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1,163 results for “demonstrators”
Simulated benchmark metagenome used to demonstrate and evaluate MGLEX software
<p>This is a mock dataset of 120 000 artificial contigs of 1 kb length derived by simulating reads from 295 unique genomes and 44 species with each two or three strain genomes using the ART read simulator (Huang et al., 2012) and a lognormal abundance distribution. Genomes were chosen according to the CAMI2015 (www.cami-challenge.org) medium complexity toy dataset. The dataset contains four replicate samples with varied abundances and corresponding sequence feature files in MGLEX v0.1.1 format to use for genome reconstruction. Our aim was to create a benchmark dataset under controlled settings, minimizing potential biases introduced by specific software. This package also includes MGLEX benchmark scripts.</p>
Data from "Asymmetries in behavioral and neural responses to spectral cues demonstrate the generality of auditory looming bias"
<p>Supporting material for Baumgartner et al. (2017): "Asymmetries in behavioral and neural responses to spectral cues demonstrate the generality of auditory looming bias" in Proc Natl Acad Sci USA; www.pnas.org/cgi/doi/10.1073/pnas.1703247114</p>
Cryo-4D-STEM datasets on cells and cellular organelles for demonstrating a dose-Efficient cryo-EM technique: tilt-Corrected Scanning Transmission Electron Microscopy
<p>This upload contains three 4D-STEM datasets in .raw format for demonstrating a dose-efficient cryo-EM technique for thick samples: tilt-corrected Scanning Transmission Electron Microscopy (tcBF-STEM). The dataset dimension is 128130256*256. Data were acquired on vitrified intact E.coli cells and isolated human cell organelles. This upload also contains the EFTEM images in .mrc acqired in the same ROI as the 4D-STEM dataset. </p> <p>It also contains analysis of the manuscript's Fig 3 and Ext. data fig 8. </p>
Intelligent Energy Systems Ontology: Local flexibility market and power system co-simulation demonstration
<p>The Intelligent Energy Systems Ontology (IESO) provides semantic interoperability within a society of multi-agent systems (MAS) developed in the scope of power and energy systems (PES). It leverages the knowledge from existing and publicly available semantic models developed for specific PES subdomains to accomplish a shared vocabulary among the agents of the MAS community, overcoming heterogeneity among the reused ontologies. IESO provides agents with semantic reasoning, constraints validation, and data uniformization. The use of IESO is demonstrated through the simulation of the management of a rural distribution network, considering the validation of the grid’s technical constraints. This dataset publishes files demonstrating: i) a snapshot of the initial semantic knowledge base (KB); ii) queries to the KB to get services inputs; iii) conversions between syntactic and semantic models; <br> iv) constraints validations; v) automatic conversion of units of measure.</p>
Demonstration of semantic and inter-input constraints on software in OWL 2 and SPARQL for fulfilling the M1 Machine FAIR Use Case
<p>This video demonstrates using hypothetical examples how to (1) find a valid dataset for input into a software using OWL 2 classification inference, (2) validly combine two software using OWL 2 subsumption inference to infer that the output of software 1 is valid input to software 2, and (3) combine OWL 2 inference with a SPARQL query to find two datasets that satisfy a software's inter-input constraints.</p>
Auto-MISCHBARES: Tutorial & Demonstration
<p>Tutorial and demonstration of Auto-MISCHBARES.</p> <p> </p> <p>Additional links:</p> <p>MISCHBARES: <a title="MISCHBARES" href="https://github.com/fuzhanrahmanian/MISCHBARES">https://github.com/fuzhanrahmanian/MISCHBARES</a></p> <p>MADAP: <a title="MADAP" href="https://github.com/fuzhanrahmanian/MADAP">https://github.com/fuzhanrahmanian/MADAP</a></p> <p>HELAO: <a title="HELAO" href="https://github.com/helgestein/helao-pub">https://github.com/helgestein/helao-pub</a></p> <p> </p>
Data for: D3.6 - Assessment of organoleptic and nutritional quality of fish products from the demonstration tests
<p>Data for: D3.6 - Assessment of organoleptic and nutritional quality of fish products from the demonstration tests </p> <p>https://ifishienci.eu/wp-content/uploads/2024/01/iFishIENCi_D3.6.pdf</p> <p>Corresponding Author</p> <p>Name: Anneli Rost<br>ttz Bremerhaven, Germany<br>Address: Knurrhahnstraß 22-24 /Packhalle X 27572 Bremerhaven<br>Email: arost@ttz-bremerhaven.de</p>
Data from: Insights from a 31-year study demonstrate an inverse correlation between recreational activities and red deer fecundity, with body weight as a mediator
<p>Human activity is omnipresent in our landscapes. Animals can perceive risk from humans similar to predation risk, which could affect their fitness. We assessed the influence of the relative intensity of recreational activities on body weight and pregnancy rates of red deer (<em>Cervus elaphus</em>) between 1985 and 2015. We hypothesized that stress, as a result of recreational activities, affects pregnancy rates of red deer directly and indirectly via a reduction in body weight. Furthermore, we expected non-motorized recreational activities to have a larger negative effect on both body weight and fecundity, compared to motorized recreational activities. The intensity of recreational activities was recorded through visual observations. We obtained pregnancy data from female red deer that were shot during the regular hunting season. Additionally, age and body weight were determined through post-mortem examination. We used two generalized linear mixed models (GLMM) to test the effect of different types of recreation on 1) pregnancy rates and 2) body weight of red deer. Recreation had a direct negative correlation with the fecundity of red deer, with body weight as a mediator as expected. Besides, we found a negative effect of non-motorized recreation on fecundity and body weight and no significant effect of motorized recreation. Our results support the concept of humans as an important stressor affecting wild animal populations at a population level and plead to regulate recreational activities in protected areas that are sensitive. The fear humans induce in large-bodied herbivores and its consequences for fitness may have strong implications for animal populations.</p>
