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.
5,023
datasets available to search
ShareScore release 0.9.0
Dataset results
5,023 results for “landing”
FIGURE 9 in Trans-Japan Sea land-bridge disjunction: A case of vicariance in the subterranean genus Nipponasellus (Crustacea, Isopoda, Asellidae) in a largescale biogeographical context
FIGURE 9. Scanning electron micrographs of pleopod 2 of Nipponasellus sudzukhensis spec. nov., adult male: (A) pair of pleopods 2, ventral view; (B) endopodite of pleopod 2, right; (C, D) appendix masculina enlarged, right and left.
FIGURE 8. Nipponasellus sudzukhensis spec. nov., holotype X54657 in Trans-Japan Sea land-bridge disjunction: A case of vicariance in the subterranean genus Nipponasellus (Crustacea, Isopoda, Asellidae) in a largescale biogeographical context
FIGURE 8. Nipponasellus sudzukhensis spec. nov., holotype X54657/Cr-2475-FEFU, male, 5.0 mm: (A) pleotelson, ventral view (g.p., genital papilla); (B) pleopod 1; (C) pleopod 2, with schematic outline of details of appendix masculina; (D) pleopod 3; (E) pleopod 4; (F) pleopod 5; (G) uropod. Paratype X54658/Cr-2476-FEFU, female, 6.0 mm: (H) pleopod 2.
FIGURE 12. Nipponasellus matsumotoi spec. nov., holotype X54659 in Trans-Japan Sea land-bridge disjunction: A case of vicariance in the subterranean genus Nipponasellus (Crustacea, Isopoda, Asellidae) in a largescale biogeographical context
FIGURE 12. Nipponasellus matsumotoi spec. nov., holotype X54659/Cr-2477-FEFU, male, 5.5 mm: (A) pleotelson, ventral view, with enlarged section of coxopodite 7 (g.p., genital papilla); (B) pleopod 1; (C) pleopod 2, with schematic outline of details of appendix masculina; (D) pleopod 3; (E) pleopod 4; (F) pleopod 5; (G) uropod. Paratype X54660/Cr-2478-FEFU, female, 4.2 mm: (H) pleopod 2.
FIGURE 3 in Trans-Japan Sea land-bridge disjunction: A case of vicariance in the subterranean genus Nipponasellus (Crustacea, Isopoda, Asellidae) in a largescale biogeographical context
FIGURE 3. Consensus tree of genera and informal groups of Asellidae showing distribution of character states, with Nipponasellus highlighted in gray and Asellus-pattern highlighted in green. Additive characters nos. 2, 7, 8, 9, 10, 11. Black rectangles – unreversed apomorphies, empty rectangles – homoplasious apomorphies.
FIGURE 10. Nipponasellus matsumotoi spec. nov., holotype X54659 in Trans-Japan Sea land-bridge disjunction: A case of vicariance in the subterranean genus Nipponasellus (Crustacea, Isopoda, Asellidae) in a largescale biogeographical context
FIGURE 10. Nipponasellus matsumotoi spec. nov., holotype X54659/Cr-2477-FEFU, male, 5.5 mm: (A) antennula; (B) antenna; (C) mandible, left; (D) mandible, right; (E) labium; (F) labrum; (G) maxillula, with enlarged outer plate; (H) maxilla, with enlarged inner plate; (I) maxilliped.
FIGURE 2 in Trans-Japan Sea land-bridge disjunction: A case of vicariance in the subterranean genus Nipponasellus (Crustacea, Isopoda, Asellidae) in a largescale biogeographical context
FIGURE 2. Worldwide distribution (A) Asellus macrogroup (green dots) and Calasellus-Columbasellus macrogroup (blue dots); (B) Caecidotea-Proasellus macrogroup (maroon dots), showing localization of Nipponasellus (orange dots). Shaded area indicates hypothetical outline of Late Paleozoic Angarida (Siberia) mainland (after Ganelin 2021).
FIGURE 7. Nipponasellus sudzukhensis spec. nov., holotype X54657 in Trans-Japan Sea land-bridge disjunction: A case of vicariance in the subterranean genus Nipponasellus (Crustacea, Isopoda, Asellidae) in a largescale biogeographical context
FIGURE 7. Nipponasellus sudzukhensis spec. nov., holotype X54657/Cr-2475-FEFU, male, 5.0 mm: (A) pereopod 1, with enlarged palm; (B) pereopod 2; (C) pereopod 3; (D) pereopod 4; (E) pereopod 5; (F) pereopod 6; (G) pereopod 7.
