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150 results for “deforestation”
Processed dataset for "Links between deforestation, conservation areas, and conservation funding in major deforestation regions of South America"
<p>Processed dataset used for the models in "Links between deforestation, conservation areas, and conservation funding in major deforestation regions of South America"</p> <p>Authors: Siyu Qin, Ana Buchadas, Patrick Meyfroidt, Yifan He, Arash Ghoddousi, Florian Pötzschner, Matthias Baumann, Tobias Kuemmerle</p> <p> </p> <p> </p>
DeDuCE: Deforestation and carbon emissions due to agriculture and forestry activities from 2001-2022
<h2>Overview</h2> <p>This dataset provides country-level estimates of agriculture and forestry-driven deforestation and associated carbon emissions for the period 2001-2022. A sub-national level attribution dataset is available for Brazil. Generated by the Deforestation Driver and Carbon Emission (DeDuCE) model, it amalgamates remotely sensed datasets with extensive agricultural statistics to estimate deforestation attributable to agricultural and forestry activities globally. Developed utilizing Google Earth Engine and Python, DeDuCE comprehensively covers over 9300 unique country-commodity footprints across <strong>179 countries and 184 commodities</strong> within the specified period, presenting an unmatched scope and granularity of data.</p> <h2>Documentation</h2> <p>The manuscript detailing the dataset is currently archived at EarthArXiV: <strong><em>Singh, C., & Persson, U. M. (2024). Global patterns of commodity-driven deforestation and associated carbon emissions</em></strong>. <a href="https://doi.org/10.31223/X5T69B" target="_blank" rel="noopener">https://doi.org/10.31223/X5T69B</a></p> <p>The insights from this dataset can also be viewed at: <strong><a href="https://www.deforestationfootprint.earth" target="_blank" rel="noopener">https://www.deforestationfootprint.earth</a></strong></p> <h2>Repository contents</h2> <p>The input and output/data generated by the model are archived here at <strong>Zenodo, </strong>and their description is available in <strong>'README (files in the directory).txt'</strong>.</p> <p>The columns of the (final) dataset '<em>DeDuCE_Deforestation_attribution_v1.0.1 (2001-2022).xlsx</em>' in the folder <em><strong>'Final Attribution Results'</strong></em> represent the following:</p> <ul> <li><strong>Continent/Country group: </strong>All countries are divided into 8 geographical regions</li> <li><strong>ISO: </strong>Three-letter country codes defined by ISO</li> <li><strong>Producer country: </strong>Country of deforestation</li> <li><strong>Year: </strong>Year of deforestation, ranges from 2001-2022 </li> <li><strong>Commodity group: </strong>All commodities are divided into 11 commodity groups</li> <li><strong>Commodity: </strong>Name of commodity aligning with FAOSTAT</li> <li><strong>Deforestation attribution, unamortized (ha): </strong>Annual deforestation estimates</li> <li><strong>Deforestation risk, amortized (ha): </strong>5-year amortised deforestation estimates</li> <li><strong>Deforestation emissions excl. peat drainage, unamortized (MtCO2): </strong>Annual estimates of carbon emissions (based on AGB, BGB, deadwood, litter, soil organic carbon and carbon stock of replacing commodity)</li> <li><strong>Deforestation emissions excl. peat drainage, amortized (MtCO2): </strong>5-year amortised carbon emission estimates, excluding carbon emissions from peatland drainage<strong> </strong></li> <li><strong>Peatland drainage emissions (MtCO2): </strong>Annual estimates of carbon emissions from peatland drainage<strong> </strong></li> <li><strong>Deforestation emissions incl. peat drainage, amortized (MtCO2): </strong>5-year amortised carbon emission estimates, including emissions from peatland drainage</li> <li><strong>Quality Index: </strong>Flagging deforestation estimates </li> </ul> <h2>Contact</h2> <p>If you have any questions, you can contact us at: </p> <p> Chandrakant Singh and U. Martin Persson <br> <em><strong><a href="mailto:chandrakant.singh@chalmers.se;martin.persson@chalmers.se">Email</a></strong>: chandrakant.singh@chalmers.se and martin.persson@chalmers.se </em><br> Physical Resource Theory, Department of Space, Earth & Environment, <br> Chalmers University of Technology, Gothenburg, Sweden</p>
