Find research datasets worth reusing

opencc-by-4.0Dec 2009View details →

Figure 1 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation

Figure 1. Terrestrial biodiversity in the Middle East is strongly influenced by seasonality: Desert area in northern Saudi Arabia after the winter rainfall (photo F. Krupp).

Does ring current heating generate the observed O+ shell?: Numerical information for the figures

This archive contains data files needed to reproduce selected figures from the following manuscript: Does ring current heating generate the observed O+ shell? by J. Krall, J. D. Huba, and M.-C. Fok This manuscript was submitted to Geophyscial Research Letters in April, 2020 In addition, this archive contains CIMI code output giving Coulomb heating losses from the ring current and a SAMI3 subroutine that describes the heating function used to mimic ring current heating.

opencc-by-4.0Nov 2019View details →

generative commons database

This dataset is one of the deliverables of the H2020 project gE.CO Living Lab (generative European commons Living Lab). It collects communities of citizens that renovated urban spaces to provide welfare services, as well as urban policies adopted to support these initiatives. The dataset includes cased based in 16 different countries in Europe and more than 200 entities.

Dataset for "Fmmgen: Automatic Code Generation of Operators for Cartesian Fast Multipole and Barnes-Hut Methods"

This repository provides the dataset, build and run scripts for the paper "Fmmgen: Automatic Code Generation of Cartesian Fast Multipole and Barnes-Hut Operators", by Ryan Alexander Pepper and Hans Fangohr. Organisation The repository is organised as follows: sim-scripts sim-scripts/ contains the source code, build and run scripts for running the FMM calculations described in the paper. To reproduce the results from the paper, you need as prerequisites: * An installation and license of the Intel Compiler (Parallel Studio 2019 Update 3 was used for the paper). * An installation of the GNU compiler suite. * GNU Make * Python 3.6 * A copy of fmmgen v1.0 (available at https://github.com/rpep/fmmgen or https://zenodo.org/record/3842591) * An installation of Fidimag v3.0 (available at http://github.com/computationalmodelling/fidimag or http://dx.doi.org/10.5281/zenodo.3841935) With these prerequisites, simply run from the sim-scripts directory: ``` # To build the executables make build # To run the studies make run ``` The four scripts are: * run-harmonic-cse-comparison.sh - Runs the Fast Multipole Method for 50000 Coulomb particles, varying the order of expansion, and evaluating the performance benefits of various optimisation strategies introduced in the code generation stage. * run-scaling-comparison.sh - Runs comparisons between the Barnes-Hut and Fast Multipole Methods for different expansion orders and values of theta, the 'opening angle' parameter. * run-error-comparison.sh - Runs the FMM and Barnes-Hut calculations, saving the fields and performing the direct calculation, allowing evaluation of the errors for the two methods. * run-fidimag-tests.sh - Runs the Fidimag scaling tests for a series of magnetic dipoles placed on a lattice. results results contains the output data from the sim-scripts scripts. Note: running the scripts will overwrite this data! figure-scripts This contains Python scripts needed to reproduce the figures from the paper. These generated figures are included in the repository for convenience. To run these scripts, you require: * Python >= 3.6 * Matplotlib >= 3.1.1 * Seaborn >= 0.9.1 * NumPy >= 1.17.1 figures This contains the output figures included in the paper.

Test Suites from Test-Generation Tools (Test-Comp 2019)

