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1,045 results for “Generated Data”
Read-mapping for next-generation sequencing data (Drosophila melanogaster)
<p>Code, logs and quality-control data for whole-genome resequencing of Sussex-LH<sub>M</sub> and RG <em>Drosophila melanogaster</em>.</p> <p>Mapping code is in the archive lhm_mapping_scripts.zip</p> <p>Mapping logs are in the in the archive lhm_mapping_logs.zip</p> <p>Other zip archives contain the quality control data.</p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p>
Read-mapping for next-generation sequencing data (Wolbachia)
<p>Code, log files and QC data. NCBI SRA accession number SRP091004. Note that sequencing Wolbachia was not a central aim of the project, and was undertaken in order to maximise the amount of information that could be extracted from the raw genome sequence data targetted at the fruit-fly host.</p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p>
Genotype reproducibility testing in next-generation sequencing data
<p>Code, log and results summary for testing the reproducibility of genotypes with three pairs of hemiclones in the Sussex LH<sub>M </sub><em>D.melanogaster </em>population sample. Discovery and genotyping of genomic sequence variants was done using GATK HaplotypeCaller, and Genomestrip. Numerical comparison of genotype calls within each pairs of hemiclone individuals was performed using GATK GenotypeConcordance.</p> <p> </p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p>
Test Data Generation from Business Rules
<p><strong>Overview of Data</strong></p> <p>The site includes data only for the two subjects: Ceu-pacific and JBilling. For both the subjects, the “<em>.model” shows the model created from the business rules obtained from respective websites, and “</em>_HighLevelTests.csv” shows the tests generated. Among csv files, we show tests generated by both BUSTER and Exhaust as well.</p> <p><strong>Paper Abstract</strong></p> <p>Test cases that drive an application under test via its graphical user interface (GUI) consist of sequences of steps that perform actions on, or verify the state of, the application user interface. Such tests can be hard to maintain, especially if they are not properly modularized—that is, common steps occur in many test cases, which can make test maintenance cumbersome and expensive. Performing modularization manually can take up considerable human effort. To address this, we present an automated approach for modularizing GUI test cases. Our approach consists of multiple phases. In the first phase, it analyzes individual test cases to partition test steps into candidate subroutines, based on how user-interface elements are accessed in the steps. This phase can analyze the test cases only or also leverage execution traces of the tests, which involves a cost-accuracy tradeoff. In the second phase, the technique compares candidate subroutines across test cases, and refines them to compute the final set of subroutines. In the last phase, it creates callable subroutines, with parameterized data and control flow, and refactors the original tests to call the subroutines with context-specific data and control parameters. Our empirical results, collected using open-source applications, illustrate the effectiveness of the approach.</p>
Data for article: Time-Resolved Spectroscopic Investigation of Charge Trapping in Carbon Nitrides Photocatalysts for Hydrogen Generation
<p>This is the data presented in the article titled 'Time-Resolved Spectroscopic Investigation of Charge Trapping in Carbon Nitrides Photocatalysts for Hydrogen Generation', published in the Journal of the American Chemical Society. DOI:10.1021/jacs.7b01547</p> <p>http://pubs.acs.org/doi/abs/10.1021/jacs.7b01547</p> <p> </p>
Data for: Multi-generational fitness effects of natural immigration indicate strong heterosis and epistatic breakdown in a wild bird population
