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Figure 4 in Density estimations of the Asiatic black bear: application of the random encounter model
Figure 4. Coefficient of variation of the estimated density and its confidence interval when the number of cameras is varied from 1 to 100. The coefficients of variation are calculated for each session. The black straight line indicates a border at CV = 0.2.
Application of Machine Learning in Chinese Medicine Differentiation of Dampness-heat Pattern in Patients with Type 2 Diabetes Mellitus
<p>Contains a compressed file of algorithm code and raw Excel data</p>
Raw data and alignments for: Application of palaeogenetic techniques to historic mollusc shells reveals phylogeographic structure in a New Zealand abalone
<p>Natural history collections worldwide contain a plethora of mollusc shells. Recent studies have detailed the sequencing of DNA extracted from shells up to thousands of years old and from various taphonomic and preservational contexts. However, previous approaches have largely addressed methodological rather than evolutionary research questions. Here we report the generation of DNA sequence data from mollusc shells using such techniques, applied to <em>Haliotis virginea</em> Gmelin, 1791, a New Zealand abalone, in which morphological variation has led to the recognition of several forms and subspecies. We successfully recovered near-complete mitogenomes from 22 specimens including 12 dry-preserved shells up to 60 years old. We used a combination of palaeogenetic techniques that have not previously been applied to shell, including DNA extraction optimized for ultra-short fragments and hybridization-capture of single-stranded DNA libraries. Phylogenetic analyses revealed three major, well-supported clades comprising samples from: 1) the Three Kings Islands; 2) the Auckland, Chatham and Antipodes Islands; and 3) mainland New Zealand and Campbell Island. This phylogeographic structure does not correspond to the currently recognized forms. Critically, our non-reliance on freshly collected or ethanol-preserved samples enabled inclusion of topotypes of all recognized subspecies as well as additional difficult-to-sample populations. Broader application of these comparatively cost-effective and reliable methods to modern, historical, archaeological and palaeontological shell samples has the potential to revolutionize invertebrate genetic research.</p>
Selected data analysed in the JGR Atmosphere manuscript " An application of the maximum entropy production method in the WRF Noah land surface model"
<p>The control experiment (hereafter WRF-CTL) and the MEP experiment (hereafter WRF-MEP) simulations results interpolated to the observation stations. The simulation period was 1 June to 31 August 2015 with 30 hours from 12:00 UTC (20:00 Beijing time (BJT)) each day, and the latest 24-hour outputs are provided.</p>
Teaching software processes from different application domains
<p>In a current application development scenario in different environments, technologies and contexts, such as IoT, Blockchain, Machine Learning and Cloud Computing, there is a need for particular solutions for domain-specific software development processes. The proper definition of software processes requires understanding the involved teams and organization’s particularities and specialized technical knowledge in Software Engineering. Although it is an essential part of Software Engineering, many university curricula do not dedicate as much effort to teaching software processes, focusing more on the basic principles of Software Engineering, such as requirements, architecture and programming languages. Another important aspect of software processes is modeling. The modeling of a software process provides a basis for managing, automating and supporting the software processes improvement. In this context, teaching software processes modeling becomes challenging, mainly due to the great emphasis on theory and few practices. This work presents an experience report teaching the definition and modeling of software processes in different domains. We apply in the discipline of software processes a practice for defining and modeling processes in various application domains, such as: IoT, cloud, mobile, critical systems, self-adaptive systems and games. The processes were modeled in the EPF composer tool based on references from the literature for each domain. In the end, we evaluated the process modeling practice with the students. We concluded that the modeling tool and the maturity in the domain are essential for the good performance of the process.<br> </p>
Clinical, technical, and implementation characteristics of real-world health applications using FHIR
<p>Fast Healthcare Interoperability Resources (FHIR) has received vast support globally, with a growing number of use cases implemented throughout various healthcare settings. To assess the use of real-world FHIR applications (apps) implemented in practice, we distributed an electronic survey to FHIR developers and implementers. The data includes characteristics of 112 FHIR apps, including health domain, target audience, terminologies, FHIR specifications, implementation details, and types of organizations developing with FHIR. This repository includes the survey dataset and survey codebook.</p>
DATASET - Evaluation of a novel inorganic scintillator for applications in LDR brachytherapy using both TE-cooled and room temperature SiPMs
<p>This dataset includes the raw data and the analyzed data which are included in the conference paper entitled: "Evaluation of a novel inorganic scintillator for applications in LDR brachytherapy using both TE-cooled and room temperature SiPMs".</p> <p> </p> <p>Agnese Giazc, Simona Comettic, Romualdo Santoroc, Peter Woulfea,b, Massimo Cacciac, and Sinead O’ Keeffeb<br> aDepartment of Medical Physics, Galway Clinic, Galway H91 HHT0, Ireland; bOptical</p>
Application of machine learning to improve appropriateness of treatment in an orthopaedic setting of personalized medicine
<p>Raw Data</p>
Scalable mixed model approaches for set-based association studies on large-scale categorical data analysis and its application to 450k exome sequencing data in UK Biobank
<p>The ongoing release of large-scale sequencing data in the UK Biobank allows for identifying associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a novel method for rare-variant association tests, POLMM-GENE, in which a proportional odds logistic mixed model was used to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole exome-sequencing data for 5 ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.</p>
FIGURE 2 in Biogeography, Speciation and Taxonomy within the genus Bactrocera Macquart with application to the Bactrocera dorsalis (Hendel) complex of fruit flies (Diptera Tephritidae: Dacinae)
FIGURE 2. The female reproductive system of Bactrocera tryoni (Froggatt) showing the position of the male phallus and preglans appendix during copulation.
