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2,031 results for “Transformer”
Dataset: error characteristics of a class 0.2S current transformer for publication
<p>Data for demonstrating typical error characteristics of a current transformer of the class 0.2 S. Data are used in a publication.</p>
Dataset for the paper: "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU."
<p>Dataset used for the development of scenarios in the publication "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU. Sustainability 2018, 10, 3685." and used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of other secondary data (from papers, reports) specified in the dataset (under tab "input codes")</p>
Data set for "Distinct contributions of whisker sensory cortex and tongue-jaw motor cortex in a goal-directed sensorimotor transformation"
<p>Data set for: Mayrhofer JM, El-Boustani S, Foustoukos G, Auffret M, Tamura K, Petersen CCH (2019) Distinct contributions of whisker sensory cortex and tongue-jaw motor cortex in a goal-directed sensorimotor transformation. Neuron https://doi.org/10.1016/j.neuron.2019.07.008</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2019_Mayrhofer_Neuron.pdf" is the Open Access pdf file of the manuscript published in Neuron.</p> <p>2. The file named "Mayrhofer_data_code.zip" (~20 GB) is a zipped version of a folder "Mayrhofer_data_code" (~57 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. The analysis code is in a subfolder named "MatlabCode", and the specific code for generating each figure panel is in a sub-subfolder named "Figures_tjM1_paper". When running the code, you need to set the Matlab file path to be "Mayrhofer_data_code". In addition, you should add the folder "Mayrhofer_data_code" with subfolders in Matlab "Set Path". The figures will be saved in a subfolder named "Figures". Some parts of the code rely upon previous results, and need to be executed sequentially in the order of the figure panels in the journal publication.</p>
Social networks and transformative behaviors in a grassland social-ecological system
<p>Dataframe for analysis presented in Nesbitt et al.'s <span>Social networks and transformative behaviors in a grassland social-ecological system published in People and Nature. Dataframe includes responses from an ego network survey administered to Nebraska (USA) ranchers in 2021. </span></p> <p><span>Metadata describes each variable in further detail including the question number from the survey.</span></p> <p> </p>
Plasmid Maps for a Nuclear Transformation Vector in Chlamydomonas reinhardtii for the Expression and Secretion of the Plastic-Degrading Enzyme (PHL7)
<p><strong>pJP32PHL7 Vector:</strong></p> <ul> <li> <p><strong>Size:</strong> 5692 bp</p> </li> <li> <p><strong>Key Features:</strong></p> <ul> <li><strong>HSP70 Promoter:</strong> A heat shock protein promoter fused with the <em>rbcS2</em> promoter to drive expression of downstream genes.</li> <li><strong>Ble Resistance Gene:</strong> Confers resistance to bleomycin, useful for selection in <em>Chlamydomonas reinhardtii</em>.</li> <li><strong>PHL7 Gene:</strong> Encodes the plastic-degrading enzyme PHL7, inserted downstream of the <em>F2A</em> site for expression in the host.</li> <li><strong>Intron Sequences:</strong> Contains multiple <em>rbcS2</em> introns for enhancing expression in <em>Chlamydomonas</em>.</li> <li><strong>Selectable Marker (AmpR):</strong> Confers ampicillin resistance for selection in <em>E. coli</em>.</li> <li><strong>Replication Origin:</strong> Includes <em>ori</em> and <em>F1 ori</em> for replication in <em>E. coli</em>.</li> </ul> <p> </p> </li> <li> <p><strong>Applications:</strong> This vector is designed for nuclear transformation in <em>Chlamydomonas reinhardtii</em>, enabling the expression and secretion of the plastic-degrading enzyme (PHL7) under the control of a hybrid <em>HSP70</em>rbcS2 promoter.