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Supplemental materials of the Castaño-Sánchez et. al. (2023) article (Agricultural Systems) containing the IFSM model input parameters not included in the main text, and the Criollo ranches survey form
CONTEXT: The southwestern United States is experiencing an increasingly warmer and drier climate that is affecting cattle production systems of the region. Adaptation strategies are needed that will not compromise environmental quality or profitability. Options include the use of desert-adapted beef cattle biotypes, such as Rarámuri Criollo cattle, and crossbreds of Criollo with more traditional British breeds. Currently, most calves raised in the Southwest are grain finished, often with irrigated crops produced in the hydrologically-threatened Ogallala Aquifer region. A viable alternative may be grass finishing with the rainfed forage of the arid and semi-arid rangeland of the Southwest or in the temperate grasslands of the Northern Plains. OBJECTIVE: Compare the environmental impacts and production costs of grain-finishing in Texas and grass-finishing in the Northern plains and the Southwest with traditional Angus cattle vs. Criollo and Criollo x Angus cattle. METHODS: Nine supply chain strategies were simulated using the Integrated Farm System Model to compare farm-gate life cycle intensities of greenhouse gas emissions (carbon footprint), fossil energy footprint, nitrogen footprint, blue water footprint and production costs using representative (appropriate soils, climate, and management) ranch and feedlot operations. RESULTS AND CONCLUSIONS: For both finishing options (grass, grain), Criollo x Angus cattle had the best environmental (3%-27% lower), and production cost (4-23% lower) outcomes followed by pure Criollo and then Angus cattle. Crossbred production combined the lower feed supplementation requirements of Criollo cows with heavier final carcasses of offspring from Angus genetics. Crossbred cattle with grass finishing in the Southwest or Northern Plains outperformed on most environmental variables as well as production costs, mostly due to reduced external input requirements (primarily feed). A downside for grass-finished crossbreds was greater carbon fo
Dataset containing the results of the selection process of DSH and CSS articles 2018-2020
<p>Dataset containing the results of the selection process of Digital Scholarship of Humanities and Computational Social Science articles. It shows which articles use data and have a clear data section. These are used to create a corpus to help build and validate and evaluate the data model of data scopes.</p>
Supplementary Material Containing DFT Structure Files and Convergence Tests for our μSR study on Fe2O3
<p>Supplemental material for <em>Local Electronic Structure and Dynamics of Muon-Polaron Complexes in Fe<sub>2</sub>O<sub>3</sub></em></p> <p>M. H. Dehn,<sup>1,2,3</sup> J. K. Shenton,<sup>4,*</sup> D. J. Arseneau,<sup>3</sup> W. A. MacFarlane,<sup>2,3,5</sup> G. D. Morris,<sup>3</sup> A. Maigné,<sup>2</sup> N. A. Spaldin<sup>4</sup> and R. F. Kiefl<sup>1,2,3</sup></p> <p><sup>1</sup>Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada<br> <sup>2</sup>Stewart Blusson Quantum Matter Institute, University of British Columbia, Vancouver, BC V6T 1Z4, Canada<br> <sup>3</sup>Triumf, Vancouver, BC V6T 2A3, Canada<br> <sup>4</sup>Department of Materials, ETH Zurich, CH-8093 Zürich, Switzerland<br> <sup>5</sup>Department of Chemistry, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada<br> <sup>*</sup> For queries about the supplemental material in this repository contact <a href="mailto:john.shenton@mat.ethz.ch">J. Kane Shenton</a>.</p> <p>In these notebooks we provide supplemental material for our work on understanding the behaviour of muon-polaron complexes in Fe<sub>2</sub>O<sub>3</sub>.</p> <p>We provide VASP input and output files for each of the candidate muon stopping sites and states identified in the paper (also labelled as in the paper). We summarise the muon stopping sites and provide the code for analysing hyperfine tensors in the jupyter notebook: <a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3/blob/master/Muon-site-summary.ipynb"><code>Muon-site-summary.ipynb</code></a>. There one can also find a summary of the <strong>computational details</strong> for the paper.