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20,150 results for “change”
HD-SIM-RBV: a synthetic dataset with model-based simulations of blood volume changes during hemodialysis
<p>The HD-SIM-RBV dataset is a synthetic (model-based) dataset generated to enable the study of blood volume (BV) or relative blood volume (RBV) changes during hemodialysis (HD).</p> <p>The dataset includes the profiles of BV changes during a standard 4-hour HD session simulated using a lumped-parameter, physiologically-based model of the cardiovascular system and the whole-body water and solute kinetics in 5,000 virtual patients with randomly adjusted values of 90 physiological parameters.</p> <p>For each of the 90 selected parameters, a random value was drawn from a normal distribution with the mean equal to the baseline value used originally in the model (with a few exceptions) and the standard deviation (SD) assumed at the level of 10%, 20%, or 40% of the baseline value, depending on the nature of the given parameter and the likelihood of its variation in the population (for some parameters, SD was set below 10% - see Parameters.xlsx). Only values within ±2SD from the mean were accepted. </p> <p>Ultrafiltration was set randomly within ±1 L from the assigned fluid overload. All other parameters as well as dialysis settings were kept constant for all virtual patients (at the levels used in our previous work - see the references below).</p> <p> </p> <p>When using the dataset, please cite the associated conference paper:</p> <p>Pstras L, Waniewski J. A Model-Based Dataset for In-Silico Exploration of the Patterns of Relative Blood Volume Changes During Hemodialysis. 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, 149-150, 2023, doi: 10.1109/IEEECONF58974.2023.10404528.</p>
Database of fitted spectra for: Changing-Look AGNs - I. Tracking the transition on the main sequence of quasars
<h3>Results from the spectral fitting for a sample of changing-look active galactic nuclei (AGNs) with SDSS spectroscopy using PyQSOFit.</h3>
Dataset of "From Gas to Solution: The Changing Neutral Structure of Proline Upon Solvation"
Liquid-jet photoelectron spectroscopy (LJ-PES) and electronic-structure theory were employed to investigate the chemical and structural properties of the amino acid L-proline in aqueous solution for its three ionized states (protonated, zwitterionic, deprotonated). This is the first PES study of this amino acid in its most biologically relevant environment. Proline's structure in the aqueous phase under neutral conditions is zwitterionic, distinctly different from the non-ionic neutral form in the gas phase. By analyzing the carbon 1s and nitrogen 1s core-levels as well as the valence spectra of aqueous-phase proline, we found that the electronic structure is dominated by the protonation state of each constituent molecular site (the carboxyl and amine) with small yet noticeable interference across the molecule. The site-specific nature of the core-level spectra enables probing of individual molecular constituents. The valence photoelectron spectra are more difficult to interpret because of overlapping signals of proline with the solvent and pH-adjusting agents (HCl and NaOH). Yet we are able to reveal subtle effects of specific (hydrogen-bonding) interaction with the solvent on the electronic structure. We also demonstrate that the relevant conformational space is much smaller for aqueous-phase proline than it is for its gas phase analogue. This study suggests that caution must be taken when comparing photoelectron spectra for gaseous and aqueous-phase molecules, particularly if those molecules are readily protonated / deprotonated in solution.
Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode
<p>This dataset accompanies Wright et al. (2022): Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode, Climate of the Past.</p> <p>This dataset contains:</p> <ul> <li><strong>Solar constant experiments</strong>: monthly files for sea level pressure (psl), surface stress east (tax), surface stress north (tay), screen temperature (tsc), and temperature at X pressure (t[0-18]) for solar constant experiments, specifically <ul> <li>control</li> <li>S+1</li> <li>S+3</li> <li>S+7</li> <li>S+35</li> <li>S-3</li> <li>S-7</li> <li>S-15</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Transient experiments</strong>: sea level pressure (psl) and screen temperature (tsc) files covering 1-2000 CE using: <ul> <li>Steinhilber_x2 solar forcing (monthly files)</li> <li>Shapiro solar forcing (monthly files)</li> </ul> </li> </ul> <p>These transient experiments are run as an Orbital-Greenhouse gases-Solar forcing experiment, and complement Phipps et al. (2013) (https://zenodo.org/record/3908927)</p> <p> </p>
A Benchmark dataset on Semantic Change in Scholarly Publications on Disability
<p>This is a benchmark dataset for semantic shift detection in disability-related corpora, including collected title and abstract text from PubMed and ArXiv, annotation sets based on domain experts and LLMs, and extracted KGs (Wikidata entity claims). The corpus from PubMed covers the period from the 1900s to 2023, while the corpus from ArXiv covers the period from the 1990s to 2023. The corpus was filtered based on 16 disability-related target words. In the annotation sets, '1' indicates that a semantic shift occurred for a target word, while '0' indicates the opposite. In particular, the LLM-based annotation sets include their generated text, and we used the Llama2 and GPT-4 models. '7b' refers to the parameter size of the Llama2 model. Graph_data.zip contains Wikidata entity claims.</p>
Ice sheet surface elevation change from ablation stake measurements on bare ice in the western Greenland ablation zone during July 2016
<p>Measurements of ice surface elevation change from a network of twelve bamboo ablation stakes installed in the western Greenland ice sheet ablation zone (67.0496o N, 49.0201o W, 1215 m a.s.l.). Stakes were installed by drilling 3 m deep holes into the ice, inserting the bamboo stakes, and allowing them to freeze into the ice for 24 hours. Following the 24 hour freeze-in period, measurements of the distance from the top of the stake to its base were recorded at nominal 3 hour intervals continuously from 12:00 local time (UTC-2) on 6 July 2016 to 23:00 local time on 12 July 2016. Prior to each measurement, a 24×24 cm square wooden ablation board was placed at the base of the stake and oriented to true north. This board operated as a datum from which the stake height above the ice surface was measured.</p>
Tabular data of the Proto-Austronesian-to-Enggano sound changes derived from Nothofer (1992: 21)
