Find research datasets worth reusing

opencc-by-4.0Nov 2022View details →

Segmentation Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon

The zip file here contains 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. A total of 1,054 unique images were labeled. 946 images were annotated by a single labeler. 95 images were annotated by two labelers. 11 images were annotated by three labelers. 2 images were annotated by five labelers. All authors contributed to labeling, and all labeling was done with an open-source labeling tool (Buscombe et al., 2022). All pixels in each image are labeled with one of four classes: 0 (water), 1 (bare sand), 2 (vegetation - both sparse and dense), 4 (the built environment - buildings, roads, parking lots, boats, etc.) The csv file provided here is a list of each image file name (which includes the anonymized labeler ID), the name of the image without the labeler ID, the name of the corresponding NOAA jpg, the NOAA flight name, the storm name, the latitude and longitude of the image, and a column stating if the image has been labeled multiple times.  Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b). The images included in this data release correspond to original NOAA images that have been resized and then split into quadrants (using ImageMagick). The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right. The resize command used was: #to resize and then quarter #Dir structure is: # --Desktop #    |- originals #    |- resized #    |- quarters `cd originals` `mogrify -resize 2000x2000 -path ../resized *.jpg` #then quarter them `cd ..` `cd resized` `mogrify -crop 2x2@ +repage -path ../quarters *.jpg` For full size images, please download the jpegs directly from NOAA.

opencc-by-4.0Dec 2022View details →

Tropical cyclone and equatorial wave data in ERA5 (1980-2018)

The dataset consists of global tropical cyclone (TC) track and equatorial wave data derived from ERA5 (1980-2018). The dataset was originally used to produce the paper "Equatorial waves as useful precursors to tropical cyclone occurrence and intensification" published in Nature Communications in 2023. See the Methods section in the article for details. Note: the TC data are produced by Kevin Hodges (Reading University) using the TRACK method (Hodges and Emerton, 2015; Hodges et al., 2017); the wave data are produced by Gui-Ying Yang (Reading University) based on the method in Yang et al., 2003. ERA5 data is generated by ECMWF and distributed by the C3S CDS. ERA5 data used in this paper are archived from <a href="https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset">https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset</a>.   TC track data (ERA5 TC-matched.zip) TCs are identified and tracked by cyclonic vorticity centres in the ECMWF fifth generation climate reanalysis (ERA5) from six‐hourly atmospheric data, using the TRACK method (Hodges and Emerton, 2015; Hodges et al., 2017). The TRACK scheme used in this paper includes the following processes. First, the vertical average of the relative vorticity between 850 and 600 hPa is obtained. This is then spatially filtered using spherical harmonics to T63 resolution; the large‐scale background with total wavenumbers n ≤ 5 is removed. Vorticity maxima in the Northern Hemisphere and minima in the Southern Hemisphere are determined on the T63 grid and then used as starting points to obtain the off‐grid locations using B‐spline interpolation and maximisation methods. Then, in the first instance, all positive vorticity centres that exceed 0.5 Cyclonic Vorticity Unit (CVU, with 1.0 CVU=1.0*10-5 s-1) in the range 0°–60°N (negative vorticity centres that are below -0.5 CVU in the range 0°–60°S) are identified through the data time series. The tracking is performed by first initialising a set of tracks using a nearest neighbour method and then refining them by minimising a cost function for track smoothness subject to adaptive constraints on track smoothness and displacement distance in a time step. After the tracking is complete, the full T63 vorticity maxima at levels from 850 hPa up to 200 hPa (850, 700, 600, 500, 400, 300 and 200 hPa) are added to the tracks using a recursive search within a 5° radius (geodesic) of the tracked centre. This is used to test for the existence of a coherent vertical structure and a warm core. A matching process is applied to match the ERA5 tracks against the observed tracks, the Best Track from IBTrACS. An ERA5 track is matched to an IBTrACS track if the mean spatial separation is ≤5° over the corresponding paired track points and it is the track with the smallest separation. This process ensures that the ERA5 tracks are those storms actually ‘observed’ in IBTrACS (but they may have different lifecycle). This means that the ERA5 storm tracks have an extended lifecycle consistent, allowing the analysis of the “pre-TC” features. ERA5 “pre-TCG” is the first point of the above identified TC track in ERA5, which is at an earlier stage than the genesis in IBTrACS (normally the first track point reaching the tropical storm intensity). On average, the ERA5 pre-TCG events are 4.6 days earlier in time, and 2.7 CVU weaker in relative vorticity of the vortex, than the TCG events at the observed TCG time.   Wave data (qvr_coefficient_YYYY_k3-40_p2-10_28plev_era5.nc) In this dataset, dynamical equatorial waves are derived by projecting global wind and geopotential height data onto an orthogonal basis defined by the horizontal equatorial wave structures obtained from the theory of disturbances to a resting atmosphere on the equatorial β-plane. Six‐hourly horizontal winds and geopotential height in ERA5 are used here. This method identifies horizontal wind (u, v) and geopotential height (Z) structures associated with distinct equatorial waves. Potential equatorial waves are identified by projecting u, v and Z in the tropics (24°S–24°N) at each pressure level onto the different equatorial wave modes, using their sinusoidal structure in the zonal direction and parabolic cylinder functions in the meridional direction. These basis functions used for the wave projection are orthogonal, meaning that the wave structures here are orthogonal since they are pre-described as a series of the basis functions. In the parabolic cylinder functions, the meridional trapping scale is y0=6°. Before the projection, a broad-band spectral filter, with wavenumber 3 to 40 and period 2 to 10 days, is applied to separate eastward and westward moving waves. This ERA5 wave dataset contains three equatorial wave modes: westward-moving mixed Rossby-gravity (WMRG) and meridional mode number n=1 and 2 Rossby (R1 and R2) waves. The dataset spans 39 years from 1980 to 2018 covering all seasons, with a 6-hourly interval at 1° resolution on 28 pressure levels from 1000 to 70 hPa.    Reference  Hodges, K. I., & Emerton, R. (2015). The prediction of Northern Hemisphere tropical cyclone extended life cycles by the ECMWF ensemble and deterministic prediction systems. Part I: Tropical cyclone stage. Monthly Weather Review, 143(12), 5091-5114. Hodges, K., Cobb, A., & Vidale, P. L. (2017). How well are tropical cyclones represented in reanalysis datasets?. Journal of Climate, 30(14), 5243-5264. Yang, G. Y., Hoskins, B., & Slingo, J. (2003). Convectively coupled equatorial waves: A new methodology for identifying wave structures in observational data. Journal of the atmospheric sciences, 60(14), 1637-1654. Feng, X., Yang, G.Y., Hodges, K., & Methven, J. (2023). Equatorial waves as useful precursors to tropical cyclone occurrence and intensification. Nature Communications.

