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2,863 results for “Challenge”
Dream challenge dataset
<p>one hot encoded dataset for synapse dream challenge</p>
Dream Challenge dataset (h5) (0-1mi)
<p>DREAM challenge</p>
Ecological and metabolic traits of non-aposematic species challenge the crypsis-aposematic duality of Neotropical poison frogs
<p>Within the context of animal defence strategies, most species are believed to be either cryptic or aposematic. In this sense, dendrobatid frogs are among the best-studied vertebrate systems in terms of colouration, behaviour, and chemical defence. Though a dual criterium, cryptic-palatable and conspicuous-toxic, was used to classify species within this clade, there are notorious exceptions that do not fit into this duality. In particular, some species assigned to the cryptic-palatable category possess colourful spots that markedly contrast the rest of the body's dark coloration and contain toxic substances. Here, we investigate whether two pairs of syntopic dendrobatids, in which one of the members within each pair has colour spots and the other a homogeneous dark colour, differ in the activity pattern, dietary specialization, foraging activity, and metabolic rate. In agreement with our prediction, we found that the coloured species in one of the pairs is more active and has a higher dietary selectivity and prey accumulation capacity. However, no association was not found for the other species pair. Furthermore, contrary to our prediction, species with coloured spots have a lower metabolic rate than their syntopic homogenous peers. Overall, our findings suggest that species with coloured spots could be classified in a different category based on ecological and metabolic traits while opening exciting perspectives for understanding the evolution of aposematism in amphibians.</p>
Telomere length correlates with physiological and behavioural responses of a long-lived seabird to an ecologically-relevant challenge
<p>Determinants of individual variation in reallocation of limited resources towards self-maintenance versus reproduction are not well known. We tested the hypothesis that individual heterogeneity in long-term "somatic state" a) explains variation in endocrine and behavioural responses to environmental challenges, and b) is associated with variation in strategies for allocating to self-maintenance versus reproduction. We used relative telomere length as an indicator of somatic state and experimentally generated an abrupt short-term reduction of food availability (withdrawal of food supplementation) for free-living seabirds (Black-legged kittiwakes, Rissa tridactyla). Incubating male kittiwakes responded to withdrawal by increasing circulating corticosterone and losing more weight compared to continuously supplemented controls. Males with longer telomeres increased time in directed travel regardless of treatment, while experiencing smaller increases in corticosterone. Males with longer telomeres fledged more chicks in the control group and tended to be more likely to return regardless of treatment. This study supports the hypothesis that somatic state can explain variation in short-term physiological and behavioural responses to challenges, and longer-term consequences for fitness. Male kittiwakes with longer telomeres appear to have prioritized investment in self over investment in offspring under challenging conditions.</p>
Trojan Detection Challenge Datasets
<p><strong>NOTE: To download test networks, please use the Zenodo records for the individual tracks linked to below. This Zenodo record only contains the training and validation networks.</strong></p> <p> </p> <p>This contains the training and validation datasets for the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai">competition website</a>.</p> <p>For participants in the detection track who are downloading the updated validation set, you can redownload the full dataset here, or you can download the new files only from this Zenodo upload: <a href="https://zenodo.org/record/6899223">https://zenodo.org/record/6899223</a>. Participants in other tracks do not need to redownload the dataset.</p> <p>To download the datasets for individual tracks, please see the following Zenodo uploads. These include the test networks but are otherwise identical to the subfolders in this upload:</p> <ul> <li>Detection: <a href="https://zenodo.org/record/7059487">https://zenodo.org/record/7059487</a></li> <li>Target Label Prediction: <a href="https://zenodo.org/record/7063081">https://zenodo.org/record/7063081</a></li> <li>Trigger Synthesis: <a href="https://zenodo.org/record/7063103">https://zenodo.org/record/7063103</a></li> <li>Evasive Trojans: <a href="https://zenodo.org/record/7063166">https://zenodo.org/record/7063166</a></li> </ul> <p>The Final Round networks are not included in this record. They can be downloaded here: <a href="https://zenodo.org/record/7269120">https://zenodo.org/record/7269120</a></p>
