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FIGURE 5 in Systematics of the Podarcis hispanicus complex (Sauria, Lacertidae) III: valid nomina of the western and central Iberian forms

FIGURE 5. Variation in females, undersides and juveniles of Podarcis guadarramae guadarramae. A) Female. Spain, El Tiemblo (province of Ávila). 27.06.2001 (BEV.2007). Photo P. Geniez. B) Female with the "guadarramae striped pattern". Spain, Navalmoral (province of Ávila), 27.6.2001 (BEV.1999). Photo P. Geniez. C) Typical underside of a male, Spain, Navalilla, in the village (province of Segovia), 13.05.1996 (BEV.4487). Photo P. Geniez. D) Male with a white underside. Spain, San Martin de Valdeiglesias (province of Madrid), 27.06.2001 (BEV.2018). Photo P. Geniez. E) Juvenile male. Spain, San Martin de Valdeiglesias (province of Madrid), 27.06.2001 (BEV.2025). Photo P. Geniez. F) Juvenile female. Spain, La Alberca, Iglesia de Nuestra Señora de la Asunción (province of Salamanca), 18.09.2007 (PGe.790). Photo P.-A. Crochet.

FIGURE 8 in Systematics of the Podarcis hispanicus complex (Sauria, Lacertidae) III: valid nomina of the western and central Iberian forms

FIGURE 8. Variation in males of Podarcis guadarramae lusitanicus. A) Portugal, Serra da Estrela, just north of the Lagoa de Viriato (district of Castelo Branco), 1579 m, 21.08.2008 (PGe.796). Photo O. Buisson. B) and C) Portugal, Serra da Estrela, top of the Vale Glacirio do Zêzere, crossroad N 338 x N 339, 1800 m (district of Guarda), 10.05.1985 (respectively BEV.3982 and 3981). Photos P. Geniez. D). Spain, 200 m E. Castro Baroña (42 km SW. Santiago, province of Pontevedra), 12.10.2006 (PGe.1035). Photo J. Speybroeck. E) and F). Spain, 1 km ESE. Viana do Bolo (province of Orense), 734 m, 1.05.2012 (respectively PGe.1134 and 1132). Photos J. Speybroeck (E) and W. Beukema (F).

FIGURE 7 in Systematics of the Podarcis hispanicus complex (Sauria, Lacertidae) III: valid nomina of the western and central Iberian forms

FIGURE 7. Holotype of Podarcis guadarramae lusitanicus, male MNHN 2012.0263. Portugal, 1 km past Âncora towards Póvoa do Varzim (district of Viana do Castelo), 14.05.1985. Photos P. Geniez. Note the very flattened head, the very large black spots on the pileus, the lack of vertebral stripe, the very broad black supradorsolateral stripes invading a large part of the pale green dorsum and the light dorsolateral stripes made of weakly elongated spots.

FIGURE 4 in Systematics of the Podarcis hispanicus complex (Sauria, Lacertidae) III: valid nomina of the western and central Iberian forms

FIGURE 4. Variation in males of Podarcis guadarramae guadarramae. For GPS coordinates (WGS84 in decimal degrees), see Appendix 1. A) Spain, Sierra de Gredos, 1.5 km N. Laguna Grande, 1,897 m (province of Ávila), 17.08.2007 (PGe.1107). Photo O. Buisson. B) Spain, Sierra de Guadarrama, Boca del Asno, 7 km past La Granja de San Ildefonso towards Puerto de Navacerrada (province of Segovia), 19.08.1990 (PGe.1028). Photo P. Geniez. C) Spain, Hiendelaencina, in the village, Plaza Major, 1,080 m (province of Guadalajara), 17.07.2010 (BEV.10983). Photo P. Geniez & P.-A. Crochet. D) Spain, San Martín de Valdeiglesias (province of Madrid), 27.06.2001 (BEV.2021). Photo P. Geniez. E) Spain, El Tiemblo (province of Ávila), 27.06.2001 (BEV.2009). Photo P. Geniez. F) Specimen with greenish tint on the back. Spain, Sierra de Gredos, 4 km NE. Laguna Grande, 2000 m (province of Ávila), 16.07.1992 (PGe.1035). Photo P. Geniez.

opencc-by-sa-4.0Dec 2018View details →

Replication Kit: "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"

