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Structure of the Canadian Forest Fire Weather Index System: the model and its components
<p>This material is part of:</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br><strong>Structure of the Canadian Forest Fire Weather Index system: the model and its components</strong> — The <a href="../record/10806780#preview-iframe">figure below</a> (formats: <a href="../record/10806780/files/FWI-sys_simple_diagram.png?download=1">PNG</a> or <a href="../record/10806780/files/FWI-sys_simple_diagram.pdf?download=1">PDF</a>) shows the logical subdivision of the Canadian Forest Fire Weather Index system (FWI-sys) in components.</p> <p> </p> <p>The Canadian FWI-sys (De Groot,1987; Van Wagner,1987) is an index of fire danger by weather designed to consider the effects on vegetation fuels of the sequence of weather conditions. It is designed to estimate a uniform numerical rating for the relative fire potential accounting for the local sequence of temperature, wind speed, relative humidity, and precipitation, for the day in which the rating is estimated but also modelling the dynamics of the previous days. In addition, the variable amount of possible drying due to the varying solar irradiation in different seasons is taken into account by adjusting the parameters per each month of the year.<br><br>The system is standardised to consider the behaviour of a reference typology of vegetation fuel (mature pine stand) regardless of other non-weather factors which may locally influence the fire danger, such as the specific topography or the pattern, composition, and structure of vegetation assemblages. Therefore, FWI-sys is suitable to support the harmonised comparison among variable weather conditions, either spatially (comparing different spatial regions) or temporally (comparing the same region over time).<br><br>The FWI-sys components are organised in three layers, processing at the daily frequency weather information (either from observations, reanalysis, forecast, or climate scenarios) and estimating from it a final standard aggregated numerical rating of fire intensity.<br><br>The required input variables are</p> <ul> <li>Temperature T (nominally, FWI-sys requires T at noon)</li> <li>Wind speed W (nominally, FWI-sys requires T at noon)</li> <li>Relative humidity</li> <li>Precipitation (24-hour rainfall)</li> <li>Month of the year</li> </ul> <p>The FWI-sys was originally designed to fit the Candian conditions. Following its success, adaptations of the system were studied for different areas of the globe. This implies that the parameters used inside the FWI-sys globally also depend on the latitude (Alexander, 2008).</p> <p>The first layer of components (the <em>fuel moisture codes</em>: Fine Fuel Moisture Content, FFMC; Duff Moisture Code, DMC; Drought Code, DC) is composed by dynamic variables. This means that the value of each component for a given day depends also on the value of the same component the day before. The dynamic components with longer memory of their past history also approximate the seasonal changes in solar radiation, by considering the month of the year (see Figure, bottom left).</p> <ul> <li><strong>Fine Fuel Moisture Code (FFMC)</strong> : provides a numerical rating of the moisture content of the top litter and other cured fine fuels, indicating the relative ease of ignition and flammability of fine fuel.</li> <li><strong>Duff Moisture Code (DMC)</strong> : models a standard moisture content of loosely-compacted organic layers of moderate depth (duff layers and medium-sized woody material). This component of the FWI-sys represents wooden fuels of intermediate thickness.</li> <li><strong>Drought Code (DC)</strong> : models a standard moisture content of deeper, compact, organic layers. This component of the FWI-sys is able to track seasonal drought effects on coarse wooden fuels.</li> </ul> <p> </p> <p>The second layer of components (the<em> fire behaviour indices</em>: Initial Spread Index, ISI; Buildup Index, BUI; Fire Weather Index, FWI) mathematically is composed by stateless D-TM components. This means that these components do not have an internal memory of the past conditions, while instead they rely on the combined information offered by the different temporal inertia of the fuel moisture codes, which they process as input information.