This thesis-mpc-dataset-public-readme.txt file was generated on 2020-10-20 by Masud Petronia

GENERAL INFORMATION
1. Title of Dataset: Data underlying the thesis: Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data
2. Author Information A. Principal Investigator Contact Information Name: Masud Petronia Institution: TU Delft, Faculty of Technology, Policy and Management Address: Mekelweg 5, 2628 CD Delft, Netherlands Email: masud.petronia@gmail.com ORCID: https://orcid.org/0000-0003-2798-046X
3: Description of dataset: This dataset contains perceptual data of firms' willingness to contribute protected data through multi party computation (MPC). Petronia (2020, ch. 6) draws several conclusions from this dataset and provides recommendations for future research Petronia (2020, ch. 7.4).
4. Date of data collection: July-August 2020
5. Geographic location of data collection: Netherlands
6. Information about funding sources that supported the collection of the data: Horizon 2020 Research and Innovation Programme, Grant Agreement no 825225 – Safe Data Enabled Economic Development (SAFE-DEED), from the H2020-ICT-2018-2

SHARING/ACCESS INFORMATION
1. Licenses/restrictions placed on the data: CC 0
2. Links to publications that cite or use the data: Petronia, M. N. (2020). Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data (Master's thesis). Retrieved from http://resolver.tudelft.nl/uuid:b0de4a4b-f5a3-44b8-baa4-a6416cebe26f
3. Was data derived from another source? No
4. Citation for this dataset: Petronia, M. N. (2020). Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data (Master's thesis). Retrieved from https://data.4tu.nl/. doi:10.4121/13102430

DATA & FILE OVERVIEW
1. File List: thesis-mpc-dataset-public.xlsxthesis-mpc-dataset-public-readme.txt (this document)
2. Relationship between files: Dataset metadata and instructions
3. Additional related data collected that was not included in the current data package: Occupation and role of respondents (traceable to unique reference), removed for privacy reasons.
4. Are there multiple versions of the dataset? No

METHODOLOGICAL INFORMATION
1. Description of methods used for collection/generation of data: A pre- and post test experimental design. For more information; see Petronia (2020, ch. 5)
2. Methods for processing the data: Full instructions are provided by Petronia (2020, ch. 6)
3. Instrument- or software-specific information needed to interpret the data: Microsoft Excel can be used to convert the dataset to other formats.
4. Environmental/experimental conditions: This dataset comprises three datasets collected through three channels. These channels are Prolific (incentive), LinkedIn/Twitter (voluntarily), and respondents in a lab setting (voluntarily). For more information; see Petronia (2020, ch. 6.1)
5. Describe any quality-assurance procedures performed on the data: A thorough examination of consistency and reliability is performed. For more information; see Petronia (2020, ch. 6).
6. People involved with sample collection, processing, analysis and/or submission: See Petronia (2020, ch. 6)

DATA-SPECIFIC INFORMATION
1. Number of variables: see worksheet experiment_matrix of thesis-mpc-dataset-public.xlsx
2. Number of cases/rows: see worksheet experiment_matrix of thesis-mpc-dataset-public.xlsx
3. Variable List: see worksheet labels of thesis-mpc-dataset-public.xlsx
4. Missing data codes: see worksheet comments of thesis-mpc-dataset-public.xlsx
5. Specialized formats or other abbreviations used: Multiparty computation (MPC) and Trusted Third Party (TTP).

INSTRUCTIONS
1. Petronia (2020, ch. 6) describes associated tests and respective syntax.

The data in this collection comprises the bibliographic metadata for all UK doctoral theses listed in EThOS, the UK's national thesis service. We estimate the data covers around 98% of all PhDs ever awarded by UK Higher Education institutions, dating back to 1787. Thesis metadata from every PhD-awarding university in the UK is included. You can investigate and re-use this unique collection of UK universities' PhD thesis data to analyse trends in postgraduate research, make connections between researchers, apply large data analysis, improve citation of theses and many more applications.

To identify relevant actors for the governance of co-produced forest nature's contributions to people (NCP) the researchers conducted a social-network analysis based on 39 semi-structured interviews with foresters and conservation managers. These interviews were conducted across three case study sites in Germany: Schorfheide-Chorin in the Northeast, Hainich-Dün in the Centre, and Schwäbische Alb in the Southwest. All three case study sites belong to the large-scale and long-term research platform Biodiversity Exploratories. The researchers employed a predefined coding set to analyse the interviews and grasp the relationships between different actors based on the anthropogenic capitals they used to co-produce forest nature's contributions to people (NCP). To secure the interviewees anonymity this coding cannot be published. Therefore, this data set is limited to this coding set.

Dataset created as part of the Master Thesis "Business Intelligence – Automation of Data Marts modeling and its data processing".

Lucerne University of Applied Sciences and Arts

Master of Science in Applied Information and Data Science (MScIDS)

Autumn Semester 2022

Change log Version 1.1:

The following SQL scripts were added:

    Index
    Type
    Name


    1
    View
    pg.dictionary_table


    2
    View
    pg.dictionary_column


    3
    View
    pg.dictionary_relation


    4
    View
    pg.accesslayer_table


    5
    View
    pg.accesslayer_column


    6
    View
    pg.accesslayer_relation


    7
    View
    pg.accesslayer_fact_candidate


    8
    Stored Procedure
    pg.get_fact_candidate


    9
    Stored Procedure
    pg.get_dimension_candidate


    10
    Stored Procedure
    pg.get_columns

Scripts are based on Microsoft SQL Server Version 2017 and compatible with a data warehouse built with Datavault Builder. Data warehouse objects scripts of the sample data warehouse are restricted and cannot be shared.

