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497 results for “campaign”
Advanced Persistent Threats (APTs) campaigns database
<p>A manually curated Neo4j database of APTs campaigns spanning from 2008 to 2020 and the related targeted software products. The repository contains the database and the raw data.</p> <p>The dataset is part of the paper "Software Updates Strategies: a Quantitative Evaluation against Advanced Persistent Threats" published at the IEEE TSE.</p> <p>DOI: <a href="https://doi.org/10.1109/TSE.2022.3176674">10.1109/TSE.2022.3176674</a></p> <p>Preprint: <a href="https://arxiv.org/abs/2205.07759">https://arxiv.org/abs/2205.07759</a></p> <p>GitHub page: <a href="https://github.com/giorgioditizio/APTs-database">APT database</a></p>
Simulated XES event log of a marketing campaign system
<p>We relied on the interview-driven methodology defined in our previous work \cite{benvenuti2022}, which allowed us to specify various simulation scenarios to frame the boundaries of all possible pipeline executions.</p> <p>Then, we generated this simulated event logs in the traditional XES format using the Simio (https://www.simio.com/), obtaining 10,000 execution traces compliant with the simulation scenarios. In the picture it is shown the Directly-Follow Graph (DFG) representing the pipeline structure, discovered by feeding a process discovery tool (https://fluxicon.com/disco/) with the simulated event log.</p> <p>The pipeline is triggered when the system receives a request to model a new marketing campaign or to report on how an already existing one is performing. In both cases, the first two steps of the pipeline are querying the required data and to apply specific transformations on it. Then, if the request was for a report there is the need of merging the queried data, while for the request of a model an algorithm to compute it is launched. Next, if the request was for a model, the result needs to be stored.<br> Finally, the pipeline ends with either the report or the model being generated.</p>
Data from the NASCENT campaign used in the publications: "Conditions favorable for secondary ice production in Arctic mixed-phase clouds" and "Understanding the history of two complex ice crystal habits deduced from a holographic imager"
<p>This repository contains the data from the Ny‐Ålesund AeroSol Cloud ExperimeNT campaign (NASCENT). This data were used to produce the figures in the two papers:</p> <p>(1) Pasquier, J. T., Henneberger, J., Ramelli, F., Korolev, A.,Wieder, J., Lauber, A., Li, G., David, R. O., Carlsen, T., Gierens, R., Maturilli, M., and Lohmann, U.: Understanding the history of two complex ice crystal habits deduced from a holographic imager, Geophys. Res. Lett., accepted, 2022</p> <p> </p> <p>(2) Pasquier J. T., Henneberger J., Ramelli F., Lauber A., David O. D., Wieder J., Carlsen T., Gierens R., Maturilli M., and Lohmann U.:Conditions favorable for secondary ice production in Arctic mixed-phase clouds, ACP, accepted.</p> <p>More information can be found in the README files.</p> <p> </p> <p>The scripts to reproduced the Figures are available on Zenodo</p> <p>(1) https://doi.org/10.5281/zenodo.7402296</p> <p>(2) https://doi.org/10.5281/zenodo.7407107</p>
Simulated HPLC–DAD datasets saved as MOCCA campaigns
<p>Supplemental data for the publication: <strong>Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening, </strong>submitted by the authors to<strong> </strong><em>ACS Central Science December 2022.</em></p> <p>The data set contains simulated HPLC–DAD datasets saved as MOCCA campaigns, which were used for validation and benchmarking. The files are saved as Python Pickle Files. The zip file contains all the simulated HPLC data one-step all-files download. </p>
Seaglider (SG640) dataset collected during the ROAM-MIZ field campaign in the Southern Ocean
<p>This dataset is a part of the Robotic Observations And Modelling in the Marginal Ice Zone (ROAM-MIZ, <a href="http://www.roammiz.com">www.roammiz.com</a>) project and contains the temperature, salinity, dissolved oxygen, fluorescence, and backscatter profiles from a Seaglider (SG640), which was deployed at the Prime Meridian in the northeastern Weddell Sea (0.02W and 55.01S) from 20th October 2019 to 18th February 2020.</p> <p>The raw data has been processed to L1 by applying the Kongsberg Seaglider Basestation code following the methods of Garau et al. (2011).</p> <p>Garau, B., S. Ruiz, W. G. Zhang, A. Pascual, E. Heslop, J. Kerfoot, and J. Tintoré, 2011: Thermal Lag Correction on Slocum CTD Glider, <em>Data. J. Atmos. Oceanic Technol</em>, 28, 1065–1071. DOI: <a href="https://doi.org/10.1175/JTECH-D-10-05030.1">https://doi.org/10.1175/JTECH-D-10-05030.1</a></p>
