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2,516 results for “Seri”
FIGURE 3 in A new species and typification in Pedicularis series Curvipes (Orobanchaceae)
FIGURE 3. Distribution of Pedicularis husainiana P. Agnihotri, D. Husain, D. Sahoo & S.K. Barik, sp. nov.
FIGURE 4. Hypericum bilgehan-bilgilii. A. habit B. leaf C. calyx D. flower E. pistil, F. capsule, G in A new species from southern Anatolia (Dedegöl Mountain Series-Çürük Mountain) in Turkey: Hypericum bilgehan-bilgilii (Hypericaceae)
FIGURE 4. Hypericum bilgehan-bilgilii. A. habit B. leaf C. calyx D. flower E. pistil, F. capsule, G. seed. (Drawn by İsa Başköse).
FIGURE 3. Hypericum bilgehan-bilgilii. A. flowering time B–C. flower D. leaves E. habitat F in A new species from southern Anatolia (Dedegöl Mountain Series-Çürük Mountain) in Turkey: Hypericum bilgehan-bilgilii (Hypericaceae)
FIGURE 3. Hypericum bilgehan-bilgilii. A. flowering time B–C. flower D. leaves E. habitat F. fruit (capsule) G. seed (photos by A. Savran)
FIGURE 2 in A new species from southern Anatolia (Dedegöl Mountain Series-Çürük Mountain) in Turkey: Hypericum bilgehan-bilgilii (Hypericaceae)
FIGURE 2. The type specimens of Hypericum bilgehan-bilgilii (A, ANK), H. pallens (B, BM), and H. ternatum (C, K)
Total Electron Content and Magnetic Field TIme Series Survey Plots during THEMIS/CMO/FAIR Magnetic Conjunctions
<p>These survey plots are for time intervals corresponding to magnetic conjunctions between (1) NASA's Time History of Events and Macroscale Interactions during Substorms (THEMIS) satellites, (2) USGS College Alaska (CMO) magnetic observatory, and (3) the FAIR GNSS receiver near Fairbanks, Alaska. Magnetic field measurements are used from THEMIS (~3s sampling interval) and CMO (1s sampling interval), while 1s Total Electron Content (TEC) measurements are used from high-rate RINEX data for FAIR obtained from the NASA Crustal Dynamics Data Information System (CDDIS) archive of space geodesy data. The plots also contain solar wind measurements and geomagnetic activity indices from NASA's OMNIWeb (https://omniweb.gsfc.nasa.gov/) database for the same magnetic conjunction intervals. </p> <p>The time series stackplots during each magnetic conjunction show the three components of Interplanetary Magnetic Field, Sym-H index, THEMIS satellite magnetic field perturbation, CMO ground-based magnetometer magnetic field perturbation, TEC perturbation from different GPS satellites-FAIR receiver pairs (specific GPS satellite varies from event to event), and elevation angle for the same GPS satellites.</p>
EXTRA DATA 2/2 Microscopy image data of a titration series with DNA surface strands and condensate-forming DNA nanomotifs (repeat)
<p>Extra data that did not fit the file size limit, for the main repository</p> <p>10.5281/zenodo.13788846</p> <pre><a href="https://doi.org/10.5281/zenodo.13788846">https://doi.org/10.5281/zenodo.13788846</a></pre>
EXTRA DATA 1/2 Microscopy image data of a titration series with DNA surface strands and condensate-forming DNA nanomotifs (repeat)
<p>Extra data that did not fit the file size limit, for the main repository</p> <p>10.5281/zenodo.13788846</p> <pre><a href="https://doi.org/10.5281/zenodo.13788846">https://doi.org/10.5281/zenodo.13788846</a></pre>
EXTRA DATA 1/1 Microscopy image data of a titration series with DNA surface strands and condensate-forming DNA nanomotifs
<p>Extra data that did not fit the file size limit, for the main repository</p> <p>10.5281/zenodo.13788484</p> <pre><a href="https://doi.org/10.5281/zenodo.13788484" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13788484</a></pre>
EXTRA DATA 2/2 Microscopy image data of a titration series with DNA surface strands and condensate-forming DNA nanomotifs
<p>Extra data that did not fit the file size limit, for the main repository</p> <p>10.5281/zenodo.13788484</p> <pre><a href="https://doi.org/10.5281/zenodo.13788484" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13788484</a></pre>
Microscopy image data of a titration series with DNA surface strands and condensate-forming DNA nanomotifs
