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394 results for “thesis”
Mass spectrometric investigation of pharmaceuticals in environmental matrices - homogenate analysis: Thesis Data 2
<p>RT Thesis - Mass spectrometric investigation of pharmaceuticals in environmental matrices - homogenate analysis_data set 4 contains Mass Lynx data sets part 2</p> <p> </p> <p>RT Thesis - Mass spectrometric investigation of pharmaceuticals in environmental matrices - homogenate analysis_data set 5 contains Mass Lynx data sets part 3</p>
JespervdVen_Bachelor_Thesis_MPingestion_2022
<p>These measurements include the data on green fluorescent microplastics ingested by Daphnia magna during grazing experiments with different MP biofouling times in natural Dutch water in Amsterdam. Biofouling experiment were done between 03/2022 - 07/2022 and biofouling times ranged from 0-4 weeks of biofouling, with each 1 week difference, and 0 being pristine microplastics</p>
Supporting Information for "Investigation of Hikurangi subduction zone slow slip events using onshore and onshore geodetic data" PhD thesis
<p>The data sets included here are those inverted using the TDEFNODE (McCaffrey et al., 2009) inversion code in the PhD thesis "Investigation of Hikurangi subduction zone slow slip events using onshore and onshore geodetic data" to obtain geodetic slip models of the 2013-2016 and February-July 2019 periods at the Hikurangi subduction zone.</p> <p> </p> <p><em>The 2013-2016 period captured the 2013 Kāpiti and 2014/2015 Manawatū slow slip events (SSEs), in addition to the 2013 Cook Strait, 2013 Lake Grassmere, and 2014 Eketāhuna earthquakes. The data related to this model are:</em></p> <p><strong>campaign_gps_2013.ts</strong><br> -campaign GPS time series<br> -columns: Year East East_sigma North North_sigma Up Up_sigma<br> -input using TDEFNODE command ts1</p> <p><strong>coseismic_displacements_2013_07_21.ds </strong><br> -coseismic displacements for Cook Strait earthquake (Hamling et al., 2014)<br> -columns: Longitude Latitude Disp_East Disp_North Sigma_East Sigma_North Site Disp_Up Sigma_Up Time1 Time2<br> -input using TDEFNODE command ds2</p> <p><strong>coseismic_displacements_2013_08_16.ds </strong><br> -coseismic displacements for Lake Grassmere earthquake (Hamling et al., 2014)<br> -columns: Longitude Latitude Disp_East Disp_North Sigma_East Sigma_North Site Disp_Up Sigma_Up Time1 Time2<br> -input using TDEFNODE command ds2</p> <p><strong>onshore_gnss_kapiti_manawatu_2013_2016.ts</strong><br> -GNSS time series<br> -columns: Year East East_sigma North North_sigma Up Up_sigma<br> -input using TDEFNODE command ts1</p> <p><strong>LOS_coseismic_2013_08_16.is<br> -</strong>coseismic Line of Sight displacements for Lake Grassmere earthquake (Hamling et al., 2014)<br> -convention: negative displacement equivalent to ground moving towards the satellite<br> -columns: Longitude Latitude LineOfSight_disp sigma Unit_x Unit_y Unit_z<br> -input using TDEFNODE command is1</p> <p> </p> <p><em>Data related to the February-July 2019 SSE model are:</em></p> <p><strong>onshore_gnss_east_coast_sse_2019.ts</strong><br> -GNSS time series<br> -columns: Year East East_sigma North North_sigma Up Up_sigma<br> -input using TDEFNODE command ts4</p> <p><strong>seafloor_displacement_gisborne.ts</strong><br> -seafloor pressure time series<br> -convention: positive change equivalent to seafloor uplift<br> -columns: Year East East_sigma North North_sigma Up Up_sigma<br> -input using TDEFNODE command ts4</p> <p><strong>seafloor_displacement_hawkebay.ts</strong><br> -seafloor pressure time series<br> -convention: positive change equivalent to seafloor uplift<br> -columns: Year East East_sigma North North_sigma Up Up_sigma<br> -input using TDEFNODE command ts4</p> <p><strong>LOS_SSE_2019.is</strong><br> -SSE-related Line of Sight displacement<br> -convention: positive displacement equivalent to ground moving away from satellite<br> -columns: Longitude Latitude LineOfSight_disp sigma Unit_x Unit_y Unit_z<br> -input using TDEFNODE command is1</p> <p> </p> <p>The TDEFNODE manual can be found here:<br> https://robmccaffrey.github.io/TDEFNODE/manual/tdefnode_manual.html</p> <p>The header lines in the time series files (.ts) take the site inter-SSE rates from the model of Wallace et al. (2012).