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497 results for “campaign”
Balloon-borne Aerosol-Cloud Interaction Studies (BACIS): Field campaigns to understand and quantify aerosol effects on clouds
<p>The dataset uploaded on Zenodo consists of in-situ measurements from specialised balloon borne sondes used in the field campaign named BACIS (Balloon borne Aerosol Cloud Interaction Studies) conducted from Gadanki, a location in Southern Peninsular India. Note that raw data from the observations is only uploaded here. There is lot more post processing of data carried out during our analysis for brining out meaningful data. Hence the users are cautioned in using the raw data for analysis and kindly advised to approach us for the post processed, quality controlled data. </p>
LiftWEC deliverable D4.4: Dataset from 2D experimental test campaign, with calculated hydrodynamic forces
<p><em>This dataset contains 2-dimensional wave tank testing data for a wave-driven rotating hydrofoil model. The model tested is composed of one or two hydrofoils rotating around a horizontal axis, perpendicular to the wave direction. The model was tested in a range of regular and irregular seas. The data contains measurements of the model in the wave tank including; wave measurement, rotor position, forces on the hydrofoils, and torque on the power take off. </em> <em>This data is the first of two sets of wave tank data generated for the LiftWEC H2020 research project. This first set consists of results for the device tested in 2D, while the second set will contain results for tests conducted in 3D. </em><em>This new version contains all data from version 1 of the first set, which consists of measurement from the experimental testing, plus results from the data analysis calculating the hydrodynamic forces. These forces are calculated by removing the static force and the centrifugal force. For a complete description of the test campaign, readers are directed to "LiftWEC Deliverable D4.4. </em> Report on physical modelling of 2D LiftWEC concepts <em>"</em></p>
Deep decarbonisation for refineries starts in Ireland: introducing the REALISE pilot campaigns
<p>This webinar shared the cutting-edge research on CO<sub>2</sub> capture solvent stability and solvent management being carried out in a real-life refinery setting in order to support industry’s decarbonisation ambitions. The event introduced the scientists engaged in solvent testing campaigns at Irving Oil Whitegate Refinery in Ireland and SINTEF’s CO<sub>2</sub> laboratories at Tiller in Norway. It included a short film and a panel Q&A. </p> <p><strong>Programme</strong></p> <ul> <li>Welcome, introduction & REALISE CCUS project overview – Peter van Os, TNO</li> <li>Solvent testing in a real-life refinery setting: introduction – Juliana Monteiro, TNO</li> <li>Demonstration at Irving Oil Whitegate Refinery: video premiere and insights – Eirini Skylogianni, TNO</li> <li>Demonstration goals at SINTEF’s Tiller CO<sub>2</sub> labs – Thor Mejdell, SINTEF</li> <li>Panel Q&A</li> </ul>
QA4EO-WP2230-D2.2.3.3 Comparison dataset from the OHP campaign
<p>Within the QA4EO project a Precision FilterRadiometer (PFR) traveling standard was installed at the European calibration site of AERONET Europe to provide continuous traceability of aerosol optical depth measurements to the World reference of AOD maintained at Davos through a PFR-Triad. The objective is to homogenize the two global monitoring networks of passive remote sensing of aerosol optical properties, which serve as fiducial reference for satellite derived aerosol properties.</p> <p>The PFR traveling standard was installed collaboration of the laboratory of atmospheric optics (LOA,CNRS) at the Observatoire de Haute province (OHP) in southern France in July 2020 and operated continuously with minor interruptions until September 2021. During clear sky periods the measurements of spectral solar irradiance are used to retrieve aerosol optical depth from the master instrument of AERONET-Europe and the PFR. The online comparison (on going activity) can be found in https://www.pmodwrc.ch/en/world-radiation-center-2/worcc/gaw-pfr/ohp/ </p> <p>The dataset consists of the AOD data of the AERONET-Europe ( 20200101_20221231_OHP_OBSERVATOIRE.lev20 ) and the GAW-PFR network ( OHP_PFR_N14_202007_202102.zip ) and the synchronizes common dataset of the Cimel and PFR instruments QA4EO_WP2230_OHP_Campaign_2020_2021_level2.mat. the fields of the matlabfile are described in the corresponding readme file. </p>