FIGURE 6. Nipponasellus sudzukhensis spec. nov., holotype X54657 in Trans-Japan Sea land-bridge disjunction: A case of vicariance in the subterranean genus Nipponasellus (Crustacea, Isopoda, Asellidae) in a largescale biogeographical context
FIGURE 6. Nipponasellus sudzukhensis spec. nov., holotype X54657/Cr-2475-FEFU, male, 5.0 mm: (A) antennula; (B) antenna; (C) mandible, left; (D) mandible, right; (E) labium; (F) maxillula, with enlarged outer plate; (G) maxilla; (H) maxilliped.
Evaluation and uncertainty analysis of the land surface hydrology in LS3MIP models over China
<p>The attached is the dataset assoicated with the paper titled "Evaluation and uncertainty analysis of the land surface hydrology in LS3MIP models over China" which was submitted to Journal of Earth and Space Science.</p><p>The Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) offers valuable land surface hydrology products from the land modules of current Earth System Models (ESMs). In this paper, historical LS3MIP hydrological variables including precipitation (PR), evapotranspiration (ET), soil moisture (SM), total runoff (Ro), and snow cover fraction (SCF) were extensively evaluated with various high-quality reference datasets over Chinese mainland. The six ESMs in LS3MIP were driven by four meteorological forcing datasets. The results indicated that the LS3MIP multi-model means (MMEs) of most variables are underestimated overall, while they show high spatial consistency in term of linear trends, with the percentage area ranging 56% ~ 85% between simulations and reference datasets. After computing and ranking multi statistical metrics (bias, correlation coefficient, normalized standard deviation, and unbiased root-mean-square biases), it is found that the CESM2 model produces the best performance of land surface hydrological variables, while as the meteorological forcing dataset GSWP3 exhibits the highest quality. Furthermore, the analysis of variance method (ANOVA) was then used to trace sources of the uncertainty of the LS3MIP hydrological variables for 1900–2012 (1948–2012 for Ro). In ANOVA, the simulation uncertainties may be decomposed into three sources: model, atmospheric forcing datasets and their interactions. In LS3MIP historical hydrological variables over China, model uncertainty is the dominant factor overall although it shows regional differences, and the dependence of uncertainty on the model differs among hydrological regimes. This highlights the urgent requirements to improve the land surface model representation in future research.</p>
FIGURE 1 in Early Land Plants Today (ELPT): How many liverwort species are there?
FIGURE 1. Number of novel liverwort species, excluding new combinations, which have been described over the last 250 years, with an inset of the number described from 2001–2009.
FIGURE 3 in Early Land Plants Today (ELPT): How many liverwort species are there?
FIGURE 3. The relationship between synonymy rate and number of species. Synonymy rates for individual monographs from Table 4 are displayed as dots. The current best estimate for the number of liverwort species, 7,486, is indicated by the arrow. Upper and lower estimates (the 95% confidence interval) of 5,536 and 9,432, respectively are shown by dashed lines.
FIGURE 2 in Early Land Plants Today (ELPT): How many liverwort species are there?
FIGURE 2. The geographical distribution of the almost 670 new species that have been described from 1990 to 2009. Dot size represents the number of new species in each area (maximum 81 in New Zealand).
FIGURE 3 in A special issue of Phytotaxa dedicated to Bryophytes: The closest living relatives of early land plants
FIGURE 3. Sphagnum pulchrum (Lindberg 1880: 25) Warnstorf (1900: 42), Alaska (Photo: Blanka Shaw). Sphagnum comprises a speciose clade of mosses that dominates many wetland ecosystems, especially in the boreal zone of the Northern Hemisphere (Shaw et al. 2003). In particular, Sphagnum is an important and conspicuous component in peatlands, which perform a significant global function in regulating the Earth's atmospheric chemistry as well as providing valuable economic commodities (Rochefort 2000). Sphagnum holds an interesting position amongst mosses, with Cox et al. (2004) indicating that Sphagnum and Takakia form a clade sister to all remaining mosses.
FIGURE 2 in A special issue of Phytotaxa dedicated to Bryophytes: The closest living relatives of early land plants
FIGURE 2. Leiosporoceros dussii (Stephani 1893: 142) Hässel (1986: 255), Panama (Photo: Juan Carlos Villarreal). Leiosporoceros is monotypic genus sister to all other hornworts. In the past decade the number of wellrecognized hornwort genera has increased from 6 to 14. Phaeomegaceros is one of the newly-erected genera and as the name implies, this genus has features that are diagnostic of Megaceros (no pyrenoid and single antheridia per cavity) and others shared with Phaeoceros (especially the presence of stomata). Despite the delineation of new genera, hornworts have low species numbers and, alarmingly, this distinct and small group has not been monographed worldwide. Because current phylogenies place them as sister to tracheophytes, hornworts are a critical group for understanding the evolution of plant form (Villarreal et al. 2010). Hornworts have a unique combination of morphological and developmental traits that have long fascinated scientists. Most hornworts have an algal-like chloroplast and exhibit a carbon concentration mechanism not seen in other land plants (e.g., Hanson et al. 2002, Meyer et al. 2008). Interestingly, a cyanobacterial association is ubiquitous in hornwort gametophytes.
