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1,780 results for “quantitative analysis”
Quantitative Content Analysis Data for Hand Labeling Road Surface Conditions in New York State Department of Transportation Camera Images
<p><strong>Foundational Codebook and Data: </strong></p> <p>Traffic camera images from the New York State Department of Transportation (511ny.org) are used to create a hand-labeled dataset of images classified into to one of six road surface conditions: 1) severe snow, 2) snow, 3) wet, 4) dry, 5) poor visibility, or 6) obstructed. Six labelers (authors Sutter, Wirz, Przybylo, Cains, Radford, and Evans) went through a series of four labeling trials where reliability across all six labelers were assessed using the Krippendorff’s alpha (KA) metric (Krippendorff, 2007). The online tool by Dr. Freelon (Freelon, 2013; Freelon, 2010) was used to calculate reliability metrics after each trial, and the group achieved inter-coder reliability with KA of 0.888 on the 4th trial. This process is known as quantitative content analysis, and three pieces of data used in this process are shared, including: 1) a PDF of the codebook which serves as a set of rules for labeling images, 2) images from each of the four labeling trials, including the use of New York State Mesonet weather observation data (Brotzge et al., 2020), and 3) an Excel spreadsheet including the calculated inter-coder reliability (ICR) metrics and other summaries used to asses reliability after each trial. The data are included in NYSDOT_quantitative_content_analysis.zip.</p> <p>The broader purpose of this work is that the six human labelers, after achieving inter-coder reliability, can then label large sets of images independently, each contributing to the creation of larger labeled dataset used for training supervised machine learning models to predict road surface conditions from camera images. The xCITE lab (xCITE, 2023) is used to store camera images from 511ny.org, and the lab provides computing resources for training machine learning models.</p> <p><strong>Obstructed Class Variation: </strong></p> <p>There are many applications for labeling roadside camera images, and as a variation of the foundational codebook, an addendum codebook provides another version of labeling the obstructed class. Specifically, this variation prioritizes labeling an image as “obstructed” only in extreme circumstances where there is a camera- or image- specific problem that prevents the assessment of any road surfaces. For labelers who want to use this version of the obstructed class (in this document) and also the other five weather-related classes (in the foundational codebook), the guidance is to use both documents in tandem, making sure to use the obstructed rules/definitions in this document while disregarding the obstructed rules/definitions in the foundational codebook. Alternatively, this codebook may be used alone in applications where the goal is to solely classify obstructed vs not obstructed. To ensure reliability and quality of this variation, quantitative content analysis was conducted on this addendum codebook, just as it was for the foundational codebook. Two labelers were tested with a sample of 30 images and achieved inter-coder reliability with Krippendorff's Alpha of 0.934 after one trial. The data, including the addendum codebook and labeling trial data (images and results) are included in ObstructedVariation_quantitative_content_analysis.zip.</p> <p>This material is based upon work supported by the U.S. National Science Foundation under Grant No. RISE-2019758.</p>
R Code and Re-analyzed Datasets for: Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses
<p>This submission includes all the scripts and data analyzed in the manuscript "Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses". This manuscript is a technical note on how genome formula data can be analyzed. There are no new experimental data in the manuscript, as published datasets are re-analyzed. Here we reproduce those datasets as formatted for our analysis, for the convenience of the reader. Please consult the README.txt file first.</p> <p>The corresponding paper was published in Viruses <em>16</em>(2): 270. (<a href="https://doi.org/10.3390/v16020270">https://doi.org/10.3390/v16020270</a>).</p> <p>This is the second version of the code, corresponding to the final version of the paper. The intial restricted version for review had a DOI 10.5281/zenodo.10355273.</p> <p> </p>
Two datasets to illustrate quantitative analysis methods for fluorescent calcium measurements
