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394 results for “thesis”
PhD Thesis Portfolio
<p>Artistic portfolio for the PhD thesis "A Continually Receding Horizon: Making, performing, and improvising with semi-autonomous double bass feedback instruments".</p>
Candidate lists from bachelor thesis"Finding stars that are likely to be from accreted satellite galaxies"
<p>Candidate stars for the moving groups GSE, ED-2, MMH-1 and the Helmi streams, for more details see "Finding stars that are likely to be from accreted satellite galaxies" (lup.lub.lu.se/student-papers/).</p>
Digital Appendix of "Continuous Rationale Management" Doctoral Thesis
<p>Digital appendix to the doctoral thesis "Continuous Rationale Management".</p> <p>Kleebaum A <strong>Continuous Rationale Management</strong> PhD Thesis, Heidelberg University, Germany, 2023, <a href="https://doi.org/10.11588/heidok.00033470">https://doi.org/10.11588/heidok.00033470</a></p> <p>The appendix contains the following artifacts for the chapters:</p> <ul> <li>Chapter 4: Protocol of systematic mapping study and R script for analysis.</li> <li>Chapter 9: Anonymized decision knowledge documentation of the case study projects and R script for analysis.</li> <li>Chapter 10: Ground truth data used to evaluate the automatic text classification.</li> <li>Chapter 11: R script for analysis of usage frequencies from log files.</li> </ul> <p>The source code of the ConDec plug-ins is published as follows:</p> <ul> <li>ConDec Jira: <a href="https://doi.org/10.5281/zenodo.7953646">https://doi.org/10.5281/zenodo.7953646</a></li> <li>ConDec Confluence: <a href="https://doi.org/10.5281/zenodo.7948296">https://doi.org/10.5281/zenodo.7948296</a></li> <li>ConDec Bitbucket: <a href="https://doi.org/10.5281/zenodo.6866299">https://doi.org/10.5281/zenodo.6866299</a></li> <li>ConDec Eclipse: <a href="https://doi.org/10.5281/zenodo.7958350">https://doi.org/10.5281/zenodo.7958350</a></li> <li>ConDec Visual Studio Code: <a href="https://doi.org/10.5281/zenodo.7954331">https://doi.org/10.5281/zenodo.7954331</a></li> <li>ConDec Slack: <a href="https://doi.org/10.5281/zenodo.7955051">https://doi.org/10.5281/zenodo.7955051</a></li> </ul> <p>The respective binary releases are provided in GitHub: <a href="https://github.com/cures-hub">https://github.com/cures-hub</a></p>
Generalised Kerr Microcomb Physics in Synchronously Driven Systems: Thesis Simulation Programs
<p>This depot inludes Matlab programs and functions for many of the figures and examples presented in the 2023 thesis of Miles H. Anderson from EPFL. Every script file is intended to be runable as is. The programs are tested with Matlab 2020b, and some of them make use of the Signal Processing package.</p> <p>A guide on the program structure is given in the Appendix chapter of the thesis, which accompanies the example programs found in the Appendix Demos folder.</p> <p>Each main folder corresponds to a chapter in the thesis.</p> <p>Some of the programs might contain errors and unexplained sections. Earnest inquiries about the programs might be answered by Miles at his email: miles.anderson@epfl.ch.</p> <p>Supervisor: Prof. Tobias J. Kippenberg, Laboratory of Photonic and Quantum Measurements (LPQM) at EPFL.</p>
Summers, J. Doctoral thesis (2023) – Supplementary A
<p>Supplementary material A for Doctoral thesis 'Evolution of developmental regulation in a simple multicellular life cycle' (2023).</p> <p>Joanna Amelia Summers, Max Planck Institute for Evolutionary Biology (officially: Christian-Albrecht University of Kiel).</p>
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>
Supplemental datasets for Chapter 2 of Thesis
