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Figure 1 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management
Figure 1. Ultrasonographic images of beluga fetuses. All images have yellow caliper lines used to measure dimensions. Biparietal diameter (A, B) at two different stages of gestation show the ovoid shaped skull and echo produced from falx (arrows) located midline between the parietal bones (arrowheads). The thoracic diameter (C) as measured between the yellow caliper marks (arrowheads) on the lateral side of the fetal thorax (d1 = 6.66 cm) at the level of the heart (white arrow) and thoracic circumference (c = 24.04 cm) determined by using the elliptical measurement caliper function to include the dorsal to ventral diameter (d2 = 8.67 cm). The total length of a fetus (D) which is bent in utero, thus requiring the addition of two separate measurements (arrowheads), 1) 8.38 cm from the cranial most aspect of the skull to mid abdomen and 2) 6.91 cm from mid abdomen to distal most portion of the peduncle for a total length of 15.29 cm.
Fig. 4 in Greenhouse evaluation of neonate and adult applications of Coleomegilla maculata (Coleoptera: Coccinellidae) to control twospotted spider mite infestations
Fig. 4. Spider mite infestation levels in each treatment 20 d afer treatment application. Data represent the non-transformed mean number of mites, at specified developmental stage, per square cm of leaf surface (abaxial) ± SEM (n = 4). Different letters above bars indicate significant difference (P <0.05, Holm-Sidak multiple comparisons).
Fig. 2 in Greenhouse evaluation of neonate and adult applications of Coleomegilla maculata (Coleoptera: Coccinellidae) to control twospotted spider mite infestations
Fig. 2. Temperature fluctuations (only daily high temperatures are charted) during the course of the experiment.
Fig. 3. Representative samples from initial experiment interrupted and lef untended for 17 d in Greenhouse evaluation of neonate and adult applications of Coleomegilla maculata (Coleoptera: Coccinellidae) to control twospotted spider mite infestations
Fig. 3. Representative samples from initial experiment interrupted and lef untended for 17 d. Plants treated with lady beetles recovered from extreme stress; infested and untreated plants could not grow new leaves (no recovery).
Electronic appendix for "Are we there yet? A critical experimental assessment of the application of induced polarization for monitoring geochemical processes", Strobel, C., Störiko, A, Olaf, O.A. & Mellage, A.
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Interactive map of Heat Stress Compensability Classification (HSCC) application in 96 United States cities.
<p>This repository includes the interactive map in format .html of the very first application of the <strong>Heat Stress Compensability Classification (HSCC) in 96 cities in the United States </strong>showing the proportion of days with compensable and uncompensable heat stress from the top 10th percentile of hottest days from 2005-2020 in each place.</p> <p>This map offers the detailed results of the very first application of the classification system as in the journal article: <strong>The Development of an Adaptive Heat Stress Compensability Classification Applied to the United States</strong>, published in the 4th SNP special issue in the International Journal of Biometeorology. The results of this visualization were obtained from open-source data and coding packages such as Folium, and the model results were obtained by applying the Python Human Heat Balance (PyHHB) on weather dataset freely available.</p> <p>The interactive map offers a detailed visualization of the results from each of the cities, allowing you to see 3 tabs when the icon of the pie chart from each location is clicked.</p> <p><strong>Tab statistics:</strong> Detail per city of Figure 4b of related paper.</p> <p><strong>Tab Histogram 2D: </strong>Details per city of Fig 6 of related paper</p> <p><strong>Tab How to read: </strong>Figure 2 in related paper.</p> <p>Please for questions related to this dataset/code contact Gisel Guzman-Echavarria (gguzma20@asu.edu).</p> <p>Guzman-Echavarria, G., & Vanos, J. (2023). PyHHB: Physiological-based estimations of human survivability and liveability to heat in a changing climate (Nature Communications (1.0.0)). Zenodo. https://doi.org/10.5281/zenodo.10020137</p> <p> </p> <p> </p>
Fig. 5. The Mantel test compares a in Application of allozyme markers for screening of turbot populations along Western Black Sea coast
Fig. 5. The Mantel test compares a genetic distance (Y matrix) with a geographical distance (X matrix) in kilometers to test correlation between genetics and geographical location.
