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246 results for “Latency”
Demonstrating real-time and low-latency quantum error correction with superconducting qubits
<p>Data associated with results presented in "Demonstrating real-time and low-latency quantum error correction with superconducting qubits".</p> <p>HDF5 files include raw data collected during experiments. Datasets for experiments performed with different number of measurement rounds are saved in separate groups. The group attributes contain information including the total number of measurement rounds. Groups also contain the stim circuits associated with each experiment, which are used for software decoding, and qubit_mappings, which maps each stim coordinate to the corresponding qubit ID on the Ankaa-2 device. Each group has a hard_measurements and soft_measurements group containing the hard and soft measurement results. Measurement results are grouped in datasets per qubit, storing results in the order of measurement execution during the experiment, and with each row representing a separate repetition of the experiment.</p> <p>When decoding with the FPGA decoder we also store the decoder register outcomes in decoder_shot_results. In particular, the first column indicates the logical correction computed by the FPGA decoder – values 0 and 2 correspond to no logical error detected and 1 corresponds to logical error being detected by the decoder.</p> <p>The HDF5 file with data for the fast-feedback experiment ("fast_feedback_raw_data.h5") includes the reference_data group storing reference data. It contains the "delays" group (used to measure T1 in FigS4(d)), "measurement_fidelity" group (used to calculate measurement confusion matrix in Fig S4e, and "double_measurement" group (used to compute post-measurement state distribution in Fig S4f).</p> <p>Also included are files containing the logical error probabilities (LEPs), and CSV files containing timings, both containing data used to plot figures.</p>
Datasets of synthetic task graphs for evaluating a reliability and latency multi-objective task allocation framework
<p>These datasets of synthetic task graphs were generated to evaluate the performance and scalability of a multi-objective task allocation approach for workflow applications of various structures and sizes in a system based on the edge-hub-cloud paradigm. The targeted architecture comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objectives were the maximization of the overall reliability and the minimization of the overall latency of the application, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device. Each task had a different vulnerability factor (i.e., probability of failure) on each device.</p> <p>We generated nine task graphs of serial, parallel, and mixed (a combination of serial and parallel) structure with 10, 100, and 1000 nodes, utilizing the Task Graphs For Free (TGFF) random task graph generator [1]. Additional task parameters (e.g., execution time, power consumption, vulnerability factor, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt.</p> <p>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10357101.</p> <p>References:</p> <p>[1] R. P. Dick, D. L. Rhodes and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE'98), Seattle, WA, USA, 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p>
Supporting Datasets produced in Allen et al. (2018) Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data"
<p><strong>Supporting datasets for Allen et al. (2018) - Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, <em>Geophysical Research Letters</em>, <a href="https://doi.org/10.1002/2018GL077914">https://doi.org/10.1002/2018GL077914</a></strong></p> <p>The code used to produce these data is available as a Github repository, permanently hosted on Zenodo: <a href="https://doi.org/10.5281/zenodo.1219784">https://doi.org/10.5281/zenodo.1219784</a></p> <p><strong>Abstract</strong></p> <p>Earth-orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real-time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet, the temporal requirements for access to satellite-based river data remain uncharacterized for time-sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low-latency/near-real-time satellite products, with an emphasis on the forthcoming SWOT satellite. We apply a kinematic wave model to a global hydrography dataset and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4 and 3 days to reach their basin terminus, the next downstream city and the next downstream dam respectively. Our findings suggest that a recently-proposed ≤2-day latency for a low-latency SWOT product is potentially useful for real-time river applications.