Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
66
datasets available to search
ShareScore release 0.9.0
Dataset results
66 results for “Master Thesis”
Archive for Master's thesis
<p>This archive contains model forcing and output for the Shyft model, along with scripts of the related data processing. The structure of the archive (folders) is as follows:</p> <ol> <li>"lalm" and "elverum: contain the meteorological forcing data for the two catchments Lalm and Elverum, respectively.</li> <li>"model forcing for historical periods": contains the bias corrected climate model data representing the three historical periods.</li> <li>"model simulation output": contains the model output data for the simulations of the three historical periods.</li> <li>"shyft workspace": contains the data processing scripts. </li> </ol> <p>1. This dataset consists of the folders: "senorge", "era5" and "hysn5". The included data variables are: temperature, precipitation, wind speed, relative humidity and radiation. Temperature and precipitation are found in "senorge". Wind speed is found in "era5". Lastly, relative humidity and radiation are found in "hysn5". The dataset is of the netCDF-format. The folders contain data that was downloaded from the sources: SeNorge2018 (The Norwegian Meteorological institute, 2022), ERA5-land (Muñoz, 2019; Muñoz, 2021) and HYSN5 (Haddeland, 2022). The data is described as follows:</p> <p>temperature: </p> <ul> <li>Description: daily mean air temperature </li> <li>Unit: degrees Celsius</li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>precipitation: </p> <ul> <li>Description: daily mean precipitation</li> <li>Unit: mm/day</li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>wind speed: </p> <ul> <li>Description: daily mean wind speed </li> <li>Unit: m/s</li> <li>Spatial resolution: 0.1x0.1 degree (native resolution of 9 km)</li> <li>Grid mapping: EPSG:4326</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>relative humidity: </p> <ul> <li>Description: daily mean near-surface relative humidity </li> <li>Unit: %</li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>radiation: </p> <ul> <li>Description: daily mean surface downwelling shortwave radiation</li> <li>Unit: W/m<sup>2 </sup></li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>2. This dataset contains climate model data for the three historical periods: Medieval Warm Period (MWP; 1000-1150 AD), Little Ice Age (LIA; 1600-1750 AD) and Industrial Time (IT; 1800-1950 AD). The data covers the two catchments Lalm (L) and Elverum (E) for simulations using both low solar variability (Solar 1; S1) and high solar variability (Solar 2; S2). The data consists of the variables: temperature (temp), precipitation (prec), wind speed (wind), relative humidity (humi) and radiation (radi). The dataset is of the netCDF-format. The related source data is not published here, due to licences. Contact Lu Li at the NORCE research centre regarding data accessibility. The data is described as follows:</p> <p>temperature: </p> <ul> <li>Description: daily mean air temperature </li> <li>Unit: degrees Celsius</li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>precipitation: </p> <ul> <li>Description: daily mean precipitation</li> <li>Unit: mm/hour</li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>wind speed: </p> <ul> <li>Description: daily mean wind speed </li> <li>Unit: m/s</li> <li>Spatial resolution: 0.1x0.1 degree (native resolution of 9 km)</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>relative humidity: </p> <ul> <li>Description: daily mean near-surface relative humidity </li> <li>Unit: -</li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>radiation: </p> <ul> <li>Description: daily mean surface downwelling shortwave radiation</li> <li>Unit: W/m<sup>2 </sup></li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>3. This dataset contains time series data for the three historical periods: Medieval Warm Period (MWP; 1000-1150 AD), Little Ice Age (LIA; 1600-1750 AD) and Industrial Time (IT; 1800-1950 AD), which are output from the Shyft model. The data covers the two catchments Lalm (L) and Elverum (E) for simulations using both low solar variability (Solar 1; S1) and high solar variability (Solar 2; S2). The data consists of the variables: discharge, temperature, precipitation, wind_speed, relative_humidity and radiation, snow water equivalent (SWE) and snow covered area (SCA). The dataset is of the csv-format. </p> <p>NB: the datetime index of the data suggests that the data covers the period of 1700-1850, however this is only true for IT. This inconsistency is caused by a limitation of datetime64 in pandas, which does not