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4,694 results for “data analysis”
A hierarchical graph-based model for mobility data representation and analysis
<p>Hierarchical representations of transportation networks should provide a better understanding of mobility patterns and the underlying structures at various abstraction levels. A hierarchical graph-based model allows representing moving objects and trajectories according to multiple spatial, temporal and semantic scales. The latter model is implemented here in a Neo4j graph database (version 4.4.0) and experimented with historical maritime data covering Brittany Bay in France.</p>
Fig. 3 in Analysis of biodiversity data suggests that mammal species are hidden in predictable places
Fig. 3. Consensus results of species delimitation analyses. Phylogenetic distribution of hidden diversity estimated from strict consensus of delimitation results (SI Appendix, Table S1). Each silhouette represents a mammalian order with its shadow reflecting the ratio of predicted species to recognized species. Striped silhouettes represent orders with conflicting delimitation results that were not included in the predictive analysis. Phylogeny was adapted from ref. 31.
Fig. 4 in Analysis of biodiversity data suggests that mammal species are hidden in predictable places
Fig. 4. Important predictors of hidden species in mammals. (A) From Top to Bottom, the 50 most important predictive variables (judged by MDA), for the consensus random forest classification model. In both plots, variables are color coded by life history, geographic, climatic, taxonomic, and environmental. (B) Boxplots representing values of the top predictive variables for species included in the consensus model. Values from species identified as hidden are shown at the Bottom of each plot (labeled "H"), and values from species not identified as hidden are shown Above (labeled "NH"). Outliers are excluded from boxplots.
Fig. 1 in Analysis of biodiversity data suggests that mammal species are hidden in predictable places
Fig. 1. Predictive modeling workflow. The framework proposed for identifying named mammal species that are likely to contain hidden diversity utilizes barcoding gene sequences and machine learning models built from environmental, geographic, climatic, taxonomic, and life history variables.
Fig. 2 in Analysis of biodiversity data suggests that mammal species are hidden in predictable places
Fig. 2. Scope of the dataset. Genetic sequences for ∼70% of currently recognized mammalian species were obtained. All mammalian orders are represented, with 23 orders containing sequences from both COI and cytb and 4 having only sequences from cytb. (A) Circle plots reflect species representation for the COI and cytb genes in each order. Dark bars represent the species present in the dataset and light bars represent species for which no genetic data are available. (B) Blue bars represent the proportion of the sequence database represented by each order, and gray bars represent the proportion of recognized species in each order. (C) A total of 3,205,630 geographic occurrence records were obtained for species present in the genetic database.
Archive of the analysis results of the microtremor data obtained from a seismic array with a radius of 0.58 m distributed to the participants of the blind prediction experiments for the ESG6 symposium
<p>This is a supplemental material of the paper "Array-size dependency of the upper limit wavelength normalized by array radius for the standard spatial autocorrelation method" by Ikuo Cho, published in Earth, Planets and Space. It consists of the analysis results of the microtremor data observed using a seismic array with a radius of 0.58 m, which were distributed to the participants of the blind prediction experiments in ESG6. It involves all analysis results and script files to draw Figure 1 of the paper. See the "Availability of data and materials" section of the paper to download the original observed data and analysis code. See the main text of the paper for the details of the analysis.</p>
Local chromatin context dictates the genetic determinants of the heterochromatin spreading reaction. Analysis Code, Numerical and Primary data.