FIGURE 1 in Biogeography, Speciation and Taxonomy within the genus Bactrocera Macquart with application to the Bactrocera dorsalis (Hendel) complex of fruit flies (Diptera Tephritidae: Dacinae)
FIGURE 1. The phallus and preglans appendix of the male aedeagus: (a) Bactrocera dorsalis (Hendel), (b) Bactrocera occipitalis (Bezzi), (c) Bactrocera papayae Drew & Hancock, (d) Bactrocera philippinensis Drew & Hancock, (e) Bactrocera invadens Drew, Tsuruta & White.
Gamma-Ray Glow simulation library for the paper "Library of simulated gamma-ray glows and application to previous airborne observations" by D. Sarria et al.
<p><strong>Gamma ray glow simulation library </strong>for the paper "Library of simulated gamma-ray glows and application to previous airborne observations" by <em>D. Sarria et al.</em></p> <p> </p> <p><strong>Description of the files:</strong></p> <ul> <li> `glow_database.mat` : MATLAB .mat file. Contains the gamma-ray glow simulation library, that contains energy spectra of photons, electrons and positrons for different production altitude, record altitudes and applied potential differences (E-field). It is a MATLAB structure with 5 indices : <ul> <li>TYPE : photon, electrons or positrons</li> <li>POTENTIAL (in MV), among the list: [-220 -210 -190 -175 -150 -120 -80 -50 -40 -30 -20 -10 0 10 20 30 40 50 80 120 150 175 190 210 220]</li> <li>RECORD position relative to center of E-field region (in km), among the list: [0 1 -1 2 -2 3 -3 4 -4 5 -5 6 -6 7 -7 8 -8]</li> <li>E-field region CENTER altitude (in km), among the list [6 8 10 12 14 15 16]</li> <li>Note: some parameter combinations were not calculated and are left empty (e.g. record altitudes above 20 km)</li> </ul> </li> </ul>
Data repository of the paper "Library of simulated gamma-ray glows and application to previous airborne observations" by D. Sarria et al.
<p><strong>Data repository of the paper "Library of simulated gamma-ray glows and application to previous airborne observations" by <em>D. Sarria et al.</em></strong></p> <p> </p>
Applications of machine learning tools for ultra-sensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of tuberculosis
Background <p>Tuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention. </p> Methods and Findings <p>A single-molecule fluorescence immunosorbent assay was used to detect the Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration assay data and then tested against the ground truth patient data. The semiautomatic model differed from the automatic model by an expert review step in the former, which calibrated the lower threshold to determine single molecules from background noise. The semiautomatic model was found to provide 88.89% clinical sensitivity, while the automatic model resulted in 77.78% clinical sensitivity.</p> Conclusions <p>The semiautomatic model outperformed the automatic model in clinical sensitivity as a result of the expert intervention applied during calibration and both models vastly outperformed manual expert counting in terms of time-to-detection and completion of analysis. Meanwhile, the clinical sensitivity of the automatic model could be improved significantly with a larger training dataset. In short, semiautomatic, and automatic Gaussian Mixture Models have a place in supporting rapid detection of Tuberculosis in resource-limited settings without sacrificing clinical sensitivity.</p>
A study on the Application of Markov Chains to Prioritize Test Cases
<p>The entire results and the inputs to run the experiment are available in this dataset. The data is composed of five folders one for each case study and one for the running example presented in Section 3. Within each folder, it is possible do see the input files, as well as the results, both for H1 and H2. The input formats can be consulted to understanding and potentially replication of the study. This dataset also contains the algorithm, in Python. The algorithm is available on GitHub, we did not put the link because of the blind review.</p>
EVALUATION OF RADAR-BASED PRECIPITATION DATASETS FOR APPLICATIONS IN THE LOUISIANA COASTAL MASTER PLAN
<p>A suite of hydrological and ecological models is used in the Louisiana Coastal Master Plan (CMP) Integrated Compartment Model (ICM) to assess potential benefits of restoration and protection projects. A main driving input for these models is precipitation. Since precipitation is considered a major source of freshwater in coastal Louisiana (50-60 inches/year), accurate information about its magnitudes and spatial and temporal distributions is critical for successful implementation of the ICM and other CMP-related modeling studies. Accurate information on precipitation is also critical for establishing reliable and representative water budget analyses for coastal Louisiana. In contrast to the sparse availability of rain gauges, weather