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23 results for “Distribution Grid”
Global taxonomic occurrence grids using GBIF data for species distribution models.
<p>To achieve large geographic coverage, species occurrence databases that are composed of ad hoc species data collections such as that provided by the Global Biodiversity Information Facility (GBIF) are often used. A drawback to using these data is their geographic sampling bias, in which some regions are more intensively sampled than others, while other areas have very little to none reported sampling effort. Uneven sampling effort can mislead conclusions about biodiversity patterns and species distributions (Gotelli & Colwell, 2001; Lobo, 2008).</p> <p>Here we provide taxonomic occurrence grids to help mitigate the effects of sampling bias in species distribution modeling. These grids can be used to exclude areas of (a custom-defined) low sampling effort from the background when sampling for pseudo-absences’ (Phillips et al., 2009; Barbet-Massin et al.,2012). The occurrence grids have a 1 degree spatial resolution using WGS 84 as the geographic coordinate system. Each 1 degree grid cell contains the number of records present in GBIF corresponding to a specific taxonomic group: plants, mammals, reptiles, amphibians, birds and molluscs.</p> <p>To construct the occurrence grids, we used the 1- by 1-degree world latitude and longitude vector grid provided by ESRI (Redlands, California). It has a custom license which permits it reuse as long as ESRI is cited. It was downloaded from : <a href="https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7">https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7</a></p> <p>To map spatial sampling effort, the number of georeferenced occurrences corresponding to each taxonomic group contained by each 1- by 1-degree grid cell were counted. The grids were then converted to GeoTIFFs. The raster values correspond to the number of occurrences reported for the grid cells. For the purposes of the <a href="https://osf.io/7dpgr/">TrIAS project</a>, grid cells with fewer than 5 occurrences were removed. The TrIAS taxonomic occurrence grids are used as inputs to the TrIAS risk modelling and mapping workflow: https://github.com/trias-project/risk-modelling-and-mapping. Full (with all grid cells containing at least one occurrence) taxonomic occurrence grids are also provided.</p> <p>GBIF data for each taxonomic group were downloaded using the following criteria: “Basis of Record”: Observation, Machine Observation, Human Observation, Specimen, Material sample, Literature Occurrence, Unknown evidence., "HasCoordinate is true", "HasGeospatialIssue is false", "TaxonKey is Amphibia", "Year 1975-2005".</p> <p><strong>Raster Attributes</strong></p> <table> <tbody> <tr> <td> <p>Attribute</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p>OID</p> </td> <td> <p>numeric row ID</p> </td> </tr> <tr> <td> <p>Value</p> </td> <td> <p>the number of records contained in the grid cell</p> </td> </tr> <tr> <td> <p>Count</p> </td> <td> <p>the number of times the value appears in the raster</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p>The extent of each taxonomic occurrence grid:</p> <ul> <li> <p>longitude -180.0; latitude -90.0 (southwest corner)</p> </li> <li> <p>longitude 180.0; latitude 90.0 (northeast corner)</p> </li> </ul> <p> </p> <p><strong>Files:</strong></p> <p>TrIAS taxonomic occurrence grids</p> <p>amphib_1deg_min5.tif</p> <p>birds_1deg_min5.tif</p> <p>mammals_1deg_min5.tif</p> <p>molluscs_1deg_min5.tif</p> <p>reptiles_1deg_min5.tif</p> <p> </p> <p>Raw taxonomic occurrence grids</p> <p>amphib_1deg_grid.tif</p> <p>birds_1deg_grid.tif</p> <p>mammals_1deg_grid.tif</p> <p>molluscs_1deg_grid.tif</p> <p>reptiles_1deg_grid.tif</p> <p><br> </p> <p> </p> <p> </p>
Dataset for KIOS CoE Sandboxing use-case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids
<p>These datasets <span> illustrate two primary scenarios (S1-S2) concerning the operation of the sandboxing use case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids. These scenarios examine the functioning of an active distribution grid and microgrid system, along with the effects of certain cyber-attacks in this context. The demonstration of each scenario is detailed in selected time-series plots which were described in detail in Section </span><span>1.3 of the supporting document of SUC5 (</span><span>accompanied by an in-depth analysis of the processes and an impact assessment). A</span><span>ll data captured during the execution of each scenario was collected, including electrical measurements, reference and set-point signals. </span></p> <ul> <li><span><span><strong>SUC5/S1 datasets/<span>MITM with FDI</span> cyber-attack</strong><span><strong> in an active distribution grid (grid-connected)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) of the KIOS CoE Sandboxing environment for cyber-physical analysis of EPES, which examines the operation of an active distribution grid, when the distribution grid is interconnected with the main grid. Specifically, this dataset corresponds to the first scenario (S1) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the active power set-point allocated to BSS inverter controller from the secondary controller. More details about the scenario related to this dataset can be found in Section 1.3 of the supporting document. The dataset includes electrical measurements of the active power generated by the BSS inverter (connected at bus 2), and the active power set-point before and after the attack. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> <li><span><span><span><strong>SUC5/S2 datasets/MITM with FDI cyber-attack in a microgrid (islanding mode)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) which investigates the operation of a microgrid during islanding mode. This dataset corresponds to the second scenario (S2) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the frequency reference signal, exchanged between the higher-level controller (tertiary controller) and the microgrid local controller (secondary V-f controller). More details about the scenario related to this dataset can be found in Section 1.3 of this supporting document. The dataset includes electrical measurements of the microgrid frequency, the reference frequency value generated by the tertiary controller, as well as the attacked frequency reference value. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> </ul>
Dataset to Study Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets
<p>We publish the dataset used to study Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets (as part of chapter 7 of deliverable D3.3 of the OneNet project).</p> <p>The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to two distribution networks: the Matpower systems 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Injections and loads of the nodes are adapted to create an anticipated imbalance in the interconnected system, resolved by flexibility. In addition, the lines’ upper limits are adjusted to create anticipated congestion in the networks. The interconnected system is fully represented in "Network_case_A_B_C.xlsx" (upward balancing need) and "Network_case_D.xlsx" (downward balancing need).<br>Upward and downward flexibility bids are randomly generated and allocated to the nodes. </p> <p>7 bids lists are available in this dataset. </p> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
Distribution grid data generated by ding0
<p>Distribution grid data generated with ding0 in the <a href="https://ego-n.org/" target="_blank" rel="noopener">eGo^n project</a>.<br>Data from pre-release v0.3.0-alpha using branch <em>ding0_run/2023_04_06</em>, head: <a href="https://github.com/openego/ding0/tree/9fe5f1c3785ccb2f5afa56fe25e4f2290df76e5c" target="_blank" rel="noopener">9fe5f1c3785ccb2f5afa56fe25e4f2290df76e5c</a>.</p> <p>Input data from eGon-data, branch <a href="https://github.com/openego/eGon-data/tree/continuous-integration/run-everything-2022-11-10" target="_blank" rel="noopener">run-everything-2022-11-10</a>.</p> <p>See <code>README.md</code> for details.</p> <p> </p> <p> </p>
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).
Linked collectors and determiners for: Florabank2: a grid-based database on distribution of bryophytes in the northern part of Belgium (Flanders and the Brussels Capital region).
Natural history specimen data linked to collectors and determiners held within, "Florabank2: a grid-based database on distribution of bryophytes in the northern part of Belgium (Flanders and the Brussels Capital region)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1e9b6eff-af44-4e48-90f0-35ca8d2cdb7b">https://bionomia.net/dataset/1e9b6eff-af44-4e48-90f0-35ca8d2cdb7b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1e9b6eff-af44-4e48-90f0-35ca8d2cdb7b">https://gbif.org/dataset/1e9b6eff-af44-4e48-90f0-35ca8d2cdb7b</a>. Formatted as a Frictionless Data package.
Fig. 2. Forty-one grid cells randomly set within a 10 in Factors limiting the current distribution of the introduced Java sparrow (Lonchura oryzivora) in Bangkok, Thailand
Fig. 2. Forty-one grid cells randomly set within a 10-km radius around Don Muang airport, Bangkok, Thailand, used for surveying Java sparrows. Don Muang was the site where Java sparrows were first recorded in Thailand in 1924. The study area includes three provinces; Bangkok, Pathum Thani, and Nonthaburi. The primary land use was urban, while paddyfields were mostly located on the eastern side of the study area. Different symbols (circles, squares and triangles) represent the number of occasions (out of 5 possible visits to a location) on which the sparrow was detected. Size of symbols of Java sparrow detection points was related to the number of birds detected; smallest size indicated only 1 bird was detected, medium size indicated 2–10 birds were detected, and largest size indicated a group of more than 40 birds was detected.