<p>The dataset contains microscopy image data of DNA-nanomotifs (color channel 2) that can form liquid-like condensates, especially in association with target DNA strands that can act as condensation surfaces (color channel 1). The data were recorded on a VisiTech iSIM microscopy with a 40X water immersion objective (Apo LWD 40x WI λS DIC N2, NA 1.15). Images were recorded using dual ORCA-Flash4.0 V3 cameras with simultaneous two-channel acquisition to avoid displacement of objects due to consecutive acquisition of color channels. The file names contain the flow channel number, the presence of condensation surfaces (SX4), and the amount of added nanomotif solution in microliters (NX in µl).</p> <p>The image files are in the .nd2 Nikon proprietary format, but can be read with the OME bioformats libraries.</p> <p>Scripts for the analysis of these data are available via a GitHub repository: <a href="https://github.com/lhilbert/SurfacesAndCondensates" target="_blank" rel="noopener">https://github.com/lhilbert/SurfacesAndCondensates</a></p> <p>This repository contains data recorded on 13 March, 2023</p> <p>The file size of data were too large, so additional repositories were opened. The complete date need to be also downloaded from these additional repositories:</p> <p>EXTRA DATA 1/2: 10.5281/zenodo.13788608 </p> <pre><a href="https://doi.org/10.5281/zenodo.13788608" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13788608</a></pre> <p>EXTRA DATA 2/2: 10.5281/zenodo.13788711</p> <pre><a href="https://doi.org/10.5281/zenodo.13788711">https://doi.org/10.5281/zenodo.13788711</a></pre> <p>There is another data set that contains a repeat of the same conditions, recorded on 10 March, 2023</p>
Time series transcriptomes resolve metabolic pathways underlying crocin's anti-cancer activity: Control FASTQ sequencing reads
<p><span>Natural products like saffron show promise in treating hepatocellular carcinoma (HCC), but their mechanisms remain unclear. Here, we used time-series transcriptomics to elucidate crocin's anti-cancer mechanisms in HCC cells. We treated HepG2 cells with 1 and 2 mM crocin for 2, 6, 12, and 24 hours and analyzed transcriptomic profiles at each timepoint. The strongest transcriptional response occurred at 2 hours with 1 mM crocin, with diminishing effects at later timepoints. We observed upregulation of metabolic-, adhesion-, and endocytosis-related genes across all timepoints. Pathway analysis revealed activation of DNA damage checkpoints and senescence while proliferation pathways were suppressed. Notably, 52 genes involved in non-alcoholic fatty liver disease were downregulated at 24 hours (FDR p = 8 × 10⁻⁸), suggesting reversal of carcinogenic pathways. Strikingly, crocin consistently downregulated spliceosomal machinery genes across all timepoints while upregulating senescence and autophagy pathways. This spliceosome targeting represents a clinically relevant mechanism, as aberrant splicing drives oncogenesis in more than 90% of cancers. The transcription factor PAX5 was significantly upregulated while oncogenic ELK1 targets were downregulated. Our findings show that crocin treatment is accompanied by HCC cell senescence induction through coordinated spliceosome disruption and metabolic reprogramming, providing novel therapeutic targets for hepatocellular carcinoma.</span></p> <p><strong><span>Keywords: </span></strong><span>Hepatocellular carcinoma (HCC), crocin, transcriptomics, spliceosome, senescence, natural anti-cancer compounds</span></p>
FIGURE 5. Mitotic metaphase plate with 2n in Notes on Crocus L. Series Flavi Mathew (Iridaceae) and a new species with unique corm structure
FIGURE 5. Mitotic metaphase plate with 2n = 8 chromosomes (a), and idiogram (b) of C. asymmetricus. Scale bar = 10 µm.
FIGURE 1 in Notes on Crocus L. Series Flavi Mathew (Iridaceae) and a new species with unique corm structure
FIGURE 1. Crocus asymmetricus from its locus classicus: habit (A, B); peeled corm (C); corms with tunics (D).
FIGURE 3 in Notes on Crocus L. Series Flavi Mathew (Iridaceae) and a new species with unique corm structure
FIGURE 3. Illustration of Crocus antalyensis: A. Habitus; B. Flower dissection; C. Leaf cross section; D. Corm: D1. General view, D2. Young (above) and mature (below) corm; E. Tunics (E1, E2, E3). Drawing made from specimen ISTF 41144.