<p>This repository provides the <a href="https://github.com/engganolang/PAN-reflexes-in-Enggano-by-Nothofer/blob/main/Nothofer_1992_21_reg-sound-reflex-of-PAN-in-ENO.tsv">digitised, tabular version</a> of the regular sound changes from Proto-Austronesian into Enggano presented in linear order in Nothofer (<a href="https://github.com/engganolang/PAN-reflexes-in-Enggano-by-Nothofer#ref-nothofer1992">1992, 21</a>). The original, image-scanned version can be seen on the <a href="https://github.com/engganolang/PAN-reflexes-in-Enggano-by-Nothofer/blob/main/README.md">README page</a> of this repository.</p> <h2>Update for version 1.0.1</h2> <ul> <li>Additional entries to capture optionality of nasalisation on the vowels.</li> </ul> <h2>How to cite</h2> <p>Cite this repository AND the original source (Nothofer 1992) as follows:</p> <blockquote> <p>Rajeg, Gede Primahadi Wijaya. 2024. Tabular data of the Proto-Austronesian-to-Enggano sound changes derived from Nothofer (1992: 21).</p> </blockquote> <blockquote> <p>Nothofer, Bernd. 1992. "Lehnwörter Im Enggano." In Kölner Beiträge Aus Malaiologie Und Ethnologie Zu Ehren von Professor Dr. Irene Hilgers-Hesse, edited by F. Schulze and Kurt Tauchmann, 1:21–32. Kölner Südostasien Studien. Bonn: Holos.</p> </blockquote>
Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux -- Supplemental Data Set: Sea Level Sensitivity Kernels
<p><strong>Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux<br> SUPPLEMENTAL DATA SET: SEA LEVEL SENSITIVITY KERNELS</strong></p> <p>To accompany</p> <p> Jerry X. Mitrovica, Carling C. Hay, Robert E. Kopp, Christopher Harig, and<br> Konstantin Laytchev (2018). Quantifying the Sensitivity of Sea Level Change<br> in Coastal Localities to the Geometry of Polar Ice Mass Flux. Journal of<br> Climate. doi: 10.1175/JCLI-D-17-0465.1.</p> <p>We provide sea level kernels for ~740 tide gauge sites in the Permanent Service for Mean Sea Level (PSMSL) database (Holgate et al., 2013). Kernels associated with sensitivities to Greenland and Alaskan glacier melt are given on a spatial grid covering the globe, with 512 latitude rows (i=1,512) and 1024 longitude (j=1,1024) columns.</p> <p>Longitude values are evenly spaced moving eastward from Greenwich (the jth grid point has an east longitude value of (j-1)×360°/1024). Latitude values are Gauss-Legendre points beginning close to the North Pole and ending near the South Pole. Kernels associated with sensitivities to Antarctic melt are given on a spatial grid covering the globe, with 256 (Gauss-Legendre) latitude rows (i=1,256) and 512 longitude (j=1,512) columns. Longitude values are evenly spaced moving eastward from Greenwich.</p> <p>The format of the files is: </p> <p> grid_sitenumber_region.txt</p> <p>where “region” is either “green” (Greenland), “ant” (Antarctic) or “Alaska” (Alaska). The list of sites (and site numbers) is provided in the sites.txt file. The first 8 sites in this list were test sites and can be ignored.</p>
Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023)
<h3>Background</h3> <p>Human-induced land use change (LUC), driven by activities such as forestry, logging, and the production of agricultural commodities (e.g. fruits, nuts, and meat) significantly impacts the Global Commons, encompassing the climate system, ice sheets, land biosphere, oceans, and the ozone layer. The convertion of natural forests into areas dedicated to these activities lead to disrupted ecosystems (Foley et al. 2005), severely degraded biodiversity (Newbold et al. 2015), and the release of substantial amounts of greenhouse gases (GHGs) into the atmosphere (Hong et al. 2021), further exacerbating climate change and ocean acidification (Doney et al. 2009). The expansion of the agricultural frontier is identified as the predominant direct cause of deforestation globally, with other industries like timber and mining also playing significant roles (Curtis et al. 2018). To achieve global climate targets, forestry, and other land use GHG emissions must decrease along a nonlinear trajectory and reach carbon neutrality by 2050 (Rockström et al. 2017). However, to successfully address this road map, improving our understanding of deforestation drivers is urgently needed.</p> <h3>Summary</h3> <p>This dataset is the result of data processing performed to estimate the extent to which commodities and other agricultural products have replaced forests, while mapping the CO2 emission impact making use of the best available spatially explicit data. Results are reported globally for 52 products at national level, as well as agroecological and thermal zones (FAO & IIASA) and a 50km cell vector grid.</p> <p>In order to detect spatially-explicit deforestation drivers, the current extent of commodities and agricultural products was overlapped with global annual tree cover loss in the 10-year period from 2014 to 2023. Carbon stocks in the deforested areas were then assumed to have been emmited into the atmosphere. Recent, detailed crop and pasture maps for relevant commodities were used whenever available, and coarser resolution datasets were used as supplements when needed. Operations were performed in Google Earth Engine.</p> <h3>Datasets used</h3> <p><em>Forest and biomass carbon distribution</em></p> <p>The <a href="https://earthenginepartners.appspot.com/science-2013-global-forest">Global Forest Change</a> dataset (Hansen et al., 2013) is used to estimate deforestation between 2014 and 2023. This tree cover loss dataset measures the first instance of complete removal of tree cover canopy at a 30-meter resolution for all woody vegetation over 5 meters in height.</p> <p>The <a href="https://data-gis.unep-wcmc.org/portal/home/item.html?id=374a99fc76574f72bb8c71af7b428d0a">WCMC Above and Below Ground Biomass Carbon Density </a>(Soto-Navarro et al., 2020), for reference year 2010 at 300m pixel, is overlapped with resulting deforested areas pixels to dermine the biomass carbon present in the areas before deforestation.</p> <p><em>Generalized deforestation drivers</em></p> <p><a href="https://data.globalforestwatch.org/documents/ff304784a9f04ac4a45a40f60bae5b26/about">Tree cover loss by dominant driver</a> (Curtis et al., 2022) in 2023 is used to determine wide categories of deforestation drivers (commodities, shifting agriculture, forestry, wildfire and urbanization). Pixels indicating deforestation in the Global Forest Change dataset (Hansen et al., 2013) that overlap the commodities and shifting agriculture pixels from this dataset (Curtis et al., 2022) have their drivers further detailed with the data sources listed in the below.</p> <p><a href="http://www.earthstat.org/">EarthStat</a> pasture areas layer (Ramankutty et al., 2008) is used to identify areas for which specific livestock categories are to be defined. The project provides pasture areas for reference year 2000 at ~10km resolution.</p> <p><em>Detailed deforestation drivers</em></p> <p>The <a href="https://earthobservations.org/geoglam.php">Group on Earth Observations Global Agricultural Monitoring</a> (GEOGLAM) commodity distibution layer (Becker-Reshef et al., 2023) is used to identify specific commodities (winter wheat, spring wheat, maize, rice and soybean) to deforestation pixels pertaining to the "commodities" class. The ressource provides commodity distribution mapping at 5km pixel resolution. Values are provided as percentage of pixel area occupied by given crop.</p> <p>The <a href="https://mapspam.info/">Spatial Production Allocation Model (SPAM)</a> physical area layer (You et al., 2014) for reference year 2020 is used to detail drivers pertaining to the "shifting agriculture" class. The dataset covers 46 crops and crop groups at ~9km pixel resolution. Values are provided as percentage of pixel area occupied by given crop or crop group.