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4,694 results for “data analysis”
Supporting isotopic data for: Amino acid isotope analysis reveals variation in gut microbial contribution to host protein metabolism in a wild small mammal community
<p>Herbivory is a dominant feeding strategy among animals, yet herbivores are often protein limited. The gut microbiome is hypothesized to help maintain host protein balance by provisioning essential macromolecules, but this has never been tested in wild consumers. Using amino acid carbon (δ<sup>13</sup>C) and nitrogen (δ<sup>15</sup>N) isotope analysis, we estimated the proportional contributions of essential amino acids (AA<sub>ESS</sub>) synthesized by gut microbes to five co-occurring desert rodents representing herbivorous, omnivorous, and insectivorous functional groups. We found that herbivorous rodents occupying lower trophic positions (<em>Dipodomys </em>spp.) routed a substantial proportion (~40–50%) of their AA<sub>ESS</sub> from gut microbes, while higher trophic level omnivores (<em>Peromyscus </em>spp.) and insectivores (<em>Onychomys arenicola</em>) obtained most of their AA<sub>ESS</sub> (~58%) from plant-based energy channels but still received ~20% of their AA<sub>ESS</sub> from gut microbes. These findings empirically demonstrate that gut microbes play a key functional role in host protein metabolism in wild animals. </p>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", main figures PART 1
<p>This deposit contains the supporting records of images and image analysis presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218</p> <p>Associated Zenodo repositories:</p> <table> <thead> <tr> <th scope="col">Description</th> <th scope="col">DOI</th> </tr> </thead> <tbody> <tr> <td>Main figures PART 1, Figure 1,2,3,5</td> <td>10.5281/zenodo.7653239</td> </tr> <tr> <td>Main figures PART 2, Figure 6</td> <td>10.5281/zenodo.7900973</td> </tr> <tr> <td>Supplemental 3DTC figures: S1, S4, S5, S7, S8, S9</td> <td>10.5281/zenodo.7894632</td> </tr> </tbody> </table> <p>Contents:1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses for figures 2, 3 and 5. Figure 6 analyses are included in a compansion repository: 10.5281/zenodo.7900973. Contents of zip files by figure contain at a minimum the .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/). Additional files may include gate files (.vtg) or max projections (.tif).</p> <p>2) a collection of zip files containing the RNAScope image files shown in: Figure 1 P,Q. The supplemental figure data for RNAScope. Figures S1,S4 and S5 are found in: 10.5281/zenodo.7894633.</p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p>
Data and code for "Brehm et al. (2023) The complexity of micro- and nanoplastic research in the genus Daphnia – A systematic review of study variability and a meta-analysis of immobilization rates"
<p>All data and R code for</p> <p><strong>Brehm, J., Ritschar, S., Laforsch, C. and Mair, M.M. (2023). The complexity of micro- and nanoplastic research in the genus <em>Daphnia</em> – A systematic review of study variability and a meta-analysis of immobilization rates. <em>Journal of Hazardous Materials</em> 458: 131839. (<a href="https://doi.org/10.1016/j.jhazmat.2023.131839">https://doi.org/10.1016/j.jhazmat.2023.131839</a>)</strong></p> <p><em>Abstract</em></p> <p>In recent years, the number of publications on nano- and microplastic particles (NMPs) effects on freshwater organisms has increased rapidly. Freshwater crustaceans of the genus <em>Daphnia</em> are widely used in ecotoxicological research as model organisms for assessing the impact of NMPs. However, the diversity of experimental designs in these studies makes conclusions about the general impact of NMPs on <em>Daphnia</em> challenging. To approach this, we systematically reviewed the literature on NMP effects on <em>Daphnia</em> and summarized the diversity of test organisms, experimental conditions, NMP properties and measured endpoints to identify gaps in our knowledge of NMP effects on <em>Daphnia</em>. We use a meta-analysis on mortality and immobilization rates extracted from the compiled literature to illustrate how NMP properties, study parameters and the biology of <em>Daphnia</em> can impact outcomes in toxicity bioassays. In addition, we investigate the extent to which the available data can be used to predict the toxicity of untested NMPs based on the extracted parameters. Based on our results, we argue that focusing on a more diverse set of NMP properties combined with a more detailed characterization of the particles in future studies will help to fill current research gaps, improve predictive models and allow the identification of NMP properties linked to toxicity.</p>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", Supplemental 3DTC figures
<p>This deposit contains the supporting records of analysis for 3D cytometry presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218 found in supplemental figures.</p> <p>Contents:</p> <p>1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses by figures in the supplemental figure data for 3D tissue cytometry. The main figure data is found at: 10.5281/zenodo.7653239 and 10.5281/zenodo.7900973.</p> <p>2) a collection of zip files containing the RNAScope image files shown in: Figures S1,S4 and S5. The main RNAScope figure data is found at: 10.5281/zenodo.7653239</p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p>