Trojan Detection Challenge Datasets (New Detection Track Validation Set)
<p>This upload contains the new version of the detection track validation set for the Trojan Detection Challenge NeurIPS 2022 competition. This is meant for participants in the detection who do not want to redownload the full dataset. For the full dataset including data for other tracks, see <a href="https://zenodo.org/record/6894041">https://zenodo.org/record/6894041</a>.</p>
[Supplementary material] Online Testing of RESTful APIs: Promises and Challenges
<p>This is the supplementary material of the paper entitled <em>Online Testing of RESTful APIs: Promises and Challenges</em>.</p> <p><strong>NOTE: An online easier-to-navigate version of this supplementary material is available at <a href="https://anonymous.4open.science/r/fse2022-supplementary-material-1172">https://anonymous.4open.science/r/fse2022-supplementary-material-1172</a>.</strong></p>
Dataset for Stack overflow Manual Results about challenges in developing Spark applications
<p>This dataset contains Stack Overflow manual study results for the paper "An Empirical Study on the Challenges that Developers Encounter When Developing Apache Spark Applications".</p> <ul> <li>the <em>data</em> folder contains the <em>Stackoverflow Manual Results.csv </em>file that is the manual analysis result for the Stack Overflow posts. The CSV file contains information on the classification of the data, the reasons and the number of views, etc. </li> </ul> <p> </p> <ul> <li>the <em>scripts</em> folder contains the python and SQL files that are used for data collection and data analysis. <ul> <li><em>query_data.sql</em> is used to collect data from the Stack Exchange website.</li> <li><em>sample.py</em> is used to sample data for the manual analysis in the paper.</li> <li><em>common_issue.py</em> is used to study the percentage of common issues in rq1. </li> <li><em>popularity.py </em>is used to calculate the average of normalized view counts in rq2.</li> <li><em>popularity_difficulty.py</em> is used to calculate the average of raw view counts and the median hours to receive an answer in rq2.</li> <li><em>root_cuase.py</em> is used to study the percentage of root causes in rq3. </li> </ul> </li> </ul>
GENEA Challenge 2022 Dataset Files
<p>This Zenodo repository contains the main dataset for the GENEA 2022 challenge, which is based on the Talking With Hands 16.2M data.</p> <p><strong>Contents:</strong></p> <p>The "trn" and "val" zip files contain audio files (in WAV format), time-aligned transcriptions (in TSV format), and motion files (in BVH format) for the training and validation datasets, respectively.</p> <p>The "tst" zip file contains audio files (in WAV format) and transcriptions (in TSV format) for the test set, but no motion. The corresponding test motion is available at:</p> <p>https://doi.org/10.5281/zenodo.6976463</p> <p>Each zip file also contains a "metadata.csv" file that contains information for all files regarding the speaker ID and whether or not the motion files contain finger motion.</p> <p>Note that the speech audio in the data sometimes has been replaced by silence for the purpose of anonymisation.<br> <br> <strong>Data processing scripts:</strong></p> <p>We provide a number of optional scripts for encoding and processing the challenge data:</p> <p>Audio: Scripts for extracting basic audio features, such as spectrograms, prosodic features, and mel-frequency cepstral coefficients (MFCCs) can be found <a href="https://github.com/genea-workshop/Speech_driven_gesture_generation_with_autoencoder/blob/GENEA_2022/data_processing/tools.py#L105">at this link</a>.</p> <p>Text: A script to encode text transcriptions to word vectors using FastText is available. <a href="https://gist.github.com/youngwoo-yoon/c9e5d5b04994478011911f88eb15caa4"><code>tsv2wordvectors.py</code></a></p> <p>Motion: If you wish to encode the joint angles from the BVH files to and from an exponential map representation, you can use scripts by <a href="https://github.com/simonalexanderson">Simon Alexanderson</a> based on the <a href="https://omid.al/projects/pymo/">PyMo library</a>, which are available here:</p> <ul> <li><a href="https://github.com/genea-workshop/Speech_driven_gesture_generation_with_autoencoder/blob/GENEA_2022/data_processing/bvh2features.py"><code>bvh2features.py</code></a></li> <li><a href="https://github.com/genea-workshop/Speech_driven_gesture_generation_with_autoencoder/blob/GENEA_2022/data_processing/features2bvh.py"><code>features2bvh.py</code></a></li> </ul> <p><strong>Attribution:</strong></p> <p>If you use this material, please cite our latest paper on the GENEA Challenge 2022. At the time of writing (2022-08-16), that is our ACM ICMI 2022 paper:</p> <p>Youngwoo Yoon, Pieter Wolfert, Taras Kucherenko, Carla Viegas, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2022. The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI '22). ACM.