Find research datasets worth reusing

opencc-by-4.0Mar 2024View details →

Integrated Preservation of Water Activity as Key to Intensified Chemoenzymatic Synthesis of Bio-Based Styrene Derivatives

The valorization of lignin-derived feedstocks by catalytic means enables their defunctionalization and upgrading to valuable products. However, the development of productive, safe, and low-waste processes remains challenging. This paper explores the industrial potential of a chemoenzymatic reaction performing the decarboxylation of bio-based phenolic acids in wet cyclopentyl methyl ether (CPME) by immobilized phenolic acid decarboxylase from Bacillus subtilis, followed by a base-catalyzed acylation. Key-to-success is the continuous control of water activity, which fluctuates along the reaction progress, particularly at high substrate loadings (triggered by different hydrophilicities of substrate and product). A combination of experimentation, thermodynamic equilibrium calculations, and MD simulations revealed the change in water activity which guided the integration of water reservoirs and allowed process intensification of the previously limiting enzymatic step. With this, the highly concentrated sequential two-step cascade (400 g·L–1) achieves full conversions and affords products in less than 3 h. The chemical step is versatile, accepting different acyl donors, leading to a range of industrially sound products. Importantly, the finding that water activity changes in intensified processes is an academic insight that might explain other deactivations of enzymes when used in non-conventional media.

opencc-by-4.0Jul 2022View details →

Intensified atmospheric branch of the hydrological cycle over the Tibetan Plateau during the Last Interglacial from a dynamical downscaling perspective

We provide the datasets run for investigating the atmospheric branch of the hydrological cycle over the Tibetan Plateau during the Last Interglacial (127 ka), based on the  mesoscale Weather Research and Forecasting (WRF) model driven by the Community Earth System Model (CESM). We upload summer mean of the model outputs from the WRF over the Tibetan Plateau used in estimating the atmospheric branch of the hydrological cycle.

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-4.0Aug 2024View details →

Intensified, Kilogram-Scaled, and Environment-Friendly: Chemoenzymatic Synthesis of Bio-Based Acylated Hydroxystyrenes

Lignin-derived styrene derivatives are versatile building blocks for the manufacture of bio-based polymers. As shown previously, phenol-protected hydroxystyrenes are accessible under industrially-sound conditions (>100 g/L, >95% yield) by subjecting biogenic phenolic acids to enzymatic decarboxylation and base-catalyzed acylation in non-aqueous media (wet cyclopentyl methyl ether, CPME). Herein, we demonstrate the production of 1 kg of 4-acetoxy-3-methoxystyrene in a 10 L reactor and present practical adjustments to the up- and downstream processing that warrant a straightforward process and high isolated yields. Additionally, an environmental assessment is conducted, starting with a thorough E factor analysis to identify the sources that contribute most to the environmental burden (solvent and downstream processing). Also, the total CO2 production of the process is studied, including contributions from energy use and the treatment of generated wastes. The energy impact is evaluated through thermodynamic analysis, and the environmental footprint contributions by wastes – organic and aqueous fractions – are assessed based on CO2 emissions from solvent incineration and wastewater treatment, respectively. Overall, the holistic assessment of the process, its optimization, scale-up, product isolation, and environmental analysis indicate the feasibility of multi-step chemoenzymatic reactions to deliver high volume, low-value chemicals from biorefineries.

opencc-by-4.0Sep 2024View details →

Data and code for "Negative effects of allelopathic plant invasion intensify as the growth season progresses"

Initial release for TT23_ms_data. Repository contains data and code for "Negative effects of allelopathic plant invasion intensify as the growth season progresses" by Perkowski et al. (in prep). Manuscript plots and tables are also included in release.

dryad40/100

Data for: Functional redundancy of weed seed predation is reduced by intensified agriculture

Intensive agriculture, a driver of biodiversity loss, can diminish ecosystem functions and their stability. Biodiversity can increase functional redundancy and is expected to stabilize ecosystem functions. Few studies however have explored how agricultural intensity affects functional redundancy and its link with ecosystem function stability. Here, within a continent-wide study, we assess how the functional redundancy of seed predation is affected by agricultural intensity and landscape simplification. By combining carabid abundances with molecular gut content data, functional redundancy of seed predation was quantified for 65 weed genera across 60 fields in four European countries. Across weed genera, functional redundancy was reduced with high field management intensity and simplified crop rotations. Moreover, functional redundancy increased the spatial stability of weed seed predation within fields. We found that ecosystem functions are vulnerable to disturbance in intensively managed agroecosystems, providing empirical evidence of the importance of biodiversity for stable ecosystem functions across space.

