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9,882 results for “scales”
WALOWA (WAve LOads on WAlls) - Large-scale Experiments in the Delta Flume on Overtopping Wave Loads on Vertical Walls
<p>Coasts of low lying countries are often comprised of a gentle foreshore and shallow waters, followed by a dike and a promenade. At the end of the promenade buildings or storm walls are constructed. This setting makes it possible for waves to overtop the dike and impact on the storm wall or building. Especially during storm season the overtopping waves induce large loads on these structures. New scenarios for climate change and sea level rise make it worthwhile to invest in research regarding overtopping wave loads.</p> <p>Within the European project 'Wave Loads on Walls' (WaLoWa) model tests in the Delta flume (The Netherlands) were conducted. It is the aim to study overtopping wave loads on storm walls and buildings. The project is coordinated by Ghent University (Belgium), in cooperation with TU Delft (The Netherlands), RWTH Aachen (Germany), University of Bari, University of L'Aquila, University of Calabria and University of Florence (Italy) and Flanders Hydraulics Research (Belgium). The project is financed by a grant by Hydralab+ in the framework of the EC Horizon 2020 program.</p> <p>A model geometry comprised of a sandy beach, a sloping dike, promenade and wall structure was built into the Delta flume. The beach alone consists of 1000m³ sand material and was an essential part of the structure, to obtain the broken wave conditions similar to reality. Waves representing a storm with a 1000 year return period and an additional water level to account for sea level rise result in the tested superstorm conditions.</p> <p>Measurements of the water surface elevation were taken close to the paddle, along the mildly sloping foreshore and at the dike toe location by resistance type wave gauges mounted to the flume side wall. The bathymetry of the sandy foreshore was measured by a mechanical profiler before and after the test. The overtopping flow properties thickness and velocity were measured by resistance type wave gauges, ultra-sonic distance sensors, paddle wheels and an electro-magnetic current meter installed along the promenade. Finally, the impact forces and pressures on the wall were measured by compression load cells and pressure sensors respectively. The data-set was complemented by a number of synoptic measurements, such as laser scan profiles, GoPro images, High-speed camera images, Digital camera images. Due to its large storage size, these data are provided on request.</p>
Validation of Emission Spectroscopy Gas Temperature Measurements Using a Standard Flame Traceable to the International Temperature Scale of 1990 (ITS-90)
<p>Data underpinning the associated publication (https://doi.org/10.1007/s10765-019-2557-6) on accurate traceable measurement of post-flame temperatures.</p>
Intensified, Kilogram-Scaled, and Environment-Friendly: Chemoenzymatic Synthesis of Bio-Based Acylated Hydroxystyrenes
<p>Lignin-derived styrene derivatives are versatile building blocks for the manufacture of bio-based polymers. As shown previously, phenol-protected hydroxystyrenes are accessible under industrially-sound conditions (>100 g/L, >95% yield) by subjecting biogenic phenolic acids to enzymatic decarboxylation and base-catalyzed acylation in non-aqueous media (wet cyclopentyl methyl ether, CPME). Herein, we demonstrate the production of 1 kg of 4-acetoxy-3-methoxystyrene in a 10 L reactor and present practical adjustments to the up- and downstream processing that warrant a straightforward process and high isolated yields. Additionally, an environmental assessment is conducted, starting with a thorough E factor analysis to identify the sources that contribute most to the environmental burden (solvent and downstream processing). Also, the total CO2 production of the process is studied, including contributions from energy use and the treatment of generated wastes. The energy impact is evaluated through thermodynamic analysis, and the environmental footprint contributions by wastes – organic and aqueous fractions – are assessed based on CO2 emissions from solvent incineration and wastewater treatment, respectively. Overall, the holistic assessment of the process, its optimization, scale-up, product isolation, and environmental analysis indicate the feasibility of multi-step chemoenzymatic reactions to deliver high volume, low-value chemicals from biorefineries.</p>
Research Data/Code for "Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification"
<p>This repository contains research data and code for supplementing the manuscript <br>"Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification" <br>by L. Gossel, E. Corbean, S. Dübal, P. Brand, M. Fricke, H. Nicolai, C. Hasse, S. Hartl, S. Ulbrich, and D. Bothe. </p> <p>There is a corresponding preprint available on Arxiv: https://doi.org/10.48550/arXiv.2404.13092</p> <p><br>Users are referred to the manuscript for background information. This repository shall enable reproduction of the reported results and does not stand alone. </p> <p>Please read important information in the README in the top-level directory. </p> <p>Funded by the Hessian Ministry of Higher Education, Research, Science and the Arts - cluster project Clean Circles. </p>
Data, multiscale dataset and supplementary information for 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process'
<p>This repository contains the analytical Supplementary Information, the data, the multiscale dataset and the codes used to construct the dataset and plot figures used in the manuscript 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process' (accepted in Earth and Planetary Science Letters). </p> <p> </p> <p> </p>
UnientrezDB: Large-scale Gene Ontology Annotation Dataset and Evaluation Benchmarks with Unified Entrez Gene Identifiers
<p>Our work focuses on providing a comprehensive dataset and benchmarks for evaluating gene ontology annotations using a unified system of Entrez Gene Identifiers.</p>
Cortisol in fish scales remains stable during extended periods of storage.