</p> </li> </ul> <p><strong>pJP32PHL7dg Vector:</strong></p> <ul> <li> <p><strong>Size:</strong> 5692 bp</p> </li> <li> <p><strong>Key Features:</strong></p> <ul> <li><strong>HSP70 Promoter:</strong> Retains the HSP70 and <em>rbcS2</em> fusion promoter for gene expression.</li> <li><strong>LacZ Alpha Fragment:</strong> Includes a LacZ alpha fragment for blue/white screening.</li> <li><strong>PHL7 Gene:</strong> Encodes the plastic-degrading enzyme PHL7, linked downstream of the <em>F2A</em> site, allowing for expression in the host.</li> <li><strong>Ble Resistance Gene:</strong> Also confers bleomycin resistance for selection in <em>Chlamydomonas</em>.</li> <li><strong>Selectable Marker (AmpR):</strong> Confers ampicillin resistance for selection in <em>E. coli</em>.</li> <li><strong>Intron Sequences:</strong> Contains <em>rbcS2</em> introns for optimizing gene expression in the host organism.</li> </ul> <p> </p> </li> <li> <p><strong>Applications:</strong> The pJP32PHL7dg vector is similarly designed for nuclear transformation in <em>Chlamydomonas reinhardtii.</em> It also facilitates the expression and secretion of the plastic-degrading enzyme PHL7, driven by the hybrid <em>HSP70</em>rbcS2 promoter, but without glycosilation sites.</p> </li> </ul>
Transformers for Modeling Physical Systems
<p>Data set associated with the publication <a href="https://arxiv.org/abs/2010.03957">Transformers for Modeling Physical Systems</a>. Transformers are widely used in natural language processing due to their ability to model longer-term dependencies in text. Although these models achieve state-of-the-art performance for many language related tasks, their applicability outside of the natural language processing field has been minimal. In this work, we propose the use of transformer models for the prediction of dynamical systems representative of physical phenomena. </p> <p>This data set includes data in HDF5 files for:</p> <p>Lorenz ODE:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/lorenz_training_rk.tar.gz?versionId=c4bd1230-3b22-4d2e-83f0-357146a90423">lorenz_training_rk.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/lorenz_valid_rk.tar.gz?versionId=3cf95dac-a75d-42d8-85ab-615f7a2ad67f">lorenz_valid_rk.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/lorenz_test_rk.tar.gz?versionId=bbb4bd3d-33c9-4903-98ed-f7a926dc95db">lorenz_test_rk.tar.gz</a></li> </ul> <p>Flow Around a Cylinder:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/cylinder_training.tar.gz?versionId=25bd1f3a-03b7-44d0-aa3f-afaffa6cd706">cylinder_training.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/cylinder_valid.tar.gz?versionId=58a6e98d-be41-4603-91cb-9522d848cf57">cylinder_valid.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/cylinder_test.tar.gz?versionId=a3c03293-0278-40ee-b181-2eb53a8d0b47">cylinder_test.tar.gz</a></li> </ul> <p>Gray-Scott Reaction-Diffusion:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/grayscott_training.tar.gz">grayscott_training.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/grayscott_valid.tar.gz?versionId=3bb8aa25-c9c8-494e-a206-e8d1fdb8a88f">grayscott_valid.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/grayscott_test.tar.gz?versionId=d9cee8a6-b22f-44b9-ae1b-2433caf77e34">grayscott_test.tar.gz</a></li> </ul> <p>Rossler ODE:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/rossler_training.tar.gz?versionId=d44be9cf-8fa7-4eb2-8b6e-8ac129822f51">rossler_training.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/rossler_valid.tar.gz?versionId=93c658e3-235a-4150-b233-ed514d6c7467">rossler_valid.tar.gz</a></li> </ul> <p>As well as several pretrained embedding models for the Google Collab notebooks on <a href="https://github.com/zabaras/transformer-physx/">Github</a>:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/embedding_lorenz_pretrained.pth?versionId=cd30ff4e-34b3-4070-b346-718fb8526dac">embedding_lorenz_pretrained.pth</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/embedding_cylinder_pretrained.pth?versionId=69552efb-09e4-49d9-a224-ccc7cff91b86">embedding_cylinder_pretrained.pth</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/embedding_rossler_pretrained.pth?versionId=e7d1f4aa-3f31-45fe-91e4-8411e9cd634f">embedding_rossler_pretrained.pth</a></li> </ul> <p>See the Github repository for code base: <a href="https://github.com/zabaras/transformer-physx/">https://github.com/zabaras/transformer-physx/</a></p>
