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31 results for “Use categories”
Raw and processed GO term data to support running GCEA analyses using ensemble-based nulls, as described in the manuscript, 'Overcoming bias in gene category enrichment analyses of brain-wide transcriptomic data'.
<p>Data to support a toolbox for performing gene category enrichment analyses, including against ensembles of null phenotypes.</p> <p>Descriptions of how these data files can be used for this purpose are in the documentation for the toolbox, at https://github.com/benfulcher/GCEA_FalsePositives</p>
High resolution and high cadence time series of land surface categories, land use land cover, and land use land cover changes
<p>A prototype of monthly, 10 m resolution land surface categories, land use land cover (LULC) cover, and LULC change maps derived from Sentinel-2 data over three areas within Belgium, Portugal, and Sicily for the period 2018-2020. The LULC and LULC change maps were independently validated by IIASA. All products were generated within the framework of the RapidAI4EO project, funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356.</p> <p>The data description can be found below. The validation report of the LULC and LULC change maps can be found in validation_LULC.pdf and validation_change.pdf, respectively, and the validation dataset can be found in Lesiv <em>et al.</em> (2023).</p> <p><strong>Data description</strong></p> <p>Increasing the cadence of the land cover updates from the typical (multi-)annual to monthly cadence poses several challenges. First, several land cover types are difficult to discriminate without any knowledge of temporal dynamics. For instance, croplands are characterized by a dynamic of vegetation growth and a harvest period (i.e. cycles of bare soil, sparsely vegetated and vegetated periods). This contrasts with grasslands that often lack the harvest period resulting in a bare soil cover. Without this temporal information, it is difficult to distinguish a vegetated cropland field from grassland. Second, phenological changes may introduce a large intra-class variability and thus also confusion between classes. For example, the shedding of leaves during autumn or wilting of herbaceous vegetation in dry summer periods introduces spectral variability within land cover classes.</p> <p>To overcome these challenges, we developed a workflow with two main phases. The first phase aims to map land surface categories (LSC) at a monthly resolution. The next phase uses the resulting monthly LSC probability time series to classify land cover.</p> <p><strong><em>Land surface category (LSC)</em></strong></p> <p>These LSC represent basic, observable bio-geophysical properties (categories) of the Earth surface that can be predicted directly from individual monthly composites. LSC classes contain a set of vegetated and non-vegetated surface categories.</p> <p>Discrete LSC classification legend:</p> <table> <tbody> <tr> <td> <p>Map code</p> </td> <td> <p>Land cover class</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>Tree (leaf-on)</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>Shrubland (leaf-on)</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>Woody vegetation (leaf-off)</p> </td> </tr> <tr> <td> <p>15</p> </td> <td> <p>Wilted herbaceous vegetation</p> </td> </tr> <tr> <td> <p>21</p> </td> <td> <p>Bare/sparse vegetation</p> </td> </tr> <tr> <td> <p>22</p> </td> <td> <p>Water</p> </td> </tr> <tr> <td> <p>24</p> </td> <td> <p>Built-up</p> </td> </tr> </tbody> </table> <p>In order to predict the LSC, we trained a CatBoost model (Dorogush et al., 2018) using a DEM, spectral bands and vegetation indices, country, the timing (month) of the spectral data, and the pseudo-probability of a U-Net model trained to segment built-up surfaces as input. Labels were derived by post-processing the land cover labels of the ESA WorldCover product (Zanaga et al., 2021). Please note that the collection of these labels was suboptimal, likely having an impact on the LULC and change maps generated in the prototype.</p> <p><strong><em>Land use land cover</em></strong></p> <p>After predicting LSC over the three AOI’s, we trained a CatBoost model using the LSC probabilities over a window of one year, country, and the timing (month) as independent variable. The use of LSC probabilities over multiple months allows to incorporate information about dynamics, which is necessary to discriminate some classes (e.g. cropland and grassland or cropland and bare). Similar to the LSC labels, the LULC labels were derived from the ESA WorldCover product v100 (year 2020), resulting in a similar legend system.