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51,102 results for “analysis”
Data from Automated plankton image analysis using convolutional neural networks
<p>Datasets and code from Luo et al., "Automated plankton image analysis using convolutional neural networks." Limnology and Oceanography Methods.</p> <p>Data include:</p> <p>1) 42,564 item training library, sorted in 108 classes,</p> <p>2) 42,548 item test set for filtering thresholds, sorted into 38 groups. These images are independent from the training library, and are used for setting the thresholds for post-classification filtering.<br> CSV file: Luo_etal_FT_images_pred.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p>3) 75,000 item fully random, validated set for confusion matrix calculations, sorted into 38 groups. This set is a representation of the full dataset, selected at random after classification. <br> CSV file: Luo_etal_confusionmatrix_images.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p> </p> <p>Scripts and programs:</p> <p>1) Segmentation.zip contains the scripts and executables for the segmentation program.</p> <p>2) Plankton_template.zip contains the archived version of the SparseConvNet program used in manuscript (current version available at: https://github.com/btgraham/SparseConvNet or https://github.com/facebookresearch/SparseConvNet)<br> Note that google-sparsehash is necessary for running SparseConvNet.<br> Also, plankton_epoch-150.cnn are the weights from the training used in the manuscript, and should be placed in the /weights folder if you want to replicate the classifications.</p>
Datasets for paper 'Cabello, V., Renner, A., Giampietro, M. 2019. Relational analysis of the resource nexus in arid land crop production. Advances in Water Resources 130:258-629'
<p>Datasets produced for the paper Cabello, V., Renner, A., Giampietro, M. 2019.<em> </em>Relational analysis of the resource nexus in arid land crop production. <em>Advances in Water Resources </em>130:258-269</p>
ExcapeDB: An integrated large scale dataset facilitating Big Data analysis in chemogenomics
<p>ExcapeDB: An integrated large scale dataset facilitating Big Data analysis in chemogenomics</p> <p>Supplementary file (full dataset download)</p> <p>- v2 with SMILES errors fixed (19.01.2019)</p>
Synthetic dataset accompanying Neural Image Compression for Gigapixel Histopathology Image Analysis
<p>This dataset was used to develop and evaluate the main method proposed in the paper "Neural Image Compression for Gigapixel Histopathology Image Analysis" published in IEEE Transactions on Pattern Analysis and Machine Intelligence with DOI 10.1109/TPAMI.2019.2936841. Please refer to the paper for a detailed description of the dataset.</p> <p>The dataset consists of a set of 50000 images and 50000 associated ground truth masks, distributed into training and test partitions. The name of each file follows the pattern "{id}_{tilted_label}_{nontilted_label}_{tilted_size}_{nontilted_size}_{kind}.png" where:<br> * id: unique identifier within each partition.<br> * tilted_label: image-level label corresponding to the tilted rectangle.<br> * nontilted_label: image-level label corresponding to the non-tilted rectangle.<br> * tilted_size: longest size of the tilted rectangle.<br> * nontilted_size: longest size of the non-tilted rectangle.<br> * kind: either "tile" or "mask" image type.</p> <p>The images are distributed into several data partitions used during cross-validation and fully described in "mnist_folds_set.json". Please rename "mnist_folds_set.json.removethis" into "mnist_folds_set.json".</p> <p>The code to recreate this dataset can be found in https://github.com/davidtellez/neural-image-compression.</p>
Data on robotic grinding of Inconel 718 part with 3M Cubitron II 984F belt for tool wear and material removal analysis
