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5,155 results for “Data Base”
Hand gesture dataset based on sEMG data captured from the Technaid human-robot interaction system
<p>Two files with a dataset of five different/independent hand gestures are provided. The data were generated in a sEMG system with two bracelets (eight sEMG sensors and six sEMG sensors) worn in the right forearm of a human. The Technaid human-robot interaction system was used to captured the data. The file "datasetForSegmentation.mat" was used to train a classifier whose purpose is the execution of Segmentation process. On the other hand, the file "datasetForRecognition.mat" was used to train a classifier whose purpose is the execution of gesture Recognition process.</p> <p> </p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2018_DiDonFranceesco
<p>The experimental calibration of the system for measuring bed shear stresses under currents was carried out at the Hydraulic Laboratory of the University of Catania.</p> <p>In this experimental campaign the magnet S0805 and S0808 were used. The tests were conducted for several bottom configurations (smooth bottom; thin sand d<sub>50</sub>=0.24 mm; coarse sand d<sub>50</sub>=0.56 mm; and mixed sand 70% thin sand and 30% coarse sand). The goals of such tests were: to study the effects of the type of magnets and to carry out a preliminary analysis the ferrofluid behavior over sandy bottom.</p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2017_Privitera
<p>The experimental calibration of the system for measuring bed shear stresses under currents was carried out at the Hydraulic Laboratory of the University of Catania.</p> <p>In this experimental campaign magnet type S0805 and a number of magnets equal to 2,3 and 4 were used. The tests were conducted both over a fixed bed (Perspex<sup>©</sup>) and in the presence of mobile beds. The goals of such tests were: to study of the velocity profiles for some fixed and mobile bottoms; to study the effects of the number of magnets on the ferrofluid behavior; preliminary analysis of the bed shear stress over sandy bottom.</p>
Arm gesture dataset based on IMU data captured from the Technaid human-robot interaction system
<p>Two files with a dataset of ten different/independent hand gestures are provided (seven static gestures and three dynamic gestures). The data were generated in a IMU system with five sensors worn in the right forearm, right arm, chest, left arm and left forearm of a human. The Technaid human-robot interaction system was used to captured the data. The file "datasetStaticGestures.mat" was used to train and test a classifier whose purpose is the recognition of static gestures. On the other hand, the file "datasetDynamicGestures.mat" was used to train and test a classifier whose purpose is the recognition of dynamic gestures. The latter file contains an extra class (gesture) which represents non-gestures.</p>
Determination of rapeseed areas based on Sentinel-2 data
<p>Determination of rapeseed areas on the basis of Sentinel-2 data. The areas were determined using supervised classifications.</p> <p>Useful presentation:</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/2_Klasyfikacja.pdf</p>
Evaluating data-flow coverage in spectrum-based fault localization
<p>This release contains files with the results of the experiment comparing the use of data- and control-flow spectra for Spectrum-based Fault Localization. It also has instructions to run Jaguar to perform experiments. The subject programs used in the experiment are public available in our GitHub repository.</p>
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models
<p><strong>Baum_et_al_2019_Supplementary_Figures.pdf: </strong>Supplementary Figures S1-S4. Legends are included under each figure.</p> <p><strong>sbm-for-correlation-based-networks-master.zip: </strong>Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.</p>
S39 | KEMIWWSUS | Wastewater Suspect List based on Swedish Product Data
<p>This is the collection associated with list S39 KEMIWWSUS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S39</p> <p>KEMIWWSUS</p> <p><strong>Wastewater Suspect List based on Swedish Product Data</strong></p> <p>Wastewater Suspect List <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/Suspects_WasteWater_Sweden_KEMI20190212.xlsx">Original File with Mapped DTXSIDs</a> (12/02/2019)</p> <p>KEMIWWSUS <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/KEMIWWSUS_InChIKeys_12022019.txt">InChIKeys</a> (12/02/2019)</p> <p>A prioritized list of 1,123 substances relevant for wastewater based on Swedish product registry data, including scores. Provided by Stellan Fischer, KEMI.</p> <p>Update 14/11/2019: added CSV version and resulting updated XLSX.</p> <p> </p>
Respondents' perspectives on the impact of digital data-based health services on disaster risk management in Indonesia.
