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243 results for “categories”
Emotion Category and Face Perception Task Optimized for Multivariate Pattern Analysis
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GESIS - Leibniz Institute for the Social Sciences data access categories
<p>Replication code for extracting and analysing data access categories from the oai-pmh feed provided by the GESIS - Leibniz Institute for the Social Sciences DBK data catalogue. The code utilises the dc_oai-de feed to extract metadata about objects in the data catalogue, this is then edited to retain and summarise information on the four data access categories used by the archive. The oai-pmh metadata is available from GESIS under a CC0 licence.</p> <p>The .csv files extracted from the oai-pmh feed and edited to correct for missing records is also included for replication.</p>
Four Essential Components for FAIR Data: Capability & Category-Specific Requirements
<p>Adapted from Bailo (2019) and Peng (2023), this diagram illustrates FAIR requirements specific to data, metadata, and infrastructure, aligned with the definitions of individual FAIR principles. It highlights the critical role of enterprise capabilities—including processes, systems, standards, tools, and skills—in supporting FAIR data. These four components are essential for systematically enhancing the overall FAIRness of an organization's scientific data collection</p> <p> </p>
Dissecting the FAIR Guiding Principles - Key Categories, Core Concepts, Focus Elements, and Harmonized Indicators
<p>A comprehensive workbook created to facilitate and document the process of decomposing the FAIR Guiding Principles and mapping them to key categories, requirements, core concepts, focus elements, and harmonized indicators. It also contains a complete list of the indicators.</p>
Triangle of Biomedicine Framework to Analyze the Citations' Impact on Categories Dissemination in the PubMed Database
<p>This is the data and the most relevant script of the paper 'Triangle of Biomedicine Framework to Analyze the Citations’ Impact on Categories Dissemination in the PubMed Database'.</p>
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </p>
Uncovering the Semantics of Wikipedia Categories - Axioms and Assertions
<p>Resulting axioms and assertions from applying the Cat2Ax approach to the DBpedia knowledge graph.<br> The methodology is described in the conference publication "N. Heist, H. Paulheim: Uncovering the Semantics of Wikipedia Categories, International Semantic Web Conference, 2019".</p>
Grammatical category and the neural processing of phrases - EEG data
<p>EEG data presented in the paper</p> <p>Grammar, lexical category and the neural processing of phrases</p> <p>Amelia Burroughs, Nina Kazanina, Conor Houghton</p> <p>abstract:</p> <p><br> The interlocking roles of lexical, syntactic and semantic processing in language comprehension has been the subject of longstanding debate. Recently, the cortical response to a frequency-tagged linguistic stimulus has been shown to track the rate of phrase and sentence, as well as syllable, presentation. This could be interpreted as evidence for the hierarchical processing of speech, or as a response to the repetition of grammatical category. To examine the extent to which hierarchical structure plays a role in language processing we record EEG from human participants as they listen to isochronous streams of monosyllabic words. Comparing responses to sequences in which grammatical category is strictly alternating and chosen such that two-word phrases can be grammatically constructed - cold food loud room - or is absent - rough give ill tell - showed cortical entrainment at the two-word phrase rate was only present in the grammatical condition. Thus, grammatical category repetition alone does not yield entertainment at higher level than a word. On the other hand, cortical entrainment was reduced for the mixed-phrase condition that contained two-word phrases but no grammatical category repetition - that word send less - which is not what would be expected if the measured entrainment reflected purely abstract hierarchical syntactic units. Our results support a model in which word-level grammatical category information is required to build larger units.</p> <p>_______________________________</p> <p>all the corresponding code is at</p> <p>github.com/conorhoughton/NeuralProcessingOfPhrases</p> <p> </p>