<p>Dataset to accompany the manuscript: <a href="https://doi.org/10.1093/conphys/coae065">https://doi.org/10.1093/conphys/coae065</a></p> <p>The dataset contains two data files describing cortisol concentrations in the scales of adult salmon and a "Read Me" file that explains the data structure and source. </p>
The geographic scale of population level variation in growth and nodulation differs for two species of the prairie clover
<p>Zenodo deposit for Pozzi et al (2024) AJB</p> <p>The geographic scale of population level variation in growth and nodulation differs for two species of the prairie clover</p> <p><strong> _____________________________________________________________________________________________________________________________________</strong></p> <p><strong>The geographic scale of population level variation in growth and nodulation differs for two species of the prairie clover</strong></p> <p>Adrien C.M. Pozzi<sup>1,2</sup>, Ruth G. Shaw<sup>1</sup>, Georgiana May<sup>1,3</sup></p> <p><sup>1</sup> Department of Ecology, Evolution and Behavior, University of Minnesota Twin-Cities, St Paul, MN 55108. <sup>2</sup> Current affiliation: Universite Claude Bernard Lyon 1, Laboratoire d'Ecologie Microbienne, UMR CNRS 5557, UMR INRAE 1418, VetAgro Sup, 69622 Villeurbanne, France. ORCID: 0000-0001-6765-4293. <sup>3</sup> Correspondence: Georgiana May (gmay@umn.edu)</p> <p><em><strong>Keywords:</strong></em></p> <p>conservation; <em>Dalea</em> spp.; habitat fragmentation; mutualism; nitrogen-fixing symbiosis; population level variation; native prairie legume; restoration; rhizobia</p> <p><em><strong>Description:</strong></em></p> <p>This Zenodo deposit is part of the MN LCCMR Healthy Prairies project granted to R. Shaw and G. May, UMN Twin-Cities. It contains the following files:</p> <p><span><span>·<span> </span></span></span>“Metadata & data” spreadsheet. <em>Contains metadata and data about the Twin Valley experiment (including the position of plants, an intermediary census, growth and nodulation traits for harvested plants, data on bacterial isolates from root nodules, and source modifiers for GenBank accessions OQ732394-OQ732572).</em></p> <p><span><span>·<span> </span></span></span>“Blast alignments output.txt” text file. <em>Contains ouput of Blastn alignements for the 16S rRNA genes of bacterial isolates against the rRNA_typestrains/16S_ribosomal_RNA 16S ribosomal RNA (Bacteria and Archaea type strains) database, to determine the genus as recommended by GenBank during sequence submission.</em></p> <p><span><span>·<span> </span></span></span>“General script.R”. <em>R script of the general statistical analyses produced for publication.</em></p> <p><span><span>·<span> </span></span></span>“General environment.RData”.<em> The companion RData (environment) of the above R script.</em></p> <p><span><span>·<span> </span></span></span>“Trait model script.R”. <em>R script of the plant trait models produced for publication</em></p> <p><span><span>·<span> </span></span></span>“Trait model environment.RData”. <em>The companion RData (environment) of the above R script.</em></p> <p><strong><em>Acknowledgments:</em></strong></p> <p>The authors thank members of the May Lab (Mai Beauclaire, Em Daily, Mara Demers, Kane Keller, Cedric Ndinga-Muniania, Liam Vertal, Monica Watson) for their help in field and lab work. We thank members of the Healthy Prairies project (Shelby Flint, Anna Peschel, Bill Peterson) who provided much of the infrastructure that made this project possible, as well as help in setting up field experiments. We also thank volunteer undergrads from University of Minnesota Morris (Amelia Nelson, Emily Job, Lily Fulton) for assistance in measuring harvested plants and counting nodules. Funding for this project was provided by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources (LCCMR) Project 00086965, Healthy Prairies.</p> <p> </p>
Regional scale shear wave velocity profiles for ground response analyses and uncertainties evaluations – the Piedmont Region (NW Italy) Database
