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6,452 results for “Application”
3D printed biomedical devices and their applications: A review on state-of-the-art technologies, existing challenges, and future perspectives
<p>This repository consists of the data that have been utilized to imagine and subsequently construct Fig. 1, Fig. 3 , Fig. 5 , Fig. 6 and Fig. 7 of the article " H. B. Mamo, M. Adamiak and A. Kunwar. 3D printed biomedical devices and their applications: A review on state-of-the-art technologies, existing challenges, and future perspectives, Journal of the Mechanical Behavior of Biomedical Materials, 143 (2023) 105930. doi: 10.1016/j.jmbbm.2023.105930 ".</p> <p> Brief introduction of the files contained in this repository</p> <ol> <li> acronyms.csv: This file consists of the list of acronyms associated with materials and techniques used in 3D printing of biomedical devices.</li> <li>fig1a-data.csv: The csv file provides a relative ranking of different types of 3D printing techniques for biomedical applications based upon merits and limitations (wherever applicable). The technique listed as Rank 1 is considered as the most commonly used 3D printing procedure.</li> <li> fig1b-data.csv: The csv file enlists the benfits of the 3D printing techniques in biomedical applications as compared to subtractive manufacturing methods.</li> <li> fig3-data.csv: The file contains a comparison between conventional and customized tablet printing methods. The illustration is made through the manufacturing or printing of pharmaceutical tablets or pills. This illustation also applies to the production of pharmaceutical capsules. It thus illustrates that customized medicine is enabled using 3D printing technology.</li> <li> fig5-data.csv: This file enumerates the major challenges associated with 3D printing of biomedical devices.</li> <li> fig6-data.csv: The file enlists the roles of wearable smarts, cloud-based platforms and physicians in context of the hospitals implementing IoMT.</li> <li> fig7-data.csv: The aspects of IoMT Sensors,IoMT Platforms,3D Printers,Design and Prototypes within the integrated 3D printing-IoMT ecosystem are listed in the file.</li> </ol>
Groundwater level data used in the manuscript titled "An explainable Bayesian TimesNet for probabilistic groundwater level prediction with application to semi-arid regions"
<p>The standardized semimonthly groundwater levels collected from 30 monitoring wells in Dalad County, China. The data were obtained from the Ministry of Water Resources of China and the groundwater yearbooks. The data are used in our submitted manuscript titled "An explainable Bayesian TimesNet for probabilistic groundwater level prediction with application to semi-arid regions". If you find this dataset useful for your research, please consider to cite our manuscript upon publication. </p>
Dataset for the publication: Potential of alcohol fuels in active and passive pre-chamber applications in a passenger car spark-ignition engine
<div> <p>The dataset covers the research data of the publication "Potential of alcohol fuels in active and passive pre-chamber applications in a passenger car spark-ignition engine" in International Journal of Engine Research (DOI: 10.1177/14680874211053168).</p> </div>
SPT results - Simulation of zonation-function relationships in the liver using coupled multiscale models: Application to drug-induced liver injury
<p>Results of the study "Simulation of zonation-function relationships in the liver using coupled multiscale models: Application to drug-induced liver injury"</p>
Dataset for publication "Chemical-Dealloying-Derived PtPdPb-Based Multimetallic Nanoparticles: Dimethyl Ether Electrocatalysis and Fuel Cell Application" in ACS Applied Materials & Interfaces 2023, 15, 49, 56930–56944
<p>Dataset for publication "Chemical-Dealloying-Derived PtPdPb-Based Multimetallic Nanoparticles: Dimethyl Ether Electrocatalysis and Fuel Cell Application" in ACS Applied Materials & Interfaces 2023, 15, 49, 56930–56944, https://doi.org/10.1021/acsami.3c11003</p> <p>Dataset contains XRD, SAXS, Particle size distribution, HRTEM, Electrochemical characterisation, Fuel cell current-voltage curve, DFT calculations, Elemental maps and electrode stability measurements made on pristine and chemically dealloyed carbon supported Pt2PdPb2 nanoparticles.</p>
Data for Hogan et al. 2024: "Basal bark herbicide treatment of Lonicera maackii (Amur honeysuckle) is effective regardless of application timing, with limited nontarget effects on native plant diversity"
