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28,952 results for “Distributed”
Spatial dataset for ecological niche and spatial distribution modeling of Herichthys bartoni (Cichliformes: Cichlidae) in the Media Luna spring, Mexico
<p>Dataset for the endangered endemic cichlid <em>Herichthys bartoni</em> in the Media Luna spring, Mexico. This data includes occurrences records by species life stage (adult, juvenile and fry), in three field sessions corresponding to the summer period, in the years 1999, 2009 and 2019.</p> <p>For more information about the codes where the previous datasets could be used, visit the following repository with URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Likewise, the UC and WDp variables used to run the ecological niche and spatial distribution model, by summer period, can be found in the following repository wirh URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p>
Nairobi_Street_Trees_Distribution_Diversity
<p>Input data and code to accompany the paper:</p> <p>Alice Gerow, Vivian Kathambi, Dexter Locke, Mark Ashton, Craig Brodersen. Street tree communities reflect socioeconomic inequalities and legacy effects of colonial planning in Nairobi, Kenya. Urban Forestry & Urban Greening. <a href="https://doi.org/10.1016/j.ufug.2024.128530">https://doi.org/10.1016/j.ufug.2024.128530</a></p> <p>The input data consists in street tree observations collected during a field survey conducted between June and August 2023 in Nairobi, Kenya. The code includes descriptive tables and plots, statistical tests, and alpha and beta diversity metrics and visualizations used to compare ecological communities across social groups.</p>
Data from: Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and extent
<p>These datasets support the scientific article "Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and area extent" published in 2025 (Journal of Sea Research, 205, 102580; <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.seares.2025.102580" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.seares.2025.102580</a>). It contains detailed data on the distribution and area extent of intertidal and subtidal seagrass meadows in the four main wetlands of the Algarve region (Southern Portugal): Ria de Alvor, Arade Estuary, Ria Formosa, Guadiana Estuary.</p> <p>The data is composed by 5 datasets with the following variables:</p> <p><strong>1) data_field_points.csv</strong></p> <p>Contains data points based on field surveys.</p> <ul> <li>dataset_id [character] - Unique identifier for the data set.</li> <li>data_id [character] - Unique identifier for the data point.</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>quadrat_id [character] - Name of the quadrat as recorded in the field.</li> <li>photo_id [character] - Name of the pictured associated to the observation.</li> <li>sampling_id - Name of the observation as recorded in the field.</li> <li>date [date] - Date of observation (YYYY-MM-DD).</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>habitat_class [factor] - Type of habitat: unvegetated, seagrass intertidal, seagrass subtidal, seagrass unknown, salt marsh low, caulerpa.</li> <li>species [factor] - Vegetation species: No vegetation, <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified species, <em>Caulerpa prolifera</em>, <em>Sporobolus maritimus</em>.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> <li>method [factor] - Method used for the observation: boat and camera, boat and snorkelling, kayak and camera, on foot.</li> <li>survey_area [character] - Number of the survey area.</li> <li>site [character] - Name of the site.</li> <li>observers [character] - Name of the researcher(s) who collected the data.</li> </ul> <p> </p> <p><strong>2) data_compilation_records.csv</strong></p> <p>Contains information on the records (i.e. sources) screened during the systematic review for the compilation od seagrass occurrence data.</p> <ul> <li>record_id [character] - unique id for the compiled records.</li> <li>short_citation [character] - short citation of the record, with author and publication year.</li> <li>included [boolean] - whereas the record was used to extract data or informacion.</li> <li>record_type [factor] - type of record: journal article, book or book chapter, PhD or MSc thesis, report, others.</li> <li>publication_year [integer] - year of the publication of the record, YYYY.</li> <li>title_record [character] - title of the record.</li> <li>link [character] - link to access the record, if available (DOI, handle, others URLs).