<p>Dataset S01. Mutants with significant fitness differences identified from randomization tests, grouped according to function, metabolism, and direction of fitness differences.</p> <p>Dataset S02. Normalized transposon insertions reads that are curated to the central 90% of coding regions.</p> <p>Dataset S03. Mean relative fitness (W) of mutants from four biological replicates. P-values were calculated for 10000 permutations of randomization tests, and were adjusted using a Benjamini-Hochberg correction for multiple comparisons.</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>
Supplementary material 1 for Thesis Chapter 2 - An obligate aerobe hybridises hydrogen fermentation and carbon storage to adapt to hypoxia
<p>Supplementary material for paired comparative metabolomics and proteomics on <em>Mycobacterium smegmatis </em>mc<sup>2</sup>155 during hypoxia, as part of chapter 2 for the thesis "Biochemistry and physiology of mycobacterial adaptations to energy starvation".</p> <p>Description below is identical to that provided in 'Summary.docx'. </p> <p>Proteomics_analysis.xlsx</p> <p>Includes raw and annotated data for comparative proteomics experiments for chapter 2.</p> <p>The tab ‘Annotated comparisons’ contains fold change and p values for the comparisons for each protein from <em>Mycobacterium smegmatis </em>mc<sup>2</sup>155 derived from LFQ-Analyst. Functional annotations are derived from KEGG pathways and modules, which utilise the spreadsheets in ‘MSMEG gene annotation’ ‘Protein ids to KEGG pathway’ and ‘KEGG Pathway and Modules’ to link KEGG annotations to MSMEG_XXXX gene identifiers and MSMEG_XXXX to Uniprot ID. Output from LFQ-Analyst is provided in the ‘Full_dataset’, ‘Imputed_matrix’ and ‘Original_matrix’ tabs.</p> <p>Data provided by the Monash Proteomics and Metabolomics Facility for upload into LFQ-analyst are provided as the ‘combined_protein.tsv’ and ‘LFQ-Analyst_experimental_design.txt’.</p> <p> </p> <p>Metabolism_analysis.xlsx</p> <p>Includes annotated data for comparative metabolomics experiments for chapter 2. Within the spreadsheet, TR refers to transition, ST refers to stationary phase and EXP refers to exponential phase. The tabs ‘TRvsEXP’, ‘STvsTR’ and ‘STvsEXP’ contain fold change and p values for each metabolite detected for each comparison. The remaining tabs categorise the metabolites based on KEGG database and IDEOM annotations. For broader categories (‘Lipid metabolism’,’ Carbohydrate metabolism’, ‘Cofactor metabolism’, ‘Nucleotide metabolism’, ‘Amino acid metabolism’ and ‘Peptides’ tabs), annotations were derived directly from filtering the ‘Map’ column of ‘Comparisons’ tab of the IDEOM worksheet (IDEOM_analysis.xlsb). Screenshots are pasted into each tab to show the filtering settings. The remaining tabs comprise narrower categories which were manually annotated with reference to KEGG pathways and maps, and also include rows corresponding to the proteomics data for these categories, so the proteomics and metabolomics data can be interpreted together. The ‘Proteomics’ tab contains the proteomics data referenced by these tabs, which is a copy of the ‘Annotated comparisons’ tab from the ‘Proteomics_analysis.xlsx’ file. A value of ‘N’ indicates the metabolite or protein (at least according to the name in the same row) was not found in these datasets.</p> <p>The IDEOM worksheet (IDEOM_analysis.xlsb) was provided by the Monash Proteomics and Metabolomics Facility and was used for further analysis and for annotations. ‘Data_for_MA_no_normalization.csv’ was also provided by the Monash Proteomics and Metabolomics Facility for upload into Metaboanalyst (https://www.metaboanalyst.ca/).</p>
DNA alignments for MSc Thesis, University of Edinburgh.
<p>DNA alignments compiled for MSc Thesis, University of Edinburgh. Myanmar Ancestral Area Reconstruction.</p>
PHYLIP format text files for BioGeoBEARS analysis. MSc thesis, University of Edinburgh.