<p>The databases provide detailed information for the Piedmont region in Northwest Italy, offering a view of its geological and geophysical characteristics:</p> <ul> <li><strong>Geological-Geomorphological Database</strong>: Includes 13 distinct Geological-Geomorphological Domains (GGD) in shapefile format. It supports spatial analysis and visualization, based on data from the Geological Map of the Piedmont Region at a 1:250,000 scale.</li> <li><strong>Geotechnical Database</strong>: Contains geotechnical data on bedrock depth and texture attributes derived from available logs in CSV format. Georeferenced using UTM coordinates (WGS84 UTM32N), it includes depth values and texture codes (C for clay, G for gravel, S for sand, R for rock, X for not available).</li> <li><strong>Geophysical Database</strong>: Provides data on shear wave velocity (Vs) profiles in CSV format. Georeferenced with UTM coordinates (WGS84 UTM32N), it includes layer interface depth and shear wave velocity above each layer.</li> </ul>
Supplementary Datasets for the Paper "A new view of seismicity under Mt. Etna volcano, Italy, 2014-2023 from multi-scale high-precision earthquake relocations"
<p>Supplementary Datasets for the Paper <br><strong>Mapping finite-fault earthquake slip with spatial correlation between seismicity and point-source Coulomb failure stress change </strong><br>by Anthony Lomax, Tiziana Tuvè, Elisabetta Giampiccolo, Ornella Cocina<br>DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">https://doi.org/xxxx</a></p> <p><strong>20240724A_Etna_Seismicity_2014-2023_INGV-OE_NLL-SC.csv</strong> is the catalog of NLL-SC relocations presented in the paper in CSV (.csv) format.</p> <p><strong>File_S1_catalog_config_run.zip</strong> includes the relocated NLL-SC catalog in CSV (.csv) and NLL-Hypocenter (.hyp) formats, along with pick data, configuration and other files used to run the NLL-SC relocations presented in the paper.</p>
Vaccine Attitudes Examination (VAX) scale dataset in Spain
<p>This dataset contains data collected between November 15, 2021, and March 15, 2022. Demographic variables, data on vaccinated people, reasons for not getting vaccinated, and responses to items on the Vaccination Attitudes Examination (VAX) scale are included. Although the language of the open answers is Spanish, the name of the variables and the value labels have been written in English to facilitate their understanding.<br>Data and codebooks are provided in csv format, following the FAIR principles.<br>Three files are provided:<br>1. VAX data, with the data related to sample characteristics and the answers to the questionnaire items.<br>2. Database codebook of variables, with information of the labels of the variables of the VAX data file.<br>3. Variables values codebook, with the labels of the values of the variables in the VAX data file.</p>
Dataset - Papyrus 2024 - A large scale curated dataset aimed at bioactivity predictions
<p><strong>This update of release 2024.1 fixes the following:</strong></p> <ul> <li>Metadata in the columns <em>type_IC50</em>, <em>type_EC50</em>, <em>type_KD</em>, <em>type_Ki</em>, and <em>type_other</em> did not contain multiple values when multiple pChEMBL values where available but reported only a single value. This fix ensures all values are reported.</li> <li>Molecules were incorrectly standardized and mixtures were included in the dataset. Standardization (using the <a href="https://github.com/OlivierBeq/papyrus_structure_pipeline" target="_blank" rel="noopener">papyrus_structure_pipeline</a>) is now correctly enforced and mixtures have been removed.</li> </ul> <p><strong>Changes since version 05.6</strong></p> <ul> <li>ChEMBL data was updated to ChEMBL version 34</li> <li>data from the IUPHAR/BPS Guide to PHARMACOLOGY has been included</li> <li>data from Pickett et al.'s publication on MMP-12 has been included (<a href="https://doi.org/10.1021/ml100191f">ACS Med Chem Lett. 2011 Jan 13; 2(1): 28–33. DOI: 10.1021/ml100191f</a>)</li> </ul> <p><strong>Papyrus++:</strong></p> <p>Previous versions mistakenly considered a deviation of 2 log units around compound-target pairs to determine the reproducibility of assays (see published article for more details). This has been fixed to 0.5 log units to ensure data points fall within a maximum range of 1 log unit. As a result, the number of entries in the Papyrus++ set from this release has drastically reduced compared to previous releases.</p>
