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2,863 results for “Challenge”
Figure 1 from: Despot-Belmonte K, Neßhöver C, Saarenmaa H, Regan E, Meyer C, Martins E, Groom Q, Hoffmann A, Caine A, Bowles-Newark N, Bae H, Canhos D, Stenzel S, Bowler D, Schneider A, V. Weatherdon L, S. Martin C (2017) Biodiversity data provision and decision-making - addressing the challenges. Research Ideas and Outcomes 3: e12165. https://doi.org/10.3897/rio.3.e12165
Figure 1 - The main users of biodiversity data.
Figure 3 from: Despot-Belmonte K, Neßhöver C, Saarenmaa H, Regan E, Meyer C, Martins E, Groom Q, Hoffmann A, Caine A, Bowles-Newark N, Bae H, Canhos D, Stenzel S, Bowler D, Schneider A, V. Weatherdon L, S. Martin C (2017) Biodiversity data provision and decision-making - addressing the challenges. Research Ideas and Outcomes 3: e12165. https://doi.org/10.3897/rio.3.e12165
Figure 3 - Discussing the development of the "Red Book" of Brazilian plants - case study.
Figure 4b from: Despot-Belmonte K, Neßhöver C, Saarenmaa H, Regan E, Meyer C, Martins E, Groom Q, Hoffmann A, Caine A, Bowles-Newark N, Bae H, Canhos D, Stenzel S, Bowler D, Schneider A, V. Weatherdon L, S. Martin C (2017) Biodiversity data provision and decision-making - addressing the challenges. Research Ideas and Outcomes 3: e12165. https://doi.org/10.3897/rio.3.e12165
Figure 4b - Developing the brief
Figure 4a from: Despot-Belmonte K, Neßhöver C, Saarenmaa H, Regan E, Meyer C, Martins E, Groom Q, Hoffmann A, Caine A, Bowles-Newark N, Bae H, Canhos D, Stenzel S, Bowler D, Schneider A, V. Weatherdon L, S. Martin C (2017) Biodiversity data provision and decision-making - addressing the challenges. Research Ideas and Outcomes 3: e12165. https://doi.org/10.3897/rio.3.e12165
Figure 4a - Workshop participants
Figure 1 from: Ek K, Goytia S, Lundmark C, Nysten-Haarala S, Pettersson M, Sandström A, Söderasp J, Stage J (2017) Challenges in Swedish hydropower – politics, economics and rights. Research Ideas and Outcomes 3: e21305. https://doi.org/10.3897/rio.3.e21305
Figure 1 - The challenges of two parallel water management systems.
Fetal Tissue Annotation Challenge (FeTA) Biometry - MICCAI 2024
<p>This dataset is the training dataset for the biometry task of FeTA Challenge held at the MICCAI 2024 Conference (Task 2). This derived dataset contains biometric measurement masks of five anatomical regions done on T2-weighted fetal brain super-resolution reconstructions: </p> <p>0. Non-relevant</p> <p>1. Height of the vermis</p> <p>2. Length of the corpus callosum</p> <p>3. Brain biparietal diameter</p> <p>4. Skull biparietal diameter</p> <p>5. Tranverse cerebellar diameter</p> <p>The data are released along a transform file that can be used to re-align the reconstructed T2w images into the plane that was used to execute the biometry.</p> <p>The goal of this dataset is to encourage research groups to develop automated biometry methods that are robust across a range of gestational agesm, multiple centers, a variety of brain pathologies as well as normally developing fetal brains. </p> <p>See fetachallenge.github.io for more details, and to register for the FeTA Challenge. Read carefully the README.md file for further details.</p> <p>If you use this dataset, please cite this repository 10.5281/zenodo.11192452.</p>
DCASE 2024 Challenge Task 10 Evaluation Dataset: Acoustic-based Traffic Monitoring
<h3><strong>Directory structure:</strong></h3> <p><strong>locX.zip</strong><br>|----- test [<em>test flac files inside</em>]</p> <p> </p> <p><strong>NOTE:</strong> We split zip files for large size folders (i.e., loc1, loc3, loc6). Please make sure to download <em><strong>all splits</strong></em> before unzipping the data! For example, run </p> <p><code>zip -s 0 loc6.zip --out unsplit-loc6.zip</code></p> <p><code>unzip unsplit-loc6.zip</code></p> <p>in your terminal once you have downloaded <code>loc6.z01</code>, <code>loc6.z02</code> and <code>loc6.zip</code>.</p> <p> </p> <h3><strong>Contact:</strong></h3> <p>If there is any question, please contact:</p> <ul> <li>Luca Bondi: <a href="mailto:luca.bondi@us.bosch.com">luca </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:luca.bondi@us.bosch.com"> bondi@us (dot) bosch (dot) com</a></li> <li>Shabnam Ghaffarzadegan: <a href="mailto:shabnam.ghaffarzadegan@us.bosch.com">shabnam </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> ghaffarzadegan@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> com</a></li> <li>Wei-Cheng (Winston) Lin: <a href="mailto:winston.lin@us.bosch.com">wei-cheng </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> lin@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> com</a></li> </ul> <p> </p> <h3><strong>Acknowledgement:</strong></h3> <p><em>This work has partially received funding from the European Union's Horizon 2020 research and innovation programme under the Marie </em><em>Skłodowska</em><em>-Curie grant agreement No. 956962 and from the European Research Council under the European Union's Horizon 2020 research and innovation program / ERC Consolidator Grant: SONORA (no. 773268). This work reflects only the authors' views and the Union is not liable for any use that may be made of the contained information.</em></p>
