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Quantifying the magnitude of storm events that have impacted the Virginia Coast Reserve (2009-2024) using the Cumulative Storm Impact Index (CSII)
This dataset contains a record of storm events along with quantified magnitudes that have impacted the Virginia Coast Reserve between 2009- 2024, minus 2010. We retrieved hourly water level data and monthly datums from the NOAA Tides and Currents database (tidesandcurrents.noaa.gov) for the tide station located in Wachapreague, VA (Station 8631044) to quantify the magnitude of storms using 1) the Storm Erosion Potential Index (SEPI; Zhang et al. 2001), and 2) the Cumulative Storm Impact Index (CSII; Fenster and Dominguez 2022). CSII incorporates the timing and magnitude of previous storms as a measure of cumulative impact, or "storminess". We identified storm events based on storm surge that exceeded two standard deviations (> 2SD) of the average surge and storm tide that exceeded the annual average Mean High Water (MHW) of a semi-diurnal tide (12 hours; SEPI). We then calculated the CSII for each storm as the sum of the SEPI and an exponentially decaying weighting factor (delta) from the previous storm's CSII that accounts for beach recovery that may have occurred between storm events. Here we use delta = 0.3 to best capture storm clustering during the 15 year period (Fenster and Dominguez 2022). Years missing >10% of data were excluded. For detailed methods on the data retrieval process, identifying storms, and quantifying storm magnitude, see Fenster and Dominguez (2022) and Dominguez et al. (2024). We identified a total of 208 storm events with an average of 14.3 events per year +/- 2.3 (SD) and an average annual CSII of 428.1 (m2hr) +/- 196.1 (SD).
Cumulated dam impact in France and the Iberian Peninsula (SUDOANG project)
<h2><strong>1. SUDOANG PROJECT</strong></h2><p>The SUDOANG project has provided common tools and assessment methods to managers to support the eel conservation in the SUDOE zone (Southern France, Spain and Portugal). One of the goals of the project was to develop an eel abundance and distribution <a href="https://zenodo.org/record/7546419">atlas</a> in the three countries, based on the results of the implementation of Eel Density Analysis (<a href="https://sudoang.eu/wp-content/uploads/2022/02/E411_Briand_et_al_2022_EDA_report_opt-1.pdf">EDA</a>). This model extrapolates eel abundance from a range of river segments sampled by electrofishing, to the whole river and lake network, by considering how eel abundance, size and sex vary according to different parameters related to eel habitat. To do this, we have created a dataset of "cumulated dam impact" which compiles different ways of calculating cumulated height from the sea.</p><h2><strong>2. SUDOANG DATABASE</strong></h2><p>The dataset on cumulated impact was first derived from information on obstacles collected by the SUDOANG project. Obstacles data for the three countries were imported in the SUDOANG database (<a href="https://sudoang.eu/wp-content/uploads/2020/11/E221_data_collection_storage-1.html#3_data_import_on_physical_obstacles_in_spain_and_portugal">deliverable 2.2.1</a>), whose structure is inherited from the DataBase for EEl (DBEEL), developed during a European research project (POSE - Pilot projects to estimate potential and actual escapement of silver eel, Walker et al., 2011). This database is designed to contain all data relative to eel biology and anthropogenic pressures applying to eel. During the course of SUDOANG, this database was used and ameliorated. </p><p>In France the obstacles were compiled from three pre-existing different databases:</p><ul><li>the Referential of flow obstacles (<a href="https://professionnels.ofb.fr/fr/node/367">ROE</a>) ,</li><li>the Information of Ecological Continuity (<a href="https://professionnels.ofb.fr/en/node/731">ICE</a>) and</li><li>the Flow Obstruction Database (Base de Données des Obstacles à l'Ecoulement, (BDOE).</li></ul><p>The data we have integrated into the SUDOANG 1.0.4. database came from a database dump of the 12th September 2019. The inventory includes bridges that have a significant impact on river continuity.