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532 results for “extent”
E4warning_Vector_Raster_Extents_AdminUnits
<p><strong>E4Warning Vector and raster Analysis extents and admin units</strong></p> <p>E4warning standard extents and polygons were defined at the start of the project. There sets of standatd geographies are provided.</p> <p> </p> <p><strong>File Names:</strong></p> <div>a) Folder POLYGONS/e4vnpolygons0921.zip: admin unit Polygon shapes – </div> <div>E4VNDATAPOLYGONS = deslivered NUTS 3 and GAUL 2 depending on country good for standardised data entry. Geographic projection</div> <div>E4VNMAPPOLYGONS = deslivered Admin units chosen to be more or less equal size – best for visibility in maps</div> <div>b) Folder POLYGONS/e4grid10ddclp.zip = decimal degree grids – 0.1 degree (about 10km)</div> <div>c) Folder RASTERS: e4vnextentland10km.zip = 0.08333 deg resolution extent, 1 = land, 0 - water</div> <div>d) Folder RASTERS: e4vnextentlandwater1km.zip = 0.008333 deg resolution extent, 1 = land, 0 - water</div> <div>e) Folder RASTERS: e4vnextentlandwater5km.zip =0.0416667 deg resolution extent, 1 = land, 0 - water</div> <div>g) Folder RASTERS: e4vnextentpcland10km.zip = 0.08333 deg resolution extent, value = PC of pixel that is land</div>
Scripts and datas for "Climate-driven projections of future global wetlands extent"
<p>Computations scripts (1, 2), associated input dataset (3), and output datasets for wetland fractions (4, 5) used and presented in the study:</p> <p><em><strong>L. Hardouin, B. Decharme, J. Colin, C. Delire: </strong>Climate-driven projections of future global wetlands extent.</em></p> <p>The calculation and input scripts include:<br><em>1_var_comput </em>: Calculation of the main variables used to diagnose wetlands: depth of the "active" layer d_wtl, liquid water content w_l, ice content and maximum content in the layer d_wtl.</p> <p><em>2_TOPMODEL </em>: The scripts used to diagnose the wetland fraction and to calibrate the models using the TOPMODEL approach. In this folder, the mean, maximum, minimum, standard deviation and skewness datasets of the topographic indices at the grid-cell level are also included.</p> <p><em>3_alpha_and_beta </em>: Calibrated alpha and beta parameters used to obtain the historical and projected wetland fractions with the calibrated version.</p> <p>The outputs datasets contain:</p> <p><em>4_fwtl_model_period </em>: The fraction of wetlands computed from each model in the calibrated version, for the historical period and the 4 SSPs scenarios presented in the submitted work.</p> <p><em>5_not_calibrated_fwtl_model_period </em>: The fraction of wetlands computed from each model in the uncalibrated version with alpha=0.65, for the historical period and the 4 SSPs scenarios, where only the historical period is used in the submitted work.</p> <p> </p> <p>Additional data not created by the authors are needed to reproduce the study (see the Open research section in the submitted article). Feel free to contact the authors (lucas.hardouin@meteo.fr) for any help or questions.</p>
Global cropland extent (fractions) annual 2000-2022 at 250 m and 1 km
<p>Global cropland extent annual for 2000-2022 based on the <a href="https://glad.umd.edu/dataset/croplands">Potapov et al. (2021)</a>. Cropland defined as: land used for annual and perennial herbaceous crops for human consumption, forage (including hay), and biofuel. Perennial woody crops, permanent pastures, and shifting cultivation are excluded from the definition. The original 30-m resolution data (0/1 values) was interpolated from time-series 2003, 2007, 2011, 2015, 2019 to annual values 2000 to 2022 using linear interpolation. All values shown are in principle fractions 0-100%. The 30-m and 100-m resoluton images (COGs) are too large for Zenodo but you can access them from URLs in the filenames_openlandmap_cropland.txt file. See for example (drop the URL in QGIS):</p> <ul> <li>https://s3.eu-central-1.wasabisys.com/openlandmap/layers30m/cropland_glad.potapov.et.al_p_30m_s_20030101_20031231_go_epsg.4326_v20240624.tif (3.2GB)</li> <li>https://s3.eu-central-1.wasabisys.com/openlandmap/layers100m/cropland_glad.potapov.et.al_p_100m_s_20030101_20031231_go_epsg.4326_v20240624.tif (2.3GB)</li> </ul> <p><strong>Disclaimer</strong>: linear interpolation has limited accuracy and is basically only used to gap-fill the missing years. The remaining missing values in the maps can be ALL consider to be 0 value for cropland. A more detailed up-to-date cropland map of the world is provided by <a href="https://doi.org/10.5194/essd-15-5491-2023">van Tricht et al., (2023)</a>, however only single year (2021) has been mapped at 10-m resolution within the <a href="../doi/10.5281/zenodo.7875104">WorldCereal project</a>.