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 1 Angustopila coprologos n in The world's tiniest land snails from Laos and Vietnam (Gastropoda, Pulmonata, Hypselostomatidae)
FIGURE 1 Angustopila coprologos n. sp. (holotype).
FIGURE 3 Angustopila psammion n in The world's tiniest land snails from Laos and Vietnam (Gastropoda, Pulmonata, Hypselostomatidae)
FIGURE 3 Angustopila psammion n. sp. A: paratype, B–I: holotype.
Land capability for agriculture (partial cover)
<p>The Land capability for agriculture (partial cover) spatial dataset provides information on the types of crops that may be grown in different areas dependent on environmental and soil characteristics. This map covers much of the productive agricultural land in Scotland and it can be used to determine the areas most suited to growing crops or grazing livestock. </p> <p>The Land capability for agriculture map (partial cover) was originally mapped at 1:50 000 scale by field survey and was subsequently digitised. It shows the distribution of the different land classes across virtually all of Scotland’s cultivated agricultural land and adjacent uplands. The map should be cited as: 'Soil Survey of Scotland Staff (1984-87). Land Capability for Agriculture maps of Scotland at a scale of 1:50 000. Macaulay Institute for Soil Research, Aberdeen. 10.5281/zenodo.6322760'.</p> <p>The digital dataset contains information on the 'class' of soil. Soil classes range from Class 1 (land capable of producing a wide range of crops) to Class 7 (land of very little agricultural value). Land within Class 3 is subdivided to provide further information on potential yields; Classes 4 and 5 are further divided to provide information on grasslands; Class 6 is divided on the quality of the natural vegetation for grazing. Classes 1 to 3.1 are known as prime agricultural land.</p> <p>There is an accompanying booklet that describes the classification in more detail and set out the rules and guidelines to be used. This booklet should be referenced as: Bibby, J.S., Douglas, H.A., Thomasson, A.J. and Robertson, J.S. (1991) Land capability classification for agriculture. Soil Survey of Scotland Monograph. The Macaulay Institute for Soil Research. Aberdeen. ISBN -0-7084-0508-8.</p> <p>The spatial dataset is provided under the James Hutton Institute open data licence included within the zipped dataset.</p> <p>The maintenance of this dataset is funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at <a href="https://www.hutton.ac.uk/learning/natural-resource-datasets/soilshutton/soils-maps-scotland/download">https://www.hutton.ac.uk/learning/natural-resource-datasets/soilshutton/soils-maps-scotland/download </a>or viewed at https://soils.environment.gov.scot.</p> <p>THE CLASSES<br> Class 1. Land capable of producing a very wide range of crops with high yields<br> Class 2. Land capable of producing a wide range of crops with yields less high than Class 1.<br> Class 3. Land capable of producing good yields from a moderate range of crops.<br> Class 4. Land capable of producing a narrow range of crops.<br> Class 5. Land suited only to improved grassland and rough grazing.<br> Class 6. Land capable only of use as rough grazing.<br> Class 7. Land of very limited agricultural value.</p>
Land Capability for Agriculture (LCA)
<p>The National scale land capability for agriculture spatial dataset provides information on the types of crops that may be grown in different areas dependent on environmental and soil characteristics. This map covers the entire country and it can be used to determine the areas most suited to growing crops or grazing livestock. </p> <p>The digital dataset contains information on the 'class' of soil. Soil classes range from Class 1 (land capable of producing a wide range of crops) to Class 7 (land of very little agricultural value). Land within Class 3 is subdivided to provide further information on potential yields; Classes 4 and 5 are further divided to provide information on grasslands; Class 6 is divided on the quality of the natural vegetation for grazing. Classes 1 to 3.1 are known as prime agricultural land.</p> <p>The Land Capability for Agriculture assessment was carried out in 1981 using data collected between 1978 and 1981. The National scale land capability for agriculture map was then created in 1983 at a scale of 1:250 000. The map should be cited as: 'Soil Survey of Scotland Staff (1981). Land Capability for Agriculture maps of Scotland at a scale of 1:250 000. Macaulay Institute for Soil Research, Aberdeen.10.5281/zenodo.6322683'.</p> <p>There is an accompanying booklet that describes the classification in more detail and set out the rules and guidelines to be used. This booklet should be referenced as: Bibby, J.S., Douglas, H.A., Thomasson, A.J. and Robertson, J.S. (1991) Land capability classification for agriculture. Soil Survey of Scotland Monograph. The Macaulay Institute for Soil Research. Aberdeen. ISBN -0-7084-0508-8.</p> <p>The spatial dataset is provided under the James Hutton Institute open data licence included within the zipped dataset.</p> <p>The maintenance of this dataset is funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at https://www.hutton.ac.uk/soil-maps/ or viewed at https://soils.environment.gov.scot.</p> <p>THE CLASSES<br>Class 1. Land capable of producing a very wide range of crops with high yields<br>Class 2. Land capable of producing a wide range of crops with yields less high than Class 1.<br>Class 3. Land capable of producing good yields from a moderate range of crops.<br>Class 4. Land capable of producing a narrow range of crops.<br>Class 5. Land suited only to improved grassland and rough grazing.<br>Class 6. Land capable only of use as rough grazing.<br>Class 7. Land of very limited agricultural value.</p>