Training Data for "Metatranscriptomics analysis using microbiome RNASeq data"
<p>Microbiomes play a critical role in host health, disease, and the environment.. Functional microbiome analysis which estimates the functional groups expressed by microbial community enables researchers to look beyond taxonomic composition and correlation with the condition under study. Using microbial community RNA-Seq data and subsequent metatranscriptomics workflows to elucidate the functional complement of the microbiome is gaining interest in the field.<br> This tutorial from Galaxy training network will introduce researchers to the basic concepts of metatranscriptomics data analysis. It takes in paired-end datasets of raw shotgun sequences (in FastQ format) as an input and:</p> <ol> <li>preprocess</li> <li>extract and analyze the community structure (taxonomic information)</li> <li>extract and analyze the community functions (functional information)</li> <li>combine taxonomic and functional information to offer insights into taxonomic contribution to a function or functions expressed by a particular taxonomy.</li> </ol> <p>The dataset used in the tutorial comes from a time-serie analysis of a microbial community inside a bioreactor (Kunath et al, ISME, 2018). Only the data for one time point (1st) and a biological replicate (A) is analyzed here, after having been trimmed out the original file for the purpose of saving time and resources.</p>
Global trade data of highly hazardous chemicals after data treatment and Python codes used for error analysis and mirror analysis
<p>This database includes all global trade data of the three types of highly hazaroudous chemicals listed under the Rotterdam Convention after error analysis and mirror analysis. Python codes used for error analysis and mirror analysis are also available.</p> <p> </p> <p> Corresponding authors: <a href="mailto:hongyan.zou@tjnu.edu.cn">hongyan.zou@tjnu.edu.cn</a>; <a href="mailto:Zhanyun.wang@empa.ch">Zhanyun.wang@empa.ch</a></p>
Deliverable D6.2 "TOOL FOR PERFORMANCE ASSESSMENT"- AHP Data analysis Excels Annex 1
<p>This Excel file will introduce the AHP data analysis (Annex 1) in deliverable D6.2 "TOOL FOR PERFORMANCE ASSESSMENT".</p>
Data and results of "Retractions in Arts and Humanities: an analysis of the retraction notices"
<p>This repository contains the datasets and visualizations generated in our work: <strong>"Retractions in Arts and Humanities: an analysis of the retraction notices"</strong>.</p> <p><strong>Note:</strong> the data are all contained inside the <strong><em>data.zip</em> </strong>file. You need to unzip the container to get access to all the files and directories listed below.</p> <p><strong>Metadata</strong></p> <p>The directory <em><strong>metadata/ </strong></em>contains a CSV with the citation count of all the retracted papers we have considered. Metadata retrieved from Retraction Watch cannot be published in this repository due to copy rights issues. </p> <p><strong>Content analysis</strong></p> <p>We run a topic modeling analysis on the content of the retraction notices. The topic modeling analysis has been done using MITAO, a tool for mashing up automatic text analysis tools and creating a completely customizable visual workflow [1]. The topic modeling data and results are separated into the following directories/files: </p> <ul> <li> <p><em><strong>workflow/ </strong></em>contains the workflow used in MITAO.</p> </li> <li> <p><em><strong>datasets_and_views/: </strong></em>the datasets and visualizations generated using MITAO. </p> </li> <li> <p><em><strong>ldamodel_corpus_dict/: </strong></em>it contains the dictionary, the LDA topic model, and the tokenized and vectorized corpus.</p> </li> <li><em><strong>rawdata/: </strong></em>the textual collection, metadata, and stopwords used as input in the workflow of MITAO</li> </ul> <p><strong>References</strong></p> <p>[1] Ferri, P., Heibi, I., Pareschi, L., & Peroni, S. (2020). MITAO: A User Friendly and Modular Software for Topic Modelling [JD]. PuntOorg International Journal, 5(2), 135–149. <a href="https://doi.org/10.19245/25.05.pij.5.2.3">https://doi.org/10.19245/25.05.pij.5.2.3</a></p>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", main figures PART 2
<p>This deposit contains the supporting records of analysis for 3D cytometry presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218</p> <p>Contents:</p> <p>1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses for figure 6 analyses.</p> <p>Contents of zip files by figure contain at a minimum the .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/). Additional files may include gate files (.vtg) or max projections (.tif).</p> <p> </p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p> <p> </p>
D-DUST Analysis Ready Data Repository