</p> <p>The <a href="https://www.fao.org/livestock-systems/global-distributions/en/">Gridded Livestock of the World (GLW3)</a> (Gilbert et al., 2022) is used to determine which species (cattle, goat, sheep or horse) of livestock is raised in areas identified as pasture in the EarthStat layer and pertaining to the "commodities" class. The project provides livestock distribution for reference year 2015 at ~9km resolution. Values are provided as number of individuals located within the pixel. Values were converted into percentage of pixel area covered by grazing field for given species based on species density thresholds.</p> <h3>Data processing</h3> <p>Most of data processing takes place in Google Earth Engine, with scripts redacted in javascript. In summary, two strategies were implemented:</p> <p><strong>Proportional driver distribution strategy</strong>: When deforestation pixels (Hansen et al., 2013) overlapped with pixels from at least one of the detailed deforestation drivers data sources, the driver describe in the latter were associated with that deforested area. Whenever more than one of these data sources had non-null pixels overlapping the area, a proportional distribution was assumed (i.e. if SPAM indicated 100% of the area to be covered by cowpea crops, GEOGLAM 100% by maize, and GLW3 100% by cattle grazing fields, the pixel is assumed to have 33.3% of its deforested area associated with each of these drivers).</p> <p><strong>Main driver strategy</strong>: When deforestation pixels did not overlap with any non-null pixels from any of the detailed drivers sources, the pixel is assumed to have the entirety of its deforested area associated with one single main driver resulting from a crop-livestock mosaic. The mosaic is created by taking the highest value from each of the crop or livestock distribution rasters, and then assigning the raster category to be the new pixel value, ultimately creating a category raster layer containing the main crop, crop group or livestock species occupying that pixel area. Null or zero values in this mosaic are filled-in by nearest neighbour analysis, to a limit of 20 pixels expansion. This was enough to ensure that all deforestation pixels had at least one detailed driver with which it could be associated. The logic behind this operation resides in the fact that the deforestation layer (Hansen et al., 2013) has a larger temporal coverage (with the more recent data point being the reference year 2023), while the detailed driver layers can be as old as reference year 2015. This means we're assuming the main deforestation drivers continued to expand their limits to neighbouring areas during the years for which no data is available.</p> <p>Resulting rasters from both strategies are put together and a zonal statistics operation is performed in order to populate the vector grid cells.</p> <h3><strong>Files</strong></h3> <p>This repository contains the following files:</p> <ul> <li><em>deforested_area_by_LUC_driver_2014_2023</em>.CSV contains the deforested area (hectares) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>carbon_emissions_by_LUC_driver_2014_2023</em>.CSV contains the carbon emitted (Mg CO2 eq.) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>spatial_grid</em>.gpkg contains the raw 50km cell grid, with identification of country (iso3 and name fields), region, and FAO agroecological zone (zone field) and thermal zone (thermal field), in Geopackage format. In order to visualize the data in a map, the user will need to join one of the csv files to this geopackage file by basing the join on the 'id' field.</li> <li><em>summary_showcase</em>.png is an image showcasing maps created using the database, as well as a diagram showing the datasets used to create the final dataset.</li> </ul> <h3><strong>How to cite</strong></h3> <p>Iablonovski, G.; Berthet, E. C.; Roberts, S. (2024). Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023) [Data set]. Zenodo. https://zenodo.org/doi/10.5281/zenodo.13308514</p> <h3>Authors and contact</h3> <p>Authors: Guilherme Iablonovski*, Etienne Charles Berthet, Sophie Roberts</p> <p>*Corresponding author: Guilherme Iablonovski (guilherme.iablonovski@unsdsn.org)</p>
Arctic Coastal Change
<p><strong>General description</strong><br>The Arctic Coastal Change image collection consists of multitemporal coastal land cover data for the entire Arctic coast for the period 1984–2023. Coastal land cover is divided into land, water, and ice. Land cover is averaged over five-year time-steps, starting from 1984–1988 and ending in 2019–2023. The dataset has been produced using the full Landsat collection in Google Earth Engine (namely, Landsat 5, Landsat 7 and Landsat 8).</p> <p>This is considered a beta version, as the paper describing the data processing is currently under review.</p> <p><strong>Data structure</strong><br>The pan-Arctic dataset has been divided into 24 large tiles. The tiles are described in the accompanying vector file '<em>ACC_tiles.geojson</em>' and distributed as zip files. The dataset consists of one image for each of the eight time-steps, indicated with the image property 'timestep'. Each image has two bands: 1) land cover and 2) the number of valid observations. The spatial resolution of the dataset is 30 meters.</p> <p>The pixel and property values indicate following:</p> <p>Band: '<em>landcover</em>'<br>1 = land<br>2 = water<br>3 = ice<br>NA = not enough valid satellite data for reliable land cover classification or outside 10-km coastal zone</p> <p>Band: '<em>observations</em>'<br>1 = 5–9<br>2 = 10–14<br>3 = 15–29<br>4 = 30 or more<br>NA = less than 5 valid observations</p> <p>Property: '<em>timestep</em>'<br>1984 = time-step 1984–1988<br>1989 = time-step 1989–1993<br>1994 = time-step 1994–1998<br>1999 = time-step 1999–2003<br>2004 = time-step 2004–2008<br>2009 = time-step 2009–2013<br>2014 = time-step 2014–2018<br>2019 = time-step 2019–2023</p>
A novel approach to the detection of unusual mitochondrial protein change suggests hypometabolism of ancestral simians: Supplemental Files