VeloCycle-estimated cell cycle phases of single cells from a genome-scale perturb-seq performed in K562
<p>Continuous cell cycle phase position between 0 and 2π estimated by <em>VeloCycle </em>(Lederer et al., <em>Nature Methods</em> 2024) for single cells of the perturb-seq performed in the K562 CML cell line by Replogle et al., Cell 2022. </p> <p>Underlies the cell cycle imbalances inferred in Pulver & Forey et al., 2024.</p> <p>Method manuscript exerpt:</p> <p>"For analysis on the genome-wide perturb-seq dataset of K562 cells (Replogle <em>et al.</em>, 2022), a transfer learning approach was applied. Condition-independent estimation of the periodic Fourier series components would be especially challenging on Perturb-seq knockdown conditions containing either (1) very few cells or (2) cells belonging to just one phase of the cell cycle. To infer accurate cell cycle phases for these cells, we first performed manifold-learning for 5,000 training steps to estimate the gene harmonic coefficients (ν0, ν1sin, ν1cos) on a larger set of non-targeting control (NT) K562 cells (75,328 cells), which are more evenly distributed throughout the various phases of the cell cycle. Next, we ran manifold-learning again for 5,000 training steps, but on the entire perturb-seq dataset of 1,971,608 cells and 4,127 gene knockdown conditions (with at least 75 cells per condition). This time, we conditioned <em>VeloCycle</em> on the gene harmonic coefficients learned in the first step. This allowed cells belonging to each stratified gene knockdown condition to be assigned to a position on the cell cycle manifold, while restricting those assignments such that they were based on gene expression patterns earned on a larger and more informative dataset (allowing for batch effect expression differences)."</p>
Characterizing cell-type spatial relationships across length scales in spatially resolved omics data: data repository
<h1>CRAWDAD</h1> <p>Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we develop CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package with source code and additional documentation at https://jef.works/CRAWDAD/.</p> <p>During CRAWDAD's development, we generated simulated datasets and new cell-type annotations for human spleen data, provided here. The external datasets such as the mouse cerebellum, mouse embryo, mouse brain, and human breast cancer data used in the paper can be found in their original publication. See more information in CRAWDAD's data availability statement.</p> <h2>Simulated Datasets</h2> <ul> <li>sim.csv: the simulated data. Used in Figure 1 b-g, Supplementary Figure 1 a-c, and Supplementary Figure 9 a-b.</li> <li>ext_sim.csv: the extended simulated data. Used in Supplementary Figure 1 d-f.</li> <li>null_sim_visualization.csv: the null simulated data. Used to generate the plots Supplementary Figure 2 a-d.</li> <li>null_sim_1.csv - null_sim_10.csv: the 10 null simulated datasets. Used to quantitatively compare CRAWDAD, Squidpy’s co-occurrence implementation, and Ripley’s K Cross.</li> </ul> <h2>HuBMAP Datasets</h2> <ul> <li>pkhl.csv: annotated cell types and positions of sample HBM389.PKHL.936 from donor HBM966.VNKN.965. Used in Figure 5 a-h, Supplementary Figure 5 a, Supplementary Figure 7 a-c, and Supplementary Figure 8 c. doi:10.35079/HBM389.PKHL.936</li> <li>xxcd.csv: annotated cell types and positions of sample HBM772.XXCD.697 from donor HBM966.VNKN.965. Used in Figure 5 d-h, Supplementary Figure 5 a-c, and Supplementary Figure 7 a-c. doi:10.35079/HBM772.XXCD.697</li> <li>fsld.csv: annotated cell types and positions of sample HBM342.FSLD.938 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM342.FSLD.938</li> <li>pbvn.csv: annotated cell types and positions of sample HBM825.PBVN.284 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM825.PBVN.284</li> <li>ksfb.csv: annotated cell types and positions of sample HBM556.KSFB.592 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM556.KSFB.592</li> <li>ngpl.csv: annotated cell types and positions of sample HBM568.NGPL.345 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM568.NGPL.345</li> </ul> <h2>External Datasets</h2> <ul> <li>Mouse cerebellum: Used in Figure 2 a-e, Supplementary Figure 3 a-b, Supplementary Figure 4 a-d, and Supplementary Figure 8 a.