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2,863 results for “Challenge”
BRAIN Journal-A New Challenge for Information Mining-Figure 4. Results of Weka's Clustering
<p>In the Figure 4 we show that, in a particular cluster, attributes are grouped in the "good" attribute of "Expertise with technology” with the "Low" attribute of "Student's performance" together. Thus, we can deduce that the level of Student's Performance is influenced by other factors over "Expertise with technology" of teacher. These factors can be searched inside the cluster, providing useful information to a significant exploration. These aspects are not deducible only by exploration through the portal and, for this reason, the clustering technique allows to user to navigate better during the search. </p>
BRAIN Journal-A New Challenge for Information Mining-Figure 5: New Approach Rich Data Exploration
<p>In the following figure (see Figure 5) it has represented a scheme of the new approach proposed to Rich Data Set's Exploration.</p> <p>Other experiments are running in order to validate our idea, both in order to optimize this clustering model by applying new algorithms and distance measures to the datasets presented here, and both applying these techniques to a different domain from the didactic one. Other experiments are also conducted to improve user exploration by skillfully combining multiple methods and exploration techniques through the application of a variety of models such as the Association Rule to extract hidden relationships and association rules between data and Artificial Neural Network mechanisms of learning applicable to classification and forecasting problems. </p>
BRAIN Journal-A New Challenge for Information Mining-Figure 3: Clustered Instances
<p> We tested the algorithm with different values of K, to find the optimal centroids. In general, as you know, there is no method for determining the exact value of K, but an accurate estimate can be obtained, for example, monitoring the value of the sum of squared error (SSE) for some values of k (for example 2, 4, 6, 8, etc.). The SSE is defined as the sum of the squared distance between each member of the cluster and its centroid. Mathematically, we can write (1): ( , ) (1) 1 2 K i c x i i SSE dist x c In our case we estimated in k = 8 the best number of cluster. We obtained the following clustered instances.</p>
BRAIN Journal-A New Challenge for Information Mining-Figure 2. A ScreenShot of L4All Portal
<p> Simple selection or complex selection operations, with boolean operators, are possible. Each widget shows the value of the attributes for the current state of the dataset with different visualization. The current set of objects is shown on a “canvas” (see Figure 2 - right side of the interface). Thanks to advanced Human-Computer Interaction mechanisms, the portal can support sophisticated exploration activities in the cycle . Based on L4All, a number of scientific investigations by different research groups took place: on the relation between different forms of group-work and inclusion, on digital storytelling and related benefits, etc. (Di Blas, Paolini, 2013; Falcinelli, 2012; Falcinelli, Laici, 2012). </p>
BRAIN Journal-A New Challenge for Information Mining-Figure 1. kinds of information needs (Wildemuth and Freund,2012)
<p>The taxonomy of tasks, related to the two different kinds of information needs is illustrated in Figure 1. </p> <p>In the precision-oriented information needs category, the task’s goal is to locate one resource and get information about its attributes or metadata while in the recall-oriented information needs category the task’s goal is to locate (and get information about) a set of resources. In this category we can distinguish goals that require accessing sets of resources just in groups, or in groups accompanied by count information for getting an overview of a set of resources, e.g. as in Faceted Dynamic Taxonomies (FDT). Furthermore, we may have goals that require more complex aggregated results like those provided by data warehouses. For instance, aggregations of arithmetic (min, max, average) and Boolean functions over the numeric attributes of the documents in the answers of free-text queries. Moreover, counts are computed and displayed over combinations (pairs, triples, quadruplets, etc.) of attributes (of grouping criteria in general). In comparison to OnLine Analytical Processing (OLAP) queries, in exploratory search the information demand in unknown a priori (in OLAP it is known and the schema is fixed) and the objective is not only to compute and see various aggregate values (e.g. sales per month and department), but also to support a flexible process for finding the desired individual resources (Tzitzikas et al., 2016).</p>
LHCb Particle Identification Compression Challenge
<p>A dataset containing Particle Identification information for a challenge to compress the contained features. </p> <p>An example of how to use this dataset and a compression benchmark is given here: https://github.com/weissercn/LHCb_PID_Compression</p> <p>-999 is the missing value identifier.</p> <p>The goal is to compress features with the name of the form S[n]x[m], where [n] and [m] are integers. Features of the form S[n]aux[m], where [n] and [m] are integers, contain information present at any time during compression and decompression. These features do not have to be compressed. The feature labelled 'pid' is the truth information of which type of particle an example is. This information is not present in real running conditions, is not present during either compression or decompression and can only be used to evaluate how well the autoencoder did. An example of this is in the example code.<br> </p>
Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus
<p>The Webis-Editorial-Quality-18 corpus is a novel corpus with 1000 news editorials. The aim of this Corpus is to study a new notion for news editorials quality. It contains the quality assessments of 1000 news editorials, each annotated by three liberals and three conservatives. The annotators also reported free-text reasons for the effects they observed.</p>
ICDAR2015 competition on smartphone document capture and OCR (SmartDoc) - Challenge 2
<p><strong>ICDAR2015 competition on smartphone document capture and OCR (SmartDoc)</strong></p> <p><strong>Challenge 2: MOBILE OCR COMPETITION</strong></p> <p>The goal of the competition is to extract the textual content from document images which are captured by mobile phones. The images are taken under varying conditions to provide a challenging input. The dataset was prepared for ICDAR2015-SmartDoc competition. For more details about the dataset please visit the competition's website:</p> <p>https://sites.google.com/site/icdar15smartdoc/home</p> <p>http://smartdoc.univ-lr.fr</p> <p>You may also refer to the following paper for more details on the ICDAR2015-SmartDoc competition:</p> <p>Jean-Christophe Burie, Joseph Chazalon, Mickaël Coustaty, Sébastien Eskenazi, Muhammad Muzzamil Luqman, Maroua Mehri, Nibal Nayef, Jean-Marc OGIER, Sophea Prum and Marçal Rusinol: “ICDAR2015 Competition on Smartphone Document Capture and OCR (SmartDoc)”, In 13th International Conference on Document Analysis and Recognition (ICDAR), 2015.</p> <p><strong>If you use this dataset, please send us a short email at <icdar.smartdoc (at) gmail.com> to tell us why it was useful to you, and whether you have results or publications we can reference on our website. Thank you!</strong></p>
DCASE2017 Challenge Submissions Package
<p>This package contains all submitted systems to DCASE2017 Challenge: system outputs and technical reports describing the systems. This package is meant to archive the DCASE0217 Challenge outcome and enable later evaluation with additional evaluation metrics.</p> <p>When referring to the <strong>DCASE2017 Challenge Submissions Package</strong> use the following:</p> <p>A. Mesaros, A. Diment, T. Heittola, B. Elizalde, E. Vincent, B. Raj, and T. Virtanen. <em>Sound Event Detection in the DCASE 2017 Challenge</em>. IEEE/ACM Transactions on Audio, Speech, and Language Processing, accepted</p>
A Multi-Challenge Clustering Benchmark Dataset Embedding Large Differences in Spatial Extent
<p>This artificial clustering benchmark dataset was designed manually and draws its inspiration from structural aspects that can be seen in principal component plots of hyperspectral image data. Distance-separated, density-separated, gradient-separated as well as connected clusters have been placed into the dataset. Following the notion that clusters may vary significantly with respect to their spatial extent the respective separability problems are scaled at different levels and only become visible by magnifying certain parts of the dataset. Another special aspect of this dataset is that cluster borders have been kept rather ambiguous which, in our opinion, better resembles the situation in spectroscopic data.</p>
Figure 6 in Towards a global synthesis of Collembola knowledge - challenges and potential solutions
Figure 6. Example of a spatially-explicit soil sampling design, based on five nested triangles. Dots with numbers represent soil core samples, distributed between thee base poles (A, B, C). Distances between the samples follow a decimal logarithmic scale. The largest triangle edge length is 10 meters. Note that some samples are directly next to each other. A more extensive design based on triangles or rectangles can be found in Saraeva et al. (2015) or Widenfalk et al. (2015), respectively.
Figure 4 in Towards a global synthesis of Collembola knowledge - challenges and potential solutions
Figure 4. Ecosystem processes that can be affected by different functional groups of Collembola. Soil-dwelling Collembola such as Onychiuridae are associated with roots and involved in rhizosphere processes. Many litter-dwelling Isotomidae and Tomoceridae are associated with decomposition processes by grazing and transformation of litter. Most of Hypogastruridae and Neanuridae and litter-dwelling Entomobryidae are largely microbivores and impact directly microbial communities in the litter. Surface-dwelling Symphypleona, Entomobryidae and Paronellidae affect microbial biofilms aboveground. Collembola, especially large surface-dwelling species, support various groups of terrestrial invertebrate predators. Summarised from Potapov et al. (2020, 2016) and Rusek (1998).
Figure 1 in Towards a global synthesis of Collembola knowledge - challenges and potential solutions
Figure 1. Trends in Collembola taxonomy. New species description accumulated up till the 1960's but stays linear afterwards (A); Most of species were recorded from Asia, Europe and North America (B); While the rate of species description stays unchanged for the last 70 years, the number of described taxa per taxonomist (first authorship) declines (C); The number of species described is driven by a low number of productive taxonomists, with the first 23 highly productive taxonomists (5.6 % of all first authors of species descriptions) being responsible for ca. 50 % of total known Collembola species (D). All estimations for the panels A, B and C were calculated in 5-year intervals. For the panel D only persons listed in the authorship of the species were included in the analysis. The figure is produced based on the data from September, 2020 available from http://collembola.org (Bellinger et al. 1996-2020).