GPS data for COPEX campaign carried out in 2002 in Brazil by INPE (Boa Vista Station)
<p>This repository provides the GPS data acquired in Boa Vista station during the Conjugate Point Equatorial Experiment (COPEX) campaign in 2002. In the folders it can find the raw intensity data (FSL file) and also the summary files (SUM files), containing only with S4 index with time and satellite ID. More information about the campaign can be found in Muella et al. (2008), Abdu et al. (2009) and de Paula et al. (2010). Information about the data recording format and reading extraction procedure of this dataset can be found in Appendix B of Beach (1998). More details regarding the receiver used and extraction tools can be found at: <a href="https://gps.ece.cornell.edu/tools.php">https://gps.ece.cornell.edu/tools.php</a></p> <p>Abdu, M. A., Batista, I. S., Reinisch, B. W., De Souza, J. R., Sobral, J. H. A., Pedersen, T. R., ... & Groves, K. M. (2009). Conjugate Point Equatorial Experiment (COPEX) campaign in Brazil: Electrodynamics highlights on spread F development conditions and day‐to‐day variability. Journal of Geophysical Research: Space Physics, 114(A4).</p> <p>Beach, T. L. (1998). Global Positioning System studies of equatorial scintillations. Cornell University.</p> <p>De Paula, E. R., Muella, M. T. A. H., Sobral, J. H. A., Abdu, M. A., Batista, I. S., Beach, T. L., & Groves, K. M. (2010). Magnetic conjugate point observations of kilometer and hundred‐meter scale irregularities and zonal drifts. Journal of Geophysical Research: Space Physics, 115(A8).</p> <p>Muella, M. T. A. H., De Paula, E. R., Kantor, I. J., Batista, I. S., Sobral, J. H. A., Abdu, M. A., ... & Smorigo, P. F. (2008). GPS L-band scintillations and ionospheric irregularity zonal drifts inferred at equatorial and low-latitude regions. Journal of Atmospheric and Solar-Terrestrial Physics, 70(10), 1261-1272.</p>
GPS data for COPEX campaign carried out in 2002 in Brazil by INPE (Campo Grande Station)
<p>This repository provides the GPS data acquired in Campo Grande station during the Conjugate Point Equatorial Experiment (COPEX) campaign in 2002. In the folders it can be found the raw intensity data (FSL file) and also the summary files (SUM files), containing only with S4 index with time and satellite ID. More information about the campaign can be found in Muella et al (2008), Abdu et al (2009) and de Paula et al (2010). Information about the data recording format and reading extraction procedure of this dataset can be found in Appendix B of Beach (1998). More details regarding the receiver used and extraction tools can be found at: <a href="https://gps.ece.cornell.edu/tools.php">https://gps.ece.cornell.edu/tools.php</a></p> <p>Abdu, M. A., Batista, I. S., Reinisch, B. W., De Souza, J. R., Sobral, J. H. A., Pedersen, T. R., ... & Groves, K. M. (2009). Conjugate Point Equatorial Experiment (COPEX) campaign in Brazil: Electrodynamics highlights on spread F development conditions and day‐to‐day variability. Journal of Geophysical Research: Space Physics, 114(A4).</p> <p>Beach, T. L. (1998). Global Positioning System studies of equatorial scintillations. Cornell University.</p> <p>De Paula, E. R., Muella, M. T. A. H., Sobral, J. H. A., Abdu, M. A., Batista, I. S., Beach, T. L., & Groves, K. M. (2010). Magnetic conjugate point observations of kilometer and hundred‐meter scale irregularities and zonal drifts. Journal of Geophysical Research: Space Physics, 115(A8).</p> <p>Muella, M. T. A. H., De Paula, E. R., Kantor, I. J., Batista, I. S., Sobral, J. H. A., Abdu, M. A., ... & Smorigo, P. F. (2008). GPS L-band scintillations and ionospheric irregularity zonal drifts inferred at equatorial and low-latitude regions. Journal of Atmospheric and Solar-Terrestrial Physics, 70(10), 1261-1272.</p>