</li> <li>Mouse embryo: Used in Figure 2 f-j, Supplementary Figure 3 c-d, Supplementary Figure 4 e-h, and Supplementary Figure 8 b.</li> <li>Human breast cancer: Used in Figure 3 a-c.</li> <li>Mouse brains: Used in Figure 4 a-e.</li> </ul>
Dataset for "Scaling of ultrashort-pulsed laser structuring processes for electromobility applications using a spatial light modulator"
<p>The dataset represents the experimental data for publication "<span>Scaling of ultrashort-pulsed laser structuring processes for electromobility applications using a spatial light modulator</span>"</p>
Deep Learning Reach-level Estimates of Mean River Depth at the Conterminous United States Spatial Scale
<p>Abstract: Estimates of riverine channel geometry play a vital role in the physical representation of stream networks in models used to predict flood and drought conditions, manage water resources, and increase our knowledge of fluvial conditions under a changing climate. A well established body of literature exists that explains the relationship between channel geometry parameters width, depth, and velocity to instantaneous river discharge using a log-log linear power-law regression. In this study, a state-of-the-art deep learning regression model is presented and compared against the power-law method to evaluate their ability to estimate cross-sectional mean river depth. Results reveal three key findings, the neural network: (1) decreases RMSE by 22% verse a CONUS scale power-law equation, (2) reduces prediction variance across Strahler stream orders, and (3) generally outperforms regional power-law equations with an average decrease in RMSE of 8.7% Lastly, a reach-level CONUS dataset of estimated mean river depth is delivered.</p> <p> </p> <p>The deep learning model was trained using the following features:</p> <ul> <li>AI - Mean aridity index of unit catchment - Trabucco and Zomer, 2019</li> <li>area - Upstream drainage area (km2) - P. Lin et al., 2020</li> <li>CLY - Mean clay content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>DOR - Stream segment degree of dam regulation (Scale 0. – 100.) - Grill et al., 2019</li> <li>Elev - Stream segment mean elevation - P. Lin et al., 2020</li> <li>K - Mean bedrock permeability of unit catchment surrounding stream segment - Huscroft et al., 2018</li> <li>LAI - Mean leaf area index of unit catchment - Zhu et al., 2013</li> <li>order - Strahler-Horton stream order - P. Lin et al., 2020</li> <li>P - Mean bedrock porosity of unit catchment - Huscroft et al., 2018</li> <li>QMEAN - Stream segment mean annual discharge (m3/s) - P. Lin et al., 2019</li> <li>Sin - Stream segment sinuosity - P. Lin et al., 2020</li> <li>Slp - Stream segment mean longitudinal slope - P. Lin et al., 2020</li> <li>SLT - Mean silt content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>SND - Mean sand content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>stream_wdth_va - Measured stream cross-sectional width (m) - Canova et al., 2016</li> <li>Urb - Mean urban fraction of unit catchment - Liu et al., 2018</li> </ul> <p>The deep learning model was trained using the following label:</p> <ul> <li>mean_depth_va - Measured stream mean depth (m) - Canova et al., 2016</li> </ul> <p>Predictions of mean depth were made by replacing stream_wdth_va from Canova et al., (2016) with bankfull width estimates (width_m) from P. Lin et al., (2020). Missing records from the P. Lin et al., (2020) dataset were excluded when making predictions, thus there are missing reaches in the dataset.</p>
MultiSubs: A Large-scale Multimodal and Multilingual Dataset
