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2,322 results for “Circulation”
Contributions of Anomalous Large-Scale Circulations to the Absence of Tropical Cyclones over the Western North Pacific in July 2020
<p>The datasets are for the article 'Contributions of Anomalous Large-Scale Circulations to the Absence of Tropical Cyclones over the Western North Pacific in July 2020', including the WRF Initial conditions (horizontal wind at 850 hPa, geopotential height at 850 and 200 hPa) of sensitivity experiments (CTRL, W_WNPSH, W_SAH, W_TUTT, and S_SASM) in July 2020, and the formation records of the simulated TC formation in all experiments.</p>
Fig. 2 in Circulation Pathways Of Trematodes Of Freshwater Gastropod Mollusks In Forest Biocenoses Of The Ukrainian Polissia
Fig. 2. Two-host life cycles of trematodes: а — alternation hosts; b — proportion of different classes of definitive hosts in life cycles.
Fig. 3 in Circulation Pathways Of Trematodes Of Freshwater Gastropod Mollusks In Forest Biocenoses Of The Ukrainian Polissia
Fig. 3. Three-host life cycle of trematodes: А — second intermediate hosts are aquatic invertebrates; В — second intermediate hosts are amphibiontic invertebrates; С — second intermediate hosts are vertebrates; а — alternation hosts; b — biological structure of helminth fauna.
Dataset: Mirror symmetric on-chip frequency circulation of light
<p>The calibrated dataset comprising main text figure 4 and supplementary figure S8 for the paper "Mirror symmetric on-chip frequency circulation of light." The CSV files contain isolation and insertion loss data. The ".m" file contain scripts for plotting the CSV content in MATLAB as heatmaps. Additional details describing the dataset and usage instructions are described in the "readme.txt" file.</p>
Number of imperial portraits (average), per year of reign (N=1625), excluding emperors that ruled less than a year and imperial portraits that circulated prior to the emperor's accession
<p>The figure presented here includes the average number of sculptural portraits of Roman emperors (mostly carved from marble or casted in bronze), per year of rule, that were collected for the purposes of analyzing the representation of Roman emperors in freestanding sculpture. PhD dissertation: S. Heijnen (2022), Portraying Change: The Representation of Roman Emperors in Freestanding Sculpture (ca. 50 BC - ca. 400 AD). Dissertation. Radboud University.</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>
Evolution of ocean circulation in the North Atlantic Ocean during the Miocene: impact of the Greenland Ice Sheet and the Eastern Tethys Seaway
<p>This dataset contains atmosphere and ocean outputs (NetCDF files) from modeling experiments with realistic early Miocene paleogeography as well as sensitivity to Greenland Ice Sheet and Eastern Tethys Seaway. The set of simulation targets the evolution of the North Atlantic Deep Water during the Miocene. The simulations have been run using the IPSL-CM5A2 General Circulation Model (Sepulchre et al. 2020 - IPSL-CM5A2 – an Earth system model designed for multi-millennial climate simulations, GMD). It includes 3 ocean-atmosphere simulations. Data are monthly averages over the last 100 years of the simulations. </p>
Dataset for "Air-sea interactions on Titan: effect of radiative transfer on the lake evaporation and atmospheric circulation"
<p>These documents are supplements to the "Air-sea interactions on Titan: effect of radiative transfer on the lake evaporation and atmospheric circulation" paper published by the same authors in The Planetary Science Journal in 2022.</p> <p>Are made available:</p> <p>-the Supporting Information document on the performed sensitivity study,<br> "paper_mtWRF_lake_RT_220825_SI.pdf"</p> <p>-the Fortran source code of the radiative transfer module developed for this work,<br> "module_ra_gray.F"</p> <p>-all the netCDF simulation outputs and a list describing their parameters,<br> "run-##.nc.gz"<br> "list_simulations_2D_paper2022_RT_zenodo.pdf"</p> <p>-the Python codes to plot figures from the netCDF output files,<br> "mtwrf_analysis_#D_#.py"</p>
