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3,101 results for “tables”
Coastal marine megaripple fields are metabolic hotspots - Data for figures and tables
This dataset contains the data used to create figures 2-5 in the paper: Berg, P. in press. Coastal marine megaripple fields are metabolic hotspots with highly dynamic oxygen exchange. L&O Letters (publication expected in 2023). Details on the nature of the data and how it was collected are included in the paper.
Dataset for Creating a Scholarly Knowledge Graph from Survey Article Tables
<ul> <li><strong>Selected papers.csv</strong><br> This file lists all selected survey papers used to create the knowledge graph. The file contains paper titles, table references (of the tables that are extracted), sources and the reference to the survey paper (either a DOI, or a full textual reference)<br> </li> <li><strong>ORKG comparisons.csv</strong><br> All comparisons imported in the ORKG are listed in this file. Per survey paper, multiple tables could be extracted, and therefore multiple comparisons are created. The files lists the internal IDs and the URL to the comparisons. <br> </li> <li><strong>Ingested papers.csv</strong><br> A full list of all individual papers extracted from the survey articles. The file contains paper titles and their respective URL in the ORKG. </li> </ul>
Tree measurements and summaries of the field plots used to develop Rojo and Montero (1996) yield tables for Pinus sylvestris L. in central Spain
<p>Tree measurements for principal trees and trees marked for thinning and summaries of the Pinus silvestris L. plots measured for the construction of Rojo and Montero (1996) Pinus sylvestris L. yield tables for central Spain. PRM_Functions.R contains R functions implementing parameter recovery methods to transform Rojo and Montero (1996) Pinus sylvestris L. yield tables into a diameter distribution model.</p> <p><strong>Trees.csv: </strong>Comma separated file with headers in the first row. Each record represents a measured tree. Fields:</p> <ul> <li>"PlotID": Identifier of the plot where the tree was measured</li> <li>"Type": Code indicating if the tree was marked for thinning.</li> <li>"ID_tree" Tree_Identifier</li> <li>"DBH1": First Diameter at breast height measurement for the tree.(mm)</li> <li> "DBH2" Second diameter at breast height measurement for the tree. The second measurement was taken in the direction perpendicular to the first measurement. (mm)</li> <li>"DBHmean": Mean of DBH 1 and DBH 2 <strong>and converted to cm</strong> (cm)</li> </ul> <p><strong>Plot_summaries.csv: </strong>Comma separated file with headers in the first row. Data digitized from Annex II of Rojo and Montero (1996). Each record contains different forest attributes of the plot. Fields:</p> <ul> <li>"PlotID": Identifier of the plot where the tree was measured</li> <li>"Age": Age of the plot determined from tree cores (Years)</li> <li>"Ho" Assman Dominant height for the plot (meters)</li> <li>"SiteIndex": Site index for the plot in meters. Site index is defined as the dominant height in meters measured or expected for the plot for an Age of 100 years.</li> <li>"MeanH" Mean tree height (m)</li> <li>"Dg" Quadratic mean diameter (cm)</li> <li>"Do" Dominant diameter. Mean diameter of the 100 largest trees of a hectare (cm)</li> <li>"N" Stand density (trees per hectare)</li> <li>"G" Plot basal area (m<sup>2</sup>/ha)</li> <li>"V" Total plot volume per unit area (m<sup>3</sup>/ha)</li> <li>"DeltaV" Periodic increment of merchantable volume (m<sup>3</sup>/ha)</li> <li>"Bark" Average percentage of total volume that is Bark. (%)</li> </ul> <p><strong>PRM_Functions.R: </strong>R functions to solve parameter recovery systems of equations based on mean and quadratic mean diameter and dominant diameter, quadratic mean diameter and stand density. Details provided as comments.</p> <p><strong>References</strong></p> <p>Rojo Alberto, Montero G (1996) El pino silvestre en la Sierra de Guadarrama: historia y selvicultura de los Pinares de Cercedilla, Navacerrada y Valsain. Ministerio de Agricultura, Pesca y Alimentación, Secretaria General Tecnica, Centro de Publicaciones, Madrid</p>
Global physical input-output tables for iron and steel (2008-2017).