</p> <p>The temporal interpolation was implemented using terra package ii.e. using the following fuction:</p> <pre><code>library(terra) y.l = c(2003, 2007, 2011, 2015, 2019) out.years = 2000:2022 i = parallel::mclapply(y.l, function(x){system(paste0('gdal_translate Global_cropland_', x, '.vrt Global_cropland_', x, '.tif -co TILED=YES -co BIGTIFF=YES -co COMPRESS=DEFLATE -co ZLEVEL=9 -co BLOCKXSIZE=1024 -co BLOCKYSIZE=1024 -co NUM_THREADS=8 -co SPARSE_OK=TRUE -a_nodata 255 -scale 0 1 0 100 -ot Byte'))}, mc.cores = length(y.l)) ## land mask at 1 deg (100x100km) ---- x = parallel::mclapply(y.l, function(x){system(paste0("gdal_translate Global_cropland_", x, ".tif Global_cropland_", x, "_1d.tif -tr 1 1 -r average -co BIGTIFF=YES -ot Byte -co NUM_THREADS=10"))}, mc.cores = length(y.l)) ## 2 hrs g1 = terra::rast(paste0("Global_cropland_", y.l, "_1d.tif")) gs = sum(g1, na.rm=TRUE) plot(gs) gs.p <- as.polygons(gs, values = TRUE, extent=FALSE, dissolve=FALSE, na.rm=TRUE) ## Input layers: r = terra::rast(paste0("Global_cropland_", y.l, ".tif")) int.mc = function(r, tile, y.l, out.years=2000:2022){ bb = paste(as.vector(ext(tile)), collapse = ".") if(any(!file.exists(paste0("./tmp/", out.years, "/Global_cropland_", out.years, "_", bb, ".tif")))){ r.t = terra::crop(r, ext(tile)) ## each tile is 16M pixels r.x = as.data.frame(r.t, xy=TRUE, na.rm=FALSE) rs = rowSums(r.x[,-c(1:2)], na.rm=TRUE) ## if sum is == 0 means no cropland throughout the time-series sel = which(rs>0) ## extract complete values: r.x0 = r.x[sel,-c(1:2)] r.x0[is.na(r.x0)] = 0 ## interpolate between values: t1s = as.data.frame(t(apply(r.x0, 1, function(y){ try( approx(y.l, as.vector(y), xout=out.years, rule=2)$y ) }))) ## write to GeoTIFFs t1s$x <- r.x$x[sel]; t1s$y <- r.x$y[sel] ## convert to RasterLayer: r.x = rast(t1s[,c("x","y",paste0("V", 1:length(out.years)))], type="xyz", crs="+proj=longlat +datum=WGS84 +no_defs") for(j in 1:length(out.years)){ writeRaster(r.x[[j]], filename=paste0("./tmp/", out.years[j], "/Global_cropland_", out.years[j], "_", bb, ".tif"), gdal=c("COMPRESS=DEFLATE"), datatype='INT1U', NAflag=0, overwrite=FALSE) } } } ## test it: #int.mc(r, tile=gs.p[1000], y.l) ## run in parallel ---- ## takes 12 hrs... 1TB RAM i = parallel::mclapply(sample(1:length(gs.p)), function(x){try( int.mc(r, tile=gs.p[x], y.l) )}, mc.cores = 70) </code></pre>
Global Mangrove Watch: Annual Mangrove Extent
<p>To improve the resolution and local relevance of the Global Mangrove Watch (GMW) baseline, a new layer has been created for 2020. Using Copernicus Sentinel-2 satellite imagery, processed to a pixel resolution of 10 m, the mangrove extent has been completely remapped and revised with many areas which were not previously mapped now included within the new map. This has increased the spatial resolution of the mapping from a pixel resolution of 25 m to 10 m, allowing finer features to be mapped, such as fringing and riverine mangroves.</p> <p><span>Using the same pre-processed Sentinel-2 data as used for the ESA WorldCover 2020 map (Zanaga et al., 2021), over 30,000 machine learning models were trained to classify mangroves using over 5 million reference points for mangrove and non-mangrove classes. The reference points were generated using the existing CoastTrain reference dataset (Murray et al., 2022), sampling from the GMW v3.0 mangrove extent layers, user feedback and extensive quality assurance and refinement during the production of the classification. </span></p>
Global tree cover extent for 2014
<p>Global tree cover extent for 2014 based on loss between 2001–2014 (defined as any change below the 10% threshold of tree cover) subtracted from 1 km tree cover extent map for 2000.</p>
Data from: Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and extent
<p>These datasets support the scientific article "Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and area extent" published in 2025 (Journal of Sea Research, 205, 102580; <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.seares.2025.102580" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.seares.2025.102580</a>). It contains detailed data on the distribution and area extent of intertidal and subtidal seagrass meadows in the four main wetlands of the Algarve region (Southern Portugal): Ria de Alvor, Arade Estuary, Ria Formosa, Guadiana Estuary.</p> <p>The data is composed by 5 datasets with the following variables:</p> <p><strong>1) data_field_points.csv</strong></p> <p>Contains data points based on field surveys.</p> <ul> <li>dataset_id [character] - Unique identifier for the data set.</li> <li>data_id [character] - Unique identifier for the data point.</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>quadrat_id [character] - Name of the quadrat as recorded in the field.</li> <li>photo_id [character] - Name of the pictured associated to the observation.</li> <li>sampling_id - Name of the observation as recorded in the field.</li> <li>date [date] - Date of observation (YYYY-MM-DD).</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>habitat_class [factor] - Type of habitat: unvegetated, seagrass intertidal, seagrass subtidal, seagrass unknown, salt marsh low, caulerpa.</li> <li>species [factor] - Vegetation species: No vegetation, <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified species, <em>Caulerpa prolifera</em>, <em>Sporobolus maritimus</em>.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> <li>method [factor] - Method used for the observation: boat and camera, boat and snorkelling, kayak and camera, on foot.</li> <li>survey_area [character] - Number of the survey area.</li> <li>site [character] - Name of the site.</li> <li>observers [character] - Name of the researcher(s) who collected the data.</li> </ul> <p> </p> <p><strong>2) data_compilation_records.csv</strong></p> <p>Contains information on the records (i.e. sources) screened during the systematic review for the compilation od seagrass occurrence data.</p> <ul> <li>record_id [character] - unique id for the compiled records.</li> <li>short_citation [character] - short citation of the record, with author and publication year.