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2,085 results for “predictors”
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-01-01/2022-02-28): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2022-01-01/2022-02-28.</p> <h2><strong>As a Part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-07-01/2022-08-31): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2022-07-01/2022-08-31.</p> <h2><strong>As a Part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-09-01/2022-10-31): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2022-09-01/2022-10-31.</p> <h2><strong>As a Part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - longterm predictor (2000-2022): Spectral indices P50
<h2><strong>Data Information</strong></h2> <p>This dataset includes long-term P50 values for NDVI, NDWI and BSF.</p> <h2><strong>As a Part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-03-01/2000-04-30): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2000-03-01/2000-04-30.</p> <h2><strong>As a Part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
The effectiveness of freshwater connectivity as a predictor of species distribution
<p>The attached dataset contains three dataframes used in the affiliated papers.</p> <p>1) DirectSlopeData.rda - Recolonisation success of two species, northern pike and European perch, in rotenone-treated lakes in Sweden, alongside connectivity parameters for the associated lakes.</p> <p>2) HPD.rda - Credible intervals for the beta estimates generated by the BORAL model in 3.</p> <p>3) Presence/absence data for seven species in lakes throughout the Kautokeino catchment in Northern Norway, alongside selected environmental covariates for associated lakes.</p>
The Brief Symptom Inventory in the Swiss general population: Presentation of norm scores and predictors of psychological distress: Data supporting the publication
This is the dataset on which the following publication is based: • Michel G, Baenziger J, Brodbeck J, Mader L, Kuehni CE, Roser K (2024). The Brief Symptom Inventory in the Swiss general population: Presentation of norm scores and predictors of psychological distress. PLOS One. 19(7), e0305192. Doi: 10.1371/journal.pone.0305192, https://doi.org/10.1371/journal.pone.0305192 A description of the sample and the data collection procedure is available in the publication. The dataset contains the following variables: • Socio-demographic characteristics of the sample - Weights according to representative general population sample - Sex from Swiss Federal Statistical Office (SFSO) - Age at study (rounded to integer) - Age categories (10-year age groups) - Language questionnaire (German/Rumantsch, French, Italian) - Nationality from SFSO - Migration background - Education - Employment status • Original and prepared data on the Brief Symptom Inventory A detailed data dictionary is available in a separate excel file. Version • 1.0 (15 August 2024)
An Empirical Evaluation of the "Cognitive Complexity" Measure as a Predictor of Code Understandability
<p>The paper provides an evaluation of the “Cognitive Complexity” measure as an indicator of code understandability.<br> The evaluation is performed via an empirical study.<br> ""Cognitive Complexity" is compared with traditional code measure, like LoC and McCabe's complexity.</p> <p>The data used in the empirical study are provided.</p>
Landuse/Landcover predictors for invasive species distribution modelling in Europe.
<p><strong>Description</strong></p> <p>This data set contains a set of predictors characterizing land use/land cover derived from the CORINE dataset, anthropogenic pressure from the global terrestrial human footprint dataset, and the distance to the nearest waterbody, for continental Europe. All have been aligned with the 1 km<sup>2</sup> EEA Reference Grid. The climate variables based on historical (1976-2005) and future (2040-2070) scenarios are available from De Troch et al., 2020 also via Zenodo. These rasters represent the habitat and anthropogenic predictors needed in the Tracking Invasive Alien Species (TrIAS) workflow for invasive species distribution modelling (wiSDM).</p> <p><strong>Geographic coverage</strong></p> <p>Europe</p> <p><strong>Methods</strong></p> <p>Land use classes were extracted from the CORINE06 100 m GeoTiff downloaded from Copernicus. The percentage of each 1 km<sup>2</sup> EEA Reference Grid cell occupied by coniferous forest, deciduous forest, wetlands, grasslands and agriculture was calculated. Multiple land use sub-classes were aggregated for the following categories: agriculture, wetlands, grasslands (Table 1). These data layers have been processed in R to replace all NAs that are within the European landmass, with zeros to distinguish them from the ocean, which remain NA, as in the CORINE dataset. In this context, a zero reflects the absence of a given land cover attribute. </p> <p>The mean anthropogenic pressure per 1km<sup>2 </sup>EEA Reference Grid cell was extracted from the global terrestrial human footprint dataset (Venter et al, 2016). Distance to the nearest waterbody within each 1km<sup>2</sup> EEA Reference Grid cell was calculated using the 2016 Surface Water Bodies shapefile available from the EEA (https://www.eea.europa.eu/data-and-maps/data/wise-wfd-spatial/surface-water-body). </p> <p> </p> <table> <tbody> <tr> <td>Land Use Class</td> <td>CORINE LABEL</td> </tr> <tr> <td>Agriculture</td> <td>Non-irrigated arable land (211), Rice fields (213),Vineyards (221),Fruit trees and berry plantations (222),Olive groves (223),Pastures (231),Annual crops associated with permanent crops (241),Complex cultivation patterns (242),Land principally occupied by agriculture, with significant areas of natural vegetation (243)</td> </tr> <tr> <td> </td> </tr> <tr> <td> </td> </tr> <tr> <td>Coniferous forest</td> <td>Coniferous forest (312)</td> </tr> <tr> <td>Deciduous forest</td> <td>Broad-leaved forest (311)</td> </tr> <tr> <td>Grassland</td> <td>Natural grasslands (321), Moors and heathland, (322) Sclerophyllous vegetation (323)</td> </tr> <tr> <td>Wetland</td> <td>Inland marshes (411), Peat bogs (412)</td> </tr> </tbody> </table> <p>Table 1. How the the original land use/land cover types as labelled in CORINE were combined (or not).