<p><span>The fitness of immigrants and their descendants produced within recipient populations fundamentally underpins the genetic </span><span>and population dynamic</span><span> consequences of immigration. </span><span>I</span><span>mmigrants can </span><span>in principle </span><span>induce contrasting genetic effects on fitness across generations, reflecting multi-faceted additive, dominance, and epistatic effects. Y</span><span>et, full multi-generational and sex-specific fitness effects of regular immigration have not been quantified within naturally structured systems, precluding inference on underlying genetic architectures </span><span>and population outcomes</span><span>. We used four decades of song sparrow </span><span>(<em>Melospiza melodia</em>)</span> <span>life-history and pedigree data to quantify fitness of natural immigrants, natives, and their F1, F2, and backcross descendants, and test for evidence of non-additive genetic effects. Values of key fitness components (including adult lifetime reproductive success and zygote survival) of F1 offspring of immigrant-native matings substantially exceeded their parent mean, indicating strong heterosis. Meanwhile, F2 offspring of F1-F1 matings had notably low values, indicating surprisingly strong epistatic breakdown. Further, magnitudes of effects varied among fitness components, and</span> <span>differed between female</span><span>s</span><span> and male</span><span>s</span><span> descendants. These results demonstrate that strong non-additive genetic effects on fitness can arise within </span><span>weakly </span><span>structured </span><span>and fragmented </span><span>populations </span><span>experiencing </span><span>frequent </span><span>natural </span><span>immigration. </span><span>Such effects will substantially affect the net </span><span>degree of effective gene flow and resulting local genetic introgression and adaptation.</span></p>
Fig. 2. Bayesian consensus tree generated from partial 28S in Relationships Of The Heteronchocleidids (Heteronchocleidus, Eutrianchoratus And Trianchoratus) As Inferred From Ribosomal Dna Nucleotide Sequence Data
Fig. 2. Bayesian consensus tree generated from partial 28S rDNA sequences (D1 domain) with Diplectanum spp. and Gyrodactylus spp. as outgroups. Values shown at each node refer to Bayesian (BI) posterior probabilities/maximum likelihood (ML) percentages of the bootstrap values with 100 replicates. Bootstrap values lower than 50 are given as dashes (-).
The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"
<p>The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"</p><p>A video file (minimum play resolution is HD to see the mesh) showing the movement of the LCA throughout the heart cycle and .STL files for 10--100% (increment of 10\%) of the heart cycle phase.</p>
Data for SI-Hg D2 validation report for the calibration of elemental mercury gas generators including information on repeatability, reproducibility and uncertainty evaluation at emission and ambient levels extended to the sub ng/m3 level
<p>In deliverable 2 of the SI-Hg project the first validation results of the SI-Hg calibration protocol are reported. Within the SI-Hg project a protocol for the metrological calibration of elemental mercury gas generators used in the field was developed. For the validation the output of two different mercury gas generators was calibrated according to the protocol. As metrological reference standard the primary mercury gas standard from the Van Swinden Laboratory (VSL) was used. The measurements described in the protocol could be performed during the validation and the data was processed using a script to determine the output of the candidate generator and the uncertainty of the mercury concentration. Based on the validation measurements and data processing several improvements for the calibration protocol were identified and were used to improve the calibration protocol. </p><p>In this repository data obtained during the validation is published. The files of the following comparisons between reference generator and candidate generator can be found in this repository:</p><ul><li>VSL vs VSL<ul><li>m1<ul><li>09022022 calibration mercury gas generator VSL vs VSL m1</li><li>VSL_vs_VSL_m1</li></ul></li><li>m2 <ul><li>05072022 calibration mercury gas generator VSL vs VSL m2</li><li>VSL_vs_VSL_m2</li></ul></li><li>m3<ul><li>07072022 calibration mercury gas generator VSL vs VSL m3</li><li>VSL_vs_VSL_m3</li></ul></li></ul></li><li>VSL vs PSA before modification<ul><li>m1<ul><li>15032022 calibration mercury gas generator VSL vs PSA fixed m1</li><li>single_point_VSL_vs_PSA_fixed_m1_4</li><li>single_point_VSL_vs_PSA_fixed_m1_6</li><li>single_point_VSL_vs_PSA_fixed_m1_8</li><li>single_point_VSL_vs_PSA_fixed_m1_12</li></ul></li><li>m2<ul><li>28032022 calibration mercury gas generator VSL vs PSA fixed