Audio files for spectrum analysis demonstration
<div>This data set consists of 6 real-world audio files (in .wav format, 48000Hz, mono) that are carefully crafted as examples for spectral analysis (i.e for teaching or as sample test data for algorithms).</div> <div> </div> <div>The files have clear discernible sound with an added true random background ambient noise (composed of: distant fan noise + distant street traffic + close harddisk clicking noise). The sound is clearly discernible by a human, despite the noise. </div> <div> </div> <div>- There are some musical sounds (the note G3 on several instruments; fundamental frequency 196Hz) on a tubular bell, classical piano, trumpet, violin. The sounds of the instrument was generated from MIDI banks with FluidSynth software, played on a loudspeaker and re-recorded with an analogical microphone (with the ambient noises). Audio processing was performed with Tenacity software;</div> <div>- Sample from human speech (wovel “o”), with the same processing as above; </div> <div>- The “Noise” file is purely digitally generated (white noise).</div> <div> </div> <div> </div> <div>Each audio set is composed of three files: </div> <div>- The audio file (*.wav), each sampled at 48000 Hz, Mono. </div> <div>- an amplitude file (*_amplitude.csv, corresponding linear amplitudes recorded by the microphone of the .wav file). Numeric format in simple text format (.csv) with labeled column names. </div> <div>-a spectrum file (*_spectrum.csv, frequency/amplitude(dB) ) with the results of a FFT (Fast Fourier Transform). Numeric format in simple text format (.csv) with labeled column names. </div> <div> </div> <div>Detailed description of each set is provided below.</div> <div> </div> <div> <ul> <li><strong>Bell_G3.wav</strong></li> <li>Bell_G3_amplitude.csv:<br>Length processed: 113851 samples 2.37190 seconds.<br>Sample Rate: 48000 Hz. <br>Sample values on linear scale. 1 channel (mono).<br>Length processed: 113851 samples, 2.37190 seconds.<br>Peak amplitude: 0.59001 (linear) -4.58276 dB. <br>Unweighted RMS: -19.87796 dB.<br>DC offset: 0.00069 linear, -63.18732 dB.</li> <li>Bell_G3_spectrum.csv:<br>FFT transform (Hz / dB)</li> </ul> </div> <div> <ul> <li><strong>Noise.wav</strong></li> <li>Noise_amplitude.csv:<br>Length processed: 31765 samples 0.66177 seconds.<br>Sample Rate: 48000 Hz. <br>Sample values on linear scale. 1 channel (mono).<br>Length processed: 31765 samples, 0.66177 seconds.<br>Peak amplitude: 0.52797 (linear) -5.54788 dB.<br>Unweighted RMS: -16.61153 dB.<br>DC offset: -0.00013 linear, -77.53051 dB</li> <li>Noise_spectrum.csv)<br>FFT transform (Hz / dB)</li> </ul> </div> <div> <ul> <li><strong>Piano_G3.wav</strong></li> <li>Piano_G3_amplitude.csv:<br>Sample Rate: 48000 Hz.<br>Sample values on linear scale. 1 channel (mono).<br>Length processed: 133063 samples, 2.77215 seconds.<br>Peak amplitude: 0.41070 (linear) -7.72946 dB.<br>Unweighted RMS: -23.95101 dB.<br>DC offset: 0.00030 linear, -70.58058 dB.</li> <li>Piano_G3_spectrum.csv:<br>FFT transform (Hz / dB)</li> </ul> </div> <div> <ul> <li><strong>Trumpet_G3.wav</strong></li> <li>Trumpet_G3_amplitude.csv:<br>Sample Rate: 48000 Hz.<br>Sample values on linear scale. 1 channel (mono).<br>Length processed: 66931 samples, 1.39440 seconds.<br>Peak amplitude: 0.29551 (linear) -10.58853 dB. <br>Unweighted RMS: -22.00611 dB.<br>DC offset: 0.00076 linear, -62.39740 dB.</li> <li>Trumpet_G3_spectrum.csv:<br>FFT transform (Hz / dB)</li> </ul> </div> <div> <ul> <li><strong>Violin_G3.wav</strong></li> <li>Violin_G3_amplitude.csv:<br>Sample Rate: 48000 Hz.<br>Sample values on linear scale. 1 channel (mono).<br>Length processed: 59252 samples, 1.23442 seconds.<br>Peak amplitude: 0.41633 (linear) -7.61119 dB.<br>Unweighted RMS: -18.36738 dB.<br>DC offset: 0.00021 linear, -73.46784 dB.</li> <li>Violin_G3_spectrum.csv:<br>FFT transform (Hz / dB)</li> </ul> </div> <div> <ul> <li><strong>Wovel_O.wav</strong></li> <li>Wovel_O_amplitude.csv<br>Sample Rate: 48000 Hz.<br>Sample values on linear scale. 1 channel (mono).<br>Length processed: 4975 samples, 0.10365 seconds.<br>Peak amplitude: 0.46174 (linear) -6.71214 dB. <br>Unweighted RMS: -14.88086 dB.<br>DC offset: 0.00009 linear, -80.54917 dB.</li> <li>Wovel_O_spectrum.csv:<br>FFT transform (Hz / dB)</li> </ul> </div> <div> </div> <div>These files are created by A. Iftime and released under Creative Commons Licence, 2024. </div> <div> </div> <div>You might cite the dataset as: </div> <div>“Audio files for spectrum analysis demonstration” [dataset] (2024), in “Medical Biophysics for 1st year medical students”, by Călinescu O., Babeș R., Iftime A., Băran I., Ionescu D., Ganea C., in publishing </div>
Demonstration of "RO-Crate for Testbeds: Automated Packaging of Experimental Results"
<p>Demonstrative experiment data of the paper "RO-Crate for Testbeds: Automated Packaging of Experimental Results".</p> <p>Demonstrator shows experimental artifacts as a RO-Crate package.</p>