<p><strong>Supplementary Fig. S1</strong>: θ<sub>evo</sub> calculated for each analyzed edge for specific OXPHOS complexes. Analyses were performed as in fig. 1F, except that SPCSs calculated from mtDNA-encoded protein positions in Complex I, Complex III, Complex IV, or Complex V were used to generate θevo values.</p> <p><strong>Supplementary Fig. S2</strong>: Mammalian orders differ in their propensity for potentially efficacious mitochondrial protein substitutions within specific OXPHOS complexes (median calculations). Analysis was performed as in fig. 2A, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V polypeptides.</p> <p><strong>Supplementary Fig. S3</strong>: Mammalian orders differ in their propensity for potentially efficacious mitochondrial protein substitutions within specific OXPHOS complexes (median confidence intervals). Analysis was performed as in (<em>A</em>) fig. 2B or (<em>B</em>) fig. 2C, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V proteins.</p> <p><strong>Supplementary Fig. S4</strong>: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median calculations). Analysis was performed as in fig. 3A, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V subunits.</p> <p><strong>Supplementary Fig. S5</strong>: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median confidence intervals ordered by lower 90% median confidence limit). Analysis was performed as in fig. 3B, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V proteins.</p> <p><strong>Supplementary Fig. S6</strong>: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median confidence intervals ordered by upper 90% median confidence limit). Analysis was performed as in fig. 3C, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V polypeptides.</p> <p>---</p> <p><strong>Supplementary File 1</strong>: All predicted protein substitutions along all edges at positions containing less than 2% gaps across input and ancestral sequences are listed, along with associated taxonomy information, TSS, and branch length. All alignment positions refer to Bos taurus reference sequences.</p> <p><strong>Supplementary File 2</strong>: The TSS calculated for each mitochondrial protein alignment position. All alignment positions refer to Bos taurus reference sequences.</p> <p><strong>Supplementary File 3</strong>: SPCS and θevo outputs are provided for analyses across all mitochondria-encoded positions, as well as for focused analyses of specific OXPHOS complexes and individual proteins.</p> <p><strong>Supplementary File 4</strong>: A GenBank flat file containing RefSeq entries for mammalian mtDNAs, as well as the entry for the reptile Anolis punctatus.</p> <p><strong>Supplementary File 5</strong>: A maximum likelihood inferred tree generated by a RAxML-NG analysis of concatenated and aligned protein coding sequences from mammalian and Anolis punctatusmtDNAs.</p> <p><strong>Supplementary File 6</strong>: Bootstrap replicates were generated from the alignment of concatenated protein coding sequences. Felsenstein’s Bootstrap Proportions (Felsenstein 1985) were calculated and used to label the maximum likelihood inferred tree of mammalian mtDNAs.</p> <p><strong>Supplementary File 7</strong>: Bootstrap replicates were generated using concatenated mammalian mtDNA coding sequences. Transfer Bootstrap Expectations (Lemoine 2018) were calculated and used to label the maximum likelihood inferred tree of mammalian mtDNAs.</p> <p><strong>Supplementary File 8</strong>: PAGAN tree output produced using aligned amino acid sequences and the rooted maximum likelihood inferred tree as input.</p>
Macroeconomic assessment of Climate Change Impacts
<p>Macroeconomic assessment of impacts on: Agriculture, Fishery, Forestry, Sea level rise, Riverine floods, Transport, Energy supply, Energy demand, Labour productivity, plus compounded assessment of all impacts</p>
Dataset for "Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity."
<p>This repository contains the tropical Pacific sea surface temperature and global precipitation data from the CESM1 time slice experiments, which were used for the analysis presented in Karamperidou & DiNezio (2022), Nature Communications (https://www.nature.com/articles/s41467-022-34880-8)</p> <p> </p> <p>From Karamperidou & DiNezio (2022):</p> <p>“To assess the response of ENSO flavors to orbital forcing over the past 12,000 years (12ka), we use a suite of time-slice experiments in 3ka intervals with version 1 of the Community Earth System Model (CESM1). Each experiment is 400-600 years long and was run until the surface climate and oceanic processes controlling tropical climate, such as the depth of the thermocline in the equatorial Pacific or the Atlantic Meridional Overturning Circulation (AMOC), have reached equilibrium. All simulations exhibit minimal drift in global mean surface temperature (less than 0.05<sup>o</sup>C per century), tropical mean surface temperature (less than 0.04<sup>o</sup>C per century), the depth of the equatorial thermocline in the Pacific (less than 0.3m per century), and the strength of the AMOC (less than 0.25 Sv per century) during the periods used in the analyses. With the exception of the 12 ka BP interval which includes ice sheet changes and lower greenhouse gases, the primary forcing in the 0, 3, 6, and 9 ka BP intervals is changes in Earth's precession, and each simulation branched off its preceding one, starting from 0ka sequentially through the Holocene. The maximum TOA energetic imbalance does not exceed 0.45 Wm<sup>-2</sup>, which is much smaller than the imposed radiative forcing.”</p> <p> </p> <p> </p> <p>Karamperidou, C., DiNezio, P.N. Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity. <em>Nat Commun</em> <strong>13</strong>, 7244 (2022). https://doi.org/10.1038/s41467-022-34880-8</p>
Forestry roads in the Purapel fluvial catchment and related changes in sediment connectivity
<p>This dataset contains georeferenced data of forestry roads and sediment connectivity in the Purapel catchment, which drains the Chilean Coastal Range. The forestry road network consists of all the dirt and gravel roads mapped in QGIS by observing open satellite images and vectorial data available during January 2021. The observed data are maps that were listed in the QGIS OpenLayers plugin (<a href="https://github.com/sourcepole/qgis-openlayers-plugin">https://github.com/sourcepole/qgis-openlayers-plugin</a>), such as Google Satellite (Map data ©2015 Google) and OpenStreetMap <sup>1</sup>, the road network of the Chilean Congress National Library (<a href="https://www.bcn.cl/siit/mapas_vectoriales">https://www.bcn.cl/siit/mapas_vectoriales</a>) and compositions of Sentinel 2 images (European Space Agency, courtesy of the U.S. Geological Survey) of the post-2017 fire period.</p> <p>Sediment Connectivity maps were calculated on a 5 m resolution LiDAR DTM using the Connectivity Index<sup> 2</sup>. The maps were derived from the stand-alone, free and open-source executable SedInConnect 2.3<sup> 3</sup> using the Weighting factor of <sup>2</sup> and two different targets, which are available as tif files:</p> <ul> <li>ICs.tif contains <em>IC<sub>s</sub></em>, the Connectivity Index to the stream network.</li> <li>ICrs.tif contains <em>ICr<sub>s</sub></em>, the Connectivity Index to the road and the stream network.</li> </ul> <p>Here, the Road Connectivity, <em>RC </em>(dimensionless) is defined as the difference between both previous maps, with the aim to describe the change in sediment connectivity due to forestry road network:</p> <ul> <li><em>RC = IC<sub>rs</sub> - IC<sub>s</sub></em></li> </ul> <p>It is available as RC.tif file. The area of<em> high RC </em>was defined using the percentile 95 (3.12). File RC95.tif is a mask of <em>RC </em><em>≥</em><em> 3.12</em>.</p> <p>The contributing area <em>CA </em>(m<sup>2</sup>) was calculated using the multiple flow D-infinity approach <sup>4</sup> using TauDEM (https://hydrology.usu.edu/taudem/taudem5/downloads.html).