This file describes the contents of an archive of the 1st Competition on Software Testing (Test-Comp 2019) <a href="https://test-comp.sosy-lab.org/2019/">https://test-comp.sosy-lab.org/2019/</a> The competition was run by Dirk Beyer, LMU Munich, Germany. More information is available in the following article: Dirk Beyer. First International Competition on Software Testing: Test-Comp 2019. International Journal on Software Tools for Technology Transfer, 2020. Copyright (C) Dirk Beyer <a href="https://www.sosy-lab.org/people/beyer/">https://www.sosy-lab.org/people/beyer/</a> SPDX-License-Identifier: CC-BY-4.0 <a href="https://spdx.org/licenses/CC-BY-4.0.html">https://spdx.org/licenses/CC-BY-4.0.html</a>   Contents: LICENSE.txt         specifies the license README.txt          this file witnessFileByHash/  This directory contains test suites (witnesses for coverage).                     Each witness in this directory is stored in a file whose name is the SHA2 256-bit hash                     of its contents followed by the filename extension .zip.                     The format of each test suite is described on the format web page:                     https://gitlab.com/sosy-lab/software/test-format                     A test suite contains also metadata in order to relate it                     to the test problem for which it was produced. witnessInfoByHash/  This directory contains for each test suite (witness) in directory witnessFileByHash/                     a record in JSON format (also using the SHA2 256-bit hash of the witness as filename,                     with .json as filename extension) that contains the meta data. witnessListByProgramHashJSON/                     For convenient access to all test suites for a certain program, this directory represents                     a function that maps each program (via its SHA2 256-bit hash) to a set of test suites                     (JSON records for test suites as described above) that the test tools have produced                     for that program.  For each program for which test suites exist, the directory contains                     a JSON file (using the SHA2 256-bit hash of the program as filename, with .json as                     filename extension) that contains all JSON records for test suites  for that program. A similar data structure was used by SV-COMP and is described in the following article: Dirk Beyer. A Data Set of Program Invariants and Error Paths. In Proceedings of the 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR 2019, Montreal, Canada, May 26-27), pages 111-115, 2019. IEEE. <a href="https://doi.org/10.1109/MSR.2019.00026">https://doi.org/10.1109/MSR.2019.00026</a>   Overview over archives from Test-Comp 2019 that are available at Zenodo: <a href="https://doi.org/10.5281/zenodo.3856669">https://doi.org/10.5281/zenodo.3856669</a>   Witness store (containing the generated test suites) <a href="https://doi.org/10.5281/zenodo.3856661">https://doi.org/10.5281/zenodo.3856661</a>   Results (XML result files, log files, file mappings, HTML tables) <a href="https://doi.org/10.5281/zenodo.3856478">https://doi.org/10.5281/zenodo.3856478</a>   Test tasks, version testcomp19 <a href="https://doi.org/10.5281/zenodo.2561835">https://doi.org/10.5281/zenodo.2561835</a>   BenchExec, version 1.18 All benchmarks were executed for Test-Comp 2019, <a href="https://test-comp.sosy-lab.org/2019/">https://test-comp.sosy-lab.org/2019/</a> by Dirk Beyer, LMU Munich based on the components git@github.com:sosy-lab/sv-benchmarks.git  testcomp19-0-g6a770a9c1 git@gitlab.com:sosy-lab/test-comp/bench-defs.git  testcomp19-0-g1677027 git@github.com:sosy-lab/benchexec.git  1.18-0-gff72868 Feel free to contact me in case of questions: <a href="https://www.sosy-lab.org/people/beyer/">https://www.sosy-lab.org/people/beyer/</a>

opencc-by-4.0Jun 2020View details →

MAOn: A Data-driven Methodology to Generate Living Ontologies

This repository presents the MAnto Lite ontology created with our MAOn methodology in the context of transport and public and the accessibility it provides. Besides, a set of annotated data with the ontology as a validation method is presented. The MAOn methodology is characterized by being data-based, by creating live ontologies and by a thorough evaluation process of the created ontology.

opencc-by-4.0Jun 2020View details →

Amorphous carbon films generated through simulated deposition with GAP from 1eV to 100eV

These amorphous carbon films were generated following the deposition protocol and methodology outlined by Caro et al., Phys. Rev. Lett. 120, 166101 (2018) and, in more detail, in Caro et al. Phys. Rev. B 102, 174201 (2020). Briefly, the structures were generated by depositing monoenergetic C atoms on a preexisting diamond (111) substrate. The interatomic potential used was the a-C GAP of Deringer and Csányi [Phys. Rev. B 95, 094203 (2017)] and the molecular dynamics simulations (MD) were carried out with QUIP's GAP implementation [http://libatoms.github.io/] using LAMMPS [https://lammps.sandia.gov/] as MD engine. The final structures of the deposition simulations at 1, 2, 3, 4, 5, 6.5, 8, 10, 20, 60 and 100eV are provided in extended XYZ format, with obvious naming convention.

