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.
9
datasets available to search
ShareScore release 0.9.0
Dataset results
9 results for “Weight Space”
Plant aboveground biomass dry weight record for Space for Time plots in PIE LTER.
Aboveground biomass measurements were conducted annually near peak biomass to evaluate aboveground plant production and determine differences in relation to other biotic and abiotic factors. In a 0.053 m2 plot, aboveground biomass was clipped to the soil surface at Space For Time plots, dried, and weighed to capture dry weight.
A dataset of 150000 terminal weighted projective spaces
<p><strong>Weighted projective spaces with at worst terminal singularities</strong></p> <p>A dataset of 150000 randomly generated weighted projective spaces with at worst terminal singularities, in dimensions 1 to 10.</p> <p>The data consists of the plain text files "rank_1_dim_N.txt" where N, which is the dimension of the weighted projective space, is in the range 1 to 10. Each line of the file is a sequence of weights of length N+1. For example, the first line of "rank_1_dim_4.txt" is:</p> <p>[1,2,5,14,21]</p> <p>and this corresponds to the 4-dimensional weighted projective space P(1,2,5,14,21).</p> <p>For details, see the paper:</p> <p>"Machine learning the dimension of a Fano variety", Tom Coates, Alexander M. Kasprzyk, and Sara Veneziale, <em>Nature Communications</em>, <strong>14:</strong>5526 (2023). doi:10.1038/s41467-023-41157-1</p> <p>Magma code capable of generating this dataset is in the file "generate_rank_1.m".</p> <p>If you make use of this data, please cite the above paper and the DOI for this data:</p> <p>doi:10.5281/zenodo.5790079</p>
Model Zoo Dataset Samples for Scalable Weight Space Learning
<p>This dataset contains small versions of model zoo datasets for our ICML 2024 paper "Towards Scalable and Versatile Weight Space Learning". These datasets are intended for testing and rapid pipeline evaluation of the code in the <a title="https://github.com/HSG-AIML/SANE" href="https://github.com/HSG-AIML/SANE">corresponding </a><a href="https://github.com/HSG-AIML/SANE">repository</a>. For full model zoos, please see <a href="modelzoos.cc">modelzoos.cc</a>.</p>
Model Weights for "Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting"
<p>Model weights for use with the SatIQ fingerprinting models used in the paper “Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting”. The models are used to authenticate Iridium satellites from high sample rate message headers.</p> <p>The data collection and model code can be found at the following URL: <a href="https://github.com/ssloxford/SatIQ">https://github.com/ssloxford/SatIQ</a></p> <p>The preprint is available on arXiv at the following URL: <a href="https://arxiv.org/abs/2305.06947">https://arxiv.org/abs/2305.06947</a></p> <p>The final trained model is <code>ae-triplet-final.h5</code>. The others are from the additional experiments and analyses described in the paper, and are included for completeness.</p> <p>When using this data, please cite the following paper: “Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting”. The BibTeX entry is given below:</p> <pre><code>@inproceedings{smailesWatch2023, author = {Smailes, Joshua and K{\"o}hler, Sebastian and Birnbach, Simon and Strohmeier, Martin and Martinovic, Ivan}, title = {{Watch This Space}: {Securing Satellite Communication through Resilient Transmitter Fingerprinting}}, year = {2023}, publisher = {Association for Computing Machinery}, booktitle = {Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security}, location = {Copenhagen, Denmark}, series = {CCS '23} }</code></pre> <p> </p>
Respiratory-triggered MRCP acquisition at 3T using a T2-weighted TSE (3D SPACE) sequence for Deep Learning-based reconstruction of MRCP
<h1>Description</h1> <p>This dataset is the sample data for MRCP_DLRecon (<a href="https://github.com/JinhoKim46/MRCP_DLRecon">GitHub</a>). <br>Place this dataset in the "Sample_data/" directory along with the "dataset.csv" file. </p> <h1>Data</h1> <p>The provided 3D MRCP data were acquired at 3T (Skyra, Siemens Healthineers AG, Erlangen) using the 3D-SPACE (3D T2w TSE) sequence for a single healthy volunteer. We provide two 2x and one 6x 3D MRCP data to ensure various training and testing scenarios. Each data in the HDF5 format contains the following structures:</p> <ul> <li>Datasets <ul> <li><strong>grappa</strong>: target data (y * x *<em> </em>slice)</li> <li><strong>kdata_raw</strong>: Raw <em>k</em>-space data (x2 or x6) (nCoil * PE * RO * slice)</li> <li><strong>kdata_fs</strong>: Fully-sampled k-space data from <strong>kdata_raw</strong> using GRAPPA (nCoil ×× PE ×× RO ×× Slice)</li> <li><strong>sm_espirit</strong>: ESPIRiT-based sensitivity maps (nCoil * y * x * slice)</li> </ul> </li> </ul> <h1>Citation</h1> <p>Please cite the following <a href="https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.70002" target="_blank" rel="noopener">paper</a> if this dataset is helpful for your research :)</p> <blockquote> <p>Kim, J., Nickel, M. and Knoll, F. (2025), Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data. NMR in Biomedicine, 38: e70002. https://doi.org/10.1002/nbm.70002 </p> </blockquote>
Raw k-space data for free-breathing diffusion weighted sequence
Open the record for dataset details and reuse information.