</p> <p>The file CA_RC95.tif contains the contributing area (m<sup>2</sup>) of the surfaces with highest changes in sediment connectivity due to the road network. That is:</p> <ul> <li><em>CA_RC95 = </em>{<em>CA </em>|<em> RC </em><em>≥</em><em> 3.12</em>}</li> </ul> <p>The landscape distribution of those surfaces, in terms of proximity to the hilltops and valleys, is described by the density plot of the raster file CA_RC95.tif in R:</p> <pre><code>library("raster") library("ggplot2") CA_RC95<-raster("CA_RC95.tif") CA_RC95<-CA_RC95*0.0025 df = as.data.frame(CA_RC95) df = na.omit(df) ggplot(df,aes(CA_RC95)) + geom_histogram(aes(y=..count..*25),binwidth = 50)+ geom_density(aes(y=50 * ..count..*25), col="blue",size=2, adjust=10000)+ xlab("Contributing Area [ha] \n Hilltop Valley") + ylab("Area [m2]")+ theme(axis.text.x = element_text(face="bold", size=30), plot.title = element_text(color="black", size=40, face="bold",hjust=0.5), axis.title.x=element_text(color="blue", size=40, face="bold"), axis.text.y = element_text(face="bold", size=30), axis.title.y=element_text(color="blue", size=40, face="bold"))+ scale_y_continuous(trans = 'log10')+ ggtitle("Upstream area of surfaces with \n High Road Connectivity (RC > 3.12)") </code></pre> <p>Bibliography</p> <p>1. OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org. https://www.openstreetmap.org/ (2017).</p> <p>2. Cavalli, M., Trevisani, S., Comiti, F. & Marchi, L. Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments. <em>Geomorphology</em> <strong>188</strong>, 31–41 (2013).</p> <p>3. Crema, S. & Cavalli, M. SedInConnect: a stand-alone, free and open source tool for the assessment of sediment connectivity. <em>Computers and Geosciences</em> <strong>111</strong>, 39–45 (2018).</p> <p>4. Tarboton, D. G. A new method for the determination of flow directions and upslope areas in grid digital elevation models. <em>Water Resources Research</em> <strong>33</strong>, 309–319 (1997). </p>
Data and code for: Little directional change in the timing of Arctic spring phenology over the past twenty-five years
<p>Data and code accompanying the publication: Little directional change in the timing of Arctic spring phenology over the past twenty-five years.</p> <p>This resource contains 1. R-scripts to calculate yearly phenologies from raw temporally explicit flowerin, arthropod observation and bird nesting data from Zackenberg. The raw data is openly accessible through the Greenland Ecosystem Monitoring database (https://data.g-e-m.dk/), as well as an R-script to carry out most of the analyses presented in the publication. To facilitate the use of the analysis script, pre-produced annual phenologies of focal taxa are included as csv-tables.</p>
Climate change velocity metrics calculated for three climate variables across Finland
<p>This dataset contains files that show the climate change velocity metrics calculated for three climate variables across Finland. The climate velocities were used to study the magnitude of projected climatic changes in a nation-wide Natura 2000 protected area (PA) network (Heikkinen et al., 2020). Using fine-resolution climate data that describes the present-day and future topoclimates and their spatio-temporal variation, the study explored the rate of climatic changes in protected areas on an ecologically relevant, but yet poorly explored scale. The velocities for the three climate variables were developed in the following work, where in-depth description of the different steps in velocity metrics calculation and a number of visualisations of their spatial variation across Finland are provided:</p><p>Risto K. Heikkinen 1, Niko Leikola 1, Juha Aalto 2,3, Kaisu Aapala 1, Saija Kuusela 1, Miska Luoto 2 & Raimo Virkkala 1 2020: Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678. https://doi.org/10.1038/s41598-020-58638-8</p><p>1 Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland</p><p>2 Department of Geosciences and Geography, University of Helsinki, FI-00014, Helsinki, Finland</p><p>3 Finnish Meteorological Institute, FI-00101, Helsinki, Finland </p><p>The dataset includes GIS compatible geotiff files describing the nine spatial climate velocity surfaces calculated across the whole of Finland at 50 m × 50 m spatial resolution. These nine different velocity surfaces consist of velocity metric values measured for each 50-m grid cell separately for the three different climate variables and in relation to the three different future climate scenarios (RCP2.6, RCP4.5 and RCP8.5). The baseline climate data for the study were the monthly temperature and precipitation data averaged for the period from 1981 to 2010 modelled at a resolution of 50-m, based on which estimates for the annual temperature sum above 5 °C (growing degree days, GDD, °C), the mean January temperature (TJan, °C) and the annual climatic water balance (WAB, the difference between annual precipitation and potential evapotranspiration; mm) were calculated. Corresponding future climate surfaces were produced using an ensemble of 23 global climate models for the years 2070–2099 (Taylor et al. 2012) and the three RCPs. The data for the three climate variables for 1981–2010 and under the three RCPs will be made available in separately via METIS - FMI's Research Data repository service (Aalto et al., in prep.). </p><p>The climate velocity surfaces included in the present data repository were developed using climate-analog approach (Hamann et al. 2015; Batllori et al. 2017; Brito-Morales et al. 2018), whereby velocity metrics for the 50-m grid cells were measured based on the distance between climatically similar cells under the baseline and the future climates, calculated separately for the three climate variables. In Heikkinen et al. (2020), the spatial data for the Natura 2000 protected areas were used to assess their exposure to climate change. The full data on N2K areas can be downloaded from the following link: https://ckan.ymparisto.fi/dataset/%7BED80465E-135B-4391-AA8A-FE2038FB224D%7D. However, note that the N2K areas including multiple physically separate patches were treated as separate polygons in Heikkinen et al. (2020), and a minimum size requirement of 2 hectares were requested. Moreover, the digital elevation model (DEM) data for Finland (which were dissected to Natura 2000 polygons to examine their elevational variation and its relationships to topoclimatic variation) can be downloaded from the following link: https://ckan.ymparisto.fi/en/dataset/dem25_astergdem25. </p><p>The coordinate system for the climate velocity data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)). Summary of the key settings and elements of the study are provided below. A detailed treatment is provided in Heikkinen et al. (2020).</p><p>Code to the files (four files per each velocity layer: *.tif, *.tfw. *.ovr and *.tif.aux.xml) in the dataset: </p><p>(a) Velocity of GDD with respect to RCP2.6 future climate (Fig 2a in Heikkinen et al. 2020). Name of the file: GDDRCP26.*</p><p>(b) Velocity of GDD with respect to RCP4.5 future climate (Fig. 2b in Heikkinen et al. 2020). Name of the file: GDDRCP45.*</p><p>(c) Velocity of GDD with respect to RCP8.5 future climate (Fig. 2c in Heikkinen et al. 2020). Name of the file: GDDRCP85.