GPS data for COPEX campaign carried out in 2002 in Brazil by INPE (Cachimbo Station)
<p>This repository provides the GPS data acquired in Cachimbo station during the Conjugate Point Equatorial Experiment (COPEX) campaign in 2002. In the folders it can find the raw intensity data (FSL file) and also the summary files (SUM files), containing only with S4 index with time and satellite ID. More information about the campaign can be found in Muella et al. (2008), Abdu et al. (2009) and de Paula et al. (2010). Information about the data recording format and reading extraction procedure of this dataset can be found in Appendix B of Beach (1998). More details regarding the receiver used and extraction tools can be found at: <a href="https://gps.ece.cornell.edu/tools.php">https://gps.ece.cornell.edu/tools.php</a></p> <p>Abdu, M. A., Batista, I. S., Reinisch, B. W., De Souza, J. R., Sobral, J. H. A., Pedersen, T. R., ... & Groves, K. M. (2009). Conjugate Point Equatorial Experiment (COPEX) campaign in Brazil: Electrodynamics highlights on spread F development conditions and day‐to‐day variability. Journal of Geophysical Research: Space Physics, 114(A4).</p> <p>Beach, T. L. (1998). Global Positioning System studies of equatorial scintillations. Cornell University.</p> <p>De Paula, E. R., Muella, M. T. A. H., Sobral, J. H. A., Abdu, M. A., Batista, I. S., Beach, T. L., & Groves, K. M. (2010). Magnetic conjugate point observations of kilometer and hundred‐meter scale irregularities and zonal drifts. Journal of Geophysical Research: Space Physics, 115(A8).</p> <p>Muella, M. T. A. H., De Paula, E. R., Kantor, I. J., Batista, I. S., Sobral, J. H. A., Abdu, M. A., ... & Smorigo, P. F. (2008). GPS L-band scintillations and ionospheric irregularity zonal drifts inferred at equatorial and low-latitude regions. Journal of Atmospheric and Solar-Terrestrial Physics, 70(10), 1261-1272.</p>
Plume and wind data from ROMEO campaign on 17.10.2019.
<p>Measurements from the oil well 1474 in Darmanesti, Parhova County, Romania. Dataset contains plume transect measurements taken by the team from TNO during the ROMEO measurement campaign. Included in the dataset are the measurements from a tracer (N2O), emitted next to the oil well. The wind data measured on-site with a 3D sonic is also added as 1 min averages of 25 Hz measurements of the three wind components.</p>
TEAMx-PC22 (TEAMx pre-campaign 2022) – video clip
<p><strong>ABSTRACT</strong></p> <p>This video clip gives a brief overview of the measurement activities during the TEAMx pre-campaign in summer 2022 (TEAMx-PC22). Although it shows some of the main measurement locations and instruments in the lower Inn Valley (Austria), it is far from being complete. Unfortunately, there are no impressions of the measurements conducted in the exit region of the Inn Valley, near Innsbruck, or at other sites in the Alps. More information on TEAMx (Multi-scale transport and exchange processes in the atmosphere over mountains – programme and experiment) including the upcoming main campaign (TEAMx Observational Campaign TOC) can be found here: <a href="http://www.teamx-programme.org/">http://www.teamx-programme.org/</a></p> <p><strong>DATA SET DESCRIPTION</strong></p> <p>The video is provided in two different resolutions and thus two different file sizes (highres=934 MB and lowres=239 MB). The video was assembled from multiple clips recorded with a DJI Mini 2 drone using the online video editor FlexClip.</p>
TEAMx-PC22 (TEAMx pre-campaign 2022) - 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 a single TAR file containing 1536 hourly NetCDF files. Within these, radial velocities from KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159 Doppler wind lidars, as well as coplanar-retrieved horizontal wind speed components in their common scanning plane are stored. </p> <p>The time period is 29 June 2022, 00:00 UTC - 31 August 2022, 23:58 UTC.</p> <p>More details about the variables, lidar locations, scan details, as well as post-processing can be found in the NetCDF metadata. The wind fields stored in the NetCDF files are also available in daily animation form under an accompanying Zenodo Video/Audio data set (DOI: 10.5281/zenodo.7212837).</p>