<p>Uploaded under this Zenodo DOI is the following:</p> <p>1. the Analysis Code used for Flow Cytometry analysis in the paper, GO complex analysis (Figure 3) and Hit visualization (Figure 1, 2 S1, S4 Figs).</p> <p>2. The primary Flow Cytometry data from both the initial screen (ScreenFlowFCS) and validation experiments (ValidationFlowFCS) are included as .zip files.</p> <p>3. a .zip folder is uploaded that contains all the analysis code for the ChIP-Seq experiments. </p> <p>4. Excel worksheets that contain the numerical source data for all qPCR bar plots.</p>
R_JAGS code for estimation and analysis of species-area-relationship (SAR) parameters from NEON (National Ecological Observatory Network) data on plant surveys
<p><span>Invasive species science is heavily geared toward the invasive agent. </span>However, management to protect native species also requires a proactive approach focused on understanding the features affecting community vulnerability to invasion impacts<span>. </span><span>Vulnerability </span><span>is likely the result of </span><span>factors acting across spatial scales, from </span><span>local to regional, and it is the combined effects of these factors that will determine the magnitude of vulnerability.</span><span> We introduce an analytical framework that quantifies the scale-dependent impact of biological invasions from the shape of the native species-area-relationship (SAR). We leverage newly available, biogeographically extensive vegetation data from the US National Ecological Observatory Network to assess plant community vulnerability to invasion impact as a function of factors acting across scales. We analyzed more than 1000 SARs widely distributed across the USA along environmental gradients and under different levels of invasion. </span>Results show that a decrease in native richness is consistently associated with invasive species cover<span>, but it is only at relatively high levels of invasion that native richness is compromised. After accounting for variation in baseline ecosystem diversity, net primary productivity, and human modification, ecoregions that are colder and wetter seem to be most vulnerable to losses of native plant species at the local level, while warmer and wetter areas seem most susceptible at the landscape level. We also document how the combined effects of cross-scale factors result in a heterogenous spatial pattern of vulnerability. </span><span>This pattern </span><span>cannot be predicted by analyses at any single scale, underscoring the importance of accounting for factors acting across scales. Simultaneously assessing differences in vulnerability between distinct plant communities at local, landscape and regional scales provided outputs that can be used to inform policy and management aimed at reducing vulnerability to the impact of plant invasions.</span></p>
Unit Test Trace Analysis Data
<p>In order to find performance changes at code level, Peass (https://github.com/DaGeRe/peass) uses unit tests as proxy for the performance of a software. The performance of a unit test may not change if the called source stays the same. Therefore, PeASS' regression test selection identifies which tests to execute. For further analysis of the performance changes, the changed source is analysed and methods, which are called and which contain source code changes, are extracted.</p> <p>This dataset provides the results of the regression test selection and source code analysis of Apache Commons Compress, CSV, DBCP, fileupload, imaging, IO, JCS, numbers, pool and text and httpcomponents-core.</p> <p>To execute the analysis, execute the following steps:</p> <pre><code class="language-bash">tar -xvf peass_metadata_commons.tar # extract folder chmod +x getCalls.sh # Make script executable, since Zenodo provides scripts without x flag ./getCalls.sh # Execute analysis</code></pre> <p>This consumes a significant amount of hard disc space and contains long filenames; therefore execution on eCryptfs might yield problems.</p>
