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61,240 results for “two”
WikiMed and PubMedDS: Two large-scale datasets for medical concept extraction and normalization research
<p>Two large-scale, automatically-created datasets of medical concept mentions, linked to the <a href="https://uts.nlm.nih.gov/uts/umls/home">Unified Medical Language System (UMLS)</a>.</p> <p><strong>WikiMed</strong></p> <p>Derived from Wikipedia data. Mappings of Wikipedia page identifiers to UMLS Concept Unique Identifiers (CUIs) was extracted by crosswalking Wikipedia, Wikidata, Freebase, and the NCBI Taxonomy to reach existing mappings to UMLS CUIs. This created a 1:1 mapping of approximately 60,500 Wikipedia pages to UMLS CUIs. Links to these pages were then extracted as mentions of the corresponding UMLS CUIs.</p> <p>WikiMed contains:</p> <ul> <li>393,618 Wikipedia page texts</li> <li>1,067,083 mentions of medical concepts</li> <li>57,739 unique UMLS CUIs</li> </ul> <p>Manual evaluation of 100 random samples of WikiMed found 91% accuracy in the automatic annotations at the level of UMLS CUIs, and 95% accuracy in terms of semantic type.</p> <p><strong>PubMedDS</strong></p> <p>Derived from biomedical literature abstracts from <a href="https://pubmed.ncbi.nlm.nih.gov/">PubMed</a>. Mentions were automatically identified using distant supervision based on Medical Subject Heading (MeSH) headers assigned to the papers in PubMed, and recognition of medical concept mentions using the high-performance <a href="https://allenai.github.io/scispacy/">scispaCy</a> model. MeSH header codes are included as well as their mappings to UMLS CUIs.</p> <p>PubMedDS contains:</p> <ul> <li>13,197,430 abstract texts</li> <li>57,943,354 medical concept mentions</li> <li>44,881 unique UMLS CUIs</li> </ul> <p>Comparison with existing manually-annotated datasets (NCBI Disease Corpus, BioCDR, and MedMentions) found 75-90% precision in automatic annotations. Please note this dataset is <em>not </em>a comprehensive annotation of medical concept mentions in these abstracts (only mentions located through distant supervision from MeSH headers were included), but is intended as data for <em>concept n</em><em>ormalization</em> research.</p> <p>Due to its size, PubMedDS is distributed as 30 individual files of approximately 1.5 million mentions each.</p> <p><strong>Data format</strong></p> <p>Both datasets use JSON format with one document per line. Each document has the following structure:</p> <pre><code class="language-json">{ "_id": "A unique identifier of each document", "text": "Contains text over which mentions are ", "title": "Title of Wikipedia/PubMed Article", "split": "[Not in PubMedDS] Dataset split: <train/test/valid>", "mentions": [ { "mention": "Surface form of the mention", "start_offset": "Character offset indicating start of the mention", "end_offset": "Character offset indicating end of the mention", "link_id": "UMLS CUI. In case of multiple CUIs, they are concatenated using '|', i.e., CUI1|CUI2|..." }, {} ] }</code></pre> <p><strong>Version history</strong></p> <table align="left"> <thead> <tr> <th scope="col">Version</th> <th scope="col">Notes</th> </tr> </thead> <tbody> <tr> <td>1.0.0</td> <td>Initial release</td> </tr> <tr> <td>1.0.1</td> <td>Corrected duplication error in WikiMed.zip file</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p>
Basis sets for the 1s2 ground states of two-electron rare gas ions
<p>This file contains the exponents of Gaussian basis functions optimized in the Dirac-Coulomb approximation for the two-electron ions of the rare gases, He, Ne, Ar, Kr, Rn, and Og. The basis sets are of double-zeta (2z) to septuple-zeta (7z) quality, and include correlating sets:1p (2z), 2p1d (3z), 3p2d1f (4z), 4p3d2f1g (5g), 5p4d3f2g1h (6z), and 6p5d4f3g2h1i (7z). The basis sets are included with the DIRAC program.</p>
Emissions from building materials - concentration of micropollutants and heavy metals in stormwater runoff of two new development areas in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (27) and heavy metals (7) for stormwater runoff from different sampling points at two test sites (A and B) in Berlin, Germany. Both sites are new development areas of similar size that were both constructed in 2017 (1 – 1.5 years prior to the start of the monitoring campaign). Composite samples of individual rain events were taken at three sampling points of each test site: façade runoff, roof runoff and corresponding stormwater runoff from the catchment area. Samples were taken as part of the research project BaSaR (<a href="http://www.kompetenz-wasser.de/en/forschung/projekte/basar/">www.kompetenz-wasser.de/en/forschung/projekte/basar/</a>) of Kompetenzzentrum Wasser Berlin, Ostschweizer Fachhochschule and Berliner Wasserbetriebe. More information including sampling and analytical methods are detailed in the corresponding journal paper "Emissions from building materials – a thread for the environment?", submitted to the MDPI-journal <em>Water</em>.