m2</li><li>single_point_VSL_vs_PSA_fixed_m2_4</li><li>single_point_VSL_vs_PSA_fixed_m2_6</li><li>single_point_VSL_vs_PSA_fixed_m2_8</li><li>single_point_VSL_vs_PSA_fixed_m2_12</li></ul></li><li>m3 <ul><li>06042022 calibration mercury gas generator VSL vs PSA fixed m3</li><li>single_point_VSL_vs_PSA_fixed_m3_4</li><li>single_point_VSL_vs_PSA_fixed_m3_6</li><li>single_point_VSL_vs_PSA_fixed_m3_8</li><li>single_point_VSL_vs_PSA_fixed_m3_12</li></ul></li><li>m4 <ul><li>12042022 calibration mercury gas generator VSL vs PSA fixed m4</li><li>single_point_VSL_vs_PSA_fixed_m4_4</li><li>single_point_VSL_vs_PSA_fixed_m4_6</li><li>single_point_VSL_vs_PSA_fixed_m4_8</li><li>single_point_VSL_vs_PSA_fixed_m4_12</li></ul></li><li>less tubing <ul><li>14042022 calibration mercury gas generator VSL vs PSA fixed less tubing</li><li>single_point_VSL_vs_PSA_fixed_less_tubing</li></ul></li><li>less tubing and air as complementary gas <ul><li>19042022 calibration mercury gas generator VSL vs PSA fixed less tubing in air</li><li>single_point_VSL_vs_PSA_fixed_less_tubing_air</li></ul></li></ul></li><li>VSL vs PSA after modification<ul><li>m1 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m1 20230324</li><li>PSA_fixed_air_m1_9</li><li>PSA_fixed_air_m1_11</li><li>PSA_fixed_air_m1_14</li></ul></li><li>m2 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m2 20230327</li><li>PSA_fixed_air_m2_9</li><li>PSA_fixed_air_m2_11</li><li>PSA_fixed_air_m2_14</li></ul></li><li>m3 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m3 20230329</li><li>PSA_fixed_air_m3_9</li><li>PSA_fixed_air_m3_11</li><li>PSA_fixed_air_m3_14</li></ul></li><li>m4 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m4 20230907</li><li>PSA_fixed_air_m4_9</li><li>PSA_fixed_air_m4_11</li><li>PSA_fixed_air_m4_14</li></ul></li><li>m5 air as complemantary gas <ul><li>Calibration PSA fixed mercury gas generator air m5 20230911</li><li>PSA_fixed_air_m5_9</li><li>PSA_fixed_air_m5_11</li><li>PSA_fixed_air_m5_14</li></ul></li><li>m1 nitrogen (N2) as complementary gas<ul><li>Calibration PSA fixed mercury gas generator nitrogen m1 20230330</li><li>PSA_fixed_N2_m1_9</li><li>PSA_fixed_N2_m1_11</li><li>PSA_fixed_N2_m1_14</li></ul></li><li>m2 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m2 20230331</li><li>PSA_fixed_N2_m2_9</li><li>PSA_fixed_N2_m2_11</li><li>PSA_fixed_N2_m2_14</li></ul></li><li>m3 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m3 20230405</li><li>PSA_fixed_N2_m3_9</li><li>PSA_fixed_N2_m3_11</li><li>PSA_fixed_N2_m3_14</li></ul></li><li>measurement at TUV<ul><li>PSA_Fixed_at_TUV</li></ul></li></ul></li></ul>
Data for PSA 10.536 Elemental Hg generator performance evaluation report
<p>During the SI-Hg performance evaluation of elemental mercury gas generators on the market three generators were tested, e.g., PSA 10.536 elemental Hg generator, bell-jar and Tekran Model 3425. Key characteristics were determined e.g.; the stabilisation period, short-term drift, precision, i.e., reproducibility and repeatability of the concentration generated, linearity, bias, sensitivity to sample gas pressure, sensitivity to surrounding temperature and sensitivity to electrical voltage. All three generators could be tested according to the calibration protocol developed within the project. The results obtained with the different gas generator clearly shows the importance of a metrological calibration. All three candidate generators show a different bias for the setpoint compared to the calibrated output. </p><p>The data obtained during the performance evaluation of the PSA 10.536 elemental Hg generator is published in this repository. The files of the following experiments can be found here:</p><ul><li>range1 m1<ul><li>Calibration_PSA_range1_m1_20221130</li><li>multi_point_calibration_PSA_range1_m1</li></ul></li><li>range1 m2<ul><li>Calibration_PSA_range1_m2_20221209</li><li>multi_point_calibration_PSA_range1_m2</li></ul></li><li>range1 m3<ul><li>Calibration_PSA_range1_m3_20221214</li><li>multi_point_calibration_PSA_range1_m3</li></ul></li><li>range1 m4<ul><li>Calibration_PSA_range1_m4_20230915</li><li>multi_point_calibration_PSA_range1_m4</li></ul></li><li>range1 m5<ul><li>Calibration_PSA_range1_m5_20230919</li><li>multi_point_calibration_PSA_range1_m5</li></ul></li><li>range2 m1<ul><li>Calibration_PSA_range2 m1 20221006</li><li>multi_point_calibration_PSA_range2_m1</li></ul></li><li>range2 m2<ul><li>Calibration_PSA_range2 m2 20221011</li><li>multi_point_calibration_PSA_range2_m2</li></ul></li><li>range2 