Area Estimates of Forest Degradation and Deforestation in the Country of Georgia by Region
<p>Area estimates of forest degradation and deforestation in the country of Georgia by region from 1987 to 2019. Unit is square kilometers.</p> <p>georgia_forest_def_0512.csv: Area estimates of deforestation</p> <p>georgia_forest_deg_0512.csv: Area estimates of forest degradation</p> <p>Please cite the data as: <a href="https://www.sciencedirect.com/science/article/pii/S0034425721003680">Chen, S., Woodcock, C.E., Bullock, E.L., Arévalo, P., Torchinava, P., Peng, S. and Olofsson, P., 2021. Monitoring temperate forest degradation on Google Earth Engine using Landsat time series analysis. Remote Sensing of Environment, 265, p.112648.</a></p> <p><a href="https://authors.elsevier.com/a/1devg7qzStnwW">Click here to get 50-day free access without registration</a></p>
Figure 4 in Deforestation affects biogeographical regionalization: a case study contrasting potential and extant distributions of Mexican terrestrial mammals
Figure 4. Map of Mexican provinces based on models of species' distributions in t1: baj, Baja California Chiapas; ist, Isthmus of Tehuantepec; mgu, Mexican Gulf; mpa, Mexican Pacific Coast; mpl, Mexican Sierra Madre Occidental; sms, Sierra Madre del Sur; son, Sonora; tam, Tamaulipas; vol, Transmexican
Figure 2 in Deforestation affects biogeographical regionalization: a case study contrasting potential and extant distributions of Mexican terrestrial mammals
Figure 2. Simplified consensus cladograms of (a) t1 (natural vegetation), and (b) t2 (land use and vegetation map based on the 2000 Inventario Forestal Nacional (SEMARNAT 2001).
Figure 1 in Deforestation affects biogeographical regionalization: a case study contrasting potential and extant distributions of Mexican terrestrial mammals
Figure 1. Ecological niche modelling projected as potential (based on the natural vegetation, t1) and extant (based on land use and natural vegetation, t2) distribution of two terrestrial mammals. Black areas depict part of distributions of (a) Chaetodipus spinatus in the Baja California Peninsula, and (b) Cabassous centralis in Chiapas.
Fig. 1 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change
Fig. 1. Camera-trap image of mainland serow (Capricornis sumatraensis) from the lowlands of the Pasoh Forest Reserve in Peninsular Malaysia at an elevation of ~100 m.
Fig. 4 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change
Fig. 4. Regional-scale relationships between serow captures and covariates. Displayed are the variables within the top-performing multivariate model as assessed by lower AICc scores. All covariates are averaged at a 20-km radius around the study area.
Fig. 5 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change
Fig. 5. The relationships between serow predicted abundance and habitat variables at the local scale from Royle-Nichols hierarchical models. Oil palm, roughness and Human Footprint Index were in the top performing multivariate model.
Fig. 3 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change
Fig. 3. Presence of the serow within its Southeast Asian range. Panel a) shows the IUCN Red List range extent of occurrence (EOO; shaded orange), and the occurrence records coloured by the data source. Panel b) shows the jackknife-based assessment of variable importance. The blue bars showing the explanatory power in the model using only the denoted variable, while the teal bars show the predictive power of the full model without the denoted variable, highlighting whether the variable captures unique information. Panel c) shows the probability of presence of the serow from Maxent modelling mapped within the Southeast Asian region covered by this study. Panel d) shows the forest cover in 2015 that is potentially occupied within the EOO. Panel e) is the Maxent probability of presence of serow inside the remaining forested areas within Southeast Asia. Original artwork courtesy of Tamzin Barber (https://www.talkinganimals.com.au/).
Higher temperature variability in deforested mountain regions impacts the competitive advantage of nocturnal species
Open the record for dataset details and reuse information.
Data and code for: Nature-based climate solutions can help mitigate the radiative forcing that follows deforestation
Open the record for dataset details and reuse information.
