Skip to main content
Powered by ShareScore

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.

48

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

48 results for “DPC”

Learn how ShareScore rates datasets ↗
zenodo48/100

dpc

<p>COVID-19 data for Italy from 2020-02-24 to 2023-11-01, including tot_cases, tot_deaths</p> <p>Files:</p> <ul> <li>".pkl" Cache file backup</li> <li>Dataframe exported "PyCoa-DF.csv"</li> <li>Original database backup&nbsp;</li> </ul>

opencc-by-4.0May 2024View details →
zenodo40/100

Fig. 78. Stereogenys cromeri Andrews, 1901. DPC 4120. A in Evolution Of The Side-Necked Turtles: The Family Podocnemididae

Fig. 78. Stereogenys cromeri Andrews, 1901. DPC 4120. A, dorsal; B, ventral; C, right lateral; D, anterior; E, left lateral; F, posterior. [M. Rightmyer, del.]

opencc-by-4.0Apr 2011View details →
zenodo40/100

Fig. 77. Stereogenys cromeri Andrews, 1901. DPC 4120. A in Evolution Of The Side-Necked Turtles: The Family Podocnemididae

Fig. 77. Stereogenys cromeri Andrews, 1901. DPC 4120. A, dorsal; B, ventral; C, right lateral; D, anterior; E, left lateral; F, posterior. [E. Heck, del.]

opencc-by-4.0Apr 2011View details →
zenodo40/100

Fig. 7. DPC 24108A in The oldest and youngest records of afrosoricid placentals from the Fayum Depression of northern Egypt

Fig. 7. DPC 24108A, right distal humerus attributed to the afrosoricid placental Dilambdogale gheerbranti gen. et sp. nov., from the earliest late Eocene (earliest Priabonian) locality BQ−2, Birket Qarun Formation, Fayum Depression, northern Egypt.

opencc-by-4.0Jun 2010View details →
zenodo40/100

Exploring deep learning models for 4D-STEM-DPC data processing

<p>This repository contains scanning transmission electron microscopy data and processing files used in the journal publication&nbsp;<strong>"Exploring deep learning models for 4D-STEM-DPC data processing"</strong>. DOI: <a href="https://doi.org/10.1016/j.ultramic.2024.114058">10.1016/j.ultramic.2024.114058</a></p> <p><strong>Prerequisites</strong></p> <p>The scripts presented below require certain open-source Python packages to run. Library versions used to run the scripts are:</p> <ul> <li>hyperspy 1.7.1</li> <li>pyxem 0.14.2</li> <li>fpd 0.2.5</li> <li>pytorch 1.12.1 (cudatoolkit 11.6.0)</li> <li>jupyterlab 4.0.7</li> </ul> <p><strong>Data files</strong></p> <p>Three zipped folders are included. Two of them contain the training- and inference data for the neural networks, aptly named&nbsp;<em>training_data.zip</em> and&nbsp;<em>inference_data.zip</em>. PyTorch state dictionaries for trained models are included in the&nbsp;<em>models.zip</em> folder.</p> <p><strong>Processing scripts</strong></p> <p>All scripts are included in an IPython notebook format (.ipynb extension). The notebooks&nbsp;<em>Segmentation.ipynb</em> and&nbsp;<em>Regression.ipynb</em> contain the code for training and inference of the segmentation and regression models, respectively. The&nbsp;<em>Training_data_creation.ipynb<strong>&nbsp;</strong></em>notebook contains the code to preprocess the training data for both neural network models. The <em>Standard_algorithms.ipynb</em> notebook has the code for doing center of mass and edge filtering/disc detection algorithms for STEM-DPC processing.</p>

opencc-by-4.0Mar 2024View details →
zenodo40/100

Tilt stacks of various ASI systems using continous STEM-DPC with an ADF detector in JEOL JEM-2100F

<p>Processed data (GIF-format) of tilt stacks using the ADF detector to acquire images of ASI systems while continuously tilting the specimen&nbsp;in LMSTEM. The structures shown are pinwheel, square and kagome lattices with&nbsp;different dimensions, milled into a 20nm thick layer of permalloy. Bitmaps used for FIB milling can be found in a separate folder.</p> <p><strong>Lattices</strong><br> pw = pinwheel<br> sq = square<br> kag = kagome</p> <p><strong>Sizes of each island</strong><br> XL = 1800nm x 600nm<br> L = 900nm x 300nm<br> M = 600nm x 200nm<br> S = 450nm x 150nm<br> XS = 225nm x 75nm</p> <p><br> We acknowledge the support from the Research Council of Norway for the Norwegian Center for Transmission Electron Microscopy, NORTEM (197405), the Norwegian Micro- and Nano-Fabrication Facility, NorFab (295864), and In-situ Correlated Nanoscale Imaging of Magnetic Fields in Functional Materials, InCoMa (315475).</p>

