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4,325 results for “real”
PM_033454_P_Vila_Real_Mateus
<u>File Name</u>: PM_033454_P_Vila_Real_Mateus.jpg <br><u>Sublocation</u>: Casa de Mateus (Palácio de Mateus) <br><u>Location</u>: Mateus <br><u>Province</u>: Norte, Vila Real <br><u>Country</u>: Portugal <br><u>Header</u>: Palácio (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus). Exterior. Os edifícios anexos e a capela. Barroco. Século 18. <br><u>Description</u>: Palace (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus) Exterior The annex buildings and the chapel Baroque 18th century <br><u>Keywords</u>: Baroque, Cultural heritage, Europe, Mateus, Monuments, Norte, Palace, Portugal, Styles, Vila Real <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_033453_P_Vila_Real_Mateus
<u>File Name</u>: PM_033453_P_Vila_Real_Mateus.jpg <br><u>Sublocation</u>: Casa de Mateus (Palácio de Mateus) <br><u>Location</u>: Mateus <br><u>Province</u>: Norte, Vila Real <br><u>Country</u>: Portugal <br><u>Header</u>: Palácio (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus). Exterior. A fachada e a lagoa. Barroco. Século 18. <br><u>Description</u>: Palace (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus) Exterior The facade and the pond Baroque 18th century <br><u>Keywords</u>: Baroque, Cultural heritage, Europe, Mateus, Monuments, Norte, Palace, Portugal, Public garden/park, Styles, Thematic, Vila Real <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
DESED_real
<p><strong>If you are in this page, you have to know that the content of this page is also in github associated.</strong></p> <p>Link to the associated github repository: <a href="https://github.com/turpaultn/Desed">https://github.com/turpaultn/Desed</a></p> <p>Link to the paper: <a href="https://hal.inria.fr/hal-02160855"><em>https://hal.inria.fr/hal-02160855</em></a></p> <p>Domestic Environment Sound Event Detection (DESED) dataset.</p> <p><strong>Description</strong></p> <p>This dataset is the real part of the DESED dataset. It is a subpart of Audioset.</p> <p>There is the material to:</p> <ul> <li>Download the metadata of the subset of Audioset used in DCASE 2019 task 4.</li> </ul> <p><strong>Files:</strong></p> <ol> <li><strong>DESED_real_metadata.tar.gz</strong> : Annotations of subset of Audioset.</li> <li><strong>audioset_strong.tsv: </strong>Metadata of an Audioset subpart strongly annotated (Shawn et al.)<sup>1</sup>. This TSV file is a modified version of "Audioset strong" curated for our needs: <ol> <li>Labels converted from the ontology</li> <li>Filename changed (Y added and timestamp going from 30000 to 30.000_40.000)</li> <li>Only keeping the files available for us (some Audioset files are unavailable)</li> </ol> </li> </ol> <p><strong>To download the associated soundfile, </strong>please visit<strong>: </strong><a href="https://github.com/turpaultn/DESED">https://github.com/turpaultn/DESED</a> .</p> <p> </p> <p><sup>1</sup>Shawn Hershey, Daniel P W Ellis, Eduardo Fonseca, Aren Jansen, Caroline Liu, R Channing Moore, and Manoj Plakal. <em>The benefit of temporally-strong labels in audio event classification.</em> In 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2021.</p> <ul> </ul>
Detection of Real-World Influence through Social Media
<p><strong>Description. </strong>This dataset corresponds to the resources produced for the following conference paper and its extended version:</p> <ol> <li>J.-V. Cossu, N. Dugué, and V. Labatut, “Detecting Real-World Influence Through Twitter,” in <em>2nd European Network Intelligence Conference (ENIC)</em>, 2015, pp. 83–90. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01164453">hal-01164453</a>⟩ DOI: <a href="http://doi.org/10.1109/ENIC.2015.20">10.1109/ENIC.2015.20</a></li> <li>J.-V. Cossu, V. Labatut, and N. Dugué, “A Review of Features for the Discrimination of Twitter Users: Application to the Prediction of Offline Influence,” <em>Social Network Analysis and Mining </em>6:25, 2016. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01203171">hal-01203171</a>⟩ DOI: <a href="http://doi.org/10.1007/s13278-016-0329-x">10.1007/s13278-016-0329-x</a></li> </ol> <p>Raw data are available through the official RepLab page: <a href="http://nlp.uned.es/replab2014/">http://nlp.uned.es/replab2014/</a> (follow <a href="http://nlp.uned.es/replab2014/replab2014-dataset.tar.gz">http://nlp.uned.es/replab2014/replab2014-dataset.tar.gz</a>)</p> <p><strong>Source code. </strong>The source code used to generate these output is available on GitHub: <a href="https://github.com/CompNet/Influence">https://github.com/CompNet/Influence</a></p> <p><strong>Funding. </strong>This work was partly funded by the French National Research Agency (ANR), through the project <a href="https://anr.fr/Project-ANR-12-CORD-0002">ImagiWeb ANR-12-CORD-0002</a>.</p> <p><strong>Contact. </strong>Jean-Valère Cossu <<a href="mailto:jean-valere.cossu@alumni.univ-avignon.fr">jean-valere.cossu@alumni.univ-avignon.fr</a>></p> <p><strong>Citation. </strong>If you use these data, please cite paper [1] above.</p> <p><br><code>@InProceedings{Cossu2015,</code><br><code> author = {Cossu, Jean-Valère and Dugué, Nicolas and Labatut, Vincent},</code><br><code> title = {Detecting Real-World Influence Through {Twitter}},</code><br><code> booktitle = {2\textsuperscript{nd} European Network Intelligence Conference},</code><br><code> year = {2015},</code><br><code> pages = {83-90},</code><br><code> address = {Karlskrona, SE},</code><br><code> publisher = {IEEE Publishing},</code><br><code> doi = {10.1109/ENIC.2015.20},</code><br><code>}</code></p> <p><strong>Details. </strong>This archive contains all ranking outputs formatted according to the TREC-EVAL tool format. These outputs consist for each domain in a ranked list of user from the most influential to the least influential. For a classification-type evaluation, just consider that users having a score higher than 0.5 are influential.</p> <p>File names correspond to the system (those starting with Cos*, indicate: the method BoT for Bag-of-Tweets, UaD for User-as-Document; the use of the Tweet-Selection strategy files denoted Artex; the learning process with Global or separated models which are noted Multi and last but not least the decision strategy for Bag-of-Tweets: Counting or Sum) or feature name. Files starting with out_* contain the results of logistic regression ranking outputs. Files matrix_auto.dat and matrix_bank.dat contain the data used to feed the PLS model (code: plspm4influence.R).</p> <p>RepLab 2014 uses Twitter data in English and Spanish. The balance between both languages depends on the availability of data for each of the profiles included in the dataset.</p> <p>The training dataset consists of 7,000 Twitter profiles (all with at least 1,000 followers) related to the automotive and banking domains, evaluation is performed separately. Each profile consists of (i) author name; (ii) profile URL and (iii) the last 600 tweets published by the author at crawling time and have been manually labelled by reputation experts either as “opinion maker” (i.e. authors with reputational influence) or “non-opinion maker”. The objective is to find out which authors have more reputational influence (who the opinion makers are) and which profiles are less influential or have no influence at all. </p> <p>Since Twitter ToS do not allow redistribution of tweets, only tweets ids and screen names are provided. Replab organizers provide details about how to download the tweets.</p>
Exploring Housing Affordability in Illinois: An In-Depth Study of the State's Real Estate Market
<p>“Exploring Housing Affordability in Illinois: An In-Depth Study of the State’s Real Estate Market” focuses on the Illinois housing market from 2013 to 2022, mainly targeting housing affordability. Housing has been a cornerstone of stability in anyone’s life throughout history. Yet today, housing affordability has emerged as a critical societal issue impacting numerous individuals and families statewide. This study aims to get an overview of the trends of Illinois housing affordability over time across different counties in Illinois. It involves a comprehensive analysis of median home value and median incomes across Illinois counties, using data from two authoritative sources: the Census Bureau and Zillow. By providing insights, we can analyze and study the hidden factors that influence housing affordability over time and forecast future trends.</p>
Georeferenced real estate data for Addis Ababa