</p> <p>You can find the latest information and a BibTeX file on the project website:</p> <p>https://youngwoo-yoon.github.io/GENEAchallenge2022/</p> <p>Also cite the paper about the original dataset from Meta Research:</p> <p>Gilwoo Lee, Zhiwei Deng, Shugao Ma, Takaaki Shiratori, Siddhartha S. Srinivasa, and Yaser Sheikh. 2019. Talking With Hands 16.2M: A large-scale dataset of synchronized body-finger motion and audio for conversational motion analysis and synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV ’19). IEEE, 763–772.</p> <p> </p> <p>The motion and audio files are based on the Talking With Hands 16.2M dataset at https://github.com/facebookresearch/TalkingWithHands32M/. All material is available under a CC BY NC 4.0 international license, with the text provided in LICENSE.txt.</p> <p> </p> <p>To find more GENEA Challenge 2022 material on the web, please see:</p> <p>* <a href="https://youngwoo-yoon.github.io/GENEAchallenge2022/">https://youngwoo-yoon.github.io/GENEAchallenge2022/</a></p> <p>* <a href="https://genea-workshop.github.io/2022/challenge/">https://genea-workshop.github.io/2022/challenge/</a></p> <p>If you have any questions or comments, please contact:</p> <p>* The GENEA Challenge & Workshop organisers <genea-contact@googlegroups.com</p>
The Impending Opacity Challenge in Exoplanet Atmospheric Characterization
<p>Cross-section required for running retrieval with https://github.com/disruptiveplanets/tierra.</p> <p>These cross-sections were generated using https://github.com/disruptiveplanets/TierraCrossSection</p> <p> </p> <p> </p>
Exon-based phylogenomics and the relationships of African cichlids: Tackling the challenges of reconstructing phylogenies with repeated rapid radiations
<p>African cichlids (subfamily: Pseudocrenilabrinae) are among the most diverse vertebrates, and their propensity for repeated rapid radiation has made them a celebrated model system in evolutionary research. Nonetheless, despite numerous studies, phylogenetic uncertainty persists, and riverine lineages remain comparatively underrepresented in higher-level phylogenetic studies. Heterogeneous gene histories resulting from incomplete lineage sorting (ILS) and hybridization are likely sources of uncertainty, especially during episodes of rapid speciation. We investigate relationships of Pseudocrenilabrinae and its close relatives while accounting for multiple sources of genetic discordance using species tree and hybrid network analyses with hundreds of single-copy exons. We improve sequence recovery for distant relatives, thereby extending the taxonomic reach of our probes, with a hybrid reference guided/<em>de novo</em> assembly approach. Our analyses provide robust hypotheses for most higher-level relationships and reveal widespread gene heterogeneity, including in riverine taxa. ILS and past hybridization are identified as sources of genetic discordance in different lineages. Sampling of various Blenniiformes (formerly Ovalentaria) adds strong phylogenomic support for convict blennies (Pholidichthyidae) as sister to Cichlidae, and points to other potentially useful protein-coding markers across the order. A reliable phylogeny with representatives from diverse environments will support ongoing taxonomic and comparative evolutionary research in the cichlid model system.</p>
Blinded Predictions and Post-hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
<p>Training and test datasets and scripts for training models.</p>
Fig. 5 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 5. Visualization of a feature with high importance and a feature with low importance from a configuration B. The importance of these features for the identification accuracy was determined using permutation tests (see the methods). For each taxon or group (rows) several randomly selected specimens (columns) are shown. For the two selected Global Average Pooling layer features, the corresponding features of the preceding (Max Pooling) layer are visualized as those show specific image parts that had higher activations.Yellow represents the maximal activation strength; dark blue represents the minimal activation strength.