<p>Data to accompany the following publication, currently in press:</p> <p>Hogan, K.F.E., K.Baker, E.M. Bach, N.A. Barber. Basal bark herbicide treatment of Lonicera maackii (Amur honeysuckle) is e ective regardless of application timing, with limited nontarget e ects on native plant diversity. In press, Ecological Solutions and Evidence</p> <p> </p> <p>Paper abstract: </p> <p><span>Managers tasked with controlling invasive species require effective methods that are quick and easy to use without inflicting extensive nontarget damage, while also being compatible with other scheduled management responsibilities. <em>Lonicera maackii</em> (Amur honeysuckle) is a non-native shrub that has invaded eastern and midwestern North American deciduous forests, altering ecosystem function and reducing biodiversity. </span><span>This study explores prescribed fire and seasonal basal applications of triclopyr ester as control methods and examines the extent of nontarget damage. We used paired-split plots to implement basal bark treatments in different seasons within burned and unburned units, and we tracked individual<em> L. maackii</em> to determine mortality and hyperlocal impacts of management. </span><span>Basal bark treatments killed 98.4% of <em>L. maackii</em> across seasonal timings. Nontarget plant cover immediately declined similarly for all herbicide application seasons, but the early- and late-spring treatments showing signs of recovery within four years. Meanwhile, species richness showed biologically small but statistically different declines across all treatment times. Prescribed fire did not impact <em>L. maackii </em>mortality or interact with herbicide efficacy. </span><span>Basal bark applications of triclopyr are an effective means of <em>L. maackii</em> control regardless of application timing, which allows managers to implement it at their convenience to avoid interfering with other management tasks that have time constraints.</span></p>
Supplementary data for the paper: "Enhancing concrete durability in chloride-rich environments through manual application of healing agents"
<p>Supplementary data for the paper: “Enhancing concrete durability in chloride-rich environments through manual application of healing agents”.<br><br>Open data concerning experimental work. The paperinvestigates the use of three potential healing agents: water-repellent agent (WRA), sodium silicate (SS), and polyurethane (PU). The agents are tested separately under two conditions: exposure to a 3.3% NaCl concentration at 20°C and exposure to cyclic freeze-thaw conditions. The agents are manually injected into the cracks and evaluated for their healing efficiency using the capillary absorption test and microscopy analysis. Additionally, their resistance to freeze-thaw cycles by monitoring mass loss and chloride bulk diffusion is measured using sprayed silver nitrate, titration, and EDX mapping. </p>
quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data
<p>This page contains the code and datasets used in "quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data".</p> <p>File <strong>test_datasets.zip</strong> containes three datasets:</p> <ul> <li><em>D1.RData</em>: scRNA-seq omics data derived from Salcher et. al (2022)</li> <li><em>D2.RData</em>: scRNA-seq omics data derived from Pineda et al. (2024)</li> <li><em>D3.RData</em>: in silico WGS SNP data.</li> </ul> <p>File <strong>test_scripts.zip</strong> containes the code to reproduce the results.</p>
Dataset for Femtosecond-Laser-Surface-Nanostructured Glass in BIPV Applications
<p><strong>Summary:</strong></p> <p>This is the collection of datasets for the article titled “Femtosecond-laser-surface-nanostructured glass for building integrated solar concentrator”, published in Materials&Design (ISSN 0264-1275).</p> <p><strong>Methods:</strong></p> <p>The experimental and calculation methods for generating the datasets are described in the original paper.</p> <p><strong>File Description:</strong></p> <p>Unzip "Laser glass.7z" using free and open source software 7-zip. The file contains following content:</p> <p>\IMG_SEM_1\<br>- This folder comprises raw Scanning Electron Microscope (SEM) images captured from the surface normal perspective. Each image is in .tif format with a resolution of 1024 × 768 pixels. The file name begins with the laser scan speed, ranging from 200 to 2000 (in mm/s). The laser scan speed is followed by the zooming rate. Some file names include "_2" at the end, signifying images taken from a different area or captured with adjusted brightness/contrast settings. Partial sections of images taken at a 5000x zoom level were used in Figure 1 in original paper.</p> <p>\IMG_SEM_2\<br>- This folder comprises raw cross-sectional Scanning Electron Microscope (SEM) images taken after cutting the sample through laser textured area. Each image is in .tif format with a resolution of 1024 × 768 pixels. The file name begins with the laser scan speed, ranging from 200 to 1800 (in mm/s). </p> <p>\XRD\<br>- This folder contains raw X-ray Diffraction (XRD) data utilized for creating Figure 2a. Additionally, the folder includes automatically generated metadata files that detail the measurement conditions. Within this folder, Ref.csv provides the XRD spectrum of the unprocessed substrate, while Main.csv contains the spectrum of a sample processed with a laser at a speed of 200 mm/s.