</p> <p> </p> <p><em>References</em></p> <p>Hamling, I. J., D’Anastasio, E., Wallace, L. M., Ellis, S., Motagh, M., Samsonov, S., Palmer, N., and Hreinsdóttir, S. (2014). Crustal deformation and stress transfer during a propagating earthquake sequence: The 2013 Cook Strait sequence, central New Zealand. <em>Journal of Geophysical Research: Solid Earth</em>, <strong>119</strong>(7):6080–6092.</p> <p>McCaffrey, R. (2009). Time-dependent inversion of three-component continuous GPS for steady and transient sources in northern Cascadia. <em>Geophysical Research Letters</em>, <strong>36</strong>(L07304).</p> <p>Wallace, L. M., Barnes, P., Beavan, J., Van Dissen, R., Litchfield, N., Mountjoy, J., Langridge, R., Lamarche, G., and Pondard, N. (2012). The kinematics of a transition from subduction to strike-slip: An example from the central New Zealand plate boundary. <em>Journal of Geophysical Research: Solid Earth</em>, <strong>117</strong>(B2).</p>
Landsat 8 and SRTM Data Processed for Soil Classification (Yuri Coelho's Thesis)
<p>This dataset was processed for the Senior Thesis of Yuri Coelho.</p> <p>The dataset is composed by the processed rasters that are used in the Senior Thesis. The objective of the dataset is to classify satellite data into Soil Classes.</p>
Data and Code: Detecting Blue Whale Calls in the Northeast Pacific Using Seismic Systems (Undergraduate Thesis)
<p><strong>SeismoData.ipynb</strong>: This Jupyter notebook is adapted from seismosocialdistancing.ipynb created by Thomas Lecocq, Fred Massin and Claudio Satriano. SeismoData.ipynb was used to retrieve seismic waveform data from the Incorporated Research Institutions for Seismology (IRIS) Data Management Center (DMC) (https://ds.iris.edu/ds/ nodes/dmc/) and convert files from miniSEED to SAC format. It was also used to preview waveform and spectrogram plots.</p> <p><strong>BlueWhaleDetectionResults_J53A_Dec132011.mat</strong>: This .mat file summarizes whale detection results from OBS J53A on December 13th 2011. The objective was to calibrate a detection algoritm created for Northwest Atlantic blue whale A calls by Plourde and Nedimovic (2022), so that it can target Northeast Pacific blue whale B calls using seismometers off Washington and California. Three tests were performed to find optimal parameters. <em>BlueWhaleDetections_J53A_Test1</em><strong> </strong>are the detection results of a control that uses Northwest Atlantic blue whale parameters (16.25-18Hz frequency and 68-78s period ranges). <em>BlueWhaleDetections_J53A_Test2</em> <strong> </strong>are the detection results using Northeast Pacific blue whale B call parameters (14-17Hz frequency and 45-55s period ranges). <em>BlueWhaleDetections_J53A_Test2 </em>are the detection results using Northeast Pacific blue whale B call and C call parameters (10.5-12Hz and 14-17Hz frequency and 45-55s period ranges). <em>BlueWhaleDetections_J53A_SCC</em> are the 95% probability detection results from the Wilcock and Hilmo (2021) blue whale catalogue created using spectrogram cross-correlation. </p> <p><strong>NEPBlueWhaleMATLABcodes.zip</strong>: Contains scripts to run the recurrence interval power ratio method created by Plourde and Nedimovic (2022), adjusted to detect Northeast Pacific blue whale B calls and plot waveforms/spectrograms. First run <em>DetectBlueWhales.m </em>to calculate the recurrence power ratio every 12 minutes, then run C<em>reateBlueWhaleDetectionList.m </em>to classify detections with high power ratios and likely blue whale call detections.