FIGURE 2 in Notes on Crocus L. Series Flavi Mathew (Iridaceae) and a new species with unique corm structure
FIGURE 2. Illustration of C. asymmetricus: A. Habitus; B. Flower dissection; C. Leaf cross section; D. Corm: D1. General view, D2. Young (above) and mature corm (below); E. Corm tunics (E1 and E2); F. Basal tunic. Drawing made from specimen ISTF 41370.
FIGURE 4 in Notes on Crocus L. Series Flavi Mathew (Iridaceae) and a new species with unique corm structure
FIGURE 4. Phylogenetic tree obtained by Bayesian inference of the nuclear rDNA ITS. Numbers along branches are posterior probabilities.
Weather- and climate-driven power supply and demand time series for European countries
<p>This repository contains times series of wind power generation, solar power generation, hydropower inflow, heating demand, and cooling demand that use both historical and projected climate variables. Historical climate data are from the ERA5 reanalysis (1940-2023), while projected climate variables are from three climate models of the CMIP5 EURO-CORDEX (2006-2100) and three representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5). The time series are at country level for the EU27 countries, Balkan countries, the United Kingdom, Norway, and Switzerland.</p> <p>Each country has the following files for the historical period, where ** is the country’s ISO Alpha-2 code:</p> <p>1) **__ERA5__wind__capacity_factor_time_series__onshore.nc<br>2) **__ERA5__wind__capacity_factor_time_series__offshore.nc<br>3) **__ERA5__solar__capacity_factor_time_series.nc<br>4) **__ERA5__hydropower__inflow_time_series__conventional_and_pumped_storage.nc<br>5) **__ERA5__hydropower__inflow_time_series__run_of_river.nc<br>6) **__ERA5__heating__demand_time_series__residential_space.nc<br>7) **__ERA5__heating__demand_time_series__services_space.nc<br>8) **__ERA5__cooling__demand_time_series.nc</p> <p>Each country has also the following files for the climate projections, where ** is the country’s ISO Alpha-2 code, #_# is the value of the RCP scenario (e.g., 2_6 is RCP 2.6), ££££__&&&& is the name of the combination of global-regional climate models (e.g., CNRM_CERFACS_CM5__CNRM_ALADIN63):</p> <p>9) **__CORDEX__RCP_#_#__££££__&&&&__wind__capacity_factor_time_series__onshore.nc<br>10) **__CORDEX__RCP_#_#__££££__&&&&__wind__capacity_factor_time_series__offshore.nc<br>11) **__CORDEX__RCP_#_#__££££__&&&&__solar__capacity_factor_time_series.nc<br>12) **__CORDEX__RCP_#_#__££££__&&&&__hydropower__inflow_time_series__conventional_and_pumped_storage.nc<br>13) **__CORDEX__RCP_#_#__££££__&&&&__hydropower__inflow_time_series__run_of_river.nc<br>14) **__CORDEX__RCP_#_#__££££__&&&&__heating__demand_time_series__residential_space.nc<br>15) **__CORDEX__RCP_#_#__££££__&&&&__heating__demand_time_series__services_space.nc<br>16) **__CORDEX__RCP_#_#__££££__&&&&__cooling__demand_time_series.nc</p> <p>The repository also contains the caliration coefficients that resulted from the calibration procedure for the countries in which data were available.</p> <p> </p>
GENERACION DE SERIES DE TIEMPO CLIMATICAS FUTURAS DEL PROYECTO ARES PRD: MODELOS DE SISTEMAS ENERGÉTICOS A MEDIDA PARA LA PLANIFICACIÓN ENERGÉTICA EN BOLIVIA
<p>Estos datos pertenecen al estudio realizado para el Centro Universitario de Investigación en Energías (CUIE) de la Universidad Mayor de San Simón UMSS. Las series (precipitación y temperatura) pertenecen a dos escenarios futuros (CMIP6) el SSP1-2.6 y el SSP5-8.5. El periodo tomado en cuenta en las series es 2021-2050. El método de reducción de escala para producir las series a nivel de cuenca fue el de Perturbación por Cuantiles (precipitación) y Delta Change (Temperatura). Esta metodología fué aplicada a 256 cuencas en Bolivia que fueron determinadas en el WEAP del balance hídrico nacional 2024. Cada uno de los archivos se describen a continuación:</p> <ul> <li>WEAP series: Series de precipitación y temperatura futuras definidas para su uso en las cuencas del WEAP. El archivo consta de 3 escenarios, definidos en el estudio como Escenarios de Impacto a Medida para Bolivia (EIMB): alto impacto, medio impacto y bajo impacto. El archivo también cuenta con gráficos, para cada una de las 256 cuencas, que resumen estos escenarios de manera gráfica. </li> <li>Downscaling: Este archivo contiene las series utilizadas para el desarrollo de los EIMB. Es decir, contiene las "downscaled series" o series a las cuales fué aplicado el proceso de reducción de escala de los 13 modelos globales que comprenden este estudio. Dentro se encuentran cada una de las 256 cuencas con sus 13 series y dos escenarios futuros. En total el archivo contiene 256*13*2 = 6656 series. Asímismo, contiene dos carpetas denominadas "Climate Perturbation Tool ssp126" y "Climate Perturbation Tool ssp585" las cuales contienen el codigo utilizado para el proceso de reducción de escalas. Las carpetas internas tienen el orden necesario para que el codigo funcione. El archivo de texto "catchment_order.txt" posee el código considerado para nombrar las series de las cuencas (ver el primer carácter numérico).