handle dates prior to the year 1678. </p> <p>The data is described as follows:</p> <p>discharge: </p> <ul> <li>Description: daily mean air temperature </li> <li>Unit: degrees Celsius</li> <li>Dimension: time</li> </ul> <p>temperature: </p> <ul> <li>Description: daily mean air temperature </li> <li>Unit: degrees Celsius</li> <li>Dimension: time</li> </ul> <p>precipitation: </p> <ul> <li>Description: daily mean precipitation</li> <li>Unit: mm/hour</li> <li>Dimension: time</li> </ul> <p>wind_speed: </p> <ul> <li>Description: daily mean wind speed </li> <li>Unit: m/s</li> <li>Dimension: time</li> </ul> <p>relative_humidity: </p> <ul> <li>Description: daily mean near-surface relative humidity </li> <li>Unit: -</li> <li>Dimension: time</li> </ul> <p>radiation: </p> <ul> <li>Description: daily mean surface downwelling shortwave radiation</li> <li>Unit: W/m<sup>2 </sup></li> <li>Dimension: time</li> </ul> <p>SWE: </p> <ul> <li>Description: daily mean snow water equivalent </li> <li>Unit: mm</li> <li>Dimension: time</li> </ul> <p>SCA: </p> <ul> <li>Description: daily mean snow covered area (% of total catchment area)</li> <li>Unit: -</li> <li>Dimension: time</li> </ul> <p>4. The scripts make up the workflow of the thesis. In order to reproduce the results, the first script has to be run firstly, then the second script is applied on the output from the first etc. Keep in mind that manual adjustments inside the scripts are required in order to obtain some of the results. The scripts are described as follows:</p> <ol> <li>"Subsetting_data.ipynb": This script subsets forcing data (temperature, precipitation, wind speed, relative humidity and radiation) from the sources (SeNorge2018, ERA5-Land and HySN5) to the catchments of Lalm and Elverum.</li> <li>"convert_netcdf.ipynb": This script converts netCDF-files of temperature, precipitation, wind speed, relative humidity and radiation to fit as model forcing to the Shyft modeling framework. It also creates a cell data file containing information about the catchments (Lalm and Elverum) forest, lake and glacier fraction, which are required in Shyft. </li> <li>"QDM_lalm.ipynb" and "QDM_elverum.ipynb": These scripts perform the bias correction approach, Quantile Delta Mapping (QDM), on the climate model data (temperature, precipitation, wind speed, relative humidity and radiation).</li> <li>"extract_historical_periods.ipynb": This script extracts the three historical periods of 1000-1150 (Medieval Warm Period), 1600-1750 (Little Ice Age) and 1800-1950 (Industrial Time) from the climate model data (temperature, precipitation, wind speed, relative humidity and radiation). </li> <li>"calibration_lalm.ipynb" and "calibration_elverum.ipynb": Scripts that runs the calibration of Lalm and Elverum catchment using the Shyft model, respectively.*</li> <li>"simulation_lalm.ipynb" and "simulation_elverum.ipynb": Scripts that runs the simulation of Lalm and Elverum catchment using the Shyft model, respectively.*</li> <li>"data_analysis.ipynb": This script contains the data analysis performed on the Shyft model simulation output. The analysis includes: calculations of mean monthly values of the climate variables (discharge, temperature, precipitation, snow water equivalent and snow covered area), decadal time series of the climate variables, calculations of mean floods and 100-year floods, flood and extreme precipitation frequency analysis, calculation of season index, estimation of flood generating processes, plotting of flood roses and estimation of Standardised Precipitation Index. </li> </ol> <p>*For the Shyft model configuration, simulation and calibration files (yaml-files) are included in the folder "yaml_lalm" and "yaml_elverum" for the two catchments. These yaml-files are described as follows: </p> <ul> <li>simulation.yaml: is used for configuration of the model simulation </li> <li>calibration.yaml: is used for configuration of the model calibration</li> <li>calibrated_model.yaml: contains the calibrated model parameters</li> <li>datasets.yaml: contains the paths to the data variables </li> <li>interpolation.yaml: contains the interpolation methods and parameters</li> <li>region.yaml: contains the modeling domain</li> </ul> <p>References: </p> <p>Haddeland, I. (2022). HySN2018v2005ERA5 (Version 1) [Data set]. Zenodo. (Accessed on: 19-09-2022). doi: https://doi.org/10.5281/zenodo.5947547.</p> <p>Muñoz Sabater, J. (2019). ERA5-Land hourly data from 1981 to present [Dataset]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on: 19-09-2022). doi: https://doi.org/10.24381/cds.e2161bac.</p> <p>Muñoz Sabater, J. (2021). ERA5-Land hourly data from 1950 to 1980 [Data set]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on: 19-09-2022). doi: https://doi.org/10.24381/cds.e2161bac.</p> <p>The Norwegian Meteorological institute, MET Norway (2022). Norwegian observational gridded climate datasets [Data set]. Thredds.met. (Accessed on: 05-09-2022). url: https://thredds.met.no/thredds/catalog/senorge/seNorge_ 2018/Archive/catalog.html.</p>