*</p><p>(d) Velocity of mean January temperature with respect to RCP2.6 future climate (Fig. 2d in Heikkinen et al. 2020). Name of the file: TJanRCP26.*</p><p>(e) Velocity of mean January temperature with respect to RCP4.5 future climate (Fig. 2e in Heikkinen et al. 2020). Name of the file: TJanRCP45.*</p><p>(f) Velocity of mean January temperature with respect to RCP8.5 future climate (Fig. 2f in Heikkinen et al. 2020). Name of the file: TJanRCP85.*</p><p>(g) Velocity of climatic water balance with respect to RCP2.6 future climate (Fig. 2g in Heikkinen et al. 2020). Name of the file: WABRCP26.*</p><p>(h) Velocity of climatic water balance with respect to RCP4.5 future climate (Fig. 2h in Heikkinen et al. 2020). Name of the file: WABRCP45.*</p><p>(i) Velocity of climatic water balance with respect to RCP8.5 future climate (Fig. 2i in Heikkinen et al. 2020). Name of the file: WABRCP85.*</p><p>Note that velocity surfaces e and f include disappearing climate conditions.</p><p><strong>Summary of the study:</strong></p><p>Climate velocity is a generic metric which provides useful information for climate-wise conservation planning to identify regions and protected areas where climate conditions are changing most rapidly, exposing them to high rates of climate displacement (Batllori et al. 2017), causing potential carry-over impacts to community structure and ecosystem functions (Ackerly et al. 2010). Climate velocity has been typically used to assess the climatic risks for species and their populations, but velocity metrics can also be used to identify protected areas which face overall difficulties in retaining ecological conditions that promote present-day biodiversity. </p><p>Earlier climate velocity assessments have focussed on the domains of the mesoclimate (resolutions of 1–100 km) or macroclimate (>100 km scales), and fine-grained (<100 m) local climatic conditions created by variation in topography ('topoclimate'; Ackerly et al. 2010; 2020) have largely been overlooked (Heikkinen et al. 2020). This omission may lead to biased exposure assessments especially in rugged terrain (Dobrowski et al. 2013; Franklin et al. 2013), as well as a limited ability to detect sites decoupled from the regional climate (Aalto et al. 2017; Lenoir et al. 2017). This study provided the first assessment of the climatic exposure risks across a national PA (Natura 2000) network based on very fine-grained velocities of three established drivers of high latitude biodiversity. </p><p>The produce fine-grain climate velocity measures, 50-m resolution monthly temperature and precipitation data averaged for 1981–2010 were first developed, and based on it, the three bioclimatic variables (growing degree days, mean January temperature and annual climatic water balance) were calculated for the whole study domain. In the next phase, similar future climate surfaces were produced based on data from an ensemble of 23 global climate models, extracted from the CMIP5 archives for the years 2070–2099 and the three RCP scenarios (RCP2.6, RCP4.5 and RCP8.5)26. In the final step, climate velocities for each the 50 x 50 m grid cells were measured using climate-analog velocity method (Hamann et al. 2015) and based on the distance between climatically similar cells under the baseline and future climates.</p><p>The results revealed notable spatial differences in the high velocity areas for the three bioclimatic variables, indicating contrasting exposure risks in protected areas situated in different areas. Moreover, comparisons of the 50-m baseline and future climate surfaces revealed a potential wholesale disappearance of current topoclimatic temperature conditions from almost all the studied PAs by the end of this century.</p><p><strong>Calculation of climate change velocity metrics for the three climate variables</strong></p><p>The overall process of calculation of climate velocities included three main steps. </p><p>(1) In the first step, we developed high-resolution monthly average temperature and precipitation data averaged over the years 1981–2010 and across the study domain at a spatial resolution of 50 × 50 m. This was done by building topoclimatic models based on climate data sourced from 313 meteorological stations (European Climate Assessment and Dataset [ECA&D]) (Klok et al. 2009). Our station network and modelling domain covered the whole of Finland with an additional 100 km buffer. However, it was also extended to cover large parts of northern Sweden and Norway for areas >66.5°N, as well as selected adjacent areas in Russia (for details see Heikkinen et al. 2020). This was done to capture the present-day climate spaces in Finland which are projected to move in the future beyond the country borders but have analogous climate areas in neighbouring areas; this was done to avoid developing a large number of velocity values deemed as infinite or unknown in the data for Finland. </p><p>The 50-m resolution average air temperature data were developed for the study domain using generalized additive modelling (GAM), as implemented in the R-package mgcv version 1.8–7 (R Development Core Team 2011; Wood 2011). In this modelling we utilised variables of geographical location (latitude and longitude, included as an anisotropic interaction), topography (elevation, potential incoming solar radiation, relative elevation) and water cover (sea and lake proximity), and subsequent leave-one-out cross-validation tests to assess model performance (for full process description, see Aalto et al. 2017; Heikkinen et al. 2020). The resulting topoclimate data effectively captured the physiographic effects of solar radiation and cold-air pooling.</p><p>To produce gridded precipitation data, we applied global kriging interpolation to the data from 343 rain gauges from the ECA&D dataset. The interpolation was carried out using information on geographical location, topography (elevation and eastness index) and proximity to the sea and R package gstat. The eastness index was obtained from a sine-transforming aspect raster surface calculated from a 50 m × 50 m digital elevation model to capture the effect of prevailing westerly winds on the accumulated precipitation on windward slopes. The gridding was first run at a resolution of 500 × 500 m, whereafter gridded precipitation values were bilinearly interpolated into the same 50 × 50 m resolution as the air temperature data. </p><p>Next, the three bioclimatic variables ((i) growing degree days (GDD, °C days) indicating the accumulated warmth during the growing season; (ii) mean January air temperature - TJan, °C; (iii) climatic water balance - WAB, mm) were calculated for each 50 x 50 grid cell from the high-resolution gridded 1981–2010 ('baseline') climate data. Earlier research has demonstrated the ecological relevance of these three complementary variables which provide estimations of winter cold, seasonal warmth and moisture availability (Sykes et al. 1996; Luoto et al. 2006; Huntley et al. 2007, 2008). </p><p>Following Carter et al. (1991), GDD was calculated as the effective temperature sum above the base temperature of 5 °C as follows:</p><p><i>GDD</i>5 = <i>∑ni (Ti - Tb), if Ti -Tb > 5</i></p><p>where Ti denotes the mean temperature