opencc-by-4.0Aug 2020View details →

Replication package of "Revisiting Test Smells in Automatically Generated Tests: Limitations, Pitfalls, and Opportunities"

Abstract: Test smells attempt to capture design issues in test code that reduce their maintainability. Previous work found such smells to be highly common in automatically generated test-cases, but based this result on specific static detection rules; although these are based on the original definition of “test smells”, a recent empirical study showed that developers perceive these as overly strict and non-representative of the maintainability and quality of test suites. This leads us to investigate how effective such test smell detection tools are on automatically generated test suites. In this paper, we build a dataset of 2,340 test cases automatically generated by EVOSUITE for 100 Java classes. We performed a multi-stage, cross-validated manual analysis to identify six types of test smells and label their instances. We benchmark the performance of two test smell detection tools: one widely used in prior work, and one recently introduced with the express goal to match developer perceptions of test smells. Our results show that these test smell detection strategies poorly characterized the issues in automatically generated test suites; the older tool’s detection strategies, especially, misclassified over 70% of test smells, both missing real instances (false negatives) and marking many smell-free tests as smelly (false positives). We identify common patterns in these tests that can be used to improve the tools, refine and update the definition of certain test smells, and highlight as of yet uncharacterized issues. Our findings suggest the need for (i) more appropriate metrics to match development practice; and (ii) more accurate detection strategies, to be evaluated primarily in industrial contexts.

opencc-by-4.0Sep 2020View details →

Synthetic COVID-19 Case Reporting Data Generated from an Agent-Based Simulation Model

This is a synthetic case reporting data set for the SARS-CoV-2 epidemic in Austria. The data set statistically reproduces and synthetically augments data on reported cases and was generated with an agent-based simulation model. References to descriptions of the model and the parameterization used to generate the data set is included in the attached PDF file. The data format is described in the README file.

opencc-by-4.0Nov 2020View details →

Raman spectroscopic data derived from Calluna vulgaris charcoals, experimentally generated across a range of natural wildfire temperatures

This data has been derived from deconvolved Raman spectra, utilising two first order bands - D (Disordered) and G (Graphitic). Spectra were collected from experimentally pyrolysed charcoals, made from Calluna vulgaris (Ling Heather) separated into three main components; stem, root and flower. For each component at 250, 400, 600 and 800 degrees centigrade respectively, 5 charcoal samples (A, B, C, D, E) were analysed. Following deconvolution, median values for each spectra were produced. These correspond to parameters derived from the Raman data, including D- and G-band width (FWHM), intensity (ID/IG or 'R1') and area (AD/AG) ratios, band separation (G-D or 'RBS'), and band width ratios (D-FWHM/G-FWHM). All parameters have been compiled for each component material, and displayed graphically within this dataset.

opencc-by-4.0Sep 2020View details →

NEXT GENERATION OPTICAL ENCODER

Linear encoders provide direct position feedback to various machine tool and automation systems. Working in a linear format allows extreme length position measurement and control. José Luis de la Peña from <a href="https://www.fagorautomation.com/en/">Fagor Automation</a> explains how <a href="https://www.laser4surf.eu">Laser4Surf</a> technology will make linear encoders even more precise with the help of lasered nano strucures on the tape.