</p> <p><strong>BlueWhaleDetectionResults_CapeMendocino_Dec15to292014.mat</strong>: This .mat file summarizes the blue whale detection results from 2 OBS (FS02D and FS07D) and 1 land seismometer (CM09A) in close proximity, using Test 2 parameters. Note if there are less than 3 BWD in a given day, these are likely false detections.</p>
2D Cell Instance Segmentation Dataset of Master Thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs"
<p>This is the 2D cell instance segmentation dataset of master thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs". The data is originally from the BBBC038 dataset for the Kaggle 2018 Data Science Bowl. We keep the images with annotations and the final dataset comprises 670 images in the training set and 171 images in the test set. We also converted the dataset to MSCOCO format for convenience.</p>
3D Cell Instance Segmentation Dataset of Master Thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs"
<p>This is the 3D cell instance segmentation dataset of master thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs". The data is originally from the BBBC027 dataset comprising 30 sets of 3D image sets with high SNR level of quality. Because only the 4 to 100 slides of every 3D image set are annotated and the main difference between annotated and unannoated slides are the brightness, to avoiding misunderstanding by the algorithms, we removed the unannotated slides and separate every slide as individual images. We also converted the dataset to MSCOCO format for convenience.</p>
Supplementary material related to the thesis "Perception of airborne sounds and vibrations in crocodiles"
<p>This repository contains all datasets, statistical codes and videos examples for the 4 different studies conducted during my thesis. </p>
Dataset and code for paper 'Qu'allons devenir des pamphlets: Een digitale analyse van de politieke pamfletten tijdens de Brabantse Omwenteling (1789-1790)' (thesis project)
<p>This folder contains:</p> <ol> <li>The final version of my dissertation (<a href="../api/records/11488783/draft/files/Thesis_Oliver_Bogaerts.pdf/content" target="_blank" rel="noopener noreferrer">Thesis_Oliver_Bogaerts.pdf</a>)</li> <li>A ZIP file with the transcripts (.txt) of all pamphlets that were used for this project (<a href="../api/records/11488783/draft/files/Transcripts_thesis_Oliver_Bogaerts.zip/content" target="_blank" rel="noopener noreferrer">Transcripts_thesis_Oliver_Bogaerts.zip</a>)</li> <li>An Excel file containing metadata about the pamphlets (<a href="../api/records/11488783/draft/files/Thesis_Oliver_Bogaerts.xlsx/content" target="_blank" rel="noopener noreferrer">Thesis_Oliver_Bogaerts.xlsx</a>)</li> <li>A Jupyter notebook with code I used to develop a text classification model and multiple visualisation techniques (<a href="../api/records/11488783/draft/files/Thesis_Oliver_Bogaerts.ipynb/content" target="_blank" rel="noopener noreferrer">Thesis_Oliver_Bogaerts.ipynb</a>)</li> <li>A text file with all French stopwords that were removed during preprocessing (<a href="../api/records/11488783/draft/files/stopwords-fr.txt/content" target="_blank" rel="noopener noreferrer">stopwords-fr.txt</a>)</li> </ol>
Reshaping Foramen Magnum Research. Analyzing foramen magnum variation in modern humans using 2D osteometry and 3D geometric morphometrics – A master thesis summary and research review. Supplementary Materials.
