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1,291 results for “gap”
Figure 2 in Male homophily in South American herpetology: one of the major processes underlying the gender gap in publications
Figure 2. Bibliometric data of nine herpetological journals. (A-C) Gender ratio in papers published during 2018 and 2019, considering different taxonomic groups and sub-areas. Multi authored papers considering the journal's qualies: (D) gender composition within publishing groups, and (E-J) gender participation in protagonic roles and its effect on the proportion of female co-authors. Single authored papers: (K) gender distribution on average and discriminated by journal's qualies. Editorial Boards by journals in 2019: (L) gender proportion within each journal, and (M) percentage of similarity between members of different journals. Abbreviations: ACTH = Acta Herpetologica; AFJH = African Journal of Herpetology; BG = Biogeography; CHER = Cuadernos de Herpetología; CM = Communication; CS = Conservation; EC = Ecology; ET = Ethology; HBUL = Herpetological Bulletin; HEGI = Herpetologica; HEMO = Herpetological Monographs; HREV = Herpetological Review; MO = Morphology; PHYL = Phyllomedusa; SAJH = South American Journal of Herpetology; SI = Systematics; Q1 = 1st quartile; Q2 = 2nd quartile; Q3 = 3rd quartile; Q4 = 4 Downloaded th quartilefrom
Pervasive gaps in Amazonian ecological research
<p>Raw data and code for the analyses on research probability across the Brazilian Amazonia. Briefly, the code reproduces the Random Forest models and the intersection of research probability with susceptibility to current and future anthropogenic disturbances, performs a site-level analysis of the resulting outputs; and then illustrates the building of figures used in the main manuscript.</p> <p>The modelling framework was built using the directory structure informed in the README.pdf file. The R-code provided has steps designed to replicate the directory structure as reported, but understanding it is a good starting point to navigate the output produced. There are five zip files whose content we described below:</p> <p><strong>AvgOutputs.zip:</strong> a folder containing four files with the results layer from the research probability model. The files represent the final result for each habitat type and the average across habitat types.</p> <p><strong>Datasets.zip</strong>: represents the "Datasets" folder directory, as illustrated in the README.pdf. This zip file contains the files ‘SamplingSites.csv’, which informs metadata for the entire list of occurrence data of community data (organism group, coordinates (long and lat) and habitat); and the ‘VarImportance.csv’, which was produced in the Script2_RCode_RandomForestModels.R, and used to build Figure 3 in the manuscript.</p> <p><strong>GlobalChangeLayers.zip</strong>: folder with input and intermediary layers used for generating Figure 4. Degradation files are from Lapola et al. (2023, DOI: <a href="https://doi.org/10.1126/science.abp8622">10.1126/science.abp8622</a>), while climate layers are from IPCC interactive atlas (Gutiérrez et al. 2021, DOI: <a href="https://dx.doi.org/10.1017/9781009157896.021">10.1017/9781009157896.021</a>.).</p> <p><strong>Predictors.zip</strong>: includes the raster files produced for the "Predictors" folder. There are five rasters representing the layers: DryMonths, LandTenure, NearbtDND, ResearchEduc, and TravelTime. All rasters were generated at the spatial resolution of 1 km spatial resolution using the files in Script1_GEECode_PredictorLayerPreparation.zip. </p> <p><strong>Projections.zip:</strong> the projections of research probability for each habitat type and organism are contained in this folder. There are 11 files, named as aquatic_benthos.tif, aquatic_fishes.tif, aquatic_heteropterans.tif, aquatic_macrophytes.tif, aquatic_odonates.ti, upland_ants.tif, upland_beetles.tif, upland_birds.tif, upland_trees.tif, wetland_birds.tif, wetland_trees.tif.</p> <p><strong>RasterMasks.zip</strong>: contains the masks created for each habitat to remove areas outside each habitat and areas without forest. The files are aquatic.tif, upland.tif and wetland.tif. They were created in the Google Earth Engine platform</p> <p><strong>RData.zip:</strong> represents the "RData" folder directory, which is designed to store RData files produced while running the R-scripts. It currently includes three files to facilitate the plotting of main figures.</p> <p>Scripts: a set of seven files, described in extra details in the following.</p> <ul> <li> <p><strong>Script1_GEECode_PredictorLayerPreparation.zip</strong>: a set of three GEE-scripts developed to prepare raster masks and predictor layers in the Google Earth Engine platform.