Data and R-scripts for "Land-use trajectories for sustainable land system transformations: identifying leverage points in a global biodiversity hotspot" (V2)
<p>Sustainable land system transformations are necessary to avert biodiversity and climate collapse. However, it remains unclear where entry points for transformations exist in complex land systems. Here, we conceptualize land systems along land-use trajectories, which allows us to identify and evaluate leverage points; i.e., entry points on the trajectory where targeted interventions have particular leverage to influence land-use decisions. We apply this framework in the biodiversity hotspot Madagascar. In the Northeast, smallholder agriculture results in a land-use trajectory originating in old-growth forests, spanning forest fragments, and reaching shifting hill rice cultivation and vanilla agroforests. Integrating interdisciplinary empirical data on seven taxa, five ecosystem services, and three measures of agricultural productivity, we assess trade-offs and co-benefits of land-use decisions at three leverage points along the trajectory. These trade-offs and co-benefits differ between leverage points: two leverage points are situated at the conversion of old-growth forests and forest fragments to shifting cultivation and agroforestry, resulting in considerable trade-offs, especially between endemic biodiversity and agricultural productivity. Here, interventions enabling smallholders to conserve forests are necessary. This is urgent since ongoing forest loss threatens to eliminate these leverage points due to path-dependency. The third leverage point allows for the restoration of land under shifting cultivation through vanilla agroforests and offers co-benefits between restoration goals and agricultural productivity. The co-occurring leverage points highlight that conservation and restoration are simultaneously necessary. Methodologically, the framework shows how leverage points can be identified, evaluated, and harnessed for land system transformations under the consideration of path-dependency along trajectories.</p>
S74 | REFTPS | Transformation Products and Reactions from Literature
<p>This is the collection associated with list S74 REFTPS Transformation Products and Reactions from Literature on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>This dataset is designed to provide an entry point for users to contribute transformation products and reactions documented in the literature for addition to the NORMAN SLE, SusDat and the PubChem Transformations section.</p> <p>Change logs and version tracking at the <a href="https://gitlab.com/uniluxembourg/lcsb/eci/pubchem/-/tree/master/annotations/tps/REFTPS">ECI GitLab site</a>.</p> <p>Change log: v0.0.2 added InChIKey file. v0.1.0 added new reactions from Anca Baesu and DTXSIDs. v0.2.0 added PFAS TPs from Parviel Chirsir. v0.2.1 more PFAS TPs from Parviel. v0.3.0 Emma added HMMM TPs; v0.3.1 updated references and added new CIDs; added new MS/MS file. v0.4.0 new PFAS TPs plus MS/MS and NMR. v0.4.1 new CID added, plus CID 67543 updated to 14571268. v0.5.0 new 8:2 FT TPs plus annotation data; new structures. v0.5.1 added new CIDs. v0.5.2 added 2:2 to 6:2 FT TPs, updated ref for Bugsel. v0.5.3: added new CIDs. v0.6.0 added new structures. v0.7.0 added more new