opencc-by-4.0Feb 2023View details →

Automated monitoring of biodiversity in the tropics: A pilot study at Barro Colorado Island

Automated monitoring of biodiversity using camera systems and acoustic devises is becoming increasingly common practise. The development of camera traps for insects, however, is relatively new, and has not been tested in tropical environments. To understand both the challenges and opportunities for automated monitoring in the tropics we deployed three newly developed cameras systems for monitoring insects, with a focus on night-flying insects. In addition we deployed audible and ultrasound recording equipment. The locations of each device was changed over the 5 day pilot study, and the configuration of each system was changed to allow an assessment of the relative importance of position, light, etc. The study was undertaken on Barro Colorado Island, Panama, in January 2023. Data are aggregated into .zip files, and details of their contents, and metadata are given in read_me.txt and metadata.csv.

opencc-by-4.0Mar 2023View details →

Wind and Waves in Tropical Cyclones (1985-2022)

Dataset contains information on wind speed and wave height in Tropical Cyclones (TCs) obtained from the Best Track Data during the period from 1985 to 2022 and along-track altimeter measurements from 17 satellites in 1985-2018 (IMOS archive, Ribal and Young, 2019) and in 2020-2022 (CMEMS archive). For each TC, in which the maximum wind speed exceeded 30 m/s (1905 cases), files are created to combine altimetry data on the significant wave height and wind speed in the cyclone area (+-7 degrees from TC center) and information on each cyclone trajectory and its main characteristics (maximum wind speed, radius of maximum winds, heading velocity vector). To describe the radial distribution of wind speed, standard data on distances from the cyclone center to points with wind speeds of 34, 50, and 64 knots are approximated with the analytical function suggested by Holland (1980). For each cyclone, graphical files are provided to illustrate the evolution of every TC parameters, the quality of the wind prifile approximation, the location of altimeter tracks, and the along-track values of significant wave height and wind speed.  Files given in NetCDF and MAT formats contain -  TC coordinates, heading velocity and direction, maximum wind speed and radius of maximum wind speed every 3 hours -  parameters of wind radial distributions for TC central and far zone every 3 hours -  altimetry data: time and along-track significant wave height and wind speed in TC region (+-7 degrees from TC center) from satellites GEOSAT, ERS-1, TOPEX, ERS-2, GFO, ENVISAT, JASON-1, JASON-2, JASON-3, SARAL/AltiKa, CryoSat-2, HY-2A, HY-2B, CFOSAT, Sentinel-3A, Sentinel-3B, Sentinel-6А   To form the dataset, the NOAA archive with data on tropical cyclones (https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/netcdf/, DOI :10.48670/moi-00178) and archives CMEMS (https://resources.marine.copernicus.eu/products, DOI:10.48670/moi-00178) and IMOS (https://catalogue-imos. aodn.org.au/geonetwork/srv/rus/catalog.search#/metadata/c6d5c7b4-323e-4979-9364-cdfa59684163, DOI:10.26198/5c184f4a5cd2e) with altimetry data were used .

Hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign

We provide here the supporting dataset for our study on airborne measurements of oh hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign in 2018.

