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13,064 results for “predictions”
FIGURE 9. Maximum entropy model developed for D in A new species of Desmopachria Babington (Coleoptera: Dytiscidae) from Cuba with a prediction of its geographic distribution and notes on other Cuban species of the genus
FIGURE 9. Maximum entropy model developed for D. andreae sp. n. in Cuba. Values range from high (red areas) to low environmental suitability (blue areas).
FIGURE 8 in A new species of Desmopachria Babington (Coleoptera: Dytiscidae) from Cuba with a prediction of its geographic distribution and notes on other Cuban species of the genus
FIGURE 8. Representation of the contribution provided by the environmental variables considered to develop the MaxEnt model for D. andreae sp. n. Grey bars show the percent contribution of each variable to the model and corresponding values are given on the right axis. Jackknife results for the model (values on the left axis) are also shown for single variables (diagonal shade), for all variables except the one selected (black bars) and for all variables (white).
FIGURES 66–67. Predicted distribution. 66 in New species of Taual a Wanless, 1988 from Australia (Araneae: Salticidae: Astioida: Astiae), with a redefinition of the genus
FIGURES 66–67. Predicted distribution. 66 Tauala spp (74 records); 67 Jacksonoides spp (105 records). Darker colours = larger likelihood of occurrence.
Hora: Architecture-aware online failure prediction
<p>Complex software systems experience failures at runtime even though a lot of effort is put into the development and operation. Reactive approaches detect these failures after they have occurred and already caused serious consequences. In order to execute proactive actions, the goal of online failure prediction is to detect these failures in advance by monitoring the quality of service or the system events. Current failure prediction approaches look at the system or individual components as a monolith without considering the architecture of the system. They disregard the fact that the failure in one component can propagate through the system and cause problems in other components. In this paper, we propose a hierarchical online failure prediction approach, called HORA , which combines component failure predictors with architectural knowledge. The failure propagation is modeled using Bayesian networks which incorporate both prediction results and component dependencies extracted from the architectural models. Our approach is evaluated using Netflix’s server-side distributed RSS reader application to predict failures caused by three representative types of faults: memory leak, system overload, and sudden node crash. We compare HORA to a monolithic approach and the results show that our approach can improve the area under the ROC curve by 9.9%.</p>
Datasets for ALADIN DTI prediction method
<p>Datasets used for the evaluation of the ALADIN method.</p> <p>This is a copy of following datasets:</p> <p>http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/</p> <p>http://staff.cs.utu.fi/~aatapa/data/DrugTarget/</p>
How manual object exploration is associated with 7- to 8-month-old infants' visual prediction abilities in spatial object processing.
<p>Data set of Kubicek, C., Jovanovic, B., & Schwarzer, G. (in press). How manual object exploration is associated with 7- to 8-month-old infants’ visual prediction abilities in spatial object processing. <em>Infancy.</em></p>
Data for Less predictable global rhythms in human than wildlife contributions to soundscapes
<p>Dataset to the article: Somervuo, P., Roslin, T., Fisher, B.L. et al. Human contributions to global soundscapes are less predictable than the acoustic rhythms of wildlife. Nat Ecol Evol (2025). https://doi.org/10.1038/s41559-025-02786-5</p>
Expert and AI-generated annotations of the tissue types for the RMS-Mutation-Prediction microscopy images
<div> <p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations">https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations</a>.. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> </div> <div> <div> <p>This dataset contains 2 components:</p> <ol> <li>Annotations of multiple regions of interest performed by an expert pathologist with eight years of experience for a subset of hematoxylin and eosin (H&E) stained images from the RMS-Mutation-Prediction image collection [1,2]. Annotations were generated manually, using the Aperio ImageScope tool, to delineate regions of alveolar rhabdomyosarcoma (ARMS), embryonal rhabdomyosarcoma (ERMS), stroma, and necrosis [3]. The resulting planar contour annotations were originally stored in ImageScope-specific XML format, and subsequently converted into Digital Imaging and Communications in Medicine (DICOM) Structured Report (SR) representation using the open source conversion tool [4].