This item contains raw and processed NMR data for three small‑molecule derivatives based on a fused thioindole scaffold with a morpholine side chain:5b (CN‑0928),CN‑0928‑HCl (hydrochloride salt), andCN‑0928‑biotin (biotin conjugate).The dataset is intended for structure confirmation and reuse, and includes original Bruker vendor files (TopSpin), exported spectra, peak lists, and concise method notes.

The HalOcAt- database is a data product of the HaOcAt inititative (https://halocat.geomar.de), which was active from 2010 to 2018. The HalOcAt database was compiled as part of the doctoral thesis of F. Ziska at GEOMAR from 2010 to 2018. HalOcAt brought together global oceanic and atmospheric data sets of mainly short-lived brominated, chlorinated and iodinated trace gases, as bromoform (CHBr₃), dibromomethane (CH₂Br₂), dibromochloromethane (CHBr₂Cl), bromodichloromethane (CHBrCl₂), methyl bromide (CH₃Br), methyl iodide (CH₃I), diiodomethane (CH₂I₂), chloroiodomethane (CH₂ClI), bromoiodomethane (CH₂BrI), dichloromethane (CH₂Cl₂), chloroform (CHCl₃) , trichloroethane (C2HCl3), carbon tetrachloride (CCl4), tetrachloroethene (C2Cl4), and higher iodoalkanes (R–I).Many of the data of bromoform (CHBr₃), dibromomethane (CH₂Br₂), and methyl iodide (CH₃I), included in this zip-Archive have been used in the publication of global halocarbon emissions by Ziska, F. et al, 2013. The data and data products are attached as a supplement to the publication.In this HalOcAt data product halocarbon data from ocean and atmosphere were formatted into a uniform Excel template, which consists of three sheets. The first sheet contains the Metadata , the second and third sheet seawater, respectively air data, if available, with the columns: Sample Identifier, Sampling Gear, Date (UTC) and time, (Air sampling period), Sample start latitude (+ve N), Sample end latitude (+ve N), Sample start longitude (+ve E), Sample end longitude (+ve E), Sample depth (meter), Bottom Depth (meter), Water Temperature, in situ (°C), Water Salinity, CH3I (pM), CH2I2 (pM), C2H5I (pM), C3H7I (pM), C4H9I (pM), CH3Br (pM), CHBr3 (pM), CH2Br2 (pM), C2H5Br (pM), CH3Cl (pM), CHCl3 (pM), CH2Cl2 (pM), C2HCl3 (pM), C2Cl4 (pM), CH2ICl (pM), CH2IBr (pM), CHIBr2 (pM), CHBr2Cl (pM), CHBrCl2 (pM), Quality Control CommentsThe HalOcAt- database was curated on a best-effort basis rather than for full coverage. Missing entries are expected and are outside our responsibility. More halocarbon data in ocean and atmosphere from the involved scientists can be found on PANGAEA, other public databases or directly from the scientists.

This study addresses the discrepancies between flow structures and bed features observed in natural river confluences and those reproduced in laboratory experiments. An improved, generalized physical model is proposed to better replicate typical characteristics of natural confluences—such as rounded downstream confluence corners, high width-to-depth ratios in the downstream channel, and downstream channel widening relative to the upstream. Using flow parameters commonly found in natural settings, a series of flume experiments were conducted to measure flow structures and bed morphology. The results revealed flow and bed characteristics more representative of natural conditions, including the absence of a separation zone and the formation of a scour hole originating at the upstream junction corner. Furthermore, the study establishes the relationship between bed evolution and flow structure, identifying the dominant driver of scour hole formation as the intense turbulent kinetic energy and high shear stress within the shear layer.

本研究解决了在自然河流汇合处观察到的流动结构和河床特征与实验室实验中重现的流动结构和河床特征之间的差异。提出了一种改进的广义物理模型，以更好地复制自然汇合的典型特征，例如下游汇合角的圆形、下游河道的高宽深比以及下游河道相对于上游的加宽。使用自然环境中常见的流动参数，进行了一系列水槽实验来测量流动结构和河床形态。结果揭示了更能代表自然条件的流动和河床特征，包括没有分离区和形成源自上游交界角的冲刷孔。此外，该研究还建立了床层演化与流动结构之间的关系，确定了冲刷孔形成的主要驱动因素是剪切层内强烈的湍流动能和高剪应力。

This study addresses the discrepancies between flow structures and bed features observed in natural river confluences and those reproduced in laboratory experiments. An improved, generalized physical model is proposed to better replicate typical characteristics of natural confluences—such as rounded downstream confluence corners, high width-to-depth ratios in the downstream channel, and downstream channel widening relative to the upstream. Using flow parameters commonly found in natural settings, a series of flume experiments were conducted to measure flow structures and bed morphology. The results revealed flow and bed characteristics more representative of natural conditions, including the absence of a separation zone and the formation of a scour hole originating at the upstream junction corner. Furthermore, the study establishes the relationship between bed evolution and flow structure, identifying the dominant driver of scour hole formation as the intense turbulent kinetic energy and high shear stress within the shear layer.

All cryo-EM structural models and maps have been deposited in the Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB). The PDB codes are 9UOK, 9KHH and 9KGK. The EMDB codes are EMD-64380, EMD-62340 and EMD-62321. These data are publicly available as of the date of publication.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Based on the planting area distribution of early rice in Sanya from 2019 to 2024, the biomass density at heading stage was estimated. Our team created a Dynamic Growth Period Unification (DGU) algorithm: Based on the objective law that NDVI of rice heading stage reaches the maximum value in the whole growth period, the change trend of rice NDVI in satellite remote sensing images of the study area is analyzed and the corresponding time range of rice heading stage is extracted. This method solves the problem of inaccurate prediction of large-scale spatial rice biomass due to the difference of crop rotation time in farmers' planting patterns and different growth stages of rice varieties. Then, Sentinel-1 radar data and Sentinel-2 optical data of the corresponding time period were obtained according to this time range, and the radar data, optical data and texture information were input into four machine learning algorithms, namely random forest, support vector machine, gradient lifting decision tree and BP neural network, to develop a rice biomass density estimation model. The results show that: The random forest algorithm performed best in the prediction task of rice biomass density. Its coefficient of determination R2 was 0.746, root mean square error (RMSE) was 290.531 g/m², Bias was 6.766 g/m², and relative root mean square error (RRMSE) was 23.850%. The Bias% was 0.555%. The results showed that the biomass density of rice in Sanya fluctuated in different years and regions. The average biomass density fluctuated between 1142.218 g/m² and 1177.410 g/m², and the total biomass fluctuated between 21120.266 tons and 28696.107 tons. The average biomass density in Yazhou district fluctuated between 1143.687 g/m² and 1167.343 g/m², and the total biomass fluctuated between 7845.106 tons and 12082.793 tons. The average biomass density fluctuated between 1149.634 g/m² and 1194.170 g/m², and the total biomass fluctuated between 6710.375 tons and 10139.757 tons. The average biomass density in Jiyang District fluctuated between 1159.411 g/m² -1227.457 g/m², and the total biomass fluctuated between 1797.002 tons and 2757.501 tons. The average biomass density of Haitang district fluctuated between 1114.167 g/m² -1184.708 g/m², and the total biomass fluctuated between 3717.419 tons and 5421.217 tons.

The dataset shows the raw data for our paper 'Accelerated soil phosphorus cycling upon abrupt permafrost thaw', including a series of topsoil (0–15 cm) phosphorus (P) pools, P species, P cycling processes, and plant P-related properties.