[Dataset] An End User Based Study on Subtitling for the d/Deaf and Hard of Hearing in Turkey [Unpublished Master's Thesis]
<p>The dataset provided is from an unpublished master's thesis authored by Selma Akseki and supervised by Asst. Prof. Elif Ersözlü at Hacettepe University, Ankara (Türkiye). For more details see https://www.openaccess.hacettepe.edu.tr/xmlui/handle/11655/25767</p> <p>Abstract from the master's thesis reporting on the analysis of this dataset:</p> <p>Reception research in audiovisual translation (AVT), particularly on the<br> intersection between AVT and media accessibility (MA) has been a research<br> avenue to interest for translation scholars in the last couple of decades. However,<br> research in reception studies in countries like Turkey, where MA practices are<br> relatively new in terms of legislative mandates on the subject, are still scarce.<br> This thesis aims to contribute to the field by investigating the reception of subtitles<br> for the d/Deaf and hard of hearing (SDH) by the intended audience, Turkish<br> d/Deaf and hard of hearing (HOH) viewers. The present study places itself in the<br> intersection of Descriptive Translation Studies (DTS) and Reception Studies (RS)<br> within AVT. First, guidelines and current practices of SDH were investigated to<br> reveal the norms with a focus on specific parameters. Second, a questionnaire<br> was designed to elicit the opinions of viewers on these practices. The English<br> template of the Digital TV for All (DTV4ALL) questionnaire was adapted to the<br> Turkish context (Romero-Fresco, 2015). The project in which the original<br> questionnaire was used aimed to facilitate provision of access services and<br> provide feedback from viewers that could be relevant to stakeholders in improving<br> the quality of SDH. The Turkish questionnaire, designed with a similar objective<br> in mind, consisted of questions regarding demographic and personal data,<br> viewing habits and preferences, and opinions on particular SDH parameters.<br> Data was collected from 237 participants through online and paper<br> questionnaires. Findings were compared with previous similar studies and<br> discussed. In conclusion, as regards the specific SDH parameters investigated,<br> current practices seem to accomplish their skopos. The provision of more<br> subtitled programmes on free-to-air linear broadcast with a wider variety of types<br> of programmes, and offering of accessible versions with premieres of<br> programmes are areas that, according to the end users, could be improved on.</p>
seoruosa/instances: master-thesis-2023
<p>MMURP instances used on master thesis. Contain instances of states of Brazil: Minas Gerais, Espírito Santo, Maranhão, Roraima, Rio Grande do Sul e Tocantins.</p>
Verifying OpenJDK's LinkedList using KeY: Additional artefacts belonging to master thesis
<p>The file testcases.tar.gz contains source code files w.r.t. 64 test cases carried out for this thesis. It also contains log files that contain output of these test cases. The latter are also contained in the file Appendix.pdf, which serves as an on-line appendix for the thesis. Other sources which are of importance for the thesis can be found in https://doi.org/10.5281/zenodo.3517081. Three proof files have been re-established for the purpose of describing these proofs in the thesis. Thus: they deviate from the ones that can be found in https://doi.org/10.5281/zenodo.3517081. These three can be found in the file "Three renewed proof files.zip". It concerns proof files for lastIndexOf(Object), linkFirst(Object), and addFirst(Object). https://doi.org/10.5281/zenodo.3517081 is a link that has been created to store artefacts w.r.t. a paper for the TACAS conference in April 2020 in Dublin, Ireland. The paper carries the same title as this thesis.</p>
Expanding Search-Based Software Modularization to Enterprise-Level Projects: A Case Study at Adyen (Master's Thesis)
<p>The zip file uploaded contains the interactive 3d graphs shown in chapter 6 in the thesis. The thesis can be found on the TU Delft repository.</p>
Master's Thesis: 2D Numerical Simulations of Shallow Crustal Magma Bodies
