Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
28,952
datasets available to search
ShareScore release 0.7.1
Dataset results
28,952 results for “Distributed”
Open database on distributional information on European pollinators
<p>(abstract) This dataset was produced in the framework of the work package 1 (task 1) of the Horizon EU project Safeguard. We aimed to mobilise EU experts and data to compile and make available distributional data for bees, butterflies, moths and hoverflies. This will allow us to assess the magnitude, scale and extent of status and trends in pollinator distributions, diversity, abundance, communities and plant-pollinator networks.</p> <p>(method) Regarding distribution data for bees, UMons have been in contact with 23 bee taxonomists, 52 national champions and 5 museums. To date, we collected 52 bio-geographical databases of European bees from both restricted (i.e. databases shared under ad hoc agreement) and public (i.e. openly accessible databases) sources. Regarding distributional data for hoverflies, the starting point was the recently published in the IUCN Red List of hoverflies. To expand the number of species with precise distributional data on syrphid flies, UNSPMF further contacted taxonomists working with this species group : Gunilla Stahls from Finland; Jeroen van Steenis, Wouter van Steenis and Gerard Pennards from Netherlands; Grigory Popov from Ukraine; Santos Rojo from Spain; Axel Ssymank from Germany; Libor Mazanek from Czech Republic; Daniele Sommaggio from Italy. They provided additional data and conducted validation of the existing data, but also engaged additional experts who provided the data. For the butterflies and the moth, the data was collected by UFZ and come from an original initiative of the scientific expert on those two groups. As the publication of the row data of some databases (e.g. bees from The Netherlands) required the clustering of the spatial records to geographic grid squares (e.g. 10x10 km²), we simplified all the records in the present dataset.</p> <p>(dataset) We consider as a data, a record that includes the following information: the name of the species, the coordinates where the species was collected. Additional information were collected (e.g. collector, determinator, number of the individuals collected, sex, data owner and reference code) but were not displayed in the present dataset. The aggregation of bee databases include 4,837,731 row data for bees, 680,641 row data for hoverflies, 1,209,320 row data for butterflies and 6,862,835 row data for moths.</p>
Supplemental data for "Inequitable spatial and temporal patterns in the distribution of multiple environmental risks and benefits in Metro Vancouver"
<p><strong>DemoEnPoC2016.csv/DemoEnPoC2006.csv:</strong></p> <p>This is a table including environmental and demographic (Census variables) data at postal code level for Metro Vancouver in the year 2006 and 2016. The environmental data (SO2 metrics, PM2.5 metrics, Calculated ozone metrics, NO2 data, NDVI metrics, and Canadian Active Living Environments Index (Can-ALE) indexed to DMTI Spatial Inc. postal codes) were extracted from CANUE (Canadian Urban Environmental Health Research Consortium). The demographic data is extracted from Canadian Census analyzer (https://datacentre.chass.utoronto.ca/), the deprivation index is downloaded from from the Institut national de santé publique du Québec (INSPQ). </p> <p><strong>DGRwithLable:</strong></p> <p>This is the Dissemination Geographies Relationship File for the 2021 census year (Statistics Canada, 2021) with the lable of urban or rural, indicating which dissemination area (DA) is identified as urban and included in this study. The urban area is named as population certer. </p> <p><strong>Aggregation and SS Determination:</strong></p> <p>This script contains code for:</p> <ul> <li>Aggregating postal code level data to the Dissemination Area (DA) level.</li> <li>Eliminating rural DAs.</li> <li>Converting environmental data into ordinal categories using quartile and even break methods.</li> <li>Identifying sweet and sour spots for each DA based on these methods.</li> </ul> <p><strong>SSEJ Analysis:</strong></p> <p>This script includes code for:</p> <ul> <li>Creating violin and box plots to illustrate descriptive statistics of demographic groups across different environmental categories (sweet, sour, risky, and medium).</li> <li>Performing linear regression analyses between environmental categories and demographic variables.</li> </ul> <p><strong>SS Heatmap:</strong></p> <p>This script comprises code for:</p> <ul> <li>Summarizing the results of the linear regression analyses.</li> <li>Assessing changes in inequities among demographic groups between 2006 and 2016.</li> <li>Visualizing regression coefficients through heatmaps.</li> </ul> <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>