Demonstrations of witness visualization using the Witness Visualizer Tool
<p>We have three datasets that display visualized <a href="https://sv-comp.sosy-lab.org/">SVCOMP</a> witnesses generated with the help of the <a href="https://github.com/ispras/cv">Witness Visualizer tool</a>. Each dataset comprises two directories: <code>witnesses</code>, which contains the original witnesses provided by SVCOMP tools, and <code>visualization</code>, which contains our visual representations of the respective witnesses in HTML format. The visualization file name contains the prefix <code>error_trace-</code>, for example, <code>error_trace-witness.2ls.html</code> corresponds to a witness named <code>witness.2ls.graphml</code>.</p> <h3>1. Overall thoroughness for all SVCOMP tools (<a href="../records/10988025/files/dataset_1.zip?download=1" target="_blank" rel="noopener">dataset_1.zip</a>)</h3> <p>This dataset includes a single random witness for each SVCOMP tool, accompanied by its corresponding visualization. The visualizations showcase the various witness elements such as function calls, conditions, assumptions, thread specifics, and other operations. Cells marked with <code>+/-</code> indicate that some elements were present in the error trace, but not all of them. All witnesses are presented in the table below:</p> <table> <tbody> <tr> <td>Witness</td> <td>SV-COMP Tool</td> <td>Function calls</td> <td>Threads</td> <td>Assumptions</td> <td>Conditions</td> <td>Link to sources</td> </tr> <tr> <td>witness.2ls.graphml</td> <td>2LS</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.aprove.graphml</td> <td>AProVE (2022)</td> <td>-</td> <td>-</td> <td>-</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.brick.graphml</td> <td>BRICK</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.bubaak.graphml</td> <td>Bubaak</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.cbmc.graphml</td> <td>CBMC</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.cpa-bam-bnb.graphml</td> <td>CPA-BAM-BnB</td> <td>+</td> <td>-</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.cpa-bam-smg.graphml</td> <td>CPA-BAM-SMG</td> <td>+</td> <td>-</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.cpalockator.graphml</td> <td>CPALockator</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.cpachecker.graphml</td> <td>CPAChecker</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.crux.graphml</td> <td>Crux</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.cseq.graphml</td> <td>Cseq</td> <td>+</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.dartagnan.graphml</td> <td>Dartagnan</td> <td>-</td> <td>+</td> <td>-</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.deagle.graphml</td> <td>Deagle</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>DIVINE (until 2022)</td> <td>empty</td> </tr> <tr> <td>-</td> <td>EBF</td> <td>empty</td> </tr> <tr> <td>witness.esbmc-incr.graphml</td> <td>ESBMC-incr</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.esbmc-kind.graphml</td> <td>ESBMC-kind</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>Frama-C-SV</td> <td>empty</td> </tr> <tr> <td>witness.gazer-theta.graphml</td> <td>Gazer-Theta</td> <td>+</td> <td>-</td> <td>+</td> <td>-</td> <td>wrong path</td> </tr> <tr> <td>witness.gdart.graphml</td> <td>Gdart-LLVM</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>Goblint</td> <td>empty</td> </tr> <tr> <td>witness.graves_cpa.graphml</td> <td>Graves-CPA</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.graves_par.graphml</td> <td>Graves-Par</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>-</td> <td>Infer</td> <td>empty</td> </tr> <tr> <td>witness.korn.graphml</td> <td>Korn</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.lart.graphml</td> <td>LART (2022)</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.lazy-cseq.graphml</td> <td>Lazy-CSeq</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.lfchecker.graphml</td> <td>LF-checker</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>Locksmith</td> <td>empty</td> </tr> <tr> <td>-</td> <td>Mopsa</td> <td>empty</td> </tr> <tr> <td>witness.pesco_cpa.graphml</td> <td>PeSCo-CPA</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.pichecker.graphml</td> <td>PIChecker</td> <td>+</td> <td>-</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.pinaka.graphml</td> <td>Pinaka</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.predator.graphml</td> <td>PredatorHP</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>SESL (2022)</td> <td>empty</td> </tr> <tr> <td>witness.smack.graphml</td> <td>SMACK (until 2022)</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.symbiotic.graphml</td> <td>Symbiotic</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.theta.graphml</td> <td>Theta</td> <td>different format</td> </tr> <tr> <td>witness.uatomozer.graphml</td> <td>UAutomizer</td> <td>+/-</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.ucutter.graphml</td> <td>UgemCutter</td> <td>+/-</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.ukojak.graphml</td> <td>UKojak</td> <td>+/-</td> <td>-</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.utaipan.graphml</td> <td>UTaipan</td> <td>+/-</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.veriabs.graphml</td> <td>VeriAbs</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>wrong path</td> </tr> <tr> <td>witness.veriabsl.graphml</td> <td>VeriAbsL</td> <td>+</td> <td>-</td> <td>+</td> <td>+</td> <td>wrong path</td> </tr> <tr> <td>witness.verifuzz.graphml</td> <td>VeriFuzz</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.verioover.graphml</td> <td>VeriOover</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> </tbody> </table> <h3>2. Thoroughness by property (<a href="../records/10988025/files/dataset_2.zip?download=1" target="_blank" rel="noopener">dataset_2.zip</a>)</h3> <p>This dataset comprises a selected witness for each SVCOMP property (ReachSafety, MemSafety, Termination, NoOverflow, ConcurrencySafety). The witnesses are presented in the following table:</p> <p> </p> <table> <tbody> <tr> <td>Witness</td> <td>SV-COMP Tool</td> <td>Property</td> <td>Mandatory elements</td> <td>Description</td> </tr> <tr> <td>witness.smg_memory.graphml</td> <td>CPA-BAM-SMG</td> <td>MemSafety</td> <td>Assumptions / conditions, function calls</td> <td>There is a double free operation. Employing function calls <code>append </code>aids in comprehending the structure of the list, while assumptions reveal which branch was chosen.