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34 results for “2012-2014”
WSC - Leaf area index (LAI) at various points within Wibu field site, 2012-2014
Leaf area index (LAI) measurements collected at various points within the Wibu field site between 2012-2014. Measurements were collected approximately weekly from plant emergence until appr. 1 month past the onset of senescence. The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons; therefore, these are all LAI values for corn. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site and use of the LAI data.
Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2012-2014)
<p>This data is part of the <em>Monthly aggregated Water Vapor MODIS MCD19A2 (1 km)</em> dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.</p><p><strong>General Description</strong></p><p>The monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:</p><ul><li><strong>Monthly time-series:</strong><br>Derived from <em>MCD19A2 v061</em>, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li><strong>Yearly time-series:</strong><br>Derived from <em>monthly time-series</em>, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li><strong>Long-term data (2000-2022):</strong><br>Derived from <em>monthly time-series</em>, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul><p><strong>Data Details</strong></p><ul><li><strong>Time period:</strong> 2012–2014</li><li><strong>Type of data:</strong> Water vapor column above the ground (0.001cm)</li><li><strong>How the data was collected or derived:</strong> Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li><strong>Statistical methods used:</strong> Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li><li><strong>Coordinate reference system:</strong> EPSG:4326</li><li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.00081, 179.99994, 87.37000)</li><li><strong>Spatial resolution:</strong> 1/120 d.d. = 0.008333333 (1km)</li><li><strong>Image size:</strong> 43,200 x 17,924</li><li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li></ul><p><strong>Support</strong></p><p>If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:</p><ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul><p><strong>Name convention</strong></p><p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p><ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20120101 = 2012-01-01</li><li>Time reference end time: 20141231 = 2014-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>
Perennial grass tiller and stolon counts in plots with experimentally altered precipitation variability at the Jornada Basin LTER site, 2012-2014
This dataset contains perennial grass tiller and stolon counts collected starting in 2012 for a long-term precipitation variability manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. The study was designed to assess the effect of interannual variability in precipitation on average aboveground net primary productivity (ANPP) in Chihuahuan Desert grasslands. The study began in 2009, has five annual precipitation treatments, and contains 50 plots (10 per treatment). This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs to 2.5 x 2.5 meter plots in a desert grassland. There are high, low, and ambient (control) precipitation variability treatments. Ambient plots receive natural precipitation each year, while variability treatments alternate between 20% and 180% (high variability), or 50% and 150% (low variability) of ambient precipitation each year. Perennial grass tiller and stolon counts were made annually in each plot from 2012-2014. This is an ongoing study and the dataset will be updated as needed.
Perennial grass tiller and stolon density in plots with experimentally altered precipitation and nutrient inputs at the Jornada Basin LTER site, 2012-2014
This dataset contains perennial grass tiller and stolon counts collected starting in 2012 for a long-term precipitation and nutrient manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs, and fertilization treatments to alter nitrogen input to 2.5 x 2.5 meter plots in a desert grassland. Tillers and stolons of perennial grasses were counted in each plot in 2012, 2013 and 2014. This is an ongoing study and the dataset will be updated as needed.