</p> <p>We further provide <code>vasprun.xml</code> files for some of the tests of convergence with respect to plane-wave cutoff energy and k-point sampling density. These tests are summarised in the jupyter notebook: <a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3/blob/master/Convergence_tests-ENCUT-KPOINTS.ipynb"><code>Convergence_tests-ENCUT-KPOINTS.ipynb</code></a>.</p> <p>A major source of uncertainty stems from the choice of Hubbard U<sub>eff</sub> correction. We varied U<sub>eff</sub> in the range 3 − 6 eV to gauge the impact of this parameter on the predicted energies and precession frequencies of the four charge-neutral muon-polaron complex states. This analysis is available in the notebook: <code><a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3/blob/master/muons_wrt_U.ipynb">muons_wrt_U.ipynb</a>.</code> Although the numerical values do vary as a function of U<sub>eff</sub>, the qualitative behaviour as well as the ordering of frequencies and energy differences presented in the paper (corresponding to U<sub>eff</sub> = 4 eV) remain robust throughout the range: 3 − 5 eV which is the range typically employed for Fe <em>d</em> states.</p> <p>Finally, in the notebook: <a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3/blob/master/Separating_the_muon-polaron_complex.ipynb"><code>Separating_the_muon-polaron_complex.ipynb</code></a>, we analyse the separation of muon from the polaron in different configurations. Here again we provide the VASP input and output files as well as the code used to analyse these results.</p> <p>These jupyter notebooks may be previewed on <a href="https://github.com/Shenton-supplemental/Muons_in_Fe2O3">github</a> or via the <a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3">jupyter notebook viewer</a>. The latter does a better job of rendering the inline LaTeX and is therefore preferred.</p> <p>Note that all of files are currently compressed to save space. These must be uncompressed before the notebooks will run. In each notebook there is a cell one can run to decompress the files needed for that particular notebook.</p>
SH2-containing-inositol-5-phosphatases (INPP5D); A Target Enabling Package
<p>SH2-containing-inositol-5-phosphatases (SHIP1 and SHIP2, coded for by genes <em>INPP5D</em> and <em>INPPL1</em>, respectively) dephosphorylate phosphatidylinositol-3,4,5-trisphosphate (PI(3,4,5)P<sub>3</sub>) to produce phosphatidylinositol-3,4-bisphosphate (PI(3,4)P<sub>2</sub>). This is an important part of the PI3K/AKT/mTOR signalling pathway. The SHIPs have been linked to a range of conditions, including cancer, diabetes, hypertension, and graft versus host disease. A link has also been demonstrated by GWAS between a non-coding mutation in the <em>INPP5D</em> (SHIP1) gene and increased risk of late-onset Alzheimer’s disease. The role of SHIP1 in Alzheimer’s disease is believed to be mediated through inflammatory processes such as the regulation of microglia and cytokine release. The distinction between the effects of SHIP1 and SHIP2 on these processes is not fully understood. In this TEP we present an apo structure of the phosphatase and C2 domains of SHIP1 and a structure with a magnesium ion and a phosphate ion bound to the active site. We also present 91 fragment bound structures that may act as starting points for the modulation of SHIP1. We are also able to crystallise an equivalent construct of SHIP2 and purify a range of other inositol-5-phosphatases to serve as a selectivity panel for the development of specific compounds. An assay has also been developed.</p>
Dehydrogenase E1 and transketolase domain-containing protein 1 (DHTKD1); A Target Enabling Package
<p>Inherited mutations of the <em>GCDH </em>gene for glutaryl-CoA dehydrogenase, catalysing the sixth enzymatic step in lysine catabolism, lead to the rare neurometabolic disorder Glutaric Aciduria type 1 (GA1). There is a rationale that inhibition of the fifth lysine catabolising step, catalysed by the DHTKD1 enzyme, could provide therapeutic benefit for GA1 by means of substrate reduction. This TEP provides early tools to develop DHTKD1 inhibitors, including recombinant protein, structure, biophysical (activity and stability) assays and fragment hits of human DHTKD1. This work also reports the interaction of DHTKD1 with its functional partner DLST as a binary complex, and an EM reconstruction of the DLST catalytic core.</p>