opencc-zeroDec 2023View details →

opencc-by-4.0May 2021View details →

Dataset - Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau

Materials and data results needed to reproduce the findings of the study published in (PNAS) Proceedings of the National Academy of Sciences of the United States of America: "Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau". A. Jouberton, T. E. Shaw, E. Miles, M. McCarthy, S. Fugger, S. Ren, A. Dehecq, W. Yang and F. Pellicciotti It includes the meteorological forcing time-series, an exhaustive list of the model parameters, the outputs of TOPKAPI-ETH, and Matlab scripts allowing to reproduce the figures and compute the numbers given in the main manuscript as well as in the Supplementary Information. --------------- Contents : Folder : "Matlab_scripts"     'Climate_import.m' : Organizes meteorological forcing and generates Figure S9     'TOPKAPI_result_import.m' : Imports TOPKAPI's reference run outputs and prepares them for analysis     'Experiment_analysis.m' : Analyses the results of the forcing experiments, generates Figure 4 and Figure S25     'Main_text_results.m' : Analyses the results of TOPKAPI's reference runs, generates Figure 1D, FIgure 2 and Figure 3     'TOPKAPI_validation.m' : Compares TOPKAPI's reference run results with several validation datasets, generates the figures and performance metrics of the model calibration and validation procedure.     'Parlung_albedo_regional_analysis.m': Computes the mean glacier albedo per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the albedo of Parlung No.4 glacier.    'Parlung_GMB_regional_analysis.m': Computes the mean glacier mass balance per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the glacier mass balance of Parlung No.4 glacier.    'Precipitation_phase_sensitivity_analysis.m': Performs a sensitivity analysis on the simulated monsoon snowfall ratio per elevation band and on the attribution of glacier mass loss to precipitation phase change using Monte Carlo simulations.    'TOPKAPI_MODIS_validation.m': Compares the snow cover at Parlung No.4 catchment simulated by TOPKAPI-ETH and observed by MODIS, generates Figure S19.   Folder : "Remote_sensing" :    Sub-Folder: 'Hugonnet' = Glacier mass balance averaged over 2000-2020 covering the Southeastern Tibetan Plateau, 100m resolution, derived from Hugonnet et al. 2021    Sub-Folder: 'MODIS' = contains the snow cover at Parlung No.4 derived from the daily product MOD10A1 version 61, for the period 2000-2018    Sub-Folder: 'Regional_glacier_albedo' = contains the annual glacier surface albedo from 2000 to 2020, covering the Southeastern Tibetan Plateau, 500m resolution.    Sub-Folder: 'Shapefiles' = contains the Parlung No.4 glacier outlines in 1974 and from the RGI 6.0    'ASTER_Nyainqentanglha_15m_utm.tif' = ASTER Digital elevation model at 15m resolution covering the Southeastern Tibetan Plateau    'parlung_mask_1974.mat' = Parlung No.4 glacier mask as a matlab file    'dh_ASTER_SRTM_30m.tif' = Mean elevation change rate from 2000 to 2016 at Parlung No.4 catchment.    'Geodetic_map.mat' = Elevation change maps for the periods 1974-2000 and 1974-2014, as a matlab file    'GMB_geodetic.mat' = Geodetic mass balance (glacier-wide mean and profile per elevation band) used in Figure S13    'parlung_30m_catchment_mask.tif' = Parlung No.4 catchment mask    'parlung_1974_30m_dem.tif' = DEM of Parlung No.4 catchment, 30 m resolution    'parlung_1974_30m_gla.tif' = Parlung No. 4 glacier mask, 30m resolution    'parlung_1974_30m_glah.tif' = Reconstructed ice thickness of 1975 for Parlung No.4 glacier    'parlung_1974_2000_diff_24m.tif' = Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2000    'parlung_1974_2014_diff_24m.tif' = Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2014    'Parlung_1974_bedrock_dem_30m.tif' = Bedrock surface digital elevation model of the catchment, 30m spatial resolution    'RGI_KangriKarpo_100m_utm_id.tif' = Glacier mask covering the Kangri Karpo mountain region, 100m resolution, with glacier IDs in the attribute table    'RGI_Nyainqentanglha_100m_utm_id.tif' = = Glacier mask covering the Southeastern Tibetan Plateau, 100m resolution, with glacier IDs in the attribute table   Folder : "TOPKAPI_forcing" :     CCT_AWS4600_extended.csv : Hourly cloud cover transmissivity from 1975 to 2018 reconstructed at AWSoff location      Climate.mat : Organizes meteorological forcings, output from the matlab script 'Climate_import.m'     LR_AWS4600_extended.csv : Hourly temperature lapse-rates from 1975 to 2018 reconstructed at AWSoff location      Precipitation_AWS4600_extended.csv : Hourly precipitation from 1975 to 2018 reconstructed at AWSoff location      Ta_AWS4600_extended.csv : Hourly air temperature from 1975 to 2018 reconstructed at AWSoff location    Sub-Folder: 'National_meteorological_stations' = Contains the daily air temperature and precipitation measured at the national meteorological stations of Bomi, Zayu,    Zuogong and Basu    Sub-Folder: 'Reference_run_inputs' = Contains the input files necessary to run TOPKAPI-ETH to obtain the outputs from which the results of this study are based on.   Folder : "TOPKAPI_output":    Sub-Folder : "Forcing experiment" = organized TOPKAPI outputs from the forcing experiment    Sub-Folder :" Reference_run_outputs" = raw TOPKAPI outputs from the reference run (catchment average, spatial and grid cells)    Sub-Folder : "Reference_run_results" = organized TOPKAPI outputs from the reference run    Sub-Folder : "Snow_ice_cover" = contains TOPKAPI-ETH derived snow cover maps (daily map outputs)    Sub-Folder : "Regional_analysis" =        'Alb'= Table containing the mean glacier albedo (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)        'GMB'= Table containing the mean glacier mass balance (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)        'Hypso_xxm' = Table containing the percentage of glacier area per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m        'NormEl_100m' = Table containing the elevation per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m    Sub-Folder : "Semi_distributed_outputs" = Precipitation phase and amounts resulting from TOPKAPI-ETH simulation per elevation band, for the reference run and for the Monte Carlo sensitivity analysis   Folder : "Validation_data"    'topkapi.out_reference_discharge2016' =  raw TOPKAPI outputs run in 2016 with AWSoff air temperature    'master_file_parlung.mat' = matlab structure containing AWS measurements, necessary for running 'TOPKAPI_validation.m'    'Qdigit.mat' = Discharge measured at the Parlung No.4 glacier outlet, from Li et al., (2016)    'Parlung_Q_1970.mat'  = 'Discharge time-series used to run TOPKAPI-ETH (goes back to 1975, but filled with 0 when no measurements are available)   In order to run the Matlab scripts, it is recommended to download all folders and gather them into the same folder. Any request about data or questions on how to run the Matlab scripts can be asked to the author of the paper (at achille.jouberton@wsl.ch).