<p>Two datasets in HDF5 formats used for illustrating some quantitative data analysis methods.</p> <p><strong>CCD_calibration.hdf5</strong>: Imago/SensiCam CCD camera (Till Photonics) calibration data set. <br> Fluorescence measurments were made using a fluorescent plastic slide. 10 exposure times from 10 to 100 ms (each making an HDF5 group) were used. For each exposure time 100 exposures were performed (with 200 ms between each). The fluorescence measured in each of the 60 x 80 pixels of the camera are stored in the stack data set of each group. The time data set (a vector) of each group contains the time at which each illumination was done. These recordings were done by Andreas Pippow (Kloppenburg Laboratory Cologne University, http://cecad.uni-koeln.de/Prof-Peter-Kloppenburg.82.0.html). <br> They were used in: Sébastien Joucla, Andreas Pippow, Peter Kloppenburg and Christophe Pouzat (2010) Quantitative estimation of calcium dynamics from ratiometric measurements: A direct, non-ratioing, method. Journal of Neurophysiology 103: 1130-1144.</p> <p><strong>Data_POMC.hdf5</strong>: POMC data set recorded by Andreas Pippow (Kloppenburg Laboratory Cologne University, http://cecad.uni-koeln.de/Prof-Peter-Kloppenburg.82.0.html). 168 measurements performed with a CCD camera recording Fura-2 fluorescence (excitation wavelength: 340 nm). The size of the CCD chip is 60 x 80 pixels. A stimulation (depolarization induced calcium entry) comes at time 527. <br>Details about this data set can be found in: Joucla et al (2013) Estimating background-subtracted fluorescence transients in calcium imaging experiments: A quantitative approach. Cell Calcium. 54 (2): 71-85.</p> <p> </p>
Quantitative results of the analysis of human bioengineered tissues corresponding to the work "Development of novel squid gladius biomaterials for cornea tissue engineering"
<p>This dataset corresponds to the quantitative data generated in the work entitled "Development of novel squid gladius biomaterials for cornea tissue engineering".</p> <p>Cornea tissue engineering is strictly dependent on the development of biomaterials fulfilling the strict biocompatibility, biomechanical and optical requirements of this organ. In this work, we have generated novel biomaterials from the squid gladius (SG) and their application in cornea tissue engineering was evaluated. Results revealed that the native SG (N-SG) was biocompatible in laboratory animals, although a local inflammatory reaction was driven by the material. Cellularized biomaterials (C-SG) demonstrated that the SG provides an adequate substrate for cell attachment and growth, and corneal epithelial cells cultured on this biomaterial were able to express crystallin alpha, a marker for this type of cells. Biomechanical analyses showed that N-SG biomaterials have higher Young modulus and lower traction deformation than control native corneas (CTR), and C-SG showed similar Young modulus than CTR. Analysis of the optical properties of these samples revealed that the diffuse transmittance of N-SG and C-SG were higher than CTR, with the diffuse reflectance showing the opposite behavior. These results confirm the putative usefulness of this abundant marine-derived biomaterial that can be obtained as a byproduct of the fishing industry.</p>
The 2020 Comparison of Tools for the Analysis of Quantitative Formal Models: Results and Reproduction
<p>This archive contains detailed results from QComp 2020 as well as the necessary scripts and data to reproduce them.</p> <p>Visit http://qcomp.org for more information for QComp.</p> <p>Overview of Contents</p> <p>- `qcomp.org/` contains the state of our website from the timepoint of the competition. This includes:<br> - All benchmark files, browsable at `qcomp.org/benchmarks/index.html`<br> - Detailed competition results in a human-readable format, browsable at `https://qcomp.org/competition/2020/`<br> - `logs/` contains the raw logfiles and data gathered by our scripts<br> - `scripts/` contains scripts to replicate the whole competition<br> - `toolpackages/` contains a package for each participating tool which includes<br> - Instructions for obtaining and installing the tool<br> - a file `invocations.json` listing the commandlines used in QComp 2020<br> - a file `tool.py` providing functionalities to obtain the result from the tool output.</p>
Semi-automated Quantitative Morphometric Analysis of E18 Rat Hippocampal Neurons from 0.5 to 6 Days In Vitro
