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409 results for “information use”
FIGURE 7. Principal components analysis using shape information for Galaxiella pusilla s.s in A review of Galaxiella pusilla (Mack) (Teleostei: Galaxiidae) in south-eastern Australia with a description of a new species
FIGURE 7. Principal components analysis using shape information for Galaxiella pusilla s.s. (black) and Galaxiella toourtkoourt (grey) generated from 12 landmarks for a) females (PC1 and PC2 explain 52.0% and 14.5% of the total variance, respectively), b) males (PC1 and PC2 explain 54.4% and 14.7% of the total variance, respectively), and shape changes associated with principal component 2 for c) females and d) males.
Supplement_Stochastic_Reconstruction_and_Interpolation_of_Precipitation_Fields_Using_Combinded_Information_CML_and_RG
<p>This file includes the synthetic dataset used in the study "Stochastic Reconstruction and Interpolation of Precipitation Fields Using Combined Information of Commercial Microwave Links and Rain Gauges", including the full synthetic reference fields (VR_precip_h_30052013_02062013.nc) as well as the used synthetic rain gauge (precip_VRObs_dry+wet_30052013_02062013_Pgr1mm.csv) and commercial microwave links (VROBS_MWL_selection_Pgr1mm.csv) input data.</p> <p>The simulated ensembles of possible realizations of the precipitation fields are stored for the synthetic (simulation_VR) and real (simulation_realworld) world dataset. A corresponding description of the grid can be found in gridinfo.txt.</p>
Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory"
<p>Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory" published in the Journal of Geophysical Research Oceans. <br>URL of the manuscript: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JC020101<br>DOI of the manuscript: https://doi.org/10.1029/2023JC020101</p>
The datasets used in the Supplementary Information
<p>The datasets used in the Supplementary Information. For additional details, please refer to the ReadMe file within each subfolder.</p>
Supplementary material 1 from: Sosa-López JR, Díaz Bernal NN, Padilla E, Briones-Salas M (2023) Analysis of the effects of habitat characteristics, human disturbance and prey on felids presence using long-term community monitoring information. Nature Conservation 53: 279-295. https://doi.org/10.3897/natureconservation.53.104135
Sampling sites and dates on which the camera-traps were installed and Generalized linear mixed models (GLMM) for Puma, Bobcat and Margay
Supplementary Information: CHAPTER 3 - Classification of genomic features of plant-associated bacteria using machine learning
<p>Appendix A- List of all bacterial genomes used in orthologous genes clustering in the feature extraction step and in the further steps to build and test classifiers’ models. The list includes the isolation source information and the related category for the genome classification and features selection purposes.</p> <p>Appendix B - Distribution of genomes by phylum, family, and genus among the categories defined according to bacteria lifestyle association.</p> <p>Appendix C - Enriched orthogroups by genus according to each enrichment test (Material and Methods). Values for each test are "Y" (enriched), "N" (not enriched), or "Untested" (clusters were untested when there was insufficient phylogenetic signal, they were too small or were found in all genomes).</p> <p>Appendix D - Classification performance of random forest and logistic regression techniques applied to genus-specific datasets of genomic features (orthogroups) using both matrices from gene count number and presence/absence values. Sensitivity is a measure of how well a test identifies true positives; Specificity: is a measure how well a test or model avoids false positives; Positive Predictive Value (Pos. Pred. Value): The probability that a positive prediction is correct; Negative Predictive Value (Neg. Pred. Value): The probability that a negative prediction is correct; Precision: The accuracy of positive predictions; Recall (Sensitivity): The ability to find all relevant cases; F1 Score: A combined measure of precision and recall; Prevalence: The proportion of positive cases in the total; Detection Rate: The proportion of true positive cases identified; Detection Prevalence: The proportion of positive predictions; Balanced Accuracy: An average of sensitivity and specificity; Area Under the Curve (AUC): The overall performance of the model in distinguishing between positive and negative cases.