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>
Mapping of FoodEx2 Exposure Hierarchy with the food categories of Annex II (part D) of Regulation (EC) No 1333/2008 on food additives
<p>FoodEx2 is a comprehensive food classification and description system aimed at covering the need to describe food in data collections across different food safety domains. All foods, including beverages and food supplements reported in the EFSA Comprehensive Food Consumption Database are coded with the FoodEx2 Exposure Hierarchy. EFSA developed a mapping of all FoodEx2 basic terms reported in the Comprehensive Database with the food categories of Annex II (part D) of Regulation (EC) No 1333/2008 on food additives in order to facilitate the assessment of exposure to food additives. In particular, this mapping has been used in the Food Additives Intake Model 2.0 (FAIM), which allows the estimation of chronic dietary exposure to food additives based on use levels proposed for food categories as presented in Annex II (part D) of Regulation (EC) No 1333/2008 on food additives.</p> <p>Some of the food categories, restrictions and/or exceptions presented in the Regulation could have not been mapped with FoodEx2 basic terms and the original food descriptors and/or FoodEx2 facets might have been used to correctly map all eating events. This information might not be available in this table.</p>
RGB pixels VALUES FOR APPLES/LETTUCE AI OPTICAL RECOGNITION - 5 categories of Freshness
<p>The Datasets include RGB color pallete per pixel values for optical recognition on apples/lettuce and freshness categorized using AI Algorithm . Those Datasets are for AI Training projects . It will be used on the stage of creation, verification or optimization for new optical AI models. The tables can be used direclty on the AI tools, inserted and using the pixels colors number for every category. The freshness categories are 5, from the highest- crop day (5) to the lowest - not for eating (1).</p> <p> </p>
wiki-category-consistency-eval
<p>Experiment results produced in the context of analyzing the consistency between Wikipedia and Wikidata categories using the Wikidata JSON dump of 2022-05-02 and the Wikipedia SQL dumps of 2022-05-01.</p> <p>Detailed information can be found on the <a href="https://github.com/fusion-jena/wiki-category-consistency">Github page</a>.</p>
wiki-category-consistency-dataset
<p>Candidate generation and cleaning results produced in the context of analyzing the consistency between Wikipedia and Wikidata categories using the Wikidata JSON dump of 2022-05-02 and the Wikipedia SQL dumps of 2022-05-01.</p> <p>Detailed information can be found on the <a href="https://github.com/fusion-jena/wiki-category-consistency">Github page</a>.</p>
Crossref metadata of COCI bibliographic resources, as of November 2018 and LCC categories of the ISBN entities in the dataset
<p>The <em>all.zip</em> CSV file (zipped) contains citation counts obtained from the November 2018 dump of COCI (https://doi.org/10.6084/m9.figshare.6741422.v3) and some metadata (title, DOI, number of authors, ISBN, ISBN of the container, type of the bibliographic resource) of the related citing and cited entities obtained by using the Crossref dump downloaded in October 2018 – which is the same dump used to create the COCI data.</p> <p>In addition, it contains all the Library of Congress Classification (LCC) categories associated with each ISBN in the previous dataset (file <em>isbn_cat_lcc.csv</em>), according to the data retrieved using the services at <a href="http://classify.oclc.org/classify2/api_docs/index.html">http://classify.oclc.org/classify2/api_docs/index.html</a>. Two ancillary mapping files have been also added: one (<em>ddc_to_lcc_mapping.csv</em>) for converting a Dewey Decimal Classification (DDC) categories into LCC categories, in the case the service mentioned above returned only DDC categories for some ISBN; the other (<em>lcc_to_wos_mapping.csv</em>) to map each LCC category into the related <a href="https://images.webofknowledge.com/images/help/WOS/hp_research_areas_easca.html">Web of Science research area</a>.</p>
