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33,754 results for “Enhancers”
Рис. 4. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок приморского гребешка (номограмма для 1972 г.). Fig. 4. A grif of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the Crassostrea gigas (nomogram for 1972). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 4. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок приморского гребешка (номограмма для 1972 г.). Fig. 4. A grif of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the Crassostrea gigas (nomogram for 1972).
Рис. 3. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок тихоокеанской устрицы (номограмма) [Раков, 1977]. Fig. 3. A grid of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the giant oyster Crassostrea gigas (nomogram) [Rakov, 1977]. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 3. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок тихоокеанской устрицы (номограмма) [Раков, 1977]. Fig. 3. A grid of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the giant oyster Crassostrea gigas (nomogram) [Rakov, 1977].
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning.
Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхности: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС). Fig. 1. Average monthly sea surface water temperature in Novgorodskaya Bay: 1 – for the period 1931–1973; 2 – for 1977; 3 – for 1978; 4 – for 1979; 5 – for 1980; 6 – for 1981; 7 –spawning temperature (18ºC). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхности: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС). Fig. 1. Average monthly sea surface water temperature in Novgorodskaya Bay: 1 – for the period 1931–1973; 2 – for 1977; 3 – for 1978; 4 – for 1979; 5 – for 1980; 6 – for 1981; 7 –spawning temperature (18ºC).
UV radiation accelerates litter decomposition in a valley-type savanna by enhancing microbial community diversity and function
<p><span>We present the data of the study by Gao et al. (202</span><span>4</span><span>): <a name="_Hlk179470571"></a><a name="OLE_LINK42"></a><strong><span>UV radiation accelerates litter decomposition in a valley-type savanna by enhancing microbial community diversity and function</span></strong></span><span>.</span><span> The excel file (Raw Data) includes the following sheets: 1- Radiation (w·m<sup>-2</sup>) variation of UV-A and UV-B during the experimental period. 2- Decay constants (<em>K</em>, yr<sup>−1</sup>) and changes in the mass loss rate of litter under different UV conditions during the experimental period<span>. 3- </span>The content of lignin, cellulose, C, N and P of litter under different UV conditions during the experimental period<span>. 4-</span></span><span> </span><span>16S ASVs under different UV conditions<span>. 5-</span></span><span> </span><span>ITS ASVs under different UV conditions.</span></p>
Identification of intratumoral bacteria that enhance breast tumor metastasis
<p>This file contains the supporting datasets for the manuscript entitled Identification of intratumoral bacteria that enhance breast tumor metastasis, by Gerbec et al. File includes 8 supporting datasets as well as a legend file with the description of each individual dataset.</p>
Replication Data for Coloring Tetrahedral Semiconductors: Synthesis and Photoluminescence Enhancement of Ternary II-III2‑VI4 Colloidal Nanocrystals
<p>Ternary tetrahedral II-III2-VI4 semiconductors, where II is Zn or Cd, III In or Ga, and VI S, Se, or Te, are of interest in UV radiation detectors in medicine and space physics as well as CO2 photoreduction under visible light. We synthesize colloidal II-III2-VI4 semiconductor nanocrystals from readily available precursors and<br>ascertain their ternary nature by structural and spectroscopic methods, including 77Se solid-state NMR spectroscopy. The pyramidally shaped nanocrystals range between 2 and 12 nm and exhibit optical gaps of 2−3.9 eV. In the presence of excess anions on the particle surface, treatment with Lewis acidic, Z-type ligands results in better passivation and enhanced photoluminescence. Electronic structure calculations reveal<br>the most stable, lowest energy polymorphs and coloring patterns. This work will pave the way toward more environmentally friendly, ternary semiconductors for optoelectronics and electrocatalysis.</p>