Fig. 2 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 2. Schematic representation of the networks used. On top are the convolutional layers of VGG16, grouped into five blocks. Output of each Max Pooling layer is fed into Global Average Pooling layer. Numbers near each block name indicate number of features in the Global Average Pooling layer. Height of layers roughly corresponds to resolution (except for Global Average Pooling layer), while width roughly corresponds to the number of feature maps or features produced.Then, in approach A, outputs of five blocks are concatenated and passed to the linear classifier. In approach B, output of only one block (block 3 in the final configuration) is passed to the linear classifier. In approach C CNN outputs are as in approach A, but instead connected to a DNN with two layers of 320 fully connected (FC) neurons followed by a prediction layer (PL), with number of neurons equal to number of species classified. Finally, approach D features CNN as in approach B which is connected to DNN as in approach C.
Fig. 1 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 1. Dorsal habitus photos of males and females of Tuxedo spp., Pygovepres vaccinicola, and Phallospinophylus setosus generated for and used in this study.
Fig. 4 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 4. Validation accuracy and accuracy on test data for SVM linear classifier (A, B) and DNN approaches (C, D) for the three datasets.
Fig. 3 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 3. Identification accuracy for the male (top) and female (bottom) Tuxedo dataset for the five selected resolutions and the individual blocks 1–5 and the concatenated block.
Fig. 6 in Discovery of New Genera Challenges the Subtribal Classification of Tok-Tok Beetles (Coleoptera: Tenebrionidae: Sepidiini)
Fig. 6. Female terminalia of species representing newly established genera. (A–D) Tibiocnodes lucidus; (E–G) Tuberocnodes synhimboides. (A, B) Ovipositor; (C, G) spiculum ventrale; (D) proctiger. c, coxites; c2–c4, coxite plates 2–4; ip, inner plate of paraproct; op, outer plate of paraproct; v, valvifer.
Fig. 3 in Discovery of New Genera Challenges the Subtribal Classification of Tok-Tok Beetles (Coleoptera: Tenebrionidae: Sepidiini)
Fig. 3. Dorsal habitus and morphological features of specimens representing the following species: (A) Tibiocnodes tristis (Fåhraeus, 1870); (B) Tibiocnodes lucidus (Fåhraeus, 1870); (C) Tibiocnodes spinosus (Haag-Rutenberg,1871); (D) Ocnodes similis (Péringuey, 1899); (E) Tuberocnodes humeralis (Haag-Rutenberg, 1871); (F) Tuberocnodes procursus (Péringuey, 1899); (G) Tuberocnodes synhimboides sp. nov.—paratype. es, elytral slope; mt, metatarsi; pn, pronotum; pt, protibia; r, protibial ridge.
Fig. 2 in Discovery of New Genera Challenges the Subtribal Classification of Tok-Tok Beetles (Coleoptera: Tenebrionidae: Sepidiini)
Fig. 2. Morphology of the genus Tuberocnodes: (A) paratype of Tuberocnodes warmeloi (Koch, 1953) comb. nov. (from TMNH); (B) laterally exposed and covered with microtubercles epipleuron of Tuberocnodes synhimboides. es, elytral slope; pn, pronotum.
ScienceDex guides
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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.