opencc-by-4.0Aug 2024View details →

Typological Database of Intensifiers and Reflexives

opengpl-3.0-or-laterJul 2024View details →

Data and Analyses supporting "Human-caused ocean warming has intensified recent hurricanes"

Notebooks and data from "Human-caused ocean warming has intensified recent hurricanes", in press at Environmetal Research: Climate. A pre-print of the paper can be found at <a href="https://essopenarchive.org/users/667788/articles/1232538-human-caused-ocean-warming-has-intensified-recent-hurricanes">https://essopenarchive.org/users/667788/articles/1232538-human-caused-ocean-warming-has-intensified-recent-hurricanes</a> Included are study analysis notebooks, figure plotting notebooks supportiung the paper publication, and data file outputs forming the computation foundation of paper results. Please email <a href="mailto:dgilford@climatecentral.org" target="_blank" rel="noopener">dgilford@climatecentral.org</a> with any questions or feedback.   Funding for this work was provided by the Bezos Earth Fund, The Schmidt Family Foundation, and the CO2 Foundation.

opencc-by-4.0Sep 2021View details →

Model output for "Impact of intensifying nitrogen limitation of ocean net primary production is fingerprinted by nitrogen isotopes"

Description. The data included in this repository is output of simulations performed with the NEMO-PISCESv2 global ocean-biogeochemical model. Simulations involved forcing the NEMO-PISCESv2 with global warming associated with historical and future emissions, as well as the historical and future trends in atmospheric nitrogen deposition. Future climate change was according to the Representative Concentration Pathway 8.5 scenario (Dufresne et al., 2013; Riahi et al., 2011), which sees rapid warming during the 21st century. Historical and future atmospheric nitrogen deposition fields were created via linear interpolation of fields produced by Hauglustaine et al. (2014) at years 1850, 2000, 2030, 2050 and 2100. To represent the amplification of deposition since 1950 (Galloway 2014), 60 % of the increase between 1850 and 2000 occurred from 1950 onwards. In this study, we quantified the effect anthropogenic climate change and anthropogenic increases in atmospheric nitrogen deposition on the marine nitrogen cycle. The response of the marine nitrogen cycle to these combined stressors is highly uncertain, and we therefore employed this complex model with a strong representation of nitrogen cycling in an attempt to constrain the global behaviour of this important cycle. In addition, through the addition of nitrogen isotopes to the ocean-biogeochemical model, we also explored and described how the isotopes responded to these anthropogenic forcings, and if the isotopes uniquely fingerprinted the response for potential monitoring/detection purposes. Our abstract reads: “The open ocean nitrogen cycle is being altered by increases in anthropogenic atmospheric nitrogen deposition and climate change. How the nitrogen cycle responds will determine long-term trends in net primary production (NPP) in the nitrogen-limited low latitude ocean, but is poorly constrained by uncertainty in how the source-sink balance will evolve. Here we show that intensifying nitrogen limitation of phytoplankton, associated with near-term reductions in NPP, causes detectable declines in nitrogen isotopes (δ15N) and constitutes the primary perturbation of the 21st century nitrogen cycle. Model experiments show that ~75% of the low latitude twilight zone develops anomalously low δ15N by 2060, predominantly due to the effects of climate change that alter ocean circulation, with implications for the nitrogen sources-sink balance. Our results highlight that δ15N changes in the low latitude twilight zone may provide a useful constraint on emerging changes to nitrogen limitation and NPP over the 21st century.”   