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37 results for “Tabular data”
Tabular data of the Proto-Austronesian-to-Enggano sound changes derived from Nothofer (1992: 21)
<p>This repository provides the <a href="https://github.com/engganolang/PAN-reflexes-in-Enggano-by-Nothofer/blob/main/Nothofer_1992_21_reg-sound-reflex-of-PAN-in-ENO.tsv">digitised, tabular version</a> of the regular sound changes from Proto-Austronesian into Enggano presented in linear order in Nothofer (<a href="https://github.com/engganolang/PAN-reflexes-in-Enggano-by-Nothofer#ref-nothofer1992">1992, 21</a>). The original, image-scanned version can be seen on the <a href="https://github.com/engganolang/PAN-reflexes-in-Enggano-by-Nothofer/blob/main/README.md">README page</a> of this repository.</p> <h2>Update for version 1.0.1</h2> <ul> <li>Additional entries to capture optionality of nasalisation on the vowels.</li> </ul> <h2>How to cite</h2> <p>Cite this repository AND the original source (Nothofer 1992) as follows:</p> <blockquote> <p>Rajeg, Gede Primahadi Wijaya. 2024. Tabular data of the Proto-Austronesian-to-Enggano sound changes derived from Nothofer (1992: 21).</p> </blockquote> <blockquote> <p>Nothofer, Bernd. 1992. "Lehnwörter Im Enggano." In Kölner Beiträge Aus Malaiologie Und Ethnologie Zu Ehren von Professor Dr. Irene Hilgers-Hesse, edited by F. Schulze and Kurt Tauchmann, 1:21–32. Kölner Südostasien Studien. Bonn: Holos.</p> </blockquote>
Frictionless Tabular Data Package for GC-MS Rose scent profile data for Data published in Nature genetics, June, 2018 & Science, July 2015
<p>This dataset, in the form of a Frictionless Tabular Data Package (https://frictionlessdata.io/specs/tabular-data-package/), holds the measurements of 35 known metabolites(all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in one Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and one organism part (annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable STATO terms. The measurements over these metabolites, which were made in 2 distinct experiments, were extracted from: a supplementary material table, available from https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip and published alongside the Nature Genetics manuscript identified by the following doi: https://doi.org/10.1038/s41588-018-0110-3, published in June 2018 a supplementary material table available as a pdf from 'Biosynthesis of monoterpene scent compounds in roses' by Magnard et al, Science 03 Jul 2015 identified by the following doi: https://doi.org/10.1126/science.aab0696. This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR)and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.It is associated to the following project: https://github.com/proccaserra/rose2018ng-notebook with all the necessaryinformation, executable code and tutorials in the form of Jupyter notebooks.</p>
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</p>
Frictionless Tabular Data Package for GC-MS data from the 'Rose Genome' article published in Nature genetics, June, 2018
<p>This dataset, in the form of a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holds the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. </p> <p>The data was extracted from a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018. This supplementary material table was deposited to Zenodo and is identified by the following doi: <a href="https://doi.org/10.5281/zenodo.2598799">https://doi.org/10.5281/zenodo.2598799</a></p> <p>This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p>
Frictionless Tabular Data Package for GC-MS Rose scent profile data for Data published in Nature genetics, June, 2018 & Science, July 2015
<p>This dataset, in the form of a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holds the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. </p> <p>The data were extracted from:</p> <ul> <li>a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018</li> <li>a supplementary material table available as a pdf from "Biosynthesis of monoterpene scent compounds in roses" by Magnard et al, Science 03 Jul 2015 identified by the following doi: <a href="https://doi.org/10.1126/science.aab0696">https://doi.org/10.1126/science.aab0696</a></li> </ul> <p>This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p> <p> </p>
