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FIGURE 8. Distal ovipositor, flat mounted. A. Pegomya disticha Griffiths. B. P in Pegomya disticha Griffiths and P. cedrica Huckett (Diptera: Anthomyiidae) - first documented case of insects trespassing the silica barrier of Common scouringrush, Equisetum hyemale L.
FIGURE 8. Distal ovipositor, flat mounted. A. Pegomya disticha Griffiths. B. P. cedrica Huckett. Abbreviations: Ce = cerci, Hy = hypoproct, Tg = tergite, Stg = sternite. Same scale.
FIGURE 2. Pegomya disticha Griffiths. A in Pegomya disticha Griffiths and P. cedrica Huckett (Diptera: Anthomyiidae) - first documented case of insects trespassing the silica barrier of Common scouringrush, Equisetum hyemale L.
FIGURE 2. Pegomya disticha Griffiths. A. Intact stem with escape window (arrow). B. Opened stem showing chamber with puparium at base and larval frass. C. Transects of occupied and healthy internode. D. Two stems with the exit windows sliced open by presumed avian predator. Scale bar in millimetres.
FIGURE 6. Pegomya disticha Griffiths, male terminalia. A. Tergo-sternite VI‒VIII. B. Sternite V. C. Hypopygium, caudal view. D. Same, left lateral view. E in Pegomya disticha Griffiths and P. cedrica Huckett (Diptera: Anthomyiidae) - first documented case of insects trespassing the silica barrier of Common scouringrush, Equisetum hyemale L.
FIGURE 6. Pegomya disticha Griffiths, male terminalia. A. Tergo-sternite VI‒VIII. B. Sternite V. C. Hypopygium, caudal view. D. Same, left lateral view. E. Pregonite and postgonite, left lateral view. F. Phallus and ejaculatory pump, left lateral view. Abbreviation: Tg = tergite. Same scale.
FIGURE 9. Cephaloskeleton, 3 in Pegomya disticha Griffiths and P. cedrica Huckett (Diptera: Anthomyiidae) - first documented case of insects trespassing the silica barrier of Common scouringrush, Equisetum hyemale L.
FIGURE 9. Cephaloskeleton, 3rd instar larva. A. Pegomya disticha Griffiths, left lateral view. B. Same, dorsal view. C. P. cedrica Huckett, left lateral view. D. Same, dorsal view. Same scale.
FIGURE 10. Spiracles, 3 in Pegomya disticha Griffiths and P. cedrica Huckett (Diptera: Anthomyiidae) - first documented case of insects trespassing the silica barrier of Common scouringrush, Equisetum hyemale L.
FIGURE 10. Spiracles, 3rd instar larva. A. Pegomya disticha Griffiths, anterior spiracle. B. P. cedrica Huckett, anterior spiracle. C. P. disticha Griffiths, posterior spiracles. D. P. cedrica Huckett, posterior spiracles. Same scale.
Does Unit-Tested Code Crash? A Case Study of Eclipse: Replication Package
<p><strong>Does Unit-Tested Code Crash? A Case Study of Eclipse: Replication Package</strong></p> <p>This is a replication package associated with the paper titled “Does Unit-Tested Code Crash? A Case Study of Eclipse”. Below is a description of the package’s contents.</p> <p><strong>Data</strong></p> <p>Data files associated with the paper are provided in the <code>data</code> directory.</p> <p><strong>Text file <code>tested-crashed.txt</code></strong></p> <p>Data specifying whether methods were tested and whether they crashed (according to the criteria adopted in the study). Extracted from <code>matches.xlsx</code>. The data are used as input for Fisher’s test (RQ1).</p> <p><strong>Spreadsheet <code>matches.xlsx</code></strong></p> <p>Test coverage data and calculations associated with failed methods, class coverage, and matching method coverage results are provided in an Excel spreadsheet. Below is the description of the spreadsheet’s contents.</p> <p>Worksheet <em>Test Coverage</em></p> <p>Contains the data regarding the JaCoCo test code coverage analysis.</p> <ul> <li>Class: The name of the class in which a method appears in JVM internal form notation</li> <li>Method: The method’s name</li> <li>Parameters: The method’s arguments in JVM parameter descriptor format; required to handle Java’s {} polymporphism</li> <li>Class Has Unit Test: Whether the corresponding class has associated unit test code</li> <li>Class Unit-Test Line Density: The ratio of lines in class’s test code over those in the class’s implementation code</li> <li>Covered Instructions / Branches / Lines: As reported by JaCoCo</li> <li>Total Instructions / Branches / Lines: As reported by JaCoCo</li> <li>Covered Instructions / Branches / Lines ratio: The ratio between the two preceding values; 1 for methods without any branches</li> <li>Top-1 / Top-6 / Top-10 : In how many stack traces the method appears within; the top-10 / top-6 / the very first stack frame(s)</li> <li>Tested: TRUE if the method is considered tested by having a test code coverage above the median (0.966) and an associated test class</li> <li>Crashed: TRUE if the method has crashed as evidenced by its appearance on the topmost stack frame</li> <li>Stack trace file names: in which the method appeared</li> </ul> <p>Worksheet <em>Test Existence</em></p> <p>Contains the data of the analysis regarding the existence of test code.