</li> <li>AI-generated annotations stored as probabilistic segmentations.</li> </ol> <p><strong>WARNING</strong>: After the release of IDC v20 (v2 of this data record), it was discovered that a mistake had been made during data conversion that affected the newly-released segmentations accompanying the "RMS-Mutation-Prediction" collection. Segmentations released in v20 for this collection have the segment labels for alveolar rhabdomyosarcoma (ARMS) and embryonal rhabdomyosarcoma (ERMS) switched in the metadata relative to the correct labels. Thus segment 3 in the released files is labelled in the metadata (the SegmentSequence) as ARMS but should correctly be interpreted as ERMS, and conversely segment 4 in the released files is labelled as ERMS but should be correctly interpreted as ARMS. This mistake was fixed in the version v3 of this record (IDC data release v21).</p> <p>Many pixels from the whole slide images annotated by this dataset are not contained inside any annotation contours and are considered to belong to the background class. Other pixels are contained inside only one annotation contour and are assigned to a single class. However, cases also exist in this dataset where annotation contours overlap. In these cases, the pixels contained in multiple contours could be assigned membership in multiple classes. One example is a necrotic tissue contour overlapping an internal subregion of an area designated by a larger ARMS or ERMS annotation. The ordering of annotations in this DICOM dataset preserves the order in the original XML generated using ImageScope. These annotations were converted, in sequence, into segmentation masks and used in the training of several machine learning models. Details on the training methods and model results are presented in [1]. In the case of overlapping contours, the order in which annotations are processed may affect the generated segmentation mask if prior contours are overwritten by later contours in the sequence. It is up to the application consuming this data to decide how to interpret tissues regions annotated with multiple classes. The annotations included in this dataset are available for visualization and exploration from the National Cancer Institute Imaging Data Commons (IDC) [5] (also see IDC Portal at <a href="https://imaging.datacommons.cancer.gov/">https://imaging.datacommons.cancer.gov</a>) as of data release v18. Direct link to open the collection in IDC Portal: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations">https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations</a>.</p> </div> <div> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>pan_cancer_nuclei_seg_dicom-collection_id-idc_v19-aws.s5cmd</code> corresponds to the annotations for th eimages in the <code>collection_id</code> collection introduced in IDC data release v19. DICOM Binary segmentations were introduced in IDC v20. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <p>For each of the collections, the following manifest files are provided:</p> <ol> <li><code>rms_mutation_prediction_expert_annotations-idc_v20-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>rms_mutation_prediction_expert_annotations-idc_v20-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>rms_mutation_prediction_expert_annotations-idc_v20-dcf.dcf</code>: Gen3 manifest (for details see <a href="../records/Gen3%20manifest%20documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/imagingdatacommons/idc-index" target="_blank" rel="noopener">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code></li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <p>If you use the files referenced in the attached manifests, we ask you to cite this dataset, as well as the publication describing the original dataset <a href="https://paperpile.com/c/NHiBXI/njdR">[2]</a> and publication acknowledging IDC <a href="https://paperpile.com/c/NHiBXI/uJJZ">[5]</a>.</p> <h3>References</h3> </div> </div> <div> <p>[1] D. Milewski et al., "Predicting molecular subtype and survival of rhabdomyosarcoma patients using deep learning of H&E images: A report from the Children's Oncology Group," Clin. Cancer Res., vol. 29, no. 2, pp. 364–378, Jan. 2023, doi: 10.1158/1078-0432.CCR-22-1663.</p> <p>[2] Clunie, D., Khan, J., Milewski, D., Jung, H., Bowen, J., Lisle, C., Brown, T., Liu, Y., Collins, J., Linardic, C. M., Hawkins, D. S., Venkatramani, R., Clifford, W., Pot, D., Wagner, U., Farahani, K., Kim, E., & Fedorov, A. (2023). DICOM converted whole slide hematoxylin and eosin images of rhabdomyosarcoma from Children's Oncology Group trials [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8225132" rel="noopener">https://doi.org/10.5281/zenodo.8225132</a></p> <p>[3] Agaram NP. Evolving classification of rhabdomyosarcoma. Histopathology. 