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.

887

datasets available to search

ShareScore release 0.7.1

Reset

Dataset results

887 results for “tunnels”

Learn how ShareScore rates datasets ↗
ClinicalTrials.gov36/100

Hematological Markers in Idiopathic Carpal Tunnel Syndrome

ClinicalTrials.gov study NCT06952647. IPD Sharing: NO. Countries: 1. Publications: 10.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Pain Outcomes of Intra-operative IV Tylenol and/or IV Toradol for Carpal Tunnel and Distal Radius Fracture Surgeries

ClinicalTrials.gov study NCT02313675. IPD Sharing: Not stated. Countries: 1. Publications: 4.

restrictedIPD-UNDECIDEDFeb 2026View details →
dryad36/100

Data from: Performance-based Egress safety assessment of underground tunnels: Simulation and artificial neural network approaches

Open the record for dataset details and reuse information.

publicNov 2025View details →
dryad36/100

A novel choice test to detect the influence of fungi on the tunneling behavior of sympatric bark beetles (Coleoptera: Scolytinae)

Open the record for dataset details and reuse information.

publicMay 2025View details →
zenodo32/100

Politecnico di Milano - Wind tunnel test data on high-rise building

<p>High-resolution pressure data recorded in&nbsp;wind tunnel tests performed at the Politecnico di Milano wind tunnel&nbsp;on a generic prismatic high-rise building.<br>If you use these data, please cite:<br>Lamberti, G., Amerio, L., Pomaranzi, G., Zasso, A., &amp; Gorl&eacute;, C. (2020). Comparison of high resolution pressure measurements on a high-rise building in a closed and open-section wind tunnel. Journal of wind engineering and industrial aerodynamics, 204, 104247. DOI: 10.1016/j.jweia.2020.104247</p>

opencc-byJun 2020View details →
zenodo32/100

Online Resource 2 - Radial displacement at the tunnel wall of a tunnel excavated with a single shield TBM at the state of equilibrium (comparison between different calculation methods)

<p>The radial displacement&nbsp;at the tunnel wall&nbsp;at the state of equilibrium calculated with the various ConVergence-ConFinement (CV-CF) methods is compared with the results obtained with a 3D numerical model of a tunnel excavation. A sensibility analysis is performed in order to compare the performance of the CV-CF approaches. The choice of the values of the mechanical parameters of the ground and of the lining is carried out in an attempt to cover the large range of situations encountered within single shield TBM. The total set of results from the sensibility analysis is shown in this work. Results obtained from some empirical formula proposed by the authors are also included.</p>

opencc-by-4.0Oct 2018View details →
zenodo32/100

Online Resource 1 – Maximal hoop stress developed in the lining of a tunnel excavated with a single shield TBM at the state of equilibrium (comparison between different calculation methods)

<p>The maximal hoop stress developed in the lining of a tunnel at the state of equilibrium calculated with the various ConVergence-ConFinement (CV-CF) methods is compared with the results obtained with a 3D numerical model of a tunnel excavation. A sensibility analysis is performed in order to compare the performance of the CV-CF approaches. The choice of the values of the mechanical parameters of the ground and of the lining is carried out in an attempt to cover the large range of situations encountered within single shield TBM. The total set of results from the sensibility analysis is shown in this work. Results obtained from some empirical formula proposed by the authors are also included. <strong>A version 2 of the document with some minor corrections has been published. </strong></p>

opencc-by-4.0Oct 2018View details →
zenodo32/100

Edge-State Wave Functions from Momentum-Conserving Tunneling Spectroscopy

<p>Supporting data for</p> <p>&quot;Edge-State Wave Functions from Momentum-Conserving Tunneling Spectroscopy&quot;</p> <p>T. Patlatiuk, C. P. Scheller, D. Hill, Y. Tserkovnyak, J. C. Egues, G. Barak, A. Yacoby, L. N. Pfeiffer, K. W. West, and D. M. Zumbuhl</p> <p>&nbsp;</p> <p>This dataset consists of one zip file with the data used in the figures of the main text and another zip file for the figures of the supplementary materials. The data is stored in the HDF5 format. The axes for 1D and 2D graphs are stored internally as&nbsp;additional 1D arrays.</p>

opencc-by-4.0Aug 2020View details →
dryad32/100

Complex relationship between tunneling patterns and individual behaviors in termites

