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497 results for “campaign”
Aerosol, Temperature and Water Vapor profiling during the BIOSPHERE Athens Campaign - NTUA - Level 3 - July 2023
<p>July 2023 - Level 3 of the lidar data obtained by the EOLE and DEPOLE lidar systems in the National Technical University of Athens (NTUA), during the EURAMET European Partnership on Metrology (EPM) project BIOSPHERE.</p>
Aerosol, Temperature and Water Vapor profiling during the BIOSPHERE Athens Campaign - NTUA - Level 3 - August 2023
<p>August 2023 - Level 3 of the lidar data obtained by the EOLE and DEPOLE lidar systems in the National Technical University of Athens (NTUA), during the EURAMET European Partnership on Metrology (EPM) project BIOSPHERE.</p>
Horizon2020 [FORCeS] Puijo SMEAR-IV campaign data
<p>A 3 month long ambient measurement campaign at SMEAR-IV station (station code FI0063R; Kuopio, Finland; https://www.atm.helsinki.fi/smear/?page_id=85) with complimenting ground station measurements. Semi-urban environment. SMEAR-IV station measurement height is at 80m above ground.</p> <p>Measured quantities at SMEAR-IV: meteorological parameters, atmosheric gasses (O3, NOx, SO2), black carbon (MAAP-5012 Thermo Fischer Scientific), particle number concentration and size distribution data (DMPS). SMEAR-IV station has a switching double inlet set-up: whole air (ambient aerosol smaller than 50 um at ambient conditions), and interstitial (ambient aerosol smaller than 1 um at ambient conditions) which was utilised in black carbon measurements. </p> <p>Measured quantities at ground station: meteorological parameters, atmosheric gases (O3, NOx, SO2), particle number concentration and size distribution data (SMPS, TSI Inc.), and aerosol bulk proprties (AMS, Aerodyne Reserach Inc.). </p> <p> </p> <p>Additional data measured at the SMEAR-IV station: cloud condesation nuclei activation diameter (CCN counter, Droplet Measurement Technologies), aerosol particle bulk composition and properties (ACSM, Aerodyne Reserach Inc.), more detailed particle composition (FIGAERO-CIMS, Aerodyne Reserach Inc.), cloud droplet concentration, and visibility. </p> <p>Additional data measured at the ground station: cloud condesation nuclei activation diameter (CCN counter, Droplet Measurement Technologies), more detailed particle composition (FIGAERO-CIMS, Aerodyne Reserach Inc.), small oxygenated compounds, polar molecules, BTEX, PAHs, other aromatics (VOCUS, Aerodyne Reserach Inc.). </p> <p>Please contact creators for these data. </p> <p> </p> <p>This work was supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 821205 (FORCeS).</p>
Ice-Nucleating Particle Concentrations from the MC2/ISLAS 2021 campaign in Andenes, and NorESM2 simulations with observationally constrained INPs
<p>This dataset containts the data for the article <strong>Using a region-specific ice-nucleating particle parameterization improves the representation of Arctic clouds in a global climate model </strong>(https://doi.org/10.5194/acp-25-1617-2025), published in Atmospheric Chemistry and Physics (ACP). It consists of ice-nucleating particle (INP) measurements collected as part of the MC2/ISLAS campaign in Andenes, Norway (69<em>° </em>18’ N, 16<em>°</em> 07’ E) in 2021. Additionally, it consists of simultaneous aerosol measurements, back trajectories for the INP measurement times, and model data from the Norwegian Earth System model (Seland et al., 2020) where INP concentrations where constrained in the Arctic using these INP measurements. </p> <p><strong>Abstract:</strong></p> <p><em>Projections of global climate change and Arctic amplification are sensitive to the representation of low-level cloud phase in climate models. Ice-nucleating particles (INPs) are necessary for primary cloud ice formation at temperatures above approximately -38 °C, and thus significantly affect cloud phase and cloud radiative effect. Due to their complex and insufficiently understood variability, INPs constitute an important modelling challenge, especially in remote regions with few observations, such as the Arctic. In