</td> </tr> <tr> <td>witness.graves_overflow.graphml</td> <td>Graves-CPA</td> <td>NoOverflow</td> <td>Assumptions / conditions</td> <td>The witness showcases an explicit (<code>-2147483648</code>, which represents the minimal value for the <code>int</code> type), which has the potential to cause overflow in specific program.</td> </tr> <tr> <td>witness.cpachecker_termination.graphml</td> <td>CPAChecker</td> <td>NoTermination</td> <td>Assumptions / conditions</td> <td>There is a condition leading to an infinite loop.</td> </tr> <tr> <td>witness.cpachecker_unreach.graphml</td> <td>CPAChecker</td> <td>ReachSafety</td> <td>Function calls</td> <td>The error trace indicates a potential scenario where a <code>mutex</code> was unlocked<br>without the corresponding <code>mutex_unlock</code> operation.</td> </tr> <tr> <td>witness.cpachecker_conc.graphml</td> <td>CPAChecker</td> <td>ConcurrencySafety</td> <td>Function calls, thread operations</td> <td>The error trace illustrates the creation of threads and highlights the assignments made within each thread that ultimately resulted in the violation of the property.</td> </tr> </tbody> </table> <div> <h3>3. Known bug (<a href="../records/10988025/files/dataset_2.zip?download=1" target="_blank" rel="noopener">dataset_3.zip</a>)</h3> <p>This dataset contains witnesses for a known bug from SVCOMP (<code>linux-3.14--drivers--usb--misc--adutux.ko.cil.i</code>) involving a data race on <code>dev->udev</code>, where concurrent writes occur without corresponding locks. Only two tools were able to solve the corresponding verification task: ESBMC-kind and CPALockator. The ESBMC error trace (<code>witness.esbmc_2020.graphml</code>) includes only thread specifics and assumptions, while the CPALockator witness (<code>witness.lockator.graphml</code>) comprises all witness elements and is presented in a human-readable format.</p> <h3>4. Comparison with the validation rate</h3> <p>This section presents a comparison between witness thoroughness and the actual validation rate for each property. We considered all tools that participated in the respective category and generated at least 10 error traces, then calculated the validation rate. This comparison demonstrates how effectively thoroughness can approximate the validation rate. The following tables provide details for each property, with the relevant elements used to calculate thoroughness highlighted:</p> <ul> <li>MemSafety property:</li> </ul> <table> <tbody> <tr> <td>SV-COMP Tool</td> <td><strong>Function calls</strong></td> <td>Threads</td> <td><strong>Assumptions</strong></td> <td><strong>Conditions</strong></td> <td>Thoroughness</td> <td>Error traces</td> <td>Validation rate</td> </tr> <tr> <td>Bubaak</td> <td><strong>0</strong></td> <td>0</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>33.33</td> <td>64</td> <td>67.19</td> </tr> <tr> <td>CBMC</td> <td><strong>0</strong></td> <td>1</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>33.33</td> <td>27</td> <td>11.11</td> </tr> <tr> <td>CPA-BAM-SMG</td> <td><strong>1</strong></td> <td>0</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>46</td> <td>78.26</td> </tr> <tr> <td>CPAChecker</td> <td><strong>1</strong></td> <td>1</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>37</td> <td>67.57</td> </tr> <tr> <td>ESBMC-kind</td> <td><strong>0</strong></td> <td>1</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>33.33</td> <td>25</td> <td>20</td> </tr> <tr> <td>Graves-CPA</td> <td><strong>1</strong></td> <td>1</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>44</td> <td>56.82</td> </tr> <tr> <td>Graves-Par</td> <td><strong>1</strong></td> <td>1</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>18</td> <td>77.78</td> </tr> <tr> <td>PeSCo-CPA</td> <td><strong>1</strong></td> <td>1</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>37</td> <td>67.57</td> </tr> </tbody> </table> <ul> <li>NoOverflow property:</li> </ul> <table> <tbody> <tr> <td>SV-COMP Tool</td> <td>Function calls</td> <td>Threads</td> <td><strong>Assumptions</strong></td> <td>Conditions</td> <td>Thoroughness</td> <td>Error traces</td> <td>Validation