Data from: Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India

This dataset contains data from the following publication: Sridhar, H., Raman, T. R. S. & Mudappa, D. 2008. <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. Current Science 94: 748-757. URL: <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">https://www.currentscience.ac.in/Volumes/94/06/0748.pdf</a> URL2: <a href="https://www.jstor.org/stable/24100628">https://www.jstor.org/stable/24100628</a> Corrigendum: Sridhar, H., Raman, T. R. S. & Mudappa, D. 2009. <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">Corrigendum: mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. Current Science 97: 612-613. URL: <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">https://www.currentscience.ac.in/Volumes/97/05/0612.pdf</a> Description of dataset: The data contains detections of mammals and hornbills (and few incidental records of other species) made along line transect surveys and opportunistic surveys in the Valparai Plateau and Anamalai Tiger Reserve, Tamil Nadu, India. Further details of the Study Area and methods are available in Sridhar et al. (2008), but methods are briefly described below. Five rainforest patches were chosen within IGWLS and four privately-owned rainforest fragments in the Valparai plateau. Fifteen line transects, ranging in length from 1 to 3 km were laid across the nine sites, with the three largest sites having 2–4 transects each. The total distance covered by all transects was 32.02 km. Each transect was walked five times between September 2005 and April 2006 following standard distance sampling protocol. Two observers walked each transect at 0.75–1 km/h. For each detection, we recorded species, group size and perpendicular distance (measured using a rangefinder) from the transect. For animals which occurred in groups, perpendicular distances were measured to group centres. Apart from detections on transects, attempts were made to obtain group sizes of mammal species whenever incidentally detected. All transects were walked between 0630 and 1000 h. Indirect evidence (scat, tracks) on transects and incidental sightings (direct and indirect) of mammals were also recorded. AUTHOR #1 1. Name: Hari Sridhar 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Current Work Address: Konrad Lorenz Institute for Evolution and Cognition Research, A-3400 Klosterneuburg, Austria 4. Email address: harisridhar1982@gmail.com 5. ORCID: https://orcid.org/0000-0003-3286-0120 AUTHOR #2 1. Name: T. R. Shankar Raman 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: trsr@ncf-india.org 5. ORCID: https://orcid.org/0000-0002-1347-3953 AUTHOR #3 1. Name: Divya Mudappa 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: divya@ncf-india.org 5. ORCID: https://orcid.org/0000-0001-9708-4826 Keywords: tropical rainforest, tea plantation, coffee plantation, line transect, population density, distance sampling, Anamalai Tiger Reserve, Valparai Plateau, Anamalai Hills, Western Ghats, mammals, hornbills Geographic Coverage: 1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India 2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E) Temporal Coverage: 1. Begins: 2005-09-01 (Year, Month, Day) 2. Ends: 2006-10-31 (Year, Month, Day)   Dataset files: Besides the 00_README.txt file, the dataset includes 4 comma-delimited text (csv) files with the data in columns as explained below: 01_Transect_locations.csv — contains transect location details and descriptions 02_Transects_and_opportunistic_surveys.csv — contains main dataset of observations on line transect and opportunistic surveys 03_Opportunistic_observations_locations.csv — contains location details of opportunistic surveys 04_Lion-tailed_macaque_counts.csv — contains counts of lion-tailed macaque (Macaca silenus) troops 05_allmammals_raw.xls — raw data NOT for use, for reference only in original Microsoft Excel format   Data variables and descriptions: 01_Transect_locations.csv TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations TransectLength_m: Length of line transect in metres decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum StartLat: transect starting point latitude in decimal degrees (N), WGS84 datum StartLon: transect starting point longitude in decimal degrees (E), WGS84 datum EndLat: transect ending point latitude in decimal degrees (N), WGS84 datum EndLon: transect ending longitude in decimal degrees (E), WGS84 datum MidLat: approximate mid-way location latitude in decimal degrees (N), WGS84 datum MidLon: approximate mid-way longitude in decimal degrees (E), WGS84 datum ExtraLatLon: additional pairs of latitude and longitude