opencc-by-4.0Nov 2020View details →

Onshore & offshore WRF generated wind data

These data sets provide the WRF [1] calculated wind data for Pritzwalk (onshore) and FINO3 (offshore) as Python dictionaries.  Additionally, the files contain k-means cluster objects derived from these profiles. These data sets were used for power assessment and design exploration of Airborne Wind Energy Systems using the awebox [2] optimization toolbox.   WRF setups are described in detail and used in publication [3,4,5]. Wind data are interpolated to fixed heights of: [10,   28,   50,   70,   90,  100,  150,  200,  250,  300,  350, 400,  450,  500,  550,  600,   700,  800,  1000, 1200] meters above ground.   Onshore wind data:  <ul> <li> Location lat: 53° 10.78' N; long: 12° 11.35' E </li> <li> Time: 1 September 2015 - 31 August 2016 </li> <li> Timestep: 10 min </li> </ul> Offshore wind data:  <ul> <li> Location lat: 55° 11.7' N, long: 7° 9.5' E </li> <li> Time: 1 September 2013 - 31 August 2014 </li> <li> Timestep: 10 min </li> </ul>   The clusters are derived from both horizontal wind velocity components using the scikit-learn’s k-means clustering algorithm [6]. For our purposes, wind vectors were rotated such that the main wind speed always points in the same direction (u_main,u_deviation). [1]: <a href="https://www.mmm.ucar.edu/weather-research-and-forecasting-model"> Weather Research and Forecasting Model </a> [2]: <a href="https://github.com/awebox/awebox">awebox</a> [3]: <a href="https://doi.org/10.5194/wes-4-563-2019">Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems</a> [4]: <a href="https://doi.org/10.5194/wes-2020-120">Offshore and onshore ground-generation airborne wind energy power curve characterization </a> [5]:<a href="https://doi.org/10.5194/wes-2020-123">Ground-generation airborne wind energy design space exploration </a> [6]: <a href="https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html">sklearn.cluster.KMeans</a>

opencc-by-4.0Nov 2020View details →

Training dataset: Generation of a spectral library from HEK-Ecoli Spike-in mass spectrometry data

The five raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/). HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in the following ratios (amount in µg): Sample    HEK    E.coli    MS method Sample1    2.5      0.00        DDA Sample2    2.5      0.05        DDA Sample3    2.5      0.15        DDA Sample4    2.5      0.40        DDA Sample5    2.5      0.80        DDA Additionally, iRT peptides were added and 1µg of each samples was measured with a Q-Exactive Plus mass spectrometer. Besides the five raw files, we uploaded two fasta files that serve as human and ecoli protein sequence databases, an transition list for the iRT peptides as well as an experimental design for the MaxQuant search. Additionally, we uploaded the Galaxy MaxQuant training result files: protein groups, peptides, mqpar, msms, evidence and PTXQC.

Six questions to shifting the focus from more to more successful generational renewal in farming

One important longer-term building-block of a farming system’s resilience is generational renewal. Vice versa, farming systems are only attractive for the younger generation to enter if they offer long-term prospects. Within the EU, regional farming systems differ enormously with regard to their organisational and demographic characteristics, their production systems, and their local natural, institutional, and infrastructural conditions. Accordingly, generational renewal is multifaceted and related to many different issues, such as whether and how farms should organise farm succession, how farms can ensure the availability of a sufficient labour force, how they can adapt to and prepare for generational and demographic changes, and how the younger generation can benefit from the training and employment opportunities in the farming sector. Fuelled by key findings of the SURE-Farm Project, the aim of this business brief is to raise awareness among farmers and stakeholders of opportunities and challenges related to the various aspects of generational renewal.

opencc-by-4.0Oct 2019View details →

Using Blender EEVEE for the Generation of Real Time Background Plate in Green Screen Movie Shots

This talk will introduce you to the use of blender EEVEE for green screen shots for a short movie. Green (and blue) screen shots are notoriously difficult to get the lighting condition right, since the image from the camera is dominated by the bright green background. It is very helpful on the set to see in real time the final composition of the scene with the proper background to make adjustments of the camera and the lighting position and the lighting intensity and color. For the shots a large volume mocap solution (Optitrack) was used to track the movie camera (Arri Alexa) and the transformation data was sent to Blender to animate the virtual camera. The previously laser scanned background was rendered in real time in EEVEE. To combine the camera image and the rendered image a dedicated live-keying hardware was used. The described set-up was used in connection with the research project Virtually Real – Aesthetics and Perception of Virtual Spaces in Film by the Zurich University of the Arts and the University of Bern, funded by the Swiss National Science Foundation.