<p>This dataset contains georeferenced real estate data for Addis Ababa, used to test the gradient predictions of the monocentric city model. The data includes property address (longitude, latitude), price (rent), size, and other relevant attributes collected from 2017 to 2024.</p> <p>The <code>raw.zip</code> file contains the raw data for each provider, untouched. </p>
Conjuntos de datos sobre la comunidad educativa del Real Conservatorio Superior de Música de Madrid en el siglo XIX
<p>Estos siete conjuntos de datos extraen la información relativa a la comunidad académica (directores, profesores, alumnos, personal administrativo y cargos honoríficos) presente en el Real Conservatorio Superior de Música de Madrid entre 1830 y 1892 a partir de dos documentos custodiados en la Biblioteca y en el Archivo de dicha institución: el <em>Registro de premios y concursos</em> (RPC) y la <em>Memoria acerca de la Escuela Nacional de Música y Declamación de Madrid escrita para ser presentada en la Exposición Universal de la Música y del Teatro que ha de verificarse en Viena en el año de 1892 </em>(M).</p>
Table of associated Legendre functions of the first kind for values on the real axis
<p>This data set is a tabulation of associated Legendre functions of the first, sometimes called the regular solutions, computed for values on the real axis ranging from 0.0 to 10.0.</p> <p>It was created by a new program which has been implemented in C++ using template meta programming, to compute associated Legendre functions of the first and second kind of integer order (l) and degree (m) and complex argument (z). The mathematical equations, along with a short table of values, are given the book (see page 118 and following) </p> <p> Shanjie Zhang and Jianming Jin, Computation of Special Functions,<br> publishers: Wiley, 1996, ISBN: 0-471-11963-6,<br> LC: QA351.C45.</p> <p>The output from this new program has been verified against the tables printed in the book.</p> <p>This data set is provided because</p> <p> i) it covers a wider range of arguments than published in the tables in the book </p> <p> ii) it has many more l,m values than the tables in the book</p> <p>Note that this data set was computed using data type long double on an Intel CPU. </p>
Table of associated Legendre functions of the second kind for values on the real axis
<p>This dataset is a tabulation of associated Legendre functions of the second, sometimes called the irregular solutions, computed for values on the real axis ranging from 0.0 to 10.0.</p> <p>It was created by a new program which has been implemented in C++ using template meta programming, to compute associated Legendre functions of the first and second kind of integer order (l) and degree (m) and complex argument (z). The mathematical equations, along with a short table of values, are given the book (see page 118 and following) </p> <p> Shanjie Zhang and Jianming Jin, Computation of Special Functions,<br> publishers: Wiley, 1996, ISBN: 0-471-11963-6,<br> LC: QA351.C45.</p> <p>The output from this new program has been verified against the tables printed in the book.</p> <p>This dataset is provided because</p> <p> i) it covers a wider range of arguments than published in the tables in the book </p> <p> ii) it has many more l,m values than the tables in the book</p> <p>Note that this data set was computed using data type long double on an Intel CPU. </p> <p> </p>
Replication data for: Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study
<p>This is the replication data for the scientific paper titled "Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study" accepted for publication in the Journal of Medical Internet Research (JMIR). For details on how to use the data files, please consider the "supplementary_material.R" file or the "supplementary_material.pdf" where the variables of interest and R code are presented.</p> <p>For the code to run correctly, have the files "multilevel_labels.R" and "glm_labels.R" in the same working directory as the .R or .Rmd script. They are executed in the background, applying modifications to labels inside the regression tables. </p> <p> </p>
Perturbative gravitational wave predictions for the real scalar extended Standard Model, dataset