points along transect in decimal degrees (E, N), WGS84 datum RouteDescription: description of transect route 02_Transects_and_opportunistic_surveys.csv eventID: unique ID of sampling event corresponding to a single on-foot survey of a line transect, with elements separated by colons and last two elements referring to TransectCode and replicate survey number occurrenceID: unique ID assigned to each occurrence (detection) along line transect Sno: serial number TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations locality: name of transect Transectno: unique number assigned to each transect survey or resurvey Replicate: number indicating repeat survey of same transect SiteCategory: Category indicating whether transcet was in Protected Area or Rainforest Fragment Date: date of transect survey or opportunistic observation Weather: Weather at time of survey Habitat: Habitat where observation was made Time: time in 24 h HH:MM format verbatimIdentification: Identification as originally entered scientificName: Scientific name of species or taxon observed vernacularName: Common English name of species or taxon observed Perpdist: Perpendicular distance in metres Freshness: rating of freshness of faeces found (d=day, wk=week, mt=month) individualCount: number of individuals counted, taken as minimum 1 if not noted in field rawNumber: number as originally entered DetectionType: type of observation classified as Call, Faeces, Indirect, Sighting, Track verbatimDetection: raw entry corresponding to previous column Height: height of observed animal above ground in metres occurrenceRemarks: remarks on occurrence 03_Opportunistic_observations_locations.csv locality: name of place or transect where opportunistic observation was made decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum coordinateUncertaintyInMeters: approximate/estimated uncertainty in location coordinates (in metres) 04_Lion-tailed_macaque_counts.csv Sno: Serial number of entry Place_or_Transect: Transect (TransectCode) or place where lion-tailed macaques were counted Date: Date of observation Time: Time of observation in 24h HH:MM format Weather: Weather Groupid: ID of Lion-tailed macaque troop, if known Total: Total number of individuals recorded AM: number of adult males AF: number of  adult females A: number of adults (unsexed) SA: number of sub-adults (unsexed) SAM: number of sub-adult males SAF: number of sub-adult females JUV: number of juveniles INF: number of infants CARINF: number of infants carried by mother UNID: number of unclassified Remarks: other notes   05_allmammals_raw.xls Raw data file in Microsoft Excel format -- for reference only (not advised for use) ADDITIONAL NOTES General notes taken about survey: Pannimade transect 3/11/05 - Most giant squirrel detections were made after squirrel alarm called on seeing a soaring raptor. 36TH hpb transect - very poor visibility on one side as it is very steep Giant squirrels present within LTM  troops might go undetected. Need to look carefully and check every movement KO transect 20/01/06 - 1 GS which wasn’t detected when walking transect detected when measuring at less than 20 metres SHK transect 23/01/06 - Abandoned 100 metres from end because of elephants BAN - Ignore detections after 2.05 KM for first two replicates KSPV 26/01/2006 - 1 GS  not detected on transect detected while returning at < 40 m Anaigundi - Transect in december strayed slightly from correct path Var 30/01/2006 transect  Do not include for indirect signs encounter rate since replicates were done on consecutive days Is there a difference in visibility between wet and dry months; atleast in the more deciduous forests like varagaliar that is the case should I consider only january and afterwards for MGH  numbers since vocal activity is much higher then? TF transect 12/02/06 4 GS heard calling from coffee estate adjoining TF; could fewer detections on last transect be because they are moving into coffee, maybe because some tree is fruiting Do NL individuals move solitarily; what average group size to use visibility in BAN and VAR is much better than other sanctuary sites such as IYAK, AN, MA rained on 1st & 2nd of March after a long dry spell KSPV  31/03/06 - Could have missed some calls because of cicada noise KSWT  01/04/06 - Could have missed some calls because of cicada noise KSPV  02/04/06 - Could have missed some calls because of cicada noise KSPV 02/04/06  Do not include for indirect signs encounter rate since replicates were done on consecutive days Great hornbills seem to be more vocal during april. To do with end of nesting?? malabar grey hornbills more vocal from february onwards

