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2,863 results for “Challenge”
Meta-analysis reveals challenges and gaps for genome-to-phenome research underpinning plant drought response.
<p>Data used to identify species occurring in hyperarid environments for analyses described in "Meta-analysis reveals challenges and gaps for genome-to-phenome research underpinning plant drought response." The "PlantsLackingHumanUse_PrelimQCd_Data.csv" contains data for all plants queried, while "HyperArid_Occurrences.csv" contains the subset of data corresponding to plants occurring in hyperarid environments.</p>
EXPLORE Expert Data Challenges 2022 - Craters dataset
<p>This dataset contains Lunar Craters images and labels in COCO json format that will be used for the EXPLORE Expert Data Challenge 2022.</p> <p> </p> <p>More information at: https://exploredatachallenges.space/</p> <p> </p> <p>Source dataset is derived from Fairweather et al. (2022</p> <p>Images were processed from NASA PDS raw data and labels extracted using python scripts. </p>
EXPLORE Expert Data Challenges 2022 - Boulders dataset
<p>This dataset contains Lunar Craters images and labels in COCO json format that will be used for the EXPLORE Expert Data Challenge 2022.</p> <p> </p> <p>More information at: https://exploredatachallenges.space/</p> <p> </p> <p>Source dataset is derived from Watkins, Ryan (2019)</p> <p>Images were processed from NASA PDS raw data and labels extracted using python scripts. </p>
DCASE2016 Challenge Submissions Package
<p>This package contains all submitted systems to DCASE2016 Challenge: system outputs and technical reports describing the systems. This package is meant to archive the DCASE0216 Challenge outcome and enable later evaluation with additional evaluation metrics.</p> <p>When referring to the <strong>DCASE2016 Challenge Submissions Package</strong> use the following:</p> <p>A. Mesaros, T. Heittola, E. Benetos, P. Foster, M. Lagrange, T. Virtanen, and M. D. Plumbley. <em>Detection and classification of acoustic scenes and events: outcome of the DCASE 2016 challenge.</em> IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(2):379–393, Feb 2018.</p>
State of Practice and Challenges of Issue Management for Component-based and Service-Oriented Systems: An Empirical Study
<p>Modern software systems often consist of independently developed components, e.g., libraries or (micro-)services, that can be combined to form a larger architecture. Many of these components are developed externally, i.e., in other projects by other teams, which is particularly the case in service-oriented architectures. Even though the components are independently developed, bugs may arise that propagate along the call chains, which sometimes negatively affect the calling components. However, as such components are typically managed in separate issue management systems (IMS), identifying that a downstream component is affected by an upstream component's issues is complex and time-consuming. Even though some scientific works and IMS forums discuss how to manage such cross-component issues, there is no systematic study on the state of practice and the challenges of issue management when different components are affected. Therefore, this study aims to determine which types of components are usually included in modern software projects, how developers identify and manage issue propagation between components, and how often they face such propagations. To get these insights, we conducted an empirical study through a questionnaire with 64 industry experts and open-source contributors and interviewed seven of them. Our key takeaways help researchers and software engineers to understand possible impacts and to improve cross-component issue management. We identified, for example, that related issues currently are non-semantically linked via URLs in issue comments.</p>
Data from: Island-wide characterization of agricultural production challenges the demographic collapse hypothesis for Rapa Nui (Easter Island)
<p>Communities in resource-poor areas face health, food production, sustainability, and overall survival challenges. Consequently, they are commonly featured in global debates surrounding societal collapse. Rapa Nui (Easter Island) is often used as an example of how over-exploitation of limited resources resulted in a catastrophic population collapse. A vital component of this narrative is that the rapid rise and fall of pre-contact Rapanui population growth rates was driven by the construction and overexploitation of once extensive rock gardens. However, the extent of island-wide rock gardening, while key for understanding food systems and demography, must be better understood. Here, we use shortwave infrared (SWIR) satellite imagery and machine learning to generate an islandwide estimate of rock gardening and re-evaluate prior population size models for Rapa Nui. We show that the extent of this agricultural infrastructure is substantially less than previously claimed and likely could not have supported the large population sizes that have been assumed.</p>
The Challenges of Implementing Digital Learning Platforms in the Ministry of Information and Digitalization in Malawi
<p>This dataset was collected as part of a study exploring the implementation challenges and opportunities of digital learning platforms within the Ministry of Information and Digitalization in Malawi. The study employs a mixed-methods approach to reveal significant barriers such as internet connectivity issues, technological access limitations, and insufficient support that hinder the effective utilization of these platforms. The data includes responses from ministry personnel on their experiences with digital learning platforms, focusing on factors like support availability, time management, and motivation.</p>
