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769 results for “CULTIVATE”
Comparative gene expression profiling of human primary endotheliocytes cultivated on polyurethane-based electrospun 3D matrices and natural decellularized vein
GEO Series GSE134909. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
Gene expression profile of macrophages of monocytic origin and Kupffer cells under conditions of co-cultivation with Ito cells, endotheliocytes of sinusoidal capillaries of the liver and HGF.
GEO Series GSE236447. Mus musculus. 48 samples. Type: Expression profiling by array.
Transcriptomic and proteomic profiling of Rhodospirillum rubrum M68 cultivated in MELiSSA related culture conditions
GEO Series GSE76666. Rhodospirillum rubrum; Rhodospirillum rubrum ATCC 11170. 4 samples. Type: Expression profiling by array.
Expression data from batch cultivations of Aspergillus niger wild type and adrA, facB and creA deletion mutants
GEO Series GSE19952. Aspergillus niger; Aspergillus nidulans; Aspergillus oryzae. 36 samples. Type: Expression profiling by array.
Deep soil desiccation hinders nitrate leaching to groundwater in the global largest apple cultivation area
Open the record for dataset details and reuse information.
Solanum section Lycopersicon (cultivated and wild tomato genotypes) trichome pictures
<p><strong>Stem trichome pictures of various genotypes from the <em>Solanum</em> section Lycopersicon:</strong></p> <pre><code class="language-markdown">| accession | species | accession nr | synonym | origin | |------------|--------------------------------|--------------|---------|-------------| | LA2172 | S. arcanum | TR0009 | - | Peru | | LA1401 | S. cheesmaniae f. minor | EA00652 | - | Ecuador | | LA1840 | S. chmielewskii | - | - | unknown | | LA2695 | S. chmielewskii | EA00759 | - | Peru | | LA0407 | S. habrochaites f. glabratum | EA00558 | - | Ecuador | | LA1777 | S. habrochaites f. hirsutum | EA00703 | - | Peru | | PI134418 | S. habrochaites f. glabratum | TR00015 | LYC38 | unknown | | LYC4 | S. habrochaites f. hirsutum | TR00017 | - | unknown | | LA1718 | S. habrochaites f. glabratum | EA00699 | LYC4934 | Peru | | PI127826 | S. habrochaites f. hirsutum | - | - | Peru | | LA1364 | S. huaylasense | TR00030 | - | Peru | | Moneymaker | S. lycopersicum | - | C32 | Netherlands | | LA4024 | S. lycopersicum | TA209 | - | unknown | | LA2133 | S. neorickii/L. parviflorum | EA00729 | - | Peru | | LA0735 | S. neorickii | TR00025 | LYC140 | unknown | | LA0716 | S. pennellii | EA00585 | - | Peru | | LA1278 | S. peruvianum/pimpinellifolium | TR00005 | - | unknown | | LA1954 | S. peruvianum | EA00713 | - | Peru | | LA1578 | S. pimpinellifolium | EA00674 | - | Peru |</code></pre> <p><strong>Pictures were taken in March 2018 following this two-step protocol:</strong></p> <pre><code class="language-markdown"># Making high-quality pictures of tomato trichomes This protocol is to take high-quality pictures of trichomes and stack them into one unique final picture. The stem from the 4th leaf from the top will be used to make a picture. This protocol is composed of two parts: - Firstly: take a series of pictures (15 to 30 pictures) using a stereomicroscope - Secondly: photo stacking using Photoshop # First part: taking pictures using the Leica MZFLII 1. Turn on the microscope zoom mechanism 2. Turn on the ebq 100 lamp and the Olympus Highlight 2000 lamp 3. Make sure no filter (e.g. dsRed) is applied 4. Open the Nikon NIS-Elements program. 5. Take a piece of stem and place it horizontally (---) under the microscope. . 6. Zoom out completely. 7. Zoom in at 10,000x on the side of the branch ("labeled 10.0 on the microscope). 8. Focus on a few trichomes. Get them as much as possible in focus. This is going to be your first picture (foreground). 9. Zoom in about a few trichome layers away. There should not be too much overlap between the different trichomes (otherwise they get merged together during the stacking phase). This will be your background. 10. Zoom out to go back to the foreground (= your first picture). 11. Depending on the number of trichomes in-between your foreground and background, you need to take a variable number of pictures. - If trichome density is high, then trichomes will overlap during the stacking meaning that you need to take more pictures (~30) and reduce the distance between your foreground and background. - If trichome density is low, then trichomes won't overlap and you need less pictures (~10) # Second part: alignment of pictures using Photoshop See the trichome layer photography protocol (PDF file)made by Maurice Heilijgers (August 2018).</code></pre> <p> </p>
Differential gene expression in CHO cells cultivated in shake flasks and bioreactors
GEO Series GSE104787. Cricetulus griseus. 18 samples. Type: Expression profiling by array.
Enhanced Cell-Cell Contact and Planarian Tissue Extract increase DNA replication and cell viability for neoblast cultivation
GEO Series GSE303204. Schmidtea mediterranea. 89 samples. Type: Expression profiling by high throughput sequencing.
U.S. annual cultivated extent and maps of cropland expansion and cropland abandonment
<p>This repository will eventually contain the data layers of several connected products related to U.S. cropland extent and dynamics, including:</p> <p><br> A set of 30-m resolution maps of <strong>annual cultivated extent </strong>for the conterminous US (CONUS) for the years 1986-2018</p> <p>A 30-m resolution nationwide map of <strong>abandoned croplands </strong>in the U.S., as described by Xie et al. (in review).</p> <p>A 30-m resolution nationwide map of <strong>stable croplands</strong> in the U.S., as described by Uludere-Aragon et al. (in review).</p> <p>A 30-m resolution nationwide map of <strong>cropland expansion</strong> in the U.S. for the period 1986-2018 (in prep). </p> <p> </p> <p> </p> <p>These data were also utilized in related publications, including:</p> <p>Xie and Lark (2021). Mapping annual irrigation from Landsat imagery and environmental variables across the conterminous United States. https://doi.org/10.1016/j.rse.2021.112445</p> <p>Xie et al. (2021). Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017. https://doi.org/10.5194/essd-13-5689-2021</p> <p>O'neil et al. (in prep) Effect of marginal land definitions on biofuel supply chain optimization outcomes.</p>
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)
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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.