Extracellular polymeric substances are closely related to land cover, microbial communities, and enzyme activity in tropical soils

These are datasets and R codes linked to the paper: Extracellular polymeric substances are closely related to land cover, microbial communities, and enzyme activity in tropical soils.

Morphological cladogenesis and terminal dwarfing in extinct Late Miocene through Pliocene menardiform globorotalids: New complementary data to «Evolutionary prospection in the Neogene planktic foraminifer Globorotalia menardii and related forms from ODP Hole 925B (Céara Rise, western tropical Atlantic): evidence for gradual evolution superimposed by long distance dispersal ?, Swiss J. Palaeontology, 135:205-248»

A complementary morphometric data set is provided to the study of Knappertsbusch (2016) about the shell evolution of menardiform globorotalids (Neogene planktic foraminifera) at ODP Hole 925B from Céara Rise in the the western tropical Atlantic. The new measurements confirm splitting of extinct Globorotalia multicamerata from the G. menardii stock via the intermediate form G. limbata between about 6 Ma to 5 Ma ago. After splitting both G. limbata and G. multicamerata show gradual divergence from G. menardii in several shell parameters illustrating morphological cladogenesis. Between 2.88 Ma and 2.59 Ma the same parameters show a concerted trend towards reduced values indicating pre-extinction dwarfing. A comparison with published literature data of Delta18O trends between species, that populated the mixed layer (Globigerinoides sacculifer) and the thermocline layer (Neogloboquadrina dutertrei) at this location during those times suggests, that both divergence and subsequent dwarfing trends were probably the results of changes in upper watermass stratification. The complementary data set is provided in six zipped archives APPENDIX A, B, C, D, E and F (zipped with free software 7-Zip 22.00 (x64), 2022-06-15 from 1999-2022 Igor Pawlow), together with a description of the data in file Report_925B_suppl_1.pdf.

openmit-licenseMay 2023View details →

WTF Climate dataset: 4 years of weather data from tropical Queensland, Australia

This repository contains the code for constructing a 4-year climate dataset for the WTF project. Time series were constructed for 6 field sites in Queensland, Australia at 1 hour resolution.

opencc-by-4.0Jun 2023View details →

Extra-tropical cyclone statistics from OpenIFS aquaplanet simulations

This data set is derived from three idealised modelling experiments that were performed with the global numerical weather prediction model, OpenIFS. Three aqua-planet experiments were performed that differed only in their sea surface temperatures. There was a control simulation, case where the sea surface temperatures were warmed everywhere by 4K and a case where the polar sea surface temperatures were increased by 5K. In all three experiments, the extra-tropical cyclones were identified. The data presented in this data set is the maximum vorticity and precipitation of all of these extra-tropical cyclones. This data set is used by Sinclair and Catto (2023) in their study. Sinclair, V. A. and Catto, J. L.: The relationship between extra-tropical cyclone intensity and precipitation in idealised current and future climates, Weather Clim. Dynam. Discuss. [preprint], https://doi.org/10.5194/wcd-2022-62, in review, 2022.

opencc-by-4.0Jul 2023View details →

Pertubation Profiles Dataset used for "Convection-generated gravity waves in the tropical lower stratosphere from Aeolus wind profiling and ERA5 reanalysis"

These are the perturbation profiles, from 5km to 29.5km, with a 500m grid. In the study, we picked up the data between tropopause-1km to 22km, which was then squared, smoothed, and averaged into one value. We used a 14 points moving average for the smoothing. The data is from 2018-09 to 2022-09, based on the Aeolus L2B Rayleigh clear wind, using only quality flag 1 data. Please email me at mathieu.ratynski@estaca.eu if you're interested in the 100m resolution version, used in the final version of the manuscript.

opencc-by-4.0Jul 2023View details →

Recent Increases in Tropical Cyclone Rapid Intensification Events in Global Offshore Regions