<p><strong>Dataset:</strong> Global physical input-output tables for iron and steel</p> <p><strong>Years:</strong> 2008-2017</p> <p><strong>Base classification:</strong> 32 regions, 39 processes and 30 flows</p> <p><strong>Associated journal article: </strong>The PIOLab - Building global physical input-output tables in a virtual laboratory (forthcoming, Journal for Industrial Ecology)</p> <p><strong>Associated GitHub repository</strong>: www.github.com/fineprint-global/PIOLab</p> <p><strong>Contact:</strong> hanspeter.wieland@wu.ac.at</p> <p>The folder <em>RawData</em> contains the unprocessed results of the reconciliation run in the PIOLab. These tables (in the Tvy format) form the basis for the R scripts that are available from the GitHub repository mentioned above. Please note the instructions on GitHub for further information and how i.e. where the content of <em>RawData</em> needs to be stored in your local repository.</p> <p>The folder <em>gPSUT</em> contains the processed physical supply-use tables, including final use matrices and boundary input and output blocks. The variable names are described in detail in the method section of the journal article.</p> <p>The folder <em>gPIOT</em> contains the process-by-process IO model, which was used for the calculation of the footprint indicators in the Journal article. Please read the information on the footprint calculus in the journal article.</p> <p>The folder<em> Diagnostics </em>contains, for all years of the time series, results from the analyses of the constraint realization. The journal article presents only the diagnostic test for the year 2008.</p>
Paper Survey Table - Smart Mobility - role of mobile games
<p>This table (CSV File) compiles the papers reviewed for the survey paper "Smart Mobility, the role of Mobile Games" presented at the 8th Serious Games Development & Applications (SGDA 2017) conference.<br> It compiles 140 documents read and described to understand the role of mobile games in the promotion of smart mobility, especially based on urban cycling. </p> <p>The images provide relevant insights of the survey and support the analysis of the survey paper they are briefly described as follows: </p> <p>Chart 1: Number of papers classified by publisher and type of publication reported.<br> Chart 2: Number of participants classified by type of publication reported.<br> Chart 3: Number of papers classified by the method reported and the number of citations.<br> Chart 4: Number of papers reporting cycling classified by type of motivation used for gamification strategies<br> Chart 5: Number of papers reporting cycling classified by device and location technology reported</p> <p>The following is a short explanation of each of the columns of the table and its contents, self-explanatory titles are omitted.</p> <ul> <li>Database: source or publisher of the paper</li> <li>Type: description of the kind of publication between conference paper, book chapter, or journal paper. </li> <li>Oldest Reference: year of publication of the oldest reference cited in the paper.</li> <li>Newest Reference: year of publication of the newest reference cited in the paper.</li> <li>Citation: Classification of the number of citations that the paper has. Values: none, 1-5, 5-10, +10.</li> <li>Participants: Classification of the number of participants that the paper reports. Values: none, 1-5, 5-10, +10. </li> <li>Method Description: Short description of the method reported by the paper.</li> <li>Reported Method: Classification of the reported method of the paper in three main categories. Design, Experiment / Test, Literature review, Survey.</li> <li>Gamification - Motivation: Classification of the sources of motivation reported. Values: Intrinsic Motivation, Extrinsic Motivation, Mixed.</li> <li>Gamification - Negative Issues: Filled when the paper is reporting the analysis of negative consequences of using gamification:</li> <li>Gamification: Classification of the kind of technique reported. Values: Gamified, not Gamified</li> <li>Device and Location Usage: Classification of the use of mobile devices, wearables and location technologies. Values: No device, Device enabled, Mobile and location enabled, Device and location enabled.</li> <li>App Name: The name of the application reported by the paper when it exists.</li> </ul>