Frictionless Tabular data package for GC-MS data from Rose Genome article published in Nature genetics, June, 2018
<p>This dataset, in the form of a Frictionless Tabular Data Package (https://frictionlessdata.io/specs/tabular-data-package/), holds the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxId) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The data was extracted from a supplementary material table, available from https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip and published alongside the Nature Genetics manuscript identified by the following doi: https://doi.org/10.1038/s41588-018-0110-3, published in June 2018. This dataset is used to demonstrate how to make data Findeable, Accessible, Discoverable and Interoperable(FAIR) and how Tabular Data Package representations can be easily mobilized for re-analysis and data science. It is associated to the following project available from github at: https://github.com/proccaserra/rose2018ng-notebook with all necessary information and Jupyter notebooks.</p>
Ports, Past and Present Heritage Stories (perma.cc link tabular data)
<p>A list of 281 heritage stories from the Ports, Past and Present project written in Omeka Classic using Curatescape. This tabular data is a modified output from the <a href="https://perma.cc/">perma.cc</a> folder containing a series of WARC records captured during the archiving phase of the project.</p> <p>This CSV contains only the original urls, titles, and perma.cc links of the stories. For more information including author, metadata and date of snapshot, see the WARC record and the perma.cc header above it by clicking on permalinks.</p>
Semantic Annotation for Tabular Data with DBpedia: Adapted SemTab 2019 with DBpedia 2016-10
<p>Semantic Annotation for Tabular Data with DBpedia: Adapted SemTab 2019 with DBpedia 2016-10</p> <p>Github: https://github.com/phucty/mtab4dbpedia<br> ---------------------------------------------------------------------------------------------------------------------------------------</p> <p>CEA: </p> <ul> <li> <p>Keep only valid entities in DBpedia 2016-10</p> </li> <li> <p>Resolve percentage encoding</p> </li> <li> <p>Add missing redirect entities</p> </li> </ul> <p>CTA: </p> <ul> <li> <p>Keep only valid types</p> </li> <li> <p>Resolve transitive types (parents and equivalent types of the specific type) with DBpedia ontology 2016-10</p> </li> </ul> <p>CPA:</p> <ul> <li> <p>Add equivalent properties</p> </li> </ul> <p>Statistic of Adapted Tabular data SemTab 2019</p> <pre><code>| | CEA | | | CPA | | | CTA | | | |---------|:--------:|:-------:|:------:|:--------:|:-------:|:------:|:--------:|---------|--------| | | Orginal | Adapted | Change | Orginal | Adapted | Change | Orginal | Adapted | Change | | Round 1 | 8418 | 8406 | -0.14% | 116 | 116 | 0.00% | 120 | 120 | 0.00% | | Round 2 | 463796 | 457567 | -1.34% | 6762 | 6762 | 0.00% | 14780 | 14333 | -3.02% | | Round 3 | 406827 | 406820 | 0.00% | 7575 | 7575 | 0.00% | 5762 | 5673 | -1.54% | | Round 4 | 107352 | 107351 | 0.00% | 2747 | 2747 | 0.00% | 1732 | 1717 | -0.87% |</code></pre> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------------------<br> DBpedia 2016-10 extra resources: (Original dataset http://downloads.dbpedia.org/2016-10/)</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>File: _dbpedia_classes_2016-10.csv</p> <p>Information: DBpedia classes and parents: (We remove the abstract types: Agent, Thing)</p> <p>Total: 759 classes</p> <p>Structure: [class, parents (separate with space)] (without prefix dbo: or http://dbpedia.org/ontology/)</p> <p>Example: "City","Location Place PopulatedPlace Settlement"</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>File: _dbpedia_properties_2016-10.csv</p> <p>Information: DBpedia properties and these equivalents</p> <p>Total: 2865 properties</p> <p>Structure: [property, it’s equivalent properties] (without prefix dbo: or http://dbpedia.org/ontology/)</p> <p>Example: "restingDate","deathDate"</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>File: _dbpedia_domains_2016-10.csv</p> <p>Information: DBpedia properties and these domain types</p> <p>Total: 2421 properties (have types as their domain)</p> <p>Structure: [property, type (domain)] (without prefix dbo: or http://dbpedia.org/ontology/)</p> <p>Example: "deathDate","Person"</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>File: _dbpedia_entities_2016-10.jsonl.bz2 </p> <p>Information: DBpedia entity dump</p> <p>Format: json list bz2 (bz2 Compressed json list)</p> <p>Source: DBpedia dump 2016-10 core</p> <p>Total: 5,289,577 entities (No disambiguation entities)</p> <p>Structure:</p> <p>An entity: for example “Tokyo”: (datatype: dictionary),</p> <p>{</p> <p>'wd': 'Q1322032', (Wikidata ID, datatype: string)</p> <p>'wp': 'Tokyo', (Wikipedia ID, add prefix <a href="https://en.wikipedia.org/wiki/">https://en.wikipedia.org/wiki/</a> + wp to get the Wikipedia URL, datatype: string)</p> <p>'dp': 'Tokyo', (DBpedia ID, add prefix <a href="http://dbpedia.org/resource/">http://dbpedia.org/resource/</a> + dp to get the DBpedia URL, datatype: string)</p> <p>'label': 'Tokyo', (Entity label, datatype: string)</p> <p>'aliases': ['To-kyo', 'Tôkyô Prefecture', ..], (Other entity names, datatype: list) </p> <p>'aliases_multilingual': ['东京小子', 'طوكيو', ...], (Other entity names in multilingual, datatype: list)</p> <p>'types_specific': 'City', (Entity direct type, datatype: string) </p> <p>'types_transitive': ['Human settlement', 'City', 'PopulatedPlace', 'Location', 'Place', 'Settlement'], (Entity transitive types, datatype: list)</p> <p>'claims_entity': { (entity statements, datatype: dictionary. Keys: properties, Values: list of tail entities)</p> <p>'governingBody': ['Tokyo Metropolitan Government'], </p> <p> 'subdivision': ['Honshu', 'Kantō region'],</p> <p>...</p> <p>},</p> <p>'claims_literal': {</p> <p>'string': { (String literal: datatype: dictionary. Keys: properties, Values: list of values</p> <p>'postalCode': ['JP-13'], </p> <p>'utcOffset': ['+09:00', '+9'],</p> <p>…</p> <p>}</p> <p>'time': { (Time literal: datatype: dictionary. Keys: properties, Values: list of date time</p> <p>'populationAsOf': ['2016-07-31'], </p> <p>...</p> <p>}), </p> <p>'quantity': { (Numerical literal: datatype: dictionary. Keys: properties, Values: list of values</p> <p>populationDesity: [6224.66, 6349.0], </p> <p>'maximumElevation': [2017], </p> <p>...</p> <p>},</p> <p>'pagerank': 2.2167366040153352e-06 (Entity page rank score calculated on DBpedia Graph)</p> <p>}</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
Tabular Data for "A Comprehensive Catalog of UVIT Observations I: Catalog Description and First Release of Source Catalog (UVIT DR1)"
<p>The first comprehensive catalog of UVIT includes the sources from the observations between 2016 and 2017. </p> <p>The catalog is formatted according to the machine-readable format used by the AAS Journals and CDS/VizieR. Specific information on the structure of MRT files can be found at:</p> <p><span> </span>AAS: <a href="https://journals.aas.org/mrt-overview/"><span>https://journals.aas.org/mrt-overview/</span></a></p> <p><span> </span>CDS: <a href="http://cds.u-strasbg.fr/doc/catstd.htx"><span>http://cds.u-strasbg.fr/doc/catstd.htx</span></a></p> <p><span> </span>These files can be read in python using the astropy package<span> </span>or with the most recent version of TOPCAT (> Version 4.8)</p>
Ports, Past and Present Blog (perma.cc link tabular data)
<p>A list of blog and calendar posts from the Ports, Past and Present project written on Wordpress. This tabular data is a modified output from the <a href="https://perma.cc/">perma.cc</a> folder containing a series of WARC records captured on the 26th of June, 2023.</p>
Experimental Protocol on Creative Engagement and Meaning Creation in Interactive Experiences for Cultural Heritage. Tabular Data