</p><p>In Spain, data came from:</p><ul><li>the MITECO Ministry</li><li>the Basque Water Agency (URA) - Basque Country</li><li>the Catalan Water Agency (ACA) - Catalonia</li><li>the University of Girona - Catalonia</li><li>the University of Córdoba - Andalusia</li><li>Xunta de Galicia, Consellería de Medio Ambiente, Territorio e Vivenda - Galicia</li><li>the <a href="https://amber.international">AMBER </a>project</li></ul><p>In Portugal the data came from:</p><ul><li>the Portuguese Water Agency (APA)</li><li>MARE-ULisboa (University of Lisbon)</li><li>CIIMAR, the University of Porto</li><li>the <a href="https://amber.international">AMBER</a> project.</li></ul><p>In the case of the transboundary river Minho, the data came from:</p><ul><li>CIIMAR, the University of Porto (Portuguese area) (<a href="https://www.dgrm.mm.gov.pt/documents/20143/0/PGE+TIRM+Vers%C3%A3o+Portuguesa+Revis%C3%A3o+Novembro+2011.pdf/3c9d8b50-e5cc-2ed8-5714-90a115d4a6a5">report</a>)</li><li>EHEC, the University of Santiago de Compostela (Spanish area) (<a href="https://www.dgrm.mm.gov.pt/documents/20143/0/PGE+TIRM+Vers%C3%A3o+Portuguesa+Revis%C3%A3o+Novembro+2011.pdf/3c9d8b50-e5cc-2ed8-5714-90a115d4a6a5">report</a>)</li></ul><h2><strong>3. DATA DESCRIPTION</strong></h2><h3><strong>3.1. Data collected on artificial obstacles</strong></h3><p>Artificial obstacles were classified into 10 types according to the Adaptive Management of Barriers in European Rivers (<a href="https://amber.international">AMBER</a>) project. Some additional types (e.g., penstock pipes) were added to identify other obstacles in national databases that did not fit the AMBER classification (see the list below). Sometimes dams from different branches are connected, creating a dam-network. In those cases, we have only kept the dam(s) in the main course and use a hierarchical classification of the dams to only consider the cumulated height from the sea to the reference dam. We included only obstacles that are presently standing, <i>i.e.,</i> not planned, under construction, or destroyed. Dikes, longitudinal control structures and grates were excluded.</p><p><i>Obstacle classification according to the data collected and the AMBER project:</i></p><ul><li>BR - Bridge: A structure that is built over a river to allow people or vehicles to cross</li><li>CU - Culvert: A tunnel or pipe carrying a stream or open drain under a road or railway</li><li>DI - Dike: An embankment used to hold back water</li><li>DA - Dam: Structure that blocks the river and extends across the river bed to the flood plain</li><li>FO - Ford: A shallow crossing-place in a river</li><li>PP - Penstock: pipe Group of pipes that transport pressurised water from a reservoir (dam) to the turbines installed in a hydro-electric power plant</li><li>RR - Rock ramp: A weir made of rocks</li><li>WE - Weir: Structure across a river that does not extend to the flood plain</li><li>OT - Other: Structure that is not covered by previous definitions</li><li>UN - Unknown: Unknown</li></ul><p>We have projected obstacles on the SUDOANG river network at the nearest point within 300 m. To avoid projecting large obstacles in the wrong location in the southwestern France, SUDOANG experts have reviewed and corrected this information. We have also used an algorithm that extracts the best obstacle height data from the three existing databases in France. In the Iberian Peninsula, data providers validated and corrected obstacle location and height using a Shiny application developed by the project, in which they could directly correct the height of obstacles.</p><p>The variables in the <strong>obstacles </strong>table (csv delimiter ",") are:</p><ul><li><i>op_id</i>: Identifier of the observation place name</li><li><i>op_gis_layername</i>: Original data source</li><li><i>op_placename</i>: Name of the dam</li><li><i>op_op_id</i>: If the dam is linked within a complex (e.g. when there are multiple channels for the same river) the name of the parent dam</li><li><i>id_original</i>: Original id of the dam (in the raw table)</li><li><i>country</i>: Country code ('SP', 'ES' or 'FR')</li><li><i>dp_name</i>: Name of the data provider</li><li><i>obstruction_type_code</i>: Type of obstruction (see table obstruction type code)</li><li><i>obstruction_type_name</i>: Name of the dam</li><li><i>po_obstruction_height</i>: Difference of level of water between the downstream and the upstream part of the dam</li><li><i>po_presence_eel_pass</i>: Presence of a pass suitable for eel (see paper)</li><li><i>po_date_presence_eel_pass</i>: Date of construction