Satellite tracking data of white sharks in the southwest Indian Ocean (2012-2014)
<p>These data comprise locations and individual metadata from 34 white sharks (<em>Carcharodon carcharias</em>) instrumented March-May 2012 with telemetry devices along the coast of South Africa. These devices were SPOT5 transmitters (SPOT-257, SPOT-258; Wildlife Computers) which transmit locations via ARGOS CLS. All research methods were approved and conducted under the South African Department of Environmental Affairs: Oceans and Coasts permitting authority.</p> <p>This dataset is linked to the manuscript Kock et al. 2021 "Sex and size influence the spatiotemporal distribution of white sharks, with implications for interactions with fisheries and spatial management in the southwest Indian Ocean".</p> <p>The data are structured in long format, so that each row in the dataset represents an observation. The columns in the data are as follows.</p> <p>DeployID: This a factor variable identifying each individual shark. It has 34 levels.</p> <p>SPOT: This is a numeric variable identifying the tag number unique to each shark.</p> <p>Date: This is a date variable (POSIXct) that gives the date and time of a geographic location record in UTC time.</p> <p>Type: This is a character variable identifying the type of location record.</p> <p>Quality: This is a character variable made up of numbers and letters giving the location error associated with each location as provided by ARGOS.</p> <p>Latitude: This is a numeric variable and gives the latitude of the shark at the time of each record.</p> <p>Longitude: This is a numeric variable and gives the longitude of the shark at the time of each record.</p> <p>Area_tagged: This is a character variable that gives the area where the shark was tagged.</p> <p>Sex: This is a character variable identifying the sex of the shark, either "F" or "M" for female and male.</p> <p>TL: This is a numeric variable giving the total length of the shark in centimetres.</p> <p>Maturity: This is a character variable giving the maturity of the shark based on its total length following Malcolm et al. 2001: juveniles (male and female: 175-300 cm TL), sub-adults (male: >300-360 cm TL; females: >300-480 cm TL) and adults (male: >360 cm TL; female: >480 cm TL).</p> <p> </p>
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Leaf Litter Decomposition 2012-2014
Decomposition of leaf litter is a major source of nutrient transfer from vegetation to soils and an important carbon flux. In northern hardwood forests, litter decomposition might be affected by nutrient availability, species composition, stand age or structure, or access by soil decomposers. We investigated these factors in four stands at the Bartlett Experimental Forest in New Hampshire that have had nitrogen and phosphorus added in full factorial design since 2011. Leaf litter of early and late successional species was collected in 2012 and deployed in bags of two mesh sizes (63 µm and 2 mm) in two young and two mature stands and collected three times over the next 2 years. Decomposition was evaluated by fitting mass loss as an exponential function of time represented by growing degree days. Litter decomposed more quickly in the small mesh bags (p < 0.001), which excluded mesofauna. This result was surprising, but might be explained by the greater rigidity of the large mesh material making poor contact with the soil. The litter with a species composition characteristic of our young stands decomposed more quickly than the litter representing mature stands (p = 0.01 for species mix in the full model). The environment in which is was placed was not as important: Neither the age of the stand in which it was placed (p = 0.31), nor N addition (p = 0.59), P addition (p = 0.41), or the interaction of N and P addition (p = 0.13) were significant predictors of the decomposition rate, defined by fitting an exponential decay constant. Additional detail on the MELNHE project, including a datatable of site descriptions and a pdf file with the project description and diagram of plot configuration can be found in this data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=344 Litter was collected by Rick Bicher and sorted by species by middle school students. Litterbags were made, filled, and weighed by middle school students. Gracie Gilcrist
Nitrate (15N) Uptake from samples collected aboard Palmer LTER annual cruises off the Western Antarctic Peninsula, 2012-2014
Nitrate uptake by the bulk phytoplankton community was determined using tracer (<10%) additions of labeled 15-NO3. Samples were collected by Go-Flo from 5 depths 0, 5, 10, 20, 65 m and incubated for 24 h at light levels of 100%, 50%, 25%, 10%, and 0% surface irradiance, respectively.