<p>This is the dataset presented in "Semi-automated quantitatve evaluation of neuron developmental morphology <em>in vitro</em> using the change-point test" by AS Liao, W Cui, VS Webster-Wood, and YJ Zhang (submitted to Neuroinformatics 2022).</p>
Assessing Quality Variations in Early Career Researchers' Data Management Plans: Quantitative Data of the Content Analysis
<p>The data includes the numerical results of the ranking of the data management plans created during the Basics of Research Data Management (BRDM) courses worth 3 ECTS credits in the years 2020 - 2022. The ranking was made using the Finnish DMP Evaluation Guidance (https://doi.org/10.5281/zenodo.4729831). Additionally, the data contains the results of the analysis of the best RDM practices included in the DMPs.</p> <p>Note 1: The comma-separated coded CSV version 1 (5.2.2024) may not open correctly on MacOS. You can use the comma-delimited CSV file version 2 or 3 (31.5.2024).</p> <p>Note 2: Versions 1 (Quality_variations_in_ECRs_DMPs_data) and 3 (Quality_variations_in_ECRs_DMPs_data_ver_3) contain evaluations of DMPs, best practices for data management, as well as methods for data sharing, storage, and preservation. In version 2 (Quality_variations_in_ECRs_DMPs_data_ver_2), the methods for data sharing, storage, and preservation are missing.</p> <p>Data is related to the research article https://doi.org/10.2218/ijdc.v18i1.873.</p>
Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research"
<p>Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research" published in <em>Frontiers in Marine Science</em></p>
Data For Scalco et al. Clinicopathological correlates of quantitative Amyloid-B Pathology in the Temporal Cortex: Machine learning analysis of 131 cases from an ADRC
<p>Dataset containing 131 de-identified whole slide images (WSIs) with a respective data dictionary. </p> <p><strong>Paper</strong>: Scalco, R., Oliveira, L.C., Lai, Z. et al. Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases. acta neuropathol commun 12, 134 (2024). https://doi.org/10.1186/s40478-024-01827-7</p> <p><strong>Details</strong>: A total of 131 .svs. WSIs, de-identified using svs-deidentifier v 0.9.1-beta (https://github.com/pearcetm/svs-deidentifier/releases). Dataset is uploaded in batches due to Zenodo data upload limitations.</p> <p><strong>Slide curation/preparation</strong>: All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (<a href="https://www.ucdmc.ucdavis.edu/alzheimers/">https://www.ucdmc.ucdavis.edu/alzheimers/</a>). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 between 20x and 40x magnification.</p> <p><strong>Code:</strong> Please refer to <a href="https://github.com/ucdrubinet/BrainSec">https://github.com/ucdrubinet/BrainSec</a> and <a href="https://github.com/keiserlab/plaquebox-paper">https://github.com/keiserlab/plaquebox-paper</a></p>
Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis
<p>This repository contain datasets and results for the paper:</p> <p><strong>Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis</strong></p> <p> </p> <p><strong>Github repository for the code: </strong></p> <p><a href="https://github.com/siebeniris/QuantifyingLanguageConfusion/tree/main">Quantifying Language Confusion GitHub repo</a></p> <p> </p> <p><strong>DATA</strong> include the following datasets:</p> <p>i) raw language graphs and</p> <p>ii) the calculated language similarities from the language graphs,</p> <p>iii) <strong>MTEI</strong>: the files from the <a href="https://github.com/siebeniris/vec2text_exp/tree/aaai">experimental results of multilingual inversion attacks</a>, and calculated language confusion entropy from the data;</p> <p>iv) <strong>LCB</strong>: the files from the <a href="https://github.com/for-ai/language-confusion?tab=Apache-2.0-1-ov-file#readme">language confusion benchmark</a> and calculated language confusion entropy from the data </p> <p> </p> <p><strong>Results</strong> include aggregated results for further analysis:</p> <p>i) <strong>inversion_language_confusion</strong>: results from MTEI</p> <p>ii) <strong>prompting_language_confusion</strong>: results from LCB</p> <p> </p> <p> </p>
Quantitative results of the analysis of relevant components of the human scapholunate interosseous ligament (SLIL)