</p> <p>\PL\<br>- This folder contains photoluminescence (PL) spectra data. The file PL_1.csv was used to generate Figure 2c, while PL_2.csv was used for Figure 2d. The data in both files are organized in an X-Y-Y... format, with each dataset labeled according to the corresponding figure legend. Note that signals below 450 nm were filtered.</p> <p>\RAMAN.csv<br>- contains raw Raman spectroscopy data used to generate Figure 2b. The dataset includes three columns: the first represents the wavenumber values, which serve as the X-axis for the plot. The second and third columns correspond to the Y-axis data, labeled as "Ref" and "Main." "Ref" represents the Raman spectrum of the unprocessed substrate, while "Main" corresponds to the spectrum of a sample processed with a laser at a speed of 200 mm/s. Note the signal before ~80 cm-1 is filtered and the sharp peak at 416.433 cm-1 is due to noise and excluded from the plot. </p> <p>\Spectrophotometry\<br>- The folder contains Spectrophotometry data used to generate Figure 3. The file Spectra_1.csv includes datasets for reflectance (%R), transmittance (%T), and absorptance (%A), each labeled according to the legends of Figures 3a, 3b, and 3c, respectively. Spectra_2.csv provides data on total reflectance (R_tot_*), diffuse reflectance (R_dif_*), and their ratio (R_*%), corresponding to Figure 3d. The datasets in Spectra_2.csv are organized by processing speed *(mm/s) of the samples.</p> <p>\IV\<br>- The folder contains current-voltage (I-V) measurement data. The file IV_1.csv was used to plot Figure 4c and to calculate the relative photocurrent presented in Figure 4d. The file IV_2.csv was used to plot Supplementary Figure S2 and to calculate the optical power efficiency.</p> <p>\Contact_angle\<br>- The folder contains contact angle measurement data. Stat.csv includes advancing and receding angles shown in Figure 5c. 400_Adv.mp4 and 400_Rec.mp4 contain the advancing and receding angle of the 400 mm/s sample, respectively. </p> <p><strong>Credit:</strong></p> <p>When using the dataset/figures, please cite the original paper.</p>
A hybrid approach to the small unannotated corpus-based language comparison and its application to the Old East Slavic charters - Supplementary material 1 (Old East Slavic)
<h1>Old East Slavic charters (XII - XIV century)</h1> <h2>General description</h2> <p>A set of nine historical Old East Slavic legal texts from Smolensk, Polack and Novgorod from the end of the XII century to the first half of the XIV century. The source for Smolensk charters is Avanesov (1963), which contains original texts as well as their initial deciphering (not machine-readable). The source for Polack charters is Horoshkevich (2015), containing trascribed machine-readable texts that required only an additional check and preparation. The source for Novgorod charters is Napierskij (1857), carrying original texts and their initial non-machine-readable deciphering. All the texts underwent an additional preprocessing of reconstructed and contracted parts deletion in order to better represent the actual texts under consideration and exclude as many research biases as it is possible. The last step was a manual tokenisation and the joining of each text into a single string.</p> <p>The data statement is available among the downloadable files.</p> <h2>How-to</h2> <p>This section contains the tutorials that allow to use this data with the intended pipelines.</p> <h3>Corpus-based distance measurement package</h3> <p>The source code for package is available <a href="https://doi.org/10.5281/zenodo.13958502" target="_blank" rel="noopener">here</a>, the manual is available in the <a href="https://github.com/The-One-Who-Speaks-and-Depicts/corpus_distance/blob/dev/README.md" target="_blank" rel="noopener">README</a> section of the repository.</p> <p>To use this dataset for the measurement of distance between Smolensk, Polack and Novgorod lects, and their subsequent clusterisation, following steps should be completed:</p> <ol> <li>Download the <a href="https://github.com/The-One-Who-Speaks-and-Depicts/corpus_distance/blob/dev/example/Corpus_distance_tutorial.ipynb" target="_blank" rel="noopener">Jupyter notebook</a> that streamlines the package use.</li> <li>Download the dataset.</li> <li>Put the dataset into a selected folder on your computer (make sure there are no other files within this folder).</li> <li>Insert the path to the directory into <code>CONTENT_DIR</code> variable in the Jupyter notebook.</li> <li>Run the notebook, adjusting the parameters, if necessary.</li> </ol> <h2> </h2>
A hybrid approach to the small unannotated corpus-based language comparison and its application to the Old East Slavic charters - Supplementary material 3 (Modern standard Slavic lects)