2022 Jan;80(1):98-108. doi: 10.1111/his.14449. PMID: 34958505; PMCID: PMC9425116,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9425116/</p> <p>[4] Chris Bridge. (2024). ImagingDataCommons/idc-sm-annotations-conversion: v1.0.0 (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.10632182" rel="noopener">https://doi.org/10.5281/zenodo.10632182</a></p> <p>[5] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. Radiographics 43, (2023).</p> </div>
Model for Predicting the Hydraulic Conductivity of Frozen Soils Using the Soil Freezing Characteristic Curve
<p>This is the data used in this manuscript.</p>
Supplemental Materials - Performance Figures for "A Model for Predicting the (re)-occurrence of a ≥40% eGFR Decline in a large Population-based cohort of Persons with or At-Risk of Chronic Kidney Disease " paper
<p>The zip file contains performance metrics figures for each dynamic Bayesian Network (DBN) model, stratified by comorbidities, race, CKD stages, and ethnicity.</p> <p>Contains:</p> <ul> <li>Stratified: Bootstrapping of 1000 iterations and 1000 samples with stratified proportions (as in the original population of the test set) of rapid eGFR decliners and non-decliners.</li> </ul> <p> </p> <p>Second zip contains DBN structures as matrices for 2 periods study entry to entry period and entry period to year 1 for all sites in 2 excel files.</p>
Data for MWR article "The consequences of surface-exchange coefficient uncertainty on an otherwise highly predictable major hurricane"
<p>This repository contains the model code, initial and boundary conditions and the namelist settings needed to reproduce the results of "The consequences of surface-exchange coefficient uncertainty on an otherwise highly predictable major hurricane".</p>
Challenging Bug Prediction and Repair Models with Synthetic Bugs
Open the record for dataset details and reuse information.
Predictive Design of Ultrastretchable Electrodes with Strain-Insensitive Performance via Robotics- and Machine Learning-Integrated Workflow
Open the record for dataset details and reuse information.
Codes and data: Community size predicts temporal β-diversity at local but not regional scales
<p>UPDATED VERSION 2025-08-30 (models were updated)</p> <p>This zip file contains the codes demonstrating how I analyzed and selected publicly available and globally extensive data on fish composition and environmental variables to test the hypothesis that random fluctuations caused by demographic stochasticity in small populations might extend to communities and metacommunities, potentially affecting stability propagation across biological levels and spatial scales. The READ_ME file contains additional details about the steps I took to develop this analysis.</p> <p>This study was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES) - Finance Code 001.</p>
Supplementary Information for the manuscript: "Gene expression evolution is predicted by stronger selection at more pleiotropic genes"
<p>This repository contains the supplementary information for the manuscript "<em>Gene expression evolution is predicted by stronger selection at more pleiotropic genes</em>" (<a href="https://doi.org/10.1101/2024.07.22.604294">https://doi.org/10.1101/2024.07.22.604294</a>).</p> <p>The supplementary data "data.tar.gz" is related to the Github repository <a href="https://github.com/charlesrocabert/Koch-et-al-Gene-expression-evolution-is-predictable-and-driven-by-indirect-selection-pressures">https://github.com/charlesrocabert/Koch-et-al-Predictability-of-Gene-Expression</a>.</p> <h2>Content of the repository</h2> <ul> <li><strong>Script S1. </strong>BSFG estimates (50.9 MB).</li> <li><strong>Script S2.</strong> WGCNA analysis and results G1 (99.6 MB).</li> <li><strong>Data S1.</strong> Datasets resulting from the global genomics analysis of the output of the transcriptomics pipeline (5.9 MB).</li> <li><strong>Data S2.</strong> VCF file containing the 566,296 quality-checked SNPs (1.1 GB).</li> <li><strong>Data S3.</strong> VCF file containing the 358,142 SNPs with a call rate higher or equal to 50% (964.3 MB).</li> <li><strong>Data S4.</strong> VCF file containing the imputed genotypes (964.3 MB).</li> <li><strong>Data S5.</strong> Results of the imputation tests (479.5 kB).</li> <li><strong>Data S6.</strong> VCF file containing imputed genotypes where SNPs with a minor allele frequency lower than 0.05 have been filtered out (MAF $\leq$ 0.05) (176.0 MB).