A diet containing mango peel silage impacts upon feed intake, energy supply and growth performances of dairy male calves
<p>The major challenges for disposal of waste from fruit processing factories are high transportation costs, limited landfill availability and environmental pollution. Therefore, developing efficient waste management techniques to reduce transportation costs and environment pollution is important. Mango peels (MP) are abundant during the mango season and high in fermentable carbohydrate, which can easily breakdown and pollute the environment if a proper waste management method is not implemented. Thus, in this study, fresh MP were ensiled after sun-dried for one day and then fed to dairy male calves as the roughage source to evaluate its effect on feed intake, digestibility, energy balance, body weight gain, feed efficiency and blood metabolites. Eight growing crossbred dairy male calves (Holstein Friesians × Zebu) were allocated into two groups [Control (n = 4) and mango peel silage (MPS, n = 4)]. This experiment lasted for 12 weeks and daily feed offered and refusal were recorded to determine the daily feed intake. Digestion trial was performed at the last five days of experiment. Body weight and measurement were recorded every two weeks interval to determine the weight gain and body physical improvement. Blood was collected at the end of experiment to analyze the serum biochemical parameters. Ensiling improved the energy and protein contents and decreased fibre content of MP, thereby improving the forage quality. Feeding MPS to calves increased (<i>P</i> < 0.05) feed intake, energy supply and energy balance, changes in body measurements, weight gain, feed efficiency, and glucose concentration, as well as lowered (<i>P</i> < 0.05) the urea nitrogen concentration. Ensiling fresh MP after sun-drying for one day improved silage quality, and feeding MPS to dairy male calves as a roughage source improved feed intake, energy supply and growth performances. Therefore, ensiling fresh MP could improve the feed supply for ruminant production and be an effective waste management strategy for fruit processing businesses. </p>
Thermally mediated transmission-mode deflection of terahertz waves by lamellar metagratings containing a phase-change material
<p>The data generated by MATLAB, whcih were used to plot a part of the figures. </p> <p>Research supported by Narodowe Centrum Nauki, project no UMO-2020/39/I/ST3/02413.</p>
108 Basins containing a minimum of 50 years of daily discharge and precipitation observations
<p>Contained within this dataset are the (1) list of USGS basins used in the Livneh et al. 2024 (in-review) analysis of the intermittency-flooding relationship, (2) shapefile of the basins, and (3) the script used to generate the list of basins from the GAGES II dataset. If using these basins or the script to generate a list of basins that meet a certain set of criteria, please cite this dataset accordingly. </p>
Abstracts that contain "justice" from AGU Fall Meeting 2014-2024
<p>CSV containing a list of abstracts from AGU Fall Meetings 2014-2024 that contain the word "justice" in the title or text of the abstract. Abstracts were assembled by searching the individual Fall Meeting confex pages and copying the information directly into a CSV. </p> <p>The file contains the conference year, abstract number, full text of the abstract, and a characterization of the sector of the authors, based on listed affiliation in the AGU Fall Meeting system. The sectors include academic, governmental, NGO, commercial, informal education, and cross-sector.</p>
FASTA file containing the MYB encoding gene An2-like and Ant1 coding sequences corresponding to wild and cultivated tomato accessions