at day i, Tb represents the base temperature, and n is the length of the summation period. However, because the daily air temperature data was not available, here the GDD was estimated using monthly data as in Araújo & Luoto (2007). The WAB is the difference between the total annual precipitation sum and the potential evapotranspiration (PET), which was estimated from the monthly air temperatures following Skov and Svenning (2004): </p><p><i>PET </i>= 58.93 × <i>Tabove </i>0°<i>C </i>/ 12</p><p>(2) In the second step we developed data on future climates by using the climate projections from the ensemble of 23 global climate models (GCMs), derived from the Coupled Model Intercomparison Project phase 5 archives (Taylor et al. 2012). From these archives, we processed to predicted averaged changes in mean temperature and precipitation with respect to the baseline 1981–2010 for the years 2070–2099, and the three RCP scenarios (cf. Moss et al. 2010). As the Coupled Model Intercomparison Project phase 5 climate scenario data represent coarse-scale resolution data, we converted it to match our fine-resolution baseline climate data by interpolation. For this, the climate model data depicting the predicted change in mean temperatures and precipitation with respect to the baseline climate were bilinearly interpolated to the 50 × 50 m grid system, and the change predicted by the GCMs was added to the spatially detailed baseline climate data. After this, the bioclimatic variables were recalculated for each RCP scenario to allow the calculation of climate change velocities across the whole country and the Natura 2000 protected areas.</p><p>(3) In the third step we developed climate change velocities for the three bioclimatic variables using the climate-analog approach (Hamann et al. 2015) where velocity is calculated by measuring the distance between present-day locations with certain climatic conditions and their future climate analogues, divided by the number of years between the two points in time. Thus, we calculated climate-analog velocities for the 50-m resolution grid climate data by measuring the distance between climatically similar grid cells for the present and future climates under RCP2.6, RCP4.5 and RCP8.5. </p><p>Prior the actual climate-analog velocity measurements, the climate variable surfaces were converted from continuous values into classified variable surfaces. For this, we defined the boundary values for the variable classes so that the climatically matching grid cells had their within-class ranges as small as possible but, at the same time, avoided artefactual extreme precision. After a set of pilot reclassifications, the following within-class ranges were applied: GDD, within-class range 50 °C with 51 categories; TJan, within-class range 0.5 °C with 60 categories; WAB, within-class range 50 mm with 55 categories. Next, using the reclassified present-day and future climate surfaces the search of the minimum distances between grid cells with similar present-day and future GDD/TJan/WAB climates were executed. The search was carried out using the ArcGIS software (Desktop 10.5.1.) by employing the Euclidean distance function. The minimum distances measured for each 50-m grid cell were divided by the difference between the mean points in the two time slices, 1981–2010 and 2070–2099. </p><p>The resulting 50-m resolution climate velocity surfaces for the three climate variables are provided in the zipped files included this data repository. In Heikkinen et al. (2020), these climate velocity data were employed in a series of subsequent analyses. For example, high-velocity areas ('velocity hotspots') of the three climate variables were visually compared with each other based on maps showing their 50-m resolution velocities across mainland Finland and the degree of overlap between the present-day range and projected future range of the three climate variables were investigated in each of the 5,068 Natura 2000 polygons included in the study.</p><p><strong>References</strong></p><p>Aalto, J., Riihimäki, H., Meineri, E., Hylander, K., Luoto, M., 2017. Revealing topoclimatic heterogeneity using meteorological station data. International Journal of Climatology 37, 544-556.</p><p>Ackerly, D.D., Loarie, S.R., Cornwell, W.K., Weiss, S.B., Hamilton, H., Branciforte, R., Kraft, N.J.B., 2010. The geography of climate change: implications for conservation biogeography. Diversity and Distributions 16, 476-487.</p><p>Ackerly, D.D., Kling, M.M., Clark, M.L., Papper, P., Oldfather, M.F., Flint, A.L., Flint, L.E., 2020. Topoclimates, refugia, and biotic responses to climate change. Frontiers in Ecology and the Environment 18, 288-297.</p><p>Araujo, M.B., Luoto, M., 2007. The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography 16.</p><p>Batllori, E., Parisien, M.-A., Parks, S.A., Moritz, M.A., Miller, C., 2017. Potential relocation of climatic environments suggests high rates of climate displacement within the North American protection network. Global Change Biology 23, 3219-3230.</p><p>Brito-Morales, I., García Molinos, J., Schoeman, D.S., Burrows, M.T., Poloczanska, E.S., Brown, C.J., Ferrier, S., Harwood, T.D., Klein, C.J., McDonald-Madden, E., Moore, P.J., Pandolfi, J.M., Watson, J.E.M., Wenger, A.S., Richardson, A.J., 2018. Climate Velocity Can Inform Conservation in a Warming World. Trends in Ecology & Evolution 33, 441-457.</p><p>Carter, T.R., Porter, J.H., Parry, M.L., 1991. Climatic warming and crop potential in Europe: Prospects and uncertainties. Global Environmental Change 1, 291-312.</p><p>Dobrowski, S.Z., Abatzoglou, J., Swanson, A.K., Greenberg, J.A., Mynsberge, A.R., Holden, Z.A., Schwartz, M.K., 2013. The climate velocity of the contiguous United States during the 20th century. Global Change Biology 19, 241-251.</p><p>Franklin, J., Davis, F.W., Ikegami, M., Syphard, A.D., Flint, L.E., Flint, A.L., Hannah, L., 2013. Modeling plant species distributions under future climates: how fine scale do climate projections need to be? Global Change Biology 19, 473-483.</p><p>Hamann, A., Roberts, D.R., Barber, Q.E., Carroll, C., Nielsen, S.E., 2015. Velocity of climate change algorithms for guiding conservation and management. Global Change Biology 21, 997-1004. </p><p>Heikkinen, R.K., Leikola, N., Aalto, J., Aapala, K., Kuusela, S., Luoto, M., Virkkala, R., 2020. Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678.</p><p>Huntley, B., Green, R.E., Collingham, Y.C., Willis, S.G., 2007. A climatic atlas of European breeding birds. Durham University, The RSPB and Lynx Edicions, Barcelona.</p><p>Huntley, B., Collingham, Y.C., Willis, S.G., Green, R.E., 2008. Potential Impacts of Climatic Change on European Breeding Birds. Plos One 3.</p><p>Klok, E.J., Klein Tank, A.M.G., 2009. Updated and extended European dataset of daily climate observations. International Journal of Climatology 29, 1182-1191.</p><p>Lenoir, J., Hattab, T., Pierre, G., 2017. Climatic microrefugia under anthropogenic climate change: implications for species redistribution. Ecography 40, 253-266.</p><p>Luoto, M., Heikkinen, R.K., Pöyry, J., Saarinen, K., 2006. Determinants of biogeographical distribution of butterflies in boreal regions. Journal of Biogeography 33, 1764-1778.</p><p>Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., Wilbanks, T.J., 2010. The next generation of scenarios for climate change research and assessment. Nature 463, 747-756.</p><p>R Development Core Team, 2011. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing).</p><p>Skov, F., Svenning, J.-C., 2004. Potential impact of climatic change on the distribution of forest herbs in Europe. Ecography 27, 366-380.</p><p>Sykes, M.T., Prentice, I.C., Cramer, W., 1996. A bioclimatic model for the potential distributions of north European tree species under present and future climates. Journal of Biogeography 23, 203-233.</p><p>Taylor, K.E., Stouffer, R.J., Meehl, G.A., 2012. An Overview of CMIP5 and the Experiment Design. Bulletin of the American meteorological Society 93, 485-498.</p><p>Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society Series B 73, 3-36.</p><p> </p>
