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1,709 results for “Reflectivity”
Lineage-Specific Class-A GPCR Dynamics Reflect Diverse Chemosensory Adaptations in Lophotrochozoa
Open the record for dataset details and reuse information.
TG 114: Reasonableness and Tolerability in the System Of Radiological Protection: ICRP TG114 On-Going Reflections
<p>The model of reasonableness and tolerability of radiological risk is a conceptual framework for the implementation of the ICRP principles of optimisation of protection (guided by constraints and reference levels) and application of dose limits, based mainly on the level of exposure, and closely related to the level of risk. Discussions about reasonableness and tolerability have been part of ICRP publications for many years, including the introduction of a model of risk tolerability in Publication 60. More recently, Publication 101 developed the approach to address the implementation of the optimisation process including the involvement of stakeholders and the way to elucidate what is reasonably achievable. Further considerations have been addressed while examining the ethic, identifying four core values underpinning the system of radiological protection (Publication 138). In 2019, ICRP has set up a dedicated task group (TG114) to review the historical and current perspectives on reasonableness and tolerability in order to consolidate and clarify Publication 103, and to prepare the considerations and basis needed for development of future recommendations.</p> <p>The ICRP on-going reflections on reasonableness and tolerability in the system of radiological protection have started to addressed a series of questions including, for example: What is the link between tolerable and reasonable? What are the considerations and criteria on which the concepts of tolerability and reasonableness are based? What are some strategies to assist in balancing competing values in determining what is tolerable and/or reasonable?</p> <p>For addressing these questions, the model of reasonableness and tolerability of radiological risk is revisited with the following objectives:</p> <ul> <li>Investigate the rational for the application of the tolerability of risk as well as the borders with unacceptable level of risks and compare with the approaches adopted for managing other risks.</li> <li>Better articulate the link between tolerability and reasonableness in the process of implementation the radiological protection system, with clarification on the criteria to be considered for defining “where we don’t want to go above” and which process could be put in place for evaluating “what is reasonable”.</li> <li>Refine the radiological criteria to be considered and their link with dose limits and reference levels, relying on the radiological detriment as benchmark for tolerability and reasonableness as well as using risk comparison but without limiting to numerical criteria.</li> <li>Emphasize the importance of the application of the model for the different exposure situations, including the deliberative process with the stakeholders for the implementation of the optimisation principle, referring to good judgement, fairness, practicability, and moderateness.</li> </ul> <p>Presented by Thierry Schneider on behalf of the whole Task Group.</p>
Dataset for the article "Can we use seismic reflection data to infer the interconnectivity of fracture networks?"
<p>This package contains the effective stiffness coefficients of the fractured rock samples explored in the paper of Rubino et al. "Can we use seismic reflection data to infer the interconnectivity of fracture networks?".</p>
DReSA: Project team reflections
<p>This presentation provides thoughts and reflections from the Digital Research Skills Australaisa (DReSA) project team on DReSA. Team members highlight their perspectives on value propositions and benefits for their respective institutiosn/organisations and nationally, as well as individual reflections on collaboration and working together on the project so far.</p> <p><strong>You can watch the video on YouTube here: https://youtu.be/qqH92itI8SI </strong></p> <p> </p>
Deliverable D4.3: Andreev reflection and Coulomb blockade in a SSN SET observed
<p>Deliverable D4.3 and raw data included in it.</p>
Data from: Emerging experience-dependent dynamics in primary somatosensory cortex reflect behavioral adaptation
<p><span><span>Behavioral experience and flexibility are crucial for survival in a constantly changing environment. Despite evolutionary pressures to develop adaptive behavioral strategies in a dynamically changing sensory landscape, the underlying neural correlates have not been well explored. Here, we use genetically encoded voltage imaging to measure signals in primary somatosensory cortex (S1) during sensory learning and behavioral adaptation in the mouse. In response to changing stimulus statistics, mice adopt a strategy that modifies their detection behavior in a context dependent manner as to maintain reward expectation. Surprisingly, neuronal activity in S1 shifts from simply representing stimulus properties to transducing signals necessary for adaptive behavior in an experience dependent manner. Our results suggest that neuronal signals in S1 are part of an adaptive framework that facilitates flexible behavior as individuals gain experience, which could be part of a general scheme that dynamically distributes the neural correlates of behavior during learning. </span></span></p>
Ripples reflect a spectrum of synchronous spiking activity in human anterior temporal lobe