Data and analysis scripts of "Predictability awareness rather than mere predictability enhances the perceptual benefits for targets in auditory rhythms over targets following temporal cues"
<p>These are the data and the analysis scripts accompanying the publication </p> <p>"Predictability awareness rather than mere predictability enhances the perceptual benefits for targets in auditory rhythms over targets following temporal cues". </p> <p>Check the readMe for an instruction.</p>
Data for ZooMS analysis of avian fauna from Teotihuacan, Mexico, for Codlin et al. 2022
<p>This data is associated with a manuscript on the analysis of avian fauna via Zooarchaeology by Mass Spectrometry (ZooMS) by Codlin et al. (2022)<br> See publication for more details <a href="https://doi.org/10.1016/j.jas.2022.105692">https://doi.org/10.1016/j.jas.2022.105692</a>. Additional data will be made available on ProteomeXchange</p> <p>All samples were processed with HCl, gelatinized at 65ºC in AmBic and digested with trypsin.<br> While some samples underwent purification using a C18 ZipTip, all digested peptide solutions were diluted to various concentrations prior to spotting and analysis on a Bruker Autoflex Speed LRF MALDI-TOF Mass Spectrometer.</p> <p># Details of files uploaded</p> <p>## "Sample_details.csv"<br> Lists sample IDs and taxonomic information for modern reference specimens and archaeological specimens selected for LC-MS/MS analysis</p> <p>## "MALDI_arch_samples.zip"<br> Contains the unprocessed .MZML MALDI spectra for archaeological specimens.</p> <p> File names are composed of:<br> - MCsample#_MALDIplate#_dilutionAND/ORziptip_platelocation</p> <p>## "MALDI_modern_samples.zip"<br> Contains the unprocessed .MZML MALDI spectra for modern reference specimens.<br> All samples except MC2 are from AMNH collections. See "Sample_details.csv" for sample information</p> <p> File names are composed of:<br> - MCsample#_dilution_speciesidentification_MALDIplatelocation</p> <p>## "Curated_avian_collagen_fasta.zip"<br> Contains two .fasta files with curated avian COL1a1 and COL1a2 sequences from publicly available data.</p> <p>##"MS2_images.zip"<br> Contains MS2 images from LC-MS/MS confirmation of biomarker peaks. See "Sample_details.csv" for sample information.</p> <p> File names are composed of:<br> - COL1A2chain_markerlocation_masspeak_sample#</p> <p>##"MALDI_spectra_images.zip"<br> Contains images of representative spectra for modern and archaeological taxa identified in the study. These spectra were processed and averaged in mMass using the "MALDI-TOF Peptides" settings.<br> Spectra were aligned to more closely fit confirmed biomarker peaks for each sample. See "Sample_details.csv" for sample information.</p> <p> File names are composed of:<br> - Sample#</p> <p>## "biomarkers_list.txt"<br> Contains the list of peaks and deamidated peaks used in clustering MALDI spectra.</p>
Test data for jga-analysis per-sample workflow
<p>Test data for jga-analysis per-sample workflow.</p> <p>Please see:</p> <p>- <a href="https://github.com/biosciencedbc/jga-analysis">https://github.com/biosciencedbc/jga-analysis</a></p> <p>- <a href="https://github.com/biosciencedbc/jga-analysis/blob/main/per-sample/Workflows/per-sample.cwl">https://github.com/biosciencedbc/jga-analysis/blob/main/per-sample/Workflows/per-sample.cwl</a></p>
Datasets from "Circulating miRNA and Lung Cancer: - a More Comprehensive Analysis of Available Data"
<p>A collection of datasets on miRNA and lung cancer used in</p> <p>Berg, O.F.B.: Circulating miRNA and Lung Cancer: - a More Comprehensive Analysis of Available Data.<br> NTNU Open (2022)</p> <p> </p> <p>The datasets in this collection are processed and normalized from available raw datasets. The processing code that was used can be found on <a href="https://github.com/OleFredrik1/masterthesis">https://github.com/OleFredrik1/masterthesis</a>. The raw datasets are:</p> <p><strong>Asakura2020:</strong></p> <p>Asakura, K., Kadota, T., Matsuzaki, J., Yoshida, Y., Yamamoto, Y., Nakagawa, K., Takizawa, S., Aoki, Y., Nakamura, E., Miura, J., Sakamoto, H., Kato, K., Watanabe, S.-i., and Ochiya, T. (2020). A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy. <em>Communications Biology</em>, 3(1):1–9.