rate</td> </tr> <tr> <td>2LS</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>2071</td> <td>95.7</td> </tr> <tr> <td>Bubaak</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>2233</td> <td>94.67</td> </tr> <tr> <td>CBMC</td> <td>0</td> <td>1</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>3296</td> <td>62.14</td> </tr> <tr> <td>CPAChecker</td> <td>1</td> <td>1</td> <td><strong>1</strong></td> <td>1</td> <td>100</td> <td>196</td> <td>100</td> </tr> <tr> <td>Crux</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>222</td> <td>95.05</td> </tr> <tr> <td>ESBMC-kind</td> <td>0</td> <td>1</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>3296</td> <td>66.69</td> </tr> <tr> <td>Frama-C-SV</td> <td>0</td> <td>0</td> <td><strong>0</strong></td> <td>0</td> <td>0</td> <td>676</td> <td>0</td> </tr> <tr> <td>Graves-Par</td> <td>1</td> <td>1</td> <td><strong>1</strong></td> <td>1</td> <td>100</td> <td>750</td> <td>2</td> </tr> <tr> <td>Infer</td> <td>0</td> <td>0</td> <td><strong>0</strong></td> <td>0</td> <td>0</td> <td>583</td> <td>0</td> </tr> <tr> <td>Pinaka</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>2232</td> <td>100</td> </tr> <tr> <td>Symbiotic</td> <td>0</td> <td>1</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>1418</td> <td>100</td> </tr> <tr> <td>UAutomizer</td> <td>0.5</td> <td>1</td> <td><strong>1</strong></td> <td>1</td> <td>100</td> <td>2222</td> <td>100</td> </tr> <tr> <td>UKojak</td> <td>0.5</td> <td>0</td> <td><strong>1</strong></td> <td>1</td> <td>100</td> <td>168</td> <td>100</td> </tr> <tr> <td>UTaipan</td> <td>0.5</td> <td>1</td> <td><strong>1</strong></td> <td>1</td> <td>100</td> <td>0</td> <td>100</td> </tr> <tr> <td>VeriFuzz</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>185</td> <td>90.81</td> </tr> </tbody> </table> <ul> <li>NoTermination property:</li> </ul> <table> <tbody> <tr> <td>SV-COMP Tool</td> <td>Function calls</td> <td>Threads</td> <td><strong>Assumptions</strong></td> <td><strong>Conditions</strong></td> <td>Thoroughness</td> <td>Error traces</td> <td>Validation rate</td> </tr> <tr> <td>2LS</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>50</td> <td>663</td> <td>69.08</td> </tr> <tr> <td>Bubaak</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>50</td> <td>578</td> <td>34.78</td> </tr> <tr> <td>CPAChecker</td> <td>1</td> <td>1</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>501</td> <td>97.01</td> </tr> <tr> <td>Symbiotic</td> <td>0</td> <td>1</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>50</td> <td>591</td> <td>52.96</td> </tr> <tr> <td>UAutomizer</td> <td>0.5</td> <td>1</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>512</td> <td>98.24</td> </tr> <tr> <td>VeriFuzz</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>50</td> <td>492</td> <td>71.34</td> </tr> </tbody> </table> <ul> <li>ReachSafety property:</li> </ul> <table> <tbody> <tr> <td>SV-COMP Tool</td> <td><strong>Function calls</strong></td> <td>Threads</td> <td>Assumptions</td> <td>Conditions</td> <td>Thoroughness</td> <td>Error traces</td> <td>Validation rate</td> </tr> <tr> <td>Bubaak</td> <td><strong>0</strong></td> <td>0</td> <td>1</td> <td>0</td> <td>0</td> <td>24</td> <td>54.17</td> </tr> <tr> <td>CBMC</td> <td><strong>0</strong></td> <td>1</td> <td>1</td> <td>0</td> <td>0</td> <td>392</td> <td>1.28</td> </tr> <tr> <td>CPA-BAM-BnB</td> <td><strong>1</strong></td> <td>0</td> <td>1</td> <td>1</td> <td>100</td> <td>69</td> <td>85.51</td> </tr> <tr> <td>CPA-BAM-SMG</td> <td><strong>1</strong></td> <td>0</td> <td>1</td> <td>1</td> <td>100</td> <td>67</td> <td>85.07</td> </tr> <tr> <td>CPAChecker</td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>1</td> <td>100</td> <td>45</td> <td>88.89</td> </tr> <tr> <td>Crux</td> <td><strong>0</strong></td> <td>0</td> <td>1</td> <td>0</td> <td>0</td> <td>1572</td> <td>0.13</td> </tr> <tr> <td>ESBMC-kind</td> <td><strong>0</strong></td> <td>1</td> <td>1</td> <td>0</td> <td>0</td> <td>64</td> <td>21.88</td> </tr> <tr> <td>Graves-CPA</td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>1</td> <td>100</td> <td>66</td> <td>87.88</td> </tr> <tr> <td>Graves-Par</td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>1</td> <td>100</td> <td>24</td> <td>58.33</td> </tr> <tr> <td>PeSCo-CPA</td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>1</td> <td>100</td> <td>63</td> <td>85.71</td> </tr> </tbody> </table> <ul> <li>ConcurrencySafety property:</li> </ul> <table> <tbody> <tr> <td>SV-COMP Tool</td> <td><strong>Function calls</strong></td> <td><strong>Threads</strong></td> <td>Assumptions</td> <td>Conditions</td> <td>Thoroughness</td> <td>Error traces</td> <td>Validation rate</td> </tr> <tr> <td>CBMC</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>50</td> <td>277</td> <td>87</td> </tr> <tr> <td>CPA-Lockator</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>100</td> <td>83</td> <td>26.51</td> </tr> <tr> <td>CPAChecker</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>100</td> <td>257</td> <td>100</td> </tr> <tr> <td>Cseq</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>100</td> <td>277</td> <td>94.58</td> </tr> <tr> <td>Dartagnan</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>0</td> <td>0</td> <td>50</td> <td>281</td> <td>92.17</td> </tr> <tr> <td>Deagle</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>50</td> <td>280</td> <td>96.07</td> </tr> <tr> <td>DIVINE</td> <td><strong>0</strong></td> <td><strong>0</strong></td> <td>0</td> <td>0</td> <td>0</td> <td>230</td> <td>80.87</td> </tr> <tr> <td>EBF</td> <td><strong>0</strong></td> <td><strong>0</strong></td> <td>0</td> <td>0</td> <td>0</td> <td>282</td> <td>89.01</td> </tr> <tr> <td>ESBMC-incr</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>50</td> <td>68</td> <td>79.41</td> </tr> <tr> <td>ESBMC-kind</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>50</td> <td>263</td> <td>89.73</td> </tr> <tr> <td>Graves-CPA</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>100</td> <td>261</td> <td>99.23</td> </tr> <tr> <td>Graves-Par</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>100</td> <td>28</td> <td>100</td> </tr> <tr> <td>Infer</td> <td><strong>0</strong></td> <td><strong>0</strong></td> <td>0</td> <td>0</td> <td>0</td> <td>634</td> <td>0</td> </tr> <tr> <td>Lazy-CSeq</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>100</td> <td>274</td> <td>94.89</td> </tr> <tr> <td>LF-checker