</li> <li>included [boolean] - whereas the record was used to extract data or informacion.</li> <li>record_type [factor] - type of record: journal article, book or book chapter, PhD or MSc thesis, report, others.</li> <li>publication_year [integer] - year of the publication of the record, YYYY.</li> <li>title_record [character] - title of the record.</li> <li>link [character] - link to access the record, if available (DOI, handle, others URLs).</li> <li>full_citation [character] - full citation of the record, with authors, publication year, title, etc.</li> </ul> <p> </p> <p><strong>3) data_compilation_points_raw.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the original raw file with all the compiled points.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_clean.csv).</li> <li>included [boolean] - whether the data point was kept in the clean dataset or not: 1, the point has been validated and it is included in the final dataset; 0, the point is excluded due to unprecise location (on land, open ocean, etc.).</li> <li>reason_exclusion [character] - Reason to exclude the data point from the clean dataset.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> <li>duplicated [boolean] - whether the data point was flagged as duplicated or not.</li> </ul> <p> </p> <p><strong>4) data_compilation_points_clean.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the clean file after elimitating duplicates and points with unprobable or unprecise location.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_raw.csv).</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> </ul> <p> </p> <p><strong>5) data_compilation_extent.csv</strong></p> <p>Contains area extent data of seagrass meadows based on the systematic review.</p> <ul> <li>data_id [character] - Unique identifier for the data.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted.</li> <li>short_citation [character] - Short reference (author(s) and year).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>value [boolean] - whether the data extracted from the record is a extent value (i.e., a value of area covered by seagrasses).</li> <li>polygon [boolean] - whether the data extracted from the record is a polygon.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>area_source [numeric] - The area extent given in the record.</li> <li>area_source_cover [factor] - The cover of the wetland for the compiled extent from the record means: total, partial or unknown.</li> <li>area_gis [numeric] - The area extent obtained using GIS.</li> <li>area_gis_cover [factor] - The cover of the wetland for the obtained extent from GIS means: total, partial or unknown.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> </ul>
Supporting data: "RECEIPT D7.3: Future climate scenarios: sea level rise and sea ice extent"
<p>This is the supporting data for deliverable D7.3 from work package 7 ( Sea level rise, infrastructure and coastal flooding ) of the RECEIPT H2020 project (No 820712).</p> <p>Deliverable 7.3 describes the development of future SLR (sea level rise) and sea ice extent scenarios. Each SLR contributor (e.g. thermal expansion, instability of Antarctic and Greenland ice sheets, melting glaciers, ocean circulation and land water storage) are included in the assessment (KNMI, Task 7.4). Sea ice extent is derived from CMIP5/6.</p> <p>This dataset is composed of three compressed files:</p> <p>cmip5_zos_zostoga_v2.zip and cmip6_zos_zostoga_v2.zip: Netcdf files of ocean thermal expansion and ocean dynamics computed from zos and zostoga data from the ESGF nodes.</p> <p>data_RECEIPT_D73.zip: Netcdf files of three sea level scenarios. Data is provided globally but scenarios are designed for the European coast.</p>
Data from: Moth species richness and diversity decline in a 30-year time series in Norway, irrespective of species' latitudinal range extent and habitat
<p>Data from:</p> <p>Burner, R., V. Selås, S. Kobro, R. Jacobsen, A. Sverdrup-Thygeson. 2021. Moth species richness and abundance decline in a 30-year time series, irrespective of species’ latitudinal range extent and habitat. <em>Journal of Insect Conservation</em><br> </p> <p>Current contact info for corresponding author: Ryan C. Burner, rburner[at]usgs.gov</p> <p> </p> <p>These data consist of a 30-year time series (1984 to 2013) of moth captures from a single site in southeast Norway, along with trait data for many of the species and climate data for the site. The moths were collected and identified by Sverre Kobro for the entire 30-year period and we are grateful for his efforts. </p> <p> </p> <p>Abstract from manuscript:</p> <p><strong>Introduction</strong></p> <p>Insects are reported to be in decline around the globe, but long-term datasets are rare. The causes of these trends are elusive, with land use change and climate change among the top candidates. Yet if species traits can predict rates of population change, this can help identify underlying mechanisms. If climate change is important, for example, northern species may decline as southern species expand. Land use changes, however, may impact species that rely on certain habitats.</p> <p><strong>Aims and Methods</strong></p> <p>We present 30 years of moth captures (comprising 85,149 individuals of 885 species) from a site in southeastern Norway to test for population trends that are correlated with species traits. We use time series analyses and joint species distribution models combined with local climate and habitat data.