</p> <p>Accession ID: GSE137140</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140</a></p> <p> </p> <p><strong>Bianchi2011:</strong></p> <p>Bianchi, F., Nicassio, F., Marzi, M., Belloni, E., Dall’Olio, V., Bernard, L., Pelosi, G., Maisonneuve, P., Veronesi, G., and Di Fiore, P. P. (2011). A serum circulating miRNA diagnostic test to identify asymptomatic high-risk individuals with early stage lung cancer. <em>EMBO Molecular Medicine</em>, 3(8):495–503.</p> <p>Link: <a href="https://www.embopress.org/action/downloadSupplement?doi=10.1002%2Femmm.201100154&file=emmm_201100154_sm_suppdata2.xls">https://www.embopress.org/action/downloadSupplement?doi=10.1002%2Femmm.201100154&file=emmm_201100154_sm_suppdata2.xls</a></p> <p> </p> <p><strong>Chen2019:</strong></p> <p>Accession ID: GSE71661</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71661">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71661</a></p> <p> </p> <p><strong>Duan2021:</strong></p> <p>Duan, X., Qiao, S., Li, D., Li, S., Zheng, Z., Wang, Q., and Zhu, X. (2021). Circulating miRNAs in Serum as Biomarkers for Early Diagnosis of Non-small Cell Lung Cancer. <em>Frontiers in Genetics</em>, 12:987.</p> <p>Accession ID: GSE137140</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140</a></p> <p> </p> <p><strong>Fehlmann2020:</strong></p> <p>Fehlmann, T., Kahraman, M., Ludwig, N., Backes, C., Galata, V., Keller, V., Geffers, L., Mercaldo, N., Hornung, D., Weis, T., Kayvanpour, E., Abu-Halima, M., Deuschle, C., Schulte, C., Suenkel, U., von Thaler, A.-K., Maetzler, W., Herr, C., Fähndrich, S., Vogelmeier, C., Guimaraes, P., Hecksteden, A., Meyer, T., Metzger, F., Diener, C., Deutscher, S., Abdul-Khaliq, H., Stehle, I., Haeusler, S., Meiser, A., Groesdonk, H. V., Volk, T., Lenhof, H.-P., Katus, H., Balling, R., Meder, B., Kruger, R., Huwer, H., Bals, R., Meese, E., and Keller, A. (2020). Evaluating the Use of Circulating MicroRNA Profiles for Lung Cancer Detection in Symptomatic Patients. <em>JAMA oncology</em>, 6(5):714–723.</p> <p>Accession ID: E-MTAB-8026</p> <p>Link: <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8026/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8026/</a></p> <p> </p> <p><strong>Halvorsen2016:</strong></p> <p>Halvorsen, A. R., Bjaanæs, M., LeBlanc, M., Holm, A. M., Bolstad, N., Rubio, L., Peñalver, J. C., Cervera, J., Mojarrieta, J. C., López-Guerrero, J. A., Brustugun, O. T., and Helland, Å. (2016). A unique set of 6 circulating microRNAs for early detection of non-small cell lung cancer. <em>Oncotarget</em>, 7(24):37250–37259.</p> <p>Accession ID: GSE70080</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70080">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70080</a></p> <p> </p> <p><strong>Jin2017:</strong></p> <p>Jin, X., Chen, Y., Chen, H., Fei, S., Chen, D., Cai, X., Liu, L., Lin, B., Su, H., Zhao, L., Su, M., Pan, H., Shen, L., Xie, D., and Xie, C. (2017). Evaluation of Tumor-Derived Exosomal miRNA as Potential Diagnostic Biomarkers for Early-Stage Non–Small Cell Lung Cancer Using Next-Generation Sequencing. <em>Clinical Cancer Research</em>, 23(17):5311–5319.</p> <p>Link: <a href="https://aacrjournals.org/clincancerres/article/23/17/5311/123048/Evaluation-of-Tumor-Derived-Exosomal-miRNA-as">https://aacrjournals.org/clincancerres/article/23/17/5311/123048/Evaluation-of-Tumor-Derived-Exosomal-miRNA-as</a> (table s1)</p> <p> </p> <p><strong>Keller2009:</strong></p> <p>Keller, A., Leidinger, P., Borries, A., Wendschlag, A., Wucherpfennig, F., Scheffler, M., Huwer, H., Lenhof, H.-P., and Meese, E. (2009). miRNAs in lung cancer - Studying complex fingerprints in patient’s blood cells by microarray experiments. <em>BMC Cancer</em>, 9(1):353.</p> <p>Accession ID: GSE17681</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17681">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17681</a></p> <p> </p> <p><strong>Keller2014:</strong></p> <p>Keller, A., Leidinger, P., Vogel, B., Backes, C., ElSharawy, A., Galata, V., Mueller, S. C., Marquart, S., Schrauder, M. G., Strick, R., Bauer, A., Wischhusen, J., Beier, M., Kohlhaas, J., Katus, H. A., Hoheisel, J., Franke, A., Meder, B., and Meese, E. (2014). miRNAs can be generally associated with human pathologies as exemplified for miR-144*. <em>BMC Medicine</em>, 12(1):224.