<h1>Modern standard Slavic lects (Croatian, Slovak, Slovenian)</h1> <h2>General description</h2> <p>The dataset consists of texts, written in three modern stanard Slavic lects: Croatian, Slovak, and Slovenian. The texts are parallel in order to compensate for the possible genre influences. The text is John’s Gospel in each of the given languages.</p> <h3>Sources</h3> <p>Croatian original text is from the <a href="https://www.wordproject.org/bibles/cr/index.htm">Ivan Šarić’s translation</a> of New Testament. Slovenian text is from the <a href="https://www.bible.com/bible/2319/JHN.1.SSP">standard Slovenian translation</a> of the New Testament. Slovak text is from the modern <a href="https://svatepismo.sk/evanjelium-podla-jana-1">Catholic translation</a> of the New Testament.</p> <p>The data statement is available among the downloadable files.</p> <h2>How-to</h2> <p>This section contains the tutorials that allow to use this data with the intended pipelines.</p> <h3>Corpus-based distance measurement package</h3> <p>The source code for package is available <a href="https://doi.org/10.5281/zenodo.13958502" target="_blank" rel="noopener">here</a>, the manual is available in the <a href="https://github.com/The-One-Who-Speaks-and-Depicts/corpus_distance/blob/dev/README.md" target="_blank" rel="noopener">README</a> section of the repository.</p> <p>To use this dataset for the measurement of distance between Slovak, Croatian and Slovenian lects, and their subsequent clusterisation, following steps should be completed:</p> <ol> <li>Download the <a href="https://github.com/The-One-Who-Speaks-and-Depicts/corpus_distance/blob/dev/example/Corpus_distance_tutorial.ipynb" target="_blank" rel="noopener">Jupyter notebook</a> that streamlines the package use.</li> <li>Download the dataset.</li> <li>Put the dataset into a selected folder on your computer (make sure there are no other files within this folder).</li> <li>Insert the path to the directory into <code>CONTENT_DIR</code> variable in the Jupyter notebook.</li> <li>Run the notebook, adjusting the parameters, if necessary.</li> </ol> <h2> </h2>
Comprehensive Ethereum Execution Data for Object-Centric Process Mining of Decentralized Applications (DApps)
<p>The dataset pertains to the collection and analysis of blockchain execution data, particularly from Ethereum-based Decentralized Applications (DApps). This data includes transactions, transaction receipts, and detailed transaction traces, documenting the execution steps performed by the Ethereum Virtual Machine (EVM). Such traces are essential for understanding the interaction between smart contracts and accounts, including Contract Accounts (CAs) and Externally Owned Accounts (EOAs).</p> <p>A blockchain is an append-only ledger that chronologically records data in blocks. Each block contains transactions that signify state transitions, and transaction receipts that provide a hashed result of these transitions to ensure uniform results across different executions. The dataset includes a classification of Ethereum accounts, detailing the functions and interactions between EOAs and CAs, where CAs deploy and execute smart contract code.</p> <p>The dataset captures the granular operational data of blockchain transactions, such as function calls, contract creations, and log entries generated by smart contracts. These details are crucial for creating object-centric event logs, aiding in process mining and analysis to bridge the gap between theoretical process models and actual execution.</p> <p>Contract creations and function calls are fundamental components of the dataset. The former documents the deployment of smart contracts, including the mechanics of contract updates and additions through various design patterns. Function calls between accounts are also extensively logged, providing insights into the flow of Ethereum's native token, Ether, and other transactional data within the blockchain.</p> <p>Delegated calls and log entries represent more specialized interactions within Ethereum, where delegated calls allow contracts to use code from other contracts to manipulate their own state, supporting upgradeable contract designs. Log entries, specified within smart contract code, facilitate the communication of contract execution details to external systems.</p> <p>To handle the diverse and dynamic nature of blockchain data, the dataset employs the Object-Centric Event Log (OCEL) format. This format accommodates multiple object types in a single log, addressing issues such as event divergence and convergence, typical of traditional single-case logs. The latest version, OCEL 2.0, supports documenting dynamic object roles and relationships, improving the fidelity of logs in capturing blockchain operations.</p> <p>In summary, the dataset is structured to support a comprehensive analysis of blockchain behaviors, particularly focusing on Ethereum DApps. It is tailored to assist researchers and practitioners in understanding and analyzing the decentralized execution of smart contracts and the associated data flows within the blockchain environment.</p>
Data for a publication "Labyrinth Seal Design for Space Applications"