The #BTW17 Twitter Dataset - Recorded Tweets of the Federal Election Campaigns of 2017 for the 19th German Bundestag
<p>The German Bundestag elections are the most important democratic elections of Germany. This dataset comprises Twitter interactions related with German politicians of the most important political parties over several months in the (pre-)phase of the German election campaigns in 2017. The Twitter accounts of 364 politicians (that is approximately half of the German parliament, the German Bundestag) were followed for almost half a year. The collected data comprise of about 10 GB of Twitter raw data generated by more than 120.000 active Twitter users generating more than 1.200.000 tweets during the pre- and hot-phase of the election campaigns for the 19th German Bundestag. <br> The dataset can be used to study how political parties, their followers and supporters make use of social media channels like Twitter in the context of political election campaigns and what kind of content is shared.</p> <p>The following files contain relevant context information:</p> <ul> <li><strong>crawled-pages.json</strong> contains the URLs of the official party faction websites of the 18th German Bundestag that were crawled to identify the Twitter screennames of German politicians of all Bundestag factions. Because the <em>Alternative für Deutschland (AfD)</em> and the <em>Freie Demokratische Partei (FDP)</em> were not part of the 18th German Bundestag (but it was likely that they will enter the 19th German Bundestag) other official websites were selected to crawl for relevant and representative politicians for these both parties (in case of the <em>AfD</em> this was the website of the directorate of the <em>AfD</em> federal party and the list of members of the European Parliament, in case of the <em>FDP</em> this was the website of the executive committee of the <em>FDP</em> federal party of Germany).</li> <li><strong>followed-accounts.json</strong> contains the (manually checked and edited) crawling result of 327 Twitter screennames of politicians that have been observed via the Twitter streaming API to collect this dataset.</li> </ul>
Raw target pixel files for TRAPPIST-1 from K2 Campaign 19
<p>The Kepler spacecraft observed the TRAPPIST-1 system from Sep 7th through Sep 26th, 2018, as part of its K2 Campaign 19. To help scientists explore these data quickly, the Kepler/K2 Guest Observer Office at NASA Ames reformatted the raw data into a <em>pseudo</em> Target Pixel Files using the Kadenza tool (https://doi.org/10.5281/zenodo.344973).</p> <p>For more information about these data, visit https://keplerscience.arc.nasa.gov.</p>
Snow depth and density measurements with different snow core samplers in HARMOSNOW Field Campaigns
<p>The data correspond to snow bulk density and snow depth measured with different snow core sampler in three field campaigns carried out in mountains of Turkey, Iceland and Finland in order to assess the uncertainty of using different snow core samplers and different observers. The field campaigns were carried out in the frame of the COST project HARMOSNOW ES1404 <a href="http://harmosnow.eu/">http://harmosnow.eu/</a></p>
Video abstract for "Communicating climate change in a 'post-factual' society: Lessons learned from the Pole to Paris campaign"
<p>MPEG-4 video containing the video abstract for the publication "Communicating climate change in a 'post-factual' society: Lessons learned from the Pole to Paris campaign" in Geoscience Communication by Erlend Moster Knudsen and Oria Jamar de Bolsée, published in 2019.</p>
Infiltration campaign in the Mercier sub-catchment (Yzeron catchment, periphery of Lyon France)