PARESv3 : PArish REgistry Survey − Historical Census Table Dataset (19th, 20th centuries) − France
<h2>PARES Dataset v3</h2> <p>PARES (PArish REcord Survey) contains<strong> 535 images of handwritten census tables</strong> for years ranging from around <strong>1650 A.D. until 1850 A.D.</strong>.They come from two <strong>French cities</strong>, Vic-sur-Seille (French department of Moselle) and Echevronne (French department of Côte d'Or). While they mention very ancient times, the documents are handwritten transcriptions of even older documents and are quite recent, copied from original documents during the 1950's and 1960's for demographic studies led by the INED in France (<em>Institut National des études démographiques</em> − National Institute for Demographic Studies). These copies were made by only a few different writers.</p> <p>In this updated version of the dataset, each table row has been carefully annotated and transcribed. Please note that for each row transcription, we have specified the attribute to which each value corresponds.</p> <p>We published a paper, <a href="https://link.springer.com/article/10.1007/s10032-025-00531-z">The PARES Database: Information Extraction over Historical Parish Records,</a> in which we better describe the dataset and the tasks it's possible to run on it.</p> <p> </p>
Table of Ultracool Fundamental Properties
<p>The Table of Ultracool Fundamental Properties is a subset of The UltracoolSheet (version 2.0.0, in preparation) associated with <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230903082S/abstract">Sanghi et al. (2023)</a>: The Hawaii Infrared Parallax Program. VI. The Fundamental Properties of 1000+ Ultracool Dwarfs and Planetary-mass Objects Using Optical to Mid-IR SEDs and Comparison to BT-Settl and ATMO 2020 Model Atmospheres.</p> <p>It is a comprehensive compilation of the astrometric, photometric, spectroscopic, age properties, and fundamental parameters (bolometric luminosities, masses, radii, surface gravities, and effective temperatures) of all objects in the above paper's sample.</p> <p>Three CSV files are provided here: (1) Ultracool_Fundamental_Properties_Table.csv (with all object information); (2) AgeValues.csv (with age values for the different age assignment categories); and (3) References.csv (with citation information). The README.txt file contains a detailed description of the contents of the three CSV files.</p> <p>When using data from the Table of Ultracool Fundamental Properties, please cite the individual papers from which the data comes. Citations codes are included for all the data in the tables, and the References table translates the citation codes into ADS bibcodes, Papers citekeys, and publication titles. For research that benefits from this compilation, please cite this Zenodo post and include the following acknowledgment:</p> <p>"This work has benefitted from The UltracoolSheet at http://bit.ly/UltracoolSheet, maintained by Will Best, Trent Dupuy, Michael Liu, Aniket Sanghi, Rob Siverd, and Zhoujian Zhang, and developed from compilations by <a href="https://ui.adsabs.harvard.edu/abs/2012ApJS..201...19D/abstract">Dupuy & Liu (2012, ApJS, 201, 19)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2013Sci...341.1492D/abstract">Dupuy & Kraus (2013, Science, 341, 1492)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2016ApJ...833...96L/abstract">Liu et al. (2016, ApJ, 833, 96)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2018ApJS..234....1B/abstract">Best et al. (2018, ApJS, 234, 1</a>), <a href="https://ui.adsabs.harvard.edu/abs/2021AJ....161...42B">Best et al. (2021, AJ, 161, 42)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230903082S/abstract">Sanghi et al. (2023)</a>, and <a href="https://ui.adsabs.harvard.edu/abs/2023AJ....166..103S/abstract">Schneider et al. (2023, AJ, 166, 103)</a>."</p>