Data from: Avian ecological succession in the Amazon: a long-term case study following experimental deforestation
<p>Approximately 20% of the Brazilian Amazon has now been deforested, and the Amazon is currently experiencing the highest rates of deforestation in a decade, leading to large-scale land-use changes. Roads have consistently been implicated as drivers of ongoing Amazon deforestation and may act as corridors to facilitate species invasions. <span>Long-term data, however, are necessary to determine how ecological succession alters avian communities following deforestation and whether established roads lead to a constant influx of new species. </span> </p> <p>We used data across nearly 40 years from a large-scale deforestation experiment in the central Amazon to examine the avian colonization process in a spatial and temporal framework, considering the role that roads may play in facilitating colonization.</p> <p>Since 1979, 139 species that are not part of the original forest avifauna have been recorded, including more secondary forest species than expected based on the regional species pool. Among the 35 species considered to have colonized and become established, a disproportionate number were secondary forest birds (63%), almost all of which first appeared during the 1980s. These new residents comprise about 13% of the current community of permanent residents.</p> <p><span>Widespread generalists associated with secondary forest colonized quickly following deforestation, with few new species added after the first decade, despite a stable road connection. Few species associated with riverine forest or specialized habitats colonized, despite road connection to their preferred source habitat. </span>Colonizing species remained restricted to anthropogenic habitats and did not infiltrate old-growth forests nor displace forest birds.</p> <p>Deforestation and expansion of road networks into <i>terra firme </i>rainforest will continue to create degraded anthropogenic habitat. Even so, the initial pulse of colonization by non-primary forest bird species was not the beginning of a protracted series of invasions in this study, and the process appears to be reversible by forest succession. </p>
Mitigating the impact of bad rainy seasons in poor agricultural regions to tackle deforestation
<p>I provide the stata files that allow to reproduce the results presented in the paper <br> "Mitigating the impact of bad rainy seasons in poor agricultural regions to tackle deforestation" by Antoine Leblois.</p> <p>The replication folder contains two files:<br> 1- *.dta file: database<br> 2- *.do file: do-file containing the codes to replicate the results</p> <p>Stata 14 was used for this work.</p>
Data from: Tropical deforestation reduces plant mating quality by shifting the functional composition of pollinator communities
<p>Deforestation can impact the quality of pollen received by target plants (<i>i.e., </i>delivery of incompatible pollen, self-pollen, or pollen from closely related individuals). Such reductions in plant mating quality may be direct, when deforestation reduces plant population size and the availability of pollen donors, or indirect, when decreased mating quality results, for example, from shifts in the composition of the pollinator community. As most flowering plants depend on animal pollinators for reproduction, there is a need to understand the direct and indirect links between deforestation, pollinator community composition, and plant mating quality.</p> <p>We quantified the direct, pollen-donor-mediated and indirect, pollinator-mediated effects of deforestation on mating quality in <i>Heliconia tortuosa</i>, a tropical herb pollinated by low- and high-mobility hummingbirds. We used a confirmatory path analysis to test the hypothesis that deforestation (amount of forest cover and forest patch size) influenced mating quality (haplotype diversity of pollen pools, outcrossing, and biparental inbreeding) directly and indirectly through functional shifts in the composition of pollinator communities (proportion of high-mobility hummingbirds).</p> <p>We found that deforestation triggered functional shifts in the composition of pollinator communities, as the proportion of high-mobility hummingbirds increased significantly with the amount of forest cover and forest patch size. The composition of the pollinator community affected mating quality, as the haplotype diversity of pollen pools increased significantly with the proportion of high-mobility hummingbirds, while biparental inbreeding decreased significantly. Although we did not detect any significant direct, pollen-donor-mediated effects of deforestation on mating quality, reductions in the amount of forest cover and forest patch size resulted in functional shifts that filtered out high-mobility hummingbirds from the pollinator community, thereby reducing mating quality indirectly.