</p> <p>The use of a moving window approach to predict LULC allows to (i) incorporate temporal information that is necessary to discriminate land cover classes and (ii) is expected to lead to more consistent land cover maps. It however has the disadvantage that (i) no land cover predictions are available at the beginning and the end of the time series and (ii) the timing of the predicted land cover change is not always accurate. To resolve these issues, we applied a post-processing step that compares and integrates the LULC predictions and cleaned LSC predictions.</p> <p>Discrete LC classification legend:</p> <table> <tbody> <tr> <td> <p><strong>Map code</strong></p> </td> <td> <p><strong>Land cover class</strong></p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>Tree cover</p> </td> </tr> <tr> <td> <p>20</p> </td> <td> <p>Shrubland</p> </td> </tr> <tr> <td> <p>30</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>40</p> </td> <td> <p>Cropland</p> </td> </tr> <tr> <td> <p>50</p> </td> <td> <p>Built-up</p> </td> </tr> <tr> <td> <p>60</p> </td> <td> <p>Bare/sparse vegetation</p> </td> </tr> <tr> <td> <p>80</p> </td> <td> <p>Permanent water bodies</p> </td> </tr> <tr> <td> <p>90</p> </td> <td> <p>Herbaceous wetland</p> </td> </tr> </tbody> </table> <p><strong><em>Land use land cover change </em></strong></p> <p>Monthly change maps were finally derived from the land cover maps. The pixel values within the change maps represent the percentage of pixels that changed with respect to the previous month over an area of 90x90m. The maps contain values between 0-100, with larger values assigned to larger change patches. A value of 100 indicates that all pixels within an area of 90x90m around the pixel were flagged as change.</p> <p><strong><em>Files</em></strong></p> <p>The zip files contain the following data:</p> <ul> <li>lsc.zip: land surface category maps over the three AOI’s</li> <li>lc.zip: LULC maps over the three AOI’s</li> <li>change.zip: change maps over the three AOI’s</li> </ul> <p>These maps are generated for each month over the period 2018-2020 for each of the tiles (see tiles.gpkg for an overview of all tiles). The files names use the following naming convention: “<em>tile</em>-<em>year</em>-<em>month</em>.tif”.</p> <p><strong><em>References</em></strong></p> <p>Myroslava Lesiv, Halyna Bun, & Martina Duerauer. (2023). Validation data set on land cover changes for RapidAI4EO project [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7825963 </p> <p>Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363.</p> <p><em>Zanaga, D., </em><em>et al.</em><em>., 2021. ESA WorldCover 10 m 2020 v100. </em><a href="https://doi.org/10.5281/zenodo.5571936 "><em>https://doi.org/10.5281/zenodo.5571936 </em></a></p>
S110 | DUTCHUSE |Dutch Prioritized Chemical Use Categories
<p>This is the collection associated with list S110 DUTCHUSE Dutch Prioritized Chemical Use Categories on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>List of 1700+ compounds with monitoring data and their assigned use categories performed within Dutch prioritization exercise by Ad eco advies and Deltares (NORMAN Working Group 1).<br> The final use category was compiled from several lists, including the <a href="https://watson-cost.eu/outputs/databases/">Watson database</a>, <a href="https://www.riwa-rijn.org/en/">RIWA</a>, <a href="https://rvszoeksysteem.rivm.nl/ZZSlijst/TotaleLijst">Dutch Substances of Very High Concern (SVHC)</a>, Kennisimpuls Waterkwalititeit (KIWK), WFD priority and specific pollutants (NL), <a href="https://www.norman-network.com/nds/SLE/">NORMAN lists</a> and some expert judgement.<br> The 1700 compounds were chosen based on availability of Dutch targeted monitoring data (3 different datasets) in 2022. This list is a translation of the file kindly provided by Anja Derksen, AD eco advies<br> </p>
Compilation of Land Use Data in 21 and 37 Category Classifications - Ipswich and Parker River Watersheds - 1971, 1985, 1991, and 1999 - Vector Shapefile.
The MassGIS Land Use datalayer has 37 land use classifications interpreted from 1:25,000 aerial photography. This layer contains data for 21 and 37 category classifications for the years of 1971, 1985, 1991, and 1999. Coverage is complete for all towns that fall partially or completely within the Ipswich River and/or Parker River watersheds. Data compiled for 1971, 1985, 1991, and 1999.