</p> <p><strong>Results and Discussion</strong></p> <p>Species richness and abundance declined by 10.1% and 13.8% per decade, respectively. Capture rates declined for 19% of species during this time as well, though 6% have increased. Annual summer weather is correlated with annual rates of abundance change for many species. But, opposite to a general expectation, many species in our study responded negatively to increasing summer temperatures. Surprisingly, neither species’ northern range limits nor the habitat in which their primary food plants grow are strong predictors of their rates of change, or their responses to climatic factors. However, species with more southerly distributions are less likely to be declining. Complex and indirect effects of both land use and climate change may play a role in these declines.</p> <p><strong>Implications for insect conservation</strong></p> <p>Our results provide additional evidence for long-term declines in insect abundance. The multifaceted causes of population changes may limit the ability of species traits to reveal which species are most at risk. </p> <p> </p> <p><strong>ACKNOWLEDGEMENTS</strong></p> <p>Thanks to J. Fjelddalen, who helped with geometrid moth identifications. This project was supported by internal funding from the Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences.</p> <p> </p>
A map of global peatland extent created using machine learning (Peat-ML)
<p>Map of global peatland extent estimated by machine learning. The download includes both a netcdf file version and a GeoTIFF (as a zip archive)</p> <p>Abstract from associated paper:</p> <p>Peatlands store large amounts of soil carbon and freshwater, constituting an important component of the global carbon and<br> hydrologic cycles. Accurate information on the global extent and distribution of peatlands is presently lacking but is needed<br> by Earth System Models (ESMs) to simulate the effects of climate change on the global carbon and hydrologic balance. Here,<br> we present Peat-ML, a spatially continuous global map of peatland fractional coverage generated using machine learning<br> techniques suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model follow drivers of<br> peatland formation and include spatially distributed climate, geomorphological and soil data, along with remotely-sensed<br> vegetation indices. Available maps of peatland fractional coverage for 14 relatively extensive regions were used along with<br> mapped ecoregions of non-peatland areas to train the statistical model. In addition to qualititative comparisons to other maps<br> in the literature, we estimated model error in two ways. The first estimate used the training data in a blocked leave-one-out<br> cross-validation strategy designed to minimize the influence of spatial autocorrelation. That approach yielded an average r<sup>2</sup><br> of 0.73 with a root mean squared error and mean bias error of 9.11% and -0.36%, respectively. Our second error estimate<br> was generated by comparing Peat-ML against a high-quality, extensively ground-truthed map generated by Ducks Unlimited<br> Canada for the Canadian Boreal Plains region. This comparison suggests our map to be of comparable quality to mapping<br> products generated through more traditional approaches, at least for boreal peatlands.</p>
Nueva serie de extensión del hielo marino ártico en septiembre entre 1935 y 2014 - A new time series of September Arctic sea ice extent: 1935-2014
<p>Archivo CSV - Presentamos una nueva serie de extensión del hielo marino ártico en el mes de septiembre desde 1935 hasta 2014 que incluye datos para el sector siberiano no utilizados hasta ahora en las series que cubren el conjunto del Ártico. La nueva serie ha sido ajustada para ser consistente con los datos de satélite</p> <p>CSV file - We present a new time series of September Arctic sea ice extent from 1935 to 2014 that includes data for the Siberian sector (AARI operational charts) not used previously in the Arctic wide existing time series (Walsh, HadISST). The new record has been adjusted to be consistent with the satellite data.</p> <p> </p>
Nueva serie de extensión del hielo marino ártico en septiembre entre 1935 y 2014 - A new time series of September Arctic sea ice extent: 1935-2014
<p>Archivo NetCDF- Presentamos una nueva serie de datos raster con la extensión del hielo marino ártico en el mes de septiembre desde 1935 hasta 2014 que incluye datos para el sector siberiano no utilizados hasta ahora en las series que cubren el conjunto del Ártico. La nueva serie ha sido ajustada para ser consistente con los datos de satélite.</p> <p>NetCDF file - We present a new gridded September Arctic sea ice extent dataset from 1935 to 2014 that includes data for the Siberian sector (AARI operational charts) not used previously in the Arctic wide existing time series (Walsh, HadISST). The new record has been adjusted to be consistent with the satellite data.</p> <p> </p>
Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs target discovery in ankylosing spondylitis
