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750 results for “coherence”
Amatrice Earthquake - Sentinel-1 TOPS Descending - Coherence Map (S1A_20160821-S1B_20160827)
<p>Coherence levels of the descending S1 TOPSAR interferogram (S1A_20160821-S1B_20160827).</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p>
Dataset Analysis of the Predictions of Coherent vs. Fragmented Knowledge
<p>The database presents participant scores of ontological commitment to the causal principles of emergent processes and direct processes. The following list indicates which principles belong to inter-level direct processes, micro-level direct processes, inter-level emergent processes, and micro-level emergent processes.</p><p><strong>Micro-level Direct </strong></p><p>Special status of the organisms</p><p>Restricted interactions </p><p>Dependent interactions </p><p>Sequential interactions </p><p>Interactions stop when equilibrium is reached</p><p><strong>Inter-level Direct </strong></p><p><strong> </strong>Progressive change</p><p>Centralization</p><p>Teleology</p><p>Level correspondence </p><p><strong>Micro-level Emergent </strong></p><p>Equal status of the organisms</p><p>Independent interactions </p><p>Simultaneous interactions</p><p>Random interactions </p><p>Interactions in continuous equilibration </p><p><strong>Inter-level Emergent </strong></p><p>Proportional change</p><p>Decentralization</p><p>Non teleology</p><p>Separation of levels</p><p>In the database, the first row indicates the name of each principle and is accompanied by a number that denotes the session in which the measurement was taken. For example, "Special status of the organisms 1" and "Special status of the organisms 2" represent the estimates of ontological commitment to the micro-level direct principle "Special status of the organisms" in sessions one and two, respectively.</p>
Data for "Qubit fractionalization and emergent majorana liquid in the honeycomb floquet code induced by coherent errors and weak measurements"
<p>From the perspective of quantum many-body physics, the Floquet code of Hastings and Haah can be thought of as a measurement-only version of the Kitaev honeycomb model where a periodic sequence of two-qubit XX, YY, and ZZ measurements dynamically stabilizes a toric code state with two logical qubits. However, the most striking feature of the Kitaev model is its intrinsic fractionalization of quantum spins into an emergent gauge field and itinerant Majorana fermions that form a Dirac liquid, which is absent in the Floquet code. Here we demonstrate that by varying the measurement strength of the honeycomb Floquet code one can observe features akin to the fractionalization physics of the Kitaev model at finite temperature. Introducing coherent errors to weaken the measurements we observe three consecutive stages that reveal qubit fractionalization (for weak measurements), the formation of a Majorana liquid (for intermediate measurement strength), and Majorana pairing together with gauge ordering (for strong measurements). Our analysis is based on a mapping of the imperfect Floquet code to random Gaussian fermionic circuits (networks) that can be Monte Carlo sampled, exposing two crossover peaks. With an eye on circuit implementations, our analysis demonstrates that the Floquet code, in contrast to the toric code, does not immediately break down to a trivial state under weak measurements, but instead gives way to a long-range entangled Majorana liquid state.</p>
Data for the article "Capacitive coupling of coherent quantum phase slip qubits to a resonator"
<p>Zip file containing data for the article "Capacitive coupling of coherent quantum phase slip qubits to a resonator" published in the New Journal of Physics. The data used in the figures and tables are saved in separate txt files.</p>
Bonneville Salt Flats InSAR Pairs and Coherence Statistics Data for Radwin et al 2024 Manuscript
<p>This data repository is a supplemental dataset to a manuscript submitted to IEEE TGRS, titled "Assessing C-Band InSAR Coherence Data for Application to Study the Bonneville Salt Flats, Utah: Harnessing Cloud-Based InSAR Data Processing" by Mark Radwin, Brenda Bowen, and Richard Forster. </p> <p>This repository hosts two files: </p> <p>1)</p> <table> <tbody> <tr> <td> <div><a href="../api/records/10436440/draft/files/InSAR_PAIRS_LIST.xlsx/content" target="_blank" rel="noopener noreferrer">InSAR_PAIRS_LIST.xlsx</a> (list of InSAR pairs used in total for the study)</div> </td> </tr> </tbody> </table> <p>2) </p> <table> <tbody> <tr> <td> <div><a href="../api/records/10436440/draft/files/Radwin_etal_BSF_InSAR_Complete_Coherence_Data.xlsx/content" target="_blank" rel="noopener noreferrer">Radwin_etal_BSF_InSAR_Complete_Coherence_Data.xlsx</a> (organized collection of 12, 24, and 36 day data - seperated by tabbed sheets)</div> </td> </tr> </tbody> </table> <p>Both files provide sufficient information for others to reproduce the work. If anything is desired that is not included, please email mark.radwin@utah.edu or markradwin@gmail.com</p>
Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow</em>”</strong> in Biomedical Optics Express (doi.org/10.1364/BOE.505847<em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 5-10 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails.</p> <p>For the diffusion measurement under static conditions, there is only one file. However, for experiments involving both flowing and diffusing particles, the dataset comprises diffusion calibration, focus (beam shape) calibration, and flow measurement files. Due to the upload size limitations of the Zenodo repository, only the flow measurements corresponding to one discharge rate have been uploaded. Furthermore, only the non-dilute flow dataset has been uploaded for the same reason. However, for the dilute flow, the analysis logic remains the same, but users will need to utilize the complete g2 formula outlined in Section 2.2 of our article. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement. To conduct the analysis, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate.</p> <p>The results are plotted at the end of our analysis routines. The parameters are displayed as a function of depth. Users can readily compute the Signal-to-Noise Ratio (SNR) at each depth by utilizing the fitted autocorrelation amplitudes. Occasionally, the fitted amplitudes may surpass unity. In such instances, users can assume an extremely high (even infinite) SNR.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>Diffusion_03032023.oct</p> </td> <td> <p>Diffusion measurement file.</p> </td> <td> <p>Na=4096, Nb=1100, 5.5 kHz</p> </td> </tr> <tr> <td> <p>Diffusion_07032023.oct</p> </td> <td> <p>Diffusion calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=10, 36 kHz</p> </td> </tr> <tr> <td> <p>Waist_07032023.oct</p> </td> <td> <p>Beam waist calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=40, 36 kHz</p> </td> </tr> <tr> <td> <p>Q=2_07032023.oct</p> </td> <td> <p>Flow measurement file for a discharge rate of 2 ml/min.</p> </td> <td> <p>Na=4096, Nb=1000, 36 kHz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis, simulation and processing routines.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_diffusion.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_flow.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from flowing and diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Diffusion_parallel.py</p> </td> <td> <p>This script is for analyzing static diffusion measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Flow_parallel.py</p> </td> <td> <p>This script is for analyzing flow measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
Data for: Coherent long-term body-size responses across all Northwest Atlantic herring populations to warming and environmental change despite contrasting harvest and ecological factors
<p>Body size is a key component of individual fitness and an important factor in the structure and functioning of populations and ecosystems. Disentangling the effects of environmental change, harvest, and intra- and inter-specific trophic effects on body size remains challenging for populations in the wild. Herring in the Northwest Atlantic provide a strong basis for evaluating hypotheses related to these drivers given that they have experienced significant warming and harvest over the past century, while also having been exposed to a wide range of other selective constraints across their range. Using data on mean length-at-age 4 for the sixteen principal populations over a period of 53 cohorts (1962-2014), we fitted a series of empirical models for temporal and between-population variation in the response to changes in sea surface temperature. We find evidence for a unified cross-population response in the form of a parabolic function according to which populations in naturally warmer environments have responded more negatively to increasing temperature compared with those in colder locations. Temporal variation in residuals from this function was highly coherent among populations, further suggesting a common response to a large-scale environmental driver. The synchrony observed in this study system, despite strong differences in harvest and ecological histories among populations and over time, clearly indicates a dominant role of environmental change on size-at-age in wild populations, in contrast to commonly reported effects of fishing. This finding has important implications for the management of fisheries as it indicates that a key trait associated with population productivity may be under considerably less short-term management control than currently assumed. Our study, overall, illustrates the need for a comparative approach within species for inferences concerning the many possible effects on body size of natural and anthropogenic drivers in the wild.</p>
Room-temperature quantum coherence of entangled multiexcitons in a metal-organic framework