</p> <p>Accession ID: GSE61741</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61741">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61741</a></p> <p> </p> <p><strong>Keller2020:</strong></p> <p>Keller, A., Fehlmann, T., Backes, C., Kern, F., Gislefoss, R., Langseth, H., Rounge, T. B., Ludwig, N., and Meese, E. (2020). Competitive learning suggests circulating miRNA profiles for cancers decades prior to diagnosis. <em>RNA Biology</em>, 17(10):1416–1426.</p> <p>Link: <a href="https://www.tandfonline.com/doi/full/10.1080/15476286.2020.1771945">https://www.tandfonline.com/doi/full/10.1080/15476286.2020.1771945</a> (Supplemental Table 9)</p> <p> </p> <p><strong>Kryczka2021:</strong></p> <p>Kryczka, J., Migdalska-Sęk, M., Kordiak, J., Kiszałkiewicz, J. M., PastuszakLewandoska, D., Antczak, A., and Brzeziańska-Lasota, E. (2021). Serum Extracellular Vesicle-Derived miRNAs in Patients with Non-Small Cell Lung Cancer—Search for Non-Invasive Diagnostic Biomarkers. <em>Diagnostics</em>, 11(3):425.</p> <p>Link: <a href="https://www.mdpi.com/2075-4418/11/3/425/s1">https://www.mdpi.com/2075-4418/11/3/425/s1</a></p> <p> </p> <p><strong>Leidinger2011:</strong></p> <p>Leidinger, P., Keller, A., Borries, A., Huwer, H., Rohling, M., Huebers, J., Lenhof, H.-P., and Meese, E. (2011). Specific peripheral miRNA profiles for distinguishing lung cancer from COPD. <em>Lung Cancer</em>, 74(1):41–47.</p> <p>Accession ID: GSE24709</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24709">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24709</a></p> <p> </p> <p><strong>Leidinger2014:</strong></p> <p>Leidinger, P., Backes, C., Dahmke, I. N., Galata, V., Huwer, H., Stehle, I., Bals, R., Keller, A., and Meese, E. (2014). What makes a blood cell based miRNA expression pattern disease specific? - A miRNome analysis of blood cell subsets in lung cancer patients and healthy controls. <em>Oncotarget</em>, 5(19):9484–9497.</p> <p>Accession ID: GSE55993</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55993">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55993</a></p> <p> </p> <p><strong>Leidinger2016:</strong></p> <p>Leidinger, P., Brefort, T., Backes, C., Krapp, M., Galata, V., Beier, M., Kohlhaas, J., Huwer, H., Meese, E., and Keller, A. (2016). High-throughput qRT-PCR validation of blood microRNAs in non-small cell lung cancer. <em>Oncotarget</em>, 7(4):4611–4623.</p> <p>Link: <a href="https://www.oncotarget.com/article/6566/text/">https://www.oncotarget.com/article/6566/text/</a> (supplementary files)</p> <p> </p> <p><strong>Li2017:</strong></p> <p>Li, L.-L., Qu, L.-L., Fu, H.-J., Zheng, X.-F., Tang, C.-H., Li, X.-Y., Chen, J., Wang, W.-X., Yang, S.-X., Wang, L., Zhao, G.-H., Lv, P.-P., Zhang, M., Lei, Y.-Y., Qin, H.-F., Wang, H., Gao, H.-J., and Liu, X.-Q. (2017). Circulating microRNAs as novel biomarkers of ALK-positive non-small cell lung cancer and predictors of response to crizotinib therapy. <em>Oncotarget</em>, 8(28):45399–45414.</p> <p>Accession ID: GSE94536</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94536">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94536</a></p> <p> </p> <p><strong>Marzi2016:</strong></p> <p>Marzi, M. J., Montani, F., Carletti, R. M., Dezi, F., Dama, E., Bonizzi, G., Sandri, M. T., Rampinelli, C., Bellomi, M., Maisonneuve, P., Spaggiari, L., Veronesi, G., Bianchi, F., Di Fiore, P. P., and Nicassio, F. (2016). Optimization and Standardization of Circulating MicroRNA Detection for Clinical Application: The miR-Test Case. <em>Clinical Chemistry</em>, 62(5):743–754</p> <p>Accession ID: GSE76462</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76462">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76462</a></p> <p> </p> <p><strong>Nigita2018:</strong></p> <p>Nigita, G., Distefano, R., Veneziano, D., Romano, G., Rahman, M., Wang, K., Pass, H., Croce, C. M., Acunzo, M., and Nana-Sinkam, P. (2018). Tissue and exosomal miRNA editing in Non-Small Cell Lung Cancer. <em>Scientific Reports</em>, 8(1):10222.