<p>The following document contains the material produced for the testing of the "caring prototype" <em>MyTISSE</em>, an interactive user experience on Matisse's painting <em>Bathers by a rive</em>r (Art Institute of Chicago). Focusing on the change of the pigments made by the artist during the gestation of this canvas, the UX exploits creative engagement to catalyze meaning creation processes.</p> <p>This pubblication contains the data collected through the form. All the participants agreed to the reuse and sharing of these data for academic purposes, filling in an <em>ad hoc </em>privacy form.</p>
Quantifying Changes along Mainland Marshes in the Virginia Coast Reserve, 1957-2011: Tabular data
This dataset contains data that quantifies the long-term rate of change in the shoreline position along seaside mainland marshes of the Virginia Coast Reserve between 1957 and 2009. It also contains data that delineates potential areas available for future marsh transgression under three different sea level rise scenarios based on current land use and elevation data. Additionally, the tabular data contains sediment grain size analysis results from sediment cores collected in 2011 at six of the ten study sites, plus GPS elevation profiles of the marsh edges at those sites. Shoreline position was determined for ten individual mainland marsh sites 3-4 km in length, plus the full length of the mainland marsh edge along the entire seaside length of the peninsula, based on aerial imagery from 1957, 1966, 1989, 2002, and 2009. The ten marsh sites (north to south) were at Gargathy Bay, Cedar Island Bay, Hummock Cove, Wachapreague, North Matulakin Marsh, Short and Long Prong Marshes, Crabbing Marsh, Oyster, Mockhorn Bay, and Marion Scott Cove. Metrics of the rate of change were determined using the Digital Shoreline Analysis System (DSAS), an ArcGIS extension (Thieler et al., 2009). Given the digitized shorelines and a parallel baseline as inputs, DSAS creates evenly-spaced transects perpendicular to the baseline and calculates the rate of shoreline change using a variety of methods, including the End Point Rate (EPR), linear regression rate (LRR), and various iterative and weighted linear regressions. Statistics for these regressions are included in the transect and summary point attribute data. Areas potentially suitable for future marsh transgression based on land use were determined based on 2005 NOAA Coastal Change Analysis Program (CCAP) data and consisted of upland and wetland areas currently in forest or scrub/shrub cover and adjacent to existing marshes. The acreage of these potential marsh areas that could be inundated and unavailable for marsh transgression under
Tabular and image data of article "Morphing cholinesterase inhibitor amiridine into multipotent drugs for the treatment of Alzheimer's disease"
<p>The search for novel drugs to address the medical needs of Alzheimer’s disease (AD) is an ongoing process relying on the discovery of disease-modifying agents. Given the complexity of the disease, such an aim can be pursued by developing so-called multi-target directed ligands (MTDLs) that will impact the disease pathophysiology more<br>comprehensively. Herewith, we contemplated the therapeutic efficacy of an amiridine drug acting as a cholinesterase inhibitor by converting it into a novel class of novel MTDLs. Applying the linking approach, we have paired amiridine as a core building block with memantine/adamantylamine, trolox, and substituted benzothiazole moieties to generate novel MTDLs endowed with additional properties like N-methyl-D-aspartate (NMDA) receptor affinity, antioxidant capacity, and anti-amyloid properties, respectively. The top-ranked amiridine-based compound 5d was also inspected by in silico to reveal the butyrylcholinesterase binding differences with its close structural analogue 5b. Our study provides insight into the discovery of novel amiridinebased drugs by broadening their target-engaged profile from cholinesterase inhibitors towards MTDLs with potential implications in AD therapy.</p> <p><strong>Table 1. </strong>hBChE inhibitory activities of 5c-d, 7c and 7 g and reference compounds (amiridine hydrochloride and THA); their cytotoxicity profile on SH-SY5Y cell line, and predictions of BBB penetration.</p> <p>T<strong>able 2</strong>. Relative inhibitions (RIs) of 5c-d and 7 m and reference compound memantine at recombinant human GluN1/GluN2B NMDA receptor expressed in HEK293 cells.