Effects of sleep-restriction and light intensity on mental effort during cognitive challenge Study 1
<p>This dataset contains the data of the preliminary study in the supplements of the publication "Effects of sleep-restriction and light intensity on mental effort during cognitive challenge" by Larissa Wüst and Ruta Lasauskaite.</p>
Exploring Data Management Challenges and Solutions in Agile Software Development: A Literature Review and Practitioner Survey
<p>This comprehensive dataset for the Systematic Literature Review (SLR) and the survey responses on "Exploring Data Management Challenges and Solutions in Agile Software Development: A Literature Review and Practitioner Survey" contains detailed information on selected studies in the SLR. It features research titles, abstracts, keywords, and authors, along with Scopus-extracted details. The dataset encompasses various data types, such as business, product, process, project, and operational, and details the distribution of these studies across major data management aspects, including integration, collection, quality, and analysis. Additionally, the dataset offers insights into the quality assessment of these studies and covers other data management aspects, providing definitions, challenges, and solutions. Furthermore, it includes studies used for search validation, presenting a comprehensive overview of the distribution of study years across different data management aspects. Additionally, the survey participants' responses (with any personal information redacted or masked) are included.</p>
Method paper_Campylobacter challenge
Open the record for dataset details and reuse information.
Supplemental Material for "Overcoming challenges in variant calling: exploring sequence diversity in candidate genes for plant development in perennial ryegrass (Lolium perenne)"
<p>Supplemental materials to the manuscript.</p>
Data from: Benefits and challenges with applying unique molecular identifiers in next generation sequencing to detect low frequency mutations
Indexing individual template molecules with a unique identifier (UID) before PCR and deep sequencing is promising for detecting low frequency mutations, as true mutations could be distinguished from PCR errors or sequencing errors based on consensus among reads sharing same index. In an effort to develop a robust assay to detect from urine low-abundant bladder cancer cells carrying well-documented mutations, we have tested the idea first on a set of mock templates, with wild type and known mutants mixed at defined ratios. We have measured the combined error rate for PCR and Illumina sequencing at each nucleotide position of three exons, and demonstrated the power of a UID in distinguishing and correcting errors. In addition, we have demonstrated that PCR sampling bias, rather than PCR errors, challenges the UID-deep sequencing method in faithfully detecting low frequency mutation.
Figure 4 from: Maggioni T, Taverna A, Tatián M (2016) Redescription of the deep-sea colonial ascidian Synoicum molle (Herdman, 1886): first record since its original finding during the Challenger Expedition. Zoosystematics and Evolution 92(2): 181-185. https://doi.org/10.3897/zse.92.9521
Figure 4 - Abdomen of Synoicum molle showing the rounded smooth-walled stomach.
Figure 2 from: Maggioni T, Taverna A, Tatián M (2016) Redescription of the deep-sea colonial ascidian Synoicum molle (Herdman, 1886): first record since its original finding during the Challenger Expedition. Zoosystematics and Evolution 92(2): 181-185. https://doi.org/10.3897/zse.92.9521
Figure 2 - Colony of Synoicum molle photographed in vivo.
Figure 1 from: Maggioni T, Taverna A, Tatián M (2016) Redescription of the deep-sea colonial ascidian Synoicum molle (Herdman, 1886): first record since its original finding during the Challenger Expedition. Zoosystematics and Evolution 92(2): 181-185. https://doi.org/10.3897/zse.92.9521
Figure 1 - Study area in the SW Atlantic. The asterisk shows the sampling location.