</li> <li>GCMs Bolivia: Contiene el ensemble de los 13 modelos globales del clima operados y cortados para su uso en Bolivia según archivos GIS adjuntos. Precipitación y temperatura. Formato .nc</li> <li>GCMs cuenca: Contiene el ensemble de los 13 modelos globales del clima operados y cortados para su uso en Bolivia según archivos GIS adjuntos. Precipitación y temperatura. Formato .txt</li> <li>Comandos CDO: Contiene los comandos utilizados en el programa Climate Data Operators (CDO) para poder utilizar los modelos globales en Bolivia de manera eficiente. Así mismo cuenta con los comandos que fueron utilizados para poder calcular las imágenes espaciales del reporte final presentado para el estudio.</li> <li>coord_cuencas: Coordenadas de las macro-cuencas con las que se realizó el proceso de extracción de series de los modelos globales.</li> </ul> <p>Las series se encuentran bajo una licencia internacional Creative Commons Attribution 4.0 International. La licencia permite la re-distribución y re-uso de los datos publicados siempre y cuando estos sean debidamente citados. Para ello, se debe citar el DOI de este archivo de la siguiente manera:</p> <p><strong>Mendoza Paz S., Villazón Gómez M., Balderrama Subieta S., Quoilin S. y Huallpara A. 2024. Generación de series de tiempo climáticas futuras del proyecto ARES PRD: Modelos de sistemas energéticos a medida para la planificación energética en Bolivia. Universidad Mayor de San Simón, Centro Universitario de Investigación en Energías (CUIE). https://zenodo.org/doi/10.5281/zenodo.13820029</strong></p> <p> </p> <p> </p>
Downscaled climate time series
<p>Downscaled time series developed for the following paper: Fernández, Manquehual-Cheuque & Somos_Valenzuela (2024): Impact of Solar Radiation Management on Andean glacier-wide surface mass balance, npj Climate and Atmospheric Science, doi:<strong> </strong>10.1038/s41612-024-00807-x</p>
Generation-based and consumption-based grid emission intensity time series for German federal states 01/2020 - 07/2024
<p>The dataset contains time series estimating the consumption mix, generation-based and consumption-based grid emission intensity of German federal states for the period January 2020 to end of June 2024 as shown on the <a href="https://co2map.de">CO2Map</a> website. For an explanation of the underlying methodology see the <a href="https://co2map.de/methodology.html">CO2Map Methology</a> section, a detailed scientific article currently is in preparation.</p> <p>All time series have an hourly resolution (time stamps are given in UTC) and contain values for the 13 federal territorial states in Germany, with the city states merged into them:<br><br>BW: Baden-Wuerttemberg<br>BY: Bavaria<br>BB: Brandenburg and Berlin<br>HE: Hesse<br>MV: Mecklenburg-Western Pomerania<br>NI: Lower Saxony and Bremen<br>NW: North Rhine-Westphalia<br>RP: Rhineland-Palatinate<br>SL: Saarland<br>SN: Saxony<br>ST: Saxony-Anhalt<br>SH: Schleswig-Holstein and Hamburg<br>TH: Thuringia</p> <p><strong>"Emission intensity"</strong><br>This folder contains the following files for 2020 to 2023:<br>"region_consumption_based_emission_intensity.csv": Consumption-based emission intensity in kgCO2/kWh<br>"region_generation_based_emission_intensity.csv": Generation-based emission intensity in kgCO2/kWh</p> <p><strong>"Import tables"</strong><br>The folder contains the raw data representing the hourly consumption and storage mix including imports for federal states in MWh. This data allows to calculate generation-based and consumption-based emission intensity time series using arbitrary technology-specific emission factors for the technology types Biomass, Gas, Hard coal, Hydro, Lignite, Nuclear, Other fossil, Other renewable, Solar, Storage, Wind Offshore, Wind Onshore. Each file contains time series for one region as given above and for one year. The columns represent the hourly amount of load/storage/load+storage originating from a certain region and technology type, with the regions including local generation (i.e. the same federal state), imports from other federal states in Germany, and imports from other European countries (the origin of the imports is determined using a flow-tracing algorithm, see the <a href="https://co2map.de/methodology.html">CO2Map Methology</a> section).