<p>This data contains respondents' perspectives on the impact of digital data-based health services on disaster risk management. Digital health services are the implementation of digital, information, and communication technologies in the context of health services. Digital health services include: mHealth, Health Information Technology, Wearable Devices, Telehealth and Telemedicine, and Personalized Medicine. </p> <p>Data was collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381) would be advisable.</p>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 1)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 2)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types classification and relative entropy</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Electrochemical and Spectroscopic Data supported by Computational Models for Exploring the Metal- and Ligand-Based Oxidation of Mackinawite Nanoparticles
<p>Supporting information to our study, where under anaerobic conditions, ferrous iron reacts with sulfide producing FeS precipitate, which can then undergo a temperature, redox potential, and pH dependent maturation process resulting in the formation of oxidized mineral phases such as gregite or pyrite. The dataset provide information about the chemical speciation of iron-sulfide by cyclic voltammetry, Raman and X-ray absorption spectroscopic techniques. Nanoparticulate FeS was found to get oxidized to a Fe<sup>3+</sup> containing FeS phase at -0.5 V vs. Ag/AgCl (pH = 7) and in a concomitant oxidation step, polysulfides are proposed to give a material described as Fe<sup>2+</sup><sub>(1−3x)</sub>Fe<sup>3+</sup><sub>(2x)</sub>S<sup>2-</sup><sub>(1-y)</sub>(S<sub>n</sub><sup>2-</sup>)<sub>y</sub>. The thermodynamic differences between ligand- and metal-based oxidation processes from density functional theory can be used to describe one- and two-electron electronic and structural transformations. These findings together point to the existence of a previously unknown, metastable FeS phase located between FeS and greigite (Fe<sup>2+</sup>Fe<sup>3+</sup><sub>2</sub>S<sup>2-</sup><sub>4</sub>) along a metal oxidation path, and Fe<sup>2+</sup>S<sup>2-</sup> and pyrite (Fe<sup>2+</sup>S<sub>2</sub><sup>2-</sup>) along a ligand oxidation path, respectively.</p>
Data from: Three decades of pastoralist settlement dynamics in the Ethiopian Omo Delta based on remote sensing data
<p>Data from the paper:</p> <p><em>Amos, S., Mengistu, S., Kleinschroth, F. (2021): Three decades of pastoralist settlement dynamics in the Ethiopian Omo Delta based on remote sensing.</em></p> <p>Based on Landsat 5, 7, 8, RapidEye Ortho, and Sentinel-2 satellite imagery, we manually mapped the settlements of the Dasanech people in the most populated parts of the Omo River Delta in Ethiopia from 1992 to 2019 using QGIS. We used the data to answer the following questions: (1) How have pastoralist settlements in the delta changed in extent and persistence over the past three decades? And (2) how have the settlements changed structurally during the construction, filling, and operation of Gibe III Dam?</p> <p>We conducted two independent remote sensing analyses. Firstly, we used Landsat data from 1992 to 2019 to track land that is inhabited by pastoralists people within the evergreen part of the Delta. Secondly, the higher spatial resolution of the RapidEye Ortho (5m) and Sentinel-2 (10m) images allowed the detailed identification of settlements as well as infrastructure (tin-roof houses and road) in the Delta during a shorter period from 2009 to 2019. <strong>For more information on the data, please refer to the README.txt or the paper.</strong></p>
Multi-decade land use and land cover samples for Brazil based in a stratified sampling design and visual interpretation of Landsat data (1985 — 2018)