<p>This supplementary materials document refers to: <em>Göldner, D., 2024. Reshaping Foramen Magnum Research. Analyzing foramen magnum variation in modern humans using 2D osteometry and 3D geometric morphometrics – A master thesis summary and research review. Mitteilungen der Berliner Gesellschaft für Anthropologie, Ethnologie und Urgeschichte 44 (2023).</em></p> <p> </p> <p> </p>
Dataset for MRes Thesis: Probabilistic Operator Learning for Climate Model Parameterisation
<p>This dataset was collated for use in experiments presented in the MRes Thesis "Probabilistic Operator Learning for Climate Model Parameterisation" submitted to the University of Cambridge.<br><br>All data included was generated by other researchers, this is simply a subset to allow easy reproduction of the experiments contained in the work above.</p> <p>The data for the Burgers' equation (<a href="../api/records/12529654/draft/files/burgers_data_R10.mat/content" target="_blank" rel="noopener noreferrer">burgers_data_R10.mat</a>) and the Darcy Flow (<a href="../api/records/12529654/draft/files/piececonst_r421_N1024_smooth1.mat/content" target="_blank" rel="noopener noreferrer">piececonst_r421_N1024_smooth1.mat</a>, <a href="../api/records/12529654/draft/files/piececonst_r421_N1024_smooth2.mat/content" target="_blank" rel="noopener noreferrer">piececonst_r421_N1024_smooth2.mat</a>) experiments were generated by Lu et al. (2022). Creative Commons Attribution Non Commercial Share Alike 4.0 International applies.</p> <p>The data for the Helmholtz (<a href="../api/records/12529654/draft/files/Helmholtz_inputs.npy/content" target="_blank" rel="noopener noreferrer">Helmholtz_inputs.npy</a>, <a href="../api/records/12529654/draft/files/Helmholtz_outputs.npy/content" target="_blank" rel="noopener noreferrer">Helmholtz_outputs.npy</a>) and Navier-Stokes (<a href="../api/records/12529654/draft/files/NavierStokes_inputs.npy/content" target="_blank" rel="noopener noreferrer">NavierStokes_inputs.npy</a>, <a href="../api/records/12529654/draft/files/NavierStokes_outputs.npy/content" target="_blank" rel="noopener noreferrer">NavierStokes_outputs.npy</a>) experiments were generated by de Hoop et al. (2022). Creative Commons Attribution 4.0 International applies.</p> <p>References:</p> <div> <div> <div>1. Lu L, Meng X, Cai S, Mao Z, Goswami S, Zhang Z, et al. A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data. Computer Methods in Applied Mechanics and Engineering. 2022 Apr 1;393:114778.</div> <div> </div> <div> <div> <div> <div>2. de Hoop MV, Huang DZ, Null EQ, Stuart AM. The Cost-Accuracy Trade-Off in Operator Learning with Neural Networks. JML. 2022 Jun;1(3):299–341.</div> </div> </div> </div> </div> </div> <p> </p>
Data repository - Bachelor thesis: mapping geodiversity
<p>This repository contains all data necessary to reproduce data of the BSc. thesis of Minqiu Korevaar [13576917]. Bsc. Future Planet Studies at the University of Amsterdam. Additionally, the thesis can be requested by contacting the author.</p>
Validation Videos - Robotic System for Reproducible Mobile Networking Experimentation in Anechoic Chambers (Master Thesis)
<p><strong>Note on Robot's Referential:</strong></p> <p>The robot's referential can be inferred in the recording via the "Safety Position." The safety position is the same for both the Digital Model (Gazebo) and the Real Robot (Joint Position = [0.0, -1.57, 1.57, 0.0, 0.0, 0.0]).</p> <p>In the safety position, the robot is approximately aligned with the X-axis, with its end-effector on the positive side of the axis. The end-effector faces perpendicular to the Y-axis. The positive Z-axis points upwards towards the ceiling.</p> <p> </p>
Gamification of Student Development Projects in Software Engineering Education - Master's Thesis Dataset