</p> <ul> <li>Class: Class containing implementation code</li> <li>TestClassNames: Classes that contain tests for the above</li> <li>Number of relevant tests</li> <li>Lines in class test code</li> <li>Lines of class</li> <li>Class Unit-Test Line Density: The ratio between the two above</li> </ul> <p>Worksheet <em>Metrics</em></p> <p>Contains the derivation of metrics reported in the paper. In the cases of tables these are formatted in LaTeX for direct incorporation into the text.</p> <p><strong>Spreadsheet <code>jacoco.xlsx</code></strong></p> <p>Complete test coverage data obtained from JaCoCo are provided in an Excel spreadsheet. Below is the description of the spreadsheet’s contents.</p> <p>Worksheet <em>Data</em></p> <p>Contains the following method code coverage fields as reported by JaCoCo, as well as the calculated percentages.</p> <ul> <li>Class</li> <li>Method</li> <li>Parameters</li> <li>Covered Instructions</li> <li>Total Instructions</li> <li>% Covered Instructions</li> <li>Covered Branches</li> <li>Total Branches</li> <li>% Covered Branches</li> <li>Covered Lines</li> <li>Total Lines</li> <li>% Covered Lines</li> </ul> <p>Worksheet <em>Metrics</em></p> <p>Contains the derivation of numbers reported in the preliminary quantitative analysis and Figure 2.</p> <p>Compressed tar archive <code>eclipse-src.tar.gz</code></p> <p>Contains the Eclipse source code used for running the Eclipse tests with JaCoCo code coverage analysis. It was obtained from the Eclipse source code repositories as follows.</p> <ul> <li>Clone the Eclipse aggreagator repository into a directory named z by running: <code>git clone -b master --recursive git://git.eclipse.org/gitroot/platform/eclipse.platform.releng.aggregator.git z</code></li> <li>In the <code>z</code> directory, checking out the used release by running <code>cd z && git submodule foreach git checkout M20160212-1500</code></li> <li>Checking out the release for the main repository by running: <code>git checkout M20160212-1500</code></li> <li>Applying the patch <code>eclipse-src.diff</code></li> </ul> <p><strong>Patch file <code>eclipse-src.diff</code></strong></p> <p>See above.</p> <p><strong>Zip file <code>incidents.zip</code></strong></p> <p>Contains the 126,026 incidents (crash report stack traces and meta-data) associated with <em>EclipseProduct</em> <code>org.eclipse.epp.package.java.product</code> and <em>BuildID</em> <code>4.5.2.M20160212-1500</code>. This is a subset from the two million incidents available as the <a href="http://software-data.org/datasets/aeri-stacktraces/downloads/incidents_full.tar.bz2">AERI stack traces data set</a>.</p> <p>The subset of incidents was extracted from the full AERI data set with the following command.</p> <pre><code class="language-bash">for f in *; do grep -q org.eclipse.epp.package.java.product $f && grep -q 4.5.2.M20160212-1500 $f && mv $f selected-files/ done</code></pre> <p><br> <strong>Compressed file <code>jacoco.xml.gz</code></strong></p> <p>Contains the results of the JaCoCo code coverage analysis over the Eclipse testing.</p> <p><strong>Code</strong></p> <p>The following scripts are provided in the <code>src</code> directory</p> <ul> <li><code>extract.py</code>: script for extracting crash (incidents) and coverage (JaCoCo) data</li> <li><code>unit-tested-classes.py</code>: script for finding the classes with associated unit test code</li> <li><code>merge.py</code>: script for matching crash (incidents) with coverage (JaCoCo) data</li> <li><code>fisher.r</code>: R script for running Fisher’s test</li> </ul>
A Process for Evaluating the Energy Efficiency of Software - Case Study A
<p>This laboratory package includes the data of the case study carried out in this work.</p> <p> </p> <p> </p>
Linking individual physiological indicators to the productivity of fish populations: A case study of Atlantic herring