<p>Direct brain recordings have provided important insights into how high-frequency activity captured through intracranial EEG (iEEG) supports human memory retrieval. The extent to which such activity is comprised of transient fluctuations that reflect the dynamic coordination of underlying neurons, however, remains unclear. Here, we simultaneously record iEEG, local field potential (LFP), and single unit activity in the human temporal cortex. We demonstrate that fast oscillations within the previously identified 80–120 Hz ripple band contribute to broadband high-frequency activity in the human cortex. These ripple oscillations exhibit a spectrum of amplitudes and durations related to the amount of underlying neuronal spiking. Ripples in the macro-scale iEEG are related to the number and synchrony of ripples in the micro-scale LFP, which in turn are related to the synchrony of neuronal spiking. Our data suggest that neural activity in the human temporal lobe is organized into transient bouts of ripple oscillations that reflect underlying bursts of spiking activity.</p>
Resilience of seagrass populations to thermal stress does not reflect regional differences in ocean climate
<p>1. The prevalence of local adaptation and phenotypic plasticity among populations is critical to accurately predicting when and where climate change impacts will occur. Currently, comparisons of thermal performance between populations are untested for most marine species or overlooked by models predicting the thermal sensitivity of species to extirpation.</p> <p>2. Here we compared the ecological response and recovery of seagrass populations (<i>Posidonia oceanica</i>) to thermal stress throughout a year-long translocation experiment across a 2800 km gradient in ocean climate. Transplants in central and warm-edge locations experienced temperatures >29 ºC, representing thermal anomalies >5ºC above long-term maxima for cool-edge populations, 1.5ºC for central and <1ºC for warm-edge populations.</p> <p>3. Cool, central and warm-edge populations differed in thermal performance when grown under common conditions, but patterns contrasted with expectations based on thermal geography. Cool-edge populations did not differ from warm-edge populations under common conditions and performed significantly better than central populations in growth and survival.</p> <p>4. Our findings reveal that thermal performance does not necessarily reflect the thermal geography of a species. We demonstrate that warm-edge populations can be less sensitive to thermal stress than cooler, central populations suggesting that Mediterranean seagrasses have greater resilience to warming than current paradigms suggest.</p>
The exact and approximate solutions of phase velocities and reflection coefficients
<p>These are the scripts of the exact and approximate solutions of phase velocities and reflection coefficients in the paper "Approximate equations of PP-, PS1- and PS2-wave reflection coefficients in fluid-filled monoclinic media".</p>
Modelling the transmission component in TIR reflectance spectra of sandstones to understand the effect of surface roughness and clinging fines
<p>This dataset includes a model that combines rock surface reflection with transmission through clinging fines the surface. All IDL scripts are provided. The dataset includes the transmission input spectra, raw data of transmission measurements and SEM images of the surfaces. The reflectance spectra presented in the paper are part of a previous publication, see related identifiers for database of this dataset.</p>
Cloud-free Chinese Gaofen-1 WFV near-infrared surface reflectance over Huailai remote sensing test site throughout 2020
<p>Land surface reflectance product form the starting point for many application regions such as land cover mapping and the generation of biophysical essential climate variables (ECV). Therefore, ensuring the quality of surface reflectance products is necessary to maintain the integrity of the research outcoming of these application areas. However, ground validation of surface reflectance satellite products is challenging, because ground “truth” on a coarse grid scale based on sparse ground measurements is subject to uncertainty due to spatial heterogeneity. In order to quantify the influence of spatial heterogeneity on the uncertainty of surface reflectance ground “truth” in different sampling cases, we generated the high-resolution (16 m) near-infrared surface reflectance over Huailai remote sensing test site based on Chinese Gaofen-1 WFV Band4 data.</p> <p> </p> <p>All cloud-free GF-1 WFV images throughout the year 2020 were extracted. And there are 25 images in total, with at least one image for each month. The WFV Band4 data covering the whole Huailai test station have been processed into Analysis Ready Data (ARD) system, which aims to simplify and reduce the users’ burden by providing pre-processing such as geometric alignment, radiometric recalibration, and atmospheric correction (Zhong et al., 2021). The geometric normalization of the GF-1 WFV data was realized with the procedure developed by Shan et al. (2014). And the radiometric normalization was finished through cross-calibrating with the Landsat TM/OLI with the method proposed by Yang et al. (2015). The 25 images have been layer stacked into one file according to their acquisition time.</p> <p> </p> <p> </p> <p>Reference:</p> <p>Shan, X. J., P. Tang, and C. M. Hu (2014), An automatic geometric precision correction system based on hierarchical registration for HJ-1 A/B CCD images, Int J Remote Sens, 35(20), 7154-7178.</p> <p>Yang, A., B. Zhong et al. (2015), Cross-calibration of GF-1/WFV over a desert site using Landsat-8/OLI imagery and ZY-3/TLC data, Remote Sens., 7, 10763–10787.</p> <p>Zhong, B., A. Yang, Q. Liu, S. Wu, X. Shan, and X Mu (2021), Analysis ready data of the chinese gaofen satellite data, Remote Sens., 13, 9, 1709.</p>
Pre-Analysis Bioinformatics Files for The Microbiome and Volatile Organic Compounds Reflect the State of Decomposition in an Indoor Environment
<p>Data statistics before and after trimming, FastQC reports before and after trimming, MultiQC reports before and after trimming, commands for the Kraken2-Bracken analysis, and classification reports. Read the read.me file for file names and descriptions. </p>
SCoRe - Student Crowd Research. A german dataset of university students' written reflections on their participation in collaborative research-based learning focused on sustainability topics and using videos as a research tool.