</p> <ul> <li><strong>Initial Spread Index (ISI)</strong> : represents the expected rate of fire spread. It considers the combined effects of wind and the FFMC on the rate of spread. However, it excludes the influence of fuel moisture and availabity for the coarser wooden fuels.</li> <li><strong>Buildup Index (BUI)</strong> : combines DMC and DC to model the total amount of fuel available for combustion to the spreading fire.</li> <li><strong>Fire Weather Index (FWI)</strong> : offers a standard aggregated numerical rating of fire intensity which combines ISI and BUI.</li> </ul> <p><br>Given its structure, the model can also be interpreted as a recurrent neural network (RNN) where the input variables are transformed into the final aggregated numerical rating (FWI) by means of two hidden layers: the <em>fuel moisture codes</em> (three nodes/neurons); and the <em>fire behaviour indices</em> (two nodes/neurons).</p> <p>Note that this structure is not a simple feedforward network, as the first hidden layer is made by dynamic components (FFMC, DMC, DC, see highlighted feedack loops in <a href="../record/10806780/files/FWI-sys_simple_diagram_recurrent.png?download=1">PNG</a> format). The activation functions are complex, and the D-TM components (either dynamic or stateless) generally mix physically-based and empirical aspects. A consequence of the complexity of the FWI-sys activation functions is that a neural network with standard (e.g. sigmoidal) activation functions would need to exploit disproportionally many more additional neurons for the same FWI-sys D-TM complexity to be reasonably approximated.</p> <p> </p> <p>An additional FWI-sys component is a simple transfromation of the aggregated FWI values to better account for the nonlinear increase of fire control effort with increasing FWI values (Van Wagner, 1987):</p> <ul> <li><strong>Daily Severity Rating (DSR)</strong>: this transformation of FWI is meant to provide a measure of control difficulty:<br> DSR = 0.0272 ⋅ FWI <sup>1.77</sup><br>which easily invertible:<br> FWI = ( DSR / 0.0272 ) <sup>1 / 1.77</sup></li> </ul> <p><br><br>To cite the Figure, please refer to:<br><br>de Rigo, 2016. <strong>Structure of the Canadian Forest Fire Weather Index System: the model and its components</strong>. https://doi.org/10.5281/zenodo.6558576</p> <p>which is part of</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br> </p> <p> </p> <p><strong>References</strong></p> <p>De Groot, W.J., 1987. <strong>Interpreting the Canadian Forest Fire Weather Index (FWI) System</strong>. In: <em>Fourth Central Regional Fire Weather Committee Scientific and Technical Seminar, Proceedings</em>. Winnipeg, Manitoba, Canada, pp. 3-14. <a href="https://purl.org/INRMM-MiD/c-14176512">https://purl.org/INRMM-MiD/c-14176512</a> </p> <p>Van Wagner, C.E., 1987. <strong>Development and structure of the Canadian Forest Fire Weather Index System</strong>. <em>Forestry Technical Report</em>. Canadian Forestry Service, Ottawa, Canada. <a href="https://purl.org/INRMM-MiD/c-14168337">https://purl.org/INRMM-MiD/c-14168337</a> </p> <p>Alexander, M.E., 2008. <strong>Latitude considerations in adapting the Canadian Forest Fire Weather Index System for use in other countries</strong>. In: Lawson, B.D., Armitage, O.B. (Eds.), <em>Weather Guide for the Canadian Forest Fire Danger Rating System</em>. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, Canada, pp. 67–73. ISBN:978-1-100-11565-8 <a href="https://purl.org/INRMM-MiD/z-MBDA6A6I">https://purl.org/INRMM-MiD/z-MBDA6A6I</a></p> <p> </p>
Data from: Artificial light at night and warming impact grazing rates and gonad index of the sea urchin Centrostephanus rodgersii
<p>Artificial light at night (ALAN) is a growing threat to coastal habitats, and is likely to exacerbate the impacts of other stressors. Kelp forests are dominant habitats on temperate reefs but are declining due to ocean warming and overgrazing. We tested the independent and interactive effects of ALAN (dark vs ALAN) and warming (ambient vs warm) on grazing rates and gonad index of the sea urchin <em>Centrostephanus rodgersii. </em>Within these treatments, urchins were fed either 'fresh' kelp or 'treated' kelp. Treated kelp (<em>Ecklonia radiata</em>) was exposed to the same light and temperature combinations as urchins. We assessed photosynthetic yield, carbon and nitrogen content and C:N ratio of treated kelp to help identify potential drivers behind any effects on urchins. Grazing increased with warming and ALAN for urchins fed fresh kelp, and increased with warming for urchins fed treated kelp. Gonad index was higher in ALAN/ambient and dark/warm treatments compared to dark/ambient treatments for urchins fed fresh kelp. Kelp carbon content was higher in ALAN/ambient treatments than ALAN/warm treatments at one time point. This indicates ocean warming and ALAN may increase urchin grazing pressure on rocky reefs, an important finding for management strategies.</p>