</p> <p><i>Synthesis. </i>Deforestation primarily influenced plant mating quality through a cascading effect mediated by functional shifts in the composition of the pollinator community. Our results indicate that plant mating quality strongly depends on the composition of local pollinator communities. Functional shifts that filter out highly mobile and effective pollinators may reduce the transfer of genetically diverse pollen loads from unrelated plants. Such shifts may have pronounced effects on plant population dynamics and disrupt genetic connectivity.</p>
Data from: Deforestation risks posed by oil palm expansion in the Peruvian Amazon
Further expansion of agriculture in the tropics is likely to accelerate the loss of biodiversity. One crop of concern to conservation is African oil palm (Elaeis guineensis). We examined recent deforestation associated with oil palm in the Peruvian Amazon within the context of the region's other crops. We found more area under oil palm cultivation (845 km2 ) than did previous studies. While this comprises less than 4% of the cropland in the region, it accounted for 11% of the deforestation from agricultural expansion from 2007 to 2013. Patches of oil palm agriculture were larger and more spatially clustered than for other crops, potentially increasing their impact on local habitat fragmentation. Modeling deforestation risk for oil palm expansion using climatic and edaphic factors showed that sites at lower elevations, with higher precipitation, and lower slopes than those typically used for intensive agriculture are at long-term risk of deforestation from oil palm agriculture. Within areas at long-term risks, based on CART models, areas near urban centers, roads, and previously deforested areas are at greatest short-term risk of deforestation. Existing protected areas and officially recognized indigenous territories cover large areas at long-term risk of deforestation for oil palm (>40%). Less than 7% of these areas are under strict (IUCN I-IV) protection. Based on these findings, we suggest targeted monitoring for oil palm deforestation as well as strengthening and expanding protected areas to conserve specific habitats.
Data supplementing the article: Schultz, N.M., Lawrence P.J, Lee X., Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. Journal of Geophysical Research - Biogeosciences
<p>These data supplement the article: Schultz, N.M., Lawrence P.J, Lee X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation, under review at the Journal of Geophysical Research - Biogeosciences.</p> <p>contact: Natalie M. Schultz, natalie.schultz@yale.edu</p> <p>Below are descriptions of the data files included here:</p> <p><br> (1) Global LST data: globalLST_Forest_Open.YYYY.nc [2003-2013]</p> <p>- DayLST1/NightLST1 and DayLST2/NightLST2 are the final (after the DEM correction) LST values for forest, and open land cover classes, respectively.<br> - Count variables show the number of pixels of each land cover class in each 0.5 degree grid<br> - The average elevation of each class is given by the DEM1 and DEM2 vars<br> - The DEM correction is the dLSTdDEM vars</p> <p>(2) Global fluxes data: globalFluxes_Forest_Open.YYYY.nc [2003-2013]<br> - Again, forest class = var1, open class = var2<br> - SWRABS is absorbed solar radiation<br> - LE is the latent heat flux<br> - HP is the heating potential term, as defined in the manuscript<br> - As described for the LST data, class pixel counts and DEM data are included</p> <p>(3) climzones3.nc<br> - The delineation of the three climate zones defined in this paper</p> <p>(4) MERRA inversion data: MERRA_11yr_TS_T10M.mat [2003-2013]<br> - 11 years of daily 1am local data averaged over 8-day intervals for 2003-2013<br> - TS = surface temperature<br> - T10M = 10M air temperature (above d)</p> <p> </p> <p> </p>
Centennial deforestation impacts on soil phosphorus cycling in the Amazon rainforest