<p>This dataset is composed by 85,152 random points throughout the Brazilian territory selected according to a stratified sampling design, based in 127 regular regions and six slope classes (<a href="https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1-arc?qt-science_center_objects=0#qt-science_center_objects">SRTM</a>). Each sample was visually inspected by three independent interpreters, which associated all the land use and land cover (LULC) changes between 1985 and 2018, on a <strong>yearly basis</strong>, using as reference two <strong>Landsat</strong> images per year, a <strong>MODIS</strong> NDVI time series and high resolution images from <strong>Google Earth</strong>. </p> <p>This process was guided by a <a href="https://www.lapig.iesa.ufg.br/chave/">reference labeling protocol</a> which established the follow LULC classes:</p> <ul> <li><strong>Annual crop:</strong> Areas occupied with short to medium-term crops, usually with a vegetative cycle of less than one year, which after harvest needs to be re-planted. </li> <li><strong>Aquaculture:</strong> Artificial lakes, where aquaculture and/or salt production activities predominate</li> <li><strong>Beach and dune (Other):</strong> Sandy areas, with bright white color, where there is no vegetation predominance of any kind.</li> <li><strong>Forest formation:</strong> Vegetation types with predominance of tree species, with continuous canopy formation</li> <li><strong>Grassland formation:</strong> Grassland formations with predominance of herbaceous stratum</li> <li><strong>Mangrove (Other):</strong> Dense and Evergreen Forest formations, often flooded by tide and associated with the mangrove coastal ecosystem.</li> <li><strong>Mining (Other):</strong> Areas where clear signs of extensive mineral extractions are present, shows clear exposure of the soil by the action of heavy machinery. Only regions surrounding the AhkBrasilien (AHK) and the CPRM digital reference data were considered.</li> <li><strong>Not observed:</strong> Areas blocked by clouds or atmospheric noise, or with absence of ground observation masked out from analysis.</li> <li><strong>Other non-forest natural formations:</strong> Marshes (with fluvio-marine influence).</li> <li><strong>Other non-vegetated area (Other):</strong> Non-permeable surface areas (infrastructure, urban expansion or mining) not mapped into their classes</li> <li><strong>Pasture:</strong> Pasture areas, natural or planted, related with farming activity. In particular in the Pampa and Pantanal biomes part of the area classified as Grassland Formation also includes pasture areas.</li> <li><strong>Perennial crop:</strong> Areas occupied with crops with a long cycle (more than one year), which allow successive harvests without the need for new crop. </li> <li><strong>Rocky outcrop (Other)</strong>: Naturally exposed rocks without soil cover, often with the partial presence of rupicolous vegetation and high slope. </li> <li><strong>Salt flat (Other):</strong> "Apicuns" or Salt flats are formations often without tree vegetation, associated to a higher, hypersaline and less flooded area in the mangrove, generally in the transition between this area and the continent.</li> <li><strong>Savanna formation:</strong> Savanna formations with defined tree and shrub-herbaceous stratum</li> <li><strong>Semi-perennial crop:</strong> Cultivated areas with sugar cane</li> <li><strong>Tree plantation:</strong> Planted tree species for commercial use (e.g. Eucalyptus, Pinus and Araucaria)</li> <li><strong>Urban infrastructure:</strong> Urban areas with predominance of non-vegetated surfaces, including roads, highways and constructions.</li> <li><strong>Water:</strong> Rivers, lakes, dams, reservoir and other water bodies</li> <li><strong>Wetland:</strong> Wetlands with fluvial influence or swampy areas</li> </ul> <p>To enable a proper area estimation and accuracy assessment (<a href="https://www.tandfonline.com/doi/abs/10.1080/01431161.2014.930207">Stehman, 2014</a>) the dataset is provided with the <strong>sampling probability</strong> for each sample (<em>brazil_lulc_samples_1985_2018</em> and <em>brazil_lulc_samples_1985_2018_row_wise</em>) and the <strong>sampling weight</strong> (<em>brazil_lulc_samples_1985_2018_row_wise</em>), which was adjusted to disregard the "<strong>Not observed" </strong>class. The number of votes for the associated LULC class (visual interpretation agreement) and an indication if the sample is between two different LULC<strong> </strong>classes (<strong>border flag</strong>) are also provided.</p> <p>The samples were used to produce several <strong><a href="https://github.com/lapig-ufg/tvi-analysis">area estimation analyses</a></strong>, including land use and land cover dynamics, historical deforestation and agricultural expansion of Brazil. A publication describing in detail the methodology and the analysis is under preparation.</p>