<p>This collection of videos showcases a series of simulations conducted during my master’s thesis. The videos illustrate the evolution of temperature, water, and silica parameter fields, highlighting the internal dynamics within the systems. Key processes captured include whole-system convection, system overturn, plume formation, drip formation, and layering at the base of the systems. Additionally, two of the videos present Harker plots of the major oxides: one from the closed-system reference scenario, which contained a single magma composition, and the other from the open-system simulation, which included two magma compositions.</p>
Complete Data Set for the Master Thesis: Acceptance of Robotics in Tourism
<p>Complete data set for the master thesis "Acceptance of Robotic Technology in Tourism" including the SPSS file. </p><p><strong>Abstract</strong></p><p>Robotics, artificial intelligence, and automation have undergone significant evolution across various industries, from manufacturing to healthcare. The tourism industry is also embracing these technological advancements, accelerated by factors such as the global pandemic and labor shortages. Despite their transformative potential, the academic exploration of robots in tourism remains limited, particularly regarding customer attitudes and acceptance.</p><p>This master thesis addresses the gap in research by investigating the acceptance of robots in the tourism industry. The study aims to identify the factors influencing acceptance, explore potential initiatives for increased acceptance over time, and highlight challenges in integrating robotics into tourism. The research questions guide the exploration of these facets. The literature review reveals a scarcity of research on robot acceptance in tourism, with existing studies often focusing on engineering aspects rather than social science perspectives. To address this topic, this thesis formulates a comprehensive research goal, seeking to understand the factors shaping acceptance and offering insights into the opportunities and challenges of robot integration in tourism. The thesis then explores the fundamental aspects of robotics and their potential applications in tourism, presenting a micro, meso, and macro level analysis. A hypothesis model is developed based on prior research, laying the groundwork for data collection through surveys in three distinct scenarios. Upon gathering and processing the survey data using SPSS, the results are analyzed, with a focus on influencing factors affecting robot acceptance. The findings are compared to established models such as TAM, UTAUT, and the Service Robot Acceptance Model.</p><p>Keywords: robotics, artificial intelligence, adoption, technology acceptance model, tourism, perceived usefulness, perceived ease of use, anthropomorphism </p><p><strong>Use not permitted without permission of the author.</strong></p>
Dataset for the master thesis "The silent majority: How belonging to the majority silences French deputies"
<p>Dataset (.dta format) supporting the work "The silent majority: How belonging to the majority silences French deputies" (PSE master thesis, 2018).</p> <p>The data comes from the website <a href="https://www.nosdeputes.fr/">nosdeputes.fr</a> covering the period 2007-2017. The original provider of the data is <a href="https://www.regardscitoyens.org/#&panel1-1">RegardsCitoyens</a>.</p>
Master Thesis- Modeling of Electric Vehicle Charging Infrastructure and Comparison of Electric Vehicle Load Simulation with Empirical Charging Data
<p>All the data behind relevant plots in the thesis report are stored here</p>
Data for the publication "Estimation of the importance of natural cloud seeding" (Master thesis)
<p>This repository contains the data for the thesis: "Estimation of the importance of natural cloud seeding" by Ulrike Proske, submitted in April 2020 to the Department of Earth Sciences, ETH Zurich.</p>
Master thesis: Comparison of Machine Learning Methods for Pedestrian Trajectory Generation
<p>These videos are the result videos of the pedestrian model with trained neural networks of different Machine Learning methods in the master thesis: Comparison of Machine Learning Methods for Pedestrian Trajectory Generation.</p> <p>Turing_Learning_1: the first experiment of the Turing Learning method.</p> <p>Turing_Learning_1: the second experiment of the Turing Learning method.</p> <p>Inverse_Reinforcement_Learning_1: the first experiment of the Inverse Reinforcement Learning method.</p> <p>Inverse_Reinforcement_Learning_2: the second experiment of the Inverse Reinforcement Learning method.</p> <p>Deep_Q_Network_1: the first experiment of the Deep Q-Learning method.</p> <p>Deep_Q_Network_2: the second experiment of the Deep Q-Learning method.</p> <p>Deep_Q_Network_3: the third experiment of the Deep Q-Learning method.</p> <p>Deep_Q_Network_4: the fourth experiment of the Deep Q-Learning method.</p> <p>Policy_Distillation_1: the first experiment of the Policy Distillation method.</p> <p>Policy_Distillation_2: the second experiment of the Policy Distillation method.</p> <p>Policy_Distillation_3: the third experiment of the Policy Distillation method.</p>