</p> <p> </p> <p><strong>Description of repository datasets:</strong></p> <p>1. riverPolylines.zip contains ESRI shapefile polylines of river networks with outputs from main analysis. These continental-scale shapefiles contain the following attributes for each river segment:</p> <ul> <li>"ARCID" : unique identifier for each river segment line, defined as the river reach between river junctions/heads/mouths. The first 10 attributes are taken from Andreadis et al. (2013): https://doi.org/10.5281/zenodo.61758</li> <li>"UP_CELLS" : number of upstream cells (pixels)</li> <li>"AREA" : upstream drainage area (km<sup>2</sup>)</li> <li>"DISCHARGE" : discharge (m<sup>3</sup>/s)</li> <li>"WIDTH" : mean bankfull river width (m)</li> <li>"WIDTH5" : 5th percentile confidence interval bankfull river width (m)</li> <li>"WIDTH95" : 95th percentile confidence interval bankfull river width (m)</li> <li>"DEPTH" : mean bankfull river depth (m)</li> <li>"DEPTH5" : 5th percentile bankfull river depth (m)</li> <li>"DEPTH95" : 95th percentile confidence bankfull river depth (m)</li> <li>"LENGTH_KM" : segment length (km)</li> <li>"ORIG_FID" : original ID of segment</li> <li>"ELEV_M" : lowest elevation of segment (m). Derived from HydroSHEDS 15 sec hydrologically conditioned DEM: https://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=15demg </li> <li>"POINT_X" : longitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"POINT_Y" : latitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"SLOPE" : average slope of segment (m/m)</li> <li>"CITY_JOINS" : an index associated with how likely a city/population center is located on the segment. Population center data from: http://web.ornl.gov/sci/landscan/ and http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/ </li> <li>"CITY_POP_M" : population of joined city (max N inhabitants) </li> <li>"DAM_JOINSC" : an index associated with how likely a dam is located on the segment. Dam data from Global Reservoir and Dam (GRanD) Database: http://www.gwsp.org/products/grand-database.html </li> <li>"DAM_AREA_S" : surface area of joined dam (m<sup>2</sup>)</li> <li>"DAM_CAP_MC" : volumetric capacity of joined dam (m<sup>3</sup>)</li> <li>"CELER_MPS" : modeled river flow wave celerity (m/s)</li> <li>"PROPTIME_D" : travel time of flow wave along segment (days)</li> <li>"hBASIN" : main basin UID for the hydroBASINS dataset: http://www.hydrosheds.org/page/hydrobasins</li> <li>"GLCC" : Global Land Cover Characterization at segment centroid: https://lta.cr.usgs.gov/glcc/globdoc2_0 </li> <li>"FLOODHAZAR" : flood hazard composite index from the DFO (via NASA Sedac): http://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution</li> <li>"SWOT_TRAC_" : SWOT track density (N overpasses per orbit cycle @ segment centroid). Created using SWOTtrack SWOTtracks_sciOrbit_sept15 polygon shapefile, uploaded here.</li> <li>"UPSTR_DIST" : upstream distance to the basin outlet (km) </li> <li>"UPSTR_TIME" : upstream flow wave travel time to the basin outlet (days)</li> <li>"CITY_UPSTR" : upstream flow wave travel time to the next downstream city (days)</li> <li>"DAM_UPSTR_" : upstream flow wave travel time to the next downstream dam (days)</li> <li>"MC_WIDTH" : mean of Monte Carlo simulated bankfull widths (m)</li> <li>"MC_DEPTH" : mean of Monte Carlo simulated bankfull depths (m)</li> <li>"MC_LENCOR" : mean of Monte Carlo simulated river length correction (km)</li> <li>"MC_LENGTH" : mean of Monte Carlo simulated river length (m)</li> <li>"MC_SLOPE" : mean of Monte Carlo simulated river slope (-)</li> <li>"MC_ZSLOPE" : mean of Monte Carlo simulated minimum slope threshold (m)</li> <li>"MC_N" : mean of Monte Carlo simulated Manning’s n (s/m^(1/3))</li> <li>"CONTINENT" : integer indicating the HydroSHEDS region of shapefile</li> </ul> <p>2. hydrosheds_connectivity.zip contains network connectivity CSVs for river polyline shapefiles. The tables do not contain headers:</p> <ul> <li>Col1: segment unique identifier (UID) corresponding to the ARCID column of the riverPolylines shapefiles</li> <li>Col2: Downstream UID</li> <li>Col3: Number of upstream UIDs</li> <li>Col4 – Col12: Upstream UIDs</li> </ul> <p>3. SWOTtracks_sciOrbit_sept15_density.zip contains a polygon shapefile derived from SWOTtracks_sciOrbit_sept15_completeOrbit containing the sampling frequency of SWOT (number of observations per complete orbit cycle). Polygon attributes correspond to each unique shape formed from overlapping swaths:</p> <ul> <li>FID : unique identifier of each polygon</li> <li>CENTROID_X : polygon centroid longitude (WGS84 - decimal degrees)</li> <li>CENTROID_Y : polygon centroid latitude (WGS84 - decimal degrees)</li> <li>COUNT_count: SWOT sampling frequency (N observations per complete orbit cycle)</li> </ul> <p>4. USGS_gauge_site_information.csv : table containing the list of USGS sites analyzed in the validation and obtained from http://nwis.waterdata.usgs.gov/nwis/dv Header descriptions contained within table. </p> <p>5. validation_gaugeBasedCelerity.zip contains polyline ESRI shapefiles covering North and Central America, where USGS gauges provided gauge-based celerity estimates. These files have FIDs and attributes corresponding to riverPolylines shapefiles described above and also contrain the folllowing fields:</p> <ul> <li>GAUGE_JOIN : an index associated with how likely a gauge is located on the segment. Gauge location information is contained in USGS_gauge_site_information.csv</li> <li>GAUGE_SITE: USGS gauge site number of joined gauge</li> <li>GAUGE_HUC8: which hydrological unit code the gauge is located in</li> <li>OBS_CEL_R: gauge-based correlation score (R). Upstream and downstream gauges were compared via lagged cross correlation analysis. The calculated celerity between the paired gauges were assigned to each segment between the two gauges. If there were multiple pairs of upstream and downstream gauges, the the mean celerity value was assigned, weighted by the quality of the correlation, R. Same weighted mean was applied in assigning R. </li> <li>OBS_CEL_MPS: gauge-based celerity estimate (m/s). </li> </ul> <p>6. tab1_latencies.csv contains data shown in Table 1 of the manuscript.</p> <p>7. figS3S4_monteCarloSim_global_runMeans.csv contains the mean of the Monte Carlo simulation inputs and outputs shown in Figure S3 and Figure S4. Column headers descriptions are given in riverPolylines (dataset #1 above). Some columns have rows with all the same value because these variables did not vary between ensemble runs.</p> <p>8. figS5_travelTimeEnsembleHistograms.zip contains data shown in Figure S5. Each csv corresponds to a figure component:</p> <ul> <li>tabdTT_b.csv : basin outlet travel times for all rivers</li> <li>tabdTT_b_swot.csv : basin outlet travel times for SWOT</li> <li>tabdTT_c.csv : next downstream city travel times for all rivers</li> <li>tabdTT_c_swot.csv : next downstream city travel times for SWOT</li> <li>tabdTT_d.csv : next downstream dam travel times for all rivers</li> <li>tabdTT_d_swot.csv : next downstream dam travel times for SWOT</li> </ul>
Measuring Web Latency and Rendering Performance: Method, Tools & Longitudinal Dataset
<p>The dataset used in the paper entitled "Measuring Web Latency and Rendering Performance: Method, Tools & Longitudinal Dataset" published in IEEE Transactions for Network and Service Management. </p>
DETERMINISTIC6G COTS 5G latency measurements
<h1>Measurement setup and dataset overview</h1> <p>The dataset contains the data collected during the latency measurements performed on a COTS 5G system. The 5G network operates in band 78, in TDD mode, with a total of 106 PRBs which occupies 40 MHz of bandwidth. The latency (one-way delay) samples were collected in both uplink and downlink directions using the <a href="https://github.com/heistp/irtt" target="_blank" rel="noopener">irtt</a> tool running on the end node (connected to the UE) and the edge node (connected to the 5G gateway). The clocks in the 5G system, end node and edge node were precisely (<200ns) synchronized using Precision Time Protocol (PTP). In addition to recording send and receive timestamps, various network conditions were also recorded for each latency sample.<br>The measurements were carried out in different sessions and for each session, there are about 1M samples in total which are contained in different parquet files corresponding to different rounds (30 mins) per session. Each session corresponds to a combination of direction (uplink or downlink), UE device, a packet interval and payload length, etc. A spreadsheet (COTS5G measurement campaign.xlsx) in this directory contains the information detailed information on the combination for each session.</p> <h1>Acknowledgements </h1> <p>This work was supported by the European Commission through the H2020 project DETERMINISTIC6G (Grant Agreement no. 101096504).</p>