Individual Table for Tel Aviv 2040 in SimMobility MIT Preday
<p>This table offers insights into the synthetic population of the Tel Aviv Metropolis projected for 2040. It serves as a forecast derived from various predictions by Israeli government officials. Primarily designed for research purposes, it is especially intended to be utilized as input for the SimMobility MIT demand simulator.</p>
Preliminary table of Citizen Science initiatives for monitoring soil health
<p>This matrix is the result of collaborative work for Deliverable 1.1 (WP1; T1.1) of the ECHO project. It facilitated the creation of an overview of the current state of the art in projects, initiatives, or activities that have already involved citizens in monitoring soil health, from both inside and outside the European Union. From this, strategic recommendations for ECHO were derived, ensuring that this project not only makes a valuable contribution to the field of soil health monitoring but also sets a precedent for future citizen science endeavors.</p>
Data Tables for the CARMA Extragalactic Database for Galaxy Evolution (CARMA EDGE)
<p>Data tables accompanying the Python package <a href="https://github.com/tonywong94/edge_pydb">edge_pydb</a> which provides access to spatially resolved measurements of CO and optical emission from the CARMA EDGE sample of 125 nearby galaxies. A full description can be found in the article by <a href="https://doi.org/10.3847/1538-4365/ad20c9" target="_blank" rel="noopener">Wong et al. (2024)</a>.</p>
Data Tables for Enrichment by Extragalactic First Stars in the Large Magellanic Cloud
<p>Machine-readable data tables for the article, "Enrichment by Extragalactic First Stars in the Large Magellanic Cloud"-- DOI: 10.1038/s41550-024-02223-w. Full descriptions of the tables are in the article, but we briefly summarize here-</p> <p>Table 1 (Table1.csv): This table provides general information on the stars for which we obtained long-exposure Magellan/MIKE data to derive their detailed elemental abundances. Columns include names, coordinates, SkyMapper g magnitudes from the Gaia XP spectra, and stellar parameters and metallicities with their respective random uncertainties.</p> <p>Table 2 (Table2.csv): This table provides names, radial velocities, metallicities, and selected elemental abundances with random uncertainties for stars listed in Table 1. [C/Fe]_c indicates carbon abundances that are corrected for the evolutionary state of the star following Placco et al. (2014). Abundances that are upper limits are flagged by the ul_[X/Fe] columns and have "nan" values for the uncertainty.</p> <p>Extended Data Table 1 (Extended_Data_Table1.csv): This table summarizes all of our observations, by providing names, coordinates, SkyMapper g magnitudes from the Gaia XP spectra, exposure times, dates of observation, and the instrument for these observations. This table includes stars observed with MagE, those observed with short-exposures with MIKE for just metallicities and carbon abundances, and those flagged as more metal-rich upon initial exposure and hence, not further observed. </p> <p>Extended Data Table 2 (Extended_Data_Table2.csv): This table provides names, followed by stellar parameters, metallicities, and carbon abundances, along with their respective uncertainties, for stars observed with MagE or MIKE for short exposures to just obtain a metallicity and carbon abundance. As in Table 2, abundances that are upper limits are flagged by the ul_[C/Fe] column and "nan" entries for the uncertainty.