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.
300
datasets available to search
ShareScore release 0.7.1
Dataset results
300 results for “land change”
Code and data used for findings and figures in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region"
<p><span>This is the code and data used in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region" to generate all findings and figures.</span></p>
Figure 4 from: Razo-León AE, Vásquez-Bolaños M, Muñoz-Urias A, Huerta-Martínez FM (2018) Changes in bee community structure (Hymenoptera, Apoidea) under three different land-use conditions. Journal of Hymenoptera Research 66: 23-38. https://doi.org/10.3897/jhr.66.27367
Figure 4 Ordination diagram derived from NMDS for the different land-use areas and bee tribal data.
Figure 1 from: Razo-León AE, Vásquez-Bolaños M, Muñoz-Urias A, Huerta-Martínez FM (2018) Changes in bee community structure (Hymenoptera, Apoidea) under three different land-use conditions. Journal of Hymenoptera Research 66: 23-38. https://doi.org/10.3897/jhr.66.27367
Figure 1 Location of the APFFSQ and sampling sites.
Figure 2 from: Razo-León AE, Vásquez-Bolaños M, Muñoz-Urias A, Huerta-Martínez FM (2018) Changes in bee community structure (Hymenoptera, Apoidea) under three different land-use conditions. Journal of Hymenoptera Research 66: 23-38. https://doi.org/10.3897/jhr.66.27367
Figure 2 Rarefaction curve for bee richness among the different land-use conditions.
Data supporting the publication of "Interactive effects of climate change and land-use change on mammal range retraction in Great Britain"
<p><strong>Table S1 (Species records) provided as a separate .xlsx file in Supporting Information. </strong>List of species included in the sample with corresponding attributes, number of records and rates of change over time.</p> <p>Column A (Scientific name): species’ accepted scientific name (n = 43 species).</p> <p>Column B (Common name): species’ common name in Great Britain (n = 43 names).</p> <p>Column C (Order): species’ taxonomical Order (n = 6 Orders).</p> <p>Column D (Family): species’ taxonomical Family (n = 14 Families).</p> <p>Column E (Guild): species’ sampling guild (n = 3 Guilds, either Bats, Midlarge, or Small</p> <p>Column F (Distribution): species’ distribution status in Great Britain (n = 3 Statuses, either Native, Naturalised, or Non-Native).</p> <p>Column G (Habitat): species’ habitat preference (n = 2 Habitats, either Terrestrial or Freshwater).</p> <p>Column H (Records): total number of records per species from 1960 to 2016 (average = 10,931).</p> <p>Column I (1960s): total number of records per species from 1960 to 1969 (average = 420).</p> <p>Column J (1970s): total number of records per species from 1970 to 1979 (average = 457).</p> <p>Column K (1980s): total number of records per species from 1980 to 1989 (average = 423).</p> <p>Column L (1990s): total number of records per species from 1990 to 1999 (average = 641).</p> <p>Column M (2000s): total number of records per species from 2000 to 2010 (average = 943).</p> <p>Column N (2010s): total number of records per species from 2011 to 2016 (average = 870).</p> <p>Column O (Hectads TP1): number of hectads where the species has been recorded in Time Period 1, from 1960 to 1992 (average = 892).</p> <p>Column P (Hectads TP2): number of hectads where the species has been recorded in Time Period 2, from 2000 to 2016 (average = 1,117).</p> <p>Column Q (Hectads Total): number of hectads where the species has been recorder from 1960 to 2016 (average = 1,315).</p> <p>Column R (Extirpation rate): species’ extirpation rate, calculated as the ratio of extirpations over the sum of extirpations and persistences (average = 0.24). The sum of extirpation and persistence rates is always equal to 1.</p> <p>Column S (Persistence rate): species’ persistence rate, calculated as the ratio of persistences over the sum of extirpations and persistences (average = 0.76). The sum of persistence and extirpation rates is always equal to 1.</p> <p>Column T (Occupancy TP1): species’ occupancy estimate in Time Period 1, from 1960 to 1992, as calculated in Frescalo (average = 0.395).</p> <p>Column U (Occupancy TP2): species’ occupancy estimate in Time Period 2, from 2000 to 2016, as calculated in Frescalo (average = 0.403).</p> <p>Column V (Occupancy change): change in the species’ occupancy estimates between Time Periods 1 and 2, as calculated in Frescalo (average = 0.076).</p> <p>Column W (Occupancy change slope): average yearly change in the species’ occupancy estimates from 1960 to 2016, as calculated in Frescalo (average = -0.001).</p> <p>Column X (Frequency TP1): adjusted frequency of occurrence in Time Period 1, from 1960 to 1992, as calculated in Frescalo (average = 0.527).</p> <p>Column Y (Frequency TP2): adjusted frequency of occurrence in Time Period 2, from 2000 to 2016, as calculated in Frescalo (average = 0.461).</p> <p>Column Z (Frequency change): change in the adjusted frequency of occurrence between Time Periods 1 and 2, as calculated in Frescalo (average = -0.066).</p>
Land change dynamics in the National Natural Park System of Colombia (2000-2018)
<p>This dataset contains final and intermediate products for analysis of Land Use and Land Cover Change in the National Natural Park System of Colombia from 2000 to 2018.</p> <p>The R scripts used to produce this dataset are available at: <a href="https://doi.org/10.5281/zenodo.7562104">https://doi.org/10.5281/zenodo.7562104</a>.</p> <p>The original Landsat multi-year composites were produced using the <a href="https://www.cde.unibe.ch/research/projects/a_tool_for_satellite_image_preprocessing_and_composition/index_eng.html">Google Earth Engine Image Pre-processing Tool</a>.</p>
Data from: Exotic species enhance response diversity to land-use change but modify functional composition
Open the record for dataset details and reuse information.