Measurement Data: Latencies and Traffic Traces in Global Mobile Roaming with Regional Breakouts
<h1>A Shortcut through the IPX: Measuring Latencies in Global Mobile Roaming with Regional Breakouts</h1> <p>This repository contains a description and sample data for the Paper<em> A Shortcut through the IPX: Measuring Latencies in Global Mobile Roaming with Regional Breakouts</em> published at the Network Traffic Measurement and Analysis (TMA) Conference 2024.<br>In the provided README.md file, we present example snippets of the datasets, including an explanation of all contained fields.</p> <p>We cover the three main datasets covered in the related paper:<br>- DT1: User plane traces captured at multiple GGSN/PGW instances of a globaly operating MVNO<br>- DT2: GTP echo round trip times between visited network SGSN/SGWs and home network GGSN/PGWs<br>- DT3: IPX routing information, as extracted from BGP routing tables</p> <p>For legal reasons, we are not able to publish the secondary datasets (DT4, DT5) covered in the manuscript.</p> <p>Finally, for privacy, security, and political reasons, certain fields in each of the datasets have been anonymized. These are indicated by the `_anonymized` prefix.<br>In case of IP addresses, the anonymization ist consistent across datasets, meaning that similar IPs have been anonymized such that their values are still identical after anonymization.</p> <h3>Contact</h3> <p>For questions regarding the dataset, contact Viktoria Vomhoff (viktoria.vomhoff@uni-wuerzburg.de)</p> <p> </p>
Data from: Combining Unity with machine vision to create low latency, flexible, and simple virtual realities
Open the record for dataset details and reuse information.
Data for Manuscript "Changes in P300 Latency and Amplitude Reflect Expertise Acquisition in a Football Visuomotor Learning Task"
<p>Open-access data for the manuscript "Changes in P300 Latency and Amplitude Reflect Expertise Acquisition in a Football Visuomotor Learning Task". Includes:</p> <p>Grand Average dEEG file containing data from all days and all conditions. .csv file outputs used for all analyses. .rmd file detailing the statistical workflow used to obtain the stats presented in the manuscript.</p>
EfficientBioAI: Making Bioimaging AI Models Efficient in Energy and Latency
<p>This dataset contains trained deep learning models, dataset and experiment files for the manuscript "EfficientBioAI: Making Bioimaging AI Models Efficient in Energy and Latency". Please find the software and more information including tutorials here: <a href="https://github.com/MMV-Lab/EfficientBioAI">MMV-Lab/EfficientBioAI (github.com)</a>.</p>
A distinct isoform of lymphoid enhancer binding factor 1 (LEF1) epigenetically restricts EBV reactivation to maintain viral latency
<p>As a human tumor virus, EBV is present as a latent infection in its associated malignancies where genetic and epigenetic changes have been shown to impede cellular differentiation and viral reactivation. We reported previously that levels of the Wnt signaling effector, lymphoid enhancer binding factor 1 (LEF1) increased following EBV epithelial infection and an epigenetic reprogramming event was maintained even after loss of the viral genome. Elevated LEF1 levels are also observed in nasopharyngeal carcinoma and Burkitt lymphoma. To determine the role played by LEF1 in the EBV life cycle, we used in silico analysis of EBV type 1 and 2 genomes to identify over 20 Wnt-response elements, which suggests that LEF1 may bind directly to the EBV genome and regulate the viral life cycle. Using CUT&RUN-seq, LEF1 was shown to bind the latent EBV genome at various sites encoding viral lytic products that included the immediate early transactivator BZLF1 and viral primase BSLF1 genes. The LEF1 gene encodes various long and short protein isoforms. siRNA depletion of specific LEF1 isoforms revealed that the alternative-promoter derived isoform with an N-terminal truncation (∆N LEF1) transcriptionally repressed lytic genes associated with LEF1 binding. In addition, forced expression of the ∆N LEF1 isoform antagonized EBV reactivation. As LEF1 repression requires histone deacetylase activity through either recruitment of or direct intrinsic histone deacetylase activity, siRNA depletion of LEF1 resulted in increased histone 3 lysine 9 and lysine 27 acetylation at LEF1 binding sites and across the EBV genome. Taken together, these results indicate a novel role for LEF1 in maintaining EBV latency and restriction viral reactivation via repressive chromatin remodeling of critical lytic cycle factors.</p>