Transformer Inrush Measurements in a Distribution Grid Laboratory
<p>This data set contains measurement data of distribution transformer inrush transients obtained in the Distribution Grid Laboratory at the IAEW at RWTH Aachen University. Within the framework of the investigations, a medium voltage feeder with one or multiple medium/low voltage transformers is energised. The setup is either supplied by the public medium voltage grid or by a grid-forming converter. In the latter case, grid voltage distortions can be observed during the inrush transient due to output current limitation of the converter.</p> <p>The inrush measurements were carried out within the framework of the project DiSCo (Distribution System Inrush From Grid-Forming Converters). The authors gratefully acknowledge funding by E.ON SE as well as the technical support in development, execution and analysis of the experimental investigations by Westnetz GmbH. In addition, we would like to thank the colleagues of the Institute for Power Generation and Storage Systems (PGS) for their support and the informative discussions.</p>
Technical Solutions for Increasing DER and EV Hosting Capacity in Distribution Grids in the Czech Republic in Terms of European Project Interflex
<p>dataset for scientific paper</p>
European Archetype Distribution Grid Models
<p>This database contains publicly available representative European archetype distribution grid models, which where identified through an extensive literature review. All included grid models were converted into a standardised data format. The collection of identified grid models from the literature was also expanded through the creation of new grid models for Finland and the Netherlands. </p> <p>Currently, the databse covers grid models for the following regions: </p> <ul> <li>Germany <ul> <li>Kerber Networks (Kerber, 2011)</li> <li>Synthetic Voltage Control LV Networks (Lindner et al, 2016)</li> <li>SimBench (Meinecke et al, 2020)</li> </ul> </li> </ul> <ul> <li>Spain <ul> <li>Spanish MV Grid Model (Buigues Beraza, 2011)</li> </ul> </li> </ul> <ul> <li>UK <ul> <li>Low Voltage Network Models (Espinosa, 2015a/b)</li> <li>UKGDS (SEDG, no date)</li> </ul> </li> </ul> <ul> <li>Finland <ul> <li>Finland LV Grids Model (Newly generated)</li> </ul> </li> </ul> <ul> <li>Netherland<br> <ul> <li>Dutch MV grids (Newly generated)</li> </ul> </li> </ul> <ul> <li>Europe (general European grid models) <ul> <li>European representative Electricity Distribution Grids (Mateo et al, 2018)</li> <li>Non-Synthetic Test Systems of European Distribution Networks (Taye et al, 2024)</li> </ul> </li> </ul> <p>The database is part of the deliverable D8.2 of the DriVe2X project. The accompanying report for D8.2 provides a detailed description of the database structure and the created data format. Additionally, it introduces all grid models included as of the publication date. Some grid models from the literature required minor adjuistments to be integrated into the database. Detailed information about these adjustment is also provided in the D8.2 report. The report is currently being prepared and will be made available from the project download section as soon as it is published.</p> <p><strong>Please note: </strong><br>Despite great care, there is no claim to completeness and correctness of the grid models contained in this database. <br><br></p> <p><strong>References</strong></p> <p>DriVe2X website: <a href="https://drive2x.eu/" target="_blank" rel="noopener">Link</a><br>Cordis website: <a href="https://cordis.europa.eu/project/id/101056934" target="_blank" rel="noopener">Link</a><br>ie³ institute: <a href="https://ie3.etit.tu-dortmund.de/">Link</a></p> <p>------</p> <p>Kerber, G. (2011). <em>Aufnahmefähigkeit von Niederspannungsverteilnetzen für die Einspeisung aus Photovoltaikkleinanlagen</em> (Doctoral dissertation, Technische Universität München).</p> <p>Lindner, M., Aigner, C., Witzmann, R., Wirtz, F., Berber, I., Gödde, M., & Frings, R. (2016). ‚Aktuelle Musternetze zur Untersuchung von Spannungsproblemen in der Niederspannung‘, <em>14. Symposium Energieinnovation</em>.</p> <p>Meinecke, S., Drauz, S., Bornhorst, N., Spalthoff, C., Lauven, L.P., Cronbach, D., Menke, J.H., Kneiske, T., Braun, M., Klettke, A., Sarajlić, D., Sprey, J., Kittl, C., van Leeuwen, T., Rehtanz, C., Moser, A. (2020). <em>Simbench-dokumentation, dokumentationsversion de-1.0. 1, elektrische benchmarknetzmodelle.