this study, INP observations were carried out at Andenes, Norway in March 2021. These observations were used as a basis for an Arctic-specific and purely temperature-dependent INP parameterization, and implemented into the Norwegian Earth System Model. This implementation results in an annual average increase in cloud liquid water path (CLWP) of 70 % for the Arctic, and improves the representation of cloud phase compared to satellite observations. The change in CLWP in boreal autumn and winter is found to likely be the dominant contributor to the annual average increase in net surface cloud radiative effect of 2 W m<sup>-2</sup>. This large surface flux increase brings the simulation into better agreement with Arctic ground-based measurements. Despite that the model cannot respond fully to the INP parameterization change due to fixed sea surface temperatures, Arctic surface air temperature increases with 0.7 °C in boreal autumn. These findings indicate that INPs could have a significant impact on Arctic climate, and that a region-specific INP parameterization can be a useful tool to improve cloud representation in the Arctic region.</em></p> <p>The dataset contains three subsets:</p> <p>i) <strong>aerosol_data:</strong> Observations of Ice-Nucleating Particles (INPs) in Andenes, Norway, March 2021, as well as simultaneous aerosol measurements</p> <p>ii) <strong>INP_trajectories: </strong>Back trajectories at the time of INP measurements</p> <p>iii) <strong>model_data: </strong>Simulations with NorESM2 using the Andenes 2021 INP observations to constrain INPs in the Arctic</p> <p>Additionally, scripts for visualizing the data and for reproducing the NorESM2 model setup can be found in the folder <strong>scripts. </strong>The data folders and scripts folder should be in the same repository when running the scripts.</p> <p>Some of the scripts use other openly available datasets. The availability of these are listed below. All the specific datasets can also be provided to the user upon request. </p> <p>The CALIOP L2 data used to derive SLF metrics (used in Fig07.py) and the CERES EBAF data (used in Fig12.py) can be downloaded freely at <a href="https://search.earthdata.nasa.gov/" rel="nofollow">https://search.earthdata.nasa.gov/</a>. The derived SLF metrics can also be found at Bruno (2022), and are also described in Hofer et al. (2024) and Shaw et al. (2022). The CALIPSO-GOCCP data product (used in Fig08.py) can be downloaded from https://climserv.ipsl.polytechnique.fr/cfmip-obs/Calipso_goccp.html. The surface radiation flux (used in Fig13.py) can be downloaded freely at <a href="https://www.pangaea.de/" rel="nofollow">https://www.pangaea.de/</a>. The ERA5 data used to produce the back trajectories can be found at https://doi.org/10.24381/cds.bd0915c6. The colormap from Crameri et al. (2020) was used when preparing the figures. </p> <p>References:</p> <ul> <li>Bruno, O. (2022). Distributions of supercooled liquid fraction from CALIOP V4 [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8289058" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8289058</a></li> <li>Crameri, F., Shephard, G.E. & Heron, P.J. The misuse of colour in science communication. <em>Nat Commun</em> <strong>11</strong>, 5444 (2020). https://doi.org/10.1038/s41467-020-19160-7</li> <li>Hofer, S., Hahn, L.C., Shaw, J.K. et al. Realistic representation of mixed-phase clouds increases projected climate warming. Commun Earth Environ 5, 390 (2024). <a href="https://doi.org/10.1038/s43247-024-01524-2" rel="nofollow">https://doi.org/10.1038/s43247-024-01524-2</a></li> <li>Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geoscientific Model Development, 13, 6165–6200, <a href="https://doi.org/10.5194/gmd-13-6165-2020" rel="nofollow">https://doi.org/10.5194/gmd-13-6165-2020</a>, 2020.</li> <li>Shaw, J., McGraw, Z., Bruno, O., Storelvmo, T., & Hofer, S. (2022). Using satellite observations to evaluate model microphysical representation of Arctic mixed-phase clouds. Geophysical Research Letters, 49, e2021GL096191. <a href="https://doi.org/10.1029/2021GL096191" rel="nofollow">https://doi.org/10.1029/2021GL096191</a></li> </ul>