</p> <p>Accession ID: GSE114711</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114711">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114711</a></p> <p> </p> <p><strong>Patnaik2012:</strong></p> <p>Patnaik, S. K., Yendamuri, S., Kannisto, E., Kucharczuk, J. C., Singhal, S., and Vachani, A. (2012). MicroRNA Expression Profiles of Whole Blood in Lung Adenocarcinoma. <em>PLOS ONE</em>, 7(9):e46045.</p> <p>Accession ID: GSE27486</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27486">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27486</a></p> <p> </p> <p><strong>Patnaik2017:</strong></p> <p>Patnaik, S. K., Kannisto, E. D., Mallick, R., Vachani, A., and Yendamuri, S. (2017). Whole blood microRNA expression may not be useful for screening non-small cell lung cancer. <em>PLOS ONE</em>, 12(7):e0181926.</p> <p>Accession ID: GSE40738</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40738">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40738</a></p> <p> </p> <p><strong>Qu2017:</strong></p> <p>Qu, L., Li, L., Zheng, X., Fu, H., Tang, C., Qin, H., Li, X., Wang, H., Li, J., Wang, W., Yang, S., Wang, L., Zhao, G., Lv, P., Lei, Y., Zhang, M., Gao, H., Song, S., and Liu, X. (2017). Circulating plasma microRNAs as potential markers to identify EGFR mutation status and to monitor epidermal growth factor receptor-tyrosine kinase inhibitor treatment in patients with advanced non-small cell lung cancer. <em>Oncotarget</em>, 8(28):45807–45824.</p> <p>Accession ID: GSE93300</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93300">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93300</a></p> <p> </p> <p><strong>Reis2020:</strong></p> <p>Reis, P. P., Drigo, S. A., Carvalho, R. F., Lopez Lapa, R. M., Felix, T. F., Patel, D., Cheng, D., Pintilie, M., Liu, G., and Tsao, M.-S. (2020). Circulating miR-16-5p, miR-92a-3p, and miR-451a in Plasma from Lung Cancer Patients: Potential Application in Early Detection and a Regulatory Role in Tumorigenesis Pathways. <em>Cancers</em>, 12(8):2071.</p> <p>Accession ID: GSE152702</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152702">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152702</a></p> <p> </p> <p><strong>Wozniak2015:</strong></p> <p>Wozniak, M. B., Scelo, G., Muller, D. C., Mukeria, A., Zaridze, D., and Brennan, P. (2015). Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer. <em>PLOS ONE</em>, 10(5):e0125026.</p> <p>Accession ID: GSE64591</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64591">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64591</a></p> <p> </p> <p><strong>Yao2019:</strong></p> <p>Yao, B., Qu, S., Hu, R., Gao, W., Jin, S., Liu, M., and Zhao, Q. (2019). A panel of miRNAs derived from plasma extracellular vesicles as novel diagnostic biomarkers of lung adenocarcinoma. <em>FEBS Open Bio</em>, 9(12):2149–2158.</p> <p>Accession ID: GSE111803</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111803">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111803</a></p> <p> </p> <p><strong>Zaporozhchenko2018:</strong></p> <p>Zaporozhchenko, I. A., Morozkin, E. S., Ponomaryova, A. A., Rykova, E. Y., Cherdyntseva, N. V., Zheravin, A. A., Pashkovskaya, O. A., Pokushalov, E. A., Vlassov, V. V., and Laktionov, P. P. (2018). Profiling of 179 miRNA Expression in Blood Plasma of Lung Cancer Patients and Cancer-Free Individuals. <em>Scientific Reports</em>, 8(1):6348.</p> <p>Accession ID: E-MTAB-6304</p> <p>Link: <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6304/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6304/</a></p> <p> </p> <p>The Abdollahi2019 and Boeri2011 datasets are not included as I recived them by email and I did not recieve conformation that they were OK with me publishing the datasets.</p>
Analysis Data, "Drivers and Determinants of Strain Dynamics Following Fecal Microbiota Transplantation"
