Raw statistical data underpinning the second two PhD research objectives for the thesis entitled "Money doesn’t grow on trees: How to increase funding for the delivery of urban forest ecosystem services?". These relate to the interviews with 30 Southampton businesses, and choice experiment survey with 415 Southampton citizens.

Uganda Road Fund Allocation Formula application 2014 and 2015

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.

This data was collected and analysed as part of a PhD thesis submitted in February 2019. The PhD research was concerned with understanding the role of Ministry of Information images in British cultural memory and, through the use of surveys, interviews and reverse image lookup, investigated how the images were remembered, interpreted and used online. The data consists of six files. The first includes demographic details of participants who took part in a Mass Observation survey in 2009. The second consists off the results of a content analysis of the responses of participants in the 2009 survey. The third consists of data collected as part of a survey with volunteers which took place between March and May 2017 and the fourth consists of the results of a content analysis of the survey data. The fifth file includes data collected through a study using reverse image lookup to locate and categorise web pages containing a particular set of Ministry of Information images and the sixth consists of a the results of a content analysis of this data.

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).

This dataset contains source code and data used in the PhD thesis "Measuring the Contributions of Vision and Text Modalities in Multimodal Transformers". The dataset is split into five repositories: Code and resources related to chapter 2 of the thesis (Section 2.2., method described in "Using Scene Graph Representations and Knowledge Bases") Code and resources related to chapter 3 of the thesis (VALSE dataset). Code and resources related to chapter 4 of the thesis: MM-SHAP measure and experiments code. Code and resources related to chapter 5 of the thesis: CCSHAP measure and experiments code related to large language models (LLMs). Code and resources related to the experiments with vision and language model decoders from chapters 3, 4, and 5.

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.

This dataset includes a selection of the interview transcripts used in the PhD thesis The Many Faces of Ravana. Ravanisation: The Revitalisation of Ravana among Sinhalese Buddhists in Post-War Sri Lanka. The fieldwork for this PhD thesis was conducted in the years 2016, 2017, and 2018 in Sri Lanka to investigate the popularity of the mythological figure Ravana among the Sinhalese Buddhist majority. The main question of the thesis is: What kinds of representations of Ravana have emerged among Sinhalese Buddhists in post-war Sri Lanka (2009 onwards), how do they take shape on local levels, and how does the interest in Ravana – including these Ravana representations – relate to the hegemonic Sinhalese Buddhist ethno-nationalist perception of Sri Lanka as a Sinhalese Buddhist country?

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.

This is data from the two experiments reported in Chapter 3 ('Word length and predictability in context') of my University of Edinburgh PhD thesis (https://www.era.lib.ed.ac.uk/handle/1842/33051). The experiments are also reported in a manuscript for journal submission, entitled 'Information content affects word length in an artificial language communication task'.

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.

Data accompanying the phd thesis

The dissertation deals with collaboration within research groups and with their environment using information and communication technology. A qualitative probe research was conducted to see how the collaboration works, at what levels, what kind of information and data is shared with the use of what software tools and services. An overview of available types of collaboration software was made. Semi-structured interviews were conducted in selected research groups on the basis of which the patterns and tools for collaboration were established. The grounded theory method was chosen to process the data collected from interviews and analyses. The result of the thesis is the research cycle with marked activities in which cooperation is taking place in the studied groups. This documents the state of usage of information and communication technology in research collaboration. It was found that information and communication technology does have an impact on research work in the whole scientific cycle in the studied research groups.

These files contain raw data and statistical analysis of PhD thesis of Nikolovski Martin titled "INCREASING CRYOPRESERVING CAPACITY OF EJACULATE MEDIA FOR OVCHEPOLSKA PRAMENKA BY UTILIZING SEMINAL PLASMA AND GLUTATHIONE".

Supplementary data files for Chapter 2 of Jordan Lin's PhD Thesis. These files exceeded the size limits for the thesis.

This research aims to develop a principle-based framework for audit analytics implementation, which addresses the challenges of AA implementation and acknowledges its socio-technical complexities and interdependencies among challenges. This research relies on mixed methods to capture the phenomena from the research’s participants through various approaches, i.e., MICMAC-ISM, case study, and interview with practitioners, with literature exploration as the starting point. The raw data collected consists of multimedia data (audio and video recordings of interviews and focused group discussion), which is then transformed into a text file (transcript), complemented with a softcopy of the documents from the case study object.

The published data in this dataset, consists of the summarized or analyzed data, as the raw data (including transcript) is not allowed to be published according to the decision by the Human Research Ethics Committee pertinent to this research (Approval #1979, 14 February 2022). This dataset's published data are text files representing the summarized/analyzed raw data as an online appendices to the thesis.