Latency and energy characterization of 5G LDPC FEC Decoding on CPU and GPU
<p>CloudRIC is a system that meets specific reliability targets in 5G FEC processing while sharing pools of heterogeneous processors among DUs, which leads to more cost- and energy-efficient vRANs. The details of the solution are presented in <a title="CloudRIC: Open Radio Access Network (O-RAN) Virtualization with Shared Heterogeneous Computing" href="https://doi.org/10.1145/3636534.3649381">CloudRIC: Open Radio Access Network (O-RAN) Virtualization with Shared Heterogeneous Computing</a>. These repository provides a dataset, analyzed therein, with experiments carried out with different 5G LDPC decoding processors: (i) Intel FlexRAN library and two open-source alternative libraries on an Intel Xeon Gold 6240R CPU, and (ii) a proprietary driver on an NVIDIA GPU V100.</p> <p>See README file for a description of the dataset.</p>
Erroneous compensation for long-latency feedback delays as origin of Essential Tremor - Dataset
<p>Data associated with the publication "Erroneous compensation for long-latency feedback delays as origin of Essential Tremor" <a href="https://doi.org/10.1101/2024.01.11.575204" target="_blank" rel="noopener noreferrer"> 10.1101/2024.01.11.575204 </a></p>
Provisioning latency increases closer to roads and is associated with species-specific reproductive success in two urban adapters
<p>Most research on urban avian ecology has focused on population- and community-level phenomena, whereas fewer studies have examined how urbanization affects individual behavioral responses to a sudden and novel stimulus, and how those translate to fitness. We measured between-individual variation in provisioning latency in two urban adapters - great tits and blue tits - in response to an infrared camera installed in the nestbox, encountered when offspring in the nest were at the peak of food demand (9–10-days old). For each nestbox, we quantified urbanization as intensity in human activity, distance to road and proportion of impervious surface area. In both species, provisioning latency increased significantly closer to roads. Moreover, increased provisioning latency when exposed to a novel object was associated with higher reproductive success in great tits whose nestboxes were surrounded by high amounts of impervious surface. In contrast, increased provisioning latency was consistently associated with lower reproductive success in blue tits. Our results suggest that provisioning latency changes in relation to the environment surrounding the nest, and may be context- and species-specific when exposed to a novel stimulus, such as a novel object in the nest. To better understand the role of initial behavioral responses towards novelty across an individual's lifetime and, ultimately, its impact on fitness in the urban mosaic, further research explicitly testing different behavioral responses across the entire breeding cycle in wild model systems is needed.</p>
NANCY SNS-JU PROJECT "ITALTEL ITALIAN IN-LAB TESTBED - LATENCY METRICS"
<p>In the context of the NANCY project (https://nancy-project.eu/), this Dataset provides input data for the development of the B-RAN and attacks models for the NANCY framework, to model training and model inference functions. The data collected plays the role of ML algorithm-specific data preparation. The dataset contains time-series, collected transmitting a video content through the Italtel "VTU - video streaming and transcoding application", that can convert audio and video streams from one format to another, at multiple encodings schemes, changing resolution, bitrate, and video parameters. The data collected are related to the observation of some of the resources involved in the Usage Scenario: “Fronthaul network of fixed topology – Direct Connectivity/CoMP Connectivity”. In the Italtel Italian in-lab testbed, a MEC assisted 5G network scenario with a video streaming application for generating traffic is provided. Two different scenarios were set-up, related to downstream and upstream video flows. Each file captured is associated to a 10min video streaming of the “Big Buck Bunny” video. This video was transmitted with different resolutions, 480p, 720p, 1080p, 2160p; both in uplink (UL) and in downlink (DL); the type of metrics monitored is RTT (Round Trip Time).</p>