opencc-by-4.0Jun 2023View details →
zenodo36/100

Lock-in and Standard DPC raw images associated to publication 10.1364/PRJ.445896

<p>Raw images obtained with helicam C3 in a DPC microscope setup. The raw images were used for the figures in the publication Chiara Bonati,&nbsp;Damien Loterie,&nbsp;Timoth&eacute; Laforest,&nbsp;Christophe Moser. Lock-in incoherent differential phase contrast imaging. Photonics Research, 2022, 10(1):237.</p> <p>Contents of the data set:</p> <ol> <li><strong>Contrast comparison</strong>: images used for Figure 4. Several USAF targets of different height, introducing varying phase differences. Samples were imaged under both standard and lock-in DPC. <ol> <li><strong>Lock-in DPC</strong>: lock-in DPC image sets. Each image was collected over four cycles of 140 us each. <ol> <li><strong>5_mrad</strong>: 5 mrad sample <ul> <li><strong>Background</strong>: no sample image <ul> <li><strong>I</strong>: Frame I</li> <li><strong>Q</strong>: Frame Q</li> </ul> </li> <li><strong>Sample</strong>: sample image <ul> <li><strong>I</strong>: Frame I</li> <li><strong>Q</strong>: Frame Q</li> </ul> </li> </ul> </li> <li><strong>13dot8_mrad</strong>: 13.8 mrad sample</li> <li><strong>15dot1_mrad</strong>: 15.1 mrad sample</li> <li><strong>18dot9_mrad</strong>: 18.9 mrad sample</li> <li><strong>35dot2_mrad</strong>: 35.2 mrad sample</li> <li><strong>50dot5_mrad</strong>: 50.5 mrad sample</li> </ol> </li> <li><strong>Standard DPC: </strong>Same set of images, under standard DPC conditions. Each illumination (left and right) was recorded for 1 us. <ol> <li><strong>5_mrad</strong>: 5 mrad sample <ul> <li><strong>Background</strong>: no sample image <ul> <li><strong>L</strong>: Image under left illumination</li> <li><strong>R</strong>: Image under right illumination</li> </ul> </li> <li><strong>Sample</strong>: sample image <ul> <li><strong>L</strong>: Image under left illumination</li> <li><strong>R</strong>: Image under right illumination</li> </ul> </li> </ul> </li> <li><strong>13dot8_mrad</strong>: 13.8 mrad sample</li> <li><strong>15dot1_mrad</strong>: 15.1 mrad sample</li> <li><strong>18dot9_mrad</strong>: 18.9 mrad sample</li> <li><strong>35dot2_mrad</strong>: 35.2 mrad sample</li> <li><strong>50dot5_mrad</strong>: 50.5 mrad sample</li> </ol> </li> </ol> </li> <li><strong>Reconstruction</strong>: Image sets used for figures 5 and 6 <ol> <li><strong>USAF_50dot5mrad</strong>: Image sets of a 50.5 mrad glass USAF target. <ol> <li><strong>Illumination</strong>: Folder containing the back-focal plane characterisation of the imaging system, used to compute the transfer functions for phase reconstruction. <ul> <li>BFP: the back-focal plane aperture of the 0.4 NA objective</li> <li>P_1: reduced pupil used in the experiments</li> <li>s_L: left source</li> <li>s_R: right source</li> </ul> </li> <li><strong>Lock-in DPC</strong>: lock-in DPC image set. The equivalent exposure time was 464 us.</li> <li><strong>Standard DPC</strong>: standard DPC image set. Each illumination was exposed for 8 us.</li> </ol> </li> <li><strong>Onion</strong>: Image sets of onion epidermal cells. <ol> <li><strong>Lock-in DPC</strong>: lock-in DPC images for varying equivalent exposure times. <ul> <li>104 us</li> <li>120 us</li> <li>136 us</li> <li>152 us</li> <li>168 us</li> <li>184 us</li> <li>200 us</li> <li>216 us</li> <li>232 us</li> <li>248 us</li> </ul> </li> <li><strong>Standard DPC</strong>: Standard DPC image set at 7 us exposure time</li> </ol> </li> </ol> </li> </ol> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Dec 2021View details →
zenodo36/100