<p>Deforestation of tropical rainforests is a major land use change that alters terrestrial biogeochemical cycling at local to global scales. Deforestation and subsequent reforestation are likely to impact soil phosphorus (P) cycling, which in P-limited ecosystems such as in the Amazon basin has implications for long-term land use change and productivity. We used a 100-year observational chronosequence of primary forest conversion to pasture, as well as a 13-year-old secondary forest, to test land use change and duration effects on soil P dynamics in the Amazon basin. By combining sequential extraction and P K-edge X-ray absorption near edge structure (XANES) spectroscopy with soil phosphatase assays, we assessed pools and process rates of P cycling in surface soils. Deforestation caused increases in total P (135-398 mg kg<sup>-1</sup>), total organic P (Po) (19-168 mg kg<sup>-1</sup>), and total inorganic P (Pi) (30-113 mg kg<sup>-1</sup>) fractions in surface soils with pasture age, with concomitant increases in Pi fractions corroborated by sequential fractionation and XANES spectroscopy. Soil non-labile Po (10-148 mg kg<sup>-1</sup>) increased disproportionately compared to labile Po (from 4-5 to 7-13 mg kg<sup>-1</sup>). Soil phosphomonoesterase and phosphodiesterase binding affinity (Km) decreased while the specificity constant (Ka) increased by 83-159% in 39–100y pastures. Soil P pools and process rates reverted to magnitudes similar to primary forests within 13 years of pasture abandonment, though the relatively short but representative pre-abandonment pasture duration of our secondary forest may not enable significant deforestation effects on soil P cycling, highlighting the need to consider both pasture duration and reforestation age in evaluations of Amazon land use legacies. Although the space-for-time substitution design can entail variation in the initial soil P pools due to atmospheric P deposition, soil properties, and/or primary forest growth, the trend of P pools and process rates with pasture age still provides valuable insights.</p>
Labelled dataset to classify direct deforestation drivers in Cameroon
<p><strong>Overview</strong></p> <p>This dataset includes the images (visible bands for Landsat-8 or NICFI PlanetScope), auxiliary data (infrared, NCEP, forest gain, OpenStreetMap, SRTM, GFW), and data about forest loss (Global Forest Change) used to train, validate and test a model to classify direct deforestation drivers in Cameroon. </p> <p><strong>Description of the files</strong></p> <ul> <li>'my_examples_landsat_final_detailed.zip': Landsat-8 images, auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon (15 classes: ‘Oil palm plantation’, ‘Timber plantation’, ‘Fruit plantation (e.g. banana)’, ‘Rubber plantation’, ‘Other large-scale plantation (e.g. tea, sugarcane)’, ‘Grassland/Shrubland’, ‘Small-scale oil palm plantation’, ‘Small-scale maize plantation’, ‘Other small-scale agriculture’, ‘Mining’, ‘Selective logging’, ‘Infrastructure’, ‘Wildfire’, ‘Hunting’, ‘Other’)</li> <li>'my_examples_planet_final_detailed.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon (15 classes)</li> <li>'my_examples_landsat_final.zip': Landsat-8 images, auxiliary data and forest loss data used to train, validate and test a model for a classification of deforestation drivers by groups in Cameroon (4 classes: 'Plantation', 'Grassland/Shrubland', 'Smallholder agriculture', 'Other')</li> <li>'my_examples_planet_final.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to train, validate and test a model for a classification of deforestation drivers by groups in Cameroon (4 classes)</li> <li>'my_examples_landsat_detailed_timeseries.zip': Landsat-8 images, auxiliary data and forest loss data used to test a model for a detailed classification of deforestation drivers in Cameroon (15 classes) using multiple images and a time series analysis </li> <li>'my_examples_planet_detailed_timeseries.zip': NICFI PlanetScope images, auxiliary data and forest loss data used to test a model for a detailed classification of deforestation drivers in Cameroon (15 classes) using multiple images and a time series analysis</li> <li> <p>‘labels.zip’: in csv files, the labels for each image in each folder described above (image identified by folder and coordinates or ‘path’) and matches the format of the csv files used as inputs to train, validate and test our classification model</p> <p>For ‘labels.zip’, we have subfolders for Landsat and PlanetScope. Then, for each type of imagery, we have subfolders for ‘detailed’, ‘groups’ and ‘time series’ which correspond to the different ‘my_examples’ folders listed above. </p> <p>For each folder, subfolders named with the coordinates of the centre of the images contain each:<br>• A folder ‘images’, with a sub-folder ‘visible’ containing the PNG RGB image; and a sub-folder ‘infrared’ containing the infrared bands in a NPY file.<br>• A folder ‘auxiliary’ with topographic and forest gain information in a NPY format, OpenStreetMap and peat data in a JSON format, and a sub-folder ‘ncep’ containing all data from NCEP in a NPY format.