</p> <p><strong>Description of fields:</strong></p> <ul> <li><strong>SiteID</strong>: site identifier <ul> <li>A: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in northern part of Berlin (124 apartments)</li> <li>B: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in southeastern part of Berlin (122 appartments)</li> </ul> </li> <li><strong>SamplingPoint</strong> <ul> <li>facade runoff: runoff from plastered facade collected with gutters during individual rain events</li> <li>roof runoff: roof runoff collected from one downpipe during individual rain events</li> <li>storm sewer: stormwater runoff sampled during individual rain events in a manhole receiving runoff from the entire catchment (A or B)</li> </ul> </li> <li><strong>LocalDateTime_StartRain</strong>: start time of sampled rain event (CET / CEST)</li> <li><strong>LocalDateTime_EndRain</strong>: end time of sampled rain event (CET / CEST)</li> <li><strong>CardinalDirection</strong>: only relevant for facade runoff <ul> <li>N: runoff from facade oriented to the north</li> <li>W: runoff from facade oriented to the west</li> </ul> </li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p>One data file is provided in comma separated format:<br> "BaSaR_data.csv" contains concentrations of all samples.</p>
The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh
<p><strong>Note: Downloading the large data file could have a timeout issue. If you cannot directly download it here, please use the following link as a complementary method for getting the data. </strong></p> <p><a href="https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing">https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing</a></p> <p> </p> <p>This dataset contains the dynamic responses (acceleration records) of two passenger trains with corresponding GPS positions, environmental conditions and track maintenance schedules for a light rail network in the city of Pittsburgh, Pennsylvania in the United States of America.</p> <p>In particular, two light rail vehicles were instrumented (identified as LRV4306 and LRV4313): <br> LRV 4306 has 5 acceleration channels, corresponding to the two uni-axial accelerometers inside the train and the three channels of the tri-axial accelerometer on the wheel truck.</p> <p><em>- The last digit of each acceleration file: 1, 2, 3, 4, 5<br> - Corresponding sensor channels: tri-axial x, tri-axial y, tri-axial z, front cabinet uni-axial, back cabinet uni-axial</em></p> <p><br> LRV 4313 has 8 acceleration channels, corresponding to the two uni-axial accelerometer and the two tri-axial accelerometers inside the train.</p> <p><em>- The last digit of each acceleration file: 1, 2, 3, 4, 5, 6, 7, 8<br> - Corresponding sensor channels: front cabinet uni-axial, back cabinet uni-axial, front tri-axial x, front tri-axial y, front tri-axial z, back tri-axial x, back tri-axial y, back tri-axial z.<br> - x longitudinal (vehicle moving direction); y-axis, transverse; z-axis, vertical.</em></p> <p>The dataset contained in this repository is a condensed version of the original raw data. While the accelerometers on the train were sampled continuously, this dataset contains only those measurements for when the train was actually moving along the track (i.e. not idling at a terminal).</p> <p>The data is stored in binary MAT-files (a MATLAB/Octave data format). These files contain MATLAB objects of the class "pass", which is defined in the file pass.m that can be found in the "code" folder. Specifically, two MAT-files named "obj_dic.mat", and found in the "LRV4306" and "LRV4313" folders, contain the "pass" objects of the two trains, respectively.</p> <p>Each category is described in detail. For more detail on the regions of the track, refer to the 'region.fig' file in this folder. The track was divided into distinct regions so that the data over specific sections of track could be compared. These regions were chosen for two reasons: <br> (1) within a region, the train always followed the same track and <br> (2) there are no tunnels in them so the GPS data is relatively consistent. </p> <p>To get started, using MATLAB or Octave try running "main_script.m" in the "code" folder.</p> <p>A data descriptor paper with details of the data collection process was published.</p> <p>Please cite as</p> <p><strong>Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Scientific Data, 6, 146. <a href="https://doi.org/10.1038/s41597-019-0148-9">https://doi.org/10.1038/s41597-019-0148-9</a>(2019)</strong></p> <p><strong>Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Zenodo, <a href="https://doi.org/10.5281/zenodo.1432702">https://doi.org/10.5281/zenodo.1432702</a>(2018).</strong></p> <p>For questions or suggestions please e-mail Jingxiao Liu <liujx@stanford.edu></p>
Data set for "Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas"
<p>Data set for: Liu Y, Foustoukos G, Crochet S and Petersen CCH (2022) Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas. Front Neuroanat 15: 791015. https://doi.org/10.3389/fnana.2021.791015</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2022_Liu_FrontNeuroanat.pdf</strong>" is the Open Access pdf of the online publication in Frontiers in Neuroanatomy.</p> <p>2. The file named "<strong>Liu_data_code.zip</strong>" (~1 GB) is a zipped version of a folder ‘<em>Liu_data_code</em>’, which contains the data analyzed in the study along with the Python codes used to generate the published figures. The original high resolution image stacks obtained through whole-brain two-photon serial tomography are unfortunately too large for Zenodo, and only highly-downsampled data are included in this upload, which were used for registration with the Allen CCFv3. Instructions on how to view and analyse the anatomical data are provided in the 'README.docx' file, which you will find upon unzipping the folder.</p> <p> </p>