Replication Kit for the Paper "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects" This additional material shall provide other researchers with the ability to replicate our results. Furthermore, we want to facilitate further insights that might be generated based on our data sets. Structure The structure of the replication kit is as follows: <ul> <li>additional_visualizations: contains additional visualizations (Venn-Diagrams) for each projects for each of the data sets that we used</li> <li>data_analysis: contains python scripts that we used to analyze our raw data</li> <li>data_collection_tools: contains all source code used for the data collection, including the used versions of the <a href="https://github.com/comfort-framework">COMFORT framework</a>, the <a href="https://github.com/ftrautsch/BugFixClassifier">BugFixClassifier</a>, and the used tools of the <a href="https://github.com/smartshark">SmartSHARK environment</a>;</li> <li>mongodb_no_authors: Archived dump of our MongoDB that we created by executing our data collection tools. The "comfort" database can be restored via the mongorestore command.</li> </ul> Additional Visualizations We provide two additional visualizations for each project: 1) <project_name>\_disj\_ieee\_venn (visualizations for the DISJ data set) 2) <project_name>\_all\_ieee\_venn (visualizations for the ALL data set) For each of these data sets there exist one visualization for each project that shows four Venn-Diagrams for each of the different defect types. These Venn-Diagrams show the number of defects that were detected by either unit, or integration tests (or both). Furthermore, we added boxplots for each of the data sets (i.e., ALL and DISJ) showing the scores of unit and integration tests for each defect type. Analysis scripts Requirements: - python3.5 - tabulate - scipy - seaborn - mongoengine - pycoshark - pandas - matplotlib Both python files contain all code for the statistical analysis we performed. Data Collection Tools We provide all data collection tools that we have implemented and used throughout our paper: <ul> <li>BugFixClassifier: Used to classify our defects.</li> <li>comfort-core: Core of the comfort framework. Used to classify our tests into unit and integration tests and calculate different metrics for  these tests.</li> <li>comfort-jacoco-listner: Used to intercept the coverage collection process as we were executing the tests of our case study projects.</li> <li>jSHARK: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Java). </li> <li>pycoSHARK: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Python).</li> <li>tools-changedistiller: Version of ChangeDistiller that we used within our comfort-core framework.</li> <li>vcsSHARK: Used to collect data from the VCSs of the projects.</li> </ul>

opencc-by-4.0Oct 2022View details →

Abstraction-based Trace Generation to Validate Semantics of Formal Verifiers: Validation Model Suite

Dataset of the Scientific Students’ Association Report titled Abstraction-based Trace Generation to Validate Semantics of Formal Verifiers. These files contain the validation model test suite and the generated traces. The models and traces are in the format of the Gamma modeling tool. validation-model-suite/model/package<Letter>/model<Number> contains the files for a given model: - stm.gcd is the statemachine, - default.ggen (and in Package F also abstraction.ggen) is the Gamma script executing trace generation and the generated traces can be found in the default (and abstraction) directories. The report of Theta on possible coverage violation is in the traces directory (report.txt).   The prototype implementation of trace generation can be found at: https://github.com/AdamZsofi/gamma/tree/dev-tracegen

Nonsmooth simulations of 3D Drucker-Prager granular flows and validation against experimental column collapses

# About This archive aims to reproduce the results of the article entitled: "***Nonsmooth simulations of 3D Drucker-Prager granular flows and validation against experimental column collapses***" *by Gauthier Rousseau, Thibaut Métivet, Hugo Rousseau, Gilles Daviet, and Florence Bertails-Descoubes*   ## Contents   - The experimental data: raw videos, experimental information, velocity and profiles - The `sand6py` code which is a fork of [Daviet PhD][1] `sand6` code including [diphasic feature][2] and other new features developed for the paper (collapses scenarios with frictional door, hysteresis, python binding with `pybind11`) - Python scripts: - for performing velocimetry measurements - for running the `sand6` 3D simulations of granular column collapses - for plotting paper figures   The git repository corresponding to `sand6py` is available on [https://gitlab.com/groussea/sand6py](https://gitlab.com/groussea/sand6py)   A maintained version of sand6 is available on [https://gitlab.inria.fr/elan-public-code/sand6](https://gitlab.inria.fr/elan-public-code/sand6)   ## Figures   the figures of the article are reproducible from the python scripts in the `python/figurejfm` path   python dependencies are `matplotlib opencv jupyter vtk h5py tqdm scikit-image opyf`   You may obtain all the required dependencies in a convenient conda environment using the following command line in your terminal:   ```shell conda create --channel conda-forge -n sand6env python=3.9 matplotlib opencv jupyter vtk h5py tqdm scikit-image   conda activate sand6env ```   Corresponding e-mail: gauthier.rousseau@gmail.com   [1]: tel.archives-ouvertes.fr/tel-01684673 "*Modeling and simulating complex materials subject to frictional contact: Application to fibrous and granular media*. Diss. Ph. D. Dissertation. Université Grenoble Alpes. , 2016" [2]: https://hal.inria.fr/hal-01458951 "Simulation of Drucker–Prager granular flows inside Newtonian fluids"