of the eel (or eel compatible) pass</li><li><i>fishway_type_code</i>: Code of the fishway type</li><li><i>fishway_type_name</i>: Name of the fishway type</li><li><i>googlemapscoods</i>: Link to google map</li><li><i>x_espg_4326</i>: Longitude (with ESPG 4326)</li><li><i>y_espg_4326</i>: Latitude (with ESPG 4326)</li></ul><h3><strong>3.2. Modeling missing data and estimating the cumulative impact on obstacles</strong></h3><p>For those obstacles missing height information, we have calculated height using a Generalized Linear Models (GLM of log transformed height, <i>family = gaussian, link = identity</i>. In France the <a href="https://forgemia.inra.fr/pole-migrateurs/eda/dbeel/-/blob/main/eda2.3/report/Dams.Rmd">model</a> was based on river segment slope, river segment median flow and hydrographic basin. In the Iberian Peninsula, we have implemented a simpler <a href="https://forgemia.inra.fr/pole-migrateurs/eda/dbeel/-/blob/main/eda2.3/report/E221_data_collection_storage_sp_pt.Rmd">model</a> based on obstacle type, as information about flow or slope was not available for all river segments.</p><p>The cumulated impact of obstacles was assessed by creating a table joining each river segment with all the dams located in the downstream course. Using this, various metrics were computed using different assumptions concerning the effect of obstacles. The heights were power transformed to test for a different effect of obstacle's height (the cumulated effect of two obstacles of 1 m might be different than the cumulated effect of a single obstacle of 2 m), and functions were developed to calculate cumulated obstacle transformed variables. Other variables were also tested. In fact, tests in France have shown that factors such as presence of a fish pass, and eel passability did not improve the <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/S4/BaseEdaRiosRiversegmentsDam.R\#L624">model performance</a>. For this reason, but also because in the Iberian Peninsula this type of information was too limited, we used dam height to model the cumulative height of obstacles at a given river segment. </p><p>The variables in the <strong>cumulated_dam_impact_SUDOANG </strong>table (format Rdata - to be read with the R software, this will load as a data.frame called datadam) are:</p><ul><li><i>cs_height_08_n</i>: Cumulated height from the sea, dam height transformed with power 0.8, no prediction for missing values</li><li><i>cs_height_08_n</i>.: Same variable but truncated to 300</li><li><i>cs_height_08_p</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values</li><li><i>cs_height_08_p</i>.: Same variable but truncated to 300</li><li><i>cs_height_08_pps </i>Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_10_FR</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from France are considered when building on a transnational water course</li><li><i>cs_height_10_n</i>: Cumulated height from the sea, no transformation, no prediction for missing values</li><li><i>cs_height_10_n</i>.: Same variable but truncated to 200</li><li><i>cs_height_10_p</i>: Cumulated height from the sea, no transformation, missing height are extrapolated from two different models in France and the Iberian Peninsula <i>cs_height_10_p</i>.: Same variable but truncated to 200</li><li><i>cs_height_10_pass0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_pass1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pp</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_10_ppass0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_ppass1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pps</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_10_pscore0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_pscore1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_PT</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Portugal are considered when building on a transnational water course</li><li><i>cs_height_10_score0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_score1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_SP</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Spain are considered when building on a transnational water course</li><li><i>cs_height_12_n</i>: Cumulated height from the sea, dam height transformed with power 1.2, no prediction for missing values</li><li><i>cs_height_12_n</i>: Same variable but truncated to 500</li><li><i>cs_height_12_p</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values</li><li><i>cs_height_12_p.