Challenges faced by adolescent girls on Menstrual Hygiene Management: School-based study, Siha, Kilimanjaro, Tanzania
<p><span>Menstrual hygiene management (MHM) has received increasing attention as a public health issue globally. Governments and stakeholders have started to engage communities to address barriers and challenges faced by adolescents in and out of school. </span><span>This study, conducted in Siha District, northern Tanzania, responds to the call for evidence sensitive to local barriers and challenges to inform successful strategies in MHM. </span></p> <p><strong><span>Methods:</span></strong></p> <p><span>Institutional-based cross-sectional study which involved 400 school girls aged 10 to 19 years old who attained menarche were randomly selected in four secondary and advanced level government schools from September 2019 to January 2020. Bivariate and multivariable logistic regression analysis were employed. A P-value less than 0.05 was used to declare statistical significance.</span></p> <p><strong><span>Results:</span></strong></p> <p><span>Among all the girls who participated in the study 30% reported missing school due to menstruation while 56% of the girls reported using toilets as changing places at school. The use of reusable sanitary material was 52% compared with non-reusable materials which was 48%. In urban areas, 34.5% of students reported missing school due to menstruation compared to 25% who reported in rural areas. The findings show that school absenteeism among adolescent girls during menstruation is significantly associated with a headache (Adjusted odds ratio (AOR) = 3.3 (95% CI:1.32 – 8.23)) and abdominal waist pain (AOR = 8.50 (95% CI: 6.27 – 15.56)),</span> <span>lack of changing rooms in school (AOR = 5.85 (95% CI: 4.82 – 7.93)). In addition, the high cost of sanitary pads was mentioned as one of the main reasons for students not using sanitary pads.</span></p> <p><strong><span>Conclusion:</span></strong></p> <p><span>This study calls for promoting MHM-friendly practices in schools to create a supportive and conducive learning environment for adolescent girls. Ongoing school infrastructure improvements such as the construction of classrooms and toilets in schools should include the construction of proper changing places to reduce the number of adolescent girls who miss school due to menstruation. </span></p> <p><span> </span></p>
Trackerless 3D Freehand Ultrasound Reconstruction Challenge 2024 - Train Dataset (Part 3)
<div> <blockquote> <p><strong>This Challenge will be an open-ended challenge, and we welcome your submission. Please register your team via this <a title="https://forms.office.com/e/dPg47ktV7M" href="https://forms.office.com/e/dPg47ktV7M" target="_blank" rel="noopener">form</a>. You can submit the algorithm via this <a title="https://forms.office.com/e/QChhNkLYiu" href="https://forms.office.com/e/QChhNkLYiu" target="_blank" rel="noopener noreferrer">form</a> for TUS-REC2024 Challenge, and we will test your submitted docker on the test set.</strong></p> <p><strong>We are organising TUS-REC2025 at MICCAI2025. More information is available on the <a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/" target="_blank" rel="noopener">TUS-REC2025 challenge website</a> and <a href="https://github.com/QiLi111/TUS-REC2025-Challenge_baseline" target="_blank" rel="noopener">Baseline code repo</a>.</strong></p> </blockquote> <p><strong>This is the third part of the Challenge dataset. <a href="../doi/10.5281/zenodo.11178509" target="_blank" rel="noopener">Link</a> to first part; <a href="../doi/10.5281/zenodo.11180795" target="_blank" rel="noopener">Link</a> to second part. <a href="../doi/10.5281/zenodo.12979481" target="_blank" rel="noopener">Link</a> to validation dataset.</strong></p> <p>For detailed information please refer to the <a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/TUS-REC2024/" target="_blank" rel="noopener">Challenge website</a>. Baseline code is also provided, which can be found at this <a href="https://github.com/QiLi111/tus-rec-challenge_baseline" target="_blank" rel="noopener">repo</a>.</p> <p>Dataset structure: The dataset contains 50 .h5 files. Each corresponds to one subject, storing coordinates of landmarks for 24 scans of this subject. For each scan, the coordinates are stored in numpy array with shape of [20,3]. The first column is the index of frames; the second and third columns denote the coordinates of landmarks in the image coordinate system. </p> </div> <div> <p> </p> <p><strong>Data Usage Policy:</strong></p> <ul> <li>The training and validation data provided may be utilized within the research scope of this challenge and in subsequent research-related publications. However, commercial use of the training and validation data is prohibited. In cases where the intended use is ambiguous, participants accessing the data are requested to abstain from further distribution or use outside the scope of this challenge.</li> <li>If you use our dataset in your publication, please cite the challenge paper and some of the following optional articles: <ul> <li>Challenge paper: <ul> <li><strong>Qi Li et al. "TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker." <em>arXiv preprint arXiv:<a title="https://arxiv.org/abs/2506.21765" href="https://doi.org/10.48550/arXiv.2506.21765" target="_blank" rel="noopener">2506.21765</a></em> (2025).