AI-related patents (WIPO, category G06N) and market capitalisation by companies registering at least 2 new ones in 2019, sorted into four global regions (China, USA, EEA, rest of the world)
<p>NOTE: for some reason the pptx and previews keep getting munged on this supposedly permanent arxiv, but the data is still there, unchanged, and you can see how the pptx should look in either the jpg, or the the article.</p> <p>Datasets and presentations concerning the strength of the EU and "the rest of the world" relative to China and the USA, for the purpose of illustrating and counteracting / better informing narratives concerning a "new AI cold war". The materials authored by us may be freely used under the terms of the MIT License, which appears in its entirety in both the dataset and the presentation. The other materials are only curated by us, taken from Twitter as examples of misinformation pertaining to this concern.</p> <p>As of 28 June, this work now also appears in a formal publication: Joanna J. Bryson, Helena Malikova; Is There an AI Cold War?. <em><em>Global Perspectives</em></em> 2021; 2 (1): 24803. doi: <a href="https://doi.org/10.1525/gp.2021.24803">https://doi.org/10.1525/gp.2021.24803</a></p> <p>Authors: The original analysis was conducted primarily by Malikova in collaboration with Bryson. An associated publication is anticipated where Bryson is the lead author.</p> <p>Contributors: independently followed Malikova's procedures to check her work. Inconsistencies were triple checked and resolved.</p>
Category Theory Framework for Variability Models with Non-functional Requirements @ CAiSE 21
<p><strong>Your can watch this video in my Youtube channel:</strong></p> <p><strong><a href="https://youtu.be/rX50Q3fpMZE">https://youtu.be/rX50Q3fpMZE</a></strong></p> <p><strong>This is a Live Conference Presentation, please access and cite the published version of the respective publication:</strong></p> <p><strong><a href="https://doi.org/10.1007/978-3-030-79382-1_24">https://doi.org/10.1007/978-3-030-79382-1_24</a></strong></p> <p>In Software Product Line (SPL) engineering one uses Variability Models (VMs) as input to automated reasoners to generate optimal products according to certain Quality Attributes (QAs). Variability models, however, and more specifically those including numerical features (i.e., NVMs), do not natively support QAs, and consequently, neither do automated reasoners commonly used for variability resolution. However, those satisfiability and optimisation problems have been covered and refined in other relational models such as databases. Category Theory (CT) is an abstract mathematical theory typically used to capture the common aspects of seemingly dissimilar algebraic structures. We propose a unified relational modelling framework subsuming the structured objects of VMs and QAs and their relationships into algebraic categories. This abstraction allows a combination of automated reasoners over different domains to analyse SPLs. The solutions’ optimisation can now be natively performed by a combination of automated theorem proving, hashing, balanced-trees and chasing algorithms. We validate this approach by means of the edge computing SPL tool HADAS.</p>
UTHSC Publication Research Category (ANZSRC 2020) Co-Occurrence
<p>This data visualization is a bibliometric analysis of University of Tennessee Health Science Center publications for the years 2018-2020. It was created for senior University leadership for the purposes of strategic planning and identifying research areas of strength.</p> <p>Bibliographic data was supplied by Dimensions by Digital Science. The chord graph was created in Tableau and demonstrates relationship pairs of Fields of Research (ANZSRC 2020) categories. Each publication record may be associated with 1+ categories. The graph highlights the frequency of category pairings within a single publication record. I.e. publications categorized as "Immunology" are most commonly also categorized with "Medical Microbiology," indicating overlap in this area of research.</p> <p>This graph was created using instructions from Marc Reid's datavis.blog entry "Creating a Chord Diagram with Tableau Prep and Desktop" (<a href="https://datavis.blog/2020/07/02/creating-chord-diagram-in-tableau/">https://datavis.blog/2020/07/02/creating-chord-diagram-in-tableau/</a>).</p> <p>The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</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>