opencc-by-4.0May 2022View details →

CRM Variance Transport validation in E3SM-MMF - analysis code and condensed data

This tarball contains a collection of code and data used to performa a detailed analysis of the CRM variance transport feature added to E3SM-MMF. The data subset is condensed from the model output and observational datasets, so the ability to reproduce results directly is limited, but this archive mainly serves to document the methods used for our analysis.

Dataset to train, validate and reconstruct POC over the global ocean for 2009-2013 based on PlankTOM12

The distribution of in situ UVP5 measurements over the period 2009-2013 was used to create synthetic data by sampling a global biogeochemical ocean model PlankTOM12 at UVP5 time and location. The synthetic data set is used to train, validate and test Machine Learning methods to reconstruct particulate organic carbon concentration.  These data are part of publication at the GMD.

Data supplement for: Validating the Nernst--Planck transport model under reaction-driven flow conditions using RetroPy v1.0

This is the repository for the publication's supplementary data and plotting scripts: Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0. The dependency of the scripts can be installed using conda and pip: <pre><code>conda create -n plot numpy matplotlib==3.6.1 h5py python=3.9 conda activate plot pip install palettable</code></pre> To reproduce the figures, execute the files using python: <pre><code>python figure03.py</code></pre>

Data for DESI Publication DESI-2022-0160: DESI Survey Validation Spectra Reveal an Increasing Fraction of Recently Quenched Galaxies at z~1

This folder contains all the data on figures for DESI Publication DESI-2022-0160, which is about to be sent to final reading.

opencc-by-4.0Dec 2022View details →

Genome graphs detect human polymorphisms in active epigenomic states during influenza infection: validation

Sanger sequencing and qPCR validation data for "Genome graphs detect human polymorphisms in active epigenomic states during influenza infection" manuscript.

opencc-by-4.0Dec 2021View details →

MS2Query results 20-fold cross validation

The zip file contains the raw results of the 20-fold cross validation, the raw data downloaded from GNPS on 01-11-2022 and all the test data splits made.  The raw results are stored in JSON format. The results of MS2Query and the benchmarking methods (cosine, modified cosine and MS2Deepscore) are stored. For each test spectrum three values are stored. The first value is the predicted score, the second value the tanimoto score between the correct annotation and the prediction and the last value is a boolean showing if the predicted spectrum was an exact 2D structure match.  The figures in the MS2Query paper can be reproduced using the functions in https://github.com/iomega/ms2query/blob/main/ms2query/benchmarking/create_accuracy_vs_recall_plot.py

opencc-by-4.0Feb 2022View details →

Dataset of the publication "Theory and Experimental Validation of Two Techniques for Compensating VT Nonlinearities"

This is a dataset for paper published: G. D’Avanzo et al., "Theory and Experimental Validation of Two Techniques for Compensating VT Nonlinearities," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-12, 2022, Art no. 9001312, doi: 10.1109/TIM.2022.3147883.   Excel file provides data for VT_B.

opencc-by-4.0Dec 2022View details →

JMLKelinci+: Detecting Semantic Bugs and Covering Branches with Valid Inputs using Coverage-Guided Fuzzing and Runtime Assertion Checking