Simulations for convection during the intensive observation period of the TWP-ICE field campaign: results from cloud-resolving model and convective parameterization schemes
<p>Simulated convections from cloud-resolving model and convective parameterization schemes during the intensive observation period of the TWP-ICE field campaign, which are used to investigate the scale-awareness problem of convective parameterization schemes. The corresponded observations are also included in this dataset.</p>
Ka, W and G-band radar observations of clouds and light precipitation during the EPCAPE campaign in March and April 2023
<p>The files contained in the data sets include Ka, W and G-band radar observations of clouds and light precipitation for several days from March 23 to April 27, 2023, during the EPCAPE campaign in La Jolla, CA, USA. <br>The YYYYMMDD_HHMMSS file naming convention corresponds to the starting measurement time of the data set in UTC.<br>The CloudCube_EPCAPE_Gband_Spectra.zip folder contains G-band radar Doppler spectra in the form of calibrated reflectivity as a function of Doppler velocity and range, where the noise has been masked out. The CloudCube_EPCAPE_Gband_Spectra_Noise.zip folder contains G-band radar Doppler spectra, including noise and SNR. The CloudCube_EPCAPE_Gband_Moments.zip folder contains G-band radar Doppler spectra moments, i.e. calibrated reflectivity, mean Doppler velocity and Doppler spectrum width. The CloudCube_EPCAPE_Multifrequency.zip folder includes Ka, W and G-band calibrated reflectivity and dual-frequency reflectivity ratios. <br>These data were obtained from CloudCube, a Ka, W and G-band atmospheric profiling radar, to demonstrate synergies between multifrequency retrievals.<br>For more details about the data processing and description, please refer to: Socuellamos, J. M., Rodriguez Monje, R., Lebsock, M. D., Cooper, K. B., Beauchamp, R. M., and Umeyama, A.: Multifrequency radar observations of marine clouds during the EPCAPE campaign, Earth Syst. Sci. Data. https://doi.org/10.5194/essd-2023-454, 2024. </p>
Dataset for the MOSES heat&drought campaign 2020 (HD2020)
<p>Unprocessed CRNS rover Data from the southern Rur catchment and unprocessed stationary CRNS data from the Kleinhau Station. In addition a calibration dataset is provided for the stationary CRNS.</p>
Dataset of the experimental campaign presented in the paper entitled "Shaking table seismic experimental investigation of lightweight rigid bodies"
<p>This study presents the findings of an extensive shaking table experimental campaign conducted on nine free standing wooden specimens, aiming at providing insights on the rigid body motion of free-standing objects. The specimens, which differ in slenderness and size, are characterized by impairments in their base surface and most likely in their shapes, which also lead to asymmetric responses. The imperfections of the tested objects are an additional source of uncertainty with respect to the intrinsic chaotic character of the rigid body motion, which is a crucial factor that prevents the reproducibility of the tests and induces discrepancies between specimen responses and those of their ideal models. A contactless measurement strategy is employed to assure unaltered data acquisition. The experimental campaign includes free vibration tests, pulse excitation and natural ground motions tests; the dynamic responses of the specimens are organized and rearranged, aiming at providing a comprehensive set of data that could be employed for calibrating numerical models accounting for imperfect conditions. The damping properties of the specimens are discussed, providing a novel estimation of the coefficient of restitution based on the free vibration tests. The limits of the ideal simple rigid model are highlighted, and the roles of size factor and aspect ratio are discussed according to the obtained results.</p>
Raw high-frequency EK80 echosounder datasets from autonomous sailbuoy campaign in Lofoten/Vesterålen 2018