</em></p> <p>Buigues Beraza, G. (2011). <em>Metodología para la detección y localización de faltas en redes de distribución con puesta a tierra activa</em> (Doctoral dissertation, Universidad del País Vasco-Euskal Herriko Unibertsitatea).</p> <p>Espinosa, A. N. (2015). <em>Dissemination Document “Low Voltage Networks Models and Low Carbon Technology Profiles”</em>.</p> <p>Espinosa, A. N. (2015). <em>Low carbon technologies in low voltage distribution networks: probabilistic assessment of impacts and solutions</em>.</p> <p>Centre for Sustainable Electricity and Distributed Generation (SEDG) (no date) UKGDS [Online]. Available at: https://github.com/sedg/ukgds (Accessed: 08 March 2024)</p> <p>Mateo, C., Prettico, G., Gómez, T., Cossent, R., Gangale, F., Frías, P., & Fulli, G. (2018) 'European representative electricity distribution networks', <em>International Journal of Electrical Power & Energy Systems</em>, 99, 273-280.</p> <p>Taye, T., Mohamed, B., Suarez-Ramon, L., & Arboleyá, P. (2024) 'A set of Non-Synthetic test systems of European LV Rural, LV urban and hybrid MV/LV industrial distribution networks', <em>International Journal of Electrical Power & Energy Systems</em>, 158, 109941.</p> <p> </p>
Dataset for TECHNICAL SOLUTIONS FOR INCREASING DER HOSTING CAPACITY IN DISTRIBUTION GRIDS IN THE CZECH REPUBLIC IN TERMS OF EUROPEAN PROJECT INTERFLEX
<p>Data set for TECHNICAL SOLUTIONS FOR INCREASING DER HOSTING CAPACITY IN DISTRIBUTION GRIDS IN THE CZECH REPUBLIC IN TERMS OF EUROPEAN PROJECT INTERFLEX</p>
Dataset for Technical Solutions for Increasing PV Penetration in Distribution Grids in the Czech Republic in Terms of Interflex project
<p>Dataset for Technical Solutions for Increasing PV Penetration in Distribution Grids in the Czech Republic in Terms of Interflex project</p>
FIGURE. Maps with overlapping quadrants showing the distribution of Drosera in Brazil. a, number of species per grid of 1 degree. b, number of species per grid of 0.1 degrees. in A synopsis of the genus Drosera (Droseraceae) in Brazil
FIGURE. Maps with overlapping quadrants showing the distribution of Drosera in Brazil. a, number of species per grid of 1 degree. b, number of species per grid of 0.1 degrees.
Online companion -Technical Impacts of the Deployment of Renewable Energy Community on Electricity Distribution Grids
<p>Online companion for the article "Technical Impacts of the Deployment of Renewable Energy Community on Electricity Distribution Grids".</p>
Ithomiini grid-cell records for distribution modeling
<p> </p> <p>This repository contains georeferenced <strong>occurrence data</strong> for ithomiine butterflies (tribe Ithomiini) needed to reproduce the analyses presented in the research paper <strong>"Doré et al., 2021 - Anthropogenic pressures coincide with Neotropical biodiversity hotspots in a flagship butterfly group"</strong>: <a href="https://doi.org/10.1111/ddi.13455">https://doi.org/10.1111/ddi.13455</a>.</p> <p> </p> <p><strong>Distribution maps</strong> generated from distribution models runned on these occurrences data are available in an other associated archive: <a href="http://doi.org/10.5281/zenodo.4673446">https://doi.org/10.5281/zenodo.4673446</a>.</p> <p><strong>Mimicry classification</strong> used for theses analyses is available in this associated archive: <a href="https://doi.org/10.5281/zenodo.5497876">https://doi.org/10.5281/zenodo.5497876</a>.</p> <p><strong>Scripts</strong> to reproduce these maps are available on Github at <a href="http://github.com/MaelDore/ithomiini_diversity">https://github.com/MaelDore/ithomiini_diversity</a>.</p> <p> </p> <p>This repository contains an Excel file with the occurrence records and associated metadata in separated tabs. </p> <p> </p>
Power Quality State Estimation for Distribution Grids based on Physics-Aware Neural Networks - Harmonic State Estimation