Thesis Data Sets

## Overview

Thesis Data Sets is a dataset for object detection tasks - it contains Fruits annotations for 2,805 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 contains the extended supplementary tables from the PhD thesis entitled 'Genetic architecture of glycomic and lipidomic phenotypes in isolated populations' by Arianna Landini. Understanding how genetics contributes to the variation of complex traits and diseases is one of the key objectives of current medical studies. To date, a large portion of this genetic variation still needs to be identified, especially considering the contribution of low-frequency and rare variants. Omics data, such as proteomics and metabolomics, are extensively employed in genetic association studies as 'proxies' for traits or diseases of interest. They are regarded as 'intermediate' traits: measurable manifestations of more complex phenotypes (e.g., cholesterol levels for cardiovascular diseases), often more strongly associated with genetic variation and having a clearer functional link than the endpoint or disease of interest. Accordingly, the genetics of omics have the potential to offer insights into relevant biological mechanisms and pathways and point to new drug targets or diagnostic biomarkers. The main goal of the related research is to expand the current knowledge about the genetic architecture of protein glycomics and bile acid lipidomics, two under-studied omic traits, but which are involved in several common diseases. In summary, in my thesis I describe the genetic architecture of the protein glycome and the bile acid lipidome: the former has a higher genetic component, while the latter is largely influenced by environmental factors (e.g., sex, diet, gut flora). Despite the limited sample size, we were able to describe rare variant associations, demonstrating that isolated populations represent a useful strategy to increase statistical power. However, additional statistical power is needed to identify the possible effect of protein glycome and bile acid lipidome on complex disease. A clearer understanding of the genetic architecture of omics traits is crucial to develop informed disease screening tests, to improve disease diagnosis and prognosis, and finally to design innovative and more customised treatment strategies to enhance human health.

This dataset supports the Thesis: by Kelly, E.S. TITLE- Data Technologies for Lower Limb Orthosis Design and Assessment The thesis featured participant testing (Chapter 3) and secondary analysis of MR and clinical data (Chapters 4-6). Due to participant confidentiality constraints of the original study ethics, the imaging data are not enclosed in this raw data file. This dataset contains: A simple Excel spreadsheet containing the raw data behind the tables and figures in the linked thesis.

This dataset contains source code and data used in the PhD thesis "Metrics of Graph-Based Meaning Representations with Applications from Parsing Evaluation to Explainable NLG Evaluation and Semantic Search". The dataset is split into five repositories: S3BERT: Source code to run experiments for chapter 9 "Building efficient and effective similarity models from MR metrics". amr-metric-suite, weisfeiler-leman-amr-metrics: Source code to run metric experiments for chapters 4, 5, 6. amr-argument-sim: Source code to run experiments for chapter 8 "Exploring argumentation with MR metrics". bamboo-amr-benchmark: Benchmark for testing and developing metrics (chapter 5).

This data set contains raw data and parsed data of all experiments [1] run for the PhD thesis. They were generated using lab (see https://doi.org/10.5281/zenodo.399255).

The raw data files (sievers-phd2017-raw-data-part*.tar.gz) contain a subdirectory for each experiment, each containing a subdirectory for each planner run of the experiment, distributed over the directories runs-*. For each run, there are the input PDDL files, domain.pddl and problem.pddl, the compressed output as generated by the translator component of Fast Downward (output.sas.xz), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), and the run script "run" used to start the experiment. The latter cannot be directly used, however, because the directory containing source code and build (compiled object files) have been removed for space reasons. The code is publicly available under https://doi.org/10.5281/zenodo.1163381. The (lab) scripts for parsing run.log are also available in the main directory of each experiment. All other scripts and a corresponding lab version are available on request.

For each raw data experiment, the parsed data file (sievers-phd2017-parsed-data.tar.gz) also contains a directory of the same name, with "-eval" appended. It contains a single file called "properties" that combines all of the experiment's parsed data (which can be and was generated from the raw data using lab and the parser scripts). They are in the json format and can be used for easy manipulation of the data. The directories with the prefix "paper-" and "talk-" are combinations of other directories (using the "fetch" mechanism of lab). It is recommended to use these, because due to technical errors, the original eval directories do not contain all runs of all planners (to be more precise: they contain all runs, but a subset of the planner have not been started in these experiments for technical errors and thus considered not solving the task). The missing ones have been run separately, see the directories with "missing-runs" in their name. This is also the reason some of these directories ("paper-", "talk-") contain files named "old-properties" and "fixed-properties" besides the actual "properties". "old-properties" are those with missing/faulty runs, "fixed-properties" are as "old-properties", however with the data of faulty runs removed, and "properties" are as "fixed-properties", however with the addition of the fixed missing runs (in fact, these always contain all fixed missing runs of all experiments, for technical reasons).

The file sievers-phd2017-parsed-data-all-and-random-merge-strategies.tar.gz contains parsed data of earlier experiments (see [1]), for which no raw data has been archived. The directories contain properties files in the json format.

[1] except raw data for the parsed data "sota-symba-spmas-eval" (which in the meantime was added to a separate data set available under https://doi.org/10.5281/zenodo.1189912) and all re-used experiments from the paper "An Analysis of Merge Strategies for Merge-and-Shrink Heuristics" (Silvan Sievers, Martin Wehrle and Malte Helmert, ICAPS 2016), for which the raw data was too large to be archived.