Honey bee Seasonal mortality 2012-2014 - Epilobee analysis
<p>EPILOBEE was the first active epidemiological surveillance program implemented in 17 EU Member States, over 2 consecutive years (from autumn 2012 to summer 2014), following a harmonised protocol based on the EU reference laboratory guidelines. EFSA requested a statistical analysis on the EPILOBEE dataset to establish associations between colony mortalities and some factors including disease prevalence, the context of beekeeping and the apiary geographical distribution.The data set published is the result of the data cleaning and categorization performed on the EPILOBEE original dataset regarding seasonal mortality. The dataset comprises 4758 observations from apiaries across Europe.</p>
Honey bee Winter mortality 2012-2014 - Epilobee analysis
<p>EPILOBEE was the first active epidemiological surveillance program implemented in 17 EU Member States, over 2 consecutive years (from autumn 2012 to summer 2014), following a harmonised protocol based on the EU reference laboratory guidelines. EFSA requested a statistical analysis on the EPILOBEE dataset to establish associations between colony mortalities and some factors including disease prevalence, the context of beekeeping and the apiary geographical distribution. The data set published is the result of the data cleaning and categorization performed on the EPILOBEE original dataset regarding winter mortality. The dataset comprises 4758 observations from apiaries across Europe.</p> <p>The present dataset has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. </p> <p>The dataset is in EXCEL format.</p>
Cellulose in situ Decomposition in a Bog Exposed to Increasing Nitrogen Treatments, 2012-2014
Development of the oil sands has led to increasing atmospheric N deposition, with values as high as 17 kg N ha-1 yr-1; regional background levels <2 kg N ha-1 yr-1. Bogs, being ombrotrophic, may be especially susceptible to increasing N deposition. To examine responses to N deposition, over five years, we experimentally applied N (as NH4NO3) to a bog near Mariana Lakes, Alberta, at rates of 0, 5, 10, 15, 20, and 25 kg N ha-1 yr-1, plus controls (no water or N addition). We examined the effects of N addition on cellulose placed in the bog from 2012-2014 and collected after 5 and 17 months. Decomposition of cellulose filter paper in surface peat increased with N input. Water addition alone had no significant effect on exponential decay constants (k values). In control and 0 kg ha-1 yr-1 treatments, k values averaged 0.58 yr-1, corresponding to 42% of initial mass lost in the first year, while in the 25 kg ha-1 yr-1 treatment, k values averaged 1.27 yr-1, corresponding to 72% of initial mass lost in the first year. Assessment of decomposition and its controls may be especially important in peatlands, as the development and persistence of peat depends on an excess of NPP over decomposition throughout the peat profile. There is some evidence that increasing N deposition/availability stimulates cellulose decomposition in surface bog peat, as we found at Mariana Lakes Bog.
United States Pacific Northwest surveys of coastal foredune topography and vegetation abundance, 2012-2014
These datasets document our measurements of dune plant species abundance and topography from paired vegetation and topographic cross-shore foredune surveys in the United States Pacific Northwest (Oregon and southern Washington coastlines) in Summer 2012 and Summer 2014. In 2012, we conducted cross-shore paired topographic and vegetation surveys at 126 transect locations, and performed three replicate cross-shore transects per location (for a total of 378 transects). Of these surveys, 58 transect locations were positioned within Habitat Restoration Areas (as described in Biel et al. 2017). In 2014, we repeated these topographic and vegetation surveys at 83 of the 2012 transect locations (performing a single transect survey per location). Within each transect, we measure elevation (using RTK GPS) and plant species abundance (using 0.25 m^2 quadrats) at 5 m intervals between the vegetation line and the foredune heel. Within each quadrat, we measure the percent cover of all plant species present, and the tiller abundance of the three dune building grasses, Ammophila arenaria (invasive), Ammophila breviligulata (invasive), and Elymus mollis (native). Together, these datasets encompass measurements of elevation and plant abundance from 7953 quadrats in 2012, and 1616 quadrats in 2014.
Monthly small nekton samplings collected in the Plum Island Estuary, years 2012-2014
Monthly small nekton samplings collected by seine in the Plum Island Estuary during years 2012 - 2014. The collections were conducted in a manner similar to the Massachusetts Division of Marine Fisheries study in 1965, "A Study of the Marine Resources of the Parker River-Plum Island Sound Estuary, Jerome et al., 1968.
Groundwater Wells on Metompkin and Smith Islands, VA 2012-2014
Shallow groundwater wells are used to monitor levels of groundwater on barrier islands off the Atlantic Coast of the Delmarva Peninsula. Levels are monitored every 12-minutes.