<p>Data on robotic grinding of Inconel 718 part for tool wear and material removal analysis</p> <p>Date data was obtained: May 2019</p> <p>The performance of a metal grinding operation with a robot has been studied, specifically how the grinding capability changes as time goes by and the tool gets worn. A pneumatic grinding tool has been implemented on the robot flange and abrasive belts of 3M Cubitron II 984F have been used.</p> <p>A rectangular metallic part of known dimensions has been attached to a load cell and has been grinded several times in consecutive tests. The grinding operation consists on a straight line along the longest side of the part.<br> During each test session, the same abrasive belt was used with fixed grinding conditions (tool angle, applied force, overlap, robot feed), until the grinding time reached 20 minutes.<br> The metal part has been weighed at regular intervals with the load cell, which allowed us to measure the evolution of the removed height of material for each pass, depending on grinding time.<br> The quantity of grinded material is measured as the height reduction in the part as the robot moves over the part at certain speed.</p> <p>Test sessions were designed for 4 tool angles (25, 35, 45, 75º related to the vertical), and were repeated three times for each angle. With 75º the tool was almost horizontal and it provided the smallest material removal capability. With 25º the tool was almost perpendicular to the area being grinded and it provided the highest material removal capability. </p> <p>The data of the tests is presented in the following units:<br> - Time: seconds:<br> - Removed material height per grinding tool pass: millimeters.</p> <p>The user of the data may easily convert the removed material height per pass into removed material volume or weight per pass. Considering that the width of the grinding belt is 12.5 mm, and the length of the grinded tool is 160 mm, if the height of the removed material is multiplied to the length and width the removed volume per pass can be calculated. Multiplying the volume with the density the weight of the removed material per pass can be calculated.</p> <p>The results of different tests presented in the .xlsx document, which can be accessed using free software such as OpenOffice or LibreOffice:<br> https://www.openoffice.org<br> https://www.libreoffice.org/</p> <p><br> TECHNICAL DESCRIPTION OF THE USED DEVICES AND CONDITIONS</p> <p>- Material of the grinded part: Inconel 718, density 8.19g/cm3. <br> - Dimensions of the grinded part: 160x90x40 mm.<br> - Robot: Stäubli TX90L.<br> - Belt grinding tool: AMTRU SwingBelt 120. https://www.amtru.com<br> - Applied pneumatic pressure on the grinding tool: 9 bars.<br> - Belt speed: Maximum possible speed obtained with 7 bars pneumatic mains in the workshop.<br> - Abrasive belt: 3M Cubitron II 984F, 610x12.5 mm, 36 grit (roughing).<br> - Load cell to measure the weight of the part: HBM SP4M, capacity 3 kg, precision 0.01 g.<br> - Overlap (tool lateral displacement between two passes): 6.25 mm.<br> - Robot feed: 75 mm/s (100 mm/s for the 45º test).</p> <p>IDEKO Research Centre<br> Address: <br> Arriaga kalea 2<br> 20870, Elgoibar, SPAIN<br> Contact:<br> Asier Barrios, abarrios@ideko.es<br> Patxi Hacala, phacala@ideko.es<br> Phone: (+34) 943 74 80 00</p>
Rare Disease analysis in Mondo
<p>To answer the question of 'How many rare diseases are there?' we analyzed terms in Mondo to get a total count of Rare Diseases as defined in Mondo Disease Ontology (Mondo).</p> <p> </p> <p>Methods</p> <p>This analysis was performed on the <a href="http://purl.obolibrary.org/obo/mondo/releases/2019-09-30/mondo.json">Mondo 2019-09-30 release</a>.</p> <p><strong>1. Get all 'Disease' terms from Mondo</strong></p> <p>First we get all the terms in Mondo that are a descendants of <code>MONDO:0000001 'Disease'</code>.</p> <p>There are <code>21633</code> Mondo disease terms.</p> <p><strong>2. Filter terms that are descendants of 'disease susceptibility'</strong></p> <p>We then filter out terms that are descendants of <code>MONDO:0042489 'disease susceptibility'</code>, to avoid counting ambiguous terms that are related to disease susceptibility and not the actual disease itself.</p> <p>This gives us a list of <code>21563</code> Mondo rare disease terms.</p> <p><strong>3. Identify terms that are 'rare'</strong></p> <p>Any disease term in Mondo is considered rare if the term, or its ancestor, has modifier <code>MONDO:0021136 'Rare'</code> in the ontology.</p> <p>There are <code>12914</code> Mondo rare disease terms.</p> <p><strong>4. Consider terms in 'gard_rare' subset</strong></p> <p>There are <code>3176</code> Mondo disease terms that are in <code>gard_rare</code> subset which contains Mondo terms that are yet to be treated as 'rare'.</p> <p>We add these terms to our set of Mondo rare disease terms.</p> <p>This increases the Mondo rare disease term count to <code>13866</code>.</p> <p>But for this analysis, we are interested in terms that are both rare and are leaf nodes in the ontology.</p> <p>After considering only leaf nodes, we get <code>10394</code> as the final count of Mondo rare disease terms.</p> <p> </p> <p>Results</p> <p>all-mondo-disease-terms.tsv: As part of our analysis, we generated a TSV containing 21633 Mondo disease terms, each with annotations that signifies whether the term is a rare disease term and whether that term is a leaf node in the ontology.</p>