<br>• The forest loss pickle file delimiting the area of forest loss.</p> </li> </ul> <p><strong>Details about the images</strong></p> <ul> <li> <p>For Landsat-8 data (courtesy of the U.S. Geological Survey), this dataset contains 332x 332 pixels RGB calibrated top-of-atmosphere (TOA) reflectance images pan-sharpened to a 15 m resolution (less than 20% cloud cover)</p> </li> <li> <p>For NICFI PlanetScope data (catalog owner: Planet), this dataset contains 332x 332 pixels monthly RGB composite with a 4.77 m resolution</p> </li> </ul> <p><strong>Details about the auxiliary data</strong></p> <ul> <li>Forest gain from GFC: 30-m resolution, yearly data for 2000-2021, downloaded via Google Earth Engine</li> <li>Near infrared, shortwave infrared 1 and 2 bands from Landsat-8 TOA: 30-m resolution, data every 16 days for 2013-2023, downloaded via Google Earth Engine and selected using the same process as for Landsat-8 RGB images</li> <li> From NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products: surface level albedo and volumetric soil moisture content (depths: 0.1 m, 0.4 m, 1.0 m, 2.0m) in 0.01%; radiative fluxes (clear-sky longwave flux downward and upward, clear-sky solar flux downward and upward, direct evaporation from bare soil, longwave and shortwave radiation flux downward and upward, latent, ground and sensible heat net flux), potential evaporation rate, and sublimation in W/m²; humidity (specific, maximum specific, minimum specific) in 10-4 kg/kg; ground level precipitation in 0.1 mm; air pressure at surface level in 10 Pa; wind level (u and v component) in 0.01 m/s, water runoff at surface level in 232.01 kg/ m²; temperature in K: 22264-m resolution, available four times a day for 2011-2023, downloaded directly from the NOAA website and selected the mean of the monthly mean over 5 years before the forest loss event, the monthly maximum over 5 years before the forest loss event, and the monthly minimum over 5 years before the forest loss event for each parameter</li> <li>Closest street and closest city from OpenStreetMap in km: directly downloaded with the Nominatim API</li> <li>Altitude in m, slope and aspect in 0.01° from Shuttle Radar Topography Mission (SRTM): 30-m resolution, measured for 2000, downloaded via Google Earth Engine</li> <li>Presence of peat from GFW: 232-m resolution, measured for 2017, directly downloaded on the GFW website</li> </ul> <p><strong>Details about Global Forest Change</strong></p> <p>For each image, there is a corresponding 'forest_loss_region' .pkl file delimiting a forest loss region polygon from Global Forest Change (GFC). GFC consists of annual maps of forest cover loss with a 30-m resolution. </p> <p><strong>License</strong></p> <p>The NICFI PlanetScope images fall under the same license as the <a href="https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf">NICFI data program license agreement</a> (data in 'my_examples_planet_final.zip', 'my_examples_planet_final_detailed.zip', 'my_examples_planet_detailed_timeseries.zip': subfolders '[coordinates]'>'images'>'visible'). </p> <p><a href="https://wiki.osmfoundation.org/wiki/Licence/Attribution_Guidelines">OpenStreetMap®</a> is open data, licensed under the <a href="https://opendatacommons.org/licenses/odbl/1-0/">Open Data Commons Open Database License (ODbL)</a> by the OpenStreetMap Foundation (OSMF) (data in all 'my_examples' folders: subfolders '[coordinates]'>'auxiliary'>'closest_city.json'/'closest_street.json'). The documentation is licensed under the <a href="https://creativecommons.org/licenses/by-sa/2.0/">Creative Commons Attribution-ShareAlike 2.0 license (CC BY-SA 2.0)</a>.</p> <p>The rest of the data is under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. The data has been transformed following the code that can be found via this link: <a href="https://github.com/aedebus/Cam-ForestNet">https://github.com/aedebus/Cam-ForestNet</a> (in 'prepare_files').</p> <p> </p>
Output data from "Emissions leakage and economic losses could limit the effectiveness of deforestation-linked oil crop import restrictions"
<p>Contains a .dat file with queried output from scenario runs used in the paper, Yarlagadda, B., X. Zhao, G. Iyer, T. Wild, N. Hultman and J. Lamontagne, "Emissions leakage and economic losses could limit the effectiveness of deforestation-linked oil crops". This output can be run with the workflow in <a href="https://github.com/brinday/oilcrop-trade-deforestation-leakage">https://github.com/brinday/oilcrop-trade-deforestation-leakage</a> to generate the figures presented in the paper.</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.