<p>This dataset corresponds to the quantification results carried out for the human scapholunate interosseous ligament (SLIL) and several control tissues analyzed in the manuscript entitled "Histological characterization of the human scapholunate ligament". The SLIL plays a fundamental role in stabilizing the wrist bones, and its disruption is a frequent cause of wrist arthrosis and disfunction. Traditionally, this structure is considered to be a variety of fibrocartilaginous tissue and consists of three regions: dorsal, membranous and palmar. Despite its functional relevance, the exact composition of the human SLIL is not well understood. In the present work, we have analyzed the human SLIL and control tissues from the human hand using an array of histological, histochemical and immunohistochemical methods to characterize each region of this structure. Results reveal that the SLIL is heterogeneous, and each region can be subdivided in two zones that are histologically different to the other zones. Analysis of collagen and elastic fibers, and several proteoglycans, glycoproteins and glycosaminoglycans confirmed that the different regions can be subdivided in two zones that have their own structure and composition. In general, all parts of the SLIL resemble the histological structure of the control articular cartilage, especially the first part of the membranous region (zone M1). Cells showing a chondrocyte-like phenotype as determined by S100 were more abundant in M1, whereas the zone containing more CD73-positive stem cells was D2. These results confirm the heterogeneity of the human SLIL and could contribute to explain why certain zones of this structure are more prone to structural damage and why other zones have specific regeneration potential. The original data obtained for the quantitative analyses of each component are shown in this dataset.</p>
Polish is quantitatively different on quartzite flakes used on different worked materials [R analysis]
<p>This upload includes the following files related to the R analysis:</p> <p>- Raw data as a CSV table (processing-quartzite-final.csv), i.e. results from the ConfoMap analysis (see <a href="https://doi.org/10.5281/zenodo.3979116">https://doi.org/10.5281/zenodo.3979116</a>)</p> <p>- RStudio project (Quantification quartzite final.Rproj)</p> <p>- R scripts as R Markdown files (*.Rmd)</p> <p>- R scripts knitted to HTML files (*.html)</p> <p>- An R script (RStudioVersion.R) to write the used version of RStudio to a text file (RStudioVersion.txt)</p> <p>- Output from script #1: processing-quartzite-final.Rbin and processing-quartzite-final.xlsx</p> <p>- Output from script #2: processing-quartzite-final_summary-stats.xlsx</p> <p>- Output from script #3: all plots as PDF files.</p> <p>Note that for running the scripts, the raw data files (processing-quartzite-final.csv, .Rbin and .xlsx) should be stored in a "Data" folder within the working directory.<br> Output (processing-quartzite-final_summary-stats.xlsx and PDF plots) were saved into "Summary-stats" and "Plots" folders, respectively.</p> <p>Zenodo does not allow sub-folders, so this folder structure had to be removed.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Polish is quantitatively different on quartzite flakes used on different worked materials [ConfoMap analysis]
<p>Each surface has been processed with two templates:</p> <p>1) Extract two 50x50 µm sub-areas and extract topography layer from each sub-area. Export sub-areas as SUR files. File names start with "A35" or "VSH4".</p> <p>2) Process all extracted sub-areas for quantitative analysis. File names start with "processing-quartzite-final".</p> <p>All ConfoMap templates are saved in MNT format (including all original and processed surfaces, as well as results). Each template has also been exported to a PDF file.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p> <p>Additionally, the results of the second template are collated into "processing-quartzite-final.csv".</p>
A quantitative analysis of the interplay of environment, neighborhood and cell state in 3D spheroids - Dataset
<p>This is the umbrella archive that contains all the datasets and code associated with:</p> <p><strong>A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids</strong></p> <p> Vito RT Zanotelli<br> Matthias Leutenegger<br> Xiao‐Kang Lun<br> Fanny Georgi<br> Natalie de Souza<br> Bernd Bodenmiller</p> <p><em>Mol Syst Biol. (2020) 16: e9798</em><br> <a href="https://doi.org/10.15252/msb.20209798">https://doi.org/10.15252/msb.20209798</a></p> <p><em>Please cite this article if you re-use any of the data or code.