Thin Nickel Coatings on Stainless Steel for Enhanced Oxygen Evolution and Reduced Iron Leaching in Alkaline Water Electrolysis
<p>This is a dataset detailing the characterization of electrodeposited Ni-layers on 1 cm2 pure Ni- and stainless steel-plates. The data is available as text files (.txt), MS Excel files (.xls), Origin files (.opju), and Gamry raw data files (.DTA).</p>
Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI)
<h1>Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI)</h1> <p>The <code>Class</code> is a custom dataset class that brings together information from two distinct domains into a unified dataset. This class is designed to streamline the process of working with data from different sources and enable users to seamlessly access and analyze combined datasets.</p> <p>The Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI) is committed to utilizing the Trees & Planting Sites dataset for a comprehensive geospatial analysis of Durham's urban tree canopy. Through Python within Google Colab, our aim is to identify key locations for canopy expansion, evaluate the impact of urban development on green spaces, and deliver informed recommendations for the sustainable growth of urban tree coverage.</p> <h2><a href="https://huggingface.co/datasets/Ziyuan111/DurhamTrees#background-and-rationale" rel="nofollow"></a>Background and Rationale</h2> <p>Durham's urban tree canopy is a crucial component that contributes to environmental quality, public health, and overall city aesthetics. This canopy is under threat due to ongoing urban development and natural wear. A systematic, data-driven approach is critical for strategic planning and conservation of the urban forest to ensure its vitality for generations to come.</p> <h2><a href="https://huggingface.co/datasets/Ziyuan111/DurhamTrees#data-sources-and-methodology" rel="nofollow"></a>Data Sources and Methodology</h2> <p>These data files are from durham open.</p> <p>And for the .py file:</p> <p>The provided Python script defines a dataset class named <code>DurhamTrees</code> using the <code>datasets</code> library. This class combines information from two different domains ("class1_domain1" and "class2_domain1") and includes features from both domains.</p> <p>Trees & Planting Sites Dataset: Hosted on the Durham Open Data portal, this dataset includes location, species, size, and health of street trees, alongside designated future planting sites. Data Source: Durham Trees & Planting Sites Dataset <a href="https://live-durhamnc.opendata.arcgis.com/datasets/DurhamNC::trees-planting-sites/about" rel="nofollow">https://live-durhamnc.opendata.arcgis.com/datasets/DurhamNC::trees-planting-sites/about</a></p> <p> </p>
Enhanced Alzheimer's Brain Scan Dataset: Normal and Synthesized
<div> <div> <div> <h2>Enhanced Alzheimer's Brain Scan Dataset: Normal and Synthesized</h2> </div> </div> </div> <div> <div> <div> <div> <h2>Unveiling the Invisible: A Novel Approach to Alzheimer's Image Analysis</h2> <h3>Dataset Overview</h3> <p>This unique dataset comprises two distinct sets of brain scan images related to Alzheimer's disease:</p> <ol> <li><strong>Normal Dataset</strong>: 2000 carefully selected images for each stage of Alzheimer's progression.</li> <li><strong>Synthesized Dataset</strong>: Corresponding enhanced images using our innovative pseudo-RGB transformation technique.</li> </ol> <p>Total Images: 16,000 (8,000 normal + 8,000 synthesized)</p> <h3>Why This Dataset Matters</h3> <ol> <li><strong>Enhanced Feature Visibility</strong>: The synthesized images amplify subtle brain structures that may be indicative of Alzheimer's progression.</li> <li><strong>Multi-Perspective Analysis</strong>: By providing both normal and synthesized versions, this dataset enables researchers to compare and leverage both representations.</li> <li><strong>Balanced Categories</strong>: With equal representation across Alzheimer's stages, this dataset supports unbiased model training.</li> <li><strong>Novel Research Opportunities</strong>: The unique pseudo-RGB transformation opens doors for innovative approaches in medical image analysis.</li> </ol> <h3>Potential Applications</h3> <ul> <li>Training more accurate deep learning models for Alzheimer's detection</li> <li>Comparative studies between traditional and enhanced image analysis techniques</li> <li>Exploration of feature importance in Alzheimer's diagnosis</li> <li>Development of new visualization tools for medical professionals</li> </ul> </div> </div> </div> </div>