</li> <li><strong>Data S7.</strong> List of the 1,273 significant eQTL associations (for gene expression levels or relative fitness as phenotypes) (61.2 kB).</li> <li><strong>Data S8.</strong> List of allele frequency changes (AFCs) for every markers in HD environment, for lines L1, L2, L3, L5, L6, Mx1 and Mx2 (32.4 MB).</li> <li><strong>Data S9.</strong> List of all SNPs indicating if their AFC is significantly higher in each line and their degree of parallelism (2.4 MB).</li> <li><strong>Data S10.</strong> Excel file containing the results of the gene functional enrichment analysis of the hub and eQTL carrier genes (33.7 KB).</li> <li><strong>data.tar.gz.</strong> Dataset mandatory to re-run the genomics analysis (see <a href="https://github.com/charlesrocabert/Koch-et-al-Gene-expression-evolution-is-predictable-and-driven-by-indirect-selection-pressures">https://github.com/charlesrocabert/Koch-et-al-Predictability-of-Gene-Expression</a>) (7.8 GB).</li> </ul> <p> </p>
Predicting p53-Dependent Cell Transitions From Thermodynamic Models
<p>Matlab codes containing Grand Canonical Monte Carlo (GCMC) simulation codes and theoretical codes to study the first-order phase transition behavior of a malignant cell to a normal healthy cell. In this work, we develop thermodynamic models to determine the fate of a malignant cell as governed by the tumor suppressor p53 signaling network.</p>
Sentinel-2 derived Chlorophyll-a prediction maps for high-altitude lakes in the Sierra Nevada, Spain
<p>This dataset contains chlorophyll-a (ug/L) predictions for 4 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. Predictions were made using a simple linear regression model with field sample chlorophyll-a as the dependent variable, and the following Sentinel-2 derived spectral index as the independent variable:</p><p>B3 - (B4+((B2-B4)*((665-560)/(665-490)))</p><p>Prediction maps are included as GeoTiffs and NetCDF files. Sentinel-2 data were atmospherically corrected using the following algorithms: </p><ul><li><a href="https://github.com/acolite/acolite/releases/tag/20221114.0">ACOLITE</a> (<a href="https://doi.org/10.1016/j.rse.2018.07.015">Vanhellemont & Ruddick, 2018</a>)</li><li><a href="https://grass.osgeo.org/grass83/manuals/i.atcorr.html">6SV</a> (<a href="https://doi.org/10.1109/36.581987">Vermote et al. 2006</a>)</li></ul><p><strong>Included Lakes and and their IDs:</strong></p><ul><li>Laguna de la Caldera (ID = P-2)</li><li>Laguna-embalse de las Yeguas (ID = D-6)</li><li>Laguna de Río Seco (ID = P-8)</li><li>Laguna Larga (ID = G-7)</li></ul>
Machine Learning Dataset for Rabies Diagnosis and Outbreak Prediction
Open the record for dataset details and reuse information.
AssemblyNet: A Point Cloud Dataset for Predicting Part Directions in an Exploded Layout
<p>The AssemblyNet dataset contains 5420 samples of 512-point and 1024-point point clouds of mixed industrial assemblies and LEGO models.</p><p>The dataset contains classification and regression annotations for predicting which direction a part should be removed in to create an exploded layout.</p><p>Classification annotations use 26 classes representing 26 world space directions while regression annotations use the actual world space direction as X, Y, and Z values.</p>
A predictive model of UV-A-riboflavin crosslinking treatment on porcine corneas
<p>The crosslinking technique (CXL) is an effective low-risk therapeutic treatment of keratoconus and other ectatic disorders of the human cornea. The effect of corneal CXL is to increase the stiffness of the stroma to prevent the progression of the cornea distortion. Several clinical and experimental studies have shown that the stiffening effects predominantly localise on the anterior portion of the stroma and that the in-depth stiffening distribution is highly dependent on the duration of treatment. Yet, how the stiffening effects distribute through the cornea thickness as a function of the treatment duration is an open question. Here we propose an analytical model of the stiffening profile due to CXL-treatment as a function of the irradiation time. We consider linear and nonlinear variations of the crosslinking effects across the thickness and implement them into a finite element model of the porcine cornea. We present a time-dependent in-depth stiffening profile that allows us to predict the post-operative cornea response to physiological intraocular pressure for different irradiation times. We anticipate that this predictive model will support the development of patient specific 3D models that will allow clinicians to design customised CXL treatment, thus enhancing treatment outcomes. </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.