<p>The coding sequence (CDS) of the MYB encoding genes <em>Ant1</em> and <em>An2-like</em>. Sequences were retrieved from regions corresponding to the<em> Aft</em> locus from <em>Solanum galapagense </em>accession LA1141, <em>S. lycopersicum</em> variety OH8245, and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences were compared to available CDS available from the Sol genomics network (SGN) and the National Center for Biotechnology Information. The CDS was retrieved from <em>S. lycopersicum</em> variety Indigo Rose [MN433087 (Yan et al., 2020)], <em>S. lycopersicum</em> accession LA1996 [MN242011.1, EF433417.1( Sapir et al., 2008; Colanero et al., 2020)], and <em>S. chilense </em>accession LA1930 [MN242012.1 (Colanero et al., 2020)], The orthologous CDS corresponding to the <em>Aft </em>MYB encoding genes from <em>Solanum tuberosum</em> L. Group Phureja clone DM1-3 genome (PGSC DM v4.03 Pseudomolecules) was retrieved from the Potato Genome Sequence Consortium (PGSC: Potato Genome Sequencing Consortium et al., 2011), and the Capsicum annum cv. CM334 genome was retrieved from <em>Capsicum annuum </em>cv CM334 genome chromosome release 1.55 (Hulse-Kemp et al. 2018). These CDS were obtained using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at https://solgenomics.net/tools/blast/). Comparison of syntenic chromosomal regions using known positions of tomato, potato, and pepper markers with comparative map viewer from SGN: (available at https://solgenomics.net/cview) on chromosome 10, was used as a quality check for S.<em> tuberosom</em> and <em>C. annuum.</em> Orthologous CDS corresponding to <em>Salvia miltiorrhiza, Arabidopsis thaliana</em>, [NM_105308.2, NM_105310.4 (Teng et al., 2005, Cominelli et al., 2008; Beradini et al., 2015)] were chosen based on tomato <em>Aft</em> sequence homology and gene annotations of positive R2R3 MYB regulation of anthocyanin. The CDS corresponding to the <em>Aft</em> genes were retrieved from the CDS reference genomes available from the Sol Genomics Network SGN: Tomato Genome CDS (ITAG release 4.0), Potato PGSC DM v3.4 CDS sequences, <em>Capsicum annuum </em>cv CM334 Genome CDS (release 1.55), or from the National Center for Biotechnology Information (NCBI: https://www.ncbi.nlm.nih.gov) reference sequences (RefSeq) section of the Genbank records. When accessed from Genank records, the CDS sequence was extracted from the “features” section and exported as a FASTA file.</p>
GeoERA RESOURCE H3O-PLUS data set which contains hydraulic properties of prime aquifers and aquitards in the Dutch-Flemish-German cross-border area
<p>Dataset which contains information about hydraulic properties of harmonized hydrogeological units in the Dutch-Flemish-German cross-border region which was compiled in the GeoERA RESOURCE project under WP3 H3O-PLUS. The harmonization of the 3D geometry of the cross-border hydrogeological units in the H3O projects constituted a major step towards a common hydrogeological dataset of the Roer Valley Graben and thus the harmonization of groundwater flow models. The database that was compiled provides the characterization of these hydrogeological units with respect to their hydraulic properties, primarily their hydraulic conductivity.<br> The associated report and appendices describe the database of hydraulic properties of aquifers and aquitards based on common criteria. Attention is also given to the characterization of hydraulic properties of faults.</p>
GeoERA RESOURCE CHAKA data set which contains time series of precipitation and discharge of springs in the CHAKA pilot areas (D5.5)
<p>Dataset which contains time series of precipitation and discharge of springs in the pilot areas of the CHAKA work package of the GeoERA RESOURCE project. The file contains precipitation and spring discharge data of 16 pilot areas in the Karst & Chalk work package. A description of the application of the dataset for the characterisation of the typology of karst systems in given in the D5.3 deliverable of GeoERA RESOURCE of which the pdf is provided. Further information about the CHAKA results can be assessed though the webservices of the European Geological Data Infrastructure (EGDI). </p>
Online Resources for Strullu-Derrien et al - The 330–320 Million-Year-Old Tranchée des Malécots (Chaudefonds-sur-Layon, South of the Armorican Massif, France): a Rare Geoheritage Site Containing In Situ Palaeobotanical Remains