<p><span>Singlet fission (SF) can generate an exchange-coupled quintet triplet pair state</span><span> </span><sup><span>5</span></sup><span>TT, which could lead to the realization of quantum computing and quantum sensing using entangled multiple qubits even at room temperature. However, the observation of the quantum coherence of <sup>5</sup>TT has been limited to cryogenic temperatures, and the fundamental question is what kind of material design will enable its room-temperature quantum coherence. Here we show that the quantum coherence of SF-derived <sup>5</sup>TT in a chromophore-integrated metal-organic framework (MOF) can be over hundred nanoseconds at room temperature. The subtle motion of the chromophores in ordered domains within the MOF leads to the enough fluctuation of the exchange interaction necessary for <sup>5</sup>TT generation, but at the same time does not cause severe <sup>5</sup>TT decoherence. Furthermore, the phase and amplitude of quantum beating can be controlled by molecular motion, opening the way to room-temperature molecular quantum computing based on multiple quantum gate control.</span></p>
Harmonized data and R code for "Coherent response of zoo- and phytoplankton assemblages to global warming since the Last Glacial Maximum"
<p>Harmonized data and R code for "<em>Coherent response of zoo- and phytoplankton assemblages to global warming since the Last Glacial Maximum</em>"<br>by Tonke Strack, Lukas Jonkers, Marina C. Rillo, Karl-Heinz Baumann, Helmut Hillebrand and Michal Kucera (submitted to <em>Global Ecology and Biogeography</em>, 2024).</p> <p><strong>STRUCTURED ABSTRACT</strong><br><em>Aim</em>: We use the fossil record of different marine plankton groups to determine how their biodiversity changed during past climate warming comparable to projected future warming.<br><em>Location</em>: North Atlantic Ocean and adjacent seas. Time series cover a latitudinal range of 75°N to 6°S.<br>Time period: Past 24,000 years, i.e., from the Last Glacial Maximum (LGM) to the current warm period covering the last deglaciation.<br><em>Major taxa studied</em>: Planktonic foraminifera, dinoflagellates and coccolithophores.<br><em>Methods</em>: We analyse time series of fossil plankton communities using principal component analysis and generalised additive models to estimate the overall trend of temporal compositional change in each plankton group and identify periods of significant change. We further analyse local biodiversity change by analysing species richness, species gains and losses, and the effective number of species in each sample and compare alpha diversity to the LGM mean.<br><em>Results</em>: All plankton groups show remarkably similar trends in the rates and spatio-temporal dynamics of local biodiversity change and a pronounced non-linearity with climate change in the current warm period. Assemblages of planktonic foraminifera and dinoflagellates started to significantly change with the onset of global warming around 15,500 to 17,000 years ago and continued to change at the same pace during the current warm period until at least 5,000 years ago, while coccolithophores assemblages changed at a constant rate throughout the past 24,000 years seemingly irrespective of the prevailing temperature change.<br><em>Main conclusions</em>: The climate change during the transition from the LGM to the current warm period led to a long-lasting reshuffling of the zoo- and phytoplankton assemblages likely associated with the emergence of new ecological interactions and possibly a shift in the dominant drivers of plankton assemblage change from more abiotic-dominated causes during the last deglaciation to more biotic-dominated causes with the onset of the Holocene.</p> <p><strong>CONTENT</strong><br>This dataset includes the harmonized assemblage data of the three investigated plankton groups (planktonic foraminifera, dinoflagellates and coccolithophores) as well as all the R code needed to re-produce the results of this study and it's main figures.</p> <p>Scripts written by Tonke Strack</p> <p><br><strong>DATA SOURCES</strong><br>1) GMST: Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. <br> <em>Nature</em> 599, 239-244, doi:10.1038/s41586-021-03984-4 (2021).<br>2) WOA18: Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov, <em>Technical Editor. </em><br><em> NOAA Atlas NESDIS</em> 81, 52 (2019).