CROSS-VALIDATION OF FUNCTIONAL MRI and PARANOID-DEPRESSIVE SCALE: BRAIN SIGNATURES FROM MULTIVARIATE ANALYSIS
<p>Brain signatures identified by bottom-up unsupervised machine learning: three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI).</p> <p>We have been able to separate the two investigated clinical entities – schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.</p>
Dataset: Open access potential and uptake in the context of Plan S - a partial gap analysis
<p>Dataset belonging to the report: <a href="https://doi.org/10.5281/zenodo.3543000">Open access potential and uptake in the context of Plan S - a partial gap analysis</a></p> <p> </p> <p>On the report: </p> <p>The analysis presented in the report, carried out by Utrecht University Library, aims to provide cOAlition S, an international group of research funding organizations, with initial quantitative and descriptive data on the availability and usage of various open access options in different fields and subdisciplines, and, as far as possible, their compliance with Plan S requirements.</p> <p>Plan S, launched in September 2018, aims to accelerate a transition to full and immediate Open Access. In the guidance to implementation, released in November 2018 and updated in May 2019, a gap analysis of Open Access journals/platforms was announced. Its goal was to inform Coalition S funders on the Open Access options per field and identify fields where there is a need to increase the share of Open Access journals/platforms. </p> <p>The report should be seen as a first step: an exploration in methodology as much as in results. Subsequent interpretation (e.g. on fields where funder investment/action is needed) and decisions on next steps (e.g. on more complete and longitudinal monitoring of Plan S-compliant venues) is intentionally left to cOAlition S and its members. </p> <p> </p> <p><em>This work was commissioned on behalf of cOAlition S by the Dutch Research Council (NWO), a member of cOAlition S. Bianca Kramer and Jeroen Bosman of Utrecht University Library were appointed to lead the project.</em></p>
WDXRF analysis of Iberian potsherds from the Late Iron Age
<p>This data set contains 99 chemical analyses of ceramic potsherds from the Iberian workshop of the Mas de Moreno (Teruel, Spain). The chemical composition of the samples was obtained by wavelength-dispersive X-ray fluorescence spectrometry (WDXRF).</p> <p><strong>Method</strong></p> <p>The outer surfaces of all the samples were mechanically removed prior to analysis.</p> <p>The samples were heated to 950°C for one hour after 24h drying at 50°C, weighted for LOI calculation and ground in a tungsten carbide mortar. 0.8 g of powder was mixed with 3.2 g of Spectroflux 110 flux (Johnson Matthey; 33.5% metaborate and 66.5% lithium tetraborate) and melted in gold-platinum crucibles with an autofluxer melting device (Breitländer).</p> <p>The data were collected with a SRS 3400 (Bruker) spectrometer of 3 kW power (working at 60 kV, 100 mA max.). The spectrometer was equipped with a rhodium tube window, four analyser crystals (OVO-55, LiF200, LiF220, PET), two collimators (0.46° and 0.15°) and two sensors (a proportional Ar/CH4 gas-flow counter and a scintillation counter). The calibration of the instrument was carried out on 40 international certified reference materials.</p> <p><strong>Data description</strong></p> <ul> <li><em>sample</em>: sample reference.</li> <li><em>date</em>: date of the analysis.</li> <li><em>laboratory</em>: analysis laboratory.</li> <li><em>stratigraphy</em>: stratigraphic unit.</li> <li><em>artefact</em>: typology.</li> <li><em>part</em>: analysed part of the artefact.</li> <li><em>decoration</em>: did the sampled artefact carry a painted decoration?</li> <li><em>LOI</em>: loss on ignition (percent).