Distribution. Angola, DR Congo, Malawi, Mozambique, Tanzania, and Zambia. Description. Head-body 46:5-47-8 cm (males), 44-45-5 cm (females), tail 40-43 cm (males), 38-39 cm (females), hindfoot 8:7-9-8 cm (males), 8-9 cm (females), ear 4-7-5-4 cm (males), 5-1-5-8 cm (females); weight 1-3-2 kg. The coat color is pale ocher, with brownish or grayish tones; melanistic individuals are quite common. The throat and chest are blackish, and the ventral pelage varies from creamy white to dirty white. The stripes and spots on the body vary from different hues of brown to black. The nuchal stripes run as two parallel lines from the nape to the shoulders, where they diverge and enlarge towards the elbows; they are not so conspicuously marked as in other genet species. Below them, a pair of thinner stripes and small spots are scattered on the shoulders and sides of the neck. A third pair of thinner, parallel stripes runs down the neck between the nuchal stripes, extending to about one fourth of the mid-dorsal line, where they vanish or diverge as the first row of flank spots. The black mid-dorsal line is continuous and is flanked on each side by four rows of oblong to squared spots, and by a few small-scattered spots below. There is a dorsal erectile crest. The face has a dark mask and a pair of white sub-ocular spots. The tail has seven to nine black rings, alternating with pale rings; the intervening white spaces are pigmented with a brownish tinge on the dorsal midline. The width of the pale rings relative to the dark rings in the middle of the tail is 50-75%; the tip of the tail is dark. The hindlimbs and forelimbs are black; there are white hairs on the metacarpals and metatarsals. [he posterior parts of the feet are dark. There are two pairs of teats. The posterior chamber of the auditory bulla is not ventrally inflated and has a continuous curve line on the external side. The ratio between the inter-orbital constriction and frontal width is 1-00 + 0-12. Dental formula: 13/3, C1/1,P 4/4, M 2/2 = 40. in Viverridae
Distribution. Angola, DR Congo, Malawi, Mozambique, Tanzania, and Zambia. Description. Head-body 46:5-47-8 cm (males), 44-45-5 cm (females), tail 40-43 cm (males), 38-39 cm (females), hindfoot 8:7-9-8 cm (males), 8-9 cm (females), ear 4-7-5-4 cm (males), 5-1-5-8 cm (females); weight 1-3-2 kg. The coat color is pale ocher, with brownish or grayish tones; melanistic individuals are quite common. The throat and chest are blackish, and the ventral pelage varies from creamy white to dirty white. The stripes and spots on the body vary from different hues of brown to black. The nuchal stripes run as two parallel lines from the nape to the shoulders, where they diverge and enlarge towards the elbows; they are not so conspicuously marked as in other genet species. Below them, a pair of thinner stripes and small spots are scattered on the shoulders and sides of the neck. A third pair of thinner, parallel stripes runs down the neck between the nuchal stripes, extending to about one fourth of the mid-dorsal line, where they vanish or diverge as the first row of flank spots. The black mid-dorsal line is continuous and is flanked on each side by four rows of oblong to squared spots, and by a few small-scattered spots below. There is a dorsal erectile crest. The face has a dark mask and a pair of white sub-ocular spots. The tail has seven to nine black rings, alternating with pale rings; the intervening white spaces are pigmented with a brownish tinge on the dorsal midline. The width of the pale rings relative to the dark rings in the middle of the tail is 50-75%; the tip of the tail is dark. The hindlimbs and forelimbs are black; there are white hairs on the metacarpals and metatarsals. [he posterior parts of the feet are dark. There are two pairs of teats. The posterior chamber of the auditory bulla is not ventrally inflated and has a continuous curve line on the external side. The ratio between the inter-orbital constriction and frontal width is 1-00 + 0-12. Dental formula: 13/3, C1/1,P 4/4, M 2/2 = 40.
k-space dataset for Robust multishot diffusion-weighted imaging of the abdomen with region-based shot rejection
<p>k-space dataset for https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.30102</p>
3D Joint Space Evaluation in Knee Osteoarthritis: The Importance of Weight-bearing and Flexion
ClinicalTrials.gov study NCT05950854. IPD Sharing: UNDECIDED. 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
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.