Dataset for Diamond-coated quartz crystal microbalance sensors: Challenges in high yield production and enhanced detection of ethanol and sars-cov-2 proteins
<p>The data set to paper: </p> <p>Name: Diamond-coated quartz crystal microbalance challenges in mass production and enhanced detection of ethanol and sars-cov-2 proteins</p> <p>Authors: Tibor Izsák1*, Marian Varga1, Michal Kočí2,3, Ondrej Szabó2, Katarína Aubrechtová Dragounová2, Gabriel Vanko2, Miroslav Gál4, Jana Korčeková5, Michaela Hornychová 4, Alexandra Poturnayová5, Alexander Kromka2*</p> <p>Affiliations: 1 Department of Microelectronics and Sensors, Institute of Electrical Engineering, Slovak Academy of Sciences, Dúbravská Cesta 9, Bratislava, 841 04, Slovak Republic<br> 2 Department of Semiconductors, Institute of Physics of the Czech Academy of Sciences, Cukrovarnicka 10/112, Prague 6 162 00, Czech Republic<br> 3 Department of Microelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, Prague 6, 166 27, Czech Republic<br> 4 Faculty of Chemical and Food Technology, Slovak University of Technology, Bratislava, Slovak Republic<br> 5 Center of Biosciences, Institute of Molecular Physiology and Genetics, Slovak Academy of Sciences, Bratislava, Slovak Republic<br> *corresponding author: tibor.izsak@savba.sk</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of collection: 1. 5. 2023 - 31. 7. 2024</p> <p>Description: Figure 1: Photos of QCM substrates oriented horizontally or vertically on the substrate holder in the deposition chamber (left) and during the diamond CVD process with ignited plasma (right).<br> Figure 2: a) 3D model of the measurement setup and b) photograph of the open gas chamber with embedded QCM sample.<br> Figure 3: Photo of the a) measurement setup and b) disassembled flow cell with V-Dia-QCM. c) Side view photo of the assembled flow cell in the measurement setup.<br> Figure 4: a) SEM images revealing surface morphology and b) corresponding Raman spectra of Dia-QCM and Dia-Si substrates horizontally or vertically oriented on the substrate holder and corresponding optical photos. There is also the Raman spectrum of the bare QCM (Au-QCM) sample before the diamond deposition.<br> Figure 5: a) Raman spectra and b) SEM images depicting surface morphology of porous diamond film grown on Si (H-PorDia-Si) and QCM (H-PorDia-QCM) substrate. The inset in Fig. 5a represents the optical photo of diamond-coated QCM. Note: ‘H-’ in sample names means horizontally loaded samples.<br> Figure 6: The response delta fR of diamond-coated QCM sensors horizontally and vertically oriented, i.e., single-sided and double-sided diamond-coated QCMs, when applying periodic switching (at 3-minute intervals) of ethanol vapour (E) with various concentrations (from 10 ppm to 100 ppm) and synthetic air (Air).<br> Figure 7: a) First resonant frequency shift (delta fR) of individual QCM sensors and b) mean values of delta fR with corresponding error bars for each QCM sensor group dependent on ethanol concentration.<br> Figure 8: a) The changes of the resonant frequency, delta fR, after the addition of neutravidin (NA) dissolved in water, biotinylated 1C aptamers (1C APT) dissolved in PBS with MgCl2, and 50 pg/mL S-RBD protein in PBS. The addition of neutravidin, aptamers, proteins, and surface washings by water (H2O) or buffer (PBS) are highlighted by arrows. b) Zoom in on the highlighted area in Fig. 8a.<br> Figure 9: Decrease of the resonant frequency, fR, at various S-RBD protein concentrations. The comparison of the sensitivity of diamond and gold QCM surfaces on which S-RBD was determined is indicated in the graph legend.</p>
Dataset for publication:Dataset for publication: "Application of shot peening to improve fatigue properties via enhancement of precipitation response in high-strength Al-Cu-Li alloys"
<p>The dataset contains a set of experimental data used in preparation for the manuscript "Application of shot peening to improve fatigue properties via enhancement of precipitation response in high-strength Al-Cu-Li alloys." The dataset contains results of fatigue tests, residual stress measurements, nanoindentation measurements, and surface roughness data.</p>
Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management
<p><span>1. Species' ranges are changing at accelerating rates. Species distribution models (SDMs) are powerful tools that help rangers and decision-makers prepare for reintroductions, range shifts, reductions, and/or expansions by predicting habitat suitability across landscapes. Yet, range-expanding or -shifting species in particular face other challenges that traditional SDM procedures cannot quantify, due to large differences between a species' currently-occupied range and potential future range. The realism of SDMs is thus lost and not as useful for conservation management in practice. Here, we address these challenges with an extended assessment of habitat suitability through an <i>integrated SDM database (iSDMdb)</i>.</span></p> <p><span>2. The<i> iSDMdb</i> is a spatial database of predicted sites in a species' prediction range, derived from SDM results, and is a single spatial feature that contains additional, user-friendly data fields that synthesise and summarise SDM predictions and uncertainty, human impacts, restoration features, novel preferences in novel spaces, and management priorities. To illustrate its utility<i>,</i> we used the endangered New Zealand sea lion (<i>Phocarctos hookeri</i>). We consulted with wildlife rangers, decision-makers, and sea lion experts to supplement SDM predictions with additional, more realistic, and applicable information for management. </span></p> <p><span>3. Almost half the data fields included in this database resulted from engaging with these end-users during our study. The SDM found 395 predicted sites. However, the <i>iSDMdb</i>'s additional assessments showed that the actual suitability of most sites (90%) was questionable due to human impacts. >50% of sites contained unnatural barriers (fences, grazing grasslands), and 75% of sites had roads located within the species' range of inland movement. Just 5% of the predicted sites were mostly (>80%) protected.</span></p> <p><span>4. Integrating SDM results with supplemental assessments provides a way to address SDM limitations, especially for range-expanding or -shifting species. SDM products for conservation applications have been critiqued for lacking transparency and interpretation support, and ineffectively communicating uncertainty. The <i>iSDMdb</i> addresses these issues and enhances the practical relevance and utility of SDMs for stakeholders, rangers, and decision-makers. We exemplify how to build an <i>iSDMdb</i> using open-source tools, and how to make diverse, complex assessments more accessible for end-users.</span></p>
Zero-Shot Information Extraction to Enhance a Knowledge Graph Describing Silk Textiles - English and Spanish neighborhood sub-graphs
<p>Two language-specific sub-graphs (English and Spanish) based on the ConceptNet Knowledge Graph. These two files are required to run the code for reproducing the results reported in the paper <a href="https://aclanthology.org/2021.latechclfl-1.16/">"Zero-Shot Information Extraction to Enhancea Knowledge Graph Describing Silk Textiles"</a> at the <a href="https://sighum.wordpress.com/events/latech-clfl-2021/">LaTeCH-CLfL 2021</a> workshop co-located with <a href="https://2021.emnlp.org/">EMNLP 2021</a>.</p>
Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic (Data)
<p>Origin projects and figures used for the article "Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic", published in the proceedings of the 29th Micromechanics and Microsystems Europe Workshop; 26.08.2018 to 29.08.2018; Smolenice Castle, Slovakia.</p>
Enhancement of unsteady frequency responses of electro-thermal resonance MEMS cantilever sensors (Data)
<p>Origin projects and figures used for the article "Enhancement of unsteady frequency responses of electro-thermal resonance MEMS cantilever sensors", published in the proceedings of the 30th Micromechanics and Microsystems Europe Workshop; 18.08.2019 to 20.08.2019; Wolfson College, Oxford, United Kingdom.</p>
Metadata of "Incorporating a molecular antenna in diatom microalgae cells enhances photosynthesis"
<p>Metadata of "Incorporating a molecular antenna in diatom microalgae cells enhances photosynthesis"</p>
Datasets supporting the paper 'Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador'