</i>: Same variable but truncated to 500</li><li><i>cs_height_12_pp</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_12_pps</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_15_n</i>: Cumulated height from the sea, dam height transformed with power 1.5, no prediction for missing values</li><li><i>cs_height_15_n:</i> Same variable but truncated to 800</li><li><i>cs_height_15_p</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values</li><li><i>cs_height_15_p.:</i> Same variable but truncated to 800</li><li><i>cs_height_15_pp</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_15_pps</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure </li><li><i>cumnbdamp</i>: Cumulated number of dam from the sea</li><li><i>cumnbdamso</i>: duplicate of cumnbdamp</li><li><i>idsegment</i>: Unique identifier of the segment [data type: UUID]. Use the <a href="https://doi.org/10.5281/zenodo.7546419">Atlas</a> to link with spatial table in PostgreSQL</li></ul><h2><strong>4. VERSIONS</strong></h2><ul><li><a href="https://doi.org/10.5281/zenodo.7825552">10.5281/zenodo.7825552 </a>1.0.0 - 2023-04-15 - Initial Upload (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8348374">10.5281/zenodo.8348374</a> 1.0.1 - 2023-09-15 - Update provider and names (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8348374">10.5281/zenodo.8348374</a> 1.0.1 - 2023-11-08 - Final version (open access)</li></ul><h2><strong>5. READ MORE</strong></h2><ul><li>Atlas of European Eel Distribution (<i>Anguilla anguilla</i>) in Portugal, Spain and France (<a href="https://doi.org/10.5281/zenodo.7546419">10.5281/zenodo.7546419</a>)</li><li>Electrofishing data for eel in the Iberian Peninsula (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.8348353">10.5281/zenodo.8348353</a>)</li><li>Eel data (<i>Anguilla anguilla</i>) and associated environment variables used to fit the EDA model in the SUDOE area (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.6397009">10.5281/zenodo.6397009</a>)</li></ul><h2><strong>6. FUNDING</strong></h2><p>Project co-financed by the INTERREG SUDOE Programme through the European Regional Development Fund (ERDF).</p>
Effects of Nitrogen Fertilization on Litter and Soil Decomposition: Cumulative Microbial Respiration
The influence of inorganic nitrogen (N) inputs on decomposition is poorly understood. Some prior studies suggest that N may reduce the decomposition of substrates with high concentrations of lignin via inhibitory effects on the activity of lignin-degrading enzymes, although such inhibition has not always been demonstrated. The purpose of E145 was to study the effects of nitrogen (N) addition on decomposition of seven substrates ranging in initial lignin concentrations (from 7.4 - 25.6%) over five years in eight different grassland and forest sites in central Minnesota.
Compilation of cumulative fission yields for Xe-131, Xe-131m, Xe-133, Xe-133m, Xe-135 and Xe-135m
<p>Values on cumulative fission yields for the Xenon isotopes <sup>131</sup>Xe, <sup>131m</sup>Xe, <sup>133</sup>Xe, <sup>133m</sup>Xe, <sup>135</sup>Xe and <sup>135m</sup>Xe were compiled from the nuclear data libraries ENDF/B-VIII.0, GEFY-8.1, JEFF-3.3 and JENDL-4.0, partly available via the Nuclear Data Services of the the International Atomic Energy Agency. This dataset lists the collected values and their uncertainties as reported.</p>
Dataset Cumulative CO2 Emissions of International Transport
<p>This dataset considers the year 1783, when the first steamship was built, as the first year of the international transport CO2 emissions.</p> <p>The global cumulative CO2 emissions including international transport are converted to the 1875 baseline, similar to the Global Warming baseline (1850-1900). </p>
Winter cumulated water presence of "Marais Breton nord" calculated with the WIW
<p><span>The WIW was calculated for all SENTINEL 2 cloudless images available within the period from January to April from 2017 to 2024. All WIW rasters were then added to provide the cumulated water presence ranging from 1 to 40 corresponding to the number of dates related to available cloudless images at that period. </span>The table provides the dates of the images used. EPSG 3857</p>
Global CHIRPS MCWD (Maximum Cumulative Water Deficit) Dataset