Fig. 3 in Application of allozyme markers for screening of turbot populations along Western Black Sea coast
Fig. 3. Enzymograms of general unspecified esterases (EST) from muscle tissue of turbot (1) and haemoglobin (2), EST-2* and EST-3* - polymorphic loci, 0 – origin.
Fig. 2. A in Application of allozyme markers for screening of turbot populations along Western Black Sea coast
Fig. 2. A. Electrophoregrams on PROT from turbot (Bulgarian and Romanian coast), using different tissues:1-3 – haemoglobins, 4-5- eye (retina) and 6 – muscle, 0 – origin. B. Electrophoregrams on PROT from turbot Bulgarian and Rumanian coast haemoglobin tissue. PROT-1* and PROT-2* were polymorphic, 0 – origin.
Radiative-transfer dataset for "Distilling machine learning's added value: Pareto fronts in atmospheric applications"
<p>This dataset goes with the journal paper "Distilling machine learning's added value: Pareto fronts in atmospheric applications" by T. Beucler, A. Grundner, S. Shamekh, P. Ukkonen, M. Chantry, and R. Lagerquist.</p> <p>Subdirectory "training" contains unnormalized (in physical units) training data. Subdirectories "validation" and "testing" contain unnormalized validation and testing data. Subdirectory "training/for_pareto_paper_2024/simple" contains training data from the simple (clear-sky) dataset discussed in the paper; subdirectory "training/for_pareto_paper_2024/complex" contains training data from the complex (multi-cloud) dataset discussed in the paper. Subdirectories "validation/for_pareto_paper_2024/simple" and "validation/for_pareto_paper_2024/complex" are analogous but for the validation data; subdirectories "testing/for_pareto_paper_2024/simple" and "testing/for_pareto_paper_2024/complex" are analogous but for the testing data.</p> <p>Subdirectories beginning with "normalized_predictors" -- "normalized_predictors/training", "normalized_predictors/validation", "normalized_predictors/testing", "normalized_predictors/training/for_pareto_paper_2024/simple", "normalized_predictors/training/for_pareto_paper_2024/complex", etc. -- are analogous to the above but containing normalized predictors (in z-scores rather than physical units).</p> <p>Every file -- after unzipping, so that the extension is ".nc" rather than ".nc.gz" -- can be read by `example_io.read_file` in the ml4rt library (https://github.com/thunderhoser/ml4rt).</p>
Figure 2-Validation of a Web Application by Using a Limited Number of Web Pages
<p>Simplifying the method of verification and testing of the web applications and maximising<br> the quality of the verification is the principal objective for the time being. We consider that the<br> method we describe in the previous sections is an important step in this matter and, by combining<br> the present techniques of verification and testing with the mode of reducing the objects that need to<br> be tested we can obtain very good results in obtaining web application of very good quality that<br> function correctly. We believe that this idea of selecting certain components from a web application<br> can be developed by using other methods of comparison among the components (that can use notion<br> as those introduces in [3], [4], [7]).</p>
Exploiting the potential of neutron imaging measurements for life sciences applications at the neutron spallation source, ISIS, UK - Crabs X-ray CT
<p>A ZIP file containing slices (in TIFF format) from a X-ray CT scan of crabs from the Eocene of Spain. Specimen was scanned on a Nikon Metrology HMX ST 225 system at the Natural History Museum, London, using a 1.0 mm thick copper filter, 225 kV voltage and 180 μA current, giving a tomographic dataset with a voxel size of 46 µm.</p>
Figure 1. Schema of clientside application for semantic browsing.-Browsing Semantic Data in Slovakia
<p>With the aim primary on unstructured information extraction and refining, relationship discovery and visualization, we propose our solution for SBR in the first place. The reason for this is, primary, that HTML formatted results of SBR are very jerky and uncertainty regarding the structure of information is very high. Readers can also be pointed by J. Suchal and P. Vojtek (2009), that care should be taken towards type errors. We discuss that later. In this work, we try to fill&up the gap of visualization and, somehow limited data access offered by SBR, adapting to the problems disclaimed above. We suggest a new client& side paradigm, which does not depend on a particular website like foaf.sk. Figure 1 describes the schema briefly and the key elements are parsers with other tools on the top and structured formats, for datastore, on the bottom.</p>