structures. v 0.7.1: updated CIDs in substances, fixed PFHpA mapping in transformations (some were mismapped to CID 67819). v 0.7.2: updated Biosystem description for many records. v0.8.0: updated CID 163201609 => 166001338, adjusted last 4 MS/MS, added Barisci AOP transformations. v0.9.0 added new structures. v0.9.1 updated CIDs and added radical structures from deposition. v0.10.0 added new irgarol reaction; v0.10.1 added new CID. v0.11.0 added Avendano and Mabury transformations from Parviel. v0.12.0 added Washington MS/MS and Marjanovic MS/MS and reactions. v0.13.0 added Galaxolide transformation. v0.14.0 added Zweigle PFAS TPs with MSMS. v0.14.1 added new CIDs. v0.15.0 added antibiotic TPs from Paul Löffler, SLU, incl. entries with no CID. v0.15.1 added new CIDs. v0.16.0 added benzothiazole reactions. v0.17.0 added TooCOLD TPs from Rick. v0.18.0 added new TFA reactions. v0.19.0 added pak choi reactions, several with no CID. v0.19.1 added new CIDs. v0.20.0 added dimers from Li Ji. v0.20.1 added new CIDs. v0.21.0 added the EJ Weber PFAS libraries "EnvLib" and "MetaLib", curation by Parviel and Emma. Some new CIDs to come. v0.21.1: fixed char issues in substance & transformation files. v0.21.2 added new CIDs. v0.22.0 added "Class_parent" column to the substance deposition file to aid annotation. v0.23.0: added Parviel's zebrafish entries</p>
A computer program to calculate discrete wavelet transform for one-dimensional signals
<p>This is the most recent version of the True Basic program 'NDHAAR.TRU', which was part of the supplementary materials for the following publication: X. Dong, P. Nyren, B. Patton, A. Nyren, J. Richardson and T. Maresca, 2008. Wavelets for agriculture and biology: A tutorial with applications and outlook. BioScience 58: 445-453.</p> <p>The original version (1.0, April 8, 2008) accepts a one-dimensional signal with 1024 data points. It was previously posted at http://www.ag.ndsu.edu/CentralGrasslandsREC/wavelets-for-agriculture-and-biology</p> <p>Version 1.1 (June 1, 2010) accepts signals with a length of 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384,<br> 32768, or 65536. This version with documentation was initially posted at www.infoclearinghouse.com. Later the website was closed. Now the documentation can still be accessed at https://www.scss.tcd.ie/Khurshid.Ahmad/Research/Wavelets/wva.pdf.</p> <p>Version 1.2 is posted in this current upload. A major change in this version is the correction of a few typos existing in Version 1.1, so that the program can correctly process signals longer than 4096 (that is, with signal length as either of 8192, 16384, 32768, or 65536). Note that Version 1.1 is fine in correctly processing signals with a length at or shorter than 4096.</p> <p>Two sample input data files are included. Also included is the original supplemental material Suppl_dong_2008.pdf. The first input data file 'pdsi.txt' has a length of 1024, and the related output files are OO1.txt, OO2.txt, OO3.txt, OO4.txt and OO5.txt. These data files are discussed in the original BioScience paper as well as in Suppl_dong_2008.pdf. </p> <p>The second sample input file 'warm.txt' has a length of 65536 and the related output files are OUT_1.txt, OUT_2.txt, OUT_3.txt, OUT_4.txt, and OUT_5.txt. The sample input file warm.txt contains NDVI values of winter wheat measured at Uvalde, TX, USA, from about 8 am to 10 am on April 12, 2018. The measurement was made using an ACS-430 Crop Circle sensor mounted to a push-wheel cart. This file and the associated output files are part of the intermediate results for Supplementary Figure S2 to the article entitled "Leaf water potential of field crops estimated using NDVI in ground-based remote sensing - opportunities to increase prediction precision" (<em>PeerJ</em>. 9:e12005 DOI 10.7717/peerj.12005), which can be accessed at https://zenodo.org/record/4574674#.YD7kI2hKiUk</p>