Rates of Vertical Land Motion (VLM) on Tutuila Island, American Samoa (December 3, 2015 to August 16, 2022)
<p>[Version 4] Latest version, updated to fix errors that were found in the vlm.mat data file.</p><p>VLM rates across Tutuila Island from December 5, 2015 to August 16, 2022, derived using a redundant multi-primary variant of the PS-InSAR technique. Dataset associated with the published version of the publication "Mapping Vertical Land Motion in Challenging Terrain: Six-Year Trends on Tutuila Island, American Samoa, With PS-InSAR, GPS, Tide Gauge, and Satellite Altimetry Data" by S. A. Huang, J. M. Sauber, and R. Ray. Available at <a href="https://doi.org/10.1029/2022GL101363">https://doi.org/10.1029/2022GL101363</a>.</p><p>ampl.mat: Amplitude backscatter averaged from all scenes, multilooked for speckle reduction and resampled to the dataset size. Normalized to 1.</p><p>latvals.mat: Latitude vector associated with the dataset.</p><p>lonvals.mat: Longitude vector associated with the dataset.</p><p>plot_ps_data.m: Matlab script to visualize the data included in all the *.mat files. Requires Computer Vision Toolbox.</p><p>vlm.mat: Computed VLM rates across Tutuila Island [mm/yr]. All non-PS have an "NaN" value.</p><p>vlm_se.mat: Standard error of VLM across Tutuila Island [mm/yr]. All non-PS have an "NaN" value.</p>
Best learned models : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Best learned models (Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models) for each region based on the classification data set DS-A with seed 0 (see description <a href="https://zenodo.org/deposit/7099785">here</a>). </p><p>Models: GP non spatial, GP spatial (sum), GP spatial (product),RF non spatial, RF spatial, MLP non spatial, MLP spatial, LTAE non spatial, LTAE spatial</p><p>For further details see section VI-C of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p><p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
Response of changes in lake area to drought and land use change
<p>This is the original data from the article "Response of changes in lake area to drought and land use change"</p>
Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM
<p>We developed an attention-enhanced long and short memory (AELSTM) model for predicting vegetation LAI time series based on climatic data. The developed AELSTM model establishes the relationships between the time series of vegetation LAI and climatic variables. </p>
Data: Dryland self-expansion enabled by land-atmosphere feedbacks
<p>The repository contains the scripts and data required to reproduced the final figures in the article: Dryland self-expansion enabled by land-atmosphere feedbacks. Link to the article: <a href="https://doi.org/10.1126/science.adn6833">https://doi.org/10.1126/science.adn6833</a>.</p>
OpenET model data for assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications
<h2>Overview</h2> <p>This dataset includes daily and monthly evapotranspiration (ET) data from the remote sensing models that comprise the [OpenET](https://openetdata.org/) ensemble as described in Melton et al., 2022 (https://doi.org/10.1111/1752-1688.12956); these data were extracted at specific locations within the contiguous United States that coincide with *in situ* measurement stations, including eddy covaraiance, Bowen-ratio, and lysimeter stations. Model ET data where extracted at each site in this dataset using flux footprints as described in Volk et al., (2023) (https://doi.org/10.1016/j.agrformet.2023.109307). These model data alongside the corresponding *in situ* ET data (https://doi.org/10.1016/j.dib.2023.109274) were subsequently used in the manuscript for the OpenET Phase II Intercomparison and Accuracy Assessment (https://doi.org/10.1038/s44221-023-00181-7). </p> <h3><br>Description of the data and file structure</h3> <p>The dataset is in a compressed (zipped) archive titled "OpenET_PhaseII_model_ET_dataset", so first it needs to be downloaded and extracted. The dataset is comprised of just three files. The first file is a Microsoft Excel file "Station_metadata.xlsx" that contains information about the *in situ* ET measurement stations where the OpenET model data was extracted. This file contains information such as site ID's, coordinates, land cover information, and site principal investigator (PI) contact information. Again, the corresponding *in situ* ET data are not included in this dataset. The other two files are tab-delimited text files containing timeseries the OpenET model data themselves, namely the daily ET [mm/day] and monthly ET [mm/month] as extracted for each model and the ensemble value as used in the OpenET Phase II Intercomparison and Accuracy Assessment. </p> <h3><br>Access information and code/software</h3> <p>OpenET data that was used here was produced using operational methods that are implemented on the Google Earth Engine platform. Monthly OpenET model data can be retrieved through Google Earth Data Catalog (e.g., https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0) or through the [online data explorer](https://openetdata.org/) or using the [OpenET API](https://openetdata.org/api-info/).</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.