</li> <li><em>CaO</em>, <em>Fe<sub>2</sub>O<sub>3</sub></em>, <em>TiO<sub>2</sub></em> , <em>K<sub>2</sub>O</em>, <em>SiO<sub>2</sub></em> , <em>Al<sub>2</sub>O<sub>3</sub></em> , <em>MgO</em>, <em>MnO</em>, <em>Na<sub>2</sub>O</em>, <em>P<sub>2</sub>O<sub>5</sub></em>: oxide mass percents.</li> <li><em>Zr</em>, <em>Sr</em>, <em>Rb</em>, <em>Zn</em>, <em>Cr</em>, <em>Ni</em>, <em>La</em>, <em>Ba</em>, <em>V</em>, <em>Ce</em>, <em>Y</em>, <em>Th</em>, <em>Pb</em>, <em>Cu</em>: ppm.</li> </ul> <p>Data below the following limits should be considered unreliable:</p> <ul> <li><em>Na<sub>2</sub>O</em>: 0.5 %</li> <li><em>La</em>: 24 ppm</li> <li><em>Y</em>: 15 ppm</li> <li><em>Th</em>: 15 ppm</li> <li><em>Pb</em>: 20 ppm</li> <li><em>Cu</em>: 10 ppm</li> </ul>
Exploring the Impact of Physiotherapy on Health Outcomes in Elderly Patients with Chronic Diseases: A Cross-Sectional Analysis
<p>In this cross-sectional analysis, we investigate the transformative impact of physiotherapy on health outcomes among elderly patients grappling with chronic diseases. Physiotherapy emerges as a pivotal intervention, offering multifaceted benefits that extend beyond mere symptom management. Through tailored exercises, mobility enhancements, and targeted pain management strategies, physiotherapy not only mitigates physical limitations but also fosters greater independence and quality of life. By examining a diverse cohort of elderly individuals diagnosed with chronic conditions such as osteoarthritis and cardiovascular diseases, this study underscores the profound role of physiotherapy in promoting functional mobility, reducing healthcare burdens, and enhancing overall well-being among this vulnerable population."</p>
Campaign content analysis on X for the Castilla y León and Andalusia 2022 regional elections
<p>This dataset refers to the replication data of the article titled: "Could you teach new tricks to old dogs? An analysis of online communication of political leaders in Spanish regional elections", to be published in Frontiers in Political Sciece, after reviewing process.</p>
Predictability Limit of the 2021 Pacific Northwest Heatwave from Deep-Learning Sensitivity Analysis
<p>The attached two datasets are the optimized inputs used to analyze predictability limits in the paper Predictability Limit of the 2021 Pacific Northwest Heatwave from Deep-Learning Sensitivity Analysis. Specifically, the datasets correspond to the inputs used to produce the blue (global) and green (regional) loss curves in Figure S2. They are NetCDF files of dimensions batch (1), time (2), latitude (181), longitude (360), pressure levels (13), and may be run as Graphcast model inputs to initiate a forecast at 00 UTC 20 June 2021. Both datasets have been systematically perturbed to reduce the Graphcast model's loss function, which minimizes forecast eror as described in the manuscript. The global input seeks to reduce the loss over the entire globe, while the regional input seeks only to minimize error within the Pacific Northwest (42N to 60N and 130W to 110W). The optimized inputs result in a reduction of the loss by approximately 85% (global) and 93% (regional) when compared to a control Graphcast forecast without perturbations.</p>
Empirical datasets for "Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe's residential buildings sector"
<p>This dataset includes the empirical datasets for the manuscript: Andreas Andreou, Panagiotis Fragkos, Faidra Filippidou, Eleftheria Zisarou, Georgios Avgerinopoulos, Robert Pietzcker, Robin Hasse, Ricarda Rosemann, Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe’s residential buildings sector (under review in Environmetal Research Letters). The dataset contains one CSV file with detailed modelling results for the scenarios presented in the manuscript.</p>
Datasets and Jupyter notebook for the structural analysis of protein-RNA interface evolution