<p>We performed comprehensive epigenetic profiling in immune cell samples from patients with ankylosing spondylitis and healthy controls. <br><br><em>Note: Due to Zenodo updating their maximum file limit to 100 files, version 4 </em>(v4) <em>of this archive has been split into 5 compressed archive (tar.gz) files containing all previous and additional files. <br><br></em>Version 4 of this archive (updated 03/06/2025) adds 4 files to the archive that were omitted in previous versions which have now been made available. These were: <em><br></em></p> <ul> <li>RNA_CD8_raw_counts.txt.gz</li> <li>RNA_CD8_normalised_counts.txt.gz</li> <li>RNA_CD14_raw_counts.txt.gz</li> <li>RNA_CD14_normalised_counts.txt.gz </li> </ul> <p><em>--------------------------------------------------------------------------------------------------------------------------</em></p> <p><strong>RNA-seq/ATAC-seq/ChIPm/eRNA: </strong>Raw and normalised count data for each gene or epigenetic peak in CD4+ T cells, CD8+ T cells, and CD14+ monocytes from AS patients and healthy controls. File name is in the format: "modality_cell-type_raw/normalised_counts.txt.gz". Table S2 shows which experiments were performed on which samples. </p> <p>This data can be found in the "Raw_Counts.tar.gz" and "Normalised_Counts.tar.gz" archives. </p> <p>--------------------------------------------------------------------------------------------------------------------------</p> <p><strong>ChromHMM: </strong>We used ChromHMM to integrate epigenomic data into a 14-emission state model detailing chromatin functionality in AS patients and healthy controls. ChromHMM filenames are in the format: "ChromHMM_sampleID_celltype_n.bed.gz" where n is the number of states in the ChromHMM emission model.</p> <p>This data can be found in the "ChromHMM_AS_HV.tar.gz" archive. </p> <p>--------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Capture-C: </strong>We performed Capture-C to detect chromosome looping interactions between gene promoters and SNPs associated with ankylosing spondylitis. Capture-C count data are shown in the format: "CaptureC_celltype_gene_Pro/SNP_normalised.unionbdg". We used PeakY to calculate a score for each interaction. PeakY scores are given in the following format: "PeakY_AS/HV_celltype_tier_chrloc_gene_Pro/SNP.txt". gene_Pro and gene_SNP relate to the baitsets given in Table S8.</p> <p>This data can be found in the "CapC_Count_Data.tar.gz" and "PeakY_regions.tar.gz" archives. </p>
Examination of head versus body heading may help clarify the extent to which animal movement pathways are structured by environmental cues?
<p>Understanding the processes that determine how animals allocate time to space is a major challenge, althoughit is acknowledged that summed animal movement pathways over time must define space-time use. The criticalquestion is then, what processes structure these pathways? Following the idea that turns within pathways mightbe based on environmentally determined decisions, we equipped Arabian oryx with head- and body-mounted tagsto determine how they orientated their heads – which we posit is indicative of them assessing the environment– in relation to their movement paths, to investigate the role of environment scanning in path tortuosity. Aftersimulating predators to verify that oryx look directly at objects of interest, we recorded that, during routinemovement, > 60% of all turns in the animals' paths, before being executed, were preceded by a change in headheading that was not immediately mirrored by the body heading: The path turn angle (as indicated by the bodyheading) correlated with a prior change in head heading (with head heading being mirrored by subsequent turnsin the path) twenty-one times more than when path turns occurred due to the animals adopting a body headingthat went in the opposite direction to the change in head heading. Although we could not determine what theobjects of interest were, and therefore the proposed reasons for turning, we suggest that this reflects the use ofcephalic senses to detect advantageous environmental features (e.g. food) or to detect detrimental features (e.g.predators). The results of our pilot study suggest how turns might emerge in animal pathways and we proposethat examination of points of inflection in highly resolved animal paths could represent decisions in landscapesand their examination could enhance our understanding of how animal pathways are structured.</p>
Body size modulates the extent of seasonal diet switching by large mammalian herbivores in Yellowstone National Park
<div> <p><span>Large mammalian herbivores vary their diets markedly with changes in resource availability yet the ways that seasonal changes in individual foraging behaviors scale up to reconfigure complex trophic networks are poorly understood. Two years of dietary DNA data enabled us to quantify fine-grained dietary variation within and among populations of five large herbivore species at Yellowstone National Park, revealing remarkably strong and significant correlations between body size and five key indicators of diet seasonality (R<sup>2</sup> = 0.71–0.80). Data from GPS collars implicated seasonal changes in each species' movement- and habitat-use patterns as potential determinants of foraging constraints and specializations that give rise to the strong allometry in diet composition. Bison and elk showed relatively muted seasonal changes compared to smaller species that exhibited stronger switches. Whereas the taxonomic breadth of individual diets contracted for all species in winter, larger species generally consumed a greater functional diversity of plants and thus maintained more unique dietary niches under resource limitations.</span></p> </div>
Replication package for "To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?