</p> <p>Appendix E - Orthogroups assigned with predicted COGs as an important feature for classifying plant-associated genomes. COG categories: A - RNA processing and modification; B - Chromatin structure and dynamics; C - Energy production and conversion; D - Cell cycle control, cell division, chromosome partitioning; E - Amino acid transport and metabolism; F - Nucleotide transport and metabolism; G - Carbohydrate transport and metabolism; H - Coenzyme transport and metabolism; I - Lipid transport and metabolism; J - Translation, ribosomal structure and biogenesis; K - Transcription; L - Replication, recombination and repair; M - Cell wall/membrane/envelope biogenesis; N - Cell motility; O - Posttranslational modification, protein turnover, chaperones; P - Inorganic ion transport and metabolism; Q - Secondary metabolites biosynthesis, transport and catabolism; R - General function prediction only; S - Function unknown; T - Signal transduction mechanisms; U - Intracellular trafficking, secretion, and vesicular transport; V - Defense mechanisms; W - Extracellular structures; X - Mobilome: prophages, transposons; Y - Nuclear structure; Z - Cytoskeleton.</p>
Data used in "Biologically informed deep neural network for prostate cancer discovery" publication
<p>Data used in the publication titled "<strong>Biologically informed deep neural network for prostate cancer discovery </strong>" </p> <p>Elmarakeby, Haitham A., et al. "Biologically informed deep neural network for prostate cancer discovery." <em>Nature</em> 598.7880 (2021): 348-352.</p> <p>These datasets were derived from the following public domain resources:</p> <ol> <li>Armenia J, Wankowicz SAM, Liu D, Gao J, Kundra R, Reznik E, et al. The long tail of oncogenic drivers in prostate cancer. Nat Genet. 2018;50: 645–651. DOI: <a href="https://doi.org/10.1038/s41588-018-0078-z">10.1038/s41588-018-0078-z</a></li> <li>Fraser M, Sabelnykova VY, Yamaguchi TN, Heisler LE, Livingstone J, Huang V, et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature. 2017;541: 359–364. https://doi.org/10.1038/nature20788</li> <li>Robinson DR, Wu Y-M, Lonigro RJ, Vats P, Cobain E, Everett J, et al. Integrative clinical genomics of metastatic cancer. Nature. 2017;548: 297–303. https://doi.org/10.1038/nature23306</li> <li>Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46: D649–D655. DOI: <a href="https://doi.org/10.1093/nar/gkv1351">10.1093/nar/gkv1351</a></li> </ol> <p> </p>
LD information used in MUSSEL for EUR, AFR, AMR, EAS, and SAS
<p>Estimated LD block matrices and other LD information used in MUSSEL for EUR, AFR, AMR, EAS, and SAS for approximately 2.0 million SNPs in HapMap 3 plus MEGA. Given the limited storage space on Zenodo, we have deposited the LD information generated based on 1000 Genomes LD reference panel and provided the link to download the LD files below using:</p> <p>(1) 1000 Genomes LD reference panel:</p> <p>EUR: <a href="https://www.dropbox.com/s/wvxh4yqthm8m7uf/EUR.zip?dl=0">https://www.dropbox.com/s/wvxh4yqthm8m7uf/EUR.zip?dl=0</a> (~6.73G, unzip by: tar -zxvf EUR.tar.gz) </p> <p>AFR: <a href="https://www.dropbox.com/s/iwqg65uieevfzj2/AFR.zip?dl=0">https://www.dropbox.com/s/iwqg65uieevfzj2/AFR.zip?dl=0</a> (~7.69G, unzip by: tar -zxvf AFR.tar.gz)</p> <p>AMR: <a href="https://www.dropbox.com/s/mev5zyf4x6m076q/AMR.zip?dl=0">https://www.dropbox.com/s/mev5zyf4x6m076q/AMR.zip?dl=0</a> (~8.80G, unzip by: tar -zxvf AMR.tar.gz)</p> <p>EAS: <a href="https://www.dropbox.com/s/o28mlovtakv5n7v/EAS.zip?dl=0">https://www.dropbox.com/s/o28mlovtakv5n7v/EAS.zip?dl=0</a> (~5.63G, unzip by: tar -zxvf EAS.tar.gz)</p> <p>SAS: <a href="https://www.dropbox.com/s/idp02rgl8xv379b/SAS.zip?dl=0">https://www.dropbox.com/s/idp02rgl8xv379b/SAS.zip?dl=0</a> (~2.60G, unzip by: tar -zxvf SAS.tar.gz)</p> <p> </p> <p>(2) UK Biobank reference panel:</p> <p>EUR: <a href="https://www.dropbox.com/scl/fi/09yd12dest1tqxkt8p8ch/EUR.zip?rlkey=774vb1e5d6hfnyucilx160cyo&dl=0">https://www.dropbox.com/scl/fi/09yd12dest1tqxkt8p8ch/EUR.zip?rlkey=774vb1e5d6hfnyucilx160cyo&dl=0</a> (~13.15G, unzip by: tar -zxvf EUR.tar.gz) </p> <p>AFR: <a href="https://www.dropbox.com/scl/fi/jfymih83anr2vuevmfqok/AFR.zip?rlkey=r1lxpn1fnbk98ssf8f8ji4xkk&dl=0">https://www.dropbox.com/scl/fi/jfymih83anr2vuevmfqok/AFR.zip?rlkey=r1lxpn1fnbk98ssf8f8ji4xkk&dl=0</a> (~11.59G, unzip by: tar -zxvf AFR.tar.gz)</p> <p>AMR: <a href="https://www.dropbox.com/s/2ba4tsbhz03rg83/AMR.zip?dl=0">https://www.dropbox.com/s/2ba4tsbhz03rg83/AMR.zip?dl=0</a> (~4.88G, unzip by: tar -zxvf AMR.tar.gz)</p> <p>EAS: <a href="https://www.dropbox.com/s/uofu788707dp4xv/EAS.zip?dl=0">https://www.dropbox.com/s/uofu788707dp4xv/EAS.zip?dl=0</a> (~4.27G, unzip by: tar -zxvf EAS.tar.gz)</p> <p>SAS: <a href="https://www.dropbox.com/scl/fi/o635c86ylthbl3omfetbu/SAS.zip?rlkey=ot396toxl0phaiae15cnpbeyn&dl=0">https://www.dropbox.com/scl/fi/o635c86ylthbl3omfetbu/SAS.zip?rlkey=ot396toxl0phaiae15cnpbeyn&dl=0</a> (~11.44G, unzip by: tar -zxvf SAS.tar.gz)</p>