Cardamine pratensis early/late ecotype transects Dibbinsdale Nature Reserve 2012-2014
<p>Phenological escape, whereby species alter the timing of life-history events to avoid seasonal antagonists, is usually analyzed either as a potential evolutionary outcome given current selection coefficients or as a realized outcome in response to known enemies. We here gain mechanistic insights into the evolutionary trajectory of phenological escape in the brassicaceous herb <em>Cardamine pratensis</em>, by comparing the flowering schedules of two sympatric ecotypes in different stages of a disruptive response to egg-laying pressure imposed by the pierid butterfly <em>Anthocharis cardamines</em>, whose larvae are pre-dispersal seed predators (reducing realized fecundity by ~70%). When the focal point of highest intensity selection (peak egg-laying) occurs early in the flowering schedule, selection for late flowering dependent on reduced egg-laying combined with selection for early flowering dependent on reduced predator survival results in a symmetrical bimodal flowering curve; when the focal point occurs late, an asymmetrical flowering curve results with a large early flowering mode due to selection for reduced egg-laying augmented by selection for infested plants to outrun larval development and dehisce before seed-pod consumption. Unequal selection pressures on high and low fecundity ramets, due to asynchronous flowering and morphologically targeted (size-dependent) egg-laying, constrain phenological escape, with bimodal flowering evolving primarily in response to disruptive selection on high fecundity phenotypes. These results emphasize the importance of analyzing variation in selection coefficients among morphological phenotypes over the entire flowering schedule to predict how populations will evolve in response to altered phenologies resulting from climate change.</p>
Mosquito data at SAFE 2012-2014
<b>Description: </b><p>PhD results</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/77"><b>Impacts of tropical deforestation and fragmentation on mosquito community</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=46">here</a></p><p><b>Data worksheets: </b>There are 5 data worksheets in this dataset:</p><ol><li><p><b>Daily Human landing catches 2012-2013</b> (Worksheet DailyHLC2012-2013)</p><p>Dimensions: 94 rows by 37 columns</p><p>Description: Human landing catch data collected at the SAFE Project 2012-2013. Worksheet contains species collected per evening (18:00-23:00)</p><p>Fields: </p><ul><li><b>Date</b>: Date of the collection (Field type: Date)</li><li><b>Location</b>: SAFE Project location (2nd order) (Field type: Location)</li><li><b>Disturbance</b>: Disturbance gradient (Field type: Ordered Categorical)</li><li><b>Collector</b>: Initial of second collector during human landing catch- first collector was always the same (Field type: Categorical)</li><li><b>Moonlight</b>: Moon illumination (percentage) during the collection (does not take cloud cover into account) (Field type: Numeric)</li><li><b>Forest_cover</b>: Forest cover recorded at each site (LAI info) (Field type: Numeric)</li><li><b>An_ait_count</b>: Number of Anopheles aitkenii mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>An_bar_count</b>: Number of Anopheles barbirostris mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>An_van_count</b>: Number of Anopheles vanus mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>An_sp_count</b>: Number of Anopheles sp. (couldn't be identified to species) mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>An_bal_count</b>: Number of Anopheles balabacensis mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>An_koc_count</b>: Number of Anopheles kochi mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>An_lat_count</b>: Number of Anopheles latens mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>An_maca_count</b>: Number of Anopheles macarthuri mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>An_macu_count</b>: Number of Anopheles maculatus mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>An_tes_count</b>: Number of Anopheles tessellatus mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>An_wat_count</b>: Number of Anopheles watsonii mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Arm_ju_count</b>: Number of Armigeres jugraensis mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Arm_fl_count</b>: Number of Armigeres