</li> <li>full_citation [character] - full citation of the record, with authors, publication year, title, etc.</li> </ul> <p> </p> <p><strong>3) data_compilation_points_raw.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the original raw file with all the compiled points.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_clean.csv).</li> <li>included [boolean] - whether the data point was kept in the clean dataset or not: 1, the point has been validated and it is included in the final dataset; 0, the point is excluded due to unprecise location (on land, open ocean, etc.).</li> <li>reason_exclusion [character] - Reason to exclude the data point from the clean dataset.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> <li>duplicated [boolean] - whether the data point was flagged as duplicated or not.</li> </ul> <p> </p> <p><strong>4) data_compilation_points_clean.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the clean file after elimitating duplicates and points with unprobable or unprecise location.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_raw.csv).</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> </ul> <p> </p> <p><strong>5) data_compilation_extent.csv</strong></p> <p>Contains area extent data of seagrass meadows based on the systematic review.</p> <ul> <li>data_id [character] - Unique identifier for the data.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted.</li> <li>short_citation [character] - Short reference (author(s) and year).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>value [boolean] - whether the data extracted from the record is a extent value (i.e., a value of area covered by seagrasses).</li> <li>polygon [boolean] - whether the data extracted from the record is a polygon.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>area_source [numeric] - The area extent given in the record.</li> <li>area_source_cover [factor] - The cover of the wetland for the compiled extent from the record means: total, partial or unknown.</li> <li>area_gis [numeric] - The area extent obtained using GIS.</li> <li>area_gis_cover [factor] - The cover of the wetland for the obtained extent from GIS means: total, partial or unknown.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> </ul>
Data from: Habitat suitability models reveal extensive distribution of deep warm water coral frameworks in the Red Sea
<p>Deep-sea coral frameworks are understudied in the Red Sea, where conditions in the deep are conspicuously warm and saline compared to other basins. Habitat suitability models can be used to predict the distribution pattern of species or assemblages where direct observation is difficult. Here we show how coral frameworks, built by species within the families Caryophylliidae and Dendrophylliidae, are distributed between water depths of 150 m and 700 m in the northern Red Sea and Gulf of Aqaba. To extrapolate the known (ground-truthed) positions of these deep frameworks, we use environmental and geomorphometric variables to inform well-performing maximum entropy models. Over 250 km2 of seafloor in our study area are identified as suitable for such frameworks, equivalent to at least 35% of the area of photic-zone coral reefs in the same region. We hence contend that deep-water coral frameworks are an important and underappreciated repository of Red Sea biodiversity.</p>
Particle size and velocity distributions from a Thies Clima 3D Stereo disdrometer installed at the Casale Calore site in L'Aquila (Italy), monthly netCDF archive
<p>Disdrometric data from a Thies Clima 3D Stereo disdrometer, with 22 size classes and 20 velocity classes, located at the instrumented site of Casale Calore in L'Aquila (Italy, 42.3831 N, 13.3148 E, 683 m a.s.l.), managed by the University of L'Aquila and the Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS). </p> <p>Mid values and widths of the classes and instrument ancillary data are provided. One-minute spectra are aggregated every 5 minutes and saved in monthly netCDF files.