<p>The present repository contains data and code related to our manuscript "Structural comparison of protein-RNA homologous interfaces reveals widespread overall conservation contrasted with versatility in polar contacts". In the manuscript, we analyze the evolution of protein-RNA interfaces by building a dataset of protein-RNA interologs (homologous interfaces) and exploring how interface contacts are conserved between homologous interfaces, as well as possible explanations for non-conserved contacts.</p> <p>This repository contains the following files:</p> <ul> <li>DataAnalysisNotebook.ipynb is a Jupyter notebook to reproduce contact conservation analysis and all figures from our manuscript, and to explore data</li> <li>env.yaml is an environment file in order to build a Conda/Mamba environment to run the Jupyter notebook </li> <li>2022-02-21-PDB.csv contains data from the PDB about 3D structures of complexes containing interacting protein and RNA chains (PDB structure identifier, chain identifiers, experimental technique and resolution)</li> <li>2022-02-21-PDB_proteinchainscontactingRNAchains.groupbp.tsv contains more detailed information about interacting protein and RNA chains from these complexes (PDB and chain identifiers, protein and RNA size, interface size and number of contacts)</li> <li>2022-02-21-PDB_proteinchainscontactingRNAchains.groupbp.txt.selectXE_2.50_p30_r10_pi5_ri5_rep_bc-100.out_RNAcl_0.99.tsv contains the same detailed information, restricted to the filtered dataset used as a starting point in our interolog search pipeline</li> <li>PDBinterfaceAlign.csv contains information about the structural alignment of pairs of protein-RNA interactions (structural alignment TM-scores, sequence identity and coverage)</li> <li>DataInterologsParam.tsv contains information about a pre-filtered set of 2587 potential interologs (including interface RMSD, sequence identity and coverage and interface size)</li> <li>DataInterologsContactsFixedSASA.tsv contains detailed information about conserved and non-conserved contacts in the final set of 2022 interologs (atomic contacts, apolar contacts, hydrogen bonds, salt bridges and stacking information for aminoacid-nucleotide pairs, as well as information about whether each belongs to the interface, secondary structures, and the aminoacid surface accessibility and evolutionary conservation metrics) - compared to version 1, the calculation of solvent accessibility was fixed for a number of interolog pairs</li> <li>DataCons.csv contains precomputed contact conservation metrics for each of the 2022 interolog pairs, for fast reproduction of manuscript figures</li> <li>DataInterologsContactsResampledMaintainStructSeqId.tsv, DataInterologsContactsShuffled.tsv and DataInterologsShuffled.tsv relate to baselines computed for contact conservation assessment</li> <li>clan.txt, clan_membership.txt, ecod.latest.domains.uniq.txt, rfam_interfaces_977.txt, DataGroupsECOD.tsv, DataGroupesRFAM.tsv, DataGroupsRFAMClan.tsv, DataInterfaceGroupsECOD.tsv and DataInterfaceGroupsRFAM.tsv relate to the ECOD (respectively Rfam) classification of protein domains (respectively RNA) in protein-RNA interfaces from our dataset</li> <li>ListeIntraHbonds.pkl and ListeIntraSaltBridges.pkl are pickle-format data files containing intra-molecular hydrogen bonds and salt bridges (respectively) that are used to analyse scenarii of compensation for non-conserved polar contacts.</li> </ul>
Multi-omics analysis reveals the link between Treg distribution and therapy efficacy in Hepatocellular Carcinoma patients treated with tremelimumab plus durvalumab
<p><strong><span><span>Introduction</span></span></strong></p> <p><span>Hepatocellular carcinoma (HCC) remains a significant contributor to cancer-related deaths. Immunotherapy, either alone or in combination, has emerged as the standard treatment for advanced HCC. Notably, the combination of durvalumab (dur) and tremelimumab (trem) has received FDA approval based on findings from the HIMALAYA trial. However, comprehensive studies elucidating immune responses are lacking. We conducted a thorough analysis utilizing clinical samples from tumor biopsies to understand the mechanism of response.</span></p> <p><strong><span><span>Methods</span></span></strong></p> <p><span>Multiplexed immunofluorescence microscopy was used to analyze immune cell infiltration in primary human liver cancer samples. We developed and validated a comprehensive 37-plex antibody panel for immunofluorescence imaging of human FFPE samples. We applied highly multiplexed co-detection by indexing (CODEX) technology to simultaneously profile in situ expression of 37 proteins at sub-cellular resolution in 20 HCC patient samples using whole slide scanning. We established an image analysis pipeline to quantify all major cell populations in the human liver using supervised manual gating and unsupervised clustering algorithms using the exported matrix of the marker expression and spatial information. Clinical metadata including sex, gender, ethnicity, pretreatment, and histopathological reports are available for all patient samples.