WoS and Scopus records for the bibliometric analysis in the output D2.2 Digital transformation of research and innovation roadmap of the reSEArch-EU project
<p>These files represent the exported WoS and Scopus records, used in the output D2.2 Digital transformation of research and innovation roadmap of the Horizont project reSEArch-EU, implemented by the SEA-EU university alliance.</p>
Reference Reflectance Transformation Imaging acquisitions for RTI stitching and acquisition optimization
<p>This dataset contains 1. RTI acquisitions a canvas painting and a metal print plate in parts, for development of RTI-stitching methods. 2. Dense RTI acquisitions of brushed metal and ruse coarse metal surfaces for development of methods for determining ideal light positions in a RTI acquisitions. </p>
Stream metabolism (as resazurin-resorufin transformation) along a boreal headwater stream and its relation to groundwater organic matter supply
<p>Datasets supporting the manuscript entitled "<strong><em>Groundwater-stream connections shape the spatial patterns and rates of aquatic metabolism</em></strong>", published in L<em>imnology and Oceanography Letters</em>. Three datasets are available:</p> <ul> <li>"<em><strong>Groundwater_Characterization.csv":</strong></em> dissolved organic matter characterization of and heterotrophic activity associated with, the major water sources discharging into a headwater boreal stream during summer 2017. Major water sources are: lake water and five discrete groundwater inflows (here named as <em>discrete riparian inflow points)</em>.</li> <li>"<em><strong>Raz_Additions.csv</strong></em>": Constant-rate additions of resazurin performed in a boreal headwater stream (Krycklan catchment, Sweden) during seven dates of summer 2017. Data contains resazurin and resorufin concentrations from the surface and hyporheic water at 18 stations along a 90-m long reach. </li> <li>"<em><strong>Raz_transformation_metric.csv</strong></em>": Hydrologic and metabolic characterization of the 90-m long reach for the seven resazurin additions conducted in summer 2017. </li> </ul> <p>More information about the data can be found in the document "<strong><em>Metadata.doc</em></strong>". Information about field and laboratory procedures can be found in the main manuscript or in the document "<strong><em>Supporting_Information.doc</em></strong>".</p>
Dataset for Link between Anisotropic Electrochemistry and Surface Transformations at Single Crystal Silicon Electrodes: Implications for Lithium Ion Batteries
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Link between Anisotropic Electrochemistry and Surface Transformations at Single Crystal Silicon Electrodes: Implications for Lithium Ion Batteries"</strong></p> <p>Specifically, the following measurements are provided:</p> <ul> <li>Electrochemical measurements as cyclic voltammetry using scanning electrochemical cell microscopy for three different Si crystallographic orientations (100, 110, 311) in 1 M LiPF6 in ethylene carbonate - ethyl methyl carbonate ("SECCM/")</li> <li>Scanning electron microscopy and transmission electron microscopy imaging of pristine and cycled samples ("Images/")</li> </ul>
Diverse Title Generation for Stack Overflow Posts with Multiple Sampling Enhanced Transformer
<p>Dataset for our paper "Diverse Title Generation for Stack Overflow Posts with Multiple Sampling Enhanced Transformer"</p> <p>(<a href="https://github.com/zfj1998/M3NSCT5">zfj1998/M3NSCT5: the code base for our paper "Diverse Title Generation for Stack Overflow Posts with Multiple Sampling Enhanced Transformer" (github.com)</a>)</p> <p>Including three files representing the train/val/test datasets. Each file contains all the collected data covering eight programming languages.</p>
Emission of volatile organic compounds from residential biomass burning and their rapid chemical transformations.