<p><strong>Global CHIRPS MCWD Dataset</strong></p> <p>The MCWD (Maximum Cumulative Water Deficit) is a measure of drought severity, which corresponds to the maximum value of the monthly accumulated water deficit reached for each pixel within the year. The MCWD is a useful indicator of meteorologically induced water stress without taking into account local soil conditions and plant adaptations, which are poorly understood in Amazonia. The full method of MCWD is described in Aragão et al. (2007; <a href="https://doi.org/10.1029/2006GL028946">https://doi.org/10.1029/2006GL028946</a>). Detail about CHIRPS (Rainfall Estimates from Rain Gauge and Satellite Observations) can be found in Funk et al. (2015; <a href="https://doi.org/10.1038/sdata.2015.66">https://doi.org/10.1038/sdata.2015.66</a>).</p> <p> </p> <p><strong>Coverage:</strong> Spanning 50°S-50°N (and all longitudes/land areas)</p> <p><strong>Period:</strong> 1981 to 2020</p> <p><strong>Spatial resolution:</strong> 0.05-degree</p> <p><strong>Temporal resolution:</strong> Annual</p> <p><strong>Coordinate reference system:</strong> Geographic Coordinate System (Datum WGS84)</p> <p><strong>File format:</strong> The zip file containing 40 files (one per year) in compressed TIFF format.</p> <p><strong>Code:</strong> <a href="https://zenodo.org/record/5034650">https://zenodo.org/record/5034650</a></p> <p><strong>Dataset usage</strong>: It is free to use, but if you use this dataset in your work, please make sure to cite the repository and our paper properly. We also welcome users to invite us for collaboration.</p> <p><strong>For the use of this dataset, please cite:</strong></p> <p>Silva Junior, C.H.L. et al. Global CHIRPS MCWD (Maximum Cumulative Water Deficit) Dataset. <em>Zenodo</em> (2021). DOI: 10.5281/zenodo.4903340. <a href="https://doi.org/10.1038/s41597-020-00600-4">https://doi.org/10.5281/zenodo.4903340</a></p> <p>Silva Junior, C.H.L. et al. Fire Responses to the 2010 and 2015/2016 Amazonian Droughts. <em>Front. Earth Sci</em>. (2019). DOI: 10.3389/feart.2019.00097. <a href="https://doi.org/10.3389/feart.2019.00097">https://doi.org/10.3389/feart.2019.00097</a></p> <p>Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. <em>Scientific Data</em> (2015). DOI: 10.1038/sdata.2015.66. <a href="https://doi.org/10.1038/sdata.2015.66">https://doi.org/10.1038/sdata.2015.66</a></p>
Supplementary Data for "Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results from a Community-Partnered Low-cost Sensor Deployment"
<p>These are the underlying data sets needed to build the kriging maps and calculate dissemination block cumulative hazard indices described in the paper. There are three data sets:</p> <ol> <li><strong>"Sampling location names and coordinates.csv"</strong>: locations and IDs of the low-cost sensors and the regulatory monitoring stations used in this work.<strong> [NOTE: </strong>latitudes and longitudes for the sensor deployments have been intentionally rounded to protect the location of volunteer sensor hosts.]</li> <li><strong>"Dissemination Block Populations.csv"</strong>: These are the relevant dissemination blocks in the study domain and their associated populations. This information was originally extracted from: https://censusmapper.ca/#13/49.2430/-123.1252</li> <li><strong>"Daily average concentrations by site and pollutant.csv"</strong>: This contains the PM2.5, NO2 and O3 daily averages for the entire study period across all low-cost sensor sites and regulatory monitoring stations. Refer to "Sampling location names and coordinates.csv" to parse the labels in this data set.</li> </ol> <p>There is also a sample code in Python to construct the kriging maps provided in 2 formats. <strong>[NOTE: </strong>we have intentionally excluded uploading the exact data sets imported by this code; our original data contains exact locations of sensor host volunteers and thus cannot be shared.]</p> <ol> <li><strong>"Jain et al - GeoHealth - Kriging Script.ipynb"</strong>: A Jupyter notebook script to import the data, build kriging maps, calculate CHIs, and export the data.</li> <li><strong>" Jain et al - GeoHealth - Kriging Script.pdf"</strong>: A PDF export of the Jupyter notebook so that you can read the Python scripts even if you are not a Jupyter notebooks user.</li> </ol>
University of Michigan Biological Station cumulative food web data for terrestrial habitats, 1909-2023.