The data and scripts in this repository can be used to support the main conclusion in the manuscript "Recent Increases in Tropical Cyclone Rapid Intensification Events in Global Offshore Regions" by Li et al., submitted to Nature Communications. The global distribution and annual variability of rapid intensification (RI) events of tropical cyclones (TCs) dervied from the open-source International Best Track Archive for Climate Stewardship (IBTrACS, https://www.ncei.noaa.gov/products/international-best-track-archive) are provided. The enviromental variables, including mid-level (600 hPa), vertical wind shear (200-850 hPa), and maximum potential intensity (MPI), were also calculated using the fifth generation of ECMWF reanalysis (ERA5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) forced in different scenarios. The python script (coastal_RI_submit ipynb) can be used to reproduce figures in the article.

opencc-by-4.0Jan 2023View details →

Geographic range size and species morphology determines the organization of sponge host-guest interaction networks across tropical coral reefs (Raw data)

Datasets for the analysis developed in the Article "Geographic range size and species morphology determines the organization of sponge host-guest interaction networks across tropical coral reefs". For more information, please refer to the original publication. Network_Structural_Index_&_SpogeTraits.csv <- Structural Index for the sponge-dwelling fauna network, sponge accumulated area and sponges’ morphology. NWTA_CoralReefs_Sponges_ interactions.csv <- Relationship between host sponges and guest fauna in the Northwester Atlantic coral reefs NWTA_CoralReefs_Sponge_reacords.csv <- Sponge species incidence records in the Northwester Atlantic coral reefs sponges_morphological_description.csv <- Sponge morphological standardization Network.html <- Interactive sponge-dwelling fauna network Enjoy!

opencc-by-4.0Jul 2023View details →

Data associated with "Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones"

These data include all tropical cyclone tornado reports from 2013–2020, as part of all data from 1995–2022, included in the Storm Prediction Center (SPC) Tropical Cyclone TORnado database (TCTOR; Edwards and Mosier, 2022) used in the following publication: Schenkel, B., K. Calhoun, T. Sandmael, M. Ake, Z. Fruits, B. Kassel, and I. Schick, 2023: Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones. J. Geophys. Res.: Atmospheres, accepted. Each specific tropical cyclone tornado record has been extracted from the broader SPC tornado database, for all Atlantic and Gulf of Mexico tropical cyclones impacting the continental United States from 1995–2022. The tornado records were analyzed individually to determine their presence within the circulation envelope of either a classified or remnant tropical cyclone, without regard to fixed radii from tropical cyclone center, inland extent, temporal cutoffs before or after landfall, or other such arbitrary thresholds that may either exclude tropical cyclone events or include non-tropical cyclone tornadoes unnecessarily. These data will not be updated regularly. Citation for SPC TCTOR dataset: Edwards, R., & Mosier, R. M. (2022). Over a quarter century of TCTOR: Tropical cyclone tornadoes in the WSR-88D era [Dataset]. In Proc., 30th conf. on severe local storms (p. 171). Santa Fe, NM.

opencc-by-4.0Sep 2023View details →

Genetic diversity, population structure, and linkage disequilibrium among tropical quality protein maize (QPM) lines assessed with high-density SNP markers

The study of genetic diversity (GD), population structure, and linkage disequilibrium (LD) provides a better understanding of the genetic relationships between individuals in a population which can be utilized in crop research and improvement. Genotyping-by-sequencing (GBS) was used to detect and genotype single nucleotide polymorphisms (SNPs) in a collection of 74 quality protein maize (QPM) lines and further to characterize their genetic diversity, population structure, and linkage disequilibrium. A total of 235,214 high-quality SNPs were used for different genetic analyses except for structure analysis where 11,950 SNPs were used. Analysis of molecular variance (AMOVA) based on these SNPs revealed high genetic heterozygosity among the five populations with 1% of the total genetic variation present among the subpopulations and 99% of the variation among individuals within the populations.  Population structure analysis using Bayesian-based clustering revealed that the 74 lines could be clustered into four groups. However, neighbor-joining trees indicate the lines are grouped into three major clusters.  Further analysis using principal component analyses (PCA) clustered the genotypes into five groups which are concordant with the groups based on pedigree information. Higher genetic diversity was detected in population 1 with a GD value of 0.484 and the lowest in population 5 (0.396) and overall, with a mean of 0.434. The LD pattern in the quality protein maize was investigated and we observed a relatively rapid LD decay of 3.53kb and 10.66kb at r2 =0.2 and r2= 0.1, respectively. Our findings provide important information for future Linkage mapping studies, genome-wide association analyses, and marker-assisted selective breeding of maize as well as genomic prediction-based selection in tropical germplasm.