Using species distribution modeling to generate relative abundance information in unstable territories: conservation of Felidae in Mexico
<p>Raw data used in the abovementioned manuscript</p>
Supporting Information for "First Observations of Large Scale Traveling Ionospheric Disturbances Using Automated Amateur Radio Receiving Networks"
<p><strong>Introduction</strong></p> <p>The supporting information for this paper consists of a movie version of Figure 2 in the main paper, comparing the high frequency (HF) amateur radio observations to differential Global Navigation Satellite System (GNSS) Total Electron Content (TEC) measurements. In this revised version of the repository, the TEC data has been reprocessed such that all GNSS data with elevation angles down to 10 degrees were kept when applying the Savitzky–Golay (SG) filter. After the SG filter was applied, data with elevation angles below 30 degrees were discarded. This was done to remove artifacts observed in the original movies.</p> <p> </p> <p><strong>Movie S1 (20171103 Ham and TEC LSTID.mp4)</strong></p> <p>(Top Panel) Time series showing the TX-RX distance for 14 MHz amateur radio spots in 2 min by 25 km bins from 1200 UT 3 Nov 2017 - 0000 UT 4 Nov 2017. (Bottom Panel) differential Global Navigation Satellite System (GNSS) Total Electron Content (TEC) measurements over the Continental United States corresponding to the times indicated by the moving white vertical line in the top panel.</p> <p> </p> <p><strong>Movie S2 (20171103 Ham and TEC LSTID – With Arrow.mp4)</strong></p> <p>Same as movie S1, but with a fiducial black arrow indicating an estimated LSTID horizontal wavelength of 1681 km and propagation azimuth of 163°.</p>
Supporting Information - An experimental assessment of competitive interactions between sexual and apomictic fern gametophytes using Easy Leaf Area
<p>Supporting Information for the paper titled <strong>An experimental assessment of competitive interactions between sexual and apomictic fern gametophytes using Easy Leaf Area</strong> published in Applications in Plant Sciences.</p>
Using seabird and whale distribution models to estimate spatial consumption of krill to inform fishery management
<p>Ecosystem dynamics at the north-west Antarctic Peninsula are driven by interactions between physical and biological processes. For example, baleen whale populations are recovering from commercial harvesting against the backdrop of rapid climate change, including reduced sea-ice extent and changing ecosystem composition. Concurrently, the commercial harvesting of Antarctic krill is increasing, with the potential to increase the likelihood for competition with and between krill predators and the fishery. However, understanding the ecology, abundance, and spatial distribution of krill predators is often limited, outdated, or at spatial scales that do not match those desired for effective fisheries management. We update current knowledge of predator dependence on krill by integrating telemetry-based data, at-sea observational surveys, estimates of predator abundance, and physiological data to estimate the spatial distribution of krill consumption during the austral summer by three species of Pygoscelis penguin, 11 species of flying seabirds, one species of pinniped and two species of baleen whale. Our models show that the majority of important areas for krill-predator foraging are close to penguin breeding colonies in nearshore areas where humpback whales also regularly feed, and along the shelf-break, though we caution that not all known krill predators are included in these analyses. We show that krill consumption is highly variable across the region, and often concentrated at fine spatial scales, emphasising the need for management of the local krill fishery at relevant temporal and spatial scales. We also note that krill consumption by recovering populations of krill predators provides further evidence in support of the krill surplus hypothesis, and highlight that despite less than comprehensive data, cetaceans are likely to consume a significant proportion of the krill consumed by natural predators but are not currently considered directly in the management of the krill fishery. If management of the krill fishery is to be precautionary and operate in a way that minimises the risks to krill predator populations, it will be necessary in future analyses, to include up-to-date and precise abundance and consumption estimates for pack-ice seals, finfish, squid, and other baleen whale species not currently considered.</p>
House sparrows use learned information selectively based on whether reward is hidden or visible
