Find research datasets worth reusing

opencc-by-4.0Jan 2021View details →

Observed runoff time series from a green roof field campaign in Hannover-Herrenhausen

This dataset includes a csv file comprising observed runoff time series from a green roof field campaign, which was conducted by Prof. Dr.-Ing. Hans-Joachim Liesecke. The csv file provides runoff from 11 green roof variants (10 was excluded, since it has a different design). Rows include daily runoff totals (collected each morning, excluding weekends). Please refer to this article, which describes the dataset in more detail:  Iffland, R., Förster, K., Westerholt, D., Pesci, M. H., & Lösken, G. Robust vegetation parameterization for green roofs in EPA SWMM. Hydrology. <a href="https://doi.org/10.3390/hydrology8010012">https://doi.org/10.3390/hydrology8010012</a> The field campaign involved a total of 11 superstructures in triple repetition. In the csv file, each column represents average values computed out of three independent measurements (in mm*d-1) The individual test plots were 2 m x 2 m with a slope of 2 % and a drainage opening in the middle of the lowest point of the slope. The outflowing water was collected in non-weighable lysimeters (rain barrels) that were read and emptied at 8 A.M. every day. On weekends, readings were taken the following workday.

opencc-by-4.0Jan 2024View details →

The state of the lower Volta Delta Beaches in Ghana from field observations.

The continuous collection of data is important, particularly for developing models for local applications. It is also important to collect in situ data in this observation scarce region to improve our understanding of coastal evolution. The data presented here were planned and collected along a 90 km coastline. The experiment holds significant scientific value as it marks the first comprehensive data-gathering effort along the Volta Delta within the Bight of Benin, West Africa. Several projects, decision-makers, and scientists will make use of this data to calibrate their models. The study was executed in 2023, and it provides a comprehensive description of the morphodynamical characteristics of the study area and identifies the factors influencing the changes. The data include nearshore and riverine bathymetry, topography, sediment grain size distribution, and waves in the study area. The research experiment provides a foundation for future extensive and long-term investigations along the LVD and promotes localized investigations.

opencc-by-4.0May 2019View details →

Observations of Diurnal Coastal-Trapped Waves with a Thermocline-Intensified Velocity Field

Repository for the data presented in the JPO 2019 article: "Observations of Diurnal Coastal-Trapped Waves with a Thermocline-Intensified Velocity Field". Observations were compared against output from Ken Brink's model. The model is available from: <a href="https://darchive.mblwhoilibrary.org/handle/1912/10527">https://darchive.mblwhoilibrary.org/handle/1912/10527</a>.

opencc-by-4.0Sep 2024View details →

Neural Network and objective analysis reconstruction of 3D Mediterranean physical fields from surface satellite and in situ observations at 1/24 deg

Daily Mediterranean 3D fields of temperature, salinity and geostrophic current at 1/24° of resolution, up to 150m-depth and from 2016 to mid 2022, obtained through a 3 steps approach: (1) Temperature and salinity 3D fields have been first estimated by a machine learning approach by using mediterranean reanalysis outputs (https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R) together with satellite observations, (2) a combination of this first step with in situ observations through an Optimal interpolation to remove part of large scale biases, (3) the computation of geostrophic currents using the thermal wind equation. This work has been funded by the European Space Agency through the 4DMED-SEA project [ESA contract No. 4000141547/23/I-DT].

opencc-by-4.0Dec 2021View details →

Supporting data for Wu et al. "Penetrating Electric Field Simulated by the MAGE and Observed by ICON" paper

This dataset contains the necessary data supporting the paper titled "Penetrating Electric Field Simulated by the MAGE and Observed by ICON", to be submitted by Wu et al., 2022. The dataset includes the Multiscale Atmosphere-Geospace Environment (MAGE) model simulation for two intervals to support 2020 Sep 26 9-10 UT and 2020 Sep 24 5-6 UT. The data contain neutral wind, ion drift, and electron density output.

opencc-by-4.0Nov 2023View details →

Dataset for publication: Investigation of Hydrostatic Imbalance with Field Observations

Here is the data set for figure 8 of the publication "Investigation of Hydrostatic Imbalance with Field Observations" by Sun et al., 2023Data collected from the Halo Doppler lidars (DL) and Eddy-Covariance station (EC) (EC south, EC-S) of the Land-Atmisphere Feedback Observatory LAFO at University of Hohenheim, Stuttgart, Germany (lafo.uni-hohenheim.de, Späth et al., 2023, doi: 10.5194/gi-12-25-2023). The data cover the temporal period of 23 August 2022 as presented in figure 8 in Sun et al., 2023, doi: ***.