</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00">https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00</a></p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure:</p> <h2><strong>File "LAQ_3DS_202301_5min.nc"</strong></h2> <pre><strong> dimensions</strong>: <em>diameter </em>= 22; <em>velocity </em>= 20; <em>n_image </em>= 20; <em>y_image </em>= 12; <em>x_image </em>= 12; <em>time </em>= UNLIMITED; // (8741 currently) <strong>variables</strong>: long <em>time_UTC</em>(time=8741); :description = "Measurement time. Timestamp indicates the end of the observation interval, e.g. 01-Mar-2020 00:05:00 represents the particle counts registered between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>diameters</em>(diameter=22); :description = "Mid values of the size classes"; :units = "mm"; float <em>velocities</em>(velocity=20); :description = "Mid values of the velocity classes"; :units = "m s^-1"; float <em>diameters_width</em>(diameter=22); :description = "Width of the size classes"; :units = "mm"; float <em>velocities_width</em>(velocity=20); :description = "Width of the velocity classes"; :units = "m s^-1"; int <em>spectrum</em>(diameter=22, velocity=20, time=8741); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over 5 minutes."; :units = "counts"; :_ChunkSizes = 22U, 20U, 1U; // uint float <em>PSD</em>(diameter=22, time=8741); :description = "Particle size distribution, 5 minutes interval, normalized by the observed volume."; :units = "m^-3 mm^-1"; :_ChunkSizes = 22U, 1U; // uint double <em>monthlySpectrum</em>(diameter=22, velocity=20); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over the entire month."; :units = "counts"; double <em>monthlyPSD</em>(diameter=22); :description = "Particle size distribution for the whole month, normalized by the observed volume."; :units = "m^-3 mm^-1"; int <em>images</em>(x_image=12, y_image=12, n_image=20, time=8741); :description = "Images of samples of the detected precipitating particles. Images are 48x12 pixel maximum, for a max of 4 stacked 12x12 images. Most of the time less than 4 images are provided."; :units = "0-255 pixel values"; :_ChunkSizes = 12U, 12U, 20U, 1U; // uint int <em>image_count</em>(time=8741); :description = "How many images are registred by the instrument in the minute."; :units = "0-4 count"; :_ChunkSizes = 1024U; // uint int <em>precip_type</em>(n_image=20, time=8741); :description = "Precipitation type as classified by the instument based on shape, size, velocity and presence of water, according to the following table with 11 entries (0-10): 0-reserved value, 1-false positive, 2-rain or graupel, 3-drizzle, 4-drizzle with rain, 5-rain, 6-rain with snow, 7-snow, 8-ice prisms, 9-graupel, 10-hail."; :units = "0-10 code"; :_ChunkSizes = 20U, 1U; // uint int <em>particle_diam</em>(n_image=20, time=8741); :description = "Main diameter of the particles shown in the images."; :units = "mm"; :_ChunkSizes = 20U, 1U; // uint //<strong> global attributes</strong>: :<em>title </em>= "Thies Clima 3D Stereo disdrometer data, aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "Particle counts diveded in 22 size classes and 20 velocity classes. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Jan 2023"; :<em>institution </em>= "CNR-ISAC, Rome (IT)"; :<em>contact_person </em>= "Luca Baldini, CNR-ISAC, Rome, l.baldini@isac.cnr.it"; :<em>source </em>= "TC 3DS disdrometer at MZS (Antarctica)"; :<em>location </em>= "Mario Zucchelli Station (74°42\'S, 164°07\'E, 15 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "23-Oct-2024 11:13:22 UTC"; :<em>coverage </em>= "Monthly coverage (Jan 2023): 100 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created from raw TC telegram TDD 163, aggregated to 5min temporal resolution with a sum of the 1-minute counts if least 3 out of 5 are not NaN."; </pre> <p> </p> <p> </p>
Spatial clustering of Neobuccinum eatoni occurrence data for potential distribution modeling
<p>The occurrence dataset for <em>Neobuccinum eatoni</em> was compiled through filtration process, starting with records from the Global Biodiversity Information Facility (GBIF) and supplemented by museum specimens and additional sources like SOMBASE, iBOL, NIWA, ANTABIF, and SCAR-AntOBIS. Further data were sourced from the National Museum of Natural History in Paris, the University of Vigo, and recent fieldwork in Antarctica, Heard Island, and Kerguelen Island. Records were meticulously screened to remove misidentified specimens, inaccurate locations, duplicates, and outdated entries, ensuring accuracy and relevance. To address spatial autocorrelation, clustering methods divided the data into distinct geographic clusters, producing a refined dataset used to model <em>N. eatoni</em>'s potential distribution with enhanced predictive reliability by reducing spatial autocorrelation effects.</p>
Paleoclimate signals and groundwater age distributions from 39 public water works in the Netherlands; insights from noble gases and carbon, hydrogen and oxygen isotope tracers [Data set].