</span></p> <p><strong><span><span>Results</span></span></strong></p> <p><span><span>Using high-dimensional spatially resolved quantitative analysis of multiplexed immunofluorescence microscopy images, we generated a unique dataset and profiled the single-cell pathology landscape for human HCC treated with immunotherapy. In situ phenotyping of 400,000 single cells (including 130,000 CD45+ immune cells) allowed for the quantification of cell phenotype clusters, differential analysis of activation markers, and spatial features of each individual cell. This analysis revealed the comprehensive profile of the cell composition and spatial interactions of different cells in the TiME of patients treated with immunotherapy. Further details on the study can be obtained in our paper once it’s published.</span></span></p> <p><strong><span><span>Conclusion</span></span></strong></p> <p><span><span>We developed the CODEX panel for FFPE biopsy samples of HCC patients.</span></span></p>
Data and Analysis for Kaplanis, Denny, and Raimondi 2024, "Vertical distribution of rocky intertidal organisms shifts with sea-level variability on the Northeast Pacific Coast".
<p>This repository contains all the data and R scripts used to produce all analyses and figures for Kaplanis, Denny, and Raimondi 2024, as well as all intermediate outputs and final figures. To access this content, download and unzip the intertidalvertdist folder (for intertidal vertical distribution). The R Project is titled "intertidalvertdist". All pertinent information needed to access data, replicate the analyses, and produce figures is contained within the README file, but a brief desciption is below.</p> <p><br>Directory Architecture:</p> <p>Data:<br>Contains all data. Within this folder are two subdirectories - Raw Data, and Processed Data. Raw Data are unmanipulated, straight from the data source. Processed Data are outputs from scripted data wrangling and transformations. </p> <p>Within each of these folders are two more subdirectories: Tide Gauge Data, and MARINe Data. These are the two data sources used in this manuscript - monthly sea-level data from The National Oceanic and Atmospheric Administration Center for Operational Oceanographic Products and Services (NOAA CO-OPS) tide gauge stations, and long-term rocky intertidal biological monitoring data from Multi-Agency Rocky Intertidal Network (MARINe) survey sites.</p> <p>Scripts:<br>All R scripts are contained within the Scripts folder. The scripts have the prefix IVD (for intertidal vertical distribution), then a name that indicates the major function of the code. The scripts either downloads data, manipulates data, conducts analyses, and/or produces a figure.</p> <p>Outputs:<br>Any figures and tables from preliminary analyses, but that are not used in the final manuscript, are saved in Outputs.</p> <p>Figures:<br>All final figures and tables are contained in the Figures folder. All figures are produced by scripts, except Figs. 1 and 2, which are schematics produced manually in a graphics editor. This folder contains two other folders: Supplemenatary Figures, and Partial Regression Plots. Partial Regression plots are the same as the final Figures 8-12, except they are grouped by taxa rather than by explanatory variable.</p> <p>Data Processing Workflow - Overview: <br>Tide Gauge Data (Data/Raw Data/Tide Gauge Data/individual stations) were downloaded using the NOAA Co-Ops API URL Builder (https://tidesandcurrents.noaa.gov/api-helper/url-generator.html), merged, then analyzed. Three MARINe data sets from the Coastal Biodiversity Survey (CBS) were accessed via data requests (https://marine.ucsc.edu/explore-the-data/contact/data-request-form.html). The first MARINe dataset (Data/Raw Data/MARINe Data/CBS_Percent Cover Data, both First Sample and Full Sample) was used to determine the top ten most abundant taxa (hereafter termed “dominant taxa”) across CBS survey sites during the monitoring period of 2001-01-01 to 2021-09-30. The second MARINe dataset (Data/Raw Data/MARINe Data/CBS_Elevation Data) was used to describe the upper limits of vertical distribution of dominant taxa through time. The third MARINe dataset (Data/Raw Data/MARINe Data/CBS_Presence Data) was used to visualize latitudinal distribution of taxa.</p> <p>Location information for Tide Gauge Stations and CBS Survey Sites were assembled into a table (Data/Raw Data/CBS_Tide Gauge_Data.csv)</p> <p>Tide Gauge Data were processed first, then MARINe Data. To replicate this workflow follow the steps described in the README file, in order.</p>