opencc-by-4.0Jan 2021View details →

Global estimates of marine gross primary production based on machine‐learning upscaling of field observations

4 variables (excluding dimension variables): double GPP_LD_MLD_RF[Lon,Lat,Month]             units: mmol O2 m-2 d-1          fill value: NaN          long_name: Monthly mixed-layer integration of gross primary production trained from the                              dataset determined by the light-dark bottle incubation using Random Forest                              algorithm         coordinates: [Longitude, Latitude Month] double GPP_LD_ZEU_RF[Lon,Lat,Month]                units: mmol O2 m-2 d-1             fill value: NaN             long_name: Monthly euphotic-zone integration of gross primary production trained from                                 the dataset determined by the light-dark bottle incubation using Random                                  Forest algorithm             coordinates: [Longitude, Latitude Month] double GPP_Triple_MLD_RF[Lon,Lat,Month]                units: mmol mmol O2 m-2 d-1             fillvalue: NaN             long_name: Monthly mixed-layer integration of gross primary production trained from                                 the dataset determined by the triple isotopes of dissolved oxygen using                                 Random Forest algorithm             coordinates: [Longitude, Latitude Month]          double GPP_Triple_ZEU_RF[Lon,Lat,Month]                units: mmol O2 m-2 d-1             fill value: NaN             long_name: Monthly euphotic-zone integration of gross primary production trained from                                  the dataset determined by the triple isotopes of dissolved oxygen using                                Random Forest algorithm 3 dimensions:         Lon  Size:181             units: degree_north             long_name: Longitude         Lat  Size:91             units: degree_east             long_name: Latitude         Month  Size:13             units: Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec, Annuual_mean             long_name: Month Author: Yibin Huang & Nicolas Cassar Correspond: nicolas.cassar@duke.edu          Request_for_citation: If you use these data in publications or presentations, please cite: Huang,                                     Y., Nicholson, D., Huang, B., & Cassar, N. (2021). Global estimates of                                      marine gross primary production based on machine‐learning upscaling of                                      field observations. Global Biogeochemical Cycles, 35, e2020GB006718.                                      https://doi.org/10.1029/2020GB006718   Creation date: Dec/6th/2021

opencc-by-4.0May 2022View details →

Table of Individual MMT Hectochelle Observations of the Triangulum II Field

Complete table of observations of the Triangulum II dwarf galaxy from MMT Hectochelle used in Buttry et. al 2022

opencc-by-4.0Aug 2022View details →

◂Fig. 6 Gynoecial development, fruit and seedling of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A–F light microscopy, G–K stereo microscopy of endocarp, mesocarp removed; L–O field images; TS in horizontal orientation). A, B TS of anthetic flower %note two to three abortive ovules and strongly stained, peripheral tissue). C, D TS of anthetic flower %note two to three abortive ovules and lignifying portions of prospective mesocarp). E Young fruit %note developing endocarp and flashily pink portions of the mesocarp). F TS of postanthetic flower %note three abortive ovules and lignifying portions of prospective mesocarp). G TS of endocarp, with three developed embryos removed %note scanty endosperm). H Endocarp. J TS of endocarp. K Endocarp. L Immature fruits. M Mature fruits. N Seedlings %note short hypocotyl and long petioles of cotyledons). O Seedlings %note long hypocotyl and short petioles of cotyledons; image taken from cultivated plant, accession number 2012–0005, in the Botanical Garden Munich) %LS, longisection; TS, transverse section; ao, abortive ovule; cot, cotyledon; db, dorsal bundle; c, calyx; ec, endocarp; ens, endosperm; ex, exocarp; fr, fruit; h, hypocotyl; int, integument; lb, lateral bundle; mc, mesocarp; o, ovule; pet, petiolus; sty, style; ut, peripheral tissue; vs, ventral slit) in Observations on flower and fruit anatomy in dioecious species of Cordia (Cordiaceae, Boraginales) with evolutionary interpretations