EBSD data (with patterns) for indexing of contrast inverted patterns
<p>Data needed to reproduce results from paper:</p> <p>Grzegorz Cios, Aimo Winkelmann, Gert Nolze, Tomasz Tokarski, Benedykt R. Jany, Piotr Bała,<br>EBSD and TKD analyses using inverted contrast Kikuchi diffraction patterns and alternative measurement geometries,<br>Ultramicroscopy, 2024, 114055, ISSN 0304-3991,<br>https://doi.org/10.1016/j.ultramic.2024.114055.<br>(https://www.sciencedirect.com/science/article/pii/S0304399124001347)</p> <p><br>Abstract: Electron backscatter diffraction (EBSD) patterns can exhibit Kikuchi bands with inverted contrast due to anomalous absorption. This can be observed, for example, on samples with nanoscale topography, in case of a low tilt backscattering geometry, or for transmission Kikuchi diffraction (TKD) on thicker samples. Three examples are discussed where contrast-inverted physics-based simulated master patterns have been applied to find the correct crystal orientation. As first EBSD example, self-assembled gold nanostructures made of Au fcc and Au hcp phases on single-crystal germanium were investigated. Gold covered about 12% of the mapped area, with only two thirds being successfully interpreted using standard Hough-based indexing. The remaining third was solved by brute force indexing using a contrast-inverted master pattern. The second EBSD example deals with maps collected at a non-tilted surface instead of the commonly used 70° tilted one. As TKD example, a jet-polished foil made of duplex stainless steel 2205 was examined. The thin part close to the hole edge producing normal-contrast patterns were standard indexed. The areas of the foil that become thicker with increasing distance from the edge of the hole produce contrast-inverted patterns. They covered three times the evaluable area and were successfully processed using the contrast-inverted master pattern. In the last example, inverted patterns collected at a non-tiled sample were mathematically inverted to normal contrast, and Hough/Radon-based indexing was successfully applied.<br>Keywords: EBSD; TKD; Contrast inversion; Topography; Kikuchi diffraction</p>
Data for "Improved constraints on hematite refractive index for estimating climatic effects of dust aerosols"
<p>This repository contains calculated/simulated data on the imaginary part of the complex refractive index, single scattering albedo, and/or optical depth for dust aerosols in the visible band or at the wavelength of 550 nm.</p> <p>For detailed information on (1) the acquisition and utilization of this data, (2) comprehensive configurations for model simulations, (3) the principal findings, and (4) the methodology employed to achieve these findings, please refer to the article authored by Li, Mahowald et al. (2024; Commun. Earth Environ).</p> <p>Other datasets, including the code and laboratory observations presented in the paper, can be found elsewhere (refer to the Data and Code Availability sections of the paper).</p> <p>For any clarification regarding the data and code, inquiries related to the publication, or potential collaboration, please contact Longlei Li (<a href="mailto:ll859@cornell.edu">ll859@cornell.edu</a>) or Natalie M. Mahowald (<a href="mailto:mahowald@cornell.edu">mahowald@cornell.edu</a>).</p>
FIG. 5. Correlation between the spatial distribution index and population density. A in Demographic and spatial structure at the stage of expansion in the populations of some alien land snails in Belgorod city (Central Russian Upland)
FIG. 5. Correlation between the spatial distribution index and population density. A. For Brephulopsis cylindrica and Xeropicta derbentina at 160 plots for three years. B. For Harmozica ravergiensis in nine sites×20 plots for two years. РИС. 5. Корреляция меЖду индексом пространственного распределения и плотностью популяции. А. Для Brephulopsis cylindrica и Xeropicta derbentina на 160 плоЩадках За три года. В. Для Harmozica ravergiensis на девяти участках по 20 плоЩадок За два года.