Is there a non-invasive biomarker for the early detection of ovarian torsion? A systematic review and meta-analysis
<p>We have performed a systematic review and meta-analysis and identified multiple biomarkers that warrant further study as part of a broader diagnostic panel for ovarian torsion. These include SCUBE1, s-DD, IL-6, IMA and TNF-a. </p>
GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis
<p>This page includes legal datasets used in the paper GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis.</p> <p>The Guided Lexrank algorithm is applied to dataset <a href="../api/records/13696090/draft/files/special_appeal.csv/content" target="_blank" rel="noopener noreferrer">special_appeal.csv</a> to summarize the texts of legal documents. The obtained summary and the texts of the topics contained in dataset <a href="../api/records/13696090/draft/files/themes.csv/content" target="_blank" rel="noopener noreferrer">themes.csv</a> are submitted to the BM25 algorithm for similarity assessment. From a list of topics, the GLARE method produces a ranking with suggested topics for a given document.</p>
Data for Tradeoff Analysis of Recovery Pathways post Superstorm Sandy: a New Jersey Case Study - v0.1
<p>Data repository for the manuscript titled: <em>Tradeoff Analysis of Recovery Pathways post Superstorm Sandy: a New Jersey Case Study.</em></p> <p>This repository includes all of the input data, processed data, and output data used for the project. This is a working version of our Tradeoff Analysis ready for interested users, peer reviewers, and others to run. </p> <p>README file on GitHub: https://github.com/Laura-Geronimo/Geronimo-etal_2024_NaturalHazardsReview/blob/main/README.md</p> <p> </p>
POLCAND_ELEC (Allocation of Seats on Electoral Lists: A Dynamic Analysis of Candidate Positioning in Parliamentary Elections from 1991 to 2023)
<p>The aim of the study was to supplement and expand the data contained in the EAST PaC database (Joshua Kjerulf Dubrow: East European Parliamentarian and Candidate Data (EAST PaC), 1985 - 2015 [dane]. Institute of Philosophy and Sociology, Polish Academy of Sciences [producent], Warsaw, 2016. PADS21320. Polish Social Data Archive [dystrybutor], Repozytorium Danych Społecznych [wydawca], 2021. <a href="https://doi.org/10.18150/LSBNLO" target="_blank" rel="noopener">https://doi.org/10.18150/LSBNLO</a>, V1).</p> <p>Currently, an open-access database called EAST PaC (with a data structure similar to panel data, which allows for identifying candidates each time they participate in subsequent elections) contains information on all candidates who have ever run in elections to the Sejm in Poland, from the last elections in the People's Republic of Poland in 1985 to the elections in 2015.</p> <p>This study allowed for the expansion of previous analyses regarding the ways of forming political representation and the associated quality of political elites. This is essential for conducting a dynamic analysis of candidate positioning on electoral lists, which enables tracing the electoral activity of all candidates in elections from 1991 to 2023. It allows for illustrating the occurrence of events over time by tracking candidates' electoral activities. Many previous empirical studies on the course and consequences of parliamentary elections have significant gaps, as they are limited to analyzing the situation of parliamentarians (often focusing on only selected categories). They lack the history of candidacies in elections, even though voters' final decisions are based on the assessment of previous results of candidates and the political entities they represent.These analyses are significant for explaining the dynamics of the political system and assessing the quality of democracy. They also enable empirical verification of hypotheses concerning the periodization of the institutionalization of the electoral system in Poland from 1991 to 2023.</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.