Raw Experimental Data of the Master's Thesis: Improving Serverless Edge Computing for Network Bound Workloads
<p>Raw Experimental Data of the Master's Thesis: Improving Serverless Edge Computing for Network Bound Workloads</p> <p>Please see the README for information on parsing and data structure.<br> Some of the data might need additional explanations. In case of confusion don't hesitate to contact me under jacob.palecek@outlook.com</p>
Master-thesis supplement Del 2 CSV-files of dataset for calanus and others
<p>CSV-files of dataset for calanus and others</p>
Master Thesis - Data sets
<p>This file contains all experiments that I have run and described in my Master thesis. The folders are named in accordance with the analysis files (from the repository).</p>
Master Thesis dataset
<p>Consists of:</p> <ul> <li>Large dataset of source code metrics for a large set of notebook groups.</li> <li>Dataset of descriptive statistics for the above mentioned source code metrics</li> <li>Documnetation of implemented source code metrics</li> </ul>
Master Thesis Parkinson desease Dataset
<p>Dataset containing result from my master thesis work described in the following github repo : https://github.com/luca-farinola/Master_Thesis-Neurodevelopmental_trajectory_PD</p>
Data collected as part of a master's thesis in information governance at the Haute école de gestion de Genève
<p>The subject of the master's thesis is (in French) : "Élaboration d’un cadre d’évaluation de la maturité de la gouvernance informationnelle dans le cadre de la direction des systèmes d'information de la HES-SO Genève : Étude du contexte et recommandations."</p> <p>These files (in French) produced as part of this master's thesis contain :</p> <ul> <li>the data used to create an information governance framework adapted to the context of a public academic institution</li> <li>the list of documents (laws, regulations, publications) used to identify the actors and their roles and responsibilities within HES-SO Genève</li> <li>extraction of the roles and responsibilities of information governance within HES-SO Genève, as well as a RACI matrix</li> <li>expert opinions on a framework for assessing the information governance maturity of an information systems department within a public academic institution</li> <li>a framework for assessing the information governance maturity of an information systems department within a public academic institution</li> </ul>
Train and Test Dataset for a Neural Network for the task of RSA Segmentation in the context of a Master thesis 2024
<p>The data corresponds to the work of the master thesis "3D Segmentation of Plant Roots from MRI Images for Enhanced Automated Root Tracing using Deep Neural Networks" by Daniel Weißen from the 24/07/2024</p> <p>train_data.tar<br>- contains the training and validation data used for the DNN<br> - data_gmm contains the soil noise data based on a Gaussian Mixture Model<br> - data_FS contains the soil noise data based on the Fourier Synthesis approach described in the thesis</p> <p>test_data.tar<br>- contains the test data which is manually labeled</p> <p>segmentations.tar<br>- contains the segmentations of the trained SwinUNETR-V2+ on the real test MRIs<br> - fs_model_output: contains the Fourier Synthesis trained NNs segmentation<br> - fmm_model_output: contains the GMM trained NNs segmentation<br> - threshold_output: contains the Dice optimized threshold labeling</p> <p>FS_checkpoint.ckpt, GMM_checkpoint.ckpt<br>- contains the checkpoint for the trained SwinUNETR-V2+ on the Fourier synthesis, and GMM soil noise respectively</p>
Scalability Measurement Data for "Efficient Data Flow Constraint Analysis" Master's Thesis
<p>Raw measurement data and plots of performed scalability measurments of our proposed approach for analyzing software systems regarding data flow constraints.</p>
Supplementary information of Master's Thesis by S. Humpage and V. Chanal
<p>This spreadsheet is the supplementary information of our Master's Thesis at Chalmers University of Technology entitled "Will solvents present challenges for carbon capture? - Assessing the cost and energy implication of solvent production for carbon capture with an energy system model".</p> <p>It contains the data collection as well as some of the main calculations made in this work. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.