Dataset for the article "When possessor and argument indexes coincide. A typological survey" (Alessandra Barotto, Andrea Sansò)
<p><span>The dataset stored here contains the data underlying the article “When Possessor and Argument Indexes Coincide” (Alessandra Barotto, Andrea Sansò)<em>. </em>The dataset comes with a README file providing an explanation of the decisions made.</span></p>
CLIMATE CHANGE EFFECTS ON A SUBTROPICAL COASTAL SHALLOW LAKE FROM HEATWAVE INDEXES
<p>This zipped folder contains the files used to generate the results of this article, submitted to the journal Earth Systems and Environment.</p>
Pediatric Heat Risk Index Project
<p>The pediatric risk index project attempts to create a risk index specifically for pediatric populations using socioeconomic and health data across the United States. This new index is then overlayed with heat warming anomaly trends in this decade and future climate projections to better understand which vulnerable populations have the greatest exposure to extreme heat events and will only continue to be exposed to these events in the future. This repository contains the climate normal data, Google Earth Engine and R code for data processing, and the final data products. MODIS Land Surface Temperature Dataset and the NOAA NCEI Climate Grid Daily Temperature dataset can be accessed via Google Earth Engine with the code in this respository. This repository also has a GitHub link with the Google Earth Engine code availabile. </p>
A Global Dataset of Standardized Moisture Anomaly Index Incorporating Snow Dynamics (SZIsnow) from 1948 to 2010
<p>The SZI<sub>snow</sub> dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System Version 2 (GLDAS-2) with the Noah land surface model. This SZI<sub>snow</sub> dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZI<sub>snow</sub>, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZI<sub>snow</sub> dataset is able to well capture large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information on drought, and also can serve as a valuable resource for drought studies.</p>
Latin American and Caribbean journals indexed in Google Scholar Metrics
<p>Dataset from a study aiming to analyze the coverage of Latin American and Caribbean journals in Google Scholar Metrics (GSM). Data from 8,205 journals from 24 countries of the region were downloaded from Latindex database. A Python script was used for automated title search and data extraction (titles, h5-index, h5-median, URLs) in GSM. For the journals not found, a manual search was carried out, with attempts by variations of the title. It was found 3,070 journals indexed in GSM, which corresponds to 37.42% of the Latindex list. The search was performed on the 2021 edition of GSM, which considers articles published between 2016 and 2020 and citations registered until July 2021. The number of all types of documents published (productivity) in the h5-index period (2016-2020) in Scopus, Journal Citation Reports, and SciELO of 1,314 journals was also identified. </p> <p>The present dataset is the result of this study, which is under peer-review in a scientific journal. </p> <p>The dataset comprises titles, h5-index; h5-median, URLs of 3,070 publications from Latin America and the Caribbean identified in Google Scholar Metrics, and the respective editorial information of the publications was extracted from Latindex</p> <p>The original language of the content was kept, mainly Spanish in the case of editorial data from Latindex. The columns descriptors are also shown in English.</p> <p>The productivity data refer to the number of all types of documents published by the journals in the period 2016-2020. Data were extracted from the InCities Journal Citation Reports, Scopus, and SciELO Citation Index (Web of Science database).</p> <p>In this version 2, only the productivity data were changed, covering a larger number of journals (1,314) and including all types of documents. Other data are the same as in the first version (https://doi.org/10.5281/zenodo.5572873).</p> <p> </p> <p> </p> <p> </p> <pre> </pre> <p> </p>
Fig. 5 in Seasonal changes in the gonadossomatic index, allometric condition factor and sex ratio of an auchenipterid catfish from eastern Amazonia
Fig. 5. Bimonthly variation in the raw data (a,c) and mean values (b, d) for the condition factor (K) in juvenile (a, b) and adult (c, d) male of the Auchenipterichthys longimanus collected from July 2008 to July 2009 in the Caxiuanã National Forest.