Fig. 4 in Detection and quantification of house mouse Eimeria at the species level - Challenges and solutions for the assessment of coccidia in wildlife
Fig. 4. qPCR detection of intracellular stages of Eimeria in cecum and ileum from Mus musculus. -Delta Ct value (CtEimeria - CtMouse) from each tissue for 164 mice are plotted on the graph. The dotted line indicate the threshold of −5, values above the line are considered positive for the corresponding tissue. Circles represent negative samples, triangles indicate samples with Eimeria species identification and colors correspond to the Eimeria species identified in those samples. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Detection and quantification of house mouse Eimeria at the species level - Challenges and solutions for the assessment of coccidia in wildlife
Fig. 1. Geographical localization of house mice (Mus musculus) collected for this study and comparison of diagnostic methods for Eimeria. A) Localization from the 378 mice included in the present study, colors indicate the Eimeria species identified for each. B) Venn diagram showing the overlap between detection methods and successful genotyping identification of the isolates. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Detection and quantification of house mouse Eimeria at the species level - Challenges and solutions for the assessment of coccidia in wildlife
Fig. 3. Morphological and morphometrical characteristics of Eimeria oocyst isolated from Mus musculus. a) Photomicrographs at 1000x amplification of Eimeria oocyst from the three species isolated from Mus musculus (red = E. falciformis; green = E. ferrisi and yellow = E. vermiformis). Length/Width ratio from b) oocyst and c) sporocysts corresponding to each species (E. falciformis n = 31; E. ferrisi n = 127 and E. vermiformis n = 35). Mean ± 95% Confidence Interval is plotted. * Represent significant difference (Tukey HSD, p <0.05). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Broad tapeworms (Diphyllobothriidae), parasites of wildlife and humans: Recent progress and future challenges
Fig. 2. Microphotographs of permanent slides of diphyllobothriid tapeworms. A – Dibothriocephalus alasensis from Canis familiaris, Hooper Bay, Alaska, March 18, 1958; fixed after relaxation by R. Rausch (MSBP 17029). B – Dibothriocephalus latus from Homo sapiens, Chile, 19 November 2012; contracted clinical sample fixed with 'cold' fixative by T. Weitzel. C – Dibothriocephalus dalliae from C. familiaris, Alaska, 5 November 1970; fixed after relaxation by R. Rausch (MSBP 26232). D – Diphyllobothrium lanceolatum from Erignathus barbatus, Greenland, 7 October 1987; collected by P. Baagoe (SNM). E – Dibothriocephalus cf. nihonkaiensis from Homo sapiens, Newtok, Alaska, 26 March 1967; fixed after relaxation by R. Rausch (MSBP 26244). F – Diphyllobothrium stemmacephalum from Lagenorhynchus acutus, Wellfleet Bay, Cape Cod, Massachusetts, 1998; collected by J.N. Caira. G – Diphyllobothrium mobile from Ommatophoca rossii, Antarctica, 11 August 1901, Deutsche Südpolar- Expedition; collected by E. Dagobert von Drygalski (ZNB 5188). H – Dibothriocephalus ursi from Ursus arctos middendorfi, Karluk Lake, Kodiak Island, 9 October 1952; fixed after relaxation by R. Rausch; paratype (MSBP 3269). I – Schistocephalus sp. from C. familiaris, Newtok, Alaska, 4 April 1958; fixed after relaxation by R. Rausch (MSBP 17939). Acronyms of museum collections: MSBP – Museum of Southwestern Biology, Division of Parasitology, University of New Mexico, Albuquerque, New Mexico, U.S.A.; SNM – Swedish Museum of Natural History, Stockholm, Sweden; ZNB – Zoologische Museum Berlin, Berlin, Germany.
Fig. 4 in Global and temporal spread of a taxonomically challenging invasive ant, Brachyponera chinensis (Hymenoptera: Formicidae)
Fig. 4. Temporal distribution of reproductive individuals (female and male alates) and brood presence within Brachyponera chinensis nests (dark gray) with overall sampling period shown in light gray. Numbers indicated when sampling occurred during the yr.
Fig. 3 in Global and temporal spread of a taxonomically challenging invasive ant, Brachyponera chinensis (Hymenoptera: Formicidae)
Fig. 3. Elevation distribution of Brachyponera chinensis in its native range. Shaded areas correspond to the elevation at which B. chinensis has been collected for each specific reference. Light gray areas were sites at a given altitude that were sampled, but no B. chinensis were found. References used are presented in
Fig. 2 in Global and temporal spread of a taxonomically challenging invasive ant, Brachyponera chinensis (Hymenoptera: Formicidae)
Fig. 2. Introduced range distributions. (A-E) Spread over time of Brachyponera chinensis from the earliest record in 1932 to 2018 in the US presented by 20-yr periods, with 1 record from Washington State not displayed; (F) and along the east coast of the Black Sea in south Russia and Georgia (with color code for temporal periods similar to Figs. D, E).
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.