</p> <p><strong>Fig_1</strong>. Chemical structures of rivastigmine, galantamine, and tacrine as representatives of cholinesterase inhibitors. Approaches to novel drugs for AD treatment on the selected candidates are displayed.</p> <p><strong>Fig_2</strong>. Examples of previously published amiridine-based derivatives and design strategy applied in the current study below, using various pharmacophores.</p> <p><strong>Fig_3</strong>. Top scored docking pose of 5b (A) and 5d (B) highlighting the key findings responsible for compound activity/inactivity. For the sake of clarity, superimposed ligands are aligned in the Fig. C with respect to key amino acid residue W82 to demonstrate the binding difference. Compounds 5b and 5d are colored in salmon and yellow, respectively. Essential amino acid residues responsible for ligand anchoring are rendered in green. Important interactions of different origin are displayed with dashed black lines. The figure was created with The PyMOL Molecular Graphics System, v. 2.5.2.</p> <p><strong>Scheme 1</strong>. Preparation of the amiridine-based compounds 5a-d. Reagents and conditions: <strong>a)</strong> 2-chloroacetyl chloride (4 eq.), CHCl3, 90 ◦C, overnight, 8, 54%, 11, 90%, 12, 52%; <strong>b)</strong> CH3CN, K2CO3, KI, reflux, 3 h, 5a, 54%, 5b, 54%; <strong>c)</strong> amiridine (1.1 eq), CH3CN, K2CO3, KI, reflux, overnight, 5c, 48%, 5d, 41%.</p> <p><strong>Scheme 2</strong>. Preparation of intermediate 13 and final compound 6. Reagents and conditions: <strong>a)</strong> potassium phthalimide, CH3CN, reflux, 3 h, then an excess of NH2NH2⋅H2O, reflux, overnight, 57%; <strong>b)</strong> DMF, TEA, BOP, room temperature, 2 days, 86%.</p> <p><strong>Scheme 3</strong>. Preparation of amiridine-benzothiazole derivatives 7a-m. Reagents and conditions: <strong>a)</strong> for 7a: 2-chlorobenzothiazole, 110 ◦C, overnight, 32%; for 7b-m: corresponding 2-chlorobenzothiazole, DIPEA, 100 ◦C, overnight, 21–77%.</p>
SemTab 2024: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets - WikidataTables2024R1 and WikidataTables2024R2
<p>Data Sets from the ISWC 2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, Round 1, Wikidata Tables. Links to other datasets can be found on the challenge website: https://sem-tab-challenge.github.io/2024/ as well as the proceedings of the challenge published on CEUR.</p> <p>For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/</p> <p>For 2024 edition, see: https://sem-tab-challenge.github.io/2024/</p> <p>Note on License: This data includes data from the following sources. Refer to each source for license details:<br>- Wikidata https://www.wikidata.org/</p> <p>THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
User evaluation results for a Wikidata-centric tool for temporal data in Humanities and Cultural Heritage (June 2024): raw tabular result data and web forms for two questionnaires from five online focus group workshops
<p><strong>Introduction</strong></p> <p>This resource is created for the article: "Wikidata Visualization for Event and Temporal Data Exploration in Digital Humanities and Cultural Heritage" in Semantic Web Journal Special issue on the Semantic Web and Ontology Design for Cultural Heritage. It contains materials use for the user evaluation (June 2024) of a Wikidata visualization tool (<a title="ReKisstory" href="https://rekisstory.labs.vu.nl/" target="_blank" rel="noopener">ReKisstory</a>) described in the article. </p> <p><strong>Summary of the user evaluation</strong></p> <p>The structrue of the user evalution is summarized in the table below:</p> <table style="border-collapse: collapse; width: 99.9708%;"><colgroup><col style="width: 28.0622%;"><col style="width: 26.893%;"><col style="width: 27.0309%;"><col style="width: 17.9856%;"></colgroup> <tbody> <tr> <td> </td> <td>Objectives</td> <td>Setup </td> <td># of people participating</td> </tr> <tr> <td>Pre-workshop survey</td> <td>Understanding potential user profiles before workshop</td> <td>Online questionnaire (Q1)</td> <td>51</td> </tr> <tr> <td>Workshop (Focus Group)</td> <td>Introducing and testing the