Millennium Ecosystem Assessment: MA Rapid Land Cover Change
The Millennium Ecosystem Assessment: MA Rapid Land Cover Change provides data and information on global and regional land cover change in raster format for agriculture (cropland increase and disease), deforestation (forest mask and hotspots), desertification (hotspot areas of degraded land and degradation types), and fires (most frequent and exceptional fires). Urbanization data are in vector format for cities with the highest rate of change (1995-2015), largest cities (2000), and the overlay of these two data layers for the year 2000. This assessment identified the need to synthesize what is known about areas of rapid land cover change around the world in order to evaluate how the provision of ecosystem goods and services has changed.
Future land use/cover change (LUCC) simulated by FLUS and SD model under four SSP-RCP scenarios
Open the record for dataset details and reuse information.
Future forest age distribution after land use/cover change under four SSP-RCP scenarios
Open the record for dataset details and reuse information.
A COMPARATIVE STUDY OF ALGORITHMS FOR LAND COVER CHANGE
A COMPARATIVE STUDY OF ALGORITHMS FOR LAND COVER CHANGE SHYAM BORIAH*, VARUN MITHAL*, ASHISH GARG*, VIPIN KUMAR*, MICHAEL STEINBACH*, CHRIS POTTER**, AND STEVE KLOOSTER*** Abstract. Ecosystem-related observations from remote sensors on satellites offer huge potential for understanding the location and extent of global land cover change. This paper presents a comparative study of three time series based algorithms for detecting changes in land cover. The techniques are evaluated quantitatively using forest fire ground truth from the state of California for 2000–2009. On relatively high quality data sets, all three schemes perform reasonably well, but their ability to handle noise and natural variability in the vegetation data differs dramatically. In particular, one of the algorithms significantly outperforms the other two since it accounts for variability in the time series.
NLCD 1992/2001 Retrofit Land Cover Change Product
Developments in mapping methodology, new sources of input data, and changes in the mapping legend for the 2001 National Land Cover Database (NLCD2001) will confound any direct comparison between NLCD2001 and National Land Cover Dataset 1992 (NLCD1992). Users are cautioned that direct comparison of these two independently created land cover products is not recommended. This NLCD 1992/2001 Retrofit Land Cover Change Product was developed to offer users more accurate direct change analysis between the two products. The NLCD 1992/2001 Retrofit Land Cover Change Product uses a specially developed methodology to provide land cover change information at the Anderson Level I classification scale (Anderson et al., 1976*), relying on decision tree classification of Landsat satellite imagery from circa 1992 and 2001. Unchanged pixels between the two dates are coded with the NLCD01 Anderson Level I class code, while changed pixels are labeled with a "from-to" land cover change value. Additional details about this product are available in the metadata included in the multi-zone downloadable zip file. This product is designed for regional application only and is not recommended for local scales.
ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V004
This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).
Adaptive changes in the vestibular system of land snail to a 30-day spaceflight and readaptation on return to Earth
The vestibular system receives a permanent influence from gravity and reflexively controls equilibrium. If we assume gravity has remained constant during the species evolution will its sensory system adapt to abrupt loss of that force? We address this question in the land snail Helix lucorum exposed to 30 days of near weightlessness aboard the Bion-M1 satellite and studied geotactic behavior of postflight snails differential gene expressions in statocyst transcriptome and electrophysiological responses of mechanoreceptors to applied tilts. Each approach revealed plastic changes in the snail s vestibular system assumed in response to spaceflight. Absence of light during the mission also affected statocyst physiology as revealed by comparison to dark-conditioned control groups. Readaptation to normal tilt responses occurred at ~20 h following return to Earth. Despite the permanence of gravity the snail responded in a compensatory manner to its loss and readapted once gravity was restored.
ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V004
This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).
Investigating the Combined Impact of Climate Change and Land Use/Land Cover on Flood Vulnerability Using a Machine Learning Algorithm
<p>Using the uploaded code in preparation of the Flood vulnerability maps.</p><p> </p>
Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing
<p>This dataset contains continental (Africa) land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources. The dataset contains seven classes, prepared annually from 2000 to 2015, using high‐resolution Landsat 7 images (ETM+) and analyzed by Google Earth Engine cloud computing method. The model that generated the LULC classification was evaluated for predictive accuracy across classes as well as overall accuracy. The model achieved an overall accuracy of 88% with class-specific user’s and producer’s accuracies ranged from 84-94% and 79-96% respectively (Midekisa et al., 2017).</p> <p> </p>
Exploring the lunar regolith's thickness and dielectric properties using band-limited impedance at Chang'E-4 landing site
<p>Exploring the lunar regolith's thickness and dielectric properties using band-limited impedance at Chang'E-4 landing site</p> <p>(Journal of Geophysical Research - Planets #2022JE007540).</p>
Investigating the Combined Impact of Climate Change and Land Use/Land Cover on Flood Vulnerability Using a Machine Learning Algorithm.
<p>The used data in the paper</p>
Investigating the Combined Impact of Climate Change and Land Use/Land Cover on Flood Vulnerability Using a Machine Learning Algorithm.
<p>Flood points and Meteorological data</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.