<p>This dataset contains all questionnaire results of the Master's Thesis <em>Gamification of Student Development Projects in Software Engineering Education</em>. The thesis will be linked to this set as soon as it is completed and publically available.</p> <p>In total, five questionnaires were conducted. To analyze users and requirements, a questionnaire with students and project supervisors was conducted. The implemented tool was evaluated using a pre-, mid-, and post-study questionnaire. This dataset contains a folder for each questionnaire including results in CSV format, results in HTML format, which is more human readable, and the questionnaire in PDF format. Due to the used survey tool, <em>lamapoll</em>, some parts of the files are in German. However, the questions and responses are all in English.</p> <p>Additionally, the scripts folder contains the Python scripts that were used to produce the graphics of the thesis. The Python libraries <code>matplotlib</code>, <code>pandas</code>, <code>scipy</code>, <code>numpy</code>, and <code>plot_likert</code> are required to run the scripts. Some of them use the two provided files <code>evalutation_concat_raw_excel.csv</code>, which is a concatenation of the results of the three evaluation questionnaires, and <code>sprint_stats.csv</code>, which contains statistics of the sprints relevant to the user study.</p>
FAU-Inf2/tree-measurements: PhD Thesis Version
<p>This repository contains the evaluation results of the research paper "P. Kreutzer, G. Dotzler, M. Ring, B. M. Eskofier, M. Philippsen: Automatic Clustering of Code Changes", as well as a simple Java tool to read in the data.</p> <p>It also contains the tree differencing evaluation of the PhD thesis "G. Dotzler: Learning Code Transformations from Repositories, Friedrich-Alexander University Erlangen-Nürnberg, 2018".</p>
Additional data of the PhD thesis "Merge-and-Shrink Abstractions for Classical Planning: Theory, Strategies, and Implementation"
<p>The original data set for the thesis "Merge-and-Shrink Abstractions for Classical Planning: Theory, Strategies, and Implementation" by Sievers, 2017, available under https://doi.org/10.5281/zenodo.1164137, accidentally did not include the data of one experiment, namely the raw data of the parsed data contained in "sota-symba-spmas-eval". It can be found in this data set.</p>
Experimental data from the PhD thesis "Counterexample-guided Cartesian Abstraction Refinement and Saturated Cost Partitioning for Optimal Classical Planning"
<p>The three data sets contain the raw experiment data, parsed values and basic reports for the three parts of the thesis. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and save space. The second directory (*-eval) contains "properties" file with all parsed values and an HTML report.</p>
Associated raw data to the PhD thesis: Design and evaluation of a camera-based indoor positioning system for forklift trucks
<p>This is a test data set for marker-based augmented reality algorithms used to locate ground conveyors in an industrial environment. It was recorded in the testing area of the chair fml at TUM to develop and evaluate algorithms for locating forklift trucks in my PhD thesis "Entwicklung und Evaluierung einer kamerabasierten Lokalisierungsmethode für Flurförderzeuge" (see https://mediatum.ub.tum.de/?id=1395267 available in German only).</p>
Simulation results for PhD thesis
<p>This is the dataset used to generate the results for PhD thesis titled: "Source Location Privacy in Wireless Sensor Networks Under Practical Scenarios: Routing Protocols, Parameterisations and Trade-Offs".</p>
Dataset Master Thesis Patrick Troxler
<p>Synthetic Dataset: synthetically generated flows using rain measurements from Samedan (2006 - 2008). Resolution of 10 minutes. Header describing the data is included directly.</p> <p>Oberengadin Dataset: measured flows from the Oberengadin sewer system and rain measurements from Samedan (2012). Resolution of 10 minutes. Header describing the data is included directly.</p> <p>GNU Octave files: generated using the codes available at https://bitbucket.org/PaTroETH/data_generation/src/default/</p>
ScienceDex guides
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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.