<br>3) plankton assemblage data: individual data references provided in CoreList.csv</p> <p><br><strong>DATA</strong><br>1. Harmonized assemblage data<strong>*</strong>: <em>FullDataTable_PF_harmonized.txt</em><br>2. Core list of additional information on time series: <em>CoreList.csv</em><br>3. Reference lists for species names names: <em>ReferenceList_PlanktonicForaminifera.csv, ReferenceList_Dino.csv, ReferenceList_Cocco.csv</em></p> <p><br><strong>CODE</strong><br>1. <em>01_LoadData.R</em>: loads harmonized assemblage data from planktonic foraminifera, dinocyst and coccolithophores<br>2. <em>02_GMST_import.R</em>: loads loads the globally resolved surface temperature since the LGM from Osman et al. (2011)<br>3. <em>03_DataAnalysis_PCA_GAM.R</em>: PCA/GAM analysis on the plankton assemblage data (results shown in Figure 2 and 3), sensititvity analysis (results shown in Figure 4), and some summary statistics<br>4. <em>04_DataAnalysis_MH_GAM_AlternativeApproach.R</em>: alternative GAM approach using Morisita-Horn index (results shown in Figure S2, S3 and S4)<br>5. <em>05_DataAnalysis_BiodiversityChange.R</em>: local biodiversity change analysis of individual time series (results shown in Figure 5, 6 and S9)</p> <p><br>*Assemblage data of individual time series were manually downloaded, quality checked, taxonomically harmonized, and combined into one data file.<br>Planktonic foraminifera data were harmonized following Siccha and Kuchera (2017). We merged <em>Globigerinoides ruber ruber</em> and <em>Globigerinoides ruber </em><br><em>albus</em>, because some studies only reported them together as<em> Globigerinoides ruber</em>. Also, P/D intergrades (an informal category of morphological<br>intermediates between <em>Neogloboquadrina incompta</em> and <em>Neogloboquadrina dutertrei</em>) were merged with <em>Neogloboquadrina incompta</em>.<br>Dinocyst taxonomy was harmonized following de Vernal et al. (2020) with slight additions following Zonneveld et al. (2013). Names that could not be<br>resolved using synonym lists and assigned a harmonized name following de Vernal et al. (2020) and Zonneveld et al. (2013) were treated as unidentified<br>specimens and were excluded from the assemblage analyses. These specimens were present in 4 time series and were rare taxa (relative abundances < 3%).<br>The protoperidinoids were also excluded from further assemblage analyses as this category includes all unidentified brownish cysts (de Vernal et al., 2020).<br>Coccolithophore taxonomy follows Young et al. (2003) and coccolith countings were conducted on a scanning-electron microscope (SEM) to ensure that all<br>specimens are resolved to the species level. We merged <em>Coccolithus pelagicus</em> subspecies, because they were not distinguished in all studies. <br>Species not reported in the time series data were assumed to be absent (that is, zero abundance) which is in accordance with the completeness of the counts<br>reported in the original studies. The original data were either given in absolute or relative abundances, and after excluding unnecessary columns<br>(unidentified or rare taxa that could not be harmonised) the abundances were recalculated to 100 %. In total, 41 species of planktonic foraminifera,<br>30 species of coccolithophores and 53 species of organic-walled dinocysts were observed in our study.</p> <p><strong>REFERENCES</strong><br>de Vernal, A., Radi, T., Zaragosi, S., Van Nieuwenhove, N., Rochon, A., Allan, E., . . . Richerol, T. (2020). Distribution of common modern dinoflagellate cyst taxa in surface sediments of the Northern Hemisphere in relation to environmental parameters: The new n=1968 database. <em>Mar. Micropaleontol.</em>, 159. doi:10.1016/j.marmicro.2019.101796<br>Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. S<em>ci. Data</em> 4, 170109, doi:10.1038/sdata.2017.109 (2017).<br>Young, J. R., Geisen, M., Cros, L., Kleijne, A., Sprengel, C., Probert, I., & Østergaard, J. B. (2003). A guide to extant coccolithophore taxonomy. <em>Journal of Nannoplankton Research Special Issue</em>, 1, 1-125. doi:10.58998/jnr2297<br>Zonneveld, K. A. F., Marret, F., Versteegh, G. J. M., Bogus, K., Bonnet, S., Bouimetarhan, I., . . . Young, M. (2013). Atlas of modern dinoflagellate cyst distribution based on 2405 data points. <em>Rev. Palaeobot. Palynol.</em>, 191, 1-197. doi:10.1016/j.revpalbo.2012.08.003</p>
Frequency-comb-linearized, widely tunable lasers for coherent ranging
<p>This data contains the raw data of the experiment and the code and corresponding data of the figures in the artical.</p>
Data for: Intervalley coherence and intrinsic spin-orbit coupling in rhombohedral trilayer graphene
<p>Data files and data fitting code for manuscript "Intervalley coherence and intrinsic spin-orbit coupling in rhombohedral trilayer graphene." Analysis files Figure3.ipynb and Extracting_lambda.ipynb generate processed data for Fig 3 and extract values of lambda (spin orbit coupling strength) respectively. </p>
Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution"
<p>These datasets have been used to produce Figure 3, 4 and 5 of manuscript: "Coherent phase transfer for real-world twin-field quantum key distribution". The files contain two arrays: time in seconds and normalised intensity.</p> <p>Explanation for "Data_fig3_XXX.txt": the files contains the raw data used to produce Fig. 3. The following timespans have been used:</p> <p>Data_fig3_stabilised.txt: t_start= 0.29 s, t_stop=0.292 s</p> <p>Data_fig3_unstabilised.txt: t_start=0.00225 s, t_stop=0.00424 s</p> <p>Explanation for "Data_fig4_XXX.txt" files: the procedure to obtain the phase deviation from the normalised interference is detailed in the text (Methods section). Datasets with different sampling rate have been combined to obtain the phase deviation on the long and short term.</p> <p>Explanation for "Data_fig5.txt": the files contains the raw data used to produce Fig. 5.</p>