<p>Analysis-ready data repository (<em>D22_ARD_repository_v1_24022022.zip</em>) and Data Management Plan (<em>D-DUST_DMP_v1_22122022.pdf</em>) developed within the D-DUST Project (Data-driven moDelling of particUlate with Satellite Technology aid)</p>
curatedPCaData supplementary data table for differential gene expression analysis
<p>This tab-separated plaintext file contains differential gene expression analyses reported for the curatedPCaData data resource publication.</p>
Axiom canine microarray data from Australian dingoes and domestic dogs for admixture and population structure analysis
<p>Admixture between species is a cause for concern in wildlife management. Canids are particularly vulnerable to inter-specific hybridisation, and genetic admixture has shaped their evolutionary history. Microsatellite DNA testing, relying on a small number of genetic markers and geographically restricted reference populations, has identified extensive domestic dog admixture in Australian dingoes and driven conservation management policy. There has been concern that geographic variation in dingo genotypes could confound ancestry analyses that use a small number of genetic markers. Here we apply genome-wide single nucleotide polymorphism (SNP) genotyping to a set of 385 wild and captive dingoes from across Australia and then carry out comparisons to domestic dogs, and perform ancestry modelling and biogeographic analyses to characterize population structure in dingoes and investigate the extent of admixture between dingoes and dogs in different regions of the continent. We show that there are at least five distinct dingo populations across Australia. We observed limited evidence of dog admixture in wild dingoes, challenging previous reports regarding the occurrence and extent of dog admixture in dingoes, as our ancestry analyses show that previous assessments severely overestimate the degree of domestic dog admixture in dingo populations, particularly in southeastern Australia. These findings strongly support the use of genome-wide SNP genotyping as a refined method for wildlife managers and policy makers to assess and inform dingo management policy and legislation moving forwards.</p>
Data and analysis for orthopaedics literature review.
<p>This is a repository of all the data and analysis code to reproduce all the plots and analysis for this paper.</p> <p>To reproduce all plots in the paper, we used Python 3.9.7, install all the requirements in `requirements.txt`,open `Analysis.ipynb` and run every cell in order.</p> <p>The file 'LiteratureTaxonomy.csv' contains information on the 492 papers analysed in the paper.</p>
Data for: Dietary analysis reveals differences in the prey use of two sympatric bat species
<p>One mechanism for morphologically similar and sympatric species to avoid competition and facilitate coexistence is to feed on different prey items within different microhabitats. In the current study, we investigated and compared the diet of the two most common and similar-sized bat species in Japan – <em>Murina ussuriensis</em> (Ognev, 1913) and <em>Myotis ikonnikovi</em> (Ognev, 1912) – to gain more knowledge about the degree of overlap in their diet and their foraging behavior. We found that both bat species consumed prey from the orders of Lepidoptera and Diptera most frequently, while the proportion of Dipterans was higher in the diet of <em>M. ikonnikovi</em>. Furthermore, we found a higher prey diversity in the diet of <em>M. ikonnikovi</em> compared to that of <em>M. ussuriensis</em><em> </em>which might indicate that the former is a more generalist predator than the latter. In contrast, the diet of <em>M. ussuriensis</em> contained many Lepidopteran families. The higher probability of prey items likely captured via gleaning to occur in the diet of <em>M. ussuriensis</em> in contrast to <em>M. ikonnikovi</em> indicates that <em>M. ussuriensis</em> might switch between aerial-hawking and gleaning modes of foraging behavior. We encourage further studies across various types of habitats and seasons to investigate the flexibility of the diet composition and foraging habitat use of these two bat species.</p>
Imaging Data for: Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis
<p>This dataset contains the microfluidic microscopy time-lapse data for the publication <a href="https://www.frontiersin.org/articles/10.3389/fmicb.2023.1198170/">"Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis"</a>.</p> <p>The imaging data is recorded as raw 16-bit tif-stacks. The sequences <span>17406 - 17410 and 17411 - </span><span>17415 contain the aerobic and anaerobic conditions, respectively. For details about cultivation conditions and image processing please have a look into our <a href="http://www.frontiersin.org/articles/10.3389/fmicb.2023.1198170/">paper</a> or the public code repository <a href="https://github.com/JuBiotech/Supplement-to-Kasahara-et-al.-2023a">Supplement-to-Kasahara-et-al.-2023a</a>.</span></p>
Input and output data for the paper "Evaluating the German onshore wind auction programme: An analysis based on individual bids"
