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10 results for “fellowship”
Pre-processed (in Detectron2 and YOLO format) planetary images and boulder labels collected during the BOULDERING Marie Skłodowska-Curie Global fellowship
<p>This database contains 4976 planetary images of boulder fields located on Earth, Mars and Moon. The data was collected during the BOULDERING Marie Skłodowska-Curie Global fellowship between October 2021 and 2024. The data was already splitted into train, validation and test datasets, but feel free to re-organize the labels at your convenience. </p> <p>For each image, all of the boulder outlines within the image were carefully mapped in QGIS. More information about the labelling procedure can be found in the following manuscript (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a>). This dataset differs from the previous dataset included along with the manuscript <a href="https://zenodo.org/records/8171052">https://zenodo.org/records/8171052</a>, as it contains more mapped images, especially of boulder populations around young impact structures on the Moon (cold spots). In addition, the boulder outlines were also pre-processed so that it can be ingested directly in YOLOv8.</p> <p>A description of what is what is given in the README.txt file (in addition in how to load the custom datasets in Detectron2 and YOLO). Most of the other files are mostly self-explanatory. Please see previous dataset or manuscript for more information. If you want to have more information about specific lunar and martian planetary images, the IDs of the images are still available in the name of the file. Use this ID to find more information (e.g., M121118602_00875_image.png, ID M121118602 ca be used on https://pilot.wr.usgs.gov/). I will also upload the raw data from which this pre-processed dataset was generated (see <a href="https://zenodo.org/records/14250970">https://zenodo.org/records/14250970</a>).</p> <p>Thanks to this database, you can easily train a Detectron2 Mask R-CNN or YOLO instance segmentation models to automatically detect boulders. </p> <p><strong>How to cite:</strong></p> <p>Please refer to the "how to cite" section of the readme file of <a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth.</a></p> <p><strong>Structure:</strong></p> <pre><code>. └── boulder2024/ ├── jupyter-notebooks/ │ └── REGISTERING_BOULDER_DATASET_IN_DETECTRON2.ipynb ├── test/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── train/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── validation/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── detectron2_inst_seg_boulder_dataset.json ├── README.txt ├── yolo_inst_seg_boulder_dataset.yaml</code></pre> <p> </p> <pre><code>detectron2_inst_seg_boulder_dataset.json</code></pre> <p>is a json file containing the masks as expected by Detectron2 (see <a href="https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html">https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html</a> for more information on the format). In order to use this custom dataset, you need to register the dataset before using it in the training. There is an example how to do that in the jupyter-notebooks folder. You need to have detectron2, and all of its depedencies installed. </p> <pre><code>yolo_inst_seg_boulder_dataset.yaml</code></pre> <p>can be used as it is, however you need to update the paths in the .yaml file, to the test, train and validation folders. More information about the YOLO format can be found here (<a href="https://docs.ultralytics.com/datasets/segment/">https://docs.ultralytics.com/datasets/segment/</a>).</p>
Raw planetary images and boulder labels data (as shapefiles) collected during the BOULDERING Marie Skłodowska-Curie Global fellowship
<p>This database contains 64 large images of craters on the lunar and martian surfaces and 3 images of boulder fields on Earth (see manuscript <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a> for more information on those terrestrial locations). The data was collected during the BOULDERING Marie Skłodowska-Curie Global fellowship between October 2021 and 2024.</p> <p>For each image, the boulder outlines within specific tiles within the image were carefully mapped in QGIS. More information about the labelling procedure can be found in the following manuscript (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a>). This dataset differs from the previous dataset included along with the manuscript <a href="https://zenodo.org/records/8171052">https://zenodo.org/records/8171052</a>, as it contains more mapped images, especially of boulder populations around young impact structures on the Moon (cold spots). </p> <p>For each location, you will find a raster with a .tif format, and three shapefiles:</p> <ul> <li> <p>a boulder-mapping file, which is the manually digitized outline of boulders.