Figure 4. A graphic on values of TP, TN, FP, and FN for each different application process.-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>This study has proposed a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM) and Cognitive Development Optimization Algorithm (CoDOA). In this approach, the training process of the SVM has been supported with the CoDOA and after determining the most optimum sigma (σ) parameter of the Gauss (RBF) kernel function (so the most optimum SVM), a better classification formation has been tried to be achieved. In the context of the study, diabetes data set, which is related to Pima Indians, has been used for evaluating effectiveness of the proposed approach and after six different application processes, it was seen that the approach is well-enough on classification, which means being capable of determining diabetes. There are also some future works regarding the developed CoDOA-SVM based approach. In this context, there will be some more works for improving classification accuracy and also setting different optimization plans on i.e. different parameters of the kernel function. Additionally, it is aimed to evaluate the approach with datasets belonging to different diseases.</p>
BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 9b. Translation of a tab for all languages
<p>After checking a few (or all!) languages for instance and pressing the Ok button , we obtain the windows shown in figure 9a, or respectively 9b for all languages. Here we can add one or more missing translations, or modify one or more of the existing translations accordingly. All the data within the view cluster can be translated into any language supported by the system.</p>
BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 8. GoTo -> Translation Option
<p>For example, we select in the view cluster the tab called “Forecasting” and then choose Goto -> Translation (figure 8). After selecting Goto -> Translation, we obtain a selecting window for the desired languages where the user can check one or more languages to translate those tabs or areas into. </p>
BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 9a. Translation of a tab for certain languages
<p>After checking a few (or all!) languages for instance and pressing the Ok button , we obtain the windows shown in figure 9a, or respectively 9b for all languages. Here we can add one or more missing translations, or modify one or more of the existing translations accordingly. All the data within the view cluster can be translated into any language supported by the system.</p>
BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 7. Dialog structure of the View Cluster
<p>Field dependency has been generated automatically and as a result the following desired structure has been achieved (figure 7). After populating the database tables with data in different languages with the help of the view cluster, the popup has been adapted in order to support the translation of the tabs and areas. Supplementary internal tables have been defined in the function module POPUP_FLEX, in order to copy data from the translation tables. The corresponding SELECT statements have been embedded in TRY-CATCH blocks, in order to prevent short dumps due to faulty selection processes.</p>
BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 6. View cluster TAFC
<p>For all these tables and views, table maintenance generators have been created and activated, in order to have the possibility to manage individual datasets in every table and view. The corresponding names of those function groups for the table maintenance generators are the same names as those for the views. The purpose of these maintenance views is only to take care of the input data more efficiently. These views will be used later in the view cluster, which ensures a hierarchical order of the data. Therefore, the maintenance views will also include the predecessor, in order to facilitate linking in the field dependency tab of the view cluster (Swapna, 2007). These three maintenance views are embedded in the following view cluster (figure 6)</p>
BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 5. Maintenance Views
<p>Beside the tabs and the areas, the database table FLD also contains the fields TABLENAME and FIELD, which suggest the related parameters, whom input may be updated at runtime. Through standard SAP functionality the tables in BASIS, respectively their fields are by default translated in the login language of the user. So in the fields TABLENAME and FIELD of the FLD table, we will obtain, in the user login language, the names of the tables and fields from BASIS via the foreign keys to the table DD03L for TABLENAME and to the table DD02L for FIELD. Beside the database tables, 3 maintenance views have been created, TABV, AREV and FLDV, for the tabs, areas and fields of the popup (figure 5). We have chosen maintenance views instead of database views to be able to use them in the view cluster.</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.