A Dataset of Coordinated Cryptocurrency-Related Social Media Campaigns
<p> </p> <p>Note: This version simply updates the dataset with a new <em>Readme - Supplementary Material</em> file.</p> <p> </p> <p>The paper describing this dataset can be cited as:</p> <p>Zilius, K., Spiliotopoulos, T., & van Moorsel, A. (2023). A Dataset of Coordinated Cryptocurrency-Related Social Media Campaigns. Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 1112-1121. <a href="https://doi.org/10.1609/icwsm.v17i1.22219">https://doi.org/10.1609/icwsm.v17i1.22219</a></p> <p> </p> <p><strong>Abstract</strong></p> <p>The rise in adoption of cryptoassets has brought many new and inexperienced investors in the cryptocurrency space. These investors can be disproportionally influenced by information they receive online, and particularly from social media. This paper presents a dataset of crypto-related bounty events and the users that participate in them. These events coordinate social media campaigns to create artificial "hype" around a crypto project in order to influence the price of its token. The dataset consists of information about 15.8K cross-media bounty events, 185K participants, 10M forum comments and 82M social media URLs collected from the Bounties(Altcoins) subforum of the BitcoinTalk online forum from May 2014 to December 2022. We describe the data collection and the data processing methods employed and we present a basic characterization of the dataset. Furthermore, we discuss potential research opportunities afforded by the dataset across many disciplines and we highlight potential novel insights into how the cryptocurrency industry operates and how it interacts with its audience.</p> <p> </p>
FIG. 5 in The marine vegetation of the Kerguelen Islands: history of scientific campaigns, inventory of the flora and first analysis of its biogeographical affinities
FIG. 5. — Graph of the similarity network between regions (coloured nodes) based on the Kerguelen macroalgae (black nodes) co-occurrence dataset (using ForceAtlas2 layout algorithm). Colour codes are the same as in Figure 1. Nodes size is relative to the number of species co-occurring in the Kerguelen Islands. 51 species of doubtful taxonomic status were not considered. Sites: AMS, Amsterdam I.; SPA, Saint-Paul I.; MAR, Marion I.; PED, Prince Edwards Is.; CRO, Crozet Is.; HEA, Heard and McDonald Is.; MAQ, Macquarie I.; AUK, Auckland Is.; CAM, Campbell Is.; FUE, Fuegia; FAL, Falkland Is.; SGI, South Georgia Is.; SSI, South Shetland Is.; SOI, South Orkney Is.; APE, Antarctic Peninsula; END, Enderby L.; McR, MacRobertson L.; QMA, Queen Mary L.; WIL, Wilkes L.; ADE, Adelie L.; VIC, Victoria L.
FIG. 4 in The marine vegetation of the Kerguelen Islands: history of scientific campaigns, inventory of the flora and first analysis of its biogeographical affinities
FIG. 4. — Number of marine macroalgae species reported in the Kerguelen Islands since the Ross expedition (1840), in each phylum (Delépine 1996 is a personnal communication to J.-P. Féral).
FIG. 3. — A in The marine vegetation of the Kerguelen Islands: history of scientific campaigns, inventory of the flora and first analysis of its biogeographical affinities
FIG. 3. — A, Number of nominal species per family of marine macroalgae of the Kerguelen Islands; B, taxonomic status (accepted, uncertain, co-occurrence in the Northern Hemisphere) of the species for the three considered phyla (Chlorophyta, Ochrophyta-Phaeophycae and Rhodophyta).
FIG. 2 in The marine vegetation of the Kerguelen Islands: history of scientific campaigns, inventory of the flora and first analysis of its biogeographical affinities
FIG. 2. — Location of historical collections of macroalgae in the Kerguelen Islands. Precise locations are not given by all reports and should be partly inferred. The map shows the sites visited during 1, the Ross expedition (Hooker 1844-1847); 2, the Challenger expedition (Dickie 1876a, b, c, e); 3, the US (Farlow 1876); 4, English (Dickie 1876d, f); and 5, German (Askenasy 1889); 6, Venus transit expeditions, as well as the German south polar expedition (Foslie 1908, Reinbold 1908). 7, The Hopeful Bay (Zinova 1973) was added.