<p>PHYLIP format text files for BioGeoBEARS analysis for each dataset. MSc thesis, University of Edinburgh.</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>
Supplementary online material for PhD thesis manuscript Proteomic approaches to the characterization of tolerance and virulence in bacterial biofilms
<p>These data belong to a PhD thesis manuscript Proteomic approaches to the characterization of tolerance and virulence in bacterial biofilms.</p> <p> </p> <p><strong>Supplementary online material 1</strong></p> <p>Theoretical proteome of <em>Staphylococcus aureus </em>ATCC 25923.</p> <p><strong>Supplementary online material 2</strong></p> <p>Theoretical proteome of <em>Pseudomonas aeruginosa </em>PAO1.</p> <p><strong>Supplementary online material 3</strong></p> <p>MaxQuant (v. 1.6.1.0) output of exoproteomic analysis carried out on a dual-species biofilm model.</p> <p><strong>Supplementary online material 4</strong></p> <p>MaxQuant (v. 1.6.1.0) output of surfaceomic analysis carried out on a dual-species biofilm model</p> <p><strong>Supplementary online material 5</strong></p> <p>Curated MaxQuant (v. 1.6.1.0) output of exoproteomic analysis carried out on a dual-species biofilm model.</p> <p><strong>Supplementary online material 6</strong></p> <p>Curated MaxQuant (v. 1.6.1.0) output of surfaceomic analysis carried out on a dual-species biofilm model.</p> <p><strong>Supplementary online material 7</strong></p> <p>Protein quantification of valid identifications in LC-MS/MS analysis of <em>Staphylococcus aureus </em>and <em>Pseudomonas aeruginosa </em>dual-species biofilms.</p>
Directional Room Impulse Response Measurement - PhD Thesis Data
<p>Room acoustic simulation result database for the PhD thesis "Directional Room Impulse Response Measurement"</p>
Datasets from PhD thesis: "Integrating classical and DNA-based approaches to advance the field of paleolimnology: Case studies of a warm monomictic lake"
<p>The general objectives of my PhD were to evaluate the advantages and limitations to using DNA-based methods in paleolimnology and to evaluate the ecological trajectory of Cultus Lake, British Columbia, using both classical paleolimnological and DNA-based approaches. The datasets therein were generated for my PhD thesis.</p> <p>Firstly, a 36-month sediment trap time series was developed to evaluate which DNA taxa can be deposited in the sediments and potentially be used as indicator taxa in paleolimnological studies. Congruence between morphological and DNA identification in water and sediment trap samples was also assessed specifically for diatoms and crustaceans. The data generated include mass accumulation rate and carbon accumulation rate, carbon and nitrogen percentage, morphological counts for diatoms and cladocerans in the sediment traps, DNA quantity for water and sediment trap samples, metabarcoding of a fragment of the V7 region of the 18S rRNA gene.</p> <p>Secondly, a multi-proxy paleolimnological study was developed to evaluate the ecological changes in Cultus Lake and the potential drivers of the changes. From this study, sedimentary delta 15N, delta 13C, percentage of carbon, percentage of nitrogen, pigments were measured and diatoms and cladoceran remains were counted and identified from a sediment core collected in 2008. The data for this project are part of a publication accepted in May 2020 in Journal of Paleolimnology: Gauthier et al (In press) Ecological dynamics of a peri-urban lake: a multi-proxy paleolimnological study of Cultus Lake (British Columbia) over the past ~200 years. doi: 10.1007/s10933-020-00147-9.</p> <p>Thirdly, a paleo-genetic project was developed to compare the ecological changes observed with sedimentary DNA with those observed with classical paleolimnological approaches. The same fragment of the V7 region of the 18S rRNA gene was targeted, percentage of carbon and nitrogen were also measured as well as DNA quantity for all intervals subsampled in a sediment core collected in 2017.</p> <p>The datasets usually come with a description of the data and meaning of abbreviations whether needed. DNA datasets will be publicly released upon acceptance of the manuscripts. For more information about the projects, methods and results, please refer to my PhD thesis published at McGill University. The number in front of the name of each file represents the chapter for which the data were generated.</p>
Citation network nodelist, edgelist, and visualization (UNIGE MA Thesis)