Fig. 1 in Seasonal changes in the gonadossomatic index, allometric condition factor and sex ratio of an auchenipterid catfish from eastern Amazonia
Fig. 1. Sampling area in the National Forest of Caxiuanã, Pará State, showing the rivers where the fish were collected. Curuá River - Ferreira Penna Research Station (ECFPn), Caxiuanã River; Puraquequara River and Caquajó River. Some black spots represent more than one collection site.
Fig. 4 in Seasonal changes in the gonadossomatic index, allometric condition factor and sex ratio of an auchenipterid catfish from eastern Amazonia
Fig. 4. Bimonthly variation in the raw data (a,c) and mean values (b, d) of the gonadosomatic index (GSI) for female (a, b) and male (c, d) of the catfish Auchenipterichthys longimanus collected from July 2008 to July 2009 in the Caxiuanã National Forest.
TMax index: spatial heterogeneity of climate change as an experiential basis for skepticism
<p>To evaluate how the spatial heterogeneity of climate change affects the public’s willingness to accept scientific results that the climate is changing, we propose an index that accurately measures local changes in climate based on the number of days per year for which the year of the record high temperature is more recent than the year of the record low temperature. TMax index is calculated using the Global Historical Climatology Network (GHCN) dataset. Please refer to the PNAS paper for details on how the index is calculated.</p>
Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping
<p>Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at 5 time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into 2 groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.</p>
Data for: Sexual dichromatism may not be a good index of sexual or natural selection in the blue cardinalids (Aves: Passeriformes).
<p>More than a century ago, Darwin and Wallace started a still ongoing debate over which are the predominant forces driving sexual dichromatism (i.e., differences in body coloration between males and females): is it sexual selection on males to become more attractive, or is it natural selection on females to become more cryptic? While these are not mutually exclusive, the degree of sexual dichromatism has been extensively used as a proxy of the intensity of one type of selection (sexual) on one of the sexes (males). Here, we evaluated the relationship between sexual dichromatism and two body-color features that can be under natural and/or sexual selection in each sex: conspicuousness against the background and colorfulness (which we defined as the variety of colors and mechanisms to produce them within an individual's plumage). We focused on the 'blue clade' of the Cardinalidae bird family and considered the properties of their own visual system and those of potential raptor predators. We found that all blue cardinalids are sexually dichromatic, but levels of dichromatism vary within the clade. Males are on average more colorful than females, but neither male nor female colorfulness correlates with sexual dichromatism. Males are not more conspicuous than females against a vegetated background but are significantly more conspicuous against a nesting background than females. Yet, we found no correlation between conspicuousness and degree of sexual dichromatism. Our results suggest that, while both natural and sexual selection can drive color differences between the sexes, levels of sexual dichromatism do not necessarily reflect intensity of selection forces in this clade. Our results highlight the importance of testing assumptions regarding the relationship between sexual dichromatism and color evolution in each sex, considering the properties of different visual systems, relevant to the ecology of the study model.</p>
Land use fractions and human impact index in 101 Canadian lake watersheds
<p>Land use fractions (urban, mines, agriculture, pasture, forestry, managed grassland, water and natural landscape) and associated human impact index in 101 lake watersheds sampled as part the NSERC Canadian Lake Pulse Network project. Land use and human impact index were calculated as described in Huot et al. (2019).</p> <p>Lakes IDs with respective locations (longitude and latitude coordinates) and Continental Basin allocations can be found here: <a href="https://doi.org/10.5281/zenodo.4701262">https://doi.org/10.5281/zenodo.4701262</a></p> <p>Reference</p> <p>Huot, Y., C. A. Brown, G. Potvin, and others. 2019. The NSERC Canadian Lake Pulse Network: A national assessment of lake health providing science for water management in a changing climate. Sci. Total Environ. <strong>695</strong>: 133668. doi:10.1016/j.scitotenv.2019.133668</p>