<p>Data set for the paper "Power Quality State Estimation for Distribution Grids based on Physics-Aware Neural Networks - Harmonic State Estimation"</p> <p>This upload contains</p> <ul> <li>Training set</li> <li>Validation set</li> <li>Test set</li> <li>Admittance matrices per frequency</li> </ul> <p>used for the paper as pickle files and weights of trained models as zip files.</p> <p>Weights represent the model with the best validation loss recorded within the first 3000 Epochs of training.</p> <p>Code for reading in the data sets, preprocessing and state estimation is available in the linked repository.</p> <p>To replicate the results of the paper follow these steps:</p> <ol> <li>clone the linked repository</li> <li>save the provided pickle files in the data folder of the linked repository</li> <li>optional: unzip weights and save them in the data folder, otherwise train a model yourself instead</li> </ol> <p><strong>Version 1.1:</strong></p> <p>Added data and model weights for the IEEE33 grid to improve comparability.</p> <p>For the IEEE33 grid, all data (train, test, validation) is saved in one pickle file; see the release tag 1.1.0 in the accompanying GitHub repository for details on the data format. Moreover, the training set size of the new grid was increased from 35040 to 131400 samples to incorporate simulation results that capture a broader range of system states. </p> <p>The code was slightly updated to account for inclusion of the IEEE33 grid. Therefore, model weights and input data are now expected in either `cigrelv` or `ieee33` subfolder.</p> <p>Added Transformer and CNN model weights for IEEE33 and CNN weights for the CIGRE grid. The Transformer model is trained with a smaller batch size since the model did not fit into GPU memory using the same batch size as in other models. This change results in more gradient updates and significantly longer training times, thus the amount of epochs was reduced to achieve a fairer comparison (batch sized reduced from 16384 to 1024, epochs reduced from 3000 to 375, total amount of gradient updates increased from 27000<em> </em>to 48375). The training of the PANN model over 3000 Epochs is significantly faster than that of the Transformer model trained over 375 epochs (approximately 2.5 hours vs 12.5 hours).</p>
Linked collectors and determiners for: Florabank1 - A grid-based database on vascular plant distribution in the northern part of Belgium (Flanders and the Brussels Capital region).
Natural history specimen data linked to collectors and determiners held within, "Florabank1 - A grid-based database on vascular plant distribution in the northern part of Belgium (Flanders and the Brussels Capital region)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/271c444f-f8d8-4986-b748-e7367755c0c1">https://bionomia.net/dataset/271c444f-f8d8-4986-b748-e7367755c0c1</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/271c444f-f8d8-4986-b748-e7367755c0c1">https://gbif.org/dataset/271c444f-f8d8-4986-b748-e7367755c0c1</a>. Formatted as a Frictionless Data package.
Figure 3 from: Brosens D, Van Landuyt W, Vanhecke L (2012) Florabank1: a grid-based database on vascular plant distribution in the northern part of Belgium (Flanders and the Brussels Capital region). PhytoKeys 12: 59-67. https://doi.org/10.3897/phytokeys.12.2849
Figure 3 - Number of prospected 1 km? grids in each grid of 4?4 km for the period 1972-2004. A 1 km? grid cell is considered as prospected if at least 90 species have been recorded.
Figure 2 from: Brosens D, Van Landuyt W, Vanhecke L (2012) Florabank1: a grid-based database on vascular plant distribution in the northern part of Belgium (Flanders and the Brussels Capital region). PhytoKeys 12: 59-67. https://doi.org/10.3897/phytokeys.12.2849
Figure 2 - Number of prospected 1 km? grids in each grid of 4?4 km for the period 1939–1971. A 1 km? grid cell is considered as prospected if at least 90 species have been recorded.
Global Gridded Soil Phosphorus Distribution Maps at 0.5-degree Resolution
This data set provides estimates of different forms of naturally occurring soil phosphorus (P) including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P on a global scale at 0.5-degree resolution. The data were assembled from chronosequence information and global spatial databases to develop a map of total soil P and the distribution among mineral bound, labile, organic, occluded, and secondary P forms in soils. Uncertainty was calculated for the different forms. The data set has no explicit temporal component -- data were nominally for the pre-industrial period ca. 1850.The estimated global spatial variation and distribution of different soil P forms presented in this study will be useful for global biogeochemistry models that include P as a limiting element in biological production by providing initial estimates of the available soil P for plant uptake and microbial utilization (Yang et al., 2013).There is one netCDF data file (.nc) with this data set.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.