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.
27
datasets available to search
ShareScore release 0.7.1
Dataset results
27 results for “STIR”
Stirred suspension bioreactors maintain naïve pluripotency of human pluripotent stem cells (hPSCs)
GEO Series GSE144656. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Cold Water Immersion Stirring in Hyperthermic Individuals
ClinicalTrials.gov study NCT04613843. IPD Sharing: NO. Countries: 1. Publications: 0.
Assessment of Cellular and Tissue Characteristics in Lipoaspirates Stirred by VorFat
ClinicalTrials.gov study NCT05016674. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Gene expression in respiratory epithelial cells treated to induce StIR
GEO Series GSE13685. Mus musculus. 12 samples. Type: Expression profiling by array.
Efficient and reproducible generation of human iPSC-derived cardiomyocytes and cardiac organoids in stirred suspension systems
GEO Series GSE263372. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
RNA-sequencing analysis of naive and primed hPSCs in static and stirred suspension conditions
GEO Series GSE125041. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Scaled and Translated Image Recognition (STIR) Source Data
<p>While convolutions are known to be invariant to (discrete) translations, scaling continues to be a challenge and most image recognition networks are not invariant to them. To explore these effects, we have created the Scaled and Translated Image Recognition (STIR) dataset. This dataset contains objects of size <span class="math-tex">\(s \in [17,64]\)</span>, each randomly placed in a <span class="math-tex">\(64 \times 64\)</span> pixel image.</p> <p><strong>Original Source Data</strong></p> <ul> <li><code>dota/</code> (from <a href="https://captain-whu.github.io/DOTA/dataset.html">DOTA v1.5 Google Drive</a> website) <ul> <li><code>train/</code> <ul> <li><code>DOTA-v1.5_train.zip</code> <strong>not</strong> unzipped</li> <li><code>part1.zip</code> <strong>not</strong> unzipped</li> <li><code>part2.zip</code> <strong>not</strong> unzipped</li> <li><code>part3.zip</code> <strong>not</strong> unzipped</li> </ul> </li> <li><code>val/</code> <ul> <li><code>DOTA-v1.5_val.zip</code> <strong>not</strong> unzipped</li> <li><code>part1.zip</code> <strong>not</strong> unzipped</li> </ul> </li> </ul> </li> <li><code>fontawesome/</code> (from <a href="https://fontawesome.com/v5/download">Font Awesome</a> 5.15.3 "Free for Desktop") <ul> <li><code>svgs/</code> unzipped from archive</li> </ul> </li> <li><code>mapillary/</code> (from <a href="https://www.mapillary.com/dataset/trafficsign">Mapillary Traffic Sign Dataset</a>) <ul> <li><code>mtsd_v2_fully_annotated</code> unzipped from archive</li> <li><code>train.0.zip</code> <strong>not</strong> unzipped</li> <li><code>train.1.zip</code> <strong>not</strong> unzipped</li> <li><code>train.2.zip</code> <strong>not</strong> unzipped</li> <li><code>val.zip</code> <strong>not</strong> unzipped</li> </ul> </li> <li><code>mnist/</code> (from <a href="http://yann.lecun.com/exdb/mnist/">Yann LeCun</a> website) <ul> <li><code>t10k-images-idx3-ubyte.gz</code></li> <li><code>t10k-labels-idx1-ubyte.gz</code></li> <li><code>train-images-idx3-ubyte.gz</code></li> <li><code>train-labels-idx1-ubyte.gz</code></li> </ul> </li> </ul> <p><strong>License and Attribution</strong></p> <p>When using the original source data for your own research, please respect the individual licenses. For attribution in papers, we recommend the following citations which introduce the respective datasets.</p> <ol> <li>D. Gandy, J. Otero, E. Emanuel, F. Botsford, J. Lundien, K. Jackson, M. Wilkerson, R. Madole, J. Raphael, T. Chase, G. Taglialatela, B. Talbot, and T. Chase. Font Awesome. https://fontawesome.com/v5/download, Nov. 2022.</li> <li>Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. <em>Proc. IEEE</em>, 86(11):2278–2324, Nov. 1998.</li> <li> C. Ertler, J. Mislej, T. Ollmann, L. Porzi, G. Neuhold, and Y. Kuang. The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale. In <em>2020 16th Eur. Conf. Comput. Vision (ECCV)</em>, Glasgow, UK, Aug. 2020.</li> <li>G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang. DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. In <em>2018 IEEE/CVF Conf. Comput. Vision and Pattern Recognition (CVPR)</em>, pages 3974–3983, Salt Lake City, UT, USA, June 2018.</li> </ol>
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.