Coordinates Spatial resolution is global (90°S-90°N, 180°W-180°E, surface ocean to 5000 metres depth) and temporal resolution runs from years 1801 to 2100.   Citation. Buchanan PJ, Aumont O, Bopp L, Mahaffey C, and Tagliabue A (2021): An isotopic fingerprint of increasingly nitrogen-limited phytoplankton in a changing oceanic nitrogen cycle. Nature Communications.   Files provided. The data files provided are those that are required to create the figures for this study and/or perform key analyses (i.e. the time of emergence calculations). In the following, each figure or analysis has an associated python script and we list the data files needed to run that script. Python scripts can be found the lead authors GitHub at <a href="https://github.com/pearseb/PISCESiso_Ncycle_analysis">https://github.com/pearseb/PISCESiso_Ncycle_analysis</a>.     Put δ15NNO3 observations on model grid (process-d15Nno3_observations_on_model_grid.py): <ul> <li>“RafterTuerena_watercolumn_d15N_no3.txt”</li> </ul> Model assessment (process-model_assessment.py): <ul> <li>“ETOPO_spinup_d15Nno3.nc”</li> <li>“ETOPO_ORCA2.0_Basins_float.nc”</li> <li>“ETOPO_ORCA2.0.full_grid.nc”</li> <li>“RafterTuerena_watercolumn_d15N_no3_gridded.npz”</li> </ul> Time of emergence calculations (process-compute_toe.py): <ul> <li>“ETOPO_picontrol_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_npp.nc”</li> <li>“ETOPO_picontrol_ndep_1y_npp.nc”</li> <li>“ETOPO_future_1y_npp.nc”</li> <li>“ETOPO_future_ndep_1y_npp.nc”</li> <li>“ETOPO_picontrol_1y_nfix.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nfix.nc”</li> <li>“ETOPO_future_1y_nfix.nc”</li> <li>“ETOPO_future_ndep_1y_nfix.nc”</li> </ul> Figure 1 (fig-main1.py): <ul> <li>“ncycle_changes.nc”</li> <li>“sources_and_sinks.nc”</li> </ul> Figure 2 (fig-main2.py): <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> Figure 3 (fig-main3.py): <ul> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_fluxanalysis_results.nc”</li> <li>“figure2D_cc_din_e15n.nc”</li> </ul> Figure 4 (fig-main4.py): <ul> <li>“ETOPO_direct_indirect_effects.nc”</li> </ul> Supp Figure 1 (fig-supp1.py): <ul> <li>“figure_d15Nmaps.nc”</li> </ul> Supp Figure 2 (process-model_assessment.py): <ul> <li>Produced by process-model_assessment.py (see data above)</li> </ul> Supp Figure 3 (fig-supp3.py): <ul> <li>“d15nstats.txt”</li> </ul> Supp Figure 4 (fig-supp4.py): <ul> <li>“ndep_Tg_yr.nc”</li> </ul> Supp Figure 5 (fig-supp5.py):y <ul> <li>“ncycle_changes_climatechangeonly.nc”</li> </ul> Supp Figure 6 (fig-supp6.py): <ul> <li>“ncycle_changes_ndeponly.nc”</li> </ul> Supp Figure 7 (fig-supp7.py): <ul> <li>“figure_depthzones.nc”</li> </ul> Supp Figure 8 (fig-supp8.py): <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> Supp Figure 9 (fig-supp9.py): <ul> <li>“figure2D_ndep_no3_utz.nc”</li> </ul> Supp Figures 10 and 11 (process-0D_model_phyto_frac.py): <ul> <li>Produced by process-0D_model_phyto_frac.py and no data required.</li> </ul> Supp Figure 12 (process-compute_toe.py): <ul> <li>Produced by process-compute_toe.py (see data above)</li> </ul>   References. Dufresne, J. L., Foujols, M. A., Denvil, S., Caubel, A., Marti, O., Aumont, O., et al. (2013). Climate change projections using the IPSL-CM5 Earth System Model: From CMIP3 to CMIP5. Climate Dynamics (Vol. 40). https://doi.org/10.1007/s00382-012-1636-1 Galloway, J. N. (2014). The Global Nitrogen Cycle. In Treatise on Geochemistry (2nd ed., Vol. 10, pp. 475–498). Elsevier. https://doi.org/10.1016/B978-0-08-095975-7.00812-3 Hauglustaine, D. A., Balkanski, Y., & Schulz, M. (2014). A global model simulation of present and future nitrate aerosols and their direct radiative forcing of climate. Atmospheric Chemistry and Physics, 14(20), 11031–11063. https://doi.org/10.5194/acp-14-11031-2014 Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., et al. (2011). RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change, 109(1–2), 33–57. https://doi.org/10.1007/s10584-011-0149-y