</p> </li> <li> <p><strong>Script2_RCode_RandomForestModels.R</strong>: R-script to perform data partition, Random Forest model training, model validation, and extraction of variable importance and partial effects per predictor.</p> </li> <li> <p><strong>Script3_RCode_DeltaClimate.R</strong>: R-script to compute the metric of absolute climate change.</p> </li> <li> <p><strong>Script4_RCode_Figure1.R</strong>: R-script to build graphical pieces composing the Figure 1.</p> </li> <li> <p><strong>Script5_RCode_Figure2.R:</strong> R-script to build the Figure 2.</p> </li> <li> <p><strong>Script6_RCode_Figure3.R:</strong> R-script to build graphical pieces composing the Figure 3.</p> </li> <li> <p><strong>Script7_RCode_Figure 4.R</strong>: R-script to build graphical pieces composing the Figure 4.</p> </li> </ul> <p><strong>Shapefiles.zip:</strong> represents the "Shapefiles" folder directory, as illustrated in the README.pdf. There is one shapefile in this zip, which corresponds to the study area limits according to Bullock et al. (2020, DOI: 10.1111/gcb.15029).</p> <p> </p>
Data for 'Phase-dependent Andreev molecules and superconducting gap closing in coherently coupled Josephson junctions'
<p>An Igor file stores source data for the two figures showing the experimental results in the main manuscript and the supplementary figures. </p>
Evaluation metrics for eight types of gap-filled snow cover products in China and four schemes of combining multiple products
<p>This dataset contains the station-based evaluation metrics in terms of CK, R of SCD, and CWR values of SSD and SED for all the eight types of gap-filled products and the proposed schemes for combining multiple products. "TP" means the Tibetan Plateau.</p>
Evaluation metrics for eight types of gap-filled snow cover products in China and four schemes of combining multiple products
<p>This dataset contains the station-based evaluation metrics in terms of CK, R of SCD, and CWR values of SSD and SED for all the eight types of gap-filled products and the proposed schemes for combining multiple products. "TP" means the Tibetan Plateau.</p>
Image-guided Computational and Experimental Analyses of Fractured Patient's Bone (GAP)
ClinicalTrials.gov study NCT04787679. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Psychosocial Factors of Burn Related Injuries and Perceived Gaps in Service Delivery
ClinicalTrials.gov study NCT03094013. IPD Sharing: NO. Countries: 1. Publications: 10.
Strong Ion Gap as Prognostic Indicator for Adult Patients Admitted With Shock to the Intensive Care Units
ClinicalTrials.gov study NCT02652364. IPD Sharing: Not stated. Countries: 1. Publications: 17.
IMPlementation of Evidence Based Facility and Community Interventions to Reduce the Treatment Gap for depRESSion
ClinicalTrials.gov study NCT05890222. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Jumping Gap Dimension at Maxillary Teeth: Cone-Beam Computed Tomography (CBCT) Study
ClinicalTrials.gov study NCT04636385. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Bridging the Gap to Adult Diabetes Care
ClinicalTrials.gov study NCT03781973. IPD Sharing: NO. Countries: 1. Publications: 2.
Intervention to Bridge the Evidence-based Gap in Stroke Care Quality
ClinicalTrials.gov study NCT02212912. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Addressing Gaps in the Hypertension and Diabetes Care Continuum in Rural Bangladesh: The Dinajpur Study
ClinicalTrials.gov study NCT06258473. IPD Sharing: NO. Countries: 1. Publications: 60.
Closing the Gap Between Self-reported and Accelerometer-based Physical Activity
ClinicalTrials.gov study NCT03539237. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effects of a Novel Nutritional Product on Nutrient Gaps and Gut Health in Active Adults
ClinicalTrials.gov study NCT06521424. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Natural History of Geographic Atrophy Progression (GAP) Study
ClinicalTrials.gov study NCT00599846. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Gap Junction Potentiation of Endothelial Function With Rotigaptide
ClinicalTrials.gov study NCT00901563. IPD Sharing: Not stated. Countries: 1. Publications: 1.
CisPlatin plUs Gemcitabine and Nabpaclitaxel (GAP) as pReoperative Chemotherapy Versus Immediate Resection in patIents With resecTable BiliarY Tract Cancers (BTC) at High Risk for Recurrence
ClinicalTrials.gov study NCT06037980. IPD Sharing: NO. Countries: 1. Publications: 4.
Bridging the Health Disparities Gap in Decision-Making Among Limited English Proficient (LEP) Patients With Pelvic Floor Disorders
ClinicalTrials.gov study NCT05871268. IPD Sharing: NO. Countries: 1. Publications: 8.
Strategies to Close the Gap From Cervical Cancer Diagnosis to Treatment in Botswana
ClinicalTrials.gov study NCT05952141. IPD Sharing: NO. Countries: 2. Publications: 1.
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.