<p>The nests built by social insects are complex group-level structures that emerge from interactions among individuals following simple behavioral rules. Nest patterns vary among species, and the theory of complex systems predicts that there is no simple one-to-one relationship between variations in collective patterns and variation in individual behaviors. Therefore, a species-by-sp<span>ecies comparison of the actual building process is essential to understand the mechanism producing diverse nest patterns. </span><span><span>Here we compare tunnel formation of three termite species, and reveal two mechanisms producing interspecific variation: in one, a common behavioral rule yields distinct patterns via parameter-tuning; in the other, distinct rules produce similar patterns. </span></span><span>We found that two related species </span><span><span>transport sand in the same way using mandibles</span></span><span> but build tunnels with different degree of branching. The variation arises from different probabilities of choosing between two behavioral options at crowded tunnel faces: excavating the sidewall to make a new branch or waiting for clearance to extend the current tunnel. We further discovered that a third species independently evolved low-branched patterns using different building rules; namely a bucket-brigade that can excavate a crowded tunnel</span><span><span>. Our findings emphasize the importance of direct comparative study of collective behaviors at both individual- and group-levels</span></span><span>. </span></p>

opencc-zeroOct 2020View details →
zenodo32/100

Quantum Tunneling Photodetector - concept movie

<p>simple illustration of the idea of Quantum Tunneling Photodetector</p>

opencc-by-4.0Nov 2020View details →
dryad32/100

Data from: Rapid divergence of nesting depth and digging appendages among tunneling dung beetle populations and species

Many dung beetle communities are characterized by species that share very similar morphological, ecological, and behavioral traits and requirements yet appear to be stably maintained. Here, we document that the morphologically nearly indistinguishable, sympatric, and syntopic tunneling sister species Onthophagus taurus and Onthophagus illyricus may be avoiding competitive exclusion by nesting at remarkably different soil depths. Intriguingly, we also find rapid divergence in preferred nesting depth across native and recently established O. taurus populations. Furthermore, geometric morphometric analyses reveal that both inter- and intraspecific divergences in nesting depth are paralleled by similar changes in the shape of the primary digging appendages, the fore tibiae. Collectively, our results identify preferred nesting depth and tibial shape as surprisingly evolutionarily labile and with the potential to ease interspecific competition and/or to facilitate adaptation to local climatic conditions.

opencc-zeroDec 2014View details →
dryad32/100

Wind Tunnel Measurements of Aerodynamic Entrainment Rate of Particles

<p>This dataset is related to a wind tunnel experiment of aerodynamic entrainment rate of particles. Wind profiles are measured by pitot tube, for calibrating surface shear stress measured by Irwin sensors. Aerodynamic entrainment rate is measured through the mass difference weighting before and after the erosion event. Each case is repeated three times to find the average value and the error. The experimental setup is showed in figure 1.</p>

opencc-zeroApr 2020View details →
zenodo32/100

Figs. 55–58. Coptonotus uteq entrance tunnels. 55 in A Revision ofCoptonotusChapuis, 1869 (Coleoptera: Curculionidae: Coptonotinae) with Notes on Its Biology

Figs. 55–58. Coptonotus uteq entrance tunnels. 55) Initiation without latex; 56) Initiation with heavy latex flow;

opennotspecifiedSep 2016View details →
zenodo32/100

Real-world vehicular source indicators for exhaust and non-exhaust contribution to PM2.5 during peak and off-peak hours using tunnel measurement

<p>The data are the species concentrations, traffic and meteorological information during the sampling periods in WJL tunnel, and the estimated function of E<sub>N</sub> curve under vehicle electrification.</p>

opencc-by-4.0Oct 2023View details →
zenodo32/100

Data supporting findings for the manuscript: "An Ultra-High Vacuum Scanning Tunneling Microscope with Pulse Tube and Joule-Thomson cooling operating at sub-pm z-noise"

<p>This is the data repository for the manuscript:<br>An Ultra-High Vacuum Scanning Tunneling Microscope with Pulse Tube and Joule-Thomson cooling operating at sub-pm z-noise</p> <p>The data is contained in the zip file.</p> <p>The data is sorted in a folder structure, named after the corresponding images in the manuscript.</p> <p>The raw data and the analysis is given.&nbsp;</p>

opencc-by-4.0Nov 2024View details →
zenodo32/100

Three dimensional microscale characterization of off-axis tunnelling cracks in non-crimp fabric based composites