Land Capability for Forestry (LCF)
<p>The Land Capability Classification for Forestry is based on an assessment of the increasing degree of limitation imposed by the physical factors of soil, topography and climate on the growth of trees and on silvicultural practices. The system described is designed for use at several levels depending on the amount and accuracy of the available data and on the purpose for which the information is required. The main use of the Land Capability Classification for Forestry is as an aid to decision-making at broad planning levels, as a guide for land managers and as a statement of the natural resources of the land of Britain in terms of forestry potential for educational and general interest purposes. The system is an interpretation derived from several sources and, as with all such approaches, will be subject to some degree of arbitrary decision.</p> <p>The digital dataset contains information on the 'class' of soil. Classes range from F1 (land offering excellent flexibility for growth and management) to F7 (land unsuitable for producing trees) with seven types of limitations, these being climate, wind throw, nutrients, topography, droughtiness, wetness and soil.</p> <p>The map was based on a reclassification of the 580 soil map units of the National soil map of Scotland with limited field validation. A set of rules were applied to each of the soil map units combined with information on climate and relief to assign the map units to a capability class for forestry.</p> <p>The dataset should be cited as Soil Survey of Scotland Staff. (1988). Land Capability for Forestry of Scotland at a Scale of 1:250 000. Macaulay Land Use Research Institute, Aberdeen. 10.5281/zenodo.6322608.</p> <p>There is an accompanying handbook which describes the classification system in detail (Bibby, J.S., Heslop, R.E.F. and Hartnup, R. 1988. Land Capability Classification for Forestry in Britain. The Macaulay Land Use Research Institute, Aberdeen. ISBN-0-7084-0467-7.</p> <p>The maintenance of this dataset is funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at <a href="https://www.hutton.ac.uk/soil-maps/">https://www.hutton.ac.uk/soil-maps/ </a>or viewed at https://soils.environment.gov.scot.</p> <p>Class F1. Land with excellent flexibility for the growth and management of tree crops<br>Class F2. Land with very good flexibility for the growth and management of tree crops<br>Class F3. Land with good flexibility for the growth and management of tree crops<br>Class F4. Land with moderate flexibility for the growth and management of tree crops<br>Class F5. Land with limited flexibility for the growth and management of tree crops<br>Class F6. Land with very limited flexibility for the growth and management of tree crops<br>Class F7. Land unsuitable for producing tree crops</p>
Data for: Modeling of spatial pattern and influencing factors of cultivated land quality based on spatial-temporal big data (PONE-D-21-21084R1)
<p>The quality of cultivated land determines the production capacity of cultivated land and the level of regional development, and also directly affects the food security and ecological safety of the country. This paper starts from the perspective of spatial pattern of cultivated land quality and uses spatial autocorrelation analysis to study the spatial aggregation characteristics and differences of cultivated land quality in Henan Province at the county level scale, and also uses bivariate spatial autocorrelation to analyze the influence of neighboring influences on the quality of cultivated land in the target area. The spatial autoregressive model was used to further analyze the driving factors affecting the quality of cultivated land, and the influence of cultivated land area index was coupled in the process of rating analysis, which was finally used as a basis to propose more precise measures for the protection of cultivated land zoning. The results show that: (1) The quality of cultivated land in Henan Province has a strong spatial correlation (global Moran's I≈0.710) and shows an obvious aggregation pattern in spatial distribution; positive correlation types (high-high and low-low) are concentrated in north-central and western mountainous areas of Henan Province, respectively; negative correlation types are discrete. The negative correlation types are distributed in a discrete manner. (2) The bivariate spatial autocorrelation results