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.
3
datasets available to search
ShareScore release 0.9.0
Dataset results
3 results for “GDD”
UA - Gaussian Depth Disc (GDD dataset)
<div> <div>Dear reader,</div> <br> <div>Welcome! You must be an avid profilometry person to be interested in downloading our dataset.</div> <div>Before you start tinkering with the dataset package please do install the requirements.txt libraries for a more easy step into operating this system.</div> <div>We hope to have made the hierarchy of the package as clear as possible! Also note that this system was written in VScode.</div> <div>The Matlab script that creates the surface has been included for if you yourself would like to print one out and do in depth research!</div> <div>Find your way to the examples folder, there you can find "entire_dataset". This folder contains a script to divide the original h5 file (480x640) containing all data</div> <div>into whatever sub-options you'd like (had to be made int16 as to not exceed to 50GB limit of Zenodo, you are more than welcome to reach out for the 32int format!). We included a downsampled format of 120x160 (int32) as this makes the file size more managable for pc's with not enough RAM. An example divided dataset has already been given namely the 80/20 division of respectively training and validation data in the "example_dataset" folder, of the downsampled dataset.</div> <div>In the folder models you will find the two models mentioned in the publication related to this dataset. These two were published with the dataset since they</div> <div>had either the highest performance on the training and validation dataset (DenseNet) or on the random physical object test (CNN).</div> <div>A training script is included (training_script.py) to show you how these models were created, so if you wish to add new models to the networks.py file in the classes folder, you can!</div> <div>The validation jupyter notebook contains two visualisation tools to quickly and neatly show the performance of your model on the recorded dataset.</div> <div>Lastly to test on the recorded objects you can run the "test_physical_data.py" and "test_physical_data_sine_deivation.py" scripts.</div> <br> <div>We hope this helps you in your research and we hope it further improves any and all research within the single shot profilometry field! 😊</div> <br> <div>Kind regards,</div> <br> <div>Rhys Evans,</div> <div>InViLab,</div> <div>University of Antwerp, Belgium</div> </div>
CHclim25 - growing degree days (gdd)
<p>Growing degree days are calculated from CHclim25 average temperature layers for 3 base temperatures, 0°C, 3°C and 5°C. Monthly and yearly current average (1981-2010) and future average (2020-2049, 2045-2074, and 2070-2099)<strong> </strong>layers can be downloaded from separate zip files. </p> <p>Future layers are based on the transient daily time series of gridded climate scenarios of temperature at 0.02°D (~2.2 km) provided by the <a href="https://www.nccs.admin.ch/nccs/en/home/climate-change-and-impacts/swiss-climate-change-scenarios/ch2018---climate-scenarios-for-switzerland.html">CH2018 initiative</a>. We calculated future climatic layers for 4 GCMs (HADGEM, ECEARTH, MPIESM, and IPSL), 3 time slices (2020-2049, 2045-2074, and 2070-2099) and 3 representative concentration pathways (RCP 2.6, 4.5 and 8.5)</p> <p>The layer files are stored in compressed GeoTIFF format with the “deflate” algorithm with option “predictor2” from the GDAL. This format has a high compression ratio but allows direct import in most GIS softwares. All the maps are projected in the Swiss coordinate system CH 1903+ LV95 (epsg:2056) with a resolution of 25x25m using the extent of the digital height model DHM25 of the Swiss office for topography (swisstopo).</p>
IOP Reduction and Complications of Polymethyl Methacrylate GDD on Glaucoma Patient
ClinicalTrials.gov study NCT04052165. IPD Sharing: NO. Countries: 1. Publications: 9.
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.