<p>This package contains datasets in `Rdata` format that underlie the analyses presented in the study, "Drivers and Determinants of Strain Dynamics Following Fecal Microbiota Transplantation" by Schmidt, Li et al.<br> </p> <p>Corresponding code, referring to these datasets, is available via `github`:</p> <p>https://github.com/grp-bork/fmt_metastudy</p> <p> </p> <p>The study is available as a preprint:</p> <p>https://www.biorxiv.org/content/10.1101/2021.09.30.462010v1</p> <p> </p> <p>The present package contains processed/derived data. Metagenome-Assembled Genomes generated for the same study are available via `Zenodo` under:</p> <p>https://zenodo.org/record/5534163#.YpoRFy8RrzA<br> doi: 10.5281/zenodo.5534163</p>
Data from: Occupancy winners in tropical protected forests: a pantropical analysis
<p class="MsoNormal"><span>The structure of forest mammal communities appears surprisingly consistent across the continental tropics<span>, presumably due to convergent evolution in similar environments. W</span>hether such consistency extends to mammal occupancy, despite variation in species characteristics and context, remains unclear. Here we ask whether we can predict occupancy patterns and, if so, whether these relationships are consistent across biogeographic regions. Specifically, we assessed how mammal feeding guild, body mass and ecological specialization relate to occupancy in protected forests across the tropics. We used standardized camera-trap data (</span><span>1,002 camera-trap locations and 2-10 years of data)</span><span> and a hierarchical Bayesian occupancy model. We found that occupancy varied by regions, and </span><span>certain species characteristics</span><span> explained much of this variation. Herbivores consistently had the highest occupancy. However, only in the Neotropics did we detect a significant effect of body mass on occupancy: large mammals had lowest occupancy. Importantly, habitat specialists generally had higher occupancy than generalists, though this was reversed in the Indo-Malayan sites. We conclude that </span><span>habitat specialization is key for understanding variation in mammal occupancy across regions, and that habitat specialists often benefit more from protected areas, than do generalists. </span><span>The contrasting examples seen in the Indo-Malayan region likely reflect distinct anthropogenic pressures.</span></p>
Data and analysis code for: Global protected areas seem insufficient to safeguard half of the world's mammals from human-induced extinction
<div> <p class="normal">Protected areas (PAs) are a cornerstone of global conservation and central to international plans to minimize global extinctions. During the coming century, global ecosystem destruction and fragmentation associated with increased human population and economic activity could make the <span class="PI"></span>long-term<span class="PI"></span> survival of most terrestrial vertebrates even more dependent on PAs. However, the capacity of the current global PA network to sustain species for the long term is unknown. Here, we explore this question for all <span class="PI"></span>nonvolant terrestrial mammals<span class="ins cts-1"> for which we found sufficient data</span>, ∼4,000 species. We first estimate the potential population size of each such mammal species in each PA and then use three different criteria to estimate if solely the current global network of PAs might be sufficient for their <span class="PI"></span>long-term<span class="PI"></span> survival. Our analyses suggest that current PAs may fail to provide robust protection for about half the species analyzed, including most species currently listed as threatened with extinction and a third of species not currently listed as threatened. Hundreds of mammal species appear to have no viable protected populations. Underprotected species were found across all body sizes, taxonomic groups, and geographic regions. <span class="PI"></span>Large-bodied<span class="PI"></span> mammals, endemic species, and those in <span class="PI"></span>high-biodiversity<span class="PI"></span> tropical regions were particularly poorly protected by existing PAs. As<span class="ins cts-1"> new</span> international biodiversity targets are formulated, our results suggest that the global network of PAs must be <span class="PI"></span>greatly expanded and most importantly that PAs must be located in diverse regions that encompass species not currently protected and must be large enough to ensure that protected species can persist for the long term.</p> </div> <p class="kwd-group"></p>