High-wind events on the Southern New England continental shelf (2015-2022), their impact on shelf stratification, and corresponding high-wind event category: Dataset and Code
<p>Dataset of identified high-wind events on the Southern New England continental shelf (2015-2022), their impact on shelf stratification, and corresponding high-wind event category, as well as the associated code to reproduce the figures of accompanying publication. The data have been recorded by the Ocean Observatories Initiative (OOI) Coastal Pioneer New England Shelf Array. </p><p><i>Accompanying publication:</i> Taenzer, L.L., Gawarkiewicz, G., and Plueddemann, A. (2023). Categorization of High-Wind Events and Their Contribution to the Seasonal Breakdown of Stratification on the Southern New England Shelf. Journal of Geophysical Research: Oceans, 128, e2022JC019625. https://doi.org/10.1029/2022JC019625</p><p><i>Contact:</i> Lukas Taenzer (lukas.taenzer@whoi.edu)</p><p><strong>Structure of provided code:</strong></p><ul><li>PART A: Local high-wind ocean impact analysis</li><li>PART B: Analysis of seasonal high-wind impacts on stratification</li><li>PART C: High-wind event categorization and the impact of different categories</li></ul><p>Code has been written in MATLAB R2023a.</p><p><strong>Output:</strong></p><ul><li>Processed data of all locally detected high-wind events incl. scalar forcing and shelf impact estimates as well as their corresponding high-wind event category:<ul><li>'OOIcp_HighWindEvents_ScalarMetrics.nc' (see userflag 'save_peak_ooi')</li><li>See README_HighWindEvents_ScalarMetrics for further details and license.</li></ul></li><li>Figures 2, 3, 4, 5, 6, 7, 8, and 9 of accompanying publication<ul><li>saved as .png file (always)</li><li>saves as .eps file (see userflag 'save_fig_eps')</li></ul></li></ul><p><strong>Input for Analysis:</strong></p><ul><li>Gridded Hydrography and Bulk Air-Sea interactions time series observed by the Ocean Observatories Initiative (OOI) Coastal Pioneer New England Shelf Mooring Array (2015-2022) (Taenzer et al., 2023). The required fields to reproduce the results of the accompanying publication are provided:<ul><li>Input/OOIcp_Met_Combined.nc</li><li>Input/OOIcp_CTD_ISSM_stat.nc</li><li>Input/OOIcp_CTD_PMUI_prof.nc</li></ul></li><li>High-wind event categorization based on their spatio-temporal sea level pressure and temporal surface wind stress signatures around/at the OOI Coastal Pioneer Array location:<ul><li>Input/storm_type_2015-2021_v5.mat</li></ul></li></ul><p><strong>Additional input for reproducing figures:</strong></p><ul><li>Manually determined cyclone tracks for cyclones that occur during the fall destratification seasons 2015-2021:<ul><li>Input/stormtracks_cyclones_20152021_save.mat</li></ul></li><li>ERA5 sea level pressure data (Hersbach et al., 2018) on a 6-hour temporal and a 1°x1° spatial resolution for the time period 2015-01-01 to 2022-06-30 and across the Eastern US, Canada, and the Northwest Atlantic with the OOI Coastal Pioneer Array in the center<ul><li>Input/ERA5_6h_2015-2022_region_1x1.mat</li></ul></li></ul>
Regional greenhouse gas net emission intensities by land cover category in Finland
<p>The methods related to the data published herein are described in detail in the associated publications (Holmberg et al. 2023, Junttila et al. 2023). This file describes the datasets and the data preparation steps. The aim of this data publication is to provide regional assessments of the role of land cover in greenhouse gas emissions in Finland. The results in the publications are reported for the large administrative divisions, the NUTS 3 regions of mainland Finland (Statistics Finland 2023a). While limited by the accuracy of the methods and source data involved, these data can also be used for more local assessments, e.g., at the scale of municipalities. The data represent a temporal snapshot of land cover. Except for the soil maps, rivers and lakes, all land cover data are from the period 2015-2020 and are based on registry data or remote sensing.</p> <p><strong>Data description</strong></p> <p><em>Data format.