<p>Data set covering the meta data of the 39 well fields, the macro chemistry data and the data of the noble gases and carbon, hydrogen and oxygen isotope tracers used for assessing the paleoclimate signals and age distributions in the publication in Water Resources Research (2021)</p> <p><strong>Paleoclimate signals and groundwater age distributions from 39 public water works in the Netherlands; insights from noble gases and carbon, hydrogen and oxygen isotope tracers</strong></p> <p>Hans Peter Broers, Jürgen Sültenfuß<sup> </sup>, Werner Aeschbach, Arne Kersting,, Armin Menkovich, Jasperien de Weert and Jeroen Castelijns</p>
Parametric Study of the Radiative Load Distribution on the EU-DEMO First Wall Due to SPI-Mitigated Disruptions and in Steady-State (dataset)
<p>Database for reproducing the calculations presented in the publication "Parametric Study of the Radiative Load Distribution on the EU-DEMO First Wall Due to SPI-Mitigated Disruptions", submitted to <em>Fusion Engineering and Design</em>.</p> <p>Work carried out within the framework of the EUROfusion Consortium.</p>
spatial soil particle distribution at OAL-UK
<p>three raster files containing information on the spatial distribution of the soil's percentage of sand, silt and clay, respectively, at OAL-UK. The files were created following a digital soil mapping approach implemented through the Random Forest algorithm. More information on how the raster files were created can be found here: <a href="https://doi.org/10.1016/j.ecoleng.2017.04.066">https://doi.org/10.1016/j.ecoleng.2017.04.066</a> </p>
Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia"
<p>Data and code used for a country-wide occupancy survey of snow leopards in Mongolia, accompanying the paper "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia".</p> <p>This data contains the results of a survey of 1017 20x20km sampling units, out of a total of 1200 sampling units identified as potential snow leopard habitat (183 could not be sampled for various reasons), a near complete survey of potential snow leopard habitat in Mongolia, nearly 500,000 square kilometers, and an enormous effort by many researchers. If you make use of the data, please cite the following sources:</p> <ul> <li><em>Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia".</em> (2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. doi: https://doi.org/10.5281/zenodo.5257572</li> <li><em>Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia. </em>(2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. To appear in <em>Diversity and Distributions</em></li> </ul> <p><strong>Contents of zip file</strong></p> <p><em>Data</em></p> <p>The main dataset is contained in `data\Mongolia_occupancy_inputs.Rdata` . Please see the paper for more detail on data collection. The following objects are contained in the file:</p> <p>- Pres: presence/absence occupancy survey results, used for model fitting<br> - Site_Cov: unit-specific covariates, used for model fitting<br> - SurvCov: survey-specific covariates, used for model fitting<br> - Mongolia_studyarea: covariates for whole survey area, used for prediction<br> - Mongolia_fullrange: covariates across whole expected snow leopard range, used for prediction</p> <p><em>Code</em></p> <p>Code is cloned from the GitHub repository <a href="https://github.com/iandurbach/mongolia-occupancy">https://github.com/iandurbach/mongolia-occupancy</a>, which may contain updates. The version here reproduces the analyses in the paper above. The run these analyses:</p> <p>- run *occupancy-analysis.R* to fit the main occupancy models (these are also saved in the `\output` folder), do model selection, and plot covariate effects<br> - run *occupancy-goodness-of-fit.R* to calculate the c-hat statistic giving an indication of model fit for the best model<br> - run *comparing-maps.R* to compare the occupancy results with similar metrics generated using a presence-only analysis (using MaxEnt) or an expert map generated through qualitative discussion (reproduces Figure 3 in the paper).</p> <p>Code in *occupancy-data-preproc.R* is not needed but included for completeness. It converts the csv files in `data\csv`, which contain various input datasets used by the occupancy model, into a single .Rdata file (`data\Mongolia_occupancy_inputs.Rdata`), which is then used by the scripts above. Some minimal pre-processing (excluding ununsed variables, renaming for consistency, etc) is performed. </p>
Values, distributions and approximations of the empirical liquidity cost function for various futures contracts.