m3<ul><li>Calibration_PSA_range2 m3 20221012</li><li>multi_point_calibration_PSA_range2_m3</li></ul></li><li>short-term drift<ul><li>m1<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 1</li><li>PSA_short_term_drift_M1</li></ul></li><li>m2<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 2</li><li>PSA_short_term_drift_M2</li></ul></li><li>m3<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 3</li><li>PSA_short_term_drift_M3</li></ul></li><li>m4<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 4</li><li>PSA_short_term_drift_M4</li></ul></li></ul></li><li>stability<ul><li>Calibration mercury gas generator range2 stability 20220811</li><li>Calibration mercury gas generator stability 20221018</li></ul></li></ul>
Distribution grid data generated by ding0
<p>Distribution grid data generated with ding0 in the <a href="https://ego-n.org/" target="_blank" rel="noopener">eGo^n project</a>.<br>Data from pre-release v0.3.0-alpha using branch <em>ding0_run/2023_04_06</em>, head: <a href="https://github.com/openego/ding0/tree/9fe5f1c3785ccb2f5afa56fe25e4f2290df76e5c" target="_blank" rel="noopener">9fe5f1c3785ccb2f5afa56fe25e4f2290df76e5c</a>.</p> <p>Input data from eGon-data, branch <a href="https://github.com/openego/eGon-data/tree/continuous-integration/run-everything-2022-11-10" target="_blank" rel="noopener">run-everything-2022-11-10</a>.</p> <p>See <code>README.md</code> for details.</p> <p> </p> <p> </p>
Experimental data from laboratory studies on the generation and evolution of internal tides under various Coriolis parameters
<p>The dataset includes experimental data from laboratory studies on the generation and evolution of internal tides under various Coriolis parameters.</p> <p>"uu" and "uh" are horizontal velocities. (unit: m/s)<br>"vv" and "vh" are vertical velocities. (unit: m/s)<br>"xx" and "yy" are the horizontal and vertical coordinates, respectively. (unit: m)</p> <p>The frequency of internal tide is 0.68 rad/s.<br>The Coriolis parameters are 0, 0.13, 0.17, 0.21, 0.25, 0.29, 0.335, 0.38, 0.42, 0.46, 0.54 rad/s for f00 to f26.<br>The time interval is 0.2s.</p>
Data generated for the publication: Keeping it in the family: Using protein family templates to rescue poor AlphaFold models unliked
<p>Data and manuscript of:</p> <p>Keeping it in the family: Using protein family templates to rescue low confidence AlphaFold2 models</p> <p>Francesco Costa1, Matthias Blum1 and Alex Bateman1</p> <ol> <li>European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton. CB10 1SD. UK</li> </ol> <ul> <li>results contains the workflow results;</li> <li>AF2_seed contains results of the comparison with AF2 run with multiple seeds;</li> </ul>
The experiment data for Photonics Diffraction Generator
<p>Three h5 compiled files are represent following experimental data:<br>cas_opt: The experimentally generated handwritten digit from a cascaded PDG<br>par_opt: The experimentally generated handwritten digit from a parallelPDG<br>speckle_opt: The experimentally collected speckles</p>
Data and Code: Familial transmission of neural representations for mental arithmetic across two generations
<p>Here we provide anonymized behavioral data, individual beta maps and analyses codes used in "Familial transmission of neural representations for mental arithmetic across two generations".</p> <p>Because of anonymization concerns within the framework of EU privacy regulations (<a href="https://gdpr-info.eu/">GDPR</a>), we cannot provide raw MRI data. <br>Therefore, the fMRI data consists of individual beta maps from the first-level analysis, which correspond to the brain activity associated with increases in problem size for each operation (addition and subtraction). Maps are normalized into the MNI template. See paper for details about the preprocessing and first-level analysis.</p> <p>The dataset consists of mother-child dyads. Mothers are assigned codes of 200 or higher. Children are assigned codes below 200. Each child's code is exactly 200 less than their mother's code.</p> <p>The analyses codes require Python version 3.8.8 and Nilearn version 0.8.1.</p> <p>If you have any questions, please send an email to charlotte.constant@inserm.fr. </p> <p> </p>
Data and source code for Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary
<p>We present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods.</p>
Code to generate figures 3 and 4 of: "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."