</em></p> <p>Datasets:</p> <ul> <li>Brightfield plate images: Contains all plate acquisitions for the individual sphere plates before pooling <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991929">10.5281/zenodo.3991929</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991931">10.5281/zenodo.3991931</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.3991925">10.5281/zenodo.3991925</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991927">10.5281/zenodo.3991927</a><br> </li> </ul> </li> </ul> </li> <li>Slidescan images: Contains all fluorescent slidescan images of the cuts derived from the pooled spheroid sample blocks <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991921">10.5281/zenodo.3991921</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991923">10.5281/zenodo.3991923</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.4066430">10.5281/zenodo.4066430</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991919">10.5281/zenodo.3991919</a><br> </li> </ul> </li> </ul> </li> <li>Spillover acquisitions: Contains all spillover acquisitions associated with the two experiments: <ul> <li>4 cellline experiment: <ul> <li>p173/p176: <a href="http://doi.org/10.5281/zenodo.3991945">10.5281/zenodo.3991945</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161/p165: <a href="http://doi.org/10.5281/zenodo.3991947">10.5281/zenodo.3991947</a><br> </li> </ul> </li> </ul> </li> <li>Imaging mass cytometry acquisitions: <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991937">10.5281/zenodo.3991937</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991939">10.5281/zenodo.3991939</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.3991933">10.5281/zenodo.3991933</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991935">10.5281/zenodo.3991935</a><br> </li> </ul> </li> </ul> </li> <li>Processed data set: <ul> <li>4 cellline experiment: <a href="https://doi.org/10.5281/zenodo.3991942">10.5281/zenodo.3991942</a></li> <li>Overexpression experiment: <a href="http://doi.org/10.5281/zenodo.4271917">10.5281/zenodo.4271917</a></li> </ul> </li> </ul> <p>Code:</p> <p>A snakemake pipeline to reproduce the analyses from this raw data can be found at: <a href="http://github.com/BodenmillerGroup/SpheroidPublication">http://github.com/BodenmillerGroup/SpheroidPublication</a></p> <p>A version already containing all the containers can be found at: <a href="http://doi.org/10.5281/zenodo.4071861">10.5281/zenodo.4071861</a></p> <p> </p>
Single cell analysis by Quantitative image-based cytometry (QIBC)
<p>Quantitative image-based cytometry (QIBC): Employing automated multichannel wild-field microscopy using the Olympus ScanR screening system. This system includes an inverted motorized Olympus IX83 microscope, a motorized stage, IR-laser hardware autofocus, a fast emission filter wheel with single band emission filters. </p> <p>Images were analyzed and processed using ScanR analysis software and TIBCOSpotfire software was used to plot total nuclear pixel intensities and mean (total pixel intensities divided by nuclear area) nuclear intensities.</p>
Quantitative results of the analysis of relevant components of artificial bilayered substitutes developed by tissue engineering
<p>This dataset corresponds to the quantification results carried out for artificial bilayered substitutes developed by tissue engineering and control tissues analyzed in the manuscript entitled "<span>Spatiotemporal characterization of extracellular matrix maturation in human artificial stromal-epithelial tissue substitutes</span>". Tissue engineering techniques offer new strategies to understand complex processes in a controlled and reproducible system. In this study, we generated bilayered human tissue substitutes consisting of a cellular connective tissue with a suprajacent epithelium (full-thickness stromal-epithelial substitutes or SESS), and human tissue substitutes with an epithelial layer generated on top of an acellular biomaterial (epithelial substitutes or ESS). Both types of artificial tissues were studied at sequential time periods to analyze the maturation process of the extracellular matrix (ECM) using histochemical and immunohistochemical techniques. Results showed that both models were able to exhibit a partial development of the epithelial layer. ESS cells showed active proliferation, positive expression of KRT5 and low expression of differentiation markers, whereas SESS epithelium showed higher differentiation levels, with a progressive positive expression of KRT10 and claudin, although the differentiation levels of control native tissues were not reached. Despite the typical rete-ridges and papillae of native tissues were not found, stromal cells in SESS tended to accumulate and actively synthetize ECM components such as collagens