<p>Replication package for "To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?"</p>
To what extent naringenin binding and membrane depolarization shape mitoBK channel gating - a machine learning approach (code and dataset)
<p>The dataset consists of dwell-time series (sampling frequency 100 kHz) of the mitoBK ion channel activation modulated by the naringenin binding and membrane<br> depolarization. It also contains the code written in Python, with the use of tslearn and scikit-learn packages, classifying the dwell-time subseries into right categories.</p> <p>The dataset is organized as follows. The mitoBK_ML.zip directory consists of two directories:</p> <ol> <li><strong>dwell times </strong>containing 5 subdirectories comprising groups of dwell-time subseries obtained at different pipette potentials and naringenin concentration. First number in the name od directory stands for the applied voltage in mV, whilst the second one denotes the naringenin concentration in µmol. For instance, directory named 20_3 means that the obtained dwell-times series were obtained at 20 mV (value of pipette potential) and 3 µmol (concentration of naringenin). These subdirectories are named as follows:</li> </ol> <ul> <li><strong>1group </strong>comprising dwell time series <strong>20_3, 40_1, 60_0</strong></li> <li><strong>2group</strong> comprising dwell-time series <strong>20_10, 60_1</strong></li> <li><strong>3group</strong> comprising dwell-time series <strong>40_10</strong>, <strong>60_3</strong></li> <li><strong>naringenina</strong> comprising dwell-time series <strong>60_0, 60_10</strong></li> <li><strong>voltage</strong> comprising dwell-time series <strong>20_10, 60_10</strong></li> </ul> <p><strong>1group, 2group and 3group</strong> contain the dwell-time series with approximately the same value of open-state probability of the ion channel.</p> <p>The <strong>naringenina</strong> contains the dwell-time series with the same value of potential (60 mV) and different values of naringenin concentration (0 µmol and 10 µmol). </p> <p>The <strong>voltage </strong>contains the dwell-time series with the same value of naringenin concentration (10 µmol) and different values of applied voltage (20 mV and 60 mV).</p> <p> 2. <strong>rslt </strong>is organized analogously to <strong>dwell times. </strong>The subdirectories are empty, but they will be filled with the results after launching the Python scripts placed in the <strong>knn_ion_channel.ipynb</strong> or <strong>shapelet_ion_channel.ipynb </strong>files.</p> <p>The Python code is placed in two files:</p> <ol> <li><strong>knn_ion_channel.ipynb </strong>containing kNN (<em>k-Nearest Neighbors</em>) algorithm classifying dwell-time series belonging to one of 5 different categories enumerated above: <strong>1group, 2group, 3group, naringenina, voltage</strong>. More detailed description of the code can be found inside uploaded Jupyter notebook.</li> <li><strong>shapelet_ion_channel.ipynb </strong>containing <em>shapelet-learning algorithm</em> classifying dwell-time series belonging to one of 5 different categories enumerated above. <strong>1group, 2group, 3group, naringenina, voltage. </strong>More detailed description of the code can be found inside uploaded Jupyter notebook.</li> </ol> <p> </p> <p> </p> <p> </p>
A temporally consistent 8-day 0.05° gap-free snow cover extent dataset over the Northern Hemisphere for the period 1981–2019
<p>Northern Hemisphere (NH) snow cover extent (SCE) is one of the most important indicator of climate change for its unique surface property. However, short temporal coverage, coarse spatial resolution, and different snow discrimination approach among published SCE products hampers its detailed studies. Using the Advanced Very High Resolution Radiometer Surface Reflectance (AVHRR-SR) Climate Data Record (CDR) and several ancillary datasets, this study generated a temporally consistent 8-day 0.05° gap-free NH terrestrial SCE product for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite. This process consistent of five steps. First, a decision tree algorithm with multiple threshold tests was applied to detect SCE from daily AVHRR-SR CDR. Second, we merge two existing daily SCE products to take advantage of their spatial coverage. Third, an aggregation process was used to detect the maximum SCE in each 8-day periods. Forth, the GLASS SCE was generated with the help of snow cover probability climatology. Fifth, the validation process was carried out to evaluate the quality of GLASS SCE. Validation results by using 562 Global Historical Climatology Network stations during 1981–2017 (r=0.61, p<0.05) and MOD10C2 during 2001–2019 (r=0.97, p<0.01) proved that the GLASS SCE product is credible in snow cover frequency monitoring. Moreover, cross-comparison between GLASS SCE and surface albedo during 1982–2018 further confirmed its values in climate changes studies.