</p> </li> <li> <p>a tiles-completely-mapped file, which depicts the patches/tiles/windows on which the boulder mapping has been conducted.</p> </li> <li> <p>a global-tiles file, which shows all of the image patches/tiles/windows (pick the term you are the most familiar with) within a raster.</p> </li> </ul> <p>In addition you will find .pkl (which stands for pickle), which contains some information about the patches/tiles/windows if you would need to clip those windows out from the original raster. You can find more information in the way we process this raw data into a format which can be ingested in a deep learning model (see <a href="https://zenodo.org/records/14250874" target="_blank" rel="noopener">https://zenodo.org/records/14250874</a>) in the two following github repositories (<a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth</a> and <a href="https://github.com/astroNils/MLtools/tree/main" target="_blank" rel="noopener">https://github.com/astroNils/MLtools</a>). If you don't plan in adding more training data, you can directly used the pre-processed database (see <a href="https://zenodo.org/records/14250874" target="_blank" rel="noopener">https://zenodo.org/records/14250874</a>).</p> <p>There are multiple locations/images per planetary body. Cold spots are located on the Moon, but they are saved in a folder of their own. </p> <p>Note that the cold spots boulder mapping shapefiles are partially manually mapped, and partially originating from predictions made from a deep learning model (which explains the outline of boulders are predicted within one pixel).</p> <p><strong>How to cite:</strong></p> <p>Please refer to the "how to cite" section of the readme file of <a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth.</a></p> <p><strong>Structure:</strong></p> <pre><code>. └── raw_data/ ├── coldspots/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── earth/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── mars/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif └── moon/ └── image_name/ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp └── raster/ └── <image_name>.tif</code></pre>
Dataset: Fokker F-28 Fellowship typical fuselage surface model for crashworthiness
<p>The Fokker F-28 Fellowship aircraft is a twin-engined jetliner developed by the Dutch aircraft manufacturer Fokker in the 1960s, and introduced in 1969. It has a wingspan ranging between 23.6 m and 25.1 m, and a MTOW between 29 480 kg and 33 110 kg, depending on the variant. The fuselage outer diameter is about 3.3 m, with passengers sitting in a 2-3, single aisle configuration. A picture of the F-28 can be found at https://www.airliners.net/photo/Piedmont-Airlines/Fokker-F-28-1000-Fellowship/0451713/L.</p> <p><br> In the late 1990s, NASA purchased a complete, retired F-28 aircraft, plus three additional fuselage sections; to test them as part of the ‘Systems Approach for Crashworthiness’ program:</p> <blockquote> <p>K. E. Jackson et al. Finite Element Simulations of Two Vertical Drop Tests of F-28 Fuselage Sections. Tech. rep. Hampton, Virginia, USA: NASA Langley Research Center, Feb. 2018. URL: http://www.sti.nasa.gov.</p> </blockquote> <p>Subsequently, in 2001, a vertical drop test of one of the three sections, the typical section, was performed. The drop test that took place at the NASA LandIR research facility was carried out in a ‘clean configuration’, meaning that no luggage was present in the test section, but only 20x34 kg (75 lb) dummy masses representing the passengers. The section impacted the ground, a concrete surface, at a ver-<br> tical speed of 9.14 m s−1. The physical drop test was compared to an MSC.Dytran™ simulation that has been simultaneously developed, for validation purposes. The finite element model was developed by modifying an existing one of a similar aircraft, the Fokker 70:</p> <blockquote> <p>K. H. Lyle et al. “Application of Probability Methods to Assess Airframe Crash Modeling Uncertainty”. In: Journal of Aircraft 44.5 (Sept. 2007), pp. 1568–1573. ISSN: 15333868. DOI: 10.2514/1.27722. URL: https://arc.aiaa.org/doi/10.2514/1.27722.</p> </blockquote> <p>The model includes a four-frames section of the fuselage portion forward of the wings. Structural features, such as frames and shear<br> clips are represented using a combination of shell and beam elements. The skin stiffeners are also represented by beam elements.<br> </p> <p>The present data set includes CAD files (STP files) and Abaqus input files (INP) that were used to obtain validated acceleration measurements, when compared to NASA experiments.