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2,774 results for “AD”
Working time, energy throughput and value added embodied in production, consumption and trade by subsectors for the US, the EU, China and rest of the world (2011)
<p>This repository contains the data needed to reproduce the results in:</p> <p>Pérez-Sánchez, L., Velasco-Fernández, R., Giampietro, M., The international division of labor and embodied working time in trade for the US, the EU and China, Ecological Economics. <a href="http://doi.org/10.1016/j.ecolecon.2020.106909">https://doi.org/10.1016/j.ecolecon.2020.1069097</a></p> <p>Sources of data are specified in the dataset (under tab "references")</p> <p> </p>
ADS-C Air Traffic Data Collected by the OpenSky Network
<p>ADS-C data collected by the OpenSky Network since 7th July 2023. </p> <p>Data underlying (Version 1.1)</p> <h1>A First Look at Exploiting the Automatic Dependent Surveillance-Contract Protocol for Open Aviation Research</h1> <p>https://journals.open.tudelft.nl/joas/article/view/7229</p>
Webis Generated Native Ads 2024
<p>Version of the <a href="https://zenodo.org/records/10802427">Webis Generated Native Ads 2024</a> dataset prepared for Sub-Task 2 of the <a href="https://touche.webis.de/clef25/touche25-web/advertisement-detection.html">Advertisement in Retrieval-Augmented Generation</a> task at Touché 2025.</p> <p>The dataset contains the same data but split into JSONL-files and with separate files for responses/sentence pairs and labels.</p> <h2>Citation</h2> <pre>@InProceedings{schmidt:2024,<br> author = {Sebastian Schmidt and Ines Zelch and Janek Bevendorff and Benno Stein and Matthias Hagen and Martin Potthast},<br> booktitle = {WWW '24: Proceedings of the ACM Web Conference 2024},<br> doi = {10.1145/3589335.3651489},<br> publisher = {ACM},<br> site = {Singapore, Singapore},<br> title = {{Detecting Generated Native Ads in Conversational Search}},<br> year = 2024<br>}</pre>
Radarcape ADS-B messages captured by antenna installed at the roof of the EETAC during Nov'2023
<p>Messages collected from the ADS-B antenna at the roof of the EETAC-UPC school at Castelldefels, Barcelona, from 15th of Sep 2023 to 31st of Oct 2023</p>
Field data collected from pyroclastic and lahar deposits of the 472 AD (Pollena) and 1631 Vesuvius eruptions
<p><strong><span>Field data collected from pyroclastic and lahar deposits of the 472 AD (Pollena) and 1631 Vesuvius eruptions</span></strong></p> <p><span>Mauro A. Di Vito<sup>1</sup>, Ilaria Rucco<sup>2</sup>, Sandro de Vita<sup>1</sup>, Domenico M. Doronzo<sup>1</sup>, Marina Bisson<sup>3</sup>, Elena Zanella<sup>4</sup></span></p> <p><sup><span>1</span></sup><span> Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Vesuviano, Napoli, Italy</span></p> <p><sup><span>2</span></sup><span> Heriot-Watt University, School of Engineering and Physical Sciences, Edinburgh, United Kingdom</span></p> <p><sup><span>3</span></sup><span> Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Pisa, Italy</span></p> <p><sup><span>4</span></sup><span> Università di Torino, Dipartimento di Scienze della Terra, Torino, Italy</span></p> <p><span> </span></p> <p><span>This dataset is organized in an Excel file, and it includes all the data collected and reviewed during the last 20 years from drill cores, outcrops, archaeological excavations, stratigraphic trenches, and the existing literature. It focuses on the primary (pyroclastic) and secondary (lahar) deposits of the 472 AD (Pollena) and 1631 eruptions from the Somma-Vesuvius volcano. The aim is to collect stratigraphic, stratimetric, sedimentological, lithological and chronological data to generate distribution maps and to validate the numerical simulations and models for the risk assessment. In particular, this dataset is complementary to – and in support of – the full work by Di Vito et al. (2024), in which the distribution of those deposits all around the Somma-Vesuvius complex and further is presented and discussed. Such dataset was used to inform the shallow-water model of lahars by de’ Michieli Vitturi et al. (2024), which in turns was used by Sandri et al. (2024) to elaborate probabilistic maps of lahar invasion in the Somma-Vesuvius and Apennine areas.</span></p> <p><span>All the data are organized in columns: the first four aim to identify the sites, and so there is a numeric identification code (ID), the name of the site (NAME), and the metric coordinates (East-North) in the UTM WGS 84 – Zone 33 reference projection (X, Y). The last two columns are “MUNICIPALITY” and “PROVINCE” and give a spatial location to the points.