◂Fig. 6 Gynoecial development, fruit and seedling of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A–F light microscopy, G–K stereo microscopy of endocarp, mesocarp removed; L–O field images; TS in horizontal orientation). A, B TS of anthetic flower %note two to three abortive ovules and strongly stained, peripheral tissue). C, D TS of anthetic flower %note two to three abortive ovules and lignifying portions of prospective mesocarp). E Young fruit %note developing endocarp and flashily pink portions of the mesocarp). F TS of postanthetic flower %note three abortive ovules and lignifying portions of prospective mesocarp). G TS of endocarp, with three developed embryos removed %note scanty endosperm). H Endocarp. J TS of endocarp. K Endocarp. L Immature fruits. M Mature fruits. N Seedlings %note short hypocotyl and long petioles of cotyledons). O Seedlings %note long hypocotyl and short petioles of cotyledons; image taken from cultivated plant, accession number 2012–0005, in the Botanical Garden Munich) %LS, longisection; TS, transverse section; ao, abortive ovule; cot, cotyledon; db, dorsal bundle; c, calyx; ec, endocarp; ens, endosperm; ex, exocarp; fr, fruit; h, hypocotyl; int, integument; lb, lateral bundle; mc, mesocarp; o, ovule; pet, petiolus; sty, style; ut, peripheral tissue; vs, ventral slit)

Fig. 4 in Field and laboratory biological observations on the uncommon Cyrtocoris egeris (Heteroptera: Pentatomidae: Cyrtocorinae)

Fig. 4. Feeding damage of Cyrtocoris egeris on a stem of the Brazilian orchid tree, Bauhinia forficata (note thickening of the stem, outer bark cracked with a rugose appearance and accompanied by hatched eggs).

Fig. 3 in Field and laboratory biological observations on the uncommon Cyrtocoris egeris (Heteroptera: Pentatomidae: Cyrtocorinae)

Fig. 3. Mimetic behavior of Cyrtocoris egeris on a stem of the Brazilian orchid tree, Bauhinia forficata (note the similarity of the bug scutellar process with the plant spine).

Fig. 2 in Field and laboratory biological observations on the uncommon Cyrtocoris egeris (Heteroptera: Pentatomidae: Cyrtocorinae)

Fig. 2. (A) Egg masses, composed of hatched and unhatched eggs, of Cyrtocoris egeris on a stem of the Brazilian orchid tree, Bauhinia forficata, found in the field; (B) nymphs around the egg shells (chorions) and unhatched eggs (pinkish) shown in the upper part and hatched eggs in the lower part.

Fig. 1 in Field and laboratory biological observations on the uncommon Cyrtocoris egeris (Heteroptera: Pentatomidae: Cyrtocorinae)

Fig. 1. Gregarious behavior of nymphs and adults of Cyrtocoris egeris on stems of the Brazilian orchid tree, Bauhinia forficata, observed at Francisco Beltrão Co., Paraná State, Brazil, in 2015 and 2016; (A) large colony of late instar nymphs and adults; (B) small colony of mostly late instar nymphs.

Fig. 11 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations

Fig. 11 Rates of chatter calls in different magpie populations and individuals. a Each mark represents average chattering rate for a single bird from five populations indicated by colours. Figures are numbers for the outliers: 1, 2—jankowskii from the mixed population of Argun'; 3, 4, 5—hybrid birds from the hybridogeneous population of Kerulen. b Each mark represents average chattering rate for a series of chatterings of one selected individual representing jankowskii, leucoptera, and hybrid birds, respectively. Green mark—pair #6 jankowskii from Vladivostok; gray—pair #43 leucoptera from Tsasuchei, Transbaikalia; blue—pair #24 hybrids from Kerulen, eastern Mongolia. X-axis—number of elements per second in a total series of chattering; Y-axis— number of elements per second in a series of 5 elements of chattering

Fig. 12 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations

Fig. 12 Violin plot diagram of the chatter call speed (elements per second) of Eurasian magpie populations across regions. X-axis presents a set of populations; Y-axis—elements per second. Box outlines the interquantile range (25%, 75%), whiskers represent range without outliers, central bar is the median, red dot is the mean, and figure shape is the probability density. The brackets on the top denote statistically significant pairwise differences (GamesHowell test, p<0.05)

Fig. 9 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations

Fig. 9 Population genetic structure based on unlinked SNP markers. Scatter plots of principal component analysis (PCA) show individual variation in components one and two (a) and three and four (b). The amount of variance explained by each PC is shown in parentheses. I—leucoptera,

Fig. 7 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations

Fig. 7 Bayesian skyline plots (BSPs) for effective female population sizes for haplogroups, subspecies, and populations of Pica pica. a Comparison of 6 haplogroups, depicted in the network Fig. 4. b Comparison of 6 subspecies. c Comparison of 4 populations of P. p. jankowskii. d Comparison of 3 populations of P. p. leucoptera.