Testing to detect semantic bugs is essential, especially for critical systems. Coverage-guided fuzzing and runtime assertion checking (RAC) are two well-known approaches for detecting semantic bugs. Coverage-guided fuzzing aims to generate inputs tests with high code coverage. However, while coverage-guided fuzzers are equipped with sanitizers that can detect a fixed set of semantic bugs, they can otherwise only detect bugs that lead to a crash. Thus, the first problem we address is how to help fuzzers detect previously unknown semantic bugs that do not lead to a crash. Moreover, a coverage-guided fuzzer may not necessarily cover all branches with valid inputs, although invalid inputs are useless for detecting semantic bugs.  So, the second problem is how to guide a fuzzer to cover all branches in a program using only valid inputs.  On the other hand, RAC monitors the expected behavior of a program dynamically and can only detect a semantic bug when a valid input test shows that the program does not satisfy its specification.   Thus, the third problem is how to provide high-quality input tests for a RAC that can trigger potential bugs. The combination of a coverage-guided fuzzer and RAC solves these problems and can cover branches with valid inputs and detect semantic bugs effectively. Our study uses RAC to guarantee that only valid inputs reach the program under test using the program's specified preconditions and it also uses RAC to detect semantic bugs using specified postconditions.  A prototype tool was developed for this study, named JMLKelinci+. Our results show that combining a coverage-guided fuzzer with RAC will lead to executing the program under test only with valid inputs and that this technique can effectively detect semantic bugs.  Also, this idea improves the feedback given to a coverage-guided fuzzer, enabling it to cover all branches faster in programs with non-trivial preconditions.

FIGURE 5. Euastacus clydensis Riek, 1969. A in Redescription of Euastacus clydensis Riek, 1969 (Crustacea: Parastacidae), a valid species of spiny crayfish from southern New South Wales, Australia

FIGURE 5. Euastacus clydensis Riek, 1969. A, distribution, sampling locations (green) in Moss Vale, Jervis Bay and Batemans Bay (grey shading: the Murray-Darling Basin; dark blue dashed lines: South East Coast Drainage river catchments; and light blue lines indicate major rivers); B–D, foraging individuals and habitat, Quart Pot Creek, tributary of Buckenbowra River (specimens not collected); E, habitat, Carters Creek, tributary of Buckenbowra River.

FIGURE 4. A in Redescription of Euastacus clydensis Riek, 1969 (Crustacea: Parastacidae), a valid species of spiny crayfish from southern New South Wales, Australia

FIGURE 4. A, Euastacus clydensis Riek, 1969, male holotype (OCL 73.37 mm), Clyde River, NSW, AM P15491, right antenna (ventral view); B, E. clydensis Riek, 1969, male, OCL 71.85 mm, Currowan Creek, NSW, ACP4767, right scaphocerite (ventral view); C, E. spinifer (Heller, 1865), female, OCL 73.43 mm, Waterfall, NSW, AM G1502, right scaphocerite (ventral view).

FIGURE 3. Euastacus clydensis Riek, 1969 in Redescription of Euastacus clydensis Riek, 1969 (Crustacea: Parastacidae), a valid species of spiny crayfish from southern New South Wales, Australia

FIGURE 3. Euastacus clydensis Riek, 1969, male holotype (OCL 73.37 mm), Clyde River, NSW, AM P15491: A, anterior cephalothorax including epistome, ventral view; B, left first chela, dorsal view; C–D, right first chela, dorsal and ventral views; E, right maxilliped 3; F, cephalothorax, ventral view.

FIGURE 2. Euastacus clydensis Riek, 1969 in Redescription of Euastacus clydensis Riek, 1969 (Crustacea: Parastacidae), a valid species of spiny crayfish from southern New South Wales, Australia

FIGURE 2. Euastacus clydensis Riek, 1969, male holotype (OCL 73.37 mm), Clyde River, NSW, AM P15491: A, carapace, dorsal view; B, carapace, right lateral view; C, abdomen, right oblique dorsal view; D, abdomen, right lateral view; E, telson and uropods, dorsal view.

FIGURE 6 in Redescription of Euastacus clydensis Riek, 1969 (Crustacea: Parastacidae), a valid species of spiny crayfish from southern New South Wales, Australia

FIGURE 6. Maximum likelihood phylogeny inferred from mitochondrial COI sequence data of Euastacus spinifer (Heller, 1865), E. vesper McCormack & Ahyong, 2017, E. clydensis Riek, 1969. Values on nodes correspond to bootstrap percentages (>75%) and posterior probabilities (>0.95).