<p>This repository contains the following files associate with "The 330–320 Million-Year-Old Tranchée des Malécots (Chaudefonds-sur-Layon, South of the Armorican Massif, France): a Rare Geoheritage Site Containing In Situ Palaeobotanical Remains" by Christine Strullu-Derrien, Alan RT Spencer, Christopher J Cleal and Victor O. Leshyk.</p> <p><strong>Online Resource 1</strong> Model data as a .zip archive (301.5MB) containing .obj/.mtl and texture files for each 3D reconstruction (Models #1-4, whole site reconstruction, detailed reconstruction of the trench, and model of the mine site).</p> <p><strong>Online Resource 2</strong> Video animation showing whole site 3D model (.mp4 | 37.7MB), with quick fly-through of the Tranchée des Malécots showing exposed rock and bedding of the SW wall.</p> <p><strong>Online Resource 3</strong> Video animation showing 3D Model #1 (.mp4 | 35.1MB).</p> <p><strong>Online Resource 4</strong> Video animation showing 3D Model #2 (.mp4 | 83.5MB).</p> <p><strong>Online Resource 5</strong> Video animation showing 3D Model #3 (.mp4 | 45.3MB).</p> <p><strong>Online Resource 6</strong> Video animation showing 3D Model #4 (.mp4 | 65.8MB).</p> <p><strong>Online Resource 7</strong> Video animation showing 3D model of the Malécots mine headframe (.mp4 | 14.9.0MB).</p>
Visual-inertial input datasets for SLAM applications containing extreme and human-like motion patterns
<p>Recorded datasets in compressed rosbag format, which contain visual and IMU sensor information that are bearing high resemblance to the movement of a human player with a handheld AR-capable device.</p> <p>For machine learning training and validation tasks, separate dataset are available containing motion patterns in a wide range from steady camera image to extremely challenging movements.</p>
Synthetic cryo electron microscopy single particle images containing biomolecular complexes with continuous conformational variability used for validating DeepHEMNMA method and validation results
<p>This archive contains a synthetic dataset used for validating DeepHEMNMA method and the validation results. DeepHEMNMA is a deep learning extension of HEMNMA approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron (cryo-EM) microscopy single particle images. We provide a training set of 20,000 images and an inference set of 50,000 images. The training images were used (1) to estimate the conformational and rigid-body parameters with HEMNMA and (2) to train the neural network using the parameters previously estimated with HEMNMA (the file with the HEMNMA-estimated parameters is provided). The inference images were used to infer the parameters with the trained neural network. Also, we provide (1) the input PDB structure, its normal modes, and the conformational and rigid-body parameters used to synthesize the 20,000 training images (ground-truth parameters) and (2) the conformational and rigid-body parameters inferred from the set of 50,000 inference images.</p> <p>The DeepHEMNMA method and the method for synthesizing images have been fully described in the following article: "Hamitouche I and Jonic S (2022), DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images. Front Mol Biosci 9, 965645. <a href="https://doi.org/10.3389/fmolb.2022.965645">https://doi.org/10.3389/fmolb.2022.965645</a> (in press)". Additionally, this article describes a test of DeepHEMNMA using one experimental cryo-EM dataset (available in EMPIAR database under the accession code EMPIAR-10016). </p>
Container spreader pose tracking dataset
<p>This dataset contains image sequences that feature a moving quay crane spreader in a port environment while unloading a container cargo vessel. A container crane spreader is a device that is installed on a crane and used to lift containers after attaching onto them.</p> <p><br> The sequences were acquired from a viewpoint similar to that of the crane operator using a camera installed next to the operator’s cabin at a height of approximately 20 meters above the quay. The camera thus moves with the crane, resulting in a non-stationary image background.</p> <p>The dataset is organized into several RAR archives, one for each sequence. In addition to the undistorted image frames, it includes for every sequence a text file whose each line consists of the frame id for every image, the spreader’s bounding box and the spreader’s 6D pose (Rodrigues vector for the orientation, and the translation vector). The axis-aligned 2D bounding box is in the format <em>x0 y0 w h</em> where <em>(x0, y0)</em> is the top left corner and <em>w x h</em> its size, all in pixels. The spreader’s pose is defined with respect to the camera coordinate frame. Also included are the camera intrinsics matrix K for each sequence along with a common 3D mesh model for the spreader.