dryad40/100

Data for: Functional redundancy of weed seed predation is reduced by intensified agriculture

Open the record for dataset details and reuse information.

publicMar 2024View details →

opencc-zeroNov 2019View details →

Data from: Anthropogenic land-use change intensifies the effect of low flows on stream fishes

1. As ecosystems experience simultaneous disturbances, it is critical to understand how multiple stressors interact to affect ecological change. Land-use change (LUC) and extreme flow events are two important stressors that could interact to affect fish populations. 2. We evaluated the individual and interactive effects of discharge and LUC associated with oil and natural gas development (ONGD) on populations of two stream fishes over a seven-year period. We used repeated-state (i.e., abundance trends) and rate (i.e., colonization and persistence) responses to advance our understanding of flow-ecology relationships in a multiple-stressor framework. 3. Overall, fish abundance, colonization, and persistence declined in association with discharge. The effect of LUC associated with ONGD differed between species, with the abundance of Mottled Sculpin declining and Mountain Sucker increasing. We found both synergistic and antagonistic interactions between discharge and LUC. LUC intensified the effect of low flows for one species and lead to greater variability in responses to flows for the other species. These differences between species' responses are likely due to differences in their physiological tolerances and behavioral adaptations to disturbance. 4. Synthesis and applications. Our research provides empirical evidence for the complex interactions that can arise between discharge and anthropogenic LUC. Management efforts (e.g., silt fences, vegetation replanting, and in-stream restoration) to mitigate the effects of anthropogenic alterations and promote high-quality refuge habitats could help mitigate the effect of hydrologic extremes. Further development of flow-ecology relationships in a multiple-stressor framework will help guide management of stream fishes, and provide a better understanding of the mechanisms underlying flow-ecology relationships for different species.06-Sep-2019

opencc-zeroJul 2024View details →

Data from: Moist heatwaves intensified by entrainment of dry air that limits deep convection