</p> <p>Supplementary Data 1 (Summary_Data_1.csv or Summary_Data_1.ascii): This table provides the suite of detailed element abundances and uncertainties from the long-exposure MIKE spectra of the stars in Table 1. Columns include the name, atomic number and ionization state of the element (element), the number of features used to estimate the elemental abundance (N), the solar abundance of that element (Solar), the absolute abundance (logeps), the chemical abundance scaled by the solar abundance relative to hydrogen ([X/H]), the ratio with respect to the iron abundance ([X/Fe]), the random uncertainty ([X/H]_err) and an upper limit flag (ul), and errors from propagating the uncertainties in the individual stellar parameters and the overall systematic and total uncertainty ([X/H]_errteff, [X/H]_errlogg, [X/H]_errvt, [X/H]_errsys, [X/H]_errtot). These are followed by the same columns, but with respect to iron (e.g., [X/Fe]_errteff, [X/Fe]_errlogg). Abundances of the CH molecule are indicated by 106.0 in the "element" column. This table is provided as a machine readable csv file and as an ascii file, the latter for easier visual readability.</p> <p>Supplementary Data 2 (Summary_Data_2.csv or Summary_Data_2.ascii): This table summarizes the chemical abundances from individual absorption features for the LMC stars with long-exposure MIKE spectra. The columns include the star name, the atomic number and ionization state of the element (species), the solar abundance of that element (Solar), followed by the wavelength, excitation potential, and loggf of the line (wavelength, expot, loggf), and then the measured equivalent width (EW), absolute abundance (logeps), and a flag indicating whether the abundance is an upper limit (ul). Abundances derived via spectral synthesis have "nan" entries for expot, loggf, and EW, and abundances of the CH molecular band are indicated by 106.0. This table is provided as a machine readable csv file and as an ascii file, the latter for easier visual readability. </p>
Database of Uniaxial Shaking Table Tests for a Two-Storey Steel-Frame Structure
<h2>Description:</h2> <p>This data set contains data from experiments conducted on a two-storey steel-frame structure, involving sine-sweep, white noise, impulse, and earthquake loading. The experiments were carried out using a uniaxial shaking table of the <a href="https://www.lbb.rwth-aachen.de/go/id/eaxh/">Institute of Structural Analysis and Dynamics (LBB) - RWTH Aachen University</a>, in cooperation with the <a href="https://www.stb.rwth-aachen.de/cms/~iozv/STB/">Institute of Structural Steel (STB) - RWTH Aachen</a> and the <a href="https://www.cwe.rwth-aachen.de/home-2/">Center for Wind and Earthquake Engineering (CWE) – RWTH Aachen</a>.</p> <h3>Test structure:</h3> <p>The test structure was a two-storey, single-bay moment resisting frame (MRF) in the direction of excitation. In the perpendicular direction, a concentrically braced system provided lateral stability. The dimensions of the test structure were: 2.40 m length, 2.40 m width, and 3.78 m total height. Each floor had a distinct height: 2.02 m for the first floor and 1.76 m for the second. The moment resisting connections were realized as bolted extended unstiffened endplate joints. The column web panel was strengthened by two supplementary web plates (SWP) and continuity plates (CP). The base plate was meticulously modified to allow for rotation movement in order to approximate a pinned boundary condition. In addition, diaphragm action was ensured using diagonally arranged L-profiles in the plane of each floor. For additional mass, four steel I-profiles, each weighting 1650 kg were attached to the main structure (2 on each floor) and were secured by using steel U-profiles. An industrial pressure vessel with a self-weight of 100 kg, which remained empty for the first phase of the experimental campaign, was additionally mounted on the first floor. </p> <p>The properties of the frame structure are:</p> <ul> <li>Columns: HEA200 S355-J2 + 2 SWP + 6 CP</li> <li>Beams: HEA160 S235-JR</li> <li>Bolts: 8x M16, 10.9 HV</li> <li>Joints: Partial strength, semi-rigid</li> <li>End-plate: 272x190x8 mm</li> <li>Welds: Full penetration groove welds</li> </ul> <h3>Test setup:</h3> <p>The shaking table specifications are:</p> <ul> <li>Table size: 3.0x3.0 m</li> <li>Max. specimen mass: 10 t</li> <li>Max. overturning moment: 30 m t</li> <li>Max. actuator stroke: +/- 250 mm</li> <li>Max. table velocity: +/- 1 m/s at rated