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Output dataset for the PRA1 of Assignment 'Tipología y ciclo de vida de los datos' of UOC's Master in Data Scince
<p>Dataset containing the concatenation of the results of a web scraping process for the portals of Idealista and Fotocasa for the area of Vilanova i la Geltrú, Barcelona, compiled on on 2022-04-19 and 2022-04-25 respectively. </p>
Smile aesthetics master chart for thesis
<p>Master chart of the thesis containing responses about the percentage of kind of facial form, tooth shape and angulation of maxillary teeth w.r.t horizontal plane present in the four temperaments.</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>
[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>
Quaker Boy Inc. Grand Old Master Box Turkey Call
Quaker Boy Inc. Grand Old Master Box Turkey Call Orchard park, NY Manufactured from Cherry (Lid) and Poplar (Bottom) wood. This was a test scan to see the capabilities of the Leo. Note the scanner had some difficulty resolving areas on the lid with black ink. Scanned with an Artec Leo Processed with Artec Studio 16, Zbrush, Blender, and XNormal Source: Objaverse 1.0 / Sketchfab
Obstructive Sleep Apnea Master Protocol GPIF: A Study of Tirzepatide (LY3298176) in Participants With Obstructive Sleep Apnea
ClinicalTrials.gov study NCT05412004. IPD Sharing: YES. Countries: 10. Publications: 5.
A Study of Suboptimally Controlled Participants Previously Taking Oral or Infusion DMDs for RMS (MASTER-2)
ClinicalTrials.gov study NCT03933202. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Metformin to Augment Strength Training Effective Response in Seniors (MASTERS)
ClinicalTrials.gov study NCT02308228. IPD Sharing: NO. Countries: 1. Publications: 5.
Chronic Pain Master Protocol (CPMP): A Study of LY3016859 in Participants With Diabetic Peripheral Neuropathic Pain
ClinicalTrials.gov study NCT04476108. IPD Sharing: YES. Countries: 2. Publications: 1.
Chronic Pain Master Protocol (CPMP): A Study of LY3016859 in Participants With Osteoarthritis
ClinicalTrials.gov study NCT04456686. IPD Sharing: YES. Countries: 2. Publications: 1.
Adaptive Decision Support for Addiction Treatment Master
ClinicalTrials.gov study NCT06799117. IPD Sharing: YES. Countries: 1. Publications: 1.
Chronic Pain Master Protocol (CPMP): A Study of LY3016859 in Participants With Chronic Low Back Pain
ClinicalTrials.gov study NCT04529096. IPD Sharing: YES. Countries: 2. Publications: 1.
COVID-19 Outpatient Pragmatic Platform Study (COPPS) - Master Protocol
ClinicalTrials.gov study NCT04662086. IPD Sharing: NO. Countries: 1. Publications: 3.
Weightlifting injuries in master athletes
Open the record for dataset details and reuse information.
Crossing extreme habitat boundaries: Jack-of-all-trades facilitates invasion but is eroded by adaptation to a master-of-one
Open the record for dataset details and reuse information.
Data from: A cryptic sex-linked locus revealed by the elimination of a master sex-determining locus in medaka fish
Open the record for dataset details and reuse information.
Master spatial file for for Spatial phylogenetics of the native California flora (Thornhill et al. BMC Biology)
Open the record for dataset details and reuse information.
MODIS/ASTER (MASTER) imagery and derived data in select neighborhoods of the greater Phoenix metropolitan area
A data collection campaign using the MODIS/ASTER airborne simulator (MASTER) was conducted in the greater Phoenix metropolitan area in July 2011 to collect visible through mid-infrared multispectral imagery. High resolution (7 m/pixel) land surface temperature products for day and night periods were calculated using the mid-infrared bands of data; surface reflectance, albedo, and Normalized Difference Vegetation Index (NDVI) products were calculated using the visible through shortwave infrared band data for 41 select neighborhoods. While the full MASTER dataset has been processed to at-sensor radiance, it did not include native geolocation data. As georeferencing the entire dataset was not possible with funds available, the processed data described above were extracted for the 41 spatially discrete Phoenix Area Social Survey neighborhoods within the MASTER flight boundary.
INNAG Master-Data Spreadsheet
<p>VIDARBHA EPIGRAPHY - Inscriptions Spreadsheet with Numerus Currens.</p> <p> </p> <p> </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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.