</p> <p>For each year and each federal state there is one file for load (representing electricity consumption), one file for storage (representing storage charging) and one file for total (sum of load and storage). The file "2023_import_table_load_DE_BW.csv", for instance, contains the mix of hourly load in Baden-Wuerttemberg. The first entry 01.01.2023 00:00 (UTC) then contains the mix for the electricity consumption in the first hour (UTC) of the year 2023 in BW. The sum over all columns for this row yields 4667.80 MWh, which is the approximated total load in this hour in BW (based on a regionalization method). The largest entry in the row is from "DE_NI,HB / Wind Onshore" (614.57 MWh), which is the amount of onshore wind power generated in Lower Saxony and Bremen and consumed in Baden-Wuerttemberg (based on the flow-tracing method). The second largest entry is from "DE_BW / Wind Onshore", which is the amount of onshore wind power generated and consumed locally in Baden-Wuerttemberg. The largest import to BW from other countries in this hour has been 115.18 MWh of nuclear power from Belgium. The file "2023_import_table_load_DE_BW.csv" contains an aggregated version of the same file, with the originating regions corresponding to the same federal state, imports from other federal states in Germany, and imports from other countries (each separated per technology).</p> <p><strong>"Export tables"<br></strong>The folder "Export tables" is similar to the one with "Import tables", but gives data about the location where generation from German regions is used. It contains the raw data representing the hourly generation mix associated with the location where it is used (locally and in other regions). The columns represent the hourly amount of load/storage/load+storage originating from the given region. </p> <p>For each year and each federal state there is one file for load (representing electricity consumption), one file for storage (representing storage charging) and one file for total (sum of load and storage). The file "2023_export_table_total_DE_SH,HH.csv", for instance, contains the mix of hourly generation in Schleswig-Holstein and Hamburg which is associated with electricity consumption and storage charging both locally and abroad. The first entry 01.01.2023 00:00 (UTC) then contains the amount of generation from SH,HH used in each region in the first hour (UTC) of the year 2023. The sum over all columns for this row yields 5730.40 MWh, which is the approximated total generation in this hour in SH,HH (based on a regionalization method). Since SH,HH is a net exporter in this hour, the sum over all columns associated with SH,HH represents the local load+storage charging (1842.40 MWh). The sum over all columns with "Wind Onshore" yields the generation from onshore wind in SH,HH in this hour (4318.05 MWh) (again based on a regionalization method). The different entries for "Wind Onshore" then tell where this onshore wind is used (load and storage charging), with the main contributions locally in SH,HH (1388.33 MWh), and exports to Norway (1582.03 MWh) and UK (682.67 MWh). The destination of these exports is estimated based on the flow tracing method in the same way as the origin of imports are determined.</p> <p>For the data sources and methods used to calculate the time series contained in this dataset see the CO2Map <a href="https://co2map.de/methodology.html">Methodology </a>and <a href="https://co2map.de/about.html">About </a>sections. </p> <p>Please communicate any questions, corrections or comments to mirko.schaefer [at] inatech.uni-freiburg.de</p> <p>All authors acknowledge funding from Elektrizitätswerke Schönau (EWS) through the Sonnencent program, ID 00009280. Tim Fürmann acknowledges funding from DFG (SPP 1984), project ID 450860949. Ramiz Qussous and Robin L. Grether would like to thank the German Federal Government, the German State Governments, and the Joint Science Conference (GWK) for their funding and support as part of the NFDI4Energy consortium, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 501865131.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.