<p>In 2018, the autonomous surface vehicle sailbuoy <a href="http://sailbuoy.no/13-news/74-introducing-the-sb-echo">Echo</a> surveyed the coastal shelf surrounding the Lofoten and Vesterålen islands (aka. LoVe), Norway. The Echo was equipped with a <a href="https://www.kongsberg.com/maritime/products/ocean-science/ocean-science/es_scientific/transducers-for-scientific-echo-sounders/simrad-wbt-mini">EK80</a> high-frequency echosounder, and over the course of 131 field days, 8791 <code>.raw</code> EK80 echosounder files were captured, with a total size of ~750 GB.</p> <p>In 2022, Akvaplan-niva published the complete, raw data from Echo's LoVe 2018 campaign in their research archive on <a href="https://zenodo.org/communities/akvaplan-niva/search?page=1&size=20&q=sailbuoy%20ek80%202018">Zenodo</a>.</p> <p>EK80 datasets from Sailbuoy Echo in LoVe 2018:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.6839067">2018-03-08/2018-03-12</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839167">2018-03-13/2018-03-17</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839191">2018-03-18/2018-04-01</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839345">2018-04-02/2018-04-05</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839512">2018-04-06/2018-04-26</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839520">2018-04-27/2018-05-03</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839542">2018-05-04/2018-05-30</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839550">2018-05-31/2018-06-02</a></li> <li><a href="https://doi.org/10.5281/zenodo.6834583">2018-06-03/2018-06-06</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839566">2018-06-07/2018-06-09</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839576">2018-06-10/2018-06-12</a></li> <li><a href="https://doi.org/10.5281/zenodo.6787552">2018-06-13/2018-06-17</a></li> <li><a href="https://doi.org/10.5281/zenodo.6786850">2018-06-18/2018-06-30</a></li> <li><a href="https://doi.org/10.5281/zenodo.6822565">2018-07-01/2018-07-11</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839593">2018-07-12/2018-07-17</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839740">2018-07-18/2018-07-28</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839756">2018-07-29/2018-08-22</a></li> </ul> <p>See also: <a href="https://doi.org/10.5281/zenodo.6786918">Real-time oceanography captured by autonomous sailbuoy in Lofoten/Vesterålen 2018</a></p> <p>The sailbuoy Echo 2018 LoVe campaign was part of the research project<br> <a href="https://prosjektbanken.forskningsradet.no/en/project/FORISS/269188">Unmanned ocean vehicles, a flexible and cost-efficient offshore monitoring and data management approach</a></p>
Chemical composition of PM2.5 for Deyang campaign
<p>The file contains the concentrations of different PM2.5 compositions measured by a ToF-ACSM, including organics, nitrate, sulphate, ammonium and chloride (μg/m3). The interval of the campaign was from 18 December 2021 to 22 January 2022, adn the temporal resolution of the data is 10 min.</p>
Holocene marine tephra offshore Ecuador and Southern Colombia collected during the Amadeus and Atacames oceanographic campaigns (2005, 2012)
<p>Marine sediment cores collected during the Amadeus and Atacames oceanographic cruises reveal numerous turbidite and ash deposits, which are related to major earthquakes and volcanic eruptions that occurred along the Ecuadorian margin during the Holocene. We provide <sup>14</sup>C ages performed on planktonic foraminifera present in hemipelagic layers to constrain the age of turbidite sequences, as well as geochemical analyses of ash layers to identify their source.</p> <p>Files:</p> <p>1. Location of coring sites<br> 2. Raw 14C ages performed on planktonic foraminifera<br> 3. Depth of tephra layers<br> 4. Raw Electron Microprobe results (single glass shards)<br> 5. Raw LA-ICP-MS results (single glass shards)<br> 6. Raw ICP-AES results (bulk glass shards)</p> <p> </p>
Effectiveness of seasonal malaria chemoprevention administered in a mass campaign in the Kedougou region of Senegal in 2016: a Case-control study