Ending the Anomaly: Achieving Low Latency and Airtime Fairness in WiFi
<p>This is the dataset and companion website to the paper <a href="https://www.usenix.org/conference/atc17/program/presentation/hoilan-jorgesen">Ending the Anomaly: Achieving Low Latency and Airtime Fairness in WiFi</a> which was published at USENIX ATC 17.</p> <p>Also published at <a href="https://www.cs.kau.se/tohojo/airtime-fairness/">https://www.cs.kau.se/tohojo/airtime-fairness/</a></p>
Measuring Latency Variation in the Internet
<p>This is the companion web site and dataset to the paper "<a href="https://doi.org/10.1145/2999572.2999603">Measuring Latency Variation in the Internet</a>" which was published at ACM CoNEXT '16.</p> <p>Also published at <a href="https://www.cs.kau.se/tohojo/measuring-latency-variation/">https://www.cs.kau.se/tohojo/measuring-latency-variation/</a></p>
Dataset for ESE submission "Pull Request Latency Explained: An Empirical Overview"
<p>This is the dataset for ESE submission "Pull Request Latency Explained: An Empirical Overview".</p> <p>For research purpose, if you need `pull request id`, please request <a href="https://zenodo.org/record/7299639#.Y2mQ-HpBwUE">the column</a>.</p>
Generalized LDPC codes for ultra reliable low latency communication in 5G and beyond
<p>Fifth-generation (5G) systems aim to increase the capacity of existing mobile networks by a factor of 1000, supporting an extremely high user density, as well as numerous device- to-device and machine communications. Ultra Reliable Low Latency Communication (URLLC) constitutes one of the critical operating regimes in 5G, since it will enable low-cost and power-efficient anywhere and anytime signalling services </p> <div> <div> <p>Generalized low-density parity-check (GLDPC) codes, where single parity-check constraints on the code bits are replaced with generalized constraints (an arbitrary linear code), are a promising class of codes for low-latency communication. We have constructed quasi-cyclic GLDPC codes, where the proportion of generalized constraints is determined by an asymptotic analysis. We have analyzed the complexity and performance of the message passing decoder with various update rules (including standard full-precision sum-product and min-sum algorithms) and quantization schemes for a GLDPC code over the additive white Gaussian noise (AWGN) channel and determined a constraint-to-variable update rule based on the specific codewords of the component codes. This data set includes the simulated GLDPC code constructions and the block error rate performance, which is shown to outperform a variety of state- of-the-art code and decoder designs with suitable lengths and rates for the 5G ultra-reliable low-latency communication regime over an AWGN channel with quadrature PSK modulation.</p> </div> </div>
Data from: Short-latency preference for faces in the primate superior colliculus
<p>Face processing is fundamental to primates and has been extensively studied in higher-order visual cortex. Here we report that visual neurons in the midbrain superior colliculus (SC) display a preference for faces, that the preference emerges within 50ms of stimulus onset – well before "face patches" in visual cortex – and that this activity can distinguish faces from other visual objects with accuracies of ~80%. This short-latency preference in SC depends on signals routed through early visual cortex, because inactivating the lateral geniculate nucleus, the key relay from retina to cortex, virtually eliminates visual responses in SC, including face-related activity. These results reveal an unexpected circuit in the primate visual system for rapidly detecting faces in the periphery, complementing the higher-order areas needed for recognizing individual faces.</p>
Study to Evaluate the Effect of 2 Dosage Strengths of Lemborexant (E2006) on a Multiple Sleep Latency Test in Participants With Insomnia Disorder
ClinicalTrials.gov study NCT02350309. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.