Land Use, Anderson Level I, 7 Categories - Ipswich and Parker River Watersheds - 1971 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for the Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 7 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1971. This datalayer has complete information.
Land Use, Anderson Level I, 7 Categories - Ipswich and Parker River Watersheds - 1985 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for the Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 7 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1985. This datalayer has complete information.
Land Use, Anderson Level I, 7 Categories - Ipswich and Parker River Watersheds - 1991 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for the Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 7 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1991. This datalayer has complete information.
Land Use, Anderson Level I, 7 Categories - Ipswich and Parker River Watersheds - 1999 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for the Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 7 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1999. This datalayer has complete information.
Land Use, 21 Categories - Ipswich and Parker River Watersheds - 1971 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 21 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1971. This datalayer has complete information.
Land Use, 21 Categories - Ipswich and Parker River Watersheds - 1985 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for the Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 21 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1985. This datalayer has complete information.
Land Use, 21 Categories - Ipswich and Parker River Watersheds - 1991 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for the Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 21 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1991. This datalayer has complete information.
Land Use, 21 Categories - Ipswich and Parker River Watersheds - 1999 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 21 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1999. This datalayer has complete information.
Land Use, 37 Categories - Ipswich and Parker River Watersheds - 1985 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for the Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 37 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1985. This datalayer has complete information.
Land Use, 37 Categories - Ipswich and Parker River Watersheds - 1991 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 37 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1991. This datalayer has complete information.
Land Use, 37 Categories - Ipswich and Parker River Watersheds - 1999 - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole, MA. This layer shows the land use, 37 categories, for the towns in the Ipswich River Watershed and the Parker River Watershed for 1999. This datalayer has complete information. Provide land use information for general mapping and display, and landuse change analysis.
Use and sharing of raw data in the Journal Citation Reports' Emergency Medicine Category: Metrics and Journals including supplementary material classification sorted by quartile of the JCR emergency medicine category.
<p>Raw data belonged to the study of use and sharing of raw research data in the Journal Citation Reports' Emergency Medicine Category.</p>
Appendix. The status of global taxonomic checklist preparation for flowering plant families (based on Angiosperm Phylogeny Group II but modified to reflect circumscriptions of existing checklists). If a checklist is complete and available on the Internet then the URL is also given. The species numbers (sp. no.) given are either based on actual working lists (WL) where they exist or are based on Stevens (2006) if no WL is available. Five categories are used to describe the status of a particular working list: 1, checklist complete and accessible via the Internet now; 2, checklist available on Internet by end of 2007 (Asteraceae 2010); 3, checklist complete but not online; 4, some online lists giving partial coverage may be available; 5, no global checklist being compiled so far as known. in Towards Target 1 of the Global Strategy for Plant Conservation: A working list of all known plant species - Progress and prospects
Appendix. The status of global taxonomic checklist preparation for flowering plant families (based on Angiosperm Phylogeny Group II but modified to reflect circumscriptions of existing checklists). If a checklist is complete and available on the Internet then the URL is also given. The species numbers (sp. no.) given are either based on actual working lists (WL) where they exist or are based on Stevens (2006) if no WL is available. Five categories are used to describe the status of a particular working list: 1, checklist complete and accessible via the Internet now; 2, checklist available on Internet by end of 2007 (Asteraceae 2010); 3, checklist complete but not online; 4, some online lists giving partial coverage may be available; 5, no global checklist being compiled so far as known.
Photos of land use categories of Pedro Moncayo county
<p>Photos of land use categories of Pedro Moncayo county</p>
FIG. 5 in Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning.
FIG. 5. Influence of parasite infection on total snail abundance. Number of original and juvenile snails observed in each parasite treatment (absent, present) on experimental day 65. Plotted values are means ± 1 SE. The y-axis is log transformed.
FIG. 2 in Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning.
FIG. 2. Example Northern Leopard Frog metamorphs observed with limb deformities. (Top left) Extra limbs. (Top right) Extra limb and boney triangle. (Bottom left) Boney triangle. (Bottom right) Absent limbs.
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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
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.