Bibliographic dataset based on Scientometrics, containing provenance information compliant with the OpenCitations Data Model and non disambigued authors
<p>The dataset contains bibliographical information about scholarly works in the journal Scientometrics only if the DOI is known. The data was extracted via Crossref. It is a temporal dataset in which provenance information and change-tracking have been managed by adopting the OpenCitations Data Model. Moreover, the dataset contains information on all the cited academic works. Journals and bibliographic resources always appear unambiguously, without duplicates. On the contrary, the authors have not been disambigued. Finally, heuristics have been applied to recover the DOI of the cited works in case Crossref did not provide such information.</p>
Data base of cycles 1 and 2 of biometric variables of fuzzy model for assessing the development of the radish crop
<p>This data represent the fuzzy model developed of a Rule-Based System (RBS) to evaluation the development of the radish crop in two production cycles, for the irrigation depth at 100% of evapotranspiration. This RBS represents the function <span class="math-tex">\(f:\mathbb{R}\rightarrow\mathbb{R}^{10}\)</span>, where the domain is represented by the Days After Sowing (DAS), and counterdomain is represented by the ten biometric variables, denominated: Number of Leaves (NL), Root Length (RL), Bulb Diameter (BD), Bulb Length (BL), Green Root Weight (GRW), Green Leaf Weight (GLW), Green Bulb Weight (GBW), Dry Root Weight (DRW), Dry Leaf Weight (DLW). </p>
Prioritizing forestation based on biogeochemical and local biogeophysical impacts - data
<p>Output data produced in "Prioritizing forestation based on biogeochemical and local biogeophysical impacts"</p> <p>Contact: michael.gregory.windisch@alumni.ethz.ch; edouard.davin@wyssacademy.org</p> <p>Content: Global output data of BGC, BGP, and combined effect of forestation and forest conservation in NetCDF4 files of 0.083° resolution</p> <p>Naming Key:<br> {effect}_{LUaction}_{season}_TCR_{TCR}_{stat}.nc</p> <p>Naming List:<br> effect_list = ["dC","dT","full"] # BGC only, BGP only, Combined effect<br> LUaction_list = ["defor", "refor"] # Forest conservation action, Forest establishment action<br> season_list = ["Annual", "JJA", "DJF"] # Yearly values, Boreal summer values (June, July, August), Boreal winter values (December, January, February)<br> TCR_list = ["local", "global"] # Local climate response as translator to CO2 equivalent, Global climate response as translator to CO2 equivalent<br> stat_list = ["median", "STD"] # Median values between all input datasets, Standard deviation values between all input datasets </p>
UAV-based data for Lake Mulargia (Sardinia, Italy) (2020/09/23)
<p>This dataset contains MicaSense-derived data of Lake Mulargia (Sardinia, Italy) for the 23 September 2020. The acquisition was done by CGR Spa (Italy). Available products are: True-color image (RGB), at-sensor-radiance (TOA), and Bottom-of-atmosphere reflectance data.</p>
Data set for "Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning"
<p>Data set for: Sippy T, Chaimowitz C, Crochet S, Petersen CCH (2021) Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning. FUNCTION 2: zqab049. https://doi.org/10.1093/function/zqab049</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2021_Sippy_FUNCTION.pdf</strong>" is the Open Access pdf of the online publication in FUNCTION.</p> <p>2. The file named "<strong>Sippy_data_code.zip</strong>" (~5 GB) is a zipped version of a folder ‘<em>Sippy_data_code</em>’, which contains the data analyzed in the study along with the Matlab codes used to generate the published figures. To access the data and the codes, first unzip the file, add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘<em>Sippy_data_code</em>’). You first need to run ‘AnalyzeDataStructure.m’ and afterwards you can run the other codes. Each code computes and plots the results used in the corresponding figure. Figures are saved in the subfolder ‘Figures’.