flavus mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Coq_cr_count</b>: Number of Coquillettidia crassipes mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Cx_Cul1_count</b>: Number of Culex (Culiciomyia) mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Cx_pap_count</b>: Number of Culex papuensis mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Cx_sca_count</b>: Number of Culex scanloni mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Cx_gel_count</b>: Number of Culex gelidus mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Cx_mim_count</b>: Number of Culex mimulus mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Cx_qui_count</b>: Number of Culex quinquefasciatus mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Cx_sit_count</b>: Number of Culex sitiens mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Cx_vis_count</b>: Number of Culex vishnui mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Cx_Lop_count</b>: Number of Culex (Lophoceraomyia- couldn't be identified to species) mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Cx_bit_count</b>: Number of Culex bitaeniorhynchus mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Do_gan_count</b>: Number of Downsiomyia ganapathi mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>He_sci_count</b>: Number of Heizmannia scintillans mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Ma_ann_count</b>: Number of Mansonia annulata mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Or_sp_count</b>: Number of Orthopodomyia sp. (couldn't be identified to species) mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Pr_ost_count</b>: Number of Paraedes ostentatio mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li><li><b>Stg_al_count</b>: Number of Aedes albopictus mosquitoes landing on the collectors during the 5 hour period (Field type: Abundance)</li></ul><br></li><li><p><b>Hourly Human landing catches 2012-2013</b> (Worksheet HourlyHLC2012-2013)</p><p>Dimensions: 430 rows by 39 columns</p><p>Description: Human landing catch data collected at the SAFE Project 2012-2013. Worksheet contains species collected per hour (18:00-23:00)</p><p>Fields: </p><ul><li><b>Date</b>: Date of the collection (Field type: Date)</li><li><b>Start_Time</b>: Start time of collection (Field type: Time)</li><li><b>Location</b>: SAFE Project location (2nd order) (Field type: Location)</li><li><b>Disturbance</b>: Disturbance gradient (Field type: Ordered Categorical)</li><li><b>Collector</b>: Initial of second collector during human landing catch- first collector was always the same (Field type: Categorical)</li><li><b>Rain</b>: Level of rain recorded during collection (Field type: Ordered Categorical)</li><li><b>Moonlight</b>: Moon illumination (percentage) during the collection (does not take cloud cover into account) (Field type: Numeric)</li><li><b>Forest_cover</b>: Forest cover recorded at each site (LAI info) (Field type: Numeric)</li><li><b>An_ait_count</b>: Number of Anopheles aitkenii mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>An_bar_count</b>: Number of Anopheles barbirostris mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>An_van_count</b>: Number of Anopheles vanus mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>An_sp_count</b>: Number of Anopheles sp. (couldn't be identified to species) mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>An_bal_count</b>: Number of Anopheles balabacensis mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>An_koc_count</b>: Number of Anopheles kochi mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>An_lat_count</b>: Number of Anopheles latens mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>An_maca_count</b>: Number of Anopheles macarthuri mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>An_macu_count</b>: Number of Anopheles maculatus mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>An_tes_count</b>: Number of Anopheles tessellatus mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>An_wat_count</b>: Number of Anopheles watsonii mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Arm_ju_count</b>: Number of Armigeres jugraensis mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Arm_fl_count</b>: Number of Armigeres flavus mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Coq_cr_count</b>: Number of Coquillettidia crassipes mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Cx_Cul1_count</b>: Number of Culex (Culiciomyia) mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Cx_pap_count</b>: Number of Culex papuensis mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Cx_sca_count</b>: Number of Culex scanloni mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Cx_gel_count</b>: Number of Culex gelidus mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Cx_mim_count</b>: Number of Culex mimulus mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Cx_qui_count</b>: Number of Culex quinquefasciatus mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Cx_sit_count</b>: Number of Culex sitiens mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Cx_vis_count</b>: Number of Culex vishnui mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Cx_Lop_count</b>: Number of Culex (Lophoceraomyia- couldn't be identified to species) mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Cx_bit_count</b>: Number of Culex bitaeniorhynchus mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Do_gan_count</b>: Number of Downsiomyia ganapathi mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>He_sci_count</b>: Number of Heizmannia scintillans mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Ma_ann_count</b>: Number of Mansonia annulata mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Or_sp_count</b>: Number of Orthopodomyia sp. (couldn't be identified to species) mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Pr_ost_count</b>: Number of Paraedes ostentatio mosquitoes landing on the collectors (Field type: Abundance)</li><li><b>Stg_al_count</b>: Number of Aedes albopictus mosquitoes landing on the collectors (Field type: Abundance)</li></ul><br></li><li><p><b>Ovitrap data 2012-2013</b> (Worksheet Ovitrap2012-2013)</p><p>Dimensions: 90 rows by 23 columns</p><p>Description: Ovitrap data collected 2012 to 2013. Worksheet contains species collected per ovitrap</p><p>Fields: </p><ul><li><b>Location</b>: SAFE Project location (2nd order) (Field type: Location)</li><li><b>Disturbance</b>: Disturbance gradient (Field type: Ordered Categorical)</li><li><b>Mosquitoes_present</b>: Yes= mosquitoes were present in the ovitrap when collected (Field type: Categorical)</li><li><b>Start_Date</b>: Date Ovitrap was set out (Field type: Date)</li><li><b>End_Date</b>: Date Ovitrap was collected (Field type: Date)</li><li><b>Avg_Temp</b>: Average temperature in location whilst ovitrap was set out (Field type: Numeric)</li><li><b>Avg_RH</b>: Average RH in location whilst ovitrap was set out (Field type: Numeric)</li><li><b>Beetles_present</b>: Yes= Beetles were present in the ovitrap when collected (Field type: Categorical)</li><li><b>Tadpoles_present</b>: Yes= Tadpoles were present in the ovitrap when collected (Field type: Categorical)</li><li><b>Cx_Cul1_count</b>: Number of Cx.Cul1 identified from the ovitrap (Field type: Abundance)</li><li><b>Cx_Cul2_count</b>: Number of Cx.Cul2 identified from the ovitrap (Field type: Abundance)</li><li><b>Stg_al_count</b>: Number of Stg.al identified from the ovitrap (Field type: Abundance)</li><li><b>Arm_ju_count</b>: Number of Arm.ju identified from the ovitrap (Field type: Abundance)</li><li><b>Ze_gra_count</b>: Number of Ze.gra identified from the ovitrap (Field type: Abundance)</li><li><b>Tri_sp_count</b>: Number of Tri.sp identified from the ovitrap (Field type: Abundance)</li><li><b>Ur_sp1_count</b>: Number of Ur.sp1 identified from the ovitrap (Field type: Abundance)</li><li><b>An_bal_count</b>: Number of An.bal identified from the ovitrap (Field type: Abundance)</li><li><b>Arm_co_count</b>: Number of Arm.co identified from the ovitrap (Field type: Abundance)</li><li><b>Cx_nig_count</b>: Number of Cx.nig identified from the ovitrap (Field type: Abundance)</li><li><b>Ur_sp2_count</b>: Number of Ur.sp2 identified from the ovitrap (Field type: Abundance)</li><li><b>Cx_qui_count</b>: Number of Cx.qui identified from the ovitrap (Field type: Abundance)</li><li><b>Col_pse_count</b>: Number of Col.pse identified from the ovitrap (Field type: Abundance)</li></ul><br></li><li><p><b>Daily Human landing catches (canopy vs. ground) 2013-2014</b> (Worksheet DailyHLC2013-2014)</p><p>Dimensions: 90 rows by 36 columns</p><p>Description: Human landing catch data collected at the SAFE Project 2013-2014. Worksheet contains species collected per evening (18:00-22:00)</p><p>Fields: </p><ul><li><b>Date</b>: Date of the collection (Field type: Date)</li><li><b>Location</b>: SAFE Project location (2nd order) (Field type: Location)</li><li><b>Disturbance</b>: Disturbance gradient (Field type: Ordered Categorical)</li><li><b>Collector</b>: Initial of second collector during human landing catch- first collector was always the same (Field type: Categorical)</li><li><b>Moonlight</b>: Moon illumination (percentage) during the collection (does not take cloud cover into account) (Field type: Numeric)</li><li><b>Forest_cover</b>: Forest cover recorded at each site (LAI info) (Field type: Numeric)</li><li><b>Height</b>: Ground= ground level collection. Canopy= collection at 10m (Field type: Categorical)</li><li><b>Tree_Height</b>: Height of tree used for collection (Field type: Numeric)</li><li><b>Wind</b>: Strength of wind during collection period (Field type: Ordered Categorical)</li><li><b>Rain</b>: Level of rain recorded during collection (Field type: Ordered Categorical)</li><li><b>Temperature</b>: Average temperature in location during collection (Field type: Numeric)</li><li><b>Humidity</b>: Average RH in location during collection (Field type: Numeric)</li><li><b>Ae_orb_count</b>: Number of Am. orb mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>An_ait_count</b>: Number of An. ait mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>An_bar_count</b>: Number of An. bar mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>An_sp_count</b>: Number of An. sp mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>An_bal_count</b>: Number of An. bal mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>An_lat_count</b>: Number of An. lat mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>An_maca_count</b>: Number of An. maca mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>An_macu_count</b>: Number of An. macu mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>An_wat_count</b>: Number of An. wat mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Arm_co_count</b>: Number of Arm. co mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Arm_ju_count</b>: Number of Arm. ju mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Arm_sp_count</b>: Number of Arm. sp mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Col_pse_count</b>: Number of Col. pse mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Coq_cr_count</b>: Number of Coq. cr mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Cx_sit_count</b>: Number of Cx. sit mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Cx_vis_count</b>: Number of Cx. vis mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Cx_Lop_count</b>: Number of Cx. Lop mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Do_gan_count</b>: Number of Do. gan mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Pr_ost_count</b>: Number of Pr. ost mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Ph_pro_count</b>: Number of Ph. pho mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Stg_al_count</b>: Number of Stg. al mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Stg_sp_count</b>: Number of Stg. sp mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li><li><b>Ve_sp_count</b>: Number of Ve. sp mosquitoes landing on the collectors during the 4 hour period (Field type: Abundance)</li></ul><br></li><li><p><b>Hourly Human landing catches (canopy vs. ground) 2013-2014</b> (Worksheet HourlyHLC2013-2014)</p><p>Dimensions: 626 rows by 15 columns</p><p>Description: Human landing catch data collected at the SAFE Project 2013-2014. Worksheet contains species collected per hour (18:00-22:00)</p><p>Fields: </p><ul><li><b>Date</b>: Date of the collection (Field type: Date)</li><li><b>Start_Time</b>: Start time of collection (Field type: Time)</li><li><b>Location</b>: SAFE Project location (2nd order) (Field type: Location)</li><li><b>Disturbance</b>: Disturbance gradient (Field type: Ordered Categorical)</li><li><b>Collector</b>: Initial of second collector during human landing catch- first collector was always the same (Field type: Categorical)</li><li><b>Level</b>: Ground= ground level collection. Canopy= collection at 10m (Field type: Categorical)</li><li><b>Forest_cover</b>: Forest cover recorded at each site (LAI info) (Field type: Numeric)</li><li><b>Height</b>: Height above ground (Field type: Numeric)</li><li><b>Moonlight</b>: Moon illumination (percentage) during the collection (does not take cloud cover into account) (Field type: Numeric)</li><li><b>Wind</b>: Strength of wind during collection period (Field type: Ordered Categorical)</li><li><b>Rain</b>: Level of rain recorded during collection (Field type: Ordered Categorical)</li><li><b>Temperature</b>: Average temperature in location during collection (Field type: Numeric)</li><li><b>Humidity</b>: Average RH in location during collection (Field type: Numeric)</li><li><b>An_bal_count</b>: Number of An. bal mosquitoes landing on the collector (Field type: Abundance)</li></ul><br></li></ol><p><b>Date range: </b>2012-01-04 to 2014-07-07</p><p><b>Latitudinal extent: </b>4.6353 to 4.9654</p><p><b>Longitudinal extent: </b>116.9542 to 117.8004</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Diptera<br> -  -  -  - Culicidae<br> -  -  -  -  - <i>Aedes</i><br> -  -  -  -  -  - <i>Aedes albopictus</i><br> -  -  -  -  -  - <i>Aedes jugraensis</i> (as <i>Armigeres jugraensis</i>)<br> -  -  -  -  -  - <i>Aedes orbitae</i><br> -  -  -  -  -  - <i>Aedes papuensis</i> (as <i>Culex papuensis</i>)<br> -  -  -  -  -  - <i>Aedes prominens</i><br> -  -  -  -  -  - [Stg.sp]<br> -  -  -  -  - <i>Anopheles</i><br> -  -  -  -  -  - [An.sp]<br> -  -  -  -  -  - <i>Anopheles aitkenii</i><br> -  -  -  -  -  - <i>Anopheles balabacensis</i><br> -  -  -  -  -  - <i>Anopheles barbirostris</i><br> -  -  -  -  -  - <i>Anopheles kochi</i><br> -  -  -  -  -  - <i>Anopheles latens</i><br> -  -  -  -  -  - <i>Anopheles macarthuri</i><br> -  -  -  -  -  - <i>Anopheles maculatus</i><br> -  -  -  -  -  - <i>Anopheles tessellatus</i><br> -  -  -  -  -  - <i>Anopheles vanus</i><br> -  -  -  -  -  - <i>Anopheles watsonii</i><br> -  -  -  -  - <i>Armigeres</i><br> -  -  -  -  -  - [Arm.sp]<br> -  -  -  -  -  - <i>Armigeres confusus</i><br> -  -  -  -  -  - <i>Armigeres flavus</i><br> -  -  -  -  - <i>Collessius</i><br> -  -  -  -  -  - [<i>Collessius pseudotaeniatus</i>]<br> -  -  -  -  - <i>Coquillettidia</i><br> -  -  -  -  -  - <i>Coquillettidia crassipes</i><br> -  -  -  -  - <i>Culex</i><br> -  -  -  -  -  - <i>Culex bitaeniorhynchus</i><br> -  -  -  -  -  - <i>Culex gelidus</i><br> -  -  -  -  -  - <i>Culex mimulus</i><br> -  -  -  -  -  - <i>Culex nigropunctatus</i><br> -  -  -  -  -  - <i>Culex quinquefasciatus</i><br> -  -  -  -  -  - <i>Culex scanloni</i><br> -  -  -  -  -  - <i>Culex sitiens</i><br> -  -  -  -  -  - <i>Culex vishnui</i><br> -  -  -  -  -  - [Cx.Cul1]<br> -  -  -  -  -  - [Cx.Cul2]<br> -  -  -  -  -  - [Cx.Lop]<br> -  -  -  -  - <i>Downsiomyia</i><br> -  -  -  -  -  - [<i>Downsiomyia ganapathi</i>]<br> -  -  -  -  - <i>Heizmannia</i><br> -  -  -  -  -  - <i>Heizmannia scintillans</i><br> -  -  -  -  - <i>Mansonia</i><br> -  -  -  -  -  - <i>Mansonia annulata</i><br> -  -  -  -  - <i>Orthopodomyia</i><br> -  -  -  -  -  - [Or.sp]<br> -  -  -  -  - <i>Uranotaenia</i><br> -  -  -  -  -  - [Ur.sp1]<br> -  -  -  -  -  - [Ur.sp2]<br> -  -  -  -  - <i>Verrallina</i><br> -  -  -  -  -  - [Ve.sp]<br> -  -  -  -  - <i>Zeugnomyia</i><br> -  -  -  -  -  - <i>Zeugnomyia gracilis</i><br> -  -  -  -  - <i>Paraedes</i><br> -  -  -  -  -  - [<i>Paraedes ostentatio</i>]<br> -  -  - Hymenoptera<br> -  -  -  - Braconidae<br> -  -  -  -  - <i>Tripteroides</i><br> -  -  -  -  -  - [Tri.sp]<br></div><p></p>
2012-2014 post-eruptive intrusions at El Hierro
<p>Matlab files with LOS and GNSS data used in the manuscript "Magma flow rates and temporal evolution of the 2012-2014 post-eruptive intrusions at El Hierro, Canary Islands", JGR Solid Earth, 2019.</p> <p>GNSS data show north, east, and up displacements, and their respective uncertainties, with the number of station.</p>
Cardamine pratensis early/late ecotype transects Dibbinsdale Nature Reserve 2012-2014
Open the record for dataset details and reuse information.
Water stable isotope data for the Ogooué River (Gabon) at Lambaréné, 2012-2014.
<p>This dataset contains hydrogen and oxygen stable isotope data on surface water samples collected on the Ogooué River at Lambaréné (Gabon) between April 2012 and April 2014.</p>
2012-2014 Dataset [5/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 5/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2012-2014. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
BCC-GEOS-Chem v1.0 model output (2012-2014)
<p>The file includes the model output of the BCC-GEOS-Chem v1.0.</p>
ScienceDex guides
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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.