<p>Raw data for i) heart rate measurements of Atlantic herring larvae reared at three different temperatures (7, 11 and 15°C); ii) compilation of Baltic herring larval growth rates measured in the field and in the laboratory; and iii) time series of thermal estimates (derived from satellite sea surface temperatures) in Greifswald Bay (Baltic Sea) between 1992 and 2017, as well as three herring productivity proxies in the area: weekly larval abundance in Greifswald Bay (one of the major spawning grounds), 1 yr-old juvenile abundance in the western Baltic basin, and the recruitment estimate from the International Council for the Exploration of the Sea (ICES) Herring Assessment Working Group for the Area South of 62°N.</p>
Crowdsourcing public perceptions of urban green space quality: A case study of Rembrandt park in Amsterdam
<p><strong><em>City-dwellers are realizing the benefits of green spaces and are flocking to urban parks. City planners face the challenge of ensuring that urban green spaces are functional for all citizens. To make informed choices they need the right information and that is where the Mijn Park app can help. </em></strong></p> <p>Research shows that when considering the social functions of urban green spaces, quality is just as important as quantity. It is easy enough to map how much green spaces there are, but how do we measure their quality? How do city planners ensure that the city’s green areas are attractive, accessible and inclusive – for everyone? The Vrije Universiteit Amsterdam in collaboration with the International Institute for Applied Systems Analysis developed Mijn Park, a mobile application that will help city planners do just that. As part of the LandSense Citizen Observatory, the ‘Mijn Park’ (My Park) app asks respondents to go to several locations in a park and give subjective responses to those locations. They are then further questioned about how they use the whole park and how much they would like to see certain changes made in the park. This information provides information that can help to inform decisions about any renovations or improvements to the park.</p> <p>A pilot campaign was conducted in the summer of 2018 in Rembrandt park in Amsterdam and insights from the citizen-driven observations were shared with the Department of Planning and Sustainability of Amsterdam.</p> <p>This dataset includes responses and photographs collected by 129 unique volunteers providing 377 observations in select locations across Rembrandt Park. The following files are available:</p> <ul> <li>_Preview MijnPark-Amsterdam-LandSense.png</li> <li>Attributes-MijnPark-Amsterdam-LandSense.csv</li> <li>MijnPark-Amsterdam-LandSense.csv</li> <li>MijnPark-Amsterdam-LandSense.geoJSON</li> <li>README.txt</li> </ul> <p> </p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="https://www.vu.nl/">Vrije Universiteit</a> (VU), Amsterdam and the <a href="https://iiasa.ac.at/">International Institute for Applied Systems Analysis</a> (IIASA).</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
PUFFIN Application Case Study Data
<p>This contains the Probabilistic Urban Flash Flood Information Nexus (PUFFIN) application and its output data from the October 11th, 2018 and February 6th, 2020 Case Study events</p>
Supporting material for: A multi-batch design to deliver robust estimates of efficacy and reduce animal use – a syngeneic tumour case study
<p>Underlying data, R scripts and subsequent figures are presented to deliver a replicable and transparent analysis for the manuscript "A multi-batch design to deliver robust estimates of efficacy and reduce animal use – a syngeneic tumour case study"</p> <p>Published in Scientific Reports</p> <p>See: <a href="https://protect-de.mimecast.com/s/kCQLCgpRGySlzOwmCNma6d?domain=rdcu.be">https://rdcu.be/b3vj2</a> </p> <p> </p> <p>This is version 2 of this data. This differs from version 1 in the following</p> <p>1. The number of simulation has increased from 300 to 2000 in each simulation cycle</p> <p>2. Script has been added to generate publication level figures</p> <p> </p> <p> </p> <p> </p>
FIGURE 4. A in Taxonomic status of the Australian dingo: the case for Canis dingo Meyer, 1793
FIGURE 4. A summary of the evolutionary relationships among wolves, dingoes and modern domestic dogs. This figure is a synthesis of the various dog-origin models from vonHoldt et al. (2010), Cairns & Wilton (2016) and vonHoldt & Driscoll (2016). Dingoes and other ancient lineages of dog such as New Guinea singing dogs form a distinct lineage separate from modern domestic dogs which have undergone successive generations of artificial selection.