</p> <p>The GLASS SCE data set provides binary maps of snow cover for the Northern Hemisphere from September 1981 to the December 2019. The data are organized by year and provided in GeoTIFF formats. The gridcells were flagged as “0” if classified as "Non-snow", "1" if retrieved from AVHRR satellite observations, and "2" if filled by IMS snow climatology.</p> <p>Spatial Coverage: N: 90, S: 0, E: 180, W: -180<br> Spatial Resolution: 0.05 deg x 0.05 deg<br> Samples = 7200<br> Lines = 1800<br> Temporal Coverage: September 1981 to December 2019<br> Temporal Resolution: 8-day</p>
To What Extent Cognitive-Driven Development Improves Code Readability?
<p>Cognitive-Driven Development (CDD) is a coding design technique<br> that aims to reduce the cognitive effort that developers place in<br> understanding a given code unit (e.g., a class). By following CDD de-<br> sign practices, it is expected that the coding units to be smaller, and,<br> thus, easier to maintain and evolve. However, it is so far unknown<br> whether these smaller code units coded using CDD standards are,<br> indeed, easier to understand. In this work we aim to assess to what<br> extent CDD improves code readability. To achieve this goal, we<br> conducted a two-phase study. We start by inviting professional<br> software developers to vote (and justify their rationale) on the most<br> readable pair of code snippets (from a set of 10 pairs); one of the<br> pairs was coded using CDD practices. We received 133 answers.<br> In the second phase, we applied the state-of-the art readability<br> model on the 10-pairs of CDD-driven refactorings. We observed<br> some conflicting results. On the one hand, developers perceived<br> that seven (out of 10) CDD-driven refactorings were more readable<br> than their counterparts; for two other CDD-driven refactorings,<br> developers were undecided, while only in one of the CDD-driven<br> refactorings, developers preferred the original code snippet. On<br> the other hand, we noticed that only one CDD-driven refactorings<br> have better performance readability, assessed by state-of-the-art<br> readability models. Our results provide initial evidence that CDD<br> could be an interesting approach for software design</p>
Probing the Extent of Vertical Mixing in Brown Dwarf Atmospheres with Disequilibrium Chemistry
<p><strong>OVERVIEW</strong></p> <p>The substellar atmospheric models described in <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220814317M/abstract">Mukherjee et al. (2022)</a> are presented here. The grid of these 1D radiative-convective atmospheric models was computed using the newly released open-source climate code PICASO 3.0 (<a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>). The grid consists of four parameters – the effective temperature (T<sub>eff</sub>), gravity (log(g)), K<sub>zz</sub> in the radiative zones, and mixing length in the convective zones. Models with T<sub>eff </sub> between 400-1000 K with an increment of 25 K are included. log(g) has been varied from 4.5 to 5.5 with an increment of 0.25 dex. The K<sub>zz</sub> in the radiative zone has been varied between 1x, 0.01x, and 100x the parametrization presented in <a href="https://ui.adsabs.harvard.edu/abs/2022ExA....53..279M/abstract">Moses et al. (2021)</a>, whereas the convective mixing length has been between the atmospheric pressure scale height and 0.1x the scale height.</p> <p>There are three types of files released here – atmospheric composition files (TP_chemistry), thermal emission spectra files (spectra), and atmospheric Kzz profile files (TP_kz). </p> <p><strong>ATMOSPHERIC COMPOSITION</strong></p> <p>The atmospheric composition files are located in the folder TP_chemistry. These files have the temperature structure of the atmosphere as a function of pressure accompanied by the volume mixing ratio of 37 gases as a function of pressure. </p> <p>The TP_chemistry files are named following the format “profile_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat", where [factor1] denotes the multiplier used for the radiative zone Kzz and can vary between ‘x0pt01’, ‘x1’, and ‘x100’. [factor2] denotes the multiplier for the mixing length and can vary between ‘1’ and ‘0pt1’. [Teff] and [gravity] denote the Teff and gravity of the models used. A simple code snippet to read and plot these files is presented below.