<p>Schriftliche Reflexionen von 57 Studierenden als Prüfungsleistung im Rahmen des forschenden Lernens mit Video zu Nachhaltigkeitsthemen. Die Reflexionen wurden durch vorgegebene Fragen angeleitet. Die hier vorliegenden Texte sind, von den Studierenden selbst niedergeschriebene, Transkripte von Sprechtexten eines Self-Video-Casts. Es handelt sich dabei um eine benotete Prüfungsleistung einer universitätsübergreifenden Wahlpflicht-Lehrveranstaltung mit 1-3 Credit-Points.</p> <p>A dataset containing written reflections of 57 university students from several German universities and fields of study on their participation in collaborative research-based learning focused on sustainability topics and using videos as a research tool. These reflections were guided by given questions. The texts presented here are transcripts of spoken texts of self-video-casts, written down by the students themselves. The texts were graded as part of an inter-university elective course worth 1-3 credit points.</p>
Top of Atmospheric Reflectance (ToA) Landsat 8 OLI
<p>The data set presented here are the Top of Atmopsheric reflectance (ToA) images calculated from the Landsat 8 OLI level 2. covering path : 147 and row:47 for the cloud free images captured during year 2016. The ToA is converted from the digital numbers using Semi-Automatic Classification plugin of QGIS. The data can be readily used for the classification and feature extraction as per the use of the research. The dataset includes all the visible and near infrared bands of the Landsat (B1 to B7 Bands).. </p>
In Search of the Edge: A Bayesian Exploration of the Detectability of Red Edges in Exoplanet Reflection Spectra
<p>This repository contains surface albedos for a paper submitted to AAS journals under the same title. Here we include a a realistic Earth-like surface albedo and the raw albedo files used for it's calculation.</p>
Low-fold seismic reflection data acquired across the Northern Hikurangi subduction margin and incoming Hikurangi Plateau, New Zealand
<p>Two-dimensional seismic reflection data were acquired on two surveys of the Northern Hikurangi subduction margin, New Zealand in 2011 and 2015. The survey data were collected to support research of tectonic structure, slow slip processes, stratigraphic architecture, and thermal state of the subduction margin and incoming plate, as well as to support ocean-floor drilling associated with IODP Expeditions 372 and 375. The surveys include (1) R/V <em>Tangaroa</em> NIWA voyage TAN1114 undertaken in 2011 by the National Institute of Water and Atmospheric Research (NIWA) and GNS Science, as part of the <em>OS2020 Northern Hikurangi Margin Geohazards</em> survey; and (2) R/V <em>Rodger Revelle</em> cruise RR1508 undertaken in 2015 by Oregon State University as part of the <em>Subduction Thrust Investigation of New Zealand using Geothermics and Seismics (STINGS)</em> project (see Figure 1). TAN1114 voyage was funded by the New Zealand Government Oceans 2020 Programme, and core research programme funding by NIWA and GNS Science. Cruise RR1508 was funded by NSF grants OCE-1355878 and OCE-1355870.</p> <p> </p> <p><strong>R/V <em>Tangaroa</em> TAN1114 Seismic Data</strong></p> <p><strong>Data Acquisition:</strong> The seismic system used on R/V <em>Tangaroa</em> during the 2011 National Institute of Water and Atmospheric Research (NIWA) survey TAN1114 included a source comprising two Sodera 45/105 GI guns operated in true GI mode. The guns were deployed 35 m behind the vessel RV <em>Tangaroa</em> at 5 m water depth. Lines TAN1114-01 to -13, and part of line 14 were acquired with a shot interval of 10.8 seconds (~25 m sailing at 4.5 knots), providing a nominal coverage of 12-fold data. Part of line TAN1114-14 and lines 15-23 were acquired with a shot interval of 21.6 seconds (~50 m sailing at 4.5 knots), providing a nominal 6-fold coverage. Data were recorded on a Geometrics GeoEel 48-channel seismic streamer with 6 X 100 m active sections, and a group interval of 12.5 m. The streamer was deployed at a depth of 7.5 m, apart from line TAN1114-01 where it was towed at 5 m depth. Depth control was maintained with a CSMX depth control system including three DigiCourse 5011 compass birds. The record length was 8 s and the sample rate 2 ms. Differential GPS was used for positioning. Table 1 summarises TAN1114 recording parameters and Table 2 lists TAN1114 lines acquired and processed. TAN1114 line coordinates are detailed in Table 3.