radars can provide high-resolution rainfall estimates with excellent spatial resolutions and coverage over coastal Louisiana. The potential value of radar-based precipitation products has not been capitalized on yet by the CMP studies. Louisiana is covered by several National Weather Service (NWS) NEXRAD radar stations, with the two radar stations located near Slidell and Lake Charles being the most relevant to coastal Louisiana. The main objective of the graduate research project is to perform a regional-scale assessment of radar-rainfall datasets available over coastal Louisiana and evaluate whether they can be directly used by the CMP studies. Unlike past assessment studies, which were geared towards short-term applications (e.g., flood prediction and forecasting), the focus of this study will be on assessing accuracy aspects that are of direct relevance to the CMP applications. The study will examine (a) the reproducibility of inter-annual and intra-annual variability by the radar dataset, (b) the representation of extreme rainfall events, and (c) the representation of spatial distributions of annual and seasonal rainfall across various regions of the coast with distinctly different climatic regimes. The study will provide the CMP with quantitative assessment on the advantages and limitations of radar-based precipitation datasets and whether they can be readily used by the ICM models. This research will also provide insight on the levels of uncertainties in the radar-rainfall datasets and the implications for these uncertainties for the ICM models.</p> <p> </p> <p>Please make sure you work with the latest version of this data set only (check the panel on the right for versioning).</p>
Segway 2.0 Application Note Datasets
<p>Learned parameters and resulting segmentation corresponding to the analyses shown in the Segway 2.0 application note.</p> <p>Directory structure:</p> <p><strong>GMM</strong> (datasets corresponding to the mixture of Gaussians analysis)</p> <ul> <li>1-component <ul> <li>traindir/ <ul> <li>log/ (training log likelihood progression)</li> <li>params/ (learned parameters)</li> </ul> </li> <li>identifydir/ <ul> <li>segway.bed.gz (segmentation)</li> </ul> </li> </ul> </li> <li>3-component <ul> <li>traindir/ <ul> <li>log/ (training log likelihood progression)</li> <li>params/ (learned parameters)</li> </ul> </li> <li>identifydir/ <ul> <li>segway.bed.gz (segmentation)</li> </ul> </li> </ul> </li> </ul> <p><strong>minibatch-fixed</strong> (datasets corresponding to the minibatch learning analysis)</p> <ul> <li>fixed/ <ul> <li>traindir/ <ul> <li>log/ (training and validation log likelihood progression)</li> <li>params/ (learned parameters)</li> </ul> </li> </ul> </li> <li>minibatch/ <ul> <li>traindir/ <ul> <li>log/ (training and validation log likelihood progression)</li> <li>params/ (learned parameters)</li> </ul> </li> </ul> </li> </ul> <p><strong>TSS_prediction</strong> (datasets corresponding to the TSS prediction analysis) (where k=component number=1-5, n=random start number=1-10)</p> <ul> <li>outputs_[date]_k/ <ul> <li>traindir/ <ul> <li>log/ (training and validation log likelihood progression)</li> <li>params/ (learned parameters)</li> </ul> </li> <li>identifydir_n/ <ul> <li>segway.bed.gz (segmentation)</li> </ul> </li> </ul> </li> </ul>
Dataset for publication 'Reconnecting Stochastic Methods with Hydrogeological Applications: Uncertainty Analysis and Risk Assessment for the Design of Optimal Monitoring Networks'
<p>This dataset includes all data and information on how to reproduce the results and the figures of the paper 'Reconnecting Stochastic Methods with Hydrogeological Applications: Uncertainty Analysis and Risk Assessment for the Design of Optimal Monitoring Networks'.</p>
Supplementary material 2 from: Thiel R, Knebelsberger T (2016) How reliably can northeast Atlantic sand lances of the genera Ammodytes and Hyperoplus be distinguished? A comparative application of morphological and molecular methods. ZooKeys 617: 139-164. https://doi.org/10.3897/zookeys.617.8866
Table S2 : Explanation note: Museum IDs and collection data for specimens of Ammodytes tobianus used for morphological analyses only.
Supplementary material 1 from: Thiel R, Knebelsberger T (2016) How reliably can northeast Atlantic sand lances of the genera Ammodytes and Hyperoplus be distinguished? A comparative application of morphological and molecular methods. ZooKeys 617: 139-164. https://doi.org/10.3897/zookeys.617.8866
Table S1 : Explanation note: Supplementary metadata for specimens used for both morphological and genetic analyses; Museum and Sample IDs are specimen identifiers, BOLD Process IDs are unique codes automatically generated for each record on BOLD, GenBank Accession NOs represent sequence identifiers.
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.