<p>This study was part of the AVuPUR project (Assessing the Vulnerability of PeriUrban catchments, 2008-2011), dedicated to the observation and understanding of the impact of urbanization on the hydrological cycle in periurban catchments. We provide the results of a field campaign conducted in the Mercier catchment, a sub-catchment of the Yzeron catchment located in the Monts du Lyonnais, in the western part of Lyon city, France. The campaign was dedicated to the characterization of top soil hydraulic properties. Some data are also available at -15 and -30 cm depth.<br> The data set includes 66 sampled points with information of particle size data, dry bulk density and porosity, hydraulic conductivity at saturation and close to saturation for 46 measurement points.<br> We also provide estimates of the parameters of the retention and hydraulic conductivity curves for the various sample points.</p> <p>Sample points were selected in the Mercier catchment (6.7 km2) by combining information about geology, land use and pedology. Two types of infiltration experiments were conducted: single ring infiltration tests with a positive pressure at the soil surface (3 replicates)(Beerkan method)_ and mini-disk infiltrometers using a -20 mm suction (0 to 3 replicates).</p>
TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"
<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> time = 3672 ;<br> stations = 6 ;<br> name_strlen = 6 ;<br> depth = 3 ;<br> height = 201 ;<br> variables:<br> double time(time) ;<br> time:standard_name = "time" ;<br> time:long_name = "time of measurement" ;<br> time:units = "hours since 2016-06-01 00:00:00" ;<br> time:timezone = "UTC" ;<br> time:calendar = "proleptic_gregorian" ;<br> double lat(stations) ;<br> lat:standard_name = "latitude" ;<br> lat:long_name = "station_latitude" ;<br> lat:units = "degrees_north" ;<br> double lon(stations) ;<br> lon:standard_name = "longitude" ;<br> lon:long_name = "station_longitude" ;<br> lon:units = "degrees_east" ;<br> double elev(stations) ;<br> elev:standard_name = "altitude" ;<br> elev:long_name = "station_altitude" ;<br> elev:units = "m ASL" ;<br> double height(height) ;<br> height:standard_name = "altitude" ;<br> height:long_name = "station_altitude" ;<br> height:units = "m ASL" ;<br> double depth(depth) ;<br> depth:standard_name = "soil_depth" ;<br> depth:long_name = "soil sensor depth" ;<br> depth:units = "cm" ;<br> char station_name(name_strlen, stations) ;<br> station_name:long_name = "station_name" ;<br> station_name:cf_role = "timeseries_id" ;<br> double T(time, stations) ;<br> T:_FillValue = -9999. ;<br> T:standard_name = "temperature" ;<br> T:long_name = "2m air temperature" ;<br> T:units = "degree_Celsius" ;<br> T:source = "TERENO-preAlpine" ;<br> double Q(time, stations) ;<br> Q:_FillValue = -9999. ;<br> Q:standard_name = "mixing_ratio" ;<br> Q:long_name = "2m mixing ratio" ;<br> Q:units = "g kg-1" ;<br> Q:source = "TERENO-preAlpine" ;<br> double ET_i(time, stations) ;<br> ET_i:_FillValue = -9999. ;<br> ET_i:standard_name = "evapotranspiration_intensive" ;<br> ET_i:long_name = "lysimeter evapotranspiration intensive management" ;<br> ET_i:units = "g kg-1 h-1" ;<br> ET_i:source = "TERENO-preAlpine" ;<br> double ET_e(time, stations) ;<br> ET_e:_FillValue = -9999. ;<br> ET_e:standard_name = "evapotranspiration_extensive" ;<br> ET_e:long_name = "lysimeter evapotranspiration extensive management" ;<br> ET_e:units = "g kg-1 h-1" ;<br> ET_e:source = "TERENO-preAlpine" ;<br> double LvE_cor(time, stations) ;<br> LvE_cor:_FillValue = -9999. ;<br> LvE_cor:standard_name = "latent_heat_flux" ;<br> LvE_cor:long_name = "energy balance corrected flux tower latent heat flux" ;<br> LvE_cor:units = "W m-2" ;<br> LvE_cor:source = "TERENO-preAlpine" ;<br> double HTs_cor(time, stations) ;<br> HTs_cor:_FillValue = -9999. ;<br> HTs_cor:standard_name = "sensible_heat_flux" ;<br> HTs_cor:long_name = "energy balance corrected flux tower sensible heat flux" ;<br> HTs_cor:units = "W m-2" ;<br> HTs_cor:source = "TERENO-preAlpine" ;<br> double GHF(time, stations) ;<br> GHF:_FillValue = -9999. ;<br> GHF:standard_name = "ground_heat_flux" ;<br> GHF:long_name = "flux tower ground heat flux" ;<br> GHF:units = "W m-2" ;<br> GHF:positive = "up" ;<br> GHF:source = "TERENO-preAlpine" ;<br> double