</p> <p> </p> <p>Description; INP file; STP file</p> <p>4 frames F28 section, no friction, no damage; Abaqus_20220906_F28_4FR_JOB1; CAD_F28_4FR;<br> 5 frames F28 section, no friction, no damage; Abaqus_20220906_F28_5FR_JOB1; CAD_F28_5FR;<br> 6 frames F28 section, no friction, no damage; Abaqus_20220906_F28_6FR_JOB1; CAD_F28_6FR;<br> 4 frames F28 section, yes friction & damage; Abaqus_20221017_F28_4FR_JC_F; CAD_F28_4FR;<br> 5 frames F28 section, yes friction & damage; Abaqus_20221011_F28_JCD_Baseline; CAD_F28_5FR;<br> 5 frames F28 oval section, e = 0.20, yes friction and damage; Abaqus_20221018_F28_Oval_F_0.20; CAD_F28_5FR_e020;<br> 5 frames F28 oval section, e = 0.40, yes friction and damage; Abaqus_20221018_F28_Oval_F_0.40; CAD_F28_5FR_e040;<br> 5 frames F28 oval section, e = 0.60, yes friction and damage; Abaqus_20221018_F28_Oval_F_0.60; CAD_F28_5FR_e060;<br> 5 frames F28 oval section, e = 0.70, yes friction and damage; Abaqus_20221018_F28_Oval_F_0.70; CAD_F28_5FR_e070;<br> 5 frames F28 oval section, e = 0.75, yes friction and damage, baseline floor beam height (500mm from keel); Abaqus_20221018_F28_Oval_F_0.75; CAD_F28_5FR_e075_FBh500;<br> 5 frames F28 oval section, e = 0.75, yes friction and damage, FB height = 550mm; Abaqus_20221025_F28_Floor_550; CAD_F28_5FR_e075_FBh550;<br> 5 frames F28 oval section, e = 0.75, yes friction and damage, FB height = 600mm; Abaqus_20221025_F28_Floor_600; CAD_F28_5FR_e075_FBh600;<br> 5 frames F28 oval section, e = 0.75, yes friction and damage, FB height = 650mm; Abaqus_20221025_F28_Floor_650; CAD_F28_5FR_e075_FBh650;</p>
New Life Fellowship Terracotta Panel #2
Existing terroctta panel at New Life Fellowship Church, the former Elks Lodge. Two4Design is completting a facade restoration, with the street facade phase one of four. Source: Objaverse 1.0 / Sketchfab
Support through the Office of Awards and Fellowships
<p>ERAU’s Office of Awards & Fellowships supports all ERAU students and alumni who seek external recognition and support through fellowships, scholarships, and awards. The department can help you find these life-changing opportunities and guide you through the application process. In this workshop, we will hear from the Office of Awards & Fellowships about how they work with ERAU students (including workshops, one-on-one meetings, and more) and learn about current opportunities that may be of interest to aspiring and active undergraduate researchers. We will also discuss how to share your experiences as a student researcher in personal statements and interviews, and provide tips for building strong relationships so that you can secure strong recommendation letters.</p>
Supplementary material 1 from: Agosti D (2016) Shuttleworth Fellowship Application 2016. Research Ideas and Outcomes 2: e11014. https://doi.org/10.3897/rio.2.e11014
Curriculum Vitae
Face-to-face panel meetings versus remote evaluation of fellowship applications
<p>The data shared here has been used in the following paper: https://bmjopen.bmj.com/content/11/5/e047386.full</p> <ul> <li>proposal_id: an anonymise identifier for the proposal</li> <li>panel_id: an id referencing to the panel in which the proposal was evaluated</li> <li>referee1: grade given by referee number 1, on a 6-point scale (A, AB, B, BC, C, D)</li> <li>referee2: grade given by referee number 2, on a 6-point scale (A, AB, B, BC, C, D)</li> <li>triage: triage group of the proposal (F: funded, R: rejected, D: discussion)</li> <li>final_decision_ref: final decision using the reference method (F: funded, R: rejected)</li> <li>random_selection_ref: random selection using the reference method (TRUE/FALSE)</li> <li>final_decision_test_2r: final decision using the test method with two referees (F: funded, R: rejected)</li> <li>random_selection_test_2r: random selection using the test method with two referees (TRUE/FALSE)</li> <li>referee3: : grade given by referee number 3, on a 6-point scale (A, AB, B, BC, C, D)</li> <li>final_decision_test_3r: final decision using the test method with three referees (F: funded, R: rejected)</li> <li>random_selection_test_3r: random selection using the test method with three referees (TRUE/FALSE)</li> </ul>
New Life Fellowship Terracotta Panel #3
One of four existing terracotta panels on the New Life Fellowship Church, the former Elks Lodge. Two4Design is completting a facade restoration, with the street facade phase one of four. Source: Objaverse 1.0 / Sketchfab
Master Preceptor Fellowship Program: A Pilot Study
ClinicalTrials.gov study NCT03918122. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Cognitive Behavioral and Faith Fellowship to Improve Thy Health
ClinicalTrials.gov study NCT06292637. IPD Sharing: NO. Countries: 1. Publications: 0.
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
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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
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