tool. Obtaining feedback about it</td> <td>Online demo, testing, discussion</td> <td>16</td> </tr> <tr> <td>Post-workshop survey</td> <td>Understanding user needs after testing</td> <td>Online questionnaire (Q2)</td> <td>11</td> </tr> </tbody> </table> <p> </p> <p><strong>The structure of the data is as follows:</strong></p> <p>1. The PDF and PPTX files, containing materials prepared for the pre-workshop survey, workshop, and post-workshop survey:</p> <ul> <li>Pre-workshop survey: Questionnaire (Q1) screenshot from GoogleForms <a href="https://zenodo.org/records/14960584/files/1stQuestionnaire.pdf?download=1&preview=1">1stQuestionnaire.pdf</a></li> <li>Post-workshop survey: Questionnaire (Q2) screenshot from GoogleForms <a href="https://zenodo.org/records/14960584/files/2ndQuestionnaire.pdf?download=1&preview=1">2ndQuestionnaire.pdf</a></li> <li>Documents distributed to the participants of the Focus Group Workshop: <ul> <li>ReKisstory Compare section manual <a href="https://zenodo.org/records/14960584/files/rekisstory_compare_manual.pdf?download=1&preview=1">rekisstory_compare_manual.pdf</a></li> <li>ReKisstory Find section manual <a href="https://zenodo.org/records/14960584/files/rekisstory_find_manual.pdf?download=1&preview=1">rekisstory_find_manual.pdf</a></li> <li>ReKisstory Find example search patterns <a href="https://zenodo.org/records/14960584/files/rekisstory_group_search_example_search_patterns.pdf?download=1&preview=1">rekisstory_group_search_example_search_patterns.pdf</a></li> <li>Workshop slides <a href="https://zenodo.org/records/14960584/files/Focus_Group_Workshop_slides.pptx?download=1&preview=1">Focus_Group_Workshop_slides.pptx</a></li> </ul> </li> </ul> <p>2. The Excel spreadsheets consists of the results of two questionnaires (Q1 and Q2) (<a href="https://zenodo.org/records/14960584/files/Two_questionnaires_online_workshops.xlsx?download=1&preview=1">Two_questionnaires_online_workshops.xlsx</a>): </p> <ul> <li>Pre-workshop survey: Questionnaire (Q1), containing information about potential users: <ul> <li>Demographics</li> <li>Experience of <ul> <li>Time-related data</li> <li>Wikidata</li> <li>SPARQL</li> </ul> </li> </ul> </li> <li>Post-workshop survey: Questionnaire (Q2), containing information about feedback from the Workshop participants : <ul> <li>Comparison with Q1</li> <li>Questions about time-related functionalities</li> <li>Questions about Compare and Find searches</li> <li>Overall comment</li> </ul> </li> </ul> <p><em>Please see "Wikidata Visualization for Event and Temporal Data Exploration in Digital Humanities and Cultural Heritage" for more details</em></p>
Irregular data for testing non-tabular data reading and parsing
<p>key1#value1,value2,...,valueN</p> <p>key2#value1,value2,...,valueM</p> <p>Generated with gen_confus.jl from https://github.com/abelsiqueira/call-julia-from-python-experiments</p>
Tabular data of fast-scanning voltammetry and physiology experiments in LRRK2 KI mice
<p>The tabular data of fast-scanning cyclic voltammetry and physiology experiments from manuscript: "R1441C and G2019S LRRK2 knockin mice have distinct striatal molecular, physiological, and behavioral alterations." by Xenias et al. </p>
Tabular data of striatal motor learning experiments in LRRK2 KI mice
<p>The tabular data of the behavioral experiments described in the manuscript titled "<strong>R1441C and G2019S LRRK2 knockin mice have distinct striatal molecular, physiological, and behavioral alterations."</strong></p>
Dataset from TableLabler: Scalable Labeling of Data Tables with Language Models for Tabular Dataset Creation [Scalable Data Science]
<p>TableLabler: Scalable Labeling of Data Tables with Language Models for Tabular Dataset Creation [Scalable Data Science]</p> <p>Pre-publicatoin upload for VLDB review.</p> <p>Code is at https://github.com/RelationalAI/annotated-tables/. The Github repository name is from a previous draft version and cannot be changed. It is code for TableLabler.</p>
Tabular representation of weather and Housing data used in EDA
<p>This is the historical weather data of North America recorded in 2012.</p>
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