Optimisation of business processes tenant distribution in the Cloud with a genetic algorithm
<p>Used data and obtained results for the paper Optimisation of business processes tenant distribution in the Cloud with a genetic algorithm.</p> <p>The reader can find the following files :</p> <ul> <li>configuration_types.csv contains the cloud resource types (the name is the EC2 instance for database, and for the BPM engine separated by an underscore), their price and their capacity</li> <li>tenants_uni.csv contains the customers and their minimum and maximum BPM task throughput</li> <li>results_[number of tenants]_seg.csv files contain the results for the previous heuristic (segmentation only)</li> <li>results_<em>[number of tenants]</em>_ga_<em>[duration]</em>.csv files contain the results for the genetic algorithm coupled to the iterative heuristic tests</li> <li>solver_<em>[number of tenants]</em>_ga_<em>[duration]</em>.csv files contain the results for the genetic algorithm coupled to the restricted model solved tests</li> </ul>
Improving Hypernymy Extraction with Distributional Semantic Classes
<p>In this paper, we show for the first time how distributionally-induced semantic classes can be helpful for extraction of hypernyms. We present a method for (1) inducing sense-aware semantic classes using distributional semantics and (2) using these induced semantic classes for filtering noisy hypernymy relations. Denoising of hypernyms is performed by labeling each semantic class with its hypernyms. On one hand, this allows us to filter out wrong extractions using the global structure of the distributionally similar senses. On the other hand, we infer missing hypernyms via label propagation to cluster terms. We conduct a large-scale crowdsourcing study showing that processing of automatically extracted hypernyms using our approach improves the quality of the hypernymy extraction both in terms of precision and recall. Furthermore, we show the utility of our method in the domain taxonomy induction task, achieving the state-of-the-art results on a benchmarking dataset.</p> <p>This particular page contains datasets related to the paper. Namely the input induced word senses, a database of hypernyms, and the output clusters of senses labeled with hypernyms -- the distributional semantic classes. The semantic classes are of two granularities, as described in the paper (coarse and fine grained). </p>
Data and code for: Habitat preference of an herbivore shapes the habitat distribution of its host plant
<p>Initial release of analysis and code for:</p> <p>Alexandre, N. M., P. T. Humphrey, A. D. Gloss, J. Lee, J. Frazier, H. A. Affeldt III, and N. K. Whiteman. 2018. Habitat preference of an herbivore shapes the habitat distribution of its host plant. Ecosphere 00(00):e02372. (full citation pending)</p> <p>Release published to accompany corrected proofs on 2018-Jul-26.</p>
Dataset of E. huxleyi blooms: spatio-temporal distribution and their impact on high-latitudinal marine environments (1998-2016)
<p>Dataset of coccolithophore blooms in polar seas of the Northern Hemisphere, viz. the North, Labrador (with adjacent North Atlantic open waters), Norwegian, Barents, Greenland and Bering seas are presented for the period 1998-2016. Seas are divided into 4 regions, for each of them continuous data series (as 8-days composites) are published, including information about bloom spatial masks, coccolith concentration, particulate inorganic carbon content and CO<sub>2</sub> partial pressure in water increment driven by coccolithophores.</p> <p>Datasets are published as NetCDF files with full metadata/descriptions and with GDAL support.</p> <p>Additional information (regions configuration, data access instructions) is provided alongside the data.</p> <p>Naming convention is: <strong>niersc_cocco_<version of dataset>_<region>_<start date>_<end date>.nc</strong></p>
Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response