load</li> <li>Max. table acceleration: +/- 1g at rated load</li> <li>Test frequency: 0 to 50 Hz</li> </ul> <p>The instrumentation scheme of the test setup consisted mainly of accelerometers, displacement tranducers and strain gauges, measuring the excitation provided by the shaking table and the response of the structure. Regarding the global response of the test structure, the recordings of the accelerometers and displacement tranducers indicated in the uploaded file 'Instrumentation_Scheme_v1.0.0.pdf' are provided. </p> <p>The properties of the accelerometers are:</p> <ul> <li>Type: M3701-series</li> <li>Manufacturer: PCB Piezotronics, Inc.</li> <li>Measurement range: +/- 3g</li> <li>Frequency range: 0-500 Hz</li> <li>Sensitivity: 900 mV/g</li> <li>Resolution: 2.2e-5g</li> <li>Noise: 1 µg/Hz<sup>-0.5</sup></li> </ul> <p>The properties of the displacement tranducers are:</p> <ul> <li>Type: LZW-M-500</li> <li>Manufacturer: WayCon Positionsmesstechnik GmbH</li> <li>Measurement range: +/- 250 mm</li> <li>Linearity: +/- 0.05%</li> <li>Repeatability: 0.01 mm</li> <li>Displacement force: ≤15 N</li> <li>Displacement speed: ≤5 m/s</li> </ul> <h2>Files:</h2> <ul> <li>Load_Protocols_v1.0.0.pdf <ul> <li>.pdf file listing all load protocols applied to the structure.</li> </ul> </li> <li>Time_Histories_v1.0.0.pdf <ul> <li>.pdf file displaying the acceleration and displacement time histories according to the load protocols.</li> </ul> </li> <li>Data_v1.0.0.zip <ul> <li>Contains all data files according to the load protocols.</li> <li>The experimental data is provided as .csv files for each load protocol. The experiments are named by the load protocol. A .pdf file contains the corresponding data plots.</li> </ul> </li> <li>References_v1.0.0.bib <ul> <li>Contains a bibtex reference with the associated publications.</li> </ul> </li> <li>Shake_Table.jpg <ul> <li>Photo of the shaking table without any specimen.</li> </ul> </li> <li>Test_Structure_v1.0.0.jpg <ul> <li>Photo of the shaking table including the test structure.</li> </ul> </li> <li>Test_Structure_Sketch_v1.0.0.pdf <ul> <li>.pdf file illustrating the test structure.</li> </ul> </li> <li>Instrumentation_Scheme_v1.0.0.pdf <ul> <li>.pdf file illustrating the sensor placements on the test structure.</li> </ul> </li> </ul> <h2>File format of the data sets:</h2> <p>The data is stored in .csv files, where each file contains the following columns:</p> <ul> <li>Time: Time in seconds since the start of the test (time step equals 0.003 s).</li> <li>Acc_0: Acceleration signal measured in m/s<sup>2</sup> on the shaking table.</li> <li>Acc_1: Acceleration response of the structure measured in m/s<sup>2</sup> on the first floor.</li> <li>Acc_2: Acceleration response of the structure measured in m/s<sup>2</sup> on the second floor.</li> <li>Disp_0: Displacement signal measured mm on the shaking table.</li> <li>Disp_1: Displacement response of the structure measured in mm on the first floor.</li> <li>Disp_2: Displacement response of the structure measured in mm on the second floor.</li> </ul> <p>These data files can easily be uploaded using the pandas library in Python. For example by:</p> <pre><code>import pandas as pd df = pd.read_csv('LP01_Sweep_001.csv') time = df["Time"] acc_0 = df["Acc_0"] disp_0 = df["Disp_0"]</code></pre> <h2>Contact:</h2> <p>Please send your enquiries regarding the shaking table to <a href="dynamics@lbb.rwth-aachen.de">dynamics@lbb.rwth-aachen.de</a>. Further information can be found on our <a href="https://www.lbb.rwth-aachen.de/go/id/eaxh/">website</a>.</p> <h2>Usage/License:</h2> <ul> <li>The data is licensed under CC BY-SA 4.0.</li> <li>If you have used our data and are publishing your work, we ask you to please reference both <ul> <li>this database by its DOI, and</li> <li>any publication that is associated with the experiments. See the "References_v1.0.0.bib" for the associated publication references.</li> </ul> </li> </ul> <h2>Fundings:</h2> <ul> <li>Deutsche Forschungsgemeinschaft - <em>Grant number: INST 222/1161-1 FUGG</em>. Einaxialer Schwingtisch für dynamische Modell- und Bauteilversuche.</li> <li>Bundesministerium für Bildung und Forschung - <em>Grant number: 03G0892A</em>. ROBUST – Nutzerorientiertes Erdbebenfrühwarnsystem mit intelligenten Sensorsystemen und digitalen Bauwerksmodellen – Entwicklung Installation und Anwendung von sensorbasierten Monitoringsystemen mit BIM-Integration zur Echtzeit-Schadenerkennung in kritischen Infrastrukturen.</li> </ul> <p> </p>