and proteoglycans in the stromal area in direct contact with the epithelium (Z1 zone), whereas these components were very scarce in ESS. Regarding the basement membrane (BM), ESS showed a partially-differentiated structure containing fibronectin-1 (FN1) and perlecan (HSPG2), although the PAS staining signal was significantly lower than control native tissues. However, SESS showed higher BM differentiation, with positive expression of FN1, HSPG2, nidogen 1 (NID1), chondroitin-6-sulfate proteoglycans (CH6S), agrin (AGRN), and collagens types IV (COL-IV) and VII (COL-VII), although this structure was negative for lumican (LUM). These results confirm the relevance of epithelial-stromal interaction for ECM development and differentiation, especially regarding BM components, and suggest the usefulness of bilayered artificial tissue substitutes to reproduce ex vivo the ECM maturation and development process of human tissues. The original data obtained for the quantitative analyses of each component are shown in this dataset.</p> <p> </p>
Images supporting: Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation
<p>Two image datasets (as zip files) including all images analyzed in the manuscript Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation. Images are of pancreatic adenocarcinoma (PDAC) cystic spheroid samples grown in either BME or Matrigel. Some images have background noise in the form of iron oxide nanoparticles introduced to them.</p>
Quantitative results of the analysis of novel ossicle particles used in mandible bone regeneration
<p>Dataset corresponding to the results of the characterization analysis of novel holothurian ossicle biomaterials. These biomaterials were evaluated at three levels:</p> <p>1) Ex vivo analysis to determine thr potential cytotoxic effects of these biomaterials on human fibroblasts using LIVE/DEAD and quantification of DNA released to the medium.</p> <p>2) In vivo analysis to determine the potential systemic effects of these biomaterials grafted subcutaneously in laboratory rats.</p> <p>3) Histochemical and immunohistochemical analysis to determine the potential effects of these biomaterials on mandible bone regeneration.</p> <p>These results correspond to the publication entitled "<span>EVALUATION OF HOLOTHURIAN OSSICLES AS A BIOLOGICAL BIOMATERIAL FOR MANDIBULAR BONE REGENERATION</span>".</p>
Quantitative results of the analysis of human native and bioengineered tissues corresponding to the work "Histological, histochemical and immunohistochemical characterization of NANOULCOR nanostructured fibrin-agarose human cornea substitutes generated by tissue engineering"
<p>Dataset containing the quantitative results of the histochemical and immunohistochemical analysis of the following human tissues:</p> <ul> <li>Control native cornea (CTR-C)</li> <li>Control native limbus (CTR-L)</li> <li>Artificial cornea generated by tissue engineering (HAC)</li> </ul> <p>Each tissue type was subjected to histochemical and immunohistochemical analyses and results were quantified using ImageJ software to determine average intensities and area fractions corresponding to positive staining signal for each marker.</p>
Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis — exact and related matches
<p>Results of the 'Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis' papers. There are 2 datasets:</p> <ul> <li>'exact_matches.csv': contains detailed information about the concepts present both in OpenAlex and OpenAIRE.</li> <li>'related_matches.csv': contains detailed information about the concepts from OpenAlex and OpenAIRE that were not present in both KGs but got aligned following the algorithm presented in the paper.</li> </ul> <p>The detailed information refers to the following column:</p> <ul> <li>Category1: name of the first category</li> <li>Source1: source of the first category ('OpenAlex' or 'OpenAIRE')</li> <li>Category2: name of the second category</li> <li>Source2: source of the first category ('OpenAlex' or 'OpenAIRE')</li> <li>Similarity: semantic similarity value of the two categories</li> <li>PapersInC1: number of papers from the collected dataset belonging to the first category</li> <li>PapersInC2: number of papers from the collected dataset belonging to the second category</li> <li>PapersInBoth: number of papers from the collected dataset belonging to both of the categories</li> <li>Agreement: the value of the agreement of the categories in the tw KGs (Intersection over Union)</li> </ul>
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.