<p>The data contains measurements and derived values that are used for the manuscript "Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response, [Paper # 2018WR024171]" Currently under review at the Water Resources Research journal.</p> <p>The data is stored in netCDF files with xarray (Python), and should be readable with any other netCDF reader. </p> <ul> <li>TEMP is the measured temperature in degrees Celsius relative to the background temperature</li> <li>tempinfty is one of the calibration parameters. Represents the steady state temperature increase</li> <li>A is one of the calibration parameters. Represents the timescale in days</li> <li>b is one of the calibration parameters. Represents the scaled distance to the heat source</li> <li>err_alpha is one of the calibration parameters. Represents the autoregressive parameter</li> <li>TEMPmodel is the best fit temperature response in degrees Celsius relative to the background temperature</li> <li>Innovation is termed the noise in the article, in degrees Celsius.</li> <li>q is the estimated specific discharge in meters per day</li> <li>q_MC_XX are the confidence intervals of the estimated specific discharge calculated with Monte Carlo as presented in the article</li> <li>q_lmfit_XX are the confidence intervals of the estimated specific discharge calculated with LMFIT. Is a rough estimate for q_MC_XX calculated by lmfit (Python package).</li> </ul> <p>Time is measured in days with respect to when the heating cable is turned on.</p> <p>Additionally, a Jupyter notebook is supplemented to the article. It demonstrates the calibration routine and the calculation of the confidence interval for the temperature response at a single depth.</p>
Resource heterogeneity leads to unjust effort distribution in climate change mitigation
<p>Climate change mitigation is a shared global challenge that involves the collective action of a set of individuals with different tendencies to cooperation. However, we lack an understanding of the effect of resource inequality when diverse actors interact together toward a common goal. Here, we report the results of a collective-risk dilemma experiment in which groups of individuals were initially given either equal or unequal endowments. We found that the effort distribution was highly inequitable, with participants with fewer resources contributing significantly more to the public goods than the richer - sometimes twice as much. An unsupervised learning algorithm classified the subjects according to their individual behavior, finding the poorest participants within two "generous clusters'" and the richest into a "greedy cluster''. Our results suggest that policies would benefit from educating about fairness and reinforcing climate justice actions addressed to vulnerable people instead of focusing on understanding generic or global climate consequences.</p> <p>Vicens J, Bueno-Guerra N, Gutiérrez-Roig M, Gracia-Lázaro C, Gómez-Gardeñes J, Perelló J, et al. (2018) Resource heterogeneity leads to unjust effort distribution in climate change mitigation. PLoS ONE 13(10): e0204369. https://doi.org/10.1371/journal.pone.0204369</p>
The effectiveness of freshwater connectivity as a predictor of species distribution
<p>The attached dataset contains three dataframes used in the affiliated papers.</p> <p>1) DirectSlopeData.rda - Recolonisation success of two species, northern pike and European perch, in rotenone-treated lakes in Sweden, alongside connectivity parameters for the associated lakes.</p> <p>2) HPD.rda - Credible intervals for the beta estimates generated by the BORAL model in 3.</p> <p>3) Presence/absence data for seven species in lakes throughout the Kautokeino catchment in Northern Norway, alongside selected environmental covariates for associated lakes.</p>
Malwa site survey : archaeological site distribution maps
<p>Malwa site survey : archaeological site distribution maps. (1) mosaic based on Survey of India maps; (2) (3) study area; (4) Vidisha Raisen area.</p>
Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ
<p>This repository contains data reported in the below study:</p> <p>Atherton, J., Liu, W. and Porcar-Castell, A., 2019. Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ. <em>Remote Sensing of Environment</em>.</p> <p>Each text file contains the data-set used to produce the relevant figure (see file name). You can find the data to produce A.4. online at https://avaa.tdata.fi/web/smart/smear/ </p> <p>Please pay attention to the following before using this data.</p> <ol> <li><strong>Figure2_lampRadPanel_Wm2srnm.txt</strong>: Note that the shapes are of interest here. The magnitude is not the same as the incident light at top of canopy, as these spectra were measured in a laboratory. See paper section A.1. for more details. </li> <li><strong>Figure3_LEDIFspectra_Wm2srnm.txt</strong>: This data contains the whole observed spectrum including the non-fluorescence regions, which were saturated (warped) in the visible. The fluorescence region is approximately > 650 nm. </li> <li><strong>Figure4_AQYspectra_nm.txt</strong>: As with Figure3 the whole spectrum is included here.</li> <li><strong>FigureA3_repLEDIFspectra_[pmay/psep/usep]._nm.txt</strong>: Data from which the mean spectra (Figure3) were calculated, including the uncorrected red spectra. I have split these by canopy type to avoid name conflicts.</li> </ol> <p> </p>
Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields (data sets)
<p>Supplementary dataset for Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields. Output functionals of interest from finite element simulations together with postprocessing source code. </p>
Ecological Drivers of Invasive Lionfish (Pterois volitans and Pterois miles) Distribution Across Mesophotic Reefs in Bermuda
<p>The data and code in this document were obtained using diver-led visual surveys of mesophotic reef sites to examine how variations in potential ecological drivers may affect lionfish distribution on mesophotic reefs in Bermuda. These data and code were used for analysis and figure preparation in association with publication in Frontiers in Marine Science (Goodbody-Gringley et al. 2019). </p>
Historical distribution and current drivers of guppy occurrence in Brazil
<p>This data set was used in the paper "Historical distribution and current drivers of guppy occurrence in Brazil". The file consist of occurrence records of <em>Poecilia reticulata</em> in Brazil over different time periods. In order to evaluate the historical and current distribution of <em>P. reticulata,</em> we searched for occurrence records in two major data sources. We first compiled data from all the studies cited in the recent comprehensive review of studies of Brazilian stream fish assemblages (Dias et al., 2016). We performed this search by using electronic databases and search engines (i.e., Web of Knowledge, Google Scholar, and Scielo) to look for primary studies and published papers from Brazilian journals that provide occurrence records of <em>P. reticulata</em> in Brazil. We also used combinations of the following search terms, in English and Portuguese: “guppy fish”, “non-native”, “nonindigenous aquatic species”, and “<em>Poecilia reticulata”; </em>this second search provided additional published papers mentioning this species in Brazilian territory. Both the papers and supplementary information were screened in order to find the geographical coordinates of the sampling points where this species has been detected. As a third data source, we used all the available records of <em>P. reticulata</em> from the SpeciesLink website (http://splink.cria.org.br/, accessed 2016), which aggregates species occurrence data from major biological collections worldwide, including those from Brazilian institutions. We extracted from this platform all the associated informations (e.g., the location and the associated geographical coordinates; the sampling dates containing year, month, and day; and the names of researchers who composed the sampling teams). From these three sources, we created a database of the occurrence of <em>P. reticulata</em> in Brazil.</p> <p>Some records were excluded because the geographical coordinates and/or sampling dates were not provided in detail, or could not be determined directly or from information in the publication itself (Dias et al., 2016). Overall, incomplete and discarded records comprised only 0.7% (12 out of 1649 records) of our dataset. We further used the sampling dates and associated collector information to remove duplicate records from the database. By this means, multiple records of <em>P. reticulata</em> with the identical geographical coordinates were compared in terms of the sampling day, month, year, and collector(s). If all the information was identical, only one record was included. On the other hand, multiple records of <em>P. reticulata</em> from the same location but with different dates and collectors were retained in the final database. This final database was composed of 1402 records and was used to investigate the occurrence of <em>P. reticulata</em> over time.</p> <p>References</p> <p>Dias, M. S., J. Zuanon, T. B. A. Couto, M. Carvalho, L. N. Carvalho, H. M. V. Espírito-Santo, R. Frederico, R. P. Leitão, A. F. Mortati, T. H. S. Pires, G. Torrente-Vilara, J. do Vale, M. B. dos Anjos, F. P. Mendonça, & P. A. Tedesco, 2016. Trends in studies of Brazilian stream fish assemblages. Natureza & Conservação 14: 106–111.</p>