Spreadsheet containing the following data tables in separate tabs: 1) Table of construction components with assembly-related properties obtained from off-the-shelf product ranges. 2) Data table summarising evidence of information technology use in five construction project case studies. 3) Table of technologies which could potentially be applied to adapt existing construction plant to provide robotic handling capabilities. 4) Table of typical reach and payload capabilities of suitable construction plant.

J1mb0o/Stock-Thesis-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

Problem folders including all the input files necessary to reproduce the computations of the results related to the Reduced Order Models Chapter of N.C. Clementi PhD Thesis.

Collecting and utilizing data to understand population trends, make predictions, and guide decisions is becoming increasingly common in today's world. In particular, statistical learning allows users to infer relationships between variables, learn patterns, and predict outcomes for previously unseen data via concepts and techniques from statistics and machine learning. Although many of the results of this practice have been beneficial, the data used often contain sensitive information, such as medical records or financial information, so maintaining privacy is of paramount importance when releasing statistics, parameter estimates, and other results. Differential privacy (DP) is the state-of-the-art framework for guaranteeing privacy when releasing aggregate information and statistics from a dataset. It provides a provable bound on the incurred privacy loss via the injection of random noise, at the cost of a reduction in utility. While many works have been devoted to establishing DP guarantees for various analysis tools in the past two decades since DP's introduction, many popular statistical learning approaches still lack a DP counterpart. This dissertation addresses this issue in three original research topics, as listed below.

First, the dissertation presents the first differentially private algorithm for general weighted empirical risk minimization (wERM), along with theoretical DP guarantees. It evaluates the performance of the DP-wERM framework applied to outcome weighted learning (OWL), a method for learning individualized treatment rules, in both simulation studies and in a real clinical trial. The results demonstrate the feasibility of training OWL models via wERM with DP guarantees while maintaining sufficiently robust model performance.

Second, the dissertation presents several original approaches with proven DP guarantees for linear mixed-effects (LME) models. LME models are popular, especially among statisticians, but lack sufficient work on integrating DP. The work leverages some recent advancements in the DP literature, particularly in DP stochastic gradient descent (SGD), to estimate LME model parameters with DP guarantees with better privacy-utility trade-offs. Theoretical results for an upper bound for the mean squared error between private parameter estimates vs the true parameters for DP-SGD-based approaches are provided, and a simulation study and a real-world case study provide further empirical evidence for the feasibility of the approaches at practically reasonable privacy budgets.

Third, this dissertation introduces SAFES, a Sequential PrivAcy and Fairness Enhancing data Synthesis procedure that sequentially combines DP data synthesis with a fairness-aware data transformation. Alongside privacy, the fairness of decisions made by a statistical learning model is also crucial to address, though the vast majority of existing literature treats the two concerns independently. For methods that do consider privacy and fairness simultaneously, they often only apply to a specific machine learning task, limiting their generalizability. SAFES allows full control over the privacy-fairness-utility trade-off via tunable privacy and fairness parameters. SAFES is illustrated by combining a graphical model-based DP data synthesizer with a popular fairness-aware data pre-processing transformation, and empirical evaluations on two popular benchmark datasets demonstrate that for reasonable privacy loss, SAFES-generated synthetic data achieve significantly improved fairness metrics with relatively low utility loss.

This simple dataset contains publication statistics of Swedish PhD and Licentiate thesis in Software Engineering from 1999 to 2018. The contents of this dataset were discussed in a blog post on https://grischaliebel.de.

The data is offered in two formats, xlsx and csv, but with the same content. Names and affiliation are anonymised in the data set to prevent identification of subjects. In the following, we describe the content of the different columns in the table.

• Level: 'lic' for Licentiate theses or 'phd' for PhD theses
• Year: The year of publication of the thesis
• Included: The total number of papers included in the compilation-style thesis.
• Listed: Number of papers listed in addition to the included papers (basically "I have also published these, but they are not relevant to the thesis). Note that we cannot distinguish between cases, where no papers are listed because none are published, or because the author decided not to list them.
• IncludedPublished: The amount of included papers that are published or accepted for publication.
• IncludedSubmitted: The amount of included papers that in submission/under review.
• IncludedPublishedISI: The amount of included, published papers that are in ISI-ranked journals.
• IncludedPublishedNonISIJ: The amount of included, published papers that are in non ISI-ranked journals.
• IncludedPublishedConf: The amount of included, published papers that are in CORE-ranked conferences (any grade).
• IncludedPublishedWS: The amount of included, published papers that are in workshops. Non CORE-ranked conferences are counted as workshops as well.
• IncludedPublishedOther: The amount of included, published papers that do not fit in any other category (e.g., book chapters, technical reports).
• IncludedSubmitted*: Amount of included, submitted papers broken down by category (Journal, conference, workshop, and other).
• ListedPublished*: Amount of listed, published papers broken down by category (ISI/Non-ISI Journal, conference, workshop, and other).
• ListedSubmitted*: Amount of listed, submitted papers broken down by category (Journal, conference, workshop, and other).

This is the custom code repository for replicating the results of the thesis. Three main routines are contained within this repository.

A new quality measure is proposed in the thesis for the purposes of assessing the quality of predictors in human activity recognition problems. The related code can be found in the file: measures.py

A postprocessing scheme is proposed in the thesis to remove unrealistically short activities from the classification given by the predictor. The related code can be found in the file: postprocessing.py

A new formulation of the null hypothesis in a permutation test for no effect is proposed in the thesis. The viability of the test is presented based on the simulation study. This simulation study can be found in the files: sim_study_lin_reg.ipynb and sim_study_nn.ipynb.