<p>Batz Liñeiro, T., Müsgens, F., (2023). Energy Policy</p> <p><a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a></p> <p>ABSTRACT</p> <p>Auctions are a highly demanded policy instrument for the promotion of renewable energy sources. Their flexible structure makes them adaptable to country-specific conditions and needs. However, their success depends greatly on how those needs are operationalised in the design elements. Disaggregating data from the German onshore wind auction programme into individual projects, we evaluated the contribution of auctions to the achievement of their primary (deployment at competitive prices) and secondary (diversity) objectives and have highlighted design elements that affect the policy's success or failure. We have shown that, in the German case, the auction scheme is unable to promote wind deployment at competitive prices, and that the design elements used to promote the secondary objectives not only fall short at achieving their intended goals but create incentives for large actors to game the system.</p> <p>Description</p> <p>The data package offered in this publication comprises input, processing, and output files, accompanied by the corresponding R-codes used for data processing at different stages. Among the various data outputs, the "Auctions" sheet within the file "3 Auction Realizations Onshore Wind" holds particular significance for users. Within this sheet, users can identify the realized projects, their respective IDs, and the reported individual bid values (BV). However, it is recommended to refer to the attached publication to gain a comprehensive understanding of the bid-value identification process.</p> <p>For users seeking to update the results, the input files can be easily updated by referring to partner publications that share the same file names. These partner publications include the <a href="https://zenodo.org/record/7945029">UnitRegister</a>, <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and <a href="https://zenodo.org/record/8013071">TariffRegister </a>datasets.<br> </p>
Source data for the scientometric analysis of citizen science research publications
<p>Source data for the scientometric comparison of citizen science research (n=5749 documents) and a semi-random sample of publications (n=5734) retrieved from the Web of Science Core Collection and published between 1997-2021. The data include information on: author(s) full name(s) ("AF"); year of publication ("PY"); title ("TI"); Digital Object Identifier ("DI"); abstract ("AB"); open access indicator ("OA"); author(s) affiliation(s) ("C1"); document type ("DT"); funding entity ("FU"); funding text ("FX"); total number of citations ("TC"); and identifier of the collection ("group"): CS for citizen science and SRS for the semi-random sample collection respectively.</p>
Dryland soil restoration meta-analysis data
<p>This dataset was used to conduct a meta-analysis on soil restoration in global drylands. It includes data collected from published literature on experiments of soil-based restoration in dryland environments. Predictor variables included in this dataset are site aridity and percent soil sand, type of restoration treatment used, whether seeding or revegetating was done, and time since restoration. Response variables include the effect of soil restoration on aggregate stability, bulk density, volumetric water content, soil organic carbon, soil nitrogen, mycorrhizal colonization, and basal respiration. The calculated effect size on each of these variables is represented as the log response ratio. </p>
Evaluation Data - Systematic Assessment of Fuzzers using Mutation Analysis
<p>The Databases which contain the results of the experiments done for the paper: "Systematic Assessment of Fuzzers using Mutation Analysis"</p> <p>Contained are the databases for the basic, ASan, and 24 hour runs under the data directory. The seed<em> </em>corpora are found in the directory seeds, minimal directory contains the initial seeds used for coverage fuzzing, the coverage directory contains the seeds that resulted from coverage fuzzing.</p> <p>To reproduce the results in the paper from scratch all that is required is the seed/minimal directory, all other artifacts are produced from this data.</p> <p>Additionally, the results of the two manual analyses are contained: not_killed_24.xlsx contain the notes of the manual analysis of unkilled mutants even after the 24 hour experiment, see Section 5.2.1. CVEs.xlsx are the notes taken during for the manual analysis of which CVEs can be reproduced mutants as described in Section 5.4. The script to collect the CVEs is provided as well under cve-script.7z.</p>
Supporting data for "Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis"
<p>Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance.</p> <p>To address these challenges, we have developed a novel tool called <em>Machine Learning Made Easy </em>(MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naïve (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations.</p> <p>MLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at <a href="https://github.com/FunctionalUrology/MLme">https://github.com/FunctionalUrology/MLme</a>.</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.