SW(time, stations) ;<br> SW:_FillValue = -9999. ;<br> SW:standard_name = "short_wave_radiation" ;<br> SW:long_name = "downward short wave radiation" ;<br> SW:units = "W m-2" ;<br> SW:source = "TERENO-preAlpine" ;<br> double LW(time, stations) ;<br> LW:_FillValue = -9999. ;<br> LW:standard_name = "long_wave_radiation" ;<br> LW:long_name = "downward long wave radiation" ;<br> LW:units = "W m-2" ;<br> LW:source = "TERENO-preAlpine" ;<br> double VWC_25(time, depth) ;<br> VWC_25:_FillValue = -9999. ;<br> VWC_25:standard_name = "volumetric_water_content" ;<br> VWC_25:long_name = "DE-Fen SoilNet volumetric water content first quartile" ;<br> VWC_25:units = "vol. %" ;<br> VWC_25:source = "TERENO-preAlpine" ;<br> double VWC_50(time, depth) ;<br> VWC_50:_FillValue = -9999. ;<br> VWC_50:standard_name = "volumetric_water_content" ;<br> VWC_50:long_name = "DE-Fen SoilNet volumetric water content second quartile" ;<br> VWC_50:units = "vol. %" ;<br> VWC_50:source = "TERENO-preAlpine" ;<br> double VWC_75(time, depth) ;<br> VWC_75:_FillValue = -9999. ;<br> VWC_75:standard_name = "volumetric_water_content" ;<br> VWC_75:long_name = "DE-Fen SoilNet volumetric water content third quartile" ;<br> VWC_75:units = "vol. %" ;<br> VWC_75:source = "TERENO-preAlpine" ;<br> double T_prof(time, height) ;<br> T_prof:_FillValue = -9999. ;<br> T_prof:standard_name = "temperature_profile" ;<br> T_prof:long_name = "DE-Fen HATPRO spline interpolated temperature profile" ;<br> T_prof:units = "K" ;<br> T_prof:source = "scaleX campaign 2016" ;<br> double A_prof(time, height) ;<br> A_prof:_FillValue = -9999. ;<br> A_prof:standard_name = "humidity_profile" ;<br> A_prof:long_name = "DE-Fen HATPRO spline interpolated absolute humidity profile" ;<br> A_prof:units = "kg m-3" ;<br> A_prof:source = "scaleX campaign 2016" ;<br> double PRW(time) ;<br> PRW:_FillValue = -9999. ;<br> PRW:standard_name = "precipitable_water" ;<br> PRW:long_name = "DE-Fen HATPRO column precipitable water" ;<br> PRW:units = "kg m-2" ;<br> PRW:source = "scaleX campaign 2016" ;</p> <p>// global attributes:<br> :history = "2019-09-12: File created." ;<br> :institution = "Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research" ;<br> :Contact_person = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :Author = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :source = "https://www.tereno.net, https://scalex.imk-ifu.kit.edu" ;<br> :Conventions = "CF-1.6" ;<br> :License = "Creative Commons Attribution Non Commercial Share Alike 4.0 International" ;</p> <p> </p>
Coordinated Reply Attacks in Influence Campaigns: Characterization and Detection
<p>We release the datasets to replicate the results of `Coordinated Reply Attacks in Influence Operations:<br>Characterization and Detection'.</p> <p>See https://github.com/osome-iu/io-coordinated-replies for details.</p>
Linked collectors and determiners for: Fishes in MZNA-VERT: baseline freshwater sampling campaigns.
Natural history specimen data linked to collectors and determiners held within, "Fishes in MZNA-VERT: baseline freshwater sampling campaigns". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/5f15bccf-9f93-41c9-8de3-34887b2a76c9">https://bionomia.net/dataset/5f15bccf-9f93-41c9-8de3-34887b2a76c9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/5f15bccf-9f93-41c9-8de3-34887b2a76c9">https://gbif.org/dataset/5f15bccf-9f93-41c9-8de3-34887b2a76c9</a>. Formatted as a Frictionless Data package.
WAR_WITNESS Annotated Bibliography: Indian Military Memoirs and Campaign Narratives
<p><span>This annotated bibliography consists of military memoirs and campaign narratives authored by British military personnel stationed in India during the period 1780 to 1855. These publications were identified and analysed as part of the MSCA-funded project WAR_WITNESS. </span></p>
Tilting test campaign on masonry corners (2021-06-03, UMinho)
<p>This experimental dataset gathers the geometrical model tested, the video of the tests and the horizontal load multiplier results. Some additional figures support the experimental campaign.</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.