<p>Volatile Organic Compounds (VOCs) were monitored during the Ioannina 2022/23 winter campaign, in north-east Greece. The campaign was carried out between December 6th, 2021, and January 10th, 2022. Nitrogen oxides, carbon monoxide, carbon dioxide, methane, PM10 and Black carbon were also monitored, as well as meteorological variables. Ioannina is nested within the Dinaric mountains and suffers from intense winter pollution events, due to the topology which traps the pollution over the city. The instruments deployed included a Proton Transfer Time-of-Flight Mass Spectrometry (PTR-ToF-MS 4000 – Ionicon GmbH, Austria), a greenhouse gas monitor (G2301 – Picarro Inc., USA), a suite of carbon monoxide, ozone, nitrogen oxide analyzers (APMA-360, APNA-360 and APOA-360 – Horiba Ltd., Japan), a PM10 monitor (F-701-20 – DURAG, Germany), an aethalometer (AE33 – Magee Scientific, USA) and a weather station. Radiation, historical temperature data from the University of Ioannina, and PMF analysis results are also submitted.</p>
Dataset for the publication: Electrochemical transformation of D,L-glutamic acid into acrylonitrile
<p>The dataset comprehends the experimental data used as basis of the contribution on the electrochemical transformation of D,L-glutamic acid into acrylonitrile. The electrochemical transformation of D,L‑glutamic acid as substrate proceeds in several steps via electro-oxidative decarboxylation and non-Kolbe electrolysis. The provided data comprehend yields and faradaic efficiencies of the transformations under different reaction conditions.</p>
Report on Transformers interpretability for Natural Language Processing: A case study on Technical Debt classification
<p>Transformer models have significantly advanced the field of natural language processing (NLP), achieving exceptional results in various tasks. However, these models are often seen as "black boxes", providing limited insight into the factors influencing their predictions. It has become crucial to develop and utilise methods for interpreting and explaining these models to uncover their complex inner workings. This report discusses the latest techniques and tools that aid in a more profound understanding of transformer models within NLP. Additionally, it explores a vital industrial use case: Technical Debt (TD) classification. In this context, the report leverages transformer model interpretability tools and Retrieval Augmented Generation (RAG) to analyse and understand the characteristics of text in Github issues, distinguishing between TD and non-TD.</p> <p>This report thoroughly outlines an approach to improve the transparency and reproducibility of machine learning models, with a special emphasis on TD classification. It integrates the RAG approach and exploits feature attribution techniques, presenting a route to create AI systems that are not only high-performing but also demonstrably trustworthy and comprehensible. Through a detailed examination of word patterns in TD classification and the innovative use of the RAG approach, the research highlights a strong dedication to promoting transparency and responsibility in AI systems, potentially ushering in a new phase in machine learning research that focuses on clarity and dependability.</p>
Enhanced Westermo dataset - Transformed and Modified for Test case Selection and Priorotization in the context of Continuous Integration and Reinforcement Learning.
<p><strong>Overview</strong></p> <p>This repository contains a modified version of the existing, recently published dataset, Westermo. The initial dataset was gathered at Westermo Network Technologies AB, located in Västerås, Sweden. It encompasses over <strong>1 Million verdicts</strong> obtained from testing embedded systems, collected over a span of more than <strong>500 consecutive days</strong> of nightly testing. The dataset has been transformed and tailored specifically to cater to the research community, particularly for addressing challenges such as regression test selection, identification of flaky tests, and visualization of test results. The original dataset can be accessed through the reference provided in <strong>[1]</strong>.</p> <p>The Westermo dataset offers valuable historical information regarding the execution of test cases and their corresponding results. It serves as a valuable resource for evaluating and comparing different Test case Selection and Prioritization (TSP) techniques, enabling researchers to identify test cases that are more likely to fail during subsequent executions. Test cases in the dataset are characterized by attributes such as execution duration, previous last execution time, and the results of their recent executions.</p> <p>This dataset offers valuable historical information regarding the execution of test cases and their corresponding results. It serves as a valuable resource for evaluating and comparing different test case prioritization and selection techniques, enabling researchers to identify test cases that are more likely to fail during subsequent executions. Test cases in the dataset are characterized by attributes such as execution duration, previous last execution time, and the results of their recent executions.