<p>The figures presents the values, distributions and approximations of the empirical liquidity cost function for various futures contracts. The raw data was obtained from the LOB snapshots for the cash-settled futures contracts on the RTS index (RI), on Brent oil (BR) and FX-rate of US dollar versus Russian ruble (Si). The data corresponds to the period from 05 May 2020 to 26 Feb 2021. The tables summarize the results.</p>
Potential tree species distributions from the Last Glacial Maximum in North America
<p>Modern tree distributions modeled under current climate and predicted to past climate.</p> <p>Values of '2' represent presence.</p> <p>The column mark is current presence, while _20000 is 20 ka, _14000 is 14 ka, _13000 is 13 ka, etc.</p> <p>For quick download, the .dbf for each species can be joined to the shapefile (us_can_ecosub). Alternatively, download and use the zipped folder of shapefiles (glac_shapes)..</p>
Dataset of 20 energy prosumers with flexibility data, distributed generation and energy storage
<p>The dataset has 20 prosumers, each with three appliances to provide flexibility for DR events, two PV generation resources, and an energy storage system. The values represent a day using 15 minutes reading periods. All the values are expressed in W, and the matrixes were created as [ time_period x info].</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars
<p>The datasets here correspond to a paper by Dawkins et al., “Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars”, originally submitted in November 2022.</p> <p>The following datasets are sufficient to produce Figure 2 and 3 in the main manuscript.</p> <p>Figure 2:</p> <ul> <li>Please use the 12 individual files with filenames, “Dawkins_et_al_2022__meteor_peak_altitude__*_data.txt”. Here the asterisk should be replaced by one of the following station abbreviations: CAR, COL, CPa, DAV, KIR, KSS, ROT, SMa, SOD, SVA, TdF, and TRO.</li> <li>Each file contains 5 columns: Column 1 is year (from 1999 to 2022), Column 2 is the time series of the annual peak altitude residuals (no units), Column 3 is the corresponding standard error, Column 4 is the multilinear model fit, and Column 5 is the normalized annual solar flux (F10.7) in arbitrary units.</li> </ul> <p>Figure 3:</p> <ul> <li>Please use “Dawkins_et_al_2022__meteor_peak_altitude_trends.txt”. For ease, a description of the different columns is included within this file.</li> </ul> <p> </p>
Geographical Distribution Maps of Western Palearctic Weevil Taxa
<p>The electronic supplement belongs to the research article Schütte A, Stüben PE, Astrin JJ (2023) Molecular Weevil Identification Project: A Thoroughly Curated Barcode Release of 1300 Western Palearctic Weevil Species (Coleoptera: Curculionoidea) - Biodiversity Data Journal 11.</p> <p>The ZIP file contains 613 distribution maps from Western Palearctic weevil taxa. The distribution maps showing Europe originate from the Curculio Institute's website (www.curci.de). Additional information on distribution range and known synonyms were based on the information from the Löbl catalogs (Löbl & Smetana 2011, Löbl & Smetana 2013). The maximum distribution range of each species was measured in km with Google Earth's ruler function.</p> <p>An unzip software is needed to access the *.JPG files within the *.ZIP file. Microsoft operating systems support *.zip files natively since Windows XP. MAC operating systems offer the "archive utility" to access *.zip files. Android users must install an app like Winzip, WinRAR, or 7ZIP. The iOS 13 operating system and onwards allow unzipping *.zip archives natively (iPhone and iPad). The *.zip filetype support can be installed on Linux operating systems via the terminal command: "sudo apt-get install unzip". Command to unzip: "unzip \*.zip". The *.JPG files can be opened with any picture viewer or internet browser.</p> <p>References<br> Löbl L, Smetana A (2011) Catalogue of the Coleoptera. Vol. 7, Curculionoiodea I, Stenstrup, Apollo Books, 373 pp.<br> Löbl L, Smetana A (2013) Catalogue of the Coleoptera. Vol. 8, Curculionoiodea II, Leiden & Boston, Brill, 700 pp.</p>
Spatial distribution of housing rental value in Amsterdam 1647-1652