<p>Code to generate figures 3 and 4 of the manuscript titled "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."</p> <p> </p>
Data from: A burning issue: Savanna fire management can generate enough carbon revenue to help restore Africa's rangelands and fill Protected Area funding gaps
<p>Many savanna-dependent species in Africa including large herbivores and apex predators are at increasing risk of extinction. Achieving effective management of protected areas (PAs) in Africa where lions live will cost an estimated USD >$1-2 B/year in new funding. We explored the potential for fire management-based carbon-financing programs to fill this funding gap and benefit degrading savanna ecosystems. We demonstrated how introducing early dry season fire management programs could produce potential carbon revenues (PCR) from either a single carbon-financing method (avoided emissions) or from multiple sequestration methods ranging from USD $59.6-$655.9 M/year (at USD $5/ton) or USD $155.0 M–$1.7 B/year (at USD $13/ton). We highlighted variable but significant PCR for savanna PAs from USD $1.5–$44.4 M/year per PA. We suggest investing in fire management programs to jump-start the United Nations Decade of Ecological Restoration to help restore degraded African savannas and conserve imperiled keystone herbivores and apex predators. <br> <br> Open Access article: <a href="https://doi.org/10.1016/j.oneear.2021.11.013">https://doi.org/10.1016/j.oneear.2021.11.013</a></p>
Audio samples from generative models trained on the TIMIT speech data.
<p>This is a posting of audio snippets to accompany the paper "Benchmarking Generative Latent Variable Models for Speech".</p> <p>The snippets include samples and reconstructions. All samples are completely unconditional and utilise only the prior internal representations learned by the model. Reconstructions are computed from a given test audio snippet by first encoding it to a learned representation and then decoding that to a reconstruction of the audio.</p> <p>All models are trained on the TIMIT speech dataset (<a href="https://catalog.ldc.upenn.edu/LDC93s1">https://catalog.ldc.upenn.edu/LDC93s1</a>). Some snippets are from models trained at different temporal resolutions denoted by `s1` and `s64`. We refer to the paper for details.</p> <p>The files include:</p> <ul> <li>`clockwork-vae-s64-reconstruction-*` <ul> <li>Four reconstructions using a two-layered Clockwork VAE trained with temporal resolution s=64.</li> </ul> </li> <li>`clockwork-vae-s64-sample-*` <ul> <li>Four samples from the prior of a Clockwork VAE trained with temporal resolution s=64.</li> </ul> </li> <li>`original-*` <ul> <li>Four original samples from TIMIT corresponding in pairs to the reconstructions.</li> </ul> </li> <li>`vrnn-s64-sample-*` <ul> <li>Two samples from the prior of a VRNN trained with temporal resolution s=64.</li> </ul> </li> <li>`vrnn-s1-sample-*` <ul> <li>Two samples from the prior of a VRNN trained with temporal resolution s=1.</li> </ul> </li> <li>`srnn-s64-sample-*` <ul> <li>Two samples from the prior of a SRNN trained with temporal resolution s=64.</li> </ul> </li> <li>`srnn-s1-sample-*` <ul> <li>Two samples from the prior of a SRNN trained with temporal resolution s=1.</li> </ul> </li> <li>`wavenet-s64-sample-*` <ul> <li>Four samples from a WaveNet trained with temporal resolution s=1.</li> </ul> </li> <li>`wavenet-s1-sample-*` <ul> <li>Two samples from a WaveNet trained with temporal resolution s=64.</li> </ul> </li> </ul>
Original data and RML mapping used to generate RDF results for K-CAP 2021
<p>These materials include the following:</p> <ul> <li>The csvs used to track the accepted papers, authors and resources in K-CAP 2021</li> <li>The RML mappings generated to transform them to RDF (paths to data will have to be adjusted)</li> <li>The resultant RDF file generated when applying the mapping (out.ttl)</li> </ul>
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.