Projected changes in droughts and extreme droughts in Great Britain are strongly influenced by the choice of drought index: UKCP18-based bias adjusted potential evapotranspiration
<p>Potential evapotranspiration calculated from the UKCP18 RCM ensemble using the Penman-Monteith method as implemented by Robinson et al. (2017) and bias adjusted using Lange et al. (2019). This dataset was used for analysis of future drought characteristics in Reyniers et al. (2022). Details on the bias adjustment of this potential evapotranspiration dataset, as well as bias-adjusted precipitation and temperature from the same climate model ensemble, can be found in Reyniers et al. (2025).</p> <p>---</p> <p>Reyniers, N., Osborn, T. J., Addor, N., and Darch, G.: Projected changes in droughts and extreme droughts in Great Britain strongly influenced by the choice of drought index, Hydrol. Earth Syst. Sci., 27, 1151–1171, https://doi.org/10.5194/hess-27-1151-2023, 2023.</p> <p>Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., and He, Y.: Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain, Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, 2025. </p> <p>Robinson, E. L., Blyth, E. M., Clark, D. B., Finch, J., Rudd, A. C. (2017). Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data. HESS, <em>21</em>(2), 1189-1224.</p> <p>Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). <em>GMD,</em> <em>12</em>(7), 3055-3070.</p>
Projected changes in droughts and extreme droughts in Great Britain are strongly influenced by the choice of drought index: UKCP18-based SPI and SPEI data
<p>Standardised Precipitation Index (SPI; McKee et al., 1993) and Standardised Precipitation Evapotranspiration Index (SPEI; Vicente-Serrano et al., 2009) computed from UKCP18 Strand 3 simulations (Met Office Hadley Centre, 2018).</p> <p>This data was produced for the study by Reyniers et al. (<em>in prep</em>) analysing (diferences in) drought projections using these indicators. The methodology used to produce this data can be found there if/when the paper is accepted, however do not hesitate to reach out with any further questions. Please note the RCM data was bias adjusted prior to SPI and SPEI computation. There is one file per ensemble member containing the full simulated period on a monthly time step, using aggregation periods of 1, 3, 6, 12, 24 and 36 months for the computation of SP(E)I.</p> <p><strong>References</strong></p> <p>McKee, T. B., Doesken, N. J., Kleist, J., et al.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology, vol. 17, pp. 179–183, Boston, 1993</p> <p>Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. Centre for Environmental Data Analysis, <em>date of citation</em>. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a></p> <p>Reyniers, N., Osborn, T. J., Addor, N., Darch, G.: Projected changes in droughts and extreme droughts in Great<br> Britain are strongly influenced by the choice of drought index. Hydrology and Earth System Sciences, <em>in prep. for HESS</em></p> <p>Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, Journal of Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2009.</p>
Global Gender Gap Index
<p>The Global Gender Gap Index (by World Economic Forum) benchmarks the evolution of gender-based gaps among four key dimensions. The following information are visualized for each country: Economic Participation and Opportunity, Educational Attainment, Health and Survival, and Political Empowerment.</p> <p>The Global Gender Gap index benchmarks 156 countries, providing a tool for cross-country comparison and to prioritize the most effective policies needed to close gender gaps. The methodology of the index has remained stable since its original conception in 2006, providing a basis for robust cross-country and time-series analysis. The Global Gender Gap Index measures scores on a 0 to 100 scale and scores can be interpreted as the distance to parity (i.e. the percentage of the gender gap that has been closed).</p> <p>This dataset includes indicators from 2006 to 2021.</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.