A consistent discretization of the single-field two-phase momentum convection term for the unstructured finite volume Level Set / Front Tracking method - data
<p>Research data from the rhoLENT unstructured Level Set / Front Tracking method for simulating two-phase flows with large density ratios. </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>
Shape Optimization of Thermoacoustic Systems Using a Two-Dimensional Adjoint Helmholtz Solver
<p>rijke stands for Rijke tube and tswc stands for turbulent swirl combustor.</p> <p>mesh_*.xml contains the mesh in xml format.</p> <p>p_dir_*.xml and p_adj_*.xml contain the direct and adjoint fields in xml format.</p> <p>omega_*.txt contains the eigenvalue.</p> <p>my_dict_*.pickle contains the control points and the shape derivatives as byte streams.</p> <p>The dataset also contains the pvd (ParaView Data) files for the amplitude and phase of the direct and adjoint fields.</p>
Data from: Vegetative phenologies of lianas and trees in two Neotropical forests with contrasting rainfall regimes
<ol> <li>Among tropical forests, lianas are predicted to have a growth advantage over trees during seasonal drought, with substantial implications for tree and forest dynamics. We tested the hypotheses that lianas maintain higher water status than trees during seasonal drought and that lianas maximize leaf cover to match high, dry-season light conditions while trees are more limited by moisture availability during the dry season.</li> <li>We monitored the seasonal dynamics of predawn and midday leaf water potentials and leaf phenology for branches of 16 liana and 16 tree species in the canopy of two lowland tropical forests with contrasting rainfall regimes in Panama.</li> <li>In a wet, weakly seasonal forest, lianas maintained higher water balance than trees and maximized their leaf cover during dry-season conditions, when light availability was high, while trees experienced drought stress. In a drier, strongly seasonal forest, lianas and trees displayed similar dry season reductions in leaf cover following strong decreases in soil water availability.</li> <li>Greater soil moisture availability and a higher capacity to maintain water status allow lianas to maintain the turgor potentials critical for plant growth in a wet and weakly seasonal forest but not in a dry and strongly seasonal forest.</li> </ol>
Supplemental Material to "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments"
<p>Supplemental material to manuscript "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments" published in IMMJ "Integrating Materials and Manufacturing Innovation" 2022</p>
Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions.
<p>This dataset contains the raw experimental data and the analysis script for the paper Merl, R., Stöckl, T., Palan, S., 2022. "Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions", Journal of Banking and Finance 106490, https://doi.org/10.1016/j.jbankfin.2022.106490.</p> <p>Instructions:</p> <p>1. Unpack all files into one folder.<br> 2. Open R version 4.1.2 and set the working directory to the folder with all the files.<br> 3. Run Script.R.</p> <p>In case the SPTools package is not available from GitHub anymore, you can also find it included in this dataset so you can install it from here.</p>
Whole-genome genotype data for French Large White pigs from two distinct sampling times
<p>Genotype data at plink binary format for 36 pigs from the french Large White breed: 13 animals from the female line born in 2014 and 2015, 13 animals from the male line born between 2012 and 2016, and 10 animals from a common ancestral line, born in 1977. These genotypes were obtained from individual whole genome sequencing (WGS) data, whiwh are available at https://www.ebi.ac.uk/ena under the accession number PRJEB51909.</p> <p>Two different genotype datasets were obtained from the raw WGS:</p> <p>1) snp20_auto_cr (.bed/bim/fam): High quality autosomal SNPs, called by 3 different software, with a call rate of at least 90%</p> <p>2) all10_auto (.bed/bim/fam): All SNPs or indels called by at least one of 3 different software.</p> <p>More details about these datasets and their use can be found in the following study:</p> <p>Boitard et al (under revision): Whole-genome sequencing of cryo-preserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines.</p>
Data for "Ferrofluidic aqueous two-phase system with ultralow interfacial tension and micro-pattern formation"
<p>This dataset contains the raw data used for the publication "Ferrofluidic Aqueous Two-Phase System with Ultralow Interfacial Tension, Instabilities and Pattern Formation"</p>