<p>The data in this deposit were used in the <a href="https://archive-ouverte.unige.ch/unige:140928">author's Thesis</a> for the degree of Master of Arts in Philosophy with Specialization in the Philosophy of Science, under the supervision of Prof. Marcel Weber (Department of Philosophy, University of Geneva, Switzerland).</p> <p>This deposit contains three files: (i) a list of nodes; (ii) a list of edges; and (iii) a digital image of the network as it appears in the Appendix A of the Thesis. The lists of nodes and edges contained here were manually built by the author, and the visualization was obtained using the software Gephi.</p> <p>The nodes represent specific texts related to the historical development of Expected Utility Theory (as explained in section 3.1 of the Thesis), corresponding to the texts registered in the Citation Network Texts section of the References of the Thesis. The data and visualization in this deposit use the convention "authorsORIGINALYEAR". For example, Pareto (1909/1979) is represented as "pareto1909", and Safra et al. (1990a) as "safra.etal1990a". The color of each node depends on the subsection of the historical description (section 3.2) they appear in, corresponding to the categories of the "topic" column of the nodelist. And their sizes are proportional to how many times they were mentioned by others in the network.</p> <p>The edges represent citations between texts and their colors represent <em>mention types</em> (as defined in section 3.1), such that agreement are in green, disagreements are in light blue, and neutral mentions are in light gray. The reasons for the final categorization of potentially unclear mention types are noted in the "reasons" column.</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>
Data collection for Tsuji et al., 2020, Microbial ecology of phototrophs in Boreal Shield lakes, Chapter 3: Biogeography and activity of chlorophototrophs in the ferruginous water columns of Boreal Shield lakes (PhD thesis)
<p>This data collection includes supplementary or raw data files related to Chapter 3 of the PhD thesis of Jackson M. Tsuji, "Biogeography and activity of chlorophototrophs in the ferruginous water columns of Boreal Shield lakes" (in "Microbial ecology of phototrophs in Boreal Shield lakes"). Specifically, the following files are included:</p> <ul> <li>ASV_table_non_rarefied_counts.tsv.gz -- non-rarefied ASV table containing 16S rRNA gene amplicon data presented in this study as raw counts. Beyond the index column and sample columns, two additional columns, "Consensus.Lineage" and "Sequence" are included in the table. These columns include the taxonomic classification of the ASV (according to Silva) and the ASV sequence, respectively.</li> <li>ASV_table_non_rarefied_percent.tsv.gz -- same as above, but the data are normalized within each sample and expressed as percentages (i.e., sum to 100%).</li> <li>ASV_table_rarefied_counts.tsv.gz -- same as "ASV_table_non_rarefied_counts.tsv.gz", except that data is rarefied to 12,000 sequences per sample. Five samples were dropped due to having <12,000 sequences.</li> <li>ASV_table_rarefied_percent.tsv.gz -- same as above, but the data are normalized within each sample and expressed as percentages (i.e., sum to 100%).</li> <li>MAG_abundances_to_unassembled_reads.tsv.gz -- table like an ASV table showing the relative abundances (expressed as percentages) of metagenome-assembled genomes within metagenomes. Aside from the index column and sample columns, additional columns are included to provide the taxonomic classification of the MAGs (based on the Genome Taxonomy Database) and the CheckM statistics of the MAGs. Relative abundances of MAGs in a metagenome are calculated as the number of mapped reads to the MAGs from the given metagenome divided by the total number of unassembled metagenome reads for that metagenome (times 100%).</li> <li>MAG_abundances_to_assembled_reads.tsv.gz -- same as above, except that relative abundances are divided by the total number of unassembled metagenome reads for that metagenome that mapped to that metagenome's assembled contigs.</li> <li>core_sample_metadata.tsv -- table of core physico-chemical and geographic metadata for the samples in this study (used to build biplots presented in the chapter). Note that "nd" means "no data available", and any measurements below detection limits have been set to 0. A limited number of values were inferred from other sampling time points -- these are noted in the table for TDFe measurements, and in addition, the light attenuation coefficient for Lake 373 in Sept. 2017 was inferred from the Sept. 2016 coefficient due to no light data being available for Sept. 2017 samples.</li> <li>metadata_descriptions.tsv -- descriptions of all metadata columns in the above file.</li> </ul> <p> </p>
Persian Thesis Preparation
<p>Persian Thesis File Preparation</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.