Here, we present species and interaction lists for a food web of the aboveground terrestrial habitats at the University of Michigan Biological Station (UMBS). The site is composed predominantly of dry-mesic, northern hardwood forests with patches of wooded wetlands (hardwood conifer swamp). Taxa were sourced from lists provided by UMBS, from resident biologists’ personal observations, museum specimens, online databases, historical censuses, and BioBlitz events. Only those that could be resolved to species-level or were genera with < 20 species in the Nearctic were included. We also excluded species that do not have a significant lifestage or feeding behavior in aboveground terrestrial habitats. Our focal taxonomic groups include vascular plants, arthropods, birds, mammals, reptiles, amphibians. The majority of arthropods are insects; non-insect arthropods were highly underrepresented in our lists. Interactions were sourced from online databases, naturalist observations, and field guides and accumulated into a “metaweb” of all potential interactions between local species. Interactions were checked by experts to plausibly occur in the aboveground terrestrial environments at UMBS, given species’ phenology, traits, and habitat usage. Interactions at any taxonomic level were included, so long as they were approved to potentially occur between all species by our experts. To study the effect of taxonomic resolution on food web structure, in this dataset, we retained records at coarser taxonomic groupings even if more highly resolved records were also approved. We included all direct interactions among species in our system with a bioenergetic flow (i.e., one species consuming another), differentiated by their focal resource. We broadly categorized the resources as animal tissues, either (1) live tissues and as prey, or (2) scavenged as carrion, carcasses, or other decaying animal remains, or as plant tissues, grouped as (3) leaves and stems, including grasses, exudates, et
Cumulative PLOS ALM Report - July 2015
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Cumulative PLOS ALM Report - August 2015
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Cumulative PLOS ALM Report - September 2015
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Cumulative Data-Level Metrics (DLM) Report - September 2015
<p>Article-Level Metrics (ALM) measure the reach and online engagement of scholarly works. This DataSite Data-Level Metrics (DLM) Server report contains the cumulative stats collected for all works through September 11, 2015. Data are generated by the Lagotto open source software. Go to the Lagotto forum for questions or comments.</p>
Cumulative PLOS ALM Report - October 2015
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Cumulative PLOS ALM Report - December 2015
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Cumulative PLOS ALM Report - January 2016
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FIGURE. Annual and cumulative totals of Thysanoptera species descriptions in Brazil (image provided by Elison Lima). in All genera of the world: Order Thysanoptera (Animalia: Arthropoda: Insecta)
FIGURE. Annual and cumulative totals of Thysanoptera species descriptions in Brazil (image provided by Elison Lima).
Cumulative effects doubled the drought impacts for one-fifth of ecosystems exposed to recurrent droughts during Australia's Millennium Drought
<p><strong>Drought-induced vegetation declines have been reported across the globe and may have widespread implications for ecosystem composition, structure, and functions. Thus, it is critical to maximizing our understanding of how vegetation has responded to recent drought extremes. To date, most drought assessments have focused on the impact of a single drought event, emphasizing the importance of drought intensity for vegetation responses. However, drought timing, duration, and repeat exposure may all be important aspects of ecosystem response with the potential for non-linear effects. Cumulative effects are one such phenomenon, representing the additional decline due to repeated or prolonged exposure to drought, and indicating gradual loss of ecosystem resistance. This study quantifies the frequency and magnitude of cumulative effects among Australian ecosystems as they responded to the Millennium Drought. Three distinct biophysical variables derived from satellite remote sensing were analyzed, including fraction of photosynthetically absorbed radiation, photosynthetic vegetation cover, and canopy density derived from passive microwave data. While cumulative effects were detected in only 20% or less of the landscape exposed to repeat or long-duration drought, they approximately doubled drought impacts in the areas where they occurred. Cultivated lands and grasslands were the most susceptible to cumulative effects from recurrent droughts. Despite being relatively infrequent in forests and savannas, cumulative effects from repeated or long-duration drought caused larger additional declines in these ecosystems. Overall, our study demonstrates that the temporal sequence of drought events can strongly influence the magnitude of drought impacts, which provides important insights into modeling vegetation response to recurrent drought events</strong></p>
Revisiting the cumulative effects of drought on global gross primary productivity based on new long-term series data (1982-2018)
<p><strong>Aim:</strong> Drought has broad and deep impacts on vegetation. Studies on the effects of drought on vegetation have been conducted over years. However, global-scale and long-term (>30 years) studies on the cumulative<strong> </strong>effect of drought are still lacking. Thus, combining a new satellite based gross primary productivity (GPP) and multi-timescale Standardized Precipitation Evapotranspiration Index datasets, we investigated the cumulative effect of drought on global vegetation GPP.</p> <p><strong>Location: </strong>Global.</p> <p><strong>Time period: </strong>1982 – 2018 (37 years).</p> <p><strong>Major taxa studied: </strong>Forests and grasslands.</p> <p><strong>Method: </strong>Based on correlation analysis framework, we investigated the cumulative effect duration of drought on global vegetation GPP. Meanwhile, the variability of this cumulative effect across different elevation gradients and climatic zones was analyzed using variance analysis.</p>
X-ray imaging of 30 year old wine grape wood reveals cumulative impacts of rootstocks on scion secondary growth and harvest index
<p><span></span></p> <p><span>Annual rings from vines in a 30 year old, California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were measured at the beginning and end of the lifetime of the vineyard.</span></p> <p><span>X-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. </span></p> <p><span>Modeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius.</span></p> <p><span>Rootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species.</span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.