</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>50</td> <td>286</td> <td>85.31</td> </tr> <tr> <td>PeSCo-CPA</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>100</td> <td>256</td> <td>100</td> </tr> <tr> <td>PIChecker</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>1</td> <td>1</td> <td>50</td> <td>269</td> <td>98.14</td> </tr> <tr> <td>Symbiotic</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>50</td> <td>110</td> <td>92.73</td> </tr> <tr> <td>UAutomizer</td> <td><strong>0.5</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>75</td> <td>297</td> <td>94.95</td> </tr> <tr> <td>UgemCutter</td> <td><strong>0.5</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>75</td> <td>283</td> <td>96.47</td> </tr> <tr> <td>UTaipan</td> <td><strong>0.5</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>75</td> <td>293</td> <td>96.25</td> </tr> </tbody> </table> <h3>5. Overall distance for all possible combinations of elements for thoroughness</h3> <p>This section presents the overall difference (i.e., the sum of differences between witness thoroughness and validation rates for each tool) when thoroughness is calculated based on all possible combinations of witness elements (assumptions, conditions, thread specifics, and function calls). The set of witnesses is the same as in the previous section. The following tables provide details for each property, with the minimum difference highlighted:</p> <ul> <li>MemSafety property:</li> </ul> <table> <tbody> <tr> <td>Combination</td> <td>Overall difference</td> </tr> <tr> <td>Function calls</td> <td>250.3</td> </tr> <tr> <td>Thread specifics</td> <td>444.6</td> </tr> <tr> <td>Assumptions</td> <td>353.7</td> </tr> <tr> <td>Conditions</td> <td>250.3</td> </tr> <tr> <td>Function calls, Thread specifics</td> <td>294.6</td> </tr> <tr> <td>Assumptions, Function calls</td> <td>238.08</td> </tr> <tr> <td>Conditions, Function calls</td> <td>250.3</td> </tr> <tr> <td>Assumptions, Thread specifics</td> <td>344.6</td> </tr> <tr> <td>Conditions, Thread specifics</td> <td>294.6</td> </tr> <tr> <td>Assumptions, Conditions</td> <td>238.08</td> </tr> <tr> <td>Assumptions, Function calls, Thread specifics</td> <td>277.94</td> </tr> <tr> <td>Conditions, Function calls, Thread specifics</td> <td>244.59</td> </tr> <tr> <td><strong>Assumptions, Conditions, Function calls</strong></td> <td><strong>221.41</strong></td> </tr> <tr> <td>Assumptions, Conditions, Thread specifics</td> <td>277.94</td> </tr> <tr> <td>Assumptions, Conditions, Function calls, Thread specifics</td> <td>244.6</td> </tr> </tbody> </table> <ul> <li>NoOverflow property:</li> </ul> <table> <tbody> <tr> <td>Combination</td> <td>Overall difference</td> </tr> <tr> <td>Function calls</td> <td>953.06</td> </tr> <tr> <td>Thread specifics</td> <td>745.4</td> </tr> <tr> <td><strong>Assumptions</strong></td> <td><strong>192.94</strong></td> </tr> <tr> <td>Conditions</td> <td>803.06</td> </tr> <tr> <td>Function calls, Thread specifics</td> <td>778.06</td> </tr> <tr> <td>Assumptions, Function calls</td> <td>478.06</td> </tr> <tr> <td>Conditions, Function calls</td> <td>878.06</td> </tr> <tr> <td>Assumptions, Thread specifics</td> <td>445.4</td> </tr> <tr> <td>Conditions, Thread specifics</td> <td>703.06</td> </tr> <tr> <td>Assumptions, Conditions</td> <td>403.06</td> </tr> <tr> <td>Assumptions, Function calls, Thread specifics</td> <td>528.8</td> </tr> <tr> <td>Conditions, Function calls, Thread specifics</td> <td>786.41</td> </tr> <tr> <td>Assumptions, Conditions, Function calls</td> <td>586.43</td> </tr> <tr> <td>Assumptions, Conditions, Thread specifics</td> <td>478.79</td> </tr> <tr> <td>Assumptions, Conditions, Function calls, Thread specifics</td> <td>590.56</td> </tr> </tbody> </table> <ul> <li>NoTermination property:</li> </ul> <table> <tbody> <tr> <td>Combination</td> <td>Overall difference</td> </tr> <tr> <td>Function calls</td> <td>279.39</td> </tr> <tr> <td>Thread specifics</td> <td>226.99</td> </tr> <tr> <td>Assumptions</td> <td>176.59</td> </tr> <tr> <td>Conditions</td> <td>232.91</td> </tr> <tr> <td>Function calls, Thread specifics</td> <td>204.39</td> </tr> <tr> <td><strong>Assumptions, Function calls</strong></td> <td><strong>84.83</strong></td> </tr> <tr> <td>Conditions, Function calls</td> <td>254.39</td> </tr> <tr> <td>Assumptions, Thread specifics</td> <td>107.43</td> </tr> <tr> <td>Conditions, Thread specifics</td> <td>182.91</td> </tr> <tr> <td>Assumptions, Conditions</td> <td>63.35</td> </tr> <tr> <td>Assumptions, Function calls, Thread specifics</td> <td>106.82</td> </tr> <tr> <td>Conditions, Function calls, Thread specifics</td> <td>212.73</td> </tr> <tr> <td>Assumptions, Conditions, Function calls</td> <td>112.74</td> </tr> <tr> <td>Assumptions, Conditions, Thread specifics</td> <td>93.67</td> </tr> <tr> <td>Assumptions, Conditions, Function calls, Thread specifics</td> <td>116.89</td> </tr> </tbody> </table> <ul> <li>ReachSafety property:</li> </ul> <table> <tbody> <tr> <td>Combination</td> <td>Overall difference</td> </tr> <tr> <td><strong>Function calls</strong></td> <td><strong>212.74</strong></td> </tr> <tr> <td>Thread specifics</td> <td>607.58</td> </tr> <tr> <td>Assumptions</td> <td>557.82</td> </tr> <tr> <td>Conditions</td> <td>312.74</td> </tr> <tr> <td>Function calls, Thread specifics</td> <td>357.58</td> </tr> <tr> <td>Assumptions, Function calls</td> <td>316.16</td> </tr> <tr> <td>Conditions, Function calls</td> <td>262.74</td> </tr> <tr> <td>Assumptions, Thread specifics</td> <td>507.32</td> </tr> <tr> <td>Conditions, Thread specifics</td> <td>407.58</td> </tr> <tr> <td>Assumptions, Conditions</td> <td>366.16</td> </tr> <tr> <td>Assumptions, Function calls, Thread