</p> <p><strong>Data Processing:</strong> A total of 29 seismic lines were processed providing 1350 km of multichannel seismic reflection data. The lines were processed to post-stack time-migrated SEGY sections, using GNS Science GLOBE CLARITAS. With allowance for overlap of line segments the data were grouped into 51167 shot-point locations. Raw data were written to disk as IBM standard SEG-Y files. IBM Claritas Extended SEG-Y data were written to disk after geometry was added, after stack, and after migration. Shots were CDP sorted from disk during the stacking process to avoid creating large and unnecessary separate CDP sorted files. </p> <p>Post-stack migration (finite difference migration) has been applied to the stacked sections to produce a dip-true image, this results in clearer resolution of structural features such as faults and folds, and of detailed sedimentary features such as on-lapping and truncated reflections. Sea-floor multiple reflections disturb structural imaging especially in water depths less than 500 m. All seismic data are written to disk as processed sections in SEG-Y format. Line TAN1114-A is a composite splice including parts of lines TAN1114-4A, -6A and 7A. Details of the TAN1114 processing parameters are given in Table 4 and SEG-Y trace headers in Table 5.</p> <p> </p> <p><strong>R/V <em>Rodger Revelle</em> RR1508 Seismic Data</strong></p> <p><strong>Data Acquisition:</strong> The 2015 R/V <em>Rodger Revelle</em> survey RR1508 used a seismic system operated by Scripps Institute of Oceanography. Of two sub-regions surveyed during this cruise, only data from the northern Hikurangi margin are presented here. The seismic system used was similar to that on <em>Tangaroa</em> TAN1114, including a source comprising two Sodera 45/105 GI guns operated in true GI mode. The guns were deployed at a depth of 3.5 m and the shot spacing was 25 m. Data were recorded on a Geometrics GeoEel 48-channel seismic streamer with 6 X 100 m active sections, and a group interval of 12.5 m. The streamer was deployed at a depth of 3.5 m. During acquisition of the HKS01 lines, only the nearest 40 data channels were recorded. The record length was 8 s and the sample rate 1 ms. Differential GPS was used for positioning. Table 6 summarises RR1508 recording parameters and Table 7 lists RR1508 lines acquired and processed.</p> <p><strong>Data Processing: </strong>A total of 13 HKS01 seismic lines were processed to post-stack time-migrated SEGY sections, using GNS Science GLOBE CLARITAS. Data processing included application of geometry, sorting, trace editing, normal moveout correction, stack, filtering and finite difference migration. All seismic data are written to disk as processed sections in SEG-Y format. Details of the RR1508 processing parameters are given in Table 8 and SEG-Y trace headers in Table 9.</p> <p> </p> <p><strong>List of files</strong></p> <p>Figure 1. TAN1114 and RR1508 seismic line locations on the northern Hikurangi margin.</p> <p>Table 1. Summary of TAN1114 recording parameters.</p> <p>Table 2. Summary of TAN1114 lines acquired and processed.</p> <p>Table 3. Summary of TAN1114 line coordinates.</p> <p>Table 4. Summary of TAN1114 seismic processing sequence.</p> <p>Table 5. Summary of TAN1114 SEG-Y trace headers.</p> <p>Table 6. Summary of RR1508 recording parameters.</p> <p>Table 7. Summary of RR1508 lines acquired and processed.</p> <p>Table 8. Summary of RR1508 seismic processing sequence.</p> <p>Table 9. Summary of RR1508 SEG-Y trace headers.</p> <p> </p> <p>Processed SEGY seismic data</p> <p>TAN1114-01.sgy</p> <p>TAN1114-02.sgy</p> <p>TAN1114-03.sgy</p> <p>TAN1114-04.sgy</p> <p>TAN1114-04A.sgy</p> <p>TAN1114-05.sgy</p> <p>TAN1114-06.sgy</p> <p>TAN1114-06A.sgy</p> <p>TAN1114-07.sgy</p> <p>TAN1114-07A.sgy</p> <p>TAN1114-08.sgy</p> <p>TAN1114-09.sgy</p> <p>TAN1114-10.sgy</p> <p>TAN1114-10B.sgy</p> <p>TAN1114-11.sgy</p> <p>TAN1114-12.sgy</p> <p>TAN1114-12T.sgy</p> <p>TAN1114-13.sgy</p> <p>TAN1114-14.sgy</p> <p>TAN1114-15.sgy</p> <p>TAN1114-16.sgy</p> <p>TAN1114-17.sgy</p> <p>TAN1114-18.sgy</p> <p>TAN1114-19.sgy</p> <p>TAN1114-20.sgy</p> <p>TAN1114-21.sgy</p> <p>TAN1114-22.sgy</p> <p>TAN1114-23.sgy</p> <p>TAN1114-A.sgy</p> <p>RR1508-HKS01_01.sgy</p> <p>RR1508-HKS01_02.sgy</p> <p>RR1508-HKS01_02A.sgy</p> <p>RR1508-HKS01_03.sgy</p> <p>RR1508-HKS01_04.sgy</p> <p>RR1508-HKS01_05.sgy</p> <p>RR1508-HKS01_05A.sgy</p> <p>RR1508-HKS01_06.sgy</p> <p>RR1508-HKS01_07.sgy</p> <p>RR1508-HKS01_08.sgy</p> <p>RR1508-HKS01_09.sgy</p> <p>RR1508-HKS01_09A.sgy</p> <p>RR1508-HKS01_10.sgy</p>