show that Slope (Moran's I≈-0.505), Irrigation guarantee rate (IGR, 0.354), Urbanization rate (-0.255), Total agricultural machinery power (TAMP, 0.331) and Pesticide use (0.214) are the main influencing factors. (3) According to the absolute values of the regression coefficients, it can be seen that the magnitude of the influence of different factors on the quality of cultivated land is: Slope (0.089) >IGR (0.025) > Urbanization rate (0.002) > TAMP (0.001) > Pesticide use (1.96e-006). (4) Based on the spatial pattern presented by the spatial autocorrelation results, we proposed corresponding protection zoning measures to provide more scientific reference decisions and technical support for the implementation of refined cultivated land management in Henan Province. </p>
Global Daily Surface Blue-sky Albedo Climatology and Land Cover Climatology Dataset from 20-year MODIS Products (500m)
<p>Only the first days of each month were uploaded to Zenodo due to the data storage limitation, and the full dataset is available at http://glass.umd.edu/albedo_clim/.</p> <p>Surface albedo plays a critical role in climate, hydrological, and biogeochemical modeling and weather forecasting. Therefore, precisely mapping surface albedo climatology globally is necessary to better parameterize environmental systems. We generated a new global surface blue-sky actual and snow-free albedo climatology dataset from 20-year MODIS products from the Google Earth Engine (GEE). </p> <p>The 500m global surface blue-sky daily albedo climatology dataset follows the basic MODIS product format and employed the sinusoidal projection. It includes historical and snow-free blue-sky albedo climatology data. For application convenience, the land cover climatology of MODIS product (MCD12Q1) is also generated and attached. The International Geosphere-Biosphere Programme (IGBP) and PFT classification climatology of MCD12Q1 since 2001 were also generated.</p>
Explicitly modelling microtopography in permafrost landscapes in a land-surface model (JULES vn5.4_microtopography)
<p>Model output data, processed observational data and plotting code used to create the figures for the paper 'Explicitly modelling microtopography in permafrost landscapes in a land-surface model (JULES vn5.4_microtopography)' submitted to Geoscientific Model Development (2021). These describe the effect of explicitly representing microtopography in the JUELS land surface model on modelled snow depth, soil moisture, temperature, and methane fluxes, and compare these with observations.</p>
Fig.1 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig.1. Sampling sites for Vestia turgida in Ukraine (photo by O. Baidashnikov).
Dataset and R code from: Positive and negative effects of land abandonment on butterfly communities revealed by a hierarchical sampling design across climatic regions
<p class="MsoNormal">Land abandonment may decrease biodiversity but also provides an opportunity for rewilding. It is therefore necessary to identify areas that may benefit from traditional land management practices and those that may benefit from a lack of human intervention. In this study, we conducted comparative field surveys of butterfly occurrence in abandoned and inhabited settlements in 18 regions of diverse climatic zones in Japan to test the hypotheses that species-specific responses to land abandonment correlate with climatic niches and habitat preferences. Hierarchical models that unified species occurrence and habitat preferences revealed that negative responses to land abandonment were associated with species that have cold climatic niches and utilize open habitats, suggesting that species negatively impacted by land abandonment will decline more due to future climate warming. Maps representing species gains and losses due to land abandonment, which were created from the model estimates, showed similar geographic patterns, but some areas exhibited high species losses relative to gains. Our hierarchical modelling approach was useful for scaling up local-scale effects of land abandonment to a macro-scale assessment, which is crucial to developing spatial conservation strategies in the era of depopulation.</p>
Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 5. Partial dependence plot for terrain roughness index (tri).
Fig. 7 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 7. Partial dependence plot for silt content (SLT).
Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 6. Partial dependence plot for pH water (phh2o).
Fig. 2 in First Records Of The Land Snail Monacha Fruticola (Gastropoda, Stylommatophora, Hygromiidae) In Western Ukraine
Fig. 2. Known records of M. fruticola in Ukraine outside the Crimea.
Fig. 1 in First Records Of The Land Snail Monacha Fruticola (Gastropoda, Stylommatophora, Hygromiidae) In Western Ukraine
Fig. 1. Habitat of M. fruticola and snails of different ages: A, B — Lviv; С — Briukhovychi.
Fig. 1 in Invasive Land Snail Oxychilus Translucidus (Stylommatophora, Zonitidae) In The Catacombs Of Odesa (Ukraine)
Fig. 1. Oxychilus translucidus in Odesa Catacombs (photo by O. Kovtun).