</strong></li> </ul> </li> <li>Optional articles: <ul> <li>Qi Li, Ziyi Shen, Qianye Yang, Dean C. Barratt, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Nonrigid Reconstruction of Freehand Ultrasound without a Tracker." In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em>, pp. 689-699. Cham: Springer Nature Switzerland, 2024. doi: <a href="https://doi.org/10.1007/978-3-031-72083-3_64" target="_blank" rel="noopener">10.1007/978-3-031-72083-3_64.</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker." IEEE Transactions on Biomedical Engineering, vol. 71, no. 3, pp. 1033-1042, 2024. doi: <a href="https://ieeexplore.ieee.org/abstract/document/10288201" target="_blank" rel="noopener">10.1109/TBME.2023.3325551</a>.</li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames." In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2023. doi: <a href="https://doi.org/10.1109/ISBI53787.2023.10230773" target="_blank" rel="noopener">10.1109/ISBI53787.2023.10230773.</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction." In International Workshop on Advances in Simplifying Medical Ultrasound, pp. 142-151. Cham: Springer Nature Switzerland, 2023. doi: <a href="https://doi.org/10.1007/978-3-031-44521-7_14" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-44521-7_14.</a></li> </ul> </li> </ul> </li> </ul> </div>
Challenges and Realities in Asian Elephant Research: A Global Bibliometric Analysis Highlighting Conservation Crises and Collaborative Opportunities
<p><span>The Asian elephant (<em>Elephas maximus</em>), also known as Asiatic elephant is facing a significant decline in its wild populations due to habitat loss, fragmentation and degradation, leading to increased conflicts between humans and elephants. Preserving this iconic species is crucial, prompting researchers to explore various aspects of its biology, behavior, ecology, and conservation efforts. </span><span>In light of the profound importance of this subject</span><span>, this study utilizes a descriptive systematic literature review (SLR) to examine global research trends on Asian elephants. Analyzing a dataset of 1,780 articles spanning from 1914 to 2022, the study reveals a notable increase in publications, particularly since 2000, with 142 articles published in 2022 alone. This research provides a comprehensive overview of advancements in Asian elephant studies, promoting international collaboration and knowledge exchange among researchers. While research from leading scientific countries can aid in conservation efforts, there is a call for more inclusive, participatory and fair approaches. The study demonstrates a commitment to Equity, Inclusion, and Diversity (EID) by adopting a multifaceted approach that considers the needs, perspectives and contributions of diverse stakeholders. By addressing disparities and advocating for social justice within elephant conservation, the study emphasizes the urgent need for collective action in conservation, welfare improvement, conflict resolution, ecosystem balance, and overall enhancement of understanding about Asian elephants. Serving as a valuable resource for policymakers, conservation organizations, and researchers, the study not only synthesizes existing literature but also identifies research gaps, highlights areas requiring immediate attention, and offers collaborative opportunities for global scientists to contribute to the well-being and preservation of this majestic species.</span></p>
Figure 1 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
Figure 1. Map of Micronesia showing jurisdictions included in the FIA program and locations of Micronesia Challenge protected areas as of 2018.
Figure 2 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
Figure 2. Estimated percentage of all trees by diameter class (in inches) by jurisdiction across Micronesia. FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.
Phenotype variation in Niphargus (Amphipoda: Niphargidae): possible explanations and open challenges: data and R code
<p>Data and R code for performing the analyses of phylogenetic signal presented in the manuscript titled "Phenotype variation in Niphargus (Amphipoda: Niphargidae): possible explanations and open challenges. Data contains phylogenetic tree (Delić et al., 2023) and functional trait data in the RDS format (Premate & Fišer, 2024). The R code is available in the html format.</p> <p>References/data sources:</p> <p>Delić, T., Borko, S., Premate, E., Rexhepi, B., Alther, R., Knuesel, M., ... & Altermatt, F. (2023). Evolutionary origin of morphologically cryptic species imprints co-occurrence and sympatry patterns. <em>bioRxiv</em>, 2023-09.</p> <p>Premate, E., & Fišer, C. (2024). Functional trait dataset of European groundwater Amphipoda: Niphargidae and Typhlogammaridae. <em>Scientific Data</em>, <em>11</em>(1), 188.</p>
Figure 1 in Challenges of drug-resistant malaria
Figure 1. Different proteins present inside the parasitic organelle that contribute to drug resistance in malaria under selective drug pressure and new drugs in development, targeting the same pathway to rescue resistance. PRBC: parasitized red blood cell, ER: endoplasmic reticulum, MT: mitochondria, DHPS: dihydropterate synthetase, DHFR: dihydrofolate reductase, ATPase6: sacro/endoplasmic reticulum calcium dependent ATPase orthologue, CRT: chloroquine resistance transporter, MDR1: multidrug resistance.