Figure 4 from: Huemer P, Karsholt O (2018) Revision of the genus Megacraspedus Zeller, 1839, a challenging taxonomic tightrope of species delimitation (Lepidoptera, Gelechiidae). ZooKeys 800: 1-278. https://doi.org/10.3897/zookeys.800.26292
Figure 4 Terminology of important characters in male genitalia of Megacraspedusgranadensis sp. n.
Figure 2 from: Huemer P, Karsholt O (2018) Revision of the genus Megacraspedus Zeller, 1839, a challenging taxonomic tightrope of species delimitation (Lepidoptera, Gelechiidae). ZooKeys 800: 1-278. https://doi.org/10.3897/zookeys.800.26292
Figure 2 Adult head with pecten on antennal scape of Megacraspedusniphorrhoa (Meyrick, 1926).
Static-Analysis-Based Solutions to Security Challenges in Cloud-Native Systems: Systematic Mapping Study
<p>It Contains data for the following search query</p> <p> </p> <p>( security OR attack OR compromise OR vulnerability )<br> AND<br> defense OR prevention<br> AND<br> microservice OR cloud native<br> AND<br> static analysis</p>
Dataset of the Paper: Practices and Challenges of Using GitHub Copilot: An Empirical Study
<p>This dataset collected from Stack Overflow (SO) and GitHub Discussions was used to conduct an empirical study on the practices and challenges of using GitHub Copilot. A brief description of each document in the dataset is provided below:</p> <p><strong>1. Dataset (SO).xlsx</strong></p> <p>contains the IDs and URLs of the labelled posts which are related to Copilot from Stack Overflow, and the data extracted from these related SO posts.</p> <p><strong>2. Dataset (GitHub).xlsx</strong></p> <p>contains the discussion IDs and discussion URLs in the Copilot category of GitHub Discussions, and the data extracted from all the discussions.</p> <p><strong>3. Extracted Data (SO+GitHub).xlsx</strong></p> <p>contains the final results of data extracted from SO posts and GitHub discussions.</p>
Data produced for "Water Scarcity Challenges across Urban Regions with Expanding Irrigation"
<p>This archive includes the data and codes to produced results for the paper- <strong>Transition from Rain-fed to Irrigation-fed Agriculture in Rural Areas to Exacerbate Urban Water Scarcity</strong>. It contains 3 archive files:</p> <p><strong>1. H08_inputs_org: </strong>All the input files required for the simulation of the global hydrological model, H08 (/H08/map/org). It has the following files (for both scenarios S1, and S2) -</p> <p>a) AQUASTAT- industrial and domestic water use</p> <p>b) C05- GDP information for the countries</p> <p>c) FAO2009_Slop- data on slope</p> <p>d) GMIA5_S1- global map for irrigated areas version 5 for scenario 1</p> <p>e) GMIA5_S2- global map for irrigated areas version 5 for scenario 2</p> <p>f) GRanD- Global Reservoir and Dam (GRanD) database</p> <p>g) GSWP2_Albedo- albedo data</p> <p>h) GSWP23_SoilType- soil type data</p> <p>i) IGRAC- groundwater use for domestic and industrial sector</p> <p>j) IIASA_SSP- socio economic pathways data</p> <p>k) K14- explicit aqueduct data</p> <p>l) M08- crop distribution data</p> <p>m) OneGeology- geology units offshore</p> <p>n) R08- cropland and pastureland data</p> <p>o) WFDEI- flow direction </p> <p>p) DS02- irrigation data</p> <p><strong>2. Codes:</strong> Jupyter notebook consisting of the python codes for post-processing, and a folder named <em>files_req </em>with the required files for the notebook. </p> <p><strong>3. Outputs: </strong>This includes 4 archives</p> <p>a) arcgis_outputs- post-processing model results' shapefiles and rasters; ArcMap documents of CAD difference; and other outputs</p> <p>b) CAD_S1_S2- Raster files of monthly CAD for scenarios S1 (named as S12) and S2; raster files of total water abstraction from renewable water sources; and raster files of total water demand. </p> <p>c) diff_cad_21- Raster files of difference in CAD (CAD_S2 - CAD_S1) for with and without considering environmental flow requirements.</p> <p>e) urban_results_excel- Excel files of CAD comparison in S1 and S2; excel file of monthly water demand and abstraction in S1 and S2. </p> <p> </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.