<p>This dataset visualises the spatial distribution of the rental value in Amsterdam between 1647 and 1652. The source of rental value comes from the <em>Verponding </em>registration in Amsterdam. The <em>verponding</em> or the ‘<em>Verpondings-quohieren van den 8sten penning</em>’ was a tax in the Netherlands on the 8<sup>th</sup> penny of the rental value of immovable property that had to be paid annually. In Amsterdam, the citywide <em>verponding </em>registration started in 1647 and continued into the early 19<sup>th</sup> century. With the introduction of the cadastre system in 1810, the <em>verponding</em> came to an end.</p> <p>The original tax registration is kept in the Amsterdam City Archives (Archief nr. <a href="https://archief.amsterdam/inventarissen/details/5044/withscans/0/findingaid/5044/start/0/limit/10/flimit/5">5044</a>) and the four registration books transcribed in this dataset are Archief 5044, inventory <a href="https://archief.amsterdam/inventarissen/scans/5044/33.2/start/0/limit/10/highlight/2">255</a>, 273, <a href="https://archief.amsterdam/inventarissen/scans/5044/33.28/start/0/limit/10/highlight/4">281</a>, <a href="https://archief.amsterdam/inventarissen/scans/5044/33.31/start/0/limit/10/highlight/4">284</a>. The <em>verponding </em>was collected by districts (<em>wijken</em>). The tax collectors documented their collecting route by writing down the street or street-section names as they proceed. For each property, the collector wrote down the names of the owner and, if applicable, the renter (after ‘per’), and the estimated rental value of the property (in guilders). Next to the rental value was the tax charged (in guilders and stuivers). Below the owner/renter names and rental value were the records of tax payments by year.</p> <p>This dataset digitises four registration books of the <em>verponding </em>between 1647 and 1652 in two ways. First, it transcribes the rental value of all real estate properties listed in the registrations. The names of the owners/renters are transcribed only selectively, focusing on the properties that exceeded an annual rental value of 300 guilders. These transcriptions can be found in Verponding1647-1652.csv. For a detailed introduction to the data, see Verponding1647-1652_data_introduction.txt.</p> <p>Second, it geo-references the registrations based on the street names and the reconstruction of tax collectors’ travel routes in the <em>verponding</em>. The tax records are then plotted on the historical map of Amsterdam using the first cadaster of 1832 as a reference. Since the geo-reference is based on the street or street sections, the location of each record/house may not be the exact location but rather a close proximation of the possible locations based on the street names and the sequence of the records on the same street or street section. Therefore, this geo-referenced <em>verponding</em> can be used to visualise the rental value distribution in Amsterdam between 1647 and 1652. The preview below shows an extrapolation of rental values in Amsterdam. And for the geo-referenced GIS files, see Verponding_wijken.shp.</p> <p><strong>GIS specifications:</strong></p> <p>Coordination Reference System (CRS): Amersfoort/RD New (ESPG:28992)</p> <p>Historical map tiles <a href="https://images.diginfra.net/webmapper/maps/berckenrode/{z}/{x}/{y}.png">URL</a> (From <a href="https://tiles.amsterdamtimemachine.nl/#16/52.3691/4.8935">Amsterdam Time Machine</a>)</p> <p> </p> <p><strong>NB: This <em>verponding</em> dataset is a provisional version. The georeferenced points and the name transcriptions might contain errors and need to be treated with caution. </strong></p> <p><strong>Contributors</strong></p> <ul> <li><strong>Historical and archival research</strong>: Weixuan Li, Bart Reuvekamp</li> <li><strong>Plotting of geo-referenced points: </strong>Bart Reuvekamp</li> <li><strong>Spatial analysis</strong>: Weixuan Li</li> <li><strong>Mapping software</strong>: QGIS</li> <li><strong>Acknowledgements</strong>: Virtual Interiors project, Daan de Groot</li> </ul> <p> </p>
The Vibrio Type III Secretion System 2 is not restricted to the Vibrionaceae and encodes differentially distributed repertoires of effector proteins
<p>Supplementary Dataset for the work entitled "The Vibrio Type III Secretion System 2 is not restricted to the Vibrionaceae and encodes differentially distributed repertoires of effector proteins".</p> <p>This dataset includes files for the T3SS2 reconstructed phylogenetic tree (Newick tree and fasta file), hierarchical clustering data analysis file from MORPHEUS, Table S1 with genome accession numbers, and all the data of the absence/presence of T3SS2-related components, Table S2 with the prediction of novel effector proteins.</p>