</p> <table align="left"> <caption><strong>Table 1: Dataset Overview</strong></caption> <tbody> <tr> <td>Test Cases</td> <td>1855</td> </tr> <tr> <td>CI Cycles</td> <td>15,197</td> </tr> <tr> <td>Verdict</td> <td>1,036,818</td> </tr> <tr> <td>Failed</td> <td>5.03%</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>However, the diversity and multitude of the features in the dataset can be irrelevant to some TSP approaches. This led us to perform a dataset conversion, where we customized Westermo to have the same features from Paint Control and IOF/ROL, two widely used datasets in Reinforcement Learning based TSP approaches.</p> <p>This conversion required the combination of multiple variables and generating the target ones. When it comes to generating the “LastResults” and “Cycle” values, further analysis was required and the data handling needed an in-depth understanding of how the nightly testing was conducted. This led us to investigate what a CI cycle is in their context, and we followed their definition of a session, stating that “a session is when we run a suite of tests on one test system with a certain software version and testware version”. When splitting the data according to the 9 different systems used, we were able to generate 9 different sub-sets that fit the CI context.</p> <p> </p> <p><strong>File Format</strong></p> <p>The compressed .zip file contains 9 files, each one corresponding to each of the 9 systems. The datasets are available in CSV format, with the semicolon (;) serving as the delimiter. The columns included are represented in the table below along with their descriptions.</p> <table> <caption><strong>Table 2: Parameters of the dataset</strong></caption> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Content</th> </tr> </thead> <tbody> <tr> <td>Id</td> <td>Unique numeric identifier of the test execution </td> </tr> <tr> <td>Name</td> <td>Unique numeric identifier of the test case</td> </tr> <tr> <td>Duration</td> <td>Approximated runtime of the test case</td> </tr> <tr> <td>CalcPrio</td> <td>Priority of the test case, calculated by the prioritization algorithm (output column, initially 0)</td> </tr> <tr> <td>LastRun</td> <td>Previous last execution of the test case as date-time-string (Format: <em>YYYY-MM-DD HH:ii </em>)</td> </tr> <tr> <td>LastResults</td> <td>List of previous test results (Failed: 1, Passed: 0), ordered by ascending age. Lists are delimited by [ ].</td> </tr> <tr> <td>Verdict</td> <td> <p>Test verdict of this test execution (Failed: 1, Passed: 0)</p> </td> </tr> <tr> <td>Cycle</td> <td>The number of the CI cycle this test execution belongs to.</td> </tr> </tbody> </table> <p> </p> <p>The implications of this conversion are important as it can help the previous works to re-assess their approaches and have more data for training and testing, as well as opening a broader data spectrum for future researchers in this field to find ready-to-use, rich datasets, on which they could evaluate their approaches and contribute to the TSP community. This also addresses the limitations in the field discussed in the systematic literature review <strong>[2]</strong>, stating that future research on TSP techniques should focus on collecting data from more recent subjects in a CI context with varying failure rates and larger execution times, as reproducible studies with appropriate datasets are needed to develop a usable body of knowledge regarding TSP over time. We believe that this conversion of the Westermo dataset is our contribution to alleviating the gap for the RL-based approaches.</p> <p>The original dataset can be found <a href="https://sites.mdu.se/aidoart/results/open-source/test-results-dataset-westermo">here.</a></p>
The photooxidation of dissolved organic matter in surface waters analyzed by Fourier-transform ion cyclotron resonance mass spectrometry.
Dissolved organic matter (DOM) plays an important role in carbon cycling in natural waters. The processing of DOM in these waters can occur via photooxidation, or interaction with sunlight. This processing can lead to the production of CO2, and also the alteration of organic compounds that make up DOM. It is likely that the extent of photooxidation is at least partially determined by the chemical composition of DOM. Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was used to characterize the dissolved organic matter at the molecular level for all water samples, both before and after light exposure to better understand the photooxidation of DOM. Chemical formulas were assigned to mass to generated mass to charge ratios using a custom script in R, resulting in a list of chemical formula assignments for each DOM sample, at multiple light exposure time points.
Dataset related to the publication "Transformation Optics: Large Multiphysics Simulation of Nonlinear Optomechanical Coupling in Microstructured Resonant Cavities", DOI: 10.1109/MMM.2018.2821086
<p>This folder contains the raw data from which the graphs in paper "Transformation Optics: Large Multiphysics Simulation of Nonlinear Optomechanical Coupling in Microstructured Resonant Cavities", DOI: 10.1109/MMM.2018.2821086, have been obtained.</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.