specifics</td> <td>373.98</td> </tr> <tr> <td>Conditions, Function calls, Thread specifics</td> <td>307.56</td> </tr> <tr> <td>Assumptions, Conditions, Function calls</td> <td>299.48</td> </tr> <tr> <td>Assumptions, Conditions, Thread specifics</td> <td>407.32</td> </tr> <tr> <td>Assumptions, Conditions, Function calls, Thread specifics</td> <td>332.32</td> </tr> </tbody> </table> <ul> <li>ConcurrencySafety property:</li> </ul> <table> <tbody> <tr> <td>Combination</td> <td>Overall difference</td> </tr> <tr> <td>Function calls</td> <td>1016.62</td> </tr> <tr> <td>Thread specifics</td> <td>442.72</td> </tr> <tr> <td>Assumptions</td> <td>430.78</td> </tr> <tr> <td>Conditions</td> <td>980.44</td> </tr> <tr> <td>Function calls, Thread specifics</td> <td>637.9</td> </tr> <tr> <td>Assumptions, Function calls</td> <td>641.62</td> </tr> <tr> <td>Conditions, Function calls</td> <td>980.78</td> </tr> <tr> <td><strong>Assumptions, Thread specifics</strong></td> <td><strong>427.06</strong></td> </tr> <tr> <td>Conditions, Thread specifics</td> <td>626.72</td> </tr> <tr> <td>Assumptions, Conditions</td> <td>630.44</td> </tr> <tr> <td>Assumptions, Function calls, Thread specifics</td> <td>512.89</td> </tr> <tr> <td>Conditions, Function calls, Thread specifics</td> <td>735.42</td> </tr> <tr> <td>Assumptions, Conditions, Function calls</td> <td>739.14</td> </tr> <tr> <td>Assumptions, Conditions, Thread specifics</td> <td>510.03</td> </tr> <tr> <td>Assumptions, Conditions, Function calls, Thread specifics</td> <td>614.56</td> </tr> </tbody> </table> </div>
Historical baleen plates indicate that once abundant Antarctic blue and fin whales demonstrated distinct migratory and foraging strategies
<p>Southern hemisphere blue (<em>Balaenoptera musculus intermedia</em>) and fin (<em>Balaenoptera physalus</em>) whales are the largest predators in the Southern Ocean, with similarities in morphology and distribution. Yet, understanding of their life history and foraging is limited due to current low abundances and limited ecological data. To address these gaps, historic Antarctic blue (n = 5) and fin (n = 5) whale baleen plates, collected in 1947–1948 and recently rediscovered in the Smithsonian National Museum of Natural History, were analyzed for bulk (δ<sup>13</sup>C and δ<sup>15</sup>N) stable isotopes. Regular oscillations in isotopic ratios, interpreted as annual cycles, revealed that baleen plates contain approximately six years (14.35 ± 1.20 cm yr<sup>–1</sup>) of life history data in blue whales and four years (16.52 ± 1.86 cm yr<sup>–1</sup>) in fin whales. Isotopic results suggest that: 1) in the 1940s, blue and fin whales fed at the same trophic level but in slightly different habitats, 2) fin whales appear to have had more regular annual migrations, and 3) fin whales may have migrated to ecologically distinct sub-Antarctic waters annually while some blue whales may have resided year-round in the Southern Ocean. These results reveal differences in ecological niche and life history strategies between Antarctic blue and fin whales during a period when their populations were more abundant than today, and before major human-driven climatic changes occurred in the Southern Ocean.</p>
Hands-On! Video demonstration - Paper folding components
<p>This video demonstrates all possible actions for the <strong>Paper folding task</strong>, providing real-time examples to help you accurately identify and assess fine motor performance. Use this video alongside the written descriptions and images from the <strong>Hands-On!</strong> observation tool for a comprehensive understanding of this fine motor task. For more information on the use of this video, please refer to <strong>Hands-On!</strong> via: <a href="https://doi.org/10.5281/zenodo.14185207">https://doi.org/10.5281/zenodo.14185207</a></p>
Hands-On! Video demonstration - Writing components
<p><span>This video demonstrates all possible actions for the <strong>Writing task</strong>, providing real-time examples to help you accurately identify and assess fine motor performance. Use this video alongside the written descriptions and images from the <strong><span>Hands-On!</span></strong> observation tool for a comprehensive understanding of this fine motor task. For more information on the use of this video, please refer to <strong><span>Hands-On!</span></strong> via: <a href="https://doi.org/10.5281/zenodo.14185207">https://doi.org/10.5281/zenodo.14185207</a> </span></p>
Hands-On! Video demonstration - Paper cutting components
<p><span>This video demonstrates all possible actions for the <strong>Paper cutting task</strong>, providing real-time examples to help you accurately identify and assess fine motor performance. Use this video alongside the written descriptions and images from the <strong><span>Hands-On!</span></strong> observation tool for a comprehensive understanding of this fine motor task. For more information on the use of this video, please refer to <strong><span>Hands-On!</span></strong> via: <a href="https://doi.org/10.5281/zenodo.14185207">https://doi.org/10.5281/zenodo.14185207</a> </span></p>
Demonstrations for imitation learning for the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives"
<p>Demonstrations for the coathanger experiment in the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives". https://elib.dlr.de/205110/</p>
Variation in the location and timing of experimental severing demonstrates that the persistent rhizome serves multiple functions in a clonal forest understory herb