Pressure distribution and schlieren for various locations of transonic compressor profiles
<p>One of the primary objectives of the EU TFAST and TEAMAero projects is to study the shock wave and boundary layer interaction on the suction side of the transonic compressor blade. In this context, the test section is designed and assembled at the IMP PAN laboratory to replicate the flow structure in a transonic compressor cascade.</p> <p>The system's sensitivity to variable parameters in the test section is crucial for achieving the design conditions and ensuring that the flow is influenced solely by the target parameter under study.</p> <p><strong>The provided data</strong> pertains to a study on the system's sensitivity to small changes in the position of the profiles relative to the nozzle (~±2% x/c). The results indicated that the profile position does not significantly alter the overall flow structure or the inflow Mach number. However, it affects the Mach number distribution over the lower profile, particularly upstream of the passage shock within the range of 0.3-0.4x/c.</p> <p><span>Data can be used for CFD validation and sensitivity study. </span>The pressure files and schlieren images are grouped by date.</p> <p> </p>
Projected distribution of invasive plant species in the tropical Andes under climate change
<p>Distribution maps of 11 invasive species now and in the future (2040-70). The projections were the result of the assembly of three algorithms: Adaptive Boosting (AdaBoost), Boosted Regression Trees (BRT), and Extreme Gradient Boosting (XGBoost). Future projections were made for three global circulation models and three climate change scenarios, each with low (SSP126), medium (SSP370), and high (SSP585) levels of carbon emission.</p> <p>Habitat suitability and presence/absence maps are also included. The threshold for establishing a species as present was determined to be the value that maximized the TSS. </p> <p>For more information, see the article accompanying the dataset by González-Trujillo et al. Mapping the threat: Projecting invasive plant distribution in the tropical Andes under climate change</p> <p>List of modeled invasive plant species and their known impacts in the tropics.</p> <table> <tbody> <tr> <td> <p><strong>Species </strong></p> </td> <td> <p><strong>Biogeographic origin</strong></p> </td> <td> <p><strong>Impacts </strong></p> </td> <td> <p><strong>References</strong></p> </td> <td> <p><strong>GBIF data (DOIs)</strong></p> </td> </tr> <tr> <td> <p><em>Acacia decurrens </em></p> </td> <td> <p>Australian</p> </td> <td> <p>Create regular layers of litter on the ground, inhibit or redirect successional processes, inhibit the expression of seed banks, and limit resource supply, leading to displacement of native plants and animals and increasing the frequency of fires.</p> </td> <td> <p> (Cárdenas López et al., 2017; Le Maitre et al., 2011)</p> </td> <td> <p>https://doi.org/10.15468/dl.mjyxhw</p> </td> </tr> <tr> <td> <p><em>Acacia melanoxylon</em></p> </td> <td> <p>Australian</p> </td> <td> <p>Alter the structure and function of their ecosystems, thereby displacing their native flora. It also causes soil erosion and alters hydrological cycles, negatively affecting agriculture.</p> </td> <td> <p>(Kumschick and Jansen, 2023; Le Maitre et al., 2011)</p> <p> </p> </td> <td> <p>https://doi.org/10.15468/dl.4cugnk</p> </td> </tr> <tr> <td> <p><em>Arundo donax</em></p> <p><em> </em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter<em> </em>the natural vegetation structure, outcompete native plant species and diminish the diversity and abundance of animals such as arthropods and birds. It also drives out soil, fuels forest fires, displaces native species, and increases the invasion of ticks that affect livestock.</p> </td> <td> <p>(Cárdenas López et al., 2017; Girotto et al., 2021; Lambert et al., 2010)</p> </td> <td> <p>https://doi.org/10.15468/dl.bfep4t</p> </td> </tr> <tr> <td> <p><em>Genista monspessulana</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter fire regime and nutrient cycling displace native species and decrease native diversity by forming dense monospecific stands. It also facilitates the establishment of other invasive species and produces seeds that are toxic to livestock and humans.