FIGURE 3 in Taxonomic status of the Australian dingo: the case for Canis dingo Meyer, 1793
FIGURE 3. Genetic variation among dingoes, New Guinea singing dogs, wolves and modern domestic dogs based on genomewide single-nucleotide polymorphism (SNP) data expressed using principal components analysis (PCA) representing 841 dogs (green), 24 dingoes (black), 5 New Guinea singing dogs (orange), 14 wolves (blue). Within the dogs there were 292 breed dogs (light green) and 549 village dogs (dark green). We genotyped each of these canid samples using the Illumina CanineHD 170 SNP chip (data from Cairns 2015 and Shannon et al. 2015). We analysed genotype data in PLINK 1.9 (Chang et al. 2015) by merging the datasets and removing SNPs that were missing in more than 10% of samples. The total remaining SNP markers were 166,019. We did the PCA in PLINK 1.9 and visualised the results using R (v 3.2.1). PC1 accounts for 49.2% of variation,
FIGURE 2. Cranial 3-D in Taxonomic status of the Australian dingo: the case for Canis dingo Meyer, 1793
FIGURE 2. Cranial 3-D reconstructions of a dingo (bottom) and a free-ranging dog (top), highlighting the differences in cranial morphology mentioned in the text. The dog cranium has been scaled to match the length of the dingo cranium to facilitate the comparison of feature shape. The dingo (male) was collected from Minburra Station in South Australia. The dog (female, 14.5 kg) was collected in 1981 from the Victorian Highlands (Evan Jones Collection).
FIGURE 1 in Taxonomic status of the Australian dingo: the case for Canis dingo Meyer, 1793
FIGURE 1. An example of a typical dingo phenotype. Photograph depicts a male from K'gari-Fraser Island (Queensland) by John Williams.
GEO4962 input dataset for running CESM 2.1.0 case for the course
<p>Contains the input dataset for running CESM 2.1.0 as part of the GEO4962 course at the University of Oslo.</p>
Air contamination and covid-19 cases in Spain
<p>This data provides values of the Air Quality Index (AQI) for the most populated city in each Spanish autonomous community since 2019. The compounds selected to evaluate AQI are PM10, O3, and NO2. This dataset is oriented to people that want to evaluate quarantine effects on air pollution during a COVID-19 disease outbreak. For this reason, a table with notified cases of the disease to the Public Administration at an autonomous community-scale is also supplied with the aim of providing a framework of disease evolution. </p> <p>The air_contamination.csv dataset contains 9 variables:</p> <p> - timestamp: date in format: yyyy/mm/dd<br> - ca: autonomous community (text)<br> - ciudad: city (text)<br> - pm10: AQI value of pm10 particle (number)<br> - pm10_level: Air quality base on pm10 particles (text)<br> - o3: AQI value of o3 particle (number)<br> - o3_level: Air quality based on o3 particles (text)<br> - no2: AQI value of no2 particle (number)<br> - no2_level: Air quality based on no2 particles (text)</p> <p>The casos_covid19.csv dataset contains 4 variables:</p> <p>- comunidad: autonomous community (text)<br> - casos: Cases notified by Ministerio de Sanidad/Healthcare minister (text) <br> - casos_notificados: Cases notified by RENAVE (Red Nacional de Vigilancia Epidemiológica) a través de la plataforma SiVIES.<br> - datetime: datetime in format yyyy/mm/dd</p> <p> </p> <p>For more information about the project visit the link on [Github](<a href="https://github.com/shiny-data-scientist/webscrap_pract_1/">https://github.com/shiny-data-scientist/webscrap_pract_1/</a>)</p>
FIG. 3 in Tyrannosaur cannibalism: A case of a tooth-traced tyrannosaurid bone in the Lance Formation (Maastrichtian), Wyoming
FIG. 3.—Details of Knethichnus parallelum and Linichnus serratus traces on SWAU HRS13997. A) Most prominent K. parallelum (center) and L. serratus (bottom) examples. B) Most prominent L. serratus in more detail and a different lighting angle. Small portion of the most prominent K. parallelum is also visible in the lower right corner. C) Detailed view of the other, less prominent L. serratus (left) and K. parallelum (right) traces. All scale bars = 1 mm.