Figure 1 in Integrating cover crops for weed management in the semiarid U.S. Great Plains: opportunities and challenges
Figure 1. Map of the Great Plains showing three main regions: (1) Northern Great Plains (marked by purple line), (2) Central Great Plains (marked by red line), and (3) Southern Great Plains (marked by light blue line). Adapted from Center for Great Plains Studies, University of Nebraska–Lincoln.
Comp-het Data for "CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM"
<p>Synthetic cryo-EM datasets with simulated compositional heterogeneity and their ground truth atomic models, density maps, poses, labels, mask, and consensus volume:</p> <ul> <li>Ribosembly: 335,240 particle images (128x128, 6A/pix) containing 16 bacterial ribosome assembly states from "Cryo-em captures early ribosome assembly in action" from Qin et al. (2023)</li> <li>Tomotwin-100: 100k particle images (128x128, 9A/pix) containing a mixture of 100 different biomolecular complexes from "Tomotwin: generalized 3d localization of macromolecules in cryo-electron tomograms with structural data mining" from Rice et al. (2023)</li> </ul>
Conf-het Data for "CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM"
<p>Synthetic cryo-EM datasets with simulated conformational heterogeneity and their ground truth atomic models, density maps, poses, labels, mask, and consensus volume:</p> <ul> <li>IgG-1D: 100k particle images (128x128, 6A/pix) of IgG with conformations uniformly sampled from a simple one-dimensional continuous circular motion</li> <li>IgG-1D-noisier: The IgG-1D dataset with noise increased from SNR 0.01 to 0.005</li> <li>IgG-1D-noisiest: The IgG-1D dataset with noise increased from SNR 0.01 to 0.001</li> <li>IgG-RL: 100k particle images (128x128, 6A/pix) of IgG with conformations of its flexible linker generated by sampling backbone dihedral angles according to the Ramachandran distributions of disordered peptides</li> </ul>
Fig. 18 in New insights in the taxonomy of Lepismatidae (Insecta, Zygentoma) with an updated key to genera and future challenges
Fig. 18. Caribesella gen. nov., diagnostic characters of the head not previously illustrated. Micrograph showing part of the head chaetotaxy: the clypeal tuft of one side (white arrow) and the labrum with no tufts (blue arrow); the frontal tuft of the same side is visible (yellow arrow). Scale bar = 0.1 mm.
Fig. 19 in New insights in the taxonomy of Lepismatidae (Insecta, Zygentoma) with an updated key to genera and future challenges
Fig. 19. Caribesella gen. nov., diagnostic characters not previously illustrated, SEM photographs. A. Orbicular scales of dorsal side of the abdomen. B. An indented scale of the dorsal side of the thorax. C. Coxal scales. D. Scales on the apex of the femur. E. Scales and setae on the basal part of the tibia. F. Scales on stylus IX. G. Open anterior trichobothrial area of the pronotum. Scale bars: A = 70 μm; B, G = 50 μm; C = 60 μm; D–E = 90 μm; F = 80 μm.
Fig. 16 in New insights in the taxonomy of Lepismatidae (Insecta, Zygentoma) with an updated key to genera and future challenges
Fig. 16. Types of macrochaetae in Lepismatidae Latreille, 1802. A. Micrograph of smooth bifid macrochaetae on the mandible of Allacrotelsa kraepelini (Escherich, 1905). B. Comb of smooth and apically bifid macrochaetae of Anisolepisma aquilonaridum Smith, 2016, SEM photograph. C. Micrograph of feathered macrochaetae on the mandible of Ctenolepisma iranicum Molero, Kahrarian & Gaju, 2016. D. Feathered macrochaetae on the frontal area of the head of Hemitelsella transpectinata (Smith, 2015). E. Design of the smooth macrochetae with a round tip on the mandible of Mormisma peyerimhoffi (Silvestri, 1938), adapted from Silvestri (1938). F. Feathered/plumose macrochaeta of the cerci of Swalepisma mirabile Irish, 1988, SEM photograph. Scale bars: A, C–E = 0.1 mm; B, F = 20 μm.
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.