GBIF animal distribution in Spain
<p>CSV that contains 1.000 GBIF observations of animals than have been involved in wildlife–vehicle collision on interurban roads in Spain and a buffer of each species distribution calculated with these data. If you are interested in the whole country, please do not hesitate to contact me and I will forward it to you.</p> <p>Each record describes the observation by the following fields:</p> <ul> <li><strong> gbifid: </strong>the unique identifier for an occurrence record in GBIF.</li> <li><strong> datasetkey: </strong>the local dataset id within the GBIF network.</li> <li><strong> occurrenceid: </strong>a unique identifier for the occurrence, allowing the same occurrence to be recognized across dataset versions as well as through data downloads and use.</li> <li><strong> kingdom: </strong>the full scientific name specifying the kingdom that the occurrence's scientific name is classified under.</li> <li><strong> phylum: </strong>the full scientific name of the phylum or division in which the taxon is classified<strong>.</strong></li> <li><strong> class: </strong>the full scientific name of the class in which the taxon is classified.</li> <li><strong> order: </strong>the full scientific name of the order in which the taxon is classified.</li> <li><strong> </strong><strong>family: </strong>the full scientific name of the family in which the taxon is classified.</li> <li><strong> genus: </strong>the full scientific name of the genus in which the taxon is classified.</li> <li><strong> species: </strong>species classification key.</li> <li><strong> infraspecificepithet: </strong>the name of the lowest or terminal infraspecific epithet of the scientificName, excluding any rank designation.</li> <li><strong> taxonrank: </strong>the taxonomic rank of the supplied scientific name.</li> <li><strong> scientificname: </strong>the full scientific name of the organism, to the lowest level taxonomic rank that is possible to supply, and including authorship and year of the name where applicable.</li> <li><strong> verbatimscientificname: </strong>the taxonomic rank of the most specific name in the scientificName as it appears in the original record.</li> <li><strong> verbatimscientificnameauthorship: </strong>non described.</li> <li><strong> countrycode: </strong>a two-letter standard abbreviation for the country of the occurrence locality.</li> <li><strong> locality: </strong>the specific description of the place.</li> <li><strong> stateprovince: </strong>the name of the next smaller administrative region than country (state, province, canton, department, region, etc.) in which the Location occurs.</li> <li><strong> occurrencestatus: </strong>a statement about the presence or absence of a Taxon at a Location.</li> <li><strong> individualcount: </strong>to record the quantity of a species occurrence, e.g. as the number of individuals, percentage of vegetation coverage, or the biomass .</li> <li><strong> publishingorgkey: </strong>the publishing organization key (a uuid).</li> <li><strong> decimallatitude: </strong>the geographic latitude, resp., in decimal degrees. </li> <li><strong> decimallongitude: </strong>the geographic longitude, resp., in decimal degrees. </li> <li><strong> coordinateuncertaintyinmeters: </strong>the horizontal distance from the given decimalLatitude and decimalLongitude in meters, describing the smallest circle containing the whole of the Location.</li> <li><strong> coordinateprecision: </strong>a decimal representation of the precision of the coordinates given in the decimalLatitude and decimalLongitude.</li> <li><strong> elevation: </strong>elevation (altitude) in meters above sea level. Supports range queries.</li> <li><strong> elevationaccuracy: </strong>non described.</li> <li><strong> depth: </strong>depth in meters relative to altitude. For example 10 meters below a lake surface with given altitude. Supports range queries.</li> <li><strong> depthaccuracy: </strong>non described.</li> <li><strong> eventdate: </strong>the date or date interval during which the occurrence record was collected, following ISO 8601 date-time standard.