<p>MultiSubs is a dataset of multilingual subtitles gathered from <a href="https://opus.nlpl.eu/OpenSubtitles.php">the OPUS OpenSubtitles dataset</a>, which in turn was sourced from <a href="http://www.opensubtitles.org/">opensubtitles.org</a>. We have supplemented some text fragments (visually salient nouns in this release) within the subtitles with web images, where the word sense of the fragment has been disambiguated using a cross-lingual approach. </p> <p>Please refer to our paper for a more detailed description of the dataset:</p> <p>Josiah Wang, Pranava Madhyastha, Josiel Figueiredo, Chiraag Lala, Lucia Specia (2021). <a href="https://arxiv.org/abs/2103.01910">MultiSubs: A Large-scale Multimodal and Multilingual Dataset</a>. CoRR, abs/2103.01910. Available at: <a href="https://arxiv.org/abs/2103.01910">https://arxiv.org/abs/2103.01910</a></p>
Single objective light-sheet acquired large-scale 3D dataset
<p>This dataset covers Fig. 5 of the following publication:</p> <p>Title: Tilt-invariant scanned oblique plane illumination microscopy for large-scale volumetric imaging<br> Authors: Manish Kumar and Yevgenia Kozorovitskiy <br> Optics Letters Vol. 44, Issue 7, pp. 1706-1709 (2019)<br> https://doi.org/10.1364/OL.44.001706</p> <p>Briefly: The sample imaged is a Thy1GFP expressing transgenic mice brain slice - fixed and coverslipped. No clearing was performed for these.</p> <p>See "readme.txt" for additional info and details about how to use "shearNscaleObliq" file.</p>
Result data related to "Tröndle et al (2019) -- Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe"
<p>The files include results to out study investigating the possibility for renewable electricity autarky in Europe. For each administrative unit on the continental, national, regional, and municipal levels these files include:</p> <ol> <li>Name, country, population, current electricity demand, land cover statistics, shared coast with exclusive economic zone</li> <li>Potential in terms of area [km2], installable capacity [MW], annual electricity yield [TWh]</li> </ol> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Pfenninger, S., & Lilliestam, J. (2019). Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe. <em>Energy Strategy Reviews</em>, <em>26</em>.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 3 (2021-07-19)</p> <p>* Fix ID of EEZ in shared-coast.csv files.</p> <p>Version 2 (2019-11-08)</p> <p>* Add land cover statistics for each unit.</p>
Building Large-Scale Gene-Disease Association Datasets for Biomedical Relation Extraction
<p>This repository contains the GDAb and GDAt datasets. GDAb and GDAt are large-scale, distantly supervised, and manually enhanced datasets for Gene-Disease Association (GDA) extraction. Each dataset consists of three text files, corresponding to train, validation, and test sets, plus an additional JSON file containing the mapping between relation names and IDs. Each record in train, validation, or test files corresponds to a single GDA extracted from a sentence. Records are represented as JSON objects with the following structure:</p> <ul> <li><strong>text:</strong> sentence from which the GDA was extracted.</li> <li><strong>relation:</strong> relation name associated to the given GDA.</li> <li><strong>h: </strong>JSON object representing the gene entity, composed of: <ul> <li><strong>id: </strong>UMLS CUI associated to the gene entity.</li> <li><strong>name:</strong> UMLS preferred name associated to the gene entity.</li> <li><strong>pos: </strong>list consisting of starting position and length of the gene mention within text.</li> </ul> </li> <li><strong>t:</strong> JSON object representing the disease entity, composed of: <ul> <li><strong>id: </strong>UMLS CUI associated to the disease entity.</li> <li><strong>name:</strong> UMLS preferred name associated to the disease entity.</li> <li><strong>pos:</strong> list consisting of starting position and length of the disease mention within text.</li> </ul> </li> </ul> <p>Both datasets contain over 2,500,000 sentences and 500,000 bags.<br> The zip file consists of two folders, GDAb and GDAt, containing the files corresponding to the two datasets, respectively.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.