Three-Dimensional Thermoporoelastic Modeling of Hydrofracturing and Fluid Circulation in Hot Dry Rock: EGS Collab Experiment 1
<p>The data regarding the determined natural fractures, locations of monitoring devices, microseismic events, and well trajectories in EGS Collab Experiment 1.</p>
Data for "Symmetric instability in the Atlantic Meridional Overturning Circulation"
<p>Data for the DPhil thesis "Symmetric instability in the Atlantic Meridional Overturning Circulation".</p>
CESM2 MDM data for "Historical changes in wind driven ocean circulation can accelerate global warming" - submitted to GRL
<p>CESM2 Experiment names:</p> <ul> <li>MD = mechanically decoupled model (referred to as MDM in paper), CESM2</li> <li>FC = fully coupled model (referred to as FCM in paper), CESM2</li> </ul> <p>Decoding file names:</p> <p>Variables that are a single value per time step (e.g. global means and globally integrated values) are given in dimensions of time by ensemble member. Variables that include values at every grid point at each point in time are provided with an ensemble mean trend and an ensemble standard deviation of the trend. </p> <ul> <li>ensmean refers to ensemble mean</li> <li>ensstd refers to ensemble standard deviation</li> <li>trend refers to linear trend over 1979-2014</li> <li>annual refers to annual mean anomalies, relative to reference period of 1941-1970</li> </ul> <p>Variables:</p> <ul> <li>aice = ice area</li> <li>AMOC = Atlantic meridional overturning circulation</li> <li>N_HEAT = northward heat transport </li> <li>BSF = barotropic streamfunction </li> <li>TREFHT = reference level air temperature </li> <li>Qnet = net surface heat flux (defined as FSNS - FLNS - LHFLX - SHFLX)</li> <li>TOA = top of atmosphere radiation </li> <li>TOAC = top of atmosphere radiation, clearsky </li> <li>FLNT = net longwave flux at top of model</li> <li>FLNTC = net longwave flux at top of model, clearsky</li> <li>FSUTOA = upwelling solar flux at top of atmosphere</li> <li>FSNTOA = net solar flux at top of atmosphere</li> <li>FSNTOAC = net solar flux at top of atmosphere, clearsky</li> </ul> <p> </p> <p> </p> <p> </p>
Phanerozoic global climatic fields simulated using the mixed-layer general circulation model FOAM
<p>These files contain the output of Phanerozoic global climate simulations conducted using the “slab” mixed-layer ocean-atmosphere general circulation model FOAM. They are available every 20 Myrs between 540 Ma and 0 Ma, both included. Boundary conditions were adapted to best match each time slice. Continental reconstructions were taken from Scotese and Wright (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/). We defined pCO2 after the proxy data compilation of Foster et al. (doi:10.1038/ncomms14845) when available and Krause et al. (dot:10.1038/s41467-018-06383-y) for older time slices. Solar luminosity followed Gough et al. (doi:10.1007/BF00151270). Continental vegetation was set to Modern-like latitudinal bands between 0 Ma and 100 Ma (included), tropical evergreen, broad-leaved forest between 120 Ma and 360 Ma (included), tundra between 380 Ma and 440 Ma (included) and rocky desert afterwards. The orbital configuration was set to null eccentricity and minimum obliquity. </p> <p>The reader is referred to the associated paper for a full description of the model and boundary conditions.</p> <p>All model output file names use the following pattern: « [age]ebP2_solCgough1981_EccN_pCO2FosterKr_[model_component] _slab.nc", with [age], the age expressed in million years ago, and [model_component] being 'atmos' or 'coupl' (atmospheric component or coupler). For each time slice, the topography-bathymetry data used in FOAM is also provided (« Topobathy_[age]eb_postslarti_cor.nc »).</p>
Figure 2 in Genetic structure of Trypanosoma congolense "forest type" circulating in domestic animals and tsetse flies in the South-West region of Cameroon
Figure 2. NJ Tree based on Cavalli-Sforza and Edwards chord distance matrix of T. congolense "forest type" circulating in tsetse flies and domestic animals of Fontem.