</li> <li><strong> day: </strong>the integer day of the month on which the Event occurred.</li> <li><strong> month:</strong> the integer month in which the Event occurred.</li> <li><strong> year: </strong>the four-digit year in which the Event occurred, according to the Common Era Calendar.</li> <li><strong> taxonkey: </strong>a taxon key from the GBIF backbone.<strong> </strong></li> <li><strong> specieskey: </strong>species classification key.</li> <li><strong> basisofrecord: </strong>the type of the individual record, e.g. observation, physical specimen, fossil, living ex-situ, culture collection specimen.</li> <li><strong> institutioncode: </strong>the name (or acronym) in use by the institution having custody of the object(s) or information referred to in the record.</li> <li><strong> collectioncode: </strong>the name, acronym, coden, or initialism identifying the collection or data set from which the record was derived.</li> <li><strong> catalognumber: </strong>an identifier (preferably unique) for the record within the data set or collection.</li> <li><strong> recordnumber: </strong>an identifier given to the Occurrence at the time it was recorded. Often serves as a link between field notes and an Occurrence record, such as a specimen collector's number.</li> <li><strong> identifiedby: </strong>a list (concatenated and separated) of names of people, groups, or organizations who assigned the Taxon to the subject.</li> <li><strong> dateidentified: </strong>the date on which the subject was determined as representing the Taxon.</li> <li><strong> license: </strong>a machine-readable statement of the rights assigned to the published dataset.</li> <li><strong> rightsholder: </strong>a person or organization owning or managing rights over the resource.</li> <li><strong> recordedby: </strong>the name of the institution or organization listed as the data publisher on GBIF.org.</li> <li><strong> typestatus:</strong> a list (concatenated and separated) of nomenclatural types (type status, typified scientific name, publication) applied to the subject.</li> <li><strong> establishmentmeans: </strong>The process by which the biological individual(s) represented in the Occurrence became established at the location.</li> <li><strong> lastinterpreted: </strong>this date the record was last modified in GBIF, in ISO 8601 format: yyyy, yyyy-MM, yyyy-MM-dd, or MM-dd. </li> <li><strong> mediatype: t</strong>he kind of multimedia associated with an occurrence as defined in GBIF MediaType enum</li> <li><strong> issue:</strong> a specific interpretation issue as defined in GBIF OccurrenceIssue enum.</li> <li><strong> geom (geometry):</strong> geometry from latitude and longitude position. Developed for this project.</li> <li><strong> buff (geometry): </strong>buffer around 'geom' taking into account 'coordinateuncertaintyinmeters' and 'coordinateprecision'.<strong> </strong>Developed for this project.</li> </ul> <p>The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC).</p> <p>This dataset is the output of the animal analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.</p>
DATASET: Electric Potential Distribution Inside the Electrolyte During High Voltage Electrolysis
<p>This project contains all the data shown in the figures of the manuscript (and the supporting information) entitled:<br> 'Electric Potential Distribution Inside the Electrolyte During High Voltage Electrolysis'<br> (doi:10.26434/chemrxiv-2022-nw4sp).</p> <p>The data to each figure is provided in a subfolder with further information.</p>
Global distribution map of Rhenish stoneware during the 16th to 18th century
<p>The dataset provides a distribution map of Rhenish stonewares between the 16th and 18th century. The data was collected from published archaeological data (print and online) available to the author. According the published information the pottery was classified to different wares (Cologne, Frechen, Siegburg, Raeren, Westerwald). Values are given for individual sherd numbers. If no information was given in the publication, the value is set to "1". Bibligraphic reference is given by author - date. Full bibliographic reference can be found in the pdf-file.</p> <p>The csv-file contains next to location name, bibliographic reference and pottery counts values for longitude and latitude. The coordinate reference is WGS 84 - EPSG:4326.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.