<p>This deposit contains data from a perturbative study of cosmological phase transitions in the real singlet scalar extension of the Standard Model (xSM). The data relates to the paper "Perturbative gravitational wave predictions for the real scalar extended Standard Model". Everything is contained within the archive file <em>xsm_results.tar.gz</em>, a tarball compressed with Gzip.</p> <p>The data covers phase transition properties for a scan of 100,000 parameter points in the xSM. Further details on the contents of the dataset are explained in the <em>README.md</em> within the tarball.</p>
Global Ionosphere Maps of vertical electron content combined in real-time from the RT-GIMs of CAS, CNES, UPC-IonSAT, and WHU International GNSS Service (IGS) centers (from Dec 1, 2020, to March 1, 2021)
<p>The datasets consists on 91 daily files, in IONEX format (<a href="http://ftp.aiub.unibe.ch/ionex/draft/ionex11.pdf">http://ftp.aiub.unibe.ch/ionex/draft/ionex11.pdf</a>) , corresponding to three months of global ionospheric maps (GIM) of vertical total electron content (VTEC) computed in real-time from the assessed and combined real-time GIMs generated by four analysis centers. Indeed, the Real-Time Working Group (RTWG) of International GNSS Service (IGS) is dedicated to providing high-quality data, high-accuracy products for Global Navigation Satellite System (GNSS) navigation, positioning, timing, and Earth observations. As one of the important part of real-time products, the IGS combined Real-Time Global Ionosphere Map (RT-GIM) have been generated by real-time weighting technique with the help of RT-GIMs from IGS real-time ionosphere centers including the Chinese Academy of Sciences (CAS), Centre National d’Etudes Spatiales (CNES), Universitat Politècnica de Catalunya (UPC), and Wuhan University (WHU). Compared with IGS rapid Global Ionosphere Maps (GIMs) (corg, ehrg, emrg, esrg, igrg, jprg, uhrg, uprg, uqrg, whrg) and IGS final combined GIM (igsg), the IGS combined RT-GIM (irtg) is equivalent to the post-processed GIMs and even better than some rapid GIMs. The IGS RT-GIMs are reliable sources of real-time global VTEC information and has great potential for real-time applications including range error correction for transionospheric radio signals (such as GNSS positioning, search and rescue, air traffic, radar altimetry, and radioastronomy), the monitoring of space weather (such as geomagnetic and ionospheric storms, ionospheric disturbance) and detection of natural hazards on a global scale (such as hurricanes/typhoons, ionospheric anomalies associated with earthquakes)</p>
SIMUSAFE cyclist behavior in simulator and in real-world
<p>This dataset includes data collected by the SIMUSAFE H2020 EU project (2017-2021) during its first data acquisition cycle. Voluntary bicycle riders are the subjects in this dataset, and the dataset includes a combination of sensory and psychological characteristics data. Sensory data was recorded in one of two settings: driving around the city in reality (NDT) and driving in a simulator (NST) along routes that were designed to imitate similar in-city driving.</p> <p>Overall, the dataset consists of recordings from 7 subjects for the following durations:</p> <p><strong>Total time per user (hours):</strong></p> <p>User Overall recording duration</p> <p>USER0 0 days 17:28:06.284997888</p> <p>USER1 0 days 04:25:50.084999680</p> <p>USER2 0 days 00:11:50.677999616</p> <p>USER3 0 days 01:30:38.754000640</p> <p>USER4 0 days 02:44:50.625000192</p> <p>USER5 0 days 00:24:49.070000128</p> <p>USER6 0 days 01:15:46.702999040</p> <p><strong>Data measurements and computed attributes:</strong></p> <p><strong><em>GPS </em></strong>coordinates, acquired by a real GPS receiver in NDT and via a simulated receiver in NST. We extracted <em>velocity </em>from the GPS measurements, computed as the distance between every two subsequent coordinates divided by their corresponding timestamps. As a second derivative, <em>Acceleration</em> was then also derived from the difference of the above-mentioned velocity change between the two subsequent points divided by the time-delta.</p> <p><strong><em>Accelerometer </em></strong>data were used to compute the Euclidean norm of the acceleration (a.k.a l<sup>2</sup>-norm) over the acceleration coordinates vector (i.e., {a<sub>x</sub>; a<sub>y</sub>; a<sub>z</sub>}) at each point in time. This feature is sometimes also referred to as the <em>energy-expenditure</em> of the motion.