Tables of results for lower mantle grain size and viscosity estimates
<p>Data on diffusivity, grain size, and viscosity calculations are shown in Figs. 7-10 and Figs. S4-S5 in Okamoto and Hiraga's "A Common Diffusional Mechanism for Creep and Grain Growth in Polycrystalline Rocks: Application to Lower Mantle Viscosity Estimates".</p>
SANTOS Benchmark for Table Union Search
<p>This record contains the datasets released with <a href="https://2023.sigmod.org/">SIGMOD 2023</a> paper entitled "<a href="https://dl.acm.org/doi/10.1145/3588689">SANTOS: Relationship-based Semantic Table Union Search</a>". We release two new tabular benchmarks to evaluate the table union search problem over the data lakes. Furthermore, we also release relabeled ground truth for an existing <a href="https://github.com/RJMillerLab/table-union-search-benchmark">TUS benchmark</a> by taking the binary relationship between the columns into account. Please visit <a href="https://dl.acm.org/doi/10.1145/3588689">our paper</a> for further details.</p> <p>If you use our dataset for your work, please cite our paper as:</p> <p>Aamod Khatiwada, Grace Fan, Roee Shraga, Zixuan Chen, Wolfgang Gatterbauer, Renée J. Miller, and Mirek<br>Riedewald. 2023. SANTOS: Relationship-based Semantic Table Union Search. SIGMOD Conference 2023, ACM</p> <p>@article{DBLP:journals/pacmmod/KhatiwadaFSCGMR23,<br> author = {Aamod Khatiwada and<br> Grace Fan and<br> Roee Shraga and<br> Zixuan Chen and<br> Wolfgang Gatterbauer and<br> Ren{\'{e}}e J. Miller and<br> Mirek Riedewald},<br> title = {{SANTOS:} Relationship-based Semantic Table Union Search},<br> journal = {Proc. {ACM} Manag. Data},<br> volume = {1},<br> number = {1},<br> pages = {9:1--9:25},<br> year = {2023},<br> doi = {10.1145/3588689},<br>}</p> <p>You can find SANTOS implementation at: <a href="https://github.com/northeastern-datalab/santos">https://github.com/northeastern-datalab/santos</a></p> <p>You can find the original TUS benchmark at: <a href="https://github.com/RJMillerLab/table-union-search-benchmark">https://github.com/RJMillerLab/table-union-search-benchmark</a></p> <p>Abstract: Existing techniques for unionable table search define unionability using metadata (tables must have the same or similar schemas) or column-based metrics (for example, the values in a table should be drawn from the same domain). In this work, we introduce the use of semantic relationships between pairs of columns in a table to improve the accuracy of union search. Consequently, we introduce a new notion of unionability that considers relationships between columns, together with the semantics of columns, in a principled way. To do so, we present two new methods to discover semantic relationship between pairs of columns: The first uses an existing knowledge base (KB), the second (which we call a “synthesized KB”) uses knowledge from the data lake itself. We adopt an existing Table Union Search benchmark and present new (open) benchmarks that represent small and large real data lakes. We show that our new unionability search algorithm called SANTOS outperforms a state-of-the-art union search that uses a wide variety of column-based semantics, including word embeddings and regular expressions. We show empirically in all benchmarks that our synthesized KB improves the accuracy of union search by representing relationship semantics that may not be contained in an available KB. This result hints at a promising future of creating a synthesized KBs from data lakes with limited KB coverage and using them for union search.</p>
Kelvin (color temperature) to RGB conversion table.