CodiEsp Silver Standard: Participant predictions in eHealth CLEF2020 - Spanish clinical cases coded in ICD10 (CIE10)
<p><strong>Introduction</strong></p> <p>Predictions in the background set of <a href="https://temu.bsc.es/codiesp/">eHealth CLEF 2020 Task 1</a> participants.</p> <p> </p> <p><strong>Zip structure</strong></p> <p>One directory per CodiEsp subtask. Within each CodiEsp subtask directory, there is one directory per team that contains the prediction runs.</p> <p> </p> <p><strong>Format</strong><br> The text documents are distributed in plain text files, UTF-8 encoding.<br> The CodiEsp Silver Standard annotations have the following format:</p> <p>For the sub-tracks CodiEsp-Diagnostic and CodiEsp-Procedure, the file files have the following fields:</p> <pre>articleID ICD10-code </pre> <p>Tab-separated files for the sub-track CodiEsp-X (explainability) contain extra fields that provide the text-reference and its position:</p> <pre>articleID label ICD10-code text-reference reference-position</pre> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/codiesp/">Web</a></strong></li> <li><strong>Citation: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><strong><a href="https://zenodo.org/record/3837305#.X7T9KVlKg5k">Gold Standard corpus</a></strong></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>All credit to CodiEsp participants</p>
Could dementia be detected from UK primary care patients' records by simple automated methods earlier than by the treating physician? A retrospective case-control study - Extended Data
<p>Extended data for Article published in Wellcome Open Research (Appendices 1,2 & 3). </p> <p>Abstract of Article: </p> <p><strong>Background:</strong> Timely diagnosis of dementia is a policy priority in the United Kingdom (UK). Primary care physicians receive incentives to diagnose dementia; however, 33% of patients are still not receiving a diagnosis. We explored automating early detection of dementia using data from patients’ electronic health records (EHRs). We investigated: a) how early a machine-learning model could accurately identify dementia before the physician; b) if models could be tuned for dementia subtype; and c) what the best clinical features were for achieving detection.</p> <p><strong>Methods:</strong> Using EHRs from Clinical Practice Research Datalink in a case-control design, we selected patients aged >65y with a diagnosis of dementia recorded 2000-2012 (cases) and matched them 1:1 to controls; we also identified subsets of Alzheimer’s and vascular dementia patients. Using 77 coded concepts recorded in the 5 years before diagnosis, we trained random forest classifiers, and evaluated models using Area Under the Receiver Operating Characteristic Curve (AUC). We examined models by year prior to diagnosis, subtype, and the most important features contributing to classification.</p> <p><strong>Results:</strong> 95,202 patients (median age 83y; 64.8% female) were included (50% dementia cases). Classification of dementia cases and controls was poor 2-5 years prior to physician-recorded diagnosis (AUC range 0.55-0.65) but good in the year before (AUC: 0.84). Features indicating increasing cognitive and physical frailty dominated models 2-5 years before diagnosis; in the final year, initiation of the dementia diagnostic pathway (symptoms, screening and referral) explained the sudden increase in accuracy. No substantial differences were seen between all-cause dementia and subtypes.</p> <p><strong>Conclusions:</strong> Automated detection of dementia earlier than the treating physician may be problematic, if using only primary care data. Future work should investigate more complex modelling, benefits of linking multiple sources of healthcare data and monitoring devices, or contextualising the algorithm to those cases that the GP would need to investigate.</p>
ScienceDex guides
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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.