</p> <p><strong>Files</strong></p> <p>distance2water_EEA_1km.tif (distance to nearest waterbody)</p> <p>ESM1000m.tif (mean anthropogenic pressure)</p> <p>corine_perAgriculture.tif</p> <p>corine_perWetland.tif</p> <p>corine_pergrass.tif</p> <p>corine_perdeciduous.tif</p> <p>corine_perConiferous.tif</p> <p> </p>
Quantifying Hierarchy and Prestige in US Ballet Academies as Social Predictors of Career Success
<p>This data contains aggregated competition outcomes from 6,363 ballet students affiliated with 1,603 schools in the United States, who participated in the Youth America Grand Prix (YAGP) between 2000 and 2021.</p> <p>We adopt a network science and <em>science of science</em> approach that empowers logistic regression models and matching experiments to quantify social prestige and its influence on dancers' careers. The analysis of career success in the performing arts, like ballet, in the context of a competition setting offers a unique opportunity to investigate the social influences on success while controlling for competition performance.</p> <p>Our work reveals the importance of institutional prestige on career success in ballet and showcases the potential of network science approaches to provide quantitative viewpoints for the professional development of careers beyond science.</p>
Data and scripts for SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures
<p>This is the repository of data and scripts for the analysis of the CellminerCDB-SCLC manuscript and website (<a href="https://discover.nci.nih.gov/SclcCellMinerCDB/">https://discover.nci.nih.gov/SclcCellMinerCDB/</a>)</p> <p> </p> <p>CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) integrates 118 patient-derived cell lines with drug sensitivity and genomic datasets, including high resolution methylome and RNAseq data. CellMiner-SCLC provides a new resource for SCLC research for this “recalcitrant cancer”. Of fundamental importance, we demonstrate the reproducibility and stability of the cell line datasets from different institutions (CCLE, GDSC, CTRP, NCI and UTSW). We validate the classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 and show transcription networks connecting them with the MYC genes (MYC, MYCL1 and MYCN) and the NOTCH and HIPPO pathways. We find that the 4 subsets express specific surface markers for antibody-targeted therapies. The YAP1-driven (SCLC-Y) cell lines differ from the other subsets by expressing the NOTCH pathway, epithelial-mesenchymal-transition (EMT) and antigen-presenting machinery (APM) genes, and by responding to mTOR and AKT inhibitors, suggesting the potential of NOTCH modulators, YAP1 inhibitors and immune checkpoint inhibitors for SCLC-Y tumors.</p>
ACF database on the predictors of time to recovery and non-response to SAM treatment in the MANGO trial
<p>This database contains the variables used to analyse the predictors of time to recovery and non-response to treatment of SAM in Burkina Faso. These results are have been submitted to review in Plos One in November 2020.</p>
Importance of respiratory syncytial virus as a predictor of hospital length of stay in Bronchiolitis
<p>DATA BASE OF ARTICLE TITTLED "<strong>Importance of respiratory syncytial virus as a predictor of hospital length of stay in Bronchiolitis"</strong></p>
Improving Algorithm-Selectors and Performance-Predictors via Learning Discriminating Training Samples - Code and Data
<p>This repository contains the code and data for reproducibility of the paper 'Improving Algorithm-Selectors and Performance-Predictors via Learning Discriminating Training Samples'. </p> <p>The following files are included:</p> <ul> <li>Plots: Additional plots not in the paper;</li> <li>Code: Python scripts to generate trajectories and perform classification/regression;</li> <li>best_algo.csv : Labels for the classification;</li> <li>performances.csv : Performances used for the regression;</li> <li>SA_parameters.csv : SA parameters for all machine learning tasks;</li> <li>irace_scenario.txt : scenario used for the tuning.</li> </ul>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P50 (2001): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P50 (median) values of corresponding predictors for the year 2001. The median values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Predictors of medical staff's knowledge, attitudes, and behavior of dysphagia assessment: A cross-sectional study