</span></p> <p><span>For the two eruptions, several columns have been created:</span></p> <p><span><span>·<span> </span></span></span><span>“472_PRIM”, “1631_PRIM” and “472_ASH” and “1631_ASH” indicate, respectively, the fallout primary deposits of the eruptions and the primary ash, particularly the ash related to the last phases of the eruptions (generally phreatomagmatic).</span></p> <p><span><span>·<span> </span></span></span><span>“472_SYN” and “1631_SYN” indicate the syn-eruptive lahars related to the two eruptions, recognized from the similar composition between the primary deposit and the lahar and from the evidence of a short-term exposure between the two.</span></p> <p><span><span>·<span> </span></span></span><span>“472_POST” AND “1631_POST” indicate the post-eruptive lahars related to the two eruptions. They are considered “post” when in the deposit there are pumices belonging to older eruptions, indicating their involvement in the progressive erosion of the slopes and valleys, and when there is evidence of long periods without deposition, such as the presence of</span><span> </span><span>slightly humified surfaces or traces of human artifacts (excavations, ploughing).</span></p> <p><span><span>·<span> </span></span></span><span>“EROSION_fallout_472” refers to the sites where it was possible to find erosional unconformities between the pyroclastic deposit of the 472 AD eruption and the lahar, as well as between the lower and upper lahar flow units. The erosional features are for example the lack of one or more primary eruptive layers (eroded by the overlying deposit), a change in the granulometry, or lateral discontinuity of the deposit. </span></p> <p><span><span>·<span> </span></span></span><span>“Pdyn (kPa)”, “v (m/s)”, “C (%)” and “T” are all the parameters quantified to validate the numerical models and to assess the hazard from lahars. Pdyn is the flow dynamic pressure, which represents the capability of the flow to entrain a clast, and it depends on the velocity (v) and the flow density, which in turn results from a combination of the density of the particles and the water through the “C (%)”, that is the particle volume concentration. To calculate the flow dynamic pressure and the velocity, the parameters taken into account are the dimensions of the biggest clasts and the nature of the clasts (limestone, ceramic, brick, tephra, lava, sandstone, iron) found in the lahar deposits. The concentration is estimated considering some sites in which the flow expands in correspondence with some obstacles (for example a Roman wall). This can be assumed to be the initial height of the flow before the emplacement. Finally, “T” refers to the estimated deposition temperature of the deposit quantified by the magnetic analysis, in particular in some sites where the lahar interacted with anthropogenic structures.</span></p> <p><span><span>·<span> </span></span></span><span>“DEPOSIT (472)” indicates the type of lahar deposit (syn- or post-eruptive) of the 472 AD eruption in which the fragments were found.</span></p> <p><span><span>·<span> </span></span></span><span>“MULTIPLE LAHAR UNITS” indicates the sites in which multiple flow units are vertically identified in the lahar deposits. They are generally a result of rapid and progressive aggradation of multiple flow pulses, each one resulting from single-pulse “en masse” emplacement</span><span>.</span></p>
OPSSAT-AD - anomaly detection dataset for satellite telemetry
<p>This is the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT---a CubeSat mission that has been operated by the European Space Agency.</p> <p>It is accompanied by the paper with baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics that should always be calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible, and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way.</p> <p>The included files are:</p> <ul> <li><code>segments.csv</code> with the acquired telemetry signals from ESA OPS-SAT aircraft,</li> <li><code>dataset.csv</code> with the extracted, synthetic features are computed for each manually split and labeled telemetry segment.</li> <li>code files for data processing and example modeliing (<code>dataset_generator.ipynb</code> for data processing, <code>modeling_examples.ipynb</code> with simple examples, <code>requirements.txt</code>- with details on Python configuration, and the <code>LICENSE</code> file)</li> </ul> <p> </p> <p>Please have a look at our two papers commenting on this dataset:</p> <ul> <li>The benchmark paper with results of 30 supervised and unsupervised anomaly detection models for this collection:<br>Ruszczak, B., Kotowski. K., Nalepa, J., Evans, D.:<strong> The OPS-SAT benchmark for detecting anomalies in satellite telemetry, 2024</strong>, <a href="https://arxiv.org/abs/2407.04730" target="_blank" rel="noopener">preprint arxiv: 2407.04730</a>,</li> <li>the conference paper in which we presented some preliminary results for this dataset:<br>Ruszczak, B., Kotowski. K., Andrzejewski, J., et al.: (2023). Machine Learning Detects Anomalies in OPS-SAT Telemetry. Computational Science – ICCS 2023. LNCS, vol 14073. Springer, Cham, <a href="https://doi.org/10.1007/978-3-031-35995-8_21">DOI:10.1007/978-3-031-35995-8_21</a>.</li> </ul>