Artifact: Reflecting on Recurring Failures in IoT Development
<p>Artifact for Reflecting on Recurring Failures in IoT Development</p>
Airborne spectral reflectance dataset of submerged plastic targets in a coastal environment (SUPPLEMENTARY DATA FILES)
<p>SUPPLEMENTARY DATA FILES for the paper entitled: "Airborne spectral reflectance dataset of submerged plastic targets in a coastal environment." Data are organized in three categories:</p> <p>- orthophoto maps (date in YYYY,MM,DD e.g. 20210615_SPOTS_Ortho_UTM_35N)</p> <p>- Hyper Cubes UTM_35N NO Normalized (date in YYYY,MM,DD_Flight number_Height_UTM zone e.g. 20210615_F47_H25_UTM_35N)</p> <p>Hyper Cubes UTM_35N Normalized (date in YYYY,MM,DD_Flight number_Height_UTM zone_NORM e.g. 20210615_F47_H25_UTM_35N_NORM)</p>
Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner
<p>The spiking activity of populations of cortical neurons is well described by a small number of population-wide covariance patterns, the "latent dynamics". These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFP). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable across behaviour: in each of the primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recording.</p>
Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets
<p>Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets</p> <p>The impact of simulated rainfall on the soil surface roughness of different soil types with various initial surface states and the differences between their spectral characteristics were studied under laboratory conditions. The soil samples were collected from a horizon of fields near Poznań, western Poland. The physical and physicochemical properties of each soil sample were determined. Then, the part of the soil materials, consisting of natural aggregates, were used to form three soil surface roughness. </p> <p>An explanation of the table column names in the “soils properties.csv” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“sand” - Sand content in the soil sample in %.</p> </li> <li> <p>“silt” – Silt content in the soil sample in %.</p> </li> <li> <p>“clay” – Clay content in the soil sample in %.</p> </li> <li> <p>pHH2O” - The pH of the soil sample determined in water. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“pHKCl” – The pH of the soil sample determined in KCl. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“SOC” – Organic matter content in soil was determined by oxidation titration using K2Cr2O7 with H2SO4 on the block mineralization.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “rainfall doses.csv” file:</p> <p> </p> <ul> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p>“rainfall dose” - One-time amount of rainfall dose expressed in millimeters.</p> </li> <li> <p>“accumulated rainfall” – Summation of rainfall after each successive dose expressed in millimeters.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “soil measurements” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p> “reflectance” - The amount of radiation reflected from the soil surface under the influence of successive rainfalls and expressed in nanometres. </p> </li> <li> <p>“roughness state” - The size of the roughness: R1 is the lowest soil roughness state, R2 represents medium soil roughness, and R3 represents the greatest roughness.</p> </li> <li> <p>“T3D” - Tortuosity index is a surface roughness index. It was calculated from DEM (Digital Elevation Model). It expresses the ratio between the true surface of DEM and its flat horizontal area.</p> </li> <li> <p>“HSD” - Height Standard Deviation is the second surface roughness index. It was calculated from DEM and expressed in millimeters. </p> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><br> </p> <p> </p>
ScienceDex guides
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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.