<p><strong><span>Context</span></strong></p> <p><span>Seasonal malaria chemoprevention (SMC) with Sulfadoxine-Pyrimethamine plus Amodiaquine (SPAQ) is a malaria prevention strategy recommended since 2012 by the World Health Organization (WHO) for children under 5 years of age. In Senegal, the scaling up of the SMC has started since 2013 in the south-eastern regions of the country with an extension of the target to 10 years old children. The scaling up of SMC requires a regular evaluation of the strategy as recommended by the WHO. This study was conducted to evaluate the effectiveness of SMC. </span></p> <p><strong><span>Methodology </span></strong></p> <p><span>A case-control study was conducted in some villages of the health districts of Saraya and Kedougou in the Kedougou region from July to December 2016. A "case" was a sick child, aged 3 months to 10 years, seen in consultation and with a positive RDT. The "control" was a child of the same age group with a negative RDT and living in the same compound as the case or in a neighbouring compound. Each case was matched with two controls. Exposure to SMC was assessed by interviewing the mothers/caretakers and by checking the SMC administration card.</span></p> <p><strong><span>Results</span></strong></p> <p><span>A total of 492 children, including 164 cases and 328 controls, were recruited for our study. Their mean ages were 5.32 (+/- 2.15) and 4.44 (+/-2.25) years for the cases and the controls respectively. Male children predominated in both cases (55.49%) and controls (51.22%) (p=</span><span>0.18)</span><span>. Net ownership was 85.80% among cases and 90.85% among controls (p=0.053). The proportion of controls who received SMC was higher than that of cases (98.17% vs 85.98%; (p=1.10<sup>-7</sup>)). The protective effectiveness of SMC was 89% </span><span>(IC 95% = 84–93%)</span><span> </span><span>(OR=0.11).</span></p> <p><strong><span>Conclusion </span></strong></p> <p><span>SMC is therefore an effective strategy in the control of malaria in children. Case-control studies are a good approach for monitoring the efficacy of drugs administered during SMC.</span></p>
Data of "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework"
<p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework",<br> journal = "Engineering Fracture Mechanics",<br> year = "2022",<br> volume = "275",<br> pages = "108844 ",<br> doi = "https://doi.org/10.1016/j.engfracmech.2022.108844",<br> author = "Antoine Hilhorst, Julien Leclerc, Thomas Pardoen, Pascal J. Jacques, Ludovic Noels, Van-Dung Nguyen"</p> <p>New version following review.</p> <p> </p> <p> </p>
Video - Gasification Test Campaign (May 2022)
<p>This video presents a pilot-scale gasification test campaign conducted at VTT’s Pilot Centre Bioruukki in May 2022. The test campaign shows the process for optimizing the co-production of biochar and synthesis gas from woody residues.</p>
GLORIA 3-D temperature and ALIMA temperature measurements from flight 12 of the SouthTRAC measurement campaign
<p>This dataset consists of temperature measurements acquired from the German HALO research aircraft during a research flight over Southern Andes on 20-21 September, 2019, as part of the SouthTRAC measurement campaign. </p> <p>ALIMA lidar instrument is developed and operated by the German Aerospace center (Deutsches Zentrum für Luft- und Raumfahrt, DLR). The data included here covers the whole research flight.</p> <p>GLORIA infrared limb imaging spectrometer is jointly developed and operated by the Jülich Research Center (Forschungszentrum Jülich). The data included here is the 3-D temperature retrieval from the hexagonal flight pattern, which was flown from 02:50 UTC till 06:10 UTC on 21 Septermber, 2019.</p>
OCU settings: aerosol characterisation campaign
<p>Datasets for Section "3.1. Sample stability and general aerosol properties" of the following publication:</p> <p>"The organic coating unit, an all-in-one system for reproducible generation of secondary organic matter aerosol" by Alejandro Keller, Daniel M. Kalbermatter, Kate Wolfer, Patrik Specht, Peter Steigmeier, Julian Resch, Markus Kalberer, Tobias Hammer & Konstantina Vasilatou, DOI: 10.1080/02786826.2022.2110448.</p> <p>Aerosol properties, such as GMDmob, EC/TC mass fraction, SSA, and particle number concentration, for various operation points of the OCU at different days.</p> <p> </p>