FIG. 1 in The marine vegetation of the Kerguelen Islands: history of scientific campaigns, inventory of the flora and first analysis of its biogeographical affinities
FIG. 1. — Sub-Antarctic and Antarctic regions considered in the biogeographical analysis of the Kerguelen Islands marine macroalgae also occurring elsewhere in the Southern Ocean. Colours: orange, temperate Southern Africa; blue, sub-Antarctic Islands; light blue, sub-Antarctic New Zealand; light green, Magellanic region; green, Scotia Sea; violet, continental High Antarctic (after Spalding et al. 2007). Geographical coordinates are given in the inset.
IAQ4EDU measurement campaign
<p>This is a database including data gathered during the measurement campaign of the IAQ4EDU project (https://iaq4edu.upc.edu/). </p>
Timelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign
<p>Timelapse footage of clouds taken during the<a href="https://cloudsense.ac.uk/dcmex/"> Deep Convective Microphysics Experiment</a> (DCMEX) research project, funded by the UK Natural Environment Research Council.</p> <p>Cameras were pointed towards the Magdalena mountains. The main camera location was Socorro airport, a secondary location was Econolodge, Socorro, and a third location was Magdalena airport.</p> <p>These are a subset of the footage collected. 20s interval photographs from a number of days are available from another archive.</p> <p>During these timelapses the FAAM aircraft was flying through the clouds collecting thermodynamic, dynamics, microphysics and aerosol <a href="https://catalogue.ceda.ac.uk/uuid/b1211ad185e24b488d41dd98f957506c">measurements</a>. Radars were also sometimes operational.</p> <p>The full campaign and data is decribed in detail in <a href="https://essd.copernicus.org/articles/16/2141/2024/">Finney et al. (2024) ESSD</a>.</p> <p><strong>Video descriptions</strong></p> <p>19th July - A number of convective cloud bursts, but no anvil formed over the Magdalena mountains during this footage.</p> <p>23rd July - Cumulus are present and growing from the start of the footage. Cloud bases stay rooted to the mountain. Shear appears low until a detrainment layer is reached.</p> <p>27th July - Footage begins with clear skies. Strong low levels winds carry clouds northward. Deep convective clouds form and are detrained south westward. Later in the footage scene becomes overcast with high cloud, and this suppresses the earlier deep convection. Late in the camera 1 footage a gust front cloud passes across the scene.</p> <p>29th July - Footage taken from the Magdalena airport and begins with fast moving cumulus cloud. The cloud grows and moves over the camera.</p> <p>31st July - Cumulus clouds form almost immediately over the Magdalena mountains and steadily grow from multiple thermals. Cloud bases stay rooted to the mountain, but shear aloft carries cloud northward from a detrainment layer. The cloud takes a structure similar to anvil but winds are strong and cloud in the detrainment layer appears to be fairly long-lived, warping the anvil shape somewhat.</p> <p>2nd August - Footage begins overcast but clears. Cumulus clouds and congestus begin to form, with strong low level winds carrying them southward.</p> <p><strong>Related datasets</strong></p> <p>Facility for Airborne Atmospheric Measurements; Finney, D.; Blyth, A.; Gallagher, M.; Wu, H.; Nott, G.J.; Biggerstaff, M.; Sonnenfeld, R.G.; Daily, M.; Walker, D.; Dufton, D.; Bower, K.N.; Boeing, S.; Choularton, T.W.; Crosier, J.; Groves, J.; Field, P.; Coe, H.; Murray, B.J.; Lloyd, G.; Marsden, N.A.; Flynn, M.; Hu, K.; Thamban, N.M.; Williams, P.I.; Connolly, P.J.; McQuaid, J.B.; Robinson, J.; Cui, Z.; Burton, R.R.; Carrie, G.; Moore, R.; Abel, S.J.; Tiddeman, D.; Aulich, G.; Bennecke, D.; Kelsey, V.; Reger, R.S.; Nowakowska, K.; Bassford, J.; Morris, F.; Hampton, J. (2022): DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment. NERC EDS Centre for Environmental Data Analysis, <em>30 April 2024</em>. <a href="https://dx.doi.org/10.5285/B1211AD185E24B488D41DD98F957506C">https://dx.doi.org/10.5285/B1211AD185E24B488D41DD98F957506C</a></p> <p>Individual image archive for camera 1 - Finney, D.; Groves, J.; Walker, D.; Dufton, D.; Moore, R.; Bennecke, D.; Kelsey, V.; Reger, R.S.; Nowakowska, K.; Bassford, J.; Blyth, A. (2023): DCMEX: cloud images from the NCAS Camera 11 from the New Mexico field campaign 2022. NERC EDS Centre for Environmental Data Analysis, 15 December 2023. <a href="https://dx.doi.org/10.5285/b839ae53abf94e23b0f61560349ccda1">https://dx.doi.org/10.5285/b839ae53abf94e23b0f61560349ccda1</a></p> <p>Individual image archive for camera 2 - Finney, D.; Groves, J.; Walker, D.; Dufton, D.; Moore, R.; Bennecke, D.; Kelsey, V.; Reger, R.S.; Nowakowska, K.; Bassford, J.; Blyth, A. (2023): DCMEX: cloud images from the NCAS Camera 12 from the New Mexico field campaign 2022. NERC EDS Centre for Environmental Data Analysis, 15 December 2023. <a href="https://dx.doi.org/10.5285/d1c61edc4f554ee09ad370f6b52f82ce">https://dx.doi.org/10.5285/d1c61edc4f554ee09ad370f6b52f82ce </a></p> <p>Oklahoma University radar data - <a href="https://doi.org/10.5281/zenodo.8051426">https://doi.org/10.5281/zenodo.8051426</a></p> <p> </p>