</em> The data are distributed as GeoTiff raster files, which can be read using most GIS-software.</p> <p><em>Units and definitions</em>. The land cover net emission intensities are shared as raster data with a 250m-by-250m resolution in the ETRS-TM35FIN projected coordinate system. Negative values correspond to sinks (only sinks of C/CO<sub>2</sub> considered). The emission intensities are reported as total emission intensities in carbon dioxide equivalents (gCO<sub>2</sub>-eq m<sup>-2</sup>) based on the 100-year global warming potential as reported in the IPCC 5<sup>th</sup> assessment report (Myhre et al. 2013, p. 73). All cells which do not include emissions from the corresponding land use are classified as <code class="language-sql">NULL</code>s or <em>no data</em>, which should be taken into account if combining raster layers. Where the source data report emission coefficients in the amount of the main element (e.g. C for CO<sub>2</sub> or N for N<sub>2</sub>O) they have been converted to the amounts of the corresponding gas using the standard atomic weights of the relevant atoms (C: 12.011, O: 15.999, N: 14.007, H: 1.008) before conversion to carbon dioxide equivalents. See the related publication for the values of the emission coefficients used and further methodological details (Holmberg et al. 2023).</p> <p><em>Data processing</em>. Data processing for the production of the 250m-by-250m emission intensity raster maps was conducted using GRASS GIS 8.2 (GRASS Development Team, 2022).</p> <p>Land cover emissions derived from vector data (rivers, lakes, agricultural land) were rasterized at a resolution of 1m<sup>2</sup> with the emission intensity as the raster cell value. For rivers, linear features representing rivers having a width of 2 to 5 meters were converted first to areal features by creating a buffer of 1.75 meters to represent an average width of 3.5 meters (see <em>Rivers</em> below). The buffer was created <em>without caps</em> so that the total length of the linear segments was not changed. The buffered river features were merged with the areal features removing the potentially overlapping parts.</p> <p>For all source raster data, the data were available at a 16m-by-16m meter resolution. Emission intensities were aggregated to 250m-by-250m by first summing over the original raster cells intersecting with each aggregate cell while accounting for the proportion of each cell overlapping with the aggregated cell and then multiplying by the area of the original cell. The resulting raster values were divided by the total area of the aggregate cell to acquire average emission intensities. Hence, rasters including a lower proportion of the corresponding land use have lower emission intensities.</p> <p><strong>Thematic layers</strong></p> <p><em>Cropland</em>. CO<sub>2</sub> emissions from cropland were estimated for mineral soils and organic soils separately using emission coefficients from the national greenhouse gas inventory report for 2023. Averaged emission coefficients for the years 2010–2020 for southern and northern Finland were used for mineral soils (Statistics Finland 2023b, Table 3_App_6j). For organic soils separate emission coefficients were used for annual and perennial crops (IPCC 2014, Table 2.1). Cropland and crop data were acquired from the Finnish Food Authority’s Land parcel register for year 2020. Soils were classified into mineral and organic soils by intersecting the field parcels with the soil body layer of the Finnish soil database (Lilja et al. 2006, Lilja et al. 2017).</p> <p>Data files:</p> <ul> <li>Net missions from cropland on mineral soils: <code>cropland_mineral_250m_250m_mean.tif</code></li> <li>Net missions from cropland on organic soils: <code>cropland_organic_250m_250m_mean.tif</code></li> </ul> <p><em>Forests</em>. The net emissions from forests are estimated as the balance of carbon sequestration due to gross primary production of trees and understory vegetation and carbon loss due to harvested biomass, and emission from decomposition of harvest residues, litter, and soil organic matter. Forest productivity is modelled using the process-based forest growth model PREBAS (Minunno et al. 2016, 2019, Junttila et al. 2023, Mäkelä et al. 2023). The initial state for the forest model for the three main forestry species Scots pine, Norway spruce, and Silver birch is derived from the multi-source national forest inventory (<a href="http://kartta.luke.fi/">MS-NFI; version 2015</a>) and harvesting intensities are modelled on the basis of the Finnish national statistics (National Resources Institute Finland 2023). The PREBAS forest net emissions represent annual averages for the period 2017–2025.