Computer-aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks
<p>During laparoscopic surgery, the Veress needle is commonly used in pneumoperitoneum establishment. Precise placement of the Veress needle is still a challenge for the surgeon. In this study, a computer-aided endoscopic optical coherence tomography (OCT) system was developed to effectively and safely guide Veress needle insertion. This endoscopic system was tested by imaging subcutaneous fat, muscle, abdominal space, and the small intestine from swine samples to simulate the surgical process, including the situation with small intestine injury. Each tissue layer was visualized in OCT images with unique features and subsequently used to develop a system for automatic localization of the Veress needle tip by identifying tissue layers (or spaces) and estimating the needle-to-tissue distance. We used convolutional neural networks (CNNs) in automatic tissue classification and distance estimation. The average testing accuracy in tissue classification was 98.53±0.39%, and the average testing relative error in distance estimation reached 4.42±0.56% (36.09±4.92 μm).</p> <p>The dataset is split into two parts:<br> (1) <strong>Classification</strong>. The zip file <em>veress_classification_raw_images.zip</em> contains 40K images from 8 swine samples where there are 1K images per layer (skin, fat, muscle, abdominal space, and small intestine)<br> (2) <strong>Regression</strong>. The zip file <em>veress_regression_raw_images.zip</em><strong> </strong>contains 8K images of the abdominal space from the same 8 swine samples, and the ground truth distance labels for each sample are found in the Excel files <em>S[1-8]_distance_measurement_20210803.xlsx.</em></p>
Data from: Coherent mechanical noise cancellation and cooperativity competition in optomechanical arrays
<p>Source data for figures.</p>
Plasmonic Copper Sulfide Nanoparticles Enable Dark Contrast in Optical Coherence Tomography
<p>Dataset of https://onlinelibrary.wiley.com/doi/10.1002/adhm.201901627</p>
CW EPR data supporting : Twenty-three–millisecond electron spin coherence of erbium ions in a natural-abundance crystal
<p>Continuous Wave electron spin resonance. Data corresponding to supporting information. Sample: CaWO4 recorded at 8K</p> <p>Dataset for the reference :</p> <p>Twenty-three–millisecond electron spin coherence of erbium ions in a natural-abundance crystal</p> <p>https://doi.org/10.1126/sciadv.abj9786</p> <p> </p> <p><strong>Marianne Le Dantec</strong><strong>, Miloš Rančić</strong><strong>, Sen Lin</strong><strong>, Eric Billaud</strong><strong>, Vishal Ranjan</strong><strong>,<br> Daniel Flanigan, Sylvain Bertaina</strong><strong>, Thierry Chanelière</strong><strong>, Philippe Goldner</strong><strong>, Andreas Erb</strong><strong>, </strong><br> <strong>Ren Bao Liu , Daniel Estève , Denis Vion , Emmanuel Flurin , Patrice Bertet * </strong></p>
data for "Horizontal connectivity in V1 : Prediction of coherence in contour and motion integration'
<p>Data associated with the article : "Horizontal connectivity in V1 : Prediction of coherence in contour and motion integration", Plos One</p>
Dataset: "Generalized scaling of spin qubit coherence in over 12,000 host materials"
<p>Accompanying data for the main text and supplemental materials of <strong><em>Generalized scaling of spin qubit coherence in over 12,000 host materials</em></strong></p>
Data accompanying "Super-broadband on-chip continuous spectral translation unlocking coherent optical communications beyond conventional telecom bands"
<p>This dataset contains measurement data and processing scripts (Matlab) for the results presented in "Super-broadband on-chip continuous spectral translation unlocking coherent optical communications beyond conventional telecom bands". </p> <p>The paper can be found here:</p> <p><a href="https://www.nature.com/articles/s41467-022-31884-2">Super-broadband on-chip continuous spectral translation unlocking coherent optical communications beyond conventional telecom bands | Nature Communications</a></p> <p><a href="https://www.researchsquare.com/article/rs-1086400/v1">Activating Unconventional Wavelength Bands for Coherent Optical Communication by On-chip Continuous Spectral Translation | Research Square</a></p>
Dataset for "Tackling global challenges with coherent policies producing more researchers and diaspora engagement"
<p>A list of science policies that were published between 2002 and 2021 by the African Union, Southern African Development Community, and the Government of Zimbabwe.</p>
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.