Fig. 5 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator

Fig. 5 Lateral view of Electrophorus electricus. National Museum of Natural History, NMNH 225670, 520 mm TL. Corantijn River, Suriname

Fig. 4 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator

Fig. 4 Ecological Niche Model and electric organ discharges for species of Electrophorus. Species niche models generated by MaxEnt for Greater Amazonia: a Electrophorus electricus (red); b E. varii (yellow); and c E. voltai (blue). d Measurements of voltage of high-voltage EODs, low-voltage EODs waveforms from Sach's organ, and posterior one-third of Hunter's organ (grey lines = individually recorded fish, black lines = averaged waveform for each species). e Nearest-neighbor hierarchical clustering of prominent time-frequency features of the low-voltage Sach's organ EOD from seven individuals of Electrophorus

Fig. 3 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator

Fig. 3 Electrophorus tree of life and time of species diversification. Time-calibrated genealogy of Electrophorus based on a maximum clade credibility (MCC) species tree derived from *BEAST2.4 analyses of 10 genes (colored lines) and 94 specimens of Electrophorus (relaxed molecular clock and uncorrelated lognormal model implemented). Purple bars represent 95% highest posterior density distributions for the estimated divergence time of each major node. Voltage measurements made by us are reported below E. electricus (National Museum of Natural History, NMNH 225670, 520 mm TL, Corantijn River, Suriname), E. voltai (Museu Paraense Emílio Goeldi, MPEG 15529; holotype, 1290 mm TL), and E. varii (MPEG 25422; holotype, 1000 mm TL)

Fig. 1 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator

Fig. 1 Sampling localities and gene trees for the three species of Electrophorus. a Map of northern South America showing distributions of sampled records and type localities (indicated by numbers) for three electric eel species: Electrophorus electricus (red dots, 1 = Suriname River, Suriname); E. voltai (blue dots, 2 = Rio Ipitinga, Brazil); and E. varii (yellow dots, 3 = Rio Goiapi, Brazil). Bicolor dots (blue/yellow) indicate sympatric co-occurrence of E. voltai and E. varii. The map was created in ArcGIS (https://www.arcgis.com) with images available at Shuttle Radar Topography Mission, Global Multi-resolution Terrain Elevation Data, and HydroSHEDS database. b *BEAST2.4 species tree (top cladogram; 94 specimens: 15 E. electricus, 41 E. voltai, 38 E. varii) based on 5 mitochondrial (trees 1–5; 107 specimens: 19 E. electricus, 43 E. voltai, 45 E. varii) and 5 nuclear genes (6–10; 94 specimens). Higher shading densities represent areas where the majority of trees agree in topology and branch lengths (posterior probabilities>0.99), while lower densities represent areas of uncertainty (Supplementary Data 1)

Fig. 2 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator

Fig. 2 Key morphological features to recognize the three species of Electrophorus. Top, radiographs of lateral view of the anterior portion of body (skull and pectoral girdle highlighted red). The cleithrum lies between the fifth and sixth vertebrae (v) in Electrophorus electricus (a) and E. voltai (b) versus first and second vertebrae in E. varii (c). Bottom, illustrations of ventral view of the head, showing key features listed in Diagnoses. a top: National Museum of Natural History, NMNH 403765, 300 mm TL, Cuyuni River, Guyana; bottom: NMNH 225576, 1000 mm TL, Corantijn River, Suriname. b top: Instituto Nacional de Pesquisas de Amazônia, INPA 39009, 450 mm TL, Teles Pires River, Brazil; bottom: Academy of Natural Sciences of Drexel University, ANSP 197583 (t3539), 1280 mm TL, Xingu River, Brazil. c top: NMNH 306677, 450 mm TL, Lago Janauari, Amazon River, Brazil; bottom: NMNH 196634, 1220 mm TL, Amazon River, Brazil