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  • Price transmission in the Latin American rice market
    Wholesale rice price data from Latin American countries and international reference prices
  • Ca C code for Stable Ca and Sr isotope responses to ocean acidification during Oceanic Anoxic Event 2
    Ca - C model for Stable Ca and Sr isotope responses to ocean acidification during Oceanic Anoxic Event 2 published in Chemical Geology. Nana Yobo, L., Holmden, C., Lau, K., Karas, J., and Brandon, A. (2026). Stable Ca and Sr isotope response to acidification during Oceanic Anoxic Event 2 (OAE 2). Chemical Geology, 123475.
  • IRTF-TEXES data, March 5-6 2025
    Each folder contains observations recorded by TEXES (Texas Echelon Cross Echelle Spectrograph) on NASA's IRTF (Infrared Telescope Facility) on March 5 and 6, 2025. The slit of the spectrograph was scanned across Jupiter's mid-to-high northern latitudes with a spectrum recorded at each step thereby producing spectral maps. The goal of recording these observations was to detect the spectral features of propadiene (CH2CCH2), propene (C3H6) and propane (C3H8) at Jupiter's mid-to-high northern latitudes. Spectra were recorded in discrete settings centered at 587, 748, 843, 912 and 1248 cm-1. The 587 and 1248 cm-1 spectra capture the readily detectable features of the hydrogen S(1) quadrupole and methane emission. The 748, 843 and 912 cm-1 settings contain the spectral features of acetylene, ethane and ethylene, and the targeted spectral features of propadiene at 845.25 cm-1 and propene at 912.56 cm-1. Each .latlon.sav file represents spectra recorded by a single scan. The 'jup<setting>list' text files list all the filenames containing scans recorded in that setting. Further details are provided
  • Jupiter photochemical models at 60N
    The folder contains files of the photochemical model output presented in Moses & Poppe, 2017, Icarus 297, 33-58 but with model output at 60N. Readers are directed to Moses & Poppe, 2017, Icarus 297, 33-58 for further details of the photochemical model. The files presented here represent model output over a range of different slopes for the eddy diffusion coefficient profile at pressures lower than 0.01 mbar, which results in a range of hydrocarbon homopause levels, as listed below. The same model grid was presented in Sinclair et al., 2025, Planetary Science Journal 6:15 and Rodrìguez et al., 2024, Journal of Geophysical Research: Planets 129, e2024JE008299. Filename Homopause level (nbar) ---------- kinjup60n_kzzslope0pt0.pun 754 kinjup60n_kzzslope0pt1.pun 617 kinjup60n_kzzslope0pt2.pun 488 kinjup60n_kzzslope0pt3.pun 376 kinjup60n_kzzslope0pt4.pun 286 kinjup60n_kzzslope0pt5.pun 222 kinjup60n_kzzslope0pt6.pun 168 kinjup60n_kzzslope0pt7.pun 119 kinjup60n_kzzslope0pt8.pun 74 kinjup60n_kzzslope0pt9.pun 35 kinjup60n_kzzslope1pt0.pun 4.5 kinjup60n_kzzslope1pt1.pun 0.52 kinjup60n_kzzslope1pt2.pun 0.52
  • An agentic AI assistant for country-level economic modeling: evaluation data
    This dataset contains the supplementary materials, code, evaluation rubrics, and simulation results associated with the paper **"An agentic AI assistant for country-level economic modeling: Methods, data, and expert evaluation."**
  • Metaverse Games multi method
    Raw data
  • Safety norms, social identity, and their impact on train1 drivers’ safety behavior - Survey data
    This dataset contains online-survey data collected from German train drivers as part of a study examining the influence of perceived social norms and social identity on safety behavior intentions. Participants completed an online questionnaire assessing safety attitude, perceived team and management safety norms, perceived behavioral control, team and organizational identification, and safety behavior intentions. Safety behavior intentions were measured using a newly developed vignette-based instrument presenting scenarios reflecting realistic safety-relevant situations in railway operations. Data were collected in 2025 and analyzed using regression-based path analysis. The dataset includes all variables used in the analyses reported in the associated manuscript.
  • Infrared spectra with metal concentrations and geochemical data for scheelite-derived Technosols (Brejuí Mine, Northeastern Brazil)
    This dataset contains near-infrared (NIR) and mid-infrared (MIR) spectral data alongside total concentrations of Cd, Co, Cr, Cu, Ni, Pb, Sn, Sr, Ti, V, and Zn, major oxide compositions, and pH measurements from 120 surface soil samples (0-30 cm) collected across three Technosol sites with 2 (s2), 10 (s10), and 40 (s40) years of scheelite mining tailings accumulation at the Brejuí Mine, Currais Novos, Rio Grande do Norte, Brazil. Metal concentrations were determined by ICP-OES following open-vessel acid digestion, and spectra were acquired by FTIR in diffuse reflectance mode at 2 cm⁻¹ resolution.
  • Digitalization Scores
    This data contains all the various scores measuring Digitalization through various methods
  • BD-MediFlora-22: A 22-Species Medicinal Plant Leaf Dataset
    In traditional and modern healthcare systems, medical plants can play a vital role due to their bioactive compounds, which are used for treating various diseases and maintaining human health. The “BD-MediFlora-22: A 22-Species Medicinal Plant Leaf Dataset” is a comprehensive collection of high-resolution images of several medicinal plant leaves. 1. The “BD-MediFlora-22” dataset consists of 3096 images with 22 distinct classes. 2. The included plant leaf species are Bay leaf, Bermuda grass, China rose, Chinese motherwort, Earleaf acacia, Eucalyptus, Guava, Henna, Hill glorybower, Holy basil, Indian pennywort, Lemon, Malabar nut, Marigold, Moringa, Neem, Periwinkle, Skunk vine, Star fruit, Thumbai, and Toothache plant. 3. Each image has been standardized to a high resolution of 512*512 pixels with uniform white lighting conditions. 4. All images are stored in JPEG (.jpg) format. 5. These medical plant leaves are collected from 3 diverse geographical locations in Bangladesh: Bangladesh Agricultural University (BAU), Daffodil International University (DIU), and Horticulture Center, Mymensingh. 6. The “BD-MediFlora-22” dataset can be used in research in several domains, including medical plant classification by integrating deep learning and computer vision and AI-based herbal plant identification.
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