</p> <p>CO<sub>2</sub> emissions from decomposition on mineral soils are estimated with the soil carbon model YASSO07 (Liski et al. 2005, Tuomi et al. 2009). On drained peatlands, in addition to CO<sub>2</sub> emissions due to peat and litter decomposition, the soil emissions include the CH<sub>4</sub> and N<sub>2</sub>O emissions. The net emissions due to CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O from drained peatland (Ojanen et al. 2010, Ojanen and Minkkinen 2019, Minkkinen et al. 2020, Junttila et al. 2023) are calculated using emission coefficients for nutrient rich sites (herb-rich and blueberry type), and nutrient poor sites (lingonberry, dwarf-shrub, and lichen type).</p> <p>Data files:</p> <ul> <li>Net missions from forest on mineral soils: <code>forest_mineral_250m_250m_mean.tif</code></li> <li>Net missions from forest on organic soils: <code>forest_organic_250m_250m_mean.tif</code></li> </ul> <p><em>Lakes</em>. Emissions of CO<sub>2</sub> and CH<sub>4</sub> were estimated for lakes using size dependent emission coefficients. The lakes were classified into five size classes with emission coefficients for CO<sub>2</sub> evasion (Kortelainen et al. 2006), CH<sub>4</sub> diffusion (Juutinen et al. 2009) and ebullition (Bastviken et al. 2004) as well as the CH<sub>4</sub> emissions due to the macrophytes <em>Phragmites australis</em> and <em>Equisetum fluviatile</em> (Juutinen et al. 2003, Bergström et al. 2007, 2011). The lake date was from the <a href="https://ckan.ymparisto.fi/dataset/ranta10-rantaviiva-1-10-000">Shoreline10</a> data by the Finnish Environment Institute.</p> <p>Data files:</p> <ul> <li>Net emissions from lakes: <code>lakes_250m_250m_mean.tif</code></li> </ul> <p><em>Rivers</em>. CO<sub>2</sub> emissions from rivers were estimated using emission coefficients based on the width of the stream. The width dependent emission coefficients were derived from stream order specific emission coefficients of Swedish rivers (Humborg et al. 2010) by classifying the rivers into width groups and with the emission coefficients chosen based on the stream order specific coefficient of corresponding average width. The river emissions were calculated from the <a href="https://ckan.ymparisto.fi/dataset/ranta10-rantaviiva-1-10-000">Shoreline10</a> data by the Finnish Environment Institute which represents rivers wider than 5 m as areal features, and rivers < 5 m wide as linear features. For rivers < 5m wide, an average width of 3.5 m was assumed.</p> <p>Data files:</p> <ul> <li>Net emssions from rivers: <code>rivers_250m_250m_mean.tif</code></li> </ul> <p><em>Undrained mires</em>. Total net emissions were estimated for undrained mires in Finland using average emission coefficients for CH<sub>4</sub> (Minkkinen and Ojanen 2013), CO<sub>2</sub> (Sallantaus 1994 , Turunen et al. 2002), and N<sub>2</sub>O (Minkkinen et al. 2020). The emission coefficients represent the long term accumulation of carbon as well as the emission of CH<sub>4</sub> and from N<sub>2</sub>O peatland. Peatland sites were extracted from the multi-source national forest inventory (<a href="http://kartta.luke.fi/">MS-NFI; version 2019</a>; see also Mäkisara et al. 2022) and undrained mires were delineated using data provided by the Natural Resources Institute Finland. The undrained mires were classified into four classes using the MS-NFI data: 1) productive forested mires, 2) sedge fens, 3) other open and sparsely treed fens and 4) ombrotrophic bogs, which mainly differ in their emission coefficients for methane (Minkkinen and Ojanen 2013).</p> <p>Data files:</p> <ul> <li>Net missions from undrained mires: <code>undrained_mires_250m_250m_mean.tif</code></li> </ul>
ScienceDex guides
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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
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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.