openCC (other)Aug 2024View details →

canopy herbivory and leaf traits of tree communities in tropical montane rainforests of southern Ecuador

This dataset contains herbivory data estimated as leaf area loss [cm²] and [%] and several leaf traits measured either conventionally or via spectral sensing-based techniques from canopies of tree communities in tropical montane rainforests of the Andes in southern Ecuador between February and March in 2019. The data were used by Schön et al. (in prep) to evaluate whether leaf traits are valuable indicators of canopy herbivory mainly caused by arthropods and further, whether chemical leaf traits estimated via spectral sensing-based techniques have similar strong relations to herbivory as leaf traits measured conventionally. Herbivory was estimated with the software WinFOLIA ™ 2019a from scanned mature and sun-exposed leaves of tree canopies. Spectral sensing-based leaf traits comprising secondary plant metabolites and both structural and nutritional cell components were estimated from leaves with an OceanOptics spectrometer HDX. Conventionally measured leaf traits comprising morphological and nutritional traits were derived by applying both elemental and morphometrical analyses (e.g., ICP analysis, a digital micrometer and penetrometer). For detailed descriptions of the methodology see Schön et al. (in prep), Homeier et al. (2021), and Limberger et al. (2021). Research was conducted by the subprojects A1, B1, and B4 within the framework of the RESPECT project (Environmental changes in biodiversity hotspot ecosystems of South Ecuador: RESPonse and feedback effECTs) funded by the DFG with the grant numbers: BE1780/51-1, BE1780/51-2, Ho3296/6-1, FA 925/11-1, FA 925/11-2, FA 925/16-1, BR1293/17-1.

openCC (other)Aug 2019View details →

High-frequency water temperature and dissolved oxygen data and derived stability and metabolism metrics for nine lakes in northeastern North America for months before and after Tropical Cyclone Irene, Fall 2011

This dataset is used in the analysis published in the following manuscript: Klug, J.L., D.C. Richardson, H.A. Ewing, B.R.Hargreaves, N. R. Samal, D. Vachon, D.C. Pierson, A. E. Lindsey, D. O'Donnell, S.W. Effler, and K.C. Weathers. 2012. Ecosystem effects of a tropical cyclone on a network of lakes in northeastern North America. Environmental Science and Technology 46(21): 11693–11701. We include Quality Assurance Quality Controlled (QAQC) high-frequency dissolved oxygen, wind speed, and water temperature data from nine lakes and reservoirs in northeastern North America which were near the track of Tropical Cyclone Irene in August 2011. These data were collected using a set of in situ, automated monitoring systems associated with the Global Lake Ecological Observatory Network (GLEON) that record data at high frequency (10 min to 6 h). These sensor data were the basis for the derived measures of Schmidt stability, net ecosystem production, respiration, and gross primary production included in the dataset. We also include daily rainfall data collected at on-site or nearby weather stations. All data cover the period from 01 August through 15 October 2011.

openCC (other)Feb 2019View details →

Variation in the Composition of Understory Vegetation in a Tropical Rain Forest as a Function of Soil and Topographic Position. 1986 - 1990

Understory plants are a major contribution to the high plant species diversity of Neotropical rain forests. Shrubs, understory trees, saplings of overstory trees, and herbs occupy a habitat of generally low light levels and high humidity in which there seem to be few obvious mechanisms to support habitat partitioning. Moreover several plant families are characterized by a high number of co-occurring understory species. In 1987-1989 we sampled understory vegetation in 18 sites at the La Selva Biological Station of the Organization for Tropical Studies in Heredia Province, Costa Rica. At each site we used 20 nested quadrats to investigate the effects of soil type on replicated sites of mapped alluvial and residual volcanic soils (5 map units) and topographic positions (ridges, midslopes and flats) on composition, density and diversity of small (1m tall to 5cm dbh, 25 m2 quadrat) and large(5-10cm dbh, 100 m2 quadrat) understory plants. We also measured fine litter dry mass, extractable P, total organic matter, percent slope and percent incident light radiation in each quadrat.