<p>This CSV provides the corresponding RGB values for a specific color temperature (measured in Kelvin). It can be used to determine the appropriate RGB color values for a given color temperature. This dataset provides an approximation following the <a href="https://cie.co.at/datatable/cie-1964-colour-matching-functions-10-degree-observer">CIE 1964 colour-matching functions</a> that is intended for low to mid quality output media (such as LED lighting, consumer screens and consumer grade VR headsets). </p>
Supplementary Table 1 and data from the workshop on Digital Building Logbooks and Permit Processes for Sustainability in Sustainable Places 24.9.2024 in Luxembourg
<p>This repository contains the supplementary Table 1 and data collected during a workshop on Digital Building Logbooks and Permit Processes for Sustainability. The workshop was held in Sustainable Places on the 24th of September 2024 in Luxembourg. </p>
A High-Performance Data Processing Workflow to Incorporate Effect-Directed Analysis in Suspect and Nontarget Screening [Feature Tables]
<p>This repository is supplementary to the manuscript "High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget Screening" (DOI: 10.1021/acs.est.1c04168) and includes an overview of all measured chemical features and annotations in a waste water treatment plant (WWTP) effluent, dust standard reference material (SRM) 2585 and fetal calf serum (FCS) sample.</p> <p>Samples were measured using liquid chromatography - high resolution mass spectrometry (LC-HRMS) and fractionated into 80 micro-fractions encompassing a couple of seconds from the chromatographic run. The fractions were tested for their bioactivity in the antibiotics and the TTR-binding assay. The samples were processed separately using one, two, and three technical replicates in positive and negative ion mode. The first excel sheet includes all measured chemical features, suspect screening annotation, and corresponding bioassay responses. The second sheet includes all possible isomer annotations from the CECscreen database (DOI: <a href="https://doi.org/10.5281/zenodo.3956586">10.5281/zenodo.3956586</a>) for the annotated features. </p>
Perception and evaluation of (modified) wood by older adults from Slovenia and Norway (Datasets, R analysis code, and supplementary tables)
<p>This entry contains datasets, R analysis code, and supplementary tables for the article <em>Perception and evaluation of (modified) wood by older adults from Slovenia and Norway.</em></p> <p>The article investigates human perception and evaluation of handrails made of different materials. Our goal was to identify how older adults perceive handrails made of unmodified wood, modified wood, and steel. We examined if certain materials are more preferred than others, which material properties might be associated with differences in human preference, and what are the roles of tactile and tactile-visual domains in material perception. Our analysis is based on the results from an 11-item rating scale and a ranking task.</p>
3D-Scere static files and result table
<p>Static files, result table and graphical representations associated with the 3D-Scere project (dashboard and publication).</p> <p> </p> <p>3D_distances.parquet.gzip: three-column parquet file with 3D distances between <em>Saccharomyces cerevisiae </em>chromosomal features.</p> <p>SCERE.db: SQLite database of <em>Saccharomyces cerevisiae </em>features build from the <a href="https://www.yeastgenome.org/">SGD</a>.</p> <p>Table1.tsv: tab-separated values file with Kolmogorov Smirnov test results for <em>Saccharomyces cerevisiae </em>transcription factors.</p> <p>supplementary-data-file-S4.zip: ZIP file with graphical representations associated to each transcriptional module.</p>
Data used to create figures and tables in the ACP manuscript "Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality" by Gao et al. (2022)
<p>This dataset contains the original data that extracted from all collected papers refering applications of two-way coupled models in Asia. It is supplied to the review paper, which titled as "Review on two-way coupled meteorology and air quality models in Asia: impacts of aerosol feedbacks on meteorology and air quality". The dataset includes three excel files (in the format of xlsx) as follows:</p> <p>1. Basic information of literatures (Table S1.xlsx)</p> <p>2. Model performance metrics (Table S2.xlsx)</p> <p>3. Quantitative results of aerosol effects on meteorological and air quality variables (Table S3.xlsx)</p> <p>4. Basic information of model setup for two-way coupled model applications in Asia (Table S4.xlsx)</p> <p>5. Summary of aerosol-induced variations of simulated shortwave and longwave radiative forcing at the bottom and top of atmosphere and in the atmosphere in Asia (Table S5.xlsx)</p> <p>.</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.