LEMON2021 Ground validation campaign
<p><strong>The LEMON CRDS ULA dataset consists of geolocated observations of humidity, water vapor isotopic composition, temperature and atmospheric pressure acquired with an ultralight aircraft (ULA) over the area of Aubenas (France) between 17/09/2021 and 23/09/2021. Data is provided in NetCDF format with 3 different averaging times for each flight (2, 5, 10 seconds). Take off location is an airstrip next to the Lanas Airfield (44.5393° N, 4.3679° E, 281 m ASL). </strong></p>
TEAMx-PC22 (TEAMx pre-campaign 2022) – Vertical profiles and Multi-Point In-Situ Measurements at Nafingalm collected with the SWUF-3D UAS fleet
<p>This dataset contains aggregated measurements from a fleet of multicopter UAS. The data was measured during the period 21 June 2022 through 27 June 2022 at Nafingalm, Austria with the SWUF-3D fleet. The data was collected in association to the TEAMx-PC22 field campaign. A maximum of three UAS were operated simultaneously. Processed level-2 data is provided. For level-2 data, time synchronization between individual UAS was done through interpolation, if multiple UAS are operated simultaneously.</p> <p>In this dataset vertical profiles (swuf3dvpro) between 10m and 120m above ground level and time series of UAS hovering for approx. 10 minutes at fixed positions (swuf3dhover) are provided with a temporal resolution of 1 Hz.</p> <p>The data are provided in NetCDF format with metadata and variable descriptions in the style of the SAMD Product standard: Jahnke-Bornemann, Annika. (2022, August 18). The SAMD Product Standard (Standardized Atmospheric Measurement Data) (Version 2.2). http://doi.org/10.25592/uhhfdm.10416</p>
LiftWEC deliverable D4.7: Dataset from 3D experimental test campaign
<p>This dataset contains 3-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 time series measurements of the model in the wave tank including; wave elevation, rotor angular position, forces on the hydrofoils, torque on the power take off and position of the hexapod holding the model. This data is the second set of wave tank data generated for the LiftWEC H2020 research project. The first set consisted of results for a similar device tested in 2D, while this second set contains results for tests conducted in 3D. For a complete description of the 3D testing and dataset, readers are directed to "LiftWEC Deliverable D4.8. Report on physical modelling of 3D LiftWEC concepts" with DOI 10.5281/zenodo.7669625.</p>
Open microdata registers from the Brazilian COVID-19 vaccination campaign
<p>This repository provides the Brazilian Ministry of Health’s (MoH) open microdata registers from the national COVID-19 vaccination campaign used in the paper <em>Age reporting for the oldest old in the Brazilian COVID-19 vaccination database: what can we learn from it?</em></p> <p>The dataset was downloaded on 14 March 2022 and was initially available at openDataSus <a href="https://opendatasus.saude.gov.br/dataset/covid-19-vacinacao">Campanha Nacional de Vacinação contra Covid-19</a> under a <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0</a> license.</p> <p>The reproducible <em><code>R</code></em> code and other datasets for the paper are available on GitHub at <a href="https://github.com/demographyandme/covid-19-datasus-vacina">Age reporting for the oldest old in the Brazilian COVID-19 vaccination database: what can we learn from it?</a> </p>
Energetic Valorisation of Saltworks Bitterns via Reverse Electrodialysis: A Laboratory Experimental Campaign