Dataset: A systematic multi-technique comparison of luminescence characteristics of two reference quartz samples
<p>Original dataset corresponding to the manuscript Schmidt et al. submitted to the Journal of Luminescence. The dataset is structured as follows: </p> <ul> <li>01_original_data <ul> <li>This folder contains the original measurement data for each experiment (e.g., LM-OSL, TL), organised following the manuscript figures, e.g., Fig_4. These subfolders contain the original measurement data and the measurement sequence. For Risø readers, the output format is *.binx and *.xsyg for results from the Freiberg Instruments lexsyg readers (e.g., TR-OSL).</li> </ul> </li> <li>02_processed_data <ul> <li>Comma-separated value (*.csv) to reproduce each figure in the manuscript. </li> </ul> </li> </ul> <p> </p>
Simulation Parameters for Two Cooperative Binding Sites Sensitize PI(4,5)P2 Recognition by the Tubby Domain
<p>Dataset to perform the coarse-grained MD simulations presented in "Two cooperative binding sites sensitize PI(4,5)P2 recognition by the tubby domain". The dataset includes protein structures and GROMACS simulation files such as mdp, itp, gro, and index files for the tubby domain as well as PLC-delta1 PH domain.</p>
Data files for figures in "Characteristics of the two types of Kuroshio large meanders in the Shikoku Basin"
<p>Processed data files used to create the figures in the paper "Characteristics of the two types of Kuroshio large meanders in the Shikoku Basin".</p>
Lattice studies of the Sp(4) gauge theory with two fundamental and three antisymmetric Dirac fermions—data release
<p>This dataset contains the raw data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2202.05516">Lattice studies of the Sp(4) gauge theory with two fundamental and three antisymmetric Dirac fermions</a>. </p> <p>Included are:</p> <ul> <li>The raw log output from the configuration generation, correlation function calculation, and Dirac eigenvalue computation, as well as metadata describing the ensembles used for the mass spectrum calculation, in `raw_data.zip`. These include all numbers used in the publication (aside from fit parameters) in plaintext form.</li> <li>All numbers included in the above logs, restructured into HDF5 format for convenience, in `data.h5`.</li> <li>The fit parameters used to compute the spectrum, including the thermalisation length, and the plateau start and end points, in `fit_params.zip`.</li> <li>The data underlying tables 2–6 of the publication above, in CSV format.</li> </ul> <p>More details can be found in the file README.md.</p> <p>Version history:</p> <ul> <li>v1.1: Replace out_corr_48x24x24x24b6.5mas-1.01mf-0.71 due to a mistake where the wrong version of the measurement code was used.</li> <li>v1.0: Initial release</li> </ul>
Data for: Polystyrene nanoplastics differentially influence the outcome of infection by two microparasites of the host Daphnia magna
<p>This dataset supports the findings of the study 'Polystyrene nanoplastics differentially influence the outcome of infection by two microparasites of the host <em>Daphnia magna</em>', published in Philosophical Transactions of the Royal Society B (https://doi.org/10.1098/rstb.2022.0013).</p>
Learning to Give a Complete Argument with a Conversational Agent: An Experimental Study in Two Domains of Argumentation
<p>This data is collected to find out how having a conversation with our agent affects argumentation. To model the arguments, we used Toulmin's model of argument. Based on the model, a good argument contains 3 different parts: 1. Claim, 2. Warrant, 3. Evidence. Based on Toulmin's model, these three components are the core components of arguments. This dataset has been collected during a between-subject experiment in which the treatment groups first talked to our agent in Task 1 and received feedback on faulty structural arguments and then did Task 2 and 3 which were answering a question on the same and completely different topic in comparison to Task 1.</p>
Brewer Global Iradiance (GI) total ozone data at two Norwegian sites (2000 to 2020)
<p>Total column ozone (TCO) derived from Global Irradiance (GI) measurements from the Brewer instruments B042 in Oslo (Norway) and B104 in Andøya (Norway) from 01-01-2000 to 31-12-2020.</p> <p>The data consist of daily values averaged +/-2 hours around local noon.</p> <p>GI calibrations where performed with a clear sky direct sun (DS) measurements in 06-2001, 08-2014, 08-2016, 08-2018, and 08-2019 at Andøya, and in 08-2005, 06-2019, and 08-2019 at Oslo. The data has been calibrated with standard lamp measurements and have been homogenized with DS measurements as a function of clouds and solar zenith angle.</p> <p>The method, calibration, and homogenization is described by Bernet et al. (2022) (Appendix A).</p> <p>Responsible institute: NILU - Norwegian Institute for Air Research</p> <p>Funded by the Swiss National Science Foundation and the Norwegian Environment Agency</p> <p>Bernet, L., Svendby, T., Hansen, G., Orsolini, Y., Dahlback, A., Goutail, F., Pazmiño, A., Petkov, B., and Kylling, A., Total ozone trends at three northern high-latitude stations, 2022.</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.