Moist heatwaves in the tropics and subtropics pose substantial risks to society, yet the dynamics governing their intensity are not fully understood. The onset of deep convection arising from hot, moist near-surface air has been thought to limit the magnitude of moist heatwaves. Here, we use reanalysis data, and output from the Coupled Model Intercomparison Project Phase 6 and model entrainment perturbation experiments, to show that entrainment of unsaturated air in the lower-free troposphere (roughly 1--3 km above the surface) limits deep convection, thereby allowing much higher near-surface moist heat. Regions with large-scale subsidence and a dry lower-free troposphere, such as coastal areas adjacent to hot and arid land, are thus particularly susceptible to moist heat waves. Even in convective regions such as the northern Indian Plain, southeast Asia, and interior South America, the lower-free tropospheric dryness strongly affects the maximum surface wet-bulb temperature. As the climate warms, the dryness (relative to saturation) of the lower-free tropospheric air increases; this allows for a larger increase of extreme moist heat, further elevating the likelihood of moist heatwaves.

zenodo36/100

Elevated CO2 Intensifies Hydrological Impacts Due to Tropical Deforestation

nc files are the analyzed CESM simulation outputs and py files are the codes processing and plotting them.

opencc-by-4.0Jul 2024View details →

zenodo36/100

TSE surveillance and Atypical scrapie intensified monitoring data

GENERAL_DATA_AS_REPORT.xls: statutory TSE surveillance data of sheep and goats 2013-2019 from 22 countries. MANDATE_DATA_RAW_AS_REPORT.xls: raw data collated by the EC for the mandate EFSA-Q-2020-00662, including the results of the intensified monitoring implemented in flocks/herds from 22 countries in which a case of atypical scrapie has been confirmed MANDATE_DATA_ANALYSIS_AS_REPORT.xls: : data collated by the EC for the mandate EFSA-Q-2020-00662, including the results of the intensified monitoring implemented in flocks/herds from 22 countries in which a case of atypical scrapie has been confirmed, aggregated for analysis.

opencc-by-4.0Jul 2021View details →

opencc-zeroNov 2022View details →

Data for: Soil incubation data with intensified water variability

The dataset is from an incubation experiment which was carried out in the dark at the temperature of 15°C and the relative humidity of 40% for approximately three months. The predominant soil sampled from a temperate grassland of northern China, it is chernozem and belongs to sandy loam, with sand, silt and clay percentages of 54%, 41% and 5%, respectively. The SOC content of the 0-10 cm layer is 4.2% and the pH is 6.9. This study manipulated soil water levels and the intensity of water variability with a total of six treatments. There were four replicates for each treatment for gas sampling. The average cumulative CO2 release and the soil aggregate of the four replicates and its standard error (SE) are provided in the dataset. One additional group of destructive samplings and the soils for the gas sampling were collected at the wet end and dry end in the last drying-rewetting cycle, respectively. 14 phospholipid fatty acids (FLFA) markers were selected from the combination results of the wet and dry ends in the last drying-rewetting cycle.

zenodo36/100

Coastal phytoplankton blooms expand and intensify in the 21st century:data and code

This repository contains the relevant dataset and code for the paper Coastal phytoplankton blooms expand and intensify in the 21st century. We generated a satellite-based dataset of phytoplankton bloom occurrence to characterize the spatial and temporal patterns of algal blooms in coastal oceans globally. The dataset was derived using global, 1-km resolution daily observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA's Aqua satellite, and all 0.76 million images acquired by this satellite mission between 2003 and 2020 were used.  Please refer to the README file in code_examples.rar file for more detailed information.

opencc-by-4.0Nov 2022View details →