</p> <p>The spreader’s mesh model is supplied in PLY format. For a certain image frame, a model vertex M transforms to the camera coordinate system as R*M + t, R and t being the spreader’s pose (R is the equivalent rotation matrix). The homogeneous coordinates of that vertex’s projection on the image frame are K*(R*M + t).</p> <p><br> The dataset can support research on topics such as object localization, object detection, pose estimation, tracking, etc.<br> If you use this dataset in your research work, you are kindly asked to cite the following paper in your publications:</p> <p>M. Lourakis and M. Pateraki, "<em>Markerless Visual Tracking of a Container Crane Spreader,</em>" 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021, pp. 2579-2586, doi: <a href="https://doi.org/10.1109/ICCVW54120.2021.00291">10.1109/ICCVW54120.2021.00291</a>.</p>
Dataset of CONTAINMENT and SUPPORT in the Uralic languages of the Volga-Kama area
<p>This open access dataset contains examples of the expressions of CONTAINMENT and SUPPORT in the Uralic languages of Volga-Kama area. The exact languages and the sources of data are given in Table 1. The dataset contains data on the relational nouns (RN) and plain spatial cases expressing prototypical CONTAINMENT and SUPPORT in the languages. The RN included in the dataset are listed in Table 2, and case forms in Table 3.</p> <p> </p> <table> <tbody> <tr> <td> <p>language</p> </td> <td> <p>corpora</p> </td> </tr> <tr> <td> <p>Erzya (MdE)</p> </td> <td> <p>Syatko-subcorpus of the MokshEr corpus (MokshEr 2010)</p> </td> </tr> <tr> <td> <p>Moksha (MdM)</p> </td> <td> <p>Subcorpora in Moksha of the MokshEr corpus (MokshEr 2010)</p> </td> </tr> <tr> <td> <p>Meadow Mari (MaM)</p> </td> <td> <p>Marko East (Marko [no year]), Oncyko (Oncyko 2000), Meadow Mari corpus (Arkhangelskiy 2019b), Wanca (Meadow Mari) (Helsingin yliopisto et al. 2019)</p> </td> </tr> <tr> <td> <p>Hill Mari (MaH)</p> </td> <td> <p>Marko West (Marko [no year]), Wanca (Hill Mari) (Helsingin yliopisto et al. 2019)</p> </td> </tr> <tr> <td> <p>Udmurt (Udm)</p> </td> <td> <p>Pilot version of Udmurt corpus (relational nouns; presently included into [Arkhangelskiy 2018])</p> <p>Udmurt corpus (content nouns) (Arkhangelskiy 2018)</p> </td> </tr> <tr> <td> <p>Komi Zyrian (KoZ)</p> </td> <td> <p>Komi Zyrian Web Corpus (Arkhangelskiy 2019a), Коми корпус (Fu-Lab team 2021)</p> </td> </tr> <tr> <td> <p>Komi Permyak (KoP)</p> </td> <td> <p>Komi Permyak text collection from the University of Turku (Permyak 2009)</p> </td> </tr> </tbody> </table> <p><strong>Table 1.</strong> Languages included into the dataset and the sources of the data for each language.</p> <p> </p> <table> <tbody> <tr> <td> <p> </p> </td> <td> <p>MdE</p> </td> <td> <p>MdM</p> </td> <td> <p>MaM</p> </td> <td> <p>MaH</p> </td> <td> <p>Udm</p> </td> <td> <p>KoZ</p> </td> <td> <p>KoP</p> </td> </tr> <tr> <td> <p>containment</p> </td> <td> <p><em>pot</em>(<em>mo</em>)-</p> </td> <td> <p><em>potmə-</em></p> </td> <td> <p><em>kørgø</em>,<em> kørgə-</em></p> </td> <td> <p><em>kørgə̈-</em></p> </td> <td> <p><em>puʃk-</em></p> </td> <td> <p><em>pɨt͡ʃk-</em></p> </td> <td> <p><em>pɨt͡ʃk-</em></p> </td> </tr> <tr> <td> <p>support</p> </td> <td> <p><em>lang-</em></p> </td> <td> <p><em>lang-</em></p> </td> <td> <p><em>ymba-</em></p> </td> <td> <p><em>βə̈(l)-</em></p> </td> <td> <p><em>vɨl-</em></p> </td> <td> <p><em>vɨl-/vɨv-</em></p> </td> <td> <p><em>vɨl-/vɨv-</em></p> </td> </tr> </tbody> </table> <p><strong>Table 2.</strong> RN included in the dataset.</p> <p> </p> <table> <tbody> <tr> <td> <p> </p> </td> <td> <p>MdE</p> </td> <td> <p>MdM</p> </td> <td> <p>MaM</p> </td> <td> <p>MaH</p> </td> <td> <p>Udm</p> </td> <td> <p>KoZ</p> </td> <td> <p>KoP</p> </td> </tr> <tr> <td> <p>location</p> </td> <td> <p><em>-so</em>/<em>-se</em> (inessive)</p> </td> <td> <p><em>-sa</em> (inessive)</p> </td> <td> <p><em>-ʃte/-ʃto/-ʃtø</em> (inessive)</p> </td> <td> <p><em>-ʃtə/-ʃtə̈</em> (inessive)</p> </td> <td> <p><em>-ɨn</em> (inessive)</p> </td> <td> <p><em>-ɨn</em> (inessive)</p> </td> <td> <p><em>-ɨn</em> (inessive)</p> </td> </tr> <tr> <td> <p>source</p> </td> <td> <p><em>-sto</em>/<em>-ste</em> (elative)</p> </td> <td> <p><em>-sta</em> (elative)</p> </td> <td> <p><em>gət͡ɕ</em> (source