</p> <p><strong>Additional features:</strong></p> <p><strong><em>De/Acceleration {high / low / none}</em></strong>, computed per user per scenario. For each user, acceleration measurements were partitioned by quartiles and were computed per scenario. <em>High-Acceleration </em>was defined as values above the 3<sup>rd</sup> quartile and <em>low-acceleration</em> as values below. <em>No-acceleration</em> was denoted for the case of acceleration is equal to zero. Respectively, decelerations were computed in an equivalent manner, computed from the partitioning of negative acceleration values.</p> <p><strong>Data preparation & preprocessing</strong></p> <ul> <li>GPS coordinates were de-duplicated w.r.t subsequent entries.</li> <li>To avoid issues originating from weak/loss of GPS signal, entries were partitioned into sessions. A session is defined as a sequence of entries with time-deltas no larger than 10 seconds. Velocity & acceleration were derived based on time-deltas within sessions.</li> <li>Rows with a velocity above or equal to 50km/h were filtered out based on the assumption that a regular bike rider won't reach such speeds.</li> </ul> <p>In addition to the data sources and processing procedures mentioned above, the data has been processed according to the following. Per each subject & scenario, the data was partitioned into windows of 30 seconds using a sliding window with overlap. On each window 5 statistics were computed I.e., entropy, mean, variance, skew, kurtosis on 3 different sources: GPS-based velocity, GPS-based acceleration, and accelerometer-based magnitude. Achieving a total of 15 features.</p> <p><strong>Data Schema</strong></p> <p>The data comprises measurement data, data computed after windowing as well as subject psychometric evaluation data. Window data is computed with a sliding window of size 40 (samples) with an overlap of 20 samples. Before computing windows, the measurement data is filtered from entries with missing ‘v_gps’ (velocity computed from GPS coordinates) values.</p> <p>The measurement dataset is in the attached bicycle_cycle_1_measurement_data.csv file.</p> <p>Dataset computed with windowing is in the attached bicycle_cycle_1_windowed_data_w_computed_features.csv file.</p> <p>The psychometric evaluation dataset is in the attached bicycle_cycle_1_subject_psychometric_evaluation.csv file.</p> <p>Description of all dataset attributes in all three datasets is detailed in the Data description.docx file.</p> <p>Preliminary correlations identified in the dataset is detailed in BICYCLE-DATA-CORRELATIONS.pptx file</p>
Real-time ZTD and gradients from 161 EPN stations; year 2020
<p>This dataset contains multi-GNSS real-time products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties estimated every 1 minute over the entire year 2020 for 161 EPN (http://epncb.oma.be/) stations.</p> <p>The processing strategy can be found in https://link.springer.com/article/10.1007/s10291-020-01014-w, under to "advanced strategy" configuration, with the exception that only GPS and Galileo observations were considered.</p> <p>For convenience, products are stored in 3 formats (but each contains identical data):</p> <p>1. Standard Matlab MAT files. Each file contains a single table array (Matlab format) with the complete set of estimated parameters for a single station. Table columns are labeled and self-explanatory. A comma-delimited text file can be obtained using the in-build Matlab function "writetable.m".</p> <p>2. Self-explanatory netCDF format (<a href="https://www.unidata.ucar.edu/software/netcdf/docs/file_format_specifications.html">https://www.unidata.ucar.edu/software/netcdf/docs/file_format_specifications.html</a>), which follows the parameter naming convention of the troposphere SINEX v2 format: <a href="https://www.pecny.cz/WWW_FIL/TRO-SINEX/old/sinex_tro_2017-05-28-JD.pdf">https://www.pecny.cz/WWW_FIL/TRO-SINEX/old/sinex_tro_2017-05-28-JD.pdf</a>. Each file contains data for all stations but only for one month.</p> <p>3. Semicolon delimited text files, with a self-explanatory header line. Each file contains daily products for one station.</p> <p> </p> <p>For visualization see:</p> <p>January https://youtu.be/oU_JaSnhoYI</p> <p>February https://youtu.be/O2LnWOjudGA</p> <p>March https://youtu.be/JVvO25oYcV8</p> <p>April https://youtu.be/2wqY7BKnw8g</p> <p>May https://youtu.be/XY62BMSlbT8</p> <p>June https://youtu.be/37cxhQ-0Y7U</p> <p>July https://youtu.be/MSJVtC-DnVM</p> <p>August https://youtu.be/lfG2GnrYiBo</p> <p>September https://youtu.be/CUrdDYr_lOY</p> <p>October https://youtu.be/pKPsTjKmvtE</p> <p>November https://youtu.be/s5gbtEdwiSM</p> <p>December https://youtu.be/rrn1SuzlM2k</p>
Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery
<p><strong>Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery</strong></p> <p>This collection consists of ten open access relations commonly used by the data management community. In addition to the relations themselves (please take note of the references to the original sources below), we added three lists in this collection that describe approximate functional dependencies found in the relations. These lists are the result of a manual annotation process performed by two independent individuals by consulting the respective schemas of the relations and identifying column combinations where one column implies another based on its semantics. As an example, in the <em>claims.csv</em> file, the <em>AirportCode</em> implies <em>AirportName</em>, as each code should be unique for a given airport.</p> <p>The file <em>ground_truth.csv</em> is a comma separated file containing approximate functional dependencies. <em>table</em> describes the relation we refer to, <em>lhs</em> and <em>rhs</em> reference two columns of those relations where semantically we found that <em>lhs</em> implies <em>rhs</em>.</p> <p>The file <em>excluded_candidates.csv</em> and <em>included_candidates.csv</em> list all column combinations that were excluded or included in the manual annotation, respectively. We excluded a candidate if there was no tuple where both attributes had a value or if the <em>g3_prime</em> value was too small.</p> <p><strong>Dataset References</strong></p> <ul> <li><em>adult.csv</em>: Dua, D. and Graff, C. (2019). <a href="http://archive.ics.uci.edu/ml">UCI Machine Learning Repository</a>. Irvine, CA: University of California, School of Information and Computer Science.</li> <li><em>claims.csv</em>: TSA Claims Data 2002 to 2006, <a href="https://www.dhs.gov/tsa-claims-data">published by the U.S. Department of Homeland Security</a>.</li> <li><em>dblp10k.csv</em>: Frequency-aware Similarity Measures. Lange, Dustin; Naumann, Felix (2011). 243–248. <a href="https://hpi.de/naumann/projects/repeatability/datasets/dblp-dataset.html">Made available as DBLP Dataset 2</a>.</li> <li><em>hospital.csv</em>: Hospital dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li><em>t_biocase_...</em> files: t_bioc_... files used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li><em>tax.csv</em>: Tax dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> </ul>
D^2EPC BIM-based Digital Twin data model example and real-time building measurements
<p>An example building digital twin data model, developed within the H2020 project D^2EPC, corresponding to the first out of six Case Studies (CERTH nZEB Smart House DIH). The following files are provided:</p><p>i) The BIM-based data model of the building parameters (.json file)</p><p>ii) Building real-time collected measurements within the project (in separate .json files):</p><ul><li>Living room: CO2, temperature, humidity, luminance, presence, PM2.5, TVOCs, loudness, smoke</li><li>Office: temperature, humidity, luminance, presence</li><li>Entire ground floor: HVAC system electrical energy consumption</li><li>Entire first floor: HVAC system electrical energy consumption</li><li>Entire building: electrical energy consumption (lighting & appliances)</li><li>Building PV installation: electrical energy production</li></ul><p> </p>
SBC LTER: Land: Hydrology: Stream discharge and associated parameters at Santa Monica Creek, Via Real (SM01)
Stream Discharge and water temperature were collected with a Solinst Model 3001 LT Levelogger at Santa Monica Creek, Via Real in the Santa Barbara coastal area (site ID: SM01). Data are reported hourly. Stage values were converted to discharge using a rating curve developed with stream channel cross-sections, roughness estimates and the HEC-RAS model.