A global land surface 250-m 8-day Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product (2022-part1)
<p>The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a critical land surface variable for carbon cycle modeling and ecological monitoring. We generated the latest version (V6) of the FAPAR product at a 250-m resolution, which is also aggregated to multiple coarser resolutions (up to 0.25° and monthly), from MODIS surface reflectance data and other information, as part of the Global Land Surface Satellite (GLASS) products suite. Three existing global FAPAR products (MODIS Collection 6, GLASS V5, and PROBA-V V1) were used to generate the time-series training samples for developing the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Direct validation using high-resolution FAPAR maps from VALERI and ImagineS networks revealed that the GLASS V6 FAPAR product achieves higher accuracy than PROBA-V, MODIS, and GLASS V5, with R<sup>2</sup> of 0.80 and RMSE of 0.10-0.11 at the 250-m, 500-m, and 3-km scales, and a higher percentage (72%) of retrievals in meeting accuracy requirement of 0.1. Global spatial evaluation and temporal comparison at the Ameriflux and NEON sites indicated that the GLASS V6 FAPAR is more spatio-temporal continuous and better reflects the variations of vegetation than the GLASS V5 FAPAR. </p> <p>The temporal span of GLASS V6 FAPAR product is from 2000 to 2020 and the temporal frequency is 8-day. The 250-m and 500-m data are in the Sinusoidal projection, while the 0.05°, 0.1° and 0.25° data are in geographic latitude/longitude. Data files are provided in HDF−EOS format.</p>
A global land surface 250-m 8-day Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product (2022-part2)
<p>The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a critical land surface variable for carbon cycle modeling and ecological monitoring. We generated the latest version (V6) of the FAPAR product at a 250-m resolution, which is also aggregated to multiple coarser resolutions (up to 0.25° and monthly), from MODIS surface reflectance data and other information, as part of the Global Land Surface Satellite (GLASS) products suite. Three existing global FAPAR products (MODIS Collection 6, GLASS V5, and PROBA-V V1) were used to generate the time-series training samples for developing the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Direct validation using high-resolution FAPAR maps from VALERI and ImagineS networks revealed that the GLASS V6 FAPAR product achieves higher accuracy than PROBA-V, MODIS, and GLASS V5, with R<sup>2</sup> of 0.80 and RMSE of 0.10-0.11 at the 250-m, 500-m, and 3-km scales, and a higher percentage (72%) of retrievals in meeting accuracy requirement of 0.1. Global spatial evaluation and temporal comparison at the Ameriflux and NEON sites indicated that the GLASS V6 FAPAR is more spatio-temporal continuous and better reflects the variations of vegetation than the GLASS V5 FAPAR. </p> <p>The temporal span of GLASS V6 FAPAR product is from 2000 to 2020 and the temporal frequency is 8-day. The 250-m and 500-m data are in the Sinusoidal projection, while the 0.05°, 0.1° and 0.25° data are in geographic latitude/longitude. Data files are provided in HDF−EOS format.</p>
Spatial heterogeneity effects on land surface modeling of water and energy partitioning
<p>Related code and data used in the manuscript https://doi.org/10.5194/gmd-2022-4, <Spatial heterogeneity effects on land surface modeling of water and energy partitioning>. The latest source code of ELMv1 is available from https://github.com/E3SM-Project/E3SM (last access: September 2020). If you have any questions, please contact lingchengliwhu@gmail.com</p>
Dataset for manuscript entitled: Switchgrass cropping systems affect soil carbon and nitrogen and microbial diversity and activity on marginal lands
<p class="MsoListParagraph">Switchgrass (<em>Panicum virgatum</em> L.),<span> </span>as a dedicated bioenergy crop, can provide cellulosic feedstock for biofuel production while improving or maintaining soil quality. However, comprehensive evaluations of how switchgrass cultivation and nitrogen (N) management impact soil and plant parameters remain incomplete. We conducted<span> </span>field trials in three years (2016–2018) at six locations in the North Central Great Lakes Region to evaluate the effects of cropping systems (switchgrass, restored prairie, undisturbed control) and N rates (0, 56 kg N ha<sup>-1</sup> yr<sup>-1</sup>) on biomass yield and soil physicochemical, microbial, and enzymatic parameters. Switchgrass cropping system yielded an aboveground biomass 2.9–3.3 times higher than the other two systems (Jayawardena et al., In submission) but our study found that this biomass accumulation didn't reduce soil dissolved organic C (DOC), total dissolved N (TDN), or bacterial diversity. The annual aboveground biomass removal for bioenergy feedstock, however, reduced soil microbial biomass C (MBC) and N (MBN) and bacterial richness in the 2<sup>nd</sup> and 3<sup>rd</sup> years; despite this, continuous monocropping of switchgrass improved soil TDN, inorganic N, bacterial diversity, and shoot biomass in the 2<sup>nd</sup> and/or 3<sup>rd</sup> years when compared to the 1<sup>st</sup> year. N fertilization increased aboveground biomass yield by 1.2 times and significantly increased soil TDN, MBN, and the shoot biomass of switchgrass when compared to the unfertilized control. Locations with higher C and N contents and lower C:N ratio had higher aboveground biomass, MBC, MBN, and the activity of BG, CBH, and UREA enzymes; by contrast, locations with higher pH had higher soil TDN and activity of NAG and LAP enzymes. Our research demonstrates that switchgrass cultivation could improve or maintain soil N content and N fertilization can increase plant biomass yield. The comprehensive data also can inform future biogeochemical models to successfully implement switchgrass for bioenergy production.</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.