postposition)</p> </td> <td> <p><em>gə̈t͡s</em> (source postposition)</p> </td> <td> <p><em>-ɨɕ</em> (elative)</p> </td> <td> <p><em>-ɨɕ</em> (elative)</p> </td> <td> <p><em>-iɕ</em> (elative)</p> </td> </tr> <tr> <td> <p>goal</p> </td> <td> <p><em>-s</em> (illative)</p> </td> <td> <p><em>-s</em>/<em>-t͜s</em> (illative)</p> </td> <td> <p><em>-ʃke/-ʃko/-ʃkø/-ʃ</em>, (illative)</p> </td> <td> <p><em>-ʃkə/-ʃkə̈/-ʃ</em>, (illative)</p> </td> <td> <p><em>-e</em>/<em>-ɨ </em>(illative)</p> </td> <td> <p><em>-ɘ </em>(illative)</p> </td> <td> <p><em>-ɘ </em>(illative)</p> </td> </tr> <tr> <td> <p>path</p> </td> <td> <p><em>-ka</em>/<em>-ga</em>/<em>-va</em> (prolative)</p> </td> <td> <p><em>-ka</em>/<em>-ga</em>/<em>-va</em>/<em>-gæ</em> (prolative)</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> <td> <p><em>-ti/-eti</em>/<em>-jeti/</em><em>-ɨti</em> (prolative)</p> </td> <td> <p><em>-ɘd</em> (prolative); <em>-ti</em> (transitive)</p> </td> <td> <p>-<em>ɘt </em>(prolative); <em>-ti</em> (transitive)</p> </td> </tr> </tbody> </table> <p><strong>Table 3.</strong> Cases that have been included into the dataset. All cases do not necessary show in every set, as for some combinations of RN and case there is no data.</p> <p> </p> <p>The main purpose of the dataset is to enable the study of variation between a plain case and RN inflected in case when expressing CONTAINMENT or SUPPORT. To facilitate this each expression of relation has been given a prototypicality score 4 = most prototypical, 1 = non-prototypical, which tells if the relation between landmark and trajector expressed in the sentence is typical for the entities participating in it. The prototypicality scores are based on the pre-linguistic concepts of containment and support, which are robustly attested and therefore should be independent of any single language. The scoring is based on the authors understanding of the language external relations, and no native consultants are used to verify the results. Therefore, some caution is in order when using the dataset.</p> <p> </p> <p>The dataset contains files with data of CONTAINMENT RN, SUPPORT RN, and plain case on all the included languages. The files are named according to the scheme element_languge (e. g. Containment_Erzya for the containment data on Erzya). In addition, files named element_frequencies show the number of examples divided by case and prototypicality score for each language, and element_summary shows the total number of prototypicality scores for each language. For plain case there are also summary files for the scores of CONTAINMENT and SUPPORT separately.</p> <p> </p> <p>The dataset is annotated for following information:</p> <ol> <li>The case in which the content noun or RN is inflected.</li> <li>The predicate as inflected in the data.</li> <li>The content noun as given in the data.</li> <li>Translations of both (mainly in citation form, but in predicate sometimes with some grammatical information, cf. abbreviations below).</li> <li>The prototypicality score. In the data on plain cases the prototypicality score is given only for the clauses where the relation is either CONTAINMENT or SUPPORT (i. e. the prototypicality score indicates the prototypicality of the relation as CONTAINMENT or SUPPORT according to the type of relation expressed).</li> <li>In the data on plain cases, the relation expressed by the case is marked (CONT = CONTAINMENT, SUP = SUPPORT, N/A = some other relation).</li> <li>The original sentence context.</li> <li>Free translation. Some of the translations are done following the lexical meanings and syntactic structures of the languages, so the English is unidiomatic from time to time.</li> <li>The file name with which the original sentence can be located in the corpus.</li> </ol> <p> </p> <p>The three final columns are partly lacking at the moment from the Mari and Komi languages. The translations in the data are intended only as guidelines, and anyone using the dataset should refer to the original language data in the analysis. The data in the columns is presented according to the following conventions :</p> <ul> <li>If the predicate is in square brackets, it means that the predicate is not present in the clause with the target LM. This can be because of two reasons: 1) The predicate is given in a previous clause, and is elliptically omitted, 2) the “predicate” is copula, which is not obligatory in the present tense in the languages studied.