<p>The dataset contains two files containing the number of visits made by each bird in each of the well types, out of the first 15 visits in each test (one file for Experiment 1 and another for Experiment 2). An additional file contains our handling time samples for each of the treatments used in experiment 1 (exposed, hidden and "wrapped").</p>
ARCHI4MOM: Using Tracing Information to Extract the Architecture of Microservice-based Systems from Message-oriented Middleware
<p>The data published here are relevant for ESCA_2022 publication. </p>
Breast cancer dataset used in: Biologically informed NeuralODEs for genome-wide regulatory dynamics
<p> The original data set comes from a cross-sectional breast cancer study (GEO accession GSE7390) consisting of microarray expression values for 22000 genes from 198 breast cancer patients, that we sorted along a pseudotime axis. We noted that the same data set was also used in the PROB paper (Sun, X., et al 2021, Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples). <br><br>PROB is a GRN inference method that infers a random-walk-based pseudotime to sort cross-sectional samples and reconstruct the GRN. For consistency and convenience in pseudotime inference, we obtained the same version of this data that was already preprocessed and sorted by PROB. This was shared with us by the authors of PROB. We have uploaded the shared files here, as well as the versions obtained after pre-processing to apply PHOENIX. </p>
Metadata information for the events used in Ni et al. (2023)
Open the record for dataset details and reuse information.
The Influence of Users' Information Needs Regarding Diabetes on Their Intention to Use Health Platforms
<p>data set</p>
Checking Secure Information Flow of CoCoME using UPPAAL
<p>Model of the CoCoME case study for the UPPAAL model checker. Also includes auto-generated verification models for checking the information flow security of CoCoME, and the corresponding verification properties.</p>
Improving the use of social networks to disseminate information aboutreal estate in the Republic of Kazakhstan
<p><span>In the modern era, digital platforms, particularly social networks, have revolutionized the way governments interact with citizens. Social media has emerged as a critical tool for disseminating information quickly and efficiently, especially in areas where public awareness is essential, such as taxes, land registration, and real estate laws. In his 2024 Address to the Nation, the President of Kazakhstan underscored the importance of digitizing public services to increase transparency, improve access to information, and simplify interactions between citizens and government bodies. This aligns with the country's long-term goal of modernizing the e-government system, emphasizing the need for effective communication channels that foster civic engagement and compliance.</span></p>
Data from: Using biogeographic history to inform conservation: the case of Preble's meadow jumping mouse
The last Pleistocene deglaciation shaped temperate and boreal communities in North America. Rapid northward expansion into high latitudes created distinctive spatial genetic patterns within species that include closely related groups of populations that are now widely spread across latitudes, while longitudinally adjacent populations, especially those near the southern periphery, often are distinctive due to long-term disjunction. Across a spatial expanse that includes both recently colonized and long-occupied regions, we analyzed molecular variation in zapodid rodents to explore how past climate shifts influenced diversification in this group. By combining molecular analyses with species distribution modeling and tests of ecological interchangeability, we show that the lineage including the Preble's meadow jumping mouse (Zapus hudsonius preblei), a US federally listed taxon of conservation concern, is not restricted to the southern Rocky Mountains. Rather, populations along the Front Range are part of a single lineage that is ecologically indistinct and extends to the far north. Of the 21 lineages identified, this Northern lineage has the largest geographic range and low measures of intra-lineage genetic differentiation, consistent with recent northward expansion. Comprehensive sampling combined with coalescent-based analyses and niche modeling lead to a radically different view of geographic structure within jumping mice and indicates the need to re-evaluate their taxonomy and management. This analysis highlights a premise in conservation biology, that biogeographic history should play a central role in establishing conservation priorities.
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.