Harmful algal bloom and aquatic weeds data from the Sacramento-San Joaquin Delta, collected to evaluate the impact of the 2021 Temporary Urgency Change Order and Emergency Drought Barrier
Condition 8 of the June 2021 Temporary Urgency Change Order for the Central Valley Project (CVP) and State Water Project (SWP) requires a special study of harmful algal blooms (HABs) in the Sacramento–San Joaquin Delta (Delta) and the spread of submersed aquatic vegetation (SAV), and floating aquatic vegetation (FAV), also referred to as “aquatic weeds”. A report on the study was submitted to the State Water Resources Control Board on June 1, 2022. This data package contains all publicly available data used in the report, including visual cyanobacteria reports, cyanotoxin data, water quality, nutrients, flow/hydrodynamics, chlorophyll-a concentrations, temperature, coverage of SAV and FAV, use of herbicides, and human populations. Many of these data were derived from other datasets, though some were collected specifically for this study
LTREB: Marsh elevation change in control and fertilized plots in a Spartina alterniflora-dominated salt marsh, North Inlet, Georgetown, SC: 1990-2025.
Marsh elevation was measured with a Surface Elevation Table (SET) as a component of a long-term project seeking to understand how salt marsh primary production and sediment chemistry respond to anthropogenic (e.g. eutrophication) and natural (e.g. sea-level rise) environmental change. Feedbacks between plants, sediments, nutrients and flooding were investigated with particular attention to mechanisms that keep marshes in equilibrium with sea level. Other data collected as part of the project include aboveground annual primary productivity, plant biomass, plant density and porewater nutrient concentrations. These data have been used to develop the Marsh Equilibrium Model, an important tool for coastal resource managers. Sampling occurred at 7 Spartina alterniflora-dominated salt marsh sites in North Inlet, a relatively pristine estuary near Georgetown, SC on the SE coast of the United States. North Inlet is a tidally-dominated, bar-built estuary, with a semi-diurnal mixed tide and a tidal range of 1.4m. The 25-km2 estuary is comprised of about 20.5 km2 of intertidal salt marsh and mudflats, and 4.5 km2 of open water. Marsh elevation sampling began in 1990, 1991, 1996 or 2000, depending on the site. Sampling occurred approximately monthly or approximately annually through 2025. The study is on-going. Additionally, some plots were fertilized with nitrogen and phosphorus.
Deep-soil carbon changes at 62 European beech stands in the Vienna Woods, Austria, 1984-2022
This dataset comprises repeated soil, vegetation, and site measurements from long-term forest monitoring in the Vienna Woods (Wienerwald), Austria, part of the UNESCO Biosphere Reserve “Wienerwald” (48.1°–48.3° N, 15.8°–16.3° E). The study focuses on pure, naturally regenerated European beech (Fagus sylvatica) stands, initially sampled in 1984 and resampled in 2012 and 2022 . Elevations range from ~180 to 800 m a.s.l., with mean annual temperatures of 8–9 °C and precipitation of 600–900 mm. Soil samples were collected from three mineral soil depths (0–5 cm, 30–40 cm, and 80–90 cm) following consistent protocols across sampling years. Variables include total, organic, and inorganic carbon, total nitrogen and sulfur, exchangeable base cations (Ca, Mg, K), pH, total Fe and Mn, fine soil mass, bulk density, rock content, soil texture, and root biomass. Stocks were calculated. Leaf nutrient concentrations (C, N, S, P, Ca, Mg, K) were determined in all sampling years. Dendrochronological measurements were conducted to determine growth trends since stand establishment, and stand-level characteristics (tree density, DBH, aboveground biomass, crown vitality, slope, aspect) were recorded. Site-level climate data (mean annual temperature, annual precipitation) from 1961 to present and atmospheric deposition data for N and S (1990, 2012, 2022) were integrated from national and European gridded datasets. The dataset supports long-term assessments of soil carbon and nutrient dynamics, forest productivity, and environmental change impacts in old-growth beech forests. Data collection is complete for the 1984, 2012, and 2022 campaigns; no ongoing sampling is planned.
Ecosystem responses to changes in climate and carbon dioxide in twelve mature ecosystems ranging from prairie to forest and from the arctic to the tropics
We use the Multiple Element Limitation (MEL) model to examine the responses of twelve ecosystems - from the arctic to the tropics and from grasslands to forests - to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in annual precipitation. The ecosystems we simulated include moist acidic tundra, shrub tundra, and wet sedge tundra near Toolik Lake, Alaska, alpine dry meadow tundra near Niwot Ridge, Colorado, restored tallgrass prairie near Kellogg Biological Station, Michigan, native tallgrass prairie at the Konza Prairie, Kansas, upland and lowland boreal forest near Bonanza Creek, Alaska, temperate coniferous forest in HJ Andrews Experimental Forest, Oregon, a northern hardwood forest in Hubbard Brook Experimental Forest, New Hampshire, a transition oak-maple forest in Harvard Forest, Massachusetts, and lowland tropical rainforest near Caxiuanã National Forest, Pará, Brazil. For each of the twelve sites, we run six 100-year simulations beginning from the calibrated steady state (72 simulations total). The six simulations are: (1) increasing CO2 from 400 to 800 μmol mol-1, (2) warming from current temperatures to current plus 3.5oC, (3) decreasing precipitation from 100% to 80% of the current annual rate, (4) increasing precipitation from 100% to 120% of the current annual rate, (5) doubling of CO2, 3.5oC warming, and 20% decrease in precipitation, and (6) doubling of CO2, 3.5oC warming, and 20% increase in precipitation. This dataset consists of the MEL model Windows executable, the driver and parameter file for each site, and the output files for each of the six simulations listed above.
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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.