Figure 1 in Myxobolus spp. (Cnidaria: Myxobolidae) in the circulating blood of fishes from Goiás and Mato Grosso States, Brazil: case report
Figure 1. Blood smears of Myxobolus spp. parasites in the circulating blood of fishes from Goiás State and Mato Grosso State, Brazil (A-D): (A) Morphological type 1 (M1) of Myxobolus sp. in the blood of Tetragonopterus araguaiensis; (B-D) Morphological type 2 (M2) of Myxobolus sp. in the blood of Myleus rubriprinnis (b) and Pygocentrus nattereri (c-d). Polar capsule (PC); Sporoplasm (SP).
Figure 6. Association between survival probability estimates with circulating T4 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
Figure 6. Association between survival probability estimates with circulating T4 (nmol/L) at the time of capture/release from the best supported model: {Φ(g + T4)}.
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: input files (2 of 3)
<p>DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes the files necessary for nudging the simulated temperature and salinity towards Copernicus GLORYS12V1 reanalysis values in a simulation from 1 September to 31 December 2013.</p> <p>The remaining input files for this period are available at <a href="https://doi.org/10.5281/zenodo.12752190" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752190</a> and <a href="https://doi.org/10.5281/zenodo.12735153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12735153</a>.</p> <p>Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for phyiscs-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request.</p> <p>Sample output files (from the physics and biogeochemistry modules) are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a> and <a href="https://doi.org/10.5281/zenodo.12746262" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12746262</a>.</p>
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: input files (1 of 3)
<p>DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes most of the input files necessary for a physics-only simulation from 1 September to 31 December 2013. The remaining input files for this period, which should be placed in the directory <code>sponge/</code> within the directory tree contained in this record, are available at <a href="https://doi.org/10.5281/zenodo.12734049" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12734049</a> and <a href="https://doi.org/10.5281/zenodo.12735153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12735153</a>. Input files for the biogeochemistry module are available upon request.</p> <p>Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request.</p> <p>Sample output files (from the physics and biogeochemistry modules) are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a> and <a href="https://doi.org/10.5281/zenodo.12746262" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12746262</a>.</p>
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: output files (2 of 2)
<p>DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes daily-mean output of all (ocean circulation, sea ice, and biogeochemistry) modules for September 2015. Similar files for September 2013 are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a>.</p> <p>Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request.</p> <p>Sample input files for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752190" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752190</a>, <a href="https://doi.org/10.5281/zenodo.12734049" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12734049</a>, and <a href="https://doi.org/10.5281/zenodo.12735153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12735153</a>. Input files for the biogeochemistry module are available upon request.</p>
CESM2 data for "Internal Wind Driven Ocean Circulation Variability Delays the Time of Emergence of Externally Forced Sea Surface Temperature Trends" - submitted to GRL
<p>CESM2 Experiment names:</p> <ul> <li>MDM = mechanically decoupled model (referred to as MDM in paper)</li> <li>FCM = fully coupled model (referred to as FCM in paper)</li> </ul> <p>Details for files cesm2.[experiment name].SST.noise.nc</p> <ul> <li>These files include the unfiltered time-varying SST noise </li> <li>"noise" refers to ensemble standard deviation (no 10-yr running mean has been applied) </li> <li>"SST" is the annual mean SST</li> <li>Time period is 1900-2014</li> </ul> <p>For the ensemble mean SST, see previously created Zenodo repository by Fu et al: https://zenodo.org/records/10484207</p> <p>For other ensemble mean variables, see previously created Zenodo repository by McMonigal et al: https://zenodo.org/records/7154374</p>
Fig. 1 in Trichinella species circulating in wild boar (Sus scrofa) populations in Poland
Fig. 1. Example of electrophoretic patterns obtained from multiplex PCR on Trichinella larvae collected from wild boar. Lane 1 and 8 molecular weight marker (Fermentas 100 bp DNA Ladder); lanes 2 and 4, T. spiralis; lanes 3 and 5, T. britovi; lane 6, T. spiralis and T. britovi mixed infection; lane 7, negative control.
ScienceDex guides
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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.