<p>Concentrated bitterns discharged from saltworks have extremely high salinity, often up to 300 g/L, thus their direct disposal not only has a harmful effect on the environment, but also generates a depletion of a potential resource of renewable energy. Here, reverse electrodialysis (RED), an emerging electrochemical membrane process, is proposed to capture and convert the salinity gradient power (SGP) intrinsically conveyed by these bitterns also aiming at the reduction of concentrated salty water disposal. A laboratory-scale RED unit has been adopted to study the SGP potential of such brines, testing ion exchange membranes from different suppliers and un-der different operating conditions. Membranes supplied by Fujifilm, Fumatech, and Suez were tested, and the results were compared. The unit was fed with synthetic hypersaline solution mimicking the concentration of natural bitterns (5 mol/L of NaCl) on one side, and with variable concentration of NaCl dilute solutions (0.01–0.1 mol/L) on the other. The influence of several op-erating parameters has also been assessed, including solutions flowrate and temperature. In-creasing feed solutions’ temperature and velocity has been found to lower the stack resistance, which enhances the output performance of the RED stack. The maximum obtained power density (corrected to account for the effect of electrodic compartments, which can be very relevant in five cell pairs laboratory stacks) reached around 10.5 W/m2cellpair, with FUJIFILM Type 10 membranes, temperature of 40 °C, and a fluid velocity of 3 cm s−1 (as empty channel, considering 270 m thickness). Notably, the present study results confirm the large potential for SGP generation from hypersaline brines, thus providing useful guidance for the harvesting of SGP in seawater saltworks all around the world.</p>
Full dataset of carbonyl sulfide URBAG measurement campaign May and October 2020
<p>The following table shows the concentrations (ppt) of carbonyl sulfide (OCS) that were sampled during the URBAG measurement campaign during the months of May and October 2020 (see urbag.eu for more information). Air samples were taken at the eight sites in the Metroolitan Area of Barcelona, representing the heterogeneity of land use as follows: agricultural area (located in the Gavà and Prat areas), urban forest (Tibidabo and Collserola), urban green (Montjuic and Guinardó) and urban built environment (Sagrada Familia and Poble Nou). Wind direction is given in degrees; Wind speed m/s; temperature in ºC, and Pressure is QFE in hPa; relative humidity (RH) is in %; PBLH is extracted from WRF model and is in m. This data set is publically available as part of the Open Research section of the manuscript “Exploring the influence of land use on the urban carbonyl sulfide budget: a case study of the Metropolitan Area of Barcelona”, recently submitted to JGR Atmospheres. </p>
Radiometer network dataset of 10 Hz spectral irradiance and derived variables (LIAISE campaign)
<p><strong>Description</strong></p> <p>Measurements from the spatial network of 15 radiometers deployed at the LIAISE field campaign in 2021.</p> <p>14 July 2021 until 28 July 2021.</p> <p><strong>Dataset Contents</strong></p> <ul> <li><em>rsds: </em>Total shortwave downwelling solar irradiance @ 10 Hz, 1 sec, and 1 minute resolution (Level 2, derived dataset)</li> <li><em>prw: </em>Total column integrated water vapour @ 1 sec resolution (Level 2, derived dataset)</li> <li><em>spectrum: </em>Pre-calibrated solar spectral irradiance measurements @ 10 Hz resolution (Level 1, source dataset)</li> <li><em>raw data:</em> straight from the sensors (Level 0)</li> </ul> <p><strong>Dataset Quality</strong></p> <p>All data is quality controlled and completed with metadata and quality flags. Level 2 data is calibrated against high quality references, and derived from Level 1 data.</p> <p>Methodology, performance, and usage all described in detail in an upcoming pre-print.</p> <p><strong>References and more info</strong></p> <ul> <li><a href="https://egusphere.copernicus.org/preprints/2022/egusphere-2022-726/">Radiometer reference paper (FROST)</a></li> <li>LIAISE <a href="https://liaise.aeris-data.fr/">campaign website</a>, <a href="https://liaise.aeris-data.fr/page-catalogue/?uuid=594dbc5d-986e-4679-b0ec-7d1e29b5cab9">LIAISE database</a></li> <li>Dataset description paper: <a href="https://arxiv.org/abs/2307.06980">pre-print on Arxiv</a></li> <li>Code to produce these data <a href="https://doi.org/10.5281/zenodo.10159129">on Zenodo</a></li> </ul> <p><strong>Version History</strong></p> <p>v1.2: fixed bug of incorrect wavelength labeling, all bands but 900 and 940 nm were affected. This only concerns the 10 Hz spectrum data, rest unchanged.</p> <p>v1.1: added raw (level 0) data, fixed typo in file name for the 10 Hz spectrum zip (L2 -> L1).</p>
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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.