</li> <li>The following abbreviations are used to specify the meaning of the predicate when the English translation is ambiguous (note that the use is not checked, and the abbreviations might be lacking from some predicates):</li> </ul> <p>CAUS causative</p> <p>CONT continuative</p> <p>CVB converb</p> <p>FRQ frequentative</p> <p>INCH inchoative</p> <p>INF infinitive</p> <p>ITR intransitive</p> <p>MOM momentaneous</p> <p>NEG negative</p> <p>NMLZ nominalization</p> <p>PASS passive</p> <p>PTCP participle</p> <p>REFL reflexive</p> <p>TRA transitive</p> <p>The authors of this dataset are Tomi Koivunen and Riku Erkkilä and it is published under CC-BY-NC-ND licence. If used in a publication, please refer to this publication as well as mention the original source(s):</p> <p>This dataset has been used in following publications:</p> <p> </p> <p>References to used corpora:</p> <p>Arkhangelskiy, Timofey. 2018. <em>Udmurt corpus</em>. http://udmurt.web-corpora.net/index.html.</p> <p>Arkhangelskiy, Timofey. 2019a. <em>Komi-Zyrian corpus</em>. http://komi-zyrian.web-corpora.net/index.html.</p> <p>Arkhangelskiy, Timofey. 2019b. <em>Meadow Mari corpus</em>. http://meadow-mari.web-corpora.net/index_en.html.</p> <p>Fu-Lab team. 2021. <em>Корпус коми языка</em>. http://komicorpora.ru/.</p> <p>Helsingin yliopisto, FIN-CLARIN, H. Jauhiainen, T. Jauhiainen & K. Lindén. 2019. <em>Wanca 2016, Korp Version</em>. Kielipankki. http://urn.fi/urn:nbn:fi:lb-2019052401.</p> <p>Marko. (no year). <em>MARKO - Corpus of Mari language</em>. University of Turku.</p> <p>MokshEr, V.3. 2010. <em>Mokšan ja ersän sähköinen korpus</em>. Turun yliopisto.</p> <p>Oncyko. 2000. <em>Oncyko corpus</em>. University of Turku.</p> <p>Permyak. 2009. <em>Turku Komi-Permyak Corpus</em>. University of Turku.</p>
Synthetic business population containing simulated business variables
<p>This dataset contain simulated data for a fully synthetic business population. The dataset contains 900,000 records, each of which represents a simulated business. It resembles the real-world population of employing businesses in Australia in terms of the distribution of businesses across size categories, industry classes and geographic regions (state). The data for the population has been generated using a combination of published survey outputs available from the Australian Bureau of Statistics (ABS) website, and employee tax data and survey data sourced from the Business Longitudinal Analysis Data Environment (BLADE) in the ABS DataLab.</p>
Not the silver bullet: assessing the effects of silver-containing antimicrobial showerheads on the drinking water microbiome
<p>Demultiplexed fastq files used for sequencing analysis, processed taxonomy read data for all samples and controls, and relevant environmental metadata as described in "Not the silver bullet: assessing the effects of silver-containing antimicrobial showerheads on the drinking water microbiome".</p>
Replication data for: "How does Docker affect energy consumption? Evaluating workloads in and out of Docker containers"
<p>Database of raw power measurements and energy summaries for our Docker energy tests.</p> <p>Please cite us if you use this dataset.</p> <p>Schema</p> <pre><code>CREATE TABLE configuration( name TEXT PRIMARY KEY, description TEXT ); CREATE TABLE experiment( name TEXT PRIMARY KEY, description TEXT ); CREATE TABLE run( id PRIMARY KEY, configuration TEXT REFERENCES configuration(name) ON DELETE CASCADE ON UPDATE CASCADE, experiment TEXT REFERENCES experiment(name) ON DELETE CASCADE ON UPDATE CASCADE ); CREATE TABLE measurement( run REFERENCES run(id) ON DELETE CASCADE ON UPDATE CASCADE, timestamp REAL NOT NULL, -- Unix timestamp in milliseoncds power REAL NOT NULL ); CREATE TABLE energy( id PRIMARY KEY REFERENCES run(id), configuration TEXT REFERENCES configuration(name) ON DELETE CASCADE ON UPDATE CASCADE, experiment TEXT REFERENCES experiment(name) ON DELETE CASCADE ON UPDATE CASCADE, energy REAL NOT NULL, started REAL NOT NULL, ended REAL NOT NULL, elapsed_time REAL NOT NULL -- in milliseconds );</code></pre>
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