<p>1. In clonal plants, persistent rhizomes can serve multiple purposes, including resource storage, modulation of heterogenous resource distributions, maintenance of bud banks and promotion of recovery from disturbance. Clonal plants are commonly long-lived and, in temperate zones, often exhibit organ preformation. Thus, investigations of how the timing of disturbance to the rhizome affects plant performance must occur over multiple growing seasons, but these types of studies are rare.</p> <p>2. We conducted a field experiment to examine how the persistent rhizome supports the existing shoot, new ramet production, and recovery from damage using mayapple (<i>Podophyllum peltatum</i>; Berberidaceae), a common herbaceous perennial of low-light forest understories in Eastern North America. Mayapple maintains a long-lived rhizome and exhibits a developmentally-programmed seasonal pattern of resource transport and new ramet initiation. We varied both the position and timing of rhizome severing in rhizome systems with terminal sexual or vegetative shoots, and tracked plants for two years following severing.</p> <p>3. The location and timing of severing affected both plant persistence (production of new shoots) and performance (leaf area), with effects differing for new shoots at the front vs. the back of the rhizome system. Across years, severing location and past years' shoot size influenced plant persistence and performance, while the effect of timing of severing diminished; initial sexual status had little effect on rhizome system response that was not accounted for by initial leaf area. Severing generally led to the establishment of two independent rhizome systems. Relative to unmanipulated control systems, these two systems had more total leaf area, but less average leaf area per system.</p> <p>4. Synthesis. Our results point to the rhizome as a resource integrator that affects plant responses to disturbance immediately following damage and in subsequent growing seasons. Rhizome bud age and/or subtending rhizome size, and developmental program influence responses to disturbance. While the effects of experimental disturbance on plant performance decreased two years after disturbance, further long-term investigation is needed to fully understand the demographic consequences of damage to persistent rhizomes. </p>
SMDemoBioref: Data from the University of Florida Stan Mayfield Demonstration Biorefinery
<p>This dataset includes previously unpublished information collected during the operation of the University of Florida Stan Mayfield Demonstration Biorefinery.</p> <p><strong>Terms of Use:</strong> These data are provided as is, without any warranties of any kind. A bibliographic citation should be included in the References section of publications and other media to acknowledge the authors of this dataset. Proper citations include the authors, title, publisher, and Digital Object Identifier (DOI) and will allow the products to be discovered and re-used by others.</p>
Demonstration of kilohertz operation of hydrodynamic optical-field-ionized plasma channels
<p>The compressed file contains the raw data used in the publication "Demonstration of kilohertz operation of Hydrodynamic Optical-Field-Ionized Plasma Channels," <em>Physical Review Accelerators and Beams </em><strong>25</strong>, 011301 (2022) DOI: 10.1103/PhysRevAccelBeams.25.011301.</p> <p>Further information on the organization of the data is provided in the README file included in the compressed file.</p> <p> </p>
Natural Killer cells demonstrate distinct eQTL and transcriptome-wide disease associations, highlighting their role in autoimmunity.
<p><strong>Abstract</strong> </p> <p>Natural Killer (NK) cells are innate lymphocytes with central roles in immunosurveillance and are implicated in autoimmune pathogenesis. The degree to which regulatory variants affect NK gene expression is poorly understood. We performed expression quantitative trait locus (eQTL) mapping of negatively selected NK cells from a population of healthy Europeans (n=245). We find a significant subset of genes demonstrate eQTL specific to NK cells and these are highly informative of human disease, in particular autoimmunity. An NK cell transcriptome-wide association study (TWAS) across five common autoimmune diseases identified further novel associations at 27 genes. In addition to these <em>cis</em> observations, we find novel master-regulatory regions impacting expression of <em>trans</em> gene networks at regions including 19q13.4, the Killer cell Immunoglobulin-like Receptor (KIR) Region, <em>GNLY</em>, <em>MC1R</em> and<em> UVSSA</em>. Our findings provide new insights into the unique biology of NK cells, demonstrating markedly different eQTL from other immune cells, with implications for disease mechanisms.</p> <p><strong>Preprint</strong></p> <p>https://www.biorxiv.org/content/10.1101/2021.05.10.443088v1</p> <p><strong>Dataset</strong></p> <p>nk_raw_for_zenodo.txt: Matrix of raw gene expression at 47,209 probes in primary human NK cells from 245 healthy individuals of European ancestry. Gene expression is quantified using the Illumina HumanHT-12 v4 BeadChip gene expression array platform. Column names represent Array Address ID for each probe, and row names represent pseudonymised sample identifiers, which can be matched to sample genotypes. Sample genotypes are available at the European Genome-Phenome Archive with accession ID EGAS00000000109).</p> <p>probes_passing_QC.txt: List of probes passing quality control; probe sequences mapping to a unique genomic locus, and probe sequences not containing common genomic variation (minor allele frequency >1%), n=29,002. Column names are Array Address ID (probeID), Ensembl ID (ensembl), Gene ID (gene), and Illumina probe ID (ilmn). </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.