<p>This study aimed to develop training resources and standardize the assessment of dysphagia in patients with stroke. This study was a cross-sectional study. A total of 430 nurses and doctors from four provinces(Guangdong Province, Hunan Province, Guangxi Province, and Shaanxi Province) who were selected by convenience sampling were invited to complete the questionnaire through WeChat, DingTalk, and Tencent QQ from May 23 to 31, 2022. A self-reported questionnaire was used to assess participants' Knowledge, Attitude, and Behavior regarding dysphagia. Participants' sociodemographic, training, and nursing experience were measured using the general information sheet and assessed as potential predictors of medical staff's Knowledge, Attitudes, and Behavior of dysphagia assessment. A multiple linear regression model was used to identify the factors predicting medical staff's Knowledge, Attitudes, and Behavior regarding dysphagia assessment. The mean scores for Knowledge, Attitudes, and Behavior of dysphagia assessments were 92.654(SD 17.519). Multiple linear regression results indicated that experience in dysphagia patients' nursing, related training for dysphagia, working years in the field of dysphagia-related diseases, specialized training in geriatric, swallowing & rehabilitation, and department related to neurology, rehabilitation & elderly were significant predictors, accounting for 35.1% of the variance in scores of medical staff's Knowledge, Attitudes and Behavior of dysphagia assessment. Our findings imply that nursing experience, training, and work for patients with swallowing disorders could have positive effects on the Knowledge, Attitudes, and Behavior of medical staff regarding dysphagia assessment. Hospital administrators should provide relevant resources, such as videos of dysphagia assessment, training centers for the assessment of dysphagia, and swallowing specialist nurses. It is important that health policies fully recognize the role of training and support systems in caring for people with dysphagia.</p>
Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning"
<p>Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning". Predictors from the ERA5 reanalysis and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rda" format, to be read from R.</p>
Predictors of genomic differentiation within a hybrid taxon
<p>Hybridization is increasingly recognized as an important evolutionary force. Novel genetic methods now enable us to address how the genomes of parental species are combined in hybrid lineages. However, we still do not know the relative importance of admixed proportions, genome architecture and local selection in shaping hybrid genomes. Here, we take advantage of the genetically divergent island populations of Italian sparrow on Crete, Corsica and Sicily to investigate the predictors of genomic variation within a hybrid taxon. We test if differentiation is affected by recombination rate, selection, or variation in ancestry proportions. We find that the relationship between recombination rate and differentiation is less pronounced within hybrid lineages than between the parent species, as expected if purging of minor parent ancestry in low recombination regions reduces the variation available for differentiation. In addition, we find that differentiation between islands is correlated with differences in signatures of selection in two out of three comparisons. Signatures of selection within islands are correlated across all islands, suggesting that shared selection may mould genomic differentiation. The best predictor of strong differentiation within islands is the degree of differentiation from house sparrow, and hence loci with Spanish sparrow ancestry may vary more freely. Jointly, this suggests that constraints and selection interact in shaping the genomic landscape of differentiation in this hybrid species.</p>
Habitats as predictors in species distribution models: Shall we use continuous or binary data?
<p>The representation of a land cover type (i.e., habitat) within an area is often used as an explanatory variable in species distribution models. However, it is possible that a simple binary presence/absence of the suitable habitat might be the most important determinant of the presence/absence of some species and, thus, be a better predictor of species occurrence than the continuous parameter (area). We hypothesize that the binary predictor is more suitable for relatively rare habitats (e.g., wetlands) while for common habitats (e.g., forests) the amount of the focal habitat is a better predictor. We used the Third Atlas of Breeding Birds in the Czech Republic as the source of species distribution data and CORINE Land Cover inventory as the source of the landcover information. To test our hypothesis, we fitted generalized linear models of 32 water and 32 forest bird species. Our results show that for water bird species, models using binary predictors (presence/absence of the habitat) performed better than models with continuous predictors (i.e., the amount of the habitat); for forest species, however, we observed the opposite. Thus, future studies using habitats as predictors of species occurrences should consider the prevalence of the habitat in the landscape, and the biological role of the habitat type in the particular species' life history. In addition, performing a preliminary comparison of the performance of the binary and continuous versions of habitat predictors (e.g., using information criteria) prior to modelling, during variable selection, can be beneficial. These are simple steps that will improve explanatory and predictive performance of models of species distributions in biogeography, community ecology, macroecology, and ecological conservation.</p>
Fig. 2 in Some Factors Behind Density Dynamics Of Bat Flies (Diptera, Nycteribiidae) - Ectoparasites Of The Boreal Chiropterans: Omitted Predictors And Hurdle Model Identification
Fig. 2. Observed (bars) and expected (PMF) host infestation by Nycteribiidae bat flies: before/after (top/bottom) host mating; host females/males (left/right). No zero truncation and the used categorisation (pooled both host species and bat flies species) are the reasons of relatively bad fit to Poisson distribution.
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.