</p> <p><strong>SPECTRA</strong></p> <p>The spectra files are located in the folder "spectra_highres_1", "spectra_highres_2", "spectra_highres_3", and "spectra_highres_4". These files have the thermal emission spectra between 0.3-30 microns calculated using the computed models. The native spectral resolution of these calculations is at an R = 500,000, but <strong>please be aware that these spectra should always be binned down to a resolution of R = 50,000 or less before usage</strong>. This means that these spectra should only be used to interpret datasets with a spectral resolution of 50,000 or less. Please contact the authors if higher resolution spectra are needed. The spectra have been uploaded in three different folders to make the file sizes manageable for transfer.</p> <p>The spectra files are also similarly named using the format “spectra_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.tar.gz". These files can be directly read into a Python pandas dataframe using </p> <pre><code class="language-python">pd.read_csv(filename, compression='gzip')</code></pre> <p> The first column of the file is wavenumbers in cm<sup>-1, </sup>which can be converted to wavelength in microns by wavelength [microns] =10000/wavenumbers[cm<sup>-1</sup>]. The second column of the file is flux in erg/s/cm<sup>2</sup>/cm. Note that these fluxes need to be multiplied with R<sup>2</sup>/D<sup>2</sup> before comparing them with the typically observed flux of brown dwarfs/exoplanets. R is the radius of the object, and D is the distance here. A tutorial to convert these fluxes to other units is present in <a href="https://natashabatalha.github.io/picaso/notebooks/6_BrownDwarfs.html#Convert-to-F_\nu-Units-and-Regrid">this link</a>. A binned-down version (R=15,000) of these high-resolution spectra can also be found in the "spectra_lowres" folder. These can be used for datasets that have a maximum spectral resolution of 15,000.</p> <p><strong>K<sub>zz</sub> PROFILE</strong></p> <p>The K<sub>zz </sub>as a function of pressure for each model is presented in these files. The K<sub>zz</sub> is reported in cm<sup>2</sup>/s. These files are also similarly named using the format “kz_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat". The columns of the files are pressure in bars, the temperature in K, and Kzz in cm<sup>2</sup>/s.</p> <p> </p> <p><strong>EXAMPLE PYTHON CODE TO READ AND PLOT COMPOSITION FILES</strong></p> <pre><code class="language-python">import numpy as np import pandas as pd import matplotlib.pyplot as plt grav = np.array([316,562,1000,1780,3160]) Teff=np.array([400,425,450,475,500,525,550,575,600,625,650,675,700,725,750,775,800,825,850,875,900,925,950,975,1000]) factor1 = np.array(['x0pt01','x1','x100']) factor2 = np.array(['1','0pt1']) file ="profile_sc_qt_rz_"+factor1[0]+"_cz_"+factor2[0]+"_"+str(Teff[14])+"_grav_"+str(grav[14])+"_mh_+0.0_sm_NA.dat" df = pd.read_csv(file, sep="\t") # Plot T(P) profile plt.ylim(100,1e-4) plt.semilogy(df['temperature'],df['pressure']) plt.show() # Plot H2O mixing ratio profile plt.ylim(100,1e-4) plt.loglog(df['H2O'],df['pressure']) plt.show()</code></pre> <p><strong>CREDITS</strong></p> <p>If you use these tables, please cite <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220814317M/abstract">Mukherjee et al. (2022, Astrophysical Journal, in press.)</a></p> <p> </p>
Text-fig. 1. Locality map with the approximate extent of Clarkia Lake during Miocene times in what is today northern Idaho, USA. Black dots mark three of the localities yielding the Miocene Clarkia flora; the fossil leaf of Nymphaea sp. described here comes from locality P-33. Other symbols: Dashed lines for county boundaries; a thin dotted line for Idaho State Hwy 3; a triangle for the local peak of Bechtel Butte; and a star for the town of Clarkia. Inset: Location of the map in northern Idaho. Abbreviations: WA – Washington state, OR – Oregon, ID – Idaho, MT – Montana. Map redrawn from Ladderud et al. (2015). in First Water Lily, A Leaf Of Nymphaea Sp., From The Miocene Clarkia Flora, Northern Idaho, Usa: Occurrence, Taphonomic Observations, Floristic Implications
Text-fig. 1. Locality map with the approximate extent of Clarkia Lake during Miocene times in what is today northern Idaho, USA. Black dots mark three of the localities yielding the Miocene Clarkia flora; the fossil leaf of Nymphaea sp. described here comes from locality P-33. Other symbols: Dashed lines for county boundaries; a thin dotted line for Idaho State Hwy 3; a triangle for the local peak of Bechtel Butte; and a star for the town of Clarkia. Inset: Location of the map in northern Idaho. Abbreviations: WA – Washington state, OR – Oregon, ID – Idaho, MT – Montana. Map redrawn from Ladderud et al. (2015).
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.