<p>Datasets supporting the paper 'Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador'</p> <p>Contact: C. Scott Watson. c.s.watson@leeds.ac.uk</p> <p>DRR_greenspace<br> DRR_greenspace_polygon.shp - classified potential DRR greenspace (minimum 100 m2)<br> DRR_greenspace_zone_points.shp - classified potential DRR greenspace aggregated to zones<br> DRR_greenspace_zone_polygons_top10.shp - Top 10 maximum capacitated analysis of classified potential DRR greenspace aggregated to zones.<br> <br> Land_cover<br> rf_1986_mode_clipped.tif - 1986 land cover classification<br> rf_2020_mode_clipped.tif - 2020 land cover classification<br> landcover_classes.PNG - land cover classes<br> accuracy_assessment_points_1986 - 1986 land cover accuracy assessment points<br> accuracy_assessment_points_2020 - 2020 land cover accuracy assessment points<br> modified_urban_growth_scenario.shp - hazard-modified urban growth scenario</p> <p> </p>
Requirements (enhancements) of 64 Mozilla projects mined from Bugzilla
<p>The dataset consists of 4200 enhancements that are in some form of dependency with the others (such as blocks, depends_on etc.) This data spans from 08/05/2001 to 09/08/2019.</p> <p>This dataset is gathered using Bugzilla's REST API for 64 projects. We have id, summary, priority, severity, type, version, target_milesotne, product, depends_on, blocks fields information for each one of these enhancements.</p>
Enhanced Protein Isoform Characterization Through Long-Read Proteogenomics - Workflow Results
<pre> </pre> <p>The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g. PacBio, Oxford Nanopore) provides full-length transcript sequencing, which can be used to predict full-length proteins. Here, we describe a long-read proteogenomics approach for integrating matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data in protein inference to enable detection of protein isoforms that are intractable to MS detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.</p> <p>Companion Repositories:</p> <ol> <li><a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow GitHub Repository Release</a></li> <li><a href="https://doi.org/10.5281/zenodo.5920847">Long-Read-Proteogenomics Analysis GitHub Repository Release</a></li> </ol> <p>Companion Datasets</p> <ol> <li><a href="https://zenodo.org/deposit/5703754">Long-Read-Proteogenomics Workflow Sample and Reference Data</a></li> <li><a href="https://doi.org/10.5281/zenodo.5234651">TEST Data for Long-Read-Proteogenomics Workflow GitHub Actions</a></li> </ol> <p>This Repository contains the complete output from the execution of the <a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow</a>, using the input from <a href="https://zenodo.org/deposit/5703754">Jurkat Samples and Reference Data</a>. </p> <p>The file <em>jurkat.flnc.bam </em>was 6.5 GB had to be split into 13 separate files and for use should be rejoined -- here are the steps that were used to split the file up. </p> <p>1. Convert <em>jurkat.flnc.bam</em> (binary format) to sam file (text format) without header: <em>samtools view jurkat.flnc.bam > jurkat.flnc.sam</em></p> <p>2. Capture the header: <em>samtools view -H jurkat.flnc.bam > jurkat.flnc.header.sam</em></p> <p>3. Split <em>jurkat.flnc.sam</em> into smaller files (aim to get final size under 2GB): <em>split -l 400000 jurkat.flnc.sam jurkat.flnc.chunk.</em></p> <p>4. Convert each of these files back to bam for uploading: <em>samtools view -b jurkat.flnc.chunk.a* -o jurkat.flnc.chunk.a*.bam (*=a,b,c,d,e,f,g,h,i,j,k,l,m)</em></p> <p>After downloading, reverse this process including using the header file which is found in the LRPG-Manuscript-Results-results-results-jurkat-isoseq3-companion-files.tar.gz file></p> <p>1. Convert the bam files back to sam files: <em>samtools view jurkat.flnc.chunk.a*.bam > jurkat.flnc.chunk.a*.sam (*=a,b,c,d,e,f,g,h,i,j,k,l,m)</em></p> <p>2. Combine the header together with the sam files: <em>cat jurkat.flnc.chunk.a*sam > jurkcat.flnc.sam (</em>verified the same number of lines of the sam files is identical to the number of lines of the original without header: 4,956,761. Header file is 13 lines.</p> <p>3. Convert to bam files if desired: <em>samtools view -b jurkat.flnc.sam -o jurkat.flnc.bam</em></p> <p>4. Rehead with the header file: <em>samtools reheader -P -i jurkat.flnc.header.sam jurkat.flnc.bam</em></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.