Extended datasets from MM-IMDB and Ads-Parallelity dataset with the features from Google Cloud Vision API
<p>This is extended datasets from MM-IMDB [<a href="https://openreview.net/forum?id=S12_nquOe">Arevalo+ ICLRW'17</a>], Ads-Parallelity [<a href="https://arxiv.org/abs/1807.08205">Zhang+ BMVC'18</a>] dataset with the features from Google Cloud Vision API. These datasets are stored in jsonl (JSON Lines) format.</p> <p><strong>Abstract (from our paper):</strong></p> <p>There is increasing interest in the use of multimodal data in various web applications, such as digital advertising and e-commerce. Typical methods for extracting important information from multimodal data rely on a mid-fusion architecture that combines the feature representations from multiple encoders. However, as the number of modalities increases, several potential problems with the mid-fusion model structure arise, such as an increase in the dimensionality of the concatenated multimodal features and missing modalities. To address these problems, we propose a new concept that considers multimodal inputs as a set of sequences, namely, deep multimodal sequence sets (DM<sup>2</sup>S<sup>2</sup>). Our set-aware concept consists of three components that capture the relationships among multiple modalities: (a) a BERT-based encoder to handle the inter- and intra-order of elements in the sequences, (b) intra-modality residual attention (IntraMRA) to capture the importance of the elements in a modality, and (c) inter-modality residual attention (InterMRA) to enhance the importance of elements with modality-level granularity further. Our concept exhibits performance that is comparable to or better than the previous set-aware models. Furthermore, we demonstrate that the visualization of the learned InterMRA and IntraMRA weights can provide an interpretation of the prediction results.</p> <p><strong>Dataset (MM-IMDB and Ads-Parallelity):</strong></p> <p>We extended two multimodal datasets, namely, MM-IMDB [<a href="https://openreview.net/forum?id=S12_nquOe">Arevalo+ ICLRW'17</a>], Ads-Parallelity [<a href="https://arxiv.org/abs/1807.08205">Zhang+ BMVC'18</a>] for the empirical experiments. The MM-IMDB dataset contains 25,925 movies with multiple labels (genres). We used the original split provided in the dataset and reported the F1 scores (micro, macro, and samples) of the test set. The Ads-Parallelity dataset contains 670 images and slogans from persuasive advertisements to understand the implicit relationship (parallel and non-parallel) between these two modalities. A binary classification task is used to predict whether the text and image in the same ad convey the same message.</p> <p>We transformed the following multimodal information (i.e., visual, textual, and categorical data) into textual tokens and fed these into our proposed model. We used the <a href="https://cloud.google.com/vision">Google Cloud Vision API</a> for the visual features to obtain the following four pieces of information as tokens: (1) text from the OCR, (2) category labels from the label detection, (3) object tags from the object detection, and (4) the number of faces from the facial detection. We input the labels and object detection results as a sequence in order of confidence, as obtained from the API. We describe the visual, textual, and categorical features of each dataset below.</p> <p><em><strong>MM-IMDB</strong></em>: We used the title and plot of movies as the textual features, and the aforementioned API results based on poster images as visual features.</p> <p><em><strong>Ads-Parallelity</strong></em>: We used the same API-based visual features as in MM-IMDB. Furthermore, we used textual and categorical features consisting of textual inputs of transcriptions and messages, and categorical inputs of natural and text concrete images.</p>
Egnazia (Fasano, BR). La domus ad atrio romana nell'isolato a sud del foro, ricostruzione virtuale e backend scientifico