Data set for the journal article Structural Analysis of Metal Coordination Sites in Single-Atom Catalysts Based on Carbon Nitrides
<p>The data is organized according to the figure in the manuscript. </p>
Groundwater level change data set and SAR analysis data set from the end of 2016 to the end of 2020 in the Osaka Plain and Kyoto Basin, Japan
<p>The .dat file contains data on groundwater level changes. Groundwater level data is hourly, and each line contains data for one day (24 hours). The name of the .dat files consists of the name of the groundwater level station and the observation period. Datasets that include raw in the name contain the respective date in the first column.</p> <p>Source: Water information System, Ministry of Land, Infrastructure, Transport and Tourism, 2017-2020. Groundwater level search results, http://www1.river.go.jp/ (accessed on 20 October 2021) [Translated from Japanese.] (In Japanese).</p> <p>SLC data for InSAR analysis can be obtained by running the .py file in python. python files are provided to obtain SLC data by two orbits, Ascending and Descending, respectively.</p> <p>Source: European Space Agency (ESA), https://search.asf.alaska.edu/#/</p>
Spanish melon landraces: revealing useful diversity by genomic, morphologic, and metabolomic analysis. Supplementary data.
<p>Original data linked to the publication Spanish melon landraces: revealing useful diversity by genomic, morphologic, and metabolomic analysis. It includes, Supp. Table 1: Genomic data; Supp. Table 2: Characterization data; Supp. Table 3: sugar and acids data; Supp Table Supp. Table 4: Voaltile organic compounds data; Supp Table 5: Germplasm details; Supp. table 6: Cromatographic parameters</p>
Data for: Range and niche expansion through multiple interspecific hybridization - a genotyping by sequencing analysis of Cherleria (Caryophyllaceae)
<p><b>Background:</b> <i>Cherleria</i> (Caryophyllaceae) is a circumboreal genus that also occurs in the high mountains of the northern hemisphere. In this study, we focus on a clade that diversified in the European High Mountains, which was identified using nuclear ribosomal (nrDNA) sequence data in a previous study. With the nrDNA data, all but one species was monophyletic, with little sequence variation within most species. Here, we use genotyping by sequencing (GBS) data to determine whether the nrDNA data showed the full picture of the evolution in the genomes of these species.</p> <p><b>Results:</b> The overall relationships found with the GBS data were congruent with those from the nrDNA study. Most of the species were still monophyletic and many of the same subclades were recovered, including a clade of three narrow endemic species from Greece and a clade of largely calcifuge species. The GBS data provided additional resolution within the two species with the best sampling, <i>C. langii</i> and <i>C. laricifolia</i>, with structure that was congruent with geography. In addition, the GBS data showed significant hybridization between several species, including species whose ranges did not currently overlap.</p> <p><b>Conclusions:</b> The hybridization led us to hypothesize that lineages came in contact on the Balkan Peninsula after they diverged, even when those lineages are no longer present on the Balkan Peninsula. Hybridization may also have helped lineages expand their niches to colonize new substrates and different areas. Not only do genome-wide data provide increased phylogenetic resolution of difficult nodes, they also give evidence for a more complex evolutionary history than what can be depicted by a simple, branching phylogeny.</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.