<p>The presented dataset contains raw and processed data from evaporation tests, simulations, and surface analyses aimed at optimizing labyrinth seal configurations for space applications. It includes experimental measurements from a custom-built evaporation test rig, simulation data from COMSOL Multiphysics (using Molecular Flow and Particle Tracing Modules), and surface roughness analyses from a 3D Optical Profilometer. Supporting MATLAB scripts for data processing, along with detailed subfolders for experimental and simulation data, provide a comprehensive resource for evaluating labyrinth seal performance and minimizing lubricant evaporation.</p>
Fe-bearing magnesium silicate glasses for potential supplementary cementitious applications
<p>The enclosed raw data files include various formats from multiple characterization techniques, covering XPS, BET SSA, XRF, SEM-EDS, ICP, FTIR, XRD, PSD, DSC-TG, TEM-EDS, and Mössbauer analyses. The formats and file details are as follows:</p> <ul> <li>XPS: Provided in .VGD format.</li> <li>BET SSA: Available in .xls and .xps formats.</li> <li>XRF: Data provided in .xlsx format, with filenames containing 'XRF'.</li> <li>SEM-EDS: Reports included in .xlsx format.</li> <li>ICP: Data listed in .pdf format, with filenames including the date and project information.</li> <li>FTIR: Raw data included in .dpt files.</li> <li>XRD: Data provided in .raw and .xrdml formats.</li> <li>PSD: Included in .pdf and .xlsx files, with filenames containing 'PSD'. </li> <li>DSC-TG: Data available in .xls files, with filenames indicating 'DSC_TG'.</li> <li>TEM-EDS: Elemental maps provided in .jpg and .bmp formats.</li> <li>Mössbauer: Raw data provided in .plt files.</li> </ul> <p>Please note that the percentages in the sample names do not correspond directly to the final sample codes (e.g., 25% does not equate to the final G25 sample). This discrepancy has been corrected based on the XRF results. For clarity, refer to the file 'Chuqing Jiang Fe-Mg-Si XRF 04-04-2023 - raw data and calculation.xlsx', which includes detailed renaming of the samples.</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>
Effectively controlling for sample relatedness in large-scale GWAS: application to 79 EHR-derived longitudinal traits
<p><span>Sample relatedness is a major confounder in genome-wide association studies (GWAS), potentially leading to inflated type I error rates if not appropriately controlled. A common strategy is to incorporate a random effect related to genetic relatedness matrix (GRM) into regression models. However, this approach is challenging for large-scale GWAS of complex traits, such as longitudinal traits. Here we propose a scalable and accurate analysis framework, SPA<sub>GRM</sub>, which controls for sample relatedness via a precise approximation of the joint distribution of genotypes. SPA<sub>GRM</sub> can utilize GRM-free models and thus is applicable to various trait types and statistical methods, including linear mixed models and generalized estimation equations for longitudinal traits. A hybrid strategy incorporating saddlepoint approximation greatly increases the accuracy to analyze low-frequency and rare genetic variants, especially in unbalanced phenotypic distributions. We also introduce SPA<sub>GRM(CCT)</sub> to aggregate the results following different models via Cauchy combination test. Extensive simulations and real data analyses demonstrated that SPA<sub>GRM</sub> maintains well-controlled type I error rates and SPA<sub>GRM(CCT)</sub> can serve as a broadly effective method. Applying SPA<sub>GRM</sub> to 79 longitudinal traits extracted from</span><span> </span><span>UK Biobank primary care data, we identified 7,463 genetic loci, making a pioneering attempt to conduct GWAS for these traits as longitudinal traits.</span></p>
scmcclelland/joint-mediation-study: Data, Analysis, and Figure Scripts for "Soil organic carbon sequestration jointly-mediated by plants and microbes after compost application"
<p>This repository contains data, analysis, and figure scripts to create findings from the manuscript "Soil organic carbon sequestration jointly-mediated by plants and microbes after compost application" currently under minor revisions.</p> <p>This release includes updated code, primarily improvements to figures, and a new script for a supplementary map figure.</p>
ABSOLUT input data for an example application on the districts of Germany