TEAMx-PC22 (TEAMx pre-campaign 2022) - Animations of radial velocity and coplanar-retrieved horizontal wind fields from KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159
<p><strong>Abstract</strong></p> <p>This data set was collected during the TEAMx pre-campaign in summer 2022 (TEAMx-PC22) in the Inn Valley Target Area, Austria.</p> <p><strong>Data Description</strong></p> <p>This data set is comprised of 64 daily .mp4 files showing:</p> <ul> <li>post-processed radial velocity fields sampled by KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159</li> <li>their coplanar-retrieved horizontal wind field output</li> <li>additionally, horizontal wind speed and direction sampled by the KITcube Vaisala Windcube v2.1 (WLS7-1489) at 60-m above ground level is added as an independent measurement for subjective validation of the coplanar-retrieved wind</li> </ul> <p>The wind fields shown in the animations originate from an accompanying Zenodo data set (DOI: 10.5281/zenodo.7212801), where complete information concerning lidar locations, scan parameters, and post-processing may be found.</p>
Eddy covariance data of lower-cost and conventional setups and meteorological data, Wendhausen 2022 campaign
<p>Eddy covariance fluxes, together with ancillary meteorological data, spectra, stability and turbulence parameters, corresponding to the measurement campaign conducted in Wendhausen, Lehre, Lower Saxony (Germany) in 2022. The results were presented in the paper <em>Comparison between lower-cost and conventional eddy covariance set-ups for CO2 and evapotranspiration measurements above monocropping and agroforestry systems</em>, <a href="https://dx.doi.org/10.2139/ssrn.4632023" target="_blank" rel="noopener">http://dx.doi.org/10.2139/ssrn.4632023</a>.</p> <p>Spectra and co-spectra were averaged as presented in the paper. Flux data are not filtered or quality checked in these files, but the procedure followed for that is described in the paper as well.</p>
A Photometric, Spectroscopic, and Apsidal Motion Analysis of the F-type Eclipsing Binary BW Aquarii from K2 Campaign 3
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018AJ....156....8L/abstract">Lester & Gies (2018)</a>. MESA version 10108.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-3881/aac2ea">10.3847/1538-3881/aac2ea</a> </p>
Dataset [Study on Algorithms in Election Campaigns: Analysis with IRaMuteQ]
<p>This is the raw data behind the publication:</p> <p>Carvalho, P. R; Ramos, M. G.; Schneider, M. A. F. Study on algorithms in election campaigns: analysis with Iramuteq. XX ENANCIB 2019.</p> <p>The present work reports the exploratory empirical research carried out in <strong>the Scopus database</strong> with the objective of identifying, quantifying and analyzing the scientific production of the topic algorithms in politics in electoral campaigns, <strong>from 2008 to 2018,</strong> by means of Scientometric techniques. In addition, the Content Analysis of abstracts of the articles collected through Iramuteq, open source and free software. The methodological proposal refers to the construction of a textual corpus composed of <strong>150 articles </strong>retrieved, following analyzes such as: pre-analysis of the material; data mining; simple statistical analysis; Descending Hierarchical Classification; Factorial Correspondence Analysis; similitude analysis; and frequency analysis with word cloud visualization. The result of the study demonstrated the efficiency of the chosen keywords in the retrieval of information. In addition, we identified the convergence and repetition of themes of the articles represented by the text segments.</p> <p>Link: <a href="https://brapci.inf.br/index.php/res/v/122943">https://brapci.inf.br/index.php/res/v/122943</a></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.