MACREL software benchmark data set: Simulated metagenomes with sequencing quality, errors profile and abundance distributions derived from real samples
<p>These metagenomes were used in the benchmarking of FACS pipeline, and were designed after NGLess benchmark dataset (doi.org/10.5281/zenodo.2560288). Metagenomes were simulated with <a href="https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm">ART-bin-MountRainier-2016.06.05</a> using real abundance profiles (.abund files) available <a href="https://doi.org/10.5281/zenodo.2560288">elsewhere</a>, and <a href="http://progenomes1.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> as reference genomes. There are available metagenomes with 40, 60 and 80 M (million of reads) based in the reference genomes and abundances of the following samples:</p> <pre><code>SAMEA2466916 SAMEA2466953 SAMEA2466965 SAMEA2621107 SAMEA2621229 SAMEA2621247</code></pre> <p>To convert them from the CRAM format back to fastq files:</p> <pre><code> ## 1. converting from cram to bam format: samtools view -b -T refgenome.fa -o file.bam file.cram ## 2. sorting the bam file: samtools sort -n file.bam -o input_sorted.bam # sort reads by identifier-name (-n) ## 3. converting from bam to fastq format: bedtools bamtofastq -i input_sorted.bam -fq output_r1.fastq -fq2 output_r2.fastq </code></pre> <p> </p>
Repository of data supporting the thesis "Poly-algorithmic Techniques in Real Quantifier Elimination"
<p>Dataset of various files (as a .zip) supporting the PhD thesis "Poly-algorithmic Techniques in Real Quantifier Elimination" by Zak Tonks, University of Bath. The PhD thesis is in the area of Quantifier Elimination over the Reals (QE) in Computer Algebra. The PhD thesis concerns implementation of algorithms in Quantifier Elimination, which largely culminates in the package QuantifierElimination for the Computer Algebra software Maple. Much of this repository is output of the benchmarking of this package against various other packes in Maple and otherwise. Otherwise there are some auxiliary tools and files to assist with working with QE in Maple, converting between various formats, and understanding case studies and the package QuantifierElimination via software demoes as Maple worksheets.</p> <p>An overview of the contents of this repository (as a .zip file, which contains subdirectories described in the README):</p> <ul> <li>The example databases contributed from the project, as files that can be read into Maple defining tables of examples, and associated functions to build or examine various examples,</li> <li>A pdf file providing the references for all examples from the example databases, and typesetting of the examples as associated QE problems,</li> <li>The benchmarking data produced from the benchmarking of the project as csv (comma separated value) files, and the Excel workbooks (xlsx files) processing said data into survival plots for the thesis,</li> <li>Copies of the survival plots themselves as .png files,</li> <li>The bash and Maple scripts used to generate the raw benchmarking data, that can be reused, including documentation how to do so in the associated README,</li> <li>Other auxiliary tools allowing for conversion of QE formulae between formats (such as that of SyNRAC, RegularChains, QuantifierElimination (amongst packages in Maple), and QEPCAD B.</li> <li>Maple worksheets and the associated exported pdf files used in software demos at conferences to demonstrate features of QuantifierElimination.</li> <li>Some pdf files demonstrating early case studies on Lazard curtains generated from an early development build of QuantifierElimination.</li> </ul> <p>Lastly, there is a README with more detail on the files of the repository further. To emulate the benchmarking of the thesis, an understanding of bash and potentially Maple is assumed, but the raw data from the project is provided here. Before QuantifierElimination's official release, the source code and/or Maple package as an .mla file is available for interested parties upon request to the author Zak Tonks (<a href="mailto:zak.p.tonks@bath.edu">zak.p.tonks@bath.edu</a>). Any other queries about this data or associated work should be directed to this email address. The author's PhD supervisor's email address is <a href="mailto:J.H.Davenport@bath.ac.uk">J.H.Davenport@bath.ac.uk</a>.</p>
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