<p>Video and x-ray data-sets behind the publications</p> <p>Bangaru, A.K., Mikkelsen, L.P. and, S&oslash;rensen, B.F. Three dimensional microscale characterization of off-axis tunnelling cracks in non-crimp fabric based composites, <em>Composites Science and Technology</em>, <strong>226</strong>, 109502, <a href="https://doi.org/10.1016/j.compscitech.2022.109502">https://doi.org/10.1016/j.compscitech.2022.109502</a>, 2022</p> <p>Videos:&nbsp;</p> <ul> <li>Figure 8:&nbsp;<a href="https://zenodo.org/api/files/cb486bb8-8183-4b67-bb03-4ed5e48d6d31/Fig08_CrackBranching_v1.mpg">Fig08_CrackBranching_v1.mpg</a>, <a href="https://youtu.be/rrp8Ax1er5I">Youtube-link</a></li> <li>Figure 10a: <a href="https://zenodo.org/api/files/cb486bb8-8183-4b67-bb03-4ed5e48d6d31/Fig10_a_MatrixPenetration.mpg">Fig10_a_MatrixPenetration.mpg</a>, <a href="https://video.dtu.dk/media/Fig10_a_MatrixPenetration.mpg/0_0ypbfkex">Video-link</a></li> <li>Figure 10b:&nbsp;<a href="https://zenodo.org/api/files/cb486bb8-8183-4b67-bb03-4ed5e48d6d31/Fig10_b_MatrixPenetration_HighResolution.mpg">Fig10_b_MatrixPenetration_HighResolution.mpg</a>,&nbsp;<a href="https://video.dtu.dk/media/Fig10_b_MatrixPenetration_HighResolution/0_1bexfv16">Video-link</a></li> <li>Figure 10c:&nbsp;<a href="https://zenodo.org/api/files/cb486bb8-8183-4b67-bb03-4ed5e48d6d31/Fig10_c_CrackTwisting_HighResolution.mpg">Fig10_c_CrackTwisting_HighResolution.mpg</a>,&nbsp;<a href="https://video.dtu.dk/media/Fig10_c_CrackTwisting_HighResolution/0_yphlldlh">Video-link</a></li> <li>Figure 14:&nbsp;<a href="https://zenodo.org/api/files/cb486bb8-8183-4b67-bb03-4ed5e48d6d31/Fig14_CrackPenetration_HighResolution.mpg">Fig14_CrackPenetration_HighResolution.mpg</a>, <a href="https://youtu.be/MO0VBdyYlAw">Youtube-link</a></li> <li>Figure 15:&nbsp;<a href="https://zenodo.org/api/files/cb486bb8-8183-4b67-bb03-4ed5e48d6d31/Fig15_CrackDeflection_HighResolution.mpg">Fig15_CrackDeflection_HighResolution.mpg</a>, <a href="https://video.dtu.dk/media/Fig15_CrackDeflection_HighResolution/0_cqw7xgyg">Video-link</a></li> </ul> <p>Scan data:</p> <ul> <li>Fig08_SpecimenS1_DataSet.txm: Data behind figure 8</li> <li>Fig10_a_SpecimenS2_DataSet_LowResolution_Stitch22_FoV6.5.txm: Data behind figure&nbsp;10a</li> <li>Fig10_b_c_SpecimenS2_DataSet_HighResolution_FoV1.5.txm: Data behind figures 10b,&nbsp;10c, 11, and 13</li> <li>Fig14_SpecimenS2_DataSet_HighResolution_FoV1.5.txm: Data behind figure&nbsp;14</li> <li>Fig15_SpecimenS3_DataSet_HIghResolution_FoV1.5.txm: Data behind figure 15</li> </ul> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2021View details →
zenodo32/100

Dynamic implicit modeling of tunnel unfavorable geology based on multi-source data fusion using support vector machine

<p>This is the relevant data of the article &quot;Dynamic implicit modeling of tunnel unfavorable geology based on multi-source data fusion using support vector machine&quot;</p>

opencc-by-4.0May 2022View details →
zenodo32/100

Data from: Passive mode-locking and terahertz frequency comb generation in resonant-tunneling-diode oscillator

<p>All the raw data and processed data used in the figures in the main text and Supplementary Information in&nbsp;the article &quot;Passive mode-locking and terahertz frequency comb generation in resonant-tunneling-diode oscillator.&quot;</p>

opencc-by-4.0May 2022View details →
zenodo32/100

O2-tolerant CO dehydrogenase via tunnel redesign for the removal of CO from industrial flue gas

<p>Molecular dynamics simulations for CODH enzymes studied in &quot;<strong>O<sub>2</sub>-tolerant CO dehydrogenase via tunnel redesign for </strong><strong>the </strong><strong>removal of CO from industrial flue gas</strong><strong>&quot;.</strong></p> <p>The repository contains&nbsp;the 10&nbsp;nanoseconds of NAMD simulation of CODH enzymes.</p>

opencc-by-4.0Aug 2022View details →
zenodo32/100

Koeln Lindenthal Tunnel Entry Hole 50k

Source: Objaverse 1.0 / Sketchfab

opencc-byMar 2022View 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