</p> <p>The subfolder ‘<em>Data</em>’ contains the data structure ‘<em>Data.mat</em>’ to be analyzed, as well as a Matlab file called ‘<em>p_value_colormap.mat</em>’ used to plot the p value color bars in some figures.</p> <p>The subfolder ‘<em>Functions</em>’ contains functions called by the main codes.</p> <p>The subfolder ‘<em>Codes</em>’ contains the following codes:</p> <p><em>‘AnalyzeDataStructure.m’: </em>computes the results and saves them as a new data structure called ‘<em>Analyzed_Data</em>’, in the subfolder ‘<em>Results</em>’.</p> <p><em>‘Figure_1.m’: </em>computes and plots the results for the panels D, E and F of Figure 1.</p> <p><em>‘Figure_2.m’: </em>computes and plots the results for the panels D-G and I-K of Figure 2.</p> <p><em>‘Figure_3.m’: </em>computes and plots the results for the panels A-F of Figure 3.</p> <p><em>‘SuppFigure_2.m’: </em>computes and plots the results for the panels B, D and F of Supplementary Figure 2.</p> <p><em>‘SuppFigure_3.m’: </em>computes and plots the results for the panels A-D of Supplementary Figure 3.</p> <p><em>‘SuppFigure_4.m': </em>computes and plots the results for the panels A-C of Supplementary Figure 4.</p> <p> </p> <p>The data structures contain the following fields:</p> <p><em>‘Mouse_Name’</em>: name of the mouse.</p> <p><em>‘Mouse_RecordingDate’</em>: date of recording (YMD).</p> <p><em>‘Mouse_DateOfBirth’</em>: date of birth of the mouse (YMD).</p> <p><em>‘Mouse_Sex’</em>: sex of the mouse (F or M).</p> <p><em>‘Mouse_Genotype’</em>: genotype of the mouse (strain of the two parents): A2A-Cre = Adora2a-Cre mice; D1-Cre = Drd1a-Cre mice; TdTomato = Lox-Stop-Lox-tdTomato mice; D1TdTomato = Drd1a-tdTomato mice; D2GFP = Drd2-GFP mice.</p> <p><em>‘Mouse_Level’</em>: Training level (NAÏVE or EXPERT).</p> <p><em>‘Cell_Counter’</em>: cell recorded in a given mouse.</p> <p><em>‘Cell_Type’</em>: type of the recorded cell (dSPN, iSPN or TAN).</p> <p><em>‘Cell_TargetedBrainArea’</em>: Brain area targeted (DLS).</p> <p><em>‘Cell_Recovered’</em>: Indicate cells that have been labelled and anatomically recovered (TRUE).</p> <p><em>‘Cell_Coordinates’</em>: Cell coordinates (in mm) relative to bregma (Lateral, AP, Ventro-dorsal)</p> <p><em>‘Cell_Fluorescence’</em>: expression of the genetically encoded fluorophore (FALSE or TRUE) and fluorophore (TdTomato or GFP). A neuron recorded in a Drd1a-tdTomato x Drd2-GFP (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence= {TRUE, TdTomato} is considered as a dSPN </em>(cf <em>Cell_Type</em>).</p> <p><em>‘Sweep_Counter’</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-300 s).</p> <p><em>‘Sweep_Type’</em>: experimental condition during that sweep (characterization = electrophysiological identification of the neurons; behavior = behavioral task).</p> <p><em>‘Sweep_MembranePotential’</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>‘Sweep_CurrentInjected’</em>: current injected into the cell (pA).</p> <p><em>‘Sweep_PiezoLick’</em>: voltage signal from the piezo sensor attached to the water spout used to detect licking in behavior sweeps.</p> <p><em>‘Sweep_Trial’</em>: voltage command triggering the onset of each trial (both Catch and Stimulus trials) in behavior sweeps.</p> <p><em>‘Sweep_WhiskerStim’</em>: voltage command triggering the onset of each whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_Valve’</em>: voltage command triggering the opening of the valve delivering the reward in Hit trials.</p> <p><em>‘Sweep_SamplingRate’</em>: sampling rate (sample.s<sup>-1</sup>) of the recorded signals for each sweep.</p> <p><em>‘Sweep_TimeStamp’</em>: time at the beginning of the recorded sweep (H/min/s).</p> <p><em>‘Sweep_Reward’</em>: voltage command indicating reward availability during the response window following whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_APThresh’</em>: Threshold (V) used to detect action potentials (AP) during current injection.</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.