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50 results for “Regionalisation”
FIGURE 1 in A history of biogeographical regionalisation in Australia
FIGURE 1. Tate's regions superimposed on a 'Rain Map of Australasia' (Tate, 1889, Plate XVIII).
Early anteroposterior regionalisation of human neural crest is shaped by a pro-mesodermal factor
GEO Series GSE184622. Homo sapiens. 3 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Mesenchymal-epithelial crosstalk shapes intestinal regionalisation via Wnt and Shh signalling
GEO Series GSE183671. Mus musculus. 56 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
dsmlib - region4FLEX example (Supplementary Material for the manuscript: Assessment of the regionalised demand response potential in Germany using an open source tool and dataset)
<p>This is the supplementary material for the manuscript:<br> Heitkoetter, Wilko, et al. "Assessment of the regionalised demand response potential in Germany using an open source tool and dataset." <em>Advances in Applied Energy</em> (2020): 100001.<br> Article DOI (open access): <a href="https://doi.org/10.1016/j.adapen.2020.100001">https://doi.org/10.1016/j.adapen.2020.100001</a></p> <p><strong>REPOSITORY CONTENT</strong><br> This repository contains the dsmlib python tool for calculating regionalised load shifting potentials and cost-potential curves.<br> Further, the input data and load shifting potential results of the region4FLEX example are provided (<a href="https://wiki.openmod-initiative.org/wiki/Region4FLEX">region4FLEX model description</a>). In the region4FLEX example dsmlib is applied to the 401 German administrative districts (NUTS-3 regions) considering multiple demand sectors and technologies (see Metadata).</p> <p><strong>quick_start_dsmlib_region4FLEX.zip:</strong><br> This file contains the python dsmlib source code and the resulting average and maximum load shifting potential values per administrative district for all technologies (e.g.: see "/results/2030/extreme_values/av_values_p_max.csv" for average load increase potentials in 2030). Unless otherwise stated the units of the results are in MW for power and MWh for energy. (For more information on the results, please refer to \dsmlib-zenodo\examples\region4FLEX_dsm_potential\README.md --> section Results). The resulting time series and large-scale input data are not contained, to allow for a fast download. </p> <p><strong>full_download_dsmlib_region4FLEX.zip:</strong><br> This file contains the python dsmlib source code and the full set of input and result data.<br> For more information refer to /dsmlib/examples/region4FLEX_dsm_potential/README.md</p> <p><strong>LICENSES</strong><br> The developed source code is licensed under the GPL v3 License. All result data are licensed under the CC-BY 4.0 License.<br> The input data are licensed under different open licenses. For more information refer to the provided README files, LICENSE files and input_data_overview files.</p> <p><strong>METADATA</strong><br> <strong>Demand sectors:</strong> Residential, commercial trade and services, industry, power-to-heat, power-to-gas, e-mobility<br> <strong>Technologies:</strong> Washing, drying, cooling, ventilation, AC, air separation, cement production, pulping, paper production,<br> recycled paper production, process heat, heat pumps, resistive space heating, resistive DHW heating, power-to-heat in district heating,<br> power-to-methane, power-to-hydrogen, e-mobility <br> <strong>Energy sectors:</strong> Electricity (+ interfaces to heat, gas and transport sector)<br> <strong>Geographical scope:</strong> Germany<br> <strong>Geographical resolution:</strong> Administrative districts (NUTS-3)<br> <strong>Temporal scope:</strong> 2018, 2030<br> <strong>Temporal resolution:</strong> 15min</p> <p><strong>NEWS AND CONTACT</strong><br> This dataset will be used as part of the region4FLEX model. If you wish to receive news or have general questions please contact: wheitkoetter(at)gmail.com</p>
Mesenchymal-epithelial crosstalk shapes intestinal regionalisation via Wnt and Shh signalling [RNA-seq]
GEO Series GSE183532. Mus musculus. 54 samples. Type: Expression profiling by high throughput sequencing.
FIGURE 12. Interim Marine and Coastal Regionalisation for Australia version 4 in A history of biogeographical regionalisation in Australia
FIGURE 12. Interim Marine and Coastal Regionalisation for Australia version 4 (IMCRA). [Reproduced with permission Of Australian Government].
FIGURE 3 in Biogeographical regionalisation of Colombia: a revised area taxonomy
FIGURE 3. Biogeographic regionalisation of Colombia with two subregions (A), and six provinces with the corresponding branch lengths in the cladogram (B). IGAC´s (1997) classification of the natural regions of Colombia (C). IGAC´s (1997) map is freely available at http://www2.igac.gov.co/.
Figures 5–8 in Biogeographic regionalisation of the Baja California biogeographic province, Mexico: A review
Figures 5–8. Units within the Baja California province recognised by different authors. (5) Wiggins (1960): Central Desert area (a), Cape area (b); (6) Garcillán and Ezcurra (2003): Central Desert region (a), Vizcaíno Desert region (b), Magdalena region (c), Central Gulf Coast region (d), Cape region (e); (7) Zippin and Vanderwier (1994): Vizcaíno region (a), Central Gulf Coast region (b), Magdalena region (c), Sierra de la Giganta region (d), Cape region (e), Cape Montane region (f); (8) González-Abraham et al. (2010): Central Desert ecoregion 8a), North Pacific Islands ecoregion (b), Vizcaíno Desert ecoregion (c), Central Gulf Coast ecoregion (d), Sierra de la Giganta ecoregion (e), Magdalena Plains ecoregion (f), Tropical Scrubland ecoregion (g), Cape Low Forest ecoregion (h), Sierra de la Laguna Forest ecoregion (i).
FIGURE 5 in A history of biogeographical regionalisation in Australia
FIGURE 5. The Adelaidean and Peronian marine regions of Hedley (1904).
Figure 9 in Biogeographic regionalisation of the Baja California biogeographic province, Mexico: A review
Figure 9. The Baja California province and its eight districts, as herein proposed.
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.