</p> </td> <td> <p>(Cárdenas López et al., 2017; Herrera et al., 2016; Pauchard et al., 2008)</p> </td> <td> <p>https://doi.org/10.15468/dl.gyhnxh</p> </td> </tr> <tr> <td> <p><em>Hedychium coronarium </em></p> </td> <td> <p>Indo-Malesian</p> </td> <td> <p>Alter hydrological and nutrient cycles in soil. It forms thickets that suppress the successional and regeneration processes of native species, thus affecting the native flora and crops.</p> </td> <td> <p>(Cárdenas López et al., 2017; Costa et al., 2019)</p> </td> <td> <p>https://doi.org/10.15468/dl.6z2jgb</p> </td> </tr> <tr> <td> <p><em>Melinis minutiflora</em></p> </td> <td> <p>African</p> </td> <td> <p>Increases the occurrence of fires, displaces native species, and alters soil properties and decomposition. It also inhibits the growth of native species.</p> </td> <td> <p>(Cárdenas López et al., 2017; Nogueira et al., 2019; Sandoval et al., 2022)</p> </td> <td> <p>https://doi.org/10.15468/dl.fsqwsv</p> </td> </tr> <tr> <td> <p><em>Pteridium aquilinum</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species.</p> </td> <td> <p> (Berget et al., 2015; Cárdenas López et al., 2017; Valdez-Ramírez et al., 2020)</p> <p> </p> </td> <td> <p>https://doi.org/10.15468/dl.sp4uuv</p> </td> </tr> <tr> <td> <p><em>Ricinus communis</em></p> </td> <td> <p>African</p> </td> <td> <p>Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species.</p> </td> <td> <p>(Cárdenas López et al., 2017; Sandoval et al., 2022; Silva and Fabricante, 2022)</p> </td> <td> <p>https://doi.org/10.15468/dl.dhbphb</p> </td> </tr> <tr> <td> <p><em>Senecio madagascariensis</em></p> </td> <td> <p>African</p> </td> <td> <p>Alter soil nutrient cycles, damage to agricultural crops, and outcompete native species. It also contains substances that are toxic to both animals and humans. </p> </td> <td> <p>(Wijayabandara et al., 2021)</p> </td> <td> <p>https://doi.org/10.15468/dl.7e8eyx</p> </td> </tr> <tr> <td> <p><em>Thunbergia alata</em></p> </td> <td> <p>African</p> </td> <td> <p>Displace native species and reduce habitat heterogeneity, thereby affecting the structure and function of native ecosystems.</p> </td> <td> <p>(Cárdenas López et al., 2017; Quijano-Abril et al., 2021)</p> </td> <td> <p>https://doi.org/10.15468/dl.g9zybc</p> </td> </tr> <tr> <td> <p><em>Ulex europeaus</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Dry soil and increase the occurrence of fires. Inhibits vegetative growth, including pastures in agricultural and livestock lands.</p> </td> <td> <p>(Anderson and Anderson, 2009; Cárdenas López et al., 2017)</p> </td> <td> <p>https://doi.org/10.15468/dl.6642q9</p> </td> </tr> </tbody> </table>
Detailed insight into gillnet catches: fish directivity and micro distribution
<p>This dataset contains data for gillnets that were deployed in Římov reservoir, South Bohemia, Czech Republic (48°50'55.0"N 14°29'14.0"E). The sampling dates were recorded from July 30 to August 2, 2019. This experiment was conducted to test the bias of gillnets in relation to fish direction capture. To determine if this is a random pattern or if it follows a directional pattern. The dataset includes various terms such as eventID, eventDate, country, countryCode, geodeticDatum, decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, habitat, waterBody, locality, DEIMS.iD, basisOfRecord, minimumDepthInMeters, maximumDepthInMeters, samplingEffort, samplingProtocol, dynamicProperties, occurrenceStatus, organismQuantity, organismQuantityType, measurementValue, measurementUnit, measurementType, measurementRemarks, organismRemarks, acceptedNameUsageID, scientificName, taxonRank, class, order, family.</p>
The impact of 11 May 2024 super geomagnetic storm on the plasma distribution over the Indian equatorial/low latitude ionospheric region
<p>The file contains the data set and the software for the generation the plots used in the manuscript " The impact of 11 May 2024 super geomagnetic storm on the plasma distribution over the Indian equatorial/low latitude ionospheric region".</p>
Dataset related to the Journal Article 'Efficiency Enhancement of Marine Propellers via Reformation of Blade Tip-Rake Distribution'
<p>This Dataset contains results related to the Graphs shown in the publication titled "Efficiency Enhancement of Marine Propellers via Reformation of Blade Tip-Rake Distribution". The results refer to open water performance curves for the benchmark propeller geometries and the models with optimal tip-rake. In the Folder we provide the data for each figure in a specific folder with the number corresponding to the number of the figure in the published version of the paper. </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.