<p>These are data for running the ABSOLUT R programs published separately on Zenodo: <a href="https://doi.org/10.5281/zenodo.4468608">10.5281/zenodo.4468608</a>.</p> <p>The data published here consist of:</p> <p><strong>absolutcontrol.dat</strong> – text file (UTF-8) with case-specific settings for program execution, may be edited by the user</p> <p><strong>crop-areas.csv</strong> – CSV table of crop areas in hectares for different crops in German administrative areas. Modified from data originally provided by the statistical offices of Germany (© Statistische Ämter des Bundes und der Länder, Deutschland, 2020) and re-distributed here in this modified form also under the terms of the Data licence Germany – attribution – version 2.0, see https://www.govdata.de/dl-de/by-2-0</p> <p><strong>districtweather.zip</strong> – zipped directories DistrictWeather and DistrictFeatures. DistrictWeather contains 401 ASCII DAT files with monthly weather variables, one per district. These have been generated using rasterized weather data from the German meteorological service (Deutscher Wetterdienst, DWD), an official digital map of administrative boundaries provided by the German Federal Agency for Cartography and Geodesy, and CLC-2012 land use data provided by Copernicus. DistrictFeatures is an empty directory to be used by the corresponding program.</p> <p><strong>climatescenarios.zip</strong> - zipped directories ClimateScenarios and ClimateScenarioFeatures. ClimateScenarios contains three subdirectories with example climate scenario realisations in the same format as DistrictWeather. ClimateScenarioFeatures is an empty directory to be used by the corresponding program.</p> <p><strong>yield-indat.csv </strong>– CSV table of crop yields in dt/ha for different crops in German administrative areas, annual values for the years 1999–2020. Modified from data originally provided by the statistical offices of Germany (© Statistische Ämter des Bundes und der Länder, Deutschland, 2021) and re-distributed here in this modified form also under the terms of the Data licence Germany – attribution – version 2.0, see https://www.govdata.de/dl-de/by-2-0</p>
Data from: Metacommunity theory review and its application in community assembly of soil animals
<p>We were interested in community assembly of soil animals and performed a literature study to find out what is known about soil metacommunities. We aimed to study keywords co-occurrence relationships of scientific journal articles in the field of "metacommunity" research from 1992-2017 as a whole. We also investigated the co-occurrence of the top 20 most frequent keywords in five 5-year time periods.</p> <p> </p> <p> </p> <p>In September 2017 we searched the Web of Science with ‘metacommunity’ as the only keyword, and found 1226 English papers published in international journals between January 1992 and September 2017. And we exported these papers from Web of Science as a plain text file (.txt) with the option ‘Full Record and Cited References’, which is archived here. The text file thus contains full records and cited references (in a concise format, so without the titles of the cited papers), and each field is prefaced by a two-character field tag. Afterwards we used the software ‘Citespace’ to create Table 1 and Figure 2 in Guo et al. (2018). We created that Figure 2 using the following settings in Citespace: we choose the “co-occurrence” function and “keyword” as node types, then created keywords co-occurrence relationships (Figure 2). Next, we created lists of the top 20 keywords in different periods, which can reveal the research hotspots (Table 1). The method for extracting the top 20 most frequent keywords was as follows: We use the Web of Science database to retrieve scientific papers from 1992 to 2017, taking into account the relationship between citation and publication time. We calculated the percentage of 200 most cited papers published in each of the 5-year periods. To do so we followed the following steps:</p> <p>Step1: we selected the 200 most cited papers from the entire datset (1226 in the 1992-2017 period).</p> <p>Step 2: we calculated the percentage of those 200 papers published each 5-year period.</p> <p>Step 3: we also calculated an correction coefficient by taking, for each 5-year period, the number of top-200 most cited papers published in that period, and dividing that number by the total number of papers (out of the 1226 selected papers) published in that period.</p> <p>Step 4 Last, we calculate the real keyword frequency by multiplying the keyword frequency in specific 5-year periods with the correction coefficient.</p>
OHS data provided by Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application
<p>Images from Chinese Orbita Hyperspectral Satellites (OHS) provided by <em>the Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application</em> are shared. All the images have been radiometric calibrated and atmospheric corrected by the author.</p> <p>Paper: J. He, J. Li, Q. Yuan, H. Shen, and L. Zhang, "Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution," <em>IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)</em>, 2021.</p> <p>More information about the author can be found at https://jianghe96.github.io/</p> <p>If this dataset is helpful please cite as:</p> <pre>@article{he2021spectral, title={Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution}, author={He, Jiang and Li, Jie and Yuan, Qiangqiang and Shen, Huanfeng and Zhang, Liangpei}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2021}, }</pre>
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.