Digital vs analog DPC and 4D-STEM detector frame rates

<p>These are raw data sets (.dm3 in zipped folders) from University of Tokyo, and overview of the detector frame rates from datasheets of pixelated detectors (.csv). Used for the paper titled "Ultra-fast Digital DPC Yielding High Spatio-Temporal Resolution for Low-Dose Phase Characterisation" (preprint available on arXiv: <a title="Ultra-fast Digital DPC Yielding High Spatio-Temporal Resolution for Low-Dose Phase Characterisation" href="https://arxiv.org/abs/2405.06367" target="_blank" rel="noopener">https://arxiv.org/abs/2405.06367</a>).</p>

opencc-by-sa-4.0May 2024View details →
zenodo36/100

Fig. 82. Stereogenys cromeri Andrews, 1901. DPC 4120 in Evolution Of The Side-Necked Turtles: The Family Podocnemididae

Fig. 82. Stereogenys cromeri Andrews, 1901. DPC 4120. Endocast. [L. Starin, F. Ippolito, del.]

opencc-by-4.0Apr 2011View details →
zenodo32/100

text-fig. 34. Left theropod humeri in anterior (a-b, d) and lateral (c) views, illustrating several forelimb characters, a, Allosaurusfragilis; redrawn from Madsen (1976). B-c, Camotaurus sastrei; based on MACN CH 894. D, Deinonychus antirrhopus; redrawn (reversed) from Ostrom (1969/?). Abbreviations: af, articular facet on head of humans; dpc, deltopectoral crest; it, internal tuberosity. Scale bars represent 50 mm. in The interrelationships and evolution of basal theropod dinosaurs

text-fig. 34. Left theropod humeri in anterior (a-b, d) and lateral (c) views, illustrating several forelimb characters, a, Allosaurusfragilis; redrawn from Madsen (1976). B-c, Camotaurus sastrei; based on MACN CH 894. D, Deinonychus antirrhopus; redrawn (reversed) from Ostrom (1969/?). Abbreviations: af, articular facet on head of humans; dpc, deltopectoral crest; it, internal tuberosity. Scale bars represent 50 mm.

opennotspecifiedMay 2003View details →
zenodo32/100

QAVA-DPC: Eye-Tracking Based Quality Assessment and Visual Attention Dataset for Dynamic Point Cloud in 6 DoF

Open the record for dataset details and reuse information.

opencc-by-4.0Aug 2024View details →
ClinicalTrials.gov32/100

Long Term Follow-up of Patients With Parkinson's Disease Who Had Administered of A9-DPC in SB-PD-001 Study

ClinicalTrials.gov study NCT06477744. IPD Sharing: NO. Countries: 1. Publications: 5.

closedIPD-NOFeb 2026View details →
zenodo28/100

Supplementary figure 1. Histopathological analysis of the pseudobranch tissues in Atlantic salmon following SAV3 challenge (by cohabitation) at 16 dpc showing no histological difference between (A) Control fish (non infected) and (B) SAV3-infected fish from HD group at 16 dpc.

Open the record for dataset details and reuse information.

opencc-by-4.0Dec 2023View details →
ClinicalTrials.gov28/100

Clinical & Radiographical Evaluation of the Effect of Dycal & Biodentine in DPC in Primary Teeth

ClinicalTrials.gov study NCT02789423. IPD Sharing: NO. Countries: 0. Publications: 7.

closedIPD-NOFeb 2026View details →
geo24/100

Genes expression of WT and KLF12 overexpression mouse fetues at 9.0 dpc

GEO Series GSE166054. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenFeb 2021View details →
geo24/100

Transcriptomic analysis of 13.5dpc embryonic ovaries exposed to the acetaminophen (APAP) and ibuprofen (IBU) combination in mice

GEO Series GSE122547. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenAug 2019View details →
geo24/100

Mouse gonadal transcriptome at 12.5dpc, 13.5dpc, 16.5dpc and 6dpp.

GEO Series GSE117590. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenOct 2019View details →
geo24/100

Whole heart transcriptional response to complete and epiblast-specfic deletion of Zic3 in 15.5 dpc mouse embryos

GEO Series GSE41674. Mus musculus. 22 samples. Type: Expression profiling by array.

openGEO-OpenDec 2012View details →
geo24/100

The efficacy of novel anti-cancer agents DpC and Dp44mT in the Treatment of Estrogen Receptor Positive Breast Cancer (BC)

GEO Series GSE192942. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenFeb 2023View details →
geo24/100

Expression data from spermatogonia enriched from 12 dpc mouse testes

GEO Series GSE234896. Mus musculus. 3 samples. Type: Expression profiling by array.

openGEO-OpenJun 2024View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record