<p>La ricostruzione virtuale (Altair4 Multimedia in collaborazione con il team dell'Università di Bari guidato da Gianluca Mastrocinque), ha interessato questa dimora ancora in corso di scavo, per la quale erano già disponibili dati consistenti, che sono stati sistematizzati per la prima pubblicazione nello stesso periodo di lavorazione del 3D. Oltre alla vista dall’esterno per il rapporto con il tessuto urbano (V01), sono stati ricostruiti dall’interno 4 spazi: l’atrio da cui si ha la percezione più efficace dello sviluppo degli spazi (V02), il tablino (V03), la dispensa (V06), il sacrario (V04), vano documentato di rado, specie nella Puglia di età romana. Per ogni ambiente i dati dello scavo stratigrafico hanno permesso di ricostruire e ricollocare molti elementi della decorazione e dell’arredo, mentre altri sono stati elaborati sulla base di confronti stringenti per tipologia e cronologia, come risulta dal back-end scientifico. Ad esempio, nel sacrario, è stato ricollocato l’altare sulla base di un grande frammento reimpiegato in età tardoantica e si è realizzata l’anastilosi virtuale della statua di Demetra custodita presso il Museo, mentre nella dispensa lo studio già completo dei materiali ha consentito di ricostruire con alto grado di dettaglio suppellettili, merci e vivande. Il modello tridimensionale è stato realizzato con Autodesk 3DSTUDIO MAX, e renderizzato con V-RAY. Sono incluse nel dataset le immagini panoramiche a 360° dello stato attuale dei 5 punti, realizzate con camera INSTA360 PRO2 per le riprese a terra, e i rendering a 360° realizzati dai medesimi punti di vista. A ciascuno dei 5 punti di vista panoramici sono associate le immagini di backend, dove il livello di affidabilità della ricostruzione è indicato con colori simbolici e supportato da informazioni su fonti e processi interpretativi, secondo il metodo dell’extended matrix.</p>
WPE01 Assessing the value added of NEON for using machine learning to quantify vegetation mosaics and woody plant encroachment at Konza Prairie
Woody encroachment, or invasion of woody plants, is rapidly shifting tallgrass prairie into shrub and evergreen dominated ecosystems, mainly due to exclusion of fire. Tracking the pace and extent of woody encroachment is difficult because shrubs and small trees are much smaller than the coarse resolution (>10m2) of common remote sensed images. However, the US government has been investing in finer resolution (<2m2) remote sensing through USDA NAIP and the National Ecological Observatory Network (NEON), both of which cost multi-million dollars each year and contain different remote sensed products. We compared two methods of classification (random forests and support vector machines) with these two freely available remotely sensed aerial images to determine if and how much NEON adds to classification accuracy and determine which method of machine learning was more accurate. All models have very high overall classification accuracy (>91%), with the NEON image a few percent more accurate than NAIP. The NEON image significantly relies on canopy height (LiDAR) to make classifications, but the importance of bands is more evenly distributed during NAIP classification. Lastly, accuracy for Eastern Red Cedar specifically is high with NEON (78-84%), compared to the relatively low classification accuracy using NAIP imagery (55-61%).
Supplementary data: The added value of Bayesian inference for estimating biotransformation rates of organic contaminants in aquatic invertebrates.
<p>Supporting information for the article "<strong>The added value of Bayesian inference for estimating biotransformation rates of organic contaminants in aquatic invertebrates.</strong>"</p> <p>This provides all the R script and .csv files for each dataset. </p>
Dataset of Syro-Palestinian chamber tombs from the first millennium BC and AD
<p>The dataset is a systematic inventory of chamber tombs in Syro-Palestine from the first millennium BCE and CE. It contains information on the form, equipment and location of collective burial sites discovered in Israel, Jordan, Lebanon, Syria and parts of Turkey. This data was collected from published excavation reports and monographs on over 650 sites. This dataset can serve as a starting point for studying the regional diversity of material culture, burial practices and changes in these over the centuries. </p>
UnityMol demo movie showing custom user-added menus
<p>This video provides more detailed supportive information about using Unitymol.</p> <p> </p> <p>1) start up UnityMol</p> <p>2) activate the functionality to remote control Unitymol</p> <p>3) edit the provided example script menu-spike1.py to include the right filepath</p> <p>4) execute menu-spike1.py with python</p> <p>5) first there is only one button, allowing you to load the scene</p> <p>6) once loaded, several customized views are available through dedicated buttons</p>
RCSED - A Value-Added Reference Catalog of Spectral Energy Distributions of 800,299 Galaxies in 11 Ultraviolet, Optical, and Near-Infrared Bands: Morphologies, Colors, Ionized Gas and Stellar Populations Properties
<p>We present RCSED, the value-added Reference Catalog of Spectral Energy Distributions of galaxies, which contains homogenized spectrophotometric data for 800,299 low and intermediate redshift galaxies (0.007 < z < 0.6) selected from the Sloan Digital Sky Survey spectroscopic sample. Accessible from the Virtual Observatory (VO) and complemented with detailed information on galaxy properties obtained with the state-of-the-art data analysis, RCSED enables direct studies of galaxy formation and evolution during the last 5 Gyr. We provide tabulated color transformations for galaxies of different morphologies and luminosities and analytic expressions for the red sequence shape in different colors. RCSED comprises integrated k-corrected photometry in up-to 11 ultraviolet, optical, and near-infrared bands published by the GALEX, SDSS, and UKIDSS wide-field imaging surveys; results of the stellar population fitting of SDSS spectra including best-fitting templates, velocity dispersions, parameterized star formation histories, and stellar metallicities computed for instantaneous starburst and exponentially declining star formation models; parametric and non-parametric emission line fluxes and profiles; and gas phase metallicities. We link RCSED to the Galaxy Zoo morphological classification and galaxy bulge+disk decomposition results by Simard et al. We construct the color-magnitude, Faber-Jackson, mass-metallicity relations, compare them with the literature and discuss systematic errors of galaxy properties presented in our catalog. RCSED is accessible from the project web-site and via VO simple spectrum access and table access services using VO compliant applications. We describe several SQL query examples against the database. Finally, we briefly discuss existing and future scientific applications of RCSED and prospectives for the catalog extension to higher redshifts and different wavelengths.</p>
Supplementary Datafile for "A reform of value-added taxes on foods can have health, environmental and economic benefits in Europe"
<p>The dataset contains the results of the study "A reform of value-added taxes on foods can have health, environmental and economic benefits in Europe" by Marco Springmann, Eugenia Dinivitzer, Florian Freund, Jørgen Dejgård Jensen, and Clara G Bouyssou. </p> <p>It contains VAT rates on foods across Europe, as well as the results of reforming VAT rates for foods, including increasing rates for meat and dairy and reducing rates for fruits and vegetables. The set of results include changes in food demand, changes in environmental impacts (greenhouse gas emissions, land use, water use, and eutrophication potential), changes in diet-related mortality, changes in costs (revenues, climate change costs, costs of illness). </p>
Towards new demography proxies and regional chronologies: Radiocarbon dates from archaeological contexts located in the Czech Republic covering the period between 10,000 BC and AD 1250 (dataset)
<p>The dataset was created within the project “<em>Land use, social transformations and woodland in Central European Prehistory. Modelling approaches to human-environment interactions</em>” funded by the Czech Science Foundation (19-20970Y). This dataset represents the largest and the most comprehensive collection of archaeological radiocarbon dates from the Czech Republic to date. The dataset offers 1579 samples from 347 archaeological sites dating from Early Mesolithic (10 000 BC) to Medieval Period (AD 1250). Published in a simple spreadsheet format, the database offers researchers a quick tool for further analyses. It is important to highlight that dates we collected originated only from archaeological contexts, which means that we have excluded some radiocarbon dates produced through palaeoecological research without a direct relationship to past human activities, such as pollen records or samples from fossilized trees in river beds. The dataset is intended to be used for demographic modelling of population numbers during periods without written records, i.e. prehistory.</p>
Ed. Ad. Prager (p2113)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Ed. Ad. Prager<br><u>musiXplora-ID</u>: p2113<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/p2113">https://musixplora.de/mxp/p2113</a><br><u>Gender</u>: m<br><u>First Mentioned</u>: 1903<br><u>Sectors</u>: Zupfinstrumentenbau<br><u>Professions (Historical)</u>: Instrumentenmacher<br><u>Professions (Musical)</u>: Zupfinstrumentenbauer<br><u>Other Places of Activity</u>: Markneukirchen<br><br><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Henkel 2013</td><td>Zitherbauer und -händler nach den Weltadreßbüchern von Paul de Wit. Unveröffentliches Ms.</td><td><a href="https://musixplora.de/mxp/5001813">5001813</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Electricity consumption and real vaue-added data for the Swiss and Genevan secondary and tertiary sectors.
<p>This file contains datasets of electricity consumption and real value added (base year 2000) in the secondary and tertiary sector for Switzerland and Geneva for the years between 2000 and 2015. The structure of the Swiss and Genevan datasets has been matched to ensure comparability of results from index decomposition analyses conducted on each region. It also contains heating and cooling degrees for both Switzerland and Geneva.</p>
Models and Data for Simple Applications of BERT for Ad Hoc Document Retrieval
<p>This submission includes all pretrained models, test data and prediction files for the arXiv paper "<a href="https://arxiv.org/abs/1903.10972">Simple Applications of BERT for Ad Hoc Document Retrieval</a>". Please follow the instructions at the <a href="https://github.com/castorini/birch">Birch repo</a> to reproduce the results.</p>
Egnazia (Fasano, BR). La domus ad atrio nell'isolato a sud del foro, storytelling
<p>Nella web app ‘e-archeo’ i cinque diversi punti di vista esterni e interni alla casa sono accompagnati da uno storytelling incentrato sulle attività e sui diversi aspetti della vita che quotidianamente si svolgeva in questi spazi, intensamente frequentati fin dalle prime ore del mattino, dall’attività politica e finanziaria nel tablino, ai culti, soprattutto di Demetra, nel sacrario, alla conservazione nella ricca dispensa di prodotti che raccontano i gusti ricercati della famiglia aristocratica proprietaria della casa. La narrazione, in italiano e inglese, accompagna sia lo stato attuale sia la ricostruzione e può essere fruita in forma scritta e come audio dalla voce di attori professionisti che hanno registrato in studio.</p>
ADS v4 results for MY 34 & 35
<p>Results from the Aerosol Determination Strategy v4:</p> <p>*Extinction values comes from RCP retrievals</p> <p>*Covers MY 34 and first half of MY 35</p> <p>Columns description:</p> <p>Reff_dust : Effective radius dust (unit in μm)<br> Reff_dust_err : Effective radius error dust (unit in μm)<br> Reff_H2Oice : Effective radius water ice (unit in μm)<br> Reff_H2Oice_err : Effective radius error water ice (unit in μm)<br> Gamma : Ratio of the number densities of water ice over dust (no unit)<br> Gamma_err : Ratio of the number densities of water ice over dust (no unit)<br> Veff_dust : Effective variance of dust (no unit)<br> Veff_dust_err : Effective variance error of dust (no unit)<br> Veff_H2Oice : Effective variance of water ice (no unit)<br> Veff_H2Oice_err : Effective variance error of water ice (no unit)<br> M_Dust : Mass loading of dust (unit in g/cm^-3)<br> M_Dust_err : Mass loading error of dust (unit in g/cm^-3)<br> M_Dust_0Veff_err : Mass loading error considering ν_eff exact of dust (unit in g/cm^-3)<br> M_Dust_0VeffGamma_err : Mass loading error considering ν_eff and γ exact of dust (unit in g/cm^-3)<br> M_H2Oice : Mass loading of water ice (unit in g/cm^-3)<br> M_H2Oice_err : Mass loading error of water ice (unit in g/cm^-3)<br> M_H2Oice_0Veff_err : Mass loading error considering ν_eff exact of water ice (unit in g/cm^-3)<br> M_H2Oice_0VeffGamma_err: Mass loading error considering ν_eff and γ exact of water ice (unit in g/cm^-3)<br> N_Dust : Number density of dust (unit in #/cm^-3)<br> N_Dust_err : Number density error of dust (unit in #/cm^-3)<br> N_Dust_0Veff_err : Number density error considering ν_eff exact of dust (unit in #/cm^-3)<br> N_Dust_0VeffGamma_err : Number density error considering ν_eff and γ exact of dust (unit in #/cm^-3)<br> N_H2Oice : Number density of water ice (unit in #/cm^-3)<br> N_H2Oice_err : Number density error of water ice (unit in #/cm^-3)<br> N_H2Oice_0Veff_err : Number density error considering ν_eff exact of water ice (unit in #/cm^-3)<br> N_H2Oice_0VeffGamma_err: Number density error considering ν_eff and γ exact of water ice (unit in #/cm^-3)<br> MeritF : Value of the Merit Function<br> aerosol_type : Type of the aerosol, H2Oice (poure water ice), Dust (pure dust), Mixture_H2Oice (mixture with water ice as MSC), Mixture_Dust (mixture with dust as MSC), No_Aerosols<br> Vertical_res : Vertical resolution (unit in km)<br> extinction_121 : Extinction for order 121 (unit in km^-1)<br> extinction_134 : Extinction for